Nothing
tests/testthat/_snaps/glmm.md
In JointAI: Joint Analysis and Imputation of Incomplete Data
data_list remains the same
Code
lapply(models, "[[", "data_list")
Output
$m0a1
$m0a1$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0a1$M_lvlone
y
1 -13.0493856
1.1 -9.3335901
1.2 -22.3469852
1.3 -15.0417337
2 -12.0655434
2.1 -15.8674476
2.2 -7.8800006
3 -11.4820604
3.1 -10.5983220
3.2 -22.4519157
4 -1.2697775
4.1 -11.1215184
4.2 -3.6134138
4.3 -14.5982385
5 -6.8457515
5.1 -7.0551214
5.2 -12.3418980
5.3 -9.2366906
6 -5.1648211
7 -10.0599502
7.1 -18.3267285
7.2 -12.5138426
8 -1.6305331
8.1 -9.6520453
8.2 -1.5278462
8.3 -7.4172211
8.4 -7.1238609
8.5 -8.8706950
9 -0.1634429
9.1 -2.6034300
9.2 -6.7272369
10 -6.4172202
10.1 -11.4834569
11 -8.7911356
11.1 -19.6645080
11.2 -20.2030932
11.3 -21.3082176
11.4 -14.5802901
12 -15.2006287
13 0.8058816
13.1 -13.6379208
14 -15.3422873
14.1 -10.0965208
14.2 -16.6452027
14.3 -15.8389733
15 -8.9424594
15.1 -22.0101983
15.2 -7.3975599
15.3 -10.3567334
16 -1.9691302
16.1 -9.9308357
16.2 -6.9626923
16.3 -3.2862557
16.4 -3.3972355
16.5 -11.5767835
17 -10.5474144
17.1 -7.6215009
17.2 -16.5386939
17.3 -20.0004774
17.4 -18.8505475
18 -19.7302351
19 -14.6177568
19.1 -17.8043866
19.2 -15.1641705
19.3 -16.6898418
20 -12.9059229
20.1 -16.8191201
20.2 -6.1010131
20.3 -7.9415371
20.4 -9.3904458
20.5 -13.3504189
21 -7.6974718
21.1 -11.9335526
21.2 -12.7064929
22 -21.5022909
22.1 -12.7745451
23 -3.5146508
23.1 -4.6724048
24 -2.5619821
25 -6.2944970
25.1 -3.8630505
25.2 -14.4205140
25.3 -19.6735037
25.4 -9.0288933
25.5 -9.0509738
26 -19.7340685
26.1 -14.1692728
26.2 -17.2819976
26.3 -24.6265576
27 -7.3354999
27.1 -11.1488468
28 -11.7996597
28.1 -8.2030122
28.2 -26.4317815
28.3 -18.5016071
29 -5.8551395
29.1 -2.0209442
29.2 -5.6368080
29.3 -3.8110961
30 -12.7217702
30.1 -17.0170140
30.2 -25.4236089
31 -17.0783921
32 -18.4338764
32.1 -19.4317212
32.2 -19.4738978
32.3 -21.4922645
33 2.0838099
33.1 -13.3172274
34 -10.0296691
34.1 -25.9426553
34.2 -18.5688138
34.3 -15.4173859
35 -14.3958113
35.1 -12.9457541
35.2 -16.1380691
36 -12.8166968
36.1 -14.3989481
36.2 -12.2436943
36.3 -15.0104638
36.4 -10.1775457
37 -15.2223495
37.1 -14.7526195
37.2 -19.8168430
38 -2.7065118
39 -8.7288138
39.1 -9.2746473
39.2 -18.2695344
39.3 -13.8219083
39.4 -16.2254704
39.5 -21.7283648
40 1.8291916
40.1 -6.6916432
40.2 -1.6278171
40.3 -10.5749790
41 -3.1556121
41.1 -11.5895327
41.2 -18.9352091
41.3 -15.9788960
41.4 -9.6070508
42 -5.2159485
42.1 -15.9878743
43 -16.6104361
43.1 -9.5549441
43.2 -14.2003491
44 -8.1969033
44.1 -19.9270197
44.2 -22.6521171
44.3 -21.1903736
45 -0.5686627
45.1 -7.5645740
46 -19.1624789
46.1 -18.4487574
46.2 -15.8222682
47 -5.4165074
47.1 -15.0975029
47.2 -12.9971413
47.3 -10.6844521
47.4 -18.2214784
48 -8.3101471
48.1 -18.3854275
49 -13.0130319
50 -10.4579977
51 -19.3157621
52 -4.4747188
52.1 -4.3163827
52.2 -6.9761408
52.3 -20.1764756
52.4 -8.9036692
52.5 -5.6949642
53 -10.3141887
53.1 -8.2642654
53.2 -9.1691554
54 -6.2198754
54.1 -15.7192609
54.2 -13.0978998
54.3 -5.1195299
54.4 -16.5771751
55 -5.7348534
55.1 -7.3217494
55.2 -12.2171938
55.3 -12.9821266
55.4 -14.8599983
56 -14.1764282
56.1 -12.5343602
56.2 -8.4573382
56.3 -12.4633969
56.4 -17.3841863
56.5 -14.8147645
57 -3.1403293
57.1 -11.1509248
57.2 -6.3940143
57.3 -9.3473241
58 -12.0245677
58.1 -9.2112246
58.2 -1.2071742
58.3 -11.0141711
58.4 -5.3721214
58.5 -7.8523047
59 -13.2946560
59.1 -10.0530648
60 -19.2209402
61 -4.6699914
61.1 -3.5981894
61.2 -1.4713611
61.3 -3.8819786
61.4 0.1041413
62 -2.8591600
62.1 -6.9461986
62.2 -16.7910593
62.3 -17.9844596
63 -24.0335535
63.1 -11.7765300
64 -20.5963897
65 -2.7969169
65.1 -11.1778694
65.2 -5.2830399
65.3 -7.9353390
66 -13.2318328
66.1 -1.9090560
66.2 -16.6643889
67 -25.6073277
68 -13.4806759
68.1 -18.4557183
68.2 -13.3982327
68.3 -12.4977127
68.4 -11.7073990
69 -14.5290675
70 -15.2122709
70.1 -7.8681167
71 -10.3352703
71.1 -7.5699888
71.2 -18.4680702
71.3 -21.4316644
71.4 -8.1137650
72 -9.1848162
72.1 -23.7538846
72.2 -26.3421306
72.3 -27.2843801
72.4 -20.8541617
72.5 -12.8948965
73 -2.6091307
74 -8.2790175
75 -12.5029612
76 -6.0061671
76.1 -8.8149114
76.2 -11.8359043
77 0.4772521
78 -9.4105229
79 -1.0217265
79.1 -11.8125257
79.2 -10.5465186
80 -12.7366807
80.1 -9.0584783
80.2 -16.6381566
81 0.5547913
81.1 -4.0892715
81.2 1.8283303
81.3 -5.2166381
82 -3.0749381
82.1 -10.5506696
82.2 -18.2226347
83 -12.5872635
83.1 -11.9756502
83.2 -10.6744217
83.3 -19.2714012
84 -2.6320312
84.1 -9.8140094
85 -12.3886736
85.1 -12.9196365
85.2 -9.6433248
85.3 -6.3296340
85.4 -7.0405525
85.5 -13.6714939
86 -10.8756412
86.1 -12.0055331
86.2 -13.3724699
86.3 -13.3252145
86.4 -14.9191290
86.5 -17.7515546
87 -10.7027963
87.1 -22.4941954
87.2 -14.9616716
88 -2.2264493
88.1 -8.9626474
88.2 -2.5095281
88.3 -16.3345673
89 -11.0459647
90 -4.5610239
90.1 -11.7036651
90.2 -5.3838521
90.3 -4.1636999
91 -7.1462503
91.1 -12.8374475
91.2 -18.2576707
92 -6.4119222
93 5.2122168
93.1 3.1211725
93.2 -3.6841177
93.3 2.6223542
93.4 -11.1877696
94 -6.9602492
94.1 -7.4318416
94.2 -4.3498045
94.3 -11.6340088
94.4 -12.9357964
94.5 -14.7648530
95 -12.8849309
95.1 -9.7451502
95.2 -0.8535063
96 -4.9139832
96.1 -3.9582653
96.2 -9.6555492
96.3 -11.8690793
96.4 -11.0224373
96.5 -10.9530403
97 -9.8540471
97.1 -19.2262840
98 -11.9651231
98.1 -2.6515128
98.2 -12.2606382
99 -11.4720500
99.1 -14.0596866
99.2 -17.3939469
100 1.1005874
100.1 -3.8226248
100.2 -0.9123182
100.3 -15.8389474
100.4 -12.8093826
$m0a1$mu_reg_norm
[1] 0
$m0a1$tau_reg_norm
[1] 1e-04
$m0a1$shape_tau_norm
[1] 0.01
$m0a1$rate_tau_norm
[1] 0.01
$m0a1$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0a1$shape_diag_RinvD
[1] "0.01"
$m0a1$rate_diag_RinvD
[1] "0.001"
$m0a2
$m0a2$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0a2$M_lvlone
y
1 -13.0493856
1.1 -9.3335901
1.2 -22.3469852
1.3 -15.0417337
2 -12.0655434
2.1 -15.8674476
2.2 -7.8800006
3 -11.4820604
3.1 -10.5983220
3.2 -22.4519157
4 -1.2697775
4.1 -11.1215184
4.2 -3.6134138
4.3 -14.5982385
5 -6.8457515
5.1 -7.0551214
5.2 -12.3418980
5.3 -9.2366906
6 -5.1648211
7 -10.0599502
7.1 -18.3267285
7.2 -12.5138426
8 -1.6305331
8.1 -9.6520453
8.2 -1.5278462
8.3 -7.4172211
8.4 -7.1238609
8.5 -8.8706950
9 -0.1634429
9.1 -2.6034300
9.2 -6.7272369
10 -6.4172202
10.1 -11.4834569
11 -8.7911356
11.1 -19.6645080
11.2 -20.2030932
11.3 -21.3082176
11.4 -14.5802901
12 -15.2006287
13 0.8058816
13.1 -13.6379208
14 -15.3422873
14.1 -10.0965208
14.2 -16.6452027
14.3 -15.8389733
15 -8.9424594
15.1 -22.0101983
15.2 -7.3975599
15.3 -10.3567334
16 -1.9691302
16.1 -9.9308357
16.2 -6.9626923
16.3 -3.2862557
16.4 -3.3972355
16.5 -11.5767835
17 -10.5474144
17.1 -7.6215009
17.2 -16.5386939
17.3 -20.0004774
17.4 -18.8505475
18 -19.7302351
19 -14.6177568
19.1 -17.8043866
19.2 -15.1641705
19.3 -16.6898418
20 -12.9059229
20.1 -16.8191201
20.2 -6.1010131
20.3 -7.9415371
20.4 -9.3904458
20.5 -13.3504189
21 -7.6974718
21.1 -11.9335526
21.2 -12.7064929
22 -21.5022909
22.1 -12.7745451
23 -3.5146508
23.1 -4.6724048
24 -2.5619821
25 -6.2944970
25.1 -3.8630505
25.2 -14.4205140
25.3 -19.6735037
25.4 -9.0288933
25.5 -9.0509738
26 -19.7340685
26.1 -14.1692728
26.2 -17.2819976
26.3 -24.6265576
27 -7.3354999
27.1 -11.1488468
28 -11.7996597
28.1 -8.2030122
28.2 -26.4317815
28.3 -18.5016071
29 -5.8551395
29.1 -2.0209442
29.2 -5.6368080
29.3 -3.8110961
30 -12.7217702
30.1 -17.0170140
30.2 -25.4236089
31 -17.0783921
32 -18.4338764
32.1 -19.4317212
32.2 -19.4738978
32.3 -21.4922645
33 2.0838099
33.1 -13.3172274
34 -10.0296691
34.1 -25.9426553
34.2 -18.5688138
34.3 -15.4173859
35 -14.3958113
35.1 -12.9457541
35.2 -16.1380691
36 -12.8166968
36.1 -14.3989481
36.2 -12.2436943
36.3 -15.0104638
36.4 -10.1775457
37 -15.2223495
37.1 -14.7526195
37.2 -19.8168430
38 -2.7065118
39 -8.7288138
39.1 -9.2746473
39.2 -18.2695344
39.3 -13.8219083
39.4 -16.2254704
39.5 -21.7283648
40 1.8291916
40.1 -6.6916432
40.2 -1.6278171
40.3 -10.5749790
41 -3.1556121
41.1 -11.5895327
41.2 -18.9352091
41.3 -15.9788960
41.4 -9.6070508
42 -5.2159485
42.1 -15.9878743
43 -16.6104361
43.1 -9.5549441
43.2 -14.2003491
44 -8.1969033
44.1 -19.9270197
44.2 -22.6521171
44.3 -21.1903736
45 -0.5686627
45.1 -7.5645740
46 -19.1624789
46.1 -18.4487574
46.2 -15.8222682
47 -5.4165074
47.1 -15.0975029
47.2 -12.9971413
47.3 -10.6844521
47.4 -18.2214784
48 -8.3101471
48.1 -18.3854275
49 -13.0130319
50 -10.4579977
51 -19.3157621
52 -4.4747188
52.1 -4.3163827
52.2 -6.9761408
52.3 -20.1764756
52.4 -8.9036692
52.5 -5.6949642
53 -10.3141887
53.1 -8.2642654
53.2 -9.1691554
54 -6.2198754
54.1 -15.7192609
54.2 -13.0978998
54.3 -5.1195299
54.4 -16.5771751
55 -5.7348534
55.1 -7.3217494
55.2 -12.2171938
55.3 -12.9821266
55.4 -14.8599983
56 -14.1764282
56.1 -12.5343602
56.2 -8.4573382
56.3 -12.4633969
56.4 -17.3841863
56.5 -14.8147645
57 -3.1403293
57.1 -11.1509248
57.2 -6.3940143
57.3 -9.3473241
58 -12.0245677
58.1 -9.2112246
58.2 -1.2071742
58.3 -11.0141711
58.4 -5.3721214
58.5 -7.8523047
59 -13.2946560
59.1 -10.0530648
60 -19.2209402
61 -4.6699914
61.1 -3.5981894
61.2 -1.4713611
61.3 -3.8819786
61.4 0.1041413
62 -2.8591600
62.1 -6.9461986
62.2 -16.7910593
62.3 -17.9844596
63 -24.0335535
63.1 -11.7765300
64 -20.5963897
65 -2.7969169
65.1 -11.1778694
65.2 -5.2830399
65.3 -7.9353390
66 -13.2318328
66.1 -1.9090560
66.2 -16.6643889
67 -25.6073277
68 -13.4806759
68.1 -18.4557183
68.2 -13.3982327
68.3 -12.4977127
68.4 -11.7073990
69 -14.5290675
70 -15.2122709
70.1 -7.8681167
71 -10.3352703
71.1 -7.5699888
71.2 -18.4680702
71.3 -21.4316644
71.4 -8.1137650
72 -9.1848162
72.1 -23.7538846
72.2 -26.3421306
72.3 -27.2843801
72.4 -20.8541617
72.5 -12.8948965
73 -2.6091307
74 -8.2790175
75 -12.5029612
76 -6.0061671
76.1 -8.8149114
76.2 -11.8359043
77 0.4772521
78 -9.4105229
79 -1.0217265
79.1 -11.8125257
79.2 -10.5465186
80 -12.7366807
80.1 -9.0584783
80.2 -16.6381566
81 0.5547913
81.1 -4.0892715
81.2 1.8283303
81.3 -5.2166381
82 -3.0749381
82.1 -10.5506696
82.2 -18.2226347
83 -12.5872635
83.1 -11.9756502
83.2 -10.6744217
83.3 -19.2714012
84 -2.6320312
84.1 -9.8140094
85 -12.3886736
85.1 -12.9196365
85.2 -9.6433248
85.3 -6.3296340
85.4 -7.0405525
85.5 -13.6714939
86 -10.8756412
86.1 -12.0055331
86.2 -13.3724699
86.3 -13.3252145
86.4 -14.9191290
86.5 -17.7515546
87 -10.7027963
87.1 -22.4941954
87.2 -14.9616716
88 -2.2264493
88.1 -8.9626474
88.2 -2.5095281
88.3 -16.3345673
89 -11.0459647
90 -4.5610239
90.1 -11.7036651
90.2 -5.3838521
90.3 -4.1636999
91 -7.1462503
91.1 -12.8374475
91.2 -18.2576707
92 -6.4119222
93 5.2122168
93.1 3.1211725
93.2 -3.6841177
93.3 2.6223542
93.4 -11.1877696
94 -6.9602492
94.1 -7.4318416
94.2 -4.3498045
94.3 -11.6340088
94.4 -12.9357964
94.5 -14.7648530
95 -12.8849309
95.1 -9.7451502
95.2 -0.8535063
96 -4.9139832
96.1 -3.9582653
96.2 -9.6555492
96.3 -11.8690793
96.4 -11.0224373
96.5 -10.9530403
97 -9.8540471
97.1 -19.2262840
98 -11.9651231
98.1 -2.6515128
98.2 -12.2606382
99 -11.4720500
99.1 -14.0596866
99.2 -17.3939469
100 1.1005874
100.1 -3.8226248
100.2 -0.9123182
100.3 -15.8389474
100.4 -12.8093826
$m0a2$mu_reg_norm
[1] 0
$m0a2$tau_reg_norm
[1] 1e-04
$m0a2$shape_tau_norm
[1] 0.01
$m0a2$rate_tau_norm
[1] 0.01
$m0a2$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0a2$shape_diag_RinvD
[1] "0.01"
$m0a2$rate_diag_RinvD
[1] "0.001"
$m0a3
$m0a3$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0a3$M_lvlone
y
1 -13.0493856
1.1 -9.3335901
1.2 -22.3469852
1.3 -15.0417337
2 -12.0655434
2.1 -15.8674476
2.2 -7.8800006
3 -11.4820604
3.1 -10.5983220
3.2 -22.4519157
4 -1.2697775
4.1 -11.1215184
4.2 -3.6134138
4.3 -14.5982385
5 -6.8457515
5.1 -7.0551214
5.2 -12.3418980
5.3 -9.2366906
6 -5.1648211
7 -10.0599502
7.1 -18.3267285
7.2 -12.5138426
8 -1.6305331
8.1 -9.6520453
8.2 -1.5278462
8.3 -7.4172211
8.4 -7.1238609
8.5 -8.8706950
9 -0.1634429
9.1 -2.6034300
9.2 -6.7272369
10 -6.4172202
10.1 -11.4834569
11 -8.7911356
11.1 -19.6645080
11.2 -20.2030932
11.3 -21.3082176
11.4 -14.5802901
12 -15.2006287
13 0.8058816
13.1 -13.6379208
14 -15.3422873
14.1 -10.0965208
14.2 -16.6452027
14.3 -15.8389733
15 -8.9424594
15.1 -22.0101983
15.2 -7.3975599
15.3 -10.3567334
16 -1.9691302
16.1 -9.9308357
16.2 -6.9626923
16.3 -3.2862557
16.4 -3.3972355
16.5 -11.5767835
17 -10.5474144
17.1 -7.6215009
17.2 -16.5386939
17.3 -20.0004774
17.4 -18.8505475
18 -19.7302351
19 -14.6177568
19.1 -17.8043866
19.2 -15.1641705
19.3 -16.6898418
20 -12.9059229
20.1 -16.8191201
20.2 -6.1010131
20.3 -7.9415371
20.4 -9.3904458
20.5 -13.3504189
21 -7.6974718
21.1 -11.9335526
21.2 -12.7064929
22 -21.5022909
22.1 -12.7745451
23 -3.5146508
23.1 -4.6724048
24 -2.5619821
25 -6.2944970
25.1 -3.8630505
25.2 -14.4205140
25.3 -19.6735037
25.4 -9.0288933
25.5 -9.0509738
26 -19.7340685
26.1 -14.1692728
26.2 -17.2819976
26.3 -24.6265576
27 -7.3354999
27.1 -11.1488468
28 -11.7996597
28.1 -8.2030122
28.2 -26.4317815
28.3 -18.5016071
29 -5.8551395
29.1 -2.0209442
29.2 -5.6368080
29.3 -3.8110961
30 -12.7217702
30.1 -17.0170140
30.2 -25.4236089
31 -17.0783921
32 -18.4338764
32.1 -19.4317212
32.2 -19.4738978
32.3 -21.4922645
33 2.0838099
33.1 -13.3172274
34 -10.0296691
34.1 -25.9426553
34.2 -18.5688138
34.3 -15.4173859
35 -14.3958113
35.1 -12.9457541
35.2 -16.1380691
36 -12.8166968
36.1 -14.3989481
36.2 -12.2436943
36.3 -15.0104638
36.4 -10.1775457
37 -15.2223495
37.1 -14.7526195
37.2 -19.8168430
38 -2.7065118
39 -8.7288138
39.1 -9.2746473
39.2 -18.2695344
39.3 -13.8219083
39.4 -16.2254704
39.5 -21.7283648
40 1.8291916
40.1 -6.6916432
40.2 -1.6278171
40.3 -10.5749790
41 -3.1556121
41.1 -11.5895327
41.2 -18.9352091
41.3 -15.9788960
41.4 -9.6070508
42 -5.2159485
42.1 -15.9878743
43 -16.6104361
43.1 -9.5549441
43.2 -14.2003491
44 -8.1969033
44.1 -19.9270197
44.2 -22.6521171
44.3 -21.1903736
45 -0.5686627
45.1 -7.5645740
46 -19.1624789
46.1 -18.4487574
46.2 -15.8222682
47 -5.4165074
47.1 -15.0975029
47.2 -12.9971413
47.3 -10.6844521
47.4 -18.2214784
48 -8.3101471
48.1 -18.3854275
49 -13.0130319
50 -10.4579977
51 -19.3157621
52 -4.4747188
52.1 -4.3163827
52.2 -6.9761408
52.3 -20.1764756
52.4 -8.9036692
52.5 -5.6949642
53 -10.3141887
53.1 -8.2642654
53.2 -9.1691554
54 -6.2198754
54.1 -15.7192609
54.2 -13.0978998
54.3 -5.1195299
54.4 -16.5771751
55 -5.7348534
55.1 -7.3217494
55.2 -12.2171938
55.3 -12.9821266
55.4 -14.8599983
56 -14.1764282
56.1 -12.5343602
56.2 -8.4573382
56.3 -12.4633969
56.4 -17.3841863
56.5 -14.8147645
57 -3.1403293
57.1 -11.1509248
57.2 -6.3940143
57.3 -9.3473241
58 -12.0245677
58.1 -9.2112246
58.2 -1.2071742
58.3 -11.0141711
58.4 -5.3721214
58.5 -7.8523047
59 -13.2946560
59.1 -10.0530648
60 -19.2209402
61 -4.6699914
61.1 -3.5981894
61.2 -1.4713611
61.3 -3.8819786
61.4 0.1041413
62 -2.8591600
62.1 -6.9461986
62.2 -16.7910593
62.3 -17.9844596
63 -24.0335535
63.1 -11.7765300
64 -20.5963897
65 -2.7969169
65.1 -11.1778694
65.2 -5.2830399
65.3 -7.9353390
66 -13.2318328
66.1 -1.9090560
66.2 -16.6643889
67 -25.6073277
68 -13.4806759
68.1 -18.4557183
68.2 -13.3982327
68.3 -12.4977127
68.4 -11.7073990
69 -14.5290675
70 -15.2122709
70.1 -7.8681167
71 -10.3352703
71.1 -7.5699888
71.2 -18.4680702
71.3 -21.4316644
71.4 -8.1137650
72 -9.1848162
72.1 -23.7538846
72.2 -26.3421306
72.3 -27.2843801
72.4 -20.8541617
72.5 -12.8948965
73 -2.6091307
74 -8.2790175
75 -12.5029612
76 -6.0061671
76.1 -8.8149114
76.2 -11.8359043
77 0.4772521
78 -9.4105229
79 -1.0217265
79.1 -11.8125257
79.2 -10.5465186
80 -12.7366807
80.1 -9.0584783
80.2 -16.6381566
81 0.5547913
81.1 -4.0892715
81.2 1.8283303
81.3 -5.2166381
82 -3.0749381
82.1 -10.5506696
82.2 -18.2226347
83 -12.5872635
83.1 -11.9756502
83.2 -10.6744217
83.3 -19.2714012
84 -2.6320312
84.1 -9.8140094
85 -12.3886736
85.1 -12.9196365
85.2 -9.6433248
85.3 -6.3296340
85.4 -7.0405525
85.5 -13.6714939
86 -10.8756412
86.1 -12.0055331
86.2 -13.3724699
86.3 -13.3252145
86.4 -14.9191290
86.5 -17.7515546
87 -10.7027963
87.1 -22.4941954
87.2 -14.9616716
88 -2.2264493
88.1 -8.9626474
88.2 -2.5095281
88.3 -16.3345673
89 -11.0459647
90 -4.5610239
90.1 -11.7036651
90.2 -5.3838521
90.3 -4.1636999
91 -7.1462503
91.1 -12.8374475
91.2 -18.2576707
92 -6.4119222
93 5.2122168
93.1 3.1211725
93.2 -3.6841177
93.3 2.6223542
93.4 -11.1877696
94 -6.9602492
94.1 -7.4318416
94.2 -4.3498045
94.3 -11.6340088
94.4 -12.9357964
94.5 -14.7648530
95 -12.8849309
95.1 -9.7451502
95.2 -0.8535063
96 -4.9139832
96.1 -3.9582653
96.2 -9.6555492
96.3 -11.8690793
96.4 -11.0224373
96.5 -10.9530403
97 -9.8540471
97.1 -19.2262840
98 -11.9651231
98.1 -2.6515128
98.2 -12.2606382
99 -11.4720500
99.1 -14.0596866
99.2 -17.3939469
100 1.1005874
100.1 -3.8226248
100.2 -0.9123182
100.3 -15.8389474
100.4 -12.8093826
$m0a3$mu_reg_norm
[1] 0
$m0a3$tau_reg_norm
[1] 1e-04
$m0a3$shape_tau_norm
[1] 0.01
$m0a3$rate_tau_norm
[1] 0.01
$m0a3$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0a3$shape_diag_RinvD
[1] "0.01"
$m0a3$rate_diag_RinvD
[1] "0.001"
$m0a4
$m0a4$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0a4$M_lvlone
y
1 -13.0493856
1.1 -9.3335901
1.2 -22.3469852
1.3 -15.0417337
2 -12.0655434
2.1 -15.8674476
2.2 -7.8800006
3 -11.4820604
3.1 -10.5983220
3.2 -22.4519157
4 -1.2697775
4.1 -11.1215184
4.2 -3.6134138
4.3 -14.5982385
5 -6.8457515
5.1 -7.0551214
5.2 -12.3418980
5.3 -9.2366906
6 -5.1648211
7 -10.0599502
7.1 -18.3267285
7.2 -12.5138426
8 -1.6305331
8.1 -9.6520453
8.2 -1.5278462
8.3 -7.4172211
8.4 -7.1238609
8.5 -8.8706950
9 -0.1634429
9.1 -2.6034300
9.2 -6.7272369
10 -6.4172202
10.1 -11.4834569
11 -8.7911356
11.1 -19.6645080
11.2 -20.2030932
11.3 -21.3082176
11.4 -14.5802901
12 -15.2006287
13 0.8058816
13.1 -13.6379208
14 -15.3422873
14.1 -10.0965208
14.2 -16.6452027
14.3 -15.8389733
15 -8.9424594
15.1 -22.0101983
15.2 -7.3975599
15.3 -10.3567334
16 -1.9691302
16.1 -9.9308357
16.2 -6.9626923
16.3 -3.2862557
16.4 -3.3972355
16.5 -11.5767835
17 -10.5474144
17.1 -7.6215009
17.2 -16.5386939
17.3 -20.0004774
17.4 -18.8505475
18 -19.7302351
19 -14.6177568
19.1 -17.8043866
19.2 -15.1641705
19.3 -16.6898418
20 -12.9059229
20.1 -16.8191201
20.2 -6.1010131
20.3 -7.9415371
20.4 -9.3904458
20.5 -13.3504189
21 -7.6974718
21.1 -11.9335526
21.2 -12.7064929
22 -21.5022909
22.1 -12.7745451
23 -3.5146508
23.1 -4.6724048
24 -2.5619821
25 -6.2944970
25.1 -3.8630505
25.2 -14.4205140
25.3 -19.6735037
25.4 -9.0288933
25.5 -9.0509738
26 -19.7340685
26.1 -14.1692728
26.2 -17.2819976
26.3 -24.6265576
27 -7.3354999
27.1 -11.1488468
28 -11.7996597
28.1 -8.2030122
28.2 -26.4317815
28.3 -18.5016071
29 -5.8551395
29.1 -2.0209442
29.2 -5.6368080
29.3 -3.8110961
30 -12.7217702
30.1 -17.0170140
30.2 -25.4236089
31 -17.0783921
32 -18.4338764
32.1 -19.4317212
32.2 -19.4738978
32.3 -21.4922645
33 2.0838099
33.1 -13.3172274
34 -10.0296691
34.1 -25.9426553
34.2 -18.5688138
34.3 -15.4173859
35 -14.3958113
35.1 -12.9457541
35.2 -16.1380691
36 -12.8166968
36.1 -14.3989481
36.2 -12.2436943
36.3 -15.0104638
36.4 -10.1775457
37 -15.2223495
37.1 -14.7526195
37.2 -19.8168430
38 -2.7065118
39 -8.7288138
39.1 -9.2746473
39.2 -18.2695344
39.3 -13.8219083
39.4 -16.2254704
39.5 -21.7283648
40 1.8291916
40.1 -6.6916432
40.2 -1.6278171
40.3 -10.5749790
41 -3.1556121
41.1 -11.5895327
41.2 -18.9352091
41.3 -15.9788960
41.4 -9.6070508
42 -5.2159485
42.1 -15.9878743
43 -16.6104361
43.1 -9.5549441
43.2 -14.2003491
44 -8.1969033
44.1 -19.9270197
44.2 -22.6521171
44.3 -21.1903736
45 -0.5686627
45.1 -7.5645740
46 -19.1624789
46.1 -18.4487574
46.2 -15.8222682
47 -5.4165074
47.1 -15.0975029
47.2 -12.9971413
47.3 -10.6844521
47.4 -18.2214784
48 -8.3101471
48.1 -18.3854275
49 -13.0130319
50 -10.4579977
51 -19.3157621
52 -4.4747188
52.1 -4.3163827
52.2 -6.9761408
52.3 -20.1764756
52.4 -8.9036692
52.5 -5.6949642
53 -10.3141887
53.1 -8.2642654
53.2 -9.1691554
54 -6.2198754
54.1 -15.7192609
54.2 -13.0978998
54.3 -5.1195299
54.4 -16.5771751
55 -5.7348534
55.1 -7.3217494
55.2 -12.2171938
55.3 -12.9821266
55.4 -14.8599983
56 -14.1764282
56.1 -12.5343602
56.2 -8.4573382
56.3 -12.4633969
56.4 -17.3841863
56.5 -14.8147645
57 -3.1403293
57.1 -11.1509248
57.2 -6.3940143
57.3 -9.3473241
58 -12.0245677
58.1 -9.2112246
58.2 -1.2071742
58.3 -11.0141711
58.4 -5.3721214
58.5 -7.8523047
59 -13.2946560
59.1 -10.0530648
60 -19.2209402
61 -4.6699914
61.1 -3.5981894
61.2 -1.4713611
61.3 -3.8819786
61.4 0.1041413
62 -2.8591600
62.1 -6.9461986
62.2 -16.7910593
62.3 -17.9844596
63 -24.0335535
63.1 -11.7765300
64 -20.5963897
65 -2.7969169
65.1 -11.1778694
65.2 -5.2830399
65.3 -7.9353390
66 -13.2318328
66.1 -1.9090560
66.2 -16.6643889
67 -25.6073277
68 -13.4806759
68.1 -18.4557183
68.2 -13.3982327
68.3 -12.4977127
68.4 -11.7073990
69 -14.5290675
70 -15.2122709
70.1 -7.8681167
71 -10.3352703
71.1 -7.5699888
71.2 -18.4680702
71.3 -21.4316644
71.4 -8.1137650
72 -9.1848162
72.1 -23.7538846
72.2 -26.3421306
72.3 -27.2843801
72.4 -20.8541617
72.5 -12.8948965
73 -2.6091307
74 -8.2790175
75 -12.5029612
76 -6.0061671
76.1 -8.8149114
76.2 -11.8359043
77 0.4772521
78 -9.4105229
79 -1.0217265
79.1 -11.8125257
79.2 -10.5465186
80 -12.7366807
80.1 -9.0584783
80.2 -16.6381566
81 0.5547913
81.1 -4.0892715
81.2 1.8283303
81.3 -5.2166381
82 -3.0749381
82.1 -10.5506696
82.2 -18.2226347
83 -12.5872635
83.1 -11.9756502
83.2 -10.6744217
83.3 -19.2714012
84 -2.6320312
84.1 -9.8140094
85 -12.3886736
85.1 -12.9196365
85.2 -9.6433248
85.3 -6.3296340
85.4 -7.0405525
85.5 -13.6714939
86 -10.8756412
86.1 -12.0055331
86.2 -13.3724699
86.3 -13.3252145
86.4 -14.9191290
86.5 -17.7515546
87 -10.7027963
87.1 -22.4941954
87.2 -14.9616716
88 -2.2264493
88.1 -8.9626474
88.2 -2.5095281
88.3 -16.3345673
89 -11.0459647
90 -4.5610239
90.1 -11.7036651
90.2 -5.3838521
90.3 -4.1636999
91 -7.1462503
91.1 -12.8374475
91.2 -18.2576707
92 -6.4119222
93 5.2122168
93.1 3.1211725
93.2 -3.6841177
93.3 2.6223542
93.4 -11.1877696
94 -6.9602492
94.1 -7.4318416
94.2 -4.3498045
94.3 -11.6340088
94.4 -12.9357964
94.5 -14.7648530
95 -12.8849309
95.1 -9.7451502
95.2 -0.8535063
96 -4.9139832
96.1 -3.9582653
96.2 -9.6555492
96.3 -11.8690793
96.4 -11.0224373
96.5 -10.9530403
97 -9.8540471
97.1 -19.2262840
98 -11.9651231
98.1 -2.6515128
98.2 -12.2606382
99 -11.4720500
99.1 -14.0596866
99.2 -17.3939469
100 1.1005874
100.1 -3.8226248
100.2 -0.9123182
100.3 -15.8389474
100.4 -12.8093826
$m0a4$mu_reg_norm
[1] 0
$m0a4$tau_reg_norm
[1] 1e-04
$m0a4$shape_tau_norm
[1] 0.01
$m0a4$rate_tau_norm
[1] 0.01
$m0a4$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0a4$shape_diag_RinvD
[1] "0.01"
$m0a4$rate_diag_RinvD
[1] "0.001"
$m0b1
$m0b1$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0b1$M_lvlone
b1
1 0
1.1 1
1.2 1
1.3 0
2 1
2.1 1
2.2 1
3 1
3.1 0
3.2 0
4 1
4.1 1
4.2 0
4.3 1
5 0
5.1 1
5.2 1
5.3 1
6 0
7 1
7.1 0
7.2 1
8 0
8.1 1
8.2 1
8.3 0
8.4 0
8.5 1
9 1
9.1 1
9.2 0
10 1
10.1 1
11 1
11.1 1
11.2 1
11.3 1
11.4 1
12 1
13 0
13.1 1
14 0
14.1 1
14.2 0
14.3 0
15 0
15.1 0
15.2 0
15.3 1
16 1
16.1 0
16.2 1
16.3 1
16.4 1
16.5 0
17 0
17.1 0
17.2 1
17.3 0
17.4 1
18 1
19 1
19.1 1
19.2 1
19.3 1
20 0
20.1 1
20.2 0
20.3 0
20.4 0
20.5 0
21 1
21.1 1
21.2 0
22 0
22.1 1
23 1
23.1 1
24 0
25 0
25.1 1
25.2 1
25.3 0
25.4 0
25.5 0
26 1
26.1 1
26.2 1
26.3 0
27 1
27.1 1
28 1
28.1 0
28.2 1
28.3 1
29 1
29.1 0
29.2 0
29.3 1
30 1
30.1 1
30.2 1
31 0
32 1
32.1 1
32.2 1
32.3 1
33 0
33.1 0
34 1
34.1 0
34.2 1
34.3 1
35 1
35.1 0
35.2 1
36 0
36.1 0
36.2 1
36.3 0
36.4 1
37 1
37.1 0
37.2 0
38 1
39 1
39.1 0
39.2 0
39.3 0
39.4 1
39.5 1
40 0
40.1 0
40.2 0
40.3 1
41 1
41.1 1
41.2 0
41.3 1
41.4 1
42 1
42.1 1
43 0
43.1 0
43.2 1
44 1
44.1 0
44.2 0
44.3 1
45 1
45.1 0
46 1
46.1 0
46.2 1
47 0
47.1 0
47.2 1
47.3 0
47.4 0
48 0
48.1 1
49 0
50 1
51 1
52 1
52.1 1
52.2 0
52.3 0
52.4 1
52.5 1
53 1
53.1 1
53.2 1
54 0
54.1 1
54.2 0
54.3 1
54.4 0
55 1
55.1 1
55.2 1
55.3 0
55.4 1
56 0
56.1 1
56.2 1
56.3 0
56.4 0
56.5 1
57 1
57.1 1
57.2 0
57.3 0
58 1
58.1 1
58.2 1
58.3 1
58.4 1
58.5 1
59 0
59.1 1
60 0
61 1
61.1 1
61.2 1
61.3 0
61.4 1
62 1
62.1 0
62.2 0
62.3 1
63 0
63.1 1
64 1
65 1
65.1 1
65.2 0
65.3 0
66 1
66.1 0
66.2 0
67 0
68 0
68.1 0
68.2 0
68.3 0
68.4 1
69 1
70 1
70.1 1
71 1
71.1 1
71.2 0
71.3 0
71.4 0
72 1
72.1 1
72.2 1
72.3 0
72.4 0
72.5 1
73 1
74 1
75 0
76 1
76.1 1
76.2 1
77 1
78 1
79 0
79.1 1
79.2 0
80 1
80.1 0
80.2 1
81 1
81.1 1
81.2 1
81.3 1
82 1
82.1 1
82.2 0
83 1
83.1 0
83.2 0
83.3 1
84 1
84.1 0
85 0
85.1 0
85.2 1
85.3 1
85.4 1
85.5 1
86 0
86.1 1
86.2 1
86.3 0
86.4 1
86.5 0
87 0
87.1 1
87.2 0
88 0
88.1 0
88.2 0
88.3 0
89 1
90 0
90.1 1
90.2 1
90.3 0
91 0
91.1 0
91.2 1
92 1
93 0
93.1 1
93.2 0
93.3 1
93.4 0
94 1
94.1 0
94.2 1
94.3 0
94.4 0
94.5 0
95 1
95.1 1
95.2 0
96 1
96.1 0
96.2 0
96.3 0
96.4 0
96.5 1
97 0
97.1 0
98 0
98.1 0
98.2 0
99 1
99.1 1
99.2 1
100 0
100.1 0
100.2 1
100.3 1
100.4 1
$m0b1$mu_reg_binom
[1] 0
$m0b1$tau_reg_binom
[1] 1e-04
$m0b1$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0b1$shape_diag_RinvD
[1] "0.01"
$m0b1$rate_diag_RinvD
[1] "0.001"
$m0b2
$m0b2$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0b2$M_lvlone
b1
1 0
1.1 1
1.2 1
1.3 0
2 1
2.1 1
2.2 1
3 1
3.1 0
3.2 0
4 1
4.1 1
4.2 0
4.3 1
5 0
5.1 1
5.2 1
5.3 1
6 0
7 1
7.1 0
7.2 1
8 0
8.1 1
8.2 1
8.3 0
8.4 0
8.5 1
9 1
9.1 1
9.2 0
10 1
10.1 1
11 1
11.1 1
11.2 1
11.3 1
11.4 1
12 1
13 0
13.1 1
14 0
14.1 1
14.2 0
14.3 0
15 0
15.1 0
15.2 0
15.3 1
16 1
16.1 0
16.2 1
16.3 1
16.4 1
16.5 0
17 0
17.1 0
17.2 1
17.3 0
17.4 1
18 1
19 1
19.1 1
19.2 1
19.3 1
20 0
20.1 1
20.2 0
20.3 0
20.4 0
20.5 0
21 1
21.1 1
21.2 0
22 0
22.1 1
23 1
23.1 1
24 0
25 0
25.1 1
25.2 1
25.3 0
25.4 0
25.5 0
26 1
26.1 1
26.2 1
26.3 0
27 1
27.1 1
28 1
28.1 0
28.2 1
28.3 1
29 1
29.1 0
29.2 0
29.3 1
30 1
30.1 1
30.2 1
31 0
32 1
32.1 1
32.2 1
32.3 1
33 0
33.1 0
34 1
34.1 0
34.2 1
34.3 1
35 1
35.1 0
35.2 1
36 0
36.1 0
36.2 1
36.3 0
36.4 1
37 1
37.1 0
37.2 0
38 1
39 1
39.1 0
39.2 0
39.3 0
39.4 1
39.5 1
40 0
40.1 0
40.2 0
40.3 1
41 1
41.1 1
41.2 0
41.3 1
41.4 1
42 1
42.1 1
43 0
43.1 0
43.2 1
44 1
44.1 0
44.2 0
44.3 1
45 1
45.1 0
46 1
46.1 0
46.2 1
47 0
47.1 0
47.2 1
47.3 0
47.4 0
48 0
48.1 1
49 0
50 1
51 1
52 1
52.1 1
52.2 0
52.3 0
52.4 1
52.5 1
53 1
53.1 1
53.2 1
54 0
54.1 1
54.2 0
54.3 1
54.4 0
55 1
55.1 1
55.2 1
55.3 0
55.4 1
56 0
56.1 1
56.2 1
56.3 0
56.4 0
56.5 1
57 1
57.1 1
57.2 0
57.3 0
58 1
58.1 1
58.2 1
58.3 1
58.4 1
58.5 1
59 0
59.1 1
60 0
61 1
61.1 1
61.2 1
61.3 0
61.4 1
62 1
62.1 0
62.2 0
62.3 1
63 0
63.1 1
64 1
65 1
65.1 1
65.2 0
65.3 0
66 1
66.1 0
66.2 0
67 0
68 0
68.1 0
68.2 0
68.3 0
68.4 1
69 1
70 1
70.1 1
71 1
71.1 1
71.2 0
71.3 0
71.4 0
72 1
72.1 1
72.2 1
72.3 0
72.4 0
72.5 1
73 1
74 1
75 0
76 1
76.1 1
76.2 1
77 1
78 1
79 0
79.1 1
79.2 0
80 1
80.1 0
80.2 1
81 1
81.1 1
81.2 1
81.3 1
82 1
82.1 1
82.2 0
83 1
83.1 0
83.2 0
83.3 1
84 1
84.1 0
85 0
85.1 0
85.2 1
85.3 1
85.4 1
85.5 1
86 0
86.1 1
86.2 1
86.3 0
86.4 1
86.5 0
87 0
87.1 1
87.2 0
88 0
88.1 0
88.2 0
88.3 0
89 1
90 0
90.1 1
90.2 1
90.3 0
91 0
91.1 0
91.2 1
92 1
93 0
93.1 1
93.2 0
93.3 1
93.4 0
94 1
94.1 0
94.2 1
94.3 0
94.4 0
94.5 0
95 1
95.1 1
95.2 0
96 1
96.1 0
96.2 0
96.3 0
96.4 0
96.5 1
97 0
97.1 0
98 0
98.1 0
98.2 0
99 1
99.1 1
99.2 1
100 0
100.1 0
100.2 1
100.3 1
100.4 1
$m0b2$mu_reg_binom
[1] 0
$m0b2$tau_reg_binom
[1] 1e-04
$m0b2$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0b2$shape_diag_RinvD
[1] "0.01"
$m0b2$rate_diag_RinvD
[1] "0.001"
$m0b3
$m0b3$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0b3$M_lvlone
b1
1 0
1.1 1
1.2 1
1.3 0
2 1
2.1 1
2.2 1
3 1
3.1 0
3.2 0
4 1
4.1 1
4.2 0
4.3 1
5 0
5.1 1
5.2 1
5.3 1
6 0
7 1
7.1 0
7.2 1
8 0
8.1 1
8.2 1
8.3 0
8.4 0
8.5 1
9 1
9.1 1
9.2 0
10 1
10.1 1
11 1
11.1 1
11.2 1
11.3 1
11.4 1
12 1
13 0
13.1 1
14 0
14.1 1
14.2 0
14.3 0
15 0
15.1 0
15.2 0
15.3 1
16 1
16.1 0
16.2 1
16.3 1
16.4 1
16.5 0
17 0
17.1 0
17.2 1
17.3 0
17.4 1
18 1
19 1
19.1 1
19.2 1
19.3 1
20 0
20.1 1
20.2 0
20.3 0
20.4 0
20.5 0
21 1
21.1 1
21.2 0
22 0
22.1 1
23 1
23.1 1
24 0
25 0
25.1 1
25.2 1
25.3 0
25.4 0
25.5 0
26 1
26.1 1
26.2 1
26.3 0
27 1
27.1 1
28 1
28.1 0
28.2 1
28.3 1
29 1
29.1 0
29.2 0
29.3 1
30 1
30.1 1
30.2 1
31 0
32 1
32.1 1
32.2 1
32.3 1
33 0
33.1 0
34 1
34.1 0
34.2 1
34.3 1
35 1
35.1 0
35.2 1
36 0
36.1 0
36.2 1
36.3 0
36.4 1
37 1
37.1 0
37.2 0
38 1
39 1
39.1 0
39.2 0
39.3 0
39.4 1
39.5 1
40 0
40.1 0
40.2 0
40.3 1
41 1
41.1 1
41.2 0
41.3 1
41.4 1
42 1
42.1 1
43 0
43.1 0
43.2 1
44 1
44.1 0
44.2 0
44.3 1
45 1
45.1 0
46 1
46.1 0
46.2 1
47 0
47.1 0
47.2 1
47.3 0
47.4 0
48 0
48.1 1
49 0
50 1
51 1
52 1
52.1 1
52.2 0
52.3 0
52.4 1
52.5 1
53 1
53.1 1
53.2 1
54 0
54.1 1
54.2 0
54.3 1
54.4 0
55 1
55.1 1
55.2 1
55.3 0
55.4 1
56 0
56.1 1
56.2 1
56.3 0
56.4 0
56.5 1
57 1
57.1 1
57.2 0
57.3 0
58 1
58.1 1
58.2 1
58.3 1
58.4 1
58.5 1
59 0
59.1 1
60 0
61 1
61.1 1
61.2 1
61.3 0
61.4 1
62 1
62.1 0
62.2 0
62.3 1
63 0
63.1 1
64 1
65 1
65.1 1
65.2 0
65.3 0
66 1
66.1 0
66.2 0
67 0
68 0
68.1 0
68.2 0
68.3 0
68.4 1
69 1
70 1
70.1 1
71 1
71.1 1
71.2 0
71.3 0
71.4 0
72 1
72.1 1
72.2 1
72.3 0
72.4 0
72.5 1
73 1
74 1
75 0
76 1
76.1 1
76.2 1
77 1
78 1
79 0
79.1 1
79.2 0
80 1
80.1 0
80.2 1
81 1
81.1 1
81.2 1
81.3 1
82 1
82.1 1
82.2 0
83 1
83.1 0
83.2 0
83.3 1
84 1
84.1 0
85 0
85.1 0
85.2 1
85.3 1
85.4 1
85.5 1
86 0
86.1 1
86.2 1
86.3 0
86.4 1
86.5 0
87 0
87.1 1
87.2 0
88 0
88.1 0
88.2 0
88.3 0
89 1
90 0
90.1 1
90.2 1
90.3 0
91 0
91.1 0
91.2 1
92 1
93 0
93.1 1
93.2 0
93.3 1
93.4 0
94 1
94.1 0
94.2 1
94.3 0
94.4 0
94.5 0
95 1
95.1 1
95.2 0
96 1
96.1 0
96.2 0
96.3 0
96.4 0
96.5 1
97 0
97.1 0
98 0
98.1 0
98.2 0
99 1
99.1 1
99.2 1
100 0
100.1 0
100.2 1
100.3 1
100.4 1
$m0b3$mu_reg_binom
[1] 0
$m0b3$tau_reg_binom
[1] 1e-04
$m0b3$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0b3$shape_diag_RinvD
[1] "0.01"
$m0b3$rate_diag_RinvD
[1] "0.001"
$m0b4
$m0b4$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0b4$M_lvlone
b1
1 0
1.1 1
1.2 1
1.3 0
2 1
2.1 1
2.2 1
3 1
3.1 0
3.2 0
4 1
4.1 1
4.2 0
4.3 1
5 0
5.1 1
5.2 1
5.3 1
6 0
7 1
7.1 0
7.2 1
8 0
8.1 1
8.2 1
8.3 0
8.4 0
8.5 1
9 1
9.1 1
9.2 0
10 1
10.1 1
11 1
11.1 1
11.2 1
11.3 1
11.4 1
12 1
13 0
13.1 1
14 0
14.1 1
14.2 0
14.3 0
15 0
15.1 0
15.2 0
15.3 1
16 1
16.1 0
16.2 1
16.3 1
16.4 1
16.5 0
17 0
17.1 0
17.2 1
17.3 0
17.4 1
18 1
19 1
19.1 1
19.2 1
19.3 1
20 0
20.1 1
20.2 0
20.3 0
20.4 0
20.5 0
21 1
21.1 1
21.2 0
22 0
22.1 1
23 1
23.1 1
24 0
25 0
25.1 1
25.2 1
25.3 0
25.4 0
25.5 0
26 1
26.1 1
26.2 1
26.3 0
27 1
27.1 1
28 1
28.1 0
28.2 1
28.3 1
29 1
29.1 0
29.2 0
29.3 1
30 1
30.1 1
30.2 1
31 0
32 1
32.1 1
32.2 1
32.3 1
33 0
33.1 0
34 1
34.1 0
34.2 1
34.3 1
35 1
35.1 0
35.2 1
36 0
36.1 0
36.2 1
36.3 0
36.4 1
37 1
37.1 0
37.2 0
38 1
39 1
39.1 0
39.2 0
39.3 0
39.4 1
39.5 1
40 0
40.1 0
40.2 0
40.3 1
41 1
41.1 1
41.2 0
41.3 1
41.4 1
42 1
42.1 1
43 0
43.1 0
43.2 1
44 1
44.1 0
44.2 0
44.3 1
45 1
45.1 0
46 1
46.1 0
46.2 1
47 0
47.1 0
47.2 1
47.3 0
47.4 0
48 0
48.1 1
49 0
50 1
51 1
52 1
52.1 1
52.2 0
52.3 0
52.4 1
52.5 1
53 1
53.1 1
53.2 1
54 0
54.1 1
54.2 0
54.3 1
54.4 0
55 1
55.1 1
55.2 1
55.3 0
55.4 1
56 0
56.1 1
56.2 1
56.3 0
56.4 0
56.5 1
57 1
57.1 1
57.2 0
57.3 0
58 1
58.1 1
58.2 1
58.3 1
58.4 1
58.5 1
59 0
59.1 1
60 0
61 1
61.1 1
61.2 1
61.3 0
61.4 1
62 1
62.1 0
62.2 0
62.3 1
63 0
63.1 1
64 1
65 1
65.1 1
65.2 0
65.3 0
66 1
66.1 0
66.2 0
67 0
68 0
68.1 0
68.2 0
68.3 0
68.4 1
69 1
70 1
70.1 1
71 1
71.1 1
71.2 0
71.3 0
71.4 0
72 1
72.1 1
72.2 1
72.3 0
72.4 0
72.5 1
73 1
74 1
75 0
76 1
76.1 1
76.2 1
77 1
78 1
79 0
79.1 1
79.2 0
80 1
80.1 0
80.2 1
81 1
81.1 1
81.2 1
81.3 1
82 1
82.1 1
82.2 0
83 1
83.1 0
83.2 0
83.3 1
84 1
84.1 0
85 0
85.1 0
85.2 1
85.3 1
85.4 1
85.5 1
86 0
86.1 1
86.2 1
86.3 0
86.4 1
86.5 0
87 0
87.1 1
87.2 0
88 0
88.1 0
88.2 0
88.3 0
89 1
90 0
90.1 1
90.2 1
90.3 0
91 0
91.1 0
91.2 1
92 1
93 0
93.1 1
93.2 0
93.3 1
93.4 0
94 1
94.1 0
94.2 1
94.3 0
94.4 0
94.5 0
95 1
95.1 1
95.2 0
96 1
96.1 0
96.2 0
96.3 0
96.4 0
96.5 1
97 0
97.1 0
98 0
98.1 0
98.2 0
99 1
99.1 1
99.2 1
100 0
100.1 0
100.2 1
100.3 1
100.4 1
$m0b4$mu_reg_binom
[1] 0
$m0b4$tau_reg_binom
[1] 1e-04
$m0b4$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0b4$shape_diag_RinvD
[1] "0.01"
$m0b4$rate_diag_RinvD
[1] "0.001"
$m0c1
$m0c1$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0c1$M_lvlone
L1
1 0.09647609
1.1 0.47743206
1.2 0.49307743
1.3 0.18468863
2 0.54595313
2.1 0.21966792
2.2 0.73654737
3 0.20862809
3.1 0.24312223
3.2 0.03051627
4 0.39499609
4.1 0.72632316
4.2 0.34199228
4.3 0.38062927
5 0.62202135
5.1 0.20305630
5.2 0.41717969
5.3 0.23980703
6 0.37653463
7 0.36356663
7.1 0.06266071
7.2 0.37849716
8 0.37802506
8.1 0.61143062
8.2 0.75648801
8.3 2.54406375
8.4 1.18637590
8.5 0.05930316
9 0.95013074
9.1 0.11917116
9.2 0.86629295
10 0.23914695
10.1 0.13708051
11 0.11067204
11.1 0.23176079
11.2 0.60038623
11.3 0.42684714
11.4 0.16458522
12 0.12861686
13 1.33377494
13.1 0.37267514
14 0.48728084
14.1 0.31792264
14.2 0.89257832
14.3 0.48509920
15 0.37711346
15.1 0.24850749
15.2 0.48117461
15.3 0.42758680
16 0.43666855
16.1 0.18190724
16.2 0.18617239
16.3 1.87047608
16.4 0.41864602
16.5 0.43588009
17 0.17925916
17.1 0.32367639
17.2 0.24912593
17.3 0.56230768
17.4 0.26182608
18 0.42338083
19 0.23371438
19.1 0.45720781
19.2 1.07923724
19.3 0.74433885
20 0.23860936
20.1 1.49001161
20.2 0.82847676
20.3 0.71062057
20.4 0.58928158
20.5 0.49204025
21 0.39710041
21.1 0.63253881
21.2 0.58877978
22 0.30440876
22.1 0.42787265
23 0.15078177
23.1 0.97104584
24 0.55355206
25 0.76006220
25.1 0.42500306
25.2 0.68011522
25.3 0.38187835
25.4 0.67265847
25.5 0.09078197
26 0.17032539
26.1 0.36699769
26.2 0.19300220
26.3 1.26993276
27 0.63999648
27.1 1.14153094
28 0.39991376
28.1 0.20658853
28.2 0.42519397
28.3 1.68848543
29 0.20853337
29.1 0.32240000
29.2 0.59527557
29.3 0.34253455
30 0.70885491
30.1 0.31107139
30.2 0.46423208
31 0.54603320
32 0.48896515
32.1 0.26838930
32.2 0.33314256
32.3 0.15482204
33 0.63379200
33.1 0.53403306
34 0.30684588
34.1 0.15596697
34.2 0.73177916
34.3 0.78232073
35 0.12725486
35.1 0.32104659
35.2 0.92993903
36 0.82634942
36.1 0.15790991
36.2 0.28319688
36.3 0.30894311
36.4 0.38835761
37 0.28006122
37.1 0.51936935
37.2 0.03553058
38 0.10984700
39 1.01908377
39.1 0.58760885
39.2 0.63292533
39.3 0.42095489
39.4 0.25220230
39.5 0.51242643
40 0.70636121
40.1 1.22834105
40.2 0.81839083
40.3 0.23540757
41 0.08592119
41.1 0.22834515
41.2 1.61636130
41.3 0.15342660
41.4 0.47650400
42 0.64398703
42.1 1.15130398
43 0.79292461
43.1 0.38506794
43.2 0.11139587
44 0.89129328
44.1 0.08958946
44.2 0.85701827
44.3 0.96417530
45 0.51097634
45.1 0.98340980
46 0.44798505
46.1 0.82655580
46.2 0.37637628
47 0.41876182
47.1 0.48389648
47.2 0.02396924
47.3 1.80138667
47.4 0.61109603
48 0.19473894
48.1 0.04006959
49 0.29560575
50 0.15625313
51 0.47908892
52 1.40786781
52.1 0.35019229
52.2 0.39332493
52.3 0.51225821
52.4 0.11419627
52.5 0.55575005
53 0.13011523
53.1 0.90571584
53.2 0.50906734
54 0.46031273
54.1 0.46156182
54.2 0.52071389
54.3 0.76983675
54.4 0.52623423
55 0.60555180
55.1 0.10776713
55.2 1.03837178
55.3 0.45001542
55.4 0.65395611
56 0.07535464
56.1 0.73328954
56.2 0.27578594
56.3 0.68719648
56.4 1.57220834
56.5 0.28753078
57 0.17289659
57.1 0.72170220
57.2 1.26500225
57.3 0.20213479
58 0.13611631
58.1 0.37311297
58.2 0.72470365
58.3 1.43014769
58.4 0.78817203
58.5 0.78387559
59 0.46747067
59.1 0.04947979
60 0.16059397
61 0.29220662
61.1 0.41535569
61.2 0.73742285
61.3 0.43320659
61.4 1.19954814
62 0.20260386
62.1 0.06652907
62.2 0.25063288
62.3 0.36290927
63 0.52314649
63.1 0.25699016
64 1.02878746
65 0.45575444
65.1 0.46306113
65.2 0.42269832
65.3 0.73172542
66 0.74765742
66.1 0.25888221
66.2 0.38244280
67 0.23644835
68 0.83195685
68.1 0.68395486
68.2 0.53889898
68.3 0.33762340
68.4 0.79922369
69 0.20260053
70 1.04535151
70.1 0.03979648
71 0.56397408
71.1 0.34854738
71.2 0.97913866
71.3 0.19630242
71.4 0.31230175
72 1.04871582
72.1 0.09370234
72.2 0.72454755
72.3 0.80705501
72.4 0.40641012
72.5 0.81634161
73 0.74327324
74 0.49202243
75 0.42954173
76 1.22280380
76.1 0.09905853
76.2 0.34132786
77 1.20980413
78 0.26184214
79 0.94287180
79.1 0.08463026
79.2 0.66769705
80 0.68766428
80.1 0.95426300
80.2 1.84421668
81 0.60279596
81.1 0.73369496
81.2 0.83514184
81.3 0.91767999
82 0.46992524
82.1 0.50002097
82.2 0.43711796
83 0.46587065
83.1 0.43364034
83.2 0.23196757
83.3 0.73616193
84 0.47791427
84.1 0.05551055
85 0.27482891
85.1 1.77694842
85.2 0.71141066
85.3 0.78806704
85.4 0.80223323
85.5 0.22172219
86 0.15018053
86.1 0.31597396
86.2 0.95686193
86.3 0.11022188
86.4 0.68477369
86.5 0.33125367
87 0.29289308
87.1 0.66197512
87.2 0.30055939
88 0.22930153
88.1 1.02206005
88.2 0.52724756
88.3 0.16276648
89 0.09190440
90 0.15333982
90.1 0.42756943
90.2 0.60354432
90.3 0.41070560
91 1.01739949
91.1 0.41121541
91.2 0.08932488
92 1.08669724
93 0.30303806
93.1 0.16800845
93.2 1.29098296
93.3 0.39962093
93.4 0.88339337
94 0.23233022
94.1 0.08638527
94.2 0.43737650
94.3 0.19800807
94.4 0.42942963
94.5 0.14150685
95 1.07323107
95.1 0.26037856
95.2 0.48623052
96 0.79796998
96.1 0.30822508
96.2 0.91060931
96.3 0.26069030
96.4 0.22889234
96.5 0.97046560
97 0.16946638
97.1 0.20265816
98 1.22465795
98.1 0.15250019
98.2 0.44675949
99 0.44238919
99.1 0.63211897
99.2 0.40140806
100 0.10484468
100.1 0.56141377
100.2 0.23655004
100.3 0.74552230
100.4 0.34230391
$m0c1$mu_reg_gamma
[1] 0
$m0c1$tau_reg_gamma
[1] 1e-04
$m0c1$shape_tau_gamma
[1] 0.01
$m0c1$rate_tau_gamma
[1] 0.01
$m0c1$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0c1$shape_diag_RinvD
[1] "0.01"
$m0c1$rate_diag_RinvD
[1] "0.001"
$m0c2
$m0c2$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0c2$M_lvlone
L1
1 0.09647609
1.1 0.47743206
1.2 0.49307743
1.3 0.18468863
2 0.54595313
2.1 0.21966792
2.2 0.73654737
3 0.20862809
3.1 0.24312223
3.2 0.03051627
4 0.39499609
4.1 0.72632316
4.2 0.34199228
4.3 0.38062927
5 0.62202135
5.1 0.20305630
5.2 0.41717969
5.3 0.23980703
6 0.37653463
7 0.36356663
7.1 0.06266071
7.2 0.37849716
8 0.37802506
8.1 0.61143062
8.2 0.75648801
8.3 2.54406375
8.4 1.18637590
8.5 0.05930316
9 0.95013074
9.1 0.11917116
9.2 0.86629295
10 0.23914695
10.1 0.13708051
11 0.11067204
11.1 0.23176079
11.2 0.60038623
11.3 0.42684714
11.4 0.16458522
12 0.12861686
13 1.33377494
13.1 0.37267514
14 0.48728084
14.1 0.31792264
14.2 0.89257832
14.3 0.48509920
15 0.37711346
15.1 0.24850749
15.2 0.48117461
15.3 0.42758680
16 0.43666855
16.1 0.18190724
16.2 0.18617239
16.3 1.87047608
16.4 0.41864602
16.5 0.43588009
17 0.17925916
17.1 0.32367639
17.2 0.24912593
17.3 0.56230768
17.4 0.26182608
18 0.42338083
19 0.23371438
19.1 0.45720781
19.2 1.07923724
19.3 0.74433885
20 0.23860936
20.1 1.49001161
20.2 0.82847676
20.3 0.71062057
20.4 0.58928158
20.5 0.49204025
21 0.39710041
21.1 0.63253881
21.2 0.58877978
22 0.30440876
22.1 0.42787265
23 0.15078177
23.1 0.97104584
24 0.55355206
25 0.76006220
25.1 0.42500306
25.2 0.68011522
25.3 0.38187835
25.4 0.67265847
25.5 0.09078197
26 0.17032539
26.1 0.36699769
26.2 0.19300220
26.3 1.26993276
27 0.63999648
27.1 1.14153094
28 0.39991376
28.1 0.20658853
28.2 0.42519397
28.3 1.68848543
29 0.20853337
29.1 0.32240000
29.2 0.59527557
29.3 0.34253455
30 0.70885491
30.1 0.31107139
30.2 0.46423208
31 0.54603320
32 0.48896515
32.1 0.26838930
32.2 0.33314256
32.3 0.15482204
33 0.63379200
33.1 0.53403306
34 0.30684588
34.1 0.15596697
34.2 0.73177916
34.3 0.78232073
35 0.12725486
35.1 0.32104659
35.2 0.92993903
36 0.82634942
36.1 0.15790991
36.2 0.28319688
36.3 0.30894311
36.4 0.38835761
37 0.28006122
37.1 0.51936935
37.2 0.03553058
38 0.10984700
39 1.01908377
39.1 0.58760885
39.2 0.63292533
39.3 0.42095489
39.4 0.25220230
39.5 0.51242643
40 0.70636121
40.1 1.22834105
40.2 0.81839083
40.3 0.23540757
41 0.08592119
41.1 0.22834515
41.2 1.61636130
41.3 0.15342660
41.4 0.47650400
42 0.64398703
42.1 1.15130398
43 0.79292461
43.1 0.38506794
43.2 0.11139587
44 0.89129328
44.1 0.08958946
44.2 0.85701827
44.3 0.96417530
45 0.51097634
45.1 0.98340980
46 0.44798505
46.1 0.82655580
46.2 0.37637628
47 0.41876182
47.1 0.48389648
47.2 0.02396924
47.3 1.80138667
47.4 0.61109603
48 0.19473894
48.1 0.04006959
49 0.29560575
50 0.15625313
51 0.47908892
52 1.40786781
52.1 0.35019229
52.2 0.39332493
52.3 0.51225821
52.4 0.11419627
52.5 0.55575005
53 0.13011523
53.1 0.90571584
53.2 0.50906734
54 0.46031273
54.1 0.46156182
54.2 0.52071389
54.3 0.76983675
54.4 0.52623423
55 0.60555180
55.1 0.10776713
55.2 1.03837178
55.3 0.45001542
55.4 0.65395611
56 0.07535464
56.1 0.73328954
56.2 0.27578594
56.3 0.68719648
56.4 1.57220834
56.5 0.28753078
57 0.17289659
57.1 0.72170220
57.2 1.26500225
57.3 0.20213479
58 0.13611631
58.1 0.37311297
58.2 0.72470365
58.3 1.43014769
58.4 0.78817203
58.5 0.78387559
59 0.46747067
59.1 0.04947979
60 0.16059397
61 0.29220662
61.1 0.41535569
61.2 0.73742285
61.3 0.43320659
61.4 1.19954814
62 0.20260386
62.1 0.06652907
62.2 0.25063288
62.3 0.36290927
63 0.52314649
63.1 0.25699016
64 1.02878746
65 0.45575444
65.1 0.46306113
65.2 0.42269832
65.3 0.73172542
66 0.74765742
66.1 0.25888221
66.2 0.38244280
67 0.23644835
68 0.83195685
68.1 0.68395486
68.2 0.53889898
68.3 0.33762340
68.4 0.79922369
69 0.20260053
70 1.04535151
70.1 0.03979648
71 0.56397408
71.1 0.34854738
71.2 0.97913866
71.3 0.19630242
71.4 0.31230175
72 1.04871582
72.1 0.09370234
72.2 0.72454755
72.3 0.80705501
72.4 0.40641012
72.5 0.81634161
73 0.74327324
74 0.49202243
75 0.42954173
76 1.22280380
76.1 0.09905853
76.2 0.34132786
77 1.20980413
78 0.26184214
79 0.94287180
79.1 0.08463026
79.2 0.66769705
80 0.68766428
80.1 0.95426300
80.2 1.84421668
81 0.60279596
81.1 0.73369496
81.2 0.83514184
81.3 0.91767999
82 0.46992524
82.1 0.50002097
82.2 0.43711796
83 0.46587065
83.1 0.43364034
83.2 0.23196757
83.3 0.73616193
84 0.47791427
84.1 0.05551055
85 0.27482891
85.1 1.77694842
85.2 0.71141066
85.3 0.78806704
85.4 0.80223323
85.5 0.22172219
86 0.15018053
86.1 0.31597396
86.2 0.95686193
86.3 0.11022188
86.4 0.68477369
86.5 0.33125367
87 0.29289308
87.1 0.66197512
87.2 0.30055939
88 0.22930153
88.1 1.02206005
88.2 0.52724756
88.3 0.16276648
89 0.09190440
90 0.15333982
90.1 0.42756943
90.2 0.60354432
90.3 0.41070560
91 1.01739949
91.1 0.41121541
91.2 0.08932488
92 1.08669724
93 0.30303806
93.1 0.16800845
93.2 1.29098296
93.3 0.39962093
93.4 0.88339337
94 0.23233022
94.1 0.08638527
94.2 0.43737650
94.3 0.19800807
94.4 0.42942963
94.5 0.14150685
95 1.07323107
95.1 0.26037856
95.2 0.48623052
96 0.79796998
96.1 0.30822508
96.2 0.91060931
96.3 0.26069030
96.4 0.22889234
96.5 0.97046560
97 0.16946638
97.1 0.20265816
98 1.22465795
98.1 0.15250019
98.2 0.44675949
99 0.44238919
99.1 0.63211897
99.2 0.40140806
100 0.10484468
100.1 0.56141377
100.2 0.23655004
100.3 0.74552230
100.4 0.34230391
$m0c2$mu_reg_gamma
[1] 0
$m0c2$tau_reg_gamma
[1] 1e-04
$m0c2$shape_tau_gamma
[1] 0.01
$m0c2$rate_tau_gamma
[1] 0.01
$m0c2$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0c2$shape_diag_RinvD
[1] "0.01"
$m0c2$rate_diag_RinvD
[1] "0.001"
$m0d1
$m0d1$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0d1$M_lvlone
p1
1 5
1.1 3
1.2 8
1.3 6
2 5
2.1 3
2.2 2
3 7
3.1 2
3.2 8
4 2
4.1 4
4.2 2
4.3 6
5 6
5.1 2
5.2 3
5.3 2
6 4
7 2
7.1 6
7.2 4
8 2
8.1 2
8.2 1
8.3 2
8.4 2
8.5 4
9 3
9.1 3
9.2 2
10 4
10.1 5
11 2
11.1 4
11.2 6
11.3 2
11.4 1
12 5
13 2
13.1 6
14 3
14.1 2
14.2 4
14.3 2
15 4
15.1 7
15.2 4
15.3 3
16 3
16.1 2
16.2 5
16.3 3
16.4 2
16.5 6
17 3
17.1 1
17.2 4
17.3 5
17.4 5
18 8
19 5
19.1 6
19.2 4
19.3 3
20 5
20.1 8
20.2 3
20.3 3
20.4 3
20.5 3
21 3
21.1 3
21.2 4
22 6
22.1 3
23 3
23.1 2
24 1
25 2
25.1 0
25.2 6
25.3 6
25.4 2
25.5 2
26 6
26.1 0
26.2 1
26.3 4
27 2
27.1 4
28 5
28.1 0
28.2 7
28.3 3
29 4
29.1 1
29.2 4
29.3 3
30 5
30.1 5
30.2 6
31 1
32 2
32.1 5
32.2 5
32.3 6
33 4
33.1 7
34 2
34.1 5
34.2 6
34.3 2
35 3
35.1 2
35.2 3
36 3
36.1 1
36.2 6
36.3 4
36.4 1
37 4
37.1 6
37.2 8
38 3
39 2
39.1 3
39.2 6
39.3 4
39.4 3
39.5 6
40 1
40.1 3
40.2 0
40.3 4
41 1
41.1 4
41.2 7
41.3 5
41.4 2
42 1
42.1 3
43 5
43.1 2
43.2 3
44 3
44.1 3
44.2 3
44.3 4
45 4
45.1 2
46 8
46.1 5
46.2 5
47 3
47.1 5
47.2 5
47.3 2
47.4 5
48 2
48.1 5
49 4
50 1
51 9
52 3
52.1 3
52.2 4
52.3 11
52.4 3
52.5 3
53 5
53.1 3
53.2 2
54 1
54.1 4
54.2 2
54.3 2
54.4 6
55 1
55.1 2
55.2 2
55.3 3
55.4 5
56 5
56.1 5
56.2 2
56.3 3
56.4 6
56.5 1
57 3
57.1 6
57.2 3
57.3 2
58 6
58.1 5
58.2 2
58.3 4
58.4 4
58.5 4
59 6
59.1 4
60 7
61 6
61.1 3
61.2 2
61.3 5
61.4 4
62 1
62.1 1
62.2 2
62.3 4
63 6
63.1 2
64 2
65 3
65.1 4
65.2 2
65.3 2
66 6
66.1 0
66.2 5
67 8
68 5
68.1 5
68.2 4
68.3 3
68.4 1
69 5
70 6
70.1 2
71 4
71.1 2
71.2 5
71.3 10
71.4 2
72 2
72.1 4
72.2 8
72.3 6
72.4 4
72.5 1
73 1
74 1
75 6
76 3
76.1 4
76.2 5
77 1
78 2
79 2
79.1 6
79.2 5
80 5
80.1 1
80.2 4
81 4
81.1 5
81.2 2
81.3 5
82 1
82.1 2
82.2 5
83 5
83.1 1
83.2 1
83.3 4
84 1
84.1 5
85 6
85.1 5
85.2 3
85.3 2
85.4 2
85.5 6
86 3
86.1 3
86.2 6
86.3 5
86.4 5
86.5 4
87 3
87.1 6
87.2 2
88 1
88.1 6
88.2 1
88.3 6
89 7
90 3
90.1 8
90.2 4
90.3 2
91 4
91.1 2
91.2 5
92 3
93 3
93.1 3
93.2 4
93.3 2
93.4 6
94 2
94.1 4
94.2 2
94.3 6
94.4 5
94.5 5
95 8
95.1 4
95.2 1
96 2
96.1 3
96.2 2
96.3 6
96.4 6
96.5 3
97 2
97.1 5
98 7
98.1 2
98.2 6
99 3
99.1 4
99.2 5
100 2
100.1 3
100.2 3
100.3 7
100.4 6
$m0d1$mu_reg_poisson
[1] 0
$m0d1$tau_reg_poisson
[1] 1e-04
$m0d1$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0d1$shape_diag_RinvD
[1] "0.01"
$m0d1$rate_diag_RinvD
[1] "0.001"
$m0d2
$m0d2$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0d2$M_lvlone
p1
1 5
1.1 3
1.2 8
1.3 6
2 5
2.1 3
2.2 2
3 7
3.1 2
3.2 8
4 2
4.1 4
4.2 2
4.3 6
5 6
5.1 2
5.2 3
5.3 2
6 4
7 2
7.1 6
7.2 4
8 2
8.1 2
8.2 1
8.3 2
8.4 2
8.5 4
9 3
9.1 3
9.2 2
10 4
10.1 5
11 2
11.1 4
11.2 6
11.3 2
11.4 1
12 5
13 2
13.1 6
14 3
14.1 2
14.2 4
14.3 2
15 4
15.1 7
15.2 4
15.3 3
16 3
16.1 2
16.2 5
16.3 3
16.4 2
16.5 6
17 3
17.1 1
17.2 4
17.3 5
17.4 5
18 8
19 5
19.1 6
19.2 4
19.3 3
20 5
20.1 8
20.2 3
20.3 3
20.4 3
20.5 3
21 3
21.1 3
21.2 4
22 6
22.1 3
23 3
23.1 2
24 1
25 2
25.1 0
25.2 6
25.3 6
25.4 2
25.5 2
26 6
26.1 0
26.2 1
26.3 4
27 2
27.1 4
28 5
28.1 0
28.2 7
28.3 3
29 4
29.1 1
29.2 4
29.3 3
30 5
30.1 5
30.2 6
31 1
32 2
32.1 5
32.2 5
32.3 6
33 4
33.1 7
34 2
34.1 5
34.2 6
34.3 2
35 3
35.1 2
35.2 3
36 3
36.1 1
36.2 6
36.3 4
36.4 1
37 4
37.1 6
37.2 8
38 3
39 2
39.1 3
39.2 6
39.3 4
39.4 3
39.5 6
40 1
40.1 3
40.2 0
40.3 4
41 1
41.1 4
41.2 7
41.3 5
41.4 2
42 1
42.1 3
43 5
43.1 2
43.2 3
44 3
44.1 3
44.2 3
44.3 4
45 4
45.1 2
46 8
46.1 5
46.2 5
47 3
47.1 5
47.2 5
47.3 2
47.4 5
48 2
48.1 5
49 4
50 1
51 9
52 3
52.1 3
52.2 4
52.3 11
52.4 3
52.5 3
53 5
53.1 3
53.2 2
54 1
54.1 4
54.2 2
54.3 2
54.4 6
55 1
55.1 2
55.2 2
55.3 3
55.4 5
56 5
56.1 5
56.2 2
56.3 3
56.4 6
56.5 1
57 3
57.1 6
57.2 3
57.3 2
58 6
58.1 5
58.2 2
58.3 4
58.4 4
58.5 4
59 6
59.1 4
60 7
61 6
61.1 3
61.2 2
61.3 5
61.4 4
62 1
62.1 1
62.2 2
62.3 4
63 6
63.1 2
64 2
65 3
65.1 4
65.2 2
65.3 2
66 6
66.1 0
66.2 5
67 8
68 5
68.1 5
68.2 4
68.3 3
68.4 1
69 5
70 6
70.1 2
71 4
71.1 2
71.2 5
71.3 10
71.4 2
72 2
72.1 4
72.2 8
72.3 6
72.4 4
72.5 1
73 1
74 1
75 6
76 3
76.1 4
76.2 5
77 1
78 2
79 2
79.1 6
79.2 5
80 5
80.1 1
80.2 4
81 4
81.1 5
81.2 2
81.3 5
82 1
82.1 2
82.2 5
83 5
83.1 1
83.2 1
83.3 4
84 1
84.1 5
85 6
85.1 5
85.2 3
85.3 2
85.4 2
85.5 6
86 3
86.1 3
86.2 6
86.3 5
86.4 5
86.5 4
87 3
87.1 6
87.2 2
88 1
88.1 6
88.2 1
88.3 6
89 7
90 3
90.1 8
90.2 4
90.3 2
91 4
91.1 2
91.2 5
92 3
93 3
93.1 3
93.2 4
93.3 2
93.4 6
94 2
94.1 4
94.2 2
94.3 6
94.4 5
94.5 5
95 8
95.1 4
95.2 1
96 2
96.1 3
96.2 2
96.3 6
96.4 6
96.5 3
97 2
97.1 5
98 7
98.1 2
98.2 6
99 3
99.1 4
99.2 5
100 2
100.1 3
100.2 3
100.3 7
100.4 6
$m0d2$mu_reg_poisson
[1] 0
$m0d2$tau_reg_poisson
[1] 1e-04
$m0d2$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0d2$shape_diag_RinvD
[1] "0.01"
$m0d2$rate_diag_RinvD
[1] "0.001"
$m0e1
$m0e1$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0e1$M_lvlone
L1
1 0.09647609
1.1 0.47743206
1.2 0.49307743
1.3 0.18468863
2 0.54595313
2.1 0.21966792
2.2 0.73654737
3 0.20862809
3.1 0.24312223
3.2 0.03051627
4 0.39499609
4.1 0.72632316
4.2 0.34199228
4.3 0.38062927
5 0.62202135
5.1 0.20305630
5.2 0.41717969
5.3 0.23980703
6 0.37653463
7 0.36356663
7.1 0.06266071
7.2 0.37849716
8 0.37802506
8.1 0.61143062
8.2 0.75648801
8.3 2.54406375
8.4 1.18637590
8.5 0.05930316
9 0.95013074
9.1 0.11917116
9.2 0.86629295
10 0.23914695
10.1 0.13708051
11 0.11067204
11.1 0.23176079
11.2 0.60038623
11.3 0.42684714
11.4 0.16458522
12 0.12861686
13 1.33377494
13.1 0.37267514
14 0.48728084
14.1 0.31792264
14.2 0.89257832
14.3 0.48509920
15 0.37711346
15.1 0.24850749
15.2 0.48117461
15.3 0.42758680
16 0.43666855
16.1 0.18190724
16.2 0.18617239
16.3 1.87047608
16.4 0.41864602
16.5 0.43588009
17 0.17925916
17.1 0.32367639
17.2 0.24912593
17.3 0.56230768
17.4 0.26182608
18 0.42338083
19 0.23371438
19.1 0.45720781
19.2 1.07923724
19.3 0.74433885
20 0.23860936
20.1 1.49001161
20.2 0.82847676
20.3 0.71062057
20.4 0.58928158
20.5 0.49204025
21 0.39710041
21.1 0.63253881
21.2 0.58877978
22 0.30440876
22.1 0.42787265
23 0.15078177
23.1 0.97104584
24 0.55355206
25 0.76006220
25.1 0.42500306
25.2 0.68011522
25.3 0.38187835
25.4 0.67265847
25.5 0.09078197
26 0.17032539
26.1 0.36699769
26.2 0.19300220
26.3 1.26993276
27 0.63999648
27.1 1.14153094
28 0.39991376
28.1 0.20658853
28.2 0.42519397
28.3 1.68848543
29 0.20853337
29.1 0.32240000
29.2 0.59527557
29.3 0.34253455
30 0.70885491
30.1 0.31107139
30.2 0.46423208
31 0.54603320
32 0.48896515
32.1 0.26838930
32.2 0.33314256
32.3 0.15482204
33 0.63379200
33.1 0.53403306
34 0.30684588
34.1 0.15596697
34.2 0.73177916
34.3 0.78232073
35 0.12725486
35.1 0.32104659
35.2 0.92993903
36 0.82634942
36.1 0.15790991
36.2 0.28319688
36.3 0.30894311
36.4 0.38835761
37 0.28006122
37.1 0.51936935
37.2 0.03553058
38 0.10984700
39 1.01908377
39.1 0.58760885
39.2 0.63292533
39.3 0.42095489
39.4 0.25220230
39.5 0.51242643
40 0.70636121
40.1 1.22834105
40.2 0.81839083
40.3 0.23540757
41 0.08592119
41.1 0.22834515
41.2 1.61636130
41.3 0.15342660
41.4 0.47650400
42 0.64398703
42.1 1.15130398
43 0.79292461
43.1 0.38506794
43.2 0.11139587
44 0.89129328
44.1 0.08958946
44.2 0.85701827
44.3 0.96417530
45 0.51097634
45.1 0.98340980
46 0.44798505
46.1 0.82655580
46.2 0.37637628
47 0.41876182
47.1 0.48389648
47.2 0.02396924
47.3 1.80138667
47.4 0.61109603
48 0.19473894
48.1 0.04006959
49 0.29560575
50 0.15625313
51 0.47908892
52 1.40786781
52.1 0.35019229
52.2 0.39332493
52.3 0.51225821
52.4 0.11419627
52.5 0.55575005
53 0.13011523
53.1 0.90571584
53.2 0.50906734
54 0.46031273
54.1 0.46156182
54.2 0.52071389
54.3 0.76983675
54.4 0.52623423
55 0.60555180
55.1 0.10776713
55.2 1.03837178
55.3 0.45001542
55.4 0.65395611
56 0.07535464
56.1 0.73328954
56.2 0.27578594
56.3 0.68719648
56.4 1.57220834
56.5 0.28753078
57 0.17289659
57.1 0.72170220
57.2 1.26500225
57.3 0.20213479
58 0.13611631
58.1 0.37311297
58.2 0.72470365
58.3 1.43014769
58.4 0.78817203
58.5 0.78387559
59 0.46747067
59.1 0.04947979
60 0.16059397
61 0.29220662
61.1 0.41535569
61.2 0.73742285
61.3 0.43320659
61.4 1.19954814
62 0.20260386
62.1 0.06652907
62.2 0.25063288
62.3 0.36290927
63 0.52314649
63.1 0.25699016
64 1.02878746
65 0.45575444
65.1 0.46306113
65.2 0.42269832
65.3 0.73172542
66 0.74765742
66.1 0.25888221
66.2 0.38244280
67 0.23644835
68 0.83195685
68.1 0.68395486
68.2 0.53889898
68.3 0.33762340
68.4 0.79922369
69 0.20260053
70 1.04535151
70.1 0.03979648
71 0.56397408
71.1 0.34854738
71.2 0.97913866
71.3 0.19630242
71.4 0.31230175
72 1.04871582
72.1 0.09370234
72.2 0.72454755
72.3 0.80705501
72.4 0.40641012
72.5 0.81634161
73 0.74327324
74 0.49202243
75 0.42954173
76 1.22280380
76.1 0.09905853
76.2 0.34132786
77 1.20980413
78 0.26184214
79 0.94287180
79.1 0.08463026
79.2 0.66769705
80 0.68766428
80.1 0.95426300
80.2 1.84421668
81 0.60279596
81.1 0.73369496
81.2 0.83514184
81.3 0.91767999
82 0.46992524
82.1 0.50002097
82.2 0.43711796
83 0.46587065
83.1 0.43364034
83.2 0.23196757
83.3 0.73616193
84 0.47791427
84.1 0.05551055
85 0.27482891
85.1 1.77694842
85.2 0.71141066
85.3 0.78806704
85.4 0.80223323
85.5 0.22172219
86 0.15018053
86.1 0.31597396
86.2 0.95686193
86.3 0.11022188
86.4 0.68477369
86.5 0.33125367
87 0.29289308
87.1 0.66197512
87.2 0.30055939
88 0.22930153
88.1 1.02206005
88.2 0.52724756
88.3 0.16276648
89 0.09190440
90 0.15333982
90.1 0.42756943
90.2 0.60354432
90.3 0.41070560
91 1.01739949
91.1 0.41121541
91.2 0.08932488
92 1.08669724
93 0.30303806
93.1 0.16800845
93.2 1.29098296
93.3 0.39962093
93.4 0.88339337
94 0.23233022
94.1 0.08638527
94.2 0.43737650
94.3 0.19800807
94.4 0.42942963
94.5 0.14150685
95 1.07323107
95.1 0.26037856
95.2 0.48623052
96 0.79796998
96.1 0.30822508
96.2 0.91060931
96.3 0.26069030
96.4 0.22889234
96.5 0.97046560
97 0.16946638
97.1 0.20265816
98 1.22465795
98.1 0.15250019
98.2 0.44675949
99 0.44238919
99.1 0.63211897
99.2 0.40140806
100 0.10484468
100.1 0.56141377
100.2 0.23655004
100.3 0.74552230
100.4 0.34230391
$m0e1$mu_reg_norm
[1] 0
$m0e1$tau_reg_norm
[1] 1e-04
$m0e1$shape_tau_norm
[1] 0.01
$m0e1$rate_tau_norm
[1] 0.01
$m0e1$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0e1$shape_diag_RinvD
[1] "0.01"
$m0e1$rate_diag_RinvD
[1] "0.001"
$m0f1
$m0f1$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0f1$M_lvlone
Be1
1 0.4480520
1.1 0.4872580
1.2 0.8042241
1.3 0.8554321
2 0.9060032
2.1 0.9275039
2.2 0.9684475
3 0.5305313
3.1 0.9121229
3.2 0.9822343
4 0.3989620
4.1 0.5799009
4.2 0.8662223
4.3 0.9158089
5 0.5896069
5.1 0.7459908
5.2 0.8891508
5.3 0.8907166
6 0.7404475
7 0.9290914
7.1 0.9510258
7.2 0.9826571
8 0.5888906
8.1 0.7383562
8.2 0.7412208
8.3 0.8882677
8.4 0.9307178
8.5 0.9751765
9 0.5598906
9.1 0.9000440
9.2 0.9835368
10 0.8256582
10.1 0.9686602
11 0.6081450
11.1 0.6203091
11.2 0.7109057
11.3 0.9335259
11.4 0.9831774
12 0.5534331
13 0.3337862
13.1 0.9431649
14 0.9653479
14.1 0.9772848
14.2 0.9806705
14.3 0.9816445
15 0.4519208
15.1 0.6121121
15.2 0.6848939
15.3 0.9850242
16 0.6319642
16.1 0.8660451
16.2 0.8755852
16.3 0.9456980
16.4 0.9552169
16.5 0.9638766
17 0.7004195
17.1 0.8447710
17.2 0.9074097
17.3 0.9301938
17.4 0.9579581
18 0.8432895
19 0.5558578
19.1 0.5971935
19.2 0.8186257
19.3 0.9694859
20 0.7222660
20.1 0.7300751
20.2 0.8161188
20.3 0.8175187
20.4 0.9387767
20.5 0.9680716
21 0.7248177
21.1 0.9030819
21.2 0.9553646
22 0.8506311
22.1 0.9192797
23 0.6969316
23.1 0.8359296
24 0.8898412
25 0.4393270
25.1 0.6952775
25.2 0.7013550
25.3 0.9229146
25.4 0.9642968
25.5 0.9668809
26 0.3844839
26.1 0.8498397
26.2 0.9472023
26.3 0.9698339
27 0.9513160
27.1 0.9713089
28 0.4565391
28.1 0.8854882
28.2 0.9695846
28.3 0.9763767
29 0.6079730
29.1 0.7332778
29.2 0.7807345
29.3 0.9344282
30 0.8225127
30.1 0.9460257
30.2 0.9470397
31 0.9745123
32 0.7195703
32.1 0.8984963
32.2 0.9033895
32.3 0.9700494
33 0.3271062
33.1 0.9386866
34 0.6807359
34.1 0.9561254
34.2 0.9594764
34.3 0.9614131
35 0.6479695
35.1 0.6917668
35.2 0.9777582
36 0.4952571
36.1 0.7438280
36.2 0.7493185
36.3 0.9721512
36.4 0.9799281
37 0.7844567
37.1 0.9505294
37.2 0.9629006
38 0.5537002
39 0.4880363
39.1 0.5405940
39.2 0.6377289
39.3 0.6902395
39.4 0.9200815
39.5 0.9676849
40 0.5970791
40.1 0.8759223
40.2 0.9088713
40.3 0.9808585
41 0.7657773
41.1 0.9203076
41.2 0.9265998
41.3 0.9329089
41.4 0.9426326
42 0.4363467
42.1 0.9730745
43 0.4523650
43.1 0.5797085
43.2 0.8653434
44 0.5063579
44.1 0.8708165
44.2 0.9306269
44.3 0.9669009
45 0.3684179
45.1 0.7793063
46 0.6489748
46.1 0.8931511
46.2 0.9754655
47 0.4659563
47.1 0.8418508
47.2 0.9055038
47.3 0.9202183
47.4 0.9798157
48 0.8934160
48.1 0.8980019
49 0.8792169
50 0.6106779
51 0.6695505
52 0.8016848
52.1 0.9145302
52.2 0.9166014
52.3 0.9448693
52.4 0.9831856
52.5 0.9859644
53 0.4430250
53.1 0.9440152
53.2 0.9792363
54 0.6568450
54.1 0.7552906
54.2 0.8527773
54.3 0.8839761
54.4 0.9630372
55 0.4682570
55.1 0.5018449
55.2 0.8890551
55.3 0.9163416
55.4 0.9229283
56 0.6156368
56.1 0.8327518
56.2 0.8600168
56.3 0.9001284
56.4 0.9223855
56.5 0.9349592
57 0.3810809
57.1 0.3837051
57.2 0.6031393
57.3 0.8011333
58 0.6212946
58.1 0.7124804
58.2 0.7217629
58.3 0.8705746
58.4 0.8930050
58.5 0.9450905
59 0.7607033
59.1 0.9856252
60 0.8926604
61 0.4989113
61.1 0.8310345
61.2 0.8559453
61.3 0.9203703
61.4 0.9466752
62 0.4538041
62.1 0.4949445
62.2 0.9393143
62.3 0.9834371
63 0.8885881
63.1 0.9620223
64 0.9672991
65 0.4899624
65.1 0.7820160
65.2 0.9141166
65.3 0.9204984
66 0.9404727
66.1 0.9540581
66.2 0.9613658
67 0.9684363
68 0.3499904
68.1 0.7374372
68.2 0.7860111
68.3 0.8995662
68.4 0.9641669
69 0.9680556
70 0.3631962
70.1 0.4309940
71 0.4991001
71.1 0.6705385
71.2 0.9643633
71.3 0.9806792
71.4 0.9810444
72 0.5476810
72.1 0.6080648
72.2 0.7596830
72.3 0.9396045
72.4 0.9501505
72.5 0.9659276
73 0.9797107
74 0.6739684
75 0.9245569
76 0.7449652
76.1 0.9716113
76.2 0.9857034
77 0.5312239
78 0.5214249
79 0.3314961
79.1 0.8430143
79.2 0.9266576
80 0.5405270
80.1 0.6473533
80.2 0.8876091
81 0.3275558
81.1 0.5529946
81.2 0.9109145
81.3 0.9319014
82 0.6572741
82.1 0.7373364
82.2 0.8693680
83 0.3360995
83.1 0.8976786
83.2 0.9156363
83.3 0.9825687
84 0.8794223
84.1 0.9307356
85 0.3930294
85.1 0.7324405
85.2 0.8756930
85.3 0.9189753
85.4 0.9613144
85.5 0.9776185
86 0.5224769
86.1 0.5632108
86.2 0.6209203
86.3 0.8068072
86.4 0.8449636
86.5 0.9553382
87 0.8762447
87.1 0.9368280
87.2 0.9775674
88 0.3258678
88.1 0.4960216
88.2 0.8541774
88.3 0.9290415
89 0.4802962
90 0.3626402
90.1 0.8658220
90.2 0.8734278
90.3 0.9161187
91 0.4759845
91.1 0.8685282
91.2 0.9827553
92 0.3397660
93 0.3869728
93.1 0.5736674
93.2 0.8522942
93.3 0.8955441
93.4 0.9764547
94 0.5306638
94.1 0.5815770
94.2 0.7718092
94.3 0.9125421
94.4 0.9138265
94.5 0.9747802
95 0.7844217
95.1 0.9640897
95.2 0.9787801
96 0.3324701
96.1 0.3553187
96.2 0.4854947
96.3 0.8098962
96.4 0.8170439
96.5 0.9709596
97 0.6156077
97.1 0.9857374
98 0.3662077
98.1 0.4202527
98.2 0.9407308
99 0.4075622
99.1 0.9811408
99.2 0.9861494
100 0.5819523
100.1 0.6840806
100.2 0.8040634
100.3 0.9583620
100.4 0.9805147
$m0f1$mu_reg_beta
[1] 0
$m0f1$tau_reg_beta
[1] 1e-04
$m0f1$shape_tau_beta
[1] 0.01
$m0f1$rate_tau_beta
[1] 0.01
$m0f1$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0f1$shape_diag_RinvD
[1] "0.01"
$m0f1$rate_diag_RinvD
[1] "0.001"
$m1a
$m1a$M_id
(Intercept) C1
1 1 0.7175865
2 1 0.7507170
3 1 0.7255954
4 1 0.7469352
5 1 0.7139120
6 1 0.7332505
7 1 0.7345929
8 1 0.7652589
9 1 0.7200622
10 1 0.7423879
11 1 0.7437448
12 1 0.7446470
13 1 0.7530186
14 1 0.7093137
15 1 0.7331192
16 1 0.7011390
17 1 0.7432395
18 1 0.7545191
19 1 0.7528487
20 1 0.7612865
21 1 0.7251719
22 1 0.7300630
23 1 0.7087249
24 1 0.7391938
25 1 0.7820641
26 1 0.7118298
27 1 0.7230857
28 1 0.7489353
29 1 0.7510888
30 1 0.7300717
31 1 0.7550721
32 1 0.7321898
33 1 0.7306414
34 1 0.7427216
35 1 0.7193042
36 1 0.7312888
37 1 0.7100436
38 1 0.7670184
39 1 0.7400449
40 1 0.7397304
41 1 0.7490966
42 1 0.7419274
43 1 0.7527810
44 1 0.7408315
45 1 0.7347550
46 1 0.7332398
47 1 0.7376481
48 1 0.7346179
49 1 0.7329402
50 1 0.7260436
51 1 0.7242910
52 1 0.7298067
53 1 0.7254741
54 1 0.7542067
55 1 0.7389952
56 1 0.7520638
57 1 0.7219958
58 1 0.7259632
59 1 0.7458606
60 1 0.7672421
61 1 0.7257179
62 1 0.7189892
63 1 0.7333356
64 1 0.7320243
65 1 0.7477711
66 1 0.7343974
67 1 0.7491624
68 1 0.7482736
69 1 0.7338267
70 1 0.7607742
71 1 0.7777600
72 1 0.7408143
73 1 0.7248271
74 1 0.7364916
75 1 0.7464926
76 1 0.7355430
77 1 0.7208449
78 1 0.7373573
79 1 0.7598079
80 1 0.7360415
81 1 0.7293932
82 1 0.7279309
83 1 0.7344643
84 1 0.7384350
85 1 0.7323716
86 1 0.7576597
87 1 0.7496139
88 1 0.7275239
89 1 0.7250648
90 1 0.7335262
91 1 0.7343980
92 1 0.7380425
93 1 0.7389460
94 1 0.7259951
95 1 0.7282840
96 1 0.7281676
97 1 0.7245642
98 1 0.7526938
99 1 0.7272309
100 1 0.7383460
$m1a$M_lvlone
y
1 -13.0493856
1.1 -9.3335901
1.2 -22.3469852
1.3 -15.0417337
2 -12.0655434
2.1 -15.8674476
2.2 -7.8800006
3 -11.4820604
3.1 -10.5983220
3.2 -22.4519157
4 -1.2697775
4.1 -11.1215184
4.2 -3.6134138
4.3 -14.5982385
5 -6.8457515
5.1 -7.0551214
5.2 -12.3418980
5.3 -9.2366906
6 -5.1648211
7 -10.0599502
7.1 -18.3267285
7.2 -12.5138426
8 -1.6305331
8.1 -9.6520453
8.2 -1.5278462
8.3 -7.4172211
8.4 -7.1238609
8.5 -8.8706950
9 -0.1634429
9.1 -2.6034300
9.2 -6.7272369
10 -6.4172202
10.1 -11.4834569
11 -8.7911356
11.1 -19.6645080
11.2 -20.2030932
11.3 -21.3082176
11.4 -14.5802901
12 -15.2006287
13 0.8058816
13.1 -13.6379208
14 -15.3422873
14.1 -10.0965208
14.2 -16.6452027
14.3 -15.8389733
15 -8.9424594
15.1 -22.0101983
15.2 -7.3975599
15.3 -10.3567334
16 -1.9691302
16.1 -9.9308357
16.2 -6.9626923
16.3 -3.2862557
16.4 -3.3972355
16.5 -11.5767835
17 -10.5474144
17.1 -7.6215009
17.2 -16.5386939
17.3 -20.0004774
17.4 -18.8505475
18 -19.7302351
19 -14.6177568
19.1 -17.8043866
19.2 -15.1641705
19.3 -16.6898418
20 -12.9059229
20.1 -16.8191201
20.2 -6.1010131
20.3 -7.9415371
20.4 -9.3904458
20.5 -13.3504189
21 -7.6974718
21.1 -11.9335526
21.2 -12.7064929
22 -21.5022909
22.1 -12.7745451
23 -3.5146508
23.1 -4.6724048
24 -2.5619821
25 -6.2944970
25.1 -3.8630505
25.2 -14.4205140
25.3 -19.6735037
25.4 -9.0288933
25.5 -9.0509738
26 -19.7340685
26.1 -14.1692728
26.2 -17.2819976
26.3 -24.6265576
27 -7.3354999
27.1 -11.1488468
28 -11.7996597
28.1 -8.2030122
28.2 -26.4317815
28.3 -18.5016071
29 -5.8551395
29.1 -2.0209442
29.2 -5.6368080
29.3 -3.8110961
30 -12.7217702
30.1 -17.0170140
30.2 -25.4236089
31 -17.0783921
32 -18.4338764
32.1 -19.4317212
32.2 -19.4738978
32.3 -21.4922645
33 2.0838099
33.1 -13.3172274
34 -10.0296691
34.1 -25.9426553
34.2 -18.5688138
34.3 -15.4173859
35 -14.3958113
35.1 -12.9457541
35.2 -16.1380691
36 -12.8166968
36.1 -14.3989481
36.2 -12.2436943
36.3 -15.0104638
36.4 -10.1775457
37 -15.2223495
37.1 -14.7526195
37.2 -19.8168430
38 -2.7065118
39 -8.7288138
39.1 -9.2746473
39.2 -18.2695344
39.3 -13.8219083
39.4 -16.2254704
39.5 -21.7283648
40 1.8291916
40.1 -6.6916432
40.2 -1.6278171
40.3 -10.5749790
41 -3.1556121
41.1 -11.5895327
41.2 -18.9352091
41.3 -15.9788960
41.4 -9.6070508
42 -5.2159485
42.1 -15.9878743
43 -16.6104361
43.1 -9.5549441
43.2 -14.2003491
44 -8.1969033
44.1 -19.9270197
44.2 -22.6521171
44.3 -21.1903736
45 -0.5686627
45.1 -7.5645740
46 -19.1624789
46.1 -18.4487574
46.2 -15.8222682
47 -5.4165074
47.1 -15.0975029
47.2 -12.9971413
47.3 -10.6844521
47.4 -18.2214784
48 -8.3101471
48.1 -18.3854275
49 -13.0130319
50 -10.4579977
51 -19.3157621
52 -4.4747188
52.1 -4.3163827
52.2 -6.9761408
52.3 -20.1764756
52.4 -8.9036692
52.5 -5.6949642
53 -10.3141887
53.1 -8.2642654
53.2 -9.1691554
54 -6.2198754
54.1 -15.7192609
54.2 -13.0978998
54.3 -5.1195299
54.4 -16.5771751
55 -5.7348534
55.1 -7.3217494
55.2 -12.2171938
55.3 -12.9821266
55.4 -14.8599983
56 -14.1764282
56.1 -12.5343602
56.2 -8.4573382
56.3 -12.4633969
56.4 -17.3841863
56.5 -14.8147645
57 -3.1403293
57.1 -11.1509248
57.2 -6.3940143
57.3 -9.3473241
58 -12.0245677
58.1 -9.2112246
58.2 -1.2071742
58.3 -11.0141711
58.4 -5.3721214
58.5 -7.8523047
59 -13.2946560
59.1 -10.0530648
60 -19.2209402
61 -4.6699914
61.1 -3.5981894
61.2 -1.4713611
61.3 -3.8819786
61.4 0.1041413
62 -2.8591600
62.1 -6.9461986
62.2 -16.7910593
62.3 -17.9844596
63 -24.0335535
63.1 -11.7765300
64 -20.5963897
65 -2.7969169
65.1 -11.1778694
65.2 -5.2830399
65.3 -7.9353390
66 -13.2318328
66.1 -1.9090560
66.2 -16.6643889
67 -25.6073277
68 -13.4806759
68.1 -18.4557183
68.2 -13.3982327
68.3 -12.4977127
68.4 -11.7073990
69 -14.5290675
70 -15.2122709
70.1 -7.8681167
71 -10.3352703
71.1 -7.5699888
71.2 -18.4680702
71.3 -21.4316644
71.4 -8.1137650
72 -9.1848162
72.1 -23.7538846
72.2 -26.3421306
72.3 -27.2843801
72.4 -20.8541617
72.5 -12.8948965
73 -2.6091307
74 -8.2790175
75 -12.5029612
76 -6.0061671
76.1 -8.8149114
76.2 -11.8359043
77 0.4772521
78 -9.4105229
79 -1.0217265
79.1 -11.8125257
79.2 -10.5465186
80 -12.7366807
80.1 -9.0584783
80.2 -16.6381566
81 0.5547913
81.1 -4.0892715
81.2 1.8283303
81.3 -5.2166381
82 -3.0749381
82.1 -10.5506696
82.2 -18.2226347
83 -12.5872635
83.1 -11.9756502
83.2 -10.6744217
83.3 -19.2714012
84 -2.6320312
84.1 -9.8140094
85 -12.3886736
85.1 -12.9196365
85.2 -9.6433248
85.3 -6.3296340
85.4 -7.0405525
85.5 -13.6714939
86 -10.8756412
86.1 -12.0055331
86.2 -13.3724699
86.3 -13.3252145
86.4 -14.9191290
86.5 -17.7515546
87 -10.7027963
87.1 -22.4941954
87.2 -14.9616716
88 -2.2264493
88.1 -8.9626474
88.2 -2.5095281
88.3 -16.3345673
89 -11.0459647
90 -4.5610239
90.1 -11.7036651
90.2 -5.3838521
90.3 -4.1636999
91 -7.1462503
91.1 -12.8374475
91.2 -18.2576707
92 -6.4119222
93 5.2122168
93.1 3.1211725
93.2 -3.6841177
93.3 2.6223542
93.4 -11.1877696
94 -6.9602492
94.1 -7.4318416
94.2 -4.3498045
94.3 -11.6340088
94.4 -12.9357964
94.5 -14.7648530
95 -12.8849309
95.1 -9.7451502
95.2 -0.8535063
96 -4.9139832
96.1 -3.9582653
96.2 -9.6555492
96.3 -11.8690793
96.4 -11.0224373
96.5 -10.9530403
97 -9.8540471
97.1 -19.2262840
98 -11.9651231
98.1 -2.6515128
98.2 -12.2606382
99 -11.4720500
99.1 -14.0596866
99.2 -17.3939469
100 1.1005874
100.1 -3.8226248
100.2 -0.9123182
100.3 -15.8389474
100.4 -12.8093826
$m1a$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1a$mu_reg_norm
[1] 0
$m1a$tau_reg_norm
[1] 1e-04
$m1a$shape_tau_norm
[1] 0.01
$m1a$rate_tau_norm
[1] 0.01
$m1a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m1a$shape_diag_RinvD
[1] "0.01"
$m1a$rate_diag_RinvD
[1] "0.001"
$m1b
$m1b$M_id
(Intercept) C1
1 1 0.7175865
2 1 0.7507170
3 1 0.7255954
4 1 0.7469352
5 1 0.7139120
6 1 0.7332505
7 1 0.7345929
8 1 0.7652589
9 1 0.7200622
10 1 0.7423879
11 1 0.7437448
12 1 0.7446470
13 1 0.7530186
14 1 0.7093137
15 1 0.7331192
16 1 0.7011390
17 1 0.7432395
18 1 0.7545191
19 1 0.7528487
20 1 0.7612865
21 1 0.7251719
22 1 0.7300630
23 1 0.7087249
24 1 0.7391938
25 1 0.7820641
26 1 0.7118298
27 1 0.7230857
28 1 0.7489353
29 1 0.7510888
30 1 0.7300717
31 1 0.7550721
32 1 0.7321898
33 1 0.7306414
34 1 0.7427216
35 1 0.7193042
36 1 0.7312888
37 1 0.7100436
38 1 0.7670184
39 1 0.7400449
40 1 0.7397304
41 1 0.7490966
42 1 0.7419274
43 1 0.7527810
44 1 0.7408315
45 1 0.7347550
46 1 0.7332398
47 1 0.7376481
48 1 0.7346179
49 1 0.7329402
50 1 0.7260436
51 1 0.7242910
52 1 0.7298067
53 1 0.7254741
54 1 0.7542067
55 1 0.7389952
56 1 0.7520638
57 1 0.7219958
58 1 0.7259632
59 1 0.7458606
60 1 0.7672421
61 1 0.7257179
62 1 0.7189892
63 1 0.7333356
64 1 0.7320243
65 1 0.7477711
66 1 0.7343974
67 1 0.7491624
68 1 0.7482736
69 1 0.7338267
70 1 0.7607742
71 1 0.7777600
72 1 0.7408143
73 1 0.7248271
74 1 0.7364916
75 1 0.7464926
76 1 0.7355430
77 1 0.7208449
78 1 0.7373573
79 1 0.7598079
80 1 0.7360415
81 1 0.7293932
82 1 0.7279309
83 1 0.7344643
84 1 0.7384350
85 1 0.7323716
86 1 0.7576597
87 1 0.7496139
88 1 0.7275239
89 1 0.7250648
90 1 0.7335262
91 1 0.7343980
92 1 0.7380425
93 1 0.7389460
94 1 0.7259951
95 1 0.7282840
96 1 0.7281676
97 1 0.7245642
98 1 0.7526938
99 1 0.7272309
100 1 0.7383460
$m1b$M_lvlone
b1
1 0
1.1 1
1.2 1
1.3 0
2 1
2.1 1
2.2 1
3 1
3.1 0
3.2 0
4 1
4.1 1
4.2 0
4.3 1
5 0
5.1 1
5.2 1
5.3 1
6 0
7 1
7.1 0
7.2 1
8 0
8.1 1
8.2 1
8.3 0
8.4 0
8.5 1
9 1
9.1 1
9.2 0
10 1
10.1 1
11 1
11.1 1
11.2 1
11.3 1
11.4 1
12 1
13 0
13.1 1
14 0
14.1 1
14.2 0
14.3 0
15 0
15.1 0
15.2 0
15.3 1
16 1
16.1 0
16.2 1
16.3 1
16.4 1
16.5 0
17 0
17.1 0
17.2 1
17.3 0
17.4 1
18 1
19 1
19.1 1
19.2 1
19.3 1
20 0
20.1 1
20.2 0
20.3 0
20.4 0
20.5 0
21 1
21.1 1
21.2 0
22 0
22.1 1
23 1
23.1 1
24 0
25 0
25.1 1
25.2 1
25.3 0
25.4 0
25.5 0
26 1
26.1 1
26.2 1
26.3 0
27 1
27.1 1
28 1
28.1 0
28.2 1
28.3 1
29 1
29.1 0
29.2 0
29.3 1
30 1
30.1 1
30.2 1
31 0
32 1
32.1 1
32.2 1
32.3 1
33 0
33.1 0
34 1
34.1 0
34.2 1
34.3 1
35 1
35.1 0
35.2 1
36 0
36.1 0
36.2 1
36.3 0
36.4 1
37 1
37.1 0
37.2 0
38 1
39 1
39.1 0
39.2 0
39.3 0
39.4 1
39.5 1
40 0
40.1 0
40.2 0
40.3 1
41 1
41.1 1
41.2 0
41.3 1
41.4 1
42 1
42.1 1
43 0
43.1 0
43.2 1
44 1
44.1 0
44.2 0
44.3 1
45 1
45.1 0
46 1
46.1 0
46.2 1
47 0
47.1 0
47.2 1
47.3 0
47.4 0
48 0
48.1 1
49 0
50 1
51 1
52 1
52.1 1
52.2 0
52.3 0
52.4 1
52.5 1
53 1
53.1 1
53.2 1
54 0
54.1 1
54.2 0
54.3 1
54.4 0
55 1
55.1 1
55.2 1
55.3 0
55.4 1
56 0
56.1 1
56.2 1
56.3 0
56.4 0
56.5 1
57 1
57.1 1
57.2 0
57.3 0
58 1
58.1 1
58.2 1
58.3 1
58.4 1
58.5 1
59 0
59.1 1
60 0
61 1
61.1 1
61.2 1
61.3 0
61.4 1
62 1
62.1 0
62.2 0
62.3 1
63 0
63.1 1
64 1
65 1
65.1 1
65.2 0
65.3 0
66 1
66.1 0
66.2 0
67 0
68 0
68.1 0
68.2 0
68.3 0
68.4 1
69 1
70 1
70.1 1
71 1
71.1 1
71.2 0
71.3 0
71.4 0
72 1
72.1 1
72.2 1
72.3 0
72.4 0
72.5 1
73 1
74 1
75 0
76 1
76.1 1
76.2 1
77 1
78 1
79 0
79.1 1
79.2 0
80 1
80.1 0
80.2 1
81 1
81.1 1
81.2 1
81.3 1
82 1
82.1 1
82.2 0
83 1
83.1 0
83.2 0
83.3 1
84 1
84.1 0
85 0
85.1 0
85.2 1
85.3 1
85.4 1
85.5 1
86 0
86.1 1
86.2 1
86.3 0
86.4 1
86.5 0
87 0
87.1 1
87.2 0
88 0
88.1 0
88.2 0
88.3 0
89 1
90 0
90.1 1
90.2 1
90.3 0
91 0
91.1 0
91.2 1
92 1
93 0
93.1 1
93.2 0
93.3 1
93.4 0
94 1
94.1 0
94.2 1
94.3 0
94.4 0
94.5 0
95 1
95.1 1
95.2 0
96 1
96.1 0
96.2 0
96.3 0
96.4 0
96.5 1
97 0
97.1 0
98 0
98.1 0
98.2 0
99 1
99.1 1
99.2 1
100 0
100.1 0
100.2 1
100.3 1
100.4 1
$m1b$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1b$mu_reg_binom
[1] 0
$m1b$tau_reg_binom
[1] 1e-04
$m1b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m1b$shape_diag_RinvD
[1] "0.01"
$m1b$rate_diag_RinvD
[1] "0.001"
$m1c
$m1c$M_id
(Intercept) C1
1 1 0.7175865
2 1 0.7507170
3 1 0.7255954
4 1 0.7469352
5 1 0.7139120
6 1 0.7332505
7 1 0.7345929
8 1 0.7652589
9 1 0.7200622
10 1 0.7423879
11 1 0.7437448
12 1 0.7446470
13 1 0.7530186
14 1 0.7093137
15 1 0.7331192
16 1 0.7011390
17 1 0.7432395
18 1 0.7545191
19 1 0.7528487
20 1 0.7612865
21 1 0.7251719
22 1 0.7300630
23 1 0.7087249
24 1 0.7391938
25 1 0.7820641
26 1 0.7118298
27 1 0.7230857
28 1 0.7489353
29 1 0.7510888
30 1 0.7300717
31 1 0.7550721
32 1 0.7321898
33 1 0.7306414
34 1 0.7427216
35 1 0.7193042
36 1 0.7312888
37 1 0.7100436
38 1 0.7670184
39 1 0.7400449
40 1 0.7397304
41 1 0.7490966
42 1 0.7419274
43 1 0.7527810
44 1 0.7408315
45 1 0.7347550
46 1 0.7332398
47 1 0.7376481
48 1 0.7346179
49 1 0.7329402
50 1 0.7260436
51 1 0.7242910
52 1 0.7298067
53 1 0.7254741
54 1 0.7542067
55 1 0.7389952
56 1 0.7520638
57 1 0.7219958
58 1 0.7259632
59 1 0.7458606
60 1 0.7672421
61 1 0.7257179
62 1 0.7189892
63 1 0.7333356
64 1 0.7320243
65 1 0.7477711
66 1 0.7343974
67 1 0.7491624
68 1 0.7482736
69 1 0.7338267
70 1 0.7607742
71 1 0.7777600
72 1 0.7408143
73 1 0.7248271
74 1 0.7364916
75 1 0.7464926
76 1 0.7355430
77 1 0.7208449
78 1 0.7373573
79 1 0.7598079
80 1 0.7360415
81 1 0.7293932
82 1 0.7279309
83 1 0.7344643
84 1 0.7384350
85 1 0.7323716
86 1 0.7576597
87 1 0.7496139
88 1 0.7275239
89 1 0.7250648
90 1 0.7335262
91 1 0.7343980
92 1 0.7380425
93 1 0.7389460
94 1 0.7259951
95 1 0.7282840
96 1 0.7281676
97 1 0.7245642
98 1 0.7526938
99 1 0.7272309
100 1 0.7383460
$m1c$M_lvlone
L1
1 0.09647609
1.1 0.47743206
1.2 0.49307743
1.3 0.18468863
2 0.54595313
2.1 0.21966792
2.2 0.73654737
3 0.20862809
3.1 0.24312223
3.2 0.03051627
4 0.39499609
4.1 0.72632316
4.2 0.34199228
4.3 0.38062927
5 0.62202135
5.1 0.20305630
5.2 0.41717969
5.3 0.23980703
6 0.37653463
7 0.36356663
7.1 0.06266071
7.2 0.37849716
8 0.37802506
8.1 0.61143062
8.2 0.75648801
8.3 2.54406375
8.4 1.18637590
8.5 0.05930316
9 0.95013074
9.1 0.11917116
9.2 0.86629295
10 0.23914695
10.1 0.13708051
11 0.11067204
11.1 0.23176079
11.2 0.60038623
11.3 0.42684714
11.4 0.16458522
12 0.12861686
13 1.33377494
13.1 0.37267514
14 0.48728084
14.1 0.31792264
14.2 0.89257832
14.3 0.48509920
15 0.37711346
15.1 0.24850749
15.2 0.48117461
15.3 0.42758680
16 0.43666855
16.1 0.18190724
16.2 0.18617239
16.3 1.87047608
16.4 0.41864602
16.5 0.43588009
17 0.17925916
17.1 0.32367639
17.2 0.24912593
17.3 0.56230768
17.4 0.26182608
18 0.42338083
19 0.23371438
19.1 0.45720781
19.2 1.07923724
19.3 0.74433885
20 0.23860936
20.1 1.49001161
20.2 0.82847676
20.3 0.71062057
20.4 0.58928158
20.5 0.49204025
21 0.39710041
21.1 0.63253881
21.2 0.58877978
22 0.30440876
22.1 0.42787265
23 0.15078177
23.1 0.97104584
24 0.55355206
25 0.76006220
25.1 0.42500306
25.2 0.68011522
25.3 0.38187835
25.4 0.67265847
25.5 0.09078197
26 0.17032539
26.1 0.36699769
26.2 0.19300220
26.3 1.26993276
27 0.63999648
27.1 1.14153094
28 0.39991376
28.1 0.20658853
28.2 0.42519397
28.3 1.68848543
29 0.20853337
29.1 0.32240000
29.2 0.59527557
29.3 0.34253455
30 0.70885491
30.1 0.31107139
30.2 0.46423208
31 0.54603320
32 0.48896515
32.1 0.26838930
32.2 0.33314256
32.3 0.15482204
33 0.63379200
33.1 0.53403306
34 0.30684588
34.1 0.15596697
34.2 0.73177916
34.3 0.78232073
35 0.12725486
35.1 0.32104659
35.2 0.92993903
36 0.82634942
36.1 0.15790991
36.2 0.28319688
36.3 0.30894311
36.4 0.38835761
37 0.28006122
37.1 0.51936935
37.2 0.03553058
38 0.10984700
39 1.01908377
39.1 0.58760885
39.2 0.63292533
39.3 0.42095489
39.4 0.25220230
39.5 0.51242643
40 0.70636121
40.1 1.22834105
40.2 0.81839083
40.3 0.23540757
41 0.08592119
41.1 0.22834515
41.2 1.61636130
41.3 0.15342660
41.4 0.47650400
42 0.64398703
42.1 1.15130398
43 0.79292461
43.1 0.38506794
43.2 0.11139587
44 0.89129328
44.1 0.08958946
44.2 0.85701827
44.3 0.96417530
45 0.51097634
45.1 0.98340980
46 0.44798505
46.1 0.82655580
46.2 0.37637628
47 0.41876182
47.1 0.48389648
47.2 0.02396924
47.3 1.80138667
47.4 0.61109603
48 0.19473894
48.1 0.04006959
49 0.29560575
50 0.15625313
51 0.47908892
52 1.40786781
52.1 0.35019229
52.2 0.39332493
52.3 0.51225821
52.4 0.11419627
52.5 0.55575005
53 0.13011523
53.1 0.90571584
53.2 0.50906734
54 0.46031273
54.1 0.46156182
54.2 0.52071389
54.3 0.76983675
54.4 0.52623423
55 0.60555180
55.1 0.10776713
55.2 1.03837178
55.3 0.45001542
55.4 0.65395611
56 0.07535464
56.1 0.73328954
56.2 0.27578594
56.3 0.68719648
56.4 1.57220834
56.5 0.28753078
57 0.17289659
57.1 0.72170220
57.2 1.26500225
57.3 0.20213479
58 0.13611631
58.1 0.37311297
58.2 0.72470365
58.3 1.43014769
58.4 0.78817203
58.5 0.78387559
59 0.46747067
59.1 0.04947979
60 0.16059397
61 0.29220662
61.1 0.41535569
61.2 0.73742285
61.3 0.43320659
61.4 1.19954814
62 0.20260386
62.1 0.06652907
62.2 0.25063288
62.3 0.36290927
63 0.52314649
63.1 0.25699016
64 1.02878746
65 0.45575444
65.1 0.46306113
65.2 0.42269832
65.3 0.73172542
66 0.74765742
66.1 0.25888221
66.2 0.38244280
67 0.23644835
68 0.83195685
68.1 0.68395486
68.2 0.53889898
68.3 0.33762340
68.4 0.79922369
69 0.20260053
70 1.04535151
70.1 0.03979648
71 0.56397408
71.1 0.34854738
71.2 0.97913866
71.3 0.19630242
71.4 0.31230175
72 1.04871582
72.1 0.09370234
72.2 0.72454755
72.3 0.80705501
72.4 0.40641012
72.5 0.81634161
73 0.74327324
74 0.49202243
75 0.42954173
76 1.22280380
76.1 0.09905853
76.2 0.34132786
77 1.20980413
78 0.26184214
79 0.94287180
79.1 0.08463026
79.2 0.66769705
80 0.68766428
80.1 0.95426300
80.2 1.84421668
81 0.60279596
81.1 0.73369496
81.2 0.83514184
81.3 0.91767999
82 0.46992524
82.1 0.50002097
82.2 0.43711796
83 0.46587065
83.1 0.43364034
83.2 0.23196757
83.3 0.73616193
84 0.47791427
84.1 0.05551055
85 0.27482891
85.1 1.77694842
85.2 0.71141066
85.3 0.78806704
85.4 0.80223323
85.5 0.22172219
86 0.15018053
86.1 0.31597396
86.2 0.95686193
86.3 0.11022188
86.4 0.68477369
86.5 0.33125367
87 0.29289308
87.1 0.66197512
87.2 0.30055939
88 0.22930153
88.1 1.02206005
88.2 0.52724756
88.3 0.16276648
89 0.09190440
90 0.15333982
90.1 0.42756943
90.2 0.60354432
90.3 0.41070560
91 1.01739949
91.1 0.41121541
91.2 0.08932488
92 1.08669724
93 0.30303806
93.1 0.16800845
93.2 1.29098296
93.3 0.39962093
93.4 0.88339337
94 0.23233022
94.1 0.08638527
94.2 0.43737650
94.3 0.19800807
94.4 0.42942963
94.5 0.14150685
95 1.07323107
95.1 0.26037856
95.2 0.48623052
96 0.79796998
96.1 0.30822508
96.2 0.91060931
96.3 0.26069030
96.4 0.22889234
96.5 0.97046560
97 0.16946638
97.1 0.20265816
98 1.22465795
98.1 0.15250019
98.2 0.44675949
99 0.44238919
99.1 0.63211897
99.2 0.40140806
100 0.10484468
100.1 0.56141377
100.2 0.23655004
100.3 0.74552230
100.4 0.34230391
$m1c$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1c$mu_reg_gamma
[1] 0
$m1c$tau_reg_gamma
[1] 1e-04
$m1c$shape_tau_gamma
[1] 0.01
$m1c$rate_tau_gamma
[1] 0.01
$m1c$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m1c$shape_diag_RinvD
[1] "0.01"
$m1c$rate_diag_RinvD
[1] "0.001"
$m1d
$m1d$M_id
(Intercept) C1
1 1 0.7175865
2 1 0.7507170
3 1 0.7255954
4 1 0.7469352
5 1 0.7139120
6 1 0.7332505
7 1 0.7345929
8 1 0.7652589
9 1 0.7200622
10 1 0.7423879
11 1 0.7437448
12 1 0.7446470
13 1 0.7530186
14 1 0.7093137
15 1 0.7331192
16 1 0.7011390
17 1 0.7432395
18 1 0.7545191
19 1 0.7528487
20 1 0.7612865
21 1 0.7251719
22 1 0.7300630
23 1 0.7087249
24 1 0.7391938
25 1 0.7820641
26 1 0.7118298
27 1 0.7230857
28 1 0.7489353
29 1 0.7510888
30 1 0.7300717
31 1 0.7550721
32 1 0.7321898
33 1 0.7306414
34 1 0.7427216
35 1 0.7193042
36 1 0.7312888
37 1 0.7100436
38 1 0.7670184
39 1 0.7400449
40 1 0.7397304
41 1 0.7490966
42 1 0.7419274
43 1 0.7527810
44 1 0.7408315
45 1 0.7347550
46 1 0.7332398
47 1 0.7376481
48 1 0.7346179
49 1 0.7329402
50 1 0.7260436
51 1 0.7242910
52 1 0.7298067
53 1 0.7254741
54 1 0.7542067
55 1 0.7389952
56 1 0.7520638
57 1 0.7219958
58 1 0.7259632
59 1 0.7458606
60 1 0.7672421
61 1 0.7257179
62 1 0.7189892
63 1 0.7333356
64 1 0.7320243
65 1 0.7477711
66 1 0.7343974
67 1 0.7491624
68 1 0.7482736
69 1 0.7338267
70 1 0.7607742
71 1 0.7777600
72 1 0.7408143
73 1 0.7248271
74 1 0.7364916
75 1 0.7464926
76 1 0.7355430
77 1 0.7208449
78 1 0.7373573
79 1 0.7598079
80 1 0.7360415
81 1 0.7293932
82 1 0.7279309
83 1 0.7344643
84 1 0.7384350
85 1 0.7323716
86 1 0.7576597
87 1 0.7496139
88 1 0.7275239
89 1 0.7250648
90 1 0.7335262
91 1 0.7343980
92 1 0.7380425
93 1 0.7389460
94 1 0.7259951
95 1 0.7282840
96 1 0.7281676
97 1 0.7245642
98 1 0.7526938
99 1 0.7272309
100 1 0.7383460
$m1d$M_lvlone
p1
1 5
1.1 3
1.2 8
1.3 6
2 5
2.1 3
2.2 2
3 7
3.1 2
3.2 8
4 2
4.1 4
4.2 2
4.3 6
5 6
5.1 2
5.2 3
5.3 2
6 4
7 2
7.1 6
7.2 4
8 2
8.1 2
8.2 1
8.3 2
8.4 2
8.5 4
9 3
9.1 3
9.2 2
10 4
10.1 5
11 2
11.1 4
11.2 6
11.3 2
11.4 1
12 5
13 2
13.1 6
14 3
14.1 2
14.2 4
14.3 2
15 4
15.1 7
15.2 4
15.3 3
16 3
16.1 2
16.2 5
16.3 3
16.4 2
16.5 6
17 3
17.1 1
17.2 4
17.3 5
17.4 5
18 8
19 5
19.1 6
19.2 4
19.3 3
20 5
20.1 8
20.2 3
20.3 3
20.4 3
20.5 3
21 3
21.1 3
21.2 4
22 6
22.1 3
23 3
23.1 2
24 1
25 2
25.1 0
25.2 6
25.3 6
25.4 2
25.5 2
26 6
26.1 0
26.2 1
26.3 4
27 2
27.1 4
28 5
28.1 0
28.2 7
28.3 3
29 4
29.1 1
29.2 4
29.3 3
30 5
30.1 5
30.2 6
31 1
32 2
32.1 5
32.2 5
32.3 6
33 4
33.1 7
34 2
34.1 5
34.2 6
34.3 2
35 3
35.1 2
35.2 3
36 3
36.1 1
36.2 6
36.3 4
36.4 1
37 4
37.1 6
37.2 8
38 3
39 2
39.1 3
39.2 6
39.3 4
39.4 3
39.5 6
40 1
40.1 3
40.2 0
40.3 4
41 1
41.1 4
41.2 7
41.3 5
41.4 2
42 1
42.1 3
43 5
43.1 2
43.2 3
44 3
44.1 3
44.2 3
44.3 4
45 4
45.1 2
46 8
46.1 5
46.2 5
47 3
47.1 5
47.2 5
47.3 2
47.4 5
48 2
48.1 5
49 4
50 1
51 9
52 3
52.1 3
52.2 4
52.3 11
52.4 3
52.5 3
53 5
53.1 3
53.2 2
54 1
54.1 4
54.2 2
54.3 2
54.4 6
55 1
55.1 2
55.2 2
55.3 3
55.4 5
56 5
56.1 5
56.2 2
56.3 3
56.4 6
56.5 1
57 3
57.1 6
57.2 3
57.3 2
58 6
58.1 5
58.2 2
58.3 4
58.4 4
58.5 4
59 6
59.1 4
60 7
61 6
61.1 3
61.2 2
61.3 5
61.4 4
62 1
62.1 1
62.2 2
62.3 4
63 6
63.1 2
64 2
65 3
65.1 4
65.2 2
65.3 2
66 6
66.1 0
66.2 5
67 8
68 5
68.1 5
68.2 4
68.3 3
68.4 1
69 5
70 6
70.1 2
71 4
71.1 2
71.2 5
71.3 10
71.4 2
72 2
72.1 4
72.2 8
72.3 6
72.4 4
72.5 1
73 1
74 1
75 6
76 3
76.1 4
76.2 5
77 1
78 2
79 2
79.1 6
79.2 5
80 5
80.1 1
80.2 4
81 4
81.1 5
81.2 2
81.3 5
82 1
82.1 2
82.2 5
83 5
83.1 1
83.2 1
83.3 4
84 1
84.1 5
85 6
85.1 5
85.2 3
85.3 2
85.4 2
85.5 6
86 3
86.1 3
86.2 6
86.3 5
86.4 5
86.5 4
87 3
87.1 6
87.2 2
88 1
88.1 6
88.2 1
88.3 6
89 7
90 3
90.1 8
90.2 4
90.3 2
91 4
91.1 2
91.2 5
92 3
93 3
93.1 3
93.2 4
93.3 2
93.4 6
94 2
94.1 4
94.2 2
94.3 6
94.4 5
94.5 5
95 8
95.1 4
95.2 1
96 2
96.1 3
96.2 2
96.3 6
96.4 6
96.5 3
97 2
97.1 5
98 7
98.1 2
98.2 6
99 3
99.1 4
99.2 5
100 2
100.1 3
100.2 3
100.3 7
100.4 6
$m1d$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1d$mu_reg_poisson
[1] 0
$m1d$tau_reg_poisson
[1] 1e-04
$m1d$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m1d$shape_diag_RinvD
[1] "0.01"
$m1d$rate_diag_RinvD
[1] "0.001"
$m1e
$m1e$M_id
(Intercept) C1
1 1 0.7175865
2 1 0.7507170
3 1 0.7255954
4 1 0.7469352
5 1 0.7139120
6 1 0.7332505
7 1 0.7345929
8 1 0.7652589
9 1 0.7200622
10 1 0.7423879
11 1 0.7437448
12 1 0.7446470
13 1 0.7530186
14 1 0.7093137
15 1 0.7331192
16 1 0.7011390
17 1 0.7432395
18 1 0.7545191
19 1 0.7528487
20 1 0.7612865
21 1 0.7251719
22 1 0.7300630
23 1 0.7087249
24 1 0.7391938
25 1 0.7820641
26 1 0.7118298
27 1 0.7230857
28 1 0.7489353
29 1 0.7510888
30 1 0.7300717
31 1 0.7550721
32 1 0.7321898
33 1 0.7306414
34 1 0.7427216
35 1 0.7193042
36 1 0.7312888
37 1 0.7100436
38 1 0.7670184
39 1 0.7400449
40 1 0.7397304
41 1 0.7490966
42 1 0.7419274
43 1 0.7527810
44 1 0.7408315
45 1 0.7347550
46 1 0.7332398
47 1 0.7376481
48 1 0.7346179
49 1 0.7329402
50 1 0.7260436
51 1 0.7242910
52 1 0.7298067
53 1 0.7254741
54 1 0.7542067
55 1 0.7389952
56 1 0.7520638
57 1 0.7219958
58 1 0.7259632
59 1 0.7458606
60 1 0.7672421
61 1 0.7257179
62 1 0.7189892
63 1 0.7333356
64 1 0.7320243
65 1 0.7477711
66 1 0.7343974
67 1 0.7491624
68 1 0.7482736
69 1 0.7338267
70 1 0.7607742
71 1 0.7777600
72 1 0.7408143
73 1 0.7248271
74 1 0.7364916
75 1 0.7464926
76 1 0.7355430
77 1 0.7208449
78 1 0.7373573
79 1 0.7598079
80 1 0.7360415
81 1 0.7293932
82 1 0.7279309
83 1 0.7344643
84 1 0.7384350
85 1 0.7323716
86 1 0.7576597
87 1 0.7496139
88 1 0.7275239
89 1 0.7250648
90 1 0.7335262
91 1 0.7343980
92 1 0.7380425
93 1 0.7389460
94 1 0.7259951
95 1 0.7282840
96 1 0.7281676
97 1 0.7245642
98 1 0.7526938
99 1 0.7272309
100 1 0.7383460
$m1e$M_lvlone
L1
1 0.09647609
1.1 0.47743206
1.2 0.49307743
1.3 0.18468863
2 0.54595313
2.1 0.21966792
2.2 0.73654737
3 0.20862809
3.1 0.24312223
3.2 0.03051627
4 0.39499609
4.1 0.72632316
4.2 0.34199228
4.3 0.38062927
5 0.62202135
5.1 0.20305630
5.2 0.41717969
5.3 0.23980703
6 0.37653463
7 0.36356663
7.1 0.06266071
7.2 0.37849716
8 0.37802506
8.1 0.61143062
8.2 0.75648801
8.3 2.54406375
8.4 1.18637590
8.5 0.05930316
9 0.95013074
9.1 0.11917116
9.2 0.86629295
10 0.23914695
10.1 0.13708051
11 0.11067204
11.1 0.23176079
11.2 0.60038623
11.3 0.42684714
11.4 0.16458522
12 0.12861686
13 1.33377494
13.1 0.37267514
14 0.48728084
14.1 0.31792264
14.2 0.89257832
14.3 0.48509920
15 0.37711346
15.1 0.24850749
15.2 0.48117461
15.3 0.42758680
16 0.43666855
16.1 0.18190724
16.2 0.18617239
16.3 1.87047608
16.4 0.41864602
16.5 0.43588009
17 0.17925916
17.1 0.32367639
17.2 0.24912593
17.3 0.56230768
17.4 0.26182608
18 0.42338083
19 0.23371438
19.1 0.45720781
19.2 1.07923724
19.3 0.74433885
20 0.23860936
20.1 1.49001161
20.2 0.82847676
20.3 0.71062057
20.4 0.58928158
20.5 0.49204025
21 0.39710041
21.1 0.63253881
21.2 0.58877978
22 0.30440876
22.1 0.42787265
23 0.15078177
23.1 0.97104584
24 0.55355206
25 0.76006220
25.1 0.42500306
25.2 0.68011522
25.3 0.38187835
25.4 0.67265847
25.5 0.09078197
26 0.17032539
26.1 0.36699769
26.2 0.19300220
26.3 1.26993276
27 0.63999648
27.1 1.14153094
28 0.39991376
28.1 0.20658853
28.2 0.42519397
28.3 1.68848543
29 0.20853337
29.1 0.32240000
29.2 0.59527557
29.3 0.34253455
30 0.70885491
30.1 0.31107139
30.2 0.46423208
31 0.54603320
32 0.48896515
32.1 0.26838930
32.2 0.33314256
32.3 0.15482204
33 0.63379200
33.1 0.53403306
34 0.30684588
34.1 0.15596697
34.2 0.73177916
34.3 0.78232073
35 0.12725486
35.1 0.32104659
35.2 0.92993903
36 0.82634942
36.1 0.15790991
36.2 0.28319688
36.3 0.30894311
36.4 0.38835761
37 0.28006122
37.1 0.51936935
37.2 0.03553058
38 0.10984700
39 1.01908377
39.1 0.58760885
39.2 0.63292533
39.3 0.42095489
39.4 0.25220230
39.5 0.51242643
40 0.70636121
40.1 1.22834105
40.2 0.81839083
40.3 0.23540757
41 0.08592119
41.1 0.22834515
41.2 1.61636130
41.3 0.15342660
41.4 0.47650400
42 0.64398703
42.1 1.15130398
43 0.79292461
43.1 0.38506794
43.2 0.11139587
44 0.89129328
44.1 0.08958946
44.2 0.85701827
44.3 0.96417530
45 0.51097634
45.1 0.98340980
46 0.44798505
46.1 0.82655580
46.2 0.37637628
47 0.41876182
47.1 0.48389648
47.2 0.02396924
47.3 1.80138667
47.4 0.61109603
48 0.19473894
48.1 0.04006959
49 0.29560575
50 0.15625313
51 0.47908892
52 1.40786781
52.1 0.35019229
52.2 0.39332493
52.3 0.51225821
52.4 0.11419627
52.5 0.55575005
53 0.13011523
53.1 0.90571584
53.2 0.50906734
54 0.46031273
54.1 0.46156182
54.2 0.52071389
54.3 0.76983675
54.4 0.52623423
55 0.60555180
55.1 0.10776713
55.2 1.03837178
55.3 0.45001542
55.4 0.65395611
56 0.07535464
56.1 0.73328954
56.2 0.27578594
56.3 0.68719648
56.4 1.57220834
56.5 0.28753078
57 0.17289659
57.1 0.72170220
57.2 1.26500225
57.3 0.20213479
58 0.13611631
58.1 0.37311297
58.2 0.72470365
58.3 1.43014769
58.4 0.78817203
58.5 0.78387559
59 0.46747067
59.1 0.04947979
60 0.16059397
61 0.29220662
61.1 0.41535569
61.2 0.73742285
61.3 0.43320659
61.4 1.19954814
62 0.20260386
62.1 0.06652907
62.2 0.25063288
62.3 0.36290927
63 0.52314649
63.1 0.25699016
64 1.02878746
65 0.45575444
65.1 0.46306113
65.2 0.42269832
65.3 0.73172542
66 0.74765742
66.1 0.25888221
66.2 0.38244280
67 0.23644835
68 0.83195685
68.1 0.68395486
68.2 0.53889898
68.3 0.33762340
68.4 0.79922369
69 0.20260053
70 1.04535151
70.1 0.03979648
71 0.56397408
71.1 0.34854738
71.2 0.97913866
71.3 0.19630242
71.4 0.31230175
72 1.04871582
72.1 0.09370234
72.2 0.72454755
72.3 0.80705501
72.4 0.40641012
72.5 0.81634161
73 0.74327324
74 0.49202243
75 0.42954173
76 1.22280380
76.1 0.09905853
76.2 0.34132786
77 1.20980413
78 0.26184214
79 0.94287180
79.1 0.08463026
79.2 0.66769705
80 0.68766428
80.1 0.95426300
80.2 1.84421668
81 0.60279596
81.1 0.73369496
81.2 0.83514184
81.3 0.91767999
82 0.46992524
82.1 0.50002097
82.2 0.43711796
83 0.46587065
83.1 0.43364034
83.2 0.23196757
83.3 0.73616193
84 0.47791427
84.1 0.05551055
85 0.27482891
85.1 1.77694842
85.2 0.71141066
85.3 0.78806704
85.4 0.80223323
85.5 0.22172219
86 0.15018053
86.1 0.31597396
86.2 0.95686193
86.3 0.11022188
86.4 0.68477369
86.5 0.33125367
87 0.29289308
87.1 0.66197512
87.2 0.30055939
88 0.22930153
88.1 1.02206005
88.2 0.52724756
88.3 0.16276648
89 0.09190440
90 0.15333982
90.1 0.42756943
90.2 0.60354432
90.3 0.41070560
91 1.01739949
91.1 0.41121541
91.2 0.08932488
92 1.08669724
93 0.30303806
93.1 0.16800845
93.2 1.29098296
93.3 0.39962093
93.4 0.88339337
94 0.23233022
94.1 0.08638527
94.2 0.43737650
94.3 0.19800807
94.4 0.42942963
94.5 0.14150685
95 1.07323107
95.1 0.26037856
95.2 0.48623052
96 0.79796998
96.1 0.30822508
96.2 0.91060931
96.3 0.26069030
96.4 0.22889234
96.5 0.97046560
97 0.16946638
97.1 0.20265816
98 1.22465795
98.1 0.15250019
98.2 0.44675949
99 0.44238919
99.1 0.63211897
99.2 0.40140806
100 0.10484468
100.1 0.56141377
100.2 0.23655004
100.3 0.74552230
100.4 0.34230391
$m1e$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1e$mu_reg_norm
[1] 0
$m1e$tau_reg_norm
[1] 1e-04
$m1e$shape_tau_norm
[1] 0.01
$m1e$rate_tau_norm
[1] 0.01
$m1e$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m1e$shape_diag_RinvD
[1] "0.01"
$m1e$rate_diag_RinvD
[1] "0.001"
$m1f
$m1f$M_id
(Intercept) C1
1 1 0.7175865
2 1 0.7507170
3 1 0.7255954
4 1 0.7469352
5 1 0.7139120
6 1 0.7332505
7 1 0.7345929
8 1 0.7652589
9 1 0.7200622
10 1 0.7423879
11 1 0.7437448
12 1 0.7446470
13 1 0.7530186
14 1 0.7093137
15 1 0.7331192
16 1 0.7011390
17 1 0.7432395
18 1 0.7545191
19 1 0.7528487
20 1 0.7612865
21 1 0.7251719
22 1 0.7300630
23 1 0.7087249
24 1 0.7391938
25 1 0.7820641
26 1 0.7118298
27 1 0.7230857
28 1 0.7489353
29 1 0.7510888
30 1 0.7300717
31 1 0.7550721
32 1 0.7321898
33 1 0.7306414
34 1 0.7427216
35 1 0.7193042
36 1 0.7312888
37 1 0.7100436
38 1 0.7670184
39 1 0.7400449
40 1 0.7397304
41 1 0.7490966
42 1 0.7419274
43 1 0.7527810
44 1 0.7408315
45 1 0.7347550
46 1 0.7332398
47 1 0.7376481
48 1 0.7346179
49 1 0.7329402
50 1 0.7260436
51 1 0.7242910
52 1 0.7298067
53 1 0.7254741
54 1 0.7542067
55 1 0.7389952
56 1 0.7520638
57 1 0.7219958
58 1 0.7259632
59 1 0.7458606
60 1 0.7672421
61 1 0.7257179
62 1 0.7189892
63 1 0.7333356
64 1 0.7320243
65 1 0.7477711
66 1 0.7343974
67 1 0.7491624
68 1 0.7482736
69 1 0.7338267
70 1 0.7607742
71 1 0.7777600
72 1 0.7408143
73 1 0.7248271
74 1 0.7364916
75 1 0.7464926
76 1 0.7355430
77 1 0.7208449
78 1 0.7373573
79 1 0.7598079
80 1 0.7360415
81 1 0.7293932
82 1 0.7279309
83 1 0.7344643
84 1 0.7384350
85 1 0.7323716
86 1 0.7576597
87 1 0.7496139
88 1 0.7275239
89 1 0.7250648
90 1 0.7335262
91 1 0.7343980
92 1 0.7380425
93 1 0.7389460
94 1 0.7259951
95 1 0.7282840
96 1 0.7281676
97 1 0.7245642
98 1 0.7526938
99 1 0.7272309
100 1 0.7383460
$m1f$M_lvlone
Be1
1 0.4480520
1.1 0.4872580
1.2 0.8042241
1.3 0.8554321
2 0.9060032
2.1 0.9275039
2.2 0.9684475
3 0.5305313
3.1 0.9121229
3.2 0.9822343
4 0.3989620
4.1 0.5799009
4.2 0.8662223
4.3 0.9158089
5 0.5896069
5.1 0.7459908
5.2 0.8891508
5.3 0.8907166
6 0.7404475
7 0.9290914
7.1 0.9510258
7.2 0.9826571
8 0.5888906
8.1 0.7383562
8.2 0.7412208
8.3 0.8882677
8.4 0.9307178
8.5 0.9751765
9 0.5598906
9.1 0.9000440
9.2 0.9835368
10 0.8256582
10.1 0.9686602
11 0.6081450
11.1 0.6203091
11.2 0.7109057
11.3 0.9335259
11.4 0.9831774
12 0.5534331
13 0.3337862
13.1 0.9431649
14 0.9653479
14.1 0.9772848
14.2 0.9806705
14.3 0.9816445
15 0.4519208
15.1 0.6121121
15.2 0.6848939
15.3 0.9850242
16 0.6319642
16.1 0.8660451
16.2 0.8755852
16.3 0.9456980
16.4 0.9552169
16.5 0.9638766
17 0.7004195
17.1 0.8447710
17.2 0.9074097
17.3 0.9301938
17.4 0.9579581
18 0.8432895
19 0.5558578
19.1 0.5971935
19.2 0.8186257
19.3 0.9694859
20 0.7222660
20.1 0.7300751
20.2 0.8161188
20.3 0.8175187
20.4 0.9387767
20.5 0.9680716
21 0.7248177
21.1 0.9030819
21.2 0.9553646
22 0.8506311
22.1 0.9192797
23 0.6969316
23.1 0.8359296
24 0.8898412
25 0.4393270
25.1 0.6952775
25.2 0.7013550
25.3 0.9229146
25.4 0.9642968
25.5 0.9668809
26 0.3844839
26.1 0.8498397
26.2 0.9472023
26.3 0.9698339
27 0.9513160
27.1 0.9713089
28 0.4565391
28.1 0.8854882
28.2 0.9695846
28.3 0.9763767
29 0.6079730
29.1 0.7332778
29.2 0.7807345
29.3 0.9344282
30 0.8225127
30.1 0.9460257
30.2 0.9470397
31 0.9745123
32 0.7195703
32.1 0.8984963
32.2 0.9033895
32.3 0.9700494
33 0.3271062
33.1 0.9386866
34 0.6807359
34.1 0.9561254
34.2 0.9594764
34.3 0.9614131
35 0.6479695
35.1 0.6917668
35.2 0.9777582
36 0.4952571
36.1 0.7438280
36.2 0.7493185
36.3 0.9721512
36.4 0.9799281
37 0.7844567
37.1 0.9505294
37.2 0.9629006
38 0.5537002
39 0.4880363
39.1 0.5405940
39.2 0.6377289
39.3 0.6902395
39.4 0.9200815
39.5 0.9676849
40 0.5970791
40.1 0.8759223
40.2 0.9088713
40.3 0.9808585
41 0.7657773
41.1 0.9203076
41.2 0.9265998
41.3 0.9329089
41.4 0.9426326
42 0.4363467
42.1 0.9730745
43 0.4523650
43.1 0.5797085
43.2 0.8653434
44 0.5063579
44.1 0.8708165
44.2 0.9306269
44.3 0.9669009
45 0.3684179
45.1 0.7793063
46 0.6489748
46.1 0.8931511
46.2 0.9754655
47 0.4659563
47.1 0.8418508
47.2 0.9055038
47.3 0.9202183
47.4 0.9798157
48 0.8934160
48.1 0.8980019
49 0.8792169
50 0.6106779
51 0.6695505
52 0.8016848
52.1 0.9145302
52.2 0.9166014
52.3 0.9448693
52.4 0.9831856
52.5 0.9859644
53 0.4430250
53.1 0.9440152
53.2 0.9792363
54 0.6568450
54.1 0.7552906
54.2 0.8527773
54.3 0.8839761
54.4 0.9630372
55 0.4682570
55.1 0.5018449
55.2 0.8890551
55.3 0.9163416
55.4 0.9229283
56 0.6156368
56.1 0.8327518
56.2 0.8600168
56.3 0.9001284
56.4 0.9223855
56.5 0.9349592
57 0.3810809
57.1 0.3837051
57.2 0.6031393
57.3 0.8011333
58 0.6212946
58.1 0.7124804
58.2 0.7217629
58.3 0.8705746
58.4 0.8930050
58.5 0.9450905
59 0.7607033
59.1 0.9856252
60 0.8926604
61 0.4989113
61.1 0.8310345
61.2 0.8559453
61.3 0.9203703
61.4 0.9466752
62 0.4538041
62.1 0.4949445
62.2 0.9393143
62.3 0.9834371
63 0.8885881
63.1 0.9620223
64 0.9672991
65 0.4899624
65.1 0.7820160
65.2 0.9141166
65.3 0.9204984
66 0.9404727
66.1 0.9540581
66.2 0.9613658
67 0.9684363
68 0.3499904
68.1 0.7374372
68.2 0.7860111
68.3 0.8995662
68.4 0.9641669
69 0.9680556
70 0.3631962
70.1 0.4309940
71 0.4991001
71.1 0.6705385
71.2 0.9643633
71.3 0.9806792
71.4 0.9810444
72 0.5476810
72.1 0.6080648
72.2 0.7596830
72.3 0.9396045
72.4 0.9501505
72.5 0.9659276
73 0.9797107
74 0.6739684
75 0.9245569
76 0.7449652
76.1 0.9716113
76.2 0.9857034
77 0.5312239
78 0.5214249
79 0.3314961
79.1 0.8430143
79.2 0.9266576
80 0.5405270
80.1 0.6473533
80.2 0.8876091
81 0.3275558
81.1 0.5529946
81.2 0.9109145
81.3 0.9319014
82 0.6572741
82.1 0.7373364
82.2 0.8693680
83 0.3360995
83.1 0.8976786
83.2 0.9156363
83.3 0.9825687
84 0.8794223
84.1 0.9307356
85 0.3930294
85.1 0.7324405
85.2 0.8756930
85.3 0.9189753
85.4 0.9613144
85.5 0.9776185
86 0.5224769
86.1 0.5632108
86.2 0.6209203
86.3 0.8068072
86.4 0.8449636
86.5 0.9553382
87 0.8762447
87.1 0.9368280
87.2 0.9775674
88 0.3258678
88.1 0.4960216
88.2 0.8541774
88.3 0.9290415
89 0.4802962
90 0.3626402
90.1 0.8658220
90.2 0.8734278
90.3 0.9161187
91 0.4759845
91.1 0.8685282
91.2 0.9827553
92 0.3397660
93 0.3869728
93.1 0.5736674
93.2 0.8522942
93.3 0.8955441
93.4 0.9764547
94 0.5306638
94.1 0.5815770
94.2 0.7718092
94.3 0.9125421
94.4 0.9138265
94.5 0.9747802
95 0.7844217
95.1 0.9640897
95.2 0.9787801
96 0.3324701
96.1 0.3553187
96.2 0.4854947
96.3 0.8098962
96.4 0.8170439
96.5 0.9709596
97 0.6156077
97.1 0.9857374
98 0.3662077
98.1 0.4202527
98.2 0.9407308
99 0.4075622
99.1 0.9811408
99.2 0.9861494
100 0.5819523
100.1 0.6840806
100.2 0.8040634
100.3 0.9583620
100.4 0.9805147
$m1f$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1f$mu_reg_beta
[1] 0
$m1f$tau_reg_beta
[1] 1e-04
$m1f$shape_tau_beta
[1] 0.01
$m1f$rate_tau_beta
[1] 0.01
$m1f$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m1f$shape_diag_RinvD
[1] "0.01"
$m1f$rate_diag_RinvD
[1] "0.001"
$m2a
$m2a$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m2a$M_lvlone
y c2
1 -13.0493856 NA
1.1 -9.3335901 -0.08061445
1.2 -22.3469852 -0.26523782
1.3 -15.0417337 -0.30260393
2 -12.0655434 -0.33443795
2.1 -15.8674476 -0.11819800
2.2 -7.8800006 -0.31532280
3 -11.4820604 -0.12920657
3.1 -10.5983220 NA
3.2 -22.4519157 NA
4 -1.2697775 -0.31177403
4.1 -11.1215184 -0.23894886
4.2 -3.6134138 -0.15533613
4.3 -14.5982385 -0.14644545
5 -6.8457515 -0.28360457
5.1 -7.0551214 -0.20135143
5.2 -12.3418980 -0.28293375
5.3 -9.2366906 NA
6 -5.1648211 -0.08617066
7 -10.0599502 -0.22243495
7.1 -18.3267285 NA
7.2 -12.5138426 NA
8 -1.6305331 NA
8.1 -9.6520453 NA
8.2 -1.5278462 NA
8.3 -7.4172211 -0.35148972
8.4 -7.1238609 0.03661023
8.5 -8.8706950 -0.08424534
9 -0.1634429 NA
9.1 -2.6034300 -0.43509340
9.2 -6.7272369 -0.22527490
10 -6.4172202 NA
10.1 -11.4834569 NA
11 -8.7911356 -0.08587475
11.1 -19.6645080 -0.06157340
11.2 -20.2030932 -0.12436018
11.3 -21.3082176 -0.21377934
11.4 -14.5802901 -0.32208329
12 -15.2006287 NA
13 0.8058816 NA
13.1 -13.6379208 -0.40300449
14 -15.3422873 -0.28992072
14.1 -10.0965208 NA
14.2 -16.6452027 NA
14.3 -15.8389733 -0.21979936
15 -8.9424594 NA
15.1 -22.0101983 -0.29092263
15.2 -7.3975599 -0.19392239
15.3 -10.3567334 -0.25718384
16 -1.9691302 -0.45041108
16.1 -9.9308357 -0.07599066
16.2 -6.9626923 -0.32385667
16.3 -3.2862557 -0.38326110
16.4 -3.3972355 -0.22845856
16.5 -11.5767835 -0.25497157
17 -10.5474144 NA
17.1 -7.6215009 -0.22105143
17.2 -16.5386939 NA
17.3 -20.0004774 NA
17.4 -18.8505475 -0.15098046
18 -19.7302351 -0.09870041
19 -14.6177568 -0.26680239
19.1 -17.8043866 -0.15815241
19.2 -15.1641705 -0.14717437
19.3 -16.6898418 -0.21271374
20 -12.9059229 -0.22087628
20.1 -16.8191201 NA
20.2 -6.1010131 -0.30127439
20.3 -7.9415371 -0.11782590
20.4 -9.3904458 -0.19857957
20.5 -13.3504189 -0.24338208
21 -7.6974718 -0.31407992
21.1 -11.9335526 -0.12424941
21.2 -12.7064929 -0.27672716
22 -21.5022909 -0.23790593
22.1 -12.7745451 -0.15996535
23 -3.5146508 -0.18236682
23.1 -4.6724048 -0.20823302
24 -2.5619821 -0.29026416
25 -6.2944970 -0.36139273
25.1 -3.8630505 -0.19571118
25.2 -14.4205140 -0.21379355
25.3 -19.6735037 -0.33876012
25.4 -9.0288933 NA
25.5 -9.0509738 -0.04068446
26 -19.7340685 -0.16846716
26.1 -14.1692728 -0.10440642
26.2 -17.2819976 -0.26884827
26.3 -24.6265576 NA
27 -7.3354999 -0.19520794
27.1 -11.1488468 -0.17622638
28 -11.7996597 -0.32164962
28.1 -8.2030122 -0.27003852
28.2 -26.4317815 -0.07235801
28.3 -18.5016071 -0.13462982
29 -5.8551395 -0.32432030
29.1 -2.0209442 -0.27034171
29.2 -5.6368080 -0.10197448
29.3 -3.8110961 -0.27606945
30 -12.7217702 -0.06949300
30.1 -17.0170140 -0.11511035
30.2 -25.4236089 -0.16215882
31 -17.0783921 0.05707733
32 -18.4338764 -0.18446298
32.1 -19.4317212 -0.14270013
32.2 -19.4738978 -0.20530798
32.3 -21.4922645 -0.14705649
33 2.0838099 -0.15252819
33.1 -13.3172274 NA
34 -10.0296691 -0.30378735
34.1 -25.9426553 -0.11982431
34.2 -18.5688138 -0.24278671
34.3 -15.4173859 -0.19971833
35 -14.3958113 NA
35.1 -12.9457541 -0.24165780
35.2 -16.1380691 NA
36 -12.8166968 -0.49062180
36.1 -14.3989481 -0.25651700
36.2 -12.2436943 NA
36.3 -15.0104638 -0.30401274
36.4 -10.1775457 NA
37 -15.2223495 -0.15276529
37.1 -14.7526195 -0.30016169
37.2 -19.8168430 0.06809545
38 -2.7065118 -0.11218486
39 -8.7288138 -0.38072211
39.1 -9.2746473 -0.32094428
39.2 -18.2695344 NA
39.3 -13.8219083 -0.40173480
39.4 -16.2254704 -0.20041848
39.5 -21.7283648 -0.26027990
40 1.8291916 -0.19751956
40.1 -6.6916432 -0.08399467
40.2 -1.6278171 -0.20864416
40.3 -10.5749790 NA
41 -3.1556121 -0.26096953
41.1 -11.5895327 -0.23953874
41.2 -18.9352091 -0.03079344
41.3 -15.9788960 NA
41.4 -9.6070508 NA
42 -5.2159485 -0.16084527
42.1 -15.9878743 -0.13812521
43 -16.6104361 -0.08864017
43.1 -9.5549441 -0.12583158
43.2 -14.2003491 -0.29253959
44 -8.1969033 -0.22697597
44.1 -19.9270197 NA
44.2 -22.6521171 NA
44.3 -21.1903736 -0.40544012
45 -0.5686627 -0.19274788
45.1 -7.5645740 -0.34860483
46 -19.1624789 -0.28547861
46.1 -18.4487574 -0.21977836
46.2 -15.8222682 NA
47 -5.4165074 -0.08597098
47.1 -15.0975029 -0.35424828
47.2 -12.9971413 -0.24262576
47.3 -10.6844521 -0.30426315
47.4 -18.2214784 NA
48 -8.3101471 NA
48.1 -18.3854275 NA
49 -13.0130319 -0.42198781
50 -10.4579977 -0.19959516
51 -19.3157621 -0.16556964
52 -4.4747188 -0.07438732
52.1 -4.3163827 -0.37537080
52.2 -6.9761408 -0.24222066
52.3 -20.1764756 -0.31520603
52.4 -8.9036692 -0.44619160
52.5 -5.6949642 -0.11011682
53 -10.3141887 -0.23278716
53.1 -8.2642654 -0.28317264
53.2 -9.1691554 -0.19517481
54 -6.2198754 -0.10122856
54.1 -15.7192609 -0.28325504
54.2 -13.0978998 -0.16753120
54.3 -5.1195299 -0.22217672
54.4 -16.5771751 -0.34609328
55 -5.7348534 -0.32428190
55.1 -7.3217494 -0.24235382
55.2 -12.2171938 -0.24065814
55.3 -12.9821266 -0.23665476
55.4 -14.8599983 NA
56 -14.1764282 NA
56.1 -12.5343602 -0.30357450
56.2 -8.4573382 -0.51301630
56.3 -12.4633969 -0.23743117
56.4 -17.3841863 -0.17264917
56.5 -14.8147645 -0.39188329
57 -3.1403293 -0.18501692
57.1 -11.1509248 -0.27274841
57.2 -6.3940143 NA
57.3 -9.3473241 -0.09898509
58 -12.0245677 -0.29901358
58.1 -9.2112246 -0.35390896
58.2 -1.2071742 -0.16687336
58.3 -11.0141711 -0.11784506
58.4 -5.3721214 -0.05321983
58.5 -7.8523047 -0.54457568
59 -13.2946560 -0.27255364
59.1 -10.0530648 NA
60 -19.2209402 NA
61 -4.6699914 -0.30550120
61.1 -3.5981894 -0.35579892
61.2 -1.4713611 NA
61.3 -3.8819786 -0.34184391
61.4 0.1041413 -0.30891967
62 -2.8591600 NA
62.1 -6.9461986 -0.10504143
62.2 -16.7910593 -0.20104997
62.3 -17.9844596 -0.08138677
63 -24.0335535 -0.12036319
63.1 -11.7765300 -0.13624992
64 -20.5963897 NA
65 -2.7969169 -0.34450396
65.1 -11.1778694 -0.32514650
65.2 -5.2830399 -0.10984996
65.3 -7.9353390 -0.19275692
66 -13.2318328 NA
66.1 -1.9090560 NA
66.2 -16.6643889 -0.11687008
67 -25.6073277 NA
68 -13.4806759 -0.13605235
68.1 -18.4557183 -0.19790827
68.2 -13.3982327 -0.17750123
68.3 -12.4977127 NA
68.4 -11.7073990 -0.12570562
69 -14.5290675 -0.32152751
70 -15.2122709 -0.28190462
70.1 -7.8681167 -0.11503263
71 -10.3352703 -0.13029093
71.1 -7.5699888 NA
71.2 -18.4680702 -0.39075433
71.3 -21.4316644 -0.21401028
71.4 -8.1137650 -0.40219281
72 -9.1848162 -0.40337108
72.1 -23.7538846 -0.25978914
72.2 -26.3421306 NA
72.3 -27.2843801 -0.09809866
72.4 -20.8541617 -0.14240019
72.5 -12.8948965 -0.14794204
73 -2.6091307 -0.23509343
74 -8.2790175 -0.27963171
75 -12.5029612 -0.12905034
76 -6.0061671 0.04775562
76.1 -8.8149114 -0.19399157
76.2 -11.8359043 -0.02754574
77 0.4772521 -0.19053195
78 -9.4105229 -0.17172929
79 -1.0217265 -0.03958515
79.1 -11.8125257 -0.20328809
79.2 -10.5465186 -0.23901634
80 -12.7366807 -0.34031873
80.1 -9.0584783 -0.19526756
80.2 -16.6381566 NA
81 0.5547913 -0.18401980
81.1 -4.0892715 -0.16889476
81.2 1.8283303 -0.37343047
81.3 -5.2166381 NA
82 -3.0749381 -0.08328168
82.1 -10.5506696 -0.22167084
82.2 -18.2226347 -0.20971187
83 -12.5872635 -0.34228255
83.1 -11.9756502 -0.34075730
83.2 -10.6744217 -0.32503954
83.3 -19.2714012 NA
84 -2.6320312 -0.20676741
84.1 -9.8140094 -0.20310458
85 -12.3886736 -0.12107593
85.1 -12.9196365 NA
85.2 -9.6433248 -0.32509207
85.3 -6.3296340 NA
85.4 -7.0405525 -0.30730810
85.5 -13.6714939 NA
86 -10.8756412 -0.10854862
86.1 -12.0055331 -0.25751662
86.2 -13.3724699 -0.38943076
86.3 -13.3252145 -0.24454702
86.4 -14.9191290 -0.12338992
86.5 -17.7515546 -0.23976984
87 -10.7027963 NA
87.1 -22.4941954 -0.34366972
87.2 -14.9616716 NA
88 -2.2264493 -0.31563888
88.1 -8.9626474 -0.20304028
88.2 -2.5095281 -0.40311895
88.3 -16.3345673 -0.12308715
89 -11.0459647 -0.18527715
90 -4.5610239 -0.25029126
90.1 -11.7036651 -0.26974303
90.2 -5.3838521 -0.28804531
90.3 -4.1636999 -0.19180615
91 -7.1462503 -0.26591197
91.1 -12.8374475 -0.09153470
91.2 -18.2576707 -0.48414390
92 -6.4119222 NA
93 5.2122168 -0.11939966
93.1 3.1211725 NA
93.2 -3.6841177 -0.21089379
93.3 2.6223542 NA
93.4 -11.1877696 -0.23618836
94 -6.9602492 NA
94.1 -7.4318416 -0.10217284
94.2 -4.3498045 -0.36713471
94.3 -11.6340088 -0.13806763
94.4 -12.9357964 -0.42353804
94.5 -14.7648530 -0.15513707
95 -12.8849309 -0.24149687
95.1 -9.7451502 -0.21315958
95.2 -0.8535063 -0.15777208
96 -4.9139832 -0.16780948
96.1 -3.9582653 -0.32504815
96.2 -9.6555492 -0.20395970
96.3 -11.8690793 -0.06221501
96.4 -11.0224373 -0.14801097
96.5 -10.9530403 -0.28658893
97 -9.8540471 -0.34484656
97.1 -19.2262840 -0.35658805
98 -11.9651231 -0.36913003
98.1 -2.6515128 NA
98.2 -12.2606382 -0.17154225
99 -11.4720500 -0.24753132
99.1 -14.0596866 -0.27947829
99.2 -17.3939469 -0.09033035
100 1.1005874 -0.17326698
100.1 -3.8226248 NA
100.2 -0.9123182 -0.12072016
100.3 -15.8389474 -0.27657520
100.4 -12.8093826 -0.14631556
$m2a$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
$m2a$mu_reg_norm
[1] 0
$m2a$tau_reg_norm
[1] 1e-04
$m2a$shape_tau_norm
[1] 0.01
$m2a$rate_tau_norm
[1] 0.01
$m2a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m2a$shape_diag_RinvD
[1] "0.01"
$m2a$rate_diag_RinvD
[1] "0.001"
$m2b
$m2b$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m2b$M_lvlone
b2 c2
1 NA NA
1.1 0 -0.08061445
1.2 NA -0.26523782
1.3 0 -0.30260393
2 0 -0.33443795
2.1 NA -0.11819800
2.2 NA -0.31532280
3 0 -0.12920657
3.1 NA NA
3.2 1 NA
4 1 -0.31177403
4.1 0 -0.23894886
4.2 0 -0.15533613
4.3 0 -0.14644545
5 NA -0.28360457
5.1 0 -0.20135143
5.2 NA -0.28293375
5.3 NA NA
6 NA -0.08617066
7 NA -0.22243495
7.1 NA NA
7.2 0 NA
8 0 NA
8.1 0 NA
8.2 NA NA
8.3 1 -0.35148972
8.4 0 0.03661023
8.5 1 -0.08424534
9 0 NA
9.1 NA -0.43509340
9.2 NA -0.22527490
10 NA NA
10.1 0 NA
11 0 -0.08587475
11.1 0 -0.06157340
11.2 0 -0.12436018
11.3 0 -0.21377934
11.4 0 -0.32208329
12 0 NA
13 NA NA
13.1 0 -0.40300449
14 NA -0.28992072
14.1 NA NA
14.2 NA NA
14.3 NA -0.21979936
15 0 NA
15.1 0 -0.29092263
15.2 0 -0.19392239
15.3 0 -0.25718384
16 1 -0.45041108
16.1 NA -0.07599066
16.2 NA -0.32385667
16.3 0 -0.38326110
16.4 0 -0.22845856
16.5 NA -0.25497157
17 0 NA
17.1 0 -0.22105143
17.2 0 NA
17.3 NA NA
17.4 0 -0.15098046
18 0 -0.09870041
19 NA -0.26680239
19.1 NA -0.15815241
19.2 0 -0.14717437
19.3 1 -0.21271374
20 NA -0.22087628
20.1 0 NA
20.2 1 -0.30127439
20.3 0 -0.11782590
20.4 0 -0.19857957
20.5 0 -0.24338208
21 0 -0.31407992
21.1 0 -0.12424941
21.2 NA -0.27672716
22 0 -0.23790593
22.1 0 -0.15996535
23 0 -0.18236682
23.1 NA -0.20823302
24 0 -0.29026416
25 0 -0.36139273
25.1 NA -0.19571118
25.2 1 -0.21379355
25.3 0 -0.33876012
25.4 0 NA
25.5 NA -0.04068446
26 NA -0.16846716
26.1 0 -0.10440642
26.2 0 -0.26884827
26.3 0 NA
27 0 -0.19520794
27.1 0 -0.17622638
28 NA -0.32164962
28.1 0 -0.27003852
28.2 0 -0.07235801
28.3 0 -0.13462982
29 0 -0.32432030
29.1 0 -0.27034171
29.2 0 -0.10197448
29.3 0 -0.27606945
30 NA -0.06949300
30.1 0 -0.11511035
30.2 0 -0.16215882
31 0 0.05707733
32 0 -0.18446298
32.1 0 -0.14270013
32.2 NA -0.20530798
32.3 NA -0.14705649
33 0 -0.15252819
33.1 1 NA
34 NA -0.30378735
34.1 0 -0.11982431
34.2 NA -0.24278671
34.3 NA -0.19971833
35 0 NA
35.1 0 -0.24165780
35.2 NA NA
36 NA -0.49062180
36.1 NA -0.25651700
36.2 0 NA
36.3 0 -0.30401274
36.4 0 NA
37 0 -0.15276529
37.1 0 -0.30016169
37.2 0 0.06809545
38 0 -0.11218486
39 1 -0.38072211
39.1 0 -0.32094428
39.2 NA NA
39.3 NA -0.40173480
39.4 0 -0.20041848
39.5 1 -0.26027990
40 0 -0.19751956
40.1 1 -0.08399467
40.2 0 -0.20864416
40.3 NA NA
41 0 -0.26096953
41.1 NA -0.23953874
41.2 0 -0.03079344
41.3 NA NA
41.4 0 NA
42 0 -0.16084527
42.1 1 -0.13812521
43 0 -0.08864017
43.1 1 -0.12583158
43.2 0 -0.29253959
44 0 -0.22697597
44.1 0 NA
44.2 0 NA
44.3 0 -0.40544012
45 NA -0.19274788
45.1 1 -0.34860483
46 0 -0.28547861
46.1 0 -0.21977836
46.2 0 NA
47 0 -0.08597098
47.1 0 -0.35424828
47.2 0 -0.24262576
47.3 NA -0.30426315
47.4 0 NA
48 1 NA
48.1 1 NA
49 NA -0.42198781
50 0 -0.19959516
51 0 -0.16556964
52 0 -0.07438732
52.1 0 -0.37537080
52.2 0 -0.24222066
52.3 0 -0.31520603
52.4 0 -0.44619160
52.5 0 -0.11011682
53 0 -0.23278716
53.1 0 -0.28317264
53.2 NA -0.19517481
54 NA -0.10122856
54.1 NA -0.28325504
54.2 NA -0.16753120
54.3 NA -0.22217672
54.4 0 -0.34609328
55 0 -0.32428190
55.1 0 -0.24235382
55.2 NA -0.24065814
55.3 NA -0.23665476
55.4 0 NA
56 0 NA
56.1 NA -0.30357450
56.2 NA -0.51301630
56.3 1 -0.23743117
56.4 0 -0.17264917
56.5 0 -0.39188329
57 0 -0.18501692
57.1 0 -0.27274841
57.2 0 NA
57.3 NA -0.09898509
58 0 -0.29901358
58.1 NA -0.35390896
58.2 1 -0.16687336
58.3 1 -0.11784506
58.4 0 -0.05321983
58.5 0 -0.54457568
59 NA -0.27255364
59.1 1 NA
60 0 NA
61 NA -0.30550120
61.1 1 -0.35579892
61.2 1 NA
61.3 0 -0.34184391
61.4 0 -0.30891967
62 NA NA
62.1 1 -0.10504143
62.2 0 -0.20104997
62.3 0 -0.08138677
63 NA -0.12036319
63.1 0 -0.13624992
64 0 NA
65 0 -0.34450396
65.1 0 -0.32514650
65.2 0 -0.10984996
65.3 0 -0.19275692
66 NA NA
66.1 0 NA
66.2 0 -0.11687008
67 NA NA
68 0 -0.13605235
68.1 0 -0.19790827
68.2 NA -0.17750123
68.3 0 NA
68.4 NA -0.12570562
69 0 -0.32152751
70 0 -0.28190462
70.1 0 -0.11503263
71 0 -0.13029093
71.1 1 NA
71.2 0 -0.39075433
71.3 1 -0.21401028
71.4 0 -0.40219281
72 0 -0.40337108
72.1 0 -0.25978914
72.2 NA NA
72.3 0 -0.09809866
72.4 0 -0.14240019
72.5 0 -0.14794204
73 0 -0.23509343
74 0 -0.27963171
75 NA -0.12905034
76 0 0.04775562
76.1 0 -0.19399157
76.2 0 -0.02754574
77 NA -0.19053195
78 0 -0.17172929
79 NA -0.03958515
79.1 0 -0.20328809
79.2 NA -0.23901634
80 NA -0.34031873
80.1 0 -0.19526756
80.2 NA NA
81 0 -0.18401980
81.1 0 -0.16889476
81.2 NA -0.37343047
81.3 0 NA
82 NA -0.08328168
82.1 0 -0.22167084
82.2 1 -0.20971187
83 NA -0.34228255
83.1 0 -0.34075730
83.2 0 -0.32503954
83.3 NA NA
84 0 -0.20676741
84.1 NA -0.20310458
85 1 -0.12107593
85.1 NA NA
85.2 0 -0.32509207
85.3 0 NA
85.4 0 -0.30730810
85.5 0 NA
86 0 -0.10854862
86.1 NA -0.25751662
86.2 NA -0.38943076
86.3 0 -0.24454702
86.4 NA -0.12338992
86.5 0 -0.23976984
87 NA NA
87.1 NA -0.34366972
87.2 NA NA
88 0 -0.31563888
88.1 NA -0.20304028
88.2 0 -0.40311895
88.3 0 -0.12308715
89 0 -0.18527715
90 0 -0.25029126
90.1 0 -0.26974303
90.2 0 -0.28804531
90.3 NA -0.19180615
91 0 -0.26591197
91.1 0 -0.09153470
91.2 0 -0.48414390
92 0 NA
93 NA -0.11939966
93.1 0 NA
93.2 NA -0.21089379
93.3 0 NA
93.4 0 -0.23618836
94 NA NA
94.1 0 -0.10217284
94.2 0 -0.36713471
94.3 NA -0.13806763
94.4 0 -0.42353804
94.5 1 -0.15513707
95 0 -0.24149687
95.1 NA -0.21315958
95.2 0 -0.15777208
96 0 -0.16780948
96.1 0 -0.32504815
96.2 0 -0.20395970
96.3 NA -0.06221501
96.4 1 -0.14801097
96.5 1 -0.28658893
97 0 -0.34484656
97.1 0 -0.35658805
98 0 -0.36913003
98.1 0 NA
98.2 1 -0.17154225
99 0 -0.24753132
99.1 0 -0.27947829
99.2 0 -0.09033035
100 NA -0.17326698
100.1 NA NA
100.2 0 -0.12072016
100.3 NA -0.27657520
100.4 0 -0.14631556
$m2b$spM_lvlone
center scale
b2 NA NA
c2 -0.2237158 0.1059527
$m2b$mu_reg_norm
[1] 0
$m2b$tau_reg_norm
[1] 1e-04
$m2b$shape_tau_norm
[1] 0.01
$m2b$rate_tau_norm
[1] 0.01
$m2b$mu_reg_binom
[1] 0
$m2b$tau_reg_binom
[1] 1e-04
$m2b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m2b$shape_diag_RinvD
[1] "0.01"
$m2b$rate_diag_RinvD
[1] "0.001"
$m2c
$m2c$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m2c$M_lvlone
L1mis c2
1 1.38634787 NA
1.1 0.79402906 -0.08061445
1.2 0.53603334 -0.26523782
1.3 0.24129804 -0.30260393
2 NA -0.33443795
2.1 0.31668065 -0.11819800
2.2 0.37114414 -0.31532280
3 0.54680608 -0.12920657
3.1 0.28280274 NA
3.2 0.76277262 NA
4 0.56100366 -0.31177403
4.1 0.38514140 -0.23894886
4.2 0.04026174 -0.15533613
4.3 0.16025873 -0.14644545
5 0.21080161 -0.28360457
5.1 0.36665700 -0.20135143
5.2 0.66368829 -0.28293375
5.3 0.40788895 NA
6 0.11889539 -0.08617066
7 1.04286843 -0.22243495
7.1 0.52098933 NA
7.2 0.09858876 NA
8 0.17281472 NA
8.1 0.25970093 NA
8.2 0.30550233 NA
8.3 0.88029778 -0.35148972
8.4 0.20200392 0.03661023
8.5 NA -0.08424534
9 1.12218535 NA
9.1 0.57911079 -0.43509340
9.2 0.81350994 -0.22527490
10 0.32744766 NA
10.1 0.62912282 NA
11 0.92140073 -0.08587475
11.1 0.16012129 -0.06157340
11.2 0.16166775 -0.12436018
11.3 0.14979756 -0.21377934
11.4 0.46855190 -0.32208329
12 0.76818678 NA
13 0.34264972 NA
13.1 0.14526619 -0.40300449
14 0.80630788 -0.28992072
14.1 0.35697552 NA
14.2 0.21330192 NA
14.3 NA -0.21979936
15 0.30769119 NA
15.1 0.28349746 -0.29092263
15.2 0.64618365 -0.19392239
15.3 0.51680884 -0.25718384
16 0.71265471 -0.45041108
16.1 0.38925880 -0.07599066
16.2 0.23648869 -0.32385667
16.3 0.45048730 -0.38326110
16.4 0.23181791 -0.22845856
16.5 0.13985349 -0.25497157
17 0.25995399 NA
17.1 0.03594878 -0.22105143
17.2 0.77583623 NA
17.3 0.60015197 NA
17.4 0.13998405 -0.15098046
18 0.96475839 -0.09870041
19 0.10596495 -0.26680239
19.1 0.13338947 -0.15815241
19.2 0.41662218 -0.14717437
19.3 0.53670855 -0.21271374
20 0.41688567 -0.22087628
20.1 NA NA
20.2 0.81634101 -0.30127439
20.3 0.39232496 -0.11782590
20.4 0.57925554 -0.19857957
20.5 0.74200986 -0.24338208
21 0.24759801 -0.31407992
21.1 0.34052205 -0.12424941
21.2 0.03905058 -0.27672716
22 0.48605351 -0.23790593
22.1 0.43761071 -0.15996535
23 0.47599712 -0.18236682
23.1 0.47680301 -0.20823302
24 0.51696505 -0.29026416
25 0.59392591 -0.36139273
25.1 0.74010330 -0.19571118
25.2 NA -0.21379355
25.3 0.73081722 -0.33876012
25.4 0.29274286 NA
25.5 0.74425342 -0.04068446
26 0.20974346 -0.16846716
26.1 NA -0.10440642
26.2 0.22908815 -0.26884827
26.3 0.41880799 NA
27 0.10097167 -0.19520794
27.1 NA -0.17622638
28 NA -0.32164962
28.1 0.56052750 -0.27003852
28.2 0.15301800 -0.07235801
28.3 0.27802542 -0.13462982
29 0.43556671 -0.32432030
29.1 0.27593085 -0.27034171
29.2 0.55256871 -0.10197448
29.3 0.47272109 -0.27606945
30 0.32743933 -0.06949300
30.1 0.02231535 -0.11511035
30.2 0.12833697 -0.16215882
31 0.11126366 0.05707733
32 1.11731084 -0.18446298
32.1 0.85943330 -0.14270013
32.2 1.53730925 -0.20530798
32.3 0.43831965 -0.14705649
33 0.46726055 -0.15252819
33.1 0.76818259 NA
34 NA -0.30378735
34.1 1.14350292 -0.11982431
34.2 0.19103604 -0.24278671
34.3 NA -0.19971833
35 0.66303137 NA
35.1 NA -0.24165780
35.2 NA NA
36 0.93843318 -0.49062180
36.1 NA -0.25651700
36.2 0.29886676 NA
36.3 0.22616598 -0.30401274
36.4 0.53849566 NA
37 1.68107300 -0.15276529
37.1 1.13777638 -0.30016169
37.2 0.26931933 0.06809545
38 NA -0.11218486
39 0.14395367 -0.38072211
39.1 0.36454923 -0.32094428
39.2 1.03700002 NA
39.3 0.41320585 -0.40173480
39.4 0.20901554 -0.20041848
39.5 0.51603848 -0.26027990
40 0.33912363 -0.19751956
40.1 0.21892118 -0.08399467
40.2 0.74070896 -0.20864416
40.3 0.82927399 NA
41 0.25193679 -0.26096953
41.1 0.28760510 -0.23953874
41.2 0.45553197 -0.03079344
41.3 0.79237611 NA
41.4 0.12582175 NA
42 0.50079604 -0.16084527
42.1 0.61140760 -0.13812521
43 0.29752019 -0.08864017
43.1 0.51793497 -0.12583158
43.2 0.15152473 -0.29253959
44 0.38806434 -0.22697597
44.1 1.11140786 NA
44.2 0.39132534 NA
44.3 0.40934909 -0.40544012
45 0.68587067 -0.19274788
45.1 0.34530800 -0.34860483
46 0.71312288 -0.28547861
46.1 0.62537420 -0.21977836
46.2 0.79574391 NA
47 0.48660773 -0.08597098
47.1 0.51241790 -0.35424828
47.2 0.58869379 -0.24262576
47.3 0.22171504 -0.30426315
47.4 0.11366347 NA
48 0.19677010 NA
48.1 0.17706320 NA
49 0.30752382 -0.42198781
50 0.93663423 -0.19959516
51 0.34107606 -0.16556964
52 0.19007135 -0.07438732
52.1 0.75662940 -0.37537080
52.2 1.66104719 -0.24222066
52.3 NA -0.31520603
52.4 0.18369708 -0.44619160
52.5 0.48689343 -0.11011682
53 0.31983157 -0.23278716
53.1 0.61569501 -0.28317264
53.2 NA -0.19517481
54 1.90522418 -0.10122856
54.1 0.59484889 -0.28325504
54.2 1.47174857 -0.16753120
54.3 0.27307143 -0.22217672
54.4 0.81272938 -0.34609328
55 0.22735476 -0.32428190
55.1 0.54683512 -0.24235382
55.2 1.03503777 -0.24065814
55.3 0.30169529 -0.23665476
55.4 0.36008059 NA
56 0.14193566 NA
56.1 0.65073539 -0.30357450
56.2 0.11338262 -0.51301630
56.3 0.16820103 -0.23743117
56.4 0.27419110 -0.17264917
56.5 0.57110215 -0.39188329
57 0.85104054 -0.18501692
57.1 0.34733833 -0.27274841
57.2 1.44438762 NA
57.3 0.31836125 -0.09898509
58 0.37456898 -0.29901358
58.1 0.22120158 -0.35390896
58.2 0.78885210 -0.16687336
58.3 0.10114937 -0.11784506
58.4 0.13385114 -0.05321983
58.5 NA -0.54457568
59 0.13202156 -0.27255364
59.1 0.33371896 NA
60 0.35096579 NA
61 0.36933806 -0.30550120
61.1 0.17623067 -0.35579892
61.2 0.21286227 NA
61.3 0.12689308 -0.34184391
61.4 0.77676718 -0.30891967
62 1.38018163 NA
62.1 0.43803892 -0.10504143
62.2 0.21947900 -0.20104997
62.3 0.11571160 -0.08138677
63 0.41583568 -0.12036319
63.1 0.25598960 -0.13624992
64 0.20415642 NA
65 0.07135646 -0.34450396
65.1 0.57450574 -0.32514650
65.2 0.52562984 -0.10984996
65.3 0.21921164 -0.19275692
66 0.33281730 NA
66.1 0.03412404 NA
66.2 0.92570619 -0.11687008
67 0.15291043 NA
68 0.37543648 -0.13605235
68.1 0.20901022 -0.19790827
68.2 0.12488064 -0.17750123
68.3 0.08711204 NA
68.4 0.54611735 -0.12570562
69 0.23638239 -0.32152751
70 0.49876756 -0.28190462
70.1 0.39512615 -0.11503263
71 0.45666551 -0.13029093
71.1 0.92047456 NA
71.2 0.32792986 -0.39075433
71.3 0.95108007 -0.21401028
71.4 0.36287072 -0.40219281
72 0.12870526 -0.40337108
72.1 0.45925876 -0.25978914
72.2 0.05418867 NA
72.3 0.48937486 -0.09809866
72.4 0.64173822 -0.14240019
72.5 0.57609943 -0.14794204
73 0.17393402 -0.23509343
74 0.23990575 -0.27963171
75 0.28469861 -0.12905034
76 0.71988630 0.04775562
76.1 1.12449946 -0.19399157
76.2 0.71313766 -0.02754574
77 0.02399030 -0.19053195
78 0.42708148 -0.17172929
79 0.37579286 -0.03958515
79.1 0.78660681 -0.20328809
79.2 0.67696116 -0.23901634
80 0.34207854 -0.34031873
80.1 0.60534092 -0.19526756
80.2 0.26731034 NA
81 0.17739052 -0.18401980
81.1 0.35453673 -0.16889476
81.2 0.20244235 -0.37343047
81.3 1.26402329 NA
82 0.09303938 -0.08328168
82.1 0.27254210 -0.22167084
82.2 0.49936304 -0.20971187
83 0.21138572 -0.34228255
83.1 0.26403568 -0.34075730
83.2 0.20311133 -0.32503954
83.3 1.16864671 NA
84 1.99179346 -0.20676741
84.1 1.52199460 -0.20310458
85 NA -0.12107593
85.1 0.61458995 NA
85.2 0.07871196 -0.32509207
85.3 1.42315283 NA
85.4 0.97986129 -0.30730810
85.5 0.91792195 NA
86 0.63509597 -0.10854862
86.1 0.24546597 -0.25751662
86.2 0.53102060 -0.38943076
86.3 0.09360826 -0.24454702
86.4 0.58301186 -0.12338992
86.5 0.39146055 -0.23976984
87 NA NA
87.1 0.66043624 -0.34366972
87.2 0.13267613 NA
88 0.10696344 -0.31563888
88.1 0.13689448 -0.20304028
88.2 0.48037889 -0.40311895
88.3 0.97755681 -0.12308715
89 0.70242369 -0.18527715
90 0.40042977 -0.25029126
90.1 0.63975731 -0.26974303
90.2 0.33412775 -0.28804531
90.3 0.38399003 -0.19180615
91 0.58250391 -0.26591197
91.1 0.13223217 -0.09153470
91.2 0.46613305 -0.48414390
92 0.18997862 NA
93 1.05243347 -0.11939966
93.1 0.01479757 NA
93.2 0.50955172 -0.21089379
93.3 0.78122514 NA
93.4 0.63940704 -0.23618836
94 0.45596305 NA
94.1 0.41610667 -0.10217284
94.2 0.52744298 -0.36713471
94.3 0.70890756 -0.13806763
94.4 0.84412478 -0.42353804
94.5 0.21166602 -0.15513707
95 0.57713135 -0.24149687
95.1 0.44400207 -0.21315958
95.2 0.42397776 -0.15777208
96 0.72391015 -0.16780948
96.1 0.32593738 -0.32504815
96.2 0.23249511 -0.20395970
96.3 1.01679990 -0.06221501
96.4 0.92267953 -0.14801097
96.5 0.83843412 -0.28658893
97 0.47151154 -0.34484656
97.1 0.15596614 -0.35658805
98 0.05179545 -0.36913003
98.1 0.47332096 NA
98.2 0.19706341 -0.17154225
99 0.22574556 -0.24753132
99.1 1.00732330 -0.27947829
99.2 0.09749127 -0.09033035
100 0.22857989 -0.17326698
100.1 0.39548654 NA
100.2 NA -0.12072016
100.3 0.32695372 -0.27657520
100.4 0.10043925 -0.14631556
$m2c$spM_lvlone
center scale
L1mis 0.4818481 0.3462447
c2 -0.2237158 0.1059527
$m2c$mu_reg_norm
[1] 0
$m2c$tau_reg_norm
[1] 1e-04
$m2c$shape_tau_norm
[1] 0.01
$m2c$rate_tau_norm
[1] 0.01
$m2c$mu_reg_gamma
[1] 0
$m2c$tau_reg_gamma
[1] 1e-04
$m2c$shape_tau_gamma
[1] 0.01
$m2c$rate_tau_gamma
[1] 0.01
$m2c$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m2c$shape_diag_RinvD
[1] "0.01"
$m2c$rate_diag_RinvD
[1] "0.001"
$m2d
$m2d$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m2d$M_lvlone
p2 c2
1 2 NA
1.1 2 -0.08061445
1.2 NA -0.26523782
1.3 NA -0.30260393
2 NA -0.33443795
2.1 6 -0.11819800
2.2 3 -0.31532280
3 NA -0.12920657
3.1 NA NA
3.2 NA NA
4 NA -0.31177403
4.1 4 -0.23894886
4.2 0 -0.15533613
4.3 NA -0.14644545
5 2 -0.28360457
5.1 NA -0.20135143
5.2 7 -0.28293375
5.3 NA NA
6 NA -0.08617066
7 NA -0.22243495
7.1 NA NA
7.2 NA NA
8 1 NA
8.1 6 NA
8.2 NA NA
8.3 3 -0.35148972
8.4 2 0.03661023
8.5 1 -0.08424534
9 3 NA
9.1 NA -0.43509340
9.2 3 -0.22527490
10 3 NA
10.1 NA NA
11 1 -0.08587475
11.1 6 -0.06157340
11.2 1 -0.12436018
11.3 6 -0.21377934
11.4 NA -0.32208329
12 NA NA
13 NA NA
13.1 NA -0.40300449
14 NA -0.28992072
14.1 NA NA
14.2 2 NA
14.3 NA -0.21979936
15 NA NA
15.1 NA -0.29092263
15.2 NA -0.19392239
15.3 NA -0.25718384
16 1 -0.45041108
16.1 NA -0.07599066
16.2 2 -0.32385667
16.3 NA -0.38326110
16.4 1 -0.22845856
16.5 NA -0.25497157
17 1 NA
17.1 NA -0.22105143
17.2 3 NA
17.3 2 NA
17.4 NA -0.15098046
18 2 -0.09870041
19 NA -0.26680239
19.1 NA -0.15815241
19.2 2 -0.14717437
19.3 2 -0.21271374
20 NA -0.22087628
20.1 2 NA
20.2 NA -0.30127439
20.3 NA -0.11782590
20.4 NA -0.19857957
20.5 NA -0.24338208
21 2 -0.31407992
21.1 3 -0.12424941
21.2 2 -0.27672716
22 3 -0.23790593
22.1 3 -0.15996535
23 NA -0.18236682
23.1 5 -0.20823302
24 2 -0.29026416
25 3 -0.36139273
25.1 3 -0.19571118
25.2 3 -0.21379355
25.3 4 -0.33876012
25.4 NA NA
25.5 NA -0.04068446
26 NA -0.16846716
26.1 2 -0.10440642
26.2 NA -0.26884827
26.3 NA NA
27 1 -0.19520794
27.1 NA -0.17622638
28 0 -0.32164962
28.1 NA -0.27003852
28.2 4 -0.07235801
28.3 NA -0.13462982
29 3 -0.32432030
29.1 3 -0.27034171
29.2 3 -0.10197448
29.3 2 -0.27606945
30 NA -0.06949300
30.1 NA -0.11511035
30.2 5 -0.16215882
31 8 0.05707733
32 NA -0.18446298
32.1 2 -0.14270013
32.2 1 -0.20530798
32.3 NA -0.14705649
33 0 -0.15252819
33.1 NA NA
34 3 -0.30378735
34.1 NA -0.11982431
34.2 1 -0.24278671
34.3 2 -0.19971833
35 NA NA
35.1 NA -0.24165780
35.2 NA NA
36 5 -0.49062180
36.1 NA -0.25651700
36.2 NA NA
36.3 1 -0.30401274
36.4 1 NA
37 5 -0.15276529
37.1 NA -0.30016169
37.2 NA 0.06809545
38 0 -0.11218486
39 NA -0.38072211
39.1 1 -0.32094428
39.2 NA NA
39.3 NA -0.40173480
39.4 NA -0.20041848
39.5 NA -0.26027990
40 2 -0.19751956
40.1 4 -0.08399467
40.2 NA -0.20864416
40.3 NA NA
41 NA -0.26096953
41.1 4 -0.23953874
41.2 2 -0.03079344
41.3 3 NA
41.4 NA NA
42 3 -0.16084527
42.1 5 -0.13812521
43 4 -0.08864017
43.1 3 -0.12583158
43.2 3 -0.29253959
44 1 -0.22697597
44.1 NA NA
44.2 7 NA
44.3 NA -0.40544012
45 NA -0.19274788
45.1 NA -0.34860483
46 4 -0.28547861
46.1 6 -0.21977836
46.2 NA NA
47 NA -0.08597098
47.1 4 -0.35424828
47.2 2 -0.24262576
47.3 4 -0.30426315
47.4 NA NA
48 NA NA
48.1 6 NA
49 NA -0.42198781
50 3 -0.19959516
51 2 -0.16556964
52 3 -0.07438732
52.1 1 -0.37537080
52.2 NA -0.24222066
52.3 2 -0.31520603
52.4 3 -0.44619160
52.5 1 -0.11011682
53 3 -0.23278716
53.1 NA -0.28317264
53.2 2 -0.19517481
54 3 -0.10122856
54.1 NA -0.28325504
54.2 4 -0.16753120
54.3 0 -0.22217672
54.4 NA -0.34609328
55 NA -0.32428190
55.1 4 -0.24235382
55.2 NA -0.24065814
55.3 4 -0.23665476
55.4 3 NA
56 NA NA
56.1 2 -0.30357450
56.2 3 -0.51301630
56.3 3 -0.23743117
56.4 0 -0.17264917
56.5 NA -0.39188329
57 3 -0.18501692
57.1 4 -0.27274841
57.2 1 NA
57.3 NA -0.09898509
58 NA -0.29901358
58.1 NA -0.35390896
58.2 NA -0.16687336
58.3 3 -0.11784506
58.4 NA -0.05321983
58.5 NA -0.54457568
59 NA -0.27255364
59.1 NA NA
60 NA NA
61 2 -0.30550120
61.1 4 -0.35579892
61.2 NA NA
61.3 NA -0.34184391
61.4 NA -0.30891967
62 2 NA
62.1 NA -0.10504143
62.2 NA -0.20104997
62.3 NA -0.08138677
63 NA -0.12036319
63.1 2 -0.13624992
64 4 NA
65 NA -0.34450396
65.1 5 -0.32514650
65.2 NA -0.10984996
65.3 NA -0.19275692
66 NA NA
66.1 NA NA
66.2 NA -0.11687008
67 NA NA
68 NA -0.13605235
68.1 NA -0.19790827
68.2 NA -0.17750123
68.3 2 NA
68.4 NA -0.12570562
69 NA -0.32152751
70 4 -0.28190462
70.1 4 -0.11503263
71 4 -0.13029093
71.1 NA NA
71.2 3 -0.39075433
71.3 0 -0.21401028
71.4 0 -0.40219281
72 NA -0.40337108
72.1 8 -0.25978914
72.2 NA NA
72.3 NA -0.09809866
72.4 3 -0.14240019
72.5 NA -0.14794204
73 2 -0.23509343
74 NA -0.27963171
75 NA -0.12905034
76 1 0.04775562
76.1 0 -0.19399157
76.2 0 -0.02754574
77 2 -0.19053195
78 NA -0.17172929
79 2 -0.03958515
79.1 NA -0.20328809
79.2 2 -0.23901634
80 2 -0.34031873
80.1 NA -0.19526756
80.2 NA NA
81 NA -0.18401980
81.1 2 -0.16889476
81.2 NA -0.37343047
81.3 NA NA
82 NA -0.08328168
82.1 NA -0.22167084
82.2 4 -0.20971187
83 NA -0.34228255
83.1 NA -0.34075730
83.2 4 -0.32503954
83.3 3 NA
84 NA -0.20676741
84.1 2 -0.20310458
85 3 -0.12107593
85.1 NA NA
85.2 3 -0.32509207
85.3 NA NA
85.4 2 -0.30730810
85.5 1 NA
86 2 -0.10854862
86.1 NA -0.25751662
86.2 0 -0.38943076
86.3 0 -0.24454702
86.4 NA -0.12338992
86.5 2 -0.23976984
87 NA NA
87.1 NA -0.34366972
87.2 3 NA
88 NA -0.31563888
88.1 1 -0.20304028
88.2 1 -0.40311895
88.3 4 -0.12308715
89 NA -0.18527715
90 3 -0.25029126
90.1 NA -0.26974303
90.2 NA -0.28804531
90.3 NA -0.19180615
91 NA -0.26591197
91.1 NA -0.09153470
91.2 NA -0.48414390
92 NA NA
93 2 -0.11939966
93.1 4 NA
93.2 4 -0.21089379
93.3 NA NA
93.4 3 -0.23618836
94 4 NA
94.1 2 -0.10217284
94.2 NA -0.36713471
94.3 1 -0.13806763
94.4 NA -0.42353804
94.5 2 -0.15513707
95 3 -0.24149687
95.1 5 -0.21315958
95.2 2 -0.15777208
96 NA -0.16780948
96.1 NA -0.32504815
96.2 5 -0.20395970
96.3 1 -0.06221501
96.4 0 -0.14801097
96.5 3 -0.28658893
97 4 -0.34484656
97.1 2 -0.35658805
98 3 -0.36913003
98.1 NA NA
98.2 NA -0.17154225
99 5 -0.24753132
99.1 NA -0.27947829
99.2 NA -0.09033035
100 NA -0.17326698
100.1 4 NA
100.2 NA -0.12072016
100.3 4 -0.27657520
100.4 NA -0.14631556
$m2d$spM_lvlone
center scale
p2 2.7125749 1.6247402
c2 -0.2237158 0.1059527
$m2d$mu_reg_norm
[1] 0
$m2d$tau_reg_norm
[1] 1e-04
$m2d$shape_tau_norm
[1] 0.01
$m2d$rate_tau_norm
[1] 0.01
$m2d$mu_reg_poisson
[1] 0
$m2d$tau_reg_poisson
[1] 1e-04
$m2d$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m2d$shape_diag_RinvD
[1] "0.01"
$m2d$rate_diag_RinvD
[1] "0.001"
$m2e
$m2e$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m2e$M_lvlone
L1mis c2
1 1.38634787 NA
1.1 0.79402906 -0.08061445
1.2 0.53603334 -0.26523782
1.3 0.24129804 -0.30260393
2 NA -0.33443795
2.1 0.31668065 -0.11819800
2.2 0.37114414 -0.31532280
3 0.54680608 -0.12920657
3.1 0.28280274 NA
3.2 0.76277262 NA
4 0.56100366 -0.31177403
4.1 0.38514140 -0.23894886
4.2 0.04026174 -0.15533613
4.3 0.16025873 -0.14644545
5 0.21080161 -0.28360457
5.1 0.36665700 -0.20135143
5.2 0.66368829 -0.28293375
5.3 0.40788895 NA
6 0.11889539 -0.08617066
7 1.04286843 -0.22243495
7.1 0.52098933 NA
7.2 0.09858876 NA
8 0.17281472 NA
8.1 0.25970093 NA
8.2 0.30550233 NA
8.3 0.88029778 -0.35148972
8.4 0.20200392 0.03661023
8.5 NA -0.08424534
9 1.12218535 NA
9.1 0.57911079 -0.43509340
9.2 0.81350994 -0.22527490
10 0.32744766 NA
10.1 0.62912282 NA
11 0.92140073 -0.08587475
11.1 0.16012129 -0.06157340
11.2 0.16166775 -0.12436018
11.3 0.14979756 -0.21377934
11.4 0.46855190 -0.32208329
12 0.76818678 NA
13 0.34264972 NA
13.1 0.14526619 -0.40300449
14 0.80630788 -0.28992072
14.1 0.35697552 NA
14.2 0.21330192 NA
14.3 NA -0.21979936
15 0.30769119 NA
15.1 0.28349746 -0.29092263
15.2 0.64618365 -0.19392239
15.3 0.51680884 -0.25718384
16 0.71265471 -0.45041108
16.1 0.38925880 -0.07599066
16.2 0.23648869 -0.32385667
16.3 0.45048730 -0.38326110
16.4 0.23181791 -0.22845856
16.5 0.13985349 -0.25497157
17 0.25995399 NA
17.1 0.03594878 -0.22105143
17.2 0.77583623 NA
17.3 0.60015197 NA
17.4 0.13998405 -0.15098046
18 0.96475839 -0.09870041
19 0.10596495 -0.26680239
19.1 0.13338947 -0.15815241
19.2 0.41662218 -0.14717437
19.3 0.53670855 -0.21271374
20 0.41688567 -0.22087628
20.1 NA NA
20.2 0.81634101 -0.30127439
20.3 0.39232496 -0.11782590
20.4 0.57925554 -0.19857957
20.5 0.74200986 -0.24338208
21 0.24759801 -0.31407992
21.1 0.34052205 -0.12424941
21.2 0.03905058 -0.27672716
22 0.48605351 -0.23790593
22.1 0.43761071 -0.15996535
23 0.47599712 -0.18236682
23.1 0.47680301 -0.20823302
24 0.51696505 -0.29026416
25 0.59392591 -0.36139273
25.1 0.74010330 -0.19571118
25.2 NA -0.21379355
25.3 0.73081722 -0.33876012
25.4 0.29274286 NA
25.5 0.74425342 -0.04068446
26 0.20974346 -0.16846716
26.1 NA -0.10440642
26.2 0.22908815 -0.26884827
26.3 0.41880799 NA
27 0.10097167 -0.19520794
27.1 NA -0.17622638
28 NA -0.32164962
28.1 0.56052750 -0.27003852
28.2 0.15301800 -0.07235801
28.3 0.27802542 -0.13462982
29 0.43556671 -0.32432030
29.1 0.27593085 -0.27034171
29.2 0.55256871 -0.10197448
29.3 0.47272109 -0.27606945
30 0.32743933 -0.06949300
30.1 0.02231535 -0.11511035
30.2 0.12833697 -0.16215882
31 0.11126366 0.05707733
32 1.11731084 -0.18446298
32.1 0.85943330 -0.14270013
32.2 1.53730925 -0.20530798
32.3 0.43831965 -0.14705649
33 0.46726055 -0.15252819
33.1 0.76818259 NA
34 NA -0.30378735
34.1 1.14350292 -0.11982431
34.2 0.19103604 -0.24278671
34.3 NA -0.19971833
35 0.66303137 NA
35.1 NA -0.24165780
35.2 NA NA
36 0.93843318 -0.49062180
36.1 NA -0.25651700
36.2 0.29886676 NA
36.3 0.22616598 -0.30401274
36.4 0.53849566 NA
37 1.68107300 -0.15276529
37.1 1.13777638 -0.30016169
37.2 0.26931933 0.06809545
38 NA -0.11218486
39 0.14395367 -0.38072211
39.1 0.36454923 -0.32094428
39.2 1.03700002 NA
39.3 0.41320585 -0.40173480
39.4 0.20901554 -0.20041848
39.5 0.51603848 -0.26027990
40 0.33912363 -0.19751956
40.1 0.21892118 -0.08399467
40.2 0.74070896 -0.20864416
40.3 0.82927399 NA
41 0.25193679 -0.26096953
41.1 0.28760510 -0.23953874
41.2 0.45553197 -0.03079344
41.3 0.79237611 NA
41.4 0.12582175 NA
42 0.50079604 -0.16084527
42.1 0.61140760 -0.13812521
43 0.29752019 -0.08864017
43.1 0.51793497 -0.12583158
43.2 0.15152473 -0.29253959
44 0.38806434 -0.22697597
44.1 1.11140786 NA
44.2 0.39132534 NA
44.3 0.40934909 -0.40544012
45 0.68587067 -0.19274788
45.1 0.34530800 -0.34860483
46 0.71312288 -0.28547861
46.1 0.62537420 -0.21977836
46.2 0.79574391 NA
47 0.48660773 -0.08597098
47.1 0.51241790 -0.35424828
47.2 0.58869379 -0.24262576
47.3 0.22171504 -0.30426315
47.4 0.11366347 NA
48 0.19677010 NA
48.1 0.17706320 NA
49 0.30752382 -0.42198781
50 0.93663423 -0.19959516
51 0.34107606 -0.16556964
52 0.19007135 -0.07438732
52.1 0.75662940 -0.37537080
52.2 1.66104719 -0.24222066
52.3 NA -0.31520603
52.4 0.18369708 -0.44619160
52.5 0.48689343 -0.11011682
53 0.31983157 -0.23278716
53.1 0.61569501 -0.28317264
53.2 NA -0.19517481
54 1.90522418 -0.10122856
54.1 0.59484889 -0.28325504
54.2 1.47174857 -0.16753120
54.3 0.27307143 -0.22217672
54.4 0.81272938 -0.34609328
55 0.22735476 -0.32428190
55.1 0.54683512 -0.24235382
55.2 1.03503777 -0.24065814
55.3 0.30169529 -0.23665476
55.4 0.36008059 NA
56 0.14193566 NA
56.1 0.65073539 -0.30357450
56.2 0.11338262 -0.51301630
56.3 0.16820103 -0.23743117
56.4 0.27419110 -0.17264917
56.5 0.57110215 -0.39188329
57 0.85104054 -0.18501692
57.1 0.34733833 -0.27274841
57.2 1.44438762 NA
57.3 0.31836125 -0.09898509
58 0.37456898 -0.29901358
58.1 0.22120158 -0.35390896
58.2 0.78885210 -0.16687336
58.3 0.10114937 -0.11784506
58.4 0.13385114 -0.05321983
58.5 NA -0.54457568
59 0.13202156 -0.27255364
59.1 0.33371896 NA
60 0.35096579 NA
61 0.36933806 -0.30550120
61.1 0.17623067 -0.35579892
61.2 0.21286227 NA
61.3 0.12689308 -0.34184391
61.4 0.77676718 -0.30891967
62 1.38018163 NA
62.1 0.43803892 -0.10504143
62.2 0.21947900 -0.20104997
62.3 0.11571160 -0.08138677
63 0.41583568 -0.12036319
63.1 0.25598960 -0.13624992
64 0.20415642 NA
65 0.07135646 -0.34450396
65.1 0.57450574 -0.32514650
65.2 0.52562984 -0.10984996
65.3 0.21921164 -0.19275692
66 0.33281730 NA
66.1 0.03412404 NA
66.2 0.92570619 -0.11687008
67 0.15291043 NA
68 0.37543648 -0.13605235
68.1 0.20901022 -0.19790827
68.2 0.12488064 -0.17750123
68.3 0.08711204 NA
68.4 0.54611735 -0.12570562
69 0.23638239 -0.32152751
70 0.49876756 -0.28190462
70.1 0.39512615 -0.11503263
71 0.45666551 -0.13029093
71.1 0.92047456 NA
71.2 0.32792986 -0.39075433
71.3 0.95108007 -0.21401028
71.4 0.36287072 -0.40219281
72 0.12870526 -0.40337108
72.1 0.45925876 -0.25978914
72.2 0.05418867 NA
72.3 0.48937486 -0.09809866
72.4 0.64173822 -0.14240019
72.5 0.57609943 -0.14794204
73 0.17393402 -0.23509343
74 0.23990575 -0.27963171
75 0.28469861 -0.12905034
76 0.71988630 0.04775562
76.1 1.12449946 -0.19399157
76.2 0.71313766 -0.02754574
77 0.02399030 -0.19053195
78 0.42708148 -0.17172929
79 0.37579286 -0.03958515
79.1 0.78660681 -0.20328809
79.2 0.67696116 -0.23901634
80 0.34207854 -0.34031873
80.1 0.60534092 -0.19526756
80.2 0.26731034 NA
81 0.17739052 -0.18401980
81.1 0.35453673 -0.16889476
81.2 0.20244235 -0.37343047
81.3 1.26402329 NA
82 0.09303938 -0.08328168
82.1 0.27254210 -0.22167084
82.2 0.49936304 -0.20971187
83 0.21138572 -0.34228255
83.1 0.26403568 -0.34075730
83.2 0.20311133 -0.32503954
83.3 1.16864671 NA
84 1.99179346 -0.20676741
84.1 1.52199460 -0.20310458
85 NA -0.12107593
85.1 0.61458995 NA
85.2 0.07871196 -0.32509207
85.3 1.42315283 NA
85.4 0.97986129 -0.30730810
85.5 0.91792195 NA
86 0.63509597 -0.10854862
86.1 0.24546597 -0.25751662
86.2 0.53102060 -0.38943076
86.3 0.09360826 -0.24454702
86.4 0.58301186 -0.12338992
86.5 0.39146055 -0.23976984
87 NA NA
87.1 0.66043624 -0.34366972
87.2 0.13267613 NA
88 0.10696344 -0.31563888
88.1 0.13689448 -0.20304028
88.2 0.48037889 -0.40311895
88.3 0.97755681 -0.12308715
89 0.70242369 -0.18527715
90 0.40042977 -0.25029126
90.1 0.63975731 -0.26974303
90.2 0.33412775 -0.28804531
90.3 0.38399003 -0.19180615
91 0.58250391 -0.26591197
91.1 0.13223217 -0.09153470
91.2 0.46613305 -0.48414390
92 0.18997862 NA
93 1.05243347 -0.11939966
93.1 0.01479757 NA
93.2 0.50955172 -0.21089379
93.3 0.78122514 NA
93.4 0.63940704 -0.23618836
94 0.45596305 NA
94.1 0.41610667 -0.10217284
94.2 0.52744298 -0.36713471
94.3 0.70890756 -0.13806763
94.4 0.84412478 -0.42353804
94.5 0.21166602 -0.15513707
95 0.57713135 -0.24149687
95.1 0.44400207 -0.21315958
95.2 0.42397776 -0.15777208
96 0.72391015 -0.16780948
96.1 0.32593738 -0.32504815
96.2 0.23249511 -0.20395970
96.3 1.01679990 -0.06221501
96.4 0.92267953 -0.14801097
96.5 0.83843412 -0.28658893
97 0.47151154 -0.34484656
97.1 0.15596614 -0.35658805
98 0.05179545 -0.36913003
98.1 0.47332096 NA
98.2 0.19706341 -0.17154225
99 0.22574556 -0.24753132
99.1 1.00732330 -0.27947829
99.2 0.09749127 -0.09033035
100 0.22857989 -0.17326698
100.1 0.39548654 NA
100.2 NA -0.12072016
100.3 0.32695372 -0.27657520
100.4 0.10043925 -0.14631556
$m2e$spM_lvlone
center scale
L1mis 0.4818481 0.3462447
c2 -0.2237158 0.1059527
$m2e$mu_reg_norm
[1] 0
$m2e$tau_reg_norm
[1] 1e-04
$m2e$shape_tau_norm
[1] 0.01
$m2e$rate_tau_norm
[1] 0.01
$m2e$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m2e$shape_diag_RinvD
[1] "0.01"
$m2e$rate_diag_RinvD
[1] "0.001"
$m2f
$m2f$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m2f$M_lvlone
Be2 c2
1 4.596628e-06 NA
1.1 2.296427e-04 -0.08061445
1.2 3.455922e-10 -0.26523782
1.3 9.618613e-07 -0.30260393
2 NA -0.33443795
2.1 1.065639e-07 -0.11819800
2.2 1.320730e-03 -0.31532280
3 9.707820e-06 -0.12920657
3.1 3.645271e-05 NA
3.2 NA NA
4 5.555794e-01 -0.31177403
4.1 6.853316e-06 -0.23894886
4.2 6.324951e-02 -0.15533613
4.3 4.330745e-07 -0.14644545
5 NA -0.28360457
5.1 6.556812e-04 -0.20135143
5.2 6.963312e-06 -0.28293375
5.3 1.159006e-04 NA
6 1.509745e-02 -0.08617066
7 NA -0.22243495
7.1 1.679086e-08 NA
7.2 3.972447e-06 NA
8 9.888512e-02 NA
8.1 8.790334e-05 NA
8.2 NA NA
8.3 5.411705e-04 -0.35148972
8.4 8.446731e-04 0.03661023
8.5 2.059814e-04 -0.08424534
9 4.160033e-01 NA
9.1 NA -0.43509340
9.2 1.087331e-03 -0.22527490
10 9.321715e-04 NA
10.1 8.167897e-06 NA
11 2.528529e-04 -0.08587475
11.1 NA -0.06157340
11.2 5.587553e-10 -0.12436018
11.3 5.240776e-10 -0.21377934
11.4 2.830994e-07 -0.32208329
12 1.962202e-07 NA
13 NA NA
13.1 1.330415e-06 -0.40300449
14 5.900181e-07 -0.28992072
14.1 3.694946e-05 NA
14.2 6.871447e-08 NA
14.3 NA -0.21979936
15 1.848068e-04 NA
15.1 1.714157e-10 -0.29092263
15.2 1.088807e-03 -0.19392239
15.3 2.677330e-05 -0.25718384
16 NA -0.45041108
16.1 1.411453e-04 -0.07599066
16.2 1.897147e-03 -0.32385667
16.3 5.950632e-02 -0.38326110
16.4 3.944608e-02 -0.22845856
16.5 NA -0.25497157
17 4.808238e-05 NA
17.1 6.175264e-04 -0.22105143
17.2 2.319036e-07 NA
17.3 1.393008e-09 NA
17.4 NA -0.15098046
18 2.685853e-09 -0.09870041
19 2.949370e-07 -0.26680239
19.1 1.183423e-08 -0.15815241
19.2 7.844699e-08 -0.14717437
19.3 NA -0.21271374
20 4.920475e-06 -0.22087628
20.1 6.885500e-08 NA
20.2 9.577206e-04 -0.30127439
20.3 1.325632e-03 -0.11782590
20.4 NA -0.19857957
20.5 1.011637e-06 -0.24338208
21 3.032947e-04 -0.31407992
21.1 4.370975e-06 -0.12424941
21.2 8.793700e-06 -0.27672716
22 NA -0.23790593
22.1 7.397166e-06 -0.15996535
23 4.931346e-02 -0.18236682
23.1 3.799306e-02 -0.20823302
24 1.018950e-01 -0.29026416
25 NA -0.36139273
25.1 2.264756e-02 -0.19571118
25.2 6.622343e-07 -0.21379355
25.3 2.802504e-09 -0.33876012
25.4 1.873599e-04 NA
25.5 NA -0.04068446
26 4.587570e-09 -0.16846716
26.1 2.394334e-06 -0.10440642
26.2 4.510972e-08 -0.26884827
26.3 3.657318e-11 NA
27 NA -0.19520794
27.1 8.874134e-06 -0.17622638
28 3.673907e-06 -0.32164962
28.1 4.541426e-04 -0.27003852
28.2 2.697966e-12 -0.07235801
28.3 NA -0.13462982
29 3.282475e-03 -0.32432030
29.1 2.270717e-01 -0.27034171
29.2 9.981536e-03 -0.10197448
29.3 2.343590e-02 -0.27606945
30 NA -0.06949300
30.1 1.591483e-07 -0.11511035
30.2 1.896944e-11 -0.16215882
31 5.546285e-08 0.05707733
32 9.411981e-09 -0.18446298
32.1 1.270914e-08 -0.14270013
32.2 3.910478e-09 -0.20530798
32.3 9.124048e-10 -0.14705649
33 9.056156e-01 -0.15252819
33.1 3.047254e-06 NA
34 1.040462e-04 -0.30378735
34.1 5.714390e-12 -0.11982431
34.2 7.883166e-09 -0.24278671
34.3 3.055823e-07 -0.19971833
35 1.287796e-07 NA
35.1 1.762232e-06 -0.24165780
35.2 5.355159e-08 NA
36 7.250797e-06 -0.49062180
36.1 2.370652e-06 -0.25651700
36.2 1.537090e-05 NA
36.3 6.993214e-07 -0.30401274
36.4 4.950009e-05 NA
37 2.755165e-07 -0.15276529
37.1 3.400517e-07 -0.30016169
37.2 2.489007e-09 0.06809545
38 1.302651e-01 -0.11218486
39 4.343746e-04 -0.38072211
39.1 6.653143e-05 -0.32094428
39.2 1.940204e-09 NA
39.3 8.300468e-07 -0.40173480
39.4 7.464169e-08 -0.20041848
39.5 5.765597e-10 -0.26027990
40 9.140572e-01 -0.19751956
40.1 1.883555e-03 -0.08399467
40.2 2.303001e-01 -0.20864416
40.3 2.799910e-05 NA
41 3.700067e-02 -0.26096953
41.1 5.798225e-06 -0.23953874
41.2 1.086252e-08 -0.03079344
41.3 3.088732e-07 NA
41.4 4.549537e-05 NA
42 5.220968e-03 -0.16084527
42.1 7.264286e-08 -0.13812521
43 1.498125e-07 -0.08864017
43.1 1.316763e-04 -0.12583158
43.2 8.151771e-07 -0.29253959
44 1.032476e-03 -0.22697597
44.1 3.120174e-09 NA
44.2 2.571257e-10 NA
44.3 2.227416e-09 -0.40544012
45 3.948036e-01 -0.19274788
45.1 1.066310e-03 -0.34860483
46 2.219556e-08 -0.28547861
46.1 1.434525e-08 -0.21977836
46.2 1.523026e-07 NA
47 5.404537e-03 -0.08597098
47.1 3.739267e-07 -0.35424828
47.2 7.171916e-06 -0.24262576
47.3 3.850162e-05 -0.30426315
47.4 1.767264e-08 NA
48 1.988010e-04 NA
48.1 6.074589e-09 NA
49 1.321544e-06 -0.42198781
50 4.240393e-05 -0.19959516
51 1.986093e-09 -0.16556964
52 1.632022e-02 -0.07438732
52.1 2.653038e-02 -0.37537080
52.2 2.262881e-03 -0.24222066
52.3 6.572647e-10 -0.31520603
52.4 1.393737e-04 -0.44619160
52.5 5.069462e-03 -0.11011682
53 5.848890e-05 -0.23278716
53.1 1.878509e-04 -0.28317264
53.2 1.293417e-04 -0.19517481
54 1.818441e-03 -0.10122856
54.1 2.251839e-07 -0.28325504
54.2 5.638172e-06 -0.16753120
54.3 5.320676e-03 -0.22217672
54.4 1.491367e-07 -0.34609328
55 3.183775e-03 -0.32428190
55.1 1.183380e-03 -0.24235382
55.2 1.817077e-06 -0.24065814
55.3 1.424370e-06 -0.23665476
55.4 3.119967e-07 NA
56 1.169667e-06 NA
56.1 1.182293e-06 -0.30357450
56.2 2.087533e-04 -0.51301630
56.3 5.728251e-06 -0.23743117
56.4 4.087596e-08 -0.17264917
56.5 8.040370e-07 -0.39188329
57 1.438387e-02 -0.18501692
57.1 3.202179e-05 -0.27274841
57.2 1.486318e-03 NA
57.3 1.718412e-04 -0.09898509
58 3.114123e-05 -0.29901358
58.1 1.403881e-04 -0.35390896
58.2 2.111006e-01 -0.16687336
58.3 9.586985e-06 -0.11784506
58.4 4.073162e-03 -0.05321983
58.5 9.285307e-04 -0.54457568
59 2.711478e-06 -0.27255364
59.1 1.173472e-04 NA
60 7.579680e-09 NA
61 4.545759e-03 -0.30550120
61.1 5.936674e-02 -0.35579892
61.2 3.897281e-01 NA
61.3 6.237379e-02 -0.34184391
61.4 5.103038e-01 -0.30891967
62 3.707353e-02 NA
62.1 1.901660e-03 -0.10504143
62.2 7.844369e-08 -0.20104997
62.3 1.496168e-08 -0.08138677
63 5.101070e-11 -0.12036319
63.1 1.106013e-05 -0.13624992
64 1.685171e-09 NA
65 1.684142e-01 -0.34450396
65.1 1.413479e-05 -0.32514650
65.2 2.841196e-03 -0.10984996
65.3 3.118871e-04 -0.19275692
66 1.078473e-06 NA
66.1 1.136650e-01 NA
66.2 7.007044e-08 -0.11687008
67 4.025749e-11 NA
68 2.469503e-06 -0.13605235
68.1 1.067638e-08 -0.19790827
68.2 1.508555e-06 -0.17750123
68.3 7.862972e-06 NA
68.4 1.970326e-05 -0.12570562
69 5.089430e-07 -0.32152751
70 5.575849e-07 -0.28190462
70.1 6.115107e-04 -0.11503263
71 1.867742e-05 -0.13029093
71.1 4.616167e-04 NA
71.2 5.314611e-08 -0.39075433
71.3 1.790244e-10 -0.21401028
71.4 1.924070e-03 -0.40219281
72 6.526547e-05 -0.40337108
72.1 5.540491e-11 -0.25978914
72.2 2.391191e-12 NA
72.3 2.878783e-12 -0.09809866
72.4 1.014404e-09 -0.14240019
72.5 1.281231e-05 -0.14794204
73 6.661564e-02 -0.23509343
74 3.683842e-04 -0.27963171
75 2.274469e-06 -0.12905034
76 9.155636e-04 0.04775562
76.1 1.485365e-04 -0.19399157
76.2 3.118702e-06 -0.02754574
77 4.946432e-01 -0.19053195
78 8.533933e-05 -0.17172929
79 1.980588e-01 -0.03958515
79.1 8.624235e-06 -0.20328809
79.2 2.176176e-05 -0.23901634
80 2.929029e-06 -0.34031873
80.1 1.126162e-04 -0.19526756
80.2 9.847382e-08 NA
81 4.026095e-01 -0.18401980
81.1 2.093927e-02 -0.16889476
81.2 9.224440e-01 -0.37343047
81.3 8.175654e-03 NA
82 1.228129e-01 -0.08328168
82.1 6.656575e-05 -0.22167084
82.2 2.001426e-08 -0.20971187
83 5.690020e-06 -0.34228255
83.1 5.980615e-06 -0.34075730
83.2 1.880816e-05 -0.32503954
83.3 4.048910e-09 NA
84 6.552173e-02 -0.20676741
84.1 8.829278e-06 -0.20310458
85 4.118253e-06 -0.12107593
85.1 2.311994e-06 NA
85.2 5.182892e-05 -0.32509207
85.3 1.689467e-03 NA
85.4 1.168017e-03 -0.30730810
85.5 7.945131e-07 NA
86 2.905567e-05 -0.10854862
86.1 5.331467e-06 -0.25751662
86.2 1.761451e-06 -0.38943076
86.3 2.272397e-06 -0.24454702
86.4 4.467006e-06 -0.12338992
86.5 1.693940e-08 -0.23976984
87 6.396865e-05 NA
87.1 1.264093e-10 -0.34366972
87.2 4.933807e-07 NA
88 9.223531e-02 -0.31563888
88.1 4.654325e-05 -0.20304028
88.2 1.260399e-01 -0.40311895
88.3 8.029866e-08 -0.12308715
89 7.489307e-05 -0.18527715
90 1.100491e-02 -0.25029126
90.1 2.715349e-05 -0.26974303
90.2 5.916576e-03 -0.28804531
90.3 2.920657e-02 -0.19180615
91 2.411997e-03 -0.26591197
91.1 8.870147e-06 -0.09153470
91.2 1.652965e-08 -0.48414390
92 2.613551e-03 NA
93 9.958480e-01 -0.11939966
93.1 9.915375e-01 NA
93.2 4.861680e-02 -0.21089379
93.3 9.769008e-01 NA
93.4 5.977439e-05 -0.23618836
94 7.091952e-04 NA
94.1 6.005522e-04 -0.10217284
94.2 8.134430e-03 -0.36713471
94.3 1.747604e-05 -0.13806763
94.4 9.404259e-07 -0.42353804
94.5 6.832077e-07 -0.15513707
95 3.216011e-06 -0.24149687
95.1 6.324477e-05 -0.21315958
95.2 1.762187e-01 -0.15777208
96 1.578796e-02 -0.16780948
96.1 2.610661e-02 -0.32504815
96.2 3.941700e-05 -0.20395970
96.3 1.683671e-05 -0.06221501
96.4 1.095127e-04 -0.14801097
96.5 1.479105e-05 -0.28658893
97 2.082560e-04 -0.34484656
97.1 7.903013e-10 -0.35658805
98 1.795949e-06 -0.36913003
98.1 2.776600e-02 NA
98.2 4.050457e-06 -0.17154225
99 2.316802e-05 -0.24753132
99.1 2.206426e-06 -0.27947829
99.2 2.488411e-08 -0.09033035
100 7.572193e-01 -0.17326698
100.1 9.794641e-02 NA
100.2 4.934595e-01 -0.12072016
100.3 1.502083e-07 -0.27657520
100.4 2.515993e-06 -0.14631556
$m2f$spM_lvlone
center scale
Be2 0.04274145 0.1563798
c2 -0.22371584 0.1059527
$m2f$mu_reg_norm
[1] 0
$m2f$tau_reg_norm
[1] 1e-04
$m2f$shape_tau_norm
[1] 0.01
$m2f$rate_tau_norm
[1] 0.01
$m2f$mu_reg_beta
[1] 0
$m2f$tau_reg_beta
[1] 1e-04
$m2f$shape_tau_beta
[1] 0.01
$m2f$rate_tau_beta
[1] 0.01
$m2f$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m2f$shape_diag_RinvD
[1] "0.01"
$m2f$rate_diag_RinvD
[1] "0.001"
$m3a
$m3a$M_id
C2 (Intercept)
1 -1.381594459 1
2 0.344426024 1
3 NA 1
4 -0.228910007 1
5 NA 1
6 -2.143955482 1
7 -1.156567023 1
8 -0.598827660 1
9 NA 1
10 -1.006719032 1
11 0.239801450 1
12 -1.064969789 1
13 -0.538082688 1
14 NA 1
15 -1.781049276 1
16 NA 1
17 NA 1
18 -0.014579883 1
19 -2.121550136 1
20 NA 1
21 -0.363239698 1
22 -0.121568514 1
23 -0.951271111 1
24 NA 1
25 -0.974288621 1
26 -1.130632418 1
27 0.114339868 1
28 0.238334648 1
29 0.840744958 1
30 NA 1
31 NA 1
32 -1.466312154 1
33 -0.637352277 1
34 NA 1
35 NA 1
36 NA 1
37 NA 1
38 NA 1
39 0.006728205 1
40 NA 1
41 -1.663281353 1
42 0.161184794 1
43 0.457939180 1
44 -0.307070331 1
45 NA 1
46 -1.071668276 1
47 -0.814751321 1
48 -0.547630662 1
49 NA 1
50 -1.350213782 1
51 0.719054706 1
52 NA 1
53 -1.207130750 1
54 NA 1
55 -0.408600991 1
56 -0.271380529 1
57 -1.361925974 1
58 NA 1
59 NA 1
60 -0.323712205 1
61 NA 1
62 NA 1
63 -1.386906880 1
64 NA 1
65 NA 1
66 -0.565191691 1
67 -0.382899912 1
68 NA 1
69 -0.405642769 1
70 NA 1
71 -0.843748427 1
72 0.116003683 1
73 -0.778634325 1
74 NA 1
75 NA 1
76 NA 1
77 -0.632974758 1
78 NA 1
79 -0.778064615 1
80 NA 1
81 NA 1
82 -0.246123253 1
83 -1.239659782 1
84 -0.467772280 1
85 NA 1
86 -2.160485036 1
87 -0.657675572 1
88 NA 1
89 -0.696710744 1
90 NA 1
91 -0.179395847 1
92 -0.441545568 1
93 -0.685799334 1
94 NA 1
95 0.191929445 1
96 NA 1
97 -0.069760671 1
98 NA 1
99 NA 1
100 NA 1
$m3a$M_lvlone
y
1 -13.0493856
1.1 -9.3335901
1.2 -22.3469852
1.3 -15.0417337
2 -12.0655434
2.1 -15.8674476
2.2 -7.8800006
3 -11.4820604
3.1 -10.5983220
3.2 -22.4519157
4 -1.2697775
4.1 -11.1215184
4.2 -3.6134138
4.3 -14.5982385
5 -6.8457515
5.1 -7.0551214
5.2 -12.3418980
5.3 -9.2366906
6 -5.1648211
7 -10.0599502
7.1 -18.3267285
7.2 -12.5138426
8 -1.6305331
8.1 -9.6520453
8.2 -1.5278462
8.3 -7.4172211
8.4 -7.1238609
8.5 -8.8706950
9 -0.1634429
9.1 -2.6034300
9.2 -6.7272369
10 -6.4172202
10.1 -11.4834569
11 -8.7911356
11.1 -19.6645080
11.2 -20.2030932
11.3 -21.3082176
11.4 -14.5802901
12 -15.2006287
13 0.8058816
13.1 -13.6379208
14 -15.3422873
14.1 -10.0965208
14.2 -16.6452027
14.3 -15.8389733
15 -8.9424594
15.1 -22.0101983
15.2 -7.3975599
15.3 -10.3567334
16 -1.9691302
16.1 -9.9308357
16.2 -6.9626923
16.3 -3.2862557
16.4 -3.3972355
16.5 -11.5767835
17 -10.5474144
17.1 -7.6215009
17.2 -16.5386939
17.3 -20.0004774
17.4 -18.8505475
18 -19.7302351
19 -14.6177568
19.1 -17.8043866
19.2 -15.1641705
19.3 -16.6898418
20 -12.9059229
20.1 -16.8191201
20.2 -6.1010131
20.3 -7.9415371
20.4 -9.3904458
20.5 -13.3504189
21 -7.6974718
21.1 -11.9335526
21.2 -12.7064929
22 -21.5022909
22.1 -12.7745451
23 -3.5146508
23.1 -4.6724048
24 -2.5619821
25 -6.2944970
25.1 -3.8630505
25.2 -14.4205140
25.3 -19.6735037
25.4 -9.0288933
25.5 -9.0509738
26 -19.7340685
26.1 -14.1692728
26.2 -17.2819976
26.3 -24.6265576
27 -7.3354999
27.1 -11.1488468
28 -11.7996597
28.1 -8.2030122
28.2 -26.4317815
28.3 -18.5016071
29 -5.8551395
29.1 -2.0209442
29.2 -5.6368080
29.3 -3.8110961
30 -12.7217702
30.1 -17.0170140
30.2 -25.4236089
31 -17.0783921
32 -18.4338764
32.1 -19.4317212
32.2 -19.4738978
32.3 -21.4922645
33 2.0838099
33.1 -13.3172274
34 -10.0296691
34.1 -25.9426553
34.2 -18.5688138
34.3 -15.4173859
35 -14.3958113
35.1 -12.9457541
35.2 -16.1380691
36 -12.8166968
36.1 -14.3989481
36.2 -12.2436943
36.3 -15.0104638
36.4 -10.1775457
37 -15.2223495
37.1 -14.7526195
37.2 -19.8168430
38 -2.7065118
39 -8.7288138
39.1 -9.2746473
39.2 -18.2695344
39.3 -13.8219083
39.4 -16.2254704
39.5 -21.7283648
40 1.8291916
40.1 -6.6916432
40.2 -1.6278171
40.3 -10.5749790
41 -3.1556121
41.1 -11.5895327
41.2 -18.9352091
41.3 -15.9788960
41.4 -9.6070508
42 -5.2159485
42.1 -15.9878743
43 -16.6104361
43.1 -9.5549441
43.2 -14.2003491
44 -8.1969033
44.1 -19.9270197
44.2 -22.6521171
44.3 -21.1903736
45 -0.5686627
45.1 -7.5645740
46 -19.1624789
46.1 -18.4487574
46.2 -15.8222682
47 -5.4165074
47.1 -15.0975029
47.2 -12.9971413
47.3 -10.6844521
47.4 -18.2214784
48 -8.3101471
48.1 -18.3854275
49 -13.0130319
50 -10.4579977
51 -19.3157621
52 -4.4747188
52.1 -4.3163827
52.2 -6.9761408
52.3 -20.1764756
52.4 -8.9036692
52.5 -5.6949642
53 -10.3141887
53.1 -8.2642654
53.2 -9.1691554
54 -6.2198754
54.1 -15.7192609
54.2 -13.0978998
54.3 -5.1195299
54.4 -16.5771751
55 -5.7348534
55.1 -7.3217494
55.2 -12.2171938
55.3 -12.9821266
55.4 -14.8599983
56 -14.1764282
56.1 -12.5343602
56.2 -8.4573382
56.3 -12.4633969
56.4 -17.3841863
56.5 -14.8147645
57 -3.1403293
57.1 -11.1509248
57.2 -6.3940143
57.3 -9.3473241
58 -12.0245677
58.1 -9.2112246
58.2 -1.2071742
58.3 -11.0141711
58.4 -5.3721214
58.5 -7.8523047
59 -13.2946560
59.1 -10.0530648
60 -19.2209402
61 -4.6699914
61.1 -3.5981894
61.2 -1.4713611
61.3 -3.8819786
61.4 0.1041413
62 -2.8591600
62.1 -6.9461986
62.2 -16.7910593
62.3 -17.9844596
63 -24.0335535
63.1 -11.7765300
64 -20.5963897
65 -2.7969169
65.1 -11.1778694
65.2 -5.2830399
65.3 -7.9353390
66 -13.2318328
66.1 -1.9090560
66.2 -16.6643889
67 -25.6073277
68 -13.4806759
68.1 -18.4557183
68.2 -13.3982327
68.3 -12.4977127
68.4 -11.7073990
69 -14.5290675
70 -15.2122709
70.1 -7.8681167
71 -10.3352703
71.1 -7.5699888
71.2 -18.4680702
71.3 -21.4316644
71.4 -8.1137650
72 -9.1848162
72.1 -23.7538846
72.2 -26.3421306
72.3 -27.2843801
72.4 -20.8541617
72.5 -12.8948965
73 -2.6091307
74 -8.2790175
75 -12.5029612
76 -6.0061671
76.1 -8.8149114
76.2 -11.8359043
77 0.4772521
78 -9.4105229
79 -1.0217265
79.1 -11.8125257
79.2 -10.5465186
80 -12.7366807
80.1 -9.0584783
80.2 -16.6381566
81 0.5547913
81.1 -4.0892715
81.2 1.8283303
81.3 -5.2166381
82 -3.0749381
82.1 -10.5506696
82.2 -18.2226347
83 -12.5872635
83.1 -11.9756502
83.2 -10.6744217
83.3 -19.2714012
84 -2.6320312
84.1 -9.8140094
85 -12.3886736
85.1 -12.9196365
85.2 -9.6433248
85.3 -6.3296340
85.4 -7.0405525
85.5 -13.6714939
86 -10.8756412
86.1 -12.0055331
86.2 -13.3724699
86.3 -13.3252145
86.4 -14.9191290
86.5 -17.7515546
87 -10.7027963
87.1 -22.4941954
87.2 -14.9616716
88 -2.2264493
88.1 -8.9626474
88.2 -2.5095281
88.3 -16.3345673
89 -11.0459647
90 -4.5610239
90.1 -11.7036651
90.2 -5.3838521
90.3 -4.1636999
91 -7.1462503
91.1 -12.8374475
91.2 -18.2576707
92 -6.4119222
93 5.2122168
93.1 3.1211725
93.2 -3.6841177
93.3 2.6223542
93.4 -11.1877696
94 -6.9602492
94.1 -7.4318416
94.2 -4.3498045
94.3 -11.6340088
94.4 -12.9357964
94.5 -14.7648530
95 -12.8849309
95.1 -9.7451502
95.2 -0.8535063
96 -4.9139832
96.1 -3.9582653
96.2 -9.6555492
96.3 -11.8690793
96.4 -11.0224373
96.5 -10.9530403
97 -9.8540471
97.1 -19.2262840
98 -11.9651231
98.1 -2.6515128
98.2 -12.2606382
99 -11.4720500
99.1 -14.0596866
99.2 -17.3939469
100 1.1005874
100.1 -3.8226248
100.2 -0.9123182
100.3 -15.8389474
100.4 -12.8093826
$m3a$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m3a$mu_reg_norm
[1] 0
$m3a$tau_reg_norm
[1] 1e-04
$m3a$shape_tau_norm
[1] 0.01
$m3a$rate_tau_norm
[1] 0.01
$m3a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m3a$shape_diag_RinvD
[1] "0.01"
$m3a$rate_diag_RinvD
[1] "0.001"
$m3b
$m3b$M_id
C2 (Intercept)
1 -1.381594459 1
2 0.344426024 1
3 NA 1
4 -0.228910007 1
5 NA 1
6 -2.143955482 1
7 -1.156567023 1
8 -0.598827660 1
9 NA 1
10 -1.006719032 1
11 0.239801450 1
12 -1.064969789 1
13 -0.538082688 1
14 NA 1
15 -1.781049276 1
16 NA 1
17 NA 1
18 -0.014579883 1
19 -2.121550136 1
20 NA 1
21 -0.363239698 1
22 -0.121568514 1
23 -0.951271111 1
24 NA 1
25 -0.974288621 1
26 -1.130632418 1
27 0.114339868 1
28 0.238334648 1
29 0.840744958 1
30 NA 1
31 NA 1
32 -1.466312154 1
33 -0.637352277 1
34 NA 1
35 NA 1
36 NA 1
37 NA 1
38 NA 1
39 0.006728205 1
40 NA 1
41 -1.663281353 1
42 0.161184794 1
43 0.457939180 1
44 -0.307070331 1
45 NA 1
46 -1.071668276 1
47 -0.814751321 1
48 -0.547630662 1
49 NA 1
50 -1.350213782 1
51 0.719054706 1
52 NA 1
53 -1.207130750 1
54 NA 1
55 -0.408600991 1
56 -0.271380529 1
57 -1.361925974 1
58 NA 1
59 NA 1
60 -0.323712205 1
61 NA 1
62 NA 1
63 -1.386906880 1
64 NA 1
65 NA 1
66 -0.565191691 1
67 -0.382899912 1
68 NA 1
69 -0.405642769 1
70 NA 1
71 -0.843748427 1
72 0.116003683 1
73 -0.778634325 1
74 NA 1
75 NA 1
76 NA 1
77 -0.632974758 1
78 NA 1
79 -0.778064615 1
80 NA 1
81 NA 1
82 -0.246123253 1
83 -1.239659782 1
84 -0.467772280 1
85 NA 1
86 -2.160485036 1
87 -0.657675572 1
88 NA 1
89 -0.696710744 1
90 NA 1
91 -0.179395847 1
92 -0.441545568 1
93 -0.685799334 1
94 NA 1
95 0.191929445 1
96 NA 1
97 -0.069760671 1
98 NA 1
99 NA 1
100 NA 1
$m3b$M_lvlone
b2
1 NA
1.1 0
1.2 NA
1.3 0
2 0
2.1 NA
2.2 NA
3 0
3.1 NA
3.2 1
4 1
4.1 0
4.2 0
4.3 0
5 NA
5.1 0
5.2 NA
5.3 NA
6 NA
7 NA
7.1 NA
7.2 0
8 0
8.1 0
8.2 NA
8.3 1
8.4 0
8.5 1
9 0
9.1 NA
9.2 NA
10 NA
10.1 0
11 0
11.1 0
11.2 0
11.3 0
11.4 0
12 0
13 NA
13.1 0
14 NA
14.1 NA
14.2 NA
14.3 NA
15 0
15.1 0
15.2 0
15.3 0
16 1
16.1 NA
16.2 NA
16.3 0
16.4 0
16.5 NA
17 0
17.1 0
17.2 0
17.3 NA
17.4 0
18 0
19 NA
19.1 NA
19.2 0
19.3 1
20 NA
20.1 0
20.2 1
20.3 0
20.4 0
20.5 0
21 0
21.1 0
21.2 NA
22 0
22.1 0
23 0
23.1 NA
24 0
25 0
25.1 NA
25.2 1
25.3 0
25.4 0
25.5 NA
26 NA
26.1 0
26.2 0
26.3 0
27 0
27.1 0
28 NA
28.1 0
28.2 0
28.3 0
29 0
29.1 0
29.2 0
29.3 0
30 NA
30.1 0
30.2 0
31 0
32 0
32.1 0
32.2 NA
32.3 NA
33 0
33.1 1
34 NA
34.1 0
34.2 NA
34.3 NA
35 0
35.1 0
35.2 NA
36 NA
36.1 NA
36.2 0
36.3 0
36.4 0
37 0
37.1 0
37.2 0
38 0
39 1
39.1 0
39.2 NA
39.3 NA
39.4 0
39.5 1
40 0
40.1 1
40.2 0
40.3 NA
41 0
41.1 NA
41.2 0
41.3 NA
41.4 0
42 0
42.1 1
43 0
43.1 1
43.2 0
44 0
44.1 0
44.2 0
44.3 0
45 NA
45.1 1
46 0
46.1 0
46.2 0
47 0
47.1 0
47.2 0
47.3 NA
47.4 0
48 1
48.1 1
49 NA
50 0
51 0
52 0
52.1 0
52.2 0
52.3 0
52.4 0
52.5 0
53 0
53.1 0
53.2 NA
54 NA
54.1 NA
54.2 NA
54.3 NA
54.4 0
55 0
55.1 0
55.2 NA
55.3 NA
55.4 0
56 0
56.1 NA
56.2 NA
56.3 1
56.4 0
56.5 0
57 0
57.1 0
57.2 0
57.3 NA
58 0
58.1 NA
58.2 1
58.3 1
58.4 0
58.5 0
59 NA
59.1 1
60 0
61 NA
61.1 1
61.2 1
61.3 0
61.4 0
62 NA
62.1 1
62.2 0
62.3 0
63 NA
63.1 0
64 0
65 0
65.1 0
65.2 0
65.3 0
66 NA
66.1 0
66.2 0
67 NA
68 0
68.1 0
68.2 NA
68.3 0
68.4 NA
69 0
70 0
70.1 0
71 0
71.1 1
71.2 0
71.3 1
71.4 0
72 0
72.1 0
72.2 NA
72.3 0
72.4 0
72.5 0
73 0
74 0
75 NA
76 0
76.1 0
76.2 0
77 NA
78 0
79 NA
79.1 0
79.2 NA
80 NA
80.1 0
80.2 NA
81 0
81.1 0
81.2 NA
81.3 0
82 NA
82.1 0
82.2 1
83 NA
83.1 0
83.2 0
83.3 NA
84 0
84.1 NA
85 1
85.1 NA
85.2 0
85.3 0
85.4 0
85.5 0
86 0
86.1 NA
86.2 NA
86.3 0
86.4 NA
86.5 0
87 NA
87.1 NA
87.2 NA
88 0
88.1 NA
88.2 0
88.3 0
89 0
90 0
90.1 0
90.2 0
90.3 NA
91 0
91.1 0
91.2 0
92 0
93 NA
93.1 0
93.2 NA
93.3 0
93.4 0
94 NA
94.1 0
94.2 0
94.3 NA
94.4 0
94.5 1
95 0
95.1 NA
95.2 0
96 0
96.1 0
96.2 0
96.3 NA
96.4 1
96.5 1
97 0
97.1 0
98 0
98.1 0
98.2 1
99 0
99.1 0
99.2 0
100 NA
100.1 NA
100.2 0
100.3 NA
100.4 0
$m3b$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m3b$mu_reg_norm
[1] 0
$m3b$tau_reg_norm
[1] 1e-04
$m3b$shape_tau_norm
[1] 0.01
$m3b$rate_tau_norm
[1] 0.01
$m3b$mu_reg_binom
[1] 0
$m3b$tau_reg_binom
[1] 1e-04
$m3b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m3b$shape_diag_RinvD
[1] "0.01"
$m3b$rate_diag_RinvD
[1] "0.001"
$m3c
$m3c$M_id
C2 (Intercept)
1 -1.381594459 1
2 0.344426024 1
3 NA 1
4 -0.228910007 1
5 NA 1
6 -2.143955482 1
7 -1.156567023 1
8 -0.598827660 1
9 NA 1
10 -1.006719032 1
11 0.239801450 1
12 -1.064969789 1
13 -0.538082688 1
14 NA 1
15 -1.781049276 1
16 NA 1
17 NA 1
18 -0.014579883 1
19 -2.121550136 1
20 NA 1
21 -0.363239698 1
22 -0.121568514 1
23 -0.951271111 1
24 NA 1
25 -0.974288621 1
26 -1.130632418 1
27 0.114339868 1
28 0.238334648 1
29 0.840744958 1
30 NA 1
31 NA 1
32 -1.466312154 1
33 -0.637352277 1
34 NA 1
35 NA 1
36 NA 1
37 NA 1
38 NA 1
39 0.006728205 1
40 NA 1
41 -1.663281353 1
42 0.161184794 1
43 0.457939180 1
44 -0.307070331 1
45 NA 1
46 -1.071668276 1
47 -0.814751321 1
48 -0.547630662 1
49 NA 1
50 -1.350213782 1
51 0.719054706 1
52 NA 1
53 -1.207130750 1
54 NA 1
55 -0.408600991 1
56 -0.271380529 1
57 -1.361925974 1
58 NA 1
59 NA 1
60 -0.323712205 1
61 NA 1
62 NA 1
63 -1.386906880 1
64 NA 1
65 NA 1
66 -0.565191691 1
67 -0.382899912 1
68 NA 1
69 -0.405642769 1
70 NA 1
71 -0.843748427 1
72 0.116003683 1
73 -0.778634325 1
74 NA 1
75 NA 1
76 NA 1
77 -0.632974758 1
78 NA 1
79 -0.778064615 1
80 NA 1
81 NA 1
82 -0.246123253 1
83 -1.239659782 1
84 -0.467772280 1
85 NA 1
86 -2.160485036 1
87 -0.657675572 1
88 NA 1
89 -0.696710744 1
90 NA 1
91 -0.179395847 1
92 -0.441545568 1
93 -0.685799334 1
94 NA 1
95 0.191929445 1
96 NA 1
97 -0.069760671 1
98 NA 1
99 NA 1
100 NA 1
$m3c$M_lvlone
L1mis
1 1.38634787
1.1 0.79402906
1.2 0.53603334
1.3 0.24129804
2 NA
2.1 0.31668065
2.2 0.37114414
3 0.54680608
3.1 0.28280274
3.2 0.76277262
4 0.56100366
4.1 0.38514140
4.2 0.04026174
4.3 0.16025873
5 0.21080161
5.1 0.36665700
5.2 0.66368829
5.3 0.40788895
6 0.11889539
7 1.04286843
7.1 0.52098933
7.2 0.09858876
8 0.17281472
8.1 0.25970093
8.2 0.30550233
8.3 0.88029778
8.4 0.20200392
8.5 NA
9 1.12218535
9.1 0.57911079
9.2 0.81350994
10 0.32744766
10.1 0.62912282
11 0.92140073
11.1 0.16012129
11.2 0.16166775
11.3 0.14979756
11.4 0.46855190
12 0.76818678
13 0.34264972
13.1 0.14526619
14 0.80630788
14.1 0.35697552
14.2 0.21330192
14.3 NA
15 0.30769119
15.1 0.28349746
15.2 0.64618365
15.3 0.51680884
16 0.71265471
16.1 0.38925880
16.2 0.23648869
16.3 0.45048730
16.4 0.23181791
16.5 0.13985349
17 0.25995399
17.1 0.03594878
17.2 0.77583623
17.3 0.60015197
17.4 0.13998405
18 0.96475839
19 0.10596495
19.1 0.13338947
19.2 0.41662218
19.3 0.53670855
20 0.41688567
20.1 NA
20.2 0.81634101
20.3 0.39232496
20.4 0.57925554
20.5 0.74200986
21 0.24759801
21.1 0.34052205
21.2 0.03905058
22 0.48605351
22.1 0.43761071
23 0.47599712
23.1 0.47680301
24 0.51696505
25 0.59392591
25.1 0.74010330
25.2 NA
25.3 0.73081722
25.4 0.29274286
25.5 0.74425342
26 0.20974346
26.1 NA
26.2 0.22908815
26.3 0.41880799
27 0.10097167
27.1 NA
28 NA
28.1 0.56052750
28.2 0.15301800
28.3 0.27802542
29 0.43556671
29.1 0.27593085
29.2 0.55256871
29.3 0.47272109
30 0.32743933
30.1 0.02231535
30.2 0.12833697
31 0.11126366
32 1.11731084
32.1 0.85943330
32.2 1.53730925
32.3 0.43831965
33 0.46726055
33.1 0.76818259
34 NA
34.1 1.14350292
34.2 0.19103604
34.3 NA
35 0.66303137
35.1 NA
35.2 NA
36 0.93843318
36.1 NA
36.2 0.29886676
36.3 0.22616598
36.4 0.53849566
37 1.68107300
37.1 1.13777638
37.2 0.26931933
38 NA
39 0.14395367
39.1 0.36454923
39.2 1.03700002
39.3 0.41320585
39.4 0.20901554
39.5 0.51603848
40 0.33912363
40.1 0.21892118
40.2 0.74070896
40.3 0.82927399
41 0.25193679
41.1 0.28760510
41.2 0.45553197
41.3 0.79237611
41.4 0.12582175
42 0.50079604
42.1 0.61140760
43 0.29752019
43.1 0.51793497
43.2 0.15152473
44 0.38806434
44.1 1.11140786
44.2 0.39132534
44.3 0.40934909
45 0.68587067
45.1 0.34530800
46 0.71312288
46.1 0.62537420
46.2 0.79574391
47 0.48660773
47.1 0.51241790
47.2 0.58869379
47.3 0.22171504
47.4 0.11366347
48 0.19677010
48.1 0.17706320
49 0.30752382
50 0.93663423
51 0.34107606
52 0.19007135
52.1 0.75662940
52.2 1.66104719
52.3 NA
52.4 0.18369708
52.5 0.48689343
53 0.31983157
53.1 0.61569501
53.2 NA
54 1.90522418
54.1 0.59484889
54.2 1.47174857
54.3 0.27307143
54.4 0.81272938
55 0.22735476
55.1 0.54683512
55.2 1.03503777
55.3 0.30169529
55.4 0.36008059
56 0.14193566
56.1 0.65073539
56.2 0.11338262
56.3 0.16820103
56.4 0.27419110
56.5 0.57110215
57 0.85104054
57.1 0.34733833
57.2 1.44438762
57.3 0.31836125
58 0.37456898
58.1 0.22120158
58.2 0.78885210
58.3 0.10114937
58.4 0.13385114
58.5 NA
59 0.13202156
59.1 0.33371896
60 0.35096579
61 0.36933806
61.1 0.17623067
61.2 0.21286227
61.3 0.12689308
61.4 0.77676718
62 1.38018163
62.1 0.43803892
62.2 0.21947900
62.3 0.11571160
63 0.41583568
63.1 0.25598960
64 0.20415642
65 0.07135646
65.1 0.57450574
65.2 0.52562984
65.3 0.21921164
66 0.33281730
66.1 0.03412404
66.2 0.92570619
67 0.15291043
68 0.37543648
68.1 0.20901022
68.2 0.12488064
68.3 0.08711204
68.4 0.54611735
69 0.23638239
70 0.49876756
70.1 0.39512615
71 0.45666551
71.1 0.92047456
71.2 0.32792986
71.3 0.95108007
71.4 0.36287072
72 0.12870526
72.1 0.45925876
72.2 0.05418867
72.3 0.48937486
72.4 0.64173822
72.5 0.57609943
73 0.17393402
74 0.23990575
75 0.28469861
76 0.71988630
76.1 1.12449946
76.2 0.71313766
77 0.02399030
78 0.42708148
79 0.37579286
79.1 0.78660681
79.2 0.67696116
80 0.34207854
80.1 0.60534092
80.2 0.26731034
81 0.17739052
81.1 0.35453673
81.2 0.20244235
81.3 1.26402329
82 0.09303938
82.1 0.27254210
82.2 0.49936304
83 0.21138572
83.1 0.26403568
83.2 0.20311133
83.3 1.16864671
84 1.99179346
84.1 1.52199460
85 NA
85.1 0.61458995
85.2 0.07871196
85.3 1.42315283
85.4 0.97986129
85.5 0.91792195
86 0.63509597
86.1 0.24546597
86.2 0.53102060
86.3 0.09360826
86.4 0.58301186
86.5 0.39146055
87 NA
87.1 0.66043624
87.2 0.13267613
88 0.10696344
88.1 0.13689448
88.2 0.48037889
88.3 0.97755681
89 0.70242369
90 0.40042977
90.1 0.63975731
90.2 0.33412775
90.3 0.38399003
91 0.58250391
91.1 0.13223217
91.2 0.46613305
92 0.18997862
93 1.05243347
93.1 0.01479757
93.2 0.50955172
93.3 0.78122514
93.4 0.63940704
94 0.45596305
94.1 0.41610667
94.2 0.52744298
94.3 0.70890756
94.4 0.84412478
94.5 0.21166602
95 0.57713135
95.1 0.44400207
95.2 0.42397776
96 0.72391015
96.1 0.32593738
96.2 0.23249511
96.3 1.01679990
96.4 0.92267953
96.5 0.83843412
97 0.47151154
97.1 0.15596614
98 0.05179545
98.1 0.47332096
98.2 0.19706341
99 0.22574556
99.1 1.00732330
99.2 0.09749127
100 0.22857989
100.1 0.39548654
100.2 NA
100.3 0.32695372
100.4 0.10043925
$m3c$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m3c$mu_reg_norm
[1] 0
$m3c$tau_reg_norm
[1] 1e-04
$m3c$shape_tau_norm
[1] 0.01
$m3c$rate_tau_norm
[1] 0.01
$m3c$mu_reg_gamma
[1] 0
$m3c$tau_reg_gamma
[1] 1e-04
$m3c$shape_tau_gamma
[1] 0.01
$m3c$rate_tau_gamma
[1] 0.01
$m3c$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m3c$shape_diag_RinvD
[1] "0.01"
$m3c$rate_diag_RinvD
[1] "0.001"
$m3d
$m3d$M_id
C2 (Intercept)
1 -1.381594459 1
2 0.344426024 1
3 NA 1
4 -0.228910007 1
5 NA 1
6 -2.143955482 1
7 -1.156567023 1
8 -0.598827660 1
9 NA 1
10 -1.006719032 1
11 0.239801450 1
12 -1.064969789 1
13 -0.538082688 1
14 NA 1
15 -1.781049276 1
16 NA 1
17 NA 1
18 -0.014579883 1
19 -2.121550136 1
20 NA 1
21 -0.363239698 1
22 -0.121568514 1
23 -0.951271111 1
24 NA 1
25 -0.974288621 1
26 -1.130632418 1
27 0.114339868 1
28 0.238334648 1
29 0.840744958 1
30 NA 1
31 NA 1
32 -1.466312154 1
33 -0.637352277 1
34 NA 1
35 NA 1
36 NA 1
37 NA 1
38 NA 1
39 0.006728205 1
40 NA 1
41 -1.663281353 1
42 0.161184794 1
43 0.457939180 1
44 -0.307070331 1
45 NA 1
46 -1.071668276 1
47 -0.814751321 1
48 -0.547630662 1
49 NA 1
50 -1.350213782 1
51 0.719054706 1
52 NA 1
53 -1.207130750 1
54 NA 1
55 -0.408600991 1
56 -0.271380529 1
57 -1.361925974 1
58 NA 1
59 NA 1
60 -0.323712205 1
61 NA 1
62 NA 1
63 -1.386906880 1
64 NA 1
65 NA 1
66 -0.565191691 1
67 -0.382899912 1
68 NA 1
69 -0.405642769 1
70 NA 1
71 -0.843748427 1
72 0.116003683 1
73 -0.778634325 1
74 NA 1
75 NA 1
76 NA 1
77 -0.632974758 1
78 NA 1
79 -0.778064615 1
80 NA 1
81 NA 1
82 -0.246123253 1
83 -1.239659782 1
84 -0.467772280 1
85 NA 1
86 -2.160485036 1
87 -0.657675572 1
88 NA 1
89 -0.696710744 1
90 NA 1
91 -0.179395847 1
92 -0.441545568 1
93 -0.685799334 1
94 NA 1
95 0.191929445 1
96 NA 1
97 -0.069760671 1
98 NA 1
99 NA 1
100 NA 1
$m3d$M_lvlone
p2
1 2
1.1 2
1.2 NA
1.3 NA
2 NA
2.1 6
2.2 3
3 NA
3.1 NA
3.2 NA
4 NA
4.1 4
4.2 0
4.3 NA
5 2
5.1 NA
5.2 7
5.3 NA
6 NA
7 NA
7.1 NA
7.2 NA
8 1
8.1 6
8.2 NA
8.3 3
8.4 2
8.5 1
9 3
9.1 NA
9.2 3
10 3
10.1 NA
11 1
11.1 6
11.2 1
11.3 6
11.4 NA
12 NA
13 NA
13.1 NA
14 NA
14.1 NA
14.2 2
14.3 NA
15 NA
15.1 NA
15.2 NA
15.3 NA
16 1
16.1 NA
16.2 2
16.3 NA
16.4 1
16.5 NA
17 1
17.1 NA
17.2 3
17.3 2
17.4 NA
18 2
19 NA
19.1 NA
19.2 2
19.3 2
20 NA
20.1 2
20.2 NA
20.3 NA
20.4 NA
20.5 NA
21 2
21.1 3
21.2 2
22 3
22.1 3
23 NA
23.1 5
24 2
25 3
25.1 3
25.2 3
25.3 4
25.4 NA
25.5 NA
26 NA
26.1 2
26.2 NA
26.3 NA
27 1
27.1 NA
28 0
28.1 NA
28.2 4
28.3 NA
29 3
29.1 3
29.2 3
29.3 2
30 NA
30.1 NA
30.2 5
31 8
32 NA
32.1 2
32.2 1
32.3 NA
33 0
33.1 NA
34 3
34.1 NA
34.2 1
34.3 2
35 NA
35.1 NA
35.2 NA
36 5
36.1 NA
36.2 NA
36.3 1
36.4 1
37 5
37.1 NA
37.2 NA
38 0
39 NA
39.1 1
39.2 NA
39.3 NA
39.4 NA
39.5 NA
40 2
40.1 4
40.2 NA
40.3 NA
41 NA
41.1 4
41.2 2
41.3 3
41.4 NA
42 3
42.1 5
43 4
43.1 3
43.2 3
44 1
44.1 NA
44.2 7
44.3 NA
45 NA
45.1 NA
46 4
46.1 6
46.2 NA
47 NA
47.1 4
47.2 2
47.3 4
47.4 NA
48 NA
48.1 6
49 NA
50 3
51 2
52 3
52.1 1
52.2 NA
52.3 2
52.4 3
52.5 1
53 3
53.1 NA
53.2 2
54 3
54.1 NA
54.2 4
54.3 0
54.4 NA
55 NA
55.1 4
55.2 NA
55.3 4
55.4 3
56 NA
56.1 2
56.2 3
56.3 3
56.4 0
56.5 NA
57 3
57.1 4
57.2 1
57.3 NA
58 NA
58.1 NA
58.2 NA
58.3 3
58.4 NA
58.5 NA
59 NA
59.1 NA
60 NA
61 2
61.1 4
61.2 NA
61.3 NA
61.4 NA
62 2
62.1 NA
62.2 NA
62.3 NA
63 NA
63.1 2
64 4
65 NA
65.1 5
65.2 NA
65.3 NA
66 NA
66.1 NA
66.2 NA
67 NA
68 NA
68.1 NA
68.2 NA
68.3 2
68.4 NA
69 NA
70 4
70.1 4
71 4
71.1 NA
71.2 3
71.3 0
71.4 0
72 NA
72.1 8
72.2 NA
72.3 NA
72.4 3
72.5 NA
73 2
74 NA
75 NA
76 1
76.1 0
76.2 0
77 2
78 NA
79 2
79.1 NA
79.2 2
80 2
80.1 NA
80.2 NA
81 NA
81.1 2
81.2 NA
81.3 NA
82 NA
82.1 NA
82.2 4
83 NA
83.1 NA
83.2 4
83.3 3
84 NA
84.1 2
85 3
85.1 NA
85.2 3
85.3 NA
85.4 2
85.5 1
86 2
86.1 NA
86.2 0
86.3 0
86.4 NA
86.5 2
87 NA
87.1 NA
87.2 3
88 NA
88.1 1
88.2 1
88.3 4
89 NA
90 3
90.1 NA
90.2 NA
90.3 NA
91 NA
91.1 NA
91.2 NA
92 NA
93 2
93.1 4
93.2 4
93.3 NA
93.4 3
94 4
94.1 2
94.2 NA
94.3 1
94.4 NA
94.5 2
95 3
95.1 5
95.2 2
96 NA
96.1 NA
96.2 5
96.3 1
96.4 0
96.5 3
97 4
97.1 2
98 3
98.1 NA
98.2 NA
99 5
99.1 NA
99.2 NA
100 NA
100.1 4
100.2 NA
100.3 4
100.4 NA
$m3d$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m3d$mu_reg_norm
[1] 0
$m3d$tau_reg_norm
[1] 1e-04
$m3d$shape_tau_norm
[1] 0.01
$m3d$rate_tau_norm
[1] 0.01
$m3d$mu_reg_poisson
[1] 0
$m3d$tau_reg_poisson
[1] 1e-04
$m3d$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m3d$shape_diag_RinvD
[1] "0.01"
$m3d$rate_diag_RinvD
[1] "0.001"
$m3e
$m3e$M_id
C2 (Intercept)
1 -1.381594459 1
2 0.344426024 1
3 NA 1
4 -0.228910007 1
5 NA 1
6 -2.143955482 1
7 -1.156567023 1
8 -0.598827660 1
9 NA 1
10 -1.006719032 1
11 0.239801450 1
12 -1.064969789 1
13 -0.538082688 1
14 NA 1
15 -1.781049276 1
16 NA 1
17 NA 1
18 -0.014579883 1
19 -2.121550136 1
20 NA 1
21 -0.363239698 1
22 -0.121568514 1
23 -0.951271111 1
24 NA 1
25 -0.974288621 1
26 -1.130632418 1
27 0.114339868 1
28 0.238334648 1
29 0.840744958 1
30 NA 1
31 NA 1
32 -1.466312154 1
33 -0.637352277 1
34 NA 1
35 NA 1
36 NA 1
37 NA 1
38 NA 1
39 0.006728205 1
40 NA 1
41 -1.663281353 1
42 0.161184794 1
43 0.457939180 1
44 -0.307070331 1
45 NA 1
46 -1.071668276 1
47 -0.814751321 1
48 -0.547630662 1
49 NA 1
50 -1.350213782 1
51 0.719054706 1
52 NA 1
53 -1.207130750 1
54 NA 1
55 -0.408600991 1
56 -0.271380529 1
57 -1.361925974 1
58 NA 1
59 NA 1
60 -0.323712205 1
61 NA 1
62 NA 1
63 -1.386906880 1
64 NA 1
65 NA 1
66 -0.565191691 1
67 -0.382899912 1
68 NA 1
69 -0.405642769 1
70 NA 1
71 -0.843748427 1
72 0.116003683 1
73 -0.778634325 1
74 NA 1
75 NA 1
76 NA 1
77 -0.632974758 1
78 NA 1
79 -0.778064615 1
80 NA 1
81 NA 1
82 -0.246123253 1
83 -1.239659782 1
84 -0.467772280 1
85 NA 1
86 -2.160485036 1
87 -0.657675572 1
88 NA 1
89 -0.696710744 1
90 NA 1
91 -0.179395847 1
92 -0.441545568 1
93 -0.685799334 1
94 NA 1
95 0.191929445 1
96 NA 1
97 -0.069760671 1
98 NA 1
99 NA 1
100 NA 1
$m3e$M_lvlone
L1mis
1 1.38634787
1.1 0.79402906
1.2 0.53603334
1.3 0.24129804
2 NA
2.1 0.31668065
2.2 0.37114414
3 0.54680608
3.1 0.28280274
3.2 0.76277262
4 0.56100366
4.1 0.38514140
4.2 0.04026174
4.3 0.16025873
5 0.21080161
5.1 0.36665700
5.2 0.66368829
5.3 0.40788895
6 0.11889539
7 1.04286843
7.1 0.52098933
7.2 0.09858876
8 0.17281472
8.1 0.25970093
8.2 0.30550233
8.3 0.88029778
8.4 0.20200392
8.5 NA
9 1.12218535
9.1 0.57911079
9.2 0.81350994
10 0.32744766
10.1 0.62912282
11 0.92140073
11.1 0.16012129
11.2 0.16166775
11.3 0.14979756
11.4 0.46855190
12 0.76818678
13 0.34264972
13.1 0.14526619
14 0.80630788
14.1 0.35697552
14.2 0.21330192
14.3 NA
15 0.30769119
15.1 0.28349746
15.2 0.64618365
15.3 0.51680884
16 0.71265471
16.1 0.38925880
16.2 0.23648869
16.3 0.45048730
16.4 0.23181791
16.5 0.13985349
17 0.25995399
17.1 0.03594878
17.2 0.77583623
17.3 0.60015197
17.4 0.13998405
18 0.96475839
19 0.10596495
19.1 0.13338947
19.2 0.41662218
19.3 0.53670855
20 0.41688567
20.1 NA
20.2 0.81634101
20.3 0.39232496
20.4 0.57925554
20.5 0.74200986
21 0.24759801
21.1 0.34052205
21.2 0.03905058
22 0.48605351
22.1 0.43761071
23 0.47599712
23.1 0.47680301
24 0.51696505
25 0.59392591
25.1 0.74010330
25.2 NA
25.3 0.73081722
25.4 0.29274286
25.5 0.74425342
26 0.20974346
26.1 NA
26.2 0.22908815
26.3 0.41880799
27 0.10097167
27.1 NA
28 NA
28.1 0.56052750
28.2 0.15301800
28.3 0.27802542
29 0.43556671
29.1 0.27593085
29.2 0.55256871
29.3 0.47272109
30 0.32743933
30.1 0.02231535
30.2 0.12833697
31 0.11126366
32 1.11731084
32.1 0.85943330
32.2 1.53730925
32.3 0.43831965
33 0.46726055
33.1 0.76818259
34 NA
34.1 1.14350292
34.2 0.19103604
34.3 NA
35 0.66303137
35.1 NA
35.2 NA
36 0.93843318
36.1 NA
36.2 0.29886676
36.3 0.22616598
36.4 0.53849566
37 1.68107300
37.1 1.13777638
37.2 0.26931933
38 NA
39 0.14395367
39.1 0.36454923
39.2 1.03700002
39.3 0.41320585
39.4 0.20901554
39.5 0.51603848
40 0.33912363
40.1 0.21892118
40.2 0.74070896
40.3 0.82927399
41 0.25193679
41.1 0.28760510
41.2 0.45553197
41.3 0.79237611
41.4 0.12582175
42 0.50079604
42.1 0.61140760
43 0.29752019
43.1 0.51793497
43.2 0.15152473
44 0.38806434
44.1 1.11140786
44.2 0.39132534
44.3 0.40934909
45 0.68587067
45.1 0.34530800
46 0.71312288
46.1 0.62537420
46.2 0.79574391
47 0.48660773
47.1 0.51241790
47.2 0.58869379
47.3 0.22171504
47.4 0.11366347
48 0.19677010
48.1 0.17706320
49 0.30752382
50 0.93663423
51 0.34107606
52 0.19007135
52.1 0.75662940
52.2 1.66104719
52.3 NA
52.4 0.18369708
52.5 0.48689343
53 0.31983157
53.1 0.61569501
53.2 NA
54 1.90522418
54.1 0.59484889
54.2 1.47174857
54.3 0.27307143
54.4 0.81272938
55 0.22735476
55.1 0.54683512
55.2 1.03503777
55.3 0.30169529
55.4 0.36008059
56 0.14193566
56.1 0.65073539
56.2 0.11338262
56.3 0.16820103
56.4 0.27419110
56.5 0.57110215
57 0.85104054
57.1 0.34733833
57.2 1.44438762
57.3 0.31836125
58 0.37456898
58.1 0.22120158
58.2 0.78885210
58.3 0.10114937
58.4 0.13385114
58.5 NA
59 0.13202156
59.1 0.33371896
60 0.35096579
61 0.36933806
61.1 0.17623067
61.2 0.21286227
61.3 0.12689308
61.4 0.77676718
62 1.38018163
62.1 0.43803892
62.2 0.21947900
62.3 0.11571160
63 0.41583568
63.1 0.25598960
64 0.20415642
65 0.07135646
65.1 0.57450574
65.2 0.52562984
65.3 0.21921164
66 0.33281730
66.1 0.03412404
66.2 0.92570619
67 0.15291043
68 0.37543648
68.1 0.20901022
68.2 0.12488064
68.3 0.08711204
68.4 0.54611735
69 0.23638239
70 0.49876756
70.1 0.39512615
71 0.45666551
71.1 0.92047456
71.2 0.32792986
71.3 0.95108007
71.4 0.36287072
72 0.12870526
72.1 0.45925876
72.2 0.05418867
72.3 0.48937486
72.4 0.64173822
72.5 0.57609943
73 0.17393402
74 0.23990575
75 0.28469861
76 0.71988630
76.1 1.12449946
76.2 0.71313766
77 0.02399030
78 0.42708148
79 0.37579286
79.1 0.78660681
79.2 0.67696116
80 0.34207854
80.1 0.60534092
80.2 0.26731034
81 0.17739052
81.1 0.35453673
81.2 0.20244235
81.3 1.26402329
82 0.09303938
82.1 0.27254210
82.2 0.49936304
83 0.21138572
83.1 0.26403568
83.2 0.20311133
83.3 1.16864671
84 1.99179346
84.1 1.52199460
85 NA
85.1 0.61458995
85.2 0.07871196
85.3 1.42315283
85.4 0.97986129
85.5 0.91792195
86 0.63509597
86.1 0.24546597
86.2 0.53102060
86.3 0.09360826
86.4 0.58301186
86.5 0.39146055
87 NA
87.1 0.66043624
87.2 0.13267613
88 0.10696344
88.1 0.13689448
88.2 0.48037889
88.3 0.97755681
89 0.70242369
90 0.40042977
90.1 0.63975731
90.2 0.33412775
90.3 0.38399003
91 0.58250391
91.1 0.13223217
91.2 0.46613305
92 0.18997862
93 1.05243347
93.1 0.01479757
93.2 0.50955172
93.3 0.78122514
93.4 0.63940704
94 0.45596305
94.1 0.41610667
94.2 0.52744298
94.3 0.70890756
94.4 0.84412478
94.5 0.21166602
95 0.57713135
95.1 0.44400207
95.2 0.42397776
96 0.72391015
96.1 0.32593738
96.2 0.23249511
96.3 1.01679990
96.4 0.92267953
96.5 0.83843412
97 0.47151154
97.1 0.15596614
98 0.05179545
98.1 0.47332096
98.2 0.19706341
99 0.22574556
99.1 1.00732330
99.2 0.09749127
100 0.22857989
100.1 0.39548654
100.2 NA
100.3 0.32695372
100.4 0.10043925
$m3e$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m3e$mu_reg_norm
[1] 0
$m3e$tau_reg_norm
[1] 1e-04
$m3e$shape_tau_norm
[1] 0.01
$m3e$rate_tau_norm
[1] 0.01
$m3e$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m3e$shape_diag_RinvD
[1] "0.01"
$m3e$rate_diag_RinvD
[1] "0.001"
$m3f
$m3f$M_id
C2 (Intercept)
1 -1.381594459 1
2 0.344426024 1
3 NA 1
4 -0.228910007 1
5 NA 1
6 -2.143955482 1
7 -1.156567023 1
8 -0.598827660 1
9 NA 1
10 -1.006719032 1
11 0.239801450 1
12 -1.064969789 1
13 -0.538082688 1
14 NA 1
15 -1.781049276 1
16 NA 1
17 NA 1
18 -0.014579883 1
19 -2.121550136 1
20 NA 1
21 -0.363239698 1
22 -0.121568514 1
23 -0.951271111 1
24 NA 1
25 -0.974288621 1
26 -1.130632418 1
27 0.114339868 1
28 0.238334648 1
29 0.840744958 1
30 NA 1
31 NA 1
32 -1.466312154 1
33 -0.637352277 1
34 NA 1
35 NA 1
36 NA 1
37 NA 1
38 NA 1
39 0.006728205 1
40 NA 1
41 -1.663281353 1
42 0.161184794 1
43 0.457939180 1
44 -0.307070331 1
45 NA 1
46 -1.071668276 1
47 -0.814751321 1
48 -0.547630662 1
49 NA 1
50 -1.350213782 1
51 0.719054706 1
52 NA 1
53 -1.207130750 1
54 NA 1
55 -0.408600991 1
56 -0.271380529 1
57 -1.361925974 1
58 NA 1
59 NA 1
60 -0.323712205 1
61 NA 1
62 NA 1
63 -1.386906880 1
64 NA 1
65 NA 1
66 -0.565191691 1
67 -0.382899912 1
68 NA 1
69 -0.405642769 1
70 NA 1
71 -0.843748427 1
72 0.116003683 1
73 -0.778634325 1
74 NA 1
75 NA 1
76 NA 1
77 -0.632974758 1
78 NA 1
79 -0.778064615 1
80 NA 1
81 NA 1
82 -0.246123253 1
83 -1.239659782 1
84 -0.467772280 1
85 NA 1
86 -2.160485036 1
87 -0.657675572 1
88 NA 1
89 -0.696710744 1
90 NA 1
91 -0.179395847 1
92 -0.441545568 1
93 -0.685799334 1
94 NA 1
95 0.191929445 1
96 NA 1
97 -0.069760671 1
98 NA 1
99 NA 1
100 NA 1
$m3f$M_lvlone
Be2
1 4.596628e-06
1.1 2.296427e-04
1.2 3.455922e-10
1.3 9.618613e-07
2 NA
2.1 1.065639e-07
2.2 1.320730e-03
3 9.707820e-06
3.1 3.645271e-05
3.2 NA
4 5.555794e-01
4.1 6.853316e-06
4.2 6.324951e-02
4.3 4.330745e-07
5 NA
5.1 6.556812e-04
5.2 6.963312e-06
5.3 1.159006e-04
6 1.509745e-02
7 NA
7.1 1.679086e-08
7.2 3.972447e-06
8 9.888512e-02
8.1 8.790334e-05
8.2 NA
8.3 5.411705e-04
8.4 8.446731e-04
8.5 2.059814e-04
9 4.160033e-01
9.1 NA
9.2 1.087331e-03
10 9.321715e-04
10.1 8.167897e-06
11 2.528529e-04
11.1 NA
11.2 5.587553e-10
11.3 5.240776e-10
11.4 2.830994e-07
12 1.962202e-07
13 NA
13.1 1.330415e-06
14 5.900181e-07
14.1 3.694946e-05
14.2 6.871447e-08
14.3 NA
15 1.848068e-04
15.1 1.714157e-10
15.2 1.088807e-03
15.3 2.677330e-05
16 NA
16.1 1.411453e-04
16.2 1.897147e-03
16.3 5.950632e-02
16.4 3.944608e-02
16.5 NA
17 4.808238e-05
17.1 6.175264e-04
17.2 2.319036e-07
17.3 1.393008e-09
17.4 NA
18 2.685853e-09
19 2.949370e-07
19.1 1.183423e-08
19.2 7.844699e-08
19.3 NA
20 4.920475e-06
20.1 6.885500e-08
20.2 9.577206e-04
20.3 1.325632e-03
20.4 NA
20.5 1.011637e-06
21 3.032947e-04
21.1 4.370975e-06
21.2 8.793700e-06
22 NA
22.1 7.397166e-06
23 4.931346e-02
23.1 3.799306e-02
24 1.018950e-01
25 NA
25.1 2.264756e-02
25.2 6.622343e-07
25.3 2.802504e-09
25.4 1.873599e-04
25.5 NA
26 4.587570e-09
26.1 2.394334e-06
26.2 4.510972e-08
26.3 3.657318e-11
27 NA
27.1 8.874134e-06
28 3.673907e-06
28.1 4.541426e-04
28.2 2.697966e-12
28.3 NA
29 3.282475e-03
29.1 2.270717e-01
29.2 9.981536e-03
29.3 2.343590e-02
30 NA
30.1 1.591483e-07
30.2 1.896944e-11
31 5.546285e-08
32 9.411981e-09
32.1 1.270914e-08
32.2 3.910478e-09
32.3 9.124048e-10
33 9.056156e-01
33.1 3.047254e-06
34 1.040462e-04
34.1 5.714390e-12
34.2 7.883166e-09
34.3 3.055823e-07
35 1.287796e-07
35.1 1.762232e-06
35.2 5.355159e-08
36 7.250797e-06
36.1 2.370652e-06
36.2 1.537090e-05
36.3 6.993214e-07
36.4 4.950009e-05
37 2.755165e-07
37.1 3.400517e-07
37.2 2.489007e-09
38 1.302651e-01
39 4.343746e-04
39.1 6.653143e-05
39.2 1.940204e-09
39.3 8.300468e-07
39.4 7.464169e-08
39.5 5.765597e-10
40 9.140572e-01
40.1 1.883555e-03
40.2 2.303001e-01
40.3 2.799910e-05
41 3.700067e-02
41.1 5.798225e-06
41.2 1.086252e-08
41.3 3.088732e-07
41.4 4.549537e-05
42 5.220968e-03
42.1 7.264286e-08
43 1.498125e-07
43.1 1.316763e-04
43.2 8.151771e-07
44 1.032476e-03
44.1 3.120174e-09
44.2 2.571257e-10
44.3 2.227416e-09
45 3.948036e-01
45.1 1.066310e-03
46 2.219556e-08
46.1 1.434525e-08
46.2 1.523026e-07
47 5.404537e-03
47.1 3.739267e-07
47.2 7.171916e-06
47.3 3.850162e-05
47.4 1.767264e-08
48 1.988010e-04
48.1 6.074589e-09
49 1.321544e-06
50 4.240393e-05
51 1.986093e-09
52 1.632022e-02
52.1 2.653038e-02
52.2 2.262881e-03
52.3 6.572647e-10
52.4 1.393737e-04
52.5 5.069462e-03
53 5.848890e-05
53.1 1.878509e-04
53.2 1.293417e-04
54 1.818441e-03
54.1 2.251839e-07
54.2 5.638172e-06
54.3 5.320676e-03
54.4 1.491367e-07
55 3.183775e-03
55.1 1.183380e-03
55.2 1.817077e-06
55.3 1.424370e-06
55.4 3.119967e-07
56 1.169667e-06
56.1 1.182293e-06
56.2 2.087533e-04
56.3 5.728251e-06
56.4 4.087596e-08
56.5 8.040370e-07
57 1.438387e-02
57.1 3.202179e-05
57.2 1.486318e-03
57.3 1.718412e-04
58 3.114123e-05
58.1 1.403881e-04
58.2 2.111006e-01
58.3 9.586985e-06
58.4 4.073162e-03
58.5 9.285307e-04
59 2.711478e-06
59.1 1.173472e-04
60 7.579680e-09
61 4.545759e-03
61.1 5.936674e-02
61.2 3.897281e-01
61.3 6.237379e-02
61.4 5.103038e-01
62 3.707353e-02
62.1 1.901660e-03
62.2 7.844369e-08
62.3 1.496168e-08
63 5.101070e-11
63.1 1.106013e-05
64 1.685171e-09
65 1.684142e-01
65.1 1.413479e-05
65.2 2.841196e-03
65.3 3.118871e-04
66 1.078473e-06
66.1 1.136650e-01
66.2 7.007044e-08
67 4.025749e-11
68 2.469503e-06
68.1 1.067638e-08
68.2 1.508555e-06
68.3 7.862972e-06
68.4 1.970326e-05
69 5.089430e-07
70 5.575849e-07
70.1 6.115107e-04
71 1.867742e-05
71.1 4.616167e-04
71.2 5.314611e-08
71.3 1.790244e-10
71.4 1.924070e-03
72 6.526547e-05
72.1 5.540491e-11
72.2 2.391191e-12
72.3 2.878783e-12
72.4 1.014404e-09
72.5 1.281231e-05
73 6.661564e-02
74 3.683842e-04
75 2.274469e-06
76 9.155636e-04
76.1 1.485365e-04
76.2 3.118702e-06
77 4.946432e-01
78 8.533933e-05
79 1.980588e-01
79.1 8.624235e-06
79.2 2.176176e-05
80 2.929029e-06
80.1 1.126162e-04
80.2 9.847382e-08
81 4.026095e-01
81.1 2.093927e-02
81.2 9.224440e-01
81.3 8.175654e-03
82 1.228129e-01
82.1 6.656575e-05
82.2 2.001426e-08
83 5.690020e-06
83.1 5.980615e-06
83.2 1.880816e-05
83.3 4.048910e-09
84 6.552173e-02
84.1 8.829278e-06
85 4.118253e-06
85.1 2.311994e-06
85.2 5.182892e-05
85.3 1.689467e-03
85.4 1.168017e-03
85.5 7.945131e-07
86 2.905567e-05
86.1 5.331467e-06
86.2 1.761451e-06
86.3 2.272397e-06
86.4 4.467006e-06
86.5 1.693940e-08
87 6.396865e-05
87.1 1.264093e-10
87.2 4.933807e-07
88 9.223531e-02
88.1 4.654325e-05
88.2 1.260399e-01
88.3 8.029866e-08
89 7.489307e-05
90 1.100491e-02
90.1 2.715349e-05
90.2 5.916576e-03
90.3 2.920657e-02
91 2.411997e-03
91.1 8.870147e-06
91.2 1.652965e-08
92 2.613551e-03
93 9.958480e-01
93.1 9.915375e-01
93.2 4.861680e-02
93.3 9.769008e-01
93.4 5.977439e-05
94 7.091952e-04
94.1 6.005522e-04
94.2 8.134430e-03
94.3 1.747604e-05
94.4 9.404259e-07
94.5 6.832077e-07
95 3.216011e-06
95.1 6.324477e-05
95.2 1.762187e-01
96 1.578796e-02
96.1 2.610661e-02
96.2 3.941700e-05
96.3 1.683671e-05
96.4 1.095127e-04
96.5 1.479105e-05
97 2.082560e-04
97.1 7.903013e-10
98 1.795949e-06
98.1 2.776600e-02
98.2 4.050457e-06
99 2.316802e-05
99.1 2.206426e-06
99.2 2.488411e-08
100 7.572193e-01
100.1 9.794641e-02
100.2 4.934595e-01
100.3 1.502083e-07
100.4 2.515993e-06
$m3f$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m3f$mu_reg_norm
[1] 0
$m3f$tau_reg_norm
[1] 1e-04
$m3f$shape_tau_norm
[1] 0.01
$m3f$rate_tau_norm
[1] 0.01
$m3f$mu_reg_beta
[1] 0
$m3f$tau_reg_beta
[1] 1e-04
$m3f$shape_tau_beta
[1] 0.01
$m3f$rate_tau_beta
[1] 0.01
$m3f$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m3f$shape_diag_RinvD
[1] "0.01"
$m3f$rate_diag_RinvD
[1] "0.001"
$m4a
$m4a$M_id
B2 (Intercept) B21
1 1 1 NA
2 NA 1 NA
3 NA 1 NA
4 1 1 NA
5 1 1 NA
6 1 1 NA
7 0 1 NA
8 1 1 NA
9 1 1 NA
10 0 1 NA
11 1 1 NA
12 1 1 NA
13 1 1 NA
14 1 1 NA
15 NA 1 NA
16 1 1 NA
17 1 1 NA
18 1 1 NA
19 1 1 NA
20 0 1 NA
21 1 1 NA
22 1 1 NA
23 1 1 NA
24 NA 1 NA
25 0 1 NA
26 1 1 NA
27 1 1 NA
28 0 1 NA
29 1 1 NA
30 0 1 NA
31 0 1 NA
32 1 1 NA
33 1 1 NA
34 0 1 NA
35 1 1 NA
36 0 1 NA
37 1 1 NA
38 1 1 NA
39 1 1 NA
40 1 1 NA
41 1 1 NA
42 1 1 NA
43 1 1 NA
44 NA 1 NA
45 1 1 NA
46 1 1 NA
47 1 1 NA
48 1 1 NA
49 1 1 NA
50 1 1 NA
51 0 1 NA
52 1 1 NA
53 1 1 NA
54 0 1 NA
55 1 1 NA
56 0 1 NA
57 1 1 NA
58 NA 1 NA
59 1 1 NA
60 1 1 NA
61 0 1 NA
62 0 1 NA
63 1 1 NA
64 1 1 NA
65 1 1 NA
66 1 1 NA
67 1 1 NA
68 1 1 NA
69 NA 1 NA
70 1 1 NA
71 1 1 NA
72 1 1 NA
73 1 1 NA
74 1 1 NA
75 1 1 NA
76 1 1 NA
77 1 1 NA
78 1 1 NA
79 1 1 NA
80 1 1 NA
81 1 1 NA
82 1 1 NA
83 1 1 NA
84 1 1 NA
85 1 1 NA
86 1 1 NA
87 1 1 NA
88 1 1 NA
89 1 1 NA
90 1 1 NA
91 NA 1 NA
92 1 1 NA
93 1 1 NA
94 1 1 NA
95 1 1 NA
96 NA 1 NA
97 NA 1 NA
98 1 1 NA
99 1 1 NA
100 1 1 NA
$m4a$M_lvlone
c1 p2 c2 L1mis Be2
1 0.7592026489 2 NA 1.38634787 4.596628e-06
1.1 0.9548337990 2 -0.08061445 0.79402906 2.296427e-04
1.2 0.5612235156 NA -0.26523782 0.53603334 3.455922e-10
1.3 1.1873391025 NA -0.30260393 0.24129804 9.618613e-07
2 0.9192204198 NA -0.33443795 NA NA
2.1 -0.1870730476 6 -0.11819800 0.31668065 1.065639e-07
2.2 1.2517512331 3 -0.31532280 0.37114414 1.320730e-03
3 -0.0605087604 NA -0.12920657 0.54680608 9.707820e-06
3.1 0.3788637747 NA NA 0.28280274 3.645271e-05
3.2 0.9872578281 NA NA 0.76277262 NA
4 1.4930175328 NA -0.31177403 0.56100366 5.555794e-01
4.1 -0.7692526880 4 -0.23894886 0.38514140 6.853316e-06
4.2 0.9180841450 0 -0.15533613 0.04026174 6.324951e-02
4.3 -0.0541170782 NA -0.14644545 0.16025873 4.330745e-07
5 -0.1376784521 2 -0.28360457 0.21080161 NA
5.1 -0.2740585866 NA -0.20135143 0.36665700 6.556812e-04
5.2 0.4670496929 7 -0.28293375 0.66368829 6.963312e-06
5.3 0.1740288049 NA NA 0.40788895 1.159006e-04
6 0.9868044683 NA -0.08617066 0.11889539 1.509745e-02
7 -0.1280320918 NA -0.22243495 1.04286843 NA
7.1 0.4242971219 NA NA 0.52098933 1.679086e-08
7.2 0.0777182491 NA NA 0.09858876 3.972447e-06
8 -0.5791408712 1 NA 0.17281472 9.888512e-02
8.1 0.3128604232 6 NA 0.25970093 8.790334e-05
8.2 0.6258446356 NA NA 0.30550233 NA
8.3 -0.1040137707 3 -0.35148972 0.88029778 5.411705e-04
8.4 0.0481450285 2 0.03661023 0.20200392 8.446731e-04
8.5 0.3831763675 1 -0.08424534 NA 2.059814e-04
9 -0.1757592269 3 NA 1.12218535 4.160033e-01
9.1 -0.1791541200 NA -0.43509340 0.57911079 NA
9.2 -0.0957042935 3 -0.22527490 0.81350994 1.087331e-03
10 -0.5598409704 3 NA 0.32744766 9.321715e-04
10.1 -0.2318340451 NA NA 0.62912282 8.167897e-06
11 0.5086859475 1 -0.08587475 0.92140073 2.528529e-04
11.1 0.4951758188 6 -0.06157340 0.16012129 NA
11.2 -1.1022162541 1 -0.12436018 0.16166775 5.587553e-10
11.3 -0.0611636705 6 -0.21377934 0.14979756 5.240776e-10
11.4 -0.4971774316 NA -0.32208329 0.46855190 2.830994e-07
12 -0.2433996286 NA NA 0.76818678 1.962202e-07
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96 0.7813892121 NA -0.16780948 0.72391015 1.578796e-02
96.1 0.3391519695 NA -0.32504815 0.32593738 2.610661e-02
96.2 -0.4857246503 5 -0.20395970 0.23249511 3.941700e-05
96.3 0.8771471244 1 -0.06221501 1.01679990 1.683671e-05
96.4 1.9030768981 0 -0.14801097 0.92267953 1.095127e-04
96.5 -0.1684332749 3 -0.28658893 0.83843412 1.479105e-05
97 1.3775130083 4 -0.34484656 0.47151154 2.082560e-04
97.1 -1.7323228619 2 -0.35658805 0.15596614 7.903013e-10
98 -1.2648518889 3 -0.36913003 0.05179545 1.795949e-06
98.1 -0.9042716241 NA NA 0.47332096 2.776600e-02
98.2 -0.1560385207 NA -0.17154225 0.19706341 4.050457e-06
99 0.7993356425 5 -0.24753132 0.22574556 2.316802e-05
99.1 1.0355522332 NA -0.27947829 1.00732330 2.206426e-06
99.2 -0.1150895843 NA -0.09033035 0.09749127 2.488411e-08
100 0.0369067906 NA -0.17326698 0.22857989 7.572193e-01
100.1 1.6023713093 4 NA 0.39548654 9.794641e-02
100.2 0.8861545820 NA -0.12072016 NA 4.934595e-01
100.3 0.1277046316 4 -0.27657520 0.32695372 1.502083e-07
100.4 -0.0834577654 NA -0.14631556 0.10043925 2.515993e-06
$m4a$spM_lvlone
center scale
c1 0.25599956 0.6718095
p2 2.71257485 1.6247402
c2 -0.22371584 0.1059527
L1mis 0.48184811 0.3462447
Be2 0.04274145 0.1563798
$m4a$mu_reg_norm
[1] 0
$m4a$tau_reg_norm
[1] 1e-04
$m4a$shape_tau_norm
[1] 0.01
$m4a$rate_tau_norm
[1] 0.01
$m4a$mu_reg_gamma
[1] 0
$m4a$tau_reg_gamma
[1] 1e-04
$m4a$shape_tau_gamma
[1] 0.01
$m4a$rate_tau_gamma
[1] 0.01
$m4a$mu_reg_beta
[1] 0
$m4a$tau_reg_beta
[1] 1e-04
$m4a$shape_tau_beta
[1] 0.01
$m4a$rate_tau_beta
[1] 0.01
$m4a$mu_reg_binom
[1] 0
$m4a$tau_reg_binom
[1] 1e-04
$m4a$mu_reg_poisson
[1] 0
$m4a$tau_reg_poisson
[1] 1e-04
$m4a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m4a$shape_diag_RinvD
[1] "0.01"
$m4a$rate_diag_RinvD
[1] "0.001"
$m4b
$m4b$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m4b$M_lvlone
c1 p2 b2 c2 L1mis b21
1 0.7592026489 2 NA NA 1.38634787 NA
1.1 0.9548337990 2 0 -0.08061445 0.79402906 NA
1.2 0.5612235156 NA NA -0.26523782 0.53603334 NA
1.3 1.1873391025 NA 0 -0.30260393 0.24129804 NA
2 0.9192204198 NA 0 -0.33443795 NA NA
2.1 -0.1870730476 6 NA -0.11819800 0.31668065 NA
2.2 1.2517512331 3 NA -0.31532280 0.37114414 NA
3 -0.0605087604 NA 0 -0.12920657 0.54680608 NA
3.1 0.3788637747 NA NA NA 0.28280274 NA
3.2 0.9872578281 NA 1 NA 0.76277262 NA
4 1.4930175328 NA 1 -0.31177403 0.56100366 NA
4.1 -0.7692526880 4 0 -0.23894886 0.38514140 NA
4.2 0.9180841450 0 0 -0.15533613 0.04026174 NA
4.3 -0.0541170782 NA 0 -0.14644545 0.16025873 NA
5 -0.1376784521 2 NA -0.28360457 0.21080161 NA
5.1 -0.2740585866 NA 0 -0.20135143 0.36665700 NA
5.2 0.4670496929 7 NA -0.28293375 0.66368829 NA
5.3 0.1740288049 NA NA NA 0.40788895 NA
6 0.9868044683 NA NA -0.08617066 0.11889539 NA
7 -0.1280320918 NA NA -0.22243495 1.04286843 NA
7.1 0.4242971219 NA NA NA 0.52098933 NA
7.2 0.0777182491 NA 0 NA 0.09858876 NA
8 -0.5791408712 1 0 NA 0.17281472 NA
8.1 0.3128604232 6 0 NA 0.25970093 NA
8.2 0.6258446356 NA NA NA 0.30550233 NA
8.3 -0.1040137707 3 1 -0.35148972 0.88029778 NA
8.4 0.0481450285 2 0 0.03661023 0.20200392 NA
8.5 0.3831763675 1 1 -0.08424534 NA NA
9 -0.1757592269 3 0 NA 1.12218535 NA
9.1 -0.1791541200 NA NA -0.43509340 0.57911079 NA
9.2 -0.0957042935 3 NA -0.22527490 0.81350994 NA
10 -0.5598409704 3 NA NA 0.32744766 NA
10.1 -0.2318340451 NA 0 NA 0.62912282 NA
11 0.5086859475 1 0 -0.08587475 0.92140073 NA
11.1 0.4951758188 6 0 -0.06157340 0.16012129 NA
11.2 -1.1022162541 1 0 -0.12436018 0.16166775 NA
11.3 -0.0611636705 6 0 -0.21377934 0.14979756 NA
11.4 -0.4971774316 NA 0 -0.32208329 0.46855190 NA
12 -0.2433996286 NA 0 NA 0.76818678 NA
13 0.8799673116 NA NA NA 0.34264972 NA
13.1 0.1079022586 NA 0 -0.40300449 0.14526619 NA
14 0.9991752617 NA NA -0.28992072 0.80630788 NA
14.1 -0.1094019046 NA NA NA 0.35697552 NA
14.2 0.1518967560 2 NA NA 0.21330192 NA
14.3 0.3521012473 NA NA -0.21979936 NA NA
15 0.3464447888 NA 0 NA 0.30769119 NA
15.1 -0.4767313971 NA 0 -0.29092263 0.28349746 NA
15.2 0.5759767791 NA 0 -0.19392239 0.64618365 NA
15.3 -0.1713452662 NA 0 -0.25718384 0.51680884 NA
16 0.4564754473 1 1 -0.45041108 0.71265471 NA
16.1 1.0652558311 NA NA -0.07599066 0.38925880 NA
16.2 0.6971872493 2 NA -0.32385667 0.23648869 NA
16.3 0.5259331838 NA 0 -0.38326110 0.45048730 NA
16.4 0.2046601798 1 0 -0.22845856 0.23181791 NA
16.5 1.0718540464 NA NA -0.25497157 0.13985349 NA
17 0.6048676222 1 0 NA 0.25995399 NA
17.1 0.2323298304 NA 0 -0.22105143 0.03594878 NA
17.2 1.2617499032 3 0 NA 0.77583623 NA
17.3 -0.3913230895 2 NA NA 0.60015197 NA
17.4 0.9577299112 NA 0 -0.15098046 0.13998405 NA
18 -0.0050324072 2 0 -0.09870041 0.96475839 NA
19 -0.4187468937 NA NA -0.26680239 0.10596495 NA
19.1 -0.4478828944 NA NA -0.15815241 0.13338947 NA
19.2 -1.1966721302 2 0 -0.14717437 0.41662218 NA
19.3 -0.5877091668 2 1 -0.21271374 0.53670855 NA
20 0.6838223064 NA NA -0.22087628 0.41688567 NA
20.1 0.3278571109 2 0 NA NA NA
20.2 -0.8489831990 NA 1 -0.30127439 0.81634101 NA
20.3 1.3169975191 NA 0 -0.11782590 0.39232496 NA
20.4 0.0444804531 NA 0 -0.19857957 0.57925554 NA
20.5 -0.4535207652 NA 0 -0.24338208 0.74200986 NA
21 -0.4030302960 2 0 -0.31407992 0.24759801 NA
21.1 -0.4069674045 3 0 -0.12424941 0.34052205 NA
21.2 1.0650265940 2 NA -0.27672716 0.03905058 NA
22 -0.0673274516 3 0 -0.23790593 0.48605351 NA
22.1 0.9601388170 3 0 -0.15996535 0.43761071 NA
23 0.5556634840 NA 0 -0.18236682 0.47599712 NA
23.1 1.4407865964 5 NA -0.20823302 0.47680301 NA
24 0.3856376411 2 0 -0.29026416 0.51696505 NA
25 0.3564400705 3 0 -0.36139273 0.59392591 NA
25.1 0.0982553434 3 NA -0.19571118 0.74010330 NA
25.2 0.1928682598 3 1 -0.21379355 NA NA
25.3 -0.0192488594 4 0 -0.33876012 0.73081722 NA
25.4 0.4466012931 NA 0 NA 0.29274286 NA
25.5 1.1425193342 NA NA -0.04068446 0.74425342 NA
26 0.5341531449 NA NA -0.16846716 0.20974346 NA
26.1 1.2268695927 2 0 -0.10440642 NA NA
26.2 0.3678294939 NA 0 -0.26884827 0.22908815 NA
26.3 0.5948516018 NA 0 NA 0.41880799 NA
27 -0.3342844147 1 0 -0.19520794 0.10097167 NA
27.1 -0.4835141229 NA 0 -0.17622638 NA NA
28 -0.7145915499 0 NA -0.32164962 NA NA
28.1 0.5063671955 NA 0 -0.27003852 0.56052750 NA
28.2 -0.2067413142 4 0 -0.07235801 0.15301800 NA
28.3 0.1196789973 NA 0 -0.13462982 0.27802542 NA
29 0.1392699487 3 0 -0.32432030 0.43556671 NA
29.1 0.7960234776 3 0 -0.27034171 0.27593085 NA
29.2 1.0398214352 3 0 -0.10197448 0.55256871 NA
29.3 0.0813246429 2 0 -0.27606945 0.47272109 NA
30 -0.3296323050 NA NA -0.06949300 0.32743933 NA
30.1 1.3635850954 NA 0 -0.11511035 0.02231535 NA
30.2 0.7354171050 5 0 -0.16215882 0.12833697 NA
31 0.3708398217 8 0 0.05707733 0.11126366 NA
32 -0.0474059668 NA 0 -0.18446298 1.11731084 NA
32.1 1.2507771489 2 0 -0.14270013 0.85943330 NA
32.2 0.1142915519 1 NA -0.20530798 1.53730925 NA
32.3 0.6773270619 NA NA -0.14705649 0.43831965 NA
33 0.1774293842 0 0 -0.15252819 0.46726055 NA
33.1 0.6159606291 NA 1 NA 0.76818259 NA
34 0.8590979166 3 NA -0.30378735 NA NA
34.1 0.0546216775 NA 0 -0.11982431 1.14350292 NA
34.2 -0.0897224473 1 NA -0.24278671 0.19103604 NA
34.3 0.4163395571 2 NA -0.19971833 NA NA
35 -1.4693520528 NA 0 NA 0.66303137 NA
35.1 -0.3031734330 NA 0 -0.24165780 NA NA
35.2 -0.6045512101 NA NA NA NA NA
36 0.9823048960 5 NA -0.49062180 0.93843318 NA
36.1 1.4466051416 NA NA -0.25651700 NA NA
36.2 1.1606752905 NA 0 NA 0.29886676 NA
36.3 0.8373091576 1 0 -0.30401274 0.22616598 NA
36.4 0.2640591685 1 0 NA 0.53849566 NA
37 0.1177313455 5 0 -0.15276529 1.68107300 NA
37.1 -0.1415483779 NA 0 -0.30016169 1.13777638 NA
37.2 0.0054610124 NA 0 0.06809545 0.26931933 NA
38 0.8078948077 0 0 -0.11218486 NA NA
39 0.9876451040 NA 1 -0.38072211 0.14395367 NA
39.1 -0.3431222274 1 0 -0.32094428 0.36454923 NA
39.2 -1.7909380751 NA NA NA 1.03700002 NA
39.3 -0.1798746191 NA NA -0.40173480 0.41320585 NA
39.4 -0.1850961689 NA 0 -0.20041848 0.20901554 NA
39.5 0.4544226146 NA 1 -0.26027990 0.51603848 NA
40 0.5350190436 2 0 -0.19751956 0.33912363 NA
40.1 0.4189342752 4 1 -0.08399467 0.21892118 NA
40.2 0.4211994981 NA 0 -0.20864416 0.74070896 NA
40.3 0.0916687506 NA NA NA 0.82927399 NA
41 -0.1035047421 NA 0 -0.26096953 0.25193679 NA
41.1 -0.4684202411 4 NA -0.23953874 0.28760510 NA
41.2 0.5972615368 2 0 -0.03079344 0.45553197 NA
41.3 0.9885613862 3 NA NA 0.79237611 NA
41.4 -0.3908036794 NA 0 NA 0.12582175 NA
42 -0.0338893961 3 0 -0.16084527 0.50079604 NA
42.1 -0.4498363172 5 1 -0.13812521 0.61140760 NA
43 0.8965546110 4 0 -0.08864017 0.29752019 NA
43.1 0.6199122090 3 1 -0.12583158 0.51793497 NA
43.2 0.1804894429 3 0 -0.29253959 0.15152473 NA
44 1.3221409285 1 0 -0.22697597 0.38806434 NA
44.1 0.3416426284 NA 0 NA 1.11140786 NA
44.2 0.5706610068 7 0 NA 0.39132534 NA
44.3 1.2679497430 NA 0 -0.40544012 0.40934909 NA
45 0.1414983160 NA NA -0.19274788 0.68587067 NA
45.1 0.7220892521 NA 1 -0.34860483 0.34530800 NA
46 1.5391054233 4 0 -0.28547861 0.71312288 NA
46.1 0.3889107049 6 0 -0.21977836 0.62537420 NA
46.2 0.1248719493 NA 0 NA 0.79574391 NA
47 0.2014101100 NA 0 -0.08597098 0.48660773 NA
47.1 0.2982973539 4 0 -0.35424828 0.51241790 NA
47.2 1.1518107179 2 0 -0.24262576 0.58869379 NA
47.3 0.5196802157 4 NA -0.30426315 0.22171504 NA
47.4 0.3702301552 NA 0 NA 0.11366347 NA
48 -0.2128602862 NA 1 NA 0.19677010 NA
48.1 -0.5337239976 6 1 NA 0.17706320 NA
49 -0.5236770035 NA NA -0.42198781 0.30752382 NA
50 0.3897705981 3 0 -0.19959516 0.93663423 NA
51 -0.7213343736 2 0 -0.16556964 0.34107606 NA
52 0.3758235358 3 0 -0.07438732 0.19007135 NA
52.1 0.7138067080 1 0 -0.37537080 0.75662940 NA
52.2 0.8872895233 NA 0 -0.24222066 1.66104719 NA
52.3 -0.9664587437 2 0 -0.31520603 NA NA
52.4 0.0254566848 3 0 -0.44619160 0.18369708 NA
52.5 0.4155259424 1 0 -0.11011682 0.48689343 NA
53 0.5675736897 3 0 -0.23278716 0.31983157 NA
53.1 -0.3154088781 NA 0 -0.28317264 0.61569501 NA
53.2 0.2162315769 2 NA -0.19517481 NA NA
54 -0.0880802382 3 NA -0.10122856 1.90522418 NA
54.1 0.4129127672 NA NA -0.28325504 0.59484889 NA
54.2 1.0119546775 4 NA -0.16753120 1.47174857 NA
54.3 -0.1112901990 0 NA -0.22217672 0.27307143 NA
54.4 0.8587727145 NA 0 -0.34609328 0.81272938 NA
55 -0.0116453589 NA 0 -0.32428190 0.22735476 NA
55.1 0.5835528661 4 0 -0.24235382 0.54683512 NA
55.2 -1.0010857254 NA NA -0.24065814 1.03503777 NA
55.3 -0.4796526070 4 NA -0.23665476 0.30169529 NA
55.4 -0.1202746964 3 0 NA 0.36008059 NA
56 0.5176377612 NA 0 NA 0.14193566 NA
56.1 -1.1136932588 2 NA -0.30357450 0.65073539 NA
56.2 -0.0168103281 3 NA -0.51301630 0.11338262 NA
56.3 0.3933023606 3 1 -0.23743117 0.16820103 NA
56.4 0.3714625139 0 0 -0.17264917 0.27419110 NA
56.5 0.7811448179 NA 0 -0.39188329 0.57110215 NA
57 -1.0868304872 3 0 -0.18501692 0.85104054 NA
57.1 0.8018626997 4 0 -0.27274841 0.34733833 NA
57.2 -0.1159517011 1 0 NA 1.44438762 NA
57.3 0.6785562445 NA NA -0.09898509 0.31836125 NA
58 1.6476207996 NA 0 -0.29901358 0.37456898 NA
58.1 0.3402652711 NA NA -0.35390896 0.22120158 NA
58.2 -0.1111300753 NA 1 -0.16687336 0.78885210 NA
58.3 -0.5409234285 3 1 -0.11784506 0.10114937 NA
58.4 -0.1271327672 NA 0 -0.05321983 0.13385114 NA
58.5 0.8713264822 NA 0 -0.54457568 NA NA
59 0.4766421367 NA NA -0.27255364 0.13202156 NA
59.1 1.0028089765 NA 1 NA 0.33371896 NA
60 0.5231452932 NA 0 NA 0.35096579 NA
61 -0.7190130614 2 NA -0.30550120 0.36933806 NA
61.1 0.8353702312 4 1 -0.35579892 0.17623067 NA
61.2 1.0229058138 NA 1 NA 0.21286227 NA
61.3 1.1717723589 NA 0 -0.34184391 0.12689308 NA
61.4 -0.0629201596 NA 0 -0.30891967 0.77676718 NA
62 -0.3979137604 2 NA NA 1.38018163 NA
62.1 0.6830738372 NA 1 -0.10504143 0.43803892 NA
62.2 0.4301745954 NA 0 -0.20104997 0.21947900 NA
62.3 -0.0333139957 NA 0 -0.08138677 0.11571160 NA
63 0.3345678035 NA NA -0.12036319 0.41583568 NA
63.1 0.3643769511 2 0 -0.13624992 0.25598960 NA
64 0.3949911859 4 0 NA 0.20415642 NA
65 1.2000091513 NA 0 -0.34450396 0.07135646 NA
65.1 0.0110122646 5 0 -0.32514650 0.57450574 NA
65.2 -0.5776452043 NA 0 -0.10984996 0.52562984 NA
65.3 -0.1372183563 NA 0 -0.19275692 0.21921164 NA
66 -0.5081302805 NA NA NA 0.33281730 NA
66.1 -0.1447837412 NA 0 NA 0.03412404 NA
66.2 0.1906241379 NA 0 -0.11687008 0.92570619 NA
67 1.6716027681 NA NA NA 0.15291043 NA
68 0.5691848839 NA 0 -0.13605235 0.37543648 NA
68.1 0.1004860389 NA 0 -0.19790827 0.20901022 NA
68.2 -0.0061241827 NA NA -0.17750123 0.12488064 NA
68.3 0.7443745962 2 0 NA 0.08711204 NA
68.4 0.8726923437 NA NA -0.12570562 0.54611735 NA
69 0.0381382683 NA 0 -0.32152751 0.23638239 NA
70 0.8126204217 4 0 -0.28190462 0.49876756 NA
70.1 0.4691503050 4 0 -0.11503263 0.39512615 NA
71 -0.5529062591 4 0 -0.13029093 0.45666551 NA
71.1 -0.1103252087 NA 1 NA 0.92047456 NA
71.2 1.7178492547 3 0 -0.39075433 0.32792986 NA
71.3 -1.0118346755 0 1 -0.21401028 0.95108007 NA
71.4 1.8623785017 0 0 -0.40219281 0.36287072 NA
72 -0.4521659275 NA 0 -0.40337108 0.12870526 NA
72.1 0.1375317317 8 0 -0.25978914 0.45925876 NA
72.2 -0.4170988856 NA NA NA 0.05418867 NA
72.3 0.7107266765 NA 0 -0.09809866 0.48937486 NA
72.4 0.1451969143 3 0 -0.14240019 0.64173822 NA
72.5 1.6298050306 NA 0 -0.14794204 0.57609943 NA
73 -0.0307469467 2 0 -0.23509343 0.17393402 NA
74 0.3730017941 NA 0 -0.27963171 0.23990575 NA
75 -0.4908003566 NA NA -0.12905034 0.28469861 NA
76 -0.9888876620 1 0 0.04775562 0.71988630 NA
76.1 0.0003798292 0 0 -0.19399157 1.12449946 NA
76.2 -0.8421863763 0 0 -0.02754574 0.71313766 NA
77 -0.4986802480 2 NA -0.19053195 0.02399030 NA
78 0.0417330969 NA 0 -0.17172929 0.42708148 NA
79 -0.3767450660 2 NA -0.03958515 0.37579286 NA
79.1 0.1516000028 NA 0 -0.20328809 0.78660681 NA
79.2 -0.1888160741 2 NA -0.23901634 0.67696116 NA
80 -0.0041558414 2 NA -0.34031873 0.34207854 NA
80.1 -0.0329337062 NA 0 -0.19526756 0.60534092 NA
80.2 0.5046816157 NA NA NA 0.26731034 NA
81 -0.9493950353 NA 0 -0.18401980 0.17739052 NA
81.1 0.2443038954 2 0 -0.16889476 0.35453673 NA
81.2 0.6476958410 NA NA -0.37343047 0.20244235 NA
81.3 0.4182528210 NA 0 NA 1.26402329 NA
82 1.1088801952 NA NA -0.08328168 0.09303938 NA
82.1 0.9334157763 NA 0 -0.22167084 0.27254210 NA
82.2 0.4958140634 4 1 -0.20971187 0.49936304 NA
83 0.5104724530 NA NA -0.34228255 0.21138572 NA
83.1 -0.0513309106 NA 0 -0.34075730 0.26403568 NA
83.2 -0.2067792494 4 0 -0.32503954 0.20311133 NA
83.3 -0.0534169155 3 NA NA 1.16864671 NA
84 -0.0255753653 NA 0 -0.20676741 1.99179346 NA
84.1 -1.8234189877 2 NA -0.20310458 1.52199460 NA
85 -0.0114038622 3 1 -0.12107593 NA NA
85.1 -0.0577615939 NA NA NA 0.61458995 NA
85.2 -0.2241856342 3 0 -0.32509207 0.07871196 NA
85.3 -0.0520175929 NA 0 NA 1.42315283 NA
85.4 0.2892733846 2 0 -0.30730810 0.97986129 NA
85.5 -0.3740417009 1 0 NA 0.91792195 NA
86 0.4293735089 2 0 -0.10854862 0.63509597 NA
86.1 -0.1363456521 NA NA -0.25751662 0.24546597 NA
86.2 0.1230989293 0 NA -0.38943076 0.53102060 NA
86.3 0.3305413955 0 0 -0.24454702 0.09360826 NA
86.4 2.6003411822 NA NA -0.12338992 0.58301186 NA
86.5 -0.1420690052 2 0 -0.23976984 0.39146055 NA
87 1.0457427869 NA NA NA NA NA
87.1 -0.2973007190 NA NA -0.34366972 0.66043624 NA
87.2 0.4396872616 3 NA NA 0.13267613 NA
88 -0.0601928334 NA 0 -0.31563888 0.10696344 NA
88.1 -1.0124347595 1 NA -0.20304028 0.13689448 NA
88.2 0.5730917016 1 0 -0.40311895 0.48037889 NA
88.3 -0.0029455332 4 0 -0.12308715 0.97755681 NA
89 1.5465903721 NA 0 -0.18527715 0.70242369 NA
90 0.0626760573 3 0 -0.25029126 0.40042977 NA
90.1 1.1896872985 NA 0 -0.26974303 0.63975731 NA
90.2 0.2597888783 NA 0 -0.28804531 0.33412775 NA
90.3 0.6599799887 NA NA -0.19180615 0.38399003 NA
91 1.1213651365 NA 0 -0.26591197 0.58250391 NA
91.1 1.2046371625 NA 0 -0.09153470 0.13223217 NA
91.2 0.3395603754 NA 0 -0.48414390 0.46613305 NA
92 0.4674939332 NA 0 NA 0.18997862 NA
93 0.2677965647 2 NA -0.11939966 1.05243347 NA
93.1 1.6424445368 4 0 NA 0.01479757 NA
93.2 0.7101700066 4 NA -0.21089379 0.50955172 NA
93.3 1.1222322893 NA 0 NA 0.78122514 NA
93.4 1.4628960401 3 0 -0.23618836 0.63940704 NA
94 -0.2904211940 4 NA NA 0.45596305 NA
94.1 0.0147813580 2 0 -0.10217284 0.41610667 NA
94.2 -0.4536774482 NA 0 -0.36713471 0.52744298 NA
94.3 0.6793464917 1 NA -0.13806763 0.70890756 NA
94.4 -0.9411356550 NA 0 -0.42353804 0.84412478 NA
94.5 0.5683867264 2 1 -0.15513707 0.21166602 NA
95 0.2375652188 3 0 -0.24149687 0.57713135 NA
95.1 0.0767152977 5 NA -0.21315958 0.44400207 NA
95.2 -0.6886731251 2 0 -0.15777208 0.42397776 NA
96 0.7813892121 NA 0 -0.16780948 0.72391015 NA
96.1 0.3391519695 NA 0 -0.32504815 0.32593738 NA
96.2 -0.4857246503 5 0 -0.20395970 0.23249511 NA
96.3 0.8771471244 1 NA -0.06221501 1.01679990 NA
96.4 1.9030768981 0 1 -0.14801097 0.92267953 NA
96.5 -0.1684332749 3 1 -0.28658893 0.83843412 NA
97 1.3775130083 4 0 -0.34484656 0.47151154 NA
97.1 -1.7323228619 2 0 -0.35658805 0.15596614 NA
98 -1.2648518889 3 0 -0.36913003 0.05179545 NA
98.1 -0.9042716241 NA 0 NA 0.47332096 NA
98.2 -0.1560385207 NA 1 -0.17154225 0.19706341 NA
99 0.7993356425 5 0 -0.24753132 0.22574556 NA
99.1 1.0355522332 NA 0 -0.27947829 1.00732330 NA
99.2 -0.1150895843 NA 0 -0.09033035 0.09749127 NA
100 0.0369067906 NA NA -0.17326698 0.22857989 NA
100.1 1.6023713093 4 NA NA 0.39548654 NA
100.2 0.8861545820 NA 0 -0.12072016 NA NA
100.3 0.1277046316 4 NA -0.27657520 0.32695372 NA
100.4 -0.0834577654 NA 0 -0.14631556 0.10043925 NA
$m4b$spM_lvlone
center scale
c1 0.2559996 0.6718095
p2 2.7125749 1.6247402
b2 NA NA
c2 -0.2237158 0.1059527
L1mis 0.4818481 0.3462447
b21 NA NA
$m4b$mu_reg_norm
[1] 0
$m4b$tau_reg_norm
[1] 1e-04
$m4b$shape_tau_norm
[1] 0.01
$m4b$rate_tau_norm
[1] 0.01
$m4b$mu_reg_binom
[1] 0
$m4b$tau_reg_binom
[1] 1e-04
$m4b$mu_reg_poisson
[1] 0
$m4b$tau_reg_poisson
[1] 1e-04
$m4b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m4b$shape_diag_RinvD
[1] "0.01"
$m4b$rate_diag_RinvD
[1] "0.001"
$m4c
$m4c$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m4c$M_lvlone
c1 p2 b2 c2 L1mis b21
1 0.7592026489 2 NA NA 1.38634787 NA
1.1 0.9548337990 2 0 -0.08061445 0.79402906 NA
1.2 0.5612235156 NA NA -0.26523782 0.53603334 NA
1.3 1.1873391025 NA 0 -0.30260393 0.24129804 NA
2 0.9192204198 NA 0 -0.33443795 NA NA
2.1 -0.1870730476 6 NA -0.11819800 0.31668065 NA
2.2 1.2517512331 3 NA -0.31532280 0.37114414 NA
3 -0.0605087604 NA 0 -0.12920657 0.54680608 NA
3.1 0.3788637747 NA NA NA 0.28280274 NA
3.2 0.9872578281 NA 1 NA 0.76277262 NA
4 1.4930175328 NA 1 -0.31177403 0.56100366 NA
4.1 -0.7692526880 4 0 -0.23894886 0.38514140 NA
4.2 0.9180841450 0 0 -0.15533613 0.04026174 NA
4.3 -0.0541170782 NA 0 -0.14644545 0.16025873 NA
5 -0.1376784521 2 NA -0.28360457 0.21080161 NA
5.1 -0.2740585866 NA 0 -0.20135143 0.36665700 NA
5.2 0.4670496929 7 NA -0.28293375 0.66368829 NA
5.3 0.1740288049 NA NA NA 0.40788895 NA
6 0.9868044683 NA NA -0.08617066 0.11889539 NA
7 -0.1280320918 NA NA -0.22243495 1.04286843 NA
7.1 0.4242971219 NA NA NA 0.52098933 NA
7.2 0.0777182491 NA 0 NA 0.09858876 NA
8 -0.5791408712 1 0 NA 0.17281472 NA
8.1 0.3128604232 6 0 NA 0.25970093 NA
8.2 0.6258446356 NA NA NA 0.30550233 NA
8.3 -0.1040137707 3 1 -0.35148972 0.88029778 NA
8.4 0.0481450285 2 0 0.03661023 0.20200392 NA
8.5 0.3831763675 1 1 -0.08424534 NA NA
9 -0.1757592269 3 0 NA 1.12218535 NA
9.1 -0.1791541200 NA NA -0.43509340 0.57911079 NA
9.2 -0.0957042935 3 NA -0.22527490 0.81350994 NA
10 -0.5598409704 3 NA NA 0.32744766 NA
10.1 -0.2318340451 NA 0 NA 0.62912282 NA
11 0.5086859475 1 0 -0.08587475 0.92140073 NA
11.1 0.4951758188 6 0 -0.06157340 0.16012129 NA
11.2 -1.1022162541 1 0 -0.12436018 0.16166775 NA
11.3 -0.0611636705 6 0 -0.21377934 0.14979756 NA
11.4 -0.4971774316 NA 0 -0.32208329 0.46855190 NA
12 -0.2433996286 NA 0 NA 0.76818678 NA
13 0.8799673116 NA NA NA 0.34264972 NA
13.1 0.1079022586 NA 0 -0.40300449 0.14526619 NA
14 0.9991752617 NA NA -0.28992072 0.80630788 NA
14.1 -0.1094019046 NA NA NA 0.35697552 NA
14.2 0.1518967560 2 NA NA 0.21330192 NA
14.3 0.3521012473 NA NA -0.21979936 NA NA
15 0.3464447888 NA 0 NA 0.30769119 NA
15.1 -0.4767313971 NA 0 -0.29092263 0.28349746 NA
15.2 0.5759767791 NA 0 -0.19392239 0.64618365 NA
15.3 -0.1713452662 NA 0 -0.25718384 0.51680884 NA
16 0.4564754473 1 1 -0.45041108 0.71265471 NA
16.1 1.0652558311 NA NA -0.07599066 0.38925880 NA
16.2 0.6971872493 2 NA -0.32385667 0.23648869 NA
16.3 0.5259331838 NA 0 -0.38326110 0.45048730 NA
16.4 0.2046601798 1 0 -0.22845856 0.23181791 NA
16.5 1.0718540464 NA NA -0.25497157 0.13985349 NA
17 0.6048676222 1 0 NA 0.25995399 NA
17.1 0.2323298304 NA 0 -0.22105143 0.03594878 NA
17.2 1.2617499032 3 0 NA 0.77583623 NA
17.3 -0.3913230895 2 NA NA 0.60015197 NA
17.4 0.9577299112 NA 0 -0.15098046 0.13998405 NA
18 -0.0050324072 2 0 -0.09870041 0.96475839 NA
19 -0.4187468937 NA NA -0.26680239 0.10596495 NA
19.1 -0.4478828944 NA NA -0.15815241 0.13338947 NA
19.2 -1.1966721302 2 0 -0.14717437 0.41662218 NA
19.3 -0.5877091668 2 1 -0.21271374 0.53670855 NA
20 0.6838223064 NA NA -0.22087628 0.41688567 NA
20.1 0.3278571109 2 0 NA NA NA
20.2 -0.8489831990 NA 1 -0.30127439 0.81634101 NA
20.3 1.3169975191 NA 0 -0.11782590 0.39232496 NA
20.4 0.0444804531 NA 0 -0.19857957 0.57925554 NA
20.5 -0.4535207652 NA 0 -0.24338208 0.74200986 NA
21 -0.4030302960 2 0 -0.31407992 0.24759801 NA
21.1 -0.4069674045 3 0 -0.12424941 0.34052205 NA
21.2 1.0650265940 2 NA -0.27672716 0.03905058 NA
22 -0.0673274516 3 0 -0.23790593 0.48605351 NA
22.1 0.9601388170 3 0 -0.15996535 0.43761071 NA
23 0.5556634840 NA 0 -0.18236682 0.47599712 NA
23.1 1.4407865964 5 NA -0.20823302 0.47680301 NA
24 0.3856376411 2 0 -0.29026416 0.51696505 NA
25 0.3564400705 3 0 -0.36139273 0.59392591 NA
25.1 0.0982553434 3 NA -0.19571118 0.74010330 NA
25.2 0.1928682598 3 1 -0.21379355 NA NA
25.3 -0.0192488594 4 0 -0.33876012 0.73081722 NA
25.4 0.4466012931 NA 0 NA 0.29274286 NA
25.5 1.1425193342 NA NA -0.04068446 0.74425342 NA
26 0.5341531449 NA NA -0.16846716 0.20974346 NA
26.1 1.2268695927 2 0 -0.10440642 NA NA
26.2 0.3678294939 NA 0 -0.26884827 0.22908815 NA
26.3 0.5948516018 NA 0 NA 0.41880799 NA
27 -0.3342844147 1 0 -0.19520794 0.10097167 NA
27.1 -0.4835141229 NA 0 -0.17622638 NA NA
28 -0.7145915499 0 NA -0.32164962 NA NA
28.1 0.5063671955 NA 0 -0.27003852 0.56052750 NA
28.2 -0.2067413142 4 0 -0.07235801 0.15301800 NA
28.3 0.1196789973 NA 0 -0.13462982 0.27802542 NA
29 0.1392699487 3 0 -0.32432030 0.43556671 NA
29.1 0.7960234776 3 0 -0.27034171 0.27593085 NA
29.2 1.0398214352 3 0 -0.10197448 0.55256871 NA
29.3 0.0813246429 2 0 -0.27606945 0.47272109 NA
30 -0.3296323050 NA NA -0.06949300 0.32743933 NA
30.1 1.3635850954 NA 0 -0.11511035 0.02231535 NA
30.2 0.7354171050 5 0 -0.16215882 0.12833697 NA
31 0.3708398217 8 0 0.05707733 0.11126366 NA
32 -0.0474059668 NA 0 -0.18446298 1.11731084 NA
32.1 1.2507771489 2 0 -0.14270013 0.85943330 NA
32.2 0.1142915519 1 NA -0.20530798 1.53730925 NA
32.3 0.6773270619 NA NA -0.14705649 0.43831965 NA
33 0.1774293842 0 0 -0.15252819 0.46726055 NA
33.1 0.6159606291 NA 1 NA 0.76818259 NA
34 0.8590979166 3 NA -0.30378735 NA NA
34.1 0.0546216775 NA 0 -0.11982431 1.14350292 NA
34.2 -0.0897224473 1 NA -0.24278671 0.19103604 NA
34.3 0.4163395571 2 NA -0.19971833 NA NA
35 -1.4693520528 NA 0 NA 0.66303137 NA
35.1 -0.3031734330 NA 0 -0.24165780 NA NA
35.2 -0.6045512101 NA NA NA NA NA
36 0.9823048960 5 NA -0.49062180 0.93843318 NA
36.1 1.4466051416 NA NA -0.25651700 NA NA
36.2 1.1606752905 NA 0 NA 0.29886676 NA
36.3 0.8373091576 1 0 -0.30401274 0.22616598 NA
36.4 0.2640591685 1 0 NA 0.53849566 NA
37 0.1177313455 5 0 -0.15276529 1.68107300 NA
37.1 -0.1415483779 NA 0 -0.30016169 1.13777638 NA
37.2 0.0054610124 NA 0 0.06809545 0.26931933 NA
38 0.8078948077 0 0 -0.11218486 NA NA
39 0.9876451040 NA 1 -0.38072211 0.14395367 NA
39.1 -0.3431222274 1 0 -0.32094428 0.36454923 NA
39.2 -1.7909380751 NA NA NA 1.03700002 NA
39.3 -0.1798746191 NA NA -0.40173480 0.41320585 NA
39.4 -0.1850961689 NA 0 -0.20041848 0.20901554 NA
39.5 0.4544226146 NA 1 -0.26027990 0.51603848 NA
40 0.5350190436 2 0 -0.19751956 0.33912363 NA
40.1 0.4189342752 4 1 -0.08399467 0.21892118 NA
40.2 0.4211994981 NA 0 -0.20864416 0.74070896 NA
40.3 0.0916687506 NA NA NA 0.82927399 NA
41 -0.1035047421 NA 0 -0.26096953 0.25193679 NA
41.1 -0.4684202411 4 NA -0.23953874 0.28760510 NA
41.2 0.5972615368 2 0 -0.03079344 0.45553197 NA
41.3 0.9885613862 3 NA NA 0.79237611 NA
41.4 -0.3908036794 NA 0 NA 0.12582175 NA
42 -0.0338893961 3 0 -0.16084527 0.50079604 NA
42.1 -0.4498363172 5 1 -0.13812521 0.61140760 NA
43 0.8965546110 4 0 -0.08864017 0.29752019 NA
43.1 0.6199122090 3 1 -0.12583158 0.51793497 NA
43.2 0.1804894429 3 0 -0.29253959 0.15152473 NA
44 1.3221409285 1 0 -0.22697597 0.38806434 NA
44.1 0.3416426284 NA 0 NA 1.11140786 NA
44.2 0.5706610068 7 0 NA 0.39132534 NA
44.3 1.2679497430 NA 0 -0.40544012 0.40934909 NA
45 0.1414983160 NA NA -0.19274788 0.68587067 NA
45.1 0.7220892521 NA 1 -0.34860483 0.34530800 NA
46 1.5391054233 4 0 -0.28547861 0.71312288 NA
46.1 0.3889107049 6 0 -0.21977836 0.62537420 NA
46.2 0.1248719493 NA 0 NA 0.79574391 NA
47 0.2014101100 NA 0 -0.08597098 0.48660773 NA
47.1 0.2982973539 4 0 -0.35424828 0.51241790 NA
47.2 1.1518107179 2 0 -0.24262576 0.58869379 NA
47.3 0.5196802157 4 NA -0.30426315 0.22171504 NA
47.4 0.3702301552 NA 0 NA 0.11366347 NA
48 -0.2128602862 NA 1 NA 0.19677010 NA
48.1 -0.5337239976 6 1 NA 0.17706320 NA
49 -0.5236770035 NA NA -0.42198781 0.30752382 NA
50 0.3897705981 3 0 -0.19959516 0.93663423 NA
51 -0.7213343736 2 0 -0.16556964 0.34107606 NA
52 0.3758235358 3 0 -0.07438732 0.19007135 NA
52.1 0.7138067080 1 0 -0.37537080 0.75662940 NA
52.2 0.8872895233 NA 0 -0.24222066 1.66104719 NA
52.3 -0.9664587437 2 0 -0.31520603 NA NA
52.4 0.0254566848 3 0 -0.44619160 0.18369708 NA
52.5 0.4155259424 1 0 -0.11011682 0.48689343 NA
53 0.5675736897 3 0 -0.23278716 0.31983157 NA
53.1 -0.3154088781 NA 0 -0.28317264 0.61569501 NA
53.2 0.2162315769 2 NA -0.19517481 NA NA
54 -0.0880802382 3 NA -0.10122856 1.90522418 NA
54.1 0.4129127672 NA NA -0.28325504 0.59484889 NA
54.2 1.0119546775 4 NA -0.16753120 1.47174857 NA
54.3 -0.1112901990 0 NA -0.22217672 0.27307143 NA
54.4 0.8587727145 NA 0 -0.34609328 0.81272938 NA
55 -0.0116453589 NA 0 -0.32428190 0.22735476 NA
55.1 0.5835528661 4 0 -0.24235382 0.54683512 NA
55.2 -1.0010857254 NA NA -0.24065814 1.03503777 NA
55.3 -0.4796526070 4 NA -0.23665476 0.30169529 NA
55.4 -0.1202746964 3 0 NA 0.36008059 NA
56 0.5176377612 NA 0 NA 0.14193566 NA
56.1 -1.1136932588 2 NA -0.30357450 0.65073539 NA
56.2 -0.0168103281 3 NA -0.51301630 0.11338262 NA
56.3 0.3933023606 3 1 -0.23743117 0.16820103 NA
56.4 0.3714625139 0 0 -0.17264917 0.27419110 NA
56.5 0.7811448179 NA 0 -0.39188329 0.57110215 NA
57 -1.0868304872 3 0 -0.18501692 0.85104054 NA
57.1 0.8018626997 4 0 -0.27274841 0.34733833 NA
57.2 -0.1159517011 1 0 NA 1.44438762 NA
57.3 0.6785562445 NA NA -0.09898509 0.31836125 NA
58 1.6476207996 NA 0 -0.29901358 0.37456898 NA
58.1 0.3402652711 NA NA -0.35390896 0.22120158 NA
58.2 -0.1111300753 NA 1 -0.16687336 0.78885210 NA
58.3 -0.5409234285 3 1 -0.11784506 0.10114937 NA
58.4 -0.1271327672 NA 0 -0.05321983 0.13385114 NA
58.5 0.8713264822 NA 0 -0.54457568 NA NA
59 0.4766421367 NA NA -0.27255364 0.13202156 NA
59.1 1.0028089765 NA 1 NA 0.33371896 NA
60 0.5231452932 NA 0 NA 0.35096579 NA
61 -0.7190130614 2 NA -0.30550120 0.36933806 NA
61.1 0.8353702312 4 1 -0.35579892 0.17623067 NA
61.2 1.0229058138 NA 1 NA 0.21286227 NA
61.3 1.1717723589 NA 0 -0.34184391 0.12689308 NA
61.4 -0.0629201596 NA 0 -0.30891967 0.77676718 NA
62 -0.3979137604 2 NA NA 1.38018163 NA
62.1 0.6830738372 NA 1 -0.10504143 0.43803892 NA
62.2 0.4301745954 NA 0 -0.20104997 0.21947900 NA
62.3 -0.0333139957 NA 0 -0.08138677 0.11571160 NA
63 0.3345678035 NA NA -0.12036319 0.41583568 NA
63.1 0.3643769511 2 0 -0.13624992 0.25598960 NA
64 0.3949911859 4 0 NA 0.20415642 NA
65 1.2000091513 NA 0 -0.34450396 0.07135646 NA
65.1 0.0110122646 5 0 -0.32514650 0.57450574 NA
65.2 -0.5776452043 NA 0 -0.10984996 0.52562984 NA
65.3 -0.1372183563 NA 0 -0.19275692 0.21921164 NA
66 -0.5081302805 NA NA NA 0.33281730 NA
66.1 -0.1447837412 NA 0 NA 0.03412404 NA
66.2 0.1906241379 NA 0 -0.11687008 0.92570619 NA
67 1.6716027681 NA NA NA 0.15291043 NA
68 0.5691848839 NA 0 -0.13605235 0.37543648 NA
68.1 0.1004860389 NA 0 -0.19790827 0.20901022 NA
68.2 -0.0061241827 NA NA -0.17750123 0.12488064 NA
68.3 0.7443745962 2 0 NA 0.08711204 NA
68.4 0.8726923437 NA NA -0.12570562 0.54611735 NA
69 0.0381382683 NA 0 -0.32152751 0.23638239 NA
70 0.8126204217 4 0 -0.28190462 0.49876756 NA
70.1 0.4691503050 4 0 -0.11503263 0.39512615 NA
71 -0.5529062591 4 0 -0.13029093 0.45666551 NA
71.1 -0.1103252087 NA 1 NA 0.92047456 NA
71.2 1.7178492547 3 0 -0.39075433 0.32792986 NA
71.3 -1.0118346755 0 1 -0.21401028 0.95108007 NA
71.4 1.8623785017 0 0 -0.40219281 0.36287072 NA
72 -0.4521659275 NA 0 -0.40337108 0.12870526 NA
72.1 0.1375317317 8 0 -0.25978914 0.45925876 NA
72.2 -0.4170988856 NA NA NA 0.05418867 NA
72.3 0.7107266765 NA 0 -0.09809866 0.48937486 NA
72.4 0.1451969143 3 0 -0.14240019 0.64173822 NA
72.5 1.6298050306 NA 0 -0.14794204 0.57609943 NA
73 -0.0307469467 2 0 -0.23509343 0.17393402 NA
74 0.3730017941 NA 0 -0.27963171 0.23990575 NA
75 -0.4908003566 NA NA -0.12905034 0.28469861 NA
76 -0.9888876620 1 0 0.04775562 0.71988630 NA
76.1 0.0003798292 0 0 -0.19399157 1.12449946 NA
76.2 -0.8421863763 0 0 -0.02754574 0.71313766 NA
77 -0.4986802480 2 NA -0.19053195 0.02399030 NA
78 0.0417330969 NA 0 -0.17172929 0.42708148 NA
79 -0.3767450660 2 NA -0.03958515 0.37579286 NA
79.1 0.1516000028 NA 0 -0.20328809 0.78660681 NA
79.2 -0.1888160741 2 NA -0.23901634 0.67696116 NA
80 -0.0041558414 2 NA -0.34031873 0.34207854 NA
80.1 -0.0329337062 NA 0 -0.19526756 0.60534092 NA
80.2 0.5046816157 NA NA NA 0.26731034 NA
81 -0.9493950353 NA 0 -0.18401980 0.17739052 NA
81.1 0.2443038954 2 0 -0.16889476 0.35453673 NA
81.2 0.6476958410 NA NA -0.37343047 0.20244235 NA
81.3 0.4182528210 NA 0 NA 1.26402329 NA
82 1.1088801952 NA NA -0.08328168 0.09303938 NA
82.1 0.9334157763 NA 0 -0.22167084 0.27254210 NA
82.2 0.4958140634 4 1 -0.20971187 0.49936304 NA
83 0.5104724530 NA NA -0.34228255 0.21138572 NA
83.1 -0.0513309106 NA 0 -0.34075730 0.26403568 NA
83.2 -0.2067792494 4 0 -0.32503954 0.20311133 NA
83.3 -0.0534169155 3 NA NA 1.16864671 NA
84 -0.0255753653 NA 0 -0.20676741 1.99179346 NA
84.1 -1.8234189877 2 NA -0.20310458 1.52199460 NA
85 -0.0114038622 3 1 -0.12107593 NA NA
85.1 -0.0577615939 NA NA NA 0.61458995 NA
85.2 -0.2241856342 3 0 -0.32509207 0.07871196 NA
85.3 -0.0520175929 NA 0 NA 1.42315283 NA
85.4 0.2892733846 2 0 -0.30730810 0.97986129 NA
85.5 -0.3740417009 1 0 NA 0.91792195 NA
86 0.4293735089 2 0 -0.10854862 0.63509597 NA
86.1 -0.1363456521 NA NA -0.25751662 0.24546597 NA
86.2 0.1230989293 0 NA -0.38943076 0.53102060 NA
86.3 0.3305413955 0 0 -0.24454702 0.09360826 NA
86.4 2.6003411822 NA NA -0.12338992 0.58301186 NA
86.5 -0.1420690052 2 0 -0.23976984 0.39146055 NA
87 1.0457427869 NA NA NA NA NA
87.1 -0.2973007190 NA NA -0.34366972 0.66043624 NA
87.2 0.4396872616 3 NA NA 0.13267613 NA
88 -0.0601928334 NA 0 -0.31563888 0.10696344 NA
88.1 -1.0124347595 1 NA -0.20304028 0.13689448 NA
88.2 0.5730917016 1 0 -0.40311895 0.48037889 NA
88.3 -0.0029455332 4 0 -0.12308715 0.97755681 NA
89 1.5465903721 NA 0 -0.18527715 0.70242369 NA
90 0.0626760573 3 0 -0.25029126 0.40042977 NA
90.1 1.1896872985 NA 0 -0.26974303 0.63975731 NA
90.2 0.2597888783 NA 0 -0.28804531 0.33412775 NA
90.3 0.6599799887 NA NA -0.19180615 0.38399003 NA
91 1.1213651365 NA 0 -0.26591197 0.58250391 NA
91.1 1.2046371625 NA 0 -0.09153470 0.13223217 NA
91.2 0.3395603754 NA 0 -0.48414390 0.46613305 NA
92 0.4674939332 NA 0 NA 0.18997862 NA
93 0.2677965647 2 NA -0.11939966 1.05243347 NA
93.1 1.6424445368 4 0 NA 0.01479757 NA
93.2 0.7101700066 4 NA -0.21089379 0.50955172 NA
93.3 1.1222322893 NA 0 NA 0.78122514 NA
93.4 1.4628960401 3 0 -0.23618836 0.63940704 NA
94 -0.2904211940 4 NA NA 0.45596305 NA
94.1 0.0147813580 2 0 -0.10217284 0.41610667 NA
94.2 -0.4536774482 NA 0 -0.36713471 0.52744298 NA
94.3 0.6793464917 1 NA -0.13806763 0.70890756 NA
94.4 -0.9411356550 NA 0 -0.42353804 0.84412478 NA
94.5 0.5683867264 2 1 -0.15513707 0.21166602 NA
95 0.2375652188 3 0 -0.24149687 0.57713135 NA
95.1 0.0767152977 5 NA -0.21315958 0.44400207 NA
95.2 -0.6886731251 2 0 -0.15777208 0.42397776 NA
96 0.7813892121 NA 0 -0.16780948 0.72391015 NA
96.1 0.3391519695 NA 0 -0.32504815 0.32593738 NA
96.2 -0.4857246503 5 0 -0.20395970 0.23249511 NA
96.3 0.8771471244 1 NA -0.06221501 1.01679990 NA
96.4 1.9030768981 0 1 -0.14801097 0.92267953 NA
96.5 -0.1684332749 3 1 -0.28658893 0.83843412 NA
97 1.3775130083 4 0 -0.34484656 0.47151154 NA
97.1 -1.7323228619 2 0 -0.35658805 0.15596614 NA
98 -1.2648518889 3 0 -0.36913003 0.05179545 NA
98.1 -0.9042716241 NA 0 NA 0.47332096 NA
98.2 -0.1560385207 NA 1 -0.17154225 0.19706341 NA
99 0.7993356425 5 0 -0.24753132 0.22574556 NA
99.1 1.0355522332 NA 0 -0.27947829 1.00732330 NA
99.2 -0.1150895843 NA 0 -0.09033035 0.09749127 NA
100 0.0369067906 NA NA -0.17326698 0.22857989 NA
100.1 1.6023713093 4 NA NA 0.39548654 NA
100.2 0.8861545820 NA 0 -0.12072016 NA NA
100.3 0.1277046316 4 NA -0.27657520 0.32695372 NA
100.4 -0.0834577654 NA 0 -0.14631556 0.10043925 NA
$m4c$spM_lvlone
center scale
c1 0.2559996 0.6718095
p2 2.7125749 1.6247402
b2 NA NA
c2 -0.2237158 0.1059527
L1mis 0.4818481 0.3462447
b21 NA NA
$m4c$mu_reg_norm
[1] 0
$m4c$tau_reg_norm
[1] 1e-04
$m4c$shape_tau_norm
[1] 0.01
$m4c$rate_tau_norm
[1] 0.01
$m4c$mu_reg_gamma
[1] 0
$m4c$tau_reg_gamma
[1] 1e-04
$m4c$shape_tau_gamma
[1] 0.01
$m4c$rate_tau_gamma
[1] 0.01
$m4c$mu_reg_binom
[1] 0
$m4c$tau_reg_binom
[1] 1e-04
$m4c$mu_reg_poisson
[1] 0
$m4c$tau_reg_poisson
[1] 1e-04
$m4c$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m4c$shape_diag_RinvD
[1] "0.01"
$m4c$rate_diag_RinvD
[1] "0.001"
$m4d
$m4d$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m4d$M_lvlone
c1 p2 b2 c2 L1mis Be2 b21
1 0.7592026489 2 NA NA 1.38634787 4.596628e-06 NA
1.1 0.9548337990 2 0 -0.08061445 0.79402906 2.296427e-04 NA
1.2 0.5612235156 NA NA -0.26523782 0.53603334 3.455922e-10 NA
1.3 1.1873391025 NA 0 -0.30260393 0.24129804 9.618613e-07 NA
2 0.9192204198 NA 0 -0.33443795 NA NA NA
2.1 -0.1870730476 6 NA -0.11819800 0.31668065 1.065639e-07 NA
2.2 1.2517512331 3 NA -0.31532280 0.37114414 1.320730e-03 NA
3 -0.0605087604 NA 0 -0.12920657 0.54680608 9.707820e-06 NA
3.1 0.3788637747 NA NA NA 0.28280274 3.645271e-05 NA
3.2 0.9872578281 NA 1 NA 0.76277262 NA NA
4 1.4930175328 NA 1 -0.31177403 0.56100366 5.555794e-01 NA
4.1 -0.7692526880 4 0 -0.23894886 0.38514140 6.853316e-06 NA
4.2 0.9180841450 0 0 -0.15533613 0.04026174 6.324951e-02 NA
4.3 -0.0541170782 NA 0 -0.14644545 0.16025873 4.330745e-07 NA
5 -0.1376784521 2 NA -0.28360457 0.21080161 NA NA
5.1 -0.2740585866 NA 0 -0.20135143 0.36665700 6.556812e-04 NA
5.2 0.4670496929 7 NA -0.28293375 0.66368829 6.963312e-06 NA
5.3 0.1740288049 NA NA NA 0.40788895 1.159006e-04 NA
6 0.9868044683 NA NA -0.08617066 0.11889539 1.509745e-02 NA
7 -0.1280320918 NA NA -0.22243495 1.04286843 NA NA
7.1 0.4242971219 NA NA NA 0.52098933 1.679086e-08 NA
7.2 0.0777182491 NA 0 NA 0.09858876 3.972447e-06 NA
8 -0.5791408712 1 0 NA 0.17281472 9.888512e-02 NA
8.1 0.3128604232 6 0 NA 0.25970093 8.790334e-05 NA
8.2 0.6258446356 NA NA NA 0.30550233 NA NA
8.3 -0.1040137707 3 1 -0.35148972 0.88029778 5.411705e-04 NA
8.4 0.0481450285 2 0 0.03661023 0.20200392 8.446731e-04 NA
8.5 0.3831763675 1 1 -0.08424534 NA 2.059814e-04 NA
9 -0.1757592269 3 0 NA 1.12218535 4.160033e-01 NA
9.1 -0.1791541200 NA NA -0.43509340 0.57911079 NA NA
9.2 -0.0957042935 3 NA -0.22527490 0.81350994 1.087331e-03 NA
10 -0.5598409704 3 NA NA 0.32744766 9.321715e-04 NA
10.1 -0.2318340451 NA 0 NA 0.62912282 8.167897e-06 NA
11 0.5086859475 1 0 -0.08587475 0.92140073 2.528529e-04 NA
11.1 0.4951758188 6 0 -0.06157340 0.16012129 NA NA
11.2 -1.1022162541 1 0 -0.12436018 0.16166775 5.587553e-10 NA
11.3 -0.0611636705 6 0 -0.21377934 0.14979756 5.240776e-10 NA
11.4 -0.4971774316 NA 0 -0.32208329 0.46855190 2.830994e-07 NA
12 -0.2433996286 NA 0 NA 0.76818678 1.962202e-07 NA
13 0.8799673116 NA NA NA 0.34264972 NA NA
13.1 0.1079022586 NA 0 -0.40300449 0.14526619 1.330415e-06 NA
14 0.9991752617 NA NA -0.28992072 0.80630788 5.900181e-07 NA
14.1 -0.1094019046 NA NA NA 0.35697552 3.694946e-05 NA
14.2 0.1518967560 2 NA NA 0.21330192 6.871447e-08 NA
14.3 0.3521012473 NA NA -0.21979936 NA NA NA
15 0.3464447888 NA 0 NA 0.30769119 1.848068e-04 NA
15.1 -0.4767313971 NA 0 -0.29092263 0.28349746 1.714157e-10 NA
15.2 0.5759767791 NA 0 -0.19392239 0.64618365 1.088807e-03 NA
15.3 -0.1713452662 NA 0 -0.25718384 0.51680884 2.677330e-05 NA
16 0.4564754473 1 1 -0.45041108 0.71265471 NA NA
16.1 1.0652558311 NA NA -0.07599066 0.38925880 1.411453e-04 NA
16.2 0.6971872493 2 NA -0.32385667 0.23648869 1.897147e-03 NA
16.3 0.5259331838 NA 0 -0.38326110 0.45048730 5.950632e-02 NA
16.4 0.2046601798 1 0 -0.22845856 0.23181791 3.944608e-02 NA
16.5 1.0718540464 NA NA -0.25497157 0.13985349 NA NA
17 0.6048676222 1 0 NA 0.25995399 4.808238e-05 NA
17.1 0.2323298304 NA 0 -0.22105143 0.03594878 6.175264e-04 NA
17.2 1.2617499032 3 0 NA 0.77583623 2.319036e-07 NA
17.3 -0.3913230895 2 NA NA 0.60015197 1.393008e-09 NA
17.4 0.9577299112 NA 0 -0.15098046 0.13998405 NA NA
18 -0.0050324072 2 0 -0.09870041 0.96475839 2.685853e-09 NA
19 -0.4187468937 NA NA -0.26680239 0.10596495 2.949370e-07 NA
19.1 -0.4478828944 NA NA -0.15815241 0.13338947 1.183423e-08 NA
19.2 -1.1966721302 2 0 -0.14717437 0.41662218 7.844699e-08 NA
19.3 -0.5877091668 2 1 -0.21271374 0.53670855 NA NA
20 0.6838223064 NA NA -0.22087628 0.41688567 4.920475e-06 NA
20.1 0.3278571109 2 0 NA NA 6.885500e-08 NA
20.2 -0.8489831990 NA 1 -0.30127439 0.81634101 9.577206e-04 NA
20.3 1.3169975191 NA 0 -0.11782590 0.39232496 1.325632e-03 NA
20.4 0.0444804531 NA 0 -0.19857957 0.57925554 NA NA
20.5 -0.4535207652 NA 0 -0.24338208 0.74200986 1.011637e-06 NA
21 -0.4030302960 2 0 -0.31407992 0.24759801 3.032947e-04 NA
21.1 -0.4069674045 3 0 -0.12424941 0.34052205 4.370975e-06 NA
21.2 1.0650265940 2 NA -0.27672716 0.03905058 8.793700e-06 NA
22 -0.0673274516 3 0 -0.23790593 0.48605351 NA NA
22.1 0.9601388170 3 0 -0.15996535 0.43761071 7.397166e-06 NA
23 0.5556634840 NA 0 -0.18236682 0.47599712 4.931346e-02 NA
23.1 1.4407865964 5 NA -0.20823302 0.47680301 3.799306e-02 NA
24 0.3856376411 2 0 -0.29026416 0.51696505 1.018950e-01 NA
25 0.3564400705 3 0 -0.36139273 0.59392591 NA NA
25.1 0.0982553434 3 NA -0.19571118 0.74010330 2.264756e-02 NA
25.2 0.1928682598 3 1 -0.21379355 NA 6.622343e-07 NA
25.3 -0.0192488594 4 0 -0.33876012 0.73081722 2.802504e-09 NA
25.4 0.4466012931 NA 0 NA 0.29274286 1.873599e-04 NA
25.5 1.1425193342 NA NA -0.04068446 0.74425342 NA NA
26 0.5341531449 NA NA -0.16846716 0.20974346 4.587570e-09 NA
26.1 1.2268695927 2 0 -0.10440642 NA 2.394334e-06 NA
26.2 0.3678294939 NA 0 -0.26884827 0.22908815 4.510972e-08 NA
26.3 0.5948516018 NA 0 NA 0.41880799 3.657318e-11 NA
27 -0.3342844147 1 0 -0.19520794 0.10097167 NA NA
27.1 -0.4835141229 NA 0 -0.17622638 NA 8.874134e-06 NA
28 -0.7145915499 0 NA -0.32164962 NA 3.673907e-06 NA
28.1 0.5063671955 NA 0 -0.27003852 0.56052750 4.541426e-04 NA
28.2 -0.2067413142 4 0 -0.07235801 0.15301800 2.697966e-12 NA
28.3 0.1196789973 NA 0 -0.13462982 0.27802542 NA NA
29 0.1392699487 3 0 -0.32432030 0.43556671 3.282475e-03 NA
29.1 0.7960234776 3 0 -0.27034171 0.27593085 2.270717e-01 NA
29.2 1.0398214352 3 0 -0.10197448 0.55256871 9.981536e-03 NA
29.3 0.0813246429 2 0 -0.27606945 0.47272109 2.343590e-02 NA
30 -0.3296323050 NA NA -0.06949300 0.32743933 NA NA
30.1 1.3635850954 NA 0 -0.11511035 0.02231535 1.591483e-07 NA
30.2 0.7354171050 5 0 -0.16215882 0.12833697 1.896944e-11 NA
31 0.3708398217 8 0 0.05707733 0.11126366 5.546285e-08 NA
32 -0.0474059668 NA 0 -0.18446298 1.11731084 9.411981e-09 NA
32.1 1.2507771489 2 0 -0.14270013 0.85943330 1.270914e-08 NA
32.2 0.1142915519 1 NA -0.20530798 1.53730925 3.910478e-09 NA
32.3 0.6773270619 NA NA -0.14705649 0.43831965 9.124048e-10 NA
33 0.1774293842 0 0 -0.15252819 0.46726055 9.056156e-01 NA
33.1 0.6159606291 NA 1 NA 0.76818259 3.047254e-06 NA
34 0.8590979166 3 NA -0.30378735 NA 1.040462e-04 NA
34.1 0.0546216775 NA 0 -0.11982431 1.14350292 5.714390e-12 NA
34.2 -0.0897224473 1 NA -0.24278671 0.19103604 7.883166e-09 NA
34.3 0.4163395571 2 NA -0.19971833 NA 3.055823e-07 NA
35 -1.4693520528 NA 0 NA 0.66303137 1.287796e-07 NA
35.1 -0.3031734330 NA 0 -0.24165780 NA 1.762232e-06 NA
35.2 -0.6045512101 NA NA NA NA 5.355159e-08 NA
36 0.9823048960 5 NA -0.49062180 0.93843318 7.250797e-06 NA
36.1 1.4466051416 NA NA -0.25651700 NA 2.370652e-06 NA
36.2 1.1606752905 NA 0 NA 0.29886676 1.537090e-05 NA
36.3 0.8373091576 1 0 -0.30401274 0.22616598 6.993214e-07 NA
36.4 0.2640591685 1 0 NA 0.53849566 4.950009e-05 NA
37 0.1177313455 5 0 -0.15276529 1.68107300 2.755165e-07 NA
37.1 -0.1415483779 NA 0 -0.30016169 1.13777638 3.400517e-07 NA
37.2 0.0054610124 NA 0 0.06809545 0.26931933 2.489007e-09 NA
38 0.8078948077 0 0 -0.11218486 NA 1.302651e-01 NA
39 0.9876451040 NA 1 -0.38072211 0.14395367 4.343746e-04 NA
39.1 -0.3431222274 1 0 -0.32094428 0.36454923 6.653143e-05 NA
39.2 -1.7909380751 NA NA NA 1.03700002 1.940204e-09 NA
39.3 -0.1798746191 NA NA -0.40173480 0.41320585 8.300468e-07 NA
39.4 -0.1850961689 NA 0 -0.20041848 0.20901554 7.464169e-08 NA
39.5 0.4544226146 NA 1 -0.26027990 0.51603848 5.765597e-10 NA
40 0.5350190436 2 0 -0.19751956 0.33912363 9.140572e-01 NA
40.1 0.4189342752 4 1 -0.08399467 0.21892118 1.883555e-03 NA
40.2 0.4211994981 NA 0 -0.20864416 0.74070896 2.303001e-01 NA
40.3 0.0916687506 NA NA NA 0.82927399 2.799910e-05 NA
41 -0.1035047421 NA 0 -0.26096953 0.25193679 3.700067e-02 NA
41.1 -0.4684202411 4 NA -0.23953874 0.28760510 5.798225e-06 NA
41.2 0.5972615368 2 0 -0.03079344 0.45553197 1.086252e-08 NA
41.3 0.9885613862 3 NA NA 0.79237611 3.088732e-07 NA
41.4 -0.3908036794 NA 0 NA 0.12582175 4.549537e-05 NA
42 -0.0338893961 3 0 -0.16084527 0.50079604 5.220968e-03 NA
42.1 -0.4498363172 5 1 -0.13812521 0.61140760 7.264286e-08 NA
43 0.8965546110 4 0 -0.08864017 0.29752019 1.498125e-07 NA
43.1 0.6199122090 3 1 -0.12583158 0.51793497 1.316763e-04 NA
43.2 0.1804894429 3 0 -0.29253959 0.15152473 8.151771e-07 NA
44 1.3221409285 1 0 -0.22697597 0.38806434 1.032476e-03 NA
44.1 0.3416426284 NA 0 NA 1.11140786 3.120174e-09 NA
44.2 0.5706610068 7 0 NA 0.39132534 2.571257e-10 NA
44.3 1.2679497430 NA 0 -0.40544012 0.40934909 2.227416e-09 NA
45 0.1414983160 NA NA -0.19274788 0.68587067 3.948036e-01 NA
45.1 0.7220892521 NA 1 -0.34860483 0.34530800 1.066310e-03 NA
46 1.5391054233 4 0 -0.28547861 0.71312288 2.219556e-08 NA
46.1 0.3889107049 6 0 -0.21977836 0.62537420 1.434525e-08 NA
46.2 0.1248719493 NA 0 NA 0.79574391 1.523026e-07 NA
47 0.2014101100 NA 0 -0.08597098 0.48660773 5.404537e-03 NA
47.1 0.2982973539 4 0 -0.35424828 0.51241790 3.739267e-07 NA
47.2 1.1518107179 2 0 -0.24262576 0.58869379 7.171916e-06 NA
47.3 0.5196802157 4 NA -0.30426315 0.22171504 3.850162e-05 NA
47.4 0.3702301552 NA 0 NA 0.11366347 1.767264e-08 NA
48 -0.2128602862 NA 1 NA 0.19677010 1.988010e-04 NA
48.1 -0.5337239976 6 1 NA 0.17706320 6.074589e-09 NA
49 -0.5236770035 NA NA -0.42198781 0.30752382 1.321544e-06 NA
50 0.3897705981 3 0 -0.19959516 0.93663423 4.240393e-05 NA
51 -0.7213343736 2 0 -0.16556964 0.34107606 1.986093e-09 NA
52 0.3758235358 3 0 -0.07438732 0.19007135 1.632022e-02 NA
52.1 0.7138067080 1 0 -0.37537080 0.75662940 2.653038e-02 NA
52.2 0.8872895233 NA 0 -0.24222066 1.66104719 2.262881e-03 NA
52.3 -0.9664587437 2 0 -0.31520603 NA 6.572647e-10 NA
52.4 0.0254566848 3 0 -0.44619160 0.18369708 1.393737e-04 NA
52.5 0.4155259424 1 0 -0.11011682 0.48689343 5.069462e-03 NA
53 0.5675736897 3 0 -0.23278716 0.31983157 5.848890e-05 NA
53.1 -0.3154088781 NA 0 -0.28317264 0.61569501 1.878509e-04 NA
53.2 0.2162315769 2 NA -0.19517481 NA 1.293417e-04 NA
54 -0.0880802382 3 NA -0.10122856 1.90522418 1.818441e-03 NA
54.1 0.4129127672 NA NA -0.28325504 0.59484889 2.251839e-07 NA
54.2 1.0119546775 4 NA -0.16753120 1.47174857 5.638172e-06 NA
54.3 -0.1112901990 0 NA -0.22217672 0.27307143 5.320676e-03 NA
54.4 0.8587727145 NA 0 -0.34609328 0.81272938 1.491367e-07 NA
55 -0.0116453589 NA 0 -0.32428190 0.22735476 3.183775e-03 NA
55.1 0.5835528661 4 0 -0.24235382 0.54683512 1.183380e-03 NA
55.2 -1.0010857254 NA NA -0.24065814 1.03503777 1.817077e-06 NA
55.3 -0.4796526070 4 NA -0.23665476 0.30169529 1.424370e-06 NA
55.4 -0.1202746964 3 0 NA 0.36008059 3.119967e-07 NA
56 0.5176377612 NA 0 NA 0.14193566 1.169667e-06 NA
56.1 -1.1136932588 2 NA -0.30357450 0.65073539 1.182293e-06 NA
56.2 -0.0168103281 3 NA -0.51301630 0.11338262 2.087533e-04 NA
56.3 0.3933023606 3 1 -0.23743117 0.16820103 5.728251e-06 NA
56.4 0.3714625139 0 0 -0.17264917 0.27419110 4.087596e-08 NA
56.5 0.7811448179 NA 0 -0.39188329 0.57110215 8.040370e-07 NA
57 -1.0868304872 3 0 -0.18501692 0.85104054 1.438387e-02 NA
57.1 0.8018626997 4 0 -0.27274841 0.34733833 3.202179e-05 NA
57.2 -0.1159517011 1 0 NA 1.44438762 1.486318e-03 NA
57.3 0.6785562445 NA NA -0.09898509 0.31836125 1.718412e-04 NA
58 1.6476207996 NA 0 -0.29901358 0.37456898 3.114123e-05 NA
58.1 0.3402652711 NA NA -0.35390896 0.22120158 1.403881e-04 NA
58.2 -0.1111300753 NA 1 -0.16687336 0.78885210 2.111006e-01 NA
58.3 -0.5409234285 3 1 -0.11784506 0.10114937 9.586985e-06 NA
58.4 -0.1271327672 NA 0 -0.05321983 0.13385114 4.073162e-03 NA
58.5 0.8713264822 NA 0 -0.54457568 NA 9.285307e-04 NA
59 0.4766421367 NA NA -0.27255364 0.13202156 2.711478e-06 NA
59.1 1.0028089765 NA 1 NA 0.33371896 1.173472e-04 NA
60 0.5231452932 NA 0 NA 0.35096579 7.579680e-09 NA
61 -0.7190130614 2 NA -0.30550120 0.36933806 4.545759e-03 NA
61.1 0.8353702312 4 1 -0.35579892 0.17623067 5.936674e-02 NA
61.2 1.0229058138 NA 1 NA 0.21286227 3.897281e-01 NA
61.3 1.1717723589 NA 0 -0.34184391 0.12689308 6.237379e-02 NA
61.4 -0.0629201596 NA 0 -0.30891967 0.77676718 5.103038e-01 NA
62 -0.3979137604 2 NA NA 1.38018163 3.707353e-02 NA
62.1 0.6830738372 NA 1 -0.10504143 0.43803892 1.901660e-03 NA
62.2 0.4301745954 NA 0 -0.20104997 0.21947900 7.844369e-08 NA
62.3 -0.0333139957 NA 0 -0.08138677 0.11571160 1.496168e-08 NA
63 0.3345678035 NA NA -0.12036319 0.41583568 5.101070e-11 NA
63.1 0.3643769511 2 0 -0.13624992 0.25598960 1.106013e-05 NA
64 0.3949911859 4 0 NA 0.20415642 1.685171e-09 NA
65 1.2000091513 NA 0 -0.34450396 0.07135646 1.684142e-01 NA
65.1 0.0110122646 5 0 -0.32514650 0.57450574 1.413479e-05 NA
65.2 -0.5776452043 NA 0 -0.10984996 0.52562984 2.841196e-03 NA
65.3 -0.1372183563 NA 0 -0.19275692 0.21921164 3.118871e-04 NA
66 -0.5081302805 NA NA NA 0.33281730 1.078473e-06 NA
66.1 -0.1447837412 NA 0 NA 0.03412404 1.136650e-01 NA
66.2 0.1906241379 NA 0 -0.11687008 0.92570619 7.007044e-08 NA
67 1.6716027681 NA NA NA 0.15291043 4.025749e-11 NA
68 0.5691848839 NA 0 -0.13605235 0.37543648 2.469503e-06 NA
68.1 0.1004860389 NA 0 -0.19790827 0.20901022 1.067638e-08 NA
68.2 -0.0061241827 NA NA -0.17750123 0.12488064 1.508555e-06 NA
68.3 0.7443745962 2 0 NA 0.08711204 7.862972e-06 NA
68.4 0.8726923437 NA NA -0.12570562 0.54611735 1.970326e-05 NA
69 0.0381382683 NA 0 -0.32152751 0.23638239 5.089430e-07 NA
70 0.8126204217 4 0 -0.28190462 0.49876756 5.575849e-07 NA
70.1 0.4691503050 4 0 -0.11503263 0.39512615 6.115107e-04 NA
71 -0.5529062591 4 0 -0.13029093 0.45666551 1.867742e-05 NA
71.1 -0.1103252087 NA 1 NA 0.92047456 4.616167e-04 NA
71.2 1.7178492547 3 0 -0.39075433 0.32792986 5.314611e-08 NA
71.3 -1.0118346755 0 1 -0.21401028 0.95108007 1.790244e-10 NA
71.4 1.8623785017 0 0 -0.40219281 0.36287072 1.924070e-03 NA
72 -0.4521659275 NA 0 -0.40337108 0.12870526 6.526547e-05 NA
72.1 0.1375317317 8 0 -0.25978914 0.45925876 5.540491e-11 NA
72.2 -0.4170988856 NA NA NA 0.05418867 2.391191e-12 NA
72.3 0.7107266765 NA 0 -0.09809866 0.48937486 2.878783e-12 NA
72.4 0.1451969143 3 0 -0.14240019 0.64173822 1.014404e-09 NA
72.5 1.6298050306 NA 0 -0.14794204 0.57609943 1.281231e-05 NA
73 -0.0307469467 2 0 -0.23509343 0.17393402 6.661564e-02 NA
74 0.3730017941 NA 0 -0.27963171 0.23990575 3.683842e-04 NA
75 -0.4908003566 NA NA -0.12905034 0.28469861 2.274469e-06 NA
76 -0.9888876620 1 0 0.04775562 0.71988630 9.155636e-04 NA
76.1 0.0003798292 0 0 -0.19399157 1.12449946 1.485365e-04 NA
76.2 -0.8421863763 0 0 -0.02754574 0.71313766 3.118702e-06 NA
77 -0.4986802480 2 NA -0.19053195 0.02399030 4.946432e-01 NA
78 0.0417330969 NA 0 -0.17172929 0.42708148 8.533933e-05 NA
79 -0.3767450660 2 NA -0.03958515 0.37579286 1.980588e-01 NA
79.1 0.1516000028 NA 0 -0.20328809 0.78660681 8.624235e-06 NA
79.2 -0.1888160741 2 NA -0.23901634 0.67696116 2.176176e-05 NA
80 -0.0041558414 2 NA -0.34031873 0.34207854 2.929029e-06 NA
80.1 -0.0329337062 NA 0 -0.19526756 0.60534092 1.126162e-04 NA
80.2 0.5046816157 NA NA NA 0.26731034 9.847382e-08 NA
81 -0.9493950353 NA 0 -0.18401980 0.17739052 4.026095e-01 NA
81.1 0.2443038954 2 0 -0.16889476 0.35453673 2.093927e-02 NA
81.2 0.6476958410 NA NA -0.37343047 0.20244235 9.224440e-01 NA
81.3 0.4182528210 NA 0 NA 1.26402329 8.175654e-03 NA
82 1.1088801952 NA NA -0.08328168 0.09303938 1.228129e-01 NA
82.1 0.9334157763 NA 0 -0.22167084 0.27254210 6.656575e-05 NA
82.2 0.4958140634 4 1 -0.20971187 0.49936304 2.001426e-08 NA
83 0.5104724530 NA NA -0.34228255 0.21138572 5.690020e-06 NA
83.1 -0.0513309106 NA 0 -0.34075730 0.26403568 5.980615e-06 NA
83.2 -0.2067792494 4 0 -0.32503954 0.20311133 1.880816e-05 NA
83.3 -0.0534169155 3 NA NA 1.16864671 4.048910e-09 NA
84 -0.0255753653 NA 0 -0.20676741 1.99179346 6.552173e-02 NA
84.1 -1.8234189877 2 NA -0.20310458 1.52199460 8.829278e-06 NA
85 -0.0114038622 3 1 -0.12107593 NA 4.118253e-06 NA
85.1 -0.0577615939 NA NA NA 0.61458995 2.311994e-06 NA
85.2 -0.2241856342 3 0 -0.32509207 0.07871196 5.182892e-05 NA
85.3 -0.0520175929 NA 0 NA 1.42315283 1.689467e-03 NA
85.4 0.2892733846 2 0 -0.30730810 0.97986129 1.168017e-03 NA
85.5 -0.3740417009 1 0 NA 0.91792195 7.945131e-07 NA
86 0.4293735089 2 0 -0.10854862 0.63509597 2.905567e-05 NA
86.1 -0.1363456521 NA NA -0.25751662 0.24546597 5.331467e-06 NA
86.2 0.1230989293 0 NA -0.38943076 0.53102060 1.761451e-06 NA
86.3 0.3305413955 0 0 -0.24454702 0.09360826 2.272397e-06 NA
86.4 2.6003411822 NA NA -0.12338992 0.58301186 4.467006e-06 NA
86.5 -0.1420690052 2 0 -0.23976984 0.39146055 1.693940e-08 NA
87 1.0457427869 NA NA NA NA 6.396865e-05 NA
87.1 -0.2973007190 NA NA -0.34366972 0.66043624 1.264093e-10 NA
87.2 0.4396872616 3 NA NA 0.13267613 4.933807e-07 NA
88 -0.0601928334 NA 0 -0.31563888 0.10696344 9.223531e-02 NA
88.1 -1.0124347595 1 NA -0.20304028 0.13689448 4.654325e-05 NA
88.2 0.5730917016 1 0 -0.40311895 0.48037889 1.260399e-01 NA
88.3 -0.0029455332 4 0 -0.12308715 0.97755681 8.029866e-08 NA
89 1.5465903721 NA 0 -0.18527715 0.70242369 7.489307e-05 NA
90 0.0626760573 3 0 -0.25029126 0.40042977 1.100491e-02 NA
90.1 1.1896872985 NA 0 -0.26974303 0.63975731 2.715349e-05 NA
90.2 0.2597888783 NA 0 -0.28804531 0.33412775 5.916576e-03 NA
90.3 0.6599799887 NA NA -0.19180615 0.38399003 2.920657e-02 NA
91 1.1213651365 NA 0 -0.26591197 0.58250391 2.411997e-03 NA
91.1 1.2046371625 NA 0 -0.09153470 0.13223217 8.870147e-06 NA
91.2 0.3395603754 NA 0 -0.48414390 0.46613305 1.652965e-08 NA
92 0.4674939332 NA 0 NA 0.18997862 2.613551e-03 NA
93 0.2677965647 2 NA -0.11939966 1.05243347 9.958480e-01 NA
93.1 1.6424445368 4 0 NA 0.01479757 9.915375e-01 NA
93.2 0.7101700066 4 NA -0.21089379 0.50955172 4.861680e-02 NA
93.3 1.1222322893 NA 0 NA 0.78122514 9.769008e-01 NA
93.4 1.4628960401 3 0 -0.23618836 0.63940704 5.977439e-05 NA
94 -0.2904211940 4 NA NA 0.45596305 7.091952e-04 NA
94.1 0.0147813580 2 0 -0.10217284 0.41610667 6.005522e-04 NA
94.2 -0.4536774482 NA 0 -0.36713471 0.52744298 8.134430e-03 NA
94.3 0.6793464917 1 NA -0.13806763 0.70890756 1.747604e-05 NA
94.4 -0.9411356550 NA 0 -0.42353804 0.84412478 9.404259e-07 NA
94.5 0.5683867264 2 1 -0.15513707 0.21166602 6.832077e-07 NA
95 0.2375652188 3 0 -0.24149687 0.57713135 3.216011e-06 NA
95.1 0.0767152977 5 NA -0.21315958 0.44400207 6.324477e-05 NA
95.2 -0.6886731251 2 0 -0.15777208 0.42397776 1.762187e-01 NA
96 0.7813892121 NA 0 -0.16780948 0.72391015 1.578796e-02 NA
96.1 0.3391519695 NA 0 -0.32504815 0.32593738 2.610661e-02 NA
96.2 -0.4857246503 5 0 -0.20395970 0.23249511 3.941700e-05 NA
96.3 0.8771471244 1 NA -0.06221501 1.01679990 1.683671e-05 NA
96.4 1.9030768981 0 1 -0.14801097 0.92267953 1.095127e-04 NA
96.5 -0.1684332749 3 1 -0.28658893 0.83843412 1.479105e-05 NA
97 1.3775130083 4 0 -0.34484656 0.47151154 2.082560e-04 NA
97.1 -1.7323228619 2 0 -0.35658805 0.15596614 7.903013e-10 NA
98 -1.2648518889 3 0 -0.36913003 0.05179545 1.795949e-06 NA
98.1 -0.9042716241 NA 0 NA 0.47332096 2.776600e-02 NA
98.2 -0.1560385207 NA 1 -0.17154225 0.19706341 4.050457e-06 NA
99 0.7993356425 5 0 -0.24753132 0.22574556 2.316802e-05 NA
99.1 1.0355522332 NA 0 -0.27947829 1.00732330 2.206426e-06 NA
99.2 -0.1150895843 NA 0 -0.09033035 0.09749127 2.488411e-08 NA
100 0.0369067906 NA NA -0.17326698 0.22857989 7.572193e-01 NA
100.1 1.6023713093 4 NA NA 0.39548654 9.794641e-02 NA
100.2 0.8861545820 NA 0 -0.12072016 NA 4.934595e-01 NA
100.3 0.1277046316 4 NA -0.27657520 0.32695372 1.502083e-07 NA
100.4 -0.0834577654 NA 0 -0.14631556 0.10043925 2.515993e-06 NA
$m4d$spM_lvlone
center scale
c1 0.25599956 0.6718095
p2 2.71257485 1.6247402
b2 NA NA
c2 -0.22371584 0.1059527
L1mis 0.48184811 0.3462447
Be2 0.04274145 0.1563798
b21 NA NA
$m4d$mu_reg_norm
[1] 0
$m4d$tau_reg_norm
[1] 1e-04
$m4d$shape_tau_norm
[1] 0.01
$m4d$rate_tau_norm
[1] 0.01
$m4d$mu_reg_gamma
[1] 0
$m4d$tau_reg_gamma
[1] 1e-04
$m4d$shape_tau_gamma
[1] 0.01
$m4d$rate_tau_gamma
[1] 0.01
$m4d$mu_reg_binom
[1] 0
$m4d$tau_reg_binom
[1] 1e-04
$m4d$mu_reg_poisson
[1] 0
$m4d$tau_reg_poisson
[1] 1e-04
$m4d$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m4d$shape_diag_RinvD
[1] "0.01"
$m4d$rate_diag_RinvD
[1] "0.001"
$m5a
$m5a$M_id
M2 (Intercept) M22 M23 M24 log(C1) C1
1 NA 1 NA NA NA -0.3318617 0.7175865
2 1 1 NA NA NA -0.2867266 0.7507170
3 2 1 NA NA NA -0.3207627 0.7255954
4 2 1 NA NA NA -0.2917769 0.7469352
5 1 1 NA NA NA -0.3369956 0.7139120
6 NA 1 NA NA NA -0.3102679 0.7332505
7 NA 1 NA NA NA -0.3084388 0.7345929
8 2 1 NA NA NA -0.2675411 0.7652589
9 NA 1 NA NA NA -0.3284176 0.7200622
10 NA 1 NA NA NA -0.2978834 0.7423879
11 3 1 NA NA NA -0.2960573 0.7437448
12 NA 1 NA NA NA -0.2948450 0.7446470
13 NA 1 NA NA NA -0.2836654 0.7530186
14 2 1 NA NA NA -0.3434574 0.7093137
15 2 1 NA NA NA -0.3104469 0.7331192
16 NA 1 NA NA NA -0.3550492 0.7011390
17 3 1 NA NA NA -0.2967369 0.7432395
18 3 1 NA NA NA -0.2816747 0.7545191
19 2 1 NA NA NA -0.2838910 0.7528487
20 NA 1 NA NA NA -0.2727455 0.7612865
21 NA 1 NA NA NA -0.3213465 0.7251719
22 1 1 NA NA NA -0.3146245 0.7300630
23 2 1 NA NA NA -0.3442879 0.7087249
24 NA 1 NA NA NA -0.3021952 0.7391938
25 4 1 NA NA NA -0.2458186 0.7820641
26 NA 1 NA NA NA -0.3399165 0.7118298
27 NA 1 NA NA NA -0.3242275 0.7230857
28 2 1 NA NA NA -0.2891027 0.7489353
29 4 1 NA NA NA -0.2862314 0.7510888
30 2 1 NA NA NA -0.3146125 0.7300717
31 2 1 NA NA NA -0.2809421 0.7550721
32 3 1 NA NA NA -0.3117155 0.7321898
33 1 1 NA NA NA -0.3138326 0.7306414
34 4 1 NA NA NA -0.2974340 0.7427216
35 4 1 NA NA NA -0.3294709 0.7193042
36 NA 1 NA NA NA -0.3129468 0.7312888
37 NA 1 NA NA NA -0.3424289 0.7100436
38 NA 1 NA NA NA -0.2652444 0.7670184
39 4 1 NA NA NA -0.3010445 0.7400449
40 NA 1 NA NA NA -0.3014695 0.7397304
41 2 1 NA NA NA -0.2888874 0.7490966
42 NA 1 NA NA NA -0.2985038 0.7419274
43 NA 1 NA NA NA -0.2839809 0.7527810
44 3 1 NA NA NA -0.2999821 0.7408315
45 NA 1 NA NA NA -0.3082181 0.7347550
46 NA 1 NA NA NA -0.3102825 0.7332398
47 4 1 NA NA NA -0.3042884 0.7376481
48 3 1 NA NA NA -0.3084048 0.7346179
49 4 1 NA NA NA -0.3106911 0.7329402
50 3 1 NA NA NA -0.3201451 0.7260436
51 NA 1 NA NA NA -0.3225621 0.7242910
52 1 1 NA NA NA -0.3149755 0.7298067
53 NA 1 NA NA NA -0.3209299 0.7254741
54 NA 1 NA NA NA -0.2820889 0.7542067
55 NA 1 NA NA NA -0.3024638 0.7389952
56 NA 1 NA NA NA -0.2849341 0.7520638
57 4 1 NA NA NA -0.3257359 0.7219958
58 1 1 NA NA NA -0.3202560 0.7259632
59 2 1 NA NA NA -0.2932166 0.7458606
60 3 1 NA NA NA -0.2649529 0.7672421
61 2 1 NA NA NA -0.3205938 0.7257179
62 3 1 NA NA NA -0.3299089 0.7189892
63 2 1 NA NA NA -0.3101519 0.7333356
64 NA 1 NA NA NA -0.3119416 0.7320243
65 NA 1 NA NA NA -0.2906584 0.7477711
66 2 1 NA NA NA -0.3087049 0.7343974
67 NA 1 NA NA NA -0.2887994 0.7491624
68 3 1 NA NA NA -0.2899866 0.7482736
69 3 1 NA NA NA -0.3094824 0.7338267
70 4 1 NA NA NA -0.2734187 0.7607742
71 2 1 NA NA NA -0.2513372 0.7777600
72 NA 1 NA NA NA -0.3000053 0.7408143
73 4 1 NA NA NA -0.3218221 0.7248271
74 NA 1 NA NA NA -0.3058575 0.7364916
75 NA 1 NA NA NA -0.2923695 0.7464926
76 4 1 NA NA NA -0.3071463 0.7355430
77 2 1 NA NA NA -0.3273313 0.7208449
78 2 1 NA NA NA -0.3046827 0.7373573
79 NA 1 NA NA NA -0.2746896 0.7598079
80 1 1 NA NA NA -0.3064688 0.7360415
81 NA 1 NA NA NA -0.3155423 0.7293932
82 3 1 NA NA NA -0.3175491 0.7279309
83 4 1 NA NA NA -0.3086139 0.7344643
84 3 1 NA NA NA -0.3032222 0.7384350
85 NA 1 NA NA NA -0.3114673 0.7323716
86 3 1 NA NA NA -0.2775210 0.7576597
87 NA 1 NA NA NA -0.2881970 0.7496139
88 NA 1 NA NA NA -0.3181084 0.7275239
89 NA 1 NA NA NA -0.3214942 0.7250648
90 4 1 NA NA NA -0.3098919 0.7335262
91 NA 1 NA NA NA -0.3087042 0.7343980
92 2 1 NA NA NA -0.3037539 0.7380425
93 4 1 NA NA NA -0.3025305 0.7389460
94 NA 1 NA NA NA -0.3202120 0.7259951
95 NA 1 NA NA NA -0.3170642 0.7282840
96 NA 1 NA NA NA -0.3172240 0.7281676
97 1 1 NA NA NA -0.3221849 0.7245642
98 NA 1 NA NA NA -0.2840967 0.7526938
99 2 1 NA NA NA -0.3185112 0.7272309
100 NA 1 NA NA NA -0.3033427 0.7383460
$m5a$M_lvlone
y c2 o2 time o22 o23 o24 abs(C1 - c2)
1 -13.0493856 NA 1 0.5090421822 NA NA NA NA
1.1 -9.3335901 -0.08061445 1 0.6666076288 NA NA NA NA
1.2 -22.3469852 -0.26523782 3 2.1304941282 NA NA NA NA
1.3 -15.0417337 -0.30260393 1 2.4954441458 NA NA NA NA
2 -12.0655434 -0.33443795 4 3.0164990982 NA NA NA NA
2.1 -15.8674476 -0.11819800 4 3.2996806887 NA NA NA NA
2.2 -7.8800006 -0.31532280 2 4.1747569619 NA NA NA NA
3 -11.4820604 -0.12920657 2 0.8478727890 NA NA NA NA
3.1 -10.5983220 NA 4 3.0654308549 NA NA NA NA
3.2 -22.4519157 NA 2 4.7381553578 NA NA NA NA
4 -1.2697775 -0.31177403 4 0.3371432109 NA NA NA NA
4.1 -11.1215184 -0.23894886 3 1.0693019140 NA NA NA NA
4.2 -3.6134138 -0.15533613 NA 2.6148973033 NA NA NA NA
4.3 -14.5982385 -0.14644545 2 3.1336532847 NA NA NA NA
5 -6.8457515 -0.28360457 2 1.0762525082 NA NA NA NA
5.1 -7.0551214 -0.20135143 4 1.7912546196 NA NA NA NA
5.2 -12.3418980 -0.28293375 2 2.7960080339 NA NA NA NA
5.3 -9.2366906 NA 4 2.8119940578 NA NA NA NA
6 -5.1648211 -0.08617066 3 1.7815462884 NA NA NA NA
7 -10.0599502 -0.22243495 1 3.3074087673 NA NA NA NA
7.1 -18.3267285 NA NA 3.7008403614 NA NA NA NA
7.2 -12.5138426 NA 4 4.7716691741 NA NA NA NA
8 -1.6305331 NA 1 1.1246398522 NA NA NA NA
8.1 -9.6520453 NA 3 1.8027009873 NA NA NA NA
8.2 -1.5278462 NA 1 1.8175825174 NA NA NA NA
8.3 -7.4172211 -0.35148972 4 2.8384267003 NA NA NA NA
8.4 -7.1238609 0.03661023 3 3.3630275307 NA NA NA NA
8.5 -8.8706950 -0.08424534 3 4.4360849704 NA NA NA NA
9 -0.1634429 NA 2 0.9607803822 NA NA NA NA
9.1 -2.6034300 -0.43509340 2 2.9177753383 NA NA NA NA
9.2 -6.7272369 -0.22527490 4 4.8100892501 NA NA NA NA
10 -6.4172202 NA 1 2.2975509102 NA NA NA NA
10.1 -11.4834569 NA 4 4.1734118364 NA NA NA NA
11 -8.7911356 -0.08587475 3 1.1832662905 NA NA NA NA
11.1 -19.6645080 -0.06157340 1 1.2346051680 NA NA NA NA
11.2 -20.2030932 -0.12436018 4 1.6435316263 NA NA NA NA
11.3 -21.3082176 -0.21377934 3 3.3859017969 NA NA NA NA
11.4 -14.5802901 -0.32208329 3 4.8118087661 NA NA NA NA
12 -15.2006287 NA 3 0.9591987054 NA NA NA NA
13 0.8058816 NA NA 0.0619085738 NA NA NA NA
13.1 -13.6379208 -0.40300449 1 3.5621061502 NA NA NA NA
14 -15.3422873 -0.28992072 1 4.0364430007 NA NA NA NA
14.1 -10.0965208 NA 4 4.4710561272 NA NA NA NA
14.2 -16.6452027 NA 3 4.6359198843 NA NA NA NA
14.3 -15.8389733 -0.21979936 4 4.6886152599 NA NA NA NA
15 -8.9424594 NA 1 0.5402063532 NA NA NA NA
15.1 -22.0101983 -0.29092263 4 1.1893180816 NA NA NA NA
15.2 -7.3975599 -0.19392239 NA 1.5094739688 NA NA NA NA
15.3 -10.3567334 -0.25718384 2 4.9193474615 NA NA NA NA
16 -1.9691302 -0.45041108 NA 1.2417913869 NA NA NA NA
16.1 -9.9308357 -0.07599066 NA 2.5675726333 NA NA NA NA
16.2 -6.9626923 -0.32385667 1 2.6524101500 NA NA NA NA
16.3 -3.2862557 -0.38326110 3 3.5585018690 NA NA NA NA
16.4 -3.3972355 -0.22845856 3 3.7612454291 NA NA NA NA
16.5 -11.5767835 -0.25497157 1 3.9851612889 NA NA NA NA
17 -10.5474144 NA 3 1.5925356350 NA NA NA NA
17.1 -7.6215009 -0.22105143 2 2.4374032998 NA NA NA NA
17.2 -16.5386939 NA 2 3.0256489082 NA NA NA NA
17.3 -20.0004774 NA 3 3.3329089405 NA NA NA NA
17.4 -18.8505475 -0.15098046 1 3.8693758985 NA NA NA NA
18 -19.7302351 -0.09870041 4 2.4374292302 NA NA NA NA
19 -14.6177568 -0.26680239 1 0.9772165376 NA NA NA NA
19.1 -17.8043866 -0.15815241 NA 1.1466335913 NA NA NA NA
19.2 -15.1641705 -0.14717437 NA 2.2599126538 NA NA NA NA
19.3 -16.6898418 -0.21271374 2 4.2114245973 NA NA NA NA
20 -12.9059229 -0.22087628 1 1.7170160066 NA NA NA NA
20.1 -16.8191201 NA 4 1.7562902288 NA NA NA NA
20.2 -6.1010131 -0.30127439 3 2.2515566566 NA NA NA NA
20.3 -7.9415371 -0.11782590 3 2.2609123867 NA NA NA NA
20.4 -9.3904458 -0.19857957 1 3.4913365287 NA NA NA NA
20.5 -13.3504189 -0.24338208 3 4.1730977828 NA NA NA NA
21 -7.6974718 -0.31407992 3 1.6936582839 NA NA NA NA
21.1 -11.9335526 -0.12424941 1 2.9571191233 NA NA NA NA
21.2 -12.7064929 -0.27672716 2 3.7887385779 NA NA NA NA
22 -21.5022909 -0.23790593 4 2.4696226232 NA NA NA NA
22.1 -12.7745451 -0.15996535 NA 3.1626627257 NA NA NA NA
23 -3.5146508 -0.18236682 4 1.5414533857 NA NA NA NA
23.1 -4.6724048 -0.20823302 NA 2.3369736120 NA NA NA NA
24 -2.5619821 -0.29026416 3 2.8283136466 NA NA NA NA
25 -6.2944970 -0.36139273 1 0.5381704110 NA NA NA NA
25.1 -3.8630505 -0.19571118 3 1.6069735331 NA NA NA NA
25.2 -14.4205140 -0.21379355 2 1.6358226922 NA NA NA NA
25.3 -19.6735037 -0.33876012 1 3.2646870392 NA NA NA NA
25.4 -9.0288933 NA 1 4.0782226040 NA NA NA NA
25.5 -9.0509738 -0.04068446 NA 4.1560292873 NA NA NA NA
26 -19.7340685 -0.16846716 3 0.2412706357 NA NA NA NA
26.1 -14.1692728 -0.10440642 4 2.4451737676 NA NA NA NA
26.2 -17.2819976 -0.26884827 3 3.5988757887 NA NA NA NA
26.3 -24.6265576 NA 1 4.1822362854 NA NA NA NA
27 -7.3354999 -0.19520794 4 3.6955824879 NA NA NA NA
27.1 -11.1488468 -0.17622638 4 4.2451434687 NA NA NA NA
28 -11.7996597 -0.32164962 1 0.5746519344 NA NA NA NA
28.1 -8.2030122 -0.27003852 2 2.7943964268 NA NA NA NA
28.2 -26.4317815 -0.07235801 3 4.2108539480 NA NA NA NA
28.3 -18.5016071 -0.13462982 3 4.4705521734 NA NA NA NA
29 -5.8551395 -0.32432030 4 1.1898884235 NA NA NA NA
29.1 -2.0209442 -0.27034171 4 1.7624059319 NA NA NA NA
29.2 -5.6368080 -0.10197448 3 2.0210406382 NA NA NA NA
29.3 -3.8110961 -0.27606945 2 3.4078777023 NA NA NA NA
30 -12.7217702 -0.06949300 3 2.2635366488 NA NA NA NA
30.1 -17.0170140 -0.11511035 4 3.5938334477 NA NA NA NA
30.2 -25.4236089 -0.16215882 4 3.6138710892 NA NA NA NA
31 -17.0783921 0.05707733 2 4.3988140998 NA NA NA NA
32 -18.4338764 -0.18446298 NA 1.6745209007 NA NA NA NA
32.1 -19.4317212 -0.14270013 2 2.9128167813 NA NA NA NA
32.2 -19.4738978 -0.20530798 4 2.9676558380 NA NA NA NA
32.3 -21.4922645 -0.14705649 3 4.2099863547 NA NA NA NA
33 2.0838099 -0.15252819 4 0.0093385763 NA NA NA NA
33.1 -13.3172274 NA 4 3.4591242753 NA NA NA NA
34 -10.0296691 -0.30378735 NA 1.4998774312 NA NA NA NA
34.1 -25.9426553 -0.11982431 NA 3.8242761395 NA NA NA NA
34.2 -18.5688138 -0.24278671 NA 3.9072251692 NA NA NA NA
34.3 -15.4173859 -0.19971833 NA 3.9582124643 NA NA NA NA
35 -14.3958113 NA 4 1.3294299203 NA NA NA NA
35.1 -12.9457541 -0.24165780 1 1.5276966314 NA NA NA NA
35.2 -16.1380691 NA NA 4.5025920868 NA NA NA NA
36 -12.8166968 -0.49062180 1 0.7123168337 NA NA NA NA
36.1 -14.3989481 -0.25651700 1 1.7972493160 NA NA NA NA
36.2 -12.2436943 NA 2 1.8262697803 NA NA NA NA
36.3 -15.0104638 -0.30401274 2 4.2840119381 NA NA NA NA
36.4 -10.1775457 NA 1 4.6194464504 NA NA NA NA
37 -15.2223495 -0.15276529 4 2.0018732361 NA NA NA NA
37.1 -14.7526195 -0.30016169 2 3.6656836793 NA NA NA NA
37.2 -19.8168430 0.06809545 2 3.9663937816 NA NA NA NA
38 -2.7065118 -0.11218486 NA 0.9826511063 NA NA NA NA
39 -8.7288138 -0.38072211 NA 0.6921808305 NA NA NA NA
39.1 -9.2746473 -0.32094428 2 0.9027792048 NA NA NA NA
39.2 -18.2695344 NA 2 1.3055654289 NA NA NA NA
39.3 -13.8219083 -0.40173480 3 1.5412842878 NA NA NA NA
39.4 -16.2254704 -0.20041848 3 3.1834997435 NA NA NA NA
39.5 -21.7283648 -0.26027990 1 4.1394166439 NA NA NA NA
40 1.8291916 -0.19751956 1 1.1330395646 NA NA NA NA
40.1 -6.6916432 -0.08399467 2 2.6940994046 NA NA NA NA
40.2 -1.6278171 -0.20864416 NA 3.0396614212 NA NA NA NA
40.3 -10.5749790 NA 2 4.6762977762 NA NA NA NA
41 -3.1556121 -0.26096953 4 1.9337158254 NA NA NA NA
41.1 -11.5895327 -0.23953874 3 3.1956304458 NA NA NA NA
41.2 -18.9352091 -0.03079344 4 3.2846923557 NA NA NA NA
41.3 -15.9788960 NA NA 3.3813529415 NA NA NA NA
41.4 -9.6070508 NA 4 3.5482964432 NA NA NA NA
42 -5.2159485 -0.16084527 4 0.4859252973 NA NA NA NA
42.1 -15.9878743 -0.13812521 4 4.3293134298 NA NA NA NA
43 -16.6104361 -0.08864017 NA 0.5616614548 NA NA NA NA
43.1 -9.5549441 -0.12583158 NA 1.0743579536 NA NA NA NA
43.2 -14.2003491 -0.29253959 3 2.6131797966 NA NA NA NA
44 -8.1969033 -0.22697597 2 0.7662644819 NA NA NA NA
44.1 -19.9270197 NA 4 2.6490291790 NA NA NA NA
44.2 -22.6521171 NA NA 3.3371910988 NA NA NA NA
44.3 -21.1903736 -0.40544012 1 4.1154200875 NA NA NA NA
45 -0.5686627 -0.19274788 3 0.1957449992 NA NA NA NA
45.1 -7.5645740 -0.34860483 4 1.9963831536 NA NA NA NA
46 -19.1624789 -0.28547861 NA 1.3477755385 NA NA NA NA
46.1 -18.4487574 -0.21977836 4 2.8565793915 NA NA NA NA
46.2 -15.8222682 NA 4 4.4160729996 NA NA NA NA
47 -5.4165074 -0.08597098 3 0.6012621359 NA NA NA NA
47.1 -15.0975029 -0.35424828 2 2.4097121472 NA NA NA NA
47.2 -12.9971413 -0.24262576 1 2.9975794035 NA NA NA NA
47.3 -10.6844521 -0.30426315 2 3.1829649757 NA NA NA NA
47.4 -18.2214784 NA 1 4.6201055450 NA NA NA NA
48 -8.3101471 NA 4 2.8607365978 NA NA NA NA
48.1 -18.3854275 NA NA 2.9098354396 NA NA NA NA
49 -13.0130319 -0.42198781 4 2.7179756400 NA NA NA NA
50 -10.4579977 -0.19959516 4 1.1762060679 NA NA NA NA
51 -19.3157621 -0.16556964 4 1.4304436720 NA NA NA NA
52 -4.4747188 -0.07438732 NA 2.1266646020 NA NA NA NA
52.1 -4.3163827 -0.37537080 NA 3.1000545993 NA NA NA NA
52.2 -6.9761408 -0.24222066 3 3.1268477370 NA NA NA NA
52.3 -20.1764756 -0.31520603 NA 3.5711459327 NA NA NA NA
52.4 -8.9036692 -0.44619160 4 4.7983659909 NA NA NA NA
52.5 -5.6949642 -0.11011682 1 4.9818264414 NA NA NA NA
53 -10.3141887 -0.23278716 2 0.4965799209 NA NA NA NA
53.1 -8.2642654 -0.28317264 1 3.5505357443 NA NA NA NA
53.2 -9.1691554 -0.19517481 3 4.5790420019 NA NA NA NA
54 -6.2198754 -0.10122856 3 1.4034724841 NA NA NA NA
54.1 -15.7192609 -0.28325504 4 1.8812377600 NA NA NA NA
54.2 -13.0978998 -0.16753120 4 2.5107589352 NA NA NA NA
54.3 -5.1195299 -0.22217672 3 2.7848406672 NA NA NA NA
54.4 -16.5771751 -0.34609328 NA 4.0143877396 NA NA NA NA
55 -5.7348534 -0.32428190 4 0.6118522980 NA NA NA NA
55.1 -7.3217494 -0.24235382 1 0.7463747414 NA NA NA NA
55.2 -12.2171938 -0.24065814 4 2.8201208171 NA NA NA NA
55.3 -12.9821266 -0.23665476 NA 3.1326431572 NA NA NA NA
55.4 -14.8599983 NA 1 3.2218102901 NA NA NA NA
56 -14.1764282 NA 1 1.2231332215 NA NA NA NA
56.1 -12.5343602 -0.30357450 2 2.3573202139 NA NA NA NA
56.2 -8.4573382 -0.51301630 2 2.5674936292 NA NA NA NA
56.3 -12.4633969 -0.23743117 3 2.9507164378 NA NA NA NA
56.4 -17.3841863 -0.17264917 4 3.2272730360 NA NA NA NA
56.5 -14.8147645 -0.39188329 4 3.4175522043 NA NA NA NA
57 -3.1403293 -0.18501692 2 0.2370331448 NA NA NA NA
57.1 -11.1509248 -0.27274841 2 0.2481445030 NA NA NA NA
57.2 -6.3940143 NA 4 1.1405586067 NA NA NA NA
57.3 -9.3473241 -0.09898509 NA 2.1153886721 NA NA NA NA
58 -12.0245677 -0.29901358 1 1.2210099772 NA NA NA NA
58.1 -9.2112246 -0.35390896 2 1.6334245703 NA NA NA NA
58.2 -1.2071742 -0.16687336 2 1.6791862890 NA NA NA NA
58.3 -11.0141711 -0.11784506 4 2.6320121693 NA NA NA NA
58.4 -5.3721214 -0.05321983 1 2.8477731440 NA NA NA NA
58.5 -7.8523047 -0.54457568 NA 3.5715569824 NA NA NA NA
59 -13.2946560 -0.27255364 4 1.9023998594 NA NA NA NA
59.1 -10.0530648 NA 1 4.9736620474 NA NA NA NA
60 -19.2209402 NA 1 2.8854503250 NA NA NA NA
61 -4.6699914 -0.30550120 1 0.7213630795 NA NA NA NA
61.1 -3.5981894 -0.35579892 1 2.3186947661 NA NA NA NA
61.2 -1.4713611 NA NA 2.5077313243 NA NA NA NA
61.3 -3.8819786 -0.34184391 1 3.1731073430 NA NA NA NA
61.4 0.1041413 -0.30891967 3 3.6022726283 NA NA NA NA
62 -2.8591600 NA NA 0.5336771999 NA NA NA NA
62.1 -6.9461986 -0.10504143 NA 0.6987666548 NA NA NA NA
62.2 -16.7910593 -0.20104997 NA 3.4584309917 NA NA NA NA
62.3 -17.9844596 -0.08138677 3 4.8028772371 NA NA NA NA
63 -24.0335535 -0.12036319 4 2.8097350930 NA NA NA NA
63.1 -11.7765300 -0.13624992 3 3.9653754211 NA NA NA NA
64 -20.5963897 NA 4 4.1191305732 NA NA NA NA
65 -2.7969169 -0.34450396 2 0.7076152589 NA NA NA NA
65.1 -11.1778694 -0.32514650 1 2.0252246363 NA NA NA NA
65.2 -5.2830399 -0.10984996 3 3.1127382827 NA NA NA NA
65.3 -7.9353390 -0.19275692 NA 3.1969087943 NA NA NA NA
66 -13.2318328 NA 1 3.4943454154 NA NA NA NA
66.1 -1.9090560 NA 3 3.7677437009 NA NA NA NA
66.2 -16.6643889 -0.11687008 2 3.9486138616 NA NA NA NA
67 -25.6073277 NA 3 4.1728388879 NA NA NA NA
68 -13.4806759 -0.13605235 3 0.1291919907 NA NA NA NA
68.1 -18.4557183 -0.19790827 4 1.7809643946 NA NA NA NA
68.2 -13.3982327 -0.17750123 3 2.0493205660 NA NA NA NA
68.3 -12.4977127 NA 1 2.9406870750 NA NA NA NA
68.4 -11.7073990 -0.12570562 4 4.0406670363 NA NA NA NA
69 -14.5290675 -0.32152751 4 4.1451198701 NA NA NA NA
70 -15.2122709 -0.28190462 4 0.1992557163 NA NA NA NA
70.1 -7.8681167 -0.11503263 4 0.4829774413 NA NA NA NA
71 -10.3352703 -0.13029093 2 0.7741605386 NA NA NA NA
71.1 -7.5699888 NA NA 1.4883817220 NA NA NA NA
71.2 -18.4680702 -0.39075433 4 4.0758526395 NA NA NA NA
71.3 -21.4316644 -0.21401028 3 4.7048238723 NA NA NA NA
71.4 -8.1137650 -0.40219281 1 4.7242791823 NA NA NA NA
72 -9.1848162 -0.40337108 1 0.9321196121 NA NA NA NA
72.1 -23.7538846 -0.25978914 NA 1.1799991806 NA NA NA NA
72.2 -26.3421306 NA 4 1.8917567329 NA NA NA NA
72.3 -27.2843801 -0.09809866 1 3.4853593935 NA NA NA NA
72.4 -20.8541617 -0.14240019 3 3.6884259700 NA NA NA NA
72.5 -12.8948965 -0.14794204 1 4.0854155901 NA NA NA NA
73 -2.6091307 -0.23509343 2 4.6019889915 NA NA NA NA
74 -8.2790175 -0.27963171 4 1.4626806753 NA NA NA NA
75 -12.5029612 -0.12905034 1 3.2524286874 NA NA NA NA
76 -6.0061671 0.04775562 2 1.8074807397 NA NA NA NA
76.1 -8.8149114 -0.19399157 1 4.2685073183 NA NA NA NA
76.2 -11.8359043 -0.02754574 1 4.9688734859 NA NA NA NA
77 0.4772521 -0.19053195 3 0.8459033852 NA NA NA NA
78 -9.4105229 -0.17172929 3 0.8231094317 NA NA NA NA
79 -1.0217265 -0.03958515 NA 0.0583819521 NA NA NA NA
79.1 -11.8125257 -0.20328809 3 2.4406372628 NA NA NA NA
79.2 -10.5465186 -0.23901634 NA 3.2962526032 NA NA NA NA
80 -12.7366807 -0.34031873 2 0.8985060186 NA NA NA NA
80.1 -9.0584783 -0.19526756 3 1.3434670598 NA NA NA NA
80.2 -16.6381566 NA 1 2.8025900386 NA NA NA NA
81 0.5547913 -0.18401980 3 0.0101324962 NA NA NA NA
81.1 -4.0892715 -0.16889476 NA 0.9421709494 NA NA NA NA
81.2 1.8283303 -0.37343047 3 3.0542453879 NA NA NA NA
81.3 -5.2166381 NA 2 3.3456630446 NA NA NA NA
82 -3.0749381 -0.08328168 NA 1.3791010005 NA NA NA NA
82.1 -10.5506696 -0.22167084 3 1.7601010622 NA NA NA NA
82.2 -18.2226347 -0.20971187 1 2.6233131927 NA NA NA NA
83 -12.5872635 -0.34228255 4 0.0537394290 NA NA NA NA
83.1 -11.9756502 -0.34075730 NA 2.9061570496 NA NA NA NA
83.2 -10.6744217 -0.32503954 2 3.1189457362 NA NA NA NA
83.3 -19.2714012 NA NA 4.7663642222 NA NA NA NA
84 -2.6320312 -0.20676741 2 2.7254060237 NA NA NA NA
84.1 -9.8140094 -0.20310458 1 3.3364784659 NA NA NA NA
85 -12.3886736 -0.12107593 1 0.2977756259 NA NA NA NA
85.1 -12.9196365 NA 4 1.7394116637 NA NA NA NA
85.2 -9.6433248 -0.32509207 3 2.6846330194 NA NA NA NA
85.3 -6.3296340 NA 3 3.1608762743 NA NA NA NA
85.4 -7.0405525 -0.30730810 NA 3.9452053758 NA NA NA NA
85.5 -13.6714939 NA 2 4.5092553482 NA NA NA NA
86 -10.8756412 -0.10854862 1 0.8476278360 NA NA NA NA
86.1 -12.0055331 -0.25751662 3 1.0118629411 NA NA NA NA
86.2 -13.3724699 -0.38943076 1 1.2511159515 NA NA NA NA
86.3 -13.3252145 -0.24454702 2 2.1870554925 NA NA NA NA
86.4 -14.9191290 -0.12338992 3 2.4532935000 NA NA NA NA
86.5 -17.7515546 -0.23976984 4 3.8206058508 NA NA NA NA
87 -10.7027963 NA NA 2.7069531474 NA NA NA NA
87.1 -22.4941954 -0.34366972 3 3.4462517721 NA NA NA NA
87.2 -14.9616716 NA 3 4.5241666853 NA NA NA NA
88 -2.2264493 -0.31563888 NA 0.0005892443 NA NA NA NA
88.1 -8.9626474 -0.20304028 1 0.7116099866 NA NA NA NA
88.2 -2.5095281 -0.40311895 2 2.4952722900 NA NA NA NA
88.3 -16.3345673 -0.12308715 NA 3.2995816297 NA NA NA NA
89 -11.0459647 -0.18527715 3 0.6462086167 NA NA NA NA
90 -4.5610239 -0.25029126 2 0.1696030737 NA NA NA NA
90.1 -11.7036651 -0.26974303 2 2.5980385230 NA NA NA NA
90.2 -5.3838521 -0.28804531 2 2.6651392167 NA NA NA NA
90.3 -4.1636999 -0.19180615 4 3.1242690247 NA NA NA NA
91 -7.1462503 -0.26591197 2 0.6382618390 NA NA NA NA
91.1 -12.8374475 -0.09153470 NA 2.6224059286 NA NA NA NA
91.2 -18.2576707 -0.48414390 3 4.7772527603 NA NA NA NA
92 -6.4119222 NA 2 0.0737052364 NA NA NA NA
93 5.2122168 -0.11939966 3 0.2788909199 NA NA NA NA
93.1 3.1211725 NA 2 1.0357759963 NA NA NA NA
93.2 -3.6841177 -0.21089379 3 2.4916551099 NA NA NA NA
93.3 2.6223542 NA 2 2.8876129608 NA NA NA NA
93.4 -11.1877696 -0.23618836 4 4.4639474002 NA NA NA NA
94 -6.9602492 NA NA 0.8488043118 NA NA NA NA
94.1 -7.4318416 -0.10217284 2 1.0552454425 NA NA NA NA
94.2 -4.3498045 -0.36713471 NA 1.9445500884 NA NA NA NA
94.3 -11.6340088 -0.13806763 3 3.0710722448 NA NA NA NA
94.4 -12.9357964 -0.42353804 4 3.0872731935 NA NA NA NA
94.5 -14.7648530 -0.15513707 3 4.3805759016 NA NA NA NA
95 -12.8849309 -0.24149687 NA 2.0199063048 NA NA NA NA
95.1 -9.7451502 -0.21315958 2 4.0184444457 NA NA NA NA
95.2 -0.8535063 -0.15777208 3 4.5596531732 NA NA NA NA
96 -4.9139832 -0.16780948 3 0.0311333477 NA NA NA NA
96.1 -3.9582653 -0.32504815 NA 0.1324267720 NA NA NA NA
96.2 -9.6555492 -0.20395970 4 0.6701303425 NA NA NA NA
96.3 -11.8690793 -0.06221501 3 2.1775037691 NA NA NA NA
96.4 -11.0224373 -0.14801097 NA 2.2246142488 NA NA NA NA
96.5 -10.9530403 -0.28658893 1 4.2377650598 NA NA NA NA
97 -9.8540471 -0.34484656 2 1.1955102731 NA NA NA NA
97.1 -19.2262840 -0.35658805 1 4.9603108643 NA NA NA NA
98 -11.9651231 -0.36913003 2 0.2041732438 NA NA NA NA
98.1 -2.6515128 NA 1 0.4309578973 NA NA NA NA
98.2 -12.2606382 -0.17154225 3 3.5172611906 NA NA NA NA
99 -11.4720500 -0.24753132 NA 0.3531786101 NA NA NA NA
99.1 -14.0596866 -0.27947829 NA 4.6789444226 NA NA NA NA
99.2 -17.3939469 -0.09033035 4 4.9927084171 NA NA NA NA
100 1.1005874 -0.17326698 1 1.0691387602 NA NA NA NA
100.1 -3.8226248 NA NA 1.5109344281 NA NA NA NA
100.2 -0.9123182 -0.12072016 1 2.1502332564 NA NA NA NA
100.3 -15.8389474 -0.27657520 4 3.8745574222 NA NA NA NA
100.4 -12.8093826 -0.14631556 1 4.6567608765 NA NA NA NA
I(time^2) o22:abs(C1 - c2) o23:abs(C1 - c2) o24:abs(C1 - c2)
1 2.591239e-01 NA NA NA
1.1 4.443657e-01 NA NA NA
1.2 4.539005e+00 NA NA NA
1.3 6.227241e+00 NA NA NA
2 9.099267e+00 NA NA NA
2.1 1.088789e+01 NA NA NA
2.2 1.742860e+01 NA NA NA
3 7.188883e-01 NA NA NA
3.1 9.396866e+00 NA NA NA
3.2 2.245012e+01 NA NA NA
4 1.136655e-01 NA NA NA
4.1 1.143407e+00 NA NA NA
4.2 6.837688e+00 NA NA NA
4.3 9.819783e+00 NA NA NA
5 1.158319e+00 NA NA NA
5.1 3.208593e+00 NA NA NA
5.2 7.817661e+00 NA NA NA
5.3 7.907311e+00 NA NA NA
6 3.173907e+00 NA NA NA
7 1.093895e+01 NA NA NA
7.1 1.369622e+01 NA NA NA
7.2 2.276883e+01 NA NA NA
8 1.264815e+00 NA NA NA
8.1 3.249731e+00 NA NA NA
8.2 3.303606e+00 NA NA NA
8.3 8.056666e+00 NA NA NA
8.4 1.130995e+01 NA NA NA
8.5 1.967885e+01 NA NA NA
9 9.230989e-01 NA NA NA
9.1 8.513413e+00 NA NA NA
9.2 2.313696e+01 NA NA NA
10 5.278740e+00 NA NA NA
10.1 1.741737e+01 NA NA NA
11 1.400119e+00 NA NA NA
11.1 1.524250e+00 NA NA NA
11.2 2.701196e+00 NA NA NA
11.3 1.146433e+01 NA NA NA
11.4 2.315350e+01 NA NA NA
12 9.200622e-01 NA NA NA
13 3.832672e-03 NA NA NA
13.1 1.268860e+01 NA NA NA
14 1.629287e+01 NA NA NA
14.1 1.999034e+01 NA NA NA
14.2 2.149175e+01 NA NA NA
14.3 2.198311e+01 NA NA NA
15 2.918229e-01 NA NA NA
15.1 1.414477e+00 NA NA NA
15.2 2.278512e+00 NA NA NA
15.3 2.419998e+01 NA NA NA
16 1.542046e+00 NA NA NA
16.1 6.592429e+00 NA NA NA
16.2 7.035280e+00 NA NA NA
16.3 1.266294e+01 NA NA NA
16.4 1.414697e+01 NA NA NA
16.5 1.588151e+01 NA NA NA
17 2.536170e+00 NA NA NA
17.1 5.940935e+00 NA NA NA
17.2 9.154551e+00 NA NA NA
17.3 1.110828e+01 NA NA NA
17.4 1.497207e+01 NA NA NA
18 5.941061e+00 NA NA NA
19 9.549522e-01 NA NA NA
19.1 1.314769e+00 NA NA NA
19.2 5.107205e+00 NA NA NA
19.3 1.773610e+01 NA NA NA
20 2.948144e+00 NA NA NA
20.1 3.084555e+00 NA NA NA
20.2 5.069507e+00 NA NA NA
20.3 5.111725e+00 NA NA NA
20.4 1.218943e+01 NA NA NA
20.5 1.741475e+01 NA NA NA
21 2.868478e+00 NA NA NA
21.1 8.744554e+00 NA NA NA
21.2 1.435454e+01 NA NA NA
22 6.099036e+00 NA NA NA
22.1 1.000244e+01 NA NA NA
23 2.376079e+00 NA NA NA
23.1 5.461446e+00 NA NA NA
24 7.999358e+00 NA NA NA
25 2.896274e-01 NA NA NA
25.1 2.582364e+00 NA NA NA
25.2 2.675916e+00 NA NA NA
25.3 1.065818e+01 NA NA NA
25.4 1.663190e+01 NA NA NA
25.5 1.727258e+01 NA NA NA
26 5.821152e-02 NA NA NA
26.1 5.978875e+00 NA NA NA
26.2 1.295191e+01 NA NA NA
26.3 1.749110e+01 NA NA NA
27 1.365733e+01 NA NA NA
27.1 1.802124e+01 NA NA NA
28 3.302248e-01 NA NA NA
28.1 7.808651e+00 NA NA NA
28.2 1.773129e+01 NA NA NA
28.3 1.998584e+01 NA NA NA
29 1.415834e+00 NA NA NA
29.1 3.106075e+00 NA NA NA
29.2 4.084605e+00 NA NA NA
29.3 1.161363e+01 NA NA NA
30 5.123598e+00 NA NA NA
30.1 1.291564e+01 NA NA NA
30.2 1.306006e+01 NA NA NA
31 1.934957e+01 NA NA NA
32 2.804020e+00 NA NA NA
32.1 8.484502e+00 NA NA NA
32.2 8.806981e+00 NA NA NA
32.3 1.772399e+01 NA NA NA
33 8.720901e-05 NA NA NA
33.1 1.196554e+01 NA NA NA
34 2.249632e+00 NA NA NA
34.1 1.462509e+01 NA NA NA
34.2 1.526641e+01 NA NA NA
34.3 1.566745e+01 NA NA NA
35 1.767384e+00 NA NA NA
35.1 2.333857e+00 NA NA NA
35.2 2.027334e+01 NA NA NA
36 5.073953e-01 NA NA NA
36.1 3.230105e+00 NA NA NA
36.2 3.335261e+00 NA NA NA
36.3 1.835276e+01 NA NA NA
36.4 2.133929e+01 NA NA NA
37 4.007496e+00 NA NA NA
37.1 1.343724e+01 NA NA NA
37.2 1.573228e+01 NA NA NA
38 9.656032e-01 NA NA NA
39 4.791143e-01 NA NA NA
39.1 8.150103e-01 NA NA NA
39.2 1.704501e+00 NA NA NA
39.3 2.375557e+00 NA NA NA
39.4 1.013467e+01 NA NA NA
39.5 1.713477e+01 NA NA NA
40 1.283779e+00 NA NA NA
40.1 7.258172e+00 NA NA NA
40.2 9.239542e+00 NA NA NA
40.3 2.186776e+01 NA NA NA
41 3.739257e+00 NA NA NA
41.1 1.021205e+01 NA NA NA
41.2 1.078920e+01 NA NA NA
41.3 1.143355e+01 NA NA NA
41.4 1.259041e+01 NA NA NA
42 2.361234e-01 NA NA NA
42.1 1.874295e+01 NA NA NA
43 3.154636e-01 NA NA NA
43.1 1.154245e+00 NA NA NA
43.2 6.828709e+00 NA NA NA
44 5.871613e-01 NA NA NA
44.1 7.017356e+00 NA NA NA
44.2 1.113684e+01 NA NA NA
44.3 1.693668e+01 NA NA NA
45 3.831610e-02 NA NA NA
45.1 3.985546e+00 NA NA NA
46 1.816499e+00 NA NA NA
46.1 8.160046e+00 NA NA NA
46.2 1.950170e+01 NA NA NA
47 3.615162e-01 NA NA NA
47.1 5.806713e+00 NA NA NA
47.2 8.985482e+00 NA NA NA
47.3 1.013127e+01 NA NA NA
47.4 2.134538e+01 NA NA NA
48 8.183814e+00 NA NA NA
48.1 8.467142e+00 NA NA NA
49 7.387392e+00 NA NA NA
50 1.383461e+00 NA NA NA
51 2.046169e+00 NA NA NA
52 4.522702e+00 NA NA NA
52.1 9.610339e+00 NA NA NA
52.2 9.777177e+00 NA NA NA
52.3 1.275308e+01 NA NA NA
52.4 2.302432e+01 NA NA NA
52.5 2.481859e+01 NA NA NA
53 2.465916e-01 NA NA NA
53.1 1.260630e+01 NA NA NA
53.2 2.096763e+01 NA NA NA
54 1.969735e+00 NA NA NA
54.1 3.539056e+00 NA NA NA
54.2 6.303910e+00 NA NA NA
54.3 7.755338e+00 NA NA NA
54.4 1.611531e+01 NA NA NA
55 3.743632e-01 NA NA NA
55.1 5.570753e-01 NA NA NA
55.2 7.953081e+00 NA NA NA
55.3 9.813453e+00 NA NA NA
55.4 1.038006e+01 NA NA NA
56 1.496055e+00 NA NA NA
56.1 5.556959e+00 NA NA NA
56.2 6.592024e+00 NA NA NA
56.3 8.706727e+00 NA NA NA
56.4 1.041529e+01 NA NA NA
56.5 1.167966e+01 NA NA NA
57 5.618471e-02 NA NA NA
57.1 6.157569e-02 NA NA NA
57.2 1.300874e+00 NA NA NA
57.3 4.474869e+00 NA NA NA
58 1.490865e+00 NA NA NA
58.1 2.668076e+00 NA NA NA
58.2 2.819667e+00 NA NA NA
58.3 6.927488e+00 NA NA NA
58.4 8.109812e+00 NA NA NA
58.5 1.275602e+01 NA NA NA
59 3.619125e+00 NA NA NA
59.1 2.473731e+01 NA NA NA
60 8.325824e+00 NA NA NA
61 5.203647e-01 NA NA NA
61.1 5.376345e+00 NA NA NA
61.2 6.288716e+00 NA NA NA
61.3 1.006861e+01 NA NA NA
61.4 1.297637e+01 NA NA NA
62 2.848114e-01 NA NA NA
62.1 4.882748e-01 NA NA NA
62.2 1.196074e+01 NA NA NA
62.3 2.306763e+01 NA NA NA
63 7.894611e+00 NA NA NA
63.1 1.572420e+01 NA NA NA
64 1.696724e+01 NA NA NA
65 5.007194e-01 NA NA NA
65.1 4.101535e+00 NA NA NA
65.2 9.689140e+00 NA NA NA
65.3 1.022023e+01 NA NA NA
66 1.221045e+01 NA NA NA
66.1 1.419589e+01 NA NA NA
66.2 1.559155e+01 NA NA NA
67 1.741258e+01 NA NA NA
68 1.669057e-02 NA NA NA
68.1 3.171834e+00 NA NA NA
68.2 4.199715e+00 NA NA NA
68.3 8.647640e+00 NA NA NA
68.4 1.632699e+01 NA NA NA
69 1.718202e+01 NA NA NA
70 3.970284e-02 NA NA NA
70.1 2.332672e-01 NA NA NA
71 5.993245e-01 NA NA NA
71.1 2.215280e+00 NA NA NA
71.2 1.661257e+01 NA NA NA
71.3 2.213537e+01 NA NA NA
71.4 2.231881e+01 NA NA NA
72 8.688470e-01 NA NA NA
72.1 1.392398e+00 NA NA NA
72.2 3.578744e+00 NA NA NA
72.3 1.214773e+01 NA NA NA
72.4 1.360449e+01 NA NA NA
72.5 1.669062e+01 NA NA NA
73 2.117830e+01 NA NA NA
74 2.139435e+00 NA NA NA
75 1.057829e+01 NA NA NA
76 3.266987e+00 NA NA NA
76.1 1.822015e+01 NA NA NA
76.2 2.468970e+01 NA NA NA
77 7.155525e-01 NA NA NA
78 6.775091e-01 NA NA NA
79 3.408452e-03 NA NA NA
79.1 5.956710e+00 NA NA NA
79.2 1.086528e+01 NA NA NA
80 8.073131e-01 NA NA NA
80.1 1.804904e+00 NA NA NA
80.2 7.854511e+00 NA NA NA
81 1.026675e-04 NA NA NA
81.1 8.876861e-01 NA NA NA
81.2 9.328415e+00 NA NA NA
81.3 1.119346e+01 NA NA NA
82 1.901920e+00 NA NA NA
82.1 3.097956e+00 NA NA NA
82.2 6.881772e+00 NA NA NA
83 2.887926e-03 NA NA NA
83.1 8.445749e+00 NA NA NA
83.2 9.727823e+00 NA NA NA
83.3 2.271823e+01 NA NA NA
84 7.427838e+00 NA NA NA
84.1 1.113209e+01 NA NA NA
85 8.867032e-02 NA NA NA
85.1 3.025553e+00 NA NA NA
85.2 7.207254e+00 NA NA NA
85.3 9.991139e+00 NA NA NA
85.4 1.556465e+01 NA NA NA
85.5 2.033338e+01 NA NA NA
86 7.184729e-01 NA NA NA
86.1 1.023867e+00 NA NA NA
86.2 1.565291e+00 NA NA NA
86.3 4.783212e+00 NA NA NA
86.4 6.018649e+00 NA NA NA
86.5 1.459703e+01 NA NA NA
87 7.327595e+00 NA NA NA
87.1 1.187665e+01 NA NA NA
87.2 2.046808e+01 NA NA NA
88 3.472088e-07 NA NA NA
88.1 5.063888e-01 NA NA NA
88.2 6.226384e+00 NA NA NA
88.3 1.088724e+01 NA NA NA
89 4.175856e-01 NA NA NA
90 2.876520e-02 NA NA NA
90.1 6.749804e+00 NA NA NA
90.2 7.102967e+00 NA NA NA
90.3 9.761057e+00 NA NA NA
91 4.073782e-01 NA NA NA
91.1 6.877013e+00 NA NA NA
91.2 2.282214e+01 NA NA NA
92 5.432462e-03 NA NA NA
93 7.778015e-02 NA NA NA
93.1 1.072832e+00 NA NA NA
93.2 6.208345e+00 NA NA NA
93.3 8.338309e+00 NA NA NA
93.4 1.992683e+01 NA NA NA
94 7.204688e-01 NA NA NA
94.1 1.113543e+00 NA NA NA
94.2 3.781275e+00 NA NA NA
94.3 9.431485e+00 NA NA NA
94.4 9.531256e+00 NA NA NA
94.5 1.918945e+01 NA NA NA
95 4.080021e+00 NA NA NA
95.1 1.614790e+01 NA NA NA
95.2 2.079044e+01 NA NA NA
96 9.692853e-04 NA NA NA
96.1 1.753685e-02 NA NA NA
96.2 4.490747e-01 NA NA NA
96.3 4.741523e+00 NA NA NA
96.4 4.948909e+00 NA NA NA
96.5 1.795865e+01 NA NA NA
97 1.429245e+00 NA NA NA
97.1 2.460468e+01 NA NA NA
98 4.168671e-02 NA NA NA
98.1 1.857247e-01 NA NA NA
98.2 1.237113e+01 NA NA NA
99 1.247351e-01 NA NA NA
99.1 2.189252e+01 NA NA NA
99.2 2.492714e+01 NA NA NA
100 1.143058e+00 NA NA NA
100.1 2.282923e+00 NA NA NA
100.2 4.623503e+00 NA NA NA
100.3 1.501220e+01 NA NA NA
100.4 2.168542e+01 NA NA NA
$m5a$spM_id
center scale
M2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
log(C1) -0.3049822 0.01990873
C1 0.7372814 0.01472882
$m5a$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
o2 NA NA
time 2.5339403 1.3818094
o22 NA NA
o23 NA NA
o24 NA NA
abs(C1 - c2) 0.9613865 0.1064886
I(time^2) 8.3244468 7.0900029
o22:abs(C1 - c2) 0.2166402 0.4111132
o23:abs(C1 - c2) 0.2721613 0.4294402
o24:abs(C1 - c2) 0.2492394 0.4265852
$m5a$mu_reg_norm
[1] 0
$m5a$tau_reg_norm
[1] 1e-04
$m5a$shape_tau_norm
[1] 0.01
$m5a$rate_tau_norm
[1] 0.01
$m5a$mu_reg_multinomial
[1] 0
$m5a$tau_reg_multinomial
[1] 1e-04
$m5a$mu_reg_ordinal
[1] 0
$m5a$tau_reg_ordinal
[1] 1e-04
$m5a$mu_delta_ordinal
[1] 0
$m5a$tau_delta_ordinal
[1] 1e-04
$m5a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m5a$shape_diag_RinvD
[1] "0.01"
$m5a$rate_diag_RinvD
[1] "0.001"
$m5a$RinvD_y_id
[,1] [,2]
[1,] NA 0
[2,] 0 NA
$m5a$KinvD_y_id
id
3
$m5b
$m5b$M_id
C2 (Intercept)
1 -1.381594459 1
2 0.344426024 1
3 NA 1
4 -0.228910007 1
5 NA 1
6 -2.143955482 1
7 -1.156567023 1
8 -0.598827660 1
9 NA 1
10 -1.006719032 1
11 0.239801450 1
12 -1.064969789 1
13 -0.538082688 1
14 NA 1
15 -1.781049276 1
16 NA 1
17 NA 1
18 -0.014579883 1
19 -2.121550136 1
20 NA 1
21 -0.363239698 1
22 -0.121568514 1
23 -0.951271111 1
24 NA 1
25 -0.974288621 1
26 -1.130632418 1
27 0.114339868 1
28 0.238334648 1
29 0.840744958 1
30 NA 1
31 NA 1
32 -1.466312154 1
33 -0.637352277 1
34 NA 1
35 NA 1
36 NA 1
37 NA 1
38 NA 1
39 0.006728205 1
40 NA 1
41 -1.663281353 1
42 0.161184794 1
43 0.457939180 1
44 -0.307070331 1
45 NA 1
46 -1.071668276 1
47 -0.814751321 1
48 -0.547630662 1
49 NA 1
50 -1.350213782 1
51 0.719054706 1
52 NA 1
53 -1.207130750 1
54 NA 1
55 -0.408600991 1
56 -0.271380529 1
57 -1.361925974 1
58 NA 1
59 NA 1
60 -0.323712205 1
61 NA 1
62 NA 1
63 -1.386906880 1
64 NA 1
65 NA 1
66 -0.565191691 1
67 -0.382899912 1
68 NA 1
69 -0.405642769 1
70 NA 1
71 -0.843748427 1
72 0.116003683 1
73 -0.778634325 1
74 NA 1
75 NA 1
76 NA 1
77 -0.632974758 1
78 NA 1
79 -0.778064615 1
80 NA 1
81 NA 1
82 -0.246123253 1
83 -1.239659782 1
84 -0.467772280 1
85 NA 1
86 -2.160485036 1
87 -0.657675572 1
88 NA 1
89 -0.696710744 1
90 NA 1
91 -0.179395847 1
92 -0.441545568 1
93 -0.685799334 1
94 NA 1
95 0.191929445 1
96 NA 1
97 -0.069760671 1
98 NA 1
99 NA 1
100 NA 1
$m5b$M_lvlone
b1 L1mis Be2 c1 time abs(c1 - C2)
1 0 1.38634787 4.596628e-06 0.7592026489 0.5090421822 NA
1.1 1 0.79402906 2.296427e-04 0.9548337990 0.6666076288 NA
1.2 1 0.53603334 3.455922e-10 0.5612235156 2.1304941282 NA
1.3 0 0.24129804 9.618613e-07 1.1873391025 2.4954441458 NA
2 1 NA NA 0.9192204198 3.0164990982 NA
2.1 1 0.31668065 1.065639e-07 -0.1870730476 3.2996806887 NA
2.2 1 0.37114414 1.320730e-03 1.2517512331 4.1747569619 NA
3 1 0.54680608 9.707820e-06 -0.0605087604 0.8478727890 NA
3.1 0 0.28280274 3.645271e-05 0.3788637747 3.0654308549 NA
3.2 0 0.76277262 NA 0.9872578281 4.7381553578 NA
4 1 0.56100366 5.555794e-01 1.4930175328 0.3371432109 NA
4.1 1 0.38514140 6.853316e-06 -0.7692526880 1.0693019140 NA
4.2 0 0.04026174 6.324951e-02 0.9180841450 2.6148973033 NA
4.3 1 0.16025873 4.330745e-07 -0.0541170782 3.1336532847 NA
5 0 0.21080161 NA -0.1376784521 1.0762525082 NA
5.1 1 0.36665700 6.556812e-04 -0.2740585866 1.7912546196 NA
5.2 1 0.66368829 6.963312e-06 0.4670496929 2.7960080339 NA
5.3 1 0.40788895 1.159006e-04 0.1740288049 2.8119940578 NA
6 0 0.11889539 1.509745e-02 0.9868044683 1.7815462884 NA
7 1 1.04286843 NA -0.1280320918 3.3074087673 NA
7.1 0 0.52098933 1.679086e-08 0.4242971219 3.7008403614 NA
7.2 1 0.09858876 3.972447e-06 0.0777182491 4.7716691741 NA
8 0 0.17281472 9.888512e-02 -0.5791408712 1.1246398522 NA
8.1 1 0.25970093 8.790334e-05 0.3128604232 1.8027009873 NA
8.2 1 0.30550233 NA 0.6258446356 1.8175825174 NA
8.3 0 0.88029778 5.411705e-04 -0.1040137707 2.8384267003 NA
8.4 0 0.20200392 8.446731e-04 0.0481450285 3.3630275307 NA
8.5 1 NA 2.059814e-04 0.3831763675 4.4360849704 NA
9 1 1.12218535 4.160033e-01 -0.1757592269 0.9607803822 NA
9.1 1 0.57911079 NA -0.1791541200 2.9177753383 NA
9.2 0 0.81350994 1.087331e-03 -0.0957042935 4.8100892501 NA
10 1 0.32744766 9.321715e-04 -0.5598409704 2.2975509102 NA
10.1 1 0.62912282 8.167897e-06 -0.2318340451 4.1734118364 NA
11 1 0.92140073 2.528529e-04 0.5086859475 1.1832662905 NA
11.1 1 0.16012129 NA 0.4951758188 1.2346051680 NA
11.2 1 0.16166775 5.587553e-10 -1.1022162541 1.6435316263 NA
11.3 1 0.14979756 5.240776e-10 -0.0611636705 3.3859017969 NA
11.4 1 0.46855190 2.830994e-07 -0.4971774316 4.8118087661 NA
12 1 0.76818678 1.962202e-07 -0.2433996286 0.9591987054 NA
13 0 0.34264972 NA 0.8799673116 0.0619085738 NA
13.1 1 0.14526619 1.330415e-06 0.1079022586 3.5621061502 NA
14 0 0.80630788 5.900181e-07 0.9991752617 4.0364430007 NA
14.1 1 0.35697552 3.694946e-05 -0.1094019046 4.4710561272 NA
14.2 0 0.21330192 6.871447e-08 0.1518967560 4.6359198843 NA
14.3 0 NA NA 0.3521012473 4.6886152599 NA
15 0 0.30769119 1.848068e-04 0.3464447888 0.5402063532 NA
15.1 0 0.28349746 1.714157e-10 -0.4767313971 1.1893180816 NA
15.2 0 0.64618365 1.088807e-03 0.5759767791 1.5094739688 NA
15.3 1 0.51680884 2.677330e-05 -0.1713452662 4.9193474615 NA
16 1 0.71265471 NA 0.4564754473 1.2417913869 NA
16.1 0 0.38925880 1.411453e-04 1.0652558311 2.5675726333 NA
16.2 1 0.23648869 1.897147e-03 0.6971872493 2.6524101500 NA
16.3 1 0.45048730 5.950632e-02 0.5259331838 3.5585018690 NA
16.4 1 0.23181791 3.944608e-02 0.2046601798 3.7612454291 NA
16.5 0 0.13985349 NA 1.0718540464 3.9851612889 NA
17 0 0.25995399 4.808238e-05 0.6048676222 1.5925356350 NA
17.1 0 0.03594878 6.175264e-04 0.2323298304 2.4374032998 NA
17.2 1 0.77583623 2.319036e-07 1.2617499032 3.0256489082 NA
17.3 0 0.60015197 1.393008e-09 -0.3913230895 3.3329089405 NA
17.4 1 0.13998405 NA 0.9577299112 3.8693758985 NA
18 1 0.96475839 2.685853e-09 -0.0050324072 2.4374292302 NA
19 1 0.10596495 2.949370e-07 -0.4187468937 0.9772165376 NA
19.1 1 0.13338947 1.183423e-08 -0.4478828944 1.1466335913 NA
19.2 1 0.41662218 7.844699e-08 -1.1966721302 2.2599126538 NA
19.3 1 0.53670855 NA -0.5877091668 4.2114245973 NA
20 0 0.41688567 4.920475e-06 0.6838223064 1.7170160066 NA
20.1 1 NA 6.885500e-08 0.3278571109 1.7562902288 NA
20.2 0 0.81634101 9.577206e-04 -0.8489831990 2.2515566566 NA
20.3 0 0.39232496 1.325632e-03 1.3169975191 2.2609123867 NA
20.4 0 0.57925554 NA 0.0444804531 3.4913365287 NA
20.5 0 0.74200986 1.011637e-06 -0.4535207652 4.1730977828 NA
21 1 0.24759801 3.032947e-04 -0.4030302960 1.6936582839 NA
21.1 1 0.34052205 4.370975e-06 -0.4069674045 2.9571191233 NA
21.2 0 0.03905058 8.793700e-06 1.0650265940 3.7887385779 NA
22 0 0.48605351 NA -0.0673274516 2.4696226232 NA
22.1 1 0.43761071 7.397166e-06 0.9601388170 3.1626627257 NA
23 1 0.47599712 4.931346e-02 0.5556634840 1.5414533857 NA
23.1 1 0.47680301 3.799306e-02 1.4407865964 2.3369736120 NA
24 0 0.51696505 1.018950e-01 0.3856376411 2.8283136466 NA
25 0 0.59392591 NA 0.3564400705 0.5381704110 NA
25.1 1 0.74010330 2.264756e-02 0.0982553434 1.6069735331 NA
25.2 1 NA 6.622343e-07 0.1928682598 1.6358226922 NA
25.3 0 0.73081722 2.802504e-09 -0.0192488594 3.2646870392 NA
25.4 0 0.29274286 1.873599e-04 0.4466012931 4.0782226040 NA
25.5 0 0.74425342 NA 1.1425193342 4.1560292873 NA
26 1 0.20974346 4.587570e-09 0.5341531449 0.2412706357 NA
26.1 1 NA 2.394334e-06 1.2268695927 2.4451737676 NA
26.2 1 0.22908815 4.510972e-08 0.3678294939 3.5988757887 NA
26.3 0 0.41880799 3.657318e-11 0.5948516018 4.1822362854 NA
27 1 0.10097167 NA -0.3342844147 3.6955824879 NA
27.1 1 NA 8.874134e-06 -0.4835141229 4.2451434687 NA
28 1 NA 3.673907e-06 -0.7145915499 0.5746519344 NA
28.1 0 0.56052750 4.541426e-04 0.5063671955 2.7943964268 NA
28.2 1 0.15301800 2.697966e-12 -0.2067413142 4.2108539480 NA
28.3 1 0.27802542 NA 0.1196789973 4.4705521734 NA
29 1 0.43556671 3.282475e-03 0.1392699487 1.1898884235 NA
29.1 0 0.27593085 2.270717e-01 0.7960234776 1.7624059319 NA
29.2 0 0.55256871 9.981536e-03 1.0398214352 2.0210406382 NA
29.3 1 0.47272109 2.343590e-02 0.0813246429 3.4078777023 NA
30 1 0.32743933 NA -0.3296323050 2.2635366488 NA
30.1 1 0.02231535 1.591483e-07 1.3635850954 3.5938334477 NA
30.2 1 0.12833697 1.896944e-11 0.7354171050 3.6138710892 NA
31 0 0.11126366 5.546285e-08 0.3708398217 4.3988140998 NA
32 1 1.11731084 9.411981e-09 -0.0474059668 1.6745209007 NA
32.1 1 0.85943330 1.270914e-08 1.2507771489 2.9128167813 NA
32.2 1 1.53730925 3.910478e-09 0.1142915519 2.9676558380 NA
32.3 1 0.43831965 9.124048e-10 0.6773270619 4.2099863547 NA
33 0 0.46726055 9.056156e-01 0.1774293842 0.0093385763 NA
33.1 0 0.76818259 3.047254e-06 0.6159606291 3.4591242753 NA
34 1 NA 1.040462e-04 0.8590979166 1.4998774312 NA
34.1 0 1.14350292 5.714390e-12 0.0546216775 3.8242761395 NA
34.2 1 0.19103604 7.883166e-09 -0.0897224473 3.9072251692 NA
34.3 1 NA 3.055823e-07 0.4163395571 3.9582124643 NA
35 1 0.66303137 1.287796e-07 -1.4693520528 1.3294299203 NA
35.1 0 NA 1.762232e-06 -0.3031734330 1.5276966314 NA
35.2 1 NA 5.355159e-08 -0.6045512101 4.5025920868 NA
36 0 0.93843318 7.250797e-06 0.9823048960 0.7123168337 NA
36.1 0 NA 2.370652e-06 1.4466051416 1.7972493160 NA
36.2 1 0.29886676 1.537090e-05 1.1606752905 1.8262697803 NA
36.3 0 0.22616598 6.993214e-07 0.8373091576 4.2840119381 NA
36.4 1 0.53849566 4.950009e-05 0.2640591685 4.6194464504 NA
37 1 1.68107300 2.755165e-07 0.1177313455 2.0018732361 NA
37.1 0 1.13777638 3.400517e-07 -0.1415483779 3.6656836793 NA
37.2 0 0.26931933 2.489007e-09 0.0054610124 3.9663937816 NA
38 1 NA 1.302651e-01 0.8078948077 0.9826511063 NA
39 1 0.14395367 4.343746e-04 0.9876451040 0.6921808305 NA
39.1 0 0.36454923 6.653143e-05 -0.3431222274 0.9027792048 NA
39.2 0 1.03700002 1.940204e-09 -1.7909380751 1.3055654289 NA
39.3 0 0.41320585 8.300468e-07 -0.1798746191 1.5412842878 NA
39.4 1 0.20901554 7.464169e-08 -0.1850961689 3.1834997435 NA
39.5 1 0.51603848 5.765597e-10 0.4544226146 4.1394166439 NA
40 0 0.33912363 9.140572e-01 0.5350190436 1.1330395646 NA
40.1 0 0.21892118 1.883555e-03 0.4189342752 2.6940994046 NA
40.2 0 0.74070896 2.303001e-01 0.4211994981 3.0396614212 NA
40.3 1 0.82927399 2.799910e-05 0.0916687506 4.6762977762 NA
41 1 0.25193679 3.700067e-02 -0.1035047421 1.9337158254 NA
41.1 1 0.28760510 5.798225e-06 -0.4684202411 3.1956304458 NA
41.2 0 0.45553197 1.086252e-08 0.5972615368 3.2846923557 NA
41.3 1 0.79237611 3.088732e-07 0.9885613862 3.3813529415 NA
41.4 1 0.12582175 4.549537e-05 -0.3908036794 3.5482964432 NA
42 1 0.50079604 5.220968e-03 -0.0338893961 0.4859252973 NA
42.1 1 0.61140760 7.264286e-08 -0.4498363172 4.3293134298 NA
43 0 0.29752019 1.498125e-07 0.8965546110 0.5616614548 NA
43.1 0 0.51793497 1.316763e-04 0.6199122090 1.0743579536 NA
43.2 1 0.15152473 8.151771e-07 0.1804894429 2.6131797966 NA
44 1 0.38806434 1.032476e-03 1.3221409285 0.7662644819 NA
44.1 0 1.11140786 3.120174e-09 0.3416426284 2.6490291790 NA
44.2 0 0.39132534 2.571257e-10 0.5706610068 3.3371910988 NA
44.3 1 0.40934909 2.227416e-09 1.2679497430 4.1154200875 NA
45 1 0.68587067 3.948036e-01 0.1414983160 0.1957449992 NA
45.1 0 0.34530800 1.066310e-03 0.7220892521 1.9963831536 NA
46 1 0.71312288 2.219556e-08 1.5391054233 1.3477755385 NA
46.1 0 0.62537420 1.434525e-08 0.3889107049 2.8565793915 NA
46.2 1 0.79574391 1.523026e-07 0.1248719493 4.4160729996 NA
47 0 0.48660773 5.404537e-03 0.2014101100 0.6012621359 NA
47.1 0 0.51241790 3.739267e-07 0.2982973539 2.4097121472 NA
47.2 1 0.58869379 7.171916e-06 1.1518107179 2.9975794035 NA
47.3 0 0.22171504 3.850162e-05 0.5196802157 3.1829649757 NA
47.4 0 0.11366347 1.767264e-08 0.3702301552 4.6201055450 NA
48 0 0.19677010 1.988010e-04 -0.2128602862 2.8607365978 NA
48.1 1 0.17706320 6.074589e-09 -0.5337239976 2.9098354396 NA
49 0 0.30752382 1.321544e-06 -0.5236770035 2.7179756400 NA
50 1 0.93663423 4.240393e-05 0.3897705981 1.1762060679 NA
51 1 0.34107606 1.986093e-09 -0.7213343736 1.4304436720 NA
52 1 0.19007135 1.632022e-02 0.3758235358 2.1266646020 NA
52.1 1 0.75662940 2.653038e-02 0.7138067080 3.1000545993 NA
52.2 0 1.66104719 2.262881e-03 0.8872895233 3.1268477370 NA
52.3 0 NA 6.572647e-10 -0.9664587437 3.5711459327 NA
52.4 1 0.18369708 1.393737e-04 0.0254566848 4.7983659909 NA
52.5 1 0.48689343 5.069462e-03 0.4155259424 4.9818264414 NA
53 1 0.31983157 5.848890e-05 0.5675736897 0.4965799209 NA
53.1 1 0.61569501 1.878509e-04 -0.3154088781 3.5505357443 NA
53.2 1 NA 1.293417e-04 0.2162315769 4.5790420019 NA
54 0 1.90522418 1.818441e-03 -0.0880802382 1.4034724841 NA
54.1 1 0.59484889 2.251839e-07 0.4129127672 1.8812377600 NA
54.2 0 1.47174857 5.638172e-06 1.0119546775 2.5107589352 NA
54.3 1 0.27307143 5.320676e-03 -0.1112901990 2.7848406672 NA
54.4 0 0.81272938 1.491367e-07 0.8587727145 4.0143877396 NA
55 1 0.22735476 3.183775e-03 -0.0116453589 0.6118522980 NA
55.1 1 0.54683512 1.183380e-03 0.5835528661 0.7463747414 NA
55.2 1 1.03503777 1.817077e-06 -1.0010857254 2.8201208171 NA
55.3 0 0.30169529 1.424370e-06 -0.4796526070 3.1326431572 NA
55.4 1 0.36008059 3.119967e-07 -0.1202746964 3.2218102901 NA
56 0 0.14193566 1.169667e-06 0.5176377612 1.2231332215 NA
56.1 1 0.65073539 1.182293e-06 -1.1136932588 2.3573202139 NA
56.2 1 0.11338262 2.087533e-04 -0.0168103281 2.5674936292 NA
56.3 0 0.16820103 5.728251e-06 0.3933023606 2.9507164378 NA
56.4 0 0.27419110 4.087596e-08 0.3714625139 3.2272730360 NA
56.5 1 0.57110215 8.040370e-07 0.7811448179 3.4175522043 NA
57 1 0.85104054 1.438387e-02 -1.0868304872 0.2370331448 NA
57.1 1 0.34733833 3.202179e-05 0.8018626997 0.2481445030 NA
57.2 0 1.44438762 1.486318e-03 -0.1159517011 1.1405586067 NA
57.3 0 0.31836125 1.718412e-04 0.6785562445 2.1153886721 NA
58 1 0.37456898 3.114123e-05 1.6476207996 1.2210099772 NA
58.1 1 0.22120158 1.403881e-04 0.3402652711 1.6334245703 NA
58.2 1 0.78885210 2.111006e-01 -0.1111300753 1.6791862890 NA
58.3 1 0.10114937 9.586985e-06 -0.5409234285 2.6320121693 NA
58.4 1 0.13385114 4.073162e-03 -0.1271327672 2.8477731440 NA
58.5 1 NA 9.285307e-04 0.8713264822 3.5715569824 NA
59 0 0.13202156 2.711478e-06 0.4766421367 1.9023998594 NA
59.1 1 0.33371896 1.173472e-04 1.0028089765 4.9736620474 NA
60 0 0.35096579 7.579680e-09 0.5231452932 2.8854503250 NA
61 1 0.36933806 4.545759e-03 -0.7190130614 0.7213630795 NA
61.1 1 0.17623067 5.936674e-02 0.8353702312 2.3186947661 NA
61.2 1 0.21286227 3.897281e-01 1.0229058138 2.5077313243 NA
61.3 0 0.12689308 6.237379e-02 1.1717723589 3.1731073430 NA
61.4 1 0.77676718 5.103038e-01 -0.0629201596 3.6022726283 NA
62 1 1.38018163 3.707353e-02 -0.3979137604 0.5336771999 NA
62.1 0 0.43803892 1.901660e-03 0.6830738372 0.6987666548 NA
62.2 0 0.21947900 7.844369e-08 0.4301745954 3.4584309917 NA
62.3 1 0.11571160 1.496168e-08 -0.0333139957 4.8028772371 NA
63 0 0.41583568 5.101070e-11 0.3345678035 2.8097350930 NA
63.1 1 0.25598960 1.106013e-05 0.3643769511 3.9653754211 NA
64 1 0.20415642 1.685171e-09 0.3949911859 4.1191305732 NA
65 1 0.07135646 1.684142e-01 1.2000091513 0.7076152589 NA
65.1 1 0.57450574 1.413479e-05 0.0110122646 2.0252246363 NA
65.2 0 0.52562984 2.841196e-03 -0.5776452043 3.1127382827 NA
65.3 0 0.21921164 3.118871e-04 -0.1372183563 3.1969087943 NA
66 1 0.33281730 1.078473e-06 -0.5081302805 3.4943454154 NA
66.1 0 0.03412404 1.136650e-01 -0.1447837412 3.7677437009 NA
66.2 0 0.92570619 7.007044e-08 0.1906241379 3.9486138616 NA
67 0 0.15291043 4.025749e-11 1.6716027681 4.1728388879 NA
68 0 0.37543648 2.469503e-06 0.5691848839 0.1291919907 NA
68.1 0 0.20901022 1.067638e-08 0.1004860389 1.7809643946 NA
68.2 0 0.12488064 1.508555e-06 -0.0061241827 2.0493205660 NA
68.3 0 0.08711204 7.862972e-06 0.7443745962 2.9406870750 NA
68.4 1 0.54611735 1.970326e-05 0.8726923437 4.0406670363 NA
69 1 0.23638239 5.089430e-07 0.0381382683 4.1451198701 NA
70 1 0.49876756 5.575849e-07 0.8126204217 0.1992557163 NA
70.1 1 0.39512615 6.115107e-04 0.4691503050 0.4829774413 NA
71 1 0.45666551 1.867742e-05 -0.5529062591 0.7741605386 NA
71.1 1 0.92047456 4.616167e-04 -0.1103252087 1.4883817220 NA
71.2 0 0.32792986 5.314611e-08 1.7178492547 4.0758526395 NA
71.3 0 0.95108007 1.790244e-10 -1.0118346755 4.7048238723 NA
71.4 0 0.36287072 1.924070e-03 1.8623785017 4.7242791823 NA
72 1 0.12870526 6.526547e-05 -0.4521659275 0.9321196121 NA
72.1 1 0.45925876 5.540491e-11 0.1375317317 1.1799991806 NA
72.2 1 0.05418867 2.391191e-12 -0.4170988856 1.8917567329 NA
72.3 0 0.48937486 2.878783e-12 0.7107266765 3.4853593935 NA
72.4 0 0.64173822 1.014404e-09 0.1451969143 3.6884259700 NA
72.5 1 0.57609943 1.281231e-05 1.6298050306 4.0854155901 NA
73 1 0.17393402 6.661564e-02 -0.0307469467 4.6019889915 NA
74 1 0.23990575 3.683842e-04 0.3730017941 1.4626806753 NA
75 0 0.28469861 2.274469e-06 -0.4908003566 3.2524286874 NA
76 1 0.71988630 9.155636e-04 -0.9888876620 1.8074807397 NA
76.1 1 1.12449946 1.485365e-04 0.0003798292 4.2685073183 NA
76.2 1 0.71313766 3.118702e-06 -0.8421863763 4.9688734859 NA
77 1 0.02399030 4.946432e-01 -0.4986802480 0.8459033852 NA
78 1 0.42708148 8.533933e-05 0.0417330969 0.8231094317 NA
79 0 0.37579286 1.980588e-01 -0.3767450660 0.0583819521 NA
79.1 1 0.78660681 8.624235e-06 0.1516000028 2.4406372628 NA
79.2 0 0.67696116 2.176176e-05 -0.1888160741 3.2962526032 NA
80 1 0.34207854 2.929029e-06 -0.0041558414 0.8985060186 NA
80.1 0 0.60534092 1.126162e-04 -0.0329337062 1.3434670598 NA
80.2 1 0.26731034 9.847382e-08 0.5046816157 2.8025900386 NA
81 1 0.17739052 4.026095e-01 -0.9493950353 0.0101324962 NA
81.1 1 0.35453673 2.093927e-02 0.2443038954 0.9421709494 NA
81.2 1 0.20244235 9.224440e-01 0.6476958410 3.0542453879 NA
81.3 1 1.26402329 8.175654e-03 0.4182528210 3.3456630446 NA
82 1 0.09303938 1.228129e-01 1.1088801952 1.3791010005 NA
82.1 1 0.27254210 6.656575e-05 0.9334157763 1.7601010622 NA
82.2 0 0.49936304 2.001426e-08 0.4958140634 2.6233131927 NA
83 1 0.21138572 5.690020e-06 0.5104724530 0.0537394290 NA
83.1 0 0.26403568 5.980615e-06 -0.0513309106 2.9061570496 NA
83.2 0 0.20311133 1.880816e-05 -0.2067792494 3.1189457362 NA
83.3 1 1.16864671 4.048910e-09 -0.0534169155 4.7663642222 NA
84 1 1.99179346 6.552173e-02 -0.0255753653 2.7254060237 NA
84.1 0 1.52199460 8.829278e-06 -1.8234189877 3.3364784659 NA
85 0 NA 4.118253e-06 -0.0114038622 0.2977756259 NA
85.1 0 0.61458995 2.311994e-06 -0.0577615939 1.7394116637 NA
85.2 1 0.07871196 5.182892e-05 -0.2241856342 2.6846330194 NA
85.3 1 1.42315283 1.689467e-03 -0.0520175929 3.1608762743 NA
85.4 1 0.97986129 1.168017e-03 0.2892733846 3.9452053758 NA
85.5 1 0.91792195 7.945131e-07 -0.3740417009 4.5092553482 NA
86 0 0.63509597 2.905567e-05 0.4293735089 0.8476278360 NA
86.1 1 0.24546597 5.331467e-06 -0.1363456521 1.0118629411 NA
86.2 1 0.53102060 1.761451e-06 0.1230989293 1.2511159515 NA
86.3 0 0.09360826 2.272397e-06 0.3305413955 2.1870554925 NA
86.4 1 0.58301186 4.467006e-06 2.6003411822 2.4532935000 NA
86.5 0 0.39146055 1.693940e-08 -0.1420690052 3.8206058508 NA
87 0 NA 6.396865e-05 1.0457427869 2.7069531474 NA
87.1 1 0.66043624 1.264093e-10 -0.2973007190 3.4462517721 NA
87.2 0 0.13267613 4.933807e-07 0.4396872616 4.5241666853 NA
88 0 0.10696344 9.223531e-02 -0.0601928334 0.0005892443 NA
88.1 0 0.13689448 4.654325e-05 -1.0124347595 0.7116099866 NA
88.2 0 0.48037889 1.260399e-01 0.5730917016 2.4952722900 NA
88.3 0 0.97755681 8.029866e-08 -0.0029455332 3.2995816297 NA
89 1 0.70242369 7.489307e-05 1.5465903721 0.6462086167 NA
90 0 0.40042977 1.100491e-02 0.0626760573 0.1696030737 NA
90.1 1 0.63975731 2.715349e-05 1.1896872985 2.5980385230 NA
90.2 1 0.33412775 5.916576e-03 0.2597888783 2.6651392167 NA
90.3 0 0.38399003 2.920657e-02 0.6599799887 3.1242690247 NA
91 0 0.58250391 2.411997e-03 1.1213651365 0.6382618390 NA
91.1 0 0.13223217 8.870147e-06 1.2046371625 2.6224059286 NA
91.2 1 0.46613305 1.652965e-08 0.3395603754 4.7772527603 NA
92 1 0.18997862 2.613551e-03 0.4674939332 0.0737052364 NA
93 0 1.05243347 9.958480e-01 0.2677965647 0.2788909199 NA
93.1 1 0.01479757 9.915375e-01 1.6424445368 1.0357759963 NA
93.2 0 0.50955172 4.861680e-02 0.7101700066 2.4916551099 NA
93.3 1 0.78122514 9.769008e-01 1.1222322893 2.8876129608 NA
93.4 0 0.63940704 5.977439e-05 1.4628960401 4.4639474002 NA
94 1 0.45596305 7.091952e-04 -0.2904211940 0.8488043118 NA
94.1 0 0.41610667 6.005522e-04 0.0147813580 1.0552454425 NA
94.2 1 0.52744298 8.134430e-03 -0.4536774482 1.9445500884 NA
94.3 0 0.70890756 1.747604e-05 0.6793464917 3.0710722448 NA
94.4 0 0.84412478 9.404259e-07 -0.9411356550 3.0872731935 NA
94.5 0 0.21166602 6.832077e-07 0.5683867264 4.3805759016 NA
95 1 0.57713135 3.216011e-06 0.2375652188 2.0199063048 NA
95.1 1 0.44400207 6.324477e-05 0.0767152977 4.0184444457 NA
95.2 0 0.42397776 1.762187e-01 -0.6886731251 4.5596531732 NA
96 1 0.72391015 1.578796e-02 0.7813892121 0.0311333477 NA
96.1 0 0.32593738 2.610661e-02 0.3391519695 0.1324267720 NA
96.2 0 0.23249511 3.941700e-05 -0.4857246503 0.6701303425 NA
96.3 0 1.01679990 1.683671e-05 0.8771471244 2.1775037691 NA
96.4 0 0.92267953 1.095127e-04 1.9030768981 2.2246142488 NA
96.5 1 0.83843412 1.479105e-05 -0.1684332749 4.2377650598 NA
97 0 0.47151154 2.082560e-04 1.3775130083 1.1955102731 NA
97.1 0 0.15596614 7.903013e-10 -1.7323228619 4.9603108643 NA
98 0 0.05179545 1.795949e-06 -1.2648518889 0.2041732438 NA
98.1 0 0.47332096 2.776600e-02 -0.9042716241 0.4309578973 NA
98.2 0 0.19706341 4.050457e-06 -0.1560385207 3.5172611906 NA
99 1 0.22574556 2.316802e-05 0.7993356425 0.3531786101 NA
99.1 1 1.00732330 2.206426e-06 1.0355522332 4.6789444226 NA
99.2 1 0.09749127 2.488411e-08 -0.1150895843 4.9927084171 NA
100 0 0.22857989 7.572193e-01 0.0369067906 1.0691387602 NA
100.1 0 0.39548654 9.794641e-02 1.6023713093 1.5109344281 NA
100.2 1 NA 4.934595e-01 0.8861545820 2.1502332564 NA
100.3 1 0.32695372 1.502083e-07 0.1277046316 3.8745574222 NA
100.4 1 0.10043925 2.515993e-06 -0.0834577654 4.6567608765 NA
log(Be2) I(time^2)
1 NA 2.591239e-01
1.1 NA 4.443657e-01
1.2 NA 4.539005e+00
1.3 NA 6.227241e+00
2 NA 9.099267e+00
2.1 NA 1.088789e+01
2.2 NA 1.742860e+01
3 NA 7.188883e-01
3.1 NA 9.396866e+00
3.2 NA 2.245012e+01
4 NA 1.136655e-01
4.1 NA 1.143407e+00
4.2 NA 6.837688e+00
4.3 NA 9.819783e+00
5 NA 1.158319e+00
5.1 NA 3.208593e+00
5.2 NA 7.817661e+00
5.3 NA 7.907311e+00
6 NA 3.173907e+00
7 NA 1.093895e+01
7.1 NA 1.369622e+01
7.2 NA 2.276883e+01
8 NA 1.264815e+00
8.1 NA 3.249731e+00
8.2 NA 3.303606e+00
8.3 NA 8.056666e+00
8.4 NA 1.130995e+01
8.5 NA 1.967885e+01
9 NA 9.230989e-01
9.1 NA 8.513413e+00
9.2 NA 2.313696e+01
10 NA 5.278740e+00
10.1 NA 1.741737e+01
11 NA 1.400119e+00
11.1 NA 1.524250e+00
11.2 NA 2.701196e+00
11.3 NA 1.146433e+01
11.4 NA 2.315350e+01
12 NA 9.200622e-01
13 NA 3.832672e-03
13.1 NA 1.268860e+01
14 NA 1.629287e+01
14.1 NA 1.999034e+01
14.2 NA 2.149175e+01
14.3 NA 2.198311e+01
15 NA 2.918229e-01
15.1 NA 1.414477e+00
15.2 NA 2.278512e+00
15.3 NA 2.419998e+01
16 NA 1.542046e+00
16.1 NA 6.592429e+00
16.2 NA 7.035280e+00
16.3 NA 1.266294e+01
16.4 NA 1.414697e+01
16.5 NA 1.588151e+01
17 NA 2.536170e+00
17.1 NA 5.940935e+00
17.2 NA 9.154551e+00
17.3 NA 1.110828e+01
17.4 NA 1.497207e+01
18 NA 5.941061e+00
19 NA 9.549522e-01
19.1 NA 1.314769e+00
19.2 NA 5.107205e+00
19.3 NA 1.773610e+01
20 NA 2.948144e+00
20.1 NA 3.084555e+00
20.2 NA 5.069507e+00
20.3 NA 5.111725e+00
20.4 NA 1.218943e+01
20.5 NA 1.741475e+01
21 NA 2.868478e+00
21.1 NA 8.744554e+00
21.2 NA 1.435454e+01
22 NA 6.099036e+00
22.1 NA 1.000244e+01
23 NA 2.376079e+00
23.1 NA 5.461446e+00
24 NA 7.999358e+00
25 NA 2.896274e-01
25.1 NA 2.582364e+00
25.2 NA 2.675916e+00
25.3 NA 1.065818e+01
25.4 NA 1.663190e+01
25.5 NA 1.727258e+01
26 NA 5.821152e-02
26.1 NA 5.978875e+00
26.2 NA 1.295191e+01
26.3 NA 1.749110e+01
27 NA 1.365733e+01
27.1 NA 1.802124e+01
28 NA 3.302248e-01
28.1 NA 7.808651e+00
28.2 NA 1.773129e+01
28.3 NA 1.998584e+01
29 NA 1.415834e+00
29.1 NA 3.106075e+00
29.2 NA 4.084605e+00
29.3 NA 1.161363e+01
30 NA 5.123598e+00
30.1 NA 1.291564e+01
30.2 NA 1.306006e+01
31 NA 1.934957e+01
32 NA 2.804020e+00
32.1 NA 8.484502e+00
32.2 NA 8.806981e+00
32.3 NA 1.772399e+01
33 NA 8.720901e-05
33.1 NA 1.196554e+01
34 NA 2.249632e+00
34.1 NA 1.462509e+01
34.2 NA 1.526641e+01
34.3 NA 1.566745e+01
35 NA 1.767384e+00
35.1 NA 2.333857e+00
35.2 NA 2.027334e+01
36 NA 5.073953e-01
36.1 NA 3.230105e+00
36.2 NA 3.335261e+00
36.3 NA 1.835276e+01
36.4 NA 2.133929e+01
37 NA 4.007496e+00
37.1 NA 1.343724e+01
37.2 NA 1.573228e+01
38 NA 9.656032e-01
39 NA 4.791143e-01
39.1 NA 8.150103e-01
39.2 NA 1.704501e+00
39.3 NA 2.375557e+00
39.4 NA 1.013467e+01
39.5 NA 1.713477e+01
40 NA 1.283779e+00
40.1 NA 7.258172e+00
40.2 NA 9.239542e+00
40.3 NA 2.186776e+01
41 NA 3.739257e+00
41.1 NA 1.021205e+01
41.2 NA 1.078920e+01
41.3 NA 1.143355e+01
41.4 NA 1.259041e+01
42 NA 2.361234e-01
42.1 NA 1.874295e+01
43 NA 3.154636e-01
43.1 NA 1.154245e+00
43.2 NA 6.828709e+00
44 NA 5.871613e-01
44.1 NA 7.017356e+00
44.2 NA 1.113684e+01
44.3 NA 1.693668e+01
45 NA 3.831610e-02
45.1 NA 3.985546e+00
46 NA 1.816499e+00
46.1 NA 8.160046e+00
46.2 NA 1.950170e+01
47 NA 3.615162e-01
47.1 NA 5.806713e+00
47.2 NA 8.985482e+00
47.3 NA 1.013127e+01
47.4 NA 2.134538e+01
48 NA 8.183814e+00
48.1 NA 8.467142e+00
49 NA 7.387392e+00
50 NA 1.383461e+00
51 NA 2.046169e+00
52 NA 4.522702e+00
52.1 NA 9.610339e+00
52.2 NA 9.777177e+00
52.3 NA 1.275308e+01
52.4 NA 2.302432e+01
52.5 NA 2.481859e+01
53 NA 2.465916e-01
53.1 NA 1.260630e+01
53.2 NA 2.096763e+01
54 NA 1.969735e+00
54.1 NA 3.539056e+00
54.2 NA 6.303910e+00
54.3 NA 7.755338e+00
54.4 NA 1.611531e+01
55 NA 3.743632e-01
55.1 NA 5.570753e-01
55.2 NA 7.953081e+00
55.3 NA 9.813453e+00
55.4 NA 1.038006e+01
56 NA 1.496055e+00
56.1 NA 5.556959e+00
56.2 NA 6.592024e+00
56.3 NA 8.706727e+00
56.4 NA 1.041529e+01
56.5 NA 1.167966e+01
57 NA 5.618471e-02
57.1 NA 6.157569e-02
57.2 NA 1.300874e+00
57.3 NA 4.474869e+00
58 NA 1.490865e+00
58.1 NA 2.668076e+00
58.2 NA 2.819667e+00
58.3 NA 6.927488e+00
58.4 NA 8.109812e+00
58.5 NA 1.275602e+01
59 NA 3.619125e+00
59.1 NA 2.473731e+01
60 NA 8.325824e+00
61 NA 5.203647e-01
61.1 NA 5.376345e+00
61.2 NA 6.288716e+00
61.3 NA 1.006861e+01
61.4 NA 1.297637e+01
62 NA 2.848114e-01
62.1 NA 4.882748e-01
62.2 NA 1.196074e+01
62.3 NA 2.306763e+01
63 NA 7.894611e+00
63.1 NA 1.572420e+01
64 NA 1.696724e+01
65 NA 5.007194e-01
65.1 NA 4.101535e+00
65.2 NA 9.689140e+00
65.3 NA 1.022023e+01
66 NA 1.221045e+01
66.1 NA 1.419589e+01
66.2 NA 1.559155e+01
67 NA 1.741258e+01
68 NA 1.669057e-02
68.1 NA 3.171834e+00
68.2 NA 4.199715e+00
68.3 NA 8.647640e+00
68.4 NA 1.632699e+01
69 NA 1.718202e+01
70 NA 3.970284e-02
70.1 NA 2.332672e-01
71 NA 5.993245e-01
71.1 NA 2.215280e+00
71.2 NA 1.661257e+01
71.3 NA 2.213537e+01
71.4 NA 2.231881e+01
72 NA 8.688470e-01
72.1 NA 1.392398e+00
72.2 NA 3.578744e+00
72.3 NA 1.214773e+01
72.4 NA 1.360449e+01
72.5 NA 1.669062e+01
73 NA 2.117830e+01
74 NA 2.139435e+00
75 NA 1.057829e+01
76 NA 3.266987e+00
76.1 NA 1.822015e+01
76.2 NA 2.468970e+01
77 NA 7.155525e-01
78 NA 6.775091e-01
79 NA 3.408452e-03
79.1 NA 5.956710e+00
79.2 NA 1.086528e+01
80 NA 8.073131e-01
80.1 NA 1.804904e+00
80.2 NA 7.854511e+00
81 NA 1.026675e-04
81.1 NA 8.876861e-01
81.2 NA 9.328415e+00
81.3 NA 1.119346e+01
82 NA 1.901920e+00
82.1 NA 3.097956e+00
82.2 NA 6.881772e+00
83 NA 2.887926e-03
83.1 NA 8.445749e+00
83.2 NA 9.727823e+00
83.3 NA 2.271823e+01
84 NA 7.427838e+00
84.1 NA 1.113209e+01
85 NA 8.867032e-02
85.1 NA 3.025553e+00
85.2 NA 7.207254e+00
85.3 NA 9.991139e+00
85.4 NA 1.556465e+01
85.5 NA 2.033338e+01
86 NA 7.184729e-01
86.1 NA 1.023867e+00
86.2 NA 1.565291e+00
86.3 NA 4.783212e+00
86.4 NA 6.018649e+00
86.5 NA 1.459703e+01
87 NA 7.327595e+00
87.1 NA 1.187665e+01
87.2 NA 2.046808e+01
88 NA 3.472088e-07
88.1 NA 5.063888e-01
88.2 NA 6.226384e+00
88.3 NA 1.088724e+01
89 NA 4.175856e-01
90 NA 2.876520e-02
90.1 NA 6.749804e+00
90.2 NA 7.102967e+00
90.3 NA 9.761057e+00
91 NA 4.073782e-01
91.1 NA 6.877013e+00
91.2 NA 2.282214e+01
92 NA 5.432462e-03
93 NA 7.778015e-02
93.1 NA 1.072832e+00
93.2 NA 6.208345e+00
93.3 NA 8.338309e+00
93.4 NA 1.992683e+01
94 NA 7.204688e-01
94.1 NA 1.113543e+00
94.2 NA 3.781275e+00
94.3 NA 9.431485e+00
94.4 NA 9.531256e+00
94.5 NA 1.918945e+01
95 NA 4.080021e+00
95.1 NA 1.614790e+01
95.2 NA 2.079044e+01
96 NA 9.692853e-04
96.1 NA 1.753685e-02
96.2 NA 4.490747e-01
96.3 NA 4.741523e+00
96.4 NA 4.948909e+00
96.5 NA 1.795865e+01
97 NA 1.429245e+00
97.1 NA 2.460468e+01
98 NA 4.168671e-02
98.1 NA 1.857247e-01
98.2 NA 1.237113e+01
99 NA 1.247351e-01
99.1 NA 2.189252e+01
99.2 NA 2.492714e+01
100 NA 1.143058e+00
100.1 NA 2.282923e+00
100.2 NA 4.623503e+00
100.3 NA 1.501220e+01
100.4 NA 2.168542e+01
$m5b$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m5b$spM_lvlone
center scale
b1 NA NA
L1mis 0.48184811 0.3462447
Be2 0.04274145 0.1563798
c1 0.25599956 0.6718095
time 2.53394028 1.3818094
abs(c1 - C2) 1.12675664 0.7813693
log(Be2) -11.02063958 6.0744935
I(time^2) 8.32444679 7.0900029
$m5b$mu_reg_norm
[1] 0
$m5b$tau_reg_norm
[1] 1e-04
$m5b$shape_tau_norm
[1] 0.01
$m5b$rate_tau_norm
[1] 0.01
$m5b$mu_reg_gamma
[1] 0
$m5b$tau_reg_gamma
[1] 1e-04
$m5b$shape_tau_gamma
[1] 0.01
$m5b$rate_tau_gamma
[1] 0.01
$m5b$mu_reg_beta
[1] 0
$m5b$tau_reg_beta
[1] 1e-04
$m5b$shape_tau_beta
[1] 0.01
$m5b$rate_tau_beta
[1] 0.01
$m5b$mu_reg_binom
[1] 0
$m5b$tau_reg_binom
[1] 1e-04
$m5b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m5b$shape_diag_RinvD
[1] "0.01"
$m5b$rate_diag_RinvD
[1] "0.001"
$m5b$RinvD_b1_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m5b$KinvD_b1_id
id
4
$m6a
$m6a$M_id
C2 (Intercept) C1
1 -1.381594459 1 0.7175865
2 0.344426024 1 0.7507170
3 NA 1 0.7255954
4 -0.228910007 1 0.7469352
5 NA 1 0.7139120
6 -2.143955482 1 0.7332505
7 -1.156567023 1 0.7345929
8 -0.598827660 1 0.7652589
9 NA 1 0.7200622
10 -1.006719032 1 0.7423879
11 0.239801450 1 0.7437448
12 -1.064969789 1 0.7446470
13 -0.538082688 1 0.7530186
14 NA 1 0.7093137
15 -1.781049276 1 0.7331192
16 NA 1 0.7011390
17 NA 1 0.7432395
18 -0.014579883 1 0.7545191
19 -2.121550136 1 0.7528487
20 NA 1 0.7612865
21 -0.363239698 1 0.7251719
22 -0.121568514 1 0.7300630
23 -0.951271111 1 0.7087249
24 NA 1 0.7391938
25 -0.974288621 1 0.7820641
26 -1.130632418 1 0.7118298
27 0.114339868 1 0.7230857
28 0.238334648 1 0.7489353
29 0.840744958 1 0.7510888
30 NA 1 0.7300717
31 NA 1 0.7550721
32 -1.466312154 1 0.7321898
33 -0.637352277 1 0.7306414
34 NA 1 0.7427216
35 NA 1 0.7193042
36 NA 1 0.7312888
37 NA 1 0.7100436
38 NA 1 0.7670184
39 0.006728205 1 0.7400449
40 NA 1 0.7397304
41 -1.663281353 1 0.7490966
42 0.161184794 1 0.7419274
43 0.457939180 1 0.7527810
44 -0.307070331 1 0.7408315
45 NA 1 0.7347550
46 -1.071668276 1 0.7332398
47 -0.814751321 1 0.7376481
48 -0.547630662 1 0.7346179
49 NA 1 0.7329402
50 -1.350213782 1 0.7260436
51 0.719054706 1 0.7242910
52 NA 1 0.7298067
53 -1.207130750 1 0.7254741
54 NA 1 0.7542067
55 -0.408600991 1 0.7389952
56 -0.271380529 1 0.7520638
57 -1.361925974 1 0.7219958
58 NA 1 0.7259632
59 NA 1 0.7458606
60 -0.323712205 1 0.7672421
61 NA 1 0.7257179
62 NA 1 0.7189892
63 -1.386906880 1 0.7333356
64 NA 1 0.7320243
65 NA 1 0.7477711
66 -0.565191691 1 0.7343974
67 -0.382899912 1 0.7491624
68 NA 1 0.7482736
69 -0.405642769 1 0.7338267
70 NA 1 0.7607742
71 -0.843748427 1 0.7777600
72 0.116003683 1 0.7408143
73 -0.778634325 1 0.7248271
74 NA 1 0.7364916
75 NA 1 0.7464926
76 NA 1 0.7355430
77 -0.632974758 1 0.7208449
78 NA 1 0.7373573
79 -0.778064615 1 0.7598079
80 NA 1 0.7360415
81 NA 1 0.7293932
82 -0.246123253 1 0.7279309
83 -1.239659782 1 0.7344643
84 -0.467772280 1 0.7384350
85 NA 1 0.7323716
86 -2.160485036 1 0.7576597
87 -0.657675572 1 0.7496139
88 NA 1 0.7275239
89 -0.696710744 1 0.7250648
90 NA 1 0.7335262
91 -0.179395847 1 0.7343980
92 -0.441545568 1 0.7380425
93 -0.685799334 1 0.7389460
94 NA 1 0.7259951
95 0.191929445 1 0.7282840
96 NA 1 0.7281676
97 -0.069760671 1 0.7245642
98 NA 1 0.7526938
99 NA 1 0.7272309
100 NA 1 0.7383460
$m6a$M_lvlone
y b2 b21 time
1 -13.0493856 NA NA 0.5090421822
1.1 -9.3335901 0 NA 0.6666076288
1.2 -22.3469852 NA NA 2.1304941282
1.3 -15.0417337 0 NA 2.4954441458
2 -12.0655434 0 NA 3.0164990982
2.1 -15.8674476 NA NA 3.2996806887
2.2 -7.8800006 NA NA 4.1747569619
3 -11.4820604 0 NA 0.8478727890
3.1 -10.5983220 NA NA 3.0654308549
3.2 -22.4519157 1 NA 4.7381553578
4 -1.2697775 1 NA 0.3371432109
4.1 -11.1215184 0 NA 1.0693019140
4.2 -3.6134138 0 NA 2.6148973033
4.3 -14.5982385 0 NA 3.1336532847
5 -6.8457515 NA NA 1.0762525082
5.1 -7.0551214 0 NA 1.7912546196
5.2 -12.3418980 NA NA 2.7960080339
5.3 -9.2366906 NA NA 2.8119940578
6 -5.1648211 NA NA 1.7815462884
7 -10.0599502 NA NA 3.3074087673
7.1 -18.3267285 NA NA 3.7008403614
7.2 -12.5138426 0 NA 4.7716691741
8 -1.6305331 0 NA 1.1246398522
8.1 -9.6520453 0 NA 1.8027009873
8.2 -1.5278462 NA NA 1.8175825174
8.3 -7.4172211 1 NA 2.8384267003
8.4 -7.1238609 0 NA 3.3630275307
8.5 -8.8706950 1 NA 4.4360849704
9 -0.1634429 0 NA 0.9607803822
9.1 -2.6034300 NA NA 2.9177753383
9.2 -6.7272369 NA NA 4.8100892501
10 -6.4172202 NA NA 2.2975509102
10.1 -11.4834569 0 NA 4.1734118364
11 -8.7911356 0 NA 1.1832662905
11.1 -19.6645080 0 NA 1.2346051680
11.2 -20.2030932 0 NA 1.6435316263
11.3 -21.3082176 0 NA 3.3859017969
11.4 -14.5802901 0 NA 4.8118087661
12 -15.2006287 0 NA 0.9591987054
13 0.8058816 NA NA 0.0619085738
13.1 -13.6379208 0 NA 3.5621061502
14 -15.3422873 NA NA 4.0364430007
14.1 -10.0965208 NA NA 4.4710561272
14.2 -16.6452027 NA NA 4.6359198843
14.3 -15.8389733 NA NA 4.6886152599
15 -8.9424594 0 NA 0.5402063532
15.1 -22.0101983 0 NA 1.1893180816
15.2 -7.3975599 0 NA 1.5094739688
15.3 -10.3567334 0 NA 4.9193474615
16 -1.9691302 1 NA 1.2417913869
16.1 -9.9308357 NA NA 2.5675726333
16.2 -6.9626923 NA NA 2.6524101500
16.3 -3.2862557 0 NA 3.5585018690
16.4 -3.3972355 0 NA 3.7612454291
16.5 -11.5767835 NA NA 3.9851612889
17 -10.5474144 0 NA 1.5925356350
17.1 -7.6215009 0 NA 2.4374032998
17.2 -16.5386939 0 NA 3.0256489082
17.3 -20.0004774 NA NA 3.3329089405
17.4 -18.8505475 0 NA 3.8693758985
18 -19.7302351 0 NA 2.4374292302
19 -14.6177568 NA NA 0.9772165376
19.1 -17.8043866 NA NA 1.1466335913
19.2 -15.1641705 0 NA 2.2599126538
19.3 -16.6898418 1 NA 4.2114245973
20 -12.9059229 NA NA 1.7170160066
20.1 -16.8191201 0 NA 1.7562902288
20.2 -6.1010131 1 NA 2.2515566566
20.3 -7.9415371 0 NA 2.2609123867
20.4 -9.3904458 0 NA 3.4913365287
20.5 -13.3504189 0 NA 4.1730977828
21 -7.6974718 0 NA 1.6936582839
21.1 -11.9335526 0 NA 2.9571191233
21.2 -12.7064929 NA NA 3.7887385779
22 -21.5022909 0 NA 2.4696226232
22.1 -12.7745451 0 NA 3.1626627257
23 -3.5146508 0 NA 1.5414533857
23.1 -4.6724048 NA NA 2.3369736120
24 -2.5619821 0 NA 2.8283136466
25 -6.2944970 0 NA 0.5381704110
25.1 -3.8630505 NA NA 1.6069735331
25.2 -14.4205140 1 NA 1.6358226922
25.3 -19.6735037 0 NA 3.2646870392
25.4 -9.0288933 0 NA 4.0782226040
25.5 -9.0509738 NA NA 4.1560292873
26 -19.7340685 NA NA 0.2412706357
26.1 -14.1692728 0 NA 2.4451737676
26.2 -17.2819976 0 NA 3.5988757887
26.3 -24.6265576 0 NA 4.1822362854
27 -7.3354999 0 NA 3.6955824879
27.1 -11.1488468 0 NA 4.2451434687
28 -11.7996597 NA NA 0.5746519344
28.1 -8.2030122 0 NA 2.7943964268
28.2 -26.4317815 0 NA 4.2108539480
28.3 -18.5016071 0 NA 4.4705521734
29 -5.8551395 0 NA 1.1898884235
29.1 -2.0209442 0 NA 1.7624059319
29.2 -5.6368080 0 NA 2.0210406382
29.3 -3.8110961 0 NA 3.4078777023
30 -12.7217702 NA NA 2.2635366488
30.1 -17.0170140 0 NA 3.5938334477
30.2 -25.4236089 0 NA 3.6138710892
31 -17.0783921 0 NA 4.3988140998
32 -18.4338764 0 NA 1.6745209007
32.1 -19.4317212 0 NA 2.9128167813
32.2 -19.4738978 NA NA 2.9676558380
32.3 -21.4922645 NA NA 4.2099863547
33 2.0838099 0 NA 0.0093385763
33.1 -13.3172274 1 NA 3.4591242753
34 -10.0296691 NA NA 1.4998774312
34.1 -25.9426553 0 NA 3.8242761395
34.2 -18.5688138 NA NA 3.9072251692
34.3 -15.4173859 NA NA 3.9582124643
35 -14.3958113 0 NA 1.3294299203
35.1 -12.9457541 0 NA 1.5276966314
35.2 -16.1380691 NA NA 4.5025920868
36 -12.8166968 NA NA 0.7123168337
36.1 -14.3989481 NA NA 1.7972493160
36.2 -12.2436943 0 NA 1.8262697803
36.3 -15.0104638 0 NA 4.2840119381
36.4 -10.1775457 0 NA 4.6194464504
37 -15.2223495 0 NA 2.0018732361
37.1 -14.7526195 0 NA 3.6656836793
37.2 -19.8168430 0 NA 3.9663937816
38 -2.7065118 0 NA 0.9826511063
39 -8.7288138 1 NA 0.6921808305
39.1 -9.2746473 0 NA 0.9027792048
39.2 -18.2695344 NA NA 1.3055654289
39.3 -13.8219083 NA NA 1.5412842878
39.4 -16.2254704 0 NA 3.1834997435
39.5 -21.7283648 1 NA 4.1394166439
40 1.8291916 0 NA 1.1330395646
40.1 -6.6916432 1 NA 2.6940994046
40.2 -1.6278171 0 NA 3.0396614212
40.3 -10.5749790 NA NA 4.6762977762
41 -3.1556121 0 NA 1.9337158254
41.1 -11.5895327 NA NA 3.1956304458
41.2 -18.9352091 0 NA 3.2846923557
41.3 -15.9788960 NA NA 3.3813529415
41.4 -9.6070508 0 NA 3.5482964432
42 -5.2159485 0 NA 0.4859252973
42.1 -15.9878743 1 NA 4.3293134298
43 -16.6104361 0 NA 0.5616614548
43.1 -9.5549441 1 NA 1.0743579536
43.2 -14.2003491 0 NA 2.6131797966
44 -8.1969033 0 NA 0.7662644819
44.1 -19.9270197 0 NA 2.6490291790
44.2 -22.6521171 0 NA 3.3371910988
44.3 -21.1903736 0 NA 4.1154200875
45 -0.5686627 NA NA 0.1957449992
45.1 -7.5645740 1 NA 1.9963831536
46 -19.1624789 0 NA 1.3477755385
46.1 -18.4487574 0 NA 2.8565793915
46.2 -15.8222682 0 NA 4.4160729996
47 -5.4165074 0 NA 0.6012621359
47.1 -15.0975029 0 NA 2.4097121472
47.2 -12.9971413 0 NA 2.9975794035
47.3 -10.6844521 NA NA 3.1829649757
47.4 -18.2214784 0 NA 4.6201055450
48 -8.3101471 1 NA 2.8607365978
48.1 -18.3854275 1 NA 2.9098354396
49 -13.0130319 NA NA 2.7179756400
50 -10.4579977 0 NA 1.1762060679
51 -19.3157621 0 NA 1.4304436720
52 -4.4747188 0 NA 2.1266646020
52.1 -4.3163827 0 NA 3.1000545993
52.2 -6.9761408 0 NA 3.1268477370
52.3 -20.1764756 0 NA 3.5711459327
52.4 -8.9036692 0 NA 4.7983659909
52.5 -5.6949642 0 NA 4.9818264414
53 -10.3141887 0 NA 0.4965799209
53.1 -8.2642654 0 NA 3.5505357443
53.2 -9.1691554 NA NA 4.5790420019
54 -6.2198754 NA NA 1.4034724841
54.1 -15.7192609 NA NA 1.8812377600
54.2 -13.0978998 NA NA 2.5107589352
54.3 -5.1195299 NA NA 2.7848406672
54.4 -16.5771751 0 NA 4.0143877396
55 -5.7348534 0 NA 0.6118522980
55.1 -7.3217494 0 NA 0.7463747414
55.2 -12.2171938 NA NA 2.8201208171
55.3 -12.9821266 NA NA 3.1326431572
55.4 -14.8599983 0 NA 3.2218102901
56 -14.1764282 0 NA 1.2231332215
56.1 -12.5343602 NA NA 2.3573202139
56.2 -8.4573382 NA NA 2.5674936292
56.3 -12.4633969 1 NA 2.9507164378
56.4 -17.3841863 0 NA 3.2272730360
56.5 -14.8147645 0 NA 3.4175522043
57 -3.1403293 0 NA 0.2370331448
57.1 -11.1509248 0 NA 0.2481445030
57.2 -6.3940143 0 NA 1.1405586067
57.3 -9.3473241 NA NA 2.1153886721
58 -12.0245677 0 NA 1.2210099772
58.1 -9.2112246 NA NA 1.6334245703
58.2 -1.2071742 1 NA 1.6791862890
58.3 -11.0141711 1 NA 2.6320121693
58.4 -5.3721214 0 NA 2.8477731440
58.5 -7.8523047 0 NA 3.5715569824
59 -13.2946560 NA NA 1.9023998594
59.1 -10.0530648 1 NA 4.9736620474
60 -19.2209402 0 NA 2.8854503250
61 -4.6699914 NA NA 0.7213630795
61.1 -3.5981894 1 NA 2.3186947661
61.2 -1.4713611 1 NA 2.5077313243
61.3 -3.8819786 0 NA 3.1731073430
61.4 0.1041413 0 NA 3.6022726283
62 -2.8591600 NA NA 0.5336771999
62.1 -6.9461986 1 NA 0.6987666548
62.2 -16.7910593 0 NA 3.4584309917
62.3 -17.9844596 0 NA 4.8028772371
63 -24.0335535 NA NA 2.8097350930
63.1 -11.7765300 0 NA 3.9653754211
64 -20.5963897 0 NA 4.1191305732
65 -2.7969169 0 NA 0.7076152589
65.1 -11.1778694 0 NA 2.0252246363
65.2 -5.2830399 0 NA 3.1127382827
65.3 -7.9353390 0 NA 3.1969087943
66 -13.2318328 NA NA 3.4943454154
66.1 -1.9090560 0 NA 3.7677437009
66.2 -16.6643889 0 NA 3.9486138616
67 -25.6073277 NA NA 4.1728388879
68 -13.4806759 0 NA 0.1291919907
68.1 -18.4557183 0 NA 1.7809643946
68.2 -13.3982327 NA NA 2.0493205660
68.3 -12.4977127 0 NA 2.9406870750
68.4 -11.7073990 NA NA 4.0406670363
69 -14.5290675 0 NA 4.1451198701
70 -15.2122709 0 NA 0.1992557163
70.1 -7.8681167 0 NA 0.4829774413
71 -10.3352703 0 NA 0.7741605386
71.1 -7.5699888 1 NA 1.4883817220
71.2 -18.4680702 0 NA 4.0758526395
71.3 -21.4316644 1 NA 4.7048238723
71.4 -8.1137650 0 NA 4.7242791823
72 -9.1848162 0 NA 0.9321196121
72.1 -23.7538846 0 NA 1.1799991806
72.2 -26.3421306 NA NA 1.8917567329
72.3 -27.2843801 0 NA 3.4853593935
72.4 -20.8541617 0 NA 3.6884259700
72.5 -12.8948965 0 NA 4.0854155901
73 -2.6091307 0 NA 4.6019889915
74 -8.2790175 0 NA 1.4626806753
75 -12.5029612 NA NA 3.2524286874
76 -6.0061671 0 NA 1.8074807397
76.1 -8.8149114 0 NA 4.2685073183
76.2 -11.8359043 0 NA 4.9688734859
77 0.4772521 NA NA 0.8459033852
78 -9.4105229 0 NA 0.8231094317
79 -1.0217265 NA NA 0.0583819521
79.1 -11.8125257 0 NA 2.4406372628
79.2 -10.5465186 NA NA 3.2962526032
80 -12.7366807 NA NA 0.8985060186
80.1 -9.0584783 0 NA 1.3434670598
80.2 -16.6381566 NA NA 2.8025900386
81 0.5547913 0 NA 0.0101324962
81.1 -4.0892715 0 NA 0.9421709494
81.2 1.8283303 NA NA 3.0542453879
81.3 -5.2166381 0 NA 3.3456630446
82 -3.0749381 NA NA 1.3791010005
82.1 -10.5506696 0 NA 1.7601010622
82.2 -18.2226347 1 NA 2.6233131927
83 -12.5872635 NA NA 0.0537394290
83.1 -11.9756502 0 NA 2.9061570496
83.2 -10.6744217 0 NA 3.1189457362
83.3 -19.2714012 NA NA 4.7663642222
84 -2.6320312 0 NA 2.7254060237
84.1 -9.8140094 NA NA 3.3364784659
85 -12.3886736 1 NA 0.2977756259
85.1 -12.9196365 NA NA 1.7394116637
85.2 -9.6433248 0 NA 2.6846330194
85.3 -6.3296340 0 NA 3.1608762743
85.4 -7.0405525 0 NA 3.9452053758
85.5 -13.6714939 0 NA 4.5092553482
86 -10.8756412 0 NA 0.8476278360
86.1 -12.0055331 NA NA 1.0118629411
86.2 -13.3724699 NA NA 1.2511159515
86.3 -13.3252145 0 NA 2.1870554925
86.4 -14.9191290 NA NA 2.4532935000
86.5 -17.7515546 0 NA 3.8206058508
87 -10.7027963 NA NA 2.7069531474
87.1 -22.4941954 NA NA 3.4462517721
87.2 -14.9616716 NA NA 4.5241666853
88 -2.2264493 0 NA 0.0005892443
88.1 -8.9626474 NA NA 0.7116099866
88.2 -2.5095281 0 NA 2.4952722900
88.3 -16.3345673 0 NA 3.2995816297
89 -11.0459647 0 NA 0.6462086167
90 -4.5610239 0 NA 0.1696030737
90.1 -11.7036651 0 NA 2.5980385230
90.2 -5.3838521 0 NA 2.6651392167
90.3 -4.1636999 NA NA 3.1242690247
91 -7.1462503 0 NA 0.6382618390
91.1 -12.8374475 0 NA 2.6224059286
91.2 -18.2576707 0 NA 4.7772527603
92 -6.4119222 0 NA 0.0737052364
93 5.2122168 NA NA 0.2788909199
93.1 3.1211725 0 NA 1.0357759963
93.2 -3.6841177 NA NA 2.4916551099
93.3 2.6223542 0 NA 2.8876129608
93.4 -11.1877696 0 NA 4.4639474002
94 -6.9602492 NA NA 0.8488043118
94.1 -7.4318416 0 NA 1.0552454425
94.2 -4.3498045 0 NA 1.9445500884
94.3 -11.6340088 NA NA 3.0710722448
94.4 -12.9357964 0 NA 3.0872731935
94.5 -14.7648530 1 NA 4.3805759016
95 -12.8849309 0 NA 2.0199063048
95.1 -9.7451502 NA NA 4.0184444457
95.2 -0.8535063 0 NA 4.5596531732
96 -4.9139832 0 NA 0.0311333477
96.1 -3.9582653 0 NA 0.1324267720
96.2 -9.6555492 0 NA 0.6701303425
96.3 -11.8690793 NA NA 2.1775037691
96.4 -11.0224373 1 NA 2.2246142488
96.5 -10.9530403 1 NA 4.2377650598
97 -9.8540471 0 NA 1.1955102731
97.1 -19.2262840 0 NA 4.9603108643
98 -11.9651231 0 NA 0.2041732438
98.1 -2.6515128 0 NA 0.4309578973
98.2 -12.2606382 1 NA 3.5172611906
99 -11.4720500 0 NA 0.3531786101
99.1 -14.0596866 0 NA 4.6789444226
99.2 -17.3939469 0 NA 4.9927084171
100 1.1005874 NA NA 1.0691387602
100.1 -3.8226248 NA NA 1.5109344281
100.2 -0.9123182 0 NA 2.1502332564
100.3 -15.8389474 NA NA 3.8745574222
100.4 -12.8093826 0 NA 4.6567608765
$m6a$spM_id
center scale
C2 -0.6240921 0.68571078
(Intercept) NA NA
C1 0.7372814 0.01472882
$m6a$spM_lvlone
center scale
y -11.17337 6.249662
b2 NA NA
b21 NA NA
time 2.53394 1.381809
$m6a$mu_reg_norm
[1] 0
$m6a$tau_reg_norm
[1] 1e-04
$m6a$shape_tau_norm
[1] 0.01
$m6a$rate_tau_norm
[1] 0.01
$m6a$mu_reg_binom
[1] 0
$m6a$tau_reg_binom
[1] 1e-04
$m6a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m6a$shape_diag_RinvD
[1] "0.01"
$m6a$rate_diag_RinvD
[1] "0.001"
$m6b
$m6b$M_id
C2 (Intercept) B11
1 -1.381594459 1 1
2 0.344426024 1 1
3 NA 1 1
4 -0.228910007 1 0
5 NA 1 1
6 -2.143955482 1 1
7 -1.156567023 1 1
8 -0.598827660 1 0
9 NA 1 1
10 -1.006719032 1 1
11 0.239801450 1 0
12 -1.064969789 1 1
13 -0.538082688 1 1
14 NA 1 1
15 -1.781049276 1 1
16 NA 1 1
17 NA 1 1
18 -0.014579883 1 1
19 -2.121550136 1 1
20 NA 1 0
21 -0.363239698 1 1
22 -0.121568514 1 1
23 -0.951271111 1 1
24 NA 1 0
25 -0.974288621 1 1
26 -1.130632418 1 1
27 0.114339868 1 0
28 0.238334648 1 1
29 0.840744958 1 1
30 NA 1 1
31 NA 1 1
32 -1.466312154 1 1
33 -0.637352277 1 1
34 NA 1 1
35 NA 1 1
36 NA 1 0
37 NA 1 0
38 NA 1 1
39 0.006728205 1 1
40 NA 1 1
41 -1.663281353 1 1
42 0.161184794 1 1
43 0.457939180 1 1
44 -0.307070331 1 1
45 NA 1 0
46 -1.071668276 1 1
47 -0.814751321 1 0
48 -0.547630662 1 0
49 NA 1 1
50 -1.350213782 1 1
51 0.719054706 1 1
52 NA 1 0
53 -1.207130750 1 1
54 NA 1 1
55 -0.408600991 1 1
56 -0.271380529 1 1
57 -1.361925974 1 1
58 NA 1 1
59 NA 1 1
60 -0.323712205 1 1
61 NA 1 0
62 NA 1 1
63 -1.386906880 1 1
64 NA 1 1
65 NA 1 1
66 -0.565191691 1 0
67 -0.382899912 1 0
68 NA 1 1
69 -0.405642769 1 1
70 NA 1 1
71 -0.843748427 1 1
72 0.116003683 1 1
73 -0.778634325 1 1
74 NA 1 0
75 NA 1 1
76 NA 1 1
77 -0.632974758 1 1
78 NA 1 1
79 -0.778064615 1 1
80 NA 1 1
81 NA 1 1
82 -0.246123253 1 1
83 -1.239659782 1 0
84 -0.467772280 1 0
85 NA 1 1
86 -2.160485036 1 1
87 -0.657675572 1 1
88 NA 1 1
89 -0.696710744 1 1
90 NA 1 0
91 -0.179395847 1 1
92 -0.441545568 1 1
93 -0.685799334 1 0
94 NA 1 1
95 0.191929445 1 0
96 NA 1 0
97 -0.069760671 1 1
98 NA 1 1
99 NA 1 1
100 NA 1 1
$m6b$M_lvlone
b1 c1 time I(time^2)
1 0 0.7592026489 0.5090421822 2.591239e-01
1.1 1 0.9548337990 0.6666076288 4.443657e-01
1.2 1 0.5612235156 2.1304941282 4.539005e+00
1.3 0 1.1873391025 2.4954441458 6.227241e+00
2 1 0.9192204198 3.0164990982 9.099267e+00
2.1 1 -0.1870730476 3.2996806887 1.088789e+01
2.2 1 1.2517512331 4.1747569619 1.742860e+01
3 1 -0.0605087604 0.8478727890 7.188883e-01
3.1 0 0.3788637747 3.0654308549 9.396866e+00
3.2 0 0.9872578281 4.7381553578 2.245012e+01
4 1 1.4930175328 0.3371432109 1.136655e-01
4.1 1 -0.7692526880 1.0693019140 1.143407e+00
4.2 0 0.9180841450 2.6148973033 6.837688e+00
4.3 1 -0.0541170782 3.1336532847 9.819783e+00
5 0 -0.1376784521 1.0762525082 1.158319e+00
5.1 1 -0.2740585866 1.7912546196 3.208593e+00
5.2 1 0.4670496929 2.7960080339 7.817661e+00
5.3 1 0.1740288049 2.8119940578 7.907311e+00
6 0 0.9868044683 1.7815462884 3.173907e+00
7 1 -0.1280320918 3.3074087673 1.093895e+01
7.1 0 0.4242971219 3.7008403614 1.369622e+01
7.2 1 0.0777182491 4.7716691741 2.276883e+01
8 0 -0.5791408712 1.1246398522 1.264815e+00
8.1 1 0.3128604232 1.8027009873 3.249731e+00
8.2 1 0.6258446356 1.8175825174 3.303606e+00
8.3 0 -0.1040137707 2.8384267003 8.056666e+00
8.4 0 0.0481450285 3.3630275307 1.130995e+01
8.5 1 0.3831763675 4.4360849704 1.967885e+01
9 1 -0.1757592269 0.9607803822 9.230989e-01
9.1 1 -0.1791541200 2.9177753383 8.513413e+00
9.2 0 -0.0957042935 4.8100892501 2.313696e+01
10 1 -0.5598409704 2.2975509102 5.278740e+00
10.1 1 -0.2318340451 4.1734118364 1.741737e+01
11 1 0.5086859475 1.1832662905 1.400119e+00
11.1 1 0.4951758188 1.2346051680 1.524250e+00
11.2 1 -1.1022162541 1.6435316263 2.701196e+00
11.3 1 -0.0611636705 3.3859017969 1.146433e+01
11.4 1 -0.4971774316 4.8118087661 2.315350e+01
12 1 -0.2433996286 0.9591987054 9.200622e-01
13 0 0.8799673116 0.0619085738 3.832672e-03
13.1 1 0.1079022586 3.5621061502 1.268860e+01
14 0 0.9991752617 4.0364430007 1.629287e+01
14.1 1 -0.1094019046 4.4710561272 1.999034e+01
14.2 0 0.1518967560 4.6359198843 2.149175e+01
14.3 0 0.3521012473 4.6886152599 2.198311e+01
15 0 0.3464447888 0.5402063532 2.918229e-01
15.1 0 -0.4767313971 1.1893180816 1.414477e+00
15.2 0 0.5759767791 1.5094739688 2.278512e+00
15.3 1 -0.1713452662 4.9193474615 2.419998e+01
16 1 0.4564754473 1.2417913869 1.542046e+00
16.1 0 1.0652558311 2.5675726333 6.592429e+00
16.2 1 0.6971872493 2.6524101500 7.035280e+00
16.3 1 0.5259331838 3.5585018690 1.266294e+01
16.4 1 0.2046601798 3.7612454291 1.414697e+01
16.5 0 1.0718540464 3.9851612889 1.588151e+01
17 0 0.6048676222 1.5925356350 2.536170e+00
17.1 0 0.2323298304 2.4374032998 5.940935e+00
17.2 1 1.2617499032 3.0256489082 9.154551e+00
17.3 0 -0.3913230895 3.3329089405 1.110828e+01
17.4 1 0.9577299112 3.8693758985 1.497207e+01
18 1 -0.0050324072 2.4374292302 5.941061e+00
19 1 -0.4187468937 0.9772165376 9.549522e-01
19.1 1 -0.4478828944 1.1466335913 1.314769e+00
19.2 1 -1.1966721302 2.2599126538 5.107205e+00
19.3 1 -0.5877091668 4.2114245973 1.773610e+01
20 0 0.6838223064 1.7170160066 2.948144e+00
20.1 1 0.3278571109 1.7562902288 3.084555e+00
20.2 0 -0.8489831990 2.2515566566 5.069507e+00
20.3 0 1.3169975191 2.2609123867 5.111725e+00
20.4 0 0.0444804531 3.4913365287 1.218943e+01
20.5 0 -0.4535207652 4.1730977828 1.741475e+01
21 1 -0.4030302960 1.6936582839 2.868478e+00
21.1 1 -0.4069674045 2.9571191233 8.744554e+00
21.2 0 1.0650265940 3.7887385779 1.435454e+01
22 0 -0.0673274516 2.4696226232 6.099036e+00
22.1 1 0.9601388170 3.1626627257 1.000244e+01
23 1 0.5556634840 1.5414533857 2.376079e+00
23.1 1 1.4407865964 2.3369736120 5.461446e+00
24 0 0.3856376411 2.8283136466 7.999358e+00
25 0 0.3564400705 0.5381704110 2.896274e-01
25.1 1 0.0982553434 1.6069735331 2.582364e+00
25.2 1 0.1928682598 1.6358226922 2.675916e+00
25.3 0 -0.0192488594 3.2646870392 1.065818e+01
25.4 0 0.4466012931 4.0782226040 1.663190e+01
25.5 0 1.1425193342 4.1560292873 1.727258e+01
26 1 0.5341531449 0.2412706357 5.821152e-02
26.1 1 1.2268695927 2.4451737676 5.978875e+00
26.2 1 0.3678294939 3.5988757887 1.295191e+01
26.3 0 0.5948516018 4.1822362854 1.749110e+01
27 1 -0.3342844147 3.6955824879 1.365733e+01
27.1 1 -0.4835141229 4.2451434687 1.802124e+01
28 1 -0.7145915499 0.5746519344 3.302248e-01
28.1 0 0.5063671955 2.7943964268 7.808651e+00
28.2 1 -0.2067413142 4.2108539480 1.773129e+01
28.3 1 0.1196789973 4.4705521734 1.998584e+01
29 1 0.1392699487 1.1898884235 1.415834e+00
29.1 0 0.7960234776 1.7624059319 3.106075e+00
29.2 0 1.0398214352 2.0210406382 4.084605e+00
29.3 1 0.0813246429 3.4078777023 1.161363e+01
30 1 -0.3296323050 2.2635366488 5.123598e+00
30.1 1 1.3635850954 3.5938334477 1.291564e+01
30.2 1 0.7354171050 3.6138710892 1.306006e+01
31 0 0.3708398217 4.3988140998 1.934957e+01
32 1 -0.0474059668 1.6745209007 2.804020e+00
32.1 1 1.2507771489 2.9128167813 8.484502e+00
32.2 1 0.1142915519 2.9676558380 8.806981e+00
32.3 1 0.6773270619 4.2099863547 1.772399e+01
33 0 0.1774293842 0.0093385763 8.720901e-05
33.1 0 0.6159606291 3.4591242753 1.196554e+01
34 1 0.8590979166 1.4998774312 2.249632e+00
34.1 0 0.0546216775 3.8242761395 1.462509e+01
34.2 1 -0.0897224473 3.9072251692 1.526641e+01
34.3 1 0.4163395571 3.9582124643 1.566745e+01
35 1 -1.4693520528 1.3294299203 1.767384e+00
35.1 0 -0.3031734330 1.5276966314 2.333857e+00
35.2 1 -0.6045512101 4.5025920868 2.027334e+01
36 0 0.9823048960 0.7123168337 5.073953e-01
36.1 0 1.4466051416 1.7972493160 3.230105e+00
36.2 1 1.1606752905 1.8262697803 3.335261e+00
36.3 0 0.8373091576 4.2840119381 1.835276e+01
36.4 1 0.2640591685 4.6194464504 2.133929e+01
37 1 0.1177313455 2.0018732361 4.007496e+00
37.1 0 -0.1415483779 3.6656836793 1.343724e+01
37.2 0 0.0054610124 3.9663937816 1.573228e+01
38 1 0.8078948077 0.9826511063 9.656032e-01
39 1 0.9876451040 0.6921808305 4.791143e-01
39.1 0 -0.3431222274 0.9027792048 8.150103e-01
39.2 0 -1.7909380751 1.3055654289 1.704501e+00
39.3 0 -0.1798746191 1.5412842878 2.375557e+00
39.4 1 -0.1850961689 3.1834997435 1.013467e+01
39.5 1 0.4544226146 4.1394166439 1.713477e+01
40 0 0.5350190436 1.1330395646 1.283779e+00
40.1 0 0.4189342752 2.6940994046 7.258172e+00
40.2 0 0.4211994981 3.0396614212 9.239542e+00
40.3 1 0.0916687506 4.6762977762 2.186776e+01
41 1 -0.1035047421 1.9337158254 3.739257e+00
41.1 1 -0.4684202411 3.1956304458 1.021205e+01
41.2 0 0.5972615368 3.2846923557 1.078920e+01
41.3 1 0.9885613862 3.3813529415 1.143355e+01
41.4 1 -0.3908036794 3.5482964432 1.259041e+01
42 1 -0.0338893961 0.4859252973 2.361234e-01
42.1 1 -0.4498363172 4.3293134298 1.874295e+01
43 0 0.8965546110 0.5616614548 3.154636e-01
43.1 0 0.6199122090 1.0743579536 1.154245e+00
43.2 1 0.1804894429 2.6131797966 6.828709e+00
44 1 1.3221409285 0.7662644819 5.871613e-01
44.1 0 0.3416426284 2.6490291790 7.017356e+00
44.2 0 0.5706610068 3.3371910988 1.113684e+01
44.3 1 1.2679497430 4.1154200875 1.693668e+01
45 1 0.1414983160 0.1957449992 3.831610e-02
45.1 0 0.7220892521 1.9963831536 3.985546e+00
46 1 1.5391054233 1.3477755385 1.816499e+00
46.1 0 0.3889107049 2.8565793915 8.160046e+00
46.2 1 0.1248719493 4.4160729996 1.950170e+01
47 0 0.2014101100 0.6012621359 3.615162e-01
47.1 0 0.2982973539 2.4097121472 5.806713e+00
47.2 1 1.1518107179 2.9975794035 8.985482e+00
47.3 0 0.5196802157 3.1829649757 1.013127e+01
47.4 0 0.3702301552 4.6201055450 2.134538e+01
48 0 -0.2128602862 2.8607365978 8.183814e+00
48.1 1 -0.5337239976 2.9098354396 8.467142e+00
49 0 -0.5236770035 2.7179756400 7.387392e+00
50 1 0.3897705981 1.1762060679 1.383461e+00
51 1 -0.7213343736 1.4304436720 2.046169e+00
52 1 0.3758235358 2.1266646020 4.522702e+00
52.1 1 0.7138067080 3.1000545993 9.610339e+00
52.2 0 0.8872895233 3.1268477370 9.777177e+00
52.3 0 -0.9664587437 3.5711459327 1.275308e+01
52.4 1 0.0254566848 4.7983659909 2.302432e+01
52.5 1 0.4155259424 4.9818264414 2.481859e+01
53 1 0.5675736897 0.4965799209 2.465916e-01
53.1 1 -0.3154088781 3.5505357443 1.260630e+01
53.2 1 0.2162315769 4.5790420019 2.096763e+01
54 0 -0.0880802382 1.4034724841 1.969735e+00
54.1 1 0.4129127672 1.8812377600 3.539056e+00
54.2 0 1.0119546775 2.5107589352 6.303910e+00
54.3 1 -0.1112901990 2.7848406672 7.755338e+00
54.4 0 0.8587727145 4.0143877396 1.611531e+01
55 1 -0.0116453589 0.6118522980 3.743632e-01
55.1 1 0.5835528661 0.7463747414 5.570753e-01
55.2 1 -1.0010857254 2.8201208171 7.953081e+00
55.3 0 -0.4796526070 3.1326431572 9.813453e+00
55.4 1 -0.1202746964 3.2218102901 1.038006e+01
56 0 0.5176377612 1.2231332215 1.496055e+00
56.1 1 -1.1136932588 2.3573202139 5.556959e+00
56.2 1 -0.0168103281 2.5674936292 6.592024e+00
56.3 0 0.3933023606 2.9507164378 8.706727e+00
56.4 0 0.3714625139 3.2272730360 1.041529e+01
56.5 1 0.7811448179 3.4175522043 1.167966e+01
57 1 -1.0868304872 0.2370331448 5.618471e-02
57.1 1 0.8018626997 0.2481445030 6.157569e-02
57.2 0 -0.1159517011 1.1405586067 1.300874e+00
57.3 0 0.6785562445 2.1153886721 4.474869e+00
58 1 1.6476207996 1.2210099772 1.490865e+00
58.1 1 0.3402652711 1.6334245703 2.668076e+00
58.2 1 -0.1111300753 1.6791862890 2.819667e+00
58.3 1 -0.5409234285 2.6320121693 6.927488e+00
58.4 1 -0.1271327672 2.8477731440 8.109812e+00
58.5 1 0.8713264822 3.5715569824 1.275602e+01
59 0 0.4766421367 1.9023998594 3.619125e+00
59.1 1 1.0028089765 4.9736620474 2.473731e+01
60 0 0.5231452932 2.8854503250 8.325824e+00
61 1 -0.7190130614 0.7213630795 5.203647e-01
61.1 1 0.8353702312 2.3186947661 5.376345e+00
61.2 1 1.0229058138 2.5077313243 6.288716e+00
61.3 0 1.1717723589 3.1731073430 1.006861e+01
61.4 1 -0.0629201596 3.6022726283 1.297637e+01
62 1 -0.3979137604 0.5336771999 2.848114e-01
62.1 0 0.6830738372 0.6987666548 4.882748e-01
62.2 0 0.4301745954 3.4584309917 1.196074e+01
62.3 1 -0.0333139957 4.8028772371 2.306763e+01
63 0 0.3345678035 2.8097350930 7.894611e+00
63.1 1 0.3643769511 3.9653754211 1.572420e+01
64 1 0.3949911859 4.1191305732 1.696724e+01
65 1 1.2000091513 0.7076152589 5.007194e-01
65.1 1 0.0110122646 2.0252246363 4.101535e+00
65.2 0 -0.5776452043 3.1127382827 9.689140e+00
65.3 0 -0.1372183563 3.1969087943 1.022023e+01
66 1 -0.5081302805 3.4943454154 1.221045e+01
66.1 0 -0.1447837412 3.7677437009 1.419589e+01
66.2 0 0.1906241379 3.9486138616 1.559155e+01
67 0 1.6716027681 4.1728388879 1.741258e+01
68 0 0.5691848839 0.1291919907 1.669057e-02
68.1 0 0.1004860389 1.7809643946 3.171834e+00
68.2 0 -0.0061241827 2.0493205660 4.199715e+00
68.3 0 0.7443745962 2.9406870750 8.647640e+00
68.4 1 0.8726923437 4.0406670363 1.632699e+01
69 1 0.0381382683 4.1451198701 1.718202e+01
70 1 0.8126204217 0.1992557163 3.970284e-02
70.1 1 0.4691503050 0.4829774413 2.332672e-01
71 1 -0.5529062591 0.7741605386 5.993245e-01
71.1 1 -0.1103252087 1.4883817220 2.215280e+00
71.2 0 1.7178492547 4.0758526395 1.661257e+01
71.3 0 -1.0118346755 4.7048238723 2.213537e+01
71.4 0 1.8623785017 4.7242791823 2.231881e+01
72 1 -0.4521659275 0.9321196121 8.688470e-01
72.1 1 0.1375317317 1.1799991806 1.392398e+00
72.2 1 -0.4170988856 1.8917567329 3.578744e+00
72.3 0 0.7107266765 3.4853593935 1.214773e+01
72.4 0 0.1451969143 3.6884259700 1.360449e+01
72.5 1 1.6298050306 4.0854155901 1.669062e+01
73 1 -0.0307469467 4.6019889915 2.117830e+01
74 1 0.3730017941 1.4626806753 2.139435e+00
75 0 -0.4908003566 3.2524286874 1.057829e+01
76 1 -0.9888876620 1.8074807397 3.266987e+00
76.1 1 0.0003798292 4.2685073183 1.822015e+01
76.2 1 -0.8421863763 4.9688734859 2.468970e+01
77 1 -0.4986802480 0.8459033852 7.155525e-01
78 1 0.0417330969 0.8231094317 6.775091e-01
79 0 -0.3767450660 0.0583819521 3.408452e-03
79.1 1 0.1516000028 2.4406372628 5.956710e+00
79.2 0 -0.1888160741 3.2962526032 1.086528e+01
80 1 -0.0041558414 0.8985060186 8.073131e-01
80.1 0 -0.0329337062 1.3434670598 1.804904e+00
80.2 1 0.5046816157 2.8025900386 7.854511e+00
81 1 -0.9493950353 0.0101324962 1.026675e-04
81.1 1 0.2443038954 0.9421709494 8.876861e-01
81.2 1 0.6476958410 3.0542453879 9.328415e+00
81.3 1 0.4182528210 3.3456630446 1.119346e+01
82 1 1.1088801952 1.3791010005 1.901920e+00
82.1 1 0.9334157763 1.7601010622 3.097956e+00
82.2 0 0.4958140634 2.6233131927 6.881772e+00
83 1 0.5104724530 0.0537394290 2.887926e-03
83.1 0 -0.0513309106 2.9061570496 8.445749e+00
83.2 0 -0.2067792494 3.1189457362 9.727823e+00
83.3 1 -0.0534169155 4.7663642222 2.271823e+01
84 1 -0.0255753653 2.7254060237 7.427838e+00
84.1 0 -1.8234189877 3.3364784659 1.113209e+01
85 0 -0.0114038622 0.2977756259 8.867032e-02
85.1 0 -0.0577615939 1.7394116637 3.025553e+00
85.2 1 -0.2241856342 2.6846330194 7.207254e+00
85.3 1 -0.0520175929 3.1608762743 9.991139e+00
85.4 1 0.2892733846 3.9452053758 1.556465e+01
85.5 1 -0.3740417009 4.5092553482 2.033338e+01
86 0 0.4293735089 0.8476278360 7.184729e-01
86.1 1 -0.1363456521 1.0118629411 1.023867e+00
86.2 1 0.1230989293 1.2511159515 1.565291e+00
86.3 0 0.3305413955 2.1870554925 4.783212e+00
86.4 1 2.6003411822 2.4532935000 6.018649e+00
86.5 0 -0.1420690052 3.8206058508 1.459703e+01
87 0 1.0457427869 2.7069531474 7.327595e+00
87.1 1 -0.2973007190 3.4462517721 1.187665e+01
87.2 0 0.4396872616 4.5241666853 2.046808e+01
88 0 -0.0601928334 0.0005892443 3.472088e-07
88.1 0 -1.0124347595 0.7116099866 5.063888e-01
88.2 0 0.5730917016 2.4952722900 6.226384e+00
88.3 0 -0.0029455332 3.2995816297 1.088724e+01
89 1 1.5465903721 0.6462086167 4.175856e-01
90 0 0.0626760573 0.1696030737 2.876520e-02
90.1 1 1.1896872985 2.5980385230 6.749804e+00
90.2 1 0.2597888783 2.6651392167 7.102967e+00
90.3 0 0.6599799887 3.1242690247 9.761057e+00
91 0 1.1213651365 0.6382618390 4.073782e-01
91.1 0 1.2046371625 2.6224059286 6.877013e+00
91.2 1 0.3395603754 4.7772527603 2.282214e+01
92 1 0.4674939332 0.0737052364 5.432462e-03
93 0 0.2677965647 0.2788909199 7.778015e-02
93.1 1 1.6424445368 1.0357759963 1.072832e+00
93.2 0 0.7101700066 2.4916551099 6.208345e+00
93.3 1 1.1222322893 2.8876129608 8.338309e+00
93.4 0 1.4628960401 4.4639474002 1.992683e+01
94 1 -0.2904211940 0.8488043118 7.204688e-01
94.1 0 0.0147813580 1.0552454425 1.113543e+00
94.2 1 -0.4536774482 1.9445500884 3.781275e+00
94.3 0 0.6793464917 3.0710722448 9.431485e+00
94.4 0 -0.9411356550 3.0872731935 9.531256e+00
94.5 0 0.5683867264 4.3805759016 1.918945e+01
95 1 0.2375652188 2.0199063048 4.080021e+00
95.1 1 0.0767152977 4.0184444457 1.614790e+01
95.2 0 -0.6886731251 4.5596531732 2.079044e+01
96 1 0.7813892121 0.0311333477 9.692853e-04
96.1 0 0.3391519695 0.1324267720 1.753685e-02
96.2 0 -0.4857246503 0.6701303425 4.490747e-01
96.3 0 0.8771471244 2.1775037691 4.741523e+00
96.4 0 1.9030768981 2.2246142488 4.948909e+00
96.5 1 -0.1684332749 4.2377650598 1.795865e+01
97 0 1.3775130083 1.1955102731 1.429245e+00
97.1 0 -1.7323228619 4.9603108643 2.460468e+01
98 0 -1.2648518889 0.2041732438 4.168671e-02
98.1 0 -0.9042716241 0.4309578973 1.857247e-01
98.2 0 -0.1560385207 3.5172611906 1.237113e+01
99 1 0.7993356425 0.3531786101 1.247351e-01
99.1 1 1.0355522332 4.6789444226 2.189252e+01
99.2 1 -0.1150895843 4.9927084171 2.492714e+01
100 0 0.0369067906 1.0691387602 1.143058e+00
100.1 0 1.6023713093 1.5109344281 2.282923e+00
100.2 1 0.8861545820 2.1502332564 4.623503e+00
100.3 1 0.1277046316 3.8745574222 1.501220e+01
100.4 1 -0.0834577654 4.6567608765 2.168542e+01
$m6b$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
B11 NA NA
$m6b$spM_lvlone
center scale
b1 NA NA
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
I(time^2) 8.3244468 7.0900029
$m6b$mu_reg_norm
[1] 0
$m6b$tau_reg_norm
[1] 1e-04
$m6b$shape_tau_norm
[1] 0.01
$m6b$rate_tau_norm
[1] 0.01
$m6b$mu_reg_binom
[1] 0
$m6b$tau_reg_binom
[1] 1e-04
$m6b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m6b$shape_diag_RinvD
[1] "0.01"
$m6b$rate_diag_RinvD
[1] "0.001"
$m6b$RinvD_b1_id
[,1] [,2]
[1,] NA 0
[2,] 0 NA
$m6b$KinvD_b1_id
id
3
$m7a
$m7a$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m7a$M_lvlone
y ns(time, df = 2)1 ns(time, df = 2)2 time
1 -13.0493856 0.149679884 -0.100552161 0.5090421822
1.1 -9.3335901 0.194627180 -0.129464178 0.6666076288
1.2 -22.3469852 0.520751993 -0.255001297 2.1304941282
1.3 -15.0417337 0.560875996 -0.221882653 2.4954441458
2 -12.0655434 0.578228925 -0.112131092 3.0164990982
2.1 -15.8674476 0.569154825 -0.023537063 3.2996806887
2.2 -7.8800006 0.481017405 0.344239525 4.1747569619
3 -11.4820604 0.244887044 -0.160459809 0.8478727890
3.1 -10.5983220 0.577508632 -0.098148611 3.0654308549
3.2 -22.4519157 0.394656259 0.627350944 4.7381553578
4 -1.2697775 0.099645844 -0.067449012 0.3371432109
4.1 -11.1215184 0.303598405 -0.194123505 1.0693019140
4.2 -3.6134138 0.569124392 -0.203401494 2.6148973033
4.3 -14.5982385 0.575895782 -0.077701736 3.1336532847
5 -6.8457515 0.305385958 -0.195093572 1.0762525082
5.1 -7.0551214 0.465582593 -0.257830256 1.7912546196
5.2 -12.3418980 0.576686171 -0.167647096 2.7960080339
5.3 -9.2366906 0.577072492 -0.164051419 2.8119940578
6 -5.1648211 0.463778516 -0.257558879 1.7815462884
7 -10.0599502 0.568748124 -0.020870495 3.3074087673
7.1 -18.3267285 0.538303487 0.130118265 3.7008403614
7.2 -12.5138426 0.389177122 0.644726730 4.7716691741
8 -1.6305331 0.317728152 -0.201687154 1.1246398522
8.1 -9.6520453 0.467694470 -0.258126481 1.8027009873
8.2 -1.5278462 0.470415425 -0.258472942 1.8175825174
8.3 -7.4172211 0.577614497 -0.157954646 2.8384267003
8.4 -7.1238609 0.565587961 -0.001314572 3.3630275307
8.5 -8.8706950 0.442658778 0.472900402 4.4360849704
9 -0.1634429 0.275235172 -0.178267924 0.9607803822
9.1 -2.6034300 0.578530617 -0.138540550 2.9177753383
9.2 -6.7272369 0.382871829 0.664683848 4.8100892501
10 -6.4172202 0.541787333 -0.244015328 2.2975509102
10.1 -11.4834569 0.481204604 0.343593295 4.1734118364
11 -8.7911356 0.332434575 -0.209288887 1.1832662905
11.1 -19.6645080 0.345079810 -0.215581053 1.2346051680
11.2 -20.2030932 0.436891791 -0.251761613 1.6435316263
11.3 -21.3082176 0.564171515 0.006910827 3.3859017969
11.4 -14.5802901 0.382589098 0.665577876 4.8118087661
12 -15.2006287 0.274815717 -0.178027342 0.9591987054
13 0.8058816 0.018233542 -0.012411541 0.0619085738
13.1 -13.6379208 0.551097100 0.073656403 3.5621061502
14 -15.3422873 0.499617145 0.278805824 4.0364430007
14.1 -10.0965208 0.437265959 0.490524050 4.4710561272
14.2 -16.6452027 0.411217976 0.574584070 4.6359198843
14.3 -15.8389733 0.402713124 0.601732379 4.6886152599
15 -8.9424594 0.158648551 -0.106393710 0.5402063532
15.1 -22.0101983 0.333936709 -0.210048628 1.1893180816
15.2 -7.3975599 0.408677850 -0.242848629 1.5094739688
15.3 -10.3567334 0.364839265 0.721596571 4.9193474615
16 -1.9691302 0.346831902 -0.216433743 1.2417913869
16.1 -9.9308357 0.566162119 -0.211201284 2.5675726333
16.2 -6.9626923 0.571181790 -0.196764094 2.6524101500
16.3 -3.2862557 0.551401297 0.072233616 3.5585018690
16.4 -3.3972355 0.532104656 0.155684929 3.7612454291
16.5 -11.5767835 0.506155856 0.255104466 3.9851612889
17 -10.5474144 0.426393434 -0.248737624 1.5925356350
17.1 -7.6215009 0.555961660 -0.229444366 2.4374032998
17.2 -16.5386939 0.578122473 -0.109560659 3.0256489082
17.3 -20.0004774 0.567349735 -0.011983440 3.3329089405
17.4 -18.8505475 0.520130058 0.202825172 3.8693758985
18 -19.7302351 0.555963985 -0.229441190 2.4374292302
19 -14.6177568 0.279583980 -0.180752315 0.9772165376
19.1 -17.8043866 0.323277715 -0.204589694 1.1466335913
19.2 -15.1641705 0.537427829 -0.247084600 2.2599126538
19.3 -16.6898418 0.475869872 0.361925196 4.2114245973
20 -12.9059229 0.451491582 -0.255292945 1.7170160066
20.1 -16.8191201 0.459030382 -0.256767080 1.7562902288
20.2 -6.1010131 0.536429474 -0.247718279 2.2515566566
20.3 -7.9415371 0.537546536 -0.247007629 2.2609123867
20.4 -9.3904458 0.556795731 0.046149115 3.4913365287
20.5 -13.3504189 0.481248294 0.343442443 4.1730977828
21 -7.6974718 0.446919882 -0.254278387 1.6936582839
21.1 -11.9335526 0.578598286 -0.128309671 2.9571191233
21.2 -12.7064929 0.529164259 0.167507684 3.7887385779
22 -21.5022909 0.558761480 -0.225359062 2.4696226232
22.1 -12.7745451 0.575000638 -0.068679901 3.1626627257
23 -3.5146508 0.415586919 -0.245254258 1.5414533857
23.1 -4.6724048 0.546109336 -0.240418571 2.3369736120
24 -2.5619821 0.577421301 -0.160309425 2.8283136466
25 -6.2944970 0.158063728 -0.106013799 0.5381704110
25.1 -3.8630505 0.429395510 -0.249640384 1.6069735331
25.2 -14.4205140 0.435323733 -0.251334129 1.6358226922
25.3 -19.6735037 0.570895539 -0.035453805 3.2646870392
25.4 -9.0288933 0.494142132 0.298346817 4.0782226040
25.5 -9.0509738 0.483613070 0.335258957 4.1560292873
26 -19.7340685 0.071415518 -0.048478092 0.2412706357
26.1 -14.1692728 0.556653188 -0.228484569 2.4451737676
26.2 -17.2819976 0.547910251 0.088301869 3.5988757887
26.3 -24.6265576 0.479974382 0.347836106 4.1822362854
27 -7.3354999 0.538825626 0.127920114 3.6955824879
27.1 -11.1488468 0.471062700 0.378303733 4.2451434687
28 -11.7996597 0.168518816 -0.112783467 0.5746519344
28.1 -8.2030122 0.576644760 -0.168005735 2.7943964268
28.2 -26.4317815 0.475950630 0.361648944 4.2108539480
28.3 -18.5016071 0.437344040 0.490269509 4.4705521734
29 -5.8551395 0.334078119 -0.210119984 1.1898884235
29.1 -2.0209442 0.460187365 -0.256970126 1.7624059319
29.2 -5.6368080 0.504694417 -0.258639728 2.0210406382
29.3 -3.8110961 0.562747906 0.014911405 3.4078777023
30 -12.7217702 0.537857385 -0.246804409 2.2635366488
30.1 -17.0170140 0.548356202 0.086279523 3.5938334477
30.2 -25.4236089 0.546567486 0.094341984 3.6138710892
31 -17.0783921 0.448346737 0.454210880 4.3988140998
32 -18.4338764 0.443126000 -0.253371730 1.6745209007
32.1 -19.4317212 0.578504120 -0.139801854 2.9128167813
32.2 -19.4738978 0.578573798 -0.125503072 2.9676558380
32.3 -21.4922645 0.476073373 0.361229002 4.2099863547
33 2.0838099 0.002602024 -0.001771525 0.0093385763
33.1 -13.3172274 0.559193838 0.033934796 3.4591242753
34 -10.0296691 0.406583378 -0.242093628 1.4998774312
34.1 -25.9426553 0.525256842 0.182956598 3.8242761395
34.2 -18.5688138 0.515687377 0.219718726 3.9072251692
34.3 -15.4173859 0.509508459 0.242779926 3.9582124643
35 -14.3958113 0.367830634 -0.226255938 1.3294299203
35.1 -12.9457541 0.412628188 -0.244240345 1.5276966314
35.2 -16.1380691 0.432359407 0.506484530 4.5025920868
36 -12.8166968 0.207461863 -0.137531762 0.7123168337
36.1 -14.3989481 0.466690680 -0.257988608 1.7972493160
36.2 -12.2436943 0.471990813 -0.258654856 1.8262697803
36.3 -15.0104638 0.465437951 0.397314113 4.2840119381
36.4 -10.1775457 0.413861161 0.566121422 4.6194464504
37 -15.2223495 0.501705572 -0.259000239 2.0018732361
37.1 -14.7526195 0.541740371 0.115505450 3.6656836793
37.2 -19.8168430 0.508496877 0.246511761 3.9663937816
38 -2.7065118 0.281017855 -0.181567443 0.9826511063
39 -8.7288138 0.201820303 -0.133997235 0.6921808305
39.1 -9.2746473 0.259745822 -0.169277221 0.9027792048
39.2 -18.2695344 0.362181329 -0.223688876 1.3055654289
39.3 -13.8219083 0.415550673 -0.245241988 1.5412842878
39.4 -16.2254704 0.574282209 -0.062081620 3.1834997435
39.5 -21.7283648 0.485896134 0.327323053 4.1394166439
40 1.8291916 0.319852137 -0.202802761 1.1330395646
40.1 -6.6916432 0.573163561 -0.188911134 2.6940994046
40.2 -1.6278171 0.577934158 -0.105584603 3.0396614212
40.3 -10.5749790 0.404707547 0.595376446 4.6762977762
41 -3.1556121 0.490664220 -0.259635659 1.9337158254
41.1 -11.5895327 0.573835232 -0.058195355 3.1956304458
41.2 -18.9352091 0.569920722 -0.028672980 3.2846923557
41.3 -15.9788960 0.564458534 0.005266743 3.3813529415
41.4 -9.6070508 0.552254596 0.068217584 3.5482964432
42 -5.2159485 0.143004690 -0.096183932 0.4859252973
42.1 -15.9878743 0.458775467 0.419637911 4.3293134298
43 -16.6104361 0.164801881 -0.110382209 0.5616614548
43.1 -9.5549441 0.304899077 -0.194829719 1.0743579536
43.2 -14.2003491 0.569024005 -0.203695700 2.6131797966
44 -8.1969033 0.222475436 -0.146843207 0.7662644819
44.1 -19.9270197 0.571007011 -0.197378972 2.6490291790
44.2 -22.6521171 0.567106481 -0.010477885 3.3371910988
44.3 -21.1903736 0.489160908 0.315911546 4.1154200875
45 -0.5686627 0.057952206 -0.039378945 0.1957449992
45.1 -7.5645740 0.500839983 -0.259088640 1.9963831536
46 -19.1624789 0.372137538 -0.228173110 1.3477755385
46.1 -18.4487574 0.577917515 -0.153659415 2.8565793915
46.2 -15.8222682 0.445720715 0.462853045 4.4160729996
47 -5.4165074 0.176111398 -0.117668931 0.6012621359
47.1 -15.0975029 0.553413814 -0.232734143 2.4097121472
47.2 -12.9971413 0.578407372 -0.117380969 2.9975794035
47.3 -10.6844521 0.574301428 -0.062252183 3.1829649757
47.4 -18.2214784 0.413755560 0.566459771 4.6201055450
48 -8.3101471 0.577979039 -0.152663438 2.8607365978
48.1 -18.3854275 0.578486237 -0.140557164 2.9098354396
49 -13.0130319 0.574155546 -0.184189885 2.7179756400
50 -10.4579977 0.330678305 -0.208396558 1.1762060679
51 -19.3157621 0.391145266 -0.236186737 1.4304436720
52 -4.4747188 0.520219781 -0.255174908 2.1266646020
52.1 -4.3163827 0.576777386 -0.087908117 3.1000545993
52.2 -6.9761408 0.576087890 -0.079790252 3.1268477370
52.3 -20.1764756 0.550327680 0.077234978 3.5711459327
52.4 -8.9036692 0.384798259 0.658590390 4.7983659909
52.5 -5.6949642 0.354489561 0.754201126 4.9818264414
53 -10.3141887 0.146083609 -0.098200867 0.4965799209
53.1 -8.2642654 0.552068382 0.069097199 3.5505357443
53.2 -9.1691554 0.420309379 0.545419383 4.5790420019
54 -6.2198754 0.385017056 -0.233686763 1.4034724841
54.1 -15.7192609 0.481733587 -0.259453369 1.8812377600
54.2 -13.0978998 0.562075210 -0.219734896 2.5107589352
54.3 -5.1195299 0.576389911 -0.170117657 2.7848406672
54.4 -16.5771751 0.502454376 0.268573134 4.0143877396
55 -5.7348534 0.179124841 -0.119600382 0.6118522980
55.1 -7.3217494 0.216957704 -0.143437680 0.7463747414
55.2 -12.2171938 0.577251925 -0.162196967 2.8201208171
55.3 -12.9821266 0.575924728 -0.078012404 3.1326431572
55.4 -14.8599983 0.572799327 -0.049696750 3.2218102901
56 -14.1764282 0.342273574 -0.214205396 1.2231332215
56.1 -12.5343602 0.548240852 -0.238407170 2.3573202139
56.2 -8.4573382 0.566156836 -0.211213776 2.5674936292
56.3 -12.4633969 0.578604420 -0.130001435 2.9507164378
56.4 -17.3841863 0.572570991 -0.047904355 3.2272730360
56.5 -14.8147645 0.562101901 0.018463687 3.4175522043
57 -3.1403293 0.070163735 -0.047633296 0.2370331448
57.1 -11.1509248 0.073445459 -0.049847473 0.2481445030
57.2 -6.3940143 0.321748692 -0.203793986 1.1405586067
57.3 -9.3473241 0.518640043 -0.255666306 2.1153886721
58 -12.0245677 0.341752954 -0.213948851 1.2210099772
58.1 -9.2112246 0.434834548 -0.251198975 1.6334245703
58.2 -1.2071742 0.444054861 -0.253598973 1.6791862890
58.3 -11.0141711 0.570095212 -0.200423540 2.6320121693
58.4 -5.3721214 0.577777513 -0.155754069 2.8477731440
58.5 -7.8523047 0.550292474 0.077398043 3.5715569824
59 -13.2946560 0.485379090 -0.259595974 1.9023998594
59.1 -10.0530648 0.355842606 0.749939603 4.9736620474
60 -19.2209402 0.578284680 -0.146648540 2.8854503250
61 -4.6699914 0.209989836 -0.139109445 0.7213630795
61.1 -3.5981894 0.544136714 -0.242135262 2.3186947661
61.2 -1.4713611 0.561841394 -0.220164588 2.5077313243
61.3 -3.8819786 0.574648353 -0.065384740 3.1731073430
61.4 0.1041413 0.547608246 0.089666727 3.6022726283
62 -2.8591600 0.156772506 -0.105174496 0.5336771999
62.1 -6.9461986 0.203667658 -0.135156684 0.6987666548
62.2 -16.7910593 0.559244073 0.033674070 3.4584309917
62.3 -17.9844596 0.384057190 0.660934842 4.8028772371
63 -24.0335535 0.577020590 -0.164563724 2.8097350930
63.1 -11.7765300 0.508623090 0.246046779 3.9653754211
64 -20.5963897 0.488658680 0.317672007 4.1191305732
65 -2.7969169 0.206146395 -0.136709276 0.7076152589
65.1 -11.1778694 0.505339955 -0.258550257 2.0252246363
65.2 -5.2830399 0.576464317 -0.084086034 3.1127382827
65.3 -7.9353390 0.573786905 -0.057783902 3.1969087943
66 -13.2318328 0.556565380 0.047299962 3.4943454154
66.1 -1.9090560 0.531416264 0.158469029 3.7677437009
66.2 -16.6643889 0.510688279 0.238412563 3.9486138616
67 -25.6073277 0.481284305 0.343318093 4.1728388879
68 -13.4806759 0.038221364 -0.026000285 0.1291919907
68.1 -18.4557183 0.463670010 -0.257542029 1.7809643946
68.2 -13.3982327 0.509009225 -0.257959238 2.0493205660
68.3 -12.4977127 0.578600681 -0.132630584 2.9406870750
68.4 -11.7073990 0.499069523 0.280772218 4.0406670363
69 -14.5290675 0.485114430 0.330044236 4.1451198701
70 -15.2122709 0.058991523 -0.040082335 0.1992557163
70.1 -7.8681167 0.142152150 -0.095624828 0.4829774413
71 -10.3352703 0.224660082 -0.148186027 0.7741605386
71.1 -7.5699888 0.404061694 -0.241169325 1.4883817220
71.2 -18.4680702 0.494456145 0.297232965 4.0758526395
71.3 -21.4316644 0.400083109 0.610104851 4.7048238723
71.4 -8.1137650 0.396918394 0.620166727 4.7242791823
72 -9.1848162 0.267608644 -0.173868105 0.9321196121
72.1 -23.7538846 0.331622374 -0.208876763 1.1799991806
72.2 -26.3421306 0.483553052 -0.259535840 1.8917567329
72.3 -27.2843801 0.557250123 0.043867968 3.4853593935
72.4 -20.8541617 0.539531742 0.124935350 3.6884259700
72.5 -12.8948965 0.493186596 0.301731319 4.0854155901
73 -2.6091307 0.416653462 0.557166987 4.6019889915
74 -8.2790175 0.398374343 -0.239025266 1.4626806753
75 -12.5029612 0.571465811 -0.039566472 3.2524286874
76 -6.0061671 0.468571460 -0.258242539 1.8074807397
76.1 -8.8149114 0.467692099 0.389714552 4.2685073183
76.2 -11.8359043 0.356636147 0.747440214 4.9688734859
77 0.4772521 0.244350694 -0.160138241 0.8459033852
78 -9.4105229 0.238126409 -0.156390504 0.8231094317
79 -1.0217265 0.017185170 -0.011698166 0.0583819521
79.1 -11.8125257 0.556250717 -0.229046871 2.4406372628
79.2 -10.5465186 0.569332668 -0.024715909 3.2962526032
80 -12.7366807 0.258596092 -0.168601439 0.8985060186
80.1 -9.0584783 0.371128894 -0.227727295 1.3434670598
80.2 -16.6381566 0.576850604 -0.166175033 2.8025900386
81 0.5547913 0.002838132 -0.001932272 0.0101324962
81.1 -4.0892715 0.270289449 -0.175420790 0.9421709494
81.2 1.8283303 0.577705879 -0.101395894 3.0542453879
81.3 -5.2166381 0.566618135 -0.007488169 3.3456630446
82 -3.0749381 0.379418082 -0.231331634 1.3791010005
82.1 -10.5506696 0.459751865 -0.256894448 1.7601010622
82.2 -18.2226347 0.569608491 -0.201947653 2.6233131927
83 -12.5872635 0.015804994 -0.010758945 0.0537394290
83.1 -11.9756502 0.578462150 -0.141485904 2.9061570496
83.2 -10.6744217 0.576302350 -0.082201802 3.1189457362
83.3 -19.2714012 0.390045811 0.641974120 4.7663642222
84 -2.6320312 0.574443229 -0.182687721 2.7254060237
84.1 -9.8140094 0.567147130 -0.010728700 3.3364784659
85 -12.3886736 0.088076477 -0.059694578 0.2977756259
85.1 -12.9196365 0.455813463 -0.256169485 1.7394116637
85.2 -9.6433248 0.572741560 -0.190738107 2.6846330194
85.3 -6.3296340 0.575059310 -0.069241031 3.1608762743
85.4 -7.0405525 0.511105405 0.236864567 3.9452053758
85.5 -13.6714939 0.431317668 0.509864701 4.5092553482
86 -10.8756412 0.244820346 -0.160419833 0.8476278360
86.1 -12.0055331 0.288690155 -0.185894081 1.0118629411
86.2 -13.3724699 0.349098660 -0.217529698 1.2511159515
86.3 -13.3252145 0.528356038 -0.252035671 2.1870554925
86.4 -14.9191290 0.557364765 -0.227464370 2.4532935000
86.5 -17.7515546 0.525665875 0.181352479 3.8206058508
87 -10.7027963 0.573710436 -0.186389542 2.7069531474
87.1 -22.4941954 0.560117024 0.029108727 3.4462517721
87.2 -14.9616716 0.428980289 0.517438583 4.5241666853
88 -2.2264493 0.000000000 0.000000000 0.0005892443
88.1 -8.9626474 0.207264162 -0.137408217 0.7116099866
88.2 -2.5095281 0.560862306 -0.221906390 2.4952722900
88.3 -16.3345673 0.569159986 -0.023571162 3.2995816297
89 -11.0459647 0.188867523 -0.125814019 0.6462086167
90 -4.5610239 0.050208031 -0.034133323 0.1696030737
90.1 -11.7036651 0.568115713 -0.206252945 2.5980385230
90.2 -5.3838521 0.571820822 -0.194419415 2.6651392167
90.3 -4.1636999 0.576158893 -0.080578820 3.1242690247
91 -7.1462503 0.186618632 -0.124384029 0.6382618390
91.1 -12.8374475 0.569556927 -0.202105359 2.6224059286
91.2 -18.2576707 0.388262274 0.647624750 4.7772527603
92 -6.4119222 0.021739973 -0.014797168 0.0737052364
93 5.2122168 0.082514929 -0.055956459 0.2788909199
93.1 3.1211725 0.294925898 -0.189365721 1.0357759963
93.2 -3.6841177 0.560572971 -0.222404132 2.4916551099
93.3 2.6223542 0.578306553 -0.146114569 2.8876129608
93.4 -11.1877696 0.438366385 0.486935041 4.4639474002
94 -6.9602492 0.245140656 -0.160611785 0.8488043118
94.1 -7.4318416 0.299972348 -0.192144469 1.0552454425
94.2 -4.3498045 0.492461920 -0.259601249 1.9445500884
94.3 -11.6340088 0.577401892 -0.096499489 3.0710722448
94.4 -12.9357964 0.577068448 -0.091721448 3.0872731935
94.5 -14.7648530 0.451106514 0.445102199 4.3805759016
95 -12.8849309 0.504518977 -0.258663317 2.0199063048
95.1 -9.7451502 0.501935320 0.270450881 4.0184444457
95.2 -0.8535063 0.423384987 0.535514358 4.5596531732
96 -4.9139832 0.009083415 -0.006183955 0.0311333477
96.1 -3.9582653 0.039181462 -0.026652251 0.1324267720
96.2 -9.6555492 0.195619870 -0.130091480 0.6701303425
96.3 -11.8690793 0.527105916 -0.252589672 2.1775037691
96.4 -11.0224373 0.533135775 -0.249644636 2.2246142488
96.5 -10.9530403 0.472120477 0.374710585 4.2377650598
97 -9.8540471 0.335470540 -0.210821063 1.1955102731
97.1 -19.2262840 0.358054995 0.742971132 4.9603108643
98 -11.9651231 0.060447025 -0.041067134 0.2041732438
98.1 -2.6515128 0.127061111 -0.085685639 0.4309578973
98.2 -12.2606382 0.554775889 0.056119803 3.5172611906
99 -11.4720500 0.104348118 -0.070591602 0.3531786101
99.1 -14.0596866 0.404279323 0.596741645 4.6789444226
99.2 -17.3939469 0.352686062 0.759881258 4.9927084171
100 1.1005874 0.303556402 -0.194100667 1.0691387602
100.1 -3.8226248 0.408995749 -0.242962199 1.5109344281
100.2 -0.9123182 0.523460505 -0.254052065 2.1502332564
100.3 -15.8389474 0.519529301 0.205126241 3.8745574222
100.4 -12.8093826 0.407863056 0.585307533 4.6567608765
$m7a$spM_lvlone
center scale
y -11.173370994 6.2496619
ns(time, df = 2)1 0.430938966 0.1552899
ns(time, df = 2)2 -0.008514259 0.2716805
time 2.533940277 1.3818094
$m7a$mu_reg_norm
[1] 0
$m7a$tau_reg_norm
[1] 1e-04
$m7a$shape_tau_norm
[1] 0.01
$m7a$rate_tau_norm
[1] 0.01
$m7a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m7a$shape_diag_RinvD
[1] "0.01"
$m7a$rate_diag_RinvD
[1] "0.001"
$m7a$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m7a$KinvD_y_id
id
4
$m7b
$m7b$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m7b$M_lvlone
y bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3
1 -13.0493856 2.464812e-01 2.795125e-02 1.056568e-03
1.1 -9.3335901 3.005702e-01 4.627383e-02 2.374673e-03
1.2 -22.3469852 4.207566e-01 3.131043e-01 7.766508e-02
1.3 -15.0417337 3.751809e-01 3.748189e-01 1.248191e-01
2 -12.0655434 2.840211e-01 4.334471e-01 2.204958e-01
2.1 -15.8674476 2.280284e-01 4.443439e-01 2.886212e-01
2.2 -7.8800006 6.734283e-02 3.436638e-01 5.845947e-01
3 -11.4820604 3.510022e-01 7.175155e-02 4.889129e-03
3.1 -10.5983220 2.745130e-01 4.365427e-01 2.314032e-01
3.2 -22.4519157 7.402499e-03 1.377702e-01 8.546947e-01
4 -1.2697775 1.759000e-01 1.271593e-02 3.064145e-04
4.1 -11.1215184 3.966925e-01 1.080567e-01 9.811334e-03
4.2 -3.6134138 3.564330e-01 3.918838e-01 1.436202e-01
4.3 -14.5982385 2.611080e-01 4.400451e-01 2.472026e-01
5 -6.8457515 3.978591e-01 1.092729e-01 1.000401e-02
5.1 -7.0551214 4.425632e-01 2.475384e-01 4.615178e-02
5.2 -12.3418980 3.252782e-01 4.139339e-01 1.755844e-01
5.3 -9.2366906 3.223943e-01 4.156349e-01 1.786139e-01
6 -5.1648211 4.428374e-01 2.456041e-01 4.540519e-02
7 -10.0599502 2.264807e-01 4.443903e-01 2.906542e-01
7.1 -18.3267285 1.489135e-01 4.265274e-01 4.072291e-01
7.2 -12.5138426 5.621107e-03 1.213303e-01 8.729618e-01
8 -1.6305331 4.055465e-01 1.178507e-01 1.141571e-02
8.1 -9.6520453 4.422130e-01 2.498167e-01 4.704249e-02
8.2 -1.5278462 4.417145e-01 2.527749e-01 4.821755e-02
8.3 -7.4172211 3.175842e-01 4.183540e-01 1.836994e-01
8.4 -7.1238609 2.153406e-01 4.443014e-01 3.055682e-01
8.5 -8.8706950 3.313840e-02 2.640659e-01 7.014095e-01
9 -0.1634429 3.764000e-01 8.963850e-02 7.115710e-03
9.1 -2.6034300 3.028578e-01 4.257933e-01 1.995435e-01
9.2 -6.7272369 3.867756e-03 1.018621e-01 8.942212e-01
10 -6.4172202 4.023356e-01 3.428926e-01 9.741064e-02
10.1 -11.4834569 6.754273e-02 3.440071e-01 5.840297e-01
11 -8.7911356 4.138618e-01 1.284873e-01 1.329669e-02
11.1 -19.6645080 4.202663e-01 1.379992e-01 1.510454e-02
11.2 -20.2030932 4.443906e-01 2.179963e-01 3.564611e-02
11.3 -21.3082176 2.107621e-01 4.440456e-01 3.118469e-01
11.4 -14.5802901 3.796619e-03 1.009751e-01 8.951806e-01
12 -15.2006287 3.760749e-01 8.937848e-02 7.080604e-03
13 0.8058816 3.594997e-02 4.470732e-04 1.853264e-06
13.1 -13.6379208 1.757678e-01 4.375779e-01 3.631200e-01
14 -15.3422873 8.899359e-02 3.755915e-01 5.283862e-01
14.1 -10.0965208 2.933469e-02 2.513931e-01 7.181312e-01
14.2 -16.6452027 1.422882e-02 1.848582e-01 8.005479e-01
14.3 -15.8389733 1.045369e-02 1.611585e-01 8.281618e-01
15 -8.9424594 2.579648e-01 3.126382e-02 1.262997e-03
15.1 -22.0101983 4.146589e-01 1.295994e-01 1.350186e-02
15.2 -7.3975599 4.414565e-01 1.912323e-01 2.761298e-02
15.3 -10.3567334 6.383397e-04 4.279986e-02 9.565586e-01
16 -1.9691302 4.210987e-01 1.393442e-01 1.536996e-02
16.1 -9.9308357 3.640505e-01 3.853440e-01 1.359610e-01
16.2 -6.9626923 3.502297e-01 3.968497e-01 1.498918e-01
16.3 -3.2862557 1.764758e-01 4.377929e-01 3.620187e-01
16.4 -3.3972355 1.375222e-01 4.199669e-01 4.274999e-01
16.5 -11.5767835 9.753912e-02 3.857404e-01 5.084991e-01
17 -10.5474144 4.438097e-01 2.077898e-01 3.242877e-02
17.1 -7.6215009 3.836846e-01 3.658929e-01 1.163087e-01
17.2 -16.5386939 2.822509e-01 4.340620e-01 2.225087e-01
17.3 -20.0004774 2.213728e-01 4.444423e-01 2.974303e-01
17.4 -18.8505475 1.177221e-01 4.054379e-01 4.654462e-01
18 -19.7302351 3.836809e-01 3.658970e-01 1.163125e-01
19 -14.6177568 3.797281e-01 9.235553e-02 7.487412e-03
19.1 -17.8043866 4.087929e-01 1.218111e-01 1.209900e-02
19.2 -15.1641705 4.068733e-01 3.363802e-01 9.270014e-02
19.3 -16.6898418 6.198011e-02 3.340502e-01 6.001364e-01
20 -12.9059229 4.441175e-01 2.327127e-01 4.064630e-02
20.1 -16.8191201 4.434516e-01 2.405648e-01 4.350076e-02
20.2 -6.1010131 4.078512e-01 3.349176e-01 9.167540e-02
20.3 -7.9415371 4.067556e-01 3.365548e-01 9.282325e-02
20.4 -9.3904458 1.897411e-01 4.411553e-01 3.419010e-01
20.5 -13.3504189 6.758944e-02 3.440872e-01 5.838978e-01
21 -7.6974718 4.443436e-01 2.280367e-01 3.900939e-02
21.1 -11.9335526 2.954126e-01 4.290631e-01 2.077265e-01
21.2 -12.7064929 1.324112e-01 4.166163e-01 4.369447e-01
22 -21.5022909 3.790159e-01 3.708962e-01 1.209835e-01
22.1 -12.7745451 2.553655e-01 4.412374e-01 2.541330e-01
23 -3.5146508 4.425730e-01 1.975933e-01 2.940615e-02
23.1 -4.6724048 3.973563e-01 3.495745e-01 1.025128e-01
24 -2.5619821 3.194306e-01 4.173276e-01 1.817425e-01
25 -6.2944970 2.572266e-01 3.104254e-02 1.248755e-03
25.1 -3.8630505 4.440397e-01 2.106776e-01 3.331912e-02
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100 1.1005874 3.966649e-01 1.080282e-01 9.806841e-03
100.1 -3.8226248 4.415133e-01 1.915223e-01 2.769324e-02
100.2 -0.9123182 4.188189e-01 3.167351e-01 7.984446e-02
100.3 -15.8389474 1.167948e-01 4.046495e-01 4.673189e-01
100.4 -12.8093826 1.267180e-02 1.756288e-01 8.113946e-01
time
1 0.5090421822
1.1 0.6666076288
1.2 2.1304941282
1.3 2.4954441458
2 3.0164990982
2.1 3.2996806887
2.2 4.1747569619
3 0.8478727890
3.1 3.0654308549
3.2 4.7381553578
4 0.3371432109
4.1 1.0693019140
4.2 2.6148973033
4.3 3.1336532847
5 1.0762525082
5.1 1.7912546196
5.2 2.7960080339
5.3 2.8119940578
6 1.7815462884
7 3.3074087673
7.1 3.7008403614
7.2 4.7716691741
8 1.1246398522
8.1 1.8027009873
8.2 1.8175825174
8.3 2.8384267003
8.4 3.3630275307
8.5 4.4360849704
9 0.9607803822
9.1 2.9177753383
9.2 4.8100892501
10 2.2975509102
10.1 4.1734118364
11 1.1832662905
11.1 1.2346051680
11.2 1.6435316263
11.3 3.3859017969
11.4 4.8118087661
12 0.9591987054
13 0.0619085738
13.1 3.5621061502
14 4.0364430007
14.1 4.4710561272
14.2 4.6359198843
14.3 4.6886152599
15 0.5402063532
15.1 1.1893180816
15.2 1.5094739688
15.3 4.9193474615
16 1.2417913869
16.1 2.5675726333
16.2 2.6524101500
16.3 3.5585018690
16.4 3.7612454291
16.5 3.9851612889
17 1.5925356350
17.1 2.4374032998
17.2 3.0256489082
17.3 3.3329089405
17.4 3.8693758985
18 2.4374292302
19 0.9772165376
19.1 1.1466335913
19.2 2.2599126538
19.3 4.2114245973
20 1.7170160066
20.1 1.7562902288
20.2 2.2515566566
20.3 2.2609123867
20.4 3.4913365287
20.5 4.1730977828
21 1.6936582839
21.1 2.9571191233
21.2 3.7887385779
22 2.4696226232
22.1 3.1626627257
23 1.5414533857
23.1 2.3369736120
24 2.8283136466
25 0.5381704110
25.1 1.6069735331
25.2 1.6358226922
25.3 3.2646870392
25.4 4.0782226040
25.5 4.1560292873
26 0.2412706357
26.1 2.4451737676
26.2 3.5988757887
26.3 4.1822362854
27 3.6955824879
27.1 4.2451434687
28 0.5746519344
28.1 2.7943964268
28.2 4.2108539480
28.3 4.4705521734
29 1.1898884235
29.1 1.7624059319
29.2 2.0210406382
29.3 3.4078777023
30 2.2635366488
30.1 3.5938334477
30.2 3.6138710892
31 4.3988140998
32 1.6745209007
32.1 2.9128167813
32.2 2.9676558380
32.3 4.2099863547
33 0.0093385763
33.1 3.4591242753
34 1.4998774312
34.1 3.8242761395
34.2 3.9072251692
34.3 3.9582124643
35 1.3294299203
35.1 1.5276966314
35.2 4.5025920868
36 0.7123168337
36.1 1.7972493160
36.2 1.8262697803
36.3 4.2840119381
36.4 4.6194464504
37 2.0018732361
37.1 3.6656836793
37.2 3.9663937816
38 0.9826511063
39 0.6921808305
39.1 0.9027792048
39.2 1.3055654289
39.3 1.5412842878
39.4 3.1834997435
39.5 4.1394166439
40 1.1330395646
40.1 2.6940994046
40.2 3.0396614212
40.3 4.6762977762
41 1.9337158254
41.1 3.1956304458
41.2 3.2846923557
41.3 3.3813529415
41.4 3.5482964432
42 0.4859252973
42.1 4.3293134298
43 0.5616614548
43.1 1.0743579536
43.2 2.6131797966
44 0.7662644819
44.1 2.6490291790
44.2 3.3371910988
44.3 4.1154200875
45 0.1957449992
45.1 1.9963831536
46 1.3477755385
46.1 2.8565793915
46.2 4.4160729996
47 0.6012621359
47.1 2.4097121472
47.2 2.9975794035
47.3 3.1829649757
47.4 4.6201055450
48 2.8607365978
48.1 2.9098354396
49 2.7179756400
50 1.1762060679
51 1.4304436720
52 2.1266646020
52.1 3.1000545993
52.2 3.1268477370
52.3 3.5711459327
52.4 4.7983659909
52.5 4.9818264414
53 0.4965799209
53.1 3.5505357443
53.2 4.5790420019
54 1.4034724841
54.1 1.8812377600
54.2 2.5107589352
54.3 2.7848406672
54.4 4.0143877396
55 0.6118522980
55.1 0.7463747414
55.2 2.8201208171
55.3 3.1326431572
55.4 3.2218102901
56 1.2231332215
56.1 2.3573202139
56.2 2.5674936292
56.3 2.9507164378
56.4 3.2272730360
56.5 3.4175522043
57 0.2370331448
57.1 0.2481445030
57.2 1.1405586067
57.3 2.1153886721
58 1.2210099772
58.1 1.6334245703
58.2 1.6791862890
58.3 2.6320121693
58.4 2.8477731440
58.5 3.5715569824
59 1.9023998594
59.1 4.9736620474
60 2.8854503250
61 0.7213630795
61.1 2.3186947661
61.2 2.5077313243
61.3 3.1731073430
61.4 3.6022726283
62 0.5336771999
62.1 0.6987666548
62.2 3.4584309917
62.3 4.8028772371
63 2.8097350930
63.1 3.9653754211
64 4.1191305732
65 0.7076152589
65.1 2.0252246363
65.2 3.1127382827
65.3 3.1969087943
66 3.4943454154
66.1 3.7677437009
66.2 3.9486138616
67 4.1728388879
68 0.1291919907
68.1 1.7809643946
68.2 2.0493205660
68.3 2.9406870750
68.4 4.0406670363
69 4.1451198701
70 0.1992557163
70.1 0.4829774413
71 0.7741605386
71.1 1.4883817220
71.2 4.0758526395
71.3 4.7048238723
71.4 4.7242791823
72 0.9321196121
72.1 1.1799991806
72.2 1.8917567329
72.3 3.4853593935
72.4 3.6884259700
72.5 4.0854155901
73 4.6019889915
74 1.4626806753
75 3.2524286874
76 1.8074807397
76.1 4.2685073183
76.2 4.9688734859
77 0.8459033852
78 0.8231094317
79 0.0583819521
79.1 2.4406372628
79.2 3.2962526032
80 0.8985060186
80.1 1.3434670598
80.2 2.8025900386
81 0.0101324962
81.1 0.9421709494
81.2 3.0542453879
81.3 3.3456630446
82 1.3791010005
82.1 1.7601010622
82.2 2.6233131927
83 0.0537394290
83.1 2.9061570496
83.2 3.1189457362
83.3 4.7663642222
84 2.7254060237
84.1 3.3364784659
85 0.2977756259
85.1 1.7394116637
85.2 2.6846330194
85.3 3.1608762743
85.4 3.9452053758
85.5 4.5092553482
86 0.8476278360
86.1 1.0118629411
86.2 1.2511159515
86.3 2.1870554925
86.4 2.4532935000
86.5 3.8206058508
87 2.7069531474
87.1 3.4462517721
87.2 4.5241666853
88 0.0005892443
88.1 0.7116099866
88.2 2.4952722900
88.3 3.2995816297
89 0.6462086167
90 0.1696030737
90.1 2.5980385230
90.2 2.6651392167
90.3 3.1242690247
91 0.6382618390
91.1 2.6224059286
91.2 4.7772527603
92 0.0737052364
93 0.2788909199
93.1 1.0357759963
93.2 2.4916551099
93.3 2.8876129608
93.4 4.4639474002
94 0.8488043118
94.1 1.0552454425
94.2 1.9445500884
94.3 3.0710722448
94.4 3.0872731935
94.5 4.3805759016
95 2.0199063048
95.1 4.0184444457
95.2 4.5596531732
96 0.0311333477
96.1 0.1324267720
96.2 0.6701303425
96.3 2.1775037691
96.4 2.2246142488
96.5 4.2377650598
97 1.1955102731
97.1 4.9603108643
98 0.2041732438
98.1 0.4309578973
98.2 3.5172611906
99 0.3531786101
99.1 4.6789444226
99.2 4.9927084171
100 1.0691387602
100.1 1.5109344281
100.2 2.1502332564
100.3 3.8745574222
100.4 4.6567608765
$m7b$spM_lvlone
center scale
y -11.1733710 6.2496619
bs(time, df = 3)1 0.2549546 0.1475335
bs(time, df = 3)2 0.2657250 0.1531363
bs(time, df = 3)3 0.2453352 0.2691884
time 2.5339403 1.3818094
$m7b$mu_reg_norm
[1] 0
$m7b$tau_reg_norm
[1] 1e-04
$m7b$shape_tau_norm
[1] 0.01
$m7b$rate_tau_norm
[1] 0.01
$m7b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m7b$shape_diag_RinvD
[1] "0.01"
$m7b$rate_diag_RinvD
[1] "0.001"
$m7b$RinvD_y_id
[,1] [,2] [,3] [,4]
[1,] NA 0 0 0
[2,] 0 NA 0 0
[3,] 0 0 NA 0
[4,] 0 0 0 NA
$m7b$KinvD_y_id
id
5
$m7c
$m7c$M_id
(Intercept) C1
1 1 0.7175865
2 1 0.7507170
3 1 0.7255954
4 1 0.7469352
5 1 0.7139120
6 1 0.7332505
7 1 0.7345929
8 1 0.7652589
9 1 0.7200622
10 1 0.7423879
11 1 0.7437448
12 1 0.7446470
13 1 0.7530186
14 1 0.7093137
15 1 0.7331192
16 1 0.7011390
17 1 0.7432395
18 1 0.7545191
19 1 0.7528487
20 1 0.7612865
21 1 0.7251719
22 1 0.7300630
23 1 0.7087249
24 1 0.7391938
25 1 0.7820641
26 1 0.7118298
27 1 0.7230857
28 1 0.7489353
29 1 0.7510888
30 1 0.7300717
31 1 0.7550721
32 1 0.7321898
33 1 0.7306414
34 1 0.7427216
35 1 0.7193042
36 1 0.7312888
37 1 0.7100436
38 1 0.7670184
39 1 0.7400449
40 1 0.7397304
41 1 0.7490966
42 1 0.7419274
43 1 0.7527810
44 1 0.7408315
45 1 0.7347550
46 1 0.7332398
47 1 0.7376481
48 1 0.7346179
49 1 0.7329402
50 1 0.7260436
51 1 0.7242910
52 1 0.7298067
53 1 0.7254741
54 1 0.7542067
55 1 0.7389952
56 1 0.7520638
57 1 0.7219958
58 1 0.7259632
59 1 0.7458606
60 1 0.7672421
61 1 0.7257179
62 1 0.7189892
63 1 0.7333356
64 1 0.7320243
65 1 0.7477711
66 1 0.7343974
67 1 0.7491624
68 1 0.7482736
69 1 0.7338267
70 1 0.7607742
71 1 0.7777600
72 1 0.7408143
73 1 0.7248271
74 1 0.7364916
75 1 0.7464926
76 1 0.7355430
77 1 0.7208449
78 1 0.7373573
79 1 0.7598079
80 1 0.7360415
81 1 0.7293932
82 1 0.7279309
83 1 0.7344643
84 1 0.7384350
85 1 0.7323716
86 1 0.7576597
87 1 0.7496139
88 1 0.7275239
89 1 0.7250648
90 1 0.7335262
91 1 0.7343980
92 1 0.7380425
93 1 0.7389460
94 1 0.7259951
95 1 0.7282840
96 1 0.7281676
97 1 0.7245642
98 1 0.7526938
99 1 0.7272309
100 1 0.7383460
$m7c$M_lvlone
y c1 ns(time, df = 3)1 ns(time, df = 3)2
1 -13.0493856 0.7592026489 -0.0731022196 0.222983368
1.1 -9.3335901 0.9548337990 -0.0896372079 0.286659651
1.2 -22.3469852 0.5612235156 0.1374616725 0.538466292
1.3 -15.0417337 1.1873391025 0.3061500570 0.485312041
2 -12.0655434 0.9192204198 0.5064248381 0.388851338
2.1 -15.8674476 -0.1870730476 0.5543647993 0.348347565
2.2 -7.8800006 1.2517512331 0.3402753582 0.338334366
3 -11.4820604 -0.0605087604 -0.1024946971 0.354448579
3.1 -10.5983220 0.3788637747 0.5187768948 0.380711276
3.2 -22.4519157 0.9872578281 0.0174998856 0.391617016
4 -1.2697775 1.4930175328 -0.0508146200 0.149748191
4.1 -11.1215184 -0.7692526880 -0.1067711172 0.427124949
4.2 -3.6134138 0.9180841450 0.3598480506 0.463409709
4.3 -14.5982385 -0.0541170782 0.5333652385 0.370048079
5 -6.8457515 -0.1376784521 -0.1066695938 0.429197233
5.1 -7.0551214 -0.2740585866 0.0046159078 0.552972700
5.2 -12.3418980 0.4670496929 0.4342731827 0.428967354
5.3 -9.2366906 0.1740288049 0.4402846745 0.425938437
6 -5.1648211 0.9868044683 0.0015253168 0.552692434
7 -10.0599502 -0.1280320918 0.5547950298 0.347509550
7.1 -18.3267285 0.4242971219 0.5158768825 0.324489976
7.2 -12.5138426 0.0777182491 -0.0038356900 0.395476156
8 -1.6305331 -0.5791408712 -0.1055283475 0.443238494
8.1 -9.6520453 0.3128604232 0.0083228157 0.553246856
8.2 -1.5278462 0.6258446356 0.0132420073 0.553513388
8.3 -7.4172211 -0.1040137707 0.4499876083 0.420954925
8.4 -7.1238609 0.0481450285 0.5564354493 0.341948600
8.5 -8.8706950 0.3831763675 0.2011700353 0.359621900
9 -0.1634429 -0.1757592269 -0.1064248362 0.393057590
9.1 -2.6034300 -0.1791541200 0.4771840649 0.406264856
9.2 -6.7272369 -0.0957042935 -0.0284447928 0.399948734
10 -6.4172202 -0.5598409704 0.2139341504 0.517577740
10.1 -11.4834569 -0.2318340451 0.3409274088 0.338245451
11 -8.7911356 0.5086859475 -0.1030921083 0.459317031
11.1 -19.6645080 0.4951758188 -0.0999663372 0.472517168
11.2 -20.2030932 -1.1022162541 -0.0371358422 0.544030949
11.3 -21.3082176 -0.0611636705 0.5563809266 0.339897056
11.4 -14.5802901 -0.4971774316 -0.0295495187 0.400149982
12 -15.2006287 -0.2433996286 -0.1063939605 0.392538168
13 0.8058816 0.8799673116 -0.0095916688 0.027579300
13.1 -13.6379208 0.1079022586 0.5424520669 0.328456601
14 -15.3422873 0.9991752617 0.4032689358 0.330500975
14.1 -10.0965208 -0.1094019046 0.1809335363 0.362994245
14.2 -16.6452027 0.1518967560 0.0816318440 0.380152387
14.3 -15.8389733 0.3521012473 0.0487734088 0.385997930
15 -8.9424594 0.3464447888 -0.0767078114 0.235874789
15.1 -22.0101983 -0.4767313971 -0.1027727004 0.460916543
15.2 -7.3975599 0.5759767791 -0.0657566366 0.527699447
15.3 -10.3567334 -0.1713452662 -0.0990627407 0.412872874
16 -1.9691302 0.4564754473 -0.0994524289 0.474297164
16.1 -9.9308357 1.0652558311 0.3388926675 0.472228980
16.2 -6.9626923 0.6971872493 0.3760850293 0.456327895
16.3 -3.2862557 0.5259331838 0.5429665219 0.328616483
16.4 -3.3972355 0.2046601798 0.5003814501 0.324030412
16.5 -11.5767835 1.0718540464 0.4243928946 0.328317423
17 -10.5474144 0.6048676222 -0.0490208458 0.538702746
17.1 -7.6215009 0.2323298304 0.2793033691 0.495414246
17.2 -16.5386939 1.2617499032 0.5088484549 0.387301236
17.3 -20.0004774 -0.3913230895 0.5558624020 0.344858135
17.4 -18.8505475 0.9577299112 0.4671596304 0.324978786
18 -19.7302351 -0.0050324072 0.2793154325 0.495409834
19 -14.6177568 -0.4187468937 -0.1067031159 0.398417477
19.1 -17.8043866 -0.4478828944 -0.1047524970 0.449392800
19.2 -15.1641705 -1.1966721302 0.1964187590 0.522886360
19.3 -16.6898418 -0.5877091668 0.3222225327 0.340847993
20 -12.9059229 0.6838223064 -0.0177612481 0.549715454
20.1 -16.8191201 0.3278571109 -0.0062817988 0.551756621
20.2 -6.1010131 -0.8489831990 0.1925456183 0.524021244
20.3 -7.9415371 1.3169975191 0.1968825793 0.522749488
20.4 -9.3904458 0.0444804531 0.5508408484 0.332148944
20.5 -13.3504189 -0.4535207652 0.3410795409 0.338224725
21 -7.6974718 -0.4030302960 -0.0242133322 0.548168935
21.1 -11.9335526 -0.4069674045 0.4894906279 0.399187414
21.2 -12.7064929 1.0650265940 0.4925855178 0.324061287
22 -21.5022909 -0.0673274516 0.2942488077 0.489859980
22.1 -12.7745451 0.9601388170 0.5385758261 0.365790145
23 -3.5146508 0.5556634840 -0.0596896592 0.532269527
23.1 -4.6724048 1.4407865964 0.2323619738 0.511693737
24 -2.5619821 0.3856376411 0.4463109938 0.422857517
25 -6.2944970 0.3564400705 -0.0764769279 0.235036737
25.1 -3.8630505 0.0982553434 -0.0457828435 0.540323722
25.2 -14.4205140 0.1928682598 -0.0390131484 0.543297023
25.3 -19.6735037 -0.0192488594 0.5517873130 0.352345378
25.4 -9.0288933 0.4466012931 0.3851356619 0.332578168
25.5 -9.0509738 1.1425193342 0.3492871599 0.337117898
26 -19.7340685 0.5341531449 -0.0370005446 0.107676329
26.1 -14.1692728 1.2268695927 0.2829160492 0.494087736
26.2 -17.2819976 0.3678294939 0.5366821080 0.326994020
26.3 -24.6265576 0.5948516018 0.3366364198 0.338833066
27 -7.3354999 -0.3342844147 0.5171168233 0.324565129
27.1 -11.1488468 -0.4835141229 0.3051619029 0.343307911
28 -11.7996597 -0.7145915499 -0.0805110543 0.249962259
28.1 -8.2030122 0.5063671955 0.4336613409 0.429273232
28.2 -26.4317815 -0.2067413142 0.3225075315 0.340807567
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55 -5.7348534 -0.0116453589 -0.0843902223 0.264974033
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56 -14.1764282 0.5176377612 -0.1007474372 0.469640780
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57 -3.1403293 -1.0868304872 -0.0363728373 0.105801595
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57.3 -9.3473241 0.6785562445 0.1307695676 0.539981816
58 -12.0245677 1.6476207996 -0.1008867350 0.469103743
58.1 -9.2112246 0.3402652711 -0.0395912379 0.543063473
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58.5 -7.8523047 0.8713264822 0.5410594734 0.328051465
59 -13.2946560 0.4766421367 0.0432887218 0.553170063
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60 -19.2209402 0.5231452932 0.4664716194 0.412191135
61 -4.6699914 -0.7190130614 -0.0942894088 0.307818016
61.1 -3.5981894 0.8353702312 0.2238107295 0.514461229
61.2 -1.4713611 1.0229058138 0.3117843207 0.483120456
61.3 -3.8819786 1.1717723589 0.5403000211 0.364301147
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62 -2.8591600 -0.3979137604 -0.0759650358 0.233185122
62.1 -6.9461986 0.6830738372 -0.0924447607 0.299153085
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62.3 -17.9844596 -0.0333139957 -0.0238142803 0.399105605
63 -24.0335535 0.3345678035 0.4394416153 0.426365839
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64 -20.5963897 0.3949911859 0.3666147194 0.334859412
65 -2.7969169 1.2000091513 -0.0931799515 0.302557595
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66 -13.2318328 -0.5081302805 0.5505587192 0.331967886
66.1 -1.9090560 -0.1447837412 0.4985800130 0.324024401
66.2 -16.6643889 0.1906241379 0.4386508147 0.327018558
67 -25.6073277 1.6716027681 0.3412049232 0.338207650
68 -13.4806759 0.5691848839 -0.0200346649 0.057768712
68.1 -18.4557183 0.1004860389 0.0013416527 0.552674233
68.2 -13.3982327 -0.0061241827 0.1021380380 0.545772611
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69 -14.5290675 0.0381382683 0.3544700410 0.336430831
70 -15.2122709 0.8126204217 -0.0307235467 0.089041044
70.1 -7.8681167 0.4691503050 -0.0699732793 0.212100911
71 -10.3352703 -0.5529062591 -0.0981662227 0.327679863
71.1 -7.5699888 -0.1103252087 -0.0695059901 0.524461669
71.2 -18.4680702 1.7178492547 0.3861857626 0.332453400
71.3 -21.4316644 -1.0118346755 0.0385790226 0.387824204
71.4 -8.1137650 1.8623785017 0.0262933692 0.390032203
72 -9.1848162 -0.4521659275 -0.1057555291 0.383548046
72.1 -23.7538846 0.1375317317 -0.1032591763 0.458448763
72.2 -26.3421306 -0.4170988856 0.0393415444 0.553381188
72.3 -27.2843801 0.7107266765 0.5513813214 0.332515066
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72.5 -12.8948965 1.6298050306 0.3819329983 0.332961867
73 -2.6091307 -0.0307469467 0.1025345515 0.376470777
74 -8.2790175 0.3730017941 -0.0738083919 0.520280412
75 -12.5029612 -0.4908003566 0.5506409300 0.353824007
76 -6.0061671 -0.9888876620 0.0098906162 0.553343477
76.1 -8.8149114 0.0003798292 0.2930926278 0.345092469
76.2 -11.8359043 -0.8421863763 -0.1312549589 0.418789942
77 0.4772521 -0.4986802480 -0.1023963499 0.353748723
78 -9.4105229 0.0417330969 -0.1011875966 0.345586078
79 -1.0217265 -0.3767450660 -0.0090412342 0.025994217
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80 -12.7366807 -0.0041558414 -0.1046806598 0.372138169
80.1 -9.0584783 -0.0329337062 -0.0900832419 0.497621832
80.2 -16.6381566 0.5046816157 0.4367610654 0.427719060
81 0.5547913 -0.9493950353 -0.0014945092 0.004293764
81.1 -4.0892715 0.2443038954 -0.1060165656 0.386906369
81.2 1.8283303 0.6476958410 0.5160872572 0.382538581
81.3 -5.2166381 0.4182528210 0.5561952783 0.343596919
82 -3.0749381 1.1088801952 -0.0858371096 0.504943034
82.1 -10.5506696 0.9334157763 -0.0051252873 0.551916873
82.2 -18.2226347 0.4958140634 0.3635216183 0.461826800
83 -12.5872635 0.5104724530 -0.0083163054 0.023907290
83.1 -11.9756502 -0.0513309106 0.4733943484 0.408384331
83.2 -10.6744217 -0.2067792494 0.5304919537 0.372272641
83.3 -19.2714012 -0.0534169155 -0.0004497824 0.394862487
84 -2.6320312 -0.0255753653 0.4065375903 0.442422222
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85 -12.3886736 -0.0114038622 -0.0452387941 0.132558080
85.1 -12.9196365 -0.0577615939 -0.0113130186 0.550966098
85.2 -9.6433248 -0.2241856342 0.3897280300 0.450202261
85.3 -6.3296340 -0.0520175929 0.5382727768 0.366047208
85.4 -7.0405525 0.2892733846 0.4399457097 0.326908671
85.5 -13.6714939 -0.3740417009 0.1584688915 0.366794208
86 -10.8756412 0.4293735089 -0.1024825180 0.354361579
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86.3 -13.3252145 0.3305413955 0.1629165477 0.532204554
86.4 -14.9191290 2.6003411822 0.2866858473 0.492692574
86.5 -17.7515546 -0.1420690052 0.4829861649 0.324278938
87 -10.7027963 1.0457427869 0.3989962007 0.445945432
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87.2 -14.9616716 0.4396872616 0.1496033589 0.368308652
88 -2.2264493 -0.0601928334 0.0000000000 0.000000000
88.1 -8.9626474 -1.0124347595 -0.0935064654 0.304089792
88.2 -2.5095281 0.5730917016 0.3060711120 0.485342573
88.3 -16.3345673 -0.0029455332 0.5543589602 0.348358411
89 -11.0459647 1.5465903721 -0.0877493552 0.278640020
90 -4.5610239 0.0626760573 -0.0262292802 0.075832019
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90.2 -5.3838521 0.2597888783 0.3815099668 0.453911731
90.3 -4.1636999 0.6599799887 0.5315496142 0.371462522
91 -7.1462503 1.1213651365 -0.0869921115 0.275496513
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91.2 -18.2576707 0.3395603754 -0.0074027623 0.396123130
92 -6.4119222 0.4674939332 -0.0114311653 0.032879925
93 5.2122168 0.2677965647 -0.0425142500 0.124267794
93.1 3.1211725 1.6424445368 -0.1070466070 0.416939473
93.2 -3.6841177 0.7101700066 0.3044086294 0.485984408
93.3 2.6223542 1.1222322893 0.4672045475 0.411791889
93.4 -11.1877696 1.4628960401 0.1850733549 0.362300252
94 -6.9602492 -0.2904211940 -0.1025408741 0.354779307
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94.3 -11.6340088 0.6793464917 0.5201024995 0.379797627
94.4 -12.9357964 -0.9411356550 0.5237923368 0.377204420
94.5 -14.7648530 0.5683867264 0.2325905025 0.354495295
95 -12.8849309 0.2375652188 0.0897729510 0.547886254
95.1 -9.7451502 0.0767152977 0.4108281293 0.329687700
95.2 -0.8535063 -0.6886731251 0.1283004785 0.371978790
96 -4.9139832 0.7813892121 -0.0047820450 0.013741475
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96.2 -9.6555492 -0.4857246503 -0.0899549335 0.288037217
96.3 -11.8690793 0.8771471244 0.1585782261 0.533324704
96.4 -11.0224373 1.9030768981 0.1801061786 0.527566661
96.5 -10.9530403 -0.1684332749 0.3089317576 0.342757800
97 -9.8540471 1.3775130083 -0.1024324589 0.462541264
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98 -11.9651231 -1.2648518889 -0.0314640445 0.091227325
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98.2 -12.2606382 -0.1560385207 0.5481906379 0.330659794
99 -11.4720500 0.7993356425 -0.0530422907 0.156711586
99.1 -14.0596866 1.0355522332 0.0548371648 0.384914328
99.2 -17.3939469 -0.1150895843 -0.1467618333 0.421642124
100 1.1005874 0.0369067906 -0.1067733151 0.427076142
100.1 -3.8226248 1.6023713093 -0.0654896559 0.527917102
100.2 -0.9123182 0.8861545820 0.1462778204 0.536384181
100.3 -15.8389474 0.1277046316 0.4653994149 0.325078572
100.4 -12.8093826 -0.0834577654 0.0686909568 0.382446660
ns(time, df = 3)3 time
1 -0.145369093 0.5090421822
1.1 -0.186881442 0.6666076288
1.2 -0.349241050 2.1304941282
1.3 -0.300999737 2.4954441458
2 -0.173896710 3.0164990982
2.1 -0.079238230 3.2996806887
2.2 0.301483601 4.1747569619
3 -0.231074940 0.8478727890
3.1 -0.158750730 3.0654308549
3.2 0.590283099 4.7381553578
4 -0.097625033 0.3371432109
4.1 -0.278454698 1.0693019140
4.2 -0.277637284 2.6148973033
4.3 -0.136774026 3.1336532847
5 -0.279805678 1.0762525082
5.1 -0.360498322 1.7912546196
5.2 -0.235519853 2.7960080339
5.3 -0.231426488 2.8119940578
6 -0.360315653 1.7815462884
7 -0.076424127 3.3074087673
7.1 0.081205236 3.7008403614
7.2 0.607966686 4.7716691741
8 -0.288959568 1.1246398522
8.1 -0.360676673 1.8027009873
8.2 -0.360848815 1.8175825174
8.3 -0.224527396 2.8384267003
8.4 -0.055827230 3.3630275307
8.5 0.432934699 4.4360849704
9 -0.256245233 0.9607803822
9.1 -0.202849973 2.9177753383
9.2 0.628274517 4.8100892501
10 -0.331608701 2.2975509102
10.1 0.300822094 4.1734118364
11 -0.299441616 1.1832662905
11.1 -0.308047155 1.2346051680
11.2 -0.354668988 1.6435316263
11.3 -0.047184145 3.3859017969
11.4 0.629184194 4.8118087661
12 -0.255906608 0.9591987054
13 -0.017979717 0.0619085738
13.1 0.022585629 3.5621061502
14 0.234420981 4.0364430007
14.1 0.450908537 4.4710561272
14.2 0.536563622 4.6359198843
14.3 0.564205754 4.6886152599
15 -0.153773371 0.5402063532
15.1 -0.300484383 1.1893180816
15.2 -0.344022025 1.5094739688
15.3 0.686175505 4.9193474615
16 -0.309207584 1.2417913869
16.1 -0.287322060 2.5675726333
16.2 -0.269566289 2.6524101500
16.3 0.021104265 3.5585018690
16.4 0.107655226 3.7612454291
16.5 0.210083745 3.9851612889
17 -0.351195384 1.5925356350
17.1 -0.311040420 2.4374032998
17.2 -0.171104158 3.0256489082
17.3 -0.067055294 3.3329089405
17.4 0.156298232 3.8693758985
18 -0.311036128 2.4374292302
19 -0.259739494 0.9772165376
19.1 -0.292971732 1.1466335913
19.2 -0.336250565 2.2599126538
19.3 0.319581745 4.2114245973
20 -0.358374876 1.7170160066
20.1 -0.359705570 1.7562902288
20.2 -0.337228841 2.2515566566
20.3 -0.336132246 2.2609123867
20.4 -0.006096291 3.4913365287
20.5 0.300667673 4.1730977828
21 -0.357366657 1.6936582839
21.1 -0.191571616 2.9571191233
21.2 0.119869388 3.7887385779
22 -0.305573977 2.4696226232
22.1 -0.127130621 3.1626627257
23 -0.347001388 1.5414533857
23.1 -0.326336775 2.3369736120
24 -0.227186155 2.8283136466
25 -0.153227021 0.5381704110
25.1 -0.352252143 1.6069735331
25.2 -0.354190522 1.6358226922
25.3 -0.091832063 3.2646870392
25.4 0.254466815 4.0782226040
25.5 0.292289382 4.1560292873
26 -0.070197210 0.2412706357
26.1 -0.309746667 2.4451737676
26.2 0.037821198 3.5988757887
26.3 0.305164939 4.1822362854
27 0.078928640 3.6955824879
27.1 0.336333009 4.2451434687
28 -0.162957386 0.5746519344
28.1 -0.235929197 2.7943964268
28.2 0.319299132 4.2108539480
28.3 0.450648982 4.4705521734
29 -0.300582271 1.1898884235
29.1 -0.359871096 1.7624059319
29.2 -0.356549969 2.0210406382
29.3 -0.038787896 3.4078777023
30 -0.335820366 2.2635366488
30.1 0.035718727 3.5938334477
30.2 0.044098116 3.6138710892
31 0.413866643 4.3988140998
32 -0.356421989 1.6745209007
32.1 -0.204246665 2.9128167813
32.2 -0.188492301 2.9676558380
32.3 0.318869516 4.2099863547
33 -0.002566356 0.0093385763
33.1 -0.018862759 3.4591242753
34 -0.343075887 1.4998774312
34.1 0.135814702 3.8242761395
34.2 0.173695359 3.9072251692
34.3 0.197417584 3.9582124643
35 -0.322452391 1.3294299203
35.1 -0.345752660 1.5276966314
35.2 0.467180923 4.5025920868
36 -0.198421183 0.7123168337
36.1 -0.360596719 1.7972493160
36.2 -0.360918172 1.8262697803
36.3 0.355765584 4.2840119381
36.4 0.527945205 4.6194464504
37 -0.357475622 2.0018732361
37.1 0.066063038 3.6656836793
37.2 0.201253686 3.9663937816
38 -0.260884907 0.9826511063
39 -0.193368224 0.6921808305
39.1 -0.243562727 0.9027792048
39.2 -0.319014966 1.3055654289
39.3 -0.346986343 1.5412842878
39.4 -0.120095446 3.1834997435
39.5 0.284162218 4.1394166439
40 -0.290503017 1.1330395646
40.1 -0.260192709 2.6940994046
40.2 -0.166792072 3.0396614212
40.3 0.557734965 4.6762977762
41 -0.359898022 1.9337158254
41.1 -0.115958300 3.1956304458
41.2 -0.084662333 3.2846923557
41.3 -0.048910814 3.3813529415
41.4 0.016921661 3.5482964432
42 -0.139079780 0.4859252973
42.1 0.378571910 4.3293134298
43 -0.159507207 0.5616614548
43.1 -0.279438320 1.0743579536
43.2 -0.277998502 2.6131797966
44 -0.211710161 0.7662644819
44.1 -0.270307896 2.6490291790
44.2 -0.065469588 3.3371910988
44.3 0.272471485 4.1154200875
45 -0.057030292 0.1957449992
45.1 -0.357721087 1.9963831536
46 -0.325006430 1.3477755385
46.1 -0.219695608 2.8565793915
46.2 0.422684799 4.4160729996
47 -0.169972385 0.6012621359
47.1 -0.315522977 2.4097121472
47.2 -0.179612843 2.9975794035
47.3 -0.120277127 3.1829649757
47.4 0.528289800 4.6201055450
48 -0.218578367 2.8607365978
48.1 -0.205083750 2.9098354396
49 -0.254634636 2.7179756400
50 -0.298215668 1.1762060679
51 -0.335531551 1.4304436720
52 -0.349554303 2.1266646020
52.1 -0.147721246 3.1000545993
52.2 -0.139010728 3.1268477370
52.3 0.026310537 3.5711459327
52.4 0.622074267 4.7983659909
52.5 0.719341816 4.9818264414
53 -0.141984214 0.4965799209
53.1 0.017837917 3.5505357443
53.2 0.506858010 4.5790420019
54 -0.332277803 1.4034724841
54.1 -0.360828251 1.8812377600
54.2 -0.298206049 2.5107589352
54.3 -0.238343800 2.7848406672
54.4 0.223917052 4.0143877396
55 -0.172743982 0.6118522980
55.1 -0.206853912 0.7463747414
55.2 -0.229322660 2.8201208171
55.3 -0.137106630 3.1326431572
55.4 -0.106926420 3.2218102901
56 -0.306171957 1.2231332215
56.1 -0.323453138 2.3573202139
56.2 -0.287337760 2.5674936292
56.3 -0.193430676 2.9507164378
56.4 -0.105024091 3.2272730360
56.5 -0.035063150 3.4175522043
57 -0.068975018 0.2370331448
57.1 -0.072178206 0.2481445030
57.2 -0.291872977 1.1405586067
57.3 -0.350452633 2.1153886721
58 -0.305821847 1.2210099772
58.1 -0.354038263 1.6334245703
58.2 -0.356662058 1.6791862890
58.3 -0.273997945 2.6320121693
58.4 -0.222049115 2.8477731440
58.5 0.026480236 3.5715569824
59 -0.360551776 1.9023998594
59.1 0.715006940 4.9736620474
60 -0.211854697 2.8854503250
61 -0.200675172 0.7213630795
61.1 -0.328833062 2.3186947661
61.2 -0.298763087 2.5077313243
61.3 -0.123615504 3.1731073430
61.4 0.039239893 3.6022726283
62 -0.152019902 0.5336771999
62.1 -0.195026261 0.6987666548
62.2 -0.019135492 3.4584309917
62.3 0.624459836 4.8028772371
63 -0.232008543 2.8097350930
63.1 0.200775752 3.9653754211
64 0.274275354 4.1191305732
65 -0.197245756 0.7076152589
65.1 -0.356333749 2.0252246363
65.2 -0.143616734 3.1127382827
65.3 -0.115520552 3.1969087943
66 -0.004894436 3.4943454154
66.1 0.110532429 3.7677437009
66.2 0.192927263 3.9486138616
67 0.300540381 4.1728388879
68 -0.037661039 0.1291919907
68.1 -0.360303787 1.7809643946
68.2 -0.354990134 2.0493205660
68.3 -0.196324326 2.9406870750
68.4 0.236438945 4.0406670363
69 0.286949248 4.1451198701
70 -0.058048346 0.1992557163
70.1 -0.138274515 0.4829774413
71 -0.213623666 0.7741605386
71.1 -0.341911227 1.4883817220
71.2 0.253324633 4.0758526395
71.3 0.572728871 4.7048238723
71.4 0.582970861 4.7242791823
72 -0.250045696 0.9321196121
72.1 -0.298875568 1.1799991806
72.2 -0.360707545 1.8917567329
72.3 -0.008479051 3.4853593935
72.4 0.075836695 3.6884259700
72.5 0.257937078 4.0854155901
73 0.518824914 4.6019889915
74 -0.339185349 1.4626806753
75 -0.096185550 3.2524286874
76 -0.360739315 1.8074807397
76.1 0.347998542 4.2685073183
76.2 0.712464524 4.9688734859
77 -0.230618684 0.8459033852
78 -0.225297228 0.8231094317
79 -0.016946357 0.0583819521
79.1 -0.310503883 2.4406372628
79.2 -0.080482751 3.2962526032
80 -0.242607278 0.8985060186
80.1 -0.324413587 1.3434670598
80.2 -0.233841717 2.8025900386
81 -0.002799225 0.0101324962
81.1 -0.252235081 0.9421709494
81.2 -0.162258804 3.0542453879
81.3 -0.062321933 3.3456630446
82 -0.329186483 1.3791010005
82.1 -0.359810043 1.7601010622
82.2 -0.275856749 2.6233131927
83 -0.015585831 0.0537394290
83.1 -0.206113773 2.9061570496
83.2 -0.141595515 3.1189457362
83.3 0.605165478 4.7663642222
84 -0.252876961 2.7254060237
84.1 -0.065733726 3.3364784659
85 -0.086418319 0.2977756259
85.1 -0.359190206 1.7394116637
85.2 -0.262358242 2.6846330194
85.3 -0.127729570 3.1608762743
85.4 0.191335442 3.9452053758
85.5 0.470626547 4.5092553482
86 -0.231018222 0.8476278360
86.1 -0.266955474 1.0118629411
86.2 -0.310696855 1.2511159515
86.3 -0.344135221 2.1870554925
86.4 -0.308378023 2.4532935000
86.5 0.134159804 3.8206058508
87 -0.257217600 2.7069531474
87.1 -0.023912615 3.4462517721
87.2 0.478346358 4.5241666853
88 0.000000000 0.0005892443
88.1 -0.198244638 0.7116099866
88.2 -0.301030746 2.4952722900
88.3 -0.079274225 3.2995816297
89 -0.181653221 0.6462086167
90 -0.049437014 0.1696030737
90.1 -0.281151299 2.5980385230
90.2 -0.266749122 2.6651392167
90.3 -0.139855690 3.1242690247
91 -0.179603880 0.6382618390
91.1 -0.276049539 2.6224059286
91.2 0.610915809 4.7772527603
92 -0.021435342 0.0737052364
93 -0.081013650 0.2788909199
93.1 -0.271814501 1.0357759963
93.2 -0.301681670 2.4916551099
93.3 -0.211259675 2.8876129608
93.4 0.447248720 4.4639474002
94 -0.231290551 0.8488043118
94.1 -0.275695453 1.0552454425
94.2 -0.359604210 1.9445500884
94.3 -0.156971195 3.0710722448
94.4 -0.151822700 3.0872731935
94.5 0.404570621 4.3805759016
95 -0.356607715 2.0199063048
95.1 0.225844939 4.0184444457
95.2 0.496766490 4.5596531732
96 -0.008958452 0.0311333477
96.1 -0.038605151 0.1324267720
96.2 -0.187779516 0.6701303425
96.3 -0.345060176 2.1775037691
96.4 -0.340252993 2.2246142488
96.5 0.332658824 4.2377650598
97 -0.301543584 1.1955102731
97.1 0.707918494 4.9603108643
98 -0.059473644 0.2041732438
98.1 -0.123949646 0.4309578973
98.2 0.004310743 3.5172611906
99 -0.102164665 0.3531786101
99.1 0.559124871 4.6789444226
99.2 0.725119709 4.9927084171
100 -0.278422879 1.0691387602
100.1 -0.344163920 1.5109344281
100.2 -0.347560939 2.1502332564
100.3 0.158668905 3.8745574222
100.4 0.547483173 4.6567608765
$m7c$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m7c$spM_lvlone
center scale
y -11.17337099 6.2496619
c1 0.25599956 0.6718095
ns(time, df = 3)1 0.19883694 0.2502686
ns(time, df = 3)2 0.38513689 0.1171115
ns(time, df = 3)3 -0.07137294 0.2891059
time 2.53394028 1.3818094
$m7c$mu_reg_norm
[1] 0
$m7c$tau_reg_norm
[1] 1e-04
$m7c$shape_tau_norm
[1] 0.01
$m7c$rate_tau_norm
[1] 0.01
$m7c$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m7c$shape_diag_RinvD
[1] "0.01"
$m7c$rate_diag_RinvD
[1] "0.001"
$m7c$RinvD_y_id
[,1] [,2] [,3] [,4]
[1,] NA 0 0 0
[2,] 0 NA 0 0
[3,] 0 0 NA 0
[4,] 0 0 0 NA
$m7c$KinvD_y_id
id
5
$m7d
$m7d$M_id
C2 (Intercept) C1
1 -1.381594459 1 0.7175865
2 0.344426024 1 0.7507170
3 NA 1 0.7255954
4 -0.228910007 1 0.7469352
5 NA 1 0.7139120
6 -2.143955482 1 0.7332505
7 -1.156567023 1 0.7345929
8 -0.598827660 1 0.7652589
9 NA 1 0.7200622
10 -1.006719032 1 0.7423879
11 0.239801450 1 0.7437448
12 -1.064969789 1 0.7446470
13 -0.538082688 1 0.7530186
14 NA 1 0.7093137
15 -1.781049276 1 0.7331192
16 NA 1 0.7011390
17 NA 1 0.7432395
18 -0.014579883 1 0.7545191
19 -2.121550136 1 0.7528487
20 NA 1 0.7612865
21 -0.363239698 1 0.7251719
22 -0.121568514 1 0.7300630
23 -0.951271111 1 0.7087249
24 NA 1 0.7391938
25 -0.974288621 1 0.7820641
26 -1.130632418 1 0.7118298
27 0.114339868 1 0.7230857
28 0.238334648 1 0.7489353
29 0.840744958 1 0.7510888
30 NA 1 0.7300717
31 NA 1 0.7550721
32 -1.466312154 1 0.7321898
33 -0.637352277 1 0.7306414
34 NA 1 0.7427216
35 NA 1 0.7193042
36 NA 1 0.7312888
37 NA 1 0.7100436
38 NA 1 0.7670184
39 0.006728205 1 0.7400449
40 NA 1 0.7397304
41 -1.663281353 1 0.7490966
42 0.161184794 1 0.7419274
43 0.457939180 1 0.7527810
44 -0.307070331 1 0.7408315
45 NA 1 0.7347550
46 -1.071668276 1 0.7332398
47 -0.814751321 1 0.7376481
48 -0.547630662 1 0.7346179
49 NA 1 0.7329402
50 -1.350213782 1 0.7260436
51 0.719054706 1 0.7242910
52 NA 1 0.7298067
53 -1.207130750 1 0.7254741
54 NA 1 0.7542067
55 -0.408600991 1 0.7389952
56 -0.271380529 1 0.7520638
57 -1.361925974 1 0.7219958
58 NA 1 0.7259632
59 NA 1 0.7458606
60 -0.323712205 1 0.7672421
61 NA 1 0.7257179
62 NA 1 0.7189892
63 -1.386906880 1 0.7333356
64 NA 1 0.7320243
65 NA 1 0.7477711
66 -0.565191691 1 0.7343974
67 -0.382899912 1 0.7491624
68 NA 1 0.7482736
69 -0.405642769 1 0.7338267
70 NA 1 0.7607742
71 -0.843748427 1 0.7777600
72 0.116003683 1 0.7408143
73 -0.778634325 1 0.7248271
74 NA 1 0.7364916
75 NA 1 0.7464926
76 NA 1 0.7355430
77 -0.632974758 1 0.7208449
78 NA 1 0.7373573
79 -0.778064615 1 0.7598079
80 NA 1 0.7360415
81 NA 1 0.7293932
82 -0.246123253 1 0.7279309
83 -1.239659782 1 0.7344643
84 -0.467772280 1 0.7384350
85 NA 1 0.7323716
86 -2.160485036 1 0.7576597
87 -0.657675572 1 0.7496139
88 NA 1 0.7275239
89 -0.696710744 1 0.7250648
90 NA 1 0.7335262
91 -0.179395847 1 0.7343980
92 -0.441545568 1 0.7380425
93 -0.685799334 1 0.7389460
94 NA 1 0.7259951
95 0.191929445 1 0.7282840
96 NA 1 0.7281676
97 -0.069760671 1 0.7245642
98 NA 1 0.7526938
99 NA 1 0.7272309
100 NA 1 0.7383460
$m7d$M_lvlone
y c1 time ns(time, df = 3)1
1 -13.0493856 0.7592026489 0.5090421822 -0.0731022196
1.1 -9.3335901 0.9548337990 0.6666076288 -0.0896372079
1.2 -22.3469852 0.5612235156 2.1304941282 0.1374616725
1.3 -15.0417337 1.1873391025 2.4954441458 0.3061500570
2 -12.0655434 0.9192204198 3.0164990982 0.5064248381
2.1 -15.8674476 -0.1870730476 3.2996806887 0.5543647993
2.2 -7.8800006 1.2517512331 4.1747569619 0.3402753582
3 -11.4820604 -0.0605087604 0.8478727890 -0.1024946971
3.1 -10.5983220 0.3788637747 3.0654308549 0.5187768948
3.2 -22.4519157 0.9872578281 4.7381553578 0.0174998856
4 -1.2697775 1.4930175328 0.3371432109 -0.0508146200
4.1 -11.1215184 -0.7692526880 1.0693019140 -0.1067711172
4.2 -3.6134138 0.9180841450 2.6148973033 0.3598480506
4.3 -14.5982385 -0.0541170782 3.1336532847 0.5333652385
5 -6.8457515 -0.1376784521 1.0762525082 -0.1066695938
5.1 -7.0551214 -0.2740585866 1.7912546196 0.0046159078
5.2 -12.3418980 0.4670496929 2.7960080339 0.4342731827
5.3 -9.2366906 0.1740288049 2.8119940578 0.4402846745
6 -5.1648211 0.9868044683 1.7815462884 0.0015253168
7 -10.0599502 -0.1280320918 3.3074087673 0.5547950298
7.1 -18.3267285 0.4242971219 3.7008403614 0.5158768825
7.2 -12.5138426 0.0777182491 4.7716691741 -0.0038356900
8 -1.6305331 -0.5791408712 1.1246398522 -0.1055283475
8.1 -9.6520453 0.3128604232 1.8027009873 0.0083228157
8.2 -1.5278462 0.6258446356 1.8175825174 0.0132420073
8.3 -7.4172211 -0.1040137707 2.8384267003 0.4499876083
8.4 -7.1238609 0.0481450285 3.3630275307 0.5564354493
8.5 -8.8706950 0.3831763675 4.4360849704 0.2011700353
9 -0.1634429 -0.1757592269 0.9607803822 -0.1064248362
9.1 -2.6034300 -0.1791541200 2.9177753383 0.4771840649
9.2 -6.7272369 -0.0957042935 4.8100892501 -0.0284447928
10 -6.4172202 -0.5598409704 2.2975509102 0.2139341504
10.1 -11.4834569 -0.2318340451 4.1734118364 0.3409274088
11 -8.7911356 0.5086859475 1.1832662905 -0.1030921083
11.1 -19.6645080 0.4951758188 1.2346051680 -0.0999663372
11.2 -20.2030932 -1.1022162541 1.6435316263 -0.0371358422
11.3 -21.3082176 -0.0611636705 3.3859017969 0.5563809266
11.4 -14.5802901 -0.4971774316 4.8118087661 -0.0295495187
12 -15.2006287 -0.2433996286 0.9591987054 -0.1063939605
13 0.8058816 0.8799673116 0.0619085738 -0.0095916688
13.1 -13.6379208 0.1079022586 3.5621061502 0.5424520669
14 -15.3422873 0.9991752617 4.0364430007 0.4032689358
14.1 -10.0965208 -0.1094019046 4.4710561272 0.1809335363
14.2 -16.6452027 0.1518967560 4.6359198843 0.0816318440
14.3 -15.8389733 0.3521012473 4.6886152599 0.0487734088
15 -8.9424594 0.3464447888 0.5402063532 -0.0767078114
15.1 -22.0101983 -0.4767313971 1.1893180816 -0.1027727004
15.2 -7.3975599 0.5759767791 1.5094739688 -0.0657566366
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98.1 -2.6515128 -0.9042716241 0.4309578973 -0.0634420036
98.2 -12.2606382 -0.1560385207 3.5172611906 0.5481906379
99 -11.4720500 0.7993356425 0.3531786101 -0.0530422907
99.1 -14.0596866 1.0355522332 4.6789444226 0.0548371648
99.2 -17.3939469 -0.1150895843 4.9927084171 -0.1467618333
100 1.1005874 0.0369067906 1.0691387602 -0.1067733151
100.1 -3.8226248 1.6023713093 1.5109344281 -0.0654896559
100.2 -0.9123182 0.8861545820 2.1502332564 0.1462778204
100.3 -15.8389474 0.1277046316 3.8745574222 0.4653994149
100.4 -12.8093826 -0.0834577654 4.6567608765 0.0686909568
ns(time, df = 3)2 ns(time, df = 3)3
1 0.222983368 -0.145369093
1.1 0.286659651 -0.186881442
1.2 0.538466292 -0.349241050
1.3 0.485312041 -0.300999737
2 0.388851338 -0.173896710
2.1 0.348347565 -0.079238230
2.2 0.338334366 0.301483601
3 0.354448579 -0.231074940
3.1 0.380711276 -0.158750730
3.2 0.391617016 0.590283099
4 0.149748191 -0.097625033
4.1 0.427124949 -0.278454698
4.2 0.463409709 -0.277637284
4.3 0.370048079 -0.136774026
5 0.429197233 -0.279805678
5.1 0.552972700 -0.360498322
5.2 0.428967354 -0.235519853
5.3 0.425938437 -0.231426488
6 0.552692434 -0.360315653
7 0.347509550 -0.076424127
7.1 0.324489976 0.081205236
7.2 0.395476156 0.607966686
8 0.443238494 -0.288959568
8.1 0.553246856 -0.360676673
8.2 0.553513388 -0.360848815
8.3 0.420954925 -0.224527396
8.4 0.341948600 -0.055827230
8.5 0.359621900 0.432934699
9 0.393057590 -0.256245233
9.1 0.406264856 -0.202849973
9.2 0.399948734 0.628274517
10 0.517577740 -0.331608701
10.1 0.338245451 0.300822094
11 0.459317031 -0.299441616
11.1 0.472517168 -0.308047155
11.2 0.544030949 -0.354668988
11.3 0.339897056 -0.047184145
11.4 0.400149982 0.629184194
12 0.392538168 -0.255906608
13 0.027579300 -0.017979717
13.1 0.328456601 0.022585629
14 0.330500975 0.234420981
14.1 0.362994245 0.450908537
14.2 0.380152387 0.536563622
14.3 0.385997930 0.564205754
15 0.235874789 -0.153773371
15.1 0.460916543 -0.300484383
15.2 0.527699447 -0.344022025
15.3 0.412872874 0.686175505
16 0.474297164 -0.309207584
16.1 0.472228980 -0.287322060
16.2 0.456327895 -0.269566289
16.3 0.328616483 0.021104265
16.4 0.324030412 0.107655226
16.5 0.328317423 0.210083745
17 0.538702746 -0.351195384
17.1 0.495414246 -0.311040420
17.2 0.387301236 -0.171104158
17.3 0.344858135 -0.067055294
17.4 0.324978786 0.156298232
18 0.495409834 -0.311036128
19 0.398417477 -0.259739494
19.1 0.449392800 -0.292971732
19.2 0.522886360 -0.336250565
19.3 0.340847993 0.319581745
20 0.549715454 -0.358374876
20.1 0.551756621 -0.359705570
20.2 0.524021244 -0.337228841
20.3 0.522749488 -0.336132246
20.4 0.332148944 -0.006096291
20.5 0.338224725 0.300667673
21 0.548168935 -0.357366657
21.1 0.399187414 -0.191571616
21.2 0.324061287 0.119869388
22 0.489859980 -0.305573977
22.1 0.365790145 -0.127130621
23 0.532269527 -0.347001388
23.1 0.511693737 -0.326336775
24 0.422857517 -0.227186155
25 0.235036737 -0.153227021
25.1 0.540323722 -0.352252143
25.2 0.543297023 -0.354190522
25.3 0.352345378 -0.091832063
25.4 0.332578168 0.254466815
25.5 0.337117898 0.292289382
26 0.107676329 -0.070197210
26.1 0.494087736 -0.309746667
26.2 0.326994020 0.037821198
26.3 0.338833066 0.305164939
27 0.324565129 0.078928640
27.1 0.343307911 0.336333009
28 0.249962259 -0.162957386
28.1 0.429273232 -0.235929197
28.2 0.340807567 0.319299132
28.3 0.362944907 0.450648982
29 0.461066695 -0.300582271
29.1 0.552010523 -0.359871096
29.2 0.547810299 -0.356549969
29.3 0.338052723 -0.038787896
30 0.522389104 -0.335820366
30.1 0.327176583 0.035718727
30.2 0.326484504 0.044098116
31 0.356147774 0.413866643
32 0.546719898 -0.356421989
32.1 0.407167901 -0.204246665
32.2 0.397320853 -0.188492301
32.3 0.340746183 0.318869516
33 0.003936563 -0.002566356
33.1 0.334224366 -0.018862759
34 0.526248154 -0.343075887
34.1 0.324316326 0.135814702
34.2 0.325817315 0.173695359
34.3 0.327338410 0.197417584
35 0.494613531 -0.322452391
35.1 0.530354088 -0.345752660
35.2 0.366122990 0.467180923
36 0.304360595 -0.198421183
36.1 0.553123835 -0.360596719
36.2 0.553622593 -0.360918172
36.3 0.346311748 0.355765584
36.4 0.378356377 0.527945205
37 0.549032516 -0.357475622
37.1 0.325102184 0.066063038
37.2 0.327623070 0.201253686
38 0.400174439 -0.260884907
39 0.296609802 -0.193368224
39.1 0.373603743 -0.243562727
39.2 0.489340823 -0.319014966
39.3 0.532246449 -0.346986343
39.4 0.362843629 -0.120095446
39.5 0.336078053 0.284162218
40 0.445606008 -0.290503017
40.1 0.448397811 -0.260192709
40.2 0.384951582 -0.166792072
40.3 0.384618587 0.557734965
41 0.552281804 -0.359898022
41.1 0.361173390 -0.115958300
41.2 0.350019018 -0.084662333
41.3 0.340294264 -0.048910814
41.4 0.329085370 0.016921661
42 0.213336117 -0.139079780
42.1 0.350028134 0.378571910
43 0.244669988 -0.159507207
43.1 0.428633738 -0.279438320
43.2 0.463732274 -0.277998502
44 0.324744715 -0.211710161
44.1 0.456968690 -0.270307896
44.2 0.344429887 -0.065469588
44.3 0.334642692 0.272471485
45 0.087479438 -0.057030292
45.1 0.549358350 -0.357721087
46 0.498531201 -0.325006430
46.1 0.417554417 -0.219695608
46.2 0.357740629 0.422684799
47 0.260722648 -0.169972385
47.1 0.500069201 -0.315522977
47.2 0.392094432 -0.179612843
47.3 0.362918036 -0.120277127
47.4 0.378427928 0.528289800
48 0.416778543 -0.218578367
48.1 0.407711957 -0.205083750
49 0.443841107 -0.254634636
50 0.457436535 -0.298215668
51 0.514675808 -0.335531551
52 0.538856976 -0.349554303
52.1 0.375191670 -0.147721246
52.2 0.371072093 -0.139010728
52.3 0.328068662 0.026310537
52.4 0.398579012 0.622074267
52.5 0.420339814 0.719341816
53 0.217791262 -0.141984214
53.1 0.328980432 0.017837917
53.2 0.374021344 0.506858010
54 0.509684846 -0.332277803
54.1 0.553543641 -0.360828251
54.2 0.482577881 -0.298206049
54.3 0.431088614 -0.238343800
54.4 0.329511331 0.223917052
55 0.264974033 -0.172743982
55.1 0.317295658 -0.206853912
55.2 0.424402666 -0.229322660
55.3 0.370199481 -0.137106630
55.4 0.357686883 -0.106926420
56 0.469640780 -0.306171957
56.1 0.508537162 -0.323453138
56.2 0.472243570 -0.287337760
56.3 0.400327890 -0.193430676
56.4 0.356980364 -0.105024091
56.5 0.337279686 -0.035063150
57 0.105801595 -0.068975018
57.1 0.110715003 -0.072178206
57.2 0.447707406 -0.291872977
57.3 0.539981816 -0.350452633
58 0.469103743 -0.305821847
58.1 0.543063473 -0.354038263
58.2 0.547088143 -0.356662058
58.3 0.460186824 -0.273997945
58.4 0.419201600 -0.222049115
58.5 0.328051465 0.026480236
59 0.553170063 -0.360551776
59.1 0.419362856 0.715006940
60 0.412191135 -0.211854697
61 0.307818016 -0.200675172
61.1 0.514461229 -0.328833062
61.2 0.483120456 -0.298763087
61.3 0.364301147 -0.123615504
61.4 0.326874230 0.039239893
62 0.233185122 -0.152019902
62.1 0.299153085 -0.195026261
62.2 0.334271811 -0.019135492
62.3 0.399105605 0.624459836
63 0.426365839 -0.232008543
63.1 0.327587037 0.200775752
64 0.334859412 0.274275354
65 0.302557595 -0.197245756
65.1 0.547526242 -0.356333749
65.2 0.373224291 -0.143616734
65.3 0.360999362 -0.115520552
66 0.331967886 -0.004894436
66.1 0.324024401 0.110532429
66.2 0.327018558 0.192927263
67 0.338207650 0.300540381
68 0.057768712 -0.037661039
68.1 0.552674233 -0.360303787
68.2 0.545772611 -0.354990134
68.3 0.402123497 -0.196324326
68.4 0.330699051 0.236438945
69 0.336430831 0.286949248
70 0.089041044 -0.058048346
70.1 0.212100911 -0.138274515
71 0.327679863 -0.213623666
71.1 0.524461669 -0.341911227
71.2 0.332453400 0.253324633
71.3 0.387824204 0.572728871
71.4 0.390032203 0.582970861
72 0.383548046 -0.250045696
72.1 0.458448763 -0.298875568
72.2 0.553381188 -0.360707545
72.3 0.332515066 -0.008479051
72.4 0.324676638 0.075836695
72.5 0.332961867 0.257937078
73 0.376470777 0.518824914
74 0.520280412 -0.339185349
75 0.353824007 -0.096185550
76 0.553343477 -0.360739315
76.1 0.345092469 0.347998542
76.2 0.418789942 0.712464524
77 0.353748723 -0.230618684
78 0.345586078 -0.225297228
79 0.025994217 -0.016946357
79.1 0.494863212 -0.310503883
79.2 0.348724471 -0.080482751
80 0.372138169 -0.242607278
80.1 0.497621832 -0.324413587
80.2 0.427719060 -0.233841717
81 0.004293764 -0.002799225
81.1 0.386906369 -0.252235081
81.2 0.382538581 -0.162258804
81.3 0.343596919 -0.062321933
82 0.504943034 -0.329186483
82.1 0.551916873 -0.359810043
82.2 0.461826800 -0.275856749
83 0.023907290 -0.015585831
83.1 0.408384331 -0.206113773
83.2 0.372272641 -0.141595515
83.3 0.394862487 0.605165478
84 0.442422222 -0.252876961
84.1 0.344500819 -0.065733726
85 0.132558080 -0.086418319
85.1 0.550966098 -0.359190206
85.2 0.450202261 -0.262358242
85.3 0.366047208 -0.127729570
85.4 0.326908671 0.191335442
85.5 0.366794208 0.470626547
86 0.354361579 -0.231018222
86.1 0.409486154 -0.266955474
86.2 0.476581575 -0.310696855
86.3 0.532204554 -0.344135221
86.4 0.492692574 -0.308378023
86.5 0.324278938 0.134159804
87 0.445945432 -0.257217600
87.1 0.335124592 -0.023912615
87.2 0.368308652 0.478346358
88 0.000000000 0.000000000
88.1 0.304089792 -0.198244638
88.2 0.485342573 -0.301030746
88.3 0.348358411 -0.079274225
89 0.278640020 -0.181653221
90 0.075832019 -0.049437014
90.1 0.466568464 -0.281151299
90.2 0.453911731 -0.266749122
90.3 0.371462522 -0.139855690
91 0.275496513 -0.179603880
91.1 0.461997624 -0.276049539
91.2 0.396123130 0.610915809
92 0.032879925 -0.021435342
93 0.124267794 -0.081013650
93.1 0.416939473 -0.271814501
93.2 0.485984408 -0.301681670
93.3 0.411791889 -0.211259675
93.4 0.362300252 0.447248720
94 0.354779307 -0.231290551
94.1 0.422892510 -0.275695453
94.2 0.551883683 -0.359604210
94.3 0.379797627 -0.156971195
94.4 0.377204420 -0.151822700
94.5 0.354495295 0.404570621
95 0.547886254 -0.356607715
95.1 0.329687700 0.225844939
95.2 0.371978790 0.496766490
96 0.013741475 -0.008958452
96.1 0.059216896 -0.038605151
96.2 0.288037217 -0.187779516
96.3 0.533324704 -0.345060176
96.4 0.527566661 -0.340252993
96.5 0.342757800 0.332658824
97 0.462541264 -0.301543584
97.1 0.417765721 0.707918494
98 0.091227325 -0.059473644
98.1 0.190127826 -0.123949646
98.2 0.330659794 0.004310743
99 0.156711586 -0.102164665
99.1 0.384914328 0.559124871
99.2 0.421642124 0.725119709
100 0.427076142 -0.278422879
100.1 0.527917102 -0.344163920
100.2 0.536384181 -0.347560939
100.3 0.325078572 0.158668905
100.4 0.382446660 0.547483173
$m7d$spM_id
center scale
C2 -0.6240921 0.68571078
(Intercept) NA NA
C1 0.7372814 0.01472882
$m7d$spM_lvlone
center scale
y -11.17337099 6.2496619
c1 0.25599956 0.6718095
time 2.53394028 1.3818094
ns(time, df = 3)1 0.19883694 0.2502686
ns(time, df = 3)2 0.38513689 0.1171115
ns(time, df = 3)3 -0.07137294 0.2891059
$m7d$mu_reg_norm
[1] 0
$m7d$tau_reg_norm
[1] 1e-04
$m7d$shape_tau_norm
[1] 0.01
$m7d$rate_tau_norm
[1] 0.01
$m7d$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m7d$shape_diag_RinvD
[1] "0.01"
$m7d$rate_diag_RinvD
[1] "0.001"
$m7d$RinvD_y_id
[,1] [,2]
[1,] NA 0
[2,] 0 NA
$m7d$KinvD_y_id
id
3
$m7e
$m7e$M_id
C2 (Intercept) C1
1 -1.381594459 1 0.7175865
2 0.344426024 1 0.7507170
3 NA 1 0.7255954
4 -0.228910007 1 0.7469352
5 NA 1 0.7139120
6 -2.143955482 1 0.7332505
7 -1.156567023 1 0.7345929
8 -0.598827660 1 0.7652589
9 NA 1 0.7200622
10 -1.006719032 1 0.7423879
11 0.239801450 1 0.7437448
12 -1.064969789 1 0.7446470
13 -0.538082688 1 0.7530186
14 NA 1 0.7093137
15 -1.781049276 1 0.7331192
16 NA 1 0.7011390
17 NA 1 0.7432395
18 -0.014579883 1 0.7545191
19 -2.121550136 1 0.7528487
20 NA 1 0.7612865
21 -0.363239698 1 0.7251719
22 -0.121568514 1 0.7300630
23 -0.951271111 1 0.7087249
24 NA 1 0.7391938
25 -0.974288621 1 0.7820641
26 -1.130632418 1 0.7118298
27 0.114339868 1 0.7230857
28 0.238334648 1 0.7489353
29 0.840744958 1 0.7510888
30 NA 1 0.7300717
31 NA 1 0.7550721
32 -1.466312154 1 0.7321898
33 -0.637352277 1 0.7306414
34 NA 1 0.7427216
35 NA 1 0.7193042
36 NA 1 0.7312888
37 NA 1 0.7100436
38 NA 1 0.7670184
39 0.006728205 1 0.7400449
40 NA 1 0.7397304
41 -1.663281353 1 0.7490966
42 0.161184794 1 0.7419274
43 0.457939180 1 0.7527810
44 -0.307070331 1 0.7408315
45 NA 1 0.7347550
46 -1.071668276 1 0.7332398
47 -0.814751321 1 0.7376481
48 -0.547630662 1 0.7346179
49 NA 1 0.7329402
50 -1.350213782 1 0.7260436
51 0.719054706 1 0.7242910
52 NA 1 0.7298067
53 -1.207130750 1 0.7254741
54 NA 1 0.7542067
55 -0.408600991 1 0.7389952
56 -0.271380529 1 0.7520638
57 -1.361925974 1 0.7219958
58 NA 1 0.7259632
59 NA 1 0.7458606
60 -0.323712205 1 0.7672421
61 NA 1 0.7257179
62 NA 1 0.7189892
63 -1.386906880 1 0.7333356
64 NA 1 0.7320243
65 NA 1 0.7477711
66 -0.565191691 1 0.7343974
67 -0.382899912 1 0.7491624
68 NA 1 0.7482736
69 -0.405642769 1 0.7338267
70 NA 1 0.7607742
71 -0.843748427 1 0.7777600
72 0.116003683 1 0.7408143
73 -0.778634325 1 0.7248271
74 NA 1 0.7364916
75 NA 1 0.7464926
76 NA 1 0.7355430
77 -0.632974758 1 0.7208449
78 NA 1 0.7373573
79 -0.778064615 1 0.7598079
80 NA 1 0.7360415
81 NA 1 0.7293932
82 -0.246123253 1 0.7279309
83 -1.239659782 1 0.7344643
84 -0.467772280 1 0.7384350
85 NA 1 0.7323716
86 -2.160485036 1 0.7576597
87 -0.657675572 1 0.7496139
88 NA 1 0.7275239
89 -0.696710744 1 0.7250648
90 NA 1 0.7335262
91 -0.179395847 1 0.7343980
92 -0.441545568 1 0.7380425
93 -0.685799334 1 0.7389460
94 NA 1 0.7259951
95 0.191929445 1 0.7282840
96 NA 1 0.7281676
97 -0.069760671 1 0.7245642
98 NA 1 0.7526938
99 NA 1 0.7272309
100 NA 1 0.7383460
$m7e$M_lvlone
y c1 ns(time, df = 3)1 ns(time, df = 3)2
1 -13.0493856 0.7592026489 -0.0731022196 0.222983368
1.1 -9.3335901 0.9548337990 -0.0896372079 0.286659651
1.2 -22.3469852 0.5612235156 0.1374616725 0.538466292
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86.5 -17.7515546 -0.1420690052 0.4829861649 0.324278938
87 -10.7027963 1.0457427869 0.3989962007 0.445945432
87.1 -22.4941954 -0.2973007190 0.5542548494 0.335124592
87.2 -14.9616716 0.4396872616 0.1496033589 0.368308652
88 -2.2264493 -0.0601928334 0.0000000000 0.000000000
88.1 -8.9626474 -1.0124347595 -0.0935064654 0.304089792
88.2 -2.5095281 0.5730917016 0.3060711120 0.485342573
88.3 -16.3345673 -0.0029455332 0.5543589602 0.348358411
89 -11.0459647 1.5465903721 -0.0877493552 0.278640020
90 -4.5610239 0.0626760573 -0.0262292802 0.075832019
90.1 -11.7036651 1.1896872985 0.3524390019 0.466568464
90.2 -5.3838521 0.2597888783 0.3815099668 0.453911731
90.3 -4.1636999 0.6599799887 0.5315496142 0.371462522
91 -7.1462503 1.1213651365 -0.0869921115 0.275496513
91.1 -12.8374475 1.2046371625 0.3631264209 0.461997624
91.2 -18.2576707 0.3395603754 -0.0074027623 0.396123130
92 -6.4119222 0.4674939332 -0.0114311653 0.032879925
93 5.2122168 0.2677965647 -0.0425142500 0.124267794
93.1 3.1211725 1.6424445368 -0.1070466070 0.416939473
93.2 -3.6841177 0.7101700066 0.3044086294 0.485984408
93.3 2.6223542 1.1222322893 0.4672045475 0.411791889
93.4 -11.1877696 1.4628960401 0.1850733549 0.362300252
94 -6.9602492 -0.2904211940 -0.1025408741 0.354779307
94.1 -7.4318416 0.0147813580 -0.1069295359 0.422892510
94.2 -4.3498045 -0.4536774482 0.0593758680 0.551883683
94.3 -11.6340088 0.6793464917 0.5201024995 0.379797627
94.4 -12.9357964 -0.9411356550 0.5237923368 0.377204420
94.5 -14.7648530 0.5683867264 0.2325905025 0.354495295
95 -12.8849309 0.2375652188 0.0897729510 0.547886254
95.1 -9.7451502 0.0767152977 0.4108281293 0.329687700
95.2 -0.8535063 -0.6886731251 0.1283004785 0.371978790
96 -4.9139832 0.7813892121 -0.0047820450 0.013741475
96.1 -3.9582653 0.3391519695 -0.0205330884 0.059216896
96.2 -9.6555492 -0.4857246503 -0.0899549335 0.288037217
96.3 -11.8690793 0.8771471244 0.1585782261 0.533324704
96.4 -11.0224373 1.9030768981 0.1801061786 0.527566661
96.5 -10.9530403 -0.1684332749 0.3089317576 0.342757800
97 -9.8540471 1.3775130083 -0.1024324589 0.462541264
97.1 -19.2262840 -1.7323228619 -0.1256854518 0.417765721
98 -11.9651231 -1.2648518889 -0.0314640445 0.091227325
98.1 -2.6515128 -0.9042716241 -0.0634420036 0.190127826
98.2 -12.2606382 -0.1560385207 0.5481906379 0.330659794
99 -11.4720500 0.7993356425 -0.0530422907 0.156711586
99.1 -14.0596866 1.0355522332 0.0548371648 0.384914328
99.2 -17.3939469 -0.1150895843 -0.1467618333 0.421642124
100 1.1005874 0.0369067906 -0.1067733151 0.427076142
100.1 -3.8226248 1.6023713093 -0.0654896559 0.527917102
100.2 -0.9123182 0.8861545820 0.1462778204 0.536384181
100.3 -15.8389474 0.1277046316 0.4653994149 0.325078572
100.4 -12.8093826 -0.0834577654 0.0686909568 0.382446660
ns(time, df = 3)3 time
1 -0.145369093 0.5090421822
1.1 -0.186881442 0.6666076288
1.2 -0.349241050 2.1304941282
1.3 -0.300999737 2.4954441458
2 -0.173896710 3.0164990982
2.1 -0.079238230 3.2996806887
2.2 0.301483601 4.1747569619
3 -0.231074940 0.8478727890
3.1 -0.158750730 3.0654308549
3.2 0.590283099 4.7381553578
4 -0.097625033 0.3371432109
4.1 -0.278454698 1.0693019140
4.2 -0.277637284 2.6148973033
4.3 -0.136774026 3.1336532847
5 -0.279805678 1.0762525082
5.1 -0.360498322 1.7912546196
5.2 -0.235519853 2.7960080339
5.3 -0.231426488 2.8119940578
6 -0.360315653 1.7815462884
7 -0.076424127 3.3074087673
7.1 0.081205236 3.7008403614
7.2 0.607966686 4.7716691741
8 -0.288959568 1.1246398522
8.1 -0.360676673 1.8027009873
8.2 -0.360848815 1.8175825174
8.3 -0.224527396 2.8384267003
8.4 -0.055827230 3.3630275307
8.5 0.432934699 4.4360849704
9 -0.256245233 0.9607803822
9.1 -0.202849973 2.9177753383
9.2 0.628274517 4.8100892501
10 -0.331608701 2.2975509102
10.1 0.300822094 4.1734118364
11 -0.299441616 1.1832662905
11.1 -0.308047155 1.2346051680
11.2 -0.354668988 1.6435316263
11.3 -0.047184145 3.3859017969
11.4 0.629184194 4.8118087661
12 -0.255906608 0.9591987054
13 -0.017979717 0.0619085738
13.1 0.022585629 3.5621061502
14 0.234420981 4.0364430007
14.1 0.450908537 4.4710561272
14.2 0.536563622 4.6359198843
14.3 0.564205754 4.6886152599
15 -0.153773371 0.5402063532
15.1 -0.300484383 1.1893180816
15.2 -0.344022025 1.5094739688
15.3 0.686175505 4.9193474615
16 -0.309207584 1.2417913869
16.1 -0.287322060 2.5675726333
16.2 -0.269566289 2.6524101500
16.3 0.021104265 3.5585018690
16.4 0.107655226 3.7612454291
16.5 0.210083745 3.9851612889
17 -0.351195384 1.5925356350
17.1 -0.311040420 2.4374032998
17.2 -0.171104158 3.0256489082
17.3 -0.067055294 3.3329089405
17.4 0.156298232 3.8693758985
18 -0.311036128 2.4374292302
19 -0.259739494 0.9772165376
19.1 -0.292971732 1.1466335913
19.2 -0.336250565 2.2599126538
19.3 0.319581745 4.2114245973
20 -0.358374876 1.7170160066
20.1 -0.359705570 1.7562902288
20.2 -0.337228841 2.2515566566
20.3 -0.336132246 2.2609123867
20.4 -0.006096291 3.4913365287
20.5 0.300667673 4.1730977828
21 -0.357366657 1.6936582839
21.1 -0.191571616 2.9571191233
21.2 0.119869388 3.7887385779
22 -0.305573977 2.4696226232
22.1 -0.127130621 3.1626627257
23 -0.347001388 1.5414533857
23.1 -0.326336775 2.3369736120
24 -0.227186155 2.8283136466
25 -0.153227021 0.5381704110
25.1 -0.352252143 1.6069735331
25.2 -0.354190522 1.6358226922
25.3 -0.091832063 3.2646870392
25.4 0.254466815 4.0782226040
25.5 0.292289382 4.1560292873
26 -0.070197210 0.2412706357
26.1 -0.309746667 2.4451737676
26.2 0.037821198 3.5988757887
26.3 0.305164939 4.1822362854
27 0.078928640 3.6955824879
27.1 0.336333009 4.2451434687
28 -0.162957386 0.5746519344
28.1 -0.235929197 2.7943964268
28.2 0.319299132 4.2108539480
28.3 0.450648982 4.4705521734
29 -0.300582271 1.1898884235
29.1 -0.359871096 1.7624059319
29.2 -0.356549969 2.0210406382
29.3 -0.038787896 3.4078777023
30 -0.335820366 2.2635366488
30.1 0.035718727 3.5938334477
30.2 0.044098116 3.6138710892
31 0.413866643 4.3988140998
32 -0.356421989 1.6745209007
32.1 -0.204246665 2.9128167813
32.2 -0.188492301 2.9676558380
32.3 0.318869516 4.2099863547
33 -0.002566356 0.0093385763
33.1 -0.018862759 3.4591242753
34 -0.343075887 1.4998774312
34.1 0.135814702 3.8242761395
34.2 0.173695359 3.9072251692
34.3 0.197417584 3.9582124643
35 -0.322452391 1.3294299203
35.1 -0.345752660 1.5276966314
35.2 0.467180923 4.5025920868
36 -0.198421183 0.7123168337
36.1 -0.360596719 1.7972493160
36.2 -0.360918172 1.8262697803
36.3 0.355765584 4.2840119381
36.4 0.527945205 4.6194464504
37 -0.357475622 2.0018732361
37.1 0.066063038 3.6656836793
37.2 0.201253686 3.9663937816
38 -0.260884907 0.9826511063
39 -0.193368224 0.6921808305
39.1 -0.243562727 0.9027792048
39.2 -0.319014966 1.3055654289
39.3 -0.346986343 1.5412842878
39.4 -0.120095446 3.1834997435
39.5 0.284162218 4.1394166439
40 -0.290503017 1.1330395646
40.1 -0.260192709 2.6940994046
40.2 -0.166792072 3.0396614212
40.3 0.557734965 4.6762977762
41 -0.359898022 1.9337158254
41.1 -0.115958300 3.1956304458
41.2 -0.084662333 3.2846923557
41.3 -0.048910814 3.3813529415
41.4 0.016921661 3.5482964432
42 -0.139079780 0.4859252973
42.1 0.378571910 4.3293134298
43 -0.159507207 0.5616614548
43.1 -0.279438320 1.0743579536
43.2 -0.277998502 2.6131797966
44 -0.211710161 0.7662644819
44.1 -0.270307896 2.6490291790
44.2 -0.065469588 3.3371910988
44.3 0.272471485 4.1154200875
45 -0.057030292 0.1957449992
45.1 -0.357721087 1.9963831536
46 -0.325006430 1.3477755385
46.1 -0.219695608 2.8565793915
46.2 0.422684799 4.4160729996
47 -0.169972385 0.6012621359
47.1 -0.315522977 2.4097121472
47.2 -0.179612843 2.9975794035
47.3 -0.120277127 3.1829649757
47.4 0.528289800 4.6201055450
48 -0.218578367 2.8607365978
48.1 -0.205083750 2.9098354396
49 -0.254634636 2.7179756400
50 -0.298215668 1.1762060679
51 -0.335531551 1.4304436720
52 -0.349554303 2.1266646020
52.1 -0.147721246 3.1000545993
52.2 -0.139010728 3.1268477370
52.3 0.026310537 3.5711459327
52.4 0.622074267 4.7983659909
52.5 0.719341816 4.9818264414
53 -0.141984214 0.4965799209
53.1 0.017837917 3.5505357443
53.2 0.506858010 4.5790420019
54 -0.332277803 1.4034724841
54.1 -0.360828251 1.8812377600
54.2 -0.298206049 2.5107589352
54.3 -0.238343800 2.7848406672
54.4 0.223917052 4.0143877396
55 -0.172743982 0.6118522980
55.1 -0.206853912 0.7463747414
55.2 -0.229322660 2.8201208171
55.3 -0.137106630 3.1326431572
55.4 -0.106926420 3.2218102901
56 -0.306171957 1.2231332215
56.1 -0.323453138 2.3573202139
56.2 -0.287337760 2.5674936292
56.3 -0.193430676 2.9507164378
56.4 -0.105024091 3.2272730360
56.5 -0.035063150 3.4175522043
57 -0.068975018 0.2370331448
57.1 -0.072178206 0.2481445030
57.2 -0.291872977 1.1405586067
57.3 -0.350452633 2.1153886721
58 -0.305821847 1.2210099772
58.1 -0.354038263 1.6334245703
58.2 -0.356662058 1.6791862890
58.3 -0.273997945 2.6320121693
58.4 -0.222049115 2.8477731440
58.5 0.026480236 3.5715569824
59 -0.360551776 1.9023998594
59.1 0.715006940 4.9736620474
60 -0.211854697 2.8854503250
61 -0.200675172 0.7213630795
61.1 -0.328833062 2.3186947661
61.2 -0.298763087 2.5077313243
61.3 -0.123615504 3.1731073430
61.4 0.039239893 3.6022726283
62 -0.152019902 0.5336771999
62.1 -0.195026261 0.6987666548
62.2 -0.019135492 3.4584309917
62.3 0.624459836 4.8028772371
63 -0.232008543 2.8097350930
63.1 0.200775752 3.9653754211
64 0.274275354 4.1191305732
65 -0.197245756 0.7076152589
65.1 -0.356333749 2.0252246363
65.2 -0.143616734 3.1127382827
65.3 -0.115520552 3.1969087943
66 -0.004894436 3.4943454154
66.1 0.110532429 3.7677437009
66.2 0.192927263 3.9486138616
67 0.300540381 4.1728388879
68 -0.037661039 0.1291919907
68.1 -0.360303787 1.7809643946
68.2 -0.354990134 2.0493205660
68.3 -0.196324326 2.9406870750
68.4 0.236438945 4.0406670363
69 0.286949248 4.1451198701
70 -0.058048346 0.1992557163
70.1 -0.138274515 0.4829774413
71 -0.213623666 0.7741605386
71.1 -0.341911227 1.4883817220
71.2 0.253324633 4.0758526395
71.3 0.572728871 4.7048238723
71.4 0.582970861 4.7242791823
72 -0.250045696 0.9321196121
72.1 -0.298875568 1.1799991806
72.2 -0.360707545 1.8917567329
72.3 -0.008479051 3.4853593935
72.4 0.075836695 3.6884259700
72.5 0.257937078 4.0854155901
73 0.518824914 4.6019889915
74 -0.339185349 1.4626806753
75 -0.096185550 3.2524286874
76 -0.360739315 1.8074807397
76.1 0.347998542 4.2685073183
76.2 0.712464524 4.9688734859
77 -0.230618684 0.8459033852
78 -0.225297228 0.8231094317
79 -0.016946357 0.0583819521
79.1 -0.310503883 2.4406372628
79.2 -0.080482751 3.2962526032
80 -0.242607278 0.8985060186
80.1 -0.324413587 1.3434670598
80.2 -0.233841717 2.8025900386
81 -0.002799225 0.0101324962
81.1 -0.252235081 0.9421709494
81.2 -0.162258804 3.0542453879
81.3 -0.062321933 3.3456630446
82 -0.329186483 1.3791010005
82.1 -0.359810043 1.7601010622
82.2 -0.275856749 2.6233131927
83 -0.015585831 0.0537394290
83.1 -0.206113773 2.9061570496
83.2 -0.141595515 3.1189457362
83.3 0.605165478 4.7663642222
84 -0.252876961 2.7254060237
84.1 -0.065733726 3.3364784659
85 -0.086418319 0.2977756259
85.1 -0.359190206 1.7394116637
85.2 -0.262358242 2.6846330194
85.3 -0.127729570 3.1608762743
85.4 0.191335442 3.9452053758
85.5 0.470626547 4.5092553482
86 -0.231018222 0.8476278360
86.1 -0.266955474 1.0118629411
86.2 -0.310696855 1.2511159515
86.3 -0.344135221 2.1870554925
86.4 -0.308378023 2.4532935000
86.5 0.134159804 3.8206058508
87 -0.257217600 2.7069531474
87.1 -0.023912615 3.4462517721
87.2 0.478346358 4.5241666853
88 0.000000000 0.0005892443
88.1 -0.198244638 0.7116099866
88.2 -0.301030746 2.4952722900
88.3 -0.079274225 3.2995816297
89 -0.181653221 0.6462086167
90 -0.049437014 0.1696030737
90.1 -0.281151299 2.5980385230
90.2 -0.266749122 2.6651392167
90.3 -0.139855690 3.1242690247
91 -0.179603880 0.6382618390
91.1 -0.276049539 2.6224059286
91.2 0.610915809 4.7772527603
92 -0.021435342 0.0737052364
93 -0.081013650 0.2788909199
93.1 -0.271814501 1.0357759963
93.2 -0.301681670 2.4916551099
93.3 -0.211259675 2.8876129608
93.4 0.447248720 4.4639474002
94 -0.231290551 0.8488043118
94.1 -0.275695453 1.0552454425
94.2 -0.359604210 1.9445500884
94.3 -0.156971195 3.0710722448
94.4 -0.151822700 3.0872731935
94.5 0.404570621 4.3805759016
95 -0.356607715 2.0199063048
95.1 0.225844939 4.0184444457
95.2 0.496766490 4.5596531732
96 -0.008958452 0.0311333477
96.1 -0.038605151 0.1324267720
96.2 -0.187779516 0.6701303425
96.3 -0.345060176 2.1775037691
96.4 -0.340252993 2.2246142488
96.5 0.332658824 4.2377650598
97 -0.301543584 1.1955102731
97.1 0.707918494 4.9603108643
98 -0.059473644 0.2041732438
98.1 -0.123949646 0.4309578973
98.2 0.004310743 3.5172611906
99 -0.102164665 0.3531786101
99.1 0.559124871 4.6789444226
99.2 0.725119709 4.9927084171
100 -0.278422879 1.0691387602
100.1 -0.344163920 1.5109344281
100.2 -0.347560939 2.1502332564
100.3 0.158668905 3.8745574222
100.4 0.547483173 4.6567608765
$m7e$spM_id
center scale
C2 -0.6240921 0.68571078
(Intercept) NA NA
C1 0.7372814 0.01472882
$m7e$spM_lvlone
center scale
y -11.17337099 6.2496619
c1 0.25599956 0.6718095
ns(time, df = 3)1 0.19883694 0.2502686
ns(time, df = 3)2 0.38513689 0.1171115
ns(time, df = 3)3 -0.07137294 0.2891059
time 2.53394028 1.3818094
$m7e$mu_reg_norm
[1] 0
$m7e$tau_reg_norm
[1] 1e-04
$m7e$shape_tau_norm
[1] 0.01
$m7e$rate_tau_norm
[1] 0.01
$m7e$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m7e$shape_diag_RinvD
[1] "0.01"
$m7e$rate_diag_RinvD
[1] "0.001"
$m7e$RinvD_y_id
[,1] [,2] [,3] [,4]
[1,] NA 0 0 0
[2,] 0 NA 0 0
[3,] 0 0 NA 0
[4,] 0 0 0 NA
$m7e$KinvD_y_id
id
5
$m7f
$m7f$M_id
C2 (Intercept) C1
1 -1.381594459 1 0.7175865
2 0.344426024 1 0.7507170
3 NA 1 0.7255954
4 -0.228910007 1 0.7469352
5 NA 1 0.7139120
6 -2.143955482 1 0.7332505
7 -1.156567023 1 0.7345929
8 -0.598827660 1 0.7652589
9 NA 1 0.7200622
10 -1.006719032 1 0.7423879
11 0.239801450 1 0.7437448
12 -1.064969789 1 0.7446470
13 -0.538082688 1 0.7530186
14 NA 1 0.7093137
15 -1.781049276 1 0.7331192
16 NA 1 0.7011390
17 NA 1 0.7432395
18 -0.014579883 1 0.7545191
19 -2.121550136 1 0.7528487
20 NA 1 0.7612865
21 -0.363239698 1 0.7251719
22 -0.121568514 1 0.7300630
23 -0.951271111 1 0.7087249
24 NA 1 0.7391938
25 -0.974288621 1 0.7820641
26 -1.130632418 1 0.7118298
27 0.114339868 1 0.7230857
28 0.238334648 1 0.7489353
29 0.840744958 1 0.7510888
30 NA 1 0.7300717
31 NA 1 0.7550721
32 -1.466312154 1 0.7321898
33 -0.637352277 1 0.7306414
34 NA 1 0.7427216
35 NA 1 0.7193042
36 NA 1 0.7312888
37 NA 1 0.7100436
38 NA 1 0.7670184
39 0.006728205 1 0.7400449
40 NA 1 0.7397304
41 -1.663281353 1 0.7490966
42 0.161184794 1 0.7419274
43 0.457939180 1 0.7527810
44 -0.307070331 1 0.7408315
45 NA 1 0.7347550
46 -1.071668276 1 0.7332398
47 -0.814751321 1 0.7376481
48 -0.547630662 1 0.7346179
49 NA 1 0.7329402
50 -1.350213782 1 0.7260436
51 0.719054706 1 0.7242910
52 NA 1 0.7298067
53 -1.207130750 1 0.7254741
54 NA 1 0.7542067
55 -0.408600991 1 0.7389952
56 -0.271380529 1 0.7520638
57 -1.361925974 1 0.7219958
58 NA 1 0.7259632
59 NA 1 0.7458606
60 -0.323712205 1 0.7672421
61 NA 1 0.7257179
62 NA 1 0.7189892
63 -1.386906880 1 0.7333356
64 NA 1 0.7320243
65 NA 1 0.7477711
66 -0.565191691 1 0.7343974
67 -0.382899912 1 0.7491624
68 NA 1 0.7482736
69 -0.405642769 1 0.7338267
70 NA 1 0.7607742
71 -0.843748427 1 0.7777600
72 0.116003683 1 0.7408143
73 -0.778634325 1 0.7248271
74 NA 1 0.7364916
75 NA 1 0.7464926
76 NA 1 0.7355430
77 -0.632974758 1 0.7208449
78 NA 1 0.7373573
79 -0.778064615 1 0.7598079
80 NA 1 0.7360415
81 NA 1 0.7293932
82 -0.246123253 1 0.7279309
83 -1.239659782 1 0.7344643
84 -0.467772280 1 0.7384350
85 NA 1 0.7323716
86 -2.160485036 1 0.7576597
87 -0.657675572 1 0.7496139
88 NA 1 0.7275239
89 -0.696710744 1 0.7250648
90 NA 1 0.7335262
91 -0.179395847 1 0.7343980
92 -0.441545568 1 0.7380425
93 -0.685799334 1 0.7389460
94 NA 1 0.7259951
95 0.191929445 1 0.7282840
96 NA 1 0.7281676
97 -0.069760671 1 0.7245642
98 NA 1 0.7526938
99 NA 1 0.7272309
100 NA 1 0.7383460
$m7f$M_lvlone
y c1 time ns(time, df = 3)1
1 -13.0493856 0.7592026489 0.5090421822 -0.0731022196
1.1 -9.3335901 0.9548337990 0.6666076288 -0.0896372079
1.2 -22.3469852 0.5612235156 2.1304941282 0.1374616725
1.3 -15.0417337 1.1873391025 2.4954441458 0.3061500570
2 -12.0655434 0.9192204198 3.0164990982 0.5064248381
2.1 -15.8674476 -0.1870730476 3.2996806887 0.5543647993
2.2 -7.8800006 1.2517512331 4.1747569619 0.3402753582
3 -11.4820604 -0.0605087604 0.8478727890 -0.1024946971
3.1 -10.5983220 0.3788637747 3.0654308549 0.5187768948
3.2 -22.4519157 0.9872578281 4.7381553578 0.0174998856
4 -1.2697775 1.4930175328 0.3371432109 -0.0508146200
4.1 -11.1215184 -0.7692526880 1.0693019140 -0.1067711172
4.2 -3.6134138 0.9180841450 2.6148973033 0.3598480506
4.3 -14.5982385 -0.0541170782 3.1336532847 0.5333652385
5 -6.8457515 -0.1376784521 1.0762525082 -0.1066695938
5.1 -7.0551214 -0.2740585866 1.7912546196 0.0046159078
5.2 -12.3418980 0.4670496929 2.7960080339 0.4342731827
5.3 -9.2366906 0.1740288049 2.8119940578 0.4402846745
6 -5.1648211 0.9868044683 1.7815462884 0.0015253168
7 -10.0599502 -0.1280320918 3.3074087673 0.5547950298
7.1 -18.3267285 0.4242971219 3.7008403614 0.5158768825
7.2 -12.5138426 0.0777182491 4.7716691741 -0.0038356900
8 -1.6305331 -0.5791408712 1.1246398522 -0.1055283475
8.1 -9.6520453 0.3128604232 1.8027009873 0.0083228157
8.2 -1.5278462 0.6258446356 1.8175825174 0.0132420073
8.3 -7.4172211 -0.1040137707 2.8384267003 0.4499876083
8.4 -7.1238609 0.0481450285 3.3630275307 0.5564354493
8.5 -8.8706950 0.3831763675 4.4360849704 0.2011700353
9 -0.1634429 -0.1757592269 0.9607803822 -0.1064248362
9.1 -2.6034300 -0.1791541200 2.9177753383 0.4771840649
9.2 -6.7272369 -0.0957042935 4.8100892501 -0.0284447928
10 -6.4172202 -0.5598409704 2.2975509102 0.2139341504
10.1 -11.4834569 -0.2318340451 4.1734118364 0.3409274088
11 -8.7911356 0.5086859475 1.1832662905 -0.1030921083
11.1 -19.6645080 0.4951758188 1.2346051680 -0.0999663372
11.2 -20.2030932 -1.1022162541 1.6435316263 -0.0371358422
11.3 -21.3082176 -0.0611636705 3.3859017969 0.5563809266
11.4 -14.5802901 -0.4971774316 4.8118087661 -0.0295495187
12 -15.2006287 -0.2433996286 0.9591987054 -0.1063939605
13 0.8058816 0.8799673116 0.0619085738 -0.0095916688
13.1 -13.6379208 0.1079022586 3.5621061502 0.5424520669
14 -15.3422873 0.9991752617 4.0364430007 0.4032689358
14.1 -10.0965208 -0.1094019046 4.4710561272 0.1809335363
14.2 -16.6452027 0.1518967560 4.6359198843 0.0816318440
14.3 -15.8389733 0.3521012473 4.6886152599 0.0487734088
15 -8.9424594 0.3464447888 0.5402063532 -0.0767078114
15.1 -22.0101983 -0.4767313971 1.1893180816 -0.1027727004
15.2 -7.3975599 0.5759767791 1.5094739688 -0.0657566366
15.3 -10.3567334 -0.1713452662 4.9193474615 -0.0990627407
16 -1.9691302 0.4564754473 1.2417913869 -0.0994524289
16.1 -9.9308357 1.0652558311 2.5675726333 0.3388926675
16.2 -6.9626923 0.6971872493 2.6524101500 0.3760850293
16.3 -3.2862557 0.5259331838 3.5585018690 0.5429665219
16.4 -3.3972355 0.2046601798 3.7612454291 0.5003814501
16.5 -11.5767835 1.0718540464 3.9851612889 0.4243928946
17 -10.5474144 0.6048676222 1.5925356350 -0.0490208458
17.1 -7.6215009 0.2323298304 2.4374032998 0.2793033691
17.2 -16.5386939 1.2617499032 3.0256489082 0.5088484549
17.3 -20.0004774 -0.3913230895 3.3329089405 0.5558624020
17.4 -18.8505475 0.9577299112 3.8693758985 0.4671596304
18 -19.7302351 -0.0050324072 2.4374292302 0.2793154325
19 -14.6177568 -0.4187468937 0.9772165376 -0.1067031159
19.1 -17.8043866 -0.4478828944 1.1466335913 -0.1047524970
19.2 -15.1641705 -1.1966721302 2.2599126538 0.1964187590
19.3 -16.6898418 -0.5877091668 4.2114245973 0.3222225327
20 -12.9059229 0.6838223064 1.7170160066 -0.0177612481
20.1 -16.8191201 0.3278571109 1.7562902288 -0.0062817988
20.2 -6.1010131 -0.8489831990 2.2515566566 0.1925456183
20.3 -7.9415371 1.3169975191 2.2609123867 0.1968825793
20.4 -9.3904458 0.0444804531 3.4913365287 0.5508408484
20.5 -13.3504189 -0.4535207652 4.1730977828 0.3410795409
21 -7.6974718 -0.4030302960 1.6936582839 -0.0242133322
21.1 -11.9335526 -0.4069674045 2.9571191233 0.4894906279
21.2 -12.7064929 1.0650265940 3.7887385779 0.4925855178
22 -21.5022909 -0.0673274516 2.4696226232 0.2942488077
22.1 -12.7745451 0.9601388170 3.1626627257 0.5385758261
23 -3.5146508 0.5556634840 1.5414533857 -0.0596896592
23.1 -4.6724048 1.4407865964 2.3369736120 0.2323619738
24 -2.5619821 0.3856376411 2.8283136466 0.4463109938
25 -6.2944970 0.3564400705 0.5381704110 -0.0764769279
25.1 -3.8630505 0.0982553434 1.6069735331 -0.0457828435
25.2 -14.4205140 0.1928682598 1.6358226922 -0.0390131484
25.3 -19.6735037 -0.0192488594 3.2646870392 0.5517873130
25.4 -9.0288933 0.4466012931 4.0782226040 0.3851356619
25.5 -9.0509738 1.1425193342 4.1560292873 0.3492871599
26 -19.7340685 0.5341531449 0.2412706357 -0.0370005446
26.1 -14.1692728 1.2268695927 2.4451737676 0.2829160492
26.2 -17.2819976 0.3678294939 3.5988757887 0.5366821080
26.3 -24.6265576 0.5948516018 4.1822362854 0.3366364198
27 -7.3354999 -0.3342844147 3.6955824879 0.5171168233
27.1 -11.1488468 -0.4835141229 4.2451434687 0.3051619029
28 -11.7996597 -0.7145915499 0.5746519344 -0.0805110543
28.1 -8.2030122 0.5063671955 2.7943964268 0.4336613409
28.2 -26.4317815 -0.2067413142 4.2108539480 0.3225075315
28.3 -18.5016071 0.1196789973 4.4705521734 0.1812274493
29 -5.8551395 0.1392699487 1.1898884235 -0.1027419307
29.1 -2.0209442 0.7960234776 1.7624059319 -0.0044221127
29.2 -5.6368080 1.0398214352 2.0210406382 0.0902449711
29.3 -3.8110961 0.0813246429 3.4078777023 0.5559364351
30 -12.7217702 -0.3296323050 2.2635366488 0.1981005081
30.1 -17.0170140 1.3635850954 3.5938334477 0.5375291120
30.2 -25.4236089 0.7354171050 3.6138710892 0.5340597966
31 -17.0783921 0.3708398217 4.3988140998 0.2223657818
32 -18.4338764 -0.0474059668 1.6745209007 -0.0292943991
32.1 -19.4317212 1.2507771489 2.9128167813 0.4755751076
32.2 -19.4738978 0.1142915519 2.9676558380 0.4926434114
32.3 -21.4922645 0.6773270619 4.2099863547 0.3229405911
33 2.0838099 0.1774293842 0.0093385763 -0.0013701847
33.1 -13.3172274 0.6159606291 3.4591242753 0.5534368438
34 -10.0296691 0.8590979166 1.4998774312 -0.0674871387
34.1 -25.9426553 0.0546216775 3.8242761395 0.4818424185
34.2 -18.5688138 -0.0897224473 3.9072251692 0.4539624195
34.3 -15.4173859 0.4163395571 3.9582124643 0.4349720689
35 -14.3958113 -1.4693520528 1.3294299203 -0.0916157074
35.1 -12.9457541 -0.3031734330 1.5276966314 -0.0623564831
35.2 -16.1380691 -0.6045512101 4.5025920868 0.1624134259
36 -12.8166968 0.9823048960 0.7123168337 -0.0935638890
36.1 -14.3989481 1.4466051416 1.7972493160 0.0065488640
36.2 -12.2436943 1.1606752905 1.8262697803 0.0161646973
36.3 -15.0104638 0.8373091576 4.2840119381 0.2849748388
36.4 -10.1775457 0.2640591685 4.6194464504 0.0918066953
37 -15.2223495 0.1177313455 2.0018732361 0.0823235483
37.1 -14.7526195 -0.1415483779 3.6656836793 0.5238280708
37.2 -19.8168430 0.0054610124 3.9663937816 0.4317992421
38 -2.7065118 0.8078948077 0.9826511063 -0.1067779048
39 -8.7288138 0.9876451040 0.6921808305 -0.0918869929
39.1 -9.2746473 -0.3431222274 0.9027792048 -0.1048343071
39.2 -18.2695344 -1.7909380751 1.3055654289 -0.0940424764
39.3 -13.8219083 -0.1798746191 1.5412842878 -0.0597229655
39.4 -16.2254704 -0.1850961689 3.1834997435 0.5419341500
39.5 -21.7283648 0.4544226146 4.1394166439 0.3571597096
40 1.8291916 0.5350190436 1.1330395646 -0.1052513278
40.1 -6.6916432 0.4189342752 2.6940994046 0.3936778108
40.2 -1.6278171 0.4211994981 3.0396614212 0.5124598453
40.3 -10.5749790 0.0916687506 4.6762977762 0.0564941376
41 -3.1556121 -0.1035047421 1.9337158254 0.0551736455
41.1 -11.5895327 -0.4684202411 3.1956304458 0.5437374685
41.2 -18.9352091 0.5972615368 3.2846923557 0.5533874833
41.3 -15.9788960 0.9885613862 3.3813529415 0.5564251145
41.4 -9.6070508 -0.3908036794 3.5482964432 0.5443730748
42 -5.2159485 -0.0338893961 0.4859252973 -0.0703321485
42.1 -15.9878743 -0.4498363172 4.3293134298 0.2607797560
43 -16.6104361 0.8965546110 0.5616614548 -0.0790999084
43.1 -9.5549441 0.6199122090 1.0743579536 -0.1066987969
43.2 -14.2003491 0.1804894429 2.6131797966 0.3590962645
44 -8.1969033 1.3221409285 0.7662644819 -0.0976262102
44.1 -19.9270197 0.3416426284 2.6490291790 0.3746366123
44.2 -22.6521171 0.5706610068 3.3371910988 0.5559890192
44.3 -21.1903736 1.2679497430 4.1154200875 0.3683249841
45 -0.5686627 0.1414983160 0.1957449992 -0.0301940136
45.1 -7.5645740 0.7220892521 1.9963831536 0.0800764791
46 -19.1624789 1.5391054233 1.3477755385 -0.0895971055
46.1 -18.4487574 0.3889107049 2.8565793915 0.4564725410
46.2 -15.8222682 0.1248719493 4.4160729996 0.2125999596
47 -5.4165074 0.2014101100 0.6012621359 -0.0833108913
47.1 -15.0975029 0.2982973539 2.4097121472 0.2663955892
47.2 -12.9971413 1.1518107179 2.9975794035 0.5012527278
47.3 -10.6844521 0.5196802157 3.1829649757 0.5418520608
47.4 -18.2214784 0.3702301552 4.6201055450 0.0914005525
48 -8.3101471 -0.2128602862 2.8607365978 0.4579365172
48.1 -18.3854275 -0.5337239976 2.9098354396 0.4746016472
49 -13.0130319 -0.5236770035 2.7179756400 0.4035144992
50 -10.4579977 0.3897705981 1.1762060679 -0.1034484387
51 -19.3157621 -0.7213343736 1.4304436720 -0.0787988153
52 -4.4747188 0.3758235358 2.1266646020 0.1357604672
52.1 -4.3163827 0.7138067080 3.1000545993 0.5265788733
52.2 -6.9761408 0.8872895233 3.1268477370 0.5320547699
52.3 -20.1764756 -0.9664587437 3.5711459327 0.5411213524
52.4 -8.9036692 0.0254566848 4.7983659909 -0.0209202834
52.5 -5.6949642 0.4155259424 4.9818264414 -0.1396816763
53 -10.3141887 0.5675736897 0.4965799209 -0.0716187009
53.1 -8.2642654 -0.3154088781 3.5505357443 0.5440708053
53.2 -9.1691554 0.2162315769 4.5790420019 0.1165457070
54 -6.2198754 -0.0880802382 1.4034724841 -0.0826335785
54.1 -15.7192609 0.4129127672 1.8812377600 0.0354880963
54.2 -13.0978998 1.0119546775 2.5107589352 0.3131694667
54.3 -5.1195299 -0.1112901990 2.7848406672 0.4300121383
54.4 -16.5771751 0.8587727145 4.0143877396 0.4125104256
55 -5.7348534 -0.0116453589 0.6118522980 -0.0843902223
55.1 -7.3217494 0.5835528661 0.7463747414 -0.0962032547
55.2 -12.2171938 -1.0010857254 2.8201208171 0.4432998504
55.3 -12.9821266 -0.4796526070 3.1326431572 0.5331728179
55.4 -14.8599983 -0.1202746964 3.2218102901 0.5472394872
56 -14.1764282 0.5176377612 1.2231332215 -0.1007474372
56.1 -12.5343602 -1.1136932588 2.3573202139 0.2418874691
56.2 -8.4573382 -0.0168103281 2.5674936292 0.3388572935
56.3 -12.4633969 0.3933023606 2.9507164378 0.4875447544
56.4 -17.3841863 0.3714625139 3.2272730360 0.5479018829
56.5 -14.8147645 0.7811448179 3.4175522043 0.5556203170
57 -3.1403293 -1.0868304872 0.2370331448 -0.0363728373
57.1 -11.1509248 0.8018626997 0.2481445030 -0.0380160354
57.2 -6.3940143 -0.1159517011 1.1405586067 -0.1049831697
57.3 -9.3473241 0.6785562445 2.1153886721 0.1307695676
58 -12.0245677 1.6476207996 1.2210099772 -0.1008867350
58.1 -9.2112246 0.3402652711 1.6334245703 -0.0395912379
58.2 -1.2071742 -0.1111300753 1.6791862890 -0.0280726286
58.3 -11.0141711 -0.5409234285 2.6320121693 0.3673004631
58.4 -5.3721214 -0.1271327672 2.8477731440 0.4533451791
58.5 -7.8523047 0.8713264822 3.5715569824 0.5410594734
59 -13.2946560 0.4766421367 1.9023998594 0.0432887218
59.1 -10.0530648 1.0028089765 4.9736620474 -0.1343700478
60 -19.2209402 0.5231452932 2.8854503250 0.4664716194
61 -4.6699914 -0.7190130614 0.7213630795 -0.0942894088
61.1 -3.5981894 0.8353702312 2.3186947661 0.2238107295
61.2 -1.4713611 1.0229058138 2.5077313243 0.3117843207
61.3 -3.8819786 1.1717723589 3.1731073430 0.5403000211
61.4 0.1041413 -0.0629201596 3.6022726283 0.5361016178
62 -2.8591600 -0.3979137604 0.5336771999 -0.0759650358
62.1 -6.9461986 0.6830738372 0.6987666548 -0.0924447607
62.2 -16.7910593 0.4301745954 3.4584309917 0.5534841147
62.3 -17.9844596 -0.0333139957 4.8028772371 -0.0238142803
63 -24.0335535 0.3345678035 2.8097350930 0.4394416153
63.1 -11.7765300 0.3643769511 3.9653754211 0.4321960350
64 -20.5963897 0.3949911859 4.1191305732 0.3666147194
65 -2.7969169 1.2000091513 0.7076152589 -0.0931799515
65.1 -11.1778694 0.0110122646 2.0252246363 0.0919894525
65.2 -5.2830399 -0.5776452043 3.1127382827 0.5292334967
65.3 -7.9353390 -0.1372183563 3.1969087943 0.5439209079
66 -13.2318328 -0.5081302805 3.4943454154 0.5505587192
66.1 -1.9090560 -0.1447837412 3.7677437009 0.4985800130
66.2 -16.6643889 0.1906241379 3.9486138616 0.4386508147
67 -25.6073277 1.6716027681 4.1728388879 0.3412049232
68 -13.4806759 0.5691848839 0.1291919907 -0.0200346649
68.1 -18.4557183 0.1004860389 1.7809643946 0.0013416527
68.2 -13.3982327 -0.0061241827 2.0493205660 0.1021380380
68.3 -12.4977127 0.7443745962 2.9406870750 0.4844516704
68.4 -11.7073990 0.8726923437 4.0406670363 0.4014725605
69 -14.5290675 0.0381382683 4.1451198701 0.3544700410
70 -15.2122709 0.8126204217 0.1992557163 -0.0307235467
70.1 -7.8681167 0.4691503050 0.4829774413 -0.0699732793
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81.3 -5.2166381 0.4182528210 3.3456630446 0.5561952783
82 -3.0749381 1.1088801952 1.3791010005 -0.0858371096
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93 5.2122168 0.2677965647 0.2788909199 -0.0425142500
93.1 3.1211725 1.6424445368 1.0357759963 -0.1070466070
93.2 -3.6841177 0.7101700066 2.4916551099 0.3044086294
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100 1.1005874 0.0369067906 1.0691387602 -0.1067733151
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ns(time, df = 3)2 ns(time, df = 3)3
1 0.222983368 -0.145369093
1.1 0.286659651 -0.186881442
1.2 0.538466292 -0.349241050
1.3 0.485312041 -0.300999737
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32.3 0.340746183 0.318869516
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54.4 0.329511331 0.223917052
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94.4 0.377204420 -0.151822700
94.5 0.354495295 0.404570621
95 0.547886254 -0.356607715
95.1 0.329687700 0.225844939
95.2 0.371978790 0.496766490
96 0.013741475 -0.008958452
96.1 0.059216896 -0.038605151
96.2 0.288037217 -0.187779516
96.3 0.533324704 -0.345060176
96.4 0.527566661 -0.340252993
96.5 0.342757800 0.332658824
97 0.462541264 -0.301543584
97.1 0.417765721 0.707918494
98 0.091227325 -0.059473644
98.1 0.190127826 -0.123949646
98.2 0.330659794 0.004310743
99 0.156711586 -0.102164665
99.1 0.384914328 0.559124871
99.2 0.421642124 0.725119709
100 0.427076142 -0.278422879
100.1 0.527917102 -0.344163920
100.2 0.536384181 -0.347560939
100.3 0.325078572 0.158668905
100.4 0.382446660 0.547483173
$m7f$spM_id
center scale
C2 -0.6240921 0.68571078
(Intercept) NA NA
C1 0.7372814 0.01472882
$m7f$spM_lvlone
center scale
y -11.17337099 6.2496619
c1 0.25599956 0.6718095
time 2.53394028 1.3818094
ns(time, df = 3)1 0.19883694 0.2502686
ns(time, df = 3)2 0.38513689 0.1171115
ns(time, df = 3)3 -0.07137294 0.2891059
$m7f$mu_reg_norm
[1] 0
$m7f$tau_reg_norm
[1] 1e-04
$m7f$shape_tau_norm
[1] 0.01
$m7f$rate_tau_norm
[1] 0.01
$m7f$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m7f$shape_diag_RinvD
[1] "0.01"
$m7f$rate_diag_RinvD
[1] "0.001"
$m7f$RinvD_y_id
[,1] [,2]
[1,] NA 0
[2,] 0 NA
$m7f$KinvD_y_id
id
3
$m8a
$m8a$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m8a$M_lvlone
y c2 c1 time
1 -13.0493856 NA 0.7592026489 0.5090421822
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890
3.1 -10.5983220 NA 0.3788637747 3.0654308549
3.2 -22.4519157 NA 0.9872578281 4.7381553578
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339
5.3 -9.2366906 NA 0.1740288049 2.8119940578
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673
7.1 -18.3267285 NA 0.4242971219 3.7008403614
7.2 -12.5138426 NA 0.0777182491 4.7716691741
8 -1.6305331 NA -0.5791408712 1.1246398522
8.1 -9.6520453 NA 0.3128604232 1.8027009873
8.2 -1.5278462 NA 0.6258446356 1.8175825174
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704
9 -0.1634429 NA -0.1757592269 0.9607803822
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501
10 -6.4172202 NA -0.5598409704 2.2975509102
10.1 -11.4834569 NA -0.2318340451 4.1734118364
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661
12 -15.2006287 NA -0.2433996286 0.9591987054
13 0.8058816 NA 0.8799673116 0.0619085738
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007
14.1 -10.0965208 NA -0.1094019046 4.4710561272
14.2 -16.6452027 NA 0.1518967560 4.6359198843
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599
15 -8.9424594 NA 0.3464447888 0.5402063532
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869
16.1 -9.9308357 -0.07599066 1.0652558311 2.5675726333
16.2 -6.9626923 -0.32385667 0.6971872493 2.6524101500
16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690
16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291
16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889
17 -10.5474144 NA 0.6048676222 1.5925356350
17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998
17.2 -16.5386939 NA 1.2617499032 3.0256489082
17.3 -20.0004774 NA -0.3913230895 3.3329089405
17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985
18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302
19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376
19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913
19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538
19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973
20 -12.9059229 -0.22087628 0.6838223064 1.7170160066
20.1 -16.8191201 NA 0.3278571109 1.7562902288
20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566
20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867
20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287
20.5 -13.3504189 -0.24338208 -0.4535207652 4.1730977828
21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839
21.1 -11.9335526 -0.12424941 -0.4069674045 2.9571191233
21.2 -12.7064929 -0.27672716 1.0650265940 3.7887385779
22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232
22.1 -12.7745451 -0.15996535 0.9601388170 3.1626627257
23 -3.5146508 -0.18236682 0.5556634840 1.5414533857
23.1 -4.6724048 -0.20823302 1.4407865964 2.3369736120
24 -2.5619821 -0.29026416 0.3856376411 2.8283136466
25 -6.2944970 -0.36139273 0.3564400705 0.5381704110
25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331
25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922
25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392
25.4 -9.0288933 NA 0.4466012931 4.0782226040
25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873
26 -19.7340685 -0.16846716 0.5341531449 0.2412706357
26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676
26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887
26.3 -24.6265576 NA 0.5948516018 4.1822362854
27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879
27.1 -11.1488468 -0.17622638 -0.4835141229 4.2451434687
28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344
28.1 -8.2030122 -0.27003852 0.5063671955 2.7943964268
28.2 -26.4317815 -0.07235801 -0.2067413142 4.2108539480
28.3 -18.5016071 -0.13462982 0.1196789973 4.4705521734
29 -5.8551395 -0.32432030 0.1392699487 1.1898884235
29.1 -2.0209442 -0.27034171 0.7960234776 1.7624059319
29.2 -5.6368080 -0.10197448 1.0398214352 2.0210406382
29.3 -3.8110961 -0.27606945 0.0813246429 3.4078777023
30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488
30.1 -17.0170140 -0.11511035 1.3635850954 3.5938334477
30.2 -25.4236089 -0.16215882 0.7354171050 3.6138710892
31 -17.0783921 0.05707733 0.3708398217 4.3988140998
32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007
32.1 -19.4317212 -0.14270013 1.2507771489 2.9128167813
32.2 -19.4738978 -0.20530798 0.1142915519 2.9676558380
32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547
33 2.0838099 -0.15252819 0.1774293842 0.0093385763
33.1 -13.3172274 NA 0.6159606291 3.4591242753
34 -10.0296691 -0.30378735 0.8590979166 1.4998774312
34.1 -25.9426553 -0.11982431 0.0546216775 3.8242761395
34.2 -18.5688138 -0.24278671 -0.0897224473 3.9072251692
34.3 -15.4173859 -0.19971833 0.4163395571 3.9582124643
35 -14.3958113 NA -1.4693520528 1.3294299203
35.1 -12.9457541 -0.24165780 -0.3031734330 1.5276966314
35.2 -16.1380691 NA -0.6045512101 4.5025920868
36 -12.8166968 -0.49062180 0.9823048960 0.7123168337
36.1 -14.3989481 -0.25651700 1.4466051416 1.7972493160
36.2 -12.2436943 NA 1.1606752905 1.8262697803
36.3 -15.0104638 -0.30401274 0.8373091576 4.2840119381
36.4 -10.1775457 NA 0.2640591685 4.6194464504
37 -15.2223495 -0.15276529 0.1177313455 2.0018732361
37.1 -14.7526195 -0.30016169 -0.1415483779 3.6656836793
37.2 -19.8168430 0.06809545 0.0054610124 3.9663937816
38 -2.7065118 -0.11218486 0.8078948077 0.9826511063
39 -8.7288138 -0.38072211 0.9876451040 0.6921808305
39.1 -9.2746473 -0.32094428 -0.3431222274 0.9027792048
39.2 -18.2695344 NA -1.7909380751 1.3055654289
39.3 -13.8219083 -0.40173480 -0.1798746191 1.5412842878
39.4 -16.2254704 -0.20041848 -0.1850961689 3.1834997435
39.5 -21.7283648 -0.26027990 0.4544226146 4.1394166439
40 1.8291916 -0.19751956 0.5350190436 1.1330395646
40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046
40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212
40.3 -10.5749790 NA 0.0916687506 4.6762977762
41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254
41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458
41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557
41.3 -15.9788960 NA 0.9885613862 3.3813529415
41.4 -9.6070508 NA -0.3908036794 3.5482964432
42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973
42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298
43 -16.6104361 -0.08864017 0.8965546110 0.5616614548
43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536
43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966
44 -8.1969033 -0.22697597 1.3221409285 0.7662644819
44.1 -19.9270197 NA 0.3416426284 2.6490291790
44.2 -22.6521171 NA 0.5706610068 3.3371910988
44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875
45 -0.5686627 -0.19274788 0.1414983160 0.1957449992
45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536
46 -19.1624789 -0.28547861 1.5391054233 1.3477755385
46.1 -18.4487574 -0.21977836 0.3889107049 2.8565793915
46.2 -15.8222682 NA 0.1248719493 4.4160729996
47 -5.4165074 -0.08597098 0.2014101100 0.6012621359
47.1 -15.0975029 -0.35424828 0.2982973539 2.4097121472
47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035
47.3 -10.6844521 -0.30426315 0.5196802157 3.1829649757
47.4 -18.2214784 NA 0.3702301552 4.6201055450
48 -8.3101471 NA -0.2128602862 2.8607365978
48.1 -18.3854275 NA -0.5337239976 2.9098354396
49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400
50 -10.4579977 -0.19959516 0.3897705981 1.1762060679
51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720
52 -4.4747188 -0.07438732 0.3758235358 2.1266646020
52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993
52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370
52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327
52.4 -8.9036692 -0.44619160 0.0254566848 4.7983659909
52.5 -5.6949642 -0.11011682 0.4155259424 4.9818264414
53 -10.3141887 -0.23278716 0.5675736897 0.4965799209
53.1 -8.2642654 -0.28317264 -0.3154088781 3.5505357443
53.2 -9.1691554 -0.19517481 0.2162315769 4.5790420019
54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841
54.1 -15.7192609 -0.28325504 0.4129127672 1.8812377600
54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352
54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672
54.4 -16.5771751 -0.34609328 0.8587727145 4.0143877396
55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980
55.1 -7.3217494 -0.24235382 0.5835528661 0.7463747414
55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171
55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572
55.4 -14.8599983 NA -0.1202746964 3.2218102901
56 -14.1764282 NA 0.5176377612 1.2231332215
56.1 -12.5343602 -0.30357450 -1.1136932588 2.3573202139
56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292
56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378
56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360
56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043
57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448
57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030
57.2 -6.3940143 NA -0.1159517011 1.1405586067
57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721
58 -12.0245677 -0.29901358 1.6476207996 1.2210099772
58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703
58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890
58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693
58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440
58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824
59 -13.2946560 -0.27255364 0.4766421367 1.9023998594
59.1 -10.0530648 NA 1.0028089765 4.9736620474
60 -19.2209402 NA 0.5231452932 2.8854503250
61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795
61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661
61.2 -1.4713611 NA 1.0229058138 2.5077313243
61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430
61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283
62 -2.8591600 NA -0.3979137604 0.5336771999
62.1 -6.9461986 -0.10504143 0.6830738372 0.6987666548
62.2 -16.7910593 -0.20104997 0.4301745954 3.4584309917
62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371
63 -24.0335535 -0.12036319 0.3345678035 2.8097350930
63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211
64 -20.5963897 NA 0.3949911859 4.1191305732
65 -2.7969169 -0.34450396 1.2000091513 0.7076152589
65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363
65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827
65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943
66 -13.2318328 NA -0.5081302805 3.4943454154
66.1 -1.9090560 NA -0.1447837412 3.7677437009
66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616
67 -25.6073277 NA 1.6716027681 4.1728388879
68 -13.4806759 -0.13605235 0.5691848839 0.1291919907
68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946
68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660
68.3 -12.4977127 NA 0.7443745962 2.9406870750
68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363
69 -14.5290675 -0.32152751 0.0381382683 4.1451198701
70 -15.2122709 -0.28190462 0.8126204217 0.1992557163
70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413
71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386
71.1 -7.5699888 NA -0.1103252087 1.4883817220
71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395
71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723
71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823
72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121
72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806
72.2 -26.3421306 NA -0.4170988856 1.8917567329
72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935
72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700
72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901
73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915
74 -8.2790175 -0.27963171 0.3730017941 1.4626806753
75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874
76 -6.0061671 0.04775562 -0.9888876620 1.8074807397
76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183
76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859
77 0.4772521 -0.19053195 -0.4986802480 0.8459033852
78 -9.4105229 -0.17172929 0.0417330969 0.8231094317
79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521
79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628
79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032
80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186
80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598
80.2 -16.6381566 NA 0.5046816157 2.8025900386
81 0.5547913 -0.18401980 -0.9493950353 0.0101324962
81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494
81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879
81.3 -5.2166381 NA 0.4182528210 3.3456630446
82 -3.0749381 -0.08328168 1.1088801952 1.3791010005
82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622
82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927
83 -12.5872635 -0.34228255 0.5104724530 0.0537394290
83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496
83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362
83.3 -19.2714012 NA -0.0534169155 4.7663642222
84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237
84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659
85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259
85.1 -12.9196365 NA -0.0577615939 1.7394116637
85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194
85.3 -6.3296340 NA -0.0520175929 3.1608762743
85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758
85.5 -13.6714939 NA -0.3740417009 4.5092553482
86 -10.8756412 -0.10854862 0.4293735089 0.8476278360
86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411
86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515
86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925
86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000
86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508
87 -10.7027963 NA 1.0457427869 2.7069531474
87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721
87.2 -14.9616716 NA 0.4396872616 4.5241666853
88 -2.2264493 -0.31563888 -0.0601928334 0.0005892443
88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603
92 -6.4119222 NA 0.4674939332 0.0737052364
93 5.2122168 -0.11939966 0.2677965647 0.2788909199
93.1 3.1211725 NA 1.6424445368 1.0357759963
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099
93.3 2.6223542 NA 1.1222322893 2.8876129608
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002
94 -6.9602492 NA -0.2904211940 0.8488043118
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438
98.1 -2.6515128 NA -0.9042716241 0.4309578973
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171
100 1.1005874 -0.17326698 0.0369067906 1.0691387602
100.1 -3.8226248 NA 1.6023713093 1.5109344281
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765
$m8a$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
$m8a$mu_reg_norm
[1] 0
$m8a$tau_reg_norm
[1] 1e-04
$m8a$shape_tau_norm
[1] 0.01
$m8a$rate_tau_norm
[1] 0.01
$m8a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8a$shape_diag_RinvD
[1] "0.01"
$m8a$rate_diag_RinvD
[1] "0.001"
$m8a$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8a$KinvD_y_id
id
4
$m8b
$m8b$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m8b$M_lvlone
y c2 c1 time
1 -13.0493856 NA 0.7592026489 0.5090421822
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890
3.1 -10.5983220 NA 0.3788637747 3.0654308549
3.2 -22.4519157 NA 0.9872578281 4.7381553578
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339
5.3 -9.2366906 NA 0.1740288049 2.8119940578
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673
7.1 -18.3267285 NA 0.4242971219 3.7008403614
7.2 -12.5138426 NA 0.0777182491 4.7716691741
8 -1.6305331 NA -0.5791408712 1.1246398522
8.1 -9.6520453 NA 0.3128604232 1.8027009873
8.2 -1.5278462 NA 0.6258446356 1.8175825174
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704
9 -0.1634429 NA -0.1757592269 0.9607803822
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501
10 -6.4172202 NA -0.5598409704 2.2975509102
10.1 -11.4834569 NA -0.2318340451 4.1734118364
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661
12 -15.2006287 NA -0.2433996286 0.9591987054
13 0.8058816 NA 0.8799673116 0.0619085738
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007
14.1 -10.0965208 NA -0.1094019046 4.4710561272
14.2 -16.6452027 NA 0.1518967560 4.6359198843
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599
15 -8.9424594 NA 0.3464447888 0.5402063532
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869
16.1 -9.9308357 -0.07599066 1.0652558311 2.5675726333
16.2 -6.9626923 -0.32385667 0.6971872493 2.6524101500
16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690
16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291
16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889
17 -10.5474144 NA 0.6048676222 1.5925356350
17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998
17.2 -16.5386939 NA 1.2617499032 3.0256489082
17.3 -20.0004774 NA -0.3913230895 3.3329089405
17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985
18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302
19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376
19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913
19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538
19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973
20 -12.9059229 -0.22087628 0.6838223064 1.7170160066
20.1 -16.8191201 NA 0.3278571109 1.7562902288
20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566
20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867
20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287
20.5 -13.3504189 -0.24338208 -0.4535207652 4.1730977828
21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839
21.1 -11.9335526 -0.12424941 -0.4069674045 2.9571191233
21.2 -12.7064929 -0.27672716 1.0650265940 3.7887385779
22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232
22.1 -12.7745451 -0.15996535 0.9601388170 3.1626627257
23 -3.5146508 -0.18236682 0.5556634840 1.5414533857
23.1 -4.6724048 -0.20823302 1.4407865964 2.3369736120
24 -2.5619821 -0.29026416 0.3856376411 2.8283136466
25 -6.2944970 -0.36139273 0.3564400705 0.5381704110
25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331
25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922
25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392
25.4 -9.0288933 NA 0.4466012931 4.0782226040
25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873
26 -19.7340685 -0.16846716 0.5341531449 0.2412706357
26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676
26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887
26.3 -24.6265576 NA 0.5948516018 4.1822362854
27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879
27.1 -11.1488468 -0.17622638 -0.4835141229 4.2451434687
28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344
28.1 -8.2030122 -0.27003852 0.5063671955 2.7943964268
28.2 -26.4317815 -0.07235801 -0.2067413142 4.2108539480
28.3 -18.5016071 -0.13462982 0.1196789973 4.4705521734
29 -5.8551395 -0.32432030 0.1392699487 1.1898884235
29.1 -2.0209442 -0.27034171 0.7960234776 1.7624059319
29.2 -5.6368080 -0.10197448 1.0398214352 2.0210406382
29.3 -3.8110961 -0.27606945 0.0813246429 3.4078777023
30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488
30.1 -17.0170140 -0.11511035 1.3635850954 3.5938334477
30.2 -25.4236089 -0.16215882 0.7354171050 3.6138710892
31 -17.0783921 0.05707733 0.3708398217 4.3988140998
32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007
32.1 -19.4317212 -0.14270013 1.2507771489 2.9128167813
32.2 -19.4738978 -0.20530798 0.1142915519 2.9676558380
32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547
33 2.0838099 -0.15252819 0.1774293842 0.0093385763
33.1 -13.3172274 NA 0.6159606291 3.4591242753
34 -10.0296691 -0.30378735 0.8590979166 1.4998774312
34.1 -25.9426553 -0.11982431 0.0546216775 3.8242761395
34.2 -18.5688138 -0.24278671 -0.0897224473 3.9072251692
34.3 -15.4173859 -0.19971833 0.4163395571 3.9582124643
35 -14.3958113 NA -1.4693520528 1.3294299203
35.1 -12.9457541 -0.24165780 -0.3031734330 1.5276966314
35.2 -16.1380691 NA -0.6045512101 4.5025920868
36 -12.8166968 -0.49062180 0.9823048960 0.7123168337
36.1 -14.3989481 -0.25651700 1.4466051416 1.7972493160
36.2 -12.2436943 NA 1.1606752905 1.8262697803
36.3 -15.0104638 -0.30401274 0.8373091576 4.2840119381
36.4 -10.1775457 NA 0.2640591685 4.6194464504
37 -15.2223495 -0.15276529 0.1177313455 2.0018732361
37.1 -14.7526195 -0.30016169 -0.1415483779 3.6656836793
37.2 -19.8168430 0.06809545 0.0054610124 3.9663937816
38 -2.7065118 -0.11218486 0.8078948077 0.9826511063
39 -8.7288138 -0.38072211 0.9876451040 0.6921808305
39.1 -9.2746473 -0.32094428 -0.3431222274 0.9027792048
39.2 -18.2695344 NA -1.7909380751 1.3055654289
39.3 -13.8219083 -0.40173480 -0.1798746191 1.5412842878
39.4 -16.2254704 -0.20041848 -0.1850961689 3.1834997435
39.5 -21.7283648 -0.26027990 0.4544226146 4.1394166439
40 1.8291916 -0.19751956 0.5350190436 1.1330395646
40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046
40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212
40.3 -10.5749790 NA 0.0916687506 4.6762977762
41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254
41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458
41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557
41.3 -15.9788960 NA 0.9885613862 3.3813529415
41.4 -9.6070508 NA -0.3908036794 3.5482964432
42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973
42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298
43 -16.6104361 -0.08864017 0.8965546110 0.5616614548
43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536
43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966
44 -8.1969033 -0.22697597 1.3221409285 0.7662644819
44.1 -19.9270197 NA 0.3416426284 2.6490291790
44.2 -22.6521171 NA 0.5706610068 3.3371910988
44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875
45 -0.5686627 -0.19274788 0.1414983160 0.1957449992
45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536
46 -19.1624789 -0.28547861 1.5391054233 1.3477755385
46.1 -18.4487574 -0.21977836 0.3889107049 2.8565793915
46.2 -15.8222682 NA 0.1248719493 4.4160729996
47 -5.4165074 -0.08597098 0.2014101100 0.6012621359
47.1 -15.0975029 -0.35424828 0.2982973539 2.4097121472
47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035
47.3 -10.6844521 -0.30426315 0.5196802157 3.1829649757
47.4 -18.2214784 NA 0.3702301552 4.6201055450
48 -8.3101471 NA -0.2128602862 2.8607365978
48.1 -18.3854275 NA -0.5337239976 2.9098354396
49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400
50 -10.4579977 -0.19959516 0.3897705981 1.1762060679
51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720
52 -4.4747188 -0.07438732 0.3758235358 2.1266646020
52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993
52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370
52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327
52.4 -8.9036692 -0.44619160 0.0254566848 4.7983659909
52.5 -5.6949642 -0.11011682 0.4155259424 4.9818264414
53 -10.3141887 -0.23278716 0.5675736897 0.4965799209
53.1 -8.2642654 -0.28317264 -0.3154088781 3.5505357443
53.2 -9.1691554 -0.19517481 0.2162315769 4.5790420019
54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841
54.1 -15.7192609 -0.28325504 0.4129127672 1.8812377600
54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352
54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672
54.4 -16.5771751 -0.34609328 0.8587727145 4.0143877396
55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980
55.1 -7.3217494 -0.24235382 0.5835528661 0.7463747414
55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171
55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572
55.4 -14.8599983 NA -0.1202746964 3.2218102901
56 -14.1764282 NA 0.5176377612 1.2231332215
56.1 -12.5343602 -0.30357450 -1.1136932588 2.3573202139
56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292
56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378
56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360
56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043
57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448
57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030
57.2 -6.3940143 NA -0.1159517011 1.1405586067
57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721
58 -12.0245677 -0.29901358 1.6476207996 1.2210099772
58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703
58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890
58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693
58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440
58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824
59 -13.2946560 -0.27255364 0.4766421367 1.9023998594
59.1 -10.0530648 NA 1.0028089765 4.9736620474
60 -19.2209402 NA 0.5231452932 2.8854503250
61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795
61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661
61.2 -1.4713611 NA 1.0229058138 2.5077313243
61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430
61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283
62 -2.8591600 NA -0.3979137604 0.5336771999
62.1 -6.9461986 -0.10504143 0.6830738372 0.6987666548
62.2 -16.7910593 -0.20104997 0.4301745954 3.4584309917
62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371
63 -24.0335535 -0.12036319 0.3345678035 2.8097350930
63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211
64 -20.5963897 NA 0.3949911859 4.1191305732
65 -2.7969169 -0.34450396 1.2000091513 0.7076152589
65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363
65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827
65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943
66 -13.2318328 NA -0.5081302805 3.4943454154
66.1 -1.9090560 NA -0.1447837412 3.7677437009
66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616
67 -25.6073277 NA 1.6716027681 4.1728388879
68 -13.4806759 -0.13605235 0.5691848839 0.1291919907
68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946
68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660
68.3 -12.4977127 NA 0.7443745962 2.9406870750
68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363
69 -14.5290675 -0.32152751 0.0381382683 4.1451198701
70 -15.2122709 -0.28190462 0.8126204217 0.1992557163
70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413
71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386
71.1 -7.5699888 NA -0.1103252087 1.4883817220
71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395
71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723
71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823
72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121
72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806
72.2 -26.3421306 NA -0.4170988856 1.8917567329
72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935
72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700
72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901
73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915
74 -8.2790175 -0.27963171 0.3730017941 1.4626806753
75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874
76 -6.0061671 0.04775562 -0.9888876620 1.8074807397
76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183
76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859
77 0.4772521 -0.19053195 -0.4986802480 0.8459033852
78 -9.4105229 -0.17172929 0.0417330969 0.8231094317
79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521
79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628
79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032
80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186
80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598
80.2 -16.6381566 NA 0.5046816157 2.8025900386
81 0.5547913 -0.18401980 -0.9493950353 0.0101324962
81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494
81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879
81.3 -5.2166381 NA 0.4182528210 3.3456630446
82 -3.0749381 -0.08328168 1.1088801952 1.3791010005
82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622
82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927
83 -12.5872635 -0.34228255 0.5104724530 0.0537394290
83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496
83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362
83.3 -19.2714012 NA -0.0534169155 4.7663642222
84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237
84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659
85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259
85.1 -12.9196365 NA -0.0577615939 1.7394116637
85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194
85.3 -6.3296340 NA -0.0520175929 3.1608762743
85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758
85.5 -13.6714939 NA -0.3740417009 4.5092553482
86 -10.8756412 -0.10854862 0.4293735089 0.8476278360
86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411
86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515
86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925
86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000
86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508
87 -10.7027963 NA 1.0457427869 2.7069531474
87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721
87.2 -14.9616716 NA 0.4396872616 4.5241666853
88 -2.2264493 -0.31563888 -0.0601928334 0.0005892443
88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603
92 -6.4119222 NA 0.4674939332 0.0737052364
93 5.2122168 -0.11939966 0.2677965647 0.2788909199
93.1 3.1211725 NA 1.6424445368 1.0357759963
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099
93.3 2.6223542 NA 1.1222322893 2.8876129608
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002
94 -6.9602492 NA -0.2904211940 0.8488043118
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438
98.1 -2.6515128 NA -0.9042716241 0.4309578973
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171
100 1.1005874 -0.17326698 0.0369067906 1.0691387602
100.1 -3.8226248 NA 1.6023713093 1.5109344281
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765
$m8b$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
$m8b$mu_reg_norm
[1] 0
$m8b$tau_reg_norm
[1] 1e-04
$m8b$shape_tau_norm
[1] 0.01
$m8b$rate_tau_norm
[1] 0.01
$m8b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8b$shape_diag_RinvD
[1] "0.01"
$m8b$rate_diag_RinvD
[1] "0.001"
$m8b$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8b$KinvD_y_id
id
4
$m8c
$m8c$M_id
B2 (Intercept) B21
1 1 1 NA
2 NA 1 NA
3 NA 1 NA
4 1 1 NA
5 1 1 NA
6 1 1 NA
7 0 1 NA
8 1 1 NA
9 1 1 NA
10 0 1 NA
11 1 1 NA
12 1 1 NA
13 1 1 NA
14 1 1 NA
15 NA 1 NA
16 1 1 NA
17 1 1 NA
18 1 1 NA
19 1 1 NA
20 0 1 NA
21 1 1 NA
22 1 1 NA
23 1 1 NA
24 NA 1 NA
25 0 1 NA
26 1 1 NA
27 1 1 NA
28 0 1 NA
29 1 1 NA
30 0 1 NA
31 0 1 NA
32 1 1 NA
33 1 1 NA
34 0 1 NA
35 1 1 NA
36 0 1 NA
37 1 1 NA
38 1 1 NA
39 1 1 NA
40 1 1 NA
41 1 1 NA
42 1 1 NA
43 1 1 NA
44 NA 1 NA
45 1 1 NA
46 1 1 NA
47 1 1 NA
48 1 1 NA
49 1 1 NA
50 1 1 NA
51 0 1 NA
52 1 1 NA
53 1 1 NA
54 0 1 NA
55 1 1 NA
56 0 1 NA
57 1 1 NA
58 NA 1 NA
59 1 1 NA
60 1 1 NA
61 0 1 NA
62 0 1 NA
63 1 1 NA
64 1 1 NA
65 1 1 NA
66 1 1 NA
67 1 1 NA
68 1 1 NA
69 NA 1 NA
70 1 1 NA
71 1 1 NA
72 1 1 NA
73 1 1 NA
74 1 1 NA
75 1 1 NA
76 1 1 NA
77 1 1 NA
78 1 1 NA
79 1 1 NA
80 1 1 NA
81 1 1 NA
82 1 1 NA
83 1 1 NA
84 1 1 NA
85 1 1 NA
86 1 1 NA
87 1 1 NA
88 1 1 NA
89 1 1 NA
90 1 1 NA
91 NA 1 NA
92 1 1 NA
93 1 1 NA
94 1 1 NA
95 1 1 NA
96 NA 1 NA
97 NA 1 NA
98 1 1 NA
99 1 1 NA
100 1 1 NA
$m8c$M_lvlone
y c2 c1 time B21:c1
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 NA
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847 NA
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082 NA
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196 NA
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339 NA
5.3 -9.2366906 NA 0.1740288049 2.8119940578 NA
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884 NA
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673 NA
7.1 -18.3267285 NA 0.4242971219 3.7008403614 NA
7.2 -12.5138426 NA 0.0777182491 4.7716691741 NA
8 -1.6305331 NA -0.5791408712 1.1246398522 NA
8.1 -9.6520453 NA 0.3128604232 1.8027009873 NA
8.2 -1.5278462 NA 0.6258446356 1.8175825174 NA
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003 NA
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307 NA
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704 NA
9 -0.1634429 NA -0.1757592269 0.9607803822 NA
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383 NA
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501 NA
10 -6.4172202 NA -0.5598409704 2.2975509102 NA
10.1 -11.4834569 NA -0.2318340451 4.1734118364 NA
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905 NA
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680 NA
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263 NA
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969 NA
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661 NA
12 -15.2006287 NA -0.2433996286 0.9591987054 NA
13 0.8058816 NA 0.8799673116 0.0619085738 NA
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502 NA
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007 NA
14.1 -10.0965208 NA -0.1094019046 4.4710561272 NA
14.2 -16.6452027 NA 0.1518967560 4.6359198843 NA
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599 NA
15 -8.9424594 NA 0.3464447888 0.5402063532 NA
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816 NA
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688 NA
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615 NA
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869 NA
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99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8c$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c1 0.1798099 0.6117459
$m8c$mu_reg_norm
[1] 0
$m8c$tau_reg_norm
[1] 1e-04
$m8c$shape_tau_norm
[1] 0.01
$m8c$rate_tau_norm
[1] 0.01
$m8c$mu_reg_binom
[1] 0
$m8c$tau_reg_binom
[1] 1e-04
$m8c$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8c$shape_diag_RinvD
[1] "0.01"
$m8c$rate_diag_RinvD
[1] "0.001"
$m8c$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8c$KinvD_y_id
id
4
$m8d
$m8d$M_id
B2 (Intercept) B21
1 1 1 NA
2 NA 1 NA
3 NA 1 NA
4 1 1 NA
5 1 1 NA
6 1 1 NA
7 0 1 NA
8 1 1 NA
9 1 1 NA
10 0 1 NA
11 1 1 NA
12 1 1 NA
13 1 1 NA
14 1 1 NA
15 NA 1 NA
16 1 1 NA
17 1 1 NA
18 1 1 NA
19 1 1 NA
20 0 1 NA
21 1 1 NA
22 1 1 NA
23 1 1 NA
24 NA 1 NA
25 0 1 NA
26 1 1 NA
27 1 1 NA
28 0 1 NA
29 1 1 NA
30 0 1 NA
31 0 1 NA
32 1 1 NA
33 1 1 NA
34 0 1 NA
35 1 1 NA
36 0 1 NA
37 1 1 NA
38 1 1 NA
39 1 1 NA
40 1 1 NA
41 1 1 NA
42 1 1 NA
43 1 1 NA
44 NA 1 NA
45 1 1 NA
46 1 1 NA
47 1 1 NA
48 1 1 NA
49 1 1 NA
50 1 1 NA
51 0 1 NA
52 1 1 NA
53 1 1 NA
54 0 1 NA
55 1 1 NA
56 0 1 NA
57 1 1 NA
58 NA 1 NA
59 1 1 NA
60 1 1 NA
61 0 1 NA
62 0 1 NA
63 1 1 NA
64 1 1 NA
65 1 1 NA
66 1 1 NA
67 1 1 NA
68 1 1 NA
69 NA 1 NA
70 1 1 NA
71 1 1 NA
72 1 1 NA
73 1 1 NA
74 1 1 NA
75 1 1 NA
76 1 1 NA
77 1 1 NA
78 1 1 NA
79 1 1 NA
80 1 1 NA
81 1 1 NA
82 1 1 NA
83 1 1 NA
84 1 1 NA
85 1 1 NA
86 1 1 NA
87 1 1 NA
88 1 1 NA
89 1 1 NA
90 1 1 NA
91 NA 1 NA
92 1 1 NA
93 1 1 NA
94 1 1 NA
95 1 1 NA
96 NA 1 NA
97 NA 1 NA
98 1 1 NA
99 1 1 NA
100 1 1 NA
$m8d$M_lvlone
y c2 c1 time B21:c1
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 NA
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847 NA
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082 NA
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196 NA
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339 NA
5.3 -9.2366906 NA 0.1740288049 2.8119940578 NA
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884 NA
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673 NA
7.1 -18.3267285 NA 0.4242971219 3.7008403614 NA
7.2 -12.5138426 NA 0.0777182491 4.7716691741 NA
8 -1.6305331 NA -0.5791408712 1.1246398522 NA
8.1 -9.6520453 NA 0.3128604232 1.8027009873 NA
8.2 -1.5278462 NA 0.6258446356 1.8175825174 NA
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003 NA
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307 NA
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704 NA
9 -0.1634429 NA -0.1757592269 0.9607803822 NA
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383 NA
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501 NA
10 -6.4172202 NA -0.5598409704 2.2975509102 NA
10.1 -11.4834569 NA -0.2318340451 4.1734118364 NA
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905 NA
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680 NA
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263 NA
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969 NA
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661 NA
12 -15.2006287 NA -0.2433996286 0.9591987054 NA
13 0.8058816 NA 0.8799673116 0.0619085738 NA
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502 NA
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007 NA
14.1 -10.0965208 NA -0.1094019046 4.4710561272 NA
14.2 -16.6452027 NA 0.1518967560 4.6359198843 NA
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599 NA
15 -8.9424594 NA 0.3464447888 0.5402063532 NA
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816 NA
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688 NA
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615 NA
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869 NA
16.1 -9.9308357 -0.07599066 1.0652558311 2.5675726333 NA
16.2 -6.9626923 -0.32385667 0.6971872493 2.6524101500 NA
16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690 NA
16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291 NA
16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889 NA
17 -10.5474144 NA 0.6048676222 1.5925356350 NA
17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998 NA
17.2 -16.5386939 NA 1.2617499032 3.0256489082 NA
17.3 -20.0004774 NA -0.3913230895 3.3329089405 NA
17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985 NA
18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302 NA
19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376 NA
19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913 NA
19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538 NA
19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973 NA
20 -12.9059229 -0.22087628 0.6838223064 1.7170160066 NA
20.1 -16.8191201 NA 0.3278571109 1.7562902288 NA
20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566 NA
20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867 NA
20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287 NA
20.5 -13.3504189 -0.24338208 -0.4535207652 4.1730977828 NA
21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839 NA
21.1 -11.9335526 -0.12424941 -0.4069674045 2.9571191233 NA
21.2 -12.7064929 -0.27672716 1.0650265940 3.7887385779 NA
22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232 NA
22.1 -12.7745451 -0.15996535 0.9601388170 3.1626627257 NA
23 -3.5146508 -0.18236682 0.5556634840 1.5414533857 NA
23.1 -4.6724048 -0.20823302 1.4407865964 2.3369736120 NA
24 -2.5619821 -0.29026416 0.3856376411 2.8283136466 NA
25 -6.2944970 -0.36139273 0.3564400705 0.5381704110 NA
25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331 NA
25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922 NA
25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392 NA
25.4 -9.0288933 NA 0.4466012931 4.0782226040 NA
25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873 NA
26 -19.7340685 -0.16846716 0.5341531449 0.2412706357 NA
26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676 NA
26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887 NA
26.3 -24.6265576 NA 0.5948516018 4.1822362854 NA
27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879 NA
27.1 -11.1488468 -0.17622638 -0.4835141229 4.2451434687 NA
28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344 NA
28.1 -8.2030122 -0.27003852 0.5063671955 2.7943964268 NA
28.2 -26.4317815 -0.07235801 -0.2067413142 4.2108539480 NA
28.3 -18.5016071 -0.13462982 0.1196789973 4.4705521734 NA
29 -5.8551395 -0.32432030 0.1392699487 1.1898884235 NA
29.1 -2.0209442 -0.27034171 0.7960234776 1.7624059319 NA
29.2 -5.6368080 -0.10197448 1.0398214352 2.0210406382 NA
29.3 -3.8110961 -0.27606945 0.0813246429 3.4078777023 NA
30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488 NA
30.1 -17.0170140 -0.11511035 1.3635850954 3.5938334477 NA
30.2 -25.4236089 -0.16215882 0.7354171050 3.6138710892 NA
31 -17.0783921 0.05707733 0.3708398217 4.3988140998 NA
32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007 NA
32.1 -19.4317212 -0.14270013 1.2507771489 2.9128167813 NA
32.2 -19.4738978 -0.20530798 0.1142915519 2.9676558380 NA
32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547 NA
33 2.0838099 -0.15252819 0.1774293842 0.0093385763 NA
33.1 -13.3172274 NA 0.6159606291 3.4591242753 NA
34 -10.0296691 -0.30378735 0.8590979166 1.4998774312 NA
34.1 -25.9426553 -0.11982431 0.0546216775 3.8242761395 NA
34.2 -18.5688138 -0.24278671 -0.0897224473 3.9072251692 NA
34.3 -15.4173859 -0.19971833 0.4163395571 3.9582124643 NA
35 -14.3958113 NA -1.4693520528 1.3294299203 NA
35.1 -12.9457541 -0.24165780 -0.3031734330 1.5276966314 NA
35.2 -16.1380691 NA -0.6045512101 4.5025920868 NA
36 -12.8166968 -0.49062180 0.9823048960 0.7123168337 NA
36.1 -14.3989481 -0.25651700 1.4466051416 1.7972493160 NA
36.2 -12.2436943 NA 1.1606752905 1.8262697803 NA
36.3 -15.0104638 -0.30401274 0.8373091576 4.2840119381 NA
36.4 -10.1775457 NA 0.2640591685 4.6194464504 NA
37 -15.2223495 -0.15276529 0.1177313455 2.0018732361 NA
37.1 -14.7526195 -0.30016169 -0.1415483779 3.6656836793 NA
37.2 -19.8168430 0.06809545 0.0054610124 3.9663937816 NA
38 -2.7065118 -0.11218486 0.8078948077 0.9826511063 NA
39 -8.7288138 -0.38072211 0.9876451040 0.6921808305 NA
39.1 -9.2746473 -0.32094428 -0.3431222274 0.9027792048 NA
39.2 -18.2695344 NA -1.7909380751 1.3055654289 NA
39.3 -13.8219083 -0.40173480 -0.1798746191 1.5412842878 NA
39.4 -16.2254704 -0.20041848 -0.1850961689 3.1834997435 NA
39.5 -21.7283648 -0.26027990 0.4544226146 4.1394166439 NA
40 1.8291916 -0.19751956 0.5350190436 1.1330395646 NA
40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046 NA
40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212 NA
40.3 -10.5749790 NA 0.0916687506 4.6762977762 NA
41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254 NA
41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458 NA
41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557 NA
41.3 -15.9788960 NA 0.9885613862 3.3813529415 NA
41.4 -9.6070508 NA -0.3908036794 3.5482964432 NA
42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973 NA
42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298 NA
43 -16.6104361 -0.08864017 0.8965546110 0.5616614548 NA
43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536 NA
43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966 NA
44 -8.1969033 -0.22697597 1.3221409285 0.7662644819 NA
44.1 -19.9270197 NA 0.3416426284 2.6490291790 NA
44.2 -22.6521171 NA 0.5706610068 3.3371910988 NA
44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875 NA
45 -0.5686627 -0.19274788 0.1414983160 0.1957449992 NA
45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536 NA
46 -19.1624789 -0.28547861 1.5391054233 1.3477755385 NA
46.1 -18.4487574 -0.21977836 0.3889107049 2.8565793915 NA
46.2 -15.8222682 NA 0.1248719493 4.4160729996 NA
47 -5.4165074 -0.08597098 0.2014101100 0.6012621359 NA
47.1 -15.0975029 -0.35424828 0.2982973539 2.4097121472 NA
47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035 NA
47.3 -10.6844521 -0.30426315 0.5196802157 3.1829649757 NA
47.4 -18.2214784 NA 0.3702301552 4.6201055450 NA
48 -8.3101471 NA -0.2128602862 2.8607365978 NA
48.1 -18.3854275 NA -0.5337239976 2.9098354396 NA
49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400 NA
50 -10.4579977 -0.19959516 0.3897705981 1.1762060679 NA
51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720 NA
52 -4.4747188 -0.07438732 0.3758235358 2.1266646020 NA
52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993 NA
52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370 NA
52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327 NA
52.4 -8.9036692 -0.44619160 0.0254566848 4.7983659909 NA
52.5 -5.6949642 -0.11011682 0.4155259424 4.9818264414 NA
53 -10.3141887 -0.23278716 0.5675736897 0.4965799209 NA
53.1 -8.2642654 -0.28317264 -0.3154088781 3.5505357443 NA
53.2 -9.1691554 -0.19517481 0.2162315769 4.5790420019 NA
54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841 NA
54.1 -15.7192609 -0.28325504 0.4129127672 1.8812377600 NA
54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352 NA
54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672 NA
54.4 -16.5771751 -0.34609328 0.8587727145 4.0143877396 NA
55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980 NA
55.1 -7.3217494 -0.24235382 0.5835528661 0.7463747414 NA
55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171 NA
55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572 NA
55.4 -14.8599983 NA -0.1202746964 3.2218102901 NA
56 -14.1764282 NA 0.5176377612 1.2231332215 NA
56.1 -12.5343602 -0.30357450 -1.1136932588 2.3573202139 NA
56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292 NA
56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378 NA
56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360 NA
56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043 NA
57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448 NA
57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030 NA
57.2 -6.3940143 NA -0.1159517011 1.1405586067 NA
57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721 NA
58 -12.0245677 -0.29901358 1.6476207996 1.2210099772 NA
58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703 NA
58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890 NA
58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693 NA
58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440 NA
58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824 NA
59 -13.2946560 -0.27255364 0.4766421367 1.9023998594 NA
59.1 -10.0530648 NA 1.0028089765 4.9736620474 NA
60 -19.2209402 NA 0.5231452932 2.8854503250 NA
61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795 NA
61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661 NA
61.2 -1.4713611 NA 1.0229058138 2.5077313243 NA
61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430 NA
61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283 NA
62 -2.8591600 NA -0.3979137604 0.5336771999 NA
62.1 -6.9461986 -0.10504143 0.6830738372 0.6987666548 NA
62.2 -16.7910593 -0.20104997 0.4301745954 3.4584309917 NA
62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371 NA
63 -24.0335535 -0.12036319 0.3345678035 2.8097350930 NA
63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211 NA
64 -20.5963897 NA 0.3949911859 4.1191305732 NA
65 -2.7969169 -0.34450396 1.2000091513 0.7076152589 NA
65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363 NA
65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827 NA
65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943 NA
66 -13.2318328 NA -0.5081302805 3.4943454154 NA
66.1 -1.9090560 NA -0.1447837412 3.7677437009 NA
66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616 NA
67 -25.6073277 NA 1.6716027681 4.1728388879 NA
68 -13.4806759 -0.13605235 0.5691848839 0.1291919907 NA
68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946 NA
68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660 NA
68.3 -12.4977127 NA 0.7443745962 2.9406870750 NA
68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363 NA
69 -14.5290675 -0.32152751 0.0381382683 4.1451198701 NA
70 -15.2122709 -0.28190462 0.8126204217 0.1992557163 NA
70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413 NA
71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386 NA
71.1 -7.5699888 NA -0.1103252087 1.4883817220 NA
71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395 NA
71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723 NA
71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823 NA
72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121 NA
72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806 NA
72.2 -26.3421306 NA -0.4170988856 1.8917567329 NA
72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935 NA
72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700 NA
72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901 NA
73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915 NA
74 -8.2790175 -0.27963171 0.3730017941 1.4626806753 NA
75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874 NA
76 -6.0061671 0.04775562 -0.9888876620 1.8074807397 NA
76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183 NA
76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859 NA
77 0.4772521 -0.19053195 -0.4986802480 0.8459033852 NA
78 -9.4105229 -0.17172929 0.0417330969 0.8231094317 NA
79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521 NA
79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628 NA
79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032 NA
80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186 NA
80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598 NA
80.2 -16.6381566 NA 0.5046816157 2.8025900386 NA
81 0.5547913 -0.18401980 -0.9493950353 0.0101324962 NA
81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494 NA
81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879 NA
81.3 -5.2166381 NA 0.4182528210 3.3456630446 NA
82 -3.0749381 -0.08328168 1.1088801952 1.3791010005 NA
82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622 NA
82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927 NA
83 -12.5872635 -0.34228255 0.5104724530 0.0537394290 NA
83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496 NA
83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362 NA
83.3 -19.2714012 NA -0.0534169155 4.7663642222 NA
84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237 NA
84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659 NA
85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259 NA
85.1 -12.9196365 NA -0.0577615939 1.7394116637 NA
85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194 NA
85.3 -6.3296340 NA -0.0520175929 3.1608762743 NA
85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758 NA
85.5 -13.6714939 NA -0.3740417009 4.5092553482 NA
86 -10.8756412 -0.10854862 0.4293735089 0.8476278360 NA
86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411 NA
86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515 NA
86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925 NA
86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000 NA
86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508 NA
87 -10.7027963 NA 1.0457427869 2.7069531474 NA
87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721 NA
87.2 -14.9616716 NA 0.4396872616 4.5241666853 NA
88 -2.2264493 -0.31563888 -0.0601928334 0.0005892443 NA
88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866 NA
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900 NA
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297 NA
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167 NA
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737 NA
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230 NA
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167 NA
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247 NA
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390 NA
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286 NA
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603 NA
92 -6.4119222 NA 0.4674939332 0.0737052364 NA
93 5.2122168 -0.11939966 0.2677965647 0.2788909199 NA
93.1 3.1211725 NA 1.6424445368 1.0357759963 NA
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099 NA
93.3 2.6223542 NA 1.1222322893 2.8876129608 NA
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002 NA
94 -6.9602492 NA -0.2904211940 0.8488043118 NA
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425 NA
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884 NA
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448 NA
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935 NA
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016 NA
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048 NA
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457 NA
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732 NA
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477 NA
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720 NA
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425 NA
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691 NA
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488 NA
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598 NA
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731 NA
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643 NA
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438 NA
98.1 -2.6515128 NA -0.9042716241 0.4309578973 NA
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906 NA
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8d$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c1 0.1798099 0.6117459
$m8d$mu_reg_norm
[1] 0
$m8d$tau_reg_norm
[1] 1e-04
$m8d$shape_tau_norm
[1] 0.01
$m8d$rate_tau_norm
[1] 0.01
$m8d$mu_reg_binom
[1] 0
$m8d$tau_reg_binom
[1] 1e-04
$m8d$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8d$shape_diag_RinvD
[1] "0.01"
$m8d$rate_diag_RinvD
[1] "0.001"
$m8d$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8d$KinvD_y_id
id
4
$m8e
$m8e$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8e$M_lvlone
y c2 c1 time B21:c1
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 NA
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847 NA
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082 NA
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196 NA
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339 NA
5.3 -9.2366906 NA 0.1740288049 2.8119940578 NA
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884 NA
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673 NA
7.1 -18.3267285 NA 0.4242971219 3.7008403614 NA
7.2 -12.5138426 NA 0.0777182491 4.7716691741 NA
8 -1.6305331 NA -0.5791408712 1.1246398522 NA
8.1 -9.6520453 NA 0.3128604232 1.8027009873 NA
8.2 -1.5278462 NA 0.6258446356 1.8175825174 NA
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003 NA
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307 NA
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704 NA
9 -0.1634429 NA -0.1757592269 0.9607803822 NA
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383 NA
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501 NA
10 -6.4172202 NA -0.5598409704 2.2975509102 NA
10.1 -11.4834569 NA -0.2318340451 4.1734118364 NA
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905 NA
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680 NA
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263 NA
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969 NA
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661 NA
12 -15.2006287 NA -0.2433996286 0.9591987054 NA
13 0.8058816 NA 0.8799673116 0.0619085738 NA
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502 NA
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007 NA
14.1 -10.0965208 NA -0.1094019046 4.4710561272 NA
14.2 -16.6452027 NA 0.1518967560 4.6359198843 NA
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599 NA
15 -8.9424594 NA 0.3464447888 0.5402063532 NA
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816 NA
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688 NA
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615 NA
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869 NA
16.1 -9.9308357 -0.07599066 1.0652558311 2.5675726333 NA
16.2 -6.9626923 -0.32385667 0.6971872493 2.6524101500 NA
16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690 NA
16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291 NA
16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889 NA
17 -10.5474144 NA 0.6048676222 1.5925356350 NA
17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998 NA
17.2 -16.5386939 NA 1.2617499032 3.0256489082 NA
17.3 -20.0004774 NA -0.3913230895 3.3329089405 NA
17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985 NA
18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302 NA
19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376 NA
19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913 NA
19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538 NA
19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973 NA
20 -12.9059229 -0.22087628 0.6838223064 1.7170160066 NA
20.1 -16.8191201 NA 0.3278571109 1.7562902288 NA
20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566 NA
20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867 NA
20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287 NA
20.5 -13.3504189 -0.24338208 -0.4535207652 4.1730977828 NA
21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839 NA
21.1 -11.9335526 -0.12424941 -0.4069674045 2.9571191233 NA
21.2 -12.7064929 -0.27672716 1.0650265940 3.7887385779 NA
22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232 NA
22.1 -12.7745451 -0.15996535 0.9601388170 3.1626627257 NA
23 -3.5146508 -0.18236682 0.5556634840 1.5414533857 NA
23.1 -4.6724048 -0.20823302 1.4407865964 2.3369736120 NA
24 -2.5619821 -0.29026416 0.3856376411 2.8283136466 NA
25 -6.2944970 -0.36139273 0.3564400705 0.5381704110 NA
25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331 NA
25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922 NA
25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392 NA
25.4 -9.0288933 NA 0.4466012931 4.0782226040 NA
25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873 NA
26 -19.7340685 -0.16846716 0.5341531449 0.2412706357 NA
26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676 NA
26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887 NA
26.3 -24.6265576 NA 0.5948516018 4.1822362854 NA
27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879 NA
27.1 -11.1488468 -0.17622638 -0.4835141229 4.2451434687 NA
28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344 NA
28.1 -8.2030122 -0.27003852 0.5063671955 2.7943964268 NA
28.2 -26.4317815 -0.07235801 -0.2067413142 4.2108539480 NA
28.3 -18.5016071 -0.13462982 0.1196789973 4.4705521734 NA
29 -5.8551395 -0.32432030 0.1392699487 1.1898884235 NA
29.1 -2.0209442 -0.27034171 0.7960234776 1.7624059319 NA
29.2 -5.6368080 -0.10197448 1.0398214352 2.0210406382 NA
29.3 -3.8110961 -0.27606945 0.0813246429 3.4078777023 NA
30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488 NA
30.1 -17.0170140 -0.11511035 1.3635850954 3.5938334477 NA
30.2 -25.4236089 -0.16215882 0.7354171050 3.6138710892 NA
31 -17.0783921 0.05707733 0.3708398217 4.3988140998 NA
32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007 NA
32.1 -19.4317212 -0.14270013 1.2507771489 2.9128167813 NA
32.2 -19.4738978 -0.20530798 0.1142915519 2.9676558380 NA
32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547 NA
33 2.0838099 -0.15252819 0.1774293842 0.0093385763 NA
33.1 -13.3172274 NA 0.6159606291 3.4591242753 NA
34 -10.0296691 -0.30378735 0.8590979166 1.4998774312 NA
34.1 -25.9426553 -0.11982431 0.0546216775 3.8242761395 NA
34.2 -18.5688138 -0.24278671 -0.0897224473 3.9072251692 NA
34.3 -15.4173859 -0.19971833 0.4163395571 3.9582124643 NA
35 -14.3958113 NA -1.4693520528 1.3294299203 NA
35.1 -12.9457541 -0.24165780 -0.3031734330 1.5276966314 NA
35.2 -16.1380691 NA -0.6045512101 4.5025920868 NA
36 -12.8166968 -0.49062180 0.9823048960 0.7123168337 NA
36.1 -14.3989481 -0.25651700 1.4466051416 1.7972493160 NA
36.2 -12.2436943 NA 1.1606752905 1.8262697803 NA
36.3 -15.0104638 -0.30401274 0.8373091576 4.2840119381 NA
36.4 -10.1775457 NA 0.2640591685 4.6194464504 NA
37 -15.2223495 -0.15276529 0.1177313455 2.0018732361 NA
37.1 -14.7526195 -0.30016169 -0.1415483779 3.6656836793 NA
37.2 -19.8168430 0.06809545 0.0054610124 3.9663937816 NA
38 -2.7065118 -0.11218486 0.8078948077 0.9826511063 NA
39 -8.7288138 -0.38072211 0.9876451040 0.6921808305 NA
39.1 -9.2746473 -0.32094428 -0.3431222274 0.9027792048 NA
39.2 -18.2695344 NA -1.7909380751 1.3055654289 NA
39.3 -13.8219083 -0.40173480 -0.1798746191 1.5412842878 NA
39.4 -16.2254704 -0.20041848 -0.1850961689 3.1834997435 NA
39.5 -21.7283648 -0.26027990 0.4544226146 4.1394166439 NA
40 1.8291916 -0.19751956 0.5350190436 1.1330395646 NA
40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046 NA
40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212 NA
40.3 -10.5749790 NA 0.0916687506 4.6762977762 NA
41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254 NA
41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458 NA
41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557 NA
41.3 -15.9788960 NA 0.9885613862 3.3813529415 NA
41.4 -9.6070508 NA -0.3908036794 3.5482964432 NA
42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973 NA
42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298 NA
43 -16.6104361 -0.08864017 0.8965546110 0.5616614548 NA
43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536 NA
43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966 NA
44 -8.1969033 -0.22697597 1.3221409285 0.7662644819 NA
44.1 -19.9270197 NA 0.3416426284 2.6490291790 NA
44.2 -22.6521171 NA 0.5706610068 3.3371910988 NA
44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875 NA
45 -0.5686627 -0.19274788 0.1414983160 0.1957449992 NA
45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536 NA
46 -19.1624789 -0.28547861 1.5391054233 1.3477755385 NA
46.1 -18.4487574 -0.21977836 0.3889107049 2.8565793915 NA
46.2 -15.8222682 NA 0.1248719493 4.4160729996 NA
47 -5.4165074 -0.08597098 0.2014101100 0.6012621359 NA
47.1 -15.0975029 -0.35424828 0.2982973539 2.4097121472 NA
47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035 NA
47.3 -10.6844521 -0.30426315 0.5196802157 3.1829649757 NA
47.4 -18.2214784 NA 0.3702301552 4.6201055450 NA
48 -8.3101471 NA -0.2128602862 2.8607365978 NA
48.1 -18.3854275 NA -0.5337239976 2.9098354396 NA
49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400 NA
50 -10.4579977 -0.19959516 0.3897705981 1.1762060679 NA
51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720 NA
52 -4.4747188 -0.07438732 0.3758235358 2.1266646020 NA
52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993 NA
52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370 NA
52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327 NA
52.4 -8.9036692 -0.44619160 0.0254566848 4.7983659909 NA
52.5 -5.6949642 -0.11011682 0.4155259424 4.9818264414 NA
53 -10.3141887 -0.23278716 0.5675736897 0.4965799209 NA
53.1 -8.2642654 -0.28317264 -0.3154088781 3.5505357443 NA
53.2 -9.1691554 -0.19517481 0.2162315769 4.5790420019 NA
54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841 NA
54.1 -15.7192609 -0.28325504 0.4129127672 1.8812377600 NA
54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352 NA
54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672 NA
54.4 -16.5771751 -0.34609328 0.8587727145 4.0143877396 NA
55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980 NA
55.1 -7.3217494 -0.24235382 0.5835528661 0.7463747414 NA
55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171 NA
55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572 NA
55.4 -14.8599983 NA -0.1202746964 3.2218102901 NA
56 -14.1764282 NA 0.5176377612 1.2231332215 NA
56.1 -12.5343602 -0.30357450 -1.1136932588 2.3573202139 NA
56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292 NA
56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378 NA
56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360 NA
56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043 NA
57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448 NA
57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030 NA
57.2 -6.3940143 NA -0.1159517011 1.1405586067 NA
57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721 NA
58 -12.0245677 -0.29901358 1.6476207996 1.2210099772 NA
58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703 NA
58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890 NA
58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693 NA
58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440 NA
58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824 NA
59 -13.2946560 -0.27255364 0.4766421367 1.9023998594 NA
59.1 -10.0530648 NA 1.0028089765 4.9736620474 NA
60 -19.2209402 NA 0.5231452932 2.8854503250 NA
61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795 NA
61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661 NA
61.2 -1.4713611 NA 1.0229058138 2.5077313243 NA
61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430 NA
61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283 NA
62 -2.8591600 NA -0.3979137604 0.5336771999 NA
62.1 -6.9461986 -0.10504143 0.6830738372 0.6987666548 NA
62.2 -16.7910593 -0.20104997 0.4301745954 3.4584309917 NA
62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371 NA
63 -24.0335535 -0.12036319 0.3345678035 2.8097350930 NA
63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211 NA
64 -20.5963897 NA 0.3949911859 4.1191305732 NA
65 -2.7969169 -0.34450396 1.2000091513 0.7076152589 NA
65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363 NA
65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827 NA
65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943 NA
66 -13.2318328 NA -0.5081302805 3.4943454154 NA
66.1 -1.9090560 NA -0.1447837412 3.7677437009 NA
66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616 NA
67 -25.6073277 NA 1.6716027681 4.1728388879 NA
68 -13.4806759 -0.13605235 0.5691848839 0.1291919907 NA
68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946 NA
68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660 NA
68.3 -12.4977127 NA 0.7443745962 2.9406870750 NA
68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363 NA
69 -14.5290675 -0.32152751 0.0381382683 4.1451198701 NA
70 -15.2122709 -0.28190462 0.8126204217 0.1992557163 NA
70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413 NA
71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386 NA
71.1 -7.5699888 NA -0.1103252087 1.4883817220 NA
71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395 NA
71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723 NA
71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823 NA
72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121 NA
72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806 NA
72.2 -26.3421306 NA -0.4170988856 1.8917567329 NA
72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935 NA
72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700 NA
72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901 NA
73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915 NA
74 -8.2790175 -0.27963171 0.3730017941 1.4626806753 NA
75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874 NA
76 -6.0061671 0.04775562 -0.9888876620 1.8074807397 NA
76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183 NA
76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859 NA
77 0.4772521 -0.19053195 -0.4986802480 0.8459033852 NA
78 -9.4105229 -0.17172929 0.0417330969 0.8231094317 NA
79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521 NA
79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628 NA
79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032 NA
80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186 NA
80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598 NA
80.2 -16.6381566 NA 0.5046816157 2.8025900386 NA
81 0.5547913 -0.18401980 -0.9493950353 0.0101324962 NA
81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494 NA
81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879 NA
81.3 -5.2166381 NA 0.4182528210 3.3456630446 NA
82 -3.0749381 -0.08328168 1.1088801952 1.3791010005 NA
82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622 NA
82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927 NA
83 -12.5872635 -0.34228255 0.5104724530 0.0537394290 NA
83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496 NA
83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362 NA
83.3 -19.2714012 NA -0.0534169155 4.7663642222 NA
84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237 NA
84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659 NA
85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259 NA
85.1 -12.9196365 NA -0.0577615939 1.7394116637 NA
85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194 NA
85.3 -6.3296340 NA -0.0520175929 3.1608762743 NA
85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758 NA
85.5 -13.6714939 NA -0.3740417009 4.5092553482 NA
86 -10.8756412 -0.10854862 0.4293735089 0.8476278360 NA
86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411 NA
86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515 NA
86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925 NA
86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000 NA
86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508 NA
87 -10.7027963 NA 1.0457427869 2.7069531474 NA
87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721 NA
87.2 -14.9616716 NA 0.4396872616 4.5241666853 NA
88 -2.2264493 -0.31563888 -0.0601928334 0.0005892443 NA
88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866 NA
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900 NA
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297 NA
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167 NA
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737 NA
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230 NA
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167 NA
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247 NA
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390 NA
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286 NA
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603 NA
92 -6.4119222 NA 0.4674939332 0.0737052364 NA
93 5.2122168 -0.11939966 0.2677965647 0.2788909199 NA
93.1 3.1211725 NA 1.6424445368 1.0357759963 NA
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099 NA
93.3 2.6223542 NA 1.1222322893 2.8876129608 NA
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002 NA
94 -6.9602492 NA -0.2904211940 0.8488043118 NA
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425 NA
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884 NA
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448 NA
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935 NA
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016 NA
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048 NA
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457 NA
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732 NA
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477 NA
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720 NA
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425 NA
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691 NA
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488 NA
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598 NA
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731 NA
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643 NA
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438 NA
98.1 -2.6515128 NA -0.9042716241 0.4309578973 NA
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906 NA
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8e$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8e$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c1 0.1798099 0.6117459
$m8e$mu_reg_norm
[1] 0
$m8e$tau_reg_norm
[1] 1e-04
$m8e$shape_tau_norm
[1] 0.01
$m8e$rate_tau_norm
[1] 0.01
$m8e$mu_reg_binom
[1] 0
$m8e$tau_reg_binom
[1] 1e-04
$m8e$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8e$shape_diag_RinvD
[1] "0.01"
$m8e$rate_diag_RinvD
[1] "0.001"
$m8e$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8e$KinvD_y_id
id
4
$m8f
$m8f$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8f$M_lvlone
y c2 c1 time B21:c1
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 NA
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847 NA
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082 NA
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196 NA
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339 NA
5.3 -9.2366906 NA 0.1740288049 2.8119940578 NA
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884 NA
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673 NA
7.1 -18.3267285 NA 0.4242971219 3.7008403614 NA
7.2 -12.5138426 NA 0.0777182491 4.7716691741 NA
8 -1.6305331 NA -0.5791408712 1.1246398522 NA
8.1 -9.6520453 NA 0.3128604232 1.8027009873 NA
8.2 -1.5278462 NA 0.6258446356 1.8175825174 NA
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003 NA
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307 NA
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704 NA
9 -0.1634429 NA -0.1757592269 0.9607803822 NA
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383 NA
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501 NA
10 -6.4172202 NA -0.5598409704 2.2975509102 NA
10.1 -11.4834569 NA -0.2318340451 4.1734118364 NA
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905 NA
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680 NA
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263 NA
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969 NA
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661 NA
12 -15.2006287 NA -0.2433996286 0.9591987054 NA
13 0.8058816 NA 0.8799673116 0.0619085738 NA
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502 NA
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007 NA
14.1 -10.0965208 NA -0.1094019046 4.4710561272 NA
14.2 -16.6452027 NA 0.1518967560 4.6359198843 NA
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599 NA
15 -8.9424594 NA 0.3464447888 0.5402063532 NA
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816 NA
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688 NA
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615 NA
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869 NA
16.1 -9.9308357 -0.07599066 1.0652558311 2.5675726333 NA
16.2 -6.9626923 -0.32385667 0.6971872493 2.6524101500 NA
16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690 NA
16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291 NA
16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889 NA
17 -10.5474144 NA 0.6048676222 1.5925356350 NA
17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998 NA
17.2 -16.5386939 NA 1.2617499032 3.0256489082 NA
17.3 -20.0004774 NA -0.3913230895 3.3329089405 NA
17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985 NA
18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302 NA
19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376 NA
19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913 NA
19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538 NA
19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973 NA
20 -12.9059229 -0.22087628 0.6838223064 1.7170160066 NA
20.1 -16.8191201 NA 0.3278571109 1.7562902288 NA
20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566 NA
20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867 NA
20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287 NA
20.5 -13.3504189 -0.24338208 -0.4535207652 4.1730977828 NA
21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839 NA
21.1 -11.9335526 -0.12424941 -0.4069674045 2.9571191233 NA
21.2 -12.7064929 -0.27672716 1.0650265940 3.7887385779 NA
22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232 NA
22.1 -12.7745451 -0.15996535 0.9601388170 3.1626627257 NA
23 -3.5146508 -0.18236682 0.5556634840 1.5414533857 NA
23.1 -4.6724048 -0.20823302 1.4407865964 2.3369736120 NA
24 -2.5619821 -0.29026416 0.3856376411 2.8283136466 NA
25 -6.2944970 -0.36139273 0.3564400705 0.5381704110 NA
25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331 NA
25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922 NA
25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392 NA
25.4 -9.0288933 NA 0.4466012931 4.0782226040 NA
25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873 NA
26 -19.7340685 -0.16846716 0.5341531449 0.2412706357 NA
26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676 NA
26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887 NA
26.3 -24.6265576 NA 0.5948516018 4.1822362854 NA
27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879 NA
27.1 -11.1488468 -0.17622638 -0.4835141229 4.2451434687 NA
28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344 NA
28.1 -8.2030122 -0.27003852 0.5063671955 2.7943964268 NA
28.2 -26.4317815 -0.07235801 -0.2067413142 4.2108539480 NA
28.3 -18.5016071 -0.13462982 0.1196789973 4.4705521734 NA
29 -5.8551395 -0.32432030 0.1392699487 1.1898884235 NA
29.1 -2.0209442 -0.27034171 0.7960234776 1.7624059319 NA
29.2 -5.6368080 -0.10197448 1.0398214352 2.0210406382 NA
29.3 -3.8110961 -0.27606945 0.0813246429 3.4078777023 NA
30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488 NA
30.1 -17.0170140 -0.11511035 1.3635850954 3.5938334477 NA
30.2 -25.4236089 -0.16215882 0.7354171050 3.6138710892 NA
31 -17.0783921 0.05707733 0.3708398217 4.3988140998 NA
32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007 NA
32.1 -19.4317212 -0.14270013 1.2507771489 2.9128167813 NA
32.2 -19.4738978 -0.20530798 0.1142915519 2.9676558380 NA
32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547 NA
33 2.0838099 -0.15252819 0.1774293842 0.0093385763 NA
33.1 -13.3172274 NA 0.6159606291 3.4591242753 NA
34 -10.0296691 -0.30378735 0.8590979166 1.4998774312 NA
34.1 -25.9426553 -0.11982431 0.0546216775 3.8242761395 NA
34.2 -18.5688138 -0.24278671 -0.0897224473 3.9072251692 NA
34.3 -15.4173859 -0.19971833 0.4163395571 3.9582124643 NA
35 -14.3958113 NA -1.4693520528 1.3294299203 NA
35.1 -12.9457541 -0.24165780 -0.3031734330 1.5276966314 NA
35.2 -16.1380691 NA -0.6045512101 4.5025920868 NA
36 -12.8166968 -0.49062180 0.9823048960 0.7123168337 NA
36.1 -14.3989481 -0.25651700 1.4466051416 1.7972493160 NA
36.2 -12.2436943 NA 1.1606752905 1.8262697803 NA
36.3 -15.0104638 -0.30401274 0.8373091576 4.2840119381 NA
36.4 -10.1775457 NA 0.2640591685 4.6194464504 NA
37 -15.2223495 -0.15276529 0.1177313455 2.0018732361 NA
37.1 -14.7526195 -0.30016169 -0.1415483779 3.6656836793 NA
37.2 -19.8168430 0.06809545 0.0054610124 3.9663937816 NA
38 -2.7065118 -0.11218486 0.8078948077 0.9826511063 NA
39 -8.7288138 -0.38072211 0.9876451040 0.6921808305 NA
39.1 -9.2746473 -0.32094428 -0.3431222274 0.9027792048 NA
39.2 -18.2695344 NA -1.7909380751 1.3055654289 NA
39.3 -13.8219083 -0.40173480 -0.1798746191 1.5412842878 NA
39.4 -16.2254704 -0.20041848 -0.1850961689 3.1834997435 NA
39.5 -21.7283648 -0.26027990 0.4544226146 4.1394166439 NA
40 1.8291916 -0.19751956 0.5350190436 1.1330395646 NA
40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046 NA
40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212 NA
40.3 -10.5749790 NA 0.0916687506 4.6762977762 NA
41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254 NA
41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458 NA
41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557 NA
41.3 -15.9788960 NA 0.9885613862 3.3813529415 NA
41.4 -9.6070508 NA -0.3908036794 3.5482964432 NA
42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973 NA
42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298 NA
43 -16.6104361 -0.08864017 0.8965546110 0.5616614548 NA
43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536 NA
43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966 NA
44 -8.1969033 -0.22697597 1.3221409285 0.7662644819 NA
44.1 -19.9270197 NA 0.3416426284 2.6490291790 NA
44.2 -22.6521171 NA 0.5706610068 3.3371910988 NA
44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875 NA
45 -0.5686627 -0.19274788 0.1414983160 0.1957449992 NA
45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536 NA
46 -19.1624789 -0.28547861 1.5391054233 1.3477755385 NA
46.1 -18.4487574 -0.21977836 0.3889107049 2.8565793915 NA
46.2 -15.8222682 NA 0.1248719493 4.4160729996 NA
47 -5.4165074 -0.08597098 0.2014101100 0.6012621359 NA
47.1 -15.0975029 -0.35424828 0.2982973539 2.4097121472 NA
47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035 NA
47.3 -10.6844521 -0.30426315 0.5196802157 3.1829649757 NA
47.4 -18.2214784 NA 0.3702301552 4.6201055450 NA
48 -8.3101471 NA -0.2128602862 2.8607365978 NA
48.1 -18.3854275 NA -0.5337239976 2.9098354396 NA
49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400 NA
50 -10.4579977 -0.19959516 0.3897705981 1.1762060679 NA
51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720 NA
52 -4.4747188 -0.07438732 0.3758235358 2.1266646020 NA
52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993 NA
52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370 NA
52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327 NA
52.4 -8.9036692 -0.44619160 0.0254566848 4.7983659909 NA
52.5 -5.6949642 -0.11011682 0.4155259424 4.9818264414 NA
53 -10.3141887 -0.23278716 0.5675736897 0.4965799209 NA
53.1 -8.2642654 -0.28317264 -0.3154088781 3.5505357443 NA
53.2 -9.1691554 -0.19517481 0.2162315769 4.5790420019 NA
54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841 NA
54.1 -15.7192609 -0.28325504 0.4129127672 1.8812377600 NA
54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352 NA
54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672 NA
54.4 -16.5771751 -0.34609328 0.8587727145 4.0143877396 NA
55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980 NA
55.1 -7.3217494 -0.24235382 0.5835528661 0.7463747414 NA
55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171 NA
55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572 NA
55.4 -14.8599983 NA -0.1202746964 3.2218102901 NA
56 -14.1764282 NA 0.5176377612 1.2231332215 NA
56.1 -12.5343602 -0.30357450 -1.1136932588 2.3573202139 NA
56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292 NA
56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378 NA
56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360 NA
56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043 NA
57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448 NA
57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030 NA
57.2 -6.3940143 NA -0.1159517011 1.1405586067 NA
57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721 NA
58 -12.0245677 -0.29901358 1.6476207996 1.2210099772 NA
58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703 NA
58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890 NA
58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693 NA
58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440 NA
58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824 NA
59 -13.2946560 -0.27255364 0.4766421367 1.9023998594 NA
59.1 -10.0530648 NA 1.0028089765 4.9736620474 NA
60 -19.2209402 NA 0.5231452932 2.8854503250 NA
61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795 NA
61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661 NA
61.2 -1.4713611 NA 1.0229058138 2.5077313243 NA
61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430 NA
61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283 NA
62 -2.8591600 NA -0.3979137604 0.5336771999 NA
62.1 -6.9461986 -0.10504143 0.6830738372 0.6987666548 NA
62.2 -16.7910593 -0.20104997 0.4301745954 3.4584309917 NA
62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371 NA
63 -24.0335535 -0.12036319 0.3345678035 2.8097350930 NA
63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211 NA
64 -20.5963897 NA 0.3949911859 4.1191305732 NA
65 -2.7969169 -0.34450396 1.2000091513 0.7076152589 NA
65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363 NA
65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827 NA
65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943 NA
66 -13.2318328 NA -0.5081302805 3.4943454154 NA
66.1 -1.9090560 NA -0.1447837412 3.7677437009 NA
66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616 NA
67 -25.6073277 NA 1.6716027681 4.1728388879 NA
68 -13.4806759 -0.13605235 0.5691848839 0.1291919907 NA
68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946 NA
68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660 NA
68.3 -12.4977127 NA 0.7443745962 2.9406870750 NA
68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363 NA
69 -14.5290675 -0.32152751 0.0381382683 4.1451198701 NA
70 -15.2122709 -0.28190462 0.8126204217 0.1992557163 NA
70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413 NA
71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386 NA
71.1 -7.5699888 NA -0.1103252087 1.4883817220 NA
71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395 NA
71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723 NA
71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823 NA
72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121 NA
72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806 NA
72.2 -26.3421306 NA -0.4170988856 1.8917567329 NA
72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935 NA
72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700 NA
72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901 NA
73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915 NA
74 -8.2790175 -0.27963171 0.3730017941 1.4626806753 NA
75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874 NA
76 -6.0061671 0.04775562 -0.9888876620 1.8074807397 NA
76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183 NA
76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859 NA
77 0.4772521 -0.19053195 -0.4986802480 0.8459033852 NA
78 -9.4105229 -0.17172929 0.0417330969 0.8231094317 NA
79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521 NA
79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628 NA
79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032 NA
80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186 NA
80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598 NA
80.2 -16.6381566 NA 0.5046816157 2.8025900386 NA
81 0.5547913 -0.18401980 -0.9493950353 0.0101324962 NA
81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494 NA
81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879 NA
81.3 -5.2166381 NA 0.4182528210 3.3456630446 NA
82 -3.0749381 -0.08328168 1.1088801952 1.3791010005 NA
82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622 NA
82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927 NA
83 -12.5872635 -0.34228255 0.5104724530 0.0537394290 NA
83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496 NA
83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362 NA
83.3 -19.2714012 NA -0.0534169155 4.7663642222 NA
84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237 NA
84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659 NA
85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259 NA
85.1 -12.9196365 NA -0.0577615939 1.7394116637 NA
85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194 NA
85.3 -6.3296340 NA -0.0520175929 3.1608762743 NA
85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758 NA
85.5 -13.6714939 NA -0.3740417009 4.5092553482 NA
86 -10.8756412 -0.10854862 0.4293735089 0.8476278360 NA
86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411 NA
86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515 NA
86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925 NA
86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000 NA
86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508 NA
87 -10.7027963 NA 1.0457427869 2.7069531474 NA
87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721 NA
87.2 -14.9616716 NA 0.4396872616 4.5241666853 NA
88 -2.2264493 -0.31563888 -0.0601928334 0.0005892443 NA
88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866 NA
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900 NA
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297 NA
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167 NA
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737 NA
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230 NA
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167 NA
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247 NA
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390 NA
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286 NA
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603 NA
92 -6.4119222 NA 0.4674939332 0.0737052364 NA
93 5.2122168 -0.11939966 0.2677965647 0.2788909199 NA
93.1 3.1211725 NA 1.6424445368 1.0357759963 NA
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099 NA
93.3 2.6223542 NA 1.1222322893 2.8876129608 NA
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002 NA
94 -6.9602492 NA -0.2904211940 0.8488043118 NA
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425 NA
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884 NA
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448 NA
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935 NA
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016 NA
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048 NA
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457 NA
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732 NA
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477 NA
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720 NA
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425 NA
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691 NA
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488 NA
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598 NA
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731 NA
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643 NA
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438 NA
98.1 -2.6515128 NA -0.9042716241 0.4309578973 NA
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906 NA
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8f$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8f$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c1 0.1798099 0.6117459
$m8f$mu_reg_norm
[1] 0
$m8f$tau_reg_norm
[1] 1e-04
$m8f$shape_tau_norm
[1] 0.01
$m8f$rate_tau_norm
[1] 0.01
$m8f$mu_reg_binom
[1] 0
$m8f$tau_reg_binom
[1] 1e-04
$m8f$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8f$shape_diag_RinvD
[1] "0.01"
$m8f$rate_diag_RinvD
[1] "0.001"
$m8f$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8f$KinvD_y_id
id
4
$m8g
$m8g$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8g$M_lvlone
y c2 c1 time B21:c1
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 NA
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847 NA
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082 NA
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196 NA
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339 NA
5.3 -9.2366906 NA 0.1740288049 2.8119940578 NA
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884 NA
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673 NA
7.1 -18.3267285 NA 0.4242971219 3.7008403614 NA
7.2 -12.5138426 NA 0.0777182491 4.7716691741 NA
8 -1.6305331 NA -0.5791408712 1.1246398522 NA
8.1 -9.6520453 NA 0.3128604232 1.8027009873 NA
8.2 -1.5278462 NA 0.6258446356 1.8175825174 NA
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003 NA
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307 NA
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704 NA
9 -0.1634429 NA -0.1757592269 0.9607803822 NA
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383 NA
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501 NA
10 -6.4172202 NA -0.5598409704 2.2975509102 NA
10.1 -11.4834569 NA -0.2318340451 4.1734118364 NA
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905 NA
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680 NA
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263 NA
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969 NA
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661 NA
12 -15.2006287 NA -0.2433996286 0.9591987054 NA
13 0.8058816 NA 0.8799673116 0.0619085738 NA
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502 NA
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007 NA
14.1 -10.0965208 NA -0.1094019046 4.4710561272 NA
14.2 -16.6452027 NA 0.1518967560 4.6359198843 NA
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599 NA
15 -8.9424594 NA 0.3464447888 0.5402063532 NA
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816 NA
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688 NA
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615 NA
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869 NA
16.1 -9.9308357 -0.07599066 1.0652558311 2.5675726333 NA
16.2 -6.9626923 -0.32385667 0.6971872493 2.6524101500 NA
16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690 NA
16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291 NA
16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889 NA
17 -10.5474144 NA 0.6048676222 1.5925356350 NA
17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998 NA
17.2 -16.5386939 NA 1.2617499032 3.0256489082 NA
17.3 -20.0004774 NA -0.3913230895 3.3329089405 NA
17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985 NA
18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302 NA
19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376 NA
19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913 NA
19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538 NA
19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973 NA
20 -12.9059229 -0.22087628 0.6838223064 1.7170160066 NA
20.1 -16.8191201 NA 0.3278571109 1.7562902288 NA
20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566 NA
20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867 NA
20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287 NA
20.5 -13.3504189 -0.24338208 -0.4535207652 4.1730977828 NA
21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839 NA
21.1 -11.9335526 -0.12424941 -0.4069674045 2.9571191233 NA
21.2 -12.7064929 -0.27672716 1.0650265940 3.7887385779 NA
22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232 NA
22.1 -12.7745451 -0.15996535 0.9601388170 3.1626627257 NA
23 -3.5146508 -0.18236682 0.5556634840 1.5414533857 NA
23.1 -4.6724048 -0.20823302 1.4407865964 2.3369736120 NA
24 -2.5619821 -0.29026416 0.3856376411 2.8283136466 NA
25 -6.2944970 -0.36139273 0.3564400705 0.5381704110 NA
25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331 NA
25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922 NA
25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392 NA
25.4 -9.0288933 NA 0.4466012931 4.0782226040 NA
25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873 NA
26 -19.7340685 -0.16846716 0.5341531449 0.2412706357 NA
26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676 NA
26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887 NA
26.3 -24.6265576 NA 0.5948516018 4.1822362854 NA
27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879 NA
27.1 -11.1488468 -0.17622638 -0.4835141229 4.2451434687 NA
28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344 NA
28.1 -8.2030122 -0.27003852 0.5063671955 2.7943964268 NA
28.2 -26.4317815 -0.07235801 -0.2067413142 4.2108539480 NA
28.3 -18.5016071 -0.13462982 0.1196789973 4.4705521734 NA
29 -5.8551395 -0.32432030 0.1392699487 1.1898884235 NA
29.1 -2.0209442 -0.27034171 0.7960234776 1.7624059319 NA
29.2 -5.6368080 -0.10197448 1.0398214352 2.0210406382 NA
29.3 -3.8110961 -0.27606945 0.0813246429 3.4078777023 NA
30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488 NA
30.1 -17.0170140 -0.11511035 1.3635850954 3.5938334477 NA
30.2 -25.4236089 -0.16215882 0.7354171050 3.6138710892 NA
31 -17.0783921 0.05707733 0.3708398217 4.3988140998 NA
32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007 NA
32.1 -19.4317212 -0.14270013 1.2507771489 2.9128167813 NA
32.2 -19.4738978 -0.20530798 0.1142915519 2.9676558380 NA
32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547 NA
33 2.0838099 -0.15252819 0.1774293842 0.0093385763 NA
33.1 -13.3172274 NA 0.6159606291 3.4591242753 NA
34 -10.0296691 -0.30378735 0.8590979166 1.4998774312 NA
34.1 -25.9426553 -0.11982431 0.0546216775 3.8242761395 NA
34.2 -18.5688138 -0.24278671 -0.0897224473 3.9072251692 NA
34.3 -15.4173859 -0.19971833 0.4163395571 3.9582124643 NA
35 -14.3958113 NA -1.4693520528 1.3294299203 NA
35.1 -12.9457541 -0.24165780 -0.3031734330 1.5276966314 NA
35.2 -16.1380691 NA -0.6045512101 4.5025920868 NA
36 -12.8166968 -0.49062180 0.9823048960 0.7123168337 NA
36.1 -14.3989481 -0.25651700 1.4466051416 1.7972493160 NA
36.2 -12.2436943 NA 1.1606752905 1.8262697803 NA
36.3 -15.0104638 -0.30401274 0.8373091576 4.2840119381 NA
36.4 -10.1775457 NA 0.2640591685 4.6194464504 NA
37 -15.2223495 -0.15276529 0.1177313455 2.0018732361 NA
37.1 -14.7526195 -0.30016169 -0.1415483779 3.6656836793 NA
37.2 -19.8168430 0.06809545 0.0054610124 3.9663937816 NA
38 -2.7065118 -0.11218486 0.8078948077 0.9826511063 NA
39 -8.7288138 -0.38072211 0.9876451040 0.6921808305 NA
39.1 -9.2746473 -0.32094428 -0.3431222274 0.9027792048 NA
39.2 -18.2695344 NA -1.7909380751 1.3055654289 NA
39.3 -13.8219083 -0.40173480 -0.1798746191 1.5412842878 NA
39.4 -16.2254704 -0.20041848 -0.1850961689 3.1834997435 NA
39.5 -21.7283648 -0.26027990 0.4544226146 4.1394166439 NA
40 1.8291916 -0.19751956 0.5350190436 1.1330395646 NA
40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046 NA
40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212 NA
40.3 -10.5749790 NA 0.0916687506 4.6762977762 NA
41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254 NA
41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458 NA
41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557 NA
41.3 -15.9788960 NA 0.9885613862 3.3813529415 NA
41.4 -9.6070508 NA -0.3908036794 3.5482964432 NA
42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973 NA
42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298 NA
43 -16.6104361 -0.08864017 0.8965546110 0.5616614548 NA
43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536 NA
43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966 NA
44 -8.1969033 -0.22697597 1.3221409285 0.7662644819 NA
44.1 -19.9270197 NA 0.3416426284 2.6490291790 NA
44.2 -22.6521171 NA 0.5706610068 3.3371910988 NA
44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875 NA
45 -0.5686627 -0.19274788 0.1414983160 0.1957449992 NA
45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536 NA
46 -19.1624789 -0.28547861 1.5391054233 1.3477755385 NA
46.1 -18.4487574 -0.21977836 0.3889107049 2.8565793915 NA
46.2 -15.8222682 NA 0.1248719493 4.4160729996 NA
47 -5.4165074 -0.08597098 0.2014101100 0.6012621359 NA
47.1 -15.0975029 -0.35424828 0.2982973539 2.4097121472 NA
47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035 NA
47.3 -10.6844521 -0.30426315 0.5196802157 3.1829649757 NA
47.4 -18.2214784 NA 0.3702301552 4.6201055450 NA
48 -8.3101471 NA -0.2128602862 2.8607365978 NA
48.1 -18.3854275 NA -0.5337239976 2.9098354396 NA
49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400 NA
50 -10.4579977 -0.19959516 0.3897705981 1.1762060679 NA
51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720 NA
52 -4.4747188 -0.07438732 0.3758235358 2.1266646020 NA
52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993 NA
52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370 NA
52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327 NA
52.4 -8.9036692 -0.44619160 0.0254566848 4.7983659909 NA
52.5 -5.6949642 -0.11011682 0.4155259424 4.9818264414 NA
53 -10.3141887 -0.23278716 0.5675736897 0.4965799209 NA
53.1 -8.2642654 -0.28317264 -0.3154088781 3.5505357443 NA
53.2 -9.1691554 -0.19517481 0.2162315769 4.5790420019 NA
54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841 NA
54.1 -15.7192609 -0.28325504 0.4129127672 1.8812377600 NA
54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352 NA
54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672 NA
54.4 -16.5771751 -0.34609328 0.8587727145 4.0143877396 NA
55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980 NA
55.1 -7.3217494 -0.24235382 0.5835528661 0.7463747414 NA
55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171 NA
55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572 NA
55.4 -14.8599983 NA -0.1202746964 3.2218102901 NA
56 -14.1764282 NA 0.5176377612 1.2231332215 NA
56.1 -12.5343602 -0.30357450 -1.1136932588 2.3573202139 NA
56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292 NA
56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378 NA
56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360 NA
56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043 NA
57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448 NA
57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030 NA
57.2 -6.3940143 NA -0.1159517011 1.1405586067 NA
57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721 NA
58 -12.0245677 -0.29901358 1.6476207996 1.2210099772 NA
58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703 NA
58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890 NA
58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693 NA
58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440 NA
58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824 NA
59 -13.2946560 -0.27255364 0.4766421367 1.9023998594 NA
59.1 -10.0530648 NA 1.0028089765 4.9736620474 NA
60 -19.2209402 NA 0.5231452932 2.8854503250 NA
61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795 NA
61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661 NA
61.2 -1.4713611 NA 1.0229058138 2.5077313243 NA
61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430 NA
61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283 NA
62 -2.8591600 NA -0.3979137604 0.5336771999 NA
62.1 -6.9461986 -0.10504143 0.6830738372 0.6987666548 NA
62.2 -16.7910593 -0.20104997 0.4301745954 3.4584309917 NA
62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371 NA
63 -24.0335535 -0.12036319 0.3345678035 2.8097350930 NA
63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211 NA
64 -20.5963897 NA 0.3949911859 4.1191305732 NA
65 -2.7969169 -0.34450396 1.2000091513 0.7076152589 NA
65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363 NA
65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827 NA
65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943 NA
66 -13.2318328 NA -0.5081302805 3.4943454154 NA
66.1 -1.9090560 NA -0.1447837412 3.7677437009 NA
66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616 NA
67 -25.6073277 NA 1.6716027681 4.1728388879 NA
68 -13.4806759 -0.13605235 0.5691848839 0.1291919907 NA
68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946 NA
68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660 NA
68.3 -12.4977127 NA 0.7443745962 2.9406870750 NA
68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363 NA
69 -14.5290675 -0.32152751 0.0381382683 4.1451198701 NA
70 -15.2122709 -0.28190462 0.8126204217 0.1992557163 NA
70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413 NA
71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386 NA
71.1 -7.5699888 NA -0.1103252087 1.4883817220 NA
71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395 NA
71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723 NA
71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823 NA
72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121 NA
72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806 NA
72.2 -26.3421306 NA -0.4170988856 1.8917567329 NA
72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935 NA
72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700 NA
72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901 NA
73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915 NA
74 -8.2790175 -0.27963171 0.3730017941 1.4626806753 NA
75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874 NA
76 -6.0061671 0.04775562 -0.9888876620 1.8074807397 NA
76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183 NA
76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859 NA
77 0.4772521 -0.19053195 -0.4986802480 0.8459033852 NA
78 -9.4105229 -0.17172929 0.0417330969 0.8231094317 NA
79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521 NA
79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628 NA
79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032 NA
80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186 NA
80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598 NA
80.2 -16.6381566 NA 0.5046816157 2.8025900386 NA
81 0.5547913 -0.18401980 -0.9493950353 0.0101324962 NA
81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494 NA
81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879 NA
81.3 -5.2166381 NA 0.4182528210 3.3456630446 NA
82 -3.0749381 -0.08328168 1.1088801952 1.3791010005 NA
82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622 NA
82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927 NA
83 -12.5872635 -0.34228255 0.5104724530 0.0537394290 NA
83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496 NA
83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362 NA
83.3 -19.2714012 NA -0.0534169155 4.7663642222 NA
84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237 NA
84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659 NA
85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259 NA
85.1 -12.9196365 NA -0.0577615939 1.7394116637 NA
85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194 NA
85.3 -6.3296340 NA -0.0520175929 3.1608762743 NA
85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758 NA
85.5 -13.6714939 NA -0.3740417009 4.5092553482 NA
86 -10.8756412 -0.10854862 0.4293735089 0.8476278360 NA
86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411 NA
86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515 NA
86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925 NA
86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000 NA
86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508 NA
87 -10.7027963 NA 1.0457427869 2.7069531474 NA
87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721 NA
87.2 -14.9616716 NA 0.4396872616 4.5241666853 NA
88 -2.2264493 -0.31563888 -0.0601928334 0.0005892443 NA
88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866 NA
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900 NA
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297 NA
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167 NA
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737 NA
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230 NA
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167 NA
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247 NA
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390 NA
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286 NA
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603 NA
92 -6.4119222 NA 0.4674939332 0.0737052364 NA
93 5.2122168 -0.11939966 0.2677965647 0.2788909199 NA
93.1 3.1211725 NA 1.6424445368 1.0357759963 NA
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099 NA
93.3 2.6223542 NA 1.1222322893 2.8876129608 NA
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002 NA
94 -6.9602492 NA -0.2904211940 0.8488043118 NA
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425 NA
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884 NA
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448 NA
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935 NA
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016 NA
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048 NA
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457 NA
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732 NA
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477 NA
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720 NA
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425 NA
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691 NA
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488 NA
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598 NA
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731 NA
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643 NA
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438 NA
98.1 -2.6515128 NA -0.9042716241 0.4309578973 NA
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906 NA
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8g$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8g$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c1 0.1798099 0.6117459
$m8g$mu_reg_norm
[1] 0
$m8g$tau_reg_norm
[1] 1e-04
$m8g$shape_tau_norm
[1] 0.01
$m8g$rate_tau_norm
[1] 0.01
$m8g$mu_reg_binom
[1] 0
$m8g$tau_reg_binom
[1] 1e-04
$m8g$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8g$shape_diag_RinvD
[1] "0.01"
$m8g$rate_diag_RinvD
[1] "0.001"
$m8g$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8g$KinvD_y_id
id
4
$m8h
$m8h$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8h$M_lvlone
y c2 c1 time B21:c2
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
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88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866 NA
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900 NA
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297 NA
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167 NA
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737 NA
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230 NA
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167 NA
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247 NA
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390 NA
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286 NA
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603 NA
92 -6.4119222 NA 0.4674939332 0.0737052364 NA
93 5.2122168 -0.11939966 0.2677965647 0.2788909199 NA
93.1 3.1211725 NA 1.6424445368 1.0357759963 NA
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099 NA
93.3 2.6223542 NA 1.1222322893 2.8876129608 NA
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002 NA
94 -6.9602492 NA -0.2904211940 0.8488043118 NA
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425 NA
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884 NA
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448 NA
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935 NA
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016 NA
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048 NA
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457 NA
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732 NA
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477 NA
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720 NA
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425 NA
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691 NA
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488 NA
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598 NA
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731 NA
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643 NA
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438 NA
98.1 -2.6515128 NA -0.9042716241 0.4309578973 NA
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906 NA
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8h$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8h$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c2 -0.1770956 0.1243159
$m8h$mu_reg_norm
[1] 0
$m8h$tau_reg_norm
[1] 1e-04
$m8h$shape_tau_norm
[1] 0.01
$m8h$rate_tau_norm
[1] 0.01
$m8h$mu_reg_binom
[1] 0
$m8h$tau_reg_binom
[1] 1e-04
$m8h$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8h$shape_diag_RinvD
[1] "0.01"
$m8h$rate_diag_RinvD
[1] "0.001"
$m8h$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8h$KinvD_y_id
id
4
$m8i
$m8i$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8i$M_lvlone
y c2 c1 time B21:c2
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 NA
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847 NA
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082 NA
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196 NA
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339 NA
5.3 -9.2366906 NA 0.1740288049 2.8119940578 NA
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884 NA
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673 NA
7.1 -18.3267285 NA 0.4242971219 3.7008403614 NA
7.2 -12.5138426 NA 0.0777182491 4.7716691741 NA
8 -1.6305331 NA -0.5791408712 1.1246398522 NA
8.1 -9.6520453 NA 0.3128604232 1.8027009873 NA
8.2 -1.5278462 NA 0.6258446356 1.8175825174 NA
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003 NA
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307 NA
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704 NA
9 -0.1634429 NA -0.1757592269 0.9607803822 NA
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383 NA
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501 NA
10 -6.4172202 NA -0.5598409704 2.2975509102 NA
10.1 -11.4834569 NA -0.2318340451 4.1734118364 NA
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905 NA
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680 NA
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263 NA
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969 NA
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661 NA
12 -15.2006287 NA -0.2433996286 0.9591987054 NA
13 0.8058816 NA 0.8799673116 0.0619085738 NA
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502 NA
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007 NA
14.1 -10.0965208 NA -0.1094019046 4.4710561272 NA
14.2 -16.6452027 NA 0.1518967560 4.6359198843 NA
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599 NA
15 -8.9424594 NA 0.3464447888 0.5402063532 NA
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816 NA
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688 NA
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615 NA
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869 NA
16.1 -9.9308357 -0.07599066 1.0652558311 2.5675726333 NA
16.2 -6.9626923 -0.32385667 0.6971872493 2.6524101500 NA
16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690 NA
16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291 NA
16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889 NA
17 -10.5474144 NA 0.6048676222 1.5925356350 NA
17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998 NA
17.2 -16.5386939 NA 1.2617499032 3.0256489082 NA
17.3 -20.0004774 NA -0.3913230895 3.3329089405 NA
17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985 NA
18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302 NA
19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376 NA
19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913 NA
19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538 NA
19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973 NA
20 -12.9059229 -0.22087628 0.6838223064 1.7170160066 NA
20.1 -16.8191201 NA 0.3278571109 1.7562902288 NA
20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566 NA
20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867 NA
20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287 NA
20.5 -13.3504189 -0.24338208 -0.4535207652 4.1730977828 NA
21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839 NA
21.1 -11.9335526 -0.12424941 -0.4069674045 2.9571191233 NA
21.2 -12.7064929 -0.27672716 1.0650265940 3.7887385779 NA
22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232 NA
22.1 -12.7745451 -0.15996535 0.9601388170 3.1626627257 NA
23 -3.5146508 -0.18236682 0.5556634840 1.5414533857 NA
23.1 -4.6724048 -0.20823302 1.4407865964 2.3369736120 NA
24 -2.5619821 -0.29026416 0.3856376411 2.8283136466 NA
25 -6.2944970 -0.36139273 0.3564400705 0.5381704110 NA
25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331 NA
25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922 NA
25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392 NA
25.4 -9.0288933 NA 0.4466012931 4.0782226040 NA
25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873 NA
26 -19.7340685 -0.16846716 0.5341531449 0.2412706357 NA
26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676 NA
26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887 NA
26.3 -24.6265576 NA 0.5948516018 4.1822362854 NA
27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879 NA
27.1 -11.1488468 -0.17622638 -0.4835141229 4.2451434687 NA
28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344 NA
28.1 -8.2030122 -0.27003852 0.5063671955 2.7943964268 NA
28.2 -26.4317815 -0.07235801 -0.2067413142 4.2108539480 NA
28.3 -18.5016071 -0.13462982 0.1196789973 4.4705521734 NA
29 -5.8551395 -0.32432030 0.1392699487 1.1898884235 NA
29.1 -2.0209442 -0.27034171 0.7960234776 1.7624059319 NA
29.2 -5.6368080 -0.10197448 1.0398214352 2.0210406382 NA
29.3 -3.8110961 -0.27606945 0.0813246429 3.4078777023 NA
30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488 NA
30.1 -17.0170140 -0.11511035 1.3635850954 3.5938334477 NA
30.2 -25.4236089 -0.16215882 0.7354171050 3.6138710892 NA
31 -17.0783921 0.05707733 0.3708398217 4.3988140998 NA
32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007 NA
32.1 -19.4317212 -0.14270013 1.2507771489 2.9128167813 NA
32.2 -19.4738978 -0.20530798 0.1142915519 2.9676558380 NA
32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547 NA
33 2.0838099 -0.15252819 0.1774293842 0.0093385763 NA
33.1 -13.3172274 NA 0.6159606291 3.4591242753 NA
34 -10.0296691 -0.30378735 0.8590979166 1.4998774312 NA
34.1 -25.9426553 -0.11982431 0.0546216775 3.8242761395 NA
34.2 -18.5688138 -0.24278671 -0.0897224473 3.9072251692 NA
34.3 -15.4173859 -0.19971833 0.4163395571 3.9582124643 NA
35 -14.3958113 NA -1.4693520528 1.3294299203 NA
35.1 -12.9457541 -0.24165780 -0.3031734330 1.5276966314 NA
35.2 -16.1380691 NA -0.6045512101 4.5025920868 NA
36 -12.8166968 -0.49062180 0.9823048960 0.7123168337 NA
36.1 -14.3989481 -0.25651700 1.4466051416 1.7972493160 NA
36.2 -12.2436943 NA 1.1606752905 1.8262697803 NA
36.3 -15.0104638 -0.30401274 0.8373091576 4.2840119381 NA
36.4 -10.1775457 NA 0.2640591685 4.6194464504 NA
37 -15.2223495 -0.15276529 0.1177313455 2.0018732361 NA
37.1 -14.7526195 -0.30016169 -0.1415483779 3.6656836793 NA
37.2 -19.8168430 0.06809545 0.0054610124 3.9663937816 NA
38 -2.7065118 -0.11218486 0.8078948077 0.9826511063 NA
39 -8.7288138 -0.38072211 0.9876451040 0.6921808305 NA
39.1 -9.2746473 -0.32094428 -0.3431222274 0.9027792048 NA
39.2 -18.2695344 NA -1.7909380751 1.3055654289 NA
39.3 -13.8219083 -0.40173480 -0.1798746191 1.5412842878 NA
39.4 -16.2254704 -0.20041848 -0.1850961689 3.1834997435 NA
39.5 -21.7283648 -0.26027990 0.4544226146 4.1394166439 NA
40 1.8291916 -0.19751956 0.5350190436 1.1330395646 NA
40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046 NA
40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212 NA
40.3 -10.5749790 NA 0.0916687506 4.6762977762 NA
41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254 NA
41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458 NA
41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557 NA
41.3 -15.9788960 NA 0.9885613862 3.3813529415 NA
41.4 -9.6070508 NA -0.3908036794 3.5482964432 NA
42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973 NA
42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298 NA
43 -16.6104361 -0.08864017 0.8965546110 0.5616614548 NA
43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536 NA
43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966 NA
44 -8.1969033 -0.22697597 1.3221409285 0.7662644819 NA
44.1 -19.9270197 NA 0.3416426284 2.6490291790 NA
44.2 -22.6521171 NA 0.5706610068 3.3371910988 NA
44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875 NA
45 -0.5686627 -0.19274788 0.1414983160 0.1957449992 NA
45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536 NA
46 -19.1624789 -0.28547861 1.5391054233 1.3477755385 NA
46.1 -18.4487574 -0.21977836 0.3889107049 2.8565793915 NA
46.2 -15.8222682 NA 0.1248719493 4.4160729996 NA
47 -5.4165074 -0.08597098 0.2014101100 0.6012621359 NA
47.1 -15.0975029 -0.35424828 0.2982973539 2.4097121472 NA
47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035 NA
47.3 -10.6844521 -0.30426315 0.5196802157 3.1829649757 NA
47.4 -18.2214784 NA 0.3702301552 4.6201055450 NA
48 -8.3101471 NA -0.2128602862 2.8607365978 NA
48.1 -18.3854275 NA -0.5337239976 2.9098354396 NA
49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400 NA
50 -10.4579977 -0.19959516 0.3897705981 1.1762060679 NA
51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720 NA
52 -4.4747188 -0.07438732 0.3758235358 2.1266646020 NA
52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993 NA
52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370 NA
52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327 NA
52.4 -8.9036692 -0.44619160 0.0254566848 4.7983659909 NA
52.5 -5.6949642 -0.11011682 0.4155259424 4.9818264414 NA
53 -10.3141887 -0.23278716 0.5675736897 0.4965799209 NA
53.1 -8.2642654 -0.28317264 -0.3154088781 3.5505357443 NA
53.2 -9.1691554 -0.19517481 0.2162315769 4.5790420019 NA
54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841 NA
54.1 -15.7192609 -0.28325504 0.4129127672 1.8812377600 NA
54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352 NA
54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672 NA
54.4 -16.5771751 -0.34609328 0.8587727145 4.0143877396 NA
55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980 NA
55.1 -7.3217494 -0.24235382 0.5835528661 0.7463747414 NA
55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171 NA
55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572 NA
55.4 -14.8599983 NA -0.1202746964 3.2218102901 NA
56 -14.1764282 NA 0.5176377612 1.2231332215 NA
56.1 -12.5343602 -0.30357450 -1.1136932588 2.3573202139 NA
56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292 NA
56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378 NA
56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360 NA
56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043 NA
57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448 NA
57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030 NA
57.2 -6.3940143 NA -0.1159517011 1.1405586067 NA
57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721 NA
58 -12.0245677 -0.29901358 1.6476207996 1.2210099772 NA
58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703 NA
58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890 NA
58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693 NA
58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440 NA
58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824 NA
59 -13.2946560 -0.27255364 0.4766421367 1.9023998594 NA
59.1 -10.0530648 NA 1.0028089765 4.9736620474 NA
60 -19.2209402 NA 0.5231452932 2.8854503250 NA
61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795 NA
61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661 NA
61.2 -1.4713611 NA 1.0229058138 2.5077313243 NA
61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430 NA
61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283 NA
62 -2.8591600 NA -0.3979137604 0.5336771999 NA
62.1 -6.9461986 -0.10504143 0.6830738372 0.6987666548 NA
62.2 -16.7910593 -0.20104997 0.4301745954 3.4584309917 NA
62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371 NA
63 -24.0335535 -0.12036319 0.3345678035 2.8097350930 NA
63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211 NA
64 -20.5963897 NA 0.3949911859 4.1191305732 NA
65 -2.7969169 -0.34450396 1.2000091513 0.7076152589 NA
65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363 NA
65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827 NA
65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943 NA
66 -13.2318328 NA -0.5081302805 3.4943454154 NA
66.1 -1.9090560 NA -0.1447837412 3.7677437009 NA
66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616 NA
67 -25.6073277 NA 1.6716027681 4.1728388879 NA
68 -13.4806759 -0.13605235 0.5691848839 0.1291919907 NA
68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946 NA
68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660 NA
68.3 -12.4977127 NA 0.7443745962 2.9406870750 NA
68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363 NA
69 -14.5290675 -0.32152751 0.0381382683 4.1451198701 NA
70 -15.2122709 -0.28190462 0.8126204217 0.1992557163 NA
70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413 NA
71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386 NA
71.1 -7.5699888 NA -0.1103252087 1.4883817220 NA
71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395 NA
71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723 NA
71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823 NA
72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121 NA
72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806 NA
72.2 -26.3421306 NA -0.4170988856 1.8917567329 NA
72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935 NA
72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700 NA
72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901 NA
73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915 NA
74 -8.2790175 -0.27963171 0.3730017941 1.4626806753 NA
75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874 NA
76 -6.0061671 0.04775562 -0.9888876620 1.8074807397 NA
76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183 NA
76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859 NA
77 0.4772521 -0.19053195 -0.4986802480 0.8459033852 NA
78 -9.4105229 -0.17172929 0.0417330969 0.8231094317 NA
79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521 NA
79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628 NA
79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032 NA
80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186 NA
80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598 NA
80.2 -16.6381566 NA 0.5046816157 2.8025900386 NA
81 0.5547913 -0.18401980 -0.9493950353 0.0101324962 NA
81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494 NA
81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879 NA
81.3 -5.2166381 NA 0.4182528210 3.3456630446 NA
82 -3.0749381 -0.08328168 1.1088801952 1.3791010005 NA
82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622 NA
82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927 NA
83 -12.5872635 -0.34228255 0.5104724530 0.0537394290 NA
83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496 NA
83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362 NA
83.3 -19.2714012 NA -0.0534169155 4.7663642222 NA
84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237 NA
84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659 NA
85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259 NA
85.1 -12.9196365 NA -0.0577615939 1.7394116637 NA
85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194 NA
85.3 -6.3296340 NA -0.0520175929 3.1608762743 NA
85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758 NA
85.5 -13.6714939 NA -0.3740417009 4.5092553482 NA
86 -10.8756412 -0.10854862 0.4293735089 0.8476278360 NA
86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411 NA
86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515 NA
86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925 NA
86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000 NA
86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508 NA
87 -10.7027963 NA 1.0457427869 2.7069531474 NA
87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721 NA
87.2 -14.9616716 NA 0.4396872616 4.5241666853 NA
88 -2.2264493 -0.31563888 -0.0601928334 0.0005892443 NA
88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866 NA
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900 NA
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297 NA
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167 NA
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737 NA
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230 NA
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167 NA
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247 NA
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390 NA
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286 NA
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603 NA
92 -6.4119222 NA 0.4674939332 0.0737052364 NA
93 5.2122168 -0.11939966 0.2677965647 0.2788909199 NA
93.1 3.1211725 NA 1.6424445368 1.0357759963 NA
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099 NA
93.3 2.6223542 NA 1.1222322893 2.8876129608 NA
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002 NA
94 -6.9602492 NA -0.2904211940 0.8488043118 NA
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425 NA
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884 NA
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448 NA
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935 NA
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016 NA
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048 NA
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457 NA
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732 NA
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477 NA
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720 NA
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425 NA
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691 NA
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488 NA
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598 NA
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731 NA
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643 NA
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438 NA
98.1 -2.6515128 NA -0.9042716241 0.4309578973 NA
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906 NA
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8i$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8i$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c2 -0.1770956 0.1243159
$m8i$mu_reg_norm
[1] 0
$m8i$tau_reg_norm
[1] 1e-04
$m8i$shape_tau_norm
[1] 0.01
$m8i$rate_tau_norm
[1] 0.01
$m8i$mu_reg_binom
[1] 0
$m8i$tau_reg_binom
[1] 1e-04
$m8i$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8i$shape_diag_RinvD
[1] "0.01"
$m8i$rate_diag_RinvD
[1] "0.001"
$m8i$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8i$KinvD_y_id
id
4
$m8j
$m8j$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8j$M_lvlone
y c2 c1 time B21:c2
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 NA
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847 NA
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082 NA
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196 NA
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339 NA
5.3 -9.2366906 NA 0.1740288049 2.8119940578 NA
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884 NA
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673 NA
7.1 -18.3267285 NA 0.4242971219 3.7008403614 NA
7.2 -12.5138426 NA 0.0777182491 4.7716691741 NA
8 -1.6305331 NA -0.5791408712 1.1246398522 NA
8.1 -9.6520453 NA 0.3128604232 1.8027009873 NA
8.2 -1.5278462 NA 0.6258446356 1.8175825174 NA
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003 NA
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307 NA
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704 NA
9 -0.1634429 NA -0.1757592269 0.9607803822 NA
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383 NA
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501 NA
10 -6.4172202 NA -0.5598409704 2.2975509102 NA
10.1 -11.4834569 NA -0.2318340451 4.1734118364 NA
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905 NA
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680 NA
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263 NA
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969 NA
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661 NA
12 -15.2006287 NA -0.2433996286 0.9591987054 NA
13 0.8058816 NA 0.8799673116 0.0619085738 NA
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502 NA
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007 NA
14.1 -10.0965208 NA -0.1094019046 4.4710561272 NA
14.2 -16.6452027 NA 0.1518967560 4.6359198843 NA
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96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598 NA
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731 NA
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643 NA
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438 NA
98.1 -2.6515128 NA -0.9042716241 0.4309578973 NA
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906 NA
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8j$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8j$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c2 -0.1770956 0.1243159
$m8j$mu_reg_norm
[1] 0
$m8j$tau_reg_norm
[1] 1e-04
$m8j$shape_tau_norm
[1] 0.01
$m8j$rate_tau_norm
[1] 0.01
$m8j$mu_reg_binom
[1] 0
$m8j$tau_reg_binom
[1] 1e-04
$m8j$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8j$shape_diag_RinvD
[1] "0.01"
$m8j$rate_diag_RinvD
[1] "0.001"
$m8j$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8j$KinvD_y_id
id
4
$m8k
$m8k$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8k$M_lvlone
y c2 c1 time B21:c2
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 NA
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847 NA
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082 NA
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196 NA
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339 NA
5.3 -9.2366906 NA 0.1740288049 2.8119940578 NA
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884 NA
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673 NA
7.1 -18.3267285 NA 0.4242971219 3.7008403614 NA
7.2 -12.5138426 NA 0.0777182491 4.7716691741 NA
8 -1.6305331 NA -0.5791408712 1.1246398522 NA
8.1 -9.6520453 NA 0.3128604232 1.8027009873 NA
8.2 -1.5278462 NA 0.6258446356 1.8175825174 NA
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003 NA
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307 NA
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704 NA
9 -0.1634429 NA -0.1757592269 0.9607803822 NA
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383 NA
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501 NA
10 -6.4172202 NA -0.5598409704 2.2975509102 NA
10.1 -11.4834569 NA -0.2318340451 4.1734118364 NA
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905 NA
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680 NA
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263 NA
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969 NA
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661 NA
12 -15.2006287 NA -0.2433996286 0.9591987054 NA
13 0.8058816 NA 0.8799673116 0.0619085738 NA
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502 NA
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007 NA
14.1 -10.0965208 NA -0.1094019046 4.4710561272 NA
14.2 -16.6452027 NA 0.1518967560 4.6359198843 NA
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599 NA
15 -8.9424594 NA 0.3464447888 0.5402063532 NA
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816 NA
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688 NA
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615 NA
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869 NA
16.1 -9.9308357 -0.07599066 1.0652558311 2.5675726333 NA
16.2 -6.9626923 -0.32385667 0.6971872493 2.6524101500 NA
16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690 NA
16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291 NA
16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889 NA
17 -10.5474144 NA 0.6048676222 1.5925356350 NA
17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998 NA
17.2 -16.5386939 NA 1.2617499032 3.0256489082 NA
17.3 -20.0004774 NA -0.3913230895 3.3329089405 NA
17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985 NA
18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302 NA
19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376 NA
19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913 NA
19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538 NA
19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973 NA
20 -12.9059229 -0.22087628 0.6838223064 1.7170160066 NA
20.1 -16.8191201 NA 0.3278571109 1.7562902288 NA
20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566 NA
20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867 NA
20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287 NA
20.5 -13.3504189 -0.24338208 -0.4535207652 4.1730977828 NA
21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839 NA
21.1 -11.9335526 -0.12424941 -0.4069674045 2.9571191233 NA
21.2 -12.7064929 -0.27672716 1.0650265940 3.7887385779 NA
22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232 NA
22.1 -12.7745451 -0.15996535 0.9601388170 3.1626627257 NA
23 -3.5146508 -0.18236682 0.5556634840 1.5414533857 NA
23.1 -4.6724048 -0.20823302 1.4407865964 2.3369736120 NA
24 -2.5619821 -0.29026416 0.3856376411 2.8283136466 NA
25 -6.2944970 -0.36139273 0.3564400705 0.5381704110 NA
25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331 NA
25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922 NA
25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392 NA
25.4 -9.0288933 NA 0.4466012931 4.0782226040 NA
25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873 NA
26 -19.7340685 -0.16846716 0.5341531449 0.2412706357 NA
26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676 NA
26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887 NA
26.3 -24.6265576 NA 0.5948516018 4.1822362854 NA
27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879 NA
27.1 -11.1488468 -0.17622638 -0.4835141229 4.2451434687 NA
28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344 NA
28.1 -8.2030122 -0.27003852 0.5063671955 2.7943964268 NA
28.2 -26.4317815 -0.07235801 -0.2067413142 4.2108539480 NA
28.3 -18.5016071 -0.13462982 0.1196789973 4.4705521734 NA
29 -5.8551395 -0.32432030 0.1392699487 1.1898884235 NA
29.1 -2.0209442 -0.27034171 0.7960234776 1.7624059319 NA
29.2 -5.6368080 -0.10197448 1.0398214352 2.0210406382 NA
29.3 -3.8110961 -0.27606945 0.0813246429 3.4078777023 NA
30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488 NA
30.1 -17.0170140 -0.11511035 1.3635850954 3.5938334477 NA
30.2 -25.4236089 -0.16215882 0.7354171050 3.6138710892 NA
31 -17.0783921 0.05707733 0.3708398217 4.3988140998 NA
32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007 NA
32.1 -19.4317212 -0.14270013 1.2507771489 2.9128167813 NA
32.2 -19.4738978 -0.20530798 0.1142915519 2.9676558380 NA
32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547 NA
33 2.0838099 -0.15252819 0.1774293842 0.0093385763 NA
33.1 -13.3172274 NA 0.6159606291 3.4591242753 NA
34 -10.0296691 -0.30378735 0.8590979166 1.4998774312 NA
34.1 -25.9426553 -0.11982431 0.0546216775 3.8242761395 NA
34.2 -18.5688138 -0.24278671 -0.0897224473 3.9072251692 NA
34.3 -15.4173859 -0.19971833 0.4163395571 3.9582124643 NA
35 -14.3958113 NA -1.4693520528 1.3294299203 NA
35.1 -12.9457541 -0.24165780 -0.3031734330 1.5276966314 NA
35.2 -16.1380691 NA -0.6045512101 4.5025920868 NA
36 -12.8166968 -0.49062180 0.9823048960 0.7123168337 NA
36.1 -14.3989481 -0.25651700 1.4466051416 1.7972493160 NA
36.2 -12.2436943 NA 1.1606752905 1.8262697803 NA
36.3 -15.0104638 -0.30401274 0.8373091576 4.2840119381 NA
36.4 -10.1775457 NA 0.2640591685 4.6194464504 NA
37 -15.2223495 -0.15276529 0.1177313455 2.0018732361 NA
37.1 -14.7526195 -0.30016169 -0.1415483779 3.6656836793 NA
37.2 -19.8168430 0.06809545 0.0054610124 3.9663937816 NA
38 -2.7065118 -0.11218486 0.8078948077 0.9826511063 NA
39 -8.7288138 -0.38072211 0.9876451040 0.6921808305 NA
39.1 -9.2746473 -0.32094428 -0.3431222274 0.9027792048 NA
39.2 -18.2695344 NA -1.7909380751 1.3055654289 NA
39.3 -13.8219083 -0.40173480 -0.1798746191 1.5412842878 NA
39.4 -16.2254704 -0.20041848 -0.1850961689 3.1834997435 NA
39.5 -21.7283648 -0.26027990 0.4544226146 4.1394166439 NA
40 1.8291916 -0.19751956 0.5350190436 1.1330395646 NA
40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046 NA
40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212 NA
40.3 -10.5749790 NA 0.0916687506 4.6762977762 NA
41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254 NA
41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458 NA
41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557 NA
41.3 -15.9788960 NA 0.9885613862 3.3813529415 NA
41.4 -9.6070508 NA -0.3908036794 3.5482964432 NA
42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973 NA
42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298 NA
43 -16.6104361 -0.08864017 0.8965546110 0.5616614548 NA
43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536 NA
43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966 NA
44 -8.1969033 -0.22697597 1.3221409285 0.7662644819 NA
44.1 -19.9270197 NA 0.3416426284 2.6490291790 NA
44.2 -22.6521171 NA 0.5706610068 3.3371910988 NA
44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875 NA
45 -0.5686627 -0.19274788 0.1414983160 0.1957449992 NA
45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536 NA
46 -19.1624789 -0.28547861 1.5391054233 1.3477755385 NA
46.1 -18.4487574 -0.21977836 0.3889107049 2.8565793915 NA
46.2 -15.8222682 NA 0.1248719493 4.4160729996 NA
47 -5.4165074 -0.08597098 0.2014101100 0.6012621359 NA
47.1 -15.0975029 -0.35424828 0.2982973539 2.4097121472 NA
47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035 NA
47.3 -10.6844521 -0.30426315 0.5196802157 3.1829649757 NA
47.4 -18.2214784 NA 0.3702301552 4.6201055450 NA
48 -8.3101471 NA -0.2128602862 2.8607365978 NA
48.1 -18.3854275 NA -0.5337239976 2.9098354396 NA
49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400 NA
50 -10.4579977 -0.19959516 0.3897705981 1.1762060679 NA
51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720 NA
52 -4.4747188 -0.07438732 0.3758235358 2.1266646020 NA
52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993 NA
52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370 NA
52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327 NA
52.4 -8.9036692 -0.44619160 0.0254566848 4.7983659909 NA
52.5 -5.6949642 -0.11011682 0.4155259424 4.9818264414 NA
53 -10.3141887 -0.23278716 0.5675736897 0.4965799209 NA
53.1 -8.2642654 -0.28317264 -0.3154088781 3.5505357443 NA
53.2 -9.1691554 -0.19517481 0.2162315769 4.5790420019 NA
54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841 NA
54.1 -15.7192609 -0.28325504 0.4129127672 1.8812377600 NA
54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352 NA
54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672 NA
54.4 -16.5771751 -0.34609328 0.8587727145 4.0143877396 NA
55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980 NA
55.1 -7.3217494 -0.24235382 0.5835528661 0.7463747414 NA
55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171 NA
55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572 NA
55.4 -14.8599983 NA -0.1202746964 3.2218102901 NA
56 -14.1764282 NA 0.5176377612 1.2231332215 NA
56.1 -12.5343602 -0.30357450 -1.1136932588 2.3573202139 NA
56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292 NA
56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378 NA
56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360 NA
56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043 NA
57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448 NA
57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030 NA
57.2 -6.3940143 NA -0.1159517011 1.1405586067 NA
57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721 NA
58 -12.0245677 -0.29901358 1.6476207996 1.2210099772 NA
58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703 NA
58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890 NA
58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693 NA
58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440 NA
58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824 NA
59 -13.2946560 -0.27255364 0.4766421367 1.9023998594 NA
59.1 -10.0530648 NA 1.0028089765 4.9736620474 NA
60 -19.2209402 NA 0.5231452932 2.8854503250 NA
61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795 NA
61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661 NA
61.2 -1.4713611 NA 1.0229058138 2.5077313243 NA
61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430 NA
61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283 NA
62 -2.8591600 NA -0.3979137604 0.5336771999 NA
62.1 -6.9461986 -0.10504143 0.6830738372 0.6987666548 NA
62.2 -16.7910593 -0.20104997 0.4301745954 3.4584309917 NA
62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371 NA
63 -24.0335535 -0.12036319 0.3345678035 2.8097350930 NA
63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211 NA
64 -20.5963897 NA 0.3949911859 4.1191305732 NA
65 -2.7969169 -0.34450396 1.2000091513 0.7076152589 NA
65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363 NA
65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827 NA
65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943 NA
66 -13.2318328 NA -0.5081302805 3.4943454154 NA
66.1 -1.9090560 NA -0.1447837412 3.7677437009 NA
66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616 NA
67 -25.6073277 NA 1.6716027681 4.1728388879 NA
68 -13.4806759 -0.13605235 0.5691848839 0.1291919907 NA
68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946 NA
68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660 NA
68.3 -12.4977127 NA 0.7443745962 2.9406870750 NA
68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363 NA
69 -14.5290675 -0.32152751 0.0381382683 4.1451198701 NA
70 -15.2122709 -0.28190462 0.8126204217 0.1992557163 NA
70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413 NA
71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386 NA
71.1 -7.5699888 NA -0.1103252087 1.4883817220 NA
71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395 NA
71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723 NA
71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823 NA
72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121 NA
72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806 NA
72.2 -26.3421306 NA -0.4170988856 1.8917567329 NA
72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935 NA
72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700 NA
72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901 NA
73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915 NA
74 -8.2790175 -0.27963171 0.3730017941 1.4626806753 NA
75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874 NA
76 -6.0061671 0.04775562 -0.9888876620 1.8074807397 NA
76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183 NA
76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859 NA
77 0.4772521 -0.19053195 -0.4986802480 0.8459033852 NA
78 -9.4105229 -0.17172929 0.0417330969 0.8231094317 NA
79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521 NA
79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628 NA
79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032 NA
80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186 NA
80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598 NA
80.2 -16.6381566 NA 0.5046816157 2.8025900386 NA
81 0.5547913 -0.18401980 -0.9493950353 0.0101324962 NA
81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494 NA
81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879 NA
81.3 -5.2166381 NA 0.4182528210 3.3456630446 NA
82 -3.0749381 -0.08328168 1.1088801952 1.3791010005 NA
82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622 NA
82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927 NA
83 -12.5872635 -0.34228255 0.5104724530 0.0537394290 NA
83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496 NA
83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362 NA
83.3 -19.2714012 NA -0.0534169155 4.7663642222 NA
84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237 NA
84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659 NA
85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259 NA
85.1 -12.9196365 NA -0.0577615939 1.7394116637 NA
85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194 NA
85.3 -6.3296340 NA -0.0520175929 3.1608762743 NA
85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758 NA
85.5 -13.6714939 NA -0.3740417009 4.5092553482 NA
86 -10.8756412 -0.10854862 0.4293735089 0.8476278360 NA
86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411 NA
86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515 NA
86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925 NA
86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000 NA
86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508 NA
87 -10.7027963 NA 1.0457427869 2.7069531474 NA
87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721 NA
87.2 -14.9616716 NA 0.4396872616 4.5241666853 NA
88 -2.2264493 -0.31563888 -0.0601928334 0.0005892443 NA
88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866 NA
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900 NA
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297 NA
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167 NA
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737 NA
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230 NA
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167 NA
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247 NA
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390 NA
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286 NA
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603 NA
92 -6.4119222 NA 0.4674939332 0.0737052364 NA
93 5.2122168 -0.11939966 0.2677965647 0.2788909199 NA
93.1 3.1211725 NA 1.6424445368 1.0357759963 NA
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099 NA
93.3 2.6223542 NA 1.1222322893 2.8876129608 NA
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002 NA
94 -6.9602492 NA -0.2904211940 0.8488043118 NA
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425 NA
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884 NA
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448 NA
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935 NA
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016 NA
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048 NA
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457 NA
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732 NA
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477 NA
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720 NA
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425 NA
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691 NA
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488 NA
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598 NA
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731 NA
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643 NA
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438 NA
98.1 -2.6515128 NA -0.9042716241 0.4309578973 NA
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906 NA
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8k$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8k$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c2 -0.1770956 0.1243159
$m8k$mu_reg_norm
[1] 0
$m8k$tau_reg_norm
[1] 1e-04
$m8k$shape_tau_norm
[1] 0.01
$m8k$rate_tau_norm
[1] 0.01
$m8k$mu_reg_binom
[1] 0
$m8k$tau_reg_binom
[1] 1e-04
$m8k$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8k$shape_diag_RinvD
[1] "0.01"
$m8k$rate_diag_RinvD
[1] "0.001"
$m8k$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8k$KinvD_y_id
id
4
$m8l
$m8l$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8l$M_lvlone
y c1 time B21:c1 B21:time c1:time
1 -13.0493856 0.7592026489 0.5090421822 NA NA 3.864662e-01
1.1 -9.3335901 0.9548337990 0.6666076288 NA NA 6.364995e-01
1.2 -22.3469852 0.5612235156 2.1304941282 NA NA 1.195683e+00
1.3 -15.0417337 1.1873391025 2.4954441458 NA NA 2.962938e+00
2 -12.0655434 0.9192204198 3.0164990982 NA NA 2.772828e+00
2.1 -15.8674476 -0.1870730476 3.2996806887 NA NA -6.172813e-01
2.2 -7.8800006 1.2517512331 4.1747569619 NA NA 5.225757e+00
3 -11.4820604 -0.0605087604 0.8478727890 NA NA -5.130373e-02
3.1 -10.5983220 0.3788637747 3.0654308549 NA NA 1.161381e+00
3.2 -22.4519157 0.9872578281 4.7381553578 NA NA 4.677781e+00
4 -1.2697775 1.4930175328 0.3371432109 NA NA 5.033607e-01
4.1 -11.1215184 -0.7692526880 1.0693019140 NA NA -8.225634e-01
4.2 -3.6134138 0.9180841450 2.6148973033 NA NA 2.400696e+00
4.3 -14.5982385 -0.0541170782 3.1336532847 NA NA -1.695842e-01
5 -6.8457515 -0.1376784521 1.0762525082 NA NA -1.481768e-01
5.1 -7.0551214 -0.2740585866 1.7912546196 NA NA -4.909087e-01
5.2 -12.3418980 0.4670496929 2.7960080339 NA NA 1.305875e+00
5.3 -9.2366906 0.1740288049 2.8119940578 NA NA 4.893680e-01
6 -5.1648211 0.9868044683 1.7815462884 NA NA 1.758038e+00
7 -10.0599502 -0.1280320918 3.3074087673 NA NA -4.234545e-01
7.1 -18.3267285 0.4242971219 3.7008403614 NA NA 1.570256e+00
7.2 -12.5138426 0.0777182491 4.7716691741 NA NA 3.708458e-01
8 -1.6305331 -0.5791408712 1.1246398522 NA NA -6.513249e-01
8.1 -9.6520453 0.3128604232 1.8027009873 NA NA 5.639938e-01
8.2 -1.5278462 0.6258446356 1.8175825174 NA NA 1.137524e+00
8.3 -7.4172211 -0.1040137707 2.8384267003 NA NA -2.952355e-01
8.4 -7.1238609 0.0481450285 3.3630275307 NA NA 1.619131e-01
8.5 -8.8706950 0.3831763675 4.4360849704 NA NA 1.699803e+00
9 -0.1634429 -0.1757592269 0.9607803822 NA NA -1.688660e-01
9.1 -2.6034300 -0.1791541200 2.9177753383 NA NA -5.227315e-01
9.2 -6.7272369 -0.0957042935 4.8100892501 NA NA -4.603462e-01
10 -6.4172202 -0.5598409704 2.2975509102 NA NA -1.286263e+00
10.1 -11.4834569 -0.2318340451 4.1734118364 NA NA -9.675389e-01
11 -8.7911356 0.5086859475 1.1832662905 NA NA 6.019109e-01
11.1 -19.6645080 0.4951758188 1.2346051680 NA NA 6.113466e-01
11.2 -20.2030932 -1.1022162541 1.6435316263 NA NA -1.811527e+00
11.3 -21.3082176 -0.0611636705 3.3859017969 NA NA -2.070942e-01
11.4 -14.5802901 -0.4971774316 4.8118087661 NA NA -2.392323e+00
12 -15.2006287 -0.2433996286 0.9591987054 NA NA -2.334686e-01
13 0.8058816 0.8799673116 0.0619085738 NA NA 5.447752e-02
13.1 -13.6379208 0.1079022586 3.5621061502 NA NA 3.843593e-01
14 -15.3422873 0.9991752617 4.0364430007 NA NA 4.033114e+00
14.1 -10.0965208 -0.1094019046 4.4710561272 NA NA -4.891421e-01
14.2 -16.6452027 0.1518967560 4.6359198843 NA NA 7.041812e-01
14.3 -15.8389733 0.3521012473 4.6886152599 NA NA 1.650867e+00
15 -8.9424594 0.3464447888 0.5402063532 NA NA 1.871517e-01
15.1 -22.0101983 -0.4767313971 1.1893180816 NA NA -5.669853e-01
15.2 -7.3975599 0.5759767791 1.5094739688 NA NA 8.694220e-01
15.3 -10.3567334 -0.1713452662 4.9193474615 NA NA -8.429069e-01
16 -1.9691302 0.4564754473 1.2417913869 NA NA 5.668473e-01
16.1 -9.9308357 1.0652558311 2.5675726333 NA NA 2.735122e+00
16.2 -6.9626923 0.6971872493 2.6524101500 NA NA 1.849227e+00
16.3 -3.2862557 0.5259331838 3.5585018690 NA NA 1.871534e+00
16.4 -3.3972355 0.2046601798 3.7612454291 NA NA 7.697772e-01
16.5 -11.5767835 1.0718540464 3.9851612889 NA NA 4.271511e+00
17 -10.5474144 0.6048676222 1.5925356350 NA NA 9.632732e-01
17.1 -7.6215009 0.2323298304 2.4374032998 NA NA 5.662815e-01
17.2 -16.5386939 1.2617499032 3.0256489082 NA NA 3.817612e+00
17.3 -20.0004774 -0.3913230895 3.3329089405 NA NA -1.304244e+00
17.4 -18.8505475 0.9577299112 3.8693758985 NA NA 3.705817e+00
18 -19.7302351 -0.0050324072 2.4374292302 NA NA -1.226614e-02
19 -14.6177568 -0.4187468937 0.9772165376 NA NA -4.092064e-01
19.1 -17.8043866 -0.4478828944 1.1466335913 NA NA -5.135576e-01
19.2 -15.1641705 -1.1966721302 2.2599126538 NA NA -2.704374e+00
19.3 -16.6898418 -0.5877091668 4.2114245973 NA NA -2.475093e+00
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95 -12.8849309 0.2375652188 2.0199063048 NA NA 4.798595e-01
95.1 -9.7451502 0.0767152977 4.0184444457 NA NA 3.082762e-01
95.2 -0.8535063 -0.6886731251 4.5596531732 NA NA -3.140111e+00
96 -4.9139832 0.7813892121 0.0311333477 NA NA 2.432726e-02
96.1 -3.9582653 0.3391519695 0.1324267720 NA NA 4.491280e-02
96.2 -9.6555492 -0.4857246503 0.6701303425 NA NA -3.254988e-01
96.3 -11.8690793 0.8771471244 2.1775037691 NA NA 1.909991e+00
96.4 -11.0224373 1.9030768981 2.2246142488 NA NA 4.233612e+00
96.5 -10.9530403 -0.1684332749 4.2377650598 NA NA -7.137806e-01
97 -9.8540471 1.3775130083 1.1955102731 NA NA 1.646831e+00
97.1 -19.2262840 -1.7323228619 4.9603108643 NA NA -8.592860e+00
98 -11.9651231 -1.2648518889 0.2041732438 NA NA -2.582489e-01
98.1 -2.6515128 -0.9042716241 0.4309578973 NA NA -3.897030e-01
98.2 -12.2606382 -0.1560385207 3.5172611906 NA NA -5.488282e-01
99 -11.4720500 0.7993356425 0.3531786101 NA NA 2.823083e-01
99.1 -14.0596866 1.0355522332 4.6789444226 NA NA 4.845291e+00
99.2 -17.3939469 -0.1150895843 4.9927084171 NA NA -5.746087e-01
100 1.1005874 0.0369067906 1.0691387602 NA NA 3.945848e-02
100.1 -3.8226248 1.6023713093 1.5109344281 NA NA 2.421078e+00
100.2 -0.9123182 0.8861545820 2.1502332564 NA NA 1.905439e+00
100.3 -15.8389474 0.1277046316 3.8745574222 NA NA 4.947989e-01
100.4 -12.8093826 -0.0834577654 4.6567608765 NA NA -3.886429e-01
B21:c1:time I(time^2)
1 NA 2.591239e-01
1.1 NA 4.443657e-01
1.2 NA 4.539005e+00
1.3 NA 6.227241e+00
2 NA 9.099267e+00
2.1 NA 1.088789e+01
2.2 NA 1.742860e+01
3 NA 7.188883e-01
3.1 NA 9.396866e+00
3.2 NA 2.245012e+01
4 NA 1.136655e-01
4.1 NA 1.143407e+00
4.2 NA 6.837688e+00
4.3 NA 9.819783e+00
5 NA 1.158319e+00
5.1 NA 3.208593e+00
5.2 NA 7.817661e+00
5.3 NA 7.907311e+00
6 NA 3.173907e+00
7 NA 1.093895e+01
7.1 NA 1.369622e+01
7.2 NA 2.276883e+01
8 NA 1.264815e+00
8.1 NA 3.249731e+00
8.2 NA 3.303606e+00
8.3 NA 8.056666e+00
8.4 NA 1.130995e+01
8.5 NA 1.967885e+01
9 NA 9.230989e-01
9.1 NA 8.513413e+00
9.2 NA 2.313696e+01
10 NA 5.278740e+00
10.1 NA 1.741737e+01
11 NA 1.400119e+00
11.1 NA 1.524250e+00
11.2 NA 2.701196e+00
11.3 NA 1.146433e+01
11.4 NA 2.315350e+01
12 NA 9.200622e-01
13 NA 3.832672e-03
13.1 NA 1.268860e+01
14 NA 1.629287e+01
14.1 NA 1.999034e+01
14.2 NA 2.149175e+01
14.3 NA 2.198311e+01
15 NA 2.918229e-01
15.1 NA 1.414477e+00
15.2 NA 2.278512e+00
15.3 NA 2.419998e+01
16 NA 1.542046e+00
16.1 NA 6.592429e+00
16.2 NA 7.035280e+00
16.3 NA 1.266294e+01
16.4 NA 1.414697e+01
16.5 NA 1.588151e+01
17 NA 2.536170e+00
17.1 NA 5.940935e+00
17.2 NA 9.154551e+00
17.3 NA 1.110828e+01
17.4 NA 1.497207e+01
18 NA 5.941061e+00
19 NA 9.549522e-01
19.1 NA 1.314769e+00
19.2 NA 5.107205e+00
19.3 NA 1.773610e+01
20 NA 2.948144e+00
20.1 NA 3.084555e+00
20.2 NA 5.069507e+00
20.3 NA 5.111725e+00
20.4 NA 1.218943e+01
20.5 NA 1.741475e+01
21 NA 2.868478e+00
21.1 NA 8.744554e+00
21.2 NA 1.435454e+01
22 NA 6.099036e+00
22.1 NA 1.000244e+01
23 NA 2.376079e+00
23.1 NA 5.461446e+00
24 NA 7.999358e+00
25 NA 2.896274e-01
25.1 NA 2.582364e+00
25.2 NA 2.675916e+00
25.3 NA 1.065818e+01
25.4 NA 1.663190e+01
25.5 NA 1.727258e+01
26 NA 5.821152e-02
26.1 NA 5.978875e+00
26.2 NA 1.295191e+01
26.3 NA 1.749110e+01
27 NA 1.365733e+01
27.1 NA 1.802124e+01
28 NA 3.302248e-01
28.1 NA 7.808651e+00
28.2 NA 1.773129e+01
28.3 NA 1.998584e+01
29 NA 1.415834e+00
29.1 NA 3.106075e+00
29.2 NA 4.084605e+00
29.3 NA 1.161363e+01
30 NA 5.123598e+00
30.1 NA 1.291564e+01
30.2 NA 1.306006e+01
31 NA 1.934957e+01
32 NA 2.804020e+00
32.1 NA 8.484502e+00
32.2 NA 8.806981e+00
32.3 NA 1.772399e+01
33 NA 8.720901e-05
33.1 NA 1.196554e+01
34 NA 2.249632e+00
34.1 NA 1.462509e+01
34.2 NA 1.526641e+01
34.3 NA 1.566745e+01
35 NA 1.767384e+00
35.1 NA 2.333857e+00
35.2 NA 2.027334e+01
36 NA 5.073953e-01
36.1 NA 3.230105e+00
36.2 NA 3.335261e+00
36.3 NA 1.835276e+01
36.4 NA 2.133929e+01
37 NA 4.007496e+00
37.1 NA 1.343724e+01
37.2 NA 1.573228e+01
38 NA 9.656032e-01
39 NA 4.791143e-01
39.1 NA 8.150103e-01
39.2 NA 1.704501e+00
39.3 NA 2.375557e+00
39.4 NA 1.013467e+01
39.5 NA 1.713477e+01
40 NA 1.283779e+00
40.1 NA 7.258172e+00
40.2 NA 9.239542e+00
40.3 NA 2.186776e+01
41 NA 3.739257e+00
41.1 NA 1.021205e+01
41.2 NA 1.078920e+01
41.3 NA 1.143355e+01
41.4 NA 1.259041e+01
42 NA 2.361234e-01
42.1 NA 1.874295e+01
43 NA 3.154636e-01
43.1 NA 1.154245e+00
43.2 NA 6.828709e+00
44 NA 5.871613e-01
44.1 NA 7.017356e+00
44.2 NA 1.113684e+01
44.3 NA 1.693668e+01
45 NA 3.831610e-02
45.1 NA 3.985546e+00
46 NA 1.816499e+00
46.1 NA 8.160046e+00
46.2 NA 1.950170e+01
47 NA 3.615162e-01
47.1 NA 5.806713e+00
47.2 NA 8.985482e+00
47.3 NA 1.013127e+01
47.4 NA 2.134538e+01
48 NA 8.183814e+00
48.1 NA 8.467142e+00
49 NA 7.387392e+00
50 NA 1.383461e+00
51 NA 2.046169e+00
52 NA 4.522702e+00
52.1 NA 9.610339e+00
52.2 NA 9.777177e+00
52.3 NA 1.275308e+01
52.4 NA 2.302432e+01
52.5 NA 2.481859e+01
53 NA 2.465916e-01
53.1 NA 1.260630e+01
53.2 NA 2.096763e+01
54 NA 1.969735e+00
54.1 NA 3.539056e+00
54.2 NA 6.303910e+00
54.3 NA 7.755338e+00
54.4 NA 1.611531e+01
55 NA 3.743632e-01
55.1 NA 5.570753e-01
55.2 NA 7.953081e+00
55.3 NA 9.813453e+00
55.4 NA 1.038006e+01
56 NA 1.496055e+00
56.1 NA 5.556959e+00
56.2 NA 6.592024e+00
56.3 NA 8.706727e+00
56.4 NA 1.041529e+01
56.5 NA 1.167966e+01
57 NA 5.618471e-02
57.1 NA 6.157569e-02
57.2 NA 1.300874e+00
57.3 NA 4.474869e+00
58 NA 1.490865e+00
58.1 NA 2.668076e+00
58.2 NA 2.819667e+00
58.3 NA 6.927488e+00
58.4 NA 8.109812e+00
58.5 NA 1.275602e+01
59 NA 3.619125e+00
59.1 NA 2.473731e+01
60 NA 8.325824e+00
61 NA 5.203647e-01
61.1 NA 5.376345e+00
61.2 NA 6.288716e+00
61.3 NA 1.006861e+01
61.4 NA 1.297637e+01
62 NA 2.848114e-01
62.1 NA 4.882748e-01
62.2 NA 1.196074e+01
62.3 NA 2.306763e+01
63 NA 7.894611e+00
63.1 NA 1.572420e+01
64 NA 1.696724e+01
65 NA 5.007194e-01
65.1 NA 4.101535e+00
65.2 NA 9.689140e+00
65.3 NA 1.022023e+01
66 NA 1.221045e+01
66.1 NA 1.419589e+01
66.2 NA 1.559155e+01
67 NA 1.741258e+01
68 NA 1.669057e-02
68.1 NA 3.171834e+00
68.2 NA 4.199715e+00
68.3 NA 8.647640e+00
68.4 NA 1.632699e+01
69 NA 1.718202e+01
70 NA 3.970284e-02
70.1 NA 2.332672e-01
71 NA 5.993245e-01
71.1 NA 2.215280e+00
71.2 NA 1.661257e+01
71.3 NA 2.213537e+01
71.4 NA 2.231881e+01
72 NA 8.688470e-01
72.1 NA 1.392398e+00
72.2 NA 3.578744e+00
72.3 NA 1.214773e+01
72.4 NA 1.360449e+01
72.5 NA 1.669062e+01
73 NA 2.117830e+01
74 NA 2.139435e+00
75 NA 1.057829e+01
76 NA 3.266987e+00
76.1 NA 1.822015e+01
76.2 NA 2.468970e+01
77 NA 7.155525e-01
78 NA 6.775091e-01
79 NA 3.408452e-03
79.1 NA 5.956710e+00
79.2 NA 1.086528e+01
80 NA 8.073131e-01
80.1 NA 1.804904e+00
80.2 NA 7.854511e+00
81 NA 1.026675e-04
81.1 NA 8.876861e-01
81.2 NA 9.328415e+00
81.3 NA 1.119346e+01
82 NA 1.901920e+00
82.1 NA 3.097956e+00
82.2 NA 6.881772e+00
83 NA 2.887926e-03
83.1 NA 8.445749e+00
83.2 NA 9.727823e+00
83.3 NA 2.271823e+01
84 NA 7.427838e+00
84.1 NA 1.113209e+01
85 NA 8.867032e-02
85.1 NA 3.025553e+00
85.2 NA 7.207254e+00
85.3 NA 9.991139e+00
85.4 NA 1.556465e+01
85.5 NA 2.033338e+01
86 NA 7.184729e-01
86.1 NA 1.023867e+00
86.2 NA 1.565291e+00
86.3 NA 4.783212e+00
86.4 NA 6.018649e+00
86.5 NA 1.459703e+01
87 NA 7.327595e+00
87.1 NA 1.187665e+01
87.2 NA 2.046808e+01
88 NA 3.472088e-07
88.1 NA 5.063888e-01
88.2 NA 6.226384e+00
88.3 NA 1.088724e+01
89 NA 4.175856e-01
90 NA 2.876520e-02
90.1 NA 6.749804e+00
90.2 NA 7.102967e+00
90.3 NA 9.761057e+00
91 NA 4.073782e-01
91.1 NA 6.877013e+00
91.2 NA 2.282214e+01
92 NA 5.432462e-03
93 NA 7.778015e-02
93.1 NA 1.072832e+00
93.2 NA 6.208345e+00
93.3 NA 8.338309e+00
93.4 NA 1.992683e+01
94 NA 7.204688e-01
94.1 NA 1.113543e+00
94.2 NA 3.781275e+00
94.3 NA 9.431485e+00
94.4 NA 9.531256e+00
94.5 NA 1.918945e+01
95 NA 4.080021e+00
95.1 NA 1.614790e+01
95.2 NA 2.079044e+01
96 NA 9.692853e-04
96.1 NA 1.753685e-02
96.2 NA 4.490747e-01
96.3 NA 4.741523e+00
96.4 NA 4.948909e+00
96.5 NA 1.795865e+01
97 NA 1.429245e+00
97.1 NA 2.460468e+01
98 NA 4.168671e-02
98.1 NA 1.857247e-01
98.2 NA 1.237113e+01
99 NA 1.247351e-01
99.1 NA 2.189252e+01
99.2 NA 2.492714e+01
100 NA 1.143058e+00
100.1 NA 2.282923e+00
100.2 NA 4.623503e+00
100.3 NA 1.501220e+01
100.4 NA 2.168542e+01
$m8l$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8l$spM_lvlone
center scale
y -11.1733710 6.2496619
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c1 0.1798099 0.6117459
B21:time 2.0207975 1.5857637
c1:time 0.6507067 1.9186258
B21:c1:time 0.4612732 1.7267423
I(time^2) 8.3244468 7.0900029
$m8l$mu_reg_norm
[1] 0
$m8l$tau_reg_norm
[1] 1e-04
$m8l$shape_tau_norm
[1] 0.01
$m8l$rate_tau_norm
[1] 0.01
$m8l$mu_reg_binom
[1] 0
$m8l$tau_reg_binom
[1] 1e-04
$m8l$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8l$shape_diag_RinvD
[1] "0.01"
$m8l$rate_diag_RinvD
[1] "0.001"
$m8l$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8l$KinvD_y_id
id
4
$m8m
$m8m$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m8m$M_lvlone
y c1 b11 o1.L o1.Q c1:b11 b1
1 -13.0493856 0.7592026489 0 -7.850462e-17 -0.8164966 0.0000000000 1
1.1 -9.3335901 0.9548337990 1 -7.071068e-01 0.4082483 0.9548337990 2
1.2 -22.3469852 0.5612235156 1 -7.071068e-01 0.4082483 0.5612235156 2
1.3 -15.0417337 1.1873391025 0 -7.850462e-17 -0.8164966 0.0000000000 1
2 -12.0655434 0.9192204198 1 7.071068e-01 0.4082483 0.9192204198 2
2.1 -15.8674476 -0.1870730476 1 -7.071068e-01 0.4082483 -0.1870730476 2
2.2 -7.8800006 1.2517512331 1 -7.850462e-17 -0.8164966 1.2517512331 2
3 -11.4820604 -0.0605087604 1 -7.071068e-01 0.4082483 -0.0605087604 2
3.1 -10.5983220 0.3788637747 0 7.071068e-01 0.4082483 0.0000000000 1
3.2 -22.4519157 0.9872578281 0 -7.850462e-17 -0.8164966 0.0000000000 1
4 -1.2697775 1.4930175328 1 7.071068e-01 0.4082483 1.4930175328 2
4.1 -11.1215184 -0.7692526880 1 -7.850462e-17 -0.8164966 -0.7692526880 2
4.2 -3.6134138 0.9180841450 0 -7.071068e-01 0.4082483 0.0000000000 1
4.3 -14.5982385 -0.0541170782 1 -7.850462e-17 -0.8164966 -0.0541170782 2
5 -6.8457515 -0.1376784521 0 -7.850462e-17 -0.8164966 0.0000000000 1
5.1 -7.0551214 -0.2740585866 1 -7.850462e-17 -0.8164966 -0.2740585866 2
5.2 -12.3418980 0.4670496929 1 7.071068e-01 0.4082483 0.4670496929 2
5.3 -9.2366906 0.1740288049 1 -7.850462e-17 -0.8164966 0.1740288049 2
6 -5.1648211 0.9868044683 0 7.071068e-01 0.4082483 0.0000000000 1
7 -10.0599502 -0.1280320918 1 7.071068e-01 0.4082483 -0.1280320918 2
7.1 -18.3267285 0.4242971219 0 -7.071068e-01 0.4082483 0.0000000000 1
7.2 -12.5138426 0.0777182491 1 -7.071068e-01 0.4082483 0.0777182491 2
8 -1.6305331 -0.5791408712 0 -7.850462e-17 -0.8164966 0.0000000000 1
8.1 -9.6520453 0.3128604232 1 -7.850462e-17 -0.8164966 0.3128604232 2
8.2 -1.5278462 0.6258446356 1 7.071068e-01 0.4082483 0.6258446356 2
8.3 -7.4172211 -0.1040137707 0 7.071068e-01 0.4082483 0.0000000000 1
8.4 -7.1238609 0.0481450285 0 -7.850462e-17 -0.8164966 0.0000000000 1
8.5 -8.8706950 0.3831763675 1 -7.850462e-17 -0.8164966 0.3831763675 2
9 -0.1634429 -0.1757592269 1 -7.850462e-17 -0.8164966 -0.1757592269 2
9.1 -2.6034300 -0.1791541200 1 -7.850462e-17 -0.8164966 -0.1791541200 2
9.2 -6.7272369 -0.0957042935 0 7.071068e-01 0.4082483 0.0000000000 1
10 -6.4172202 -0.5598409704 1 -7.850462e-17 -0.8164966 -0.5598409704 2
10.1 -11.4834569 -0.2318340451 1 -7.071068e-01 0.4082483 -0.2318340451 2
11 -8.7911356 0.5086859475 1 -7.850462e-17 -0.8164966 0.5086859475 2
11.1 -19.6645080 0.4951758188 1 7.071068e-01 0.4082483 0.4951758188 2
11.2 -20.2030932 -1.1022162541 1 7.071068e-01 0.4082483 -1.1022162541 2
11.3 -21.3082176 -0.0611636705 1 -7.071068e-01 0.4082483 -0.0611636705 2
11.4 -14.5802901 -0.4971774316 1 7.071068e-01 0.4082483 -0.4971774316 2
12 -15.2006287 -0.2433996286 1 -7.071068e-01 0.4082483 -0.2433996286 2
13 0.8058816 0.8799673116 0 -7.850462e-17 -0.8164966 0.0000000000 1
13.1 -13.6379208 0.1079022586 1 -7.071068e-01 0.4082483 0.1079022586 2
14 -15.3422873 0.9991752617 0 7.071068e-01 0.4082483 0.0000000000 1
14.1 -10.0965208 -0.1094019046 1 7.071068e-01 0.4082483 -0.1094019046 2
14.2 -16.6452027 0.1518967560 0 -7.850462e-17 -0.8164966 0.0000000000 1
14.3 -15.8389733 0.3521012473 0 -7.071068e-01 0.4082483 0.0000000000 1
15 -8.9424594 0.3464447888 0 7.071068e-01 0.4082483 0.0000000000 1
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81.3 -5.2166381 0.4182528210 1 7.071068e-01 0.4082483 0.4182528210 2
82 -3.0749381 1.1088801952 1 7.071068e-01 0.4082483 1.1088801952 2
82.1 -10.5506696 0.9334157763 1 -7.850462e-17 -0.8164966 0.9334157763 2
82.2 -18.2226347 0.4958140634 0 -7.850462e-17 -0.8164966 0.0000000000 1
83 -12.5872635 0.5104724530 1 7.071068e-01 0.4082483 0.5104724530 2
83.1 -11.9756502 -0.0513309106 0 7.071068e-01 0.4082483 0.0000000000 1
83.2 -10.6744217 -0.2067792494 0 7.071068e-01 0.4082483 0.0000000000 1
83.3 -19.2714012 -0.0534169155 1 -7.850462e-17 -0.8164966 -0.0534169155 2
84 -2.6320312 -0.0255753653 1 7.071068e-01 0.4082483 -0.0255753653 2
84.1 -9.8140094 -1.8234189877 0 7.071068e-01 0.4082483 0.0000000000 1
85 -12.3886736 -0.0114038622 0 7.071068e-01 0.4082483 0.0000000000 1
85.1 -12.9196365 -0.0577615939 0 -7.850462e-17 -0.8164966 0.0000000000 1
85.2 -9.6433248 -0.2241856342 1 -7.850462e-17 -0.8164966 -0.2241856342 2
85.3 -6.3296340 -0.0520175929 1 -7.850462e-17 -0.8164966 -0.0520175929 2
85.4 -7.0405525 0.2892733846 1 -7.071068e-01 0.4082483 0.2892733846 2
85.5 -13.6714939 -0.3740417009 1 7.071068e-01 0.4082483 -0.3740417009 2
86 -10.8756412 0.4293735089 0 -7.850462e-17 -0.8164966 0.0000000000 1
86.1 -12.0055331 -0.1363456521 1 7.071068e-01 0.4082483 -0.1363456521 2
86.2 -13.3724699 0.1230989293 1 -7.071068e-01 0.4082483 0.1230989293 2
86.3 -13.3252145 0.3305413955 0 -7.071068e-01 0.4082483 0.0000000000 1
86.4 -14.9191290 2.6003411822 1 -7.071068e-01 0.4082483 2.6003411822 2
86.5 -17.7515546 -0.1420690052 0 7.071068e-01 0.4082483 0.0000000000 1
87 -10.7027963 1.0457427869 0 -7.071068e-01 0.4082483 0.0000000000 1
87.1 -22.4941954 -0.2973007190 1 -7.071068e-01 0.4082483 -0.2973007190 2
87.2 -14.9616716 0.4396872616 0 -7.071068e-01 0.4082483 0.0000000000 1
88 -2.2264493 -0.0601928334 0 7.071068e-01 0.4082483 0.0000000000 1
88.1 -8.9626474 -1.0124347595 0 -7.071068e-01 0.4082483 0.0000000000 1
88.2 -2.5095281 0.5730917016 0 -7.071068e-01 0.4082483 0.0000000000 1
88.3 -16.3345673 -0.0029455332 0 -7.071068e-01 0.4082483 0.0000000000 1
89 -11.0459647 1.5465903721 1 -7.071068e-01 0.4082483 1.5465903721 2
90 -4.5610239 0.0626760573 0 -7.850462e-17 -0.8164966 0.0000000000 1
90.1 -11.7036651 1.1896872985 1 -7.071068e-01 0.4082483 1.1896872985 2
90.2 -5.3838521 0.2597888783 1 7.071068e-01 0.4082483 0.2597888783 2
90.3 -4.1636999 0.6599799887 0 -7.071068e-01 0.4082483 0.0000000000 1
91 -7.1462503 1.1213651365 0 -7.071068e-01 0.4082483 0.0000000000 1
91.1 -12.8374475 1.2046371625 0 -7.850462e-17 -0.8164966 0.0000000000 1
91.2 -18.2576707 0.3395603754 1 -7.071068e-01 0.4082483 0.3395603754 2
92 -6.4119222 0.4674939332 1 -7.850462e-17 -0.8164966 0.4674939332 2
93 5.2122168 0.2677965647 0 -7.071068e-01 0.4082483 0.0000000000 1
93.1 3.1211725 1.6424445368 1 7.071068e-01 0.4082483 1.6424445368 2
93.2 -3.6841177 0.7101700066 0 -7.071068e-01 0.4082483 0.0000000000 1
93.3 2.6223542 1.1222322893 1 7.071068e-01 0.4082483 1.1222322893 2
93.4 -11.1877696 1.4628960401 0 -7.071068e-01 0.4082483 0.0000000000 1
94 -6.9602492 -0.2904211940 1 -7.850462e-17 -0.8164966 -0.2904211940 2
94.1 -7.4318416 0.0147813580 0 7.071068e-01 0.4082483 0.0000000000 1
94.2 -4.3498045 -0.4536774482 1 -7.071068e-01 0.4082483 -0.4536774482 2
94.3 -11.6340088 0.6793464917 0 7.071068e-01 0.4082483 0.0000000000 1
94.4 -12.9357964 -0.9411356550 0 -7.850462e-17 -0.8164966 0.0000000000 1
94.5 -14.7648530 0.5683867264 0 7.071068e-01 0.4082483 0.0000000000 1
95 -12.8849309 0.2375652188 1 7.071068e-01 0.4082483 0.2375652188 2
95.1 -9.7451502 0.0767152977 1 -7.071068e-01 0.4082483 0.0767152977 2
95.2 -0.8535063 -0.6886731251 0 -7.850462e-17 -0.8164966 0.0000000000 1
96 -4.9139832 0.7813892121 1 -7.071068e-01 0.4082483 0.7813892121 2
96.1 -3.9582653 0.3391519695 0 7.071068e-01 0.4082483 0.0000000000 1
96.2 -9.6555492 -0.4857246503 0 -7.071068e-01 0.4082483 0.0000000000 1
96.3 -11.8690793 0.8771471244 0 -7.071068e-01 0.4082483 0.0000000000 1
96.4 -11.0224373 1.9030768981 0 -7.850462e-17 -0.8164966 0.0000000000 1
96.5 -10.9530403 -0.1684332749 1 7.071068e-01 0.4082483 -0.1684332749 2
97 -9.8540471 1.3775130083 0 7.071068e-01 0.4082483 0.0000000000 1
97.1 -19.2262840 -1.7323228619 0 7.071068e-01 0.4082483 0.0000000000 1
98 -11.9651231 -1.2648518889 0 7.071068e-01 0.4082483 0.0000000000 1
98.1 -2.6515128 -0.9042716241 0 -7.071068e-01 0.4082483 0.0000000000 1
98.2 -12.2606382 -0.1560385207 0 -7.071068e-01 0.4082483 0.0000000000 1
99 -11.4720500 0.7993356425 1 -7.850462e-17 -0.8164966 0.7993356425 2
99.1 -14.0596866 1.0355522332 1 -7.850462e-17 -0.8164966 1.0355522332 2
99.2 -17.3939469 -0.1150895843 1 -7.071068e-01 0.4082483 -0.1150895843 2
100 1.1005874 0.0369067906 0 -7.071068e-01 0.4082483 0.0000000000 1
100.1 -3.8226248 1.6023713093 0 -7.850462e-17 -0.8164966 0.0000000000 1
100.2 -0.9123182 0.8861545820 1 -7.850462e-17 -0.8164966 0.8861545820 2
100.3 -15.8389474 0.1277046316 1 -7.071068e-01 0.4082483 0.1277046316 2
100.4 -12.8093826 -0.0834577654 1 -7.071068e-01 0.4082483 -0.0834577654 2
$m8m$spM_lvlone
center scale
y -11.1733710 6.2496619
c1 0.2559996 0.6718095
b11 NA NA
o1.L NA NA
o1.Q NA NA
c1:b11 0.1247936 0.5064162
b1 NA NA
$m8m$mu_reg_norm
[1] 0
$m8m$tau_reg_norm
[1] 1e-04
$m8m$shape_tau_norm
[1] 0.01
$m8m$rate_tau_norm
[1] 0.01
$m8m$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8m$shape_diag_RinvD
[1] "0.01"
$m8m$rate_diag_RinvD
[1] "0.001"
$m8m$RinvD_y_id
[,1] [,2]
[1,] NA 0
[2,] 0 NA
$m8m$KinvD_y_id
id
3
$m8n
$m8n$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8n$M_lvlone
y c1 time b1 b11 C1:time
1 -13.0493856 0.7592026489 0.5090421822 0 0 0.3652818145
1.1 -9.3335901 0.9548337990 0.6666076288 1 1 0.4783486570
1.2 -22.3469852 0.5612235156 2.1304941282 1 1 1.5288138942
1.3 -15.0417337 1.1873391025 2.4954441458 0 0 1.7906971118
2 -12.0655434 0.9192204198 3.0164990982 1 1 2.2645370243
2.1 -15.8674476 -0.1870730476 3.2996806887 1 1 2.4771262462
2.2 -7.8800006 1.2517512331 4.1747569619 1 1 3.1340608434
3 -11.4820604 -0.0605087604 0.8478727890 1 1 0.6152125819
3.1 -10.5983220 0.3788637747 3.0654308549 0 0 2.2242624781
3.2 -22.4519157 0.9872578281 4.7381553578 0 0 3.4379836560
4 -1.2697775 1.4930175328 0.3371432109 1 1 0.2518241168
4.1 -11.1215184 -0.7692526880 1.0693019140 1 1 0.7986991917
4.2 -3.6134138 0.9180841450 2.6148973033 0 0 1.9531587247
4.3 -14.5982385 -0.0541170782 3.1336532847 1 1 2.3406358046
5 -6.8457515 -0.1376784521 1.0762525082 0 0 0.7683495918
5.1 -7.0551214 -0.2740585866 1.7912546196 1 1 1.2787981866
5.2 -12.3418980 0.4670496929 2.7960080339 1 1 1.9961037166
5.3 -9.2366906 0.1740288049 2.8119940578 1 1 2.0075163311
6 -5.1648211 0.9868044683 1.7815462884 0 0 1.3063196933
7 -10.0599502 -0.1280320918 3.3074087673 1 1 2.4295989047
7.1 -18.3267285 0.4242971219 3.7008403614 0 0 2.7186109493
7.2 -12.5138426 0.0777182491 4.7716691741 1 1 3.5052341620
8 -1.6305331 -0.5791408712 1.1246398522 0 0 0.8606406530
8.1 -9.6520453 0.3128604232 1.8027009873 1 1 1.3795329695
8.2 -1.5278462 0.6258446356 1.8175825174 1 1 1.3909211928
8.3 -7.4172211 -0.1040137707 2.8384267003 0 0 2.1721312865
8.4 -7.1238609 0.0481450285 3.3630275307 0 0 2.5735867394
8.5 -8.8706950 0.3831763675 4.4360849704 1 1 3.3947534923
9 -0.1634429 -0.1757592269 0.9607803822 1 1 0.6918216822
9.1 -2.6034300 -0.1791541200 2.9177753383 1 1 2.1009798703
9.2 -6.7272369 -0.0957042935 4.8100892501 0 0 3.4635636802
10 -6.4172202 -0.5598409704 2.2975509102 1 1 1.7056739807
10.1 -11.4834569 -0.2318340451 4.1734118364 1 1 3.0982904226
11 -8.7911356 0.5086859475 1.1832662905 1 1 0.8800481723
11.1 -19.6645080 0.4951758188 1.2346051680 1 1 0.9182311964
11.2 -20.2030932 -1.1022162541 1.6435316263 1 1 1.2223681309
11.3 -21.3082176 -0.0611636705 3.3859017969 1 1 2.5182469169
11.4 -14.5802901 -0.4971774316 4.8118087661 1 1 3.5787578367
12 -15.2006287 -0.2433996286 0.9591987054 1 1 0.7142644156
13 0.8058816 0.8799673116 0.0619085738 0 0 0.0466183059
13.1 -13.6379208 0.1079022586 3.5621061502 1 1 2.6823320911
14 -15.3422873 0.9991752617 4.0364430007 0 0 2.8631042655
14.1 -10.0965208 -0.1094019046 4.4710561272 1 1 3.1713813046
14.2 -16.6452027 0.1518967560 4.6359198843 0 0 3.2883214239
14.3 -15.8389733 0.3521012473 4.6886152599 0 0 3.3256989750
15 -8.9424594 0.3464447888 0.5402063532 0 0 0.3960356618
15.1 -22.0101983 -0.4767313971 1.1893180816 0 0 0.8719119477
15.2 -7.3975599 0.5759767791 1.5094739688 0 0 1.1066243829
15.3 -10.3567334 -0.1713452662 4.9193474615 1 1 3.6064681879
16 -1.9691302 0.4564754473 1.2417913869 1 1 0.8706683147
16.1 -9.9308357 1.0652558311 2.5675726333 0 0 1.8002251917
16.2 -6.9626923 0.6971872493 2.6524101500 1 1 1.8597080795
16.3 -3.2862557 0.5259331838 3.5585018690 1 1 2.4950042800
16.4 -3.3972355 0.2046601798 3.7612454291 1 1 2.6371556877
16.5 -11.5767835 1.0718540464 3.9851612889 0 0 2.7941518196
17 -10.5474144 0.6048676222 1.5925356350 0 0 1.1836354184
17.1 -7.6215009 0.2323298304 2.4374032998 0 0 1.8115744548
17.2 -16.5386939 1.2617499032 3.0256489082 1 1 2.2487818375
17.3 -20.0004774 -0.3913230895 3.3329089405 0 0 2.4771496359
17.4 -18.8505475 0.9577299112 3.8693758985 1 1 2.8758730794
18 -19.7302351 -0.0050324072 2.4374292302 1 1 1.8390868081
19 -14.6177568 -0.4187468937 0.9772165376 1 1 0.7356962360
19.1 -17.8043866 -0.4478828944 1.1466335913 1 1 0.8632416509
19.2 -15.1641705 -1.1966721302 2.2599126538 1 1 1.7013723870
19.3 -16.6898418 -0.5877091668 4.2114245973 1 1 3.1705656887
20 -12.9059229 0.6838223064 1.7170160066 0 0 1.3071411820
20.1 -16.8191201 0.3278571109 1.7562902288 1 1 1.3370401189
20.2 -6.1010131 -0.8489831990 2.2515566566 0 0 1.7140797861
20.3 -7.9415371 1.3169975191 2.2609123867 0 0 1.7212021776
20.4 -9.3904458 0.0444804531 3.4913365287 0 0 2.6579075206
20.5 -13.3504189 -0.4535207652 4.1730977828 0 0 3.1769231897
21 -7.6974718 -0.4030302960 1.6936582839 1 1 1.2281934263
21.1 -11.9335526 -0.4069674045 2.9571191233 1 1 2.1444197467
21.2 -12.7064929 1.0650265940 3.7887385779 0 0 2.7474868216
22 -21.5022909 -0.0673274516 2.4696226232 0 0 1.8029800300
22.1 -12.7745451 0.9601388170 3.1626627257 1 1 2.3089429463
23 -3.5146508 0.5556634840 1.5414533857 1 1 1.0924663221
23.1 -4.6724048 1.4407865964 2.3369736120 1 1 1.6562712764
24 -2.5619821 0.3856376411 2.8283136466 0 0 2.0906718629
25 -6.2944970 0.3564400705 0.5381704110 0 0 0.4208837547
25.1 -3.8630505 0.0982553434 1.6069735331 1 1 1.2567562995
25.2 -14.4205140 0.1928682598 1.6358226922 1 1 1.2793181910
25.3 -19.6735037 -0.0192488594 3.2646870392 0 0 2.5531945101
25.4 -9.0288933 0.4466012931 4.0782226040 0 0 3.1894314642
25.5 -9.0509738 1.1425193342 4.1560292873 0 0 3.2502812774
26 -19.7340685 0.5341531449 0.2412706357 1 1 0.1717436194
26.1 -14.1692728 1.2268695927 2.4451737676 1 1 1.7405474628
26.2 -17.2819976 0.3678294939 3.5988757887 1 1 2.5617868987
26.3 -24.6265576 0.5948516018 4.1822362854 0 0 2.9770402626
27 -7.3354999 -0.3342844147 3.6955824879 1 1 2.6722228982
27.1 -11.1488468 -0.4835141229 4.2451434687 1 1 3.0696025919
28 -11.7996597 -0.7145915499 0.5746519344 1 1 0.4303771207
28.1 -8.2030122 0.5063671955 2.7943964268 0 0 2.0928221348
28.2 -26.4317815 -0.2067413142 4.2108539480 1 1 3.1536571778
28.3 -18.5016071 0.1196789973 4.4705521734 1 1 3.3481543470
29 -5.8551395 0.1392699487 1.1898884235 1 1 0.8937118818
29.1 -2.0209442 0.7960234776 1.7624059319 0 0 1.3237233767
29.2 -5.6368080 1.0398214352 2.0210406382 0 0 1.5179810109
29.3 -3.8110961 0.0813246429 3.4078777023 1 1 2.5596188130
30 -12.7217702 -0.3296323050 2.2635366488 1 1 1.6525441223
30.1 -17.0170140 1.3635850954 3.5938334477 1 1 2.6237562107
30.2 -25.4236089 0.7354171050 3.6138710892 1 1 2.6383851264
31 -17.0783921 0.3708398217 4.3988140998 0 0 3.3214216230
32 -18.4338764 -0.0474059668 1.6745209007 1 1 1.2260671754
32.1 -19.4317212 1.2507771489 2.9128167813 1 1 2.1327348270
32.2 -19.4738978 0.1142915519 2.9676558380 1 1 2.1728874266
32.3 -21.4922645 0.6773270619 4.2099863547 1 1 3.0825091978
33 2.0838099 0.1774293842 0.0093385763 0 0 0.0068231501
33.1 -13.3172274 0.6159606291 3.4591242753 0 0 2.5273792639
34 -10.0296691 0.8590979166 1.4998774312 1 1 1.1139914202
34.1 -25.9426553 0.0546216775 3.8242761395 0 0 2.8403726326
34.2 -18.5688138 -0.0897224473 3.9072251692 1 1 2.9019806717
34.3 -15.4173859 0.4163395571 3.9582124643 1 1 2.9398500390
35 -14.3958113 -1.4693520528 1.3294299203 1 1 0.9562645676
35.1 -12.9457541 -0.3031734330 1.5276966314 0 0 1.0988786520
35.2 -16.1380691 -0.6045512101 4.5025920868 1 1 3.2387335424
36 -12.8166968 0.9823048960 0.7123168337 0 0 0.5209093415
36.1 -14.3989481 1.4466051416 1.7972493160 0 0 1.3143083435
36.2 -12.2436943 1.1606752905 1.8262697803 1 1 1.3355306848
36.3 -15.0104638 0.8373091576 4.2840119381 0 0 3.1328500636
36.4 -10.1775457 0.2640591685 4.6194464504 1 1 3.3781495744
37 -15.2223495 0.1177313455 2.0018732361 1 1 1.4214172307
37.1 -14.7526195 -0.1415483779 3.6656836793 0 0 2.6027951471
37.2 -19.8168430 0.0054610124 3.9663937816 0 0 2.8163124234
38 -2.7065118 0.8078948077 0.9826511063 1 1 0.7537115243
39 -8.7288138 0.9876451040 0.6921808305 1 1 0.5122448722
39.1 -9.2746473 -0.3431222274 0.9027792048 0 0 0.6680971185
39.2 -18.2695344 -1.7909380751 1.3055654289 0 0 0.9661769970
39.3 -13.8219083 -0.1798746191 1.5412842878 0 0 1.1406195291
39.4 -16.2254704 -0.1850961689 3.1834997435 1 1 2.3559326511
39.5 -21.7283648 0.4544226146 4.1394166439 1 1 3.0633540486
40 1.8291916 0.5350190436 1.1330395646 0 0 0.8381437699
40.1 -6.6916432 0.4189342752 2.6940994046 0 0 1.9929071342
40.2 -1.6278171 0.4211994981 3.0396614212 0 0 2.2485298506
40.3 -10.5749790 0.0916687506 4.6762977762 1 1 3.4591994578
41 -3.1556121 -0.1035047421 1.9337158254 1 1 1.4485398595
41.1 -11.5895327 -0.4684202411 3.1956304458 1 1 2.3938357519
41.2 -18.9352091 0.5972615368 3.2846923557 0 0 2.4605517215
41.3 -15.9788960 0.9885613862 3.3813529415 1 1 2.5329598332
41.4 -9.6070508 -0.3908036794 3.5482964432 1 1 2.6580166349
42 -5.2159485 -0.0338893961 0.4859252973 1 1 0.3605213138
42.1 -15.9878743 -0.4498363172 4.3293134298 1 1 3.2120364478
43 -16.6104361 0.8965546110 0.5616614548 0 0 0.4228080758
43.1 -9.5549441 0.6199122090 1.0743579536 0 0 0.8087562626
43.2 -14.2003491 0.1804894429 2.6131797966 1 1 1.9671521198
44 -8.1969033 1.3221409285 0.7662644819 1 1 0.5676728587
44.1 -19.9270197 0.3416426284 2.6490291790 0 0 1.9624842365
44.2 -22.6521171 0.5706610068 3.3371910988 0 0 2.4722962576
44.3 -21.1903736 1.2679497430 4.1154200875 1 1 3.0488327997
45 -0.5686627 0.1414983160 0.1957449992 1 1 0.1438246201
45.1 -7.5645740 0.7220892521 1.9963831536 0 0 1.4668525369
46 -19.1624789 1.5391054233 1.3477755385 1 1 0.9882426330
46.1 -18.4487574 0.3889107049 2.8565793915 0 0 2.0945576311
46.2 -15.8222682 0.1248719493 4.4160729996 1 1 3.2380403739
47 -5.4165074 0.2014101100 0.6012621359 0 0 0.4435198614
47.1 -15.0975029 0.2982973539 2.4097121472 0 0 1.7775195440
47.2 -12.9971413 1.1518107179 2.9975794035 1 1 2.2111586981
47.3 -10.6844521 0.5196802157 3.1829649757 0 0 2.3479080100
47.4 -18.2214784 0.3702301552 4.6201055450 0 0 3.4080119947
48 -8.3101471 -0.2128602862 2.8607365978 0 0 2.1015481927
48.1 -18.3854275 -0.5337239976 2.9098354396 1 1 2.1376170787
49 -13.0130319 -0.5236770035 2.7179756400 0 0 1.9921136553
50 -10.4579977 0.3897705981 1.1762060679 1 1 0.8539769445
51 -19.3157621 -0.7213343736 1.4304436720 1 1 1.0360574125
52 -4.4747188 0.3758235358 2.1266646020 1 1 1.5520541611
52.1 -4.3163827 0.7138067080 3.1000545993 1 1 2.2624407422
52.2 -6.9761408 0.8872895233 3.1268477370 0 0 2.2819945547
52.3 -20.1764756 -0.9664587437 3.5711459327 0 0 2.6062463726
52.4 -8.9036692 0.0254566848 4.7983659909 1 1 3.5018798430
52.5 -5.6949642 0.4155259424 4.9818264414 1 1 3.6357705164
53 -10.3141887 0.5675736897 0.4965799209 1 1 0.3602558557
53.1 -8.2642654 -0.3154088781 3.5505357443 1 1 2.5758216127
53.2 -9.1691554 0.2162315769 4.5790420019 1 1 3.3219762321
54 -6.2198754 -0.0880802382 1.4034724841 0 0 1.0585083082
54.1 -15.7192609 0.4129127672 1.8812377600 1 1 1.4188420659
54.2 -13.0978998 1.0119546775 2.5107589352 0 0 1.8936311350
54.3 -5.1195299 -0.1112901990 2.7848406672 1 1 2.1003454052
54.4 -16.5771751 0.8587727145 4.0143877396 0 0 3.0276780080
55 -5.7348534 -0.0116453589 0.6118522980 1 1 0.4521559134
55.1 -7.3217494 0.5835528661 0.7463747414 1 1 0.5515673538
55.2 -12.2171938 -1.0010857254 2.8201208171 1 1 2.0840557566
55.3 -12.9821266 -0.4796526070 3.1326431572 0 0 2.3150082668
55.4 -14.8599983 -0.1202746964 3.2218102901 1 1 2.3809023504
56 -14.1764282 0.5176377612 1.2231332215 0 0 0.9198742485
56.1 -12.5343602 -1.1136932588 2.3573202139 1 1 1.7728552558
56.2 -8.4573382 -0.0168103281 2.5674936292 1 1 1.9309190783
56.3 -12.4633969 0.3933023606 2.9507164378 0 0 2.2191270894
56.4 -17.3841863 0.3714625139 3.2272730360 0 0 2.4271153024
56.5 -14.8147645 0.7811448179 3.4175522043 1 1 2.5702173815
57 -3.1403293 -1.0868304872 0.2370331448 1 1 0.1711369455
57.1 -11.1509248 0.8018626997 0.2481445030 1 1 0.1791592999
57.2 -6.3940143 -0.1159517011 1.1405586067 0 0 0.8234785742
57.3 -9.3473241 0.6785562445 2.1153886721 0 0 1.5273018303
58 -12.0245677 1.6476207996 1.2210099772 1 1 0.8864082613
58.1 -9.2112246 0.3402652711 1.6334245703 1 1 1.1858060626
58.2 -1.2071742 -0.1111300753 1.6791862890 1 1 1.2190273844
58.3 -11.0141711 -0.5409234285 2.6320121693 1 1 1.9107438714
58.4 -5.3721214 -0.1271327672 2.8477731440 1 1 2.0673783904
58.5 -7.8523047 0.8713264822 3.5715569824 1 1 2.5928187928
59 -13.2946560 0.4766421367 1.9023998594 0 0 1.4189250995
59.1 -10.0530648 1.0028089765 4.9736620474 1 1 3.7096585560
60 -19.2209402 0.5231452932 2.8854503250 0 0 2.2138389085
61 -4.6699914 -0.7190130614 0.7213630795 1 1 0.5235061310
61.1 -3.5981894 0.8353702312 2.3186947661 1 1 1.6827183986
61.2 -1.4713611 1.0229058138 2.5077313243 1 1 1.8199056209
61.3 -3.8819786 1.1717723589 3.1731073430 0 0 2.3027809373
61.4 0.1041413 -0.0629201596 3.6022726283 1 1 2.6142338858
62 -2.8591600 -0.3979137604 0.5336771999 1 1 0.3837081458
62.1 -6.9461986 0.6830738372 0.6987666548 0 0 0.5024056819
62.2 -16.7910593 0.4301745954 3.4584309917 0 0 2.4865745506
62.3 -17.9844596 -0.0333139957 4.8028772371 1 1 3.4532168882
63 -24.0335535 0.3345678035 2.8097350930 0 0 2.0604786922
63.1 -11.7765300 0.3643769511 3.9653754211 1 1 2.9079508535
64 -20.5963897 0.3949911859 4.1191305732 1 1 3.0153034804
65 -2.7969169 1.2000091513 0.7076152589 1 1 0.5291342322
65.1 -11.1778694 0.0110122646 2.0252246363 1 1 1.5144044302
65.2 -5.2830399 -0.5776452043 3.1127382827 0 0 2.3276156931
65.3 -7.9353390 -0.1372183563 3.1969087943 0 0 2.3905559682
66 -13.2318328 -0.5081302805 3.4943454154 1 1 2.5662383121
66.1 -1.9090560 -0.1447837412 3.7677437009 0 0 2.7670213119
66.2 -16.6643889 0.1906241379 3.9486138616 0 0 2.8998518941
67 -25.6073277 1.6716027681 4.1728388879 0 0 3.1261341693
68 -13.4806759 0.5691848839 0.1291919907 0 0 0.0966709548
68.1 -18.4557183 0.1004860389 1.7809643946 0 0 1.3326486232
68.2 -13.3982327 -0.0061241827 2.0493205660 0 0 1.5334524594
68.3 -12.4977127 0.7443745962 2.9406870750 0 0 2.2004384781
68.4 -11.7073990 0.8726923437 4.0406670363 1 1 3.0235244339
69 -14.5290675 0.0381382683 4.1451198701 1 1 3.0417997050
70 -15.2122709 0.8126204217 0.1992557163 1 1 0.1515886088
70.1 -7.8681167 0.4691503050 0.4829774413 1 1 0.3674367781
71 -10.3352703 -0.5529062591 0.7741605386 1 1 0.6021111272
71.1 -7.5699888 -0.1103252087 1.4883817220 1 1 1.1576038195
71.2 -18.4680702 1.7178492547 4.0758526395 0 0 3.1700352897
71.3 -21.4316644 -1.0118346755 4.7048238723 0 0 3.6592239775
71.4 -8.1137650 1.8623785017 4.7242791823 0 0 3.6743555400
72 -9.1848162 -0.4521659275 0.9321196121 1 1 0.6905275565
72.1 -23.7538846 0.1375317317 1.1799991806 1 1 0.8741602904
72.2 -26.3421306 -0.4170988856 1.8917567329 1 1 1.4014404774
72.3 -27.2843801 0.7107266765 3.4853593935 0 0 2.5820041485
72.4 -20.8541617 0.1451969143 3.6884259700 0 0 2.7324387763
72.5 -12.8948965 1.6298050306 4.0854155901 1 1 3.0265343716
73 -2.6091307 -0.0307469467 4.6019889915 1 1 3.3356465236
74 -8.2790175 0.3730017941 1.4626806753 1 1 1.0772519613
75 -12.5029612 -0.4908003566 3.2524286874 0 0 2.4279140631
76 -6.0061671 -0.9888876620 1.8074807397 1 1 1.3294798303
76.1 -8.8149114 0.0003798292 4.2685073183 1 1 3.1396707367
76.2 -11.8359043 -0.8421863763 4.9688734859 1 1 3.6548201782
77 0.4772521 -0.4986802480 0.8459033852 1 1 0.6097651292
78 -9.4105229 0.0417330969 0.8231094317 1 1 0.6069257516
79 -1.0217265 -0.3767450660 0.0583819521 0 0 0.0443590703
79.1 -11.8125257 0.1516000028 2.4406372628 1 1 1.8544155528
79.2 -10.5465186 -0.1888160741 3.2962526032 0 0 2.5045188757
80 -12.7366807 -0.0041558414 0.8985060186 1 1 0.6613377055
80.1 -9.0584783 -0.0329337062 1.3434670598 0 0 0.9888474917
80.2 -16.6381566 0.5046816157 2.8025900386 1 1 2.0628225380
81 0.5547913 -0.9493950353 0.0101324962 1 1 0.0073905742
81.1 -4.0892715 0.2443038954 0.9421709494 1 1 0.6872131160
81.2 1.8283303 0.6476958410 3.0542453879 1 1 2.2277459217
81.3 -5.2166381 0.4182528210 3.3456630446 1 1 2.4403039888
82 -3.0749381 1.1088801952 1.3791010005 1 1 1.0038902706
82.1 -10.5506696 0.9334157763 1.7601010622 1 1 1.2812319990
82.2 -18.2226347 0.4958140634 2.6233131927 0 0 1.9095908060
83 -12.5872635 0.5104724530 0.0537394290 1 1 0.0394696928
83.1 -11.9756502 -0.0513309106 2.9061570496 0 0 2.1344686414
83.2 -10.6744217 -0.2067792494 3.1189457362 0 0 2.2907543380
83.3 -19.2714012 -0.0534169155 4.7663642222 1 1 3.5007244248
84 -2.6320312 -0.0255753653 2.7254060237 1 1 2.0125352642
84.1 -9.8140094 -1.8234189877 3.3364784659 0 0 2.4637725582
85 -12.3886736 -0.0114038622 0.2977756259 0 0 0.2180824084
85.1 -12.9196365 -0.0577615939 1.7394116637 0 0 1.2738956847
85.2 -9.6433248 -0.2241856342 2.6846330194 1 1 1.9661489512
85.3 -6.3296340 -0.0520175929 3.1608762743 1 1 2.3149359808
85.4 -7.0405525 0.2892733846 3.9452053758 1 1 2.8893563314
85.5 -13.6714939 -0.3740417009 4.5092553482 1 1 3.3024505062
86 -10.8756412 0.4293735089 0.8476278360 0 0 0.6422134099
86.1 -12.0055331 -0.1363456521 1.0118629411 1 1 0.7666477221
86.2 -13.3724699 0.1230989293 1.2511159515 1 1 0.9479200743
86.3 -13.3252145 0.3305413955 2.1870554925 0 0 1.6570436997
86.4 -14.9191290 2.6003411822 2.4532935000 1 1 1.8587614954
86.5 -17.7515546 -0.1420690052 3.8206058508 0 0 2.8947188929
87 -10.7027963 1.0457427869 2.7069531474 0 0 2.0291697589
87.1 -22.4941954 -0.2973007190 3.4462517721 1 1 2.5833582987
87.2 -14.9616716 0.4396872616 4.5241666853 0 0 3.3913783218
88 -2.2264493 -0.0601928334 0.0005892443 0 0 0.0004286893
88.1 -8.9626474 -1.0124347595 0.7116099866 0 0 0.5177132747
88.2 -2.5095281 0.5730917016 2.4952722900 0 0 1.8153702347
88.3 -16.3345673 -0.0029455332 3.2995816297 0 0 2.4005245046
89 -11.0459647 1.5465903721 0.6462086167 1 1 0.4685431339
90 -4.5610239 0.0626760573 0.1696030737 0 0 0.1244083036
90.1 -11.7036651 1.1896872985 2.5980385230 1 1 1.9057294087
90.2 -5.3838521 0.2597888783 2.6651392167 1 1 1.9549495277
90.3 -4.1636999 0.6599799887 3.1242690247 0 0 2.2917332858
91 -7.1462503 1.1213651365 0.6382618390 0 0 0.4687382105
91.1 -12.8374475 1.2046371625 2.6224059286 0 0 1.9258896380
91.2 -18.2576707 0.3395603754 4.7772527603 1 1 3.5084048160
92 -6.4119222 0.4674939332 0.0737052364 1 1 0.0543975943
93 5.2122168 0.2677965647 0.2788909199 0 0 0.2060853219
93.1 3.1211725 1.6424445368 1.0357759963 1 1 0.7653825006
93.2 -3.6841177 0.7101700066 2.4916551099 0 0 1.8411985077
93.3 2.6223542 1.1222322893 2.8876129608 1 1 2.1337899668
93.4 -11.1877696 1.4628960401 4.4639474002 0 0 3.2986159518
94 -6.9602492 -0.2904211940 0.8488043118 1 1 0.6162277526
94.1 -7.4318416 0.0147813580 1.0552454425 0 0 0.7661029975
94.2 -4.3498045 -0.4536774482 1.9445500884 1 1 1.4117337932
94.3 -11.6340088 0.6793464917 3.0710722448 0 0 2.2295833342
94.4 -12.9357964 -0.9411356550 3.0872731935 0 0 2.2413451432
94.5 -14.7648530 0.5683867264 4.3805759016 0 0 3.1802765437
95 -12.8849309 0.2375652188 2.0199063048 1 1 1.4710654666
95.1 -9.7451502 0.0767152977 4.0184444457 1 1 2.9265688411
95.2 -0.8535063 -0.6886731251 4.5596531732 0 0 3.3207225043
96 -4.9139832 0.7813892121 0.0311333477 1 1 0.0226702966
96.1 -3.9582653 0.3391519695 0.1324267720 0 0 0.0964288912
96.2 -9.6555492 -0.4857246503 0.6701303425 0 0 0.4879672359
96.3 -11.8690793 0.8771471244 2.1775037691 0 0 1.5855877997
96.4 -11.0224373 1.9030768981 2.2246142488 0 0 1.6198921270
96.5 -10.9530403 -0.1684332749 4.2377650598 1 1 3.0858034196
97 -9.8540471 1.3775130083 1.1955102731 0 0 0.8662239621
97.1 -19.2262840 -1.7323228619 4.9603108643 0 0 3.5940637456
98 -11.9651231 -1.2648518889 0.2041732438 0 0 0.1536799385
98.1 -2.6515128 -0.9042716241 0.4309578973 0 0 0.3243793452
98.2 -12.2606382 -0.1560385207 3.5172611906 0 0 2.6474207553
99 -11.4720500 0.7993356425 0.3531786101 1 1 0.2568424071
99.1 -14.0596866 1.0355522332 4.6789444226 1 1 3.4026730772
99.2 -17.3939469 -0.1150895843 4.9927084171 1 1 3.6308519569
100 1.1005874 0.0369067906 1.0691387602 0 0 0.7893943379
100.1 -3.8226248 1.6023713093 1.5109344281 0 0 1.1155924065
100.2 -0.9123182 0.8861545820 2.1502332564 1 1 1.5876161457
100.3 -15.8389474 0.1277046316 3.8745574222 1 1 2.8607640137
100.4 -12.8093826 -0.0834577654 4.6567608765 1 1 3.4383008132
$m8n$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8n$spM_lvlone
center scale
y -11.1733710 6.2496619
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
b1 NA NA
b11 NA NA
C1:time 1.8687612 1.0180574
$m8n$mu_reg_norm
[1] 0
$m8n$tau_reg_norm
[1] 1e-04
$m8n$shape_tau_norm
[1] 0.01
$m8n$rate_tau_norm
[1] 0.01
$m8n$mu_reg_binom
[1] 0
$m8n$tau_reg_binom
[1] 1e-04
$m8n$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8n$shape_diag_RinvD
[1] "0.01"
$m8n$rate_diag_RinvD
[1] "0.001"
$m8n$RinvD_y_id
[,1] [,2] [,3] [,4]
[1,] NA 0 0 0
[2,] 0 NA 0 0
[3,] 0 0 NA 0
[4,] 0 0 0 NA
$m8n$KinvD_y_id
id
5
$m9a
$m9a$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m9a$M_lvlone
y c1 b11 time
1 -13.0493856 0.7592026489 0 0.5090421822
1.1 -9.3335901 0.9548337990 1 0.6666076288
1.2 -22.3469852 0.5612235156 1 2.1304941282
1.3 -15.0417337 1.1873391025 0 2.4954441458
2 -12.0655434 0.9192204198 1 3.0164990982
2.1 -15.8674476 -0.1870730476 1 3.2996806887
2.2 -7.8800006 1.2517512331 1 4.1747569619
3 -11.4820604 -0.0605087604 1 0.8478727890
3.1 -10.5983220 0.3788637747 0 3.0654308549
3.2 -22.4519157 0.9872578281 0 4.7381553578
4 -1.2697775 1.4930175328 1 0.3371432109
4.1 -11.1215184 -0.7692526880 1 1.0693019140
4.2 -3.6134138 0.9180841450 0 2.6148973033
4.3 -14.5982385 -0.0541170782 1 3.1336532847
5 -6.8457515 -0.1376784521 0 1.0762525082
5.1 -7.0551214 -0.2740585866 1 1.7912546196
5.2 -12.3418980 0.4670496929 1 2.7960080339
5.3 -9.2366906 0.1740288049 1 2.8119940578
6 -5.1648211 0.9868044683 0 1.7815462884
7 -10.0599502 -0.1280320918 1 3.3074087673
7.1 -18.3267285 0.4242971219 0 3.7008403614
7.2 -12.5138426 0.0777182491 1 4.7716691741
8 -1.6305331 -0.5791408712 0 1.1246398522
8.1 -9.6520453 0.3128604232 1 1.8027009873
8.2 -1.5278462 0.6258446356 1 1.8175825174
8.3 -7.4172211 -0.1040137707 0 2.8384267003
8.4 -7.1238609 0.0481450285 0 3.3630275307
8.5 -8.8706950 0.3831763675 1 4.4360849704
9 -0.1634429 -0.1757592269 1 0.9607803822
9.1 -2.6034300 -0.1791541200 1 2.9177753383
9.2 -6.7272369 -0.0957042935 0 4.8100892501
10 -6.4172202 -0.5598409704 1 2.2975509102
10.1 -11.4834569 -0.2318340451 1 4.1734118364
11 -8.7911356 0.5086859475 1 1.1832662905
11.1 -19.6645080 0.4951758188 1 1.2346051680
11.2 -20.2030932 -1.1022162541 1 1.6435316263
11.3 -21.3082176 -0.0611636705 1 3.3859017969
11.4 -14.5802901 -0.4971774316 1 4.8118087661
12 -15.2006287 -0.2433996286 1 0.9591987054
13 0.8058816 0.8799673116 0 0.0619085738
13.1 -13.6379208 0.1079022586 1 3.5621061502
14 -15.3422873 0.9991752617 0 4.0364430007
14.1 -10.0965208 -0.1094019046 1 4.4710561272
14.2 -16.6452027 0.1518967560 0 4.6359198843
14.3 -15.8389733 0.3521012473 0 4.6886152599
15 -8.9424594 0.3464447888 0 0.5402063532
15.1 -22.0101983 -0.4767313971 0 1.1893180816
15.2 -7.3975599 0.5759767791 0 1.5094739688
15.3 -10.3567334 -0.1713452662 1 4.9193474615
16 -1.9691302 0.4564754473 1 1.2417913869
16.1 -9.9308357 1.0652558311 0 2.5675726333
16.2 -6.9626923 0.6971872493 1 2.6524101500
16.3 -3.2862557 0.5259331838 1 3.5585018690
16.4 -3.3972355 0.2046601798 1 3.7612454291
16.5 -11.5767835 1.0718540464 0 3.9851612889
17 -10.5474144 0.6048676222 0 1.5925356350
17.1 -7.6215009 0.2323298304 0 2.4374032998
17.2 -16.5386939 1.2617499032 1 3.0256489082
17.3 -20.0004774 -0.3913230895 0 3.3329089405
17.4 -18.8505475 0.9577299112 1 3.8693758985
18 -19.7302351 -0.0050324072 1 2.4374292302
19 -14.6177568 -0.4187468937 1 0.9772165376
19.1 -17.8043866 -0.4478828944 1 1.1466335913
19.2 -15.1641705 -1.1966721302 1 2.2599126538
19.3 -16.6898418 -0.5877091668 1 4.2114245973
20 -12.9059229 0.6838223064 0 1.7170160066
20.1 -16.8191201 0.3278571109 1 1.7562902288
20.2 -6.1010131 -0.8489831990 0 2.2515566566
20.3 -7.9415371 1.3169975191 0 2.2609123867
20.4 -9.3904458 0.0444804531 0 3.4913365287
20.5 -13.3504189 -0.4535207652 0 4.1730977828
21 -7.6974718 -0.4030302960 1 1.6936582839
21.1 -11.9335526 -0.4069674045 1 2.9571191233
21.2 -12.7064929 1.0650265940 0 3.7887385779
22 -21.5022909 -0.0673274516 0 2.4696226232
22.1 -12.7745451 0.9601388170 1 3.1626627257
23 -3.5146508 0.5556634840 1 1.5414533857
23.1 -4.6724048 1.4407865964 1 2.3369736120
24 -2.5619821 0.3856376411 0 2.8283136466
25 -6.2944970 0.3564400705 0 0.5381704110
25.1 -3.8630505 0.0982553434 1 1.6069735331
25.2 -14.4205140 0.1928682598 1 1.6358226922
25.3 -19.6735037 -0.0192488594 0 3.2646870392
25.4 -9.0288933 0.4466012931 0 4.0782226040
25.5 -9.0509738 1.1425193342 0 4.1560292873
26 -19.7340685 0.5341531449 1 0.2412706357
26.1 -14.1692728 1.2268695927 1 2.4451737676
26.2 -17.2819976 0.3678294939 1 3.5988757887
26.3 -24.6265576 0.5948516018 0 4.1822362854
27 -7.3354999 -0.3342844147 1 3.6955824879
27.1 -11.1488468 -0.4835141229 1 4.2451434687
28 -11.7996597 -0.7145915499 1 0.5746519344
28.1 -8.2030122 0.5063671955 0 2.7943964268
28.2 -26.4317815 -0.2067413142 1 4.2108539480
28.3 -18.5016071 0.1196789973 1 4.4705521734
29 -5.8551395 0.1392699487 1 1.1898884235
29.1 -2.0209442 0.7960234776 0 1.7624059319
29.2 -5.6368080 1.0398214352 0 2.0210406382
29.3 -3.8110961 0.0813246429 1 3.4078777023
30 -12.7217702 -0.3296323050 1 2.2635366488
30.1 -17.0170140 1.3635850954 1 3.5938334477
30.2 -25.4236089 0.7354171050 1 3.6138710892
31 -17.0783921 0.3708398217 0 4.3988140998
32 -18.4338764 -0.0474059668 1 1.6745209007
32.1 -19.4317212 1.2507771489 1 2.9128167813
32.2 -19.4738978 0.1142915519 1 2.9676558380
32.3 -21.4922645 0.6773270619 1 4.2099863547
33 2.0838099 0.1774293842 0 0.0093385763
33.1 -13.3172274 0.6159606291 0 3.4591242753
34 -10.0296691 0.8590979166 1 1.4998774312
34.1 -25.9426553 0.0546216775 0 3.8242761395
34.2 -18.5688138 -0.0897224473 1 3.9072251692
34.3 -15.4173859 0.4163395571 1 3.9582124643
35 -14.3958113 -1.4693520528 1 1.3294299203
35.1 -12.9457541 -0.3031734330 0 1.5276966314
35.2 -16.1380691 -0.6045512101 1 4.5025920868
36 -12.8166968 0.9823048960 0 0.7123168337
36.1 -14.3989481 1.4466051416 0 1.7972493160
36.2 -12.2436943 1.1606752905 1 1.8262697803
36.3 -15.0104638 0.8373091576 0 4.2840119381
36.4 -10.1775457 0.2640591685 1 4.6194464504
37 -15.2223495 0.1177313455 1 2.0018732361
37.1 -14.7526195 -0.1415483779 0 3.6656836793
37.2 -19.8168430 0.0054610124 0 3.9663937816
38 -2.7065118 0.8078948077 1 0.9826511063
39 -8.7288138 0.9876451040 1 0.6921808305
39.1 -9.2746473 -0.3431222274 0 0.9027792048
39.2 -18.2695344 -1.7909380751 0 1.3055654289
39.3 -13.8219083 -0.1798746191 0 1.5412842878
39.4 -16.2254704 -0.1850961689 1 3.1834997435
39.5 -21.7283648 0.4544226146 1 4.1394166439
40 1.8291916 0.5350190436 0 1.1330395646
40.1 -6.6916432 0.4189342752 0 2.6940994046
40.2 -1.6278171 0.4211994981 0 3.0396614212
40.3 -10.5749790 0.0916687506 1 4.6762977762
41 -3.1556121 -0.1035047421 1 1.9337158254
41.1 -11.5895327 -0.4684202411 1 3.1956304458
41.2 -18.9352091 0.5972615368 0 3.2846923557
41.3 -15.9788960 0.9885613862 1 3.3813529415
41.4 -9.6070508 -0.3908036794 1 3.5482964432
42 -5.2159485 -0.0338893961 1 0.4859252973
42.1 -15.9878743 -0.4498363172 1 4.3293134298
43 -16.6104361 0.8965546110 0 0.5616614548
43.1 -9.5549441 0.6199122090 0 1.0743579536
43.2 -14.2003491 0.1804894429 1 2.6131797966
44 -8.1969033 1.3221409285 1 0.7662644819
44.1 -19.9270197 0.3416426284 0 2.6490291790
44.2 -22.6521171 0.5706610068 0 3.3371910988
44.3 -21.1903736 1.2679497430 1 4.1154200875
45 -0.5686627 0.1414983160 1 0.1957449992
45.1 -7.5645740 0.7220892521 0 1.9963831536
46 -19.1624789 1.5391054233 1 1.3477755385
46.1 -18.4487574 0.3889107049 0 2.8565793915
46.2 -15.8222682 0.1248719493 1 4.4160729996
47 -5.4165074 0.2014101100 0 0.6012621359
47.1 -15.0975029 0.2982973539 0 2.4097121472
47.2 -12.9971413 1.1518107179 1 2.9975794035
47.3 -10.6844521 0.5196802157 0 3.1829649757
47.4 -18.2214784 0.3702301552 0 4.6201055450
48 -8.3101471 -0.2128602862 0 2.8607365978
48.1 -18.3854275 -0.5337239976 1 2.9098354396
49 -13.0130319 -0.5236770035 0 2.7179756400
50 -10.4579977 0.3897705981 1 1.1762060679
51 -19.3157621 -0.7213343736 1 1.4304436720
52 -4.4747188 0.3758235358 1 2.1266646020
52.1 -4.3163827 0.7138067080 1 3.1000545993
52.2 -6.9761408 0.8872895233 0 3.1268477370
52.3 -20.1764756 -0.9664587437 0 3.5711459327
52.4 -8.9036692 0.0254566848 1 4.7983659909
52.5 -5.6949642 0.4155259424 1 4.9818264414
53 -10.3141887 0.5675736897 1 0.4965799209
53.1 -8.2642654 -0.3154088781 1 3.5505357443
53.2 -9.1691554 0.2162315769 1 4.5790420019
54 -6.2198754 -0.0880802382 0 1.4034724841
54.1 -15.7192609 0.4129127672 1 1.8812377600
54.2 -13.0978998 1.0119546775 0 2.5107589352
54.3 -5.1195299 -0.1112901990 1 2.7848406672
54.4 -16.5771751 0.8587727145 0 4.0143877396
55 -5.7348534 -0.0116453589 1 0.6118522980
55.1 -7.3217494 0.5835528661 1 0.7463747414
55.2 -12.2171938 -1.0010857254 1 2.8201208171
55.3 -12.9821266 -0.4796526070 0 3.1326431572
55.4 -14.8599983 -0.1202746964 1 3.2218102901
56 -14.1764282 0.5176377612 0 1.2231332215
56.1 -12.5343602 -1.1136932588 1 2.3573202139
56.2 -8.4573382 -0.0168103281 1 2.5674936292
56.3 -12.4633969 0.3933023606 0 2.9507164378
56.4 -17.3841863 0.3714625139 0 3.2272730360
56.5 -14.8147645 0.7811448179 1 3.4175522043
57 -3.1403293 -1.0868304872 1 0.2370331448
57.1 -11.1509248 0.8018626997 1 0.2481445030
57.2 -6.3940143 -0.1159517011 0 1.1405586067
57.3 -9.3473241 0.6785562445 0 2.1153886721
58 -12.0245677 1.6476207996 1 1.2210099772
58.1 -9.2112246 0.3402652711 1 1.6334245703
58.2 -1.2071742 -0.1111300753 1 1.6791862890
58.3 -11.0141711 -0.5409234285 1 2.6320121693
58.4 -5.3721214 -0.1271327672 1 2.8477731440
58.5 -7.8523047 0.8713264822 1 3.5715569824
59 -13.2946560 0.4766421367 0 1.9023998594
59.1 -10.0530648 1.0028089765 1 4.9736620474
60 -19.2209402 0.5231452932 0 2.8854503250
61 -4.6699914 -0.7190130614 1 0.7213630795
61.1 -3.5981894 0.8353702312 1 2.3186947661
61.2 -1.4713611 1.0229058138 1 2.5077313243
61.3 -3.8819786 1.1717723589 0 3.1731073430
61.4 0.1041413 -0.0629201596 1 3.6022726283
62 -2.8591600 -0.3979137604 1 0.5336771999
62.1 -6.9461986 0.6830738372 0 0.6987666548
62.2 -16.7910593 0.4301745954 0 3.4584309917
62.3 -17.9844596 -0.0333139957 1 4.8028772371
63 -24.0335535 0.3345678035 0 2.8097350930
63.1 -11.7765300 0.3643769511 1 3.9653754211
64 -20.5963897 0.3949911859 1 4.1191305732
65 -2.7969169 1.2000091513 1 0.7076152589
65.1 -11.1778694 0.0110122646 1 2.0252246363
65.2 -5.2830399 -0.5776452043 0 3.1127382827
65.3 -7.9353390 -0.1372183563 0 3.1969087943
66 -13.2318328 -0.5081302805 1 3.4943454154
66.1 -1.9090560 -0.1447837412 0 3.7677437009
66.2 -16.6643889 0.1906241379 0 3.9486138616
67 -25.6073277 1.6716027681 0 4.1728388879
68 -13.4806759 0.5691848839 0 0.1291919907
68.1 -18.4557183 0.1004860389 0 1.7809643946
68.2 -13.3982327 -0.0061241827 0 2.0493205660
68.3 -12.4977127 0.7443745962 0 2.9406870750
68.4 -11.7073990 0.8726923437 1 4.0406670363
69 -14.5290675 0.0381382683 1 4.1451198701
70 -15.2122709 0.8126204217 1 0.1992557163
70.1 -7.8681167 0.4691503050 1 0.4829774413
71 -10.3352703 -0.5529062591 1 0.7741605386
71.1 -7.5699888 -0.1103252087 1 1.4883817220
71.2 -18.4680702 1.7178492547 0 4.0758526395
71.3 -21.4316644 -1.0118346755 0 4.7048238723
71.4 -8.1137650 1.8623785017 0 4.7242791823
72 -9.1848162 -0.4521659275 1 0.9321196121
72.1 -23.7538846 0.1375317317 1 1.1799991806
72.2 -26.3421306 -0.4170988856 1 1.8917567329
72.3 -27.2843801 0.7107266765 0 3.4853593935
72.4 -20.8541617 0.1451969143 0 3.6884259700
72.5 -12.8948965 1.6298050306 1 4.0854155901
73 -2.6091307 -0.0307469467 1 4.6019889915
74 -8.2790175 0.3730017941 1 1.4626806753
75 -12.5029612 -0.4908003566 0 3.2524286874
76 -6.0061671 -0.9888876620 1 1.8074807397
76.1 -8.8149114 0.0003798292 1 4.2685073183
76.2 -11.8359043 -0.8421863763 1 4.9688734859
77 0.4772521 -0.4986802480 1 0.8459033852
78 -9.4105229 0.0417330969 1 0.8231094317
79 -1.0217265 -0.3767450660 0 0.0583819521
79.1 -11.8125257 0.1516000028 1 2.4406372628
79.2 -10.5465186 -0.1888160741 0 3.2962526032
80 -12.7366807 -0.0041558414 1 0.8985060186
80.1 -9.0584783 -0.0329337062 0 1.3434670598
80.2 -16.6381566 0.5046816157 1 2.8025900386
81 0.5547913 -0.9493950353 1 0.0101324962
81.1 -4.0892715 0.2443038954 1 0.9421709494
81.2 1.8283303 0.6476958410 1 3.0542453879
81.3 -5.2166381 0.4182528210 1 3.3456630446
82 -3.0749381 1.1088801952 1 1.3791010005
82.1 -10.5506696 0.9334157763 1 1.7601010622
82.2 -18.2226347 0.4958140634 0 2.6233131927
83 -12.5872635 0.5104724530 1 0.0537394290
83.1 -11.9756502 -0.0513309106 0 2.9061570496
83.2 -10.6744217 -0.2067792494 0 3.1189457362
83.3 -19.2714012 -0.0534169155 1 4.7663642222
84 -2.6320312 -0.0255753653 1 2.7254060237
84.1 -9.8140094 -1.8234189877 0 3.3364784659
85 -12.3886736 -0.0114038622 0 0.2977756259
85.1 -12.9196365 -0.0577615939 0 1.7394116637
85.2 -9.6433248 -0.2241856342 1 2.6846330194
85.3 -6.3296340 -0.0520175929 1 3.1608762743
85.4 -7.0405525 0.2892733846 1 3.9452053758
85.5 -13.6714939 -0.3740417009 1 4.5092553482
86 -10.8756412 0.4293735089 0 0.8476278360
86.1 -12.0055331 -0.1363456521 1 1.0118629411
86.2 -13.3724699 0.1230989293 1 1.2511159515
86.3 -13.3252145 0.3305413955 0 2.1870554925
86.4 -14.9191290 2.6003411822 1 2.4532935000
86.5 -17.7515546 -0.1420690052 0 3.8206058508
87 -10.7027963 1.0457427869 0 2.7069531474
87.1 -22.4941954 -0.2973007190 1 3.4462517721
87.2 -14.9616716 0.4396872616 0 4.5241666853
88 -2.2264493 -0.0601928334 0 0.0005892443
88.1 -8.9626474 -1.0124347595 0 0.7116099866
88.2 -2.5095281 0.5730917016 0 2.4952722900
88.3 -16.3345673 -0.0029455332 0 3.2995816297
89 -11.0459647 1.5465903721 1 0.6462086167
90 -4.5610239 0.0626760573 0 0.1696030737
90.1 -11.7036651 1.1896872985 1 2.5980385230
90.2 -5.3838521 0.2597888783 1 2.6651392167
90.3 -4.1636999 0.6599799887 0 3.1242690247
91 -7.1462503 1.1213651365 0 0.6382618390
91.1 -12.8374475 1.2046371625 0 2.6224059286
91.2 -18.2576707 0.3395603754 1 4.7772527603
92 -6.4119222 0.4674939332 1 0.0737052364
93 5.2122168 0.2677965647 0 0.2788909199
93.1 3.1211725 1.6424445368 1 1.0357759963
93.2 -3.6841177 0.7101700066 0 2.4916551099
93.3 2.6223542 1.1222322893 1 2.8876129608
93.4 -11.1877696 1.4628960401 0 4.4639474002
94 -6.9602492 -0.2904211940 1 0.8488043118
94.1 -7.4318416 0.0147813580 0 1.0552454425
94.2 -4.3498045 -0.4536774482 1 1.9445500884
94.3 -11.6340088 0.6793464917 0 3.0710722448
94.4 -12.9357964 -0.9411356550 0 3.0872731935
94.5 -14.7648530 0.5683867264 0 4.3805759016
95 -12.8849309 0.2375652188 1 2.0199063048
95.1 -9.7451502 0.0767152977 1 4.0184444457
95.2 -0.8535063 -0.6886731251 0 4.5596531732
96 -4.9139832 0.7813892121 1 0.0311333477
96.1 -3.9582653 0.3391519695 0 0.1324267720
96.2 -9.6555492 -0.4857246503 0 0.6701303425
96.3 -11.8690793 0.8771471244 0 2.1775037691
96.4 -11.0224373 1.9030768981 0 2.2246142488
96.5 -10.9530403 -0.1684332749 1 4.2377650598
97 -9.8540471 1.3775130083 0 1.1955102731
97.1 -19.2262840 -1.7323228619 0 4.9603108643
98 -11.9651231 -1.2648518889 0 0.2041732438
98.1 -2.6515128 -0.9042716241 0 0.4309578973
98.2 -12.2606382 -0.1560385207 0 3.5172611906
99 -11.4720500 0.7993356425 1 0.3531786101
99.1 -14.0596866 1.0355522332 1 4.6789444226
99.2 -17.3939469 -0.1150895843 1 4.9927084171
100 1.1005874 0.0369067906 0 1.0691387602
100.1 -3.8226248 1.6023713093 0 1.5109344281
100.2 -0.9123182 0.8861545820 1 2.1502332564
100.3 -15.8389474 0.1277046316 1 3.8745574222
100.4 -12.8093826 -0.0834577654 1 4.6567608765
$m9a$spM_lvlone
center scale
y -11.1733710 6.2496619
c1 0.2559996 0.6718095
b11 NA NA
time 2.5339403 1.3818094
$m9a$mu_reg_norm
[1] 0
$m9a$tau_reg_norm
[1] 1e-04
$m9a$shape_tau_norm
[1] 0.01
$m9a$rate_tau_norm
[1] 0.01
$m9a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m9a$group_o1
[1] 1 2 2 1 3 2 1 2 3 1 3 1 2 1 1 1 3 1 3 3 2 2 1 1 3 3 1 1 1 1 3 1 2 1 3 3 2
[38] 3 2 1 2 3 3 1 2 3 3 1 1 3 2 3 2 2 1 1 1 3 1 3 1 3 1 1 1 2 1 1 2 2 1 1 1 1
[75] 2 2 2 1 2 1 2 2 2 1 1 1 3 1 2 1 1 2 2 2 2 1 1 2 1 1 1 1 1 3 2 3 3 1 1 3 1
[112] 1 2 1 3 1 3 1 3 3 3 3 3 3 1 2 1 1 2 2 2 3 2 2 1 3 1 2 2 3 1 2 1 1 2 3 1 2
[149] 2 2 2 2 1 3 2 2 2 3 1 3 1 2 1 1 1 3 1 2 3 1 1 2 2 2 1 3 2 1 2 2 1 2 2 3 1
[186] 3 3 2 3 1 1 1 1 3 1 3 3 1 3 3 2 3 2 1 1 2 1 1 3 2 1 2 3 1 2 1 2 2 2 2 3 3
[223] 2 1 1 1 1 1 2 3 3 3 3 1 1 2 2 1 1 2 2 2 2 3 3 3 2 1 1 2 1 2 1 1 3 3 1 3 3
[260] 3 1 1 3 3 3 1 3 3 3 1 1 1 2 3 1 3 2 2 2 3 2 2 2 3 2 2 2 2 1 2 3 2 2 1 2 1
[297] 2 3 2 3 2 1 3 2 3 1 3 3 2 1 2 3 2 2 1 3 3 3 3 2 2 1 1 2 2 1 1 2 2
$m9a$shape_diag_RinvD
[1] "0.01"
$m9a$rate_diag_RinvD
[1] "0.001"
$m9b
$m9b$M_id
C2 (Intercept) C1 B11
1 -1.381594459 1 0.7175865 1
2 0.344426024 1 0.7507170 1
3 NA 1 0.7255954 1
4 -0.228910007 1 0.7469352 0
5 NA 1 0.7139120 1
6 -2.143955482 1 0.7332505 1
7 -1.156567023 1 0.7345929 1
8 -0.598827660 1 0.7652589 0
9 NA 1 0.7200622 1
10 -1.006719032 1 0.7423879 1
11 0.239801450 1 0.7437448 0
12 -1.064969789 1 0.7446470 1
13 -0.538082688 1 0.7530186 1
14 NA 1 0.7093137 1
15 -1.781049276 1 0.7331192 1
16 NA 1 0.7011390 1
17 NA 1 0.7432395 1
18 -0.014579883 1 0.7545191 1
19 -2.121550136 1 0.7528487 1
20 NA 1 0.7612865 0
21 -0.363239698 1 0.7251719 1
22 -0.121568514 1 0.7300630 1
23 -0.951271111 1 0.7087249 1
24 NA 1 0.7391938 0
25 -0.974288621 1 0.7820641 1
26 -1.130632418 1 0.7118298 1
27 0.114339868 1 0.7230857 0
28 0.238334648 1 0.7489353 1
29 0.840744958 1 0.7510888 1
30 NA 1 0.7300717 1
31 NA 1 0.7550721 1
32 -1.466312154 1 0.7321898 1
33 -0.637352277 1 0.7306414 1
34 NA 1 0.7427216 1
35 NA 1 0.7193042 1
36 NA 1 0.7312888 0
37 NA 1 0.7100436 0
38 NA 1 0.7670184 1
39 0.006728205 1 0.7400449 1
40 NA 1 0.7397304 1
41 -1.663281353 1 0.7490966 1
42 0.161184794 1 0.7419274 1
43 0.457939180 1 0.7527810 1
44 -0.307070331 1 0.7408315 1
45 NA 1 0.7347550 0
46 -1.071668276 1 0.7332398 1
47 -0.814751321 1 0.7376481 0
48 -0.547630662 1 0.7346179 0
49 NA 1 0.7329402 1
50 -1.350213782 1 0.7260436 1
51 0.719054706 1 0.7242910 1
52 NA 1 0.7298067 0
53 -1.207130750 1 0.7254741 1
54 NA 1 0.7542067 1
55 -0.408600991 1 0.7389952 1
56 -0.271380529 1 0.7520638 1
57 -1.361925974 1 0.7219958 1
58 NA 1 0.7259632 1
59 NA 1 0.7458606 1
60 -0.323712205 1 0.7672421 1
61 NA 1 0.7257179 0
62 NA 1 0.7189892 1
63 -1.386906880 1 0.7333356 1
64 NA 1 0.7320243 1
65 NA 1 0.7477711 1
66 -0.565191691 1 0.7343974 0
67 -0.382899912 1 0.7491624 0
68 NA 1 0.7482736 1
69 -0.405642769 1 0.7338267 1
70 NA 1 0.7607742 1
71 -0.843748427 1 0.7777600 1
72 0.116003683 1 0.7408143 1
73 -0.778634325 1 0.7248271 1
74 NA 1 0.7364916 0
75 NA 1 0.7464926 1
76 NA 1 0.7355430 1
77 -0.632974758 1 0.7208449 1
78 NA 1 0.7373573 1
79 -0.778064615 1 0.7598079 1
80 NA 1 0.7360415 1
81 NA 1 0.7293932 1
82 -0.246123253 1 0.7279309 1
83 -1.239659782 1 0.7344643 0
84 -0.467772280 1 0.7384350 0
85 NA 1 0.7323716 1
86 -2.160485036 1 0.7576597 1
87 -0.657675572 1 0.7496139 1
88 NA 1 0.7275239 1
89 -0.696710744 1 0.7250648 1
90 NA 1 0.7335262 0
91 -0.179395847 1 0.7343980 1
92 -0.441545568 1 0.7380425 1
93 -0.685799334 1 0.7389460 0
94 NA 1 0.7259951 1
95 0.191929445 1 0.7282840 0
96 NA 1 0.7281676 0
97 -0.069760671 1 0.7245642 1
98 NA 1 0.7526938 1
99 NA 1 0.7272309 1
100 NA 1 0.7383460 1
$m9b$M_lvlone
y time
1 -13.0493856 0.5090421822
1.1 -9.3335901 0.6666076288
1.2 -22.3469852 2.1304941282
1.3 -15.0417337 2.4954441458
2 -12.0655434 3.0164990982
2.1 -15.8674476 3.2996806887
2.2 -7.8800006 4.1747569619
3 -11.4820604 0.8478727890
3.1 -10.5983220 3.0654308549
3.2 -22.4519157 4.7381553578
4 -1.2697775 0.3371432109
4.1 -11.1215184 1.0693019140
4.2 -3.6134138 2.6148973033
4.3 -14.5982385 3.1336532847
5 -6.8457515 1.0762525082
5.1 -7.0551214 1.7912546196
5.2 -12.3418980 2.7960080339
5.3 -9.2366906 2.8119940578
6 -5.1648211 1.7815462884
7 -10.0599502 3.3074087673
7.1 -18.3267285 3.7008403614
7.2 -12.5138426 4.7716691741
8 -1.6305331 1.1246398522
8.1 -9.6520453 1.8027009873
8.2 -1.5278462 1.8175825174
8.3 -7.4172211 2.8384267003
8.4 -7.1238609 3.3630275307
8.5 -8.8706950 4.4360849704
9 -0.1634429 0.9607803822
9.1 -2.6034300 2.9177753383
9.2 -6.7272369 4.8100892501
10 -6.4172202 2.2975509102
10.1 -11.4834569 4.1734118364
11 -8.7911356 1.1832662905
11.1 -19.6645080 1.2346051680
11.2 -20.2030932 1.6435316263
11.3 -21.3082176 3.3859017969
11.4 -14.5802901 4.8118087661
12 -15.2006287 0.9591987054
13 0.8058816 0.0619085738
13.1 -13.6379208 3.5621061502
14 -15.3422873 4.0364430007
14.1 -10.0965208 4.4710561272
14.2 -16.6452027 4.6359198843
14.3 -15.8389733 4.6886152599
15 -8.9424594 0.5402063532
15.1 -22.0101983 1.1893180816
15.2 -7.3975599 1.5094739688
15.3 -10.3567334 4.9193474615
16 -1.9691302 1.2417913869
16.1 -9.9308357 2.5675726333
16.2 -6.9626923 2.6524101500
16.3 -3.2862557 3.5585018690
16.4 -3.3972355 3.7612454291
16.5 -11.5767835 3.9851612889
17 -10.5474144 1.5925356350
17.1 -7.6215009 2.4374032998
17.2 -16.5386939 3.0256489082
17.3 -20.0004774 3.3329089405
17.4 -18.8505475 3.8693758985
18 -19.7302351 2.4374292302
19 -14.6177568 0.9772165376
19.1 -17.8043866 1.1466335913
19.2 -15.1641705 2.2599126538
19.3 -16.6898418 4.2114245973
20 -12.9059229 1.7170160066
20.1 -16.8191201 1.7562902288
20.2 -6.1010131 2.2515566566
20.3 -7.9415371 2.2609123867
20.4 -9.3904458 3.4913365287
20.5 -13.3504189 4.1730977828
21 -7.6974718 1.6936582839
21.1 -11.9335526 2.9571191233
21.2 -12.7064929 3.7887385779
22 -21.5022909 2.4696226232
22.1 -12.7745451 3.1626627257
23 -3.5146508 1.5414533857
23.1 -4.6724048 2.3369736120
24 -2.5619821 2.8283136466
25 -6.2944970 0.5381704110
25.1 -3.8630505 1.6069735331
25.2 -14.4205140 1.6358226922
25.3 -19.6735037 3.2646870392
25.4 -9.0288933 4.0782226040
25.5 -9.0509738 4.1560292873
26 -19.7340685 0.2412706357
26.1 -14.1692728 2.4451737676
26.2 -17.2819976 3.5988757887
26.3 -24.6265576 4.1822362854
27 -7.3354999 3.6955824879
27.1 -11.1488468 4.2451434687
28 -11.7996597 0.5746519344
28.1 -8.2030122 2.7943964268
28.2 -26.4317815 4.2108539480
28.3 -18.5016071 4.4705521734
29 -5.8551395 1.1898884235
29.1 -2.0209442 1.7624059319
29.2 -5.6368080 2.0210406382
29.3 -3.8110961 3.4078777023
30 -12.7217702 2.2635366488
30.1 -17.0170140 3.5938334477
30.2 -25.4236089 3.6138710892
31 -17.0783921 4.3988140998
32 -18.4338764 1.6745209007
32.1 -19.4317212 2.9128167813
32.2 -19.4738978 2.9676558380
32.3 -21.4922645 4.2099863547
33 2.0838099 0.0093385763
33.1 -13.3172274 3.4591242753
34 -10.0296691 1.4998774312
34.1 -25.9426553 3.8242761395
34.2 -18.5688138 3.9072251692
34.3 -15.4173859 3.9582124643
35 -14.3958113 1.3294299203
35.1 -12.9457541 1.5276966314
35.2 -16.1380691 4.5025920868
36 -12.8166968 0.7123168337
36.1 -14.3989481 1.7972493160
36.2 -12.2436943 1.8262697803
36.3 -15.0104638 4.2840119381
36.4 -10.1775457 4.6194464504
37 -15.2223495 2.0018732361
37.1 -14.7526195 3.6656836793
37.2 -19.8168430 3.9663937816
38 -2.7065118 0.9826511063
39 -8.7288138 0.6921808305
39.1 -9.2746473 0.9027792048
39.2 -18.2695344 1.3055654289
39.3 -13.8219083 1.5412842878
39.4 -16.2254704 3.1834997435
39.5 -21.7283648 4.1394166439
40 1.8291916 1.1330395646
40.1 -6.6916432 2.6940994046
40.2 -1.6278171 3.0396614212
40.3 -10.5749790 4.6762977762
41 -3.1556121 1.9337158254
41.1 -11.5895327 3.1956304458
41.2 -18.9352091 3.2846923557
41.3 -15.9788960 3.3813529415
41.4 -9.6070508 3.5482964432
42 -5.2159485 0.4859252973
42.1 -15.9878743 4.3293134298
43 -16.6104361 0.5616614548
43.1 -9.5549441 1.0743579536
43.2 -14.2003491 2.6131797966
44 -8.1969033 0.7662644819
44.1 -19.9270197 2.6490291790
44.2 -22.6521171 3.3371910988
44.3 -21.1903736 4.1154200875
45 -0.5686627 0.1957449992
45.1 -7.5645740 1.9963831536
46 -19.1624789 1.3477755385
46.1 -18.4487574 2.8565793915
46.2 -15.8222682 4.4160729996
47 -5.4165074 0.6012621359
47.1 -15.0975029 2.4097121472
47.2 -12.9971413 2.9975794035
47.3 -10.6844521 3.1829649757
47.4 -18.2214784 4.6201055450
48 -8.3101471 2.8607365978
48.1 -18.3854275 2.9098354396
49 -13.0130319 2.7179756400
50 -10.4579977 1.1762060679
51 -19.3157621 1.4304436720
52 -4.4747188 2.1266646020
52.1 -4.3163827 3.1000545993
52.2 -6.9761408 3.1268477370
52.3 -20.1764756 3.5711459327
52.4 -8.9036692 4.7983659909
52.5 -5.6949642 4.9818264414
53 -10.3141887 0.4965799209
53.1 -8.2642654 3.5505357443
53.2 -9.1691554 4.5790420019
54 -6.2198754 1.4034724841
54.1 -15.7192609 1.8812377600
54.2 -13.0978998 2.5107589352
54.3 -5.1195299 2.7848406672
54.4 -16.5771751 4.0143877396
55 -5.7348534 0.6118522980
55.1 -7.3217494 0.7463747414
55.2 -12.2171938 2.8201208171
55.3 -12.9821266 3.1326431572
55.4 -14.8599983 3.2218102901
56 -14.1764282 1.2231332215
56.1 -12.5343602 2.3573202139
56.2 -8.4573382 2.5674936292
56.3 -12.4633969 2.9507164378
56.4 -17.3841863 3.2272730360
56.5 -14.8147645 3.4175522043
57 -3.1403293 0.2370331448
57.1 -11.1509248 0.2481445030
57.2 -6.3940143 1.1405586067
57.3 -9.3473241 2.1153886721
58 -12.0245677 1.2210099772
58.1 -9.2112246 1.6334245703
58.2 -1.2071742 1.6791862890
58.3 -11.0141711 2.6320121693
58.4 -5.3721214 2.8477731440
58.5 -7.8523047 3.5715569824
59 -13.2946560 1.9023998594
59.1 -10.0530648 4.9736620474
60 -19.2209402 2.8854503250
61 -4.6699914 0.7213630795
61.1 -3.5981894 2.3186947661
61.2 -1.4713611 2.5077313243
61.3 -3.8819786 3.1731073430
61.4 0.1041413 3.6022726283
62 -2.8591600 0.5336771999
62.1 -6.9461986 0.6987666548
62.2 -16.7910593 3.4584309917
62.3 -17.9844596 4.8028772371
63 -24.0335535 2.8097350930
63.1 -11.7765300 3.9653754211
64 -20.5963897 4.1191305732
65 -2.7969169 0.7076152589
65.1 -11.1778694 2.0252246363
65.2 -5.2830399 3.1127382827
65.3 -7.9353390 3.1969087943
66 -13.2318328 3.4943454154
66.1 -1.9090560 3.7677437009
66.2 -16.6643889 3.9486138616
67 -25.6073277 4.1728388879
68 -13.4806759 0.1291919907
68.1 -18.4557183 1.7809643946
68.2 -13.3982327 2.0493205660
68.3 -12.4977127 2.9406870750
68.4 -11.7073990 4.0406670363
69 -14.5290675 4.1451198701
70 -15.2122709 0.1992557163
70.1 -7.8681167 0.4829774413
71 -10.3352703 0.7741605386
71.1 -7.5699888 1.4883817220
71.2 -18.4680702 4.0758526395
71.3 -21.4316644 4.7048238723
71.4 -8.1137650 4.7242791823
72 -9.1848162 0.9321196121
72.1 -23.7538846 1.1799991806
72.2 -26.3421306 1.8917567329
72.3 -27.2843801 3.4853593935
72.4 -20.8541617 3.6884259700
72.5 -12.8948965 4.0854155901
73 -2.6091307 4.6019889915
74 -8.2790175 1.4626806753
75 -12.5029612 3.2524286874
76 -6.0061671 1.8074807397
76.1 -8.8149114 4.2685073183
76.2 -11.8359043 4.9688734859
77 0.4772521 0.8459033852
78 -9.4105229 0.8231094317
79 -1.0217265 0.0583819521
79.1 -11.8125257 2.4406372628
79.2 -10.5465186 3.2962526032
80 -12.7366807 0.8985060186
80.1 -9.0584783 1.3434670598
80.2 -16.6381566 2.8025900386
81 0.5547913 0.0101324962
81.1 -4.0892715 0.9421709494
81.2 1.8283303 3.0542453879
81.3 -5.2166381 3.3456630446
82 -3.0749381 1.3791010005
82.1 -10.5506696 1.7601010622
82.2 -18.2226347 2.6233131927
83 -12.5872635 0.0537394290
83.1 -11.9756502 2.9061570496
83.2 -10.6744217 3.1189457362
83.3 -19.2714012 4.7663642222
84 -2.6320312 2.7254060237
84.1 -9.8140094 3.3364784659
85 -12.3886736 0.2977756259
85.1 -12.9196365 1.7394116637
85.2 -9.6433248 2.6846330194
85.3 -6.3296340 3.1608762743
85.4 -7.0405525 3.9452053758
85.5 -13.6714939 4.5092553482
86 -10.8756412 0.8476278360
86.1 -12.0055331 1.0118629411
86.2 -13.3724699 1.2511159515
86.3 -13.3252145 2.1870554925
86.4 -14.9191290 2.4532935000
86.5 -17.7515546 3.8206058508
87 -10.7027963 2.7069531474
87.1 -22.4941954 3.4462517721
87.2 -14.9616716 4.5241666853
88 -2.2264493 0.0005892443
88.1 -8.9626474 0.7116099866
88.2 -2.5095281 2.4952722900
88.3 -16.3345673 3.2995816297
89 -11.0459647 0.6462086167
90 -4.5610239 0.1696030737
90.1 -11.7036651 2.5980385230
90.2 -5.3838521 2.6651392167
90.3 -4.1636999 3.1242690247
91 -7.1462503 0.6382618390
91.1 -12.8374475 2.6224059286
91.2 -18.2576707 4.7772527603
92 -6.4119222 0.0737052364
93 5.2122168 0.2788909199
93.1 3.1211725 1.0357759963
93.2 -3.6841177 2.4916551099
93.3 2.6223542 2.8876129608
93.4 -11.1877696 4.4639474002
94 -6.9602492 0.8488043118
94.1 -7.4318416 1.0552454425
94.2 -4.3498045 1.9445500884
94.3 -11.6340088 3.0710722448
94.4 -12.9357964 3.0872731935
94.5 -14.7648530 4.3805759016
95 -12.8849309 2.0199063048
95.1 -9.7451502 4.0184444457
95.2 -0.8535063 4.5596531732
96 -4.9139832 0.0311333477
96.1 -3.9582653 0.1324267720
96.2 -9.6555492 0.6701303425
96.3 -11.8690793 2.1775037691
96.4 -11.0224373 2.2246142488
96.5 -10.9530403 4.2377650598
97 -9.8540471 1.1955102731
97.1 -19.2262840 4.9603108643
98 -11.9651231 0.2041732438
98.1 -2.6515128 0.4309578973
98.2 -12.2606382 3.5172611906
99 -11.4720500 0.3531786101
99.1 -14.0596866 4.6789444226
99.2 -17.3939469 4.9927084171
100 1.1005874 1.0691387602
100.1 -3.8226248 1.5109344281
100.2 -0.9123182 2.1502332564
100.3 -15.8389474 3.8745574222
100.4 -12.8093826 4.6567608765
$m9b$spM_id
center scale
C2 -0.6240921 0.68571078
(Intercept) NA NA
C1 0.7372814 0.01472882
B11 NA NA
$m9b$spM_lvlone
center scale
y -11.17337 6.249662
time 2.53394 1.381809
$m9b$mu_reg_norm
[1] 0
$m9b$tau_reg_norm
[1] 1e-04
$m9b$shape_tau_norm
[1] 0.01
$m9b$rate_tau_norm
[1] 0.01
$m9b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m9b$shape_diag_RinvD
[1] "0.01"
$m9b$rate_diag_RinvD
[1] "0.001"
$m9b$RinvD_y_id
[,1] [,2]
[1,] NA 0
[2,] 0 NA
$m9b$KinvD_y_id
id
3
$m9c
$m9c$M_id
C2 (Intercept) C1 B11
1 -1.381594459 1 0.7175865 1
2 0.344426024 1 0.7507170 1
3 NA 1 0.7255954 1
4 -0.228910007 1 0.7469352 0
5 NA 1 0.7139120 1
6 -2.143955482 1 0.7332505 1
7 -1.156567023 1 0.7345929 1
8 -0.598827660 1 0.7652589 0
9 NA 1 0.7200622 1
10 -1.006719032 1 0.7423879 1
11 0.239801450 1 0.7437448 0
12 -1.064969789 1 0.7446470 1
13 -0.538082688 1 0.7530186 1
14 NA 1 0.7093137 1
15 -1.781049276 1 0.7331192 1
16 NA 1 0.7011390 1
17 NA 1 0.7432395 1
18 -0.014579883 1 0.7545191 1
19 -2.121550136 1 0.7528487 1
20 NA 1 0.7612865 0
21 -0.363239698 1 0.7251719 1
22 -0.121568514 1 0.7300630 1
23 -0.951271111 1 0.7087249 1
24 NA 1 0.7391938 0
25 -0.974288621 1 0.7820641 1
26 -1.130632418 1 0.7118298 1
27 0.114339868 1 0.7230857 0
28 0.238334648 1 0.7489353 1
29 0.840744958 1 0.7510888 1
30 NA 1 0.7300717 1
31 NA 1 0.7550721 1
32 -1.466312154 1 0.7321898 1
33 -0.637352277 1 0.7306414 1
34 NA 1 0.7427216 1
35 NA 1 0.7193042 1
36 NA 1 0.7312888 0
37 NA 1 0.7100436 0
38 NA 1 0.7670184 1
39 0.006728205 1 0.7400449 1
40 NA 1 0.7397304 1
41 -1.663281353 1 0.7490966 1
42 0.161184794 1 0.7419274 1
43 0.457939180 1 0.7527810 1
44 -0.307070331 1 0.7408315 1
45 NA 1 0.7347550 0
46 -1.071668276 1 0.7332398 1
47 -0.814751321 1 0.7376481 0
48 -0.547630662 1 0.7346179 0
49 NA 1 0.7329402 1
50 -1.350213782 1 0.7260436 1
51 0.719054706 1 0.7242910 1
52 NA 1 0.7298067 0
53 -1.207130750 1 0.7254741 1
54 NA 1 0.7542067 1
55 -0.408600991 1 0.7389952 1
56 -0.271380529 1 0.7520638 1
57 -1.361925974 1 0.7219958 1
58 NA 1 0.7259632 1
59 NA 1 0.7458606 1
60 -0.323712205 1 0.7672421 1
61 NA 1 0.7257179 0
62 NA 1 0.7189892 1
63 -1.386906880 1 0.7333356 1
64 NA 1 0.7320243 1
65 NA 1 0.7477711 1
66 -0.565191691 1 0.7343974 0
67 -0.382899912 1 0.7491624 0
68 NA 1 0.7482736 1
69 -0.405642769 1 0.7338267 1
70 NA 1 0.7607742 1
71 -0.843748427 1 0.7777600 1
72 0.116003683 1 0.7408143 1
73 -0.778634325 1 0.7248271 1
74 NA 1 0.7364916 0
75 NA 1 0.7464926 1
76 NA 1 0.7355430 1
77 -0.632974758 1 0.7208449 1
78 NA 1 0.7373573 1
79 -0.778064615 1 0.7598079 1
80 NA 1 0.7360415 1
81 NA 1 0.7293932 1
82 -0.246123253 1 0.7279309 1
83 -1.239659782 1 0.7344643 0
84 -0.467772280 1 0.7384350 0
85 NA 1 0.7323716 1
86 -2.160485036 1 0.7576597 1
87 -0.657675572 1 0.7496139 1
88 NA 1 0.7275239 1
89 -0.696710744 1 0.7250648 1
90 NA 1 0.7335262 0
91 -0.179395847 1 0.7343980 1
92 -0.441545568 1 0.7380425 1
93 -0.685799334 1 0.7389460 0
94 NA 1 0.7259951 1
95 0.191929445 1 0.7282840 0
96 NA 1 0.7281676 0
97 -0.069760671 1 0.7245642 1
98 NA 1 0.7526938 1
99 NA 1 0.7272309 1
100 NA 1 0.7383460 1
$m9c$M_lvlone
y
1 -13.0493856
1.1 -9.3335901
1.2 -22.3469852
1.3 -15.0417337
2 -12.0655434
2.1 -15.8674476
2.2 -7.8800006
3 -11.4820604
3.1 -10.5983220
3.2 -22.4519157
4 -1.2697775
4.1 -11.1215184
4.2 -3.6134138
4.3 -14.5982385
5 -6.8457515
5.1 -7.0551214
5.2 -12.3418980
5.3 -9.2366906
6 -5.1648211
7 -10.0599502
7.1 -18.3267285
7.2 -12.5138426
8 -1.6305331
8.1 -9.6520453
8.2 -1.5278462
8.3 -7.4172211
8.4 -7.1238609
8.5 -8.8706950
9 -0.1634429
9.1 -2.6034300
9.2 -6.7272369
10 -6.4172202
10.1 -11.4834569
11 -8.7911356
11.1 -19.6645080
11.2 -20.2030932
11.3 -21.3082176
11.4 -14.5802901
12 -15.2006287
13 0.8058816
13.1 -13.6379208
14 -15.3422873
14.1 -10.0965208
14.2 -16.6452027
14.3 -15.8389733
15 -8.9424594
15.1 -22.0101983
15.2 -7.3975599
15.3 -10.3567334
16 -1.9691302
16.1 -9.9308357
16.2 -6.9626923
16.3 -3.2862557
16.4 -3.3972355
16.5 -11.5767835
17 -10.5474144
17.1 -7.6215009
17.2 -16.5386939
17.3 -20.0004774
17.4 -18.8505475
18 -19.7302351
19 -14.6177568
19.1 -17.8043866
19.2 -15.1641705
19.3 -16.6898418
20 -12.9059229
20.1 -16.8191201
20.2 -6.1010131
20.3 -7.9415371
20.4 -9.3904458
20.5 -13.3504189
21 -7.6974718
21.1 -11.9335526
21.2 -12.7064929
22 -21.5022909
22.1 -12.7745451
23 -3.5146508
23.1 -4.6724048
24 -2.5619821
25 -6.2944970
25.1 -3.8630505
25.2 -14.4205140
25.3 -19.6735037
25.4 -9.0288933
25.5 -9.0509738
26 -19.7340685
26.1 -14.1692728
26.2 -17.2819976
26.3 -24.6265576
27 -7.3354999
27.1 -11.1488468
28 -11.7996597
28.1 -8.2030122
28.2 -26.4317815
28.3 -18.5016071
29 -5.8551395
29.1 -2.0209442
29.2 -5.6368080
29.3 -3.8110961
30 -12.7217702
30.1 -17.0170140
30.2 -25.4236089
31 -17.0783921
32 -18.4338764
32.1 -19.4317212
32.2 -19.4738978
32.3 -21.4922645
33 2.0838099
33.1 -13.3172274
34 -10.0296691
34.1 -25.9426553
34.2 -18.5688138
34.3 -15.4173859
35 -14.3958113
35.1 -12.9457541
35.2 -16.1380691
36 -12.8166968
36.1 -14.3989481
36.2 -12.2436943
36.3 -15.0104638
36.4 -10.1775457
37 -15.2223495
37.1 -14.7526195
37.2 -19.8168430
38 -2.7065118
39 -8.7288138
39.1 -9.2746473
39.2 -18.2695344
39.3 -13.8219083
39.4 -16.2254704
39.5 -21.7283648
40 1.8291916
40.1 -6.6916432
40.2 -1.6278171
40.3 -10.5749790
41 -3.1556121
41.1 -11.5895327
41.2 -18.9352091
41.3 -15.9788960
41.4 -9.6070508
42 -5.2159485
42.1 -15.9878743
43 -16.6104361
43.1 -9.5549441
43.2 -14.2003491
44 -8.1969033
44.1 -19.9270197
44.2 -22.6521171
44.3 -21.1903736
45 -0.5686627
45.1 -7.5645740
46 -19.1624789
46.1 -18.4487574
46.2 -15.8222682
47 -5.4165074
47.1 -15.0975029
47.2 -12.9971413
47.3 -10.6844521
47.4 -18.2214784
48 -8.3101471
48.1 -18.3854275
49 -13.0130319
50 -10.4579977
51 -19.3157621
52 -4.4747188
52.1 -4.3163827
52.2 -6.9761408
52.3 -20.1764756
52.4 -8.9036692
52.5 -5.6949642
53 -10.3141887
53.1 -8.2642654
53.2 -9.1691554
54 -6.2198754
54.1 -15.7192609
54.2 -13.0978998
54.3 -5.1195299
54.4 -16.5771751
55 -5.7348534
55.1 -7.3217494
55.2 -12.2171938
55.3 -12.9821266
55.4 -14.8599983
56 -14.1764282
56.1 -12.5343602
56.2 -8.4573382
56.3 -12.4633969
56.4 -17.3841863
56.5 -14.8147645
57 -3.1403293
57.1 -11.1509248
57.2 -6.3940143
57.3 -9.3473241
58 -12.0245677
58.1 -9.2112246
58.2 -1.2071742
58.3 -11.0141711
58.4 -5.3721214
58.5 -7.8523047
59 -13.2946560
59.1 -10.0530648
60 -19.2209402
61 -4.6699914
61.1 -3.5981894
61.2 -1.4713611
61.3 -3.8819786
61.4 0.1041413
62 -2.8591600
62.1 -6.9461986
62.2 -16.7910593
62.3 -17.9844596
63 -24.0335535
63.1 -11.7765300
64 -20.5963897
65 -2.7969169
65.1 -11.1778694
65.2 -5.2830399
65.3 -7.9353390
66 -13.2318328
66.1 -1.9090560
66.2 -16.6643889
67 -25.6073277
68 -13.4806759
68.1 -18.4557183
68.2 -13.3982327
68.3 -12.4977127
68.4 -11.7073990
69 -14.5290675
70 -15.2122709
70.1 -7.8681167
71 -10.3352703
71.1 -7.5699888
71.2 -18.4680702
71.3 -21.4316644
71.4 -8.1137650
72 -9.1848162
72.1 -23.7538846
72.2 -26.3421306
72.3 -27.2843801
72.4 -20.8541617
72.5 -12.8948965
73 -2.6091307
74 -8.2790175
75 -12.5029612
76 -6.0061671
76.1 -8.8149114
76.2 -11.8359043
77 0.4772521
78 -9.4105229
79 -1.0217265
79.1 -11.8125257
79.2 -10.5465186
80 -12.7366807
80.1 -9.0584783
80.2 -16.6381566
81 0.5547913
81.1 -4.0892715
81.2 1.8283303
81.3 -5.2166381
82 -3.0749381
82.1 -10.5506696
82.2 -18.2226347
83 -12.5872635
83.1 -11.9756502
83.2 -10.6744217
83.3 -19.2714012
84 -2.6320312
84.1 -9.8140094
85 -12.3886736
85.1 -12.9196365
85.2 -9.6433248
85.3 -6.3296340
85.4 -7.0405525
85.5 -13.6714939
86 -10.8756412
86.1 -12.0055331
86.2 -13.3724699
86.3 -13.3252145
86.4 -14.9191290
86.5 -17.7515546
87 -10.7027963
87.1 -22.4941954
87.2 -14.9616716
88 -2.2264493
88.1 -8.9626474
88.2 -2.5095281
88.3 -16.3345673
89 -11.0459647
90 -4.5610239
90.1 -11.7036651
90.2 -5.3838521
90.3 -4.1636999
91 -7.1462503
91.1 -12.8374475
91.2 -18.2576707
92 -6.4119222
93 5.2122168
93.1 3.1211725
93.2 -3.6841177
93.3 2.6223542
93.4 -11.1877696
94 -6.9602492
94.1 -7.4318416
94.2 -4.3498045
94.3 -11.6340088
94.4 -12.9357964
94.5 -14.7648530
95 -12.8849309
95.1 -9.7451502
95.2 -0.8535063
96 -4.9139832
96.1 -3.9582653
96.2 -9.6555492
96.3 -11.8690793
96.4 -11.0224373
96.5 -10.9530403
97 -9.8540471
97.1 -19.2262840
98 -11.9651231
98.1 -2.6515128
98.2 -12.2606382
99 -11.4720500
99.1 -14.0596866
99.2 -17.3939469
100 1.1005874
100.1 -3.8226248
100.2 -0.9123182
100.3 -15.8389474
100.4 -12.8093826
$m9c$spM_id
center scale
C2 -0.6240921 0.68571078
(Intercept) NA NA
C1 0.7372814 0.01472882
B11 NA NA
$m9c$mu_reg_norm
[1] 0
$m9c$tau_reg_norm
[1] 1e-04
$m9c$shape_tau_norm
[1] 0.01
$m9c$rate_tau_norm
[1] 0.01
$m9c$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m9c$shape_diag_RinvD
[1] "0.01"
$m9c$rate_diag_RinvD
[1] "0.001"
jagsmodel remains the same
Code
lapply(models, "[[", "jagsmodel")
Output
$m0a1
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
}
$m0a2
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
}
$m0a3
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
log(mu_y[i]) <- b_y_id[group_id[i], 1]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
}
$m0a4
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- 1/max(1e-10, inv_mu_y[i])
inv_mu_y[i] <- b_y_id[group_id[i], 1]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
}
$m0b1
model {
# Binomial mixed effects model for b1 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i])))
logit(mu_b1[i]) <- b_b1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ])
mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for b1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1])
}
$m0b2
model {
# Binomial mixed effects model for b1 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i])))
probit(mu_b1[i]) <- b_b1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ])
mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for b1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1])
}
$m0b3
model {
# Binomial mixed effects model for b1 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i])))
log(mu_b1[i]) <- b_b1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ])
mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for b1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1])
}
$m0b4
model {
# Binomial mixed effects model for b1 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i])))
log(mu_b1[i]) <- b_b1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ])
mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for b1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1])
}
$m0c1
model {
# Gamma mixed effects model for L1 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i])
shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2)
rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2)
mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i])
inv_mu_L1[i] <- b_L1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ])
mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for L1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1 <- sqrt(1/tau_L1)
invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1])
}
$m0c2
model {
# Gamma mixed effects model for L1 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i])
shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2)
rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2)
log(mu_L1[i]) <- b_L1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ])
mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for L1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1 <- sqrt(1/tau_L1)
invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1])
}
$m0d1
model {
# Poisson mixed effects model for p1 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i]))
log(mu_p1[i]) <- b_p1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ])
mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for p1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1])
}
$m0d2
model {
# Poisson mixed effects model for p1 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i]))
mu_p1[i] <- b_p1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ])
mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for p1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1])
}
$m0e1
model {
# Log-normal mixed effects model for L1 -----------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1)
mu_L1[i] <- b_L1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ])
mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for L1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1 <- sqrt(1/tau_L1)
invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1])
}
$m0f1
model {
# Beta mixed effects model for Be1 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15)
shape1_Be1[i] <- mu_Be1[i] * tau_Be1
shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1
logit(mu_Be1[i]) <- b_Be1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_Be1_id[ii, 1:1] ~ dnorm(mu_b_Be1_id[ii, ], invD_Be1_id[ , ])
mu_b_Be1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for Be1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta)
invD_Be1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Be1_id[1, 1] <- 1 / (invD_Be1_id[1, 1])
}
$m1a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2]
}
# Priors for the model for y
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
}
$m1b
model {
# Binomial mixed effects model for b1 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i])))
logit(mu_b1[i]) <- b_b1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ])
mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2]
}
# Priors for the model for b1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1])
}
$m1c
model {
# Gamma mixed effects model for L1 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i])
shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2)
rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2)
mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i])
inv_mu_L1[i] <- b_L1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ])
mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2]
}
# Priors for the model for L1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1 <- sqrt(1/tau_L1)
invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1])
}
$m1d
model {
# Poisson mixed effects model for p1 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i]))
log(mu_p1[i]) <- b_p1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ])
mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2]
}
# Priors for the model for p1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1])
}
$m1e
model {
# Log-normal mixed effects model for L1 -----------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1)
mu_L1[i] <- b_L1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ])
mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2]
}
# Priors for the model for L1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1 <- sqrt(1/tau_L1)
invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1])
}
$m1f
model {
# Beta mixed effects model for Be1 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15)
shape1_Be1[i] <- mu_Be1[i] * tau_Be1
shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1
logit(mu_Be1[i]) <- b_Be1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_Be1_id[ii, 1:1] ~ dnorm(mu_b_Be1_id[ii, ], invD_Be1_id[ , ])
mu_b_Be1_id[ii, 1] <- M_id[ii, 1] * beta[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2]
}
# Priors for the model for Be1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta)
invD_Be1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Be1_id[1, 1] <- 1 / (invD_Be1_id[1, 1])
}
$m2a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for y
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for c2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
}
$m2b
model {
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ])
mu_b_b2_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for b2
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for c2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
}
$m2c
model {
# Gamma mixed effects model for L1mis -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i])
inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for L1mis
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for c2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
}
$m2d
model {
# Poisson mixed effects model for p2 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p2[i]))
log(mu_p2[i]) <- b_p2_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ])
mu_b_p2_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for p2
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for c2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
}
$m2e
model {
# Log-normal mixed effects model for L1mis --------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dlnorm(mu_L1mis[i], tau_L1mis)
mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for L1mis
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for c2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
}
$m2f
model {
# Beta mixed effects model for Be2 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15)
shape1_Be2[i] <- mu_Be2[i] * tau_Be2
shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2
logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ])
mu_b_Be2_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for Be2
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta)
invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for c2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
}
$m3a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[1]
}
# Priors for the model for C2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m3b
model {
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
logit(mu_b2[i]) <- b_b2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ])
mu_b_b2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1]
}
# Priors for the model for b2
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[1]
}
# Priors for the model for C2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m3c
model {
# Gamma mixed effects model for L1mis -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i])
inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1]
}
# Priors for the model for L1mis
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[1]
}
# Priors for the model for C2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m3d
model {
# Poisson mixed effects model for p2 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p2[i]))
log(mu_p2[i]) <- b_p2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ])
mu_b_p2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1]
}
# Priors for the model for p2
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[1]
}
# Priors for the model for C2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m3e
model {
# Log-normal mixed effects model for L1mis --------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dlnorm(mu_L1mis[i], tau_L1mis)
mu_L1mis[i] <- b_L1mis_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1]
}
# Priors for the model for L1mis
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[1]
}
# Priors for the model for C2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m3f
model {
# Beta mixed effects model for Be2 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15)
shape1_Be2[i] <- mu_Be2[i] * tau_Be2
shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2
logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ])
mu_b_Be2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1]
}
# Priors for the model for Be2
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta)
invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[1]
}
# Priors for the model for C2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m4a
model {
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2]
}
# Priors for the model for c1
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Poisson mixed effects model for p2 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i]))
log(mu_p2[i]) <- b_p2_id[group_id[i], 1] +
alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ])
mu_b_p2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2]
}
# Priors for the model for p2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[6] + M_id[ii, 3] * alpha[7]
}
# Priors for the model for c2
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Gamma mixed effects model for L1mis -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i])
inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] +
alpha[12] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- M_id[ii, 2] * alpha[10] + M_id[ii, 3] * alpha[11]
}
# Priors for the model for L1mis
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
# Beta mixed effects model for Be2 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 5] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15)
shape1_Be2[i] <- mu_Be2[i] * tau_Be2
shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2
logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ])
mu_b_Be2_id[ii, 1] <- M_id[ii, 2] * alpha[13] + M_id[ii, 3] * alpha[14]
}
# Priors for the model for Be2
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta)
invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[15]
M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 15:15) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
}
$m4b
model {
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[3] * M_lvlone[i, 6] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for c1
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Poisson mixed effects model for p2 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i]))
mu_p2[i] <- b_p2_id[group_id[i], 1] +
alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[3] * M_lvlone[i, 6] +
alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ])
mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for p2
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1])
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
probit(mu_b2[i]) <- b_b2_id[group_id[i], 1] +
alpha[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 1, 1, 0)
}
for (ii in 1:100) {
b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ])
mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[5]
}
# Priors for the model for b2
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- 1/max(1e-10, inv_mu_c2[i])
inv_mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8]
}
# Priors for the model for c2
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Log-normal mixed effects model for L1mis --------------------------------------
for (i in 1:329) {
M_lvlone[i, 5] ~ dlnorm(mu_L1mis[i], tau_L1mis)
mu_L1mis[i] <- b_L1mis_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[10]
}
# Priors for the model for L1mis
for (k in 10:10) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
}
$m4c
model {
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[3] * M_lvlone[i, 6] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for c1
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Poisson mixed effects model for p2 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i]))
mu_p2[i] <- b_p2_id[group_id[i], 1] +
alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[3] * M_lvlone[i, 6] +
alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ])
mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for p2
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1])
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
log(mu_b2[i]) <- b_b2_id[group_id[i], 1] +
alpha[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 1, 1, 0)
}
for (ii in 1:100) {
b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ])
mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[5]
}
# Priors for the model for b2
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2)
log(mu_c2[i]) <- b_c2_id[group_id[i], 1] +
alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8]
}
# Priors for the model for c2
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Gamma mixed effects model for L1mis -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 5] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
log(mu_L1mis[i]) <- b_L1mis_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[10]
}
# Priors for the model for L1mis
for (k in 10:10) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
}
$m4d
model {
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[3] * M_lvlone[i, 7] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
beta[6] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for c1
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_beta[k])
tau_reg_norm_ridge_beta[k] ~ dgamma(0.01, 0.01)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Poisson mixed effects model for p2 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i]))
mu_p2[i] <- b_p2_id[group_id[i], 1] +
alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[3] * M_lvlone[i, 7] +
alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
alpha[5] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ])
mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for p2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson_ridge_alpha[k])
tau_reg_poisson_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1])
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
log(mu_b2[i]) <- b_b2_id[group_id[i], 1] +
alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[8] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
alpha[9] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 1, 1, 0)
}
for (ii in 1:100) {
b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ])
mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[6]
}
# Priors for the model for b2
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_alpha[k])
tau_reg_binom_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2)
log(mu_c2[i]) <- b_c2_id[group_id[i], 1] +
alpha[11] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
alpha[12] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[10]
}
# Priors for the model for c2
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k])
tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Gamma mixed effects model for L1mis -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 5] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
log(mu_L1mis[i]) <- b_L1mis_id[group_id[i], 1] +
alpha[14] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[13]
}
# Priors for the model for L1mis
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma_ridge_alpha[k])
tau_reg_gamma_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
# Normal mixed effects model for Be2 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 6] ~ dnorm(mu_Be2[i], tau_Be2)T(0, 1)
mu_Be2[i] <- b_Be2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ])
mu_b_Be2_id[ii, 1] <- M_id[ii, 1] * alpha[15]
}
# Priors for the model for Be2
for (k in 15:15) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k])
tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_Be2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_Be2 <- sqrt(1/tau_Be2)
invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1])
}
$m5a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[6] * M_lvlone[i, 5] + beta[7] * M_lvlone[i, 6] +
beta[8] * M_lvlone[i, 7] +
beta[9] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] +
beta[11] * (M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] +
beta[12] * (M_lvlone[i, 10] - spM_lvlone[10, 1])/spM_lvlone[10, 2] +
beta[13] * (M_lvlone[i, 11] - spM_lvlone[11, 1])/spM_lvlone[11, 2] +
beta[14] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] +
M_id[ii, 4] * beta[3] + M_id[ii, 5] * beta[4] +
(M_id[ii, 6] - spM_id[6, 1])/spM_id[6, 2] * beta[5]
mu_b_y_id[ii, 2] <- beta[10]
}
# Priors for the model for y
for (k in 1:14) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:2) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] + alpha[6] * M_lvlone[i, 5] +
alpha[7] * M_lvlone[i, 6] + alpha[8] * M_lvlone[i, 7] +
alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
M_lvlone[i, 8] <- abs(M_id[group_id[i], 7] - M_lvlone[i, 2])
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] +
M_id[ii, 4] * alpha[3] + M_id[ii, 5] * alpha[4] +
(M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[5]
}
# Priors for the model for c2
for (k in 1:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1] +
alpha[14] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- M_id[ii, 3] * alpha[10] + M_id[ii, 4] * alpha[11] +
M_id[ii, 5] * alpha[12] +
(M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[13]
}
# Priors for the model for o2
for (k in 10:14) {
alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[15] + M_id[ii, 3] * alpha[16] +
M_id[ii, 4] * alpha[17] + M_id[ii, 5] * alpha[18] +
(M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[19]
}
# Priors for the model for time
for (k in 15:19) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Multinomial logit model for M2 ------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dcat(p_M2[ii, 1:4])
p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ])))
p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ])))
p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ])))
p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ])))
log(phi_M2[ii, 1]) <- 0
log(phi_M2[ii, 2]) <- M_id[ii, 2] * alpha[20] +
(M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[21]
log(phi_M2[ii, 3]) <- M_id[ii, 2] * alpha[22] +
(M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[23]
log(phi_M2[ii, 4]) <- M_id[ii, 2] * alpha[24] +
(M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[25]
M_id[ii, 3] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 20:25) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 10] <- M_lvlone[i, 5] * M_lvlone[i, 8]
M_lvlone[i, 11] <- M_lvlone[i, 6] * M_lvlone[i, 8]
M_lvlone[i, 12] <- M_lvlone[i, 7] * M_lvlone[i, 8]
}
}
$m5b
model {
# Binomial mixed effects model for b1 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i])))
logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] +
b_b1_id[group_id[i], 2] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
b_b1_id[group_id[i], 3] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[3] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] +
beta[4] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2]
}
for (ii in 1:100) {
b_b1_id[ii, 1:3] ~ dmnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ])
mu_b_b1_id[ii, 1] <- M_id[ii, 2] * beta[1]
mu_b_b1_id[ii, 2] <- beta[5]
mu_b_b1_id[ii, 3] <- 0
}
# Priors for the model for b1
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_beta[k])
tau_reg_binom_ridge_beta[k] ~ dgamma(0.01, 0.01)
}
for (k in 1:3) {
RinvD_b1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_b1_id[1:3, 1:3] ~ dwish(RinvD_b1_id[ , ], KinvD_b1_id)
D_b1_id[1:3, 1:3] <- inverse(invD_b1_id[ , ])
# Gamma mixed effects model for L1mis -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i])
inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] +
alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[2]
}
# Priors for the model for L1mis
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma_ridge_alpha[k])
tau_reg_gamma_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
# Beta mixed effects model for Be2 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15)
shape1_Be2[i] <- mu_Be2[i] * tau_Be2
shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2
logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] +
alpha[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
M_lvlone[i, 7] <- log(M_lvlone[i, 3])
}
for (ii in 1:100) {
b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ])
mu_b_Be2_id[ii, 1] <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7]
}
# Priors for the model for Be2
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta_ridge_alpha[k])
tau_reg_beta_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta)
invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[12] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
M_lvlone[i, 6] <- abs(M_lvlone[i, 4] - M_id[group_id[i], 1])
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[10] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[11]
}
# Priors for the model for c1
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k])
tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 5] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[13] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[14]
}
# Priors for the model for time
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k])
tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
log(mu_C2[ii]) <- M_id[ii, 2] * alpha[15]
}
# Priors for the model for C2
for (k in 15:15) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k])
tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m6a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[1] * M_id[group_id[i], 2] +
beta[2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] +
beta[3] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] +
beta[4] * M_lvlone[i, 3]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- beta[5]
}
# Priors for the model for y
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
for (ii in 1:100) {
b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ])
mu_b_b2_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3]
}
# Priors for the model for b2
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[5] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6]
}
# Priors for the model for C2
for (k in 5:6) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m6b
model {
# Binomial mixed effects model for b1 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i])))
logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_b1_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[1] * M_id[group_id[i], 2] +
beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] +
beta[3] * M_id[group_id[i], 3] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_b1_id[ii, 1:2] ~ dmnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ])
mu_b_b1_id[ii, 1] <- beta[5]
mu_b_b1_id[ii, 2] <- 0
}
# Priors for the model for b1
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_beta[k])
tau_reg_binom_ridge_beta[k] ~ dgamma(0.01, 0.01)
}
for (k in 1:2) {
RinvD_b1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_b1_id[1:2, 1:2] ~ dwish(RinvD_b1_id[ , ], KinvD_b1_id)
D_b1_id[1:2, 1:2] <- inverse(invD_b1_id[ , ])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[2] +
M_id[ii, 3] * alpha[3]
}
# Priors for the model for c1
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k])
tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[6] +
M_id[ii, 3] * alpha[7]
}
# Priors for the model for time
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k])
tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[8] + M_id[ii, 3] * alpha[9]
}
# Priors for the model for C2
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k])
tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m7a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
mu_b_y_id[ii, 2] <- beta[2]
mu_b_y_id[ii, 3] <- beta[3]
}
# Priors for the model for y
for (k in 1:3) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
}
$m7b
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_y_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
mu_b_y_id[ii, 2] <- beta[2]
mu_b_y_id[ii, 3] <- beta[3]
mu_b_y_id[ii, 4] <- beta[4]
}
# Priors for the model for y
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:4) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ])
}
$m7c
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2]
mu_b_y_id[ii, 2] <- beta[4]
mu_b_y_id[ii, 3] <- beta[5]
mu_b_y_id[ii, 4] <- beta[6]
}
# Priors for the model for y
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:4) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ])
}
$m7d
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
beta[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3]
mu_b_y_id[ii, 2] <- 0
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:2) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3]
}
# Priors for the model for c1
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7]
}
# Priors for the model for time
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[8] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9]
}
# Priors for the model for C2
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m7e
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3]
mu_b_y_id[ii, 2] <- beta[5]
mu_b_y_id[ii, 3] <- beta[6]
mu_b_y_id[ii, 4] <- beta[7]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:4) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3]
}
# Priors for the model for c1
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[5] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6]
}
# Priors for the model for C2
for (k in 5:6) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m7f
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
beta[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3]
mu_b_y_id[ii, 2] <- 0
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:2) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3]
}
# Priors for the model for c1
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7]
}
# Priors for the model for time
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[8] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9]
}
# Priors for the model for C2
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m8a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
mu_b_y_id[ii, 2] <- beta[4]
mu_b_y_id[ii, 3] <- beta[3]
}
# Priors for the model for y
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for c2
for (k in 1:3) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
}
$m8b
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
mu_b_y_id[ii, 2] <- beta[4]
mu_b_y_id[ii, 3] <- beta[3]
}
# Priors for the model for y
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for c2
for (k in 1:3) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
}
$m8c
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2]
mu_b_y_id[ii, 2] <- beta[5]
mu_b_y_id[ii, 3] <- beta[3] + M_id[ii, 3] * beta[6]
}
# Priors for the model for y
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2]
}
# Priors for the model for c2
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + M_id[ii, 3] * alpha[6]
}
# Priors for the model for c1
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8]
M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 8:8) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 3] * M_lvlone[i, 3]
}
}
$m8d
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2]
mu_b_y_id[ii, 2] <- beta[5]
mu_b_y_id[ii, 3] <- beta[3] + M_id[ii, 3] * beta[6]
}
# Priors for the model for y
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2]
}
# Priors for the model for c2
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + M_id[ii, 3] * alpha[6]
}
# Priors for the model for c1
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[8] + M_id[ii, 3] * alpha[9]
}
# Priors for the model for time
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10]
M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 10:10) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 3] * M_lvlone[i, 3]
}
}
$m8e
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[6]
mu_b_y_id[ii, 3] <- beta[5]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] +
M_id[ii, 4] * alpha[8]
}
# Priors for the model for c1
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] +
M_id[ii, 4] * alpha[12]
}
# Priors for the model for time
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3]
}
}
$m8f
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[6]
mu_b_y_id[ii, 3] <- beta[5]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] +
M_id[ii, 4] * alpha[8]
}
# Priors for the model for c1
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 10:11) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3]
}
}
$m8g
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[6]
mu_b_y_id[ii, 3] <- beta[5]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 6:7) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3]
}
}
$m8h
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[6]
mu_b_y_id[ii, 3] <- beta[5]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] +
M_id[ii, 4] * alpha[8]
}
# Priors for the model for c1
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] +
M_id[ii, 4] * alpha[12]
}
# Priors for the model for time
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2]
}
}
$m8i
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[6]
mu_b_y_id[ii, 3] <- beta[5]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] +
M_id[ii, 4] * alpha[8]
}
# Priors for the model for c1
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 10:11) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2]
}
}
$m8j
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[6]
mu_b_y_id[ii, 3] <- beta[4] + M_id[ii, 4] * beta[7]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] +
M_id[ii, 4] * alpha[8]
}
# Priors for the model for c1
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] +
M_id[ii, 4] * alpha[12]
}
# Priors for the model for time
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2]
}
}
$m8k
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[6]
mu_b_y_id[ii, 3] <- beta[4] + M_id[ii, 4] * beta[7]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] +
M_id[ii, 4] * alpha[8]
}
# Priors for the model for c1
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] +
M_id[ii, 4] * alpha[12]
}
# Priors for the model for time
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2]
}
}
$m8l
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[8] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] +
beta[9] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[5] + M_id[ii, 4] * beta[7]
mu_b_y_id[ii, 3] <- 0
}
# Priors for the model for y
for (k in 1:9) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c1
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] +
M_id[ii, 4] * alpha[7]
}
# Priors for the model for time
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 4] <- M_id[group_id[i], 4] * M_lvlone[i, 2]
M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3]
M_lvlone[i, 7] <- M_id[group_id[i], 4] * M_lvlone[i, 2] * M_lvlone[i, 3]
}
}
$m8m
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] + b_y_id[group_id[i], 2] * M_lvlone[i, 3] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[4] * M_lvlone[i, 4] + beta[5] * M_lvlone[i, 5] +
beta[6] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
mu_b_y_id[ii, 2] <- beta[3]
}
# Priors for the model for y
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:2) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ])
}
$m8n
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_y_id[group_id[i], 4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[6] * M_lvlone[i, 5]
}
for (ii in 1:100) {
b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[2]
mu_b_y_id[ii, 3] <- beta[5]
mu_b_y_id[ii, 4] <- beta[7]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:4) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * M_lvlone[i, 5]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c1
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1] + alpha[9] * M_lvlone[i, 5]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] +
M_id[ii, 4] * alpha[8]
}
# Priors for the model for time
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Binomial mixed effects model for b1 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i])))
logit(mu_b1[i]) <- b_b1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ])
mu_b_b1_id[ii, 1] <- M_id[ii, 2] * alpha[10] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] +
M_id[ii, 4] * alpha[12]
}
# Priors for the model for b1
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
}
$m9a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] + b_y_o1[group_o1[i], 1] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[3] * M_lvlone[i, 3] +
beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
for (iii in 1:3) {
b_y_o1[iii, 1:1] ~ dnorm(mu_b_y_o1[iii, ], invD_y_o1[ , ])
mu_b_y_o1[iii, 1] <- 0
}
# Priors for the model for y
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
invD_y_o1[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_o1[1, 1] <- 1 / (invD_y_o1[1, 1])
}
$m9b
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] +
M_id[ii, 4] * beta[4]
mu_b_y_id[ii, 2] <- beta[5]
}
# Priors for the model for y
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:2) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] +
M_id[ii, 4] * alpha[4]
}
# Priors for the model for time
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[5] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] +
M_id[ii, 4] * alpha[7]
}
# Priors for the model for C2
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m9c
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] +
M_id[ii, 4] * beta[4]
}
# Priors for the model for y
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for C2
for (k in 1:3) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
GRcrit and MCerror give same result
Code
lapply(models0, GR_crit, multivariate = FALSE)
Output
$m0a1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m0a2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m0a3
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m0a4
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m0b1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
D_b1_id[1,1] NaN NaN
$m0b2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
D_b1_id[1,1] NaN NaN
$m0b3
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
D_b1_id[1,1] NaN NaN
$m0b4
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
D_b1_id[1,1] NaN NaN
$m0c1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_L1 NaN NaN
D_L1_id[1,1] NaN NaN
$m0c2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_L1 NaN NaN
D_L1_id[1,1] NaN NaN
$m0d1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
D_p1_id[1,1] NaN NaN
$m0d2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
D_p1_id[1,1] NaN NaN
$m0e1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_L1 NaN NaN
D_L1_id[1,1] NaN NaN
$m0f1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
tau_Be1 NaN NaN
D_Be1_id[1,1] NaN NaN
$m1a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m1b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
D_b1_id[1,1] NaN NaN
$m1c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
sigma_L1 NaN NaN
D_L1_id[1,1] NaN NaN
$m1d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
D_p1_id[1,1] NaN NaN
$m1e
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
sigma_L1 NaN NaN
D_L1_id[1,1] NaN NaN
$m1f
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
tau_Be1 NaN NaN
D_Be1_id[1,1] NaN NaN
$m2a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m2b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
D_b2_id[1,1] NaN NaN
$m2c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
sigma_L1mis NaN NaN
D_L1mis_id[1,1] NaN NaN
$m2d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
D_p2_id[1,1] NaN NaN
$m2e
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
sigma_L1mis NaN NaN
D_L1mis_id[1,1] NaN NaN
$m2f
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
tau_Be2 NaN NaN
D_Be2_id[1,1] NaN NaN
$m3a
Potential scale reduction factors:
Point est. Upper C.I.
C2 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m3b
Potential scale reduction factors:
Point est. Upper C.I.
C2 NaN NaN
D_b2_id[1,1] NaN NaN
$m3c
Potential scale reduction factors:
Point est. Upper C.I.
C2 NaN NaN
sigma_L1mis NaN NaN
D_L1mis_id[1,1] NaN NaN
$m3d
Potential scale reduction factors:
Point est. Upper C.I.
C2 NaN NaN
D_p2_id[1,1] NaN NaN
$m3e
Potential scale reduction factors:
Point est. Upper C.I.
C2 NaN NaN
sigma_L1mis NaN NaN
D_L1mis_id[1,1] NaN NaN
$m3f
Potential scale reduction factors:
Point est. Upper C.I.
C2 NaN NaN
tau_Be2 NaN NaN
D_Be2_id[1,1] NaN NaN
$m4a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
B21 NaN NaN
c2 NaN NaN
p2 NaN NaN
L1mis NaN NaN
Be2 NaN NaN
sigma_c1 NaN NaN
D_c1_id[1,1] NaN NaN
$m4b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
b21 NaN NaN
p2 NaN NaN
L1mis NaN NaN
sigma_c1 NaN NaN
D_c1_id[1,1] NaN NaN
$m4c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
b21 NaN NaN
p2 NaN NaN
L1mis NaN NaN
sigma_c1 NaN NaN
D_c1_id[1,1] NaN NaN
$m4d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
b21 NaN NaN
p2 NaN NaN
L1mis NaN NaN
Be2 NaN NaN
sigma_c1 NaN NaN
D_c1_id[1,1] NaN NaN
$m5a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
log(C1) NaN NaN
o22 NaN NaN
o23 NaN NaN
o24 NaN NaN
abs(C1 - c2) NaN NaN
time NaN NaN
I(time^2) NaN NaN
o22:abs(C1 - c2) NaN NaN
o23:abs(C1 - c2) NaN NaN
o24:abs(C1 - c2) NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
$m5b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
L1mis NaN NaN
abs(c1 - C2) NaN NaN
log(Be2) NaN NaN
time NaN NaN
D_b1_id[1,1] NaN NaN
D_b1_id[1,2] NaN NaN
D_b1_id[2,2] NaN NaN
D_b1_id[1,3] NaN NaN
D_b1_id[2,3] NaN NaN
D_b1_id[3,3] NaN NaN
$m6a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
C2 NaN NaN
b21 NaN NaN
time NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m6b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B11 NaN NaN
c1 NaN NaN
time NaN NaN
D_b1_id[1,1] NaN NaN
D_b1_id[1,2] NaN NaN
D_b1_id[2,2] NaN NaN
$m7a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
ns(time, df = 2)1 NaN NaN
ns(time, df = 2)2 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m7b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
bs(time, df = 3)1 NaN NaN
bs(time, df = 3)2 NaN NaN
bs(time, df = 3)3 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
D_y_id[1,4] NaN NaN
D_y_id[2,4] NaN NaN
D_y_id[3,4] NaN NaN
D_y_id[4,4] NaN NaN
$m7c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
c1 NaN NaN
ns(time, df = 3)1 NaN NaN
ns(time, df = 3)2 NaN NaN
ns(time, df = 3)3 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
D_y_id[1,4] NaN NaN
D_y_id[2,4] NaN NaN
D_y_id[3,4] NaN NaN
D_y_id[4,4] NaN NaN
$m7d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
C2 NaN NaN
c1 NaN NaN
ns(time, df = 3)1 NaN NaN
ns(time, df = 3)2 NaN NaN
ns(time, df = 3)3 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
$m7e
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
C2 NaN NaN
c1 NaN NaN
ns(time, df = 3)1 NaN NaN
ns(time, df = 3)2 NaN NaN
ns(time, df = 3)3 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
D_y_id[1,4] NaN NaN
D_y_id[2,4] NaN NaN
D_y_id[3,4] NaN NaN
D_y_id[4,4] NaN NaN
$m7f
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
C2 NaN NaN
c1 NaN NaN
ns(time, df = 3)1 NaN NaN
ns(time, df = 3)2 NaN NaN
ns(time, df = 3)3 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
$m8a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c1 NaN NaN
c2 NaN NaN
time NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c1 NaN NaN
c2 NaN NaN
time NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
B21 NaN NaN
c1 NaN NaN
c2 NaN NaN
time NaN NaN
B21:c1 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
B21 NaN NaN
c1 NaN NaN
c2 NaN NaN
time NaN NaN
B21:c1 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8e
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c1 NaN NaN
c2 NaN NaN
time NaN NaN
B21:c1 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8f
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c1 NaN NaN
c2 NaN NaN
time NaN NaN
B21:c1 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8g
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c1 NaN NaN
c2 NaN NaN
time NaN NaN
B21:c1 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8h
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c2 NaN NaN
c1 NaN NaN
time NaN NaN
B21:c2 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8i
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c2 NaN NaN
c1 NaN NaN
time NaN NaN
B21:c2 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8j
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c2 NaN NaN
c1 NaN NaN
time NaN NaN
B21:c2 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8k
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c2 NaN NaN
c1 NaN NaN
time NaN NaN
B21:c2 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8l
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c1 NaN NaN
time NaN NaN
B21:c1 NaN NaN
B21:time NaN NaN
c1:time NaN NaN
B21:c1:time NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8m
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c1 NaN NaN
b11 NaN NaN
o1.L NaN NaN
o1.Q NaN NaN
c1:b11 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
$m8n
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c1 NaN NaN
time NaN NaN
b11 NaN NaN
C1:time NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
D_y_id[1,4] NaN NaN
D_y_id[2,4] NaN NaN
D_y_id[3,4] NaN NaN
D_y_id[4,4] NaN NaN
$m9a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c1 NaN NaN
b11 NaN NaN
time NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_o1[1,1] NaN NaN
$m9b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
C2 NaN NaN
B11 NaN NaN
time NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
$m9c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
C2 NaN NaN
B11 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
Code
lapply(models0, MC_error)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m0a2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m0a3
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m0a4
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m0b1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
D_b1_id[1,1] 0 0 0 NaN
$m0b2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
D_b1_id[1,1] 0 0 0 NaN
$m0b3
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
D_b1_id[1,1] 0 0 0 NaN
$m0b4
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
D_b1_id[1,1] 0 0 0 NaN
$m0c1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_L1 0 0 0 NaN
D_L1_id[1,1] 0 0 0 NaN
$m0c2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_L1 0 0 0 NaN
D_L1_id[1,1] 0 0 0 NaN
$m0d1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
D_p1_id[1,1] 0 0 0 NaN
$m0d2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
D_p1_id[1,1] 0 0 0 NaN
$m0e1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_L1 0 0 0 NaN
D_L1_id[1,1] 0 0 0 NaN
$m0f1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
tau_Be1 0 0 0 NaN
D_Be1_id[1,1] 0 0 0 NaN
$m1a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m1b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
D_b1_id[1,1] 0 0 0 NaN
$m1c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
sigma_L1 0 0 0 NaN
D_L1_id[1,1] 0 0 0 NaN
$m1d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
D_p1_id[1,1] 0 0 0 NaN
$m1e
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
sigma_L1 0 0 0 NaN
D_L1_id[1,1] 0 0 0 NaN
$m1f
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
tau_Be1 0 0 0 NaN
D_Be1_id[1,1] 0 0 0 NaN
$m2a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m2b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
D_b2_id[1,1] 0 0 0 NaN
$m2c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
sigma_L1mis 0 0 0 NaN
D_L1mis_id[1,1] 0 0 0 NaN
$m2d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
D_p2_id[1,1] 0 0 0 NaN
$m2e
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
sigma_L1mis 0 0 0 NaN
D_L1mis_id[1,1] 0 0 0 NaN
$m2f
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
tau_Be2 0 0 0 NaN
D_Be2_id[1,1] 0 0 0 NaN
$m3a
est MCSE SD MCSE/SD
C2 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m3b
est MCSE SD MCSE/SD
C2 0 0 0 NaN
D_b2_id[1,1] 0 0 0 NaN
$m3c
est MCSE SD MCSE/SD
C2 0 0 0 NaN
sigma_L1mis 0 0 0 NaN
D_L1mis_id[1,1] 0 0 0 NaN
$m3d
est MCSE SD MCSE/SD
C2 0 0 0 NaN
D_p2_id[1,1] 0 0 0 NaN
$m3e
est MCSE SD MCSE/SD
C2 0 0 0 NaN
sigma_L1mis 0 0 0 NaN
D_L1mis_id[1,1] 0 0 0 NaN
$m3f
est MCSE SD MCSE/SD
C2 0 0 0 NaN
tau_Be2 0 0 0 NaN
D_Be2_id[1,1] 0 0 0 NaN
$m4a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
B21 0 0 0 NaN
c2 0 0 0 NaN
p2 0 0 0 NaN
L1mis 0 0 0 NaN
Be2 0 0 0 NaN
sigma_c1 0 0 0 NaN
D_c1_id[1,1] 0 0 0 NaN
$m4b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
b21 0 0 0 NaN
p2 0 0 0 NaN
L1mis 0 0 0 NaN
sigma_c1 0 0 0 NaN
D_c1_id[1,1] 0 0 0 NaN
$m4c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
b21 0 0 0 NaN
p2 0 0 0 NaN
L1mis 0 0 0 NaN
sigma_c1 0 0 0 NaN
D_c1_id[1,1] 0 0 0 NaN
$m4d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
b21 0 0 0 NaN
p2 0 0 0 NaN
L1mis 0 0 0 NaN
Be2 0 0 0 NaN
sigma_c1 0 0 0 NaN
D_c1_id[1,1] 0 0 0 NaN
$m5a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
log(C1) 0 0 0 NaN
o22 0 0 0 NaN
o23 0 0 0 NaN
o24 0 0 0 NaN
abs(C1 - c2) 0 0 0 NaN
time 0 0 0 NaN
I(time^2) 0 0 0 NaN
o22:abs(C1 - c2) 0 0 0 NaN
o23:abs(C1 - c2) 0 0 0 NaN
o24:abs(C1 - c2) 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
$m5b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
L1mis 0 0 0 NaN
abs(c1 - C2) 0 0 0 NaN
log(Be2) 0 0 0 NaN
time 0 0 0 NaN
D_b1_id[1,1] 0 0 0 NaN
D_b1_id[1,2] 0 0 0 NaN
D_b1_id[2,2] 0 0 0 NaN
D_b1_id[1,3] 0 0 0 NaN
D_b1_id[2,3] 0 0 0 NaN
D_b1_id[3,3] 0 0 0 NaN
$m6a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
C2 0 0 0 NaN
b21 0 0 0 NaN
time 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m6b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B11 0 0 0 NaN
c1 0 0 0 NaN
time 0 0 0 NaN
D_b1_id[1,1] 0 0 0 NaN
D_b1_id[1,2] 0 0 0 NaN
D_b1_id[2,2] 0 0 0 NaN
$m7a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
ns(time, df = 2)1 0 0 0 NaN
ns(time, df = 2)2 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m7b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
bs(time, df = 3)1 0 0 0 NaN
bs(time, df = 3)2 0 0 0 NaN
bs(time, df = 3)3 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
D_y_id[1,4] 0 0 0 NaN
D_y_id[2,4] 0 0 0 NaN
D_y_id[3,4] 0 0 0 NaN
D_y_id[4,4] 0 0 0 NaN
$m7c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
c1 0 0 0 NaN
ns(time, df = 3)1 0 0 0 NaN
ns(time, df = 3)2 0 0 0 NaN
ns(time, df = 3)3 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
D_y_id[1,4] 0 0 0 NaN
D_y_id[2,4] 0 0 0 NaN
D_y_id[3,4] 0 0 0 NaN
D_y_id[4,4] 0 0 0 NaN
$m7d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
C2 0 0 0 NaN
c1 0 0 0 NaN
ns(time, df = 3)1 0 0 0 NaN
ns(time, df = 3)2 0 0 0 NaN
ns(time, df = 3)3 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
$m7e
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
C2 0 0 0 NaN
c1 0 0 0 NaN
ns(time, df = 3)1 0 0 0 NaN
ns(time, df = 3)2 0 0 0 NaN
ns(time, df = 3)3 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
D_y_id[1,4] 0 0 0 NaN
D_y_id[2,4] 0 0 0 NaN
D_y_id[3,4] 0 0 0 NaN
D_y_id[4,4] 0 0 0 NaN
$m7f
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
C2 0 0 0 NaN
c1 0 0 0 NaN
ns(time, df = 3)1 0 0 0 NaN
ns(time, df = 3)2 0 0 0 NaN
ns(time, df = 3)3 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
$m8a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c1 0 0 0 NaN
c2 0 0 0 NaN
time 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c1 0 0 0 NaN
c2 0 0 0 NaN
time 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
B21 0 0 0 NaN
c1 0 0 0 NaN
c2 0 0 0 NaN
time 0 0 0 NaN
B21:c1 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
B21 0 0 0 NaN
c1 0 0 0 NaN
c2 0 0 0 NaN
time 0 0 0 NaN
B21:c1 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8e
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c1 0 0 0 NaN
c2 0 0 0 NaN
time 0 0 0 NaN
B21:c1 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8f
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c1 0 0 0 NaN
c2 0 0 0 NaN
time 0 0 0 NaN
B21:c1 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8g
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c1 0 0 0 NaN
c2 0 0 0 NaN
time 0 0 0 NaN
B21:c1 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8h
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c2 0 0 0 NaN
c1 0 0 0 NaN
time 0 0 0 NaN
B21:c2 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8i
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c2 0 0 0 NaN
c1 0 0 0 NaN
time 0 0 0 NaN
B21:c2 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8j
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c2 0 0 0 NaN
c1 0 0 0 NaN
time 0 0 0 NaN
B21:c2 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8k
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c2 0 0 0 NaN
c1 0 0 0 NaN
time 0 0 0 NaN
B21:c2 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8l
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c1 0 0 0 NaN
time 0 0 0 NaN
B21:c1 0 0 0 NaN
B21:time 0 0 0 NaN
c1:time 0 0 0 NaN
B21:c1:time 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8m
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c1 0 0 0 NaN
b11 0 0 0 NaN
o1.L 0 0 0 NaN
o1.Q 0 0 0 NaN
c1:b11 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
$m8n
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c1 0 0 0 NaN
time 0 0 0 NaN
b11 0 0 0 NaN
C1:time 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
D_y_id[1,4] 0 0 0 NaN
D_y_id[2,4] 0 0 0 NaN
D_y_id[3,4] 0 0 0 NaN
D_y_id[4,4] 0 0 0 NaN
$m9a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c1 0 0 0 NaN
b11 0 0 0 NaN
time 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_o1[1,1] 0 0 0 NaN
$m9b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
C2 0 0 0 NaN
B11 0 0 0 NaN
time 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
$m9c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
C2 0 0 0 NaN
B11 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
summary output remained the same
Code
lapply(models0, print)
Output
Call:
lme_imp(fixed = y ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "identity"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "inverse"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "logit"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "probit"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "log"),
n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "cloglog"),
n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "inverse"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
p1
(Intercept)
p1 (Intercept) 0
Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "identity"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
p1
(Intercept)
p1 (Intercept) 0
Call:
lognormmm_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal mixed model for "L1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
Call:
betamm_imp(fixed = Be1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta mixed model for "Be1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
Be1
(Intercept)
Be1 (Intercept) 0
Call:
lme_imp(fixed = y ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = b1 ~ C1 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
Call:
glme_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
Call:
glme_imp(fixed = p1 ~ C1 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
p1
(Intercept)
p1 (Intercept) 0
Call:
lognormmm_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal mixed model for "L1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
Call:
betamm_imp(fixed = Be1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta mixed model for "Be1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
Be1
(Intercept)
Be1 (Intercept) 0
Call:
lme_imp(fixed = y ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = b2 ~ c2 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b2"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
b2
(Intercept)
b2 (Intercept) 0
Call:
glme_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1mis"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
L1mis
(Intercept)
L1mis (Intercept) 0
Residual standard deviation:
sigma_L1mis
0
Call:
glme_imp(fixed = p2 ~ c2 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p2"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
p2
(Intercept)
p2 (Intercept) 0
Call:
lognormmm_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal mixed model for "L1mis"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
L1mis
(Intercept)
L1mis (Intercept) 0
Residual standard deviation:
sigma_L1mis
0
Call:
betamm_imp(fixed = Be2 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta mixed model for "Be2"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
Be2
(Intercept)
Be2 (Intercept) 0
Call:
lme_imp(fixed = y ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = b2 ~ 0 + C2 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b2"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
b2
(Intercept)
b2 (Intercept) 0
Call:
glme_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1mis"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
L1mis
(Intercept)
L1mis (Intercept) 0
Residual standard deviation:
sigma_L1mis
0
Call:
glme_imp(fixed = p2 ~ 0 + C2 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p2"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
p2
(Intercept)
p2 (Intercept) 0
Call:
lognormmm_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal mixed model for "L1mis"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
L1mis
(Intercept)
L1mis (Intercept) 0
Residual standard deviation:
sigma_L1mis
0
Call:
betamm_imp(fixed = Be2 ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta mixed model for "Be2"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
Be2
(Intercept)
Be2 (Intercept) 0
Call:
lme_imp(fixed = c1 ~ c2 + B2 + p2 + L1mis + Be2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(p2 = "glmm_poisson_log",
L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) B21 c2 p2 L1mis Be2
0 0 0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_inverse",
p2 = "glmm_poisson_identity", b2 = "glmm_binomial_probit",
L1mis = "glmm_lognorm"), seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) c2 b21 p2 L1mis
0 0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log",
p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log",
b2 = "glmm_binomial_log"), no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) c2 b21 p2 L1mis
0 0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + Be2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log",
p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log",
b2 = "glmm_binomial_log"), shrinkage = "ridge", seed = 2020,
warn = FALSE, mess = FALSE, trunc = list(Be2 = c(0, 1)))
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) c2 b21 p2 L1mis Be2
0 0 0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
Call:
lme_imp(fixed = y ~ M2 + o2 * abs(C1 - c2) + log(C1) + time +
I(time^2) + (time | id), data = longDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) M22 M23 M24
0 0 0 0
log(C1) o22 o23 o24
0 0 0 0
abs(C1 - c2) time I(time^2) o22:abs(C1 - c2)
0 0 0 0
o23:abs(C1 - c2) o24:abs(C1 - c2)
0 0
Random effects covariance matrix:
$id
y y
(Intercept) time
y (Intercept) 0 0
y time 0 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = b1 ~ L1mis + abs(c1 - C2) + log(Be2) + time +
(time + I(time^2) | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), shrinkage = "ridge",
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept) L1mis abs(c1 - C2) log(Be2) time
0 0 0 0 0
Random effects covariance matrix:
$id
b1 b1 b1
(Intercept) time I(time^2)
b1 (Intercept) 0 0 0
b1 time 0 0 0
b1 I(time^2) 0 0 0
Call:
lme_imp(fixed = y ~ b2 + C1 + C2 + time + (0 + time | id), data = longDF,
n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 b21 time
0 0 0 0 0
Random effects covariance matrix:
$id
y
time
y time 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = b1 ~ c1 + C2 + B1 + time + (0 + time + I(time^2) |
id), data = longDF, family = binomial(), n.adapt = 5, n.iter = 10,
shrinkage = "ridge", seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept) C2 B11 c1 time
0 0 0 0 0
Random effects covariance matrix:
$id
b1 b1
time I(time^2)
b1 time 0 0
b1 I(time^2) 0 0
Call:
lme_imp(fixed = y ~ ns(time, df = 2), data = longDF, random = ~ns(time,
df = 2) | id, n.iter = 10, seed = 2020, adapt = 5)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) ns(time, df = 2)1 ns(time, df = 2)2
0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) ns(time, df = 2)1 ns(time, df = 2)2
y (Intercept) 0 0 0
y ns(time, df = 2)1 0 0 0
y ns(time, df = 2)2 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ bs(time, df = 3), data = longDF, random = ~bs(time,
df = 3) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3
0 0 0 0
Random effects covariance matrix:
$id
y y y y
(Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3
y (Intercept) 0 0 0 0
y bs(time, df = 3)1 0 0 0 0
y bs(time, df = 3)2 0 0 0 0
y bs(time, df = 3)3 0 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + c1 + ns(time, df = 3), data = longDF,
random = ~ns(time, df = 3) | id, n.iter = 10, seed = 2020,
nadapt = 5)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 c1 ns(time, df = 3)1
0 0 0 0
ns(time, df = 3)2 ns(time, df = 3)3
0 0
Random effects covariance matrix:
$id
y y y y
(Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
y (Intercept) 0 0 0 0
y ns(time, df = 3)1 0 0 0 0
y ns(time, df = 3)2 0 0 0 0
y ns(time, df = 3)3 0 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
0 0 0
Random effects covariance matrix:
$id
y y
(Intercept) time
y (Intercept) 0 0
y time 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~ns(time, df = 3) | id, n.adapt = 5, n.iter = 10,
no_model = "time", seed = 2020)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
0 0 0
Random effects covariance matrix:
$id
y y y y
(Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
y (Intercept) 0 0 0 0
y ns(time, df = 3)1 0 0 0 0
y ns(time, df = 3)2 0 0 0 0
y ns(time, df = 3)3 0 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
0 0 0
Random effects covariance matrix:
$id
y y
(Intercept) time
y (Intercept) 0 0
y time 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time +
c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c1 c2 time
0 0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time +
c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c1 c2 time
0 0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time +
c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) B21 c1 c2 time B21:c1
0 0 0 0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time c1
y (Intercept) 0 0 0
y time 0 0 0
y c1 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time +
c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) B21 c1 c2 time B21:c1
0 0 0 0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time c1
y (Intercept) 0 0 0
y time 0 0 0
y c1 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time",
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = c("time",
"c1"), seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c1
y (Intercept) 0 0 0
y time 0 0 0
y c1 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time",
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c1
y (Intercept) 0 0 0
y time 0 0 0
y c1 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 * time, data = longDF, random = ~time +
I(time^2) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 time B21:c1
0 0 0 0 0 0
B21:time c1:time B21:c1:time
0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time I(time^2)
y (Intercept) 0 0 0
y time 0 0 0
y I(time^2) 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ c1 * b1 + o1, data = longDF, random = ~b1 |
id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c1 b11 o1.L o1.Q c1:b11
0 0 0 0 0 0
Random effects covariance matrix:
$id
y y
(Intercept) b11
y (Intercept) 0 0
y b11 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ c1 + C1 * time + b1 + B2, data = longDF,
random = ~C1 * time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 time b11
0 0 0 0 0 0
C1:time
0
Random effects covariance matrix:
$id
y y y y
(Intercept) C1 time C1:time
y (Intercept) 0 0 0 0
y C1 0 0 0 0
y time 0 0 0 0
y C1:time 0 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ c1 + b1 + time + (1 | id) + (1 | o1), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c1 b11 time
0 0 0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
$o1
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + time + (time | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = c(analysis_random = TRUE),
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 B11 time
0 0 0 0 0
Random effects covariance matrix:
$id
y y
(Intercept) time
y (Intercept) 0 0
y time 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, monitor_params = c(analysis_random = TRUE),
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 B11
0 0 0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m0a1
Call:
lme_imp(fixed = y ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m0a2
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "identity"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m0a3
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m0a4
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "inverse"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m0b1
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "logit"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
$m0b2
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "probit"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
$m0b3
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "log"),
n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
$m0b4
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "cloglog"),
n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
$m0c1
Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "inverse"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
$m0c2
Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
$m0d1
Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
p1
(Intercept)
p1 (Intercept) 0
$m0d2
Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "identity"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
p1
(Intercept)
p1 (Intercept) 0
$m0e1
Call:
lognormmm_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal mixed model for "L1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
$m0f1
Call:
betamm_imp(fixed = Be1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta mixed model for "Be1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
Be1
(Intercept)
Be1 (Intercept) 0
$m1a
Call:
lme_imp(fixed = y ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m1b
Call:
glme_imp(fixed = b1 ~ C1 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
$m1c
Call:
glme_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
$m1d
Call:
glme_imp(fixed = p1 ~ C1 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
p1
(Intercept)
p1 (Intercept) 0
$m1e
Call:
lognormmm_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal mixed model for "L1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
$m1f
Call:
betamm_imp(fixed = Be1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta mixed model for "Be1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
Be1
(Intercept)
Be1 (Intercept) 0
$m2a
Call:
lme_imp(fixed = y ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m2b
Call:
glme_imp(fixed = b2 ~ c2 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b2"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
b2
(Intercept)
b2 (Intercept) 0
$m2c
Call:
glme_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1mis"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
L1mis
(Intercept)
L1mis (Intercept) 0
Residual standard deviation:
sigma_L1mis
0
$m2d
Call:
glme_imp(fixed = p2 ~ c2 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p2"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
p2
(Intercept)
p2 (Intercept) 0
$m2e
Call:
lognormmm_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal mixed model for "L1mis"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
L1mis
(Intercept)
L1mis (Intercept) 0
Residual standard deviation:
sigma_L1mis
0
$m2f
Call:
betamm_imp(fixed = Be2 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta mixed model for "Be2"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
Be2
(Intercept)
Be2 (Intercept) 0
$m3a
Call:
lme_imp(fixed = y ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m3b
Call:
glme_imp(fixed = b2 ~ 0 + C2 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b2"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
b2
(Intercept)
b2 (Intercept) 0
$m3c
Call:
glme_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1mis"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
L1mis
(Intercept)
L1mis (Intercept) 0
Residual standard deviation:
sigma_L1mis
0
$m3d
Call:
glme_imp(fixed = p2 ~ 0 + C2 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p2"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
p2
(Intercept)
p2 (Intercept) 0
$m3e
Call:
lognormmm_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal mixed model for "L1mis"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
L1mis
(Intercept)
L1mis (Intercept) 0
Residual standard deviation:
sigma_L1mis
0
$m3f
Call:
betamm_imp(fixed = Be2 ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta mixed model for "Be2"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
Be2
(Intercept)
Be2 (Intercept) 0
$m4a
Call:
lme_imp(fixed = c1 ~ c2 + B2 + p2 + L1mis + Be2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(p2 = "glmm_poisson_log",
L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) B21 c2 p2 L1mis Be2
0 0 0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
$m4b
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_inverse",
p2 = "glmm_poisson_identity", b2 = "glmm_binomial_probit",
L1mis = "glmm_lognorm"), seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) c2 b21 p2 L1mis
0 0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
$m4c
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log",
p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log",
b2 = "glmm_binomial_log"), no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) c2 b21 p2 L1mis
0 0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
$m4d
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + Be2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log",
p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log",
b2 = "glmm_binomial_log"), shrinkage = "ridge", seed = 2020,
warn = FALSE, mess = FALSE, trunc = list(Be2 = c(0, 1)))
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) c2 b21 p2 L1mis Be2
0 0 0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
$m5a
Call:
lme_imp(fixed = y ~ M2 + o2 * abs(C1 - c2) + log(C1) + time +
I(time^2) + (time | id), data = longDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) M22 M23 M24
0 0 0 0
log(C1) o22 o23 o24
0 0 0 0
abs(C1 - c2) time I(time^2) o22:abs(C1 - c2)
0 0 0 0
o23:abs(C1 - c2) o24:abs(C1 - c2)
0 0
Random effects covariance matrix:
$id
y y
(Intercept) time
y (Intercept) 0 0
y time 0 0
Residual standard deviation:
sigma_y
0
$m5b
Call:
glme_imp(fixed = b1 ~ L1mis + abs(c1 - C2) + log(Be2) + time +
(time + I(time^2) | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), shrinkage = "ridge",
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept) L1mis abs(c1 - C2) log(Be2) time
0 0 0 0 0
Random effects covariance matrix:
$id
b1 b1 b1
(Intercept) time I(time^2)
b1 (Intercept) 0 0 0
b1 time 0 0 0
b1 I(time^2) 0 0 0
$m6a
Call:
lme_imp(fixed = y ~ b2 + C1 + C2 + time + (0 + time | id), data = longDF,
n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 b21 time
0 0 0 0 0
Random effects covariance matrix:
$id
y
time
y time 0
Residual standard deviation:
sigma_y
0
$m6b
Call:
glme_imp(fixed = b1 ~ c1 + C2 + B1 + time + (0 + time + I(time^2) |
id), data = longDF, family = binomial(), n.adapt = 5, n.iter = 10,
shrinkage = "ridge", seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept) C2 B11 c1 time
0 0 0 0 0
Random effects covariance matrix:
$id
b1 b1
time I(time^2)
b1 time 0 0
b1 I(time^2) 0 0
$m7a
Call:
lme_imp(fixed = y ~ ns(time, df = 2), data = longDF, random = ~ns(time,
df = 2) | id, n.iter = 10, seed = 2020, adapt = 5)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) ns(time, df = 2)1 ns(time, df = 2)2
0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) ns(time, df = 2)1 ns(time, df = 2)2
y (Intercept) 0 0 0
y ns(time, df = 2)1 0 0 0
y ns(time, df = 2)2 0 0 0
Residual standard deviation:
sigma_y
0
$m7b
Call:
lme_imp(fixed = y ~ bs(time, df = 3), data = longDF, random = ~bs(time,
df = 3) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3
0 0 0 0
Random effects covariance matrix:
$id
y y y y
(Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3
y (Intercept) 0 0 0 0
y bs(time, df = 3)1 0 0 0 0
y bs(time, df = 3)2 0 0 0 0
y bs(time, df = 3)3 0 0 0 0
Residual standard deviation:
sigma_y
0
$m7c
Call:
lme_imp(fixed = y ~ C1 + c1 + ns(time, df = 3), data = longDF,
random = ~ns(time, df = 3) | id, n.iter = 10, seed = 2020,
nadapt = 5)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 c1 ns(time, df = 3)1
0 0 0 0
ns(time, df = 3)2 ns(time, df = 3)3
0 0
Random effects covariance matrix:
$id
y y y y
(Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
y (Intercept) 0 0 0 0
y ns(time, df = 3)1 0 0 0 0
y ns(time, df = 3)2 0 0 0 0
y ns(time, df = 3)3 0 0 0 0
Residual standard deviation:
sigma_y
0
$m7d
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
0 0 0
Random effects covariance matrix:
$id
y y
(Intercept) time
y (Intercept) 0 0
y time 0 0
Residual standard deviation:
sigma_y
0
$m7e
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~ns(time, df = 3) | id, n.adapt = 5, n.iter = 10,
no_model = "time", seed = 2020)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
0 0 0
Random effects covariance matrix:
$id
y y y y
(Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
y (Intercept) 0 0 0 0
y ns(time, df = 3)1 0 0 0 0
y ns(time, df = 3)2 0 0 0 0
y ns(time, df = 3)3 0 0 0 0
Residual standard deviation:
sigma_y
0
$m7f
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
0 0 0
Random effects covariance matrix:
$id
y y
(Intercept) time
y (Intercept) 0 0
y time 0 0
Residual standard deviation:
sigma_y
0
$m8a
Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time +
c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c1 c2 time
0 0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
$m8b
Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time +
c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c1 c2 time
0 0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
$m8c
Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time +
c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) B21 c1 c2 time B21:c1
0 0 0 0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time c1
y (Intercept) 0 0 0
y time 0 0 0
y c1 0 0 0
Residual standard deviation:
sigma_y
0
$m8d
Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time +
c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) B21 c1 c2 time B21:c1
0 0 0 0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time c1
y (Intercept) 0 0 0
y time 0 0 0
y c1 0 0 0
Residual standard deviation:
sigma_y
0
$m8e
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
$m8f
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time",
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
$m8g
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = c("time",
"c1"), seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
$m8h
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c1
y (Intercept) 0 0 0
y time 0 0 0
y c1 0 0 0
Residual standard deviation:
sigma_y
0
$m8i
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time",
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c1
y (Intercept) 0 0 0
y time 0 0 0
y c1 0 0 0
Residual standard deviation:
sigma_y
0
$m8j
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
$m8k
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
$m8l
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 * time, data = longDF, random = ~time +
I(time^2) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 time B21:c1
0 0 0 0 0 0
B21:time c1:time B21:c1:time
0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time I(time^2)
y (Intercept) 0 0 0
y time 0 0 0
y I(time^2) 0 0 0
Residual standard deviation:
sigma_y
0
$m8m
Call:
lme_imp(fixed = y ~ c1 * b1 + o1, data = longDF, random = ~b1 |
id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c1 b11 o1.L o1.Q c1:b11
0 0 0 0 0 0
Random effects covariance matrix:
$id
y y
(Intercept) b11
y (Intercept) 0 0
y b11 0 0
Residual standard deviation:
sigma_y
0
$m8n
Call:
lme_imp(fixed = y ~ c1 + C1 * time + b1 + B2, data = longDF,
random = ~C1 * time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 time b11
0 0 0 0 0 0
C1:time
0
Random effects covariance matrix:
$id
y y y y
(Intercept) C1 time C1:time
y (Intercept) 0 0 0 0
y C1 0 0 0 0
y time 0 0 0 0
y C1:time 0 0 0 0
Residual standard deviation:
sigma_y
0
$m9a
Call:
lme_imp(fixed = y ~ c1 + b1 + time + (1 | id) + (1 | o1), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c1 b11 time
0 0 0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
$o1
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m9b
Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + time + (time | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = c(analysis_random = TRUE),
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 B11 time
0 0 0 0 0
Random effects covariance matrix:
$id
y y
(Intercept) time
y (Intercept) 0 0
y time 0 0
Residual standard deviation:
sigma_y
0
$m9c
Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, monitor_params = c(analysis_random = TRUE),
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 B11
0 0 0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Code
lapply(models0, coef)
Output
$m0a1
$m0a1$y
(Intercept) sigma_y D_y_id[1,1]
0 0 0
$m0a2
$m0a2$y
(Intercept) sigma_y D_y_id[1,1]
0 0 0
$m0a3
$m0a3$y
(Intercept) sigma_y D_y_id[1,1]
0 0 0
$m0a4
$m0a4$y
(Intercept) sigma_y D_y_id[1,1]
0 0 0
$m0b1
$m0b1$b1
(Intercept) D_b1_id[1,1]
0 0
$m0b2
$m0b2$b1
(Intercept) D_b1_id[1,1]
0 0
$m0b3
$m0b3$b1
(Intercept) D_b1_id[1,1]
0 0
$m0b4
$m0b4$b1
(Intercept) D_b1_id[1,1]
0 0
$m0c1
$m0c1$L1
(Intercept) sigma_L1 D_L1_id[1,1]
0 0 0
$m0c2
$m0c2$L1
(Intercept) sigma_L1 D_L1_id[1,1]
0 0 0
$m0d1
$m0d1$p1
(Intercept) D_p1_id[1,1]
0 0
$m0d2
$m0d2$p1
(Intercept) D_p1_id[1,1]
0 0
$m0e1
$m0e1$L1
(Intercept) sigma_L1 D_L1_id[1,1]
0 0 0
$m0f1
$m0f1$Be1
(Intercept) tau_Be1 D_Be1_id[1,1]
0 0 0
$m1a
$m1a$y
(Intercept) C1 sigma_y D_y_id[1,1]
0 0 0 0
$m1b
$m1b$b1
(Intercept) C1 D_b1_id[1,1]
0 0 0
$m1c
$m1c$L1
(Intercept) C1 sigma_L1 D_L1_id[1,1]
0 0 0 0
$m1d
$m1d$p1
(Intercept) C1 D_p1_id[1,1]
0 0 0
$m1e
$m1e$L1
(Intercept) C1 sigma_L1 D_L1_id[1,1]
0 0 0 0
$m1f
$m1f$Be1
(Intercept) C1 tau_Be1 D_Be1_id[1,1]
0 0 0 0
$m2a
$m2a$y
(Intercept) c2 sigma_y D_y_id[1,1]
0 0 0 0
$m2b
$m2b$b2
(Intercept) c2 D_b2_id[1,1]
0 0 0
$m2c
$m2c$L1mis
(Intercept) c2 sigma_L1mis D_L1mis_id[1,1]
0 0 0 0
$m2d
$m2d$p2
(Intercept) c2 D_p2_id[1,1]
0 0 0
$m2e
$m2e$L1mis
(Intercept) c2 sigma_L1mis D_L1mis_id[1,1]
0 0 0 0
$m2f
$m2f$Be2
(Intercept) c2 tau_Be2 D_Be2_id[1,1]
0 0 0 0
$m3a
$m3a$y
C2 sigma_y D_y_id[1,1]
0 0 0
$m3b
$m3b$b2
C2 D_b2_id[1,1]
0 0
$m3c
$m3c$L1mis
C2 sigma_L1mis D_L1mis_id[1,1]
0 0 0
$m3d
$m3d$p2
C2 D_p2_id[1,1]
0 0
$m3e
$m3e$L1mis
C2 sigma_L1mis D_L1mis_id[1,1]
0 0 0
$m3f
$m3f$Be2
C2 tau_Be2 D_Be2_id[1,1]
0 0 0
$m4a
$m4a$c1
(Intercept) B21 c2 p2 L1mis Be2
0 0 0 0 0 0
sigma_c1 D_c1_id[1,1]
0 0
$m4b
$m4b$c1
(Intercept) c2 b21 p2 L1mis sigma_c1
0 0 0 0 0 0
D_c1_id[1,1]
0
$m4c
$m4c$c1
(Intercept) c2 b21 p2 L1mis sigma_c1
0 0 0 0 0 0
D_c1_id[1,1]
0
$m4d
$m4d$c1
(Intercept) c2 b21 p2 L1mis Be2
0 0 0 0 0 0
sigma_c1 D_c1_id[1,1]
0 0
$m5a
$m5a$y
(Intercept) M22 M23 M24
0 0 0 0
log(C1) o22 o23 o24
0 0 0 0
abs(C1 - c2) time I(time^2) o22:abs(C1 - c2)
0 0 0 0
o23:abs(C1 - c2) o24:abs(C1 - c2) sigma_y D_y_id[1,1]
0 0 0 0
D_y_id[1,2] D_y_id[2,2]
0 0
$m5b
$m5b$b1
(Intercept) L1mis abs(c1 - C2) log(Be2) time D_b1_id[1,1]
0 0 0 0 0 0
D_b1_id[1,2] D_b1_id[2,2] D_b1_id[1,3] D_b1_id[2,3] D_b1_id[3,3]
0 0 0 0 0
$m6a
$m6a$y
(Intercept) C1 C2 b21 time sigma_y
0 0 0 0 0 0
D_y_id[1,1]
0
$m6b
$m6b$b1
(Intercept) C2 B11 c1 time D_b1_id[1,1]
0 0 0 0 0 0
D_b1_id[1,2] D_b1_id[2,2]
0 0
$m7a
$m7a$y
(Intercept) ns(time, df = 2)1 ns(time, df = 2)2 sigma_y
0 0 0 0
D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0
D_y_id[2,3] D_y_id[3,3]
0 0
$m7b
$m7b$y
(Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3
0 0 0 0
sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2]
0 0 0 0
D_y_id[1,3] D_y_id[2,3] D_y_id[3,3] D_y_id[1,4]
0 0 0 0
D_y_id[2,4] D_y_id[3,4] D_y_id[4,4]
0 0 0
$m7c
$m7c$y
(Intercept) C1 c1 ns(time, df = 3)1
0 0 0 0
ns(time, df = 3)2 ns(time, df = 3)3 sigma_y D_y_id[1,1]
0 0 0 0
D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3]
0 0 0 0
D_y_id[3,3] D_y_id[1,4] D_y_id[2,4] D_y_id[3,4]
0 0 0 0
D_y_id[4,4]
0
$m7d
$m7d$y
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 sigma_y
0 0 0 0
D_y_id[1,1] D_y_id[1,2] D_y_id[2,2]
0 0 0
$m7e
$m7e$y
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 sigma_y
0 0 0 0
D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0
D_y_id[2,3] D_y_id[3,3] D_y_id[1,4] D_y_id[2,4]
0 0 0 0
D_y_id[3,4] D_y_id[4,4]
0 0
$m7f
$m7f$y
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 sigma_y
0 0 0 0
D_y_id[1,1] D_y_id[1,2] D_y_id[2,2]
0 0 0
$m8a
$m8a$y
(Intercept) c1 c2 time sigma_y D_y_id[1,1]
0 0 0 0 0 0
D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] D_y_id[3,3]
0 0 0 0 0
$m8b
$m8b$y
(Intercept) c1 c2 time sigma_y D_y_id[1,1]
0 0 0 0 0 0
D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] D_y_id[3,3]
0 0 0 0 0
$m8c
$m8c$y
(Intercept) B21 c1 c2 time B21:c1
0 0 0 0 0 0
sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3]
0 0 0 0 0 0
D_y_id[3,3]
0
$m8d
$m8d$y
(Intercept) B21 c1 c2 time B21:c1
0 0 0 0 0 0
sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3]
0 0 0 0 0 0
D_y_id[3,3]
0
$m8e
$m8e$y
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0 0 0
D_y_id[2,3] D_y_id[3,3]
0 0
$m8f
$m8f$y
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0 0 0
D_y_id[2,3] D_y_id[3,3]
0 0
$m8g
$m8g$y
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0 0 0
D_y_id[2,3] D_y_id[3,3]
0 0
$m8h
$m8h$y
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0 0 0
D_y_id[2,3] D_y_id[3,3]
0 0
$m8i
$m8i$y
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0 0 0
D_y_id[2,3] D_y_id[3,3]
0 0
$m8j
$m8j$y
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0 0 0
D_y_id[2,3] D_y_id[3,3]
0 0
$m8k
$m8k$y
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0 0 0
D_y_id[2,3] D_y_id[3,3]
0 0
$m8l
$m8l$y
(Intercept) C1 B21 c1 time B21:c1
0 0 0 0 0 0
B21:time c1:time B21:c1:time sigma_y D_y_id[1,1] D_y_id[1,2]
0 0 0 0 0 0
D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] D_y_id[3,3]
0 0 0 0
$m8m
$m8m$y
(Intercept) c1 b11 o1.L o1.Q c1:b11
0 0 0 0 0 0
sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2]
0 0 0 0
$m8n
$m8n$y
(Intercept) C1 B21 c1 time b11
0 0 0 0 0 0
C1:time sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0 0 0
D_y_id[2,3] D_y_id[3,3] D_y_id[1,4] D_y_id[2,4] D_y_id[3,4] D_y_id[4,4]
0 0 0 0 0 0
$m9a
$m9a$y
(Intercept) c1 b11 time sigma_y D_y_id[1,1]
0 0 0 0 0 0
D_y_o1[1,1]
0
$m9b
$m9b$y
(Intercept) C1 C2 B11 time sigma_y
0 0 0 0 0 0
D_y_id[1,1] D_y_id[1,2] D_y_id[2,2]
0 0 0
$m9c
$m9c$y
(Intercept) C1 C2 B11 sigma_y D_y_id[1,1]
0 0 0 0 0 0
Code
lapply(models0, confint)
Output
$m0a1
$m0a1$y
2.5% 97.5%
(Intercept) 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m0a2
$m0a2$y
2.5% 97.5%
(Intercept) 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m0a3
$m0a3$y
2.5% 97.5%
(Intercept) 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m0a4
$m0a4$y
2.5% 97.5%
(Intercept) 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m0b1
$m0b1$b1
2.5% 97.5%
(Intercept) 0 0
D_b1_id[1,1] 0 0
$m0b2
$m0b2$b1
2.5% 97.5%
(Intercept) 0 0
D_b1_id[1,1] 0 0
$m0b3
$m0b3$b1
2.5% 97.5%
(Intercept) 0 0
D_b1_id[1,1] 0 0
$m0b4
$m0b4$b1
2.5% 97.5%
(Intercept) 0 0
D_b1_id[1,1] 0 0
$m0c1
$m0c1$L1
2.5% 97.5%
(Intercept) 0 0
sigma_L1 0 0
D_L1_id[1,1] 0 0
$m0c2
$m0c2$L1
2.5% 97.5%
(Intercept) 0 0
sigma_L1 0 0
D_L1_id[1,1] 0 0
$m0d1
$m0d1$p1
2.5% 97.5%
(Intercept) 0 0
D_p1_id[1,1] 0 0
$m0d2
$m0d2$p1
2.5% 97.5%
(Intercept) 0 0
D_p1_id[1,1] 0 0
$m0e1
$m0e1$L1
2.5% 97.5%
(Intercept) 0 0
sigma_L1 0 0
D_L1_id[1,1] 0 0
$m0f1
$m0f1$Be1
2.5% 97.5%
(Intercept) 0 0
tau_Be1 0 0
D_Be1_id[1,1] 0 0
$m1a
$m1a$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m1b
$m1b$b1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
D_b1_id[1,1] 0 0
$m1c
$m1c$L1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
sigma_L1 0 0
D_L1_id[1,1] 0 0
$m1d
$m1d$p1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
D_p1_id[1,1] 0 0
$m1e
$m1e$L1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
sigma_L1 0 0
D_L1_id[1,1] 0 0
$m1f
$m1f$Be1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
tau_Be1 0 0
D_Be1_id[1,1] 0 0
$m2a
$m2a$y
2.5% 97.5%
(Intercept) 0 0
c2 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m2b
$m2b$b2
2.5% 97.5%
(Intercept) 0 0
c2 0 0
D_b2_id[1,1] 0 0
$m2c
$m2c$L1mis
2.5% 97.5%
(Intercept) 0 0
c2 0 0
sigma_L1mis 0 0
D_L1mis_id[1,1] 0 0
$m2d
$m2d$p2
2.5% 97.5%
(Intercept) 0 0
c2 0 0
D_p2_id[1,1] 0 0
$m2e
$m2e$L1mis
2.5% 97.5%
(Intercept) 0 0
c2 0 0
sigma_L1mis 0 0
D_L1mis_id[1,1] 0 0
$m2f
$m2f$Be2
2.5% 97.5%
(Intercept) 0 0
c2 0 0
tau_Be2 0 0
D_Be2_id[1,1] 0 0
$m3a
$m3a$y
2.5% 97.5%
C2 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m3b
$m3b$b2
2.5% 97.5%
C2 0 0
D_b2_id[1,1] 0 0
$m3c
$m3c$L1mis
2.5% 97.5%
C2 0 0
sigma_L1mis 0 0
D_L1mis_id[1,1] 0 0
$m3d
$m3d$p2
2.5% 97.5%
C2 0 0
D_p2_id[1,1] 0 0
$m3e
$m3e$L1mis
2.5% 97.5%
C2 0 0
sigma_L1mis 0 0
D_L1mis_id[1,1] 0 0
$m3f
$m3f$Be2
2.5% 97.5%
C2 0 0
tau_Be2 0 0
D_Be2_id[1,1] 0 0
$m4a
$m4a$c1
2.5% 97.5%
(Intercept) 0 0
B21 0 0
c2 0 0
p2 0 0
L1mis 0 0
Be2 0 0
sigma_c1 0 0
D_c1_id[1,1] 0 0
$m4b
$m4b$c1
2.5% 97.5%
(Intercept) 0 0
c2 0 0
b21 0 0
p2 0 0
L1mis 0 0
sigma_c1 0 0
D_c1_id[1,1] 0 0
$m4c
$m4c$c1
2.5% 97.5%
(Intercept) 0 0
c2 0 0
b21 0 0
p2 0 0
L1mis 0 0
sigma_c1 0 0
D_c1_id[1,1] 0 0
$m4d
$m4d$c1
2.5% 97.5%
(Intercept) 0 0
c2 0 0
b21 0 0
p2 0 0
L1mis 0 0
Be2 0 0
sigma_c1 0 0
D_c1_id[1,1] 0 0
$m5a
$m5a$y
2.5% 97.5%
(Intercept) 0 0
M22 0 0
M23 0 0
M24 0 0
log(C1) 0 0
o22 0 0
o23 0 0
o24 0 0
abs(C1 - c2) 0 0
time 0 0
I(time^2) 0 0
o22:abs(C1 - c2) 0 0
o23:abs(C1 - c2) 0 0
o24:abs(C1 - c2) 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
$m5b
$m5b$b1
2.5% 97.5%
(Intercept) 0 0
L1mis 0 0
abs(c1 - C2) 0 0
log(Be2) 0 0
time 0 0
D_b1_id[1,1] 0 0
D_b1_id[1,2] 0 0
D_b1_id[2,2] 0 0
D_b1_id[1,3] 0 0
D_b1_id[2,3] 0 0
D_b1_id[3,3] 0 0
$m6a
$m6a$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
C2 0 0
b21 0 0
time 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m6b
$m6b$b1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B11 0 0
c1 0 0
time 0 0
D_b1_id[1,1] 0 0
D_b1_id[1,2] 0 0
D_b1_id[2,2] 0 0
$m7a
$m7a$y
2.5% 97.5%
(Intercept) 0 0
ns(time, df = 2)1 0 0
ns(time, df = 2)2 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m7b
$m7b$y
2.5% 97.5%
(Intercept) 0 0
bs(time, df = 3)1 0 0
bs(time, df = 3)2 0 0
bs(time, df = 3)3 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
D_y_id[1,4] 0 0
D_y_id[2,4] 0 0
D_y_id[3,4] 0 0
D_y_id[4,4] 0 0
$m7c
$m7c$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
c1 0 0
ns(time, df = 3)1 0 0
ns(time, df = 3)2 0 0
ns(time, df = 3)3 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
D_y_id[1,4] 0 0
D_y_id[2,4] 0 0
D_y_id[3,4] 0 0
D_y_id[4,4] 0 0
$m7d
$m7d$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
C2 0 0
c1 0 0
ns(time, df = 3)1 0 0
ns(time, df = 3)2 0 0
ns(time, df = 3)3 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
$m7e
$m7e$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
C2 0 0
c1 0 0
ns(time, df = 3)1 0 0
ns(time, df = 3)2 0 0
ns(time, df = 3)3 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
D_y_id[1,4] 0 0
D_y_id[2,4] 0 0
D_y_id[3,4] 0 0
D_y_id[4,4] 0 0
$m7f
$m7f$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
C2 0 0
c1 0 0
ns(time, df = 3)1 0 0
ns(time, df = 3)2 0 0
ns(time, df = 3)3 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
$m8a
$m8a$y
2.5% 97.5%
(Intercept) 0 0
c1 0 0
c2 0 0
time 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8b
$m8b$y
2.5% 97.5%
(Intercept) 0 0
c1 0 0
c2 0 0
time 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8c
$m8c$y
2.5% 97.5%
(Intercept) 0 0
B21 0 0
c1 0 0
c2 0 0
time 0 0
B21:c1 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8d
$m8d$y
2.5% 97.5%
(Intercept) 0 0
B21 0 0
c1 0 0
c2 0 0
time 0 0
B21:c1 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8e
$m8e$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c1 0 0
c2 0 0
time 0 0
B21:c1 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8f
$m8f$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c1 0 0
c2 0 0
time 0 0
B21:c1 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8g
$m8g$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c1 0 0
c2 0 0
time 0 0
B21:c1 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8h
$m8h$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c2 0 0
c1 0 0
time 0 0
B21:c2 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8i
$m8i$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c2 0 0
c1 0 0
time 0 0
B21:c2 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8j
$m8j$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c2 0 0
c1 0 0
time 0 0
B21:c2 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8k
$m8k$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c2 0 0
c1 0 0
time 0 0
B21:c2 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8l
$m8l$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c1 0 0
time 0 0
B21:c1 0 0
B21:time 0 0
c1:time 0 0
B21:c1:time 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8m
$m8m$y
2.5% 97.5%
(Intercept) 0 0
c1 0 0
b11 0 0
o1.L 0 0
o1.Q 0 0
c1:b11 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
$m8n
$m8n$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c1 0 0
time 0 0
b11 0 0
C1:time 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
D_y_id[1,4] 0 0
D_y_id[2,4] 0 0
D_y_id[3,4] 0 0
D_y_id[4,4] 0 0
$m9a
$m9a$y
2.5% 97.5%
(Intercept) 0 0
c1 0 0
b11 0 0
time 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_o1[1,1] 0 0
$m9b
$m9b$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
C2 0 0
B11 0 0
time 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
$m9c
$m9c$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
C2 0 0
B11 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
Code
lapply(models0, summary, missinfo = TRUE)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a1
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
level # NA % NA
id id 0 0
$m0a2
Bayesian linear mixed model fitted with JointAI
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "identity"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
level # NA % NA
id id 0 0
$m0a3
Bayesian linear mixed model fitted with JointAI
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
level # NA % NA
id id 0 0
$m0a4
Bayesian linear mixed model fitted with JointAI
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "inverse"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
level # NA % NA
id id 0 0
$m0b1
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "logit"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
b1 lvlone 0 0
level # NA % NA
id id 0 0
$m0b2
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "probit"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
b1 lvlone 0 0
level # NA % NA
id id 0 0
$m0b3
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "log"),
n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 51:60
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
b1 lvlone 0 0
level # NA % NA
id id 0 0
$m0b4
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "cloglog"),
n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 51:60
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
b1 lvlone 0 0
level # NA % NA
id id 0 0
$m0c1
Bayesian Gamma mixed model fitted with JointAI
Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "inverse"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
L1 lvlone 0 0
level # NA % NA
id id 0 0
$m0c2
Bayesian Gamma mixed model fitted with JointAI
Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
L1 lvlone 0 0
level # NA % NA
id id 0 0
$m0d1
Bayesian poisson mixed model fitted with JointAI
Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_p1_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
p1 lvlone 0 0
level # NA % NA
id id 0 0
$m0d2
Bayesian poisson mixed model fitted with JointAI
Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "identity"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_p1_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
p1 lvlone 0 0
level # NA % NA
id id 0 0
$m0e1
Bayesian log-normal mixed model fitted with JointAI
Call:
lognormmm_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
L1 lvlone 0 0
level # NA % NA
id id 0 0
$m0f1
Bayesian beta mixed model fitted with JointAI
Call:
betamm_imp(fixed = Be1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Be1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be1 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
Be1 lvlone 0 0
level # NA % NA
id id 0 0
$m1a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
$m1b
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b1 ~ C1 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
b1 lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
$m1c
Bayesian Gamma mixed model fitted with JointAI
Call:
glme_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
L1 lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
$m1d
Bayesian poisson mixed model fitted with JointAI
Call:
glme_imp(fixed = p1 ~ C1 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_p1_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
p1 lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
$m1e
Bayesian log-normal mixed model fitted with JointAI
Call:
lognormmm_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
L1 lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
$m1f
Bayesian beta mixed model fitted with JointAI
Call:
betamm_imp(fixed = Be1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Be1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be1 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
Be1 lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
$m2a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
id id 0 0
$m2b
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b2 ~ c2 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b2_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 189 57.4
Number and proportion of missing values:
level # NA % NA
c2 lvlone 66 20.1
b2 lvlone 99 30.1
level # NA % NA
id id 0 0
$m2c
Bayesian Gamma mixed model fitted with JointAI
Call:
glme_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1mis_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1mis 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 246 74.8
Number and proportion of missing values:
level # NA % NA
L1mis lvlone 20 6.08
c2 lvlone 66 20.06
level # NA % NA
id id 0 0
$m2d
Bayesian poisson mixed model fitted with JointAI
Call:
glme_imp(fixed = p2 ~ c2 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_p2_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 142 43.2
Number and proportion of missing values:
level # NA % NA
c2 lvlone 66 20.1
p2 lvlone 162 49.2
level # NA % NA
id id 0 0
$m2e
Bayesian log-normal mixed model fitted with JointAI
Call:
lognormmm_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1mis_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1mis 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 246 74.8
Number and proportion of missing values:
level # NA % NA
L1mis lvlone 20 6.08
c2 lvlone 66 20.06
level # NA % NA
id id 0 0
$m2f
Bayesian beta mixed model fitted with JointAI
Call:
betamm_imp(fixed = Be2 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Be2_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be2 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 246 74.8
Number and proportion of missing values:
level # NA % NA
Be2 lvlone 20 6.08
c2 lvlone 66 20.06
level # NA % NA
id id 0 0
$m3a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
level # NA % NA
id id 0 0
C2 id 42 42
$m3b
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b2 ~ 0 + C2 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b2_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58.0
lvlone lvlone 230 69.9
Number and proportion of missing values:
level # NA % NA
b2 lvlone 99 30.1
level # NA % NA
id id 0 0
C2 id 42 42
$m3c
Bayesian Gamma mixed model fitted with JointAI
Call:
glme_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1mis_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1mis 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58.0
lvlone lvlone 309 93.9
Number and proportion of missing values:
level # NA % NA
L1mis lvlone 20 6.08
level # NA % NA
id id 0 0
C2 id 42 42
$m3d
Bayesian poisson mixed model fitted with JointAI
Call:
glme_imp(fixed = p2 ~ 0 + C2 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_p2_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58.0
lvlone lvlone 167 50.8
Number and proportion of missing values:
level # NA % NA
p2 lvlone 162 49.2
level # NA % NA
id id 0 0
C2 id 42 42
$m3e
Bayesian log-normal mixed model fitted with JointAI
Call:
lognormmm_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1mis_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1mis 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58.0
lvlone lvlone 309 93.9
Number and proportion of missing values:
level # NA % NA
L1mis lvlone 20 6.08
level # NA % NA
id id 0 0
C2 id 42 42
$m3f
Bayesian beta mixed model fitted with JointAI
Call:
betamm_imp(fixed = Be2 ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Be2_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be2 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58.0
lvlone lvlone 309 93.9
Number and proportion of missing values:
level # NA % NA
Be2 lvlone 20 6.08
level # NA % NA
id id 0 0
C2 id 42 42
$m4a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = c1 ~ c2 + B2 + p2 + L1mis + Be2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(p2 = "glmm_poisson_log",
L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
p2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Be2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_c1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_c1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90
lvlone lvlone 125 38
Number and proportion of missing values:
level # NA % NA
c1 lvlone 0 0.00
L1mis lvlone 20 6.08
Be2 lvlone 20 6.08
c2 lvlone 66 20.06
p2 lvlone 162 49.24
level # NA % NA
id id 0 0
B2 id 10 10
$m4b
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_inverse",
p2 = "glmm_poisson_identity", b2 = "glmm_binomial_probit",
L1mis = "glmm_lognorm"), seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
p2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_c1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_c1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 98 29.8
Number and proportion of missing values:
level # NA % NA
c1 lvlone 0 0.00
L1mis lvlone 20 6.08
c2 lvlone 66 20.06
b2 lvlone 99 30.09
p2 lvlone 162 49.24
level # NA % NA
id id 0 0
$m4c
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log",
p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log",
b2 = "glmm_binomial_log"), no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
p2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_c1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_c1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 98 29.8
Number and proportion of missing values:
level # NA % NA
c1 lvlone 0 0.00
L1mis lvlone 20 6.08
c2 lvlone 66 20.06
b2 lvlone 99 30.09
p2 lvlone 162 49.24
level # NA % NA
id id 0 0
$m4d
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + Be2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log",
p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log",
b2 = "glmm_binomial_log"), shrinkage = "ridge", seed = 2020,
warn = FALSE, mess = FALSE, trunc = list(Be2 = c(0, 1)))
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
p2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Be2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_c1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_c1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 92 28
Number and proportion of missing values:
level # NA % NA
c1 lvlone 0 0.00
L1mis lvlone 20 6.08
Be2 lvlone 20 6.08
c2 lvlone 66 20.06
b2 lvlone 99 30.09
p2 lvlone 162 49.24
level # NA % NA
id id 0 0
$m5a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ M2 + o2 * abs(C1 - c2) + log(C1) + time +
I(time^2) + (time | id), data = longDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
abs(C1 - c2) 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
I(time^2) 0 0 0 0 0 NaN NaN
o22:abs(C1 - c2) 0 0 0 0 0 NaN NaN
o23:abs(C1 - c2) 0 0 0 0 0 NaN NaN
o24:abs(C1 - c2) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 56 56
lvlone lvlone 217 66
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
time lvlone 0 0.0
o2 lvlone 59 17.9
c2 lvlone 66 20.1
level # NA % NA
C1 id 0 0
id id 0 0
M2 id 44 44
$m5b
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b1 ~ L1mis + abs(c1 - C2) + log(Be2) + time +
(time + I(time^2) | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), shrinkage = "ridge",
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
abs(c1 - C2) 0 0 0 0 0 NaN NaN
log(Be2) 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1] 0 0 0 0 NaN NaN
D_b1_id[1,2] 0 0 0 0 0 NaN NaN
D_b1_id[2,2] 0 0 0 0 NaN NaN
D_b1_id[1,3] 0 0 0 0 0 NaN NaN
D_b1_id[2,3] 0 0 0 0 0 NaN NaN
D_b1_id[3,3] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58.0
lvlone lvlone 291 88.4
Number and proportion of missing values:
level # NA % NA
b1 lvlone 0 0.00
c1 lvlone 0 0.00
time lvlone 0 0.00
L1mis lvlone 20 6.08
Be2 lvlone 20 6.08
level # NA % NA
id id 0 0
C2 id 42 42
$m6a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ b2 + C1 + C2 + time + (0 + time | id), data = longDF,
n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58.0
lvlone lvlone 230 69.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
time lvlone 0 0.0
b2 lvlone 99 30.1
level # NA % NA
C1 id 0 0
id id 0 0
C2 id 42 42
$m6b
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b1 ~ c1 + C2 + B1 + time + (0 + time + I(time^2) |
id), data = longDF, family = binomial(), n.adapt = 5, n.iter = 10,
shrinkage = "ridge", seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1] 0 0 0 0 NaN NaN
D_b1_id[1,2] 0 0 0 0 0 NaN NaN
D_b1_id[2,2] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
b1 lvlone 0 0
c1 lvlone 0 0
time lvlone 0 0
level # NA % NA
B1 id 0 0
id id 0 0
C2 id 42 42
$m7a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ ns(time, df = 2), data = longDF, random = ~ns(time,
df = 2) | id, n.iter = 10, seed = 2020, adapt = 5)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
ns(time, df = 2)1 0 0 0 0 0 NaN NaN
ns(time, df = 2)2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 101:110
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
time lvlone 0 0
level # NA % NA
id id 0 0
$m7b
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ bs(time, df = 3), data = longDF, random = ~bs(time,
df = 3) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
bs(time, df = 3)1 0 0 0 0 0 NaN NaN
bs(time, df = 3)2 0 0 0 0 0 NaN NaN
bs(time, df = 3)3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
D_y_id[1,4] 0 0 0 0 0 NaN NaN
D_y_id[2,4] 0 0 0 0 0 NaN NaN
D_y_id[3,4] 0 0 0 0 0 NaN NaN
D_y_id[4,4] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
time lvlone 0 0
level # NA % NA
id id 0 0
$m7c
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + c1 + ns(time, df = 3), data = longDF,
random = ~ns(time, df = 3) | id, n.iter = 10, seed = 2020,
nadapt = 5)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
ns(time, df = 3)1 0 0 0 0 0 NaN NaN
ns(time, df = 3)2 0 0 0 0 0 NaN NaN
ns(time, df = 3)3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
D_y_id[1,4] 0 0 0 0 0 NaN NaN
D_y_id[2,4] 0 0 0 0 0 NaN NaN
D_y_id[3,4] 0 0 0 0 0 NaN NaN
D_y_id[4,4] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 101:110
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
c1 lvlone 0 0
time lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
$m7d
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
ns(time, df = 3)1 0 0 0 0 0 NaN NaN
ns(time, df = 3)2 0 0 0 0 0 NaN NaN
ns(time, df = 3)3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
c1 lvlone 0 0
time lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
C2 id 42 42
$m7e
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~ns(time, df = 3) | id, n.adapt = 5, n.iter = 10,
no_model = "time", seed = 2020)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
ns(time, df = 3)1 0 0 0 0 0 NaN NaN
ns(time, df = 3)2 0 0 0 0 0 NaN NaN
ns(time, df = 3)3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
D_y_id[1,4] 0 0 0 0 0 NaN NaN
D_y_id[2,4] 0 0 0 0 0 NaN NaN
D_y_id[3,4] 0 0 0 0 0 NaN NaN
D_y_id[4,4] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
c1 lvlone 0 0
time lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
C2 id 42 42
$m7f
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
ns(time, df = 3)1 0 0 0 0 0 NaN NaN
ns(time, df = 3)2 0 0 0 0 0 NaN NaN
ns(time, df = 3)3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
c1 lvlone 0 0
time lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
C2 id 42 42
$m8a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time +
c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Warning <simpleWarning>
There are missing values in a variable for which a random effect is
specified ("c2"). It will not be possible to re-scale the random
effects "b_y_id" and their variance covariance matrix "D_y_id" back to
the original scale of the data. If you are not interested in the
estimated random effects or their (co)variances this is not a problem.
The fixed effects estimates are not affected by this. If you are
interested in the random effects or the (co)variances you need to
specify that "time" and "c2" are not scaled (using the argument
"scale_params").
Output
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
id id 0 0
$m8b
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time +
c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Warning <simpleWarning>
There are missing values in a variable for which a random effect is
specified ("c2"). It will not be possible to re-scale the random
effects "b_y_id" and their variance covariance matrix "D_y_id" back to
the original scale of the data. If you are not interested in the
estimated random effects or their (co)variances this is not a problem.
The fixed effects estimates are not affected by this. If you are
interested in the random effects or the (co)variances you need to
specify that "time" and "c2" are not scaled (using the argument
"scale_params").
Output
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
id id 0 0
$m8c
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time +
c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
id id 0 0
B2 id 10 10
$m8d
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time +
c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
id id 0 0
B2 id 10 10
$m8e
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Warning <simpleWarning>
There are missing values in a variable for which a random effect is
specified ("c2"). It will not be possible to re-scale the random
effects "b_y_id" and their variance covariance matrix "D_y_id" back to
the original scale of the data. If you are not interested in the
estimated random effects or their (co)variances this is not a problem.
The fixed effects estimates are not affected by this. If you are
interested in the random effects or the (co)variances you need to
specify that "time" and "c2" are not scaled (using the argument
"scale_params").
Output
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m8f
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time",
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Warning <simpleWarning>
There are missing values in a variable for which a random effect is
specified ("c2"). It will not be possible to re-scale the random
effects "b_y_id" and their variance covariance matrix "D_y_id" back to
the original scale of the data. If you are not interested in the
estimated random effects or their (co)variances this is not a problem.
The fixed effects estimates are not affected by this. If you are
interested in the random effects or the (co)variances you need to
specify that "time" and "c2" are not scaled (using the argument
"scale_params").
Output
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m8g
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = c("time",
"c1"), seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Warning <simpleWarning>
There are missing values in a variable for which a random effect is
specified ("c2"). It will not be possible to re-scale the random
effects "b_y_id" and their variance covariance matrix "D_y_id" back to
the original scale of the data. If you are not interested in the
estimated random effects or their (co)variances this is not a problem.
The fixed effects estimates are not affected by this. If you are
interested in the random effects or the (co)variances you need to
specify that "time" and "c2" are not scaled (using the argument
"scale_params").
Output
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m8h
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m8i
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time",
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m8j
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Warning <simpleWarning>
There are missing values in a variable for which a random effect is
specified ("c2"). It will not be possible to re-scale the random
effects "b_y_id" and their variance covariance matrix "D_y_id" back to
the original scale of the data. If you are not interested in the
estimated random effects or their (co)variances this is not a problem.
The fixed effects estimates are not affected by this. If you are
interested in the random effects or the (co)variances you need to
specify that "time" and "c2" are not scaled (using the argument
"scale_params").
Output
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m8k
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Warning <simpleWarning>
There are missing values in a variable for which a random effect is
specified ("c2"). It will not be possible to re-scale the random
effects "b_y_id" and their variance covariance matrix "D_y_id" back to
the original scale of the data. If you are not interested in the
estimated random effects or their (co)variances this is not a problem.
The fixed effects estimates are not affected by this. If you are
interested in the random effects or the (co)variances you need to
specify that "time" and "c2" are not scaled (using the argument
"scale_params").
Output
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m8l
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 * time, data = longDF, random = ~time +
I(time^2) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
B21:time 0 0 0 0 0 NaN NaN
c1:time 0 0 0 0 0 NaN NaN
B21:c1:time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
c1 lvlone 0 0
time lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m8m
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ c1 * b1 + o1, data = longDF, random = ~b1 |
id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
b11 0 0 0 0 0 NaN NaN
o1.L 0 0 0 0 0 NaN NaN
o1.Q 0 0 0 0 0 NaN NaN
c1:b11 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
c1 lvlone 0 0
b1 lvlone 0 0
o1 lvlone 0 0
level # NA % NA
id id 0 0
$m8n
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ c1 + C1 * time + b1 + B2, data = longDF,
random = ~C1 * time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
b11 0 0 0 0 0 NaN NaN
C1:time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
D_y_id[1,4] 0 0 0 0 0 NaN NaN
D_y_id[2,4] 0 0 0 0 0 NaN NaN
D_y_id[3,4] 0 0 0 0 0 NaN NaN
D_y_id[4,4] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
c1 lvlone 0 0
time lvlone 0 0
b1 lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m9a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ c1 + b1 + time + (1 | id) + (1 | o1), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
b11 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
* For level "id":
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
* For level "o1":
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_o1[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
- o1: 3
Number and proportion of complete cases:
level # %
id id 100 100
o1 o1 3 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
c1 lvlone 0 0
b1 lvlone 0 0
time lvlone 0 0
level # NA % NA
id id 0 0
level # NA % NA
o1 o1 0 0
$m9b
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + time + (time | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = c(analysis_random = TRUE),
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
time lvlone 0 0
level # NA % NA
C1 id 0 0
B1 id 0 0
id id 0 0
C2 id 42 42
$m9c
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, monitor_params = c(analysis_random = TRUE),
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
level # NA % NA
C1 id 0 0
B1 id 0 0
id id 0 0
C2 id 42 42
Code
lapply(models0, function(x) coef(summary(x)))
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a1
$m0a1$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0a2
$m0a2$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0a3
$m0a3$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0a4
$m0a4$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0b1
$m0b1$b1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0b2
$m0b2$b1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0b3
$m0b3$b1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0b4
$m0b4$b1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0c1
$m0c1$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0c2
$m0c2$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0d1
$m0d1$p1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0d2
$m0d2$p1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0e1
$m0e1$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0f1
$m0f1$Be1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m1a
$m1a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1b
$m1b$b1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1c
$m1c$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1d
$m1d$p1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1e
$m1e$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1f
$m1f$Be1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m2a
$m2a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
$m2b
$m2b$b2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
$m2c
$m2c$L1mis
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
$m2d
$m2d$p2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
$m2e
$m2e$L1mis
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
$m2f
$m2f$Be2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
$m3a
$m3a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
$m3b
$m3b$b2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
$m3c
$m3c$L1mis
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
$m3d
$m3d$p2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
$m3e
$m3e$L1mis
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
$m3f
$m3f$Be2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
$m4a
$m4a$c1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
p2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Be2 0 0 0 0 0 NaN NaN
$m4b
$m4b$c1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
p2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
$m4c
$m4c$c1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
p2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
$m4d
$m4d$c1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
p2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Be2 0 0 0 0 0 NaN NaN
$m5a
$m5a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
abs(C1 - c2) 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
I(time^2) 0 0 0 0 0 NaN NaN
o22:abs(C1 - c2) 0 0 0 0 0 NaN NaN
o23:abs(C1 - c2) 0 0 0 0 0 NaN NaN
o24:abs(C1 - c2) 0 0 0 0 0 NaN NaN
$m5b
$m5b$b1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
abs(c1 - C2) 0 0 0 0 0 NaN NaN
log(Be2) 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
$m6a
$m6a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
$m6b
$m6b$b1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
$m7a
$m7a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
ns(time, df = 2)1 0 0 0 0 0 NaN NaN
ns(time, df = 2)2 0 0 0 0 0 NaN NaN
$m7b
$m7b$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
bs(time, df = 3)1 0 0 0 0 0 NaN NaN
bs(time, df = 3)2 0 0 0 0 0 NaN NaN
bs(time, df = 3)3 0 0 0 0 0 NaN NaN
$m7c
$m7c$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
ns(time, df = 3)1 0 0 0 0 0 NaN NaN
ns(time, df = 3)2 0 0 0 0 0 NaN NaN
ns(time, df = 3)3 0 0 0 0 0 NaN NaN
$m7d
$m7d$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
ns(time, df = 3)1 0 0 0 0 0 NaN NaN
ns(time, df = 3)2 0 0 0 0 0 NaN NaN
ns(time, df = 3)3 0 0 0 0 0 NaN NaN
$m7e
$m7e$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
ns(time, df = 3)1 0 0 0 0 0 NaN NaN
ns(time, df = 3)2 0 0 0 0 0 NaN NaN
ns(time, df = 3)3 0 0 0 0 0 NaN NaN
$m7f
$m7f$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
ns(time, df = 3)1 0 0 0 0 0 NaN NaN
ns(time, df = 3)2 0 0 0 0 0 NaN NaN
ns(time, df = 3)3 0 0 0 0 0 NaN NaN
$m8a
$m8a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
$m8b
$m8b$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
$m8c
$m8c$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
$m8d
$m8d$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
$m8e
$m8e$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
$m8f
$m8f$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
$m8g
$m8g$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
$m8h
$m8h$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c2 0 0 0 0 0 NaN NaN
$m8i
$m8i$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c2 0 0 0 0 0 NaN NaN
$m8j
$m8j$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c2 0 0 0 0 0 NaN NaN
$m8k
$m8k$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c2 0 0 0 0 0 NaN NaN
$m8l
$m8l$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
B21:time 0 0 0 0 0 NaN NaN
c1:time 0 0 0 0 0 NaN NaN
B21:c1:time 0 0 0 0 0 NaN NaN
$m8m
$m8m$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
b11 0 0 0 0 0 NaN NaN
o1.L 0 0 0 0 0 NaN NaN
o1.Q 0 0 0 0 0 NaN NaN
c1:b11 0 0 0 0 0 NaN NaN
$m8n
$m8n$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
b11 0 0 0 0 0 NaN NaN
C1:time 0 0 0 0 0 NaN NaN
$m9a
$m9a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
b11 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
$m9b
$m9b$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
$m9c
$m9c$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
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data_list remains the same
Code
lapply(models, "[[", "data_list")
Output
$m0a1
$m0a1$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0a1$M_lvlone
y
1 -13.0493856
1.1 -9.3335901
1.2 -22.3469852
1.3 -15.0417337
2 -12.0655434
2.1 -15.8674476
2.2 -7.8800006
3 -11.4820604
3.1 -10.5983220
3.2 -22.4519157
4 -1.2697775
4.1 -11.1215184
4.2 -3.6134138
4.3 -14.5982385
5 -6.8457515
5.1 -7.0551214
5.2 -12.3418980
5.3 -9.2366906
6 -5.1648211
7 -10.0599502
7.1 -18.3267285
7.2 -12.5138426
8 -1.6305331
8.1 -9.6520453
8.2 -1.5278462
8.3 -7.4172211
8.4 -7.1238609
8.5 -8.8706950
9 -0.1634429
9.1 -2.6034300
9.2 -6.7272369
10 -6.4172202
10.1 -11.4834569
11 -8.7911356
11.1 -19.6645080
11.2 -20.2030932
11.3 -21.3082176
11.4 -14.5802901
12 -15.2006287
13 0.8058816
13.1 -13.6379208
14 -15.3422873
14.1 -10.0965208
14.2 -16.6452027
14.3 -15.8389733
15 -8.9424594
15.1 -22.0101983
15.2 -7.3975599
15.3 -10.3567334
16 -1.9691302
16.1 -9.9308357
16.2 -6.9626923
16.3 -3.2862557
16.4 -3.3972355
16.5 -11.5767835
17 -10.5474144
17.1 -7.6215009
17.2 -16.5386939
17.3 -20.0004774
17.4 -18.8505475
18 -19.7302351
19 -14.6177568
19.1 -17.8043866
19.2 -15.1641705
19.3 -16.6898418
20 -12.9059229
20.1 -16.8191201
20.2 -6.1010131
20.3 -7.9415371
20.4 -9.3904458
20.5 -13.3504189
21 -7.6974718
21.1 -11.9335526
21.2 -12.7064929
22 -21.5022909
22.1 -12.7745451
23 -3.5146508
23.1 -4.6724048
24 -2.5619821
25 -6.2944970
25.1 -3.8630505
25.2 -14.4205140
25.3 -19.6735037
25.4 -9.0288933
25.5 -9.0509738
26 -19.7340685
26.1 -14.1692728
26.2 -17.2819976
26.3 -24.6265576
27 -7.3354999
27.1 -11.1488468
28 -11.7996597
28.1 -8.2030122
28.2 -26.4317815
28.3 -18.5016071
29 -5.8551395
29.1 -2.0209442
29.2 -5.6368080
29.3 -3.8110961
30 -12.7217702
30.1 -17.0170140
30.2 -25.4236089
31 -17.0783921
32 -18.4338764
32.1 -19.4317212
32.2 -19.4738978
32.3 -21.4922645
33 2.0838099
33.1 -13.3172274
34 -10.0296691
34.1 -25.9426553
34.2 -18.5688138
34.3 -15.4173859
35 -14.3958113
35.1 -12.9457541
35.2 -16.1380691
36 -12.8166968
36.1 -14.3989481
36.2 -12.2436943
36.3 -15.0104638
36.4 -10.1775457
37 -15.2223495
37.1 -14.7526195
37.2 -19.8168430
38 -2.7065118
39 -8.7288138
39.1 -9.2746473
39.2 -18.2695344
39.3 -13.8219083
39.4 -16.2254704
39.5 -21.7283648
40 1.8291916
40.1 -6.6916432
40.2 -1.6278171
40.3 -10.5749790
41 -3.1556121
41.1 -11.5895327
41.2 -18.9352091
41.3 -15.9788960
41.4 -9.6070508
42 -5.2159485
42.1 -15.9878743
43 -16.6104361
43.1 -9.5549441
43.2 -14.2003491
44 -8.1969033
44.1 -19.9270197
44.2 -22.6521171
44.3 -21.1903736
45 -0.5686627
45.1 -7.5645740
46 -19.1624789
46.1 -18.4487574
46.2 -15.8222682
47 -5.4165074
47.1 -15.0975029
47.2 -12.9971413
47.3 -10.6844521
47.4 -18.2214784
48 -8.3101471
48.1 -18.3854275
49 -13.0130319
50 -10.4579977
51 -19.3157621
52 -4.4747188
52.1 -4.3163827
52.2 -6.9761408
52.3 -20.1764756
52.4 -8.9036692
52.5 -5.6949642
53 -10.3141887
53.1 -8.2642654
53.2 -9.1691554
54 -6.2198754
54.1 -15.7192609
54.2 -13.0978998
54.3 -5.1195299
54.4 -16.5771751
55 -5.7348534
55.1 -7.3217494
55.2 -12.2171938
55.3 -12.9821266
55.4 -14.8599983
56 -14.1764282
56.1 -12.5343602
56.2 -8.4573382
56.3 -12.4633969
56.4 -17.3841863
56.5 -14.8147645
57 -3.1403293
57.1 -11.1509248
57.2 -6.3940143
57.3 -9.3473241
58 -12.0245677
58.1 -9.2112246
58.2 -1.2071742
58.3 -11.0141711
58.4 -5.3721214
58.5 -7.8523047
59 -13.2946560
59.1 -10.0530648
60 -19.2209402
61 -4.6699914
61.1 -3.5981894
61.2 -1.4713611
61.3 -3.8819786
61.4 0.1041413
62 -2.8591600
62.1 -6.9461986
62.2 -16.7910593
62.3 -17.9844596
63 -24.0335535
63.1 -11.7765300
64 -20.5963897
65 -2.7969169
65.1 -11.1778694
65.2 -5.2830399
65.3 -7.9353390
66 -13.2318328
66.1 -1.9090560
66.2 -16.6643889
67 -25.6073277
68 -13.4806759
68.1 -18.4557183
68.2 -13.3982327
68.3 -12.4977127
68.4 -11.7073990
69 -14.5290675
70 -15.2122709
70.1 -7.8681167
71 -10.3352703
71.1 -7.5699888
71.2 -18.4680702
71.3 -21.4316644
71.4 -8.1137650
72 -9.1848162
72.1 -23.7538846
72.2 -26.3421306
72.3 -27.2843801
72.4 -20.8541617
72.5 -12.8948965
73 -2.6091307
74 -8.2790175
75 -12.5029612
76 -6.0061671
76.1 -8.8149114
76.2 -11.8359043
77 0.4772521
78 -9.4105229
79 -1.0217265
79.1 -11.8125257
79.2 -10.5465186
80 -12.7366807
80.1 -9.0584783
80.2 -16.6381566
81 0.5547913
81.1 -4.0892715
81.2 1.8283303
81.3 -5.2166381
82 -3.0749381
82.1 -10.5506696
82.2 -18.2226347
83 -12.5872635
83.1 -11.9756502
83.2 -10.6744217
83.3 -19.2714012
84 -2.6320312
84.1 -9.8140094
85 -12.3886736
85.1 -12.9196365
85.2 -9.6433248
85.3 -6.3296340
85.4 -7.0405525
85.5 -13.6714939
86 -10.8756412
86.1 -12.0055331
86.2 -13.3724699
86.3 -13.3252145
86.4 -14.9191290
86.5 -17.7515546
87 -10.7027963
87.1 -22.4941954
87.2 -14.9616716
88 -2.2264493
88.1 -8.9626474
88.2 -2.5095281
88.3 -16.3345673
89 -11.0459647
90 -4.5610239
90.1 -11.7036651
90.2 -5.3838521
90.3 -4.1636999
91 -7.1462503
91.1 -12.8374475
91.2 -18.2576707
92 -6.4119222
93 5.2122168
93.1 3.1211725
93.2 -3.6841177
93.3 2.6223542
93.4 -11.1877696
94 -6.9602492
94.1 -7.4318416
94.2 -4.3498045
94.3 -11.6340088
94.4 -12.9357964
94.5 -14.7648530
95 -12.8849309
95.1 -9.7451502
95.2 -0.8535063
96 -4.9139832
96.1 -3.9582653
96.2 -9.6555492
96.3 -11.8690793
96.4 -11.0224373
96.5 -10.9530403
97 -9.8540471
97.1 -19.2262840
98 -11.9651231
98.1 -2.6515128
98.2 -12.2606382
99 -11.4720500
99.1 -14.0596866
99.2 -17.3939469
100 1.1005874
100.1 -3.8226248
100.2 -0.9123182
100.3 -15.8389474
100.4 -12.8093826
$m0a1$mu_reg_norm
[1] 0
$m0a1$tau_reg_norm
[1] 1e-04
$m0a1$shape_tau_norm
[1] 0.01
$m0a1$rate_tau_norm
[1] 0.01
$m0a1$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0a1$shape_diag_RinvD
[1] "0.01"
$m0a1$rate_diag_RinvD
[1] "0.001"
$m0a2
$m0a2$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0a2$M_lvlone
y
1 -13.0493856
1.1 -9.3335901
1.2 -22.3469852
1.3 -15.0417337
2 -12.0655434
2.1 -15.8674476
2.2 -7.8800006
3 -11.4820604
3.1 -10.5983220
3.2 -22.4519157
4 -1.2697775
4.1 -11.1215184
4.2 -3.6134138
4.3 -14.5982385
5 -6.8457515
5.1 -7.0551214
5.2 -12.3418980
5.3 -9.2366906
6 -5.1648211
7 -10.0599502
7.1 -18.3267285
7.2 -12.5138426
8 -1.6305331
8.1 -9.6520453
8.2 -1.5278462
8.3 -7.4172211
8.4 -7.1238609
8.5 -8.8706950
9 -0.1634429
9.1 -2.6034300
9.2 -6.7272369
10 -6.4172202
10.1 -11.4834569
11 -8.7911356
11.1 -19.6645080
11.2 -20.2030932
11.3 -21.3082176
11.4 -14.5802901
12 -15.2006287
13 0.8058816
13.1 -13.6379208
14 -15.3422873
14.1 -10.0965208
14.2 -16.6452027
14.3 -15.8389733
15 -8.9424594
15.1 -22.0101983
15.2 -7.3975599
15.3 -10.3567334
16 -1.9691302
16.1 -9.9308357
16.2 -6.9626923
16.3 -3.2862557
16.4 -3.3972355
16.5 -11.5767835
17 -10.5474144
17.1 -7.6215009
17.2 -16.5386939
17.3 -20.0004774
17.4 -18.8505475
18 -19.7302351
19 -14.6177568
19.1 -17.8043866
19.2 -15.1641705
19.3 -16.6898418
20 -12.9059229
20.1 -16.8191201
20.2 -6.1010131
20.3 -7.9415371
20.4 -9.3904458
20.5 -13.3504189
21 -7.6974718
21.1 -11.9335526
21.2 -12.7064929
22 -21.5022909
22.1 -12.7745451
23 -3.5146508
23.1 -4.6724048
24 -2.5619821
25 -6.2944970
25.1 -3.8630505
25.2 -14.4205140
25.3 -19.6735037
25.4 -9.0288933
25.5 -9.0509738
26 -19.7340685
26.1 -14.1692728
26.2 -17.2819976
26.3 -24.6265576
27 -7.3354999
27.1 -11.1488468
28 -11.7996597
28.1 -8.2030122
28.2 -26.4317815
28.3 -18.5016071
29 -5.8551395
29.1 -2.0209442
29.2 -5.6368080
29.3 -3.8110961
30 -12.7217702
30.1 -17.0170140
30.2 -25.4236089
31 -17.0783921
32 -18.4338764
32.1 -19.4317212
32.2 -19.4738978
32.3 -21.4922645
33 2.0838099
33.1 -13.3172274
34 -10.0296691
34.1 -25.9426553
34.2 -18.5688138
34.3 -15.4173859
35 -14.3958113
35.1 -12.9457541
35.2 -16.1380691
36 -12.8166968
36.1 -14.3989481
36.2 -12.2436943
36.3 -15.0104638
36.4 -10.1775457
37 -15.2223495
37.1 -14.7526195
37.2 -19.8168430
38 -2.7065118
39 -8.7288138
39.1 -9.2746473
39.2 -18.2695344
39.3 -13.8219083
39.4 -16.2254704
39.5 -21.7283648
40 1.8291916
40.1 -6.6916432
40.2 -1.6278171
40.3 -10.5749790
41 -3.1556121
41.1 -11.5895327
41.2 -18.9352091
41.3 -15.9788960
41.4 -9.6070508
42 -5.2159485
42.1 -15.9878743
43 -16.6104361
43.1 -9.5549441
43.2 -14.2003491
44 -8.1969033
44.1 -19.9270197
44.2 -22.6521171
44.3 -21.1903736
45 -0.5686627
45.1 -7.5645740
46 -19.1624789
46.1 -18.4487574
46.2 -15.8222682
47 -5.4165074
47.1 -15.0975029
47.2 -12.9971413
47.3 -10.6844521
47.4 -18.2214784
48 -8.3101471
48.1 -18.3854275
49 -13.0130319
50 -10.4579977
51 -19.3157621
52 -4.4747188
52.1 -4.3163827
52.2 -6.9761408
52.3 -20.1764756
52.4 -8.9036692
52.5 -5.6949642
53 -10.3141887
53.1 -8.2642654
53.2 -9.1691554
54 -6.2198754
54.1 -15.7192609
54.2 -13.0978998
54.3 -5.1195299
54.4 -16.5771751
55 -5.7348534
55.1 -7.3217494
55.2 -12.2171938
55.3 -12.9821266
55.4 -14.8599983
56 -14.1764282
56.1 -12.5343602
56.2 -8.4573382
56.3 -12.4633969
56.4 -17.3841863
56.5 -14.8147645
57 -3.1403293
57.1 -11.1509248
57.2 -6.3940143
57.3 -9.3473241
58 -12.0245677
58.1 -9.2112246
58.2 -1.2071742
58.3 -11.0141711
58.4 -5.3721214
58.5 -7.8523047
59 -13.2946560
59.1 -10.0530648
60 -19.2209402
61 -4.6699914
61.1 -3.5981894
61.2 -1.4713611
61.3 -3.8819786
61.4 0.1041413
62 -2.8591600
62.1 -6.9461986
62.2 -16.7910593
62.3 -17.9844596
63 -24.0335535
63.1 -11.7765300
64 -20.5963897
65 -2.7969169
65.1 -11.1778694
65.2 -5.2830399
65.3 -7.9353390
66 -13.2318328
66.1 -1.9090560
66.2 -16.6643889
67 -25.6073277
68 -13.4806759
68.1 -18.4557183
68.2 -13.3982327
68.3 -12.4977127
68.4 -11.7073990
69 -14.5290675
70 -15.2122709
70.1 -7.8681167
71 -10.3352703
71.1 -7.5699888
71.2 -18.4680702
71.3 -21.4316644
71.4 -8.1137650
72 -9.1848162
72.1 -23.7538846
72.2 -26.3421306
72.3 -27.2843801
72.4 -20.8541617
72.5 -12.8948965
73 -2.6091307
74 -8.2790175
75 -12.5029612
76 -6.0061671
76.1 -8.8149114
76.2 -11.8359043
77 0.4772521
78 -9.4105229
79 -1.0217265
79.1 -11.8125257
79.2 -10.5465186
80 -12.7366807
80.1 -9.0584783
80.2 -16.6381566
81 0.5547913
81.1 -4.0892715
81.2 1.8283303
81.3 -5.2166381
82 -3.0749381
82.1 -10.5506696
82.2 -18.2226347
83 -12.5872635
83.1 -11.9756502
83.2 -10.6744217
83.3 -19.2714012
84 -2.6320312
84.1 -9.8140094
85 -12.3886736
85.1 -12.9196365
85.2 -9.6433248
85.3 -6.3296340
85.4 -7.0405525
85.5 -13.6714939
86 -10.8756412
86.1 -12.0055331
86.2 -13.3724699
86.3 -13.3252145
86.4 -14.9191290
86.5 -17.7515546
87 -10.7027963
87.1 -22.4941954
87.2 -14.9616716
88 -2.2264493
88.1 -8.9626474
88.2 -2.5095281
88.3 -16.3345673
89 -11.0459647
90 -4.5610239
90.1 -11.7036651
90.2 -5.3838521
90.3 -4.1636999
91 -7.1462503
91.1 -12.8374475
91.2 -18.2576707
92 -6.4119222
93 5.2122168
93.1 3.1211725
93.2 -3.6841177
93.3 2.6223542
93.4 -11.1877696
94 -6.9602492
94.1 -7.4318416
94.2 -4.3498045
94.3 -11.6340088
94.4 -12.9357964
94.5 -14.7648530
95 -12.8849309
95.1 -9.7451502
95.2 -0.8535063
96 -4.9139832
96.1 -3.9582653
96.2 -9.6555492
96.3 -11.8690793
96.4 -11.0224373
96.5 -10.9530403
97 -9.8540471
97.1 -19.2262840
98 -11.9651231
98.1 -2.6515128
98.2 -12.2606382
99 -11.4720500
99.1 -14.0596866
99.2 -17.3939469
100 1.1005874
100.1 -3.8226248
100.2 -0.9123182
100.3 -15.8389474
100.4 -12.8093826
$m0a2$mu_reg_norm
[1] 0
$m0a2$tau_reg_norm
[1] 1e-04
$m0a2$shape_tau_norm
[1] 0.01
$m0a2$rate_tau_norm
[1] 0.01
$m0a2$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0a2$shape_diag_RinvD
[1] "0.01"
$m0a2$rate_diag_RinvD
[1] "0.001"
$m0a3
$m0a3$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0a3$M_lvlone
y
1 -13.0493856
1.1 -9.3335901
1.2 -22.3469852
1.3 -15.0417337
2 -12.0655434
2.1 -15.8674476
2.2 -7.8800006
3 -11.4820604
3.1 -10.5983220
3.2 -22.4519157
4 -1.2697775
4.1 -11.1215184
4.2 -3.6134138
4.3 -14.5982385
5 -6.8457515
5.1 -7.0551214
5.2 -12.3418980
5.3 -9.2366906
6 -5.1648211
7 -10.0599502
7.1 -18.3267285
7.2 -12.5138426
8 -1.6305331
8.1 -9.6520453
8.2 -1.5278462
8.3 -7.4172211
8.4 -7.1238609
8.5 -8.8706950
9 -0.1634429
9.1 -2.6034300
9.2 -6.7272369
10 -6.4172202
10.1 -11.4834569
11 -8.7911356
11.1 -19.6645080
11.2 -20.2030932
11.3 -21.3082176
11.4 -14.5802901
12 -15.2006287
13 0.8058816
13.1 -13.6379208
14 -15.3422873
14.1 -10.0965208
14.2 -16.6452027
14.3 -15.8389733
15 -8.9424594
15.1 -22.0101983
15.2 -7.3975599
15.3 -10.3567334
16 -1.9691302
16.1 -9.9308357
16.2 -6.9626923
16.3 -3.2862557
16.4 -3.3972355
16.5 -11.5767835
17 -10.5474144
17.1 -7.6215009
17.2 -16.5386939
17.3 -20.0004774
17.4 -18.8505475
18 -19.7302351
19 -14.6177568
19.1 -17.8043866
19.2 -15.1641705
19.3 -16.6898418
20 -12.9059229
20.1 -16.8191201
20.2 -6.1010131
20.3 -7.9415371
20.4 -9.3904458
20.5 -13.3504189
21 -7.6974718
21.1 -11.9335526
21.2 -12.7064929
22 -21.5022909
22.1 -12.7745451
23 -3.5146508
23.1 -4.6724048
24 -2.5619821
25 -6.2944970
25.1 -3.8630505
25.2 -14.4205140
25.3 -19.6735037
25.4 -9.0288933
25.5 -9.0509738
26 -19.7340685
26.1 -14.1692728
26.2 -17.2819976
26.3 -24.6265576
27 -7.3354999
27.1 -11.1488468
28 -11.7996597
28.1 -8.2030122
28.2 -26.4317815
28.3 -18.5016071
29 -5.8551395
29.1 -2.0209442
29.2 -5.6368080
29.3 -3.8110961
30 -12.7217702
30.1 -17.0170140
30.2 -25.4236089
31 -17.0783921
32 -18.4338764
32.1 -19.4317212
32.2 -19.4738978
32.3 -21.4922645
33 2.0838099
33.1 -13.3172274
34 -10.0296691
34.1 -25.9426553
34.2 -18.5688138
34.3 -15.4173859
35 -14.3958113
35.1 -12.9457541
35.2 -16.1380691
36 -12.8166968
36.1 -14.3989481
36.2 -12.2436943
36.3 -15.0104638
36.4 -10.1775457
37 -15.2223495
37.1 -14.7526195
37.2 -19.8168430
38 -2.7065118
39 -8.7288138
39.1 -9.2746473
39.2 -18.2695344
39.3 -13.8219083
39.4 -16.2254704
39.5 -21.7283648
40 1.8291916
40.1 -6.6916432
40.2 -1.6278171
40.3 -10.5749790
41 -3.1556121
41.1 -11.5895327
41.2 -18.9352091
41.3 -15.9788960
41.4 -9.6070508
42 -5.2159485
42.1 -15.9878743
43 -16.6104361
43.1 -9.5549441
43.2 -14.2003491
44 -8.1969033
44.1 -19.9270197
44.2 -22.6521171
44.3 -21.1903736
45 -0.5686627
45.1 -7.5645740
46 -19.1624789
46.1 -18.4487574
46.2 -15.8222682
47 -5.4165074
47.1 -15.0975029
47.2 -12.9971413
47.3 -10.6844521
47.4 -18.2214784
48 -8.3101471
48.1 -18.3854275
49 -13.0130319
50 -10.4579977
51 -19.3157621
52 -4.4747188
52.1 -4.3163827
52.2 -6.9761408
52.3 -20.1764756
52.4 -8.9036692
52.5 -5.6949642
53 -10.3141887
53.1 -8.2642654
53.2 -9.1691554
54 -6.2198754
54.1 -15.7192609
54.2 -13.0978998
54.3 -5.1195299
54.4 -16.5771751
55 -5.7348534
55.1 -7.3217494
55.2 -12.2171938
55.3 -12.9821266
55.4 -14.8599983
56 -14.1764282
56.1 -12.5343602
56.2 -8.4573382
56.3 -12.4633969
56.4 -17.3841863
56.5 -14.8147645
57 -3.1403293
57.1 -11.1509248
57.2 -6.3940143
57.3 -9.3473241
58 -12.0245677
58.1 -9.2112246
58.2 -1.2071742
58.3 -11.0141711
58.4 -5.3721214
58.5 -7.8523047
59 -13.2946560
59.1 -10.0530648
60 -19.2209402
61 -4.6699914
61.1 -3.5981894
61.2 -1.4713611
61.3 -3.8819786
61.4 0.1041413
62 -2.8591600
62.1 -6.9461986
62.2 -16.7910593
62.3 -17.9844596
63 -24.0335535
63.1 -11.7765300
64 -20.5963897
65 -2.7969169
65.1 -11.1778694
65.2 -5.2830399
65.3 -7.9353390
66 -13.2318328
66.1 -1.9090560
66.2 -16.6643889
67 -25.6073277
68 -13.4806759
68.1 -18.4557183
68.2 -13.3982327
68.3 -12.4977127
68.4 -11.7073990
69 -14.5290675
70 -15.2122709
70.1 -7.8681167
71 -10.3352703
71.1 -7.5699888
71.2 -18.4680702
71.3 -21.4316644
71.4 -8.1137650
72 -9.1848162
72.1 -23.7538846
72.2 -26.3421306
72.3 -27.2843801
72.4 -20.8541617
72.5 -12.8948965
73 -2.6091307
74 -8.2790175
75 -12.5029612
76 -6.0061671
76.1 -8.8149114
76.2 -11.8359043
77 0.4772521
78 -9.4105229
79 -1.0217265
79.1 -11.8125257
79.2 -10.5465186
80 -12.7366807
80.1 -9.0584783
80.2 -16.6381566
81 0.5547913
81.1 -4.0892715
81.2 1.8283303
81.3 -5.2166381
82 -3.0749381
82.1 -10.5506696
82.2 -18.2226347
83 -12.5872635
83.1 -11.9756502
83.2 -10.6744217
83.3 -19.2714012
84 -2.6320312
84.1 -9.8140094
85 -12.3886736
85.1 -12.9196365
85.2 -9.6433248
85.3 -6.3296340
85.4 -7.0405525
85.5 -13.6714939
86 -10.8756412
86.1 -12.0055331
86.2 -13.3724699
86.3 -13.3252145
86.4 -14.9191290
86.5 -17.7515546
87 -10.7027963
87.1 -22.4941954
87.2 -14.9616716
88 -2.2264493
88.1 -8.9626474
88.2 -2.5095281
88.3 -16.3345673
89 -11.0459647
90 -4.5610239
90.1 -11.7036651
90.2 -5.3838521
90.3 -4.1636999
91 -7.1462503
91.1 -12.8374475
91.2 -18.2576707
92 -6.4119222
93 5.2122168
93.1 3.1211725
93.2 -3.6841177
93.3 2.6223542
93.4 -11.1877696
94 -6.9602492
94.1 -7.4318416
94.2 -4.3498045
94.3 -11.6340088
94.4 -12.9357964
94.5 -14.7648530
95 -12.8849309
95.1 -9.7451502
95.2 -0.8535063
96 -4.9139832
96.1 -3.9582653
96.2 -9.6555492
96.3 -11.8690793
96.4 -11.0224373
96.5 -10.9530403
97 -9.8540471
97.1 -19.2262840
98 -11.9651231
98.1 -2.6515128
98.2 -12.2606382
99 -11.4720500
99.1 -14.0596866
99.2 -17.3939469
100 1.1005874
100.1 -3.8226248
100.2 -0.9123182
100.3 -15.8389474
100.4 -12.8093826
$m0a3$mu_reg_norm
[1] 0
$m0a3$tau_reg_norm
[1] 1e-04
$m0a3$shape_tau_norm
[1] 0.01
$m0a3$rate_tau_norm
[1] 0.01
$m0a3$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0a3$shape_diag_RinvD
[1] "0.01"
$m0a3$rate_diag_RinvD
[1] "0.001"
$m0a4
$m0a4$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0a4$M_lvlone
y
1 -13.0493856
1.1 -9.3335901
1.2 -22.3469852
1.3 -15.0417337
2 -12.0655434
2.1 -15.8674476
2.2 -7.8800006
3 -11.4820604
3.1 -10.5983220
3.2 -22.4519157
4 -1.2697775
4.1 -11.1215184
4.2 -3.6134138
4.3 -14.5982385
5 -6.8457515
5.1 -7.0551214
5.2 -12.3418980
5.3 -9.2366906
6 -5.1648211
7 -10.0599502
7.1 -18.3267285
7.2 -12.5138426
8 -1.6305331
8.1 -9.6520453
8.2 -1.5278462
8.3 -7.4172211
8.4 -7.1238609
8.5 -8.8706950
9 -0.1634429
9.1 -2.6034300
9.2 -6.7272369
10 -6.4172202
10.1 -11.4834569
11 -8.7911356
11.1 -19.6645080
11.2 -20.2030932
11.3 -21.3082176
11.4 -14.5802901
12 -15.2006287
13 0.8058816
13.1 -13.6379208
14 -15.3422873
14.1 -10.0965208
14.2 -16.6452027
14.3 -15.8389733
15 -8.9424594
15.1 -22.0101983
15.2 -7.3975599
15.3 -10.3567334
16 -1.9691302
16.1 -9.9308357
16.2 -6.9626923
16.3 -3.2862557
16.4 -3.3972355
16.5 -11.5767835
17 -10.5474144
17.1 -7.6215009
17.2 -16.5386939
17.3 -20.0004774
17.4 -18.8505475
18 -19.7302351
19 -14.6177568
19.1 -17.8043866
19.2 -15.1641705
19.3 -16.6898418
20 -12.9059229
20.1 -16.8191201
20.2 -6.1010131
20.3 -7.9415371
20.4 -9.3904458
20.5 -13.3504189
21 -7.6974718
21.1 -11.9335526
21.2 -12.7064929
22 -21.5022909
22.1 -12.7745451
23 -3.5146508
23.1 -4.6724048
24 -2.5619821
25 -6.2944970
25.1 -3.8630505
25.2 -14.4205140
25.3 -19.6735037
25.4 -9.0288933
25.5 -9.0509738
26 -19.7340685
26.1 -14.1692728
26.2 -17.2819976
26.3 -24.6265576
27 -7.3354999
27.1 -11.1488468
28 -11.7996597
28.1 -8.2030122
28.2 -26.4317815
28.3 -18.5016071
29 -5.8551395
29.1 -2.0209442
29.2 -5.6368080
29.3 -3.8110961
30 -12.7217702
30.1 -17.0170140
30.2 -25.4236089
31 -17.0783921
32 -18.4338764
32.1 -19.4317212
32.2 -19.4738978
32.3 -21.4922645
33 2.0838099
33.1 -13.3172274
34 -10.0296691
34.1 -25.9426553
34.2 -18.5688138
34.3 -15.4173859
35 -14.3958113
35.1 -12.9457541
35.2 -16.1380691
36 -12.8166968
36.1 -14.3989481
36.2 -12.2436943
36.3 -15.0104638
36.4 -10.1775457
37 -15.2223495
37.1 -14.7526195
37.2 -19.8168430
38 -2.7065118
39 -8.7288138
39.1 -9.2746473
39.2 -18.2695344
39.3 -13.8219083
39.4 -16.2254704
39.5 -21.7283648
40 1.8291916
40.1 -6.6916432
40.2 -1.6278171
40.3 -10.5749790
41 -3.1556121
41.1 -11.5895327
41.2 -18.9352091
41.3 -15.9788960
41.4 -9.6070508
42 -5.2159485
42.1 -15.9878743
43 -16.6104361
43.1 -9.5549441
43.2 -14.2003491
44 -8.1969033
44.1 -19.9270197
44.2 -22.6521171
44.3 -21.1903736
45 -0.5686627
45.1 -7.5645740
46 -19.1624789
46.1 -18.4487574
46.2 -15.8222682
47 -5.4165074
47.1 -15.0975029
47.2 -12.9971413
47.3 -10.6844521
47.4 -18.2214784
48 -8.3101471
48.1 -18.3854275
49 -13.0130319
50 -10.4579977
51 -19.3157621
52 -4.4747188
52.1 -4.3163827
52.2 -6.9761408
52.3 -20.1764756
52.4 -8.9036692
52.5 -5.6949642
53 -10.3141887
53.1 -8.2642654
53.2 -9.1691554
54 -6.2198754
54.1 -15.7192609
54.2 -13.0978998
54.3 -5.1195299
54.4 -16.5771751
55 -5.7348534
55.1 -7.3217494
55.2 -12.2171938
55.3 -12.9821266
55.4 -14.8599983
56 -14.1764282
56.1 -12.5343602
56.2 -8.4573382
56.3 -12.4633969
56.4 -17.3841863
56.5 -14.8147645
57 -3.1403293
57.1 -11.1509248
57.2 -6.3940143
57.3 -9.3473241
58 -12.0245677
58.1 -9.2112246
58.2 -1.2071742
58.3 -11.0141711
58.4 -5.3721214
58.5 -7.8523047
59 -13.2946560
59.1 -10.0530648
60 -19.2209402
61 -4.6699914
61.1 -3.5981894
61.2 -1.4713611
61.3 -3.8819786
61.4 0.1041413
62 -2.8591600
62.1 -6.9461986
62.2 -16.7910593
62.3 -17.9844596
63 -24.0335535
63.1 -11.7765300
64 -20.5963897
65 -2.7969169
65.1 -11.1778694
65.2 -5.2830399
65.3 -7.9353390
66 -13.2318328
66.1 -1.9090560
66.2 -16.6643889
67 -25.6073277
68 -13.4806759
68.1 -18.4557183
68.2 -13.3982327
68.3 -12.4977127
68.4 -11.7073990
69 -14.5290675
70 -15.2122709
70.1 -7.8681167
71 -10.3352703
71.1 -7.5699888
71.2 -18.4680702
71.3 -21.4316644
71.4 -8.1137650
72 -9.1848162
72.1 -23.7538846
72.2 -26.3421306
72.3 -27.2843801
72.4 -20.8541617
72.5 -12.8948965
73 -2.6091307
74 -8.2790175
75 -12.5029612
76 -6.0061671
76.1 -8.8149114
76.2 -11.8359043
77 0.4772521
78 -9.4105229
79 -1.0217265
79.1 -11.8125257
79.2 -10.5465186
80 -12.7366807
80.1 -9.0584783
80.2 -16.6381566
81 0.5547913
81.1 -4.0892715
81.2 1.8283303
81.3 -5.2166381
82 -3.0749381
82.1 -10.5506696
82.2 -18.2226347
83 -12.5872635
83.1 -11.9756502
83.2 -10.6744217
83.3 -19.2714012
84 -2.6320312
84.1 -9.8140094
85 -12.3886736
85.1 -12.9196365
85.2 -9.6433248
85.3 -6.3296340
85.4 -7.0405525
85.5 -13.6714939
86 -10.8756412
86.1 -12.0055331
86.2 -13.3724699
86.3 -13.3252145
86.4 -14.9191290
86.5 -17.7515546
87 -10.7027963
87.1 -22.4941954
87.2 -14.9616716
88 -2.2264493
88.1 -8.9626474
88.2 -2.5095281
88.3 -16.3345673
89 -11.0459647
90 -4.5610239
90.1 -11.7036651
90.2 -5.3838521
90.3 -4.1636999
91 -7.1462503
91.1 -12.8374475
91.2 -18.2576707
92 -6.4119222
93 5.2122168
93.1 3.1211725
93.2 -3.6841177
93.3 2.6223542
93.4 -11.1877696
94 -6.9602492
94.1 -7.4318416
94.2 -4.3498045
94.3 -11.6340088
94.4 -12.9357964
94.5 -14.7648530
95 -12.8849309
95.1 -9.7451502
95.2 -0.8535063
96 -4.9139832
96.1 -3.9582653
96.2 -9.6555492
96.3 -11.8690793
96.4 -11.0224373
96.5 -10.9530403
97 -9.8540471
97.1 -19.2262840
98 -11.9651231
98.1 -2.6515128
98.2 -12.2606382
99 -11.4720500
99.1 -14.0596866
99.2 -17.3939469
100 1.1005874
100.1 -3.8226248
100.2 -0.9123182
100.3 -15.8389474
100.4 -12.8093826
$m0a4$mu_reg_norm
[1] 0
$m0a4$tau_reg_norm
[1] 1e-04
$m0a4$shape_tau_norm
[1] 0.01
$m0a4$rate_tau_norm
[1] 0.01
$m0a4$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0a4$shape_diag_RinvD
[1] "0.01"
$m0a4$rate_diag_RinvD
[1] "0.001"
$m0b1
$m0b1$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0b1$M_lvlone
b1
1 0
1.1 1
1.2 1
1.3 0
2 1
2.1 1
2.2 1
3 1
3.1 0
3.2 0
4 1
4.1 1
4.2 0
4.3 1
5 0
5.1 1
5.2 1
5.3 1
6 0
7 1
7.1 0
7.2 1
8 0
8.1 1
8.2 1
8.3 0
8.4 0
8.5 1
9 1
9.1 1
9.2 0
10 1
10.1 1
11 1
11.1 1
11.2 1
11.3 1
11.4 1
12 1
13 0
13.1 1
14 0
14.1 1
14.2 0
14.3 0
15 0
15.1 0
15.2 0
15.3 1
16 1
16.1 0
16.2 1
16.3 1
16.4 1
16.5 0
17 0
17.1 0
17.2 1
17.3 0
17.4 1
18 1
19 1
19.1 1
19.2 1
19.3 1
20 0
20.1 1
20.2 0
20.3 0
20.4 0
20.5 0
21 1
21.1 1
21.2 0
22 0
22.1 1
23 1
23.1 1
24 0
25 0
25.1 1
25.2 1
25.3 0
25.4 0
25.5 0
26 1
26.1 1
26.2 1
26.3 0
27 1
27.1 1
28 1
28.1 0
28.2 1
28.3 1
29 1
29.1 0
29.2 0
29.3 1
30 1
30.1 1
30.2 1
31 0
32 1
32.1 1
32.2 1
32.3 1
33 0
33.1 0
34 1
34.1 0
34.2 1
34.3 1
35 1
35.1 0
35.2 1
36 0
36.1 0
36.2 1
36.3 0
36.4 1
37 1
37.1 0
37.2 0
38 1
39 1
39.1 0
39.2 0
39.3 0
39.4 1
39.5 1
40 0
40.1 0
40.2 0
40.3 1
41 1
41.1 1
41.2 0
41.3 1
41.4 1
42 1
42.1 1
43 0
43.1 0
43.2 1
44 1
44.1 0
44.2 0
44.3 1
45 1
45.1 0
46 1
46.1 0
46.2 1
47 0
47.1 0
47.2 1
47.3 0
47.4 0
48 0
48.1 1
49 0
50 1
51 1
52 1
52.1 1
52.2 0
52.3 0
52.4 1
52.5 1
53 1
53.1 1
53.2 1
54 0
54.1 1
54.2 0
54.3 1
54.4 0
55 1
55.1 1
55.2 1
55.3 0
55.4 1
56 0
56.1 1
56.2 1
56.3 0
56.4 0
56.5 1
57 1
57.1 1
57.2 0
57.3 0
58 1
58.1 1
58.2 1
58.3 1
58.4 1
58.5 1
59 0
59.1 1
60 0
61 1
61.1 1
61.2 1
61.3 0
61.4 1
62 1
62.1 0
62.2 0
62.3 1
63 0
63.1 1
64 1
65 1
65.1 1
65.2 0
65.3 0
66 1
66.1 0
66.2 0
67 0
68 0
68.1 0
68.2 0
68.3 0
68.4 1
69 1
70 1
70.1 1
71 1
71.1 1
71.2 0
71.3 0
71.4 0
72 1
72.1 1
72.2 1
72.3 0
72.4 0
72.5 1
73 1
74 1
75 0
76 1
76.1 1
76.2 1
77 1
78 1
79 0
79.1 1
79.2 0
80 1
80.1 0
80.2 1
81 1
81.1 1
81.2 1
81.3 1
82 1
82.1 1
82.2 0
83 1
83.1 0
83.2 0
83.3 1
84 1
84.1 0
85 0
85.1 0
85.2 1
85.3 1
85.4 1
85.5 1
86 0
86.1 1
86.2 1
86.3 0
86.4 1
86.5 0
87 0
87.1 1
87.2 0
88 0
88.1 0
88.2 0
88.3 0
89 1
90 0
90.1 1
90.2 1
90.3 0
91 0
91.1 0
91.2 1
92 1
93 0
93.1 1
93.2 0
93.3 1
93.4 0
94 1
94.1 0
94.2 1
94.3 0
94.4 0
94.5 0
95 1
95.1 1
95.2 0
96 1
96.1 0
96.2 0
96.3 0
96.4 0
96.5 1
97 0
97.1 0
98 0
98.1 0
98.2 0
99 1
99.1 1
99.2 1
100 0
100.1 0
100.2 1
100.3 1
100.4 1
$m0b1$mu_reg_binom
[1] 0
$m0b1$tau_reg_binom
[1] 1e-04
$m0b1$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0b1$shape_diag_RinvD
[1] "0.01"
$m0b1$rate_diag_RinvD
[1] "0.001"
$m0b2
$m0b2$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0b2$M_lvlone
b1
1 0
1.1 1
1.2 1
1.3 0
2 1
2.1 1
2.2 1
3 1
3.1 0
3.2 0
4 1
4.1 1
4.2 0
4.3 1
5 0
5.1 1
5.2 1
5.3 1
6 0
7 1
7.1 0
7.2 1
8 0
8.1 1
8.2 1
8.3 0
8.4 0
8.5 1
9 1
9.1 1
9.2 0
10 1
10.1 1
11 1
11.1 1
11.2 1
11.3 1
11.4 1
12 1
13 0
13.1 1
14 0
14.1 1
14.2 0
14.3 0
15 0
15.1 0
15.2 0
15.3 1
16 1
16.1 0
16.2 1
16.3 1
16.4 1
16.5 0
17 0
17.1 0
17.2 1
17.3 0
17.4 1
18 1
19 1
19.1 1
19.2 1
19.3 1
20 0
20.1 1
20.2 0
20.3 0
20.4 0
20.5 0
21 1
21.1 1
21.2 0
22 0
22.1 1
23 1
23.1 1
24 0
25 0
25.1 1
25.2 1
25.3 0
25.4 0
25.5 0
26 1
26.1 1
26.2 1
26.3 0
27 1
27.1 1
28 1
28.1 0
28.2 1
28.3 1
29 1
29.1 0
29.2 0
29.3 1
30 1
30.1 1
30.2 1
31 0
32 1
32.1 1
32.2 1
32.3 1
33 0
33.1 0
34 1
34.1 0
34.2 1
34.3 1
35 1
35.1 0
35.2 1
36 0
36.1 0
36.2 1
36.3 0
36.4 1
37 1
37.1 0
37.2 0
38 1
39 1
39.1 0
39.2 0
39.3 0
39.4 1
39.5 1
40 0
40.1 0
40.2 0
40.3 1
41 1
41.1 1
41.2 0
41.3 1
41.4 1
42 1
42.1 1
43 0
43.1 0
43.2 1
44 1
44.1 0
44.2 0
44.3 1
45 1
45.1 0
46 1
46.1 0
46.2 1
47 0
47.1 0
47.2 1
47.3 0
47.4 0
48 0
48.1 1
49 0
50 1
51 1
52 1
52.1 1
52.2 0
52.3 0
52.4 1
52.5 1
53 1
53.1 1
53.2 1
54 0
54.1 1
54.2 0
54.3 1
54.4 0
55 1
55.1 1
55.2 1
55.3 0
55.4 1
56 0
56.1 1
56.2 1
56.3 0
56.4 0
56.5 1
57 1
57.1 1
57.2 0
57.3 0
58 1
58.1 1
58.2 1
58.3 1
58.4 1
58.5 1
59 0
59.1 1
60 0
61 1
61.1 1
61.2 1
61.3 0
61.4 1
62 1
62.1 0
62.2 0
62.3 1
63 0
63.1 1
64 1
65 1
65.1 1
65.2 0
65.3 0
66 1
66.1 0
66.2 0
67 0
68 0
68.1 0
68.2 0
68.3 0
68.4 1
69 1
70 1
70.1 1
71 1
71.1 1
71.2 0
71.3 0
71.4 0
72 1
72.1 1
72.2 1
72.3 0
72.4 0
72.5 1
73 1
74 1
75 0
76 1
76.1 1
76.2 1
77 1
78 1
79 0
79.1 1
79.2 0
80 1
80.1 0
80.2 1
81 1
81.1 1
81.2 1
81.3 1
82 1
82.1 1
82.2 0
83 1
83.1 0
83.2 0
83.3 1
84 1
84.1 0
85 0
85.1 0
85.2 1
85.3 1
85.4 1
85.5 1
86 0
86.1 1
86.2 1
86.3 0
86.4 1
86.5 0
87 0
87.1 1
87.2 0
88 0
88.1 0
88.2 0
88.3 0
89 1
90 0
90.1 1
90.2 1
90.3 0
91 0
91.1 0
91.2 1
92 1
93 0
93.1 1
93.2 0
93.3 1
93.4 0
94 1
94.1 0
94.2 1
94.3 0
94.4 0
94.5 0
95 1
95.1 1
95.2 0
96 1
96.1 0
96.2 0
96.3 0
96.4 0
96.5 1
97 0
97.1 0
98 0
98.1 0
98.2 0
99 1
99.1 1
99.2 1
100 0
100.1 0
100.2 1
100.3 1
100.4 1
$m0b2$mu_reg_binom
[1] 0
$m0b2$tau_reg_binom
[1] 1e-04
$m0b2$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0b2$shape_diag_RinvD
[1] "0.01"
$m0b2$rate_diag_RinvD
[1] "0.001"
$m0b3
$m0b3$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0b3$M_lvlone
b1
1 0
1.1 1
1.2 1
1.3 0
2 1
2.1 1
2.2 1
3 1
3.1 0
3.2 0
4 1
4.1 1
4.2 0
4.3 1
5 0
5.1 1
5.2 1
5.3 1
6 0
7 1
7.1 0
7.2 1
8 0
8.1 1
8.2 1
8.3 0
8.4 0
8.5 1
9 1
9.1 1
9.2 0
10 1
10.1 1
11 1
11.1 1
11.2 1
11.3 1
11.4 1
12 1
13 0
13.1 1
14 0
14.1 1
14.2 0
14.3 0
15 0
15.1 0
15.2 0
15.3 1
16 1
16.1 0
16.2 1
16.3 1
16.4 1
16.5 0
17 0
17.1 0
17.2 1
17.3 0
17.4 1
18 1
19 1
19.1 1
19.2 1
19.3 1
20 0
20.1 1
20.2 0
20.3 0
20.4 0
20.5 0
21 1
21.1 1
21.2 0
22 0
22.1 1
23 1
23.1 1
24 0
25 0
25.1 1
25.2 1
25.3 0
25.4 0
25.5 0
26 1
26.1 1
26.2 1
26.3 0
27 1
27.1 1
28 1
28.1 0
28.2 1
28.3 1
29 1
29.1 0
29.2 0
29.3 1
30 1
30.1 1
30.2 1
31 0
32 1
32.1 1
32.2 1
32.3 1
33 0
33.1 0
34 1
34.1 0
34.2 1
34.3 1
35 1
35.1 0
35.2 1
36 0
36.1 0
36.2 1
36.3 0
36.4 1
37 1
37.1 0
37.2 0
38 1
39 1
39.1 0
39.2 0
39.3 0
39.4 1
39.5 1
40 0
40.1 0
40.2 0
40.3 1
41 1
41.1 1
41.2 0
41.3 1
41.4 1
42 1
42.1 1
43 0
43.1 0
43.2 1
44 1
44.1 0
44.2 0
44.3 1
45 1
45.1 0
46 1
46.1 0
46.2 1
47 0
47.1 0
47.2 1
47.3 0
47.4 0
48 0
48.1 1
49 0
50 1
51 1
52 1
52.1 1
52.2 0
52.3 0
52.4 1
52.5 1
53 1
53.1 1
53.2 1
54 0
54.1 1
54.2 0
54.3 1
54.4 0
55 1
55.1 1
55.2 1
55.3 0
55.4 1
56 0
56.1 1
56.2 1
56.3 0
56.4 0
56.5 1
57 1
57.1 1
57.2 0
57.3 0
58 1
58.1 1
58.2 1
58.3 1
58.4 1
58.5 1
59 0
59.1 1
60 0
61 1
61.1 1
61.2 1
61.3 0
61.4 1
62 1
62.1 0
62.2 0
62.3 1
63 0
63.1 1
64 1
65 1
65.1 1
65.2 0
65.3 0
66 1
66.1 0
66.2 0
67 0
68 0
68.1 0
68.2 0
68.3 0
68.4 1
69 1
70 1
70.1 1
71 1
71.1 1
71.2 0
71.3 0
71.4 0
72 1
72.1 1
72.2 1
72.3 0
72.4 0
72.5 1
73 1
74 1
75 0
76 1
76.1 1
76.2 1
77 1
78 1
79 0
79.1 1
79.2 0
80 1
80.1 0
80.2 1
81 1
81.1 1
81.2 1
81.3 1
82 1
82.1 1
82.2 0
83 1
83.1 0
83.2 0
83.3 1
84 1
84.1 0
85 0
85.1 0
85.2 1
85.3 1
85.4 1
85.5 1
86 0
86.1 1
86.2 1
86.3 0
86.4 1
86.5 0
87 0
87.1 1
87.2 0
88 0
88.1 0
88.2 0
88.3 0
89 1
90 0
90.1 1
90.2 1
90.3 0
91 0
91.1 0
91.2 1
92 1
93 0
93.1 1
93.2 0
93.3 1
93.4 0
94 1
94.1 0
94.2 1
94.3 0
94.4 0
94.5 0
95 1
95.1 1
95.2 0
96 1
96.1 0
96.2 0
96.3 0
96.4 0
96.5 1
97 0
97.1 0
98 0
98.1 0
98.2 0
99 1
99.1 1
99.2 1
100 0
100.1 0
100.2 1
100.3 1
100.4 1
$m0b3$mu_reg_binom
[1] 0
$m0b3$tau_reg_binom
[1] 1e-04
$m0b3$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0b3$shape_diag_RinvD
[1] "0.01"
$m0b3$rate_diag_RinvD
[1] "0.001"
$m0b4
$m0b4$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0b4$M_lvlone
b1
1 0
1.1 1
1.2 1
1.3 0
2 1
2.1 1
2.2 1
3 1
3.1 0
3.2 0
4 1
4.1 1
4.2 0
4.3 1
5 0
5.1 1
5.2 1
5.3 1
6 0
7 1
7.1 0
7.2 1
8 0
8.1 1
8.2 1
8.3 0
8.4 0
8.5 1
9 1
9.1 1
9.2 0
10 1
10.1 1
11 1
11.1 1
11.2 1
11.3 1
11.4 1
12 1
13 0
13.1 1
14 0
14.1 1
14.2 0
14.3 0
15 0
15.1 0
15.2 0
15.3 1
16 1
16.1 0
16.2 1
16.3 1
16.4 1
16.5 0
17 0
17.1 0
17.2 1
17.3 0
17.4 1
18 1
19 1
19.1 1
19.2 1
19.3 1
20 0
20.1 1
20.2 0
20.3 0
20.4 0
20.5 0
21 1
21.1 1
21.2 0
22 0
22.1 1
23 1
23.1 1
24 0
25 0
25.1 1
25.2 1
25.3 0
25.4 0
25.5 0
26 1
26.1 1
26.2 1
26.3 0
27 1
27.1 1
28 1
28.1 0
28.2 1
28.3 1
29 1
29.1 0
29.2 0
29.3 1
30 1
30.1 1
30.2 1
31 0
32 1
32.1 1
32.2 1
32.3 1
33 0
33.1 0
34 1
34.1 0
34.2 1
34.3 1
35 1
35.1 0
35.2 1
36 0
36.1 0
36.2 1
36.3 0
36.4 1
37 1
37.1 0
37.2 0
38 1
39 1
39.1 0
39.2 0
39.3 0
39.4 1
39.5 1
40 0
40.1 0
40.2 0
40.3 1
41 1
41.1 1
41.2 0
41.3 1
41.4 1
42 1
42.1 1
43 0
43.1 0
43.2 1
44 1
44.1 0
44.2 0
44.3 1
45 1
45.1 0
46 1
46.1 0
46.2 1
47 0
47.1 0
47.2 1
47.3 0
47.4 0
48 0
48.1 1
49 0
50 1
51 1
52 1
52.1 1
52.2 0
52.3 0
52.4 1
52.5 1
53 1
53.1 1
53.2 1
54 0
54.1 1
54.2 0
54.3 1
54.4 0
55 1
55.1 1
55.2 1
55.3 0
55.4 1
56 0
56.1 1
56.2 1
56.3 0
56.4 0
56.5 1
57 1
57.1 1
57.2 0
57.3 0
58 1
58.1 1
58.2 1
58.3 1
58.4 1
58.5 1
59 0
59.1 1
60 0
61 1
61.1 1
61.2 1
61.3 0
61.4 1
62 1
62.1 0
62.2 0
62.3 1
63 0
63.1 1
64 1
65 1
65.1 1
65.2 0
65.3 0
66 1
66.1 0
66.2 0
67 0
68 0
68.1 0
68.2 0
68.3 0
68.4 1
69 1
70 1
70.1 1
71 1
71.1 1
71.2 0
71.3 0
71.4 0
72 1
72.1 1
72.2 1
72.3 0
72.4 0
72.5 1
73 1
74 1
75 0
76 1
76.1 1
76.2 1
77 1
78 1
79 0
79.1 1
79.2 0
80 1
80.1 0
80.2 1
81 1
81.1 1
81.2 1
81.3 1
82 1
82.1 1
82.2 0
83 1
83.1 0
83.2 0
83.3 1
84 1
84.1 0
85 0
85.1 0
85.2 1
85.3 1
85.4 1
85.5 1
86 0
86.1 1
86.2 1
86.3 0
86.4 1
86.5 0
87 0
87.1 1
87.2 0
88 0
88.1 0
88.2 0
88.3 0
89 1
90 0
90.1 1
90.2 1
90.3 0
91 0
91.1 0
91.2 1
92 1
93 0
93.1 1
93.2 0
93.3 1
93.4 0
94 1
94.1 0
94.2 1
94.3 0
94.4 0
94.5 0
95 1
95.1 1
95.2 0
96 1
96.1 0
96.2 0
96.3 0
96.4 0
96.5 1
97 0
97.1 0
98 0
98.1 0
98.2 0
99 1
99.1 1
99.2 1
100 0
100.1 0
100.2 1
100.3 1
100.4 1
$m0b4$mu_reg_binom
[1] 0
$m0b4$tau_reg_binom
[1] 1e-04
$m0b4$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0b4$shape_diag_RinvD
[1] "0.01"
$m0b4$rate_diag_RinvD
[1] "0.001"
$m0c1
$m0c1$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0c1$M_lvlone
L1
1 0.09647609
1.1 0.47743206
1.2 0.49307743
1.3 0.18468863
2 0.54595313
2.1 0.21966792
2.2 0.73654737
3 0.20862809
3.1 0.24312223
3.2 0.03051627
4 0.39499609
4.1 0.72632316
4.2 0.34199228
4.3 0.38062927
5 0.62202135
5.1 0.20305630
5.2 0.41717969
5.3 0.23980703
6 0.37653463
7 0.36356663
7.1 0.06266071
7.2 0.37849716
8 0.37802506
8.1 0.61143062
8.2 0.75648801
8.3 2.54406375
8.4 1.18637590
8.5 0.05930316
9 0.95013074
9.1 0.11917116
9.2 0.86629295
10 0.23914695
10.1 0.13708051
11 0.11067204
11.1 0.23176079
11.2 0.60038623
11.3 0.42684714
11.4 0.16458522
12 0.12861686
13 1.33377494
13.1 0.37267514
14 0.48728084
14.1 0.31792264
14.2 0.89257832
14.3 0.48509920
15 0.37711346
15.1 0.24850749
15.2 0.48117461
15.3 0.42758680
16 0.43666855
16.1 0.18190724
16.2 0.18617239
16.3 1.87047608
16.4 0.41864602
16.5 0.43588009
17 0.17925916
17.1 0.32367639
17.2 0.24912593
17.3 0.56230768
17.4 0.26182608
18 0.42338083
19 0.23371438
19.1 0.45720781
19.2 1.07923724
19.3 0.74433885
20 0.23860936
20.1 1.49001161
20.2 0.82847676
20.3 0.71062057
20.4 0.58928158
20.5 0.49204025
21 0.39710041
21.1 0.63253881
21.2 0.58877978
22 0.30440876
22.1 0.42787265
23 0.15078177
23.1 0.97104584
24 0.55355206
25 0.76006220
25.1 0.42500306
25.2 0.68011522
25.3 0.38187835
25.4 0.67265847
25.5 0.09078197
26 0.17032539
26.1 0.36699769
26.2 0.19300220
26.3 1.26993276
27 0.63999648
27.1 1.14153094
28 0.39991376
28.1 0.20658853
28.2 0.42519397
28.3 1.68848543
29 0.20853337
29.1 0.32240000
29.2 0.59527557
29.3 0.34253455
30 0.70885491
30.1 0.31107139
30.2 0.46423208
31 0.54603320
32 0.48896515
32.1 0.26838930
32.2 0.33314256
32.3 0.15482204
33 0.63379200
33.1 0.53403306
34 0.30684588
34.1 0.15596697
34.2 0.73177916
34.3 0.78232073
35 0.12725486
35.1 0.32104659
35.2 0.92993903
36 0.82634942
36.1 0.15790991
36.2 0.28319688
36.3 0.30894311
36.4 0.38835761
37 0.28006122
37.1 0.51936935
37.2 0.03553058
38 0.10984700
39 1.01908377
39.1 0.58760885
39.2 0.63292533
39.3 0.42095489
39.4 0.25220230
39.5 0.51242643
40 0.70636121
40.1 1.22834105
40.2 0.81839083
40.3 0.23540757
41 0.08592119
41.1 0.22834515
41.2 1.61636130
41.3 0.15342660
41.4 0.47650400
42 0.64398703
42.1 1.15130398
43 0.79292461
43.1 0.38506794
43.2 0.11139587
44 0.89129328
44.1 0.08958946
44.2 0.85701827
44.3 0.96417530
45 0.51097634
45.1 0.98340980
46 0.44798505
46.1 0.82655580
46.2 0.37637628
47 0.41876182
47.1 0.48389648
47.2 0.02396924
47.3 1.80138667
47.4 0.61109603
48 0.19473894
48.1 0.04006959
49 0.29560575
50 0.15625313
51 0.47908892
52 1.40786781
52.1 0.35019229
52.2 0.39332493
52.3 0.51225821
52.4 0.11419627
52.5 0.55575005
53 0.13011523
53.1 0.90571584
53.2 0.50906734
54 0.46031273
54.1 0.46156182
54.2 0.52071389
54.3 0.76983675
54.4 0.52623423
55 0.60555180
55.1 0.10776713
55.2 1.03837178
55.3 0.45001542
55.4 0.65395611
56 0.07535464
56.1 0.73328954
56.2 0.27578594
56.3 0.68719648
56.4 1.57220834
56.5 0.28753078
57 0.17289659
57.1 0.72170220
57.2 1.26500225
57.3 0.20213479
58 0.13611631
58.1 0.37311297
58.2 0.72470365
58.3 1.43014769
58.4 0.78817203
58.5 0.78387559
59 0.46747067
59.1 0.04947979
60 0.16059397
61 0.29220662
61.1 0.41535569
61.2 0.73742285
61.3 0.43320659
61.4 1.19954814
62 0.20260386
62.1 0.06652907
62.2 0.25063288
62.3 0.36290927
63 0.52314649
63.1 0.25699016
64 1.02878746
65 0.45575444
65.1 0.46306113
65.2 0.42269832
65.3 0.73172542
66 0.74765742
66.1 0.25888221
66.2 0.38244280
67 0.23644835
68 0.83195685
68.1 0.68395486
68.2 0.53889898
68.3 0.33762340
68.4 0.79922369
69 0.20260053
70 1.04535151
70.1 0.03979648
71 0.56397408
71.1 0.34854738
71.2 0.97913866
71.3 0.19630242
71.4 0.31230175
72 1.04871582
72.1 0.09370234
72.2 0.72454755
72.3 0.80705501
72.4 0.40641012
72.5 0.81634161
73 0.74327324
74 0.49202243
75 0.42954173
76 1.22280380
76.1 0.09905853
76.2 0.34132786
77 1.20980413
78 0.26184214
79 0.94287180
79.1 0.08463026
79.2 0.66769705
80 0.68766428
80.1 0.95426300
80.2 1.84421668
81 0.60279596
81.1 0.73369496
81.2 0.83514184
81.3 0.91767999
82 0.46992524
82.1 0.50002097
82.2 0.43711796
83 0.46587065
83.1 0.43364034
83.2 0.23196757
83.3 0.73616193
84 0.47791427
84.1 0.05551055
85 0.27482891
85.1 1.77694842
85.2 0.71141066
85.3 0.78806704
85.4 0.80223323
85.5 0.22172219
86 0.15018053
86.1 0.31597396
86.2 0.95686193
86.3 0.11022188
86.4 0.68477369
86.5 0.33125367
87 0.29289308
87.1 0.66197512
87.2 0.30055939
88 0.22930153
88.1 1.02206005
88.2 0.52724756
88.3 0.16276648
89 0.09190440
90 0.15333982
90.1 0.42756943
90.2 0.60354432
90.3 0.41070560
91 1.01739949
91.1 0.41121541
91.2 0.08932488
92 1.08669724
93 0.30303806
93.1 0.16800845
93.2 1.29098296
93.3 0.39962093
93.4 0.88339337
94 0.23233022
94.1 0.08638527
94.2 0.43737650
94.3 0.19800807
94.4 0.42942963
94.5 0.14150685
95 1.07323107
95.1 0.26037856
95.2 0.48623052
96 0.79796998
96.1 0.30822508
96.2 0.91060931
96.3 0.26069030
96.4 0.22889234
96.5 0.97046560
97 0.16946638
97.1 0.20265816
98 1.22465795
98.1 0.15250019
98.2 0.44675949
99 0.44238919
99.1 0.63211897
99.2 0.40140806
100 0.10484468
100.1 0.56141377
100.2 0.23655004
100.3 0.74552230
100.4 0.34230391
$m0c1$mu_reg_gamma
[1] 0
$m0c1$tau_reg_gamma
[1] 1e-04
$m0c1$shape_tau_gamma
[1] 0.01
$m0c1$rate_tau_gamma
[1] 0.01
$m0c1$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0c1$shape_diag_RinvD
[1] "0.01"
$m0c1$rate_diag_RinvD
[1] "0.001"
$m0c2
$m0c2$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0c2$M_lvlone
L1
1 0.09647609
1.1 0.47743206
1.2 0.49307743
1.3 0.18468863
2 0.54595313
2.1 0.21966792
2.2 0.73654737
3 0.20862809
3.1 0.24312223
3.2 0.03051627
4 0.39499609
4.1 0.72632316
4.2 0.34199228
4.3 0.38062927
5 0.62202135
5.1 0.20305630
5.2 0.41717969
5.3 0.23980703
6 0.37653463
7 0.36356663
7.1 0.06266071
7.2 0.37849716
8 0.37802506
8.1 0.61143062
8.2 0.75648801
8.3 2.54406375
8.4 1.18637590
8.5 0.05930316
9 0.95013074
9.1 0.11917116
9.2 0.86629295
10 0.23914695
10.1 0.13708051
11 0.11067204
11.1 0.23176079
11.2 0.60038623
11.3 0.42684714
11.4 0.16458522
12 0.12861686
13 1.33377494
13.1 0.37267514
14 0.48728084
14.1 0.31792264
14.2 0.89257832
14.3 0.48509920
15 0.37711346
15.1 0.24850749
15.2 0.48117461
15.3 0.42758680
16 0.43666855
16.1 0.18190724
16.2 0.18617239
16.3 1.87047608
16.4 0.41864602
16.5 0.43588009
17 0.17925916
17.1 0.32367639
17.2 0.24912593
17.3 0.56230768
17.4 0.26182608
18 0.42338083
19 0.23371438
19.1 0.45720781
19.2 1.07923724
19.3 0.74433885
20 0.23860936
20.1 1.49001161
20.2 0.82847676
20.3 0.71062057
20.4 0.58928158
20.5 0.49204025
21 0.39710041
21.1 0.63253881
21.2 0.58877978
22 0.30440876
22.1 0.42787265
23 0.15078177
23.1 0.97104584
24 0.55355206
25 0.76006220
25.1 0.42500306
25.2 0.68011522
25.3 0.38187835
25.4 0.67265847
25.5 0.09078197
26 0.17032539
26.1 0.36699769
26.2 0.19300220
26.3 1.26993276
27 0.63999648
27.1 1.14153094
28 0.39991376
28.1 0.20658853
28.2 0.42519397
28.3 1.68848543
29 0.20853337
29.1 0.32240000
29.2 0.59527557
29.3 0.34253455
30 0.70885491
30.1 0.31107139
30.2 0.46423208
31 0.54603320
32 0.48896515
32.1 0.26838930
32.2 0.33314256
32.3 0.15482204
33 0.63379200
33.1 0.53403306
34 0.30684588
34.1 0.15596697
34.2 0.73177916
34.3 0.78232073
35 0.12725486
35.1 0.32104659
35.2 0.92993903
36 0.82634942
36.1 0.15790991
36.2 0.28319688
36.3 0.30894311
36.4 0.38835761
37 0.28006122
37.1 0.51936935
37.2 0.03553058
38 0.10984700
39 1.01908377
39.1 0.58760885
39.2 0.63292533
39.3 0.42095489
39.4 0.25220230
39.5 0.51242643
40 0.70636121
40.1 1.22834105
40.2 0.81839083
40.3 0.23540757
41 0.08592119
41.1 0.22834515
41.2 1.61636130
41.3 0.15342660
41.4 0.47650400
42 0.64398703
42.1 1.15130398
43 0.79292461
43.1 0.38506794
43.2 0.11139587
44 0.89129328
44.1 0.08958946
44.2 0.85701827
44.3 0.96417530
45 0.51097634
45.1 0.98340980
46 0.44798505
46.1 0.82655580
46.2 0.37637628
47 0.41876182
47.1 0.48389648
47.2 0.02396924
47.3 1.80138667
47.4 0.61109603
48 0.19473894
48.1 0.04006959
49 0.29560575
50 0.15625313
51 0.47908892
52 1.40786781
52.1 0.35019229
52.2 0.39332493
52.3 0.51225821
52.4 0.11419627
52.5 0.55575005
53 0.13011523
53.1 0.90571584
53.2 0.50906734
54 0.46031273
54.1 0.46156182
54.2 0.52071389
54.3 0.76983675
54.4 0.52623423
55 0.60555180
55.1 0.10776713
55.2 1.03837178
55.3 0.45001542
55.4 0.65395611
56 0.07535464
56.1 0.73328954
56.2 0.27578594
56.3 0.68719648
56.4 1.57220834
56.5 0.28753078
57 0.17289659
57.1 0.72170220
57.2 1.26500225
57.3 0.20213479
58 0.13611631
58.1 0.37311297
58.2 0.72470365
58.3 1.43014769
58.4 0.78817203
58.5 0.78387559
59 0.46747067
59.1 0.04947979
60 0.16059397
61 0.29220662
61.1 0.41535569
61.2 0.73742285
61.3 0.43320659
61.4 1.19954814
62 0.20260386
62.1 0.06652907
62.2 0.25063288
62.3 0.36290927
63 0.52314649
63.1 0.25699016
64 1.02878746
65 0.45575444
65.1 0.46306113
65.2 0.42269832
65.3 0.73172542
66 0.74765742
66.1 0.25888221
66.2 0.38244280
67 0.23644835
68 0.83195685
68.1 0.68395486
68.2 0.53889898
68.3 0.33762340
68.4 0.79922369
69 0.20260053
70 1.04535151
70.1 0.03979648
71 0.56397408
71.1 0.34854738
71.2 0.97913866
71.3 0.19630242
71.4 0.31230175
72 1.04871582
72.1 0.09370234
72.2 0.72454755
72.3 0.80705501
72.4 0.40641012
72.5 0.81634161
73 0.74327324
74 0.49202243
75 0.42954173
76 1.22280380
76.1 0.09905853
76.2 0.34132786
77 1.20980413
78 0.26184214
79 0.94287180
79.1 0.08463026
79.2 0.66769705
80 0.68766428
80.1 0.95426300
80.2 1.84421668
81 0.60279596
81.1 0.73369496
81.2 0.83514184
81.3 0.91767999
82 0.46992524
82.1 0.50002097
82.2 0.43711796
83 0.46587065
83.1 0.43364034
83.2 0.23196757
83.3 0.73616193
84 0.47791427
84.1 0.05551055
85 0.27482891
85.1 1.77694842
85.2 0.71141066
85.3 0.78806704
85.4 0.80223323
85.5 0.22172219
86 0.15018053
86.1 0.31597396
86.2 0.95686193
86.3 0.11022188
86.4 0.68477369
86.5 0.33125367
87 0.29289308
87.1 0.66197512
87.2 0.30055939
88 0.22930153
88.1 1.02206005
88.2 0.52724756
88.3 0.16276648
89 0.09190440
90 0.15333982
90.1 0.42756943
90.2 0.60354432
90.3 0.41070560
91 1.01739949
91.1 0.41121541
91.2 0.08932488
92 1.08669724
93 0.30303806
93.1 0.16800845
93.2 1.29098296
93.3 0.39962093
93.4 0.88339337
94 0.23233022
94.1 0.08638527
94.2 0.43737650
94.3 0.19800807
94.4 0.42942963
94.5 0.14150685
95 1.07323107
95.1 0.26037856
95.2 0.48623052
96 0.79796998
96.1 0.30822508
96.2 0.91060931
96.3 0.26069030
96.4 0.22889234
96.5 0.97046560
97 0.16946638
97.1 0.20265816
98 1.22465795
98.1 0.15250019
98.2 0.44675949
99 0.44238919
99.1 0.63211897
99.2 0.40140806
100 0.10484468
100.1 0.56141377
100.2 0.23655004
100.3 0.74552230
100.4 0.34230391
$m0c2$mu_reg_gamma
[1] 0
$m0c2$tau_reg_gamma
[1] 1e-04
$m0c2$shape_tau_gamma
[1] 0.01
$m0c2$rate_tau_gamma
[1] 0.01
$m0c2$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0c2$shape_diag_RinvD
[1] "0.01"
$m0c2$rate_diag_RinvD
[1] "0.001"
$m0d1
$m0d1$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0d1$M_lvlone
p1
1 5
1.1 3
1.2 8
1.3 6
2 5
2.1 3
2.2 2
3 7
3.1 2
3.2 8
4 2
4.1 4
4.2 2
4.3 6
5 6
5.1 2
5.2 3
5.3 2
6 4
7 2
7.1 6
7.2 4
8 2
8.1 2
8.2 1
8.3 2
8.4 2
8.5 4
9 3
9.1 3
9.2 2
10 4
10.1 5
11 2
11.1 4
11.2 6
11.3 2
11.4 1
12 5
13 2
13.1 6
14 3
14.1 2
14.2 4
14.3 2
15 4
15.1 7
15.2 4
15.3 3
16 3
16.1 2
16.2 5
16.3 3
16.4 2
16.5 6
17 3
17.1 1
17.2 4
17.3 5
17.4 5
18 8
19 5
19.1 6
19.2 4
19.3 3
20 5
20.1 8
20.2 3
20.3 3
20.4 3
20.5 3
21 3
21.1 3
21.2 4
22 6
22.1 3
23 3
23.1 2
24 1
25 2
25.1 0
25.2 6
25.3 6
25.4 2
25.5 2
26 6
26.1 0
26.2 1
26.3 4
27 2
27.1 4
28 5
28.1 0
28.2 7
28.3 3
29 4
29.1 1
29.2 4
29.3 3
30 5
30.1 5
30.2 6
31 1
32 2
32.1 5
32.2 5
32.3 6
33 4
33.1 7
34 2
34.1 5
34.2 6
34.3 2
35 3
35.1 2
35.2 3
36 3
36.1 1
36.2 6
36.3 4
36.4 1
37 4
37.1 6
37.2 8
38 3
39 2
39.1 3
39.2 6
39.3 4
39.4 3
39.5 6
40 1
40.1 3
40.2 0
40.3 4
41 1
41.1 4
41.2 7
41.3 5
41.4 2
42 1
42.1 3
43 5
43.1 2
43.2 3
44 3
44.1 3
44.2 3
44.3 4
45 4
45.1 2
46 8
46.1 5
46.2 5
47 3
47.1 5
47.2 5
47.3 2
47.4 5
48 2
48.1 5
49 4
50 1
51 9
52 3
52.1 3
52.2 4
52.3 11
52.4 3
52.5 3
53 5
53.1 3
53.2 2
54 1
54.1 4
54.2 2
54.3 2
54.4 6
55 1
55.1 2
55.2 2
55.3 3
55.4 5
56 5
56.1 5
56.2 2
56.3 3
56.4 6
56.5 1
57 3
57.1 6
57.2 3
57.3 2
58 6
58.1 5
58.2 2
58.3 4
58.4 4
58.5 4
59 6
59.1 4
60 7
61 6
61.1 3
61.2 2
61.3 5
61.4 4
62 1
62.1 1
62.2 2
62.3 4
63 6
63.1 2
64 2
65 3
65.1 4
65.2 2
65.3 2
66 6
66.1 0
66.2 5
67 8
68 5
68.1 5
68.2 4
68.3 3
68.4 1
69 5
70 6
70.1 2
71 4
71.1 2
71.2 5
71.3 10
71.4 2
72 2
72.1 4
72.2 8
72.3 6
72.4 4
72.5 1
73 1
74 1
75 6
76 3
76.1 4
76.2 5
77 1
78 2
79 2
79.1 6
79.2 5
80 5
80.1 1
80.2 4
81 4
81.1 5
81.2 2
81.3 5
82 1
82.1 2
82.2 5
83 5
83.1 1
83.2 1
83.3 4
84 1
84.1 5
85 6
85.1 5
85.2 3
85.3 2
85.4 2
85.5 6
86 3
86.1 3
86.2 6
86.3 5
86.4 5
86.5 4
87 3
87.1 6
87.2 2
88 1
88.1 6
88.2 1
88.3 6
89 7
90 3
90.1 8
90.2 4
90.3 2
91 4
91.1 2
91.2 5
92 3
93 3
93.1 3
93.2 4
93.3 2
93.4 6
94 2
94.1 4
94.2 2
94.3 6
94.4 5
94.5 5
95 8
95.1 4
95.2 1
96 2
96.1 3
96.2 2
96.3 6
96.4 6
96.5 3
97 2
97.1 5
98 7
98.1 2
98.2 6
99 3
99.1 4
99.2 5
100 2
100.1 3
100.2 3
100.3 7
100.4 6
$m0d1$mu_reg_poisson
[1] 0
$m0d1$tau_reg_poisson
[1] 1e-04
$m0d1$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0d1$shape_diag_RinvD
[1] "0.01"
$m0d1$rate_diag_RinvD
[1] "0.001"
$m0d2
$m0d2$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0d2$M_lvlone
p1
1 5
1.1 3
1.2 8
1.3 6
2 5
2.1 3
2.2 2
3 7
3.1 2
3.2 8
4 2
4.1 4
4.2 2
4.3 6
5 6
5.1 2
5.2 3
5.3 2
6 4
7 2
7.1 6
7.2 4
8 2
8.1 2
8.2 1
8.3 2
8.4 2
8.5 4
9 3
9.1 3
9.2 2
10 4
10.1 5
11 2
11.1 4
11.2 6
11.3 2
11.4 1
12 5
13 2
13.1 6
14 3
14.1 2
14.2 4
14.3 2
15 4
15.1 7
15.2 4
15.3 3
16 3
16.1 2
16.2 5
16.3 3
16.4 2
16.5 6
17 3
17.1 1
17.2 4
17.3 5
17.4 5
18 8
19 5
19.1 6
19.2 4
19.3 3
20 5
20.1 8
20.2 3
20.3 3
20.4 3
20.5 3
21 3
21.1 3
21.2 4
22 6
22.1 3
23 3
23.1 2
24 1
25 2
25.1 0
25.2 6
25.3 6
25.4 2
25.5 2
26 6
26.1 0
26.2 1
26.3 4
27 2
27.1 4
28 5
28.1 0
28.2 7
28.3 3
29 4
29.1 1
29.2 4
29.3 3
30 5
30.1 5
30.2 6
31 1
32 2
32.1 5
32.2 5
32.3 6
33 4
33.1 7
34 2
34.1 5
34.2 6
34.3 2
35 3
35.1 2
35.2 3
36 3
36.1 1
36.2 6
36.3 4
36.4 1
37 4
37.1 6
37.2 8
38 3
39 2
39.1 3
39.2 6
39.3 4
39.4 3
39.5 6
40 1
40.1 3
40.2 0
40.3 4
41 1
41.1 4
41.2 7
41.3 5
41.4 2
42 1
42.1 3
43 5
43.1 2
43.2 3
44 3
44.1 3
44.2 3
44.3 4
45 4
45.1 2
46 8
46.1 5
46.2 5
47 3
47.1 5
47.2 5
47.3 2
47.4 5
48 2
48.1 5
49 4
50 1
51 9
52 3
52.1 3
52.2 4
52.3 11
52.4 3
52.5 3
53 5
53.1 3
53.2 2
54 1
54.1 4
54.2 2
54.3 2
54.4 6
55 1
55.1 2
55.2 2
55.3 3
55.4 5
56 5
56.1 5
56.2 2
56.3 3
56.4 6
56.5 1
57 3
57.1 6
57.2 3
57.3 2
58 6
58.1 5
58.2 2
58.3 4
58.4 4
58.5 4
59 6
59.1 4
60 7
61 6
61.1 3
61.2 2
61.3 5
61.4 4
62 1
62.1 1
62.2 2
62.3 4
63 6
63.1 2
64 2
65 3
65.1 4
65.2 2
65.3 2
66 6
66.1 0
66.2 5
67 8
68 5
68.1 5
68.2 4
68.3 3
68.4 1
69 5
70 6
70.1 2
71 4
71.1 2
71.2 5
71.3 10
71.4 2
72 2
72.1 4
72.2 8
72.3 6
72.4 4
72.5 1
73 1
74 1
75 6
76 3
76.1 4
76.2 5
77 1
78 2
79 2
79.1 6
79.2 5
80 5
80.1 1
80.2 4
81 4
81.1 5
81.2 2
81.3 5
82 1
82.1 2
82.2 5
83 5
83.1 1
83.2 1
83.3 4
84 1
84.1 5
85 6
85.1 5
85.2 3
85.3 2
85.4 2
85.5 6
86 3
86.1 3
86.2 6
86.3 5
86.4 5
86.5 4
87 3
87.1 6
87.2 2
88 1
88.1 6
88.2 1
88.3 6
89 7
90 3
90.1 8
90.2 4
90.3 2
91 4
91.1 2
91.2 5
92 3
93 3
93.1 3
93.2 4
93.3 2
93.4 6
94 2
94.1 4
94.2 2
94.3 6
94.4 5
94.5 5
95 8
95.1 4
95.2 1
96 2
96.1 3
96.2 2
96.3 6
96.4 6
96.5 3
97 2
97.1 5
98 7
98.1 2
98.2 6
99 3
99.1 4
99.2 5
100 2
100.1 3
100.2 3
100.3 7
100.4 6
$m0d2$mu_reg_poisson
[1] 0
$m0d2$tau_reg_poisson
[1] 1e-04
$m0d2$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0d2$shape_diag_RinvD
[1] "0.01"
$m0d2$rate_diag_RinvD
[1] "0.001"
$m0e1
$m0e1$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0e1$M_lvlone
L1
1 0.09647609
1.1 0.47743206
1.2 0.49307743
1.3 0.18468863
2 0.54595313
2.1 0.21966792
2.2 0.73654737
3 0.20862809
3.1 0.24312223
3.2 0.03051627
4 0.39499609
4.1 0.72632316
4.2 0.34199228
4.3 0.38062927
5 0.62202135
5.1 0.20305630
5.2 0.41717969
5.3 0.23980703
6 0.37653463
7 0.36356663
7.1 0.06266071
7.2 0.37849716
8 0.37802506
8.1 0.61143062
8.2 0.75648801
8.3 2.54406375
8.4 1.18637590
8.5 0.05930316
9 0.95013074
9.1 0.11917116
9.2 0.86629295
10 0.23914695
10.1 0.13708051
11 0.11067204
11.1 0.23176079
11.2 0.60038623
11.3 0.42684714
11.4 0.16458522
12 0.12861686
13 1.33377494
13.1 0.37267514
14 0.48728084
14.1 0.31792264
14.2 0.89257832
14.3 0.48509920
15 0.37711346
15.1 0.24850749
15.2 0.48117461
15.3 0.42758680
16 0.43666855
16.1 0.18190724
16.2 0.18617239
16.3 1.87047608
16.4 0.41864602
16.5 0.43588009
17 0.17925916
17.1 0.32367639
17.2 0.24912593
17.3 0.56230768
17.4 0.26182608
18 0.42338083
19 0.23371438
19.1 0.45720781
19.2 1.07923724
19.3 0.74433885
20 0.23860936
20.1 1.49001161
20.2 0.82847676
20.3 0.71062057
20.4 0.58928158
20.5 0.49204025
21 0.39710041
21.1 0.63253881
21.2 0.58877978
22 0.30440876
22.1 0.42787265
23 0.15078177
23.1 0.97104584
24 0.55355206
25 0.76006220
25.1 0.42500306
25.2 0.68011522
25.3 0.38187835
25.4 0.67265847
25.5 0.09078197
26 0.17032539
26.1 0.36699769
26.2 0.19300220
26.3 1.26993276
27 0.63999648
27.1 1.14153094
28 0.39991376
28.1 0.20658853
28.2 0.42519397
28.3 1.68848543
29 0.20853337
29.1 0.32240000
29.2 0.59527557
29.3 0.34253455
30 0.70885491
30.1 0.31107139
30.2 0.46423208
31 0.54603320
32 0.48896515
32.1 0.26838930
32.2 0.33314256
32.3 0.15482204
33 0.63379200
33.1 0.53403306
34 0.30684588
34.1 0.15596697
34.2 0.73177916
34.3 0.78232073
35 0.12725486
35.1 0.32104659
35.2 0.92993903
36 0.82634942
36.1 0.15790991
36.2 0.28319688
36.3 0.30894311
36.4 0.38835761
37 0.28006122
37.1 0.51936935
37.2 0.03553058
38 0.10984700
39 1.01908377
39.1 0.58760885
39.2 0.63292533
39.3 0.42095489
39.4 0.25220230
39.5 0.51242643
40 0.70636121
40.1 1.22834105
40.2 0.81839083
40.3 0.23540757
41 0.08592119
41.1 0.22834515
41.2 1.61636130
41.3 0.15342660
41.4 0.47650400
42 0.64398703
42.1 1.15130398
43 0.79292461
43.1 0.38506794
43.2 0.11139587
44 0.89129328
44.1 0.08958946
44.2 0.85701827
44.3 0.96417530
45 0.51097634
45.1 0.98340980
46 0.44798505
46.1 0.82655580
46.2 0.37637628
47 0.41876182
47.1 0.48389648
47.2 0.02396924
47.3 1.80138667
47.4 0.61109603
48 0.19473894
48.1 0.04006959
49 0.29560575
50 0.15625313
51 0.47908892
52 1.40786781
52.1 0.35019229
52.2 0.39332493
52.3 0.51225821
52.4 0.11419627
52.5 0.55575005
53 0.13011523
53.1 0.90571584
53.2 0.50906734
54 0.46031273
54.1 0.46156182
54.2 0.52071389
54.3 0.76983675
54.4 0.52623423
55 0.60555180
55.1 0.10776713
55.2 1.03837178
55.3 0.45001542
55.4 0.65395611
56 0.07535464
56.1 0.73328954
56.2 0.27578594
56.3 0.68719648
56.4 1.57220834
56.5 0.28753078
57 0.17289659
57.1 0.72170220
57.2 1.26500225
57.3 0.20213479
58 0.13611631
58.1 0.37311297
58.2 0.72470365
58.3 1.43014769
58.4 0.78817203
58.5 0.78387559
59 0.46747067
59.1 0.04947979
60 0.16059397
61 0.29220662
61.1 0.41535569
61.2 0.73742285
61.3 0.43320659
61.4 1.19954814
62 0.20260386
62.1 0.06652907
62.2 0.25063288
62.3 0.36290927
63 0.52314649
63.1 0.25699016
64 1.02878746
65 0.45575444
65.1 0.46306113
65.2 0.42269832
65.3 0.73172542
66 0.74765742
66.1 0.25888221
66.2 0.38244280
67 0.23644835
68 0.83195685
68.1 0.68395486
68.2 0.53889898
68.3 0.33762340
68.4 0.79922369
69 0.20260053
70 1.04535151
70.1 0.03979648
71 0.56397408
71.1 0.34854738
71.2 0.97913866
71.3 0.19630242
71.4 0.31230175
72 1.04871582
72.1 0.09370234
72.2 0.72454755
72.3 0.80705501
72.4 0.40641012
72.5 0.81634161
73 0.74327324
74 0.49202243
75 0.42954173
76 1.22280380
76.1 0.09905853
76.2 0.34132786
77 1.20980413
78 0.26184214
79 0.94287180
79.1 0.08463026
79.2 0.66769705
80 0.68766428
80.1 0.95426300
80.2 1.84421668
81 0.60279596
81.1 0.73369496
81.2 0.83514184
81.3 0.91767999
82 0.46992524
82.1 0.50002097
82.2 0.43711796
83 0.46587065
83.1 0.43364034
83.2 0.23196757
83.3 0.73616193
84 0.47791427
84.1 0.05551055
85 0.27482891
85.1 1.77694842
85.2 0.71141066
85.3 0.78806704
85.4 0.80223323
85.5 0.22172219
86 0.15018053
86.1 0.31597396
86.2 0.95686193
86.3 0.11022188
86.4 0.68477369
86.5 0.33125367
87 0.29289308
87.1 0.66197512
87.2 0.30055939
88 0.22930153
88.1 1.02206005
88.2 0.52724756
88.3 0.16276648
89 0.09190440
90 0.15333982
90.1 0.42756943
90.2 0.60354432
90.3 0.41070560
91 1.01739949
91.1 0.41121541
91.2 0.08932488
92 1.08669724
93 0.30303806
93.1 0.16800845
93.2 1.29098296
93.3 0.39962093
93.4 0.88339337
94 0.23233022
94.1 0.08638527
94.2 0.43737650
94.3 0.19800807
94.4 0.42942963
94.5 0.14150685
95 1.07323107
95.1 0.26037856
95.2 0.48623052
96 0.79796998
96.1 0.30822508
96.2 0.91060931
96.3 0.26069030
96.4 0.22889234
96.5 0.97046560
97 0.16946638
97.1 0.20265816
98 1.22465795
98.1 0.15250019
98.2 0.44675949
99 0.44238919
99.1 0.63211897
99.2 0.40140806
100 0.10484468
100.1 0.56141377
100.2 0.23655004
100.3 0.74552230
100.4 0.34230391
$m0e1$mu_reg_norm
[1] 0
$m0e1$tau_reg_norm
[1] 1e-04
$m0e1$shape_tau_norm
[1] 0.01
$m0e1$rate_tau_norm
[1] 0.01
$m0e1$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0e1$shape_diag_RinvD
[1] "0.01"
$m0e1$rate_diag_RinvD
[1] "0.001"
$m0f1
$m0f1$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m0f1$M_lvlone
Be1
1 0.4480520
1.1 0.4872580
1.2 0.8042241
1.3 0.8554321
2 0.9060032
2.1 0.9275039
2.2 0.9684475
3 0.5305313
3.1 0.9121229
3.2 0.9822343
4 0.3989620
4.1 0.5799009
4.2 0.8662223
4.3 0.9158089
5 0.5896069
5.1 0.7459908
5.2 0.8891508
5.3 0.8907166
6 0.7404475
7 0.9290914
7.1 0.9510258
7.2 0.9826571
8 0.5888906
8.1 0.7383562
8.2 0.7412208
8.3 0.8882677
8.4 0.9307178
8.5 0.9751765
9 0.5598906
9.1 0.9000440
9.2 0.9835368
10 0.8256582
10.1 0.9686602
11 0.6081450
11.1 0.6203091
11.2 0.7109057
11.3 0.9335259
11.4 0.9831774
12 0.5534331
13 0.3337862
13.1 0.9431649
14 0.9653479
14.1 0.9772848
14.2 0.9806705
14.3 0.9816445
15 0.4519208
15.1 0.6121121
15.2 0.6848939
15.3 0.9850242
16 0.6319642
16.1 0.8660451
16.2 0.8755852
16.3 0.9456980
16.4 0.9552169
16.5 0.9638766
17 0.7004195
17.1 0.8447710
17.2 0.9074097
17.3 0.9301938
17.4 0.9579581
18 0.8432895
19 0.5558578
19.1 0.5971935
19.2 0.8186257
19.3 0.9694859
20 0.7222660
20.1 0.7300751
20.2 0.8161188
20.3 0.8175187
20.4 0.9387767
20.5 0.9680716
21 0.7248177
21.1 0.9030819
21.2 0.9553646
22 0.8506311
22.1 0.9192797
23 0.6969316
23.1 0.8359296
24 0.8898412
25 0.4393270
25.1 0.6952775
25.2 0.7013550
25.3 0.9229146
25.4 0.9642968
25.5 0.9668809
26 0.3844839
26.1 0.8498397
26.2 0.9472023
26.3 0.9698339
27 0.9513160
27.1 0.9713089
28 0.4565391
28.1 0.8854882
28.2 0.9695846
28.3 0.9763767
29 0.6079730
29.1 0.7332778
29.2 0.7807345
29.3 0.9344282
30 0.8225127
30.1 0.9460257
30.2 0.9470397
31 0.9745123
32 0.7195703
32.1 0.8984963
32.2 0.9033895
32.3 0.9700494
33 0.3271062
33.1 0.9386866
34 0.6807359
34.1 0.9561254
34.2 0.9594764
34.3 0.9614131
35 0.6479695
35.1 0.6917668
35.2 0.9777582
36 0.4952571
36.1 0.7438280
36.2 0.7493185
36.3 0.9721512
36.4 0.9799281
37 0.7844567
37.1 0.9505294
37.2 0.9629006
38 0.5537002
39 0.4880363
39.1 0.5405940
39.2 0.6377289
39.3 0.6902395
39.4 0.9200815
39.5 0.9676849
40 0.5970791
40.1 0.8759223
40.2 0.9088713
40.3 0.9808585
41 0.7657773
41.1 0.9203076
41.2 0.9265998
41.3 0.9329089
41.4 0.9426326
42 0.4363467
42.1 0.9730745
43 0.4523650
43.1 0.5797085
43.2 0.8653434
44 0.5063579
44.1 0.8708165
44.2 0.9306269
44.3 0.9669009
45 0.3684179
45.1 0.7793063
46 0.6489748
46.1 0.8931511
46.2 0.9754655
47 0.4659563
47.1 0.8418508
47.2 0.9055038
47.3 0.9202183
47.4 0.9798157
48 0.8934160
48.1 0.8980019
49 0.8792169
50 0.6106779
51 0.6695505
52 0.8016848
52.1 0.9145302
52.2 0.9166014
52.3 0.9448693
52.4 0.9831856
52.5 0.9859644
53 0.4430250
53.1 0.9440152
53.2 0.9792363
54 0.6568450
54.1 0.7552906
54.2 0.8527773
54.3 0.8839761
54.4 0.9630372
55 0.4682570
55.1 0.5018449
55.2 0.8890551
55.3 0.9163416
55.4 0.9229283
56 0.6156368
56.1 0.8327518
56.2 0.8600168
56.3 0.9001284
56.4 0.9223855
56.5 0.9349592
57 0.3810809
57.1 0.3837051
57.2 0.6031393
57.3 0.8011333
58 0.6212946
58.1 0.7124804
58.2 0.7217629
58.3 0.8705746
58.4 0.8930050
58.5 0.9450905
59 0.7607033
59.1 0.9856252
60 0.8926604
61 0.4989113
61.1 0.8310345
61.2 0.8559453
61.3 0.9203703
61.4 0.9466752
62 0.4538041
62.1 0.4949445
62.2 0.9393143
62.3 0.9834371
63 0.8885881
63.1 0.9620223
64 0.9672991
65 0.4899624
65.1 0.7820160
65.2 0.9141166
65.3 0.9204984
66 0.9404727
66.1 0.9540581
66.2 0.9613658
67 0.9684363
68 0.3499904
68.1 0.7374372
68.2 0.7860111
68.3 0.8995662
68.4 0.9641669
69 0.9680556
70 0.3631962
70.1 0.4309940
71 0.4991001
71.1 0.6705385
71.2 0.9643633
71.3 0.9806792
71.4 0.9810444
72 0.5476810
72.1 0.6080648
72.2 0.7596830
72.3 0.9396045
72.4 0.9501505
72.5 0.9659276
73 0.9797107
74 0.6739684
75 0.9245569
76 0.7449652
76.1 0.9716113
76.2 0.9857034
77 0.5312239
78 0.5214249
79 0.3314961
79.1 0.8430143
79.2 0.9266576
80 0.5405270
80.1 0.6473533
80.2 0.8876091
81 0.3275558
81.1 0.5529946
81.2 0.9109145
81.3 0.9319014
82 0.6572741
82.1 0.7373364
82.2 0.8693680
83 0.3360995
83.1 0.8976786
83.2 0.9156363
83.3 0.9825687
84 0.8794223
84.1 0.9307356
85 0.3930294
85.1 0.7324405
85.2 0.8756930
85.3 0.9189753
85.4 0.9613144
85.5 0.9776185
86 0.5224769
86.1 0.5632108
86.2 0.6209203
86.3 0.8068072
86.4 0.8449636
86.5 0.9553382
87 0.8762447
87.1 0.9368280
87.2 0.9775674
88 0.3258678
88.1 0.4960216
88.2 0.8541774
88.3 0.9290415
89 0.4802962
90 0.3626402
90.1 0.8658220
90.2 0.8734278
90.3 0.9161187
91 0.4759845
91.1 0.8685282
91.2 0.9827553
92 0.3397660
93 0.3869728
93.1 0.5736674
93.2 0.8522942
93.3 0.8955441
93.4 0.9764547
94 0.5306638
94.1 0.5815770
94.2 0.7718092
94.3 0.9125421
94.4 0.9138265
94.5 0.9747802
95 0.7844217
95.1 0.9640897
95.2 0.9787801
96 0.3324701
96.1 0.3553187
96.2 0.4854947
96.3 0.8098962
96.4 0.8170439
96.5 0.9709596
97 0.6156077
97.1 0.9857374
98 0.3662077
98.1 0.4202527
98.2 0.9407308
99 0.4075622
99.1 0.9811408
99.2 0.9861494
100 0.5819523
100.1 0.6840806
100.2 0.8040634
100.3 0.9583620
100.4 0.9805147
$m0f1$mu_reg_beta
[1] 0
$m0f1$tau_reg_beta
[1] 1e-04
$m0f1$shape_tau_beta
[1] 0.01
$m0f1$rate_tau_beta
[1] 0.01
$m0f1$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m0f1$shape_diag_RinvD
[1] "0.01"
$m0f1$rate_diag_RinvD
[1] "0.001"
$m1a
$m1a$M_id
(Intercept) C1
1 1 0.7175865
2 1 0.7507170
3 1 0.7255954
4 1 0.7469352
5 1 0.7139120
6 1 0.7332505
7 1 0.7345929
8 1 0.7652589
9 1 0.7200622
10 1 0.7423879
11 1 0.7437448
12 1 0.7446470
13 1 0.7530186
14 1 0.7093137
15 1 0.7331192
16 1 0.7011390
17 1 0.7432395
18 1 0.7545191
19 1 0.7528487
20 1 0.7612865
21 1 0.7251719
22 1 0.7300630
23 1 0.7087249
24 1 0.7391938
25 1 0.7820641
26 1 0.7118298
27 1 0.7230857
28 1 0.7489353
29 1 0.7510888
30 1 0.7300717
31 1 0.7550721
32 1 0.7321898
33 1 0.7306414
34 1 0.7427216
35 1 0.7193042
36 1 0.7312888
37 1 0.7100436
38 1 0.7670184
39 1 0.7400449
40 1 0.7397304
41 1 0.7490966
42 1 0.7419274
43 1 0.7527810
44 1 0.7408315
45 1 0.7347550
46 1 0.7332398
47 1 0.7376481
48 1 0.7346179
49 1 0.7329402
50 1 0.7260436
51 1 0.7242910
52 1 0.7298067
53 1 0.7254741
54 1 0.7542067
55 1 0.7389952
56 1 0.7520638
57 1 0.7219958
58 1 0.7259632
59 1 0.7458606
60 1 0.7672421
61 1 0.7257179
62 1 0.7189892
63 1 0.7333356
64 1 0.7320243
65 1 0.7477711
66 1 0.7343974
67 1 0.7491624
68 1 0.7482736
69 1 0.7338267
70 1 0.7607742
71 1 0.7777600
72 1 0.7408143
73 1 0.7248271
74 1 0.7364916
75 1 0.7464926
76 1 0.7355430
77 1 0.7208449
78 1 0.7373573
79 1 0.7598079
80 1 0.7360415
81 1 0.7293932
82 1 0.7279309
83 1 0.7344643
84 1 0.7384350
85 1 0.7323716
86 1 0.7576597
87 1 0.7496139
88 1 0.7275239
89 1 0.7250648
90 1 0.7335262
91 1 0.7343980
92 1 0.7380425
93 1 0.7389460
94 1 0.7259951
95 1 0.7282840
96 1 0.7281676
97 1 0.7245642
98 1 0.7526938
99 1 0.7272309
100 1 0.7383460
$m1a$M_lvlone
y
1 -13.0493856
1.1 -9.3335901
1.2 -22.3469852
1.3 -15.0417337
2 -12.0655434
2.1 -15.8674476
2.2 -7.8800006
3 -11.4820604
3.1 -10.5983220
3.2 -22.4519157
4 -1.2697775
4.1 -11.1215184
4.2 -3.6134138
4.3 -14.5982385
5 -6.8457515
5.1 -7.0551214
5.2 -12.3418980
5.3 -9.2366906
6 -5.1648211
7 -10.0599502
7.1 -18.3267285
7.2 -12.5138426
8 -1.6305331
8.1 -9.6520453
8.2 -1.5278462
8.3 -7.4172211
8.4 -7.1238609
8.5 -8.8706950
9 -0.1634429
9.1 -2.6034300
9.2 -6.7272369
10 -6.4172202
10.1 -11.4834569
11 -8.7911356
11.1 -19.6645080
11.2 -20.2030932
11.3 -21.3082176
11.4 -14.5802901
12 -15.2006287
13 0.8058816
13.1 -13.6379208
14 -15.3422873
14.1 -10.0965208
14.2 -16.6452027
14.3 -15.8389733
15 -8.9424594
15.1 -22.0101983
15.2 -7.3975599
15.3 -10.3567334
16 -1.9691302
16.1 -9.9308357
16.2 -6.9626923
16.3 -3.2862557
16.4 -3.3972355
16.5 -11.5767835
17 -10.5474144
17.1 -7.6215009
17.2 -16.5386939
17.3 -20.0004774
17.4 -18.8505475
18 -19.7302351
19 -14.6177568
19.1 -17.8043866
19.2 -15.1641705
19.3 -16.6898418
20 -12.9059229
20.1 -16.8191201
20.2 -6.1010131
20.3 -7.9415371
20.4 -9.3904458
20.5 -13.3504189
21 -7.6974718
21.1 -11.9335526
21.2 -12.7064929
22 -21.5022909
22.1 -12.7745451
23 -3.5146508
23.1 -4.6724048
24 -2.5619821
25 -6.2944970
25.1 -3.8630505
25.2 -14.4205140
25.3 -19.6735037
25.4 -9.0288933
25.5 -9.0509738
26 -19.7340685
26.1 -14.1692728
26.2 -17.2819976
26.3 -24.6265576
27 -7.3354999
27.1 -11.1488468
28 -11.7996597
28.1 -8.2030122
28.2 -26.4317815
28.3 -18.5016071
29 -5.8551395
29.1 -2.0209442
29.2 -5.6368080
29.3 -3.8110961
30 -12.7217702
30.1 -17.0170140
30.2 -25.4236089
31 -17.0783921
32 -18.4338764
32.1 -19.4317212
32.2 -19.4738978
32.3 -21.4922645
33 2.0838099
33.1 -13.3172274
34 -10.0296691
34.1 -25.9426553
34.2 -18.5688138
34.3 -15.4173859
35 -14.3958113
35.1 -12.9457541
35.2 -16.1380691
36 -12.8166968
36.1 -14.3989481
36.2 -12.2436943
36.3 -15.0104638
36.4 -10.1775457
37 -15.2223495
37.1 -14.7526195
37.2 -19.8168430
38 -2.7065118
39 -8.7288138
39.1 -9.2746473
39.2 -18.2695344
39.3 -13.8219083
39.4 -16.2254704
39.5 -21.7283648
40 1.8291916
40.1 -6.6916432
40.2 -1.6278171
40.3 -10.5749790
41 -3.1556121
41.1 -11.5895327
41.2 -18.9352091
41.3 -15.9788960
41.4 -9.6070508
42 -5.2159485
42.1 -15.9878743
43 -16.6104361
43.1 -9.5549441
43.2 -14.2003491
44 -8.1969033
44.1 -19.9270197
44.2 -22.6521171
44.3 -21.1903736
45 -0.5686627
45.1 -7.5645740
46 -19.1624789
46.1 -18.4487574
46.2 -15.8222682
47 -5.4165074
47.1 -15.0975029
47.2 -12.9971413
47.3 -10.6844521
47.4 -18.2214784
48 -8.3101471
48.1 -18.3854275
49 -13.0130319
50 -10.4579977
51 -19.3157621
52 -4.4747188
52.1 -4.3163827
52.2 -6.9761408
52.3 -20.1764756
52.4 -8.9036692
52.5 -5.6949642
53 -10.3141887
53.1 -8.2642654
53.2 -9.1691554
54 -6.2198754
54.1 -15.7192609
54.2 -13.0978998
54.3 -5.1195299
54.4 -16.5771751
55 -5.7348534
55.1 -7.3217494
55.2 -12.2171938
55.3 -12.9821266
55.4 -14.8599983
56 -14.1764282
56.1 -12.5343602
56.2 -8.4573382
56.3 -12.4633969
56.4 -17.3841863
56.5 -14.8147645
57 -3.1403293
57.1 -11.1509248
57.2 -6.3940143
57.3 -9.3473241
58 -12.0245677
58.1 -9.2112246
58.2 -1.2071742
58.3 -11.0141711
58.4 -5.3721214
58.5 -7.8523047
59 -13.2946560
59.1 -10.0530648
60 -19.2209402
61 -4.6699914
61.1 -3.5981894
61.2 -1.4713611
61.3 -3.8819786
61.4 0.1041413
62 -2.8591600
62.1 -6.9461986
62.2 -16.7910593
62.3 -17.9844596
63 -24.0335535
63.1 -11.7765300
64 -20.5963897
65 -2.7969169
65.1 -11.1778694
65.2 -5.2830399
65.3 -7.9353390
66 -13.2318328
66.1 -1.9090560
66.2 -16.6643889
67 -25.6073277
68 -13.4806759
68.1 -18.4557183
68.2 -13.3982327
68.3 -12.4977127
68.4 -11.7073990
69 -14.5290675
70 -15.2122709
70.1 -7.8681167
71 -10.3352703
71.1 -7.5699888
71.2 -18.4680702
71.3 -21.4316644
71.4 -8.1137650
72 -9.1848162
72.1 -23.7538846
72.2 -26.3421306
72.3 -27.2843801
72.4 -20.8541617
72.5 -12.8948965
73 -2.6091307
74 -8.2790175
75 -12.5029612
76 -6.0061671
76.1 -8.8149114
76.2 -11.8359043
77 0.4772521
78 -9.4105229
79 -1.0217265
79.1 -11.8125257
79.2 -10.5465186
80 -12.7366807
80.1 -9.0584783
80.2 -16.6381566
81 0.5547913
81.1 -4.0892715
81.2 1.8283303
81.3 -5.2166381
82 -3.0749381
82.1 -10.5506696
82.2 -18.2226347
83 -12.5872635
83.1 -11.9756502
83.2 -10.6744217
83.3 -19.2714012
84 -2.6320312
84.1 -9.8140094
85 -12.3886736
85.1 -12.9196365
85.2 -9.6433248
85.3 -6.3296340
85.4 -7.0405525
85.5 -13.6714939
86 -10.8756412
86.1 -12.0055331
86.2 -13.3724699
86.3 -13.3252145
86.4 -14.9191290
86.5 -17.7515546
87 -10.7027963
87.1 -22.4941954
87.2 -14.9616716
88 -2.2264493
88.1 -8.9626474
88.2 -2.5095281
88.3 -16.3345673
89 -11.0459647
90 -4.5610239
90.1 -11.7036651
90.2 -5.3838521
90.3 -4.1636999
91 -7.1462503
91.1 -12.8374475
91.2 -18.2576707
92 -6.4119222
93 5.2122168
93.1 3.1211725
93.2 -3.6841177
93.3 2.6223542
93.4 -11.1877696
94 -6.9602492
94.1 -7.4318416
94.2 -4.3498045
94.3 -11.6340088
94.4 -12.9357964
94.5 -14.7648530
95 -12.8849309
95.1 -9.7451502
95.2 -0.8535063
96 -4.9139832
96.1 -3.9582653
96.2 -9.6555492
96.3 -11.8690793
96.4 -11.0224373
96.5 -10.9530403
97 -9.8540471
97.1 -19.2262840
98 -11.9651231
98.1 -2.6515128
98.2 -12.2606382
99 -11.4720500
99.1 -14.0596866
99.2 -17.3939469
100 1.1005874
100.1 -3.8226248
100.2 -0.9123182
100.3 -15.8389474
100.4 -12.8093826
$m1a$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1a$mu_reg_norm
[1] 0
$m1a$tau_reg_norm
[1] 1e-04
$m1a$shape_tau_norm
[1] 0.01
$m1a$rate_tau_norm
[1] 0.01
$m1a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m1a$shape_diag_RinvD
[1] "0.01"
$m1a$rate_diag_RinvD
[1] "0.001"
$m1b
$m1b$M_id
(Intercept) C1
1 1 0.7175865
2 1 0.7507170
3 1 0.7255954
4 1 0.7469352
5 1 0.7139120
6 1 0.7332505
7 1 0.7345929
8 1 0.7652589
9 1 0.7200622
10 1 0.7423879
11 1 0.7437448
12 1 0.7446470
13 1 0.7530186
14 1 0.7093137
15 1 0.7331192
16 1 0.7011390
17 1 0.7432395
18 1 0.7545191
19 1 0.7528487
20 1 0.7612865
21 1 0.7251719
22 1 0.7300630
23 1 0.7087249
24 1 0.7391938
25 1 0.7820641
26 1 0.7118298
27 1 0.7230857
28 1 0.7489353
29 1 0.7510888
30 1 0.7300717
31 1 0.7550721
32 1 0.7321898
33 1 0.7306414
34 1 0.7427216
35 1 0.7193042
36 1 0.7312888
37 1 0.7100436
38 1 0.7670184
39 1 0.7400449
40 1 0.7397304
41 1 0.7490966
42 1 0.7419274
43 1 0.7527810
44 1 0.7408315
45 1 0.7347550
46 1 0.7332398
47 1 0.7376481
48 1 0.7346179
49 1 0.7329402
50 1 0.7260436
51 1 0.7242910
52 1 0.7298067
53 1 0.7254741
54 1 0.7542067
55 1 0.7389952
56 1 0.7520638
57 1 0.7219958
58 1 0.7259632
59 1 0.7458606
60 1 0.7672421
61 1 0.7257179
62 1 0.7189892
63 1 0.7333356
64 1 0.7320243
65 1 0.7477711
66 1 0.7343974
67 1 0.7491624
68 1 0.7482736
69 1 0.7338267
70 1 0.7607742
71 1 0.7777600
72 1 0.7408143
73 1 0.7248271
74 1 0.7364916
75 1 0.7464926
76 1 0.7355430
77 1 0.7208449
78 1 0.7373573
79 1 0.7598079
80 1 0.7360415
81 1 0.7293932
82 1 0.7279309
83 1 0.7344643
84 1 0.7384350
85 1 0.7323716
86 1 0.7576597
87 1 0.7496139
88 1 0.7275239
89 1 0.7250648
90 1 0.7335262
91 1 0.7343980
92 1 0.7380425
93 1 0.7389460
94 1 0.7259951
95 1 0.7282840
96 1 0.7281676
97 1 0.7245642
98 1 0.7526938
99 1 0.7272309
100 1 0.7383460
$m1b$M_lvlone
b1
1 0
1.1 1
1.2 1
1.3 0
2 1
2.1 1
2.2 1
3 1
3.1 0
3.2 0
4 1
4.1 1
4.2 0
4.3 1
5 0
5.1 1
5.2 1
5.3 1
6 0
7 1
7.1 0
7.2 1
8 0
8.1 1
8.2 1
8.3 0
8.4 0
8.5 1
9 1
9.1 1
9.2 0
10 1
10.1 1
11 1
11.1 1
11.2 1
11.3 1
11.4 1
12 1
13 0
13.1 1
14 0
14.1 1
14.2 0
14.3 0
15 0
15.1 0
15.2 0
15.3 1
16 1
16.1 0
16.2 1
16.3 1
16.4 1
16.5 0
17 0
17.1 0
17.2 1
17.3 0
17.4 1
18 1
19 1
19.1 1
19.2 1
19.3 1
20 0
20.1 1
20.2 0
20.3 0
20.4 0
20.5 0
21 1
21.1 1
21.2 0
22 0
22.1 1
23 1
23.1 1
24 0
25 0
25.1 1
25.2 1
25.3 0
25.4 0
25.5 0
26 1
26.1 1
26.2 1
26.3 0
27 1
27.1 1
28 1
28.1 0
28.2 1
28.3 1
29 1
29.1 0
29.2 0
29.3 1
30 1
30.1 1
30.2 1
31 0
32 1
32.1 1
32.2 1
32.3 1
33 0
33.1 0
34 1
34.1 0
34.2 1
34.3 1
35 1
35.1 0
35.2 1
36 0
36.1 0
36.2 1
36.3 0
36.4 1
37 1
37.1 0
37.2 0
38 1
39 1
39.1 0
39.2 0
39.3 0
39.4 1
39.5 1
40 0
40.1 0
40.2 0
40.3 1
41 1
41.1 1
41.2 0
41.3 1
41.4 1
42 1
42.1 1
43 0
43.1 0
43.2 1
44 1
44.1 0
44.2 0
44.3 1
45 1
45.1 0
46 1
46.1 0
46.2 1
47 0
47.1 0
47.2 1
47.3 0
47.4 0
48 0
48.1 1
49 0
50 1
51 1
52 1
52.1 1
52.2 0
52.3 0
52.4 1
52.5 1
53 1
53.1 1
53.2 1
54 0
54.1 1
54.2 0
54.3 1
54.4 0
55 1
55.1 1
55.2 1
55.3 0
55.4 1
56 0
56.1 1
56.2 1
56.3 0
56.4 0
56.5 1
57 1
57.1 1
57.2 0
57.3 0
58 1
58.1 1
58.2 1
58.3 1
58.4 1
58.5 1
59 0
59.1 1
60 0
61 1
61.1 1
61.2 1
61.3 0
61.4 1
62 1
62.1 0
62.2 0
62.3 1
63 0
63.1 1
64 1
65 1
65.1 1
65.2 0
65.3 0
66 1
66.1 0
66.2 0
67 0
68 0
68.1 0
68.2 0
68.3 0
68.4 1
69 1
70 1
70.1 1
71 1
71.1 1
71.2 0
71.3 0
71.4 0
72 1
72.1 1
72.2 1
72.3 0
72.4 0
72.5 1
73 1
74 1
75 0
76 1
76.1 1
76.2 1
77 1
78 1
79 0
79.1 1
79.2 0
80 1
80.1 0
80.2 1
81 1
81.1 1
81.2 1
81.3 1
82 1
82.1 1
82.2 0
83 1
83.1 0
83.2 0
83.3 1
84 1
84.1 0
85 0
85.1 0
85.2 1
85.3 1
85.4 1
85.5 1
86 0
86.1 1
86.2 1
86.3 0
86.4 1
86.5 0
87 0
87.1 1
87.2 0
88 0
88.1 0
88.2 0
88.3 0
89 1
90 0
90.1 1
90.2 1
90.3 0
91 0
91.1 0
91.2 1
92 1
93 0
93.1 1
93.2 0
93.3 1
93.4 0
94 1
94.1 0
94.2 1
94.3 0
94.4 0
94.5 0
95 1
95.1 1
95.2 0
96 1
96.1 0
96.2 0
96.3 0
96.4 0
96.5 1
97 0
97.1 0
98 0
98.1 0
98.2 0
99 1
99.1 1
99.2 1
100 0
100.1 0
100.2 1
100.3 1
100.4 1
$m1b$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1b$mu_reg_binom
[1] 0
$m1b$tau_reg_binom
[1] 1e-04
$m1b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m1b$shape_diag_RinvD
[1] "0.01"
$m1b$rate_diag_RinvD
[1] "0.001"
$m1c
$m1c$M_id
(Intercept) C1
1 1 0.7175865
2 1 0.7507170
3 1 0.7255954
4 1 0.7469352
5 1 0.7139120
6 1 0.7332505
7 1 0.7345929
8 1 0.7652589
9 1 0.7200622
10 1 0.7423879
11 1 0.7437448
12 1 0.7446470
13 1 0.7530186
14 1 0.7093137
15 1 0.7331192
16 1 0.7011390
17 1 0.7432395
18 1 0.7545191
19 1 0.7528487
20 1 0.7612865
21 1 0.7251719
22 1 0.7300630
23 1 0.7087249
24 1 0.7391938
25 1 0.7820641
26 1 0.7118298
27 1 0.7230857
28 1 0.7489353
29 1 0.7510888
30 1 0.7300717
31 1 0.7550721
32 1 0.7321898
33 1 0.7306414
34 1 0.7427216
35 1 0.7193042
36 1 0.7312888
37 1 0.7100436
38 1 0.7670184
39 1 0.7400449
40 1 0.7397304
41 1 0.7490966
42 1 0.7419274
43 1 0.7527810
44 1 0.7408315
45 1 0.7347550
46 1 0.7332398
47 1 0.7376481
48 1 0.7346179
49 1 0.7329402
50 1 0.7260436
51 1 0.7242910
52 1 0.7298067
53 1 0.7254741
54 1 0.7542067
55 1 0.7389952
56 1 0.7520638
57 1 0.7219958
58 1 0.7259632
59 1 0.7458606
60 1 0.7672421
61 1 0.7257179
62 1 0.7189892
63 1 0.7333356
64 1 0.7320243
65 1 0.7477711
66 1 0.7343974
67 1 0.7491624
68 1 0.7482736
69 1 0.7338267
70 1 0.7607742
71 1 0.7777600
72 1 0.7408143
73 1 0.7248271
74 1 0.7364916
75 1 0.7464926
76 1 0.7355430
77 1 0.7208449
78 1 0.7373573
79 1 0.7598079
80 1 0.7360415
81 1 0.7293932
82 1 0.7279309
83 1 0.7344643
84 1 0.7384350
85 1 0.7323716
86 1 0.7576597
87 1 0.7496139
88 1 0.7275239
89 1 0.7250648
90 1 0.7335262
91 1 0.7343980
92 1 0.7380425
93 1 0.7389460
94 1 0.7259951
95 1 0.7282840
96 1 0.7281676
97 1 0.7245642
98 1 0.7526938
99 1 0.7272309
100 1 0.7383460
$m1c$M_lvlone
L1
1 0.09647609
1.1 0.47743206
1.2 0.49307743
1.3 0.18468863
2 0.54595313
2.1 0.21966792
2.2 0.73654737
3 0.20862809
3.1 0.24312223
3.2 0.03051627
4 0.39499609
4.1 0.72632316
4.2 0.34199228
4.3 0.38062927
5 0.62202135
5.1 0.20305630
5.2 0.41717969
5.3 0.23980703
6 0.37653463
7 0.36356663
7.1 0.06266071
7.2 0.37849716
8 0.37802506
8.1 0.61143062
8.2 0.75648801
8.3 2.54406375
8.4 1.18637590
8.5 0.05930316
9 0.95013074
9.1 0.11917116
9.2 0.86629295
10 0.23914695
10.1 0.13708051
11 0.11067204
11.1 0.23176079
11.2 0.60038623
11.3 0.42684714
11.4 0.16458522
12 0.12861686
13 1.33377494
13.1 0.37267514
14 0.48728084
14.1 0.31792264
14.2 0.89257832
14.3 0.48509920
15 0.37711346
15.1 0.24850749
15.2 0.48117461
15.3 0.42758680
16 0.43666855
16.1 0.18190724
16.2 0.18617239
16.3 1.87047608
16.4 0.41864602
16.5 0.43588009
17 0.17925916
17.1 0.32367639
17.2 0.24912593
17.3 0.56230768
17.4 0.26182608
18 0.42338083
19 0.23371438
19.1 0.45720781
19.2 1.07923724
19.3 0.74433885
20 0.23860936
20.1 1.49001161
20.2 0.82847676
20.3 0.71062057
20.4 0.58928158
20.5 0.49204025
21 0.39710041
21.1 0.63253881
21.2 0.58877978
22 0.30440876
22.1 0.42787265
23 0.15078177
23.1 0.97104584
24 0.55355206
25 0.76006220
25.1 0.42500306
25.2 0.68011522
25.3 0.38187835
25.4 0.67265847
25.5 0.09078197
26 0.17032539
26.1 0.36699769
26.2 0.19300220
26.3 1.26993276
27 0.63999648
27.1 1.14153094
28 0.39991376
28.1 0.20658853
28.2 0.42519397
28.3 1.68848543
29 0.20853337
29.1 0.32240000
29.2 0.59527557
29.3 0.34253455
30 0.70885491
30.1 0.31107139
30.2 0.46423208
31 0.54603320
32 0.48896515
32.1 0.26838930
32.2 0.33314256
32.3 0.15482204
33 0.63379200
33.1 0.53403306
34 0.30684588
34.1 0.15596697
34.2 0.73177916
34.3 0.78232073
35 0.12725486
35.1 0.32104659
35.2 0.92993903
36 0.82634942
36.1 0.15790991
36.2 0.28319688
36.3 0.30894311
36.4 0.38835761
37 0.28006122
37.1 0.51936935
37.2 0.03553058
38 0.10984700
39 1.01908377
39.1 0.58760885
39.2 0.63292533
39.3 0.42095489
39.4 0.25220230
39.5 0.51242643
40 0.70636121
40.1 1.22834105
40.2 0.81839083
40.3 0.23540757
41 0.08592119
41.1 0.22834515
41.2 1.61636130
41.3 0.15342660
41.4 0.47650400
42 0.64398703
42.1 1.15130398
43 0.79292461
43.1 0.38506794
43.2 0.11139587
44 0.89129328
44.1 0.08958946
44.2 0.85701827
44.3 0.96417530
45 0.51097634
45.1 0.98340980
46 0.44798505
46.1 0.82655580
46.2 0.37637628
47 0.41876182
47.1 0.48389648
47.2 0.02396924
47.3 1.80138667
47.4 0.61109603
48 0.19473894
48.1 0.04006959
49 0.29560575
50 0.15625313
51 0.47908892
52 1.40786781
52.1 0.35019229
52.2 0.39332493
52.3 0.51225821
52.4 0.11419627
52.5 0.55575005
53 0.13011523
53.1 0.90571584
53.2 0.50906734
54 0.46031273
54.1 0.46156182
54.2 0.52071389
54.3 0.76983675
54.4 0.52623423
55 0.60555180
55.1 0.10776713
55.2 1.03837178
55.3 0.45001542
55.4 0.65395611
56 0.07535464
56.1 0.73328954
56.2 0.27578594
56.3 0.68719648
56.4 1.57220834
56.5 0.28753078
57 0.17289659
57.1 0.72170220
57.2 1.26500225
57.3 0.20213479
58 0.13611631
58.1 0.37311297
58.2 0.72470365
58.3 1.43014769
58.4 0.78817203
58.5 0.78387559
59 0.46747067
59.1 0.04947979
60 0.16059397
61 0.29220662
61.1 0.41535569
61.2 0.73742285
61.3 0.43320659
61.4 1.19954814
62 0.20260386
62.1 0.06652907
62.2 0.25063288
62.3 0.36290927
63 0.52314649
63.1 0.25699016
64 1.02878746
65 0.45575444
65.1 0.46306113
65.2 0.42269832
65.3 0.73172542
66 0.74765742
66.1 0.25888221
66.2 0.38244280
67 0.23644835
68 0.83195685
68.1 0.68395486
68.2 0.53889898
68.3 0.33762340
68.4 0.79922369
69 0.20260053
70 1.04535151
70.1 0.03979648
71 0.56397408
71.1 0.34854738
71.2 0.97913866
71.3 0.19630242
71.4 0.31230175
72 1.04871582
72.1 0.09370234
72.2 0.72454755
72.3 0.80705501
72.4 0.40641012
72.5 0.81634161
73 0.74327324
74 0.49202243
75 0.42954173
76 1.22280380
76.1 0.09905853
76.2 0.34132786
77 1.20980413
78 0.26184214
79 0.94287180
79.1 0.08463026
79.2 0.66769705
80 0.68766428
80.1 0.95426300
80.2 1.84421668
81 0.60279596
81.1 0.73369496
81.2 0.83514184
81.3 0.91767999
82 0.46992524
82.1 0.50002097
82.2 0.43711796
83 0.46587065
83.1 0.43364034
83.2 0.23196757
83.3 0.73616193
84 0.47791427
84.1 0.05551055
85 0.27482891
85.1 1.77694842
85.2 0.71141066
85.3 0.78806704
85.4 0.80223323
85.5 0.22172219
86 0.15018053
86.1 0.31597396
86.2 0.95686193
86.3 0.11022188
86.4 0.68477369
86.5 0.33125367
87 0.29289308
87.1 0.66197512
87.2 0.30055939
88 0.22930153
88.1 1.02206005
88.2 0.52724756
88.3 0.16276648
89 0.09190440
90 0.15333982
90.1 0.42756943
90.2 0.60354432
90.3 0.41070560
91 1.01739949
91.1 0.41121541
91.2 0.08932488
92 1.08669724
93 0.30303806
93.1 0.16800845
93.2 1.29098296
93.3 0.39962093
93.4 0.88339337
94 0.23233022
94.1 0.08638527
94.2 0.43737650
94.3 0.19800807
94.4 0.42942963
94.5 0.14150685
95 1.07323107
95.1 0.26037856
95.2 0.48623052
96 0.79796998
96.1 0.30822508
96.2 0.91060931
96.3 0.26069030
96.4 0.22889234
96.5 0.97046560
97 0.16946638
97.1 0.20265816
98 1.22465795
98.1 0.15250019
98.2 0.44675949
99 0.44238919
99.1 0.63211897
99.2 0.40140806
100 0.10484468
100.1 0.56141377
100.2 0.23655004
100.3 0.74552230
100.4 0.34230391
$m1c$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1c$mu_reg_gamma
[1] 0
$m1c$tau_reg_gamma
[1] 1e-04
$m1c$shape_tau_gamma
[1] 0.01
$m1c$rate_tau_gamma
[1] 0.01
$m1c$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m1c$shape_diag_RinvD
[1] "0.01"
$m1c$rate_diag_RinvD
[1] "0.001"
$m1d
$m1d$M_id
(Intercept) C1
1 1 0.7175865
2 1 0.7507170
3 1 0.7255954
4 1 0.7469352
5 1 0.7139120
6 1 0.7332505
7 1 0.7345929
8 1 0.7652589
9 1 0.7200622
10 1 0.7423879
11 1 0.7437448
12 1 0.7446470
13 1 0.7530186
14 1 0.7093137
15 1 0.7331192
16 1 0.7011390
17 1 0.7432395
18 1 0.7545191
19 1 0.7528487
20 1 0.7612865
21 1 0.7251719
22 1 0.7300630
23 1 0.7087249
24 1 0.7391938
25 1 0.7820641
26 1 0.7118298
27 1 0.7230857
28 1 0.7489353
29 1 0.7510888
30 1 0.7300717
31 1 0.7550721
32 1 0.7321898
33 1 0.7306414
34 1 0.7427216
35 1 0.7193042
36 1 0.7312888
37 1 0.7100436
38 1 0.7670184
39 1 0.7400449
40 1 0.7397304
41 1 0.7490966
42 1 0.7419274
43 1 0.7527810
44 1 0.7408315
45 1 0.7347550
46 1 0.7332398
47 1 0.7376481
48 1 0.7346179
49 1 0.7329402
50 1 0.7260436
51 1 0.7242910
52 1 0.7298067
53 1 0.7254741
54 1 0.7542067
55 1 0.7389952
56 1 0.7520638
57 1 0.7219958
58 1 0.7259632
59 1 0.7458606
60 1 0.7672421
61 1 0.7257179
62 1 0.7189892
63 1 0.7333356
64 1 0.7320243
65 1 0.7477711
66 1 0.7343974
67 1 0.7491624
68 1 0.7482736
69 1 0.7338267
70 1 0.7607742
71 1 0.7777600
72 1 0.7408143
73 1 0.7248271
74 1 0.7364916
75 1 0.7464926
76 1 0.7355430
77 1 0.7208449
78 1 0.7373573
79 1 0.7598079
80 1 0.7360415
81 1 0.7293932
82 1 0.7279309
83 1 0.7344643
84 1 0.7384350
85 1 0.7323716
86 1 0.7576597
87 1 0.7496139
88 1 0.7275239
89 1 0.7250648
90 1 0.7335262
91 1 0.7343980
92 1 0.7380425
93 1 0.7389460
94 1 0.7259951
95 1 0.7282840
96 1 0.7281676
97 1 0.7245642
98 1 0.7526938
99 1 0.7272309
100 1 0.7383460
$m1d$M_lvlone
p1
1 5
1.1 3
1.2 8
1.3 6
2 5
2.1 3
2.2 2
3 7
3.1 2
3.2 8
4 2
4.1 4
4.2 2
4.3 6
5 6
5.1 2
5.2 3
5.3 2
6 4
7 2
7.1 6
7.2 4
8 2
8.1 2
8.2 1
8.3 2
8.4 2
8.5 4
9 3
9.1 3
9.2 2
10 4
10.1 5
11 2
11.1 4
11.2 6
11.3 2
11.4 1
12 5
13 2
13.1 6
14 3
14.1 2
14.2 4
14.3 2
15 4
15.1 7
15.2 4
15.3 3
16 3
16.1 2
16.2 5
16.3 3
16.4 2
16.5 6
17 3
17.1 1
17.2 4
17.3 5
17.4 5
18 8
19 5
19.1 6
19.2 4
19.3 3
20 5
20.1 8
20.2 3
20.3 3
20.4 3
20.5 3
21 3
21.1 3
21.2 4
22 6
22.1 3
23 3
23.1 2
24 1
25 2
25.1 0
25.2 6
25.3 6
25.4 2
25.5 2
26 6
26.1 0
26.2 1
26.3 4
27 2
27.1 4
28 5
28.1 0
28.2 7
28.3 3
29 4
29.1 1
29.2 4
29.3 3
30 5
30.1 5
30.2 6
31 1
32 2
32.1 5
32.2 5
32.3 6
33 4
33.1 7
34 2
34.1 5
34.2 6
34.3 2
35 3
35.1 2
35.2 3
36 3
36.1 1
36.2 6
36.3 4
36.4 1
37 4
37.1 6
37.2 8
38 3
39 2
39.1 3
39.2 6
39.3 4
39.4 3
39.5 6
40 1
40.1 3
40.2 0
40.3 4
41 1
41.1 4
41.2 7
41.3 5
41.4 2
42 1
42.1 3
43 5
43.1 2
43.2 3
44 3
44.1 3
44.2 3
44.3 4
45 4
45.1 2
46 8
46.1 5
46.2 5
47 3
47.1 5
47.2 5
47.3 2
47.4 5
48 2
48.1 5
49 4
50 1
51 9
52 3
52.1 3
52.2 4
52.3 11
52.4 3
52.5 3
53 5
53.1 3
53.2 2
54 1
54.1 4
54.2 2
54.3 2
54.4 6
55 1
55.1 2
55.2 2
55.3 3
55.4 5
56 5
56.1 5
56.2 2
56.3 3
56.4 6
56.5 1
57 3
57.1 6
57.2 3
57.3 2
58 6
58.1 5
58.2 2
58.3 4
58.4 4
58.5 4
59 6
59.1 4
60 7
61 6
61.1 3
61.2 2
61.3 5
61.4 4
62 1
62.1 1
62.2 2
62.3 4
63 6
63.1 2
64 2
65 3
65.1 4
65.2 2
65.3 2
66 6
66.1 0
66.2 5
67 8
68 5
68.1 5
68.2 4
68.3 3
68.4 1
69 5
70 6
70.1 2
71 4
71.1 2
71.2 5
71.3 10
71.4 2
72 2
72.1 4
72.2 8
72.3 6
72.4 4
72.5 1
73 1
74 1
75 6
76 3
76.1 4
76.2 5
77 1
78 2
79 2
79.1 6
79.2 5
80 5
80.1 1
80.2 4
81 4
81.1 5
81.2 2
81.3 5
82 1
82.1 2
82.2 5
83 5
83.1 1
83.2 1
83.3 4
84 1
84.1 5
85 6
85.1 5
85.2 3
85.3 2
85.4 2
85.5 6
86 3
86.1 3
86.2 6
86.3 5
86.4 5
86.5 4
87 3
87.1 6
87.2 2
88 1
88.1 6
88.2 1
88.3 6
89 7
90 3
90.1 8
90.2 4
90.3 2
91 4
91.1 2
91.2 5
92 3
93 3
93.1 3
93.2 4
93.3 2
93.4 6
94 2
94.1 4
94.2 2
94.3 6
94.4 5
94.5 5
95 8
95.1 4
95.2 1
96 2
96.1 3
96.2 2
96.3 6
96.4 6
96.5 3
97 2
97.1 5
98 7
98.1 2
98.2 6
99 3
99.1 4
99.2 5
100 2
100.1 3
100.2 3
100.3 7
100.4 6
$m1d$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1d$mu_reg_poisson
[1] 0
$m1d$tau_reg_poisson
[1] 1e-04
$m1d$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m1d$shape_diag_RinvD
[1] "0.01"
$m1d$rate_diag_RinvD
[1] "0.001"
$m1e
$m1e$M_id
(Intercept) C1
1 1 0.7175865
2 1 0.7507170
3 1 0.7255954
4 1 0.7469352
5 1 0.7139120
6 1 0.7332505
7 1 0.7345929
8 1 0.7652589
9 1 0.7200622
10 1 0.7423879
11 1 0.7437448
12 1 0.7446470
13 1 0.7530186
14 1 0.7093137
15 1 0.7331192
16 1 0.7011390
17 1 0.7432395
18 1 0.7545191
19 1 0.7528487
20 1 0.7612865
21 1 0.7251719
22 1 0.7300630
23 1 0.7087249
24 1 0.7391938
25 1 0.7820641
26 1 0.7118298
27 1 0.7230857
28 1 0.7489353
29 1 0.7510888
30 1 0.7300717
31 1 0.7550721
32 1 0.7321898
33 1 0.7306414
34 1 0.7427216
35 1 0.7193042
36 1 0.7312888
37 1 0.7100436
38 1 0.7670184
39 1 0.7400449
40 1 0.7397304
41 1 0.7490966
42 1 0.7419274
43 1 0.7527810
44 1 0.7408315
45 1 0.7347550
46 1 0.7332398
47 1 0.7376481
48 1 0.7346179
49 1 0.7329402
50 1 0.7260436
51 1 0.7242910
52 1 0.7298067
53 1 0.7254741
54 1 0.7542067
55 1 0.7389952
56 1 0.7520638
57 1 0.7219958
58 1 0.7259632
59 1 0.7458606
60 1 0.7672421
61 1 0.7257179
62 1 0.7189892
63 1 0.7333356
64 1 0.7320243
65 1 0.7477711
66 1 0.7343974
67 1 0.7491624
68 1 0.7482736
69 1 0.7338267
70 1 0.7607742
71 1 0.7777600
72 1 0.7408143
73 1 0.7248271
74 1 0.7364916
75 1 0.7464926
76 1 0.7355430
77 1 0.7208449
78 1 0.7373573
79 1 0.7598079
80 1 0.7360415
81 1 0.7293932
82 1 0.7279309
83 1 0.7344643
84 1 0.7384350
85 1 0.7323716
86 1 0.7576597
87 1 0.7496139
88 1 0.7275239
89 1 0.7250648
90 1 0.7335262
91 1 0.7343980
92 1 0.7380425
93 1 0.7389460
94 1 0.7259951
95 1 0.7282840
96 1 0.7281676
97 1 0.7245642
98 1 0.7526938
99 1 0.7272309
100 1 0.7383460
$m1e$M_lvlone
L1
1 0.09647609
1.1 0.47743206
1.2 0.49307743
1.3 0.18468863
2 0.54595313
2.1 0.21966792
2.2 0.73654737
3 0.20862809
3.1 0.24312223
3.2 0.03051627
4 0.39499609
4.1 0.72632316
4.2 0.34199228
4.3 0.38062927
5 0.62202135
5.1 0.20305630
5.2 0.41717969
5.3 0.23980703
6 0.37653463
7 0.36356663
7.1 0.06266071
7.2 0.37849716
8 0.37802506
8.1 0.61143062
8.2 0.75648801
8.3 2.54406375
8.4 1.18637590
8.5 0.05930316
9 0.95013074
9.1 0.11917116
9.2 0.86629295
10 0.23914695
10.1 0.13708051
11 0.11067204
11.1 0.23176079
11.2 0.60038623
11.3 0.42684714
11.4 0.16458522
12 0.12861686
13 1.33377494
13.1 0.37267514
14 0.48728084
14.1 0.31792264
14.2 0.89257832
14.3 0.48509920
15 0.37711346
15.1 0.24850749
15.2 0.48117461
15.3 0.42758680
16 0.43666855
16.1 0.18190724
16.2 0.18617239
16.3 1.87047608
16.4 0.41864602
16.5 0.43588009
17 0.17925916
17.1 0.32367639
17.2 0.24912593
17.3 0.56230768
17.4 0.26182608
18 0.42338083
19 0.23371438
19.1 0.45720781
19.2 1.07923724
19.3 0.74433885
20 0.23860936
20.1 1.49001161
20.2 0.82847676
20.3 0.71062057
20.4 0.58928158
20.5 0.49204025
21 0.39710041
21.1 0.63253881
21.2 0.58877978
22 0.30440876
22.1 0.42787265
23 0.15078177
23.1 0.97104584
24 0.55355206
25 0.76006220
25.1 0.42500306
25.2 0.68011522
25.3 0.38187835
25.4 0.67265847
25.5 0.09078197
26 0.17032539
26.1 0.36699769
26.2 0.19300220
26.3 1.26993276
27 0.63999648
27.1 1.14153094
28 0.39991376
28.1 0.20658853
28.2 0.42519397
28.3 1.68848543
29 0.20853337
29.1 0.32240000
29.2 0.59527557
29.3 0.34253455
30 0.70885491
30.1 0.31107139
30.2 0.46423208
31 0.54603320
32 0.48896515
32.1 0.26838930
32.2 0.33314256
32.3 0.15482204
33 0.63379200
33.1 0.53403306
34 0.30684588
34.1 0.15596697
34.2 0.73177916
34.3 0.78232073
35 0.12725486
35.1 0.32104659
35.2 0.92993903
36 0.82634942
36.1 0.15790991
36.2 0.28319688
36.3 0.30894311
36.4 0.38835761
37 0.28006122
37.1 0.51936935
37.2 0.03553058
38 0.10984700
39 1.01908377
39.1 0.58760885
39.2 0.63292533
39.3 0.42095489
39.4 0.25220230
39.5 0.51242643
40 0.70636121
40.1 1.22834105
40.2 0.81839083
40.3 0.23540757
41 0.08592119
41.1 0.22834515
41.2 1.61636130
41.3 0.15342660
41.4 0.47650400
42 0.64398703
42.1 1.15130398
43 0.79292461
43.1 0.38506794
43.2 0.11139587
44 0.89129328
44.1 0.08958946
44.2 0.85701827
44.3 0.96417530
45 0.51097634
45.1 0.98340980
46 0.44798505
46.1 0.82655580
46.2 0.37637628
47 0.41876182
47.1 0.48389648
47.2 0.02396924
47.3 1.80138667
47.4 0.61109603
48 0.19473894
48.1 0.04006959
49 0.29560575
50 0.15625313
51 0.47908892
52 1.40786781
52.1 0.35019229
52.2 0.39332493
52.3 0.51225821
52.4 0.11419627
52.5 0.55575005
53 0.13011523
53.1 0.90571584
53.2 0.50906734
54 0.46031273
54.1 0.46156182
54.2 0.52071389
54.3 0.76983675
54.4 0.52623423
55 0.60555180
55.1 0.10776713
55.2 1.03837178
55.3 0.45001542
55.4 0.65395611
56 0.07535464
56.1 0.73328954
56.2 0.27578594
56.3 0.68719648
56.4 1.57220834
56.5 0.28753078
57 0.17289659
57.1 0.72170220
57.2 1.26500225
57.3 0.20213479
58 0.13611631
58.1 0.37311297
58.2 0.72470365
58.3 1.43014769
58.4 0.78817203
58.5 0.78387559
59 0.46747067
59.1 0.04947979
60 0.16059397
61 0.29220662
61.1 0.41535569
61.2 0.73742285
61.3 0.43320659
61.4 1.19954814
62 0.20260386
62.1 0.06652907
62.2 0.25063288
62.3 0.36290927
63 0.52314649
63.1 0.25699016
64 1.02878746
65 0.45575444
65.1 0.46306113
65.2 0.42269832
65.3 0.73172542
66 0.74765742
66.1 0.25888221
66.2 0.38244280
67 0.23644835
68 0.83195685
68.1 0.68395486
68.2 0.53889898
68.3 0.33762340
68.4 0.79922369
69 0.20260053
70 1.04535151
70.1 0.03979648
71 0.56397408
71.1 0.34854738
71.2 0.97913866
71.3 0.19630242
71.4 0.31230175
72 1.04871582
72.1 0.09370234
72.2 0.72454755
72.3 0.80705501
72.4 0.40641012
72.5 0.81634161
73 0.74327324
74 0.49202243
75 0.42954173
76 1.22280380
76.1 0.09905853
76.2 0.34132786
77 1.20980413
78 0.26184214
79 0.94287180
79.1 0.08463026
79.2 0.66769705
80 0.68766428
80.1 0.95426300
80.2 1.84421668
81 0.60279596
81.1 0.73369496
81.2 0.83514184
81.3 0.91767999
82 0.46992524
82.1 0.50002097
82.2 0.43711796
83 0.46587065
83.1 0.43364034
83.2 0.23196757
83.3 0.73616193
84 0.47791427
84.1 0.05551055
85 0.27482891
85.1 1.77694842
85.2 0.71141066
85.3 0.78806704
85.4 0.80223323
85.5 0.22172219
86 0.15018053
86.1 0.31597396
86.2 0.95686193
86.3 0.11022188
86.4 0.68477369
86.5 0.33125367
87 0.29289308
87.1 0.66197512
87.2 0.30055939
88 0.22930153
88.1 1.02206005
88.2 0.52724756
88.3 0.16276648
89 0.09190440
90 0.15333982
90.1 0.42756943
90.2 0.60354432
90.3 0.41070560
91 1.01739949
91.1 0.41121541
91.2 0.08932488
92 1.08669724
93 0.30303806
93.1 0.16800845
93.2 1.29098296
93.3 0.39962093
93.4 0.88339337
94 0.23233022
94.1 0.08638527
94.2 0.43737650
94.3 0.19800807
94.4 0.42942963
94.5 0.14150685
95 1.07323107
95.1 0.26037856
95.2 0.48623052
96 0.79796998
96.1 0.30822508
96.2 0.91060931
96.3 0.26069030
96.4 0.22889234
96.5 0.97046560
97 0.16946638
97.1 0.20265816
98 1.22465795
98.1 0.15250019
98.2 0.44675949
99 0.44238919
99.1 0.63211897
99.2 0.40140806
100 0.10484468
100.1 0.56141377
100.2 0.23655004
100.3 0.74552230
100.4 0.34230391
$m1e$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1e$mu_reg_norm
[1] 0
$m1e$tau_reg_norm
[1] 1e-04
$m1e$shape_tau_norm
[1] 0.01
$m1e$rate_tau_norm
[1] 0.01
$m1e$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m1e$shape_diag_RinvD
[1] "0.01"
$m1e$rate_diag_RinvD
[1] "0.001"
$m1f
$m1f$M_id
(Intercept) C1
1 1 0.7175865
2 1 0.7507170
3 1 0.7255954
4 1 0.7469352
5 1 0.7139120
6 1 0.7332505
7 1 0.7345929
8 1 0.7652589
9 1 0.7200622
10 1 0.7423879
11 1 0.7437448
12 1 0.7446470
13 1 0.7530186
14 1 0.7093137
15 1 0.7331192
16 1 0.7011390
17 1 0.7432395
18 1 0.7545191
19 1 0.7528487
20 1 0.7612865
21 1 0.7251719
22 1 0.7300630
23 1 0.7087249
24 1 0.7391938
25 1 0.7820641
26 1 0.7118298
27 1 0.7230857
28 1 0.7489353
29 1 0.7510888
30 1 0.7300717
31 1 0.7550721
32 1 0.7321898
33 1 0.7306414
34 1 0.7427216
35 1 0.7193042
36 1 0.7312888
37 1 0.7100436
38 1 0.7670184
39 1 0.7400449
40 1 0.7397304
41 1 0.7490966
42 1 0.7419274
43 1 0.7527810
44 1 0.7408315
45 1 0.7347550
46 1 0.7332398
47 1 0.7376481
48 1 0.7346179
49 1 0.7329402
50 1 0.7260436
51 1 0.7242910
52 1 0.7298067
53 1 0.7254741
54 1 0.7542067
55 1 0.7389952
56 1 0.7520638
57 1 0.7219958
58 1 0.7259632
59 1 0.7458606
60 1 0.7672421
61 1 0.7257179
62 1 0.7189892
63 1 0.7333356
64 1 0.7320243
65 1 0.7477711
66 1 0.7343974
67 1 0.7491624
68 1 0.7482736
69 1 0.7338267
70 1 0.7607742
71 1 0.7777600
72 1 0.7408143
73 1 0.7248271
74 1 0.7364916
75 1 0.7464926
76 1 0.7355430
77 1 0.7208449
78 1 0.7373573
79 1 0.7598079
80 1 0.7360415
81 1 0.7293932
82 1 0.7279309
83 1 0.7344643
84 1 0.7384350
85 1 0.7323716
86 1 0.7576597
87 1 0.7496139
88 1 0.7275239
89 1 0.7250648
90 1 0.7335262
91 1 0.7343980
92 1 0.7380425
93 1 0.7389460
94 1 0.7259951
95 1 0.7282840
96 1 0.7281676
97 1 0.7245642
98 1 0.7526938
99 1 0.7272309
100 1 0.7383460
$m1f$M_lvlone
Be1
1 0.4480520
1.1 0.4872580
1.2 0.8042241
1.3 0.8554321
2 0.9060032
2.1 0.9275039
2.2 0.9684475
3 0.5305313
3.1 0.9121229
3.2 0.9822343
4 0.3989620
4.1 0.5799009
4.2 0.8662223
4.3 0.9158089
5 0.5896069
5.1 0.7459908
5.2 0.8891508
5.3 0.8907166
6 0.7404475
7 0.9290914
7.1 0.9510258
7.2 0.9826571
8 0.5888906
8.1 0.7383562
8.2 0.7412208
8.3 0.8882677
8.4 0.9307178
8.5 0.9751765
9 0.5598906
9.1 0.9000440
9.2 0.9835368
10 0.8256582
10.1 0.9686602
11 0.6081450
11.1 0.6203091
11.2 0.7109057
11.3 0.9335259
11.4 0.9831774
12 0.5534331
13 0.3337862
13.1 0.9431649
14 0.9653479
14.1 0.9772848
14.2 0.9806705
14.3 0.9816445
15 0.4519208
15.1 0.6121121
15.2 0.6848939
15.3 0.9850242
16 0.6319642
16.1 0.8660451
16.2 0.8755852
16.3 0.9456980
16.4 0.9552169
16.5 0.9638766
17 0.7004195
17.1 0.8447710
17.2 0.9074097
17.3 0.9301938
17.4 0.9579581
18 0.8432895
19 0.5558578
19.1 0.5971935
19.2 0.8186257
19.3 0.9694859
20 0.7222660
20.1 0.7300751
20.2 0.8161188
20.3 0.8175187
20.4 0.9387767
20.5 0.9680716
21 0.7248177
21.1 0.9030819
21.2 0.9553646
22 0.8506311
22.1 0.9192797
23 0.6969316
23.1 0.8359296
24 0.8898412
25 0.4393270
25.1 0.6952775
25.2 0.7013550
25.3 0.9229146
25.4 0.9642968
25.5 0.9668809
26 0.3844839
26.1 0.8498397
26.2 0.9472023
26.3 0.9698339
27 0.9513160
27.1 0.9713089
28 0.4565391
28.1 0.8854882
28.2 0.9695846
28.3 0.9763767
29 0.6079730
29.1 0.7332778
29.2 0.7807345
29.3 0.9344282
30 0.8225127
30.1 0.9460257
30.2 0.9470397
31 0.9745123
32 0.7195703
32.1 0.8984963
32.2 0.9033895
32.3 0.9700494
33 0.3271062
33.1 0.9386866
34 0.6807359
34.1 0.9561254
34.2 0.9594764
34.3 0.9614131
35 0.6479695
35.1 0.6917668
35.2 0.9777582
36 0.4952571
36.1 0.7438280
36.2 0.7493185
36.3 0.9721512
36.4 0.9799281
37 0.7844567
37.1 0.9505294
37.2 0.9629006
38 0.5537002
39 0.4880363
39.1 0.5405940
39.2 0.6377289
39.3 0.6902395
39.4 0.9200815
39.5 0.9676849
40 0.5970791
40.1 0.8759223
40.2 0.9088713
40.3 0.9808585
41 0.7657773
41.1 0.9203076
41.2 0.9265998
41.3 0.9329089
41.4 0.9426326
42 0.4363467
42.1 0.9730745
43 0.4523650
43.1 0.5797085
43.2 0.8653434
44 0.5063579
44.1 0.8708165
44.2 0.9306269
44.3 0.9669009
45 0.3684179
45.1 0.7793063
46 0.6489748
46.1 0.8931511
46.2 0.9754655
47 0.4659563
47.1 0.8418508
47.2 0.9055038
47.3 0.9202183
47.4 0.9798157
48 0.8934160
48.1 0.8980019
49 0.8792169
50 0.6106779
51 0.6695505
52 0.8016848
52.1 0.9145302
52.2 0.9166014
52.3 0.9448693
52.4 0.9831856
52.5 0.9859644
53 0.4430250
53.1 0.9440152
53.2 0.9792363
54 0.6568450
54.1 0.7552906
54.2 0.8527773
54.3 0.8839761
54.4 0.9630372
55 0.4682570
55.1 0.5018449
55.2 0.8890551
55.3 0.9163416
55.4 0.9229283
56 0.6156368
56.1 0.8327518
56.2 0.8600168
56.3 0.9001284
56.4 0.9223855
56.5 0.9349592
57 0.3810809
57.1 0.3837051
57.2 0.6031393
57.3 0.8011333
58 0.6212946
58.1 0.7124804
58.2 0.7217629
58.3 0.8705746
58.4 0.8930050
58.5 0.9450905
59 0.7607033
59.1 0.9856252
60 0.8926604
61 0.4989113
61.1 0.8310345
61.2 0.8559453
61.3 0.9203703
61.4 0.9466752
62 0.4538041
62.1 0.4949445
62.2 0.9393143
62.3 0.9834371
63 0.8885881
63.1 0.9620223
64 0.9672991
65 0.4899624
65.1 0.7820160
65.2 0.9141166
65.3 0.9204984
66 0.9404727
66.1 0.9540581
66.2 0.9613658
67 0.9684363
68 0.3499904
68.1 0.7374372
68.2 0.7860111
68.3 0.8995662
68.4 0.9641669
69 0.9680556
70 0.3631962
70.1 0.4309940
71 0.4991001
71.1 0.6705385
71.2 0.9643633
71.3 0.9806792
71.4 0.9810444
72 0.5476810
72.1 0.6080648
72.2 0.7596830
72.3 0.9396045
72.4 0.9501505
72.5 0.9659276
73 0.9797107
74 0.6739684
75 0.9245569
76 0.7449652
76.1 0.9716113
76.2 0.9857034
77 0.5312239
78 0.5214249
79 0.3314961
79.1 0.8430143
79.2 0.9266576
80 0.5405270
80.1 0.6473533
80.2 0.8876091
81 0.3275558
81.1 0.5529946
81.2 0.9109145
81.3 0.9319014
82 0.6572741
82.1 0.7373364
82.2 0.8693680
83 0.3360995
83.1 0.8976786
83.2 0.9156363
83.3 0.9825687
84 0.8794223
84.1 0.9307356
85 0.3930294
85.1 0.7324405
85.2 0.8756930
85.3 0.9189753
85.4 0.9613144
85.5 0.9776185
86 0.5224769
86.1 0.5632108
86.2 0.6209203
86.3 0.8068072
86.4 0.8449636
86.5 0.9553382
87 0.8762447
87.1 0.9368280
87.2 0.9775674
88 0.3258678
88.1 0.4960216
88.2 0.8541774
88.3 0.9290415
89 0.4802962
90 0.3626402
90.1 0.8658220
90.2 0.8734278
90.3 0.9161187
91 0.4759845
91.1 0.8685282
91.2 0.9827553
92 0.3397660
93 0.3869728
93.1 0.5736674
93.2 0.8522942
93.3 0.8955441
93.4 0.9764547
94 0.5306638
94.1 0.5815770
94.2 0.7718092
94.3 0.9125421
94.4 0.9138265
94.5 0.9747802
95 0.7844217
95.1 0.9640897
95.2 0.9787801
96 0.3324701
96.1 0.3553187
96.2 0.4854947
96.3 0.8098962
96.4 0.8170439
96.5 0.9709596
97 0.6156077
97.1 0.9857374
98 0.3662077
98.1 0.4202527
98.2 0.9407308
99 0.4075622
99.1 0.9811408
99.2 0.9861494
100 0.5819523
100.1 0.6840806
100.2 0.8040634
100.3 0.9583620
100.4 0.9805147
$m1f$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1f$mu_reg_beta
[1] 0
$m1f$tau_reg_beta
[1] 1e-04
$m1f$shape_tau_beta
[1] 0.01
$m1f$rate_tau_beta
[1] 0.01
$m1f$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m1f$shape_diag_RinvD
[1] "0.01"
$m1f$rate_diag_RinvD
[1] "0.001"
$m2a
$m2a$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m2a$M_lvlone
y c2
1 -13.0493856 NA
1.1 -9.3335901 -0.08061445
1.2 -22.3469852 -0.26523782
1.3 -15.0417337 -0.30260393
2 -12.0655434 -0.33443795
2.1 -15.8674476 -0.11819800
2.2 -7.8800006 -0.31532280
3 -11.4820604 -0.12920657
3.1 -10.5983220 NA
3.2 -22.4519157 NA
4 -1.2697775 -0.31177403
4.1 -11.1215184 -0.23894886
4.2 -3.6134138 -0.15533613
4.3 -14.5982385 -0.14644545
5 -6.8457515 -0.28360457
5.1 -7.0551214 -0.20135143
5.2 -12.3418980 -0.28293375
5.3 -9.2366906 NA
6 -5.1648211 -0.08617066
7 -10.0599502 -0.22243495
7.1 -18.3267285 NA
7.2 -12.5138426 NA
8 -1.6305331 NA
8.1 -9.6520453 NA
8.2 -1.5278462 NA
8.3 -7.4172211 -0.35148972
8.4 -7.1238609 0.03661023
8.5 -8.8706950 -0.08424534
9 -0.1634429 NA
9.1 -2.6034300 -0.43509340
9.2 -6.7272369 -0.22527490
10 -6.4172202 NA
10.1 -11.4834569 NA
11 -8.7911356 -0.08587475
11.1 -19.6645080 -0.06157340
11.2 -20.2030932 -0.12436018
11.3 -21.3082176 -0.21377934
11.4 -14.5802901 -0.32208329
12 -15.2006287 NA
13 0.8058816 NA
13.1 -13.6379208 -0.40300449
14 -15.3422873 -0.28992072
14.1 -10.0965208 NA
14.2 -16.6452027 NA
14.3 -15.8389733 -0.21979936
15 -8.9424594 NA
15.1 -22.0101983 -0.29092263
15.2 -7.3975599 -0.19392239
15.3 -10.3567334 -0.25718384
16 -1.9691302 -0.45041108
16.1 -9.9308357 -0.07599066
16.2 -6.9626923 -0.32385667
16.3 -3.2862557 -0.38326110
16.4 -3.3972355 -0.22845856
16.5 -11.5767835 -0.25497157
17 -10.5474144 NA
17.1 -7.6215009 -0.22105143
17.2 -16.5386939 NA
17.3 -20.0004774 NA
17.4 -18.8505475 -0.15098046
18 -19.7302351 -0.09870041
19 -14.6177568 -0.26680239
19.1 -17.8043866 -0.15815241
19.2 -15.1641705 -0.14717437
19.3 -16.6898418 -0.21271374
20 -12.9059229 -0.22087628
20.1 -16.8191201 NA
20.2 -6.1010131 -0.30127439
20.3 -7.9415371 -0.11782590
20.4 -9.3904458 -0.19857957
20.5 -13.3504189 -0.24338208
21 -7.6974718 -0.31407992
21.1 -11.9335526 -0.12424941
21.2 -12.7064929 -0.27672716
22 -21.5022909 -0.23790593
22.1 -12.7745451 -0.15996535
23 -3.5146508 -0.18236682
23.1 -4.6724048 -0.20823302
24 -2.5619821 -0.29026416
25 -6.2944970 -0.36139273
25.1 -3.8630505 -0.19571118
25.2 -14.4205140 -0.21379355
25.3 -19.6735037 -0.33876012
25.4 -9.0288933 NA
25.5 -9.0509738 -0.04068446
26 -19.7340685 -0.16846716
26.1 -14.1692728 -0.10440642
26.2 -17.2819976 -0.26884827
26.3 -24.6265576 NA
27 -7.3354999 -0.19520794
27.1 -11.1488468 -0.17622638
28 -11.7996597 -0.32164962
28.1 -8.2030122 -0.27003852
28.2 -26.4317815 -0.07235801
28.3 -18.5016071 -0.13462982
29 -5.8551395 -0.32432030
29.1 -2.0209442 -0.27034171
29.2 -5.6368080 -0.10197448
29.3 -3.8110961 -0.27606945
30 -12.7217702 -0.06949300
30.1 -17.0170140 -0.11511035
30.2 -25.4236089 -0.16215882
31 -17.0783921 0.05707733
32 -18.4338764 -0.18446298
32.1 -19.4317212 -0.14270013
32.2 -19.4738978 -0.20530798
32.3 -21.4922645 -0.14705649
33 2.0838099 -0.15252819
33.1 -13.3172274 NA
34 -10.0296691 -0.30378735
34.1 -25.9426553 -0.11982431
34.2 -18.5688138 -0.24278671
34.3 -15.4173859 -0.19971833
35 -14.3958113 NA
35.1 -12.9457541 -0.24165780
35.2 -16.1380691 NA
36 -12.8166968 -0.49062180
36.1 -14.3989481 -0.25651700
36.2 -12.2436943 NA
36.3 -15.0104638 -0.30401274
36.4 -10.1775457 NA
37 -15.2223495 -0.15276529
37.1 -14.7526195 -0.30016169
37.2 -19.8168430 0.06809545
38 -2.7065118 -0.11218486
39 -8.7288138 -0.38072211
39.1 -9.2746473 -0.32094428
39.2 -18.2695344 NA
39.3 -13.8219083 -0.40173480
39.4 -16.2254704 -0.20041848
39.5 -21.7283648 -0.26027990
40 1.8291916 -0.19751956
40.1 -6.6916432 -0.08399467
40.2 -1.6278171 -0.20864416
40.3 -10.5749790 NA
41 -3.1556121 -0.26096953
41.1 -11.5895327 -0.23953874
41.2 -18.9352091 -0.03079344
41.3 -15.9788960 NA
41.4 -9.6070508 NA
42 -5.2159485 -0.16084527
42.1 -15.9878743 -0.13812521
43 -16.6104361 -0.08864017
43.1 -9.5549441 -0.12583158
43.2 -14.2003491 -0.29253959
44 -8.1969033 -0.22697597
44.1 -19.9270197 NA
44.2 -22.6521171 NA
44.3 -21.1903736 -0.40544012
45 -0.5686627 -0.19274788
45.1 -7.5645740 -0.34860483
46 -19.1624789 -0.28547861
46.1 -18.4487574 -0.21977836
46.2 -15.8222682 NA
47 -5.4165074 -0.08597098
47.1 -15.0975029 -0.35424828
47.2 -12.9971413 -0.24262576
47.3 -10.6844521 -0.30426315
47.4 -18.2214784 NA
48 -8.3101471 NA
48.1 -18.3854275 NA
49 -13.0130319 -0.42198781
50 -10.4579977 -0.19959516
51 -19.3157621 -0.16556964
52 -4.4747188 -0.07438732
52.1 -4.3163827 -0.37537080
52.2 -6.9761408 -0.24222066
52.3 -20.1764756 -0.31520603
52.4 -8.9036692 -0.44619160
52.5 -5.6949642 -0.11011682
53 -10.3141887 -0.23278716
53.1 -8.2642654 -0.28317264
53.2 -9.1691554 -0.19517481
54 -6.2198754 -0.10122856
54.1 -15.7192609 -0.28325504
54.2 -13.0978998 -0.16753120
54.3 -5.1195299 -0.22217672
54.4 -16.5771751 -0.34609328
55 -5.7348534 -0.32428190
55.1 -7.3217494 -0.24235382
55.2 -12.2171938 -0.24065814
55.3 -12.9821266 -0.23665476
55.4 -14.8599983 NA
56 -14.1764282 NA
56.1 -12.5343602 -0.30357450
56.2 -8.4573382 -0.51301630
56.3 -12.4633969 -0.23743117
56.4 -17.3841863 -0.17264917
56.5 -14.8147645 -0.39188329
57 -3.1403293 -0.18501692
57.1 -11.1509248 -0.27274841
57.2 -6.3940143 NA
57.3 -9.3473241 -0.09898509
58 -12.0245677 -0.29901358
58.1 -9.2112246 -0.35390896
58.2 -1.2071742 -0.16687336
58.3 -11.0141711 -0.11784506
58.4 -5.3721214 -0.05321983
58.5 -7.8523047 -0.54457568
59 -13.2946560 -0.27255364
59.1 -10.0530648 NA
60 -19.2209402 NA
61 -4.6699914 -0.30550120
61.1 -3.5981894 -0.35579892
61.2 -1.4713611 NA
61.3 -3.8819786 -0.34184391
61.4 0.1041413 -0.30891967
62 -2.8591600 NA
62.1 -6.9461986 -0.10504143
62.2 -16.7910593 -0.20104997
62.3 -17.9844596 -0.08138677
63 -24.0335535 -0.12036319
63.1 -11.7765300 -0.13624992
64 -20.5963897 NA
65 -2.7969169 -0.34450396
65.1 -11.1778694 -0.32514650
65.2 -5.2830399 -0.10984996
65.3 -7.9353390 -0.19275692
66 -13.2318328 NA
66.1 -1.9090560 NA
66.2 -16.6643889 -0.11687008
67 -25.6073277 NA
68 -13.4806759 -0.13605235
68.1 -18.4557183 -0.19790827
68.2 -13.3982327 -0.17750123
68.3 -12.4977127 NA
68.4 -11.7073990 -0.12570562
69 -14.5290675 -0.32152751
70 -15.2122709 -0.28190462
70.1 -7.8681167 -0.11503263
71 -10.3352703 -0.13029093
71.1 -7.5699888 NA
71.2 -18.4680702 -0.39075433
71.3 -21.4316644 -0.21401028
71.4 -8.1137650 -0.40219281
72 -9.1848162 -0.40337108
72.1 -23.7538846 -0.25978914
72.2 -26.3421306 NA
72.3 -27.2843801 -0.09809866
72.4 -20.8541617 -0.14240019
72.5 -12.8948965 -0.14794204
73 -2.6091307 -0.23509343
74 -8.2790175 -0.27963171
75 -12.5029612 -0.12905034
76 -6.0061671 0.04775562
76.1 -8.8149114 -0.19399157
76.2 -11.8359043 -0.02754574
77 0.4772521 -0.19053195
78 -9.4105229 -0.17172929
79 -1.0217265 -0.03958515
79.1 -11.8125257 -0.20328809
79.2 -10.5465186 -0.23901634
80 -12.7366807 -0.34031873
80.1 -9.0584783 -0.19526756
80.2 -16.6381566 NA
81 0.5547913 -0.18401980
81.1 -4.0892715 -0.16889476
81.2 1.8283303 -0.37343047
81.3 -5.2166381 NA
82 -3.0749381 -0.08328168
82.1 -10.5506696 -0.22167084
82.2 -18.2226347 -0.20971187
83 -12.5872635 -0.34228255
83.1 -11.9756502 -0.34075730
83.2 -10.6744217 -0.32503954
83.3 -19.2714012 NA
84 -2.6320312 -0.20676741
84.1 -9.8140094 -0.20310458
85 -12.3886736 -0.12107593
85.1 -12.9196365 NA
85.2 -9.6433248 -0.32509207
85.3 -6.3296340 NA
85.4 -7.0405525 -0.30730810
85.5 -13.6714939 NA
86 -10.8756412 -0.10854862
86.1 -12.0055331 -0.25751662
86.2 -13.3724699 -0.38943076
86.3 -13.3252145 -0.24454702
86.4 -14.9191290 -0.12338992
86.5 -17.7515546 -0.23976984
87 -10.7027963 NA
87.1 -22.4941954 -0.34366972
87.2 -14.9616716 NA
88 -2.2264493 -0.31563888
88.1 -8.9626474 -0.20304028
88.2 -2.5095281 -0.40311895
88.3 -16.3345673 -0.12308715
89 -11.0459647 -0.18527715
90 -4.5610239 -0.25029126
90.1 -11.7036651 -0.26974303
90.2 -5.3838521 -0.28804531
90.3 -4.1636999 -0.19180615
91 -7.1462503 -0.26591197
91.1 -12.8374475 -0.09153470
91.2 -18.2576707 -0.48414390
92 -6.4119222 NA
93 5.2122168 -0.11939966
93.1 3.1211725 NA
93.2 -3.6841177 -0.21089379
93.3 2.6223542 NA
93.4 -11.1877696 -0.23618836
94 -6.9602492 NA
94.1 -7.4318416 -0.10217284
94.2 -4.3498045 -0.36713471
94.3 -11.6340088 -0.13806763
94.4 -12.9357964 -0.42353804
94.5 -14.7648530 -0.15513707
95 -12.8849309 -0.24149687
95.1 -9.7451502 -0.21315958
95.2 -0.8535063 -0.15777208
96 -4.9139832 -0.16780948
96.1 -3.9582653 -0.32504815
96.2 -9.6555492 -0.20395970
96.3 -11.8690793 -0.06221501
96.4 -11.0224373 -0.14801097
96.5 -10.9530403 -0.28658893
97 -9.8540471 -0.34484656
97.1 -19.2262840 -0.35658805
98 -11.9651231 -0.36913003
98.1 -2.6515128 NA
98.2 -12.2606382 -0.17154225
99 -11.4720500 -0.24753132
99.1 -14.0596866 -0.27947829
99.2 -17.3939469 -0.09033035
100 1.1005874 -0.17326698
100.1 -3.8226248 NA
100.2 -0.9123182 -0.12072016
100.3 -15.8389474 -0.27657520
100.4 -12.8093826 -0.14631556
$m2a$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
$m2a$mu_reg_norm
[1] 0
$m2a$tau_reg_norm
[1] 1e-04
$m2a$shape_tau_norm
[1] 0.01
$m2a$rate_tau_norm
[1] 0.01
$m2a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m2a$shape_diag_RinvD
[1] "0.01"
$m2a$rate_diag_RinvD
[1] "0.001"
$m2b
$m2b$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m2b$M_lvlone
b2 c2
1 NA NA
1.1 0 -0.08061445
1.2 NA -0.26523782
1.3 0 -0.30260393
2 0 -0.33443795
2.1 NA -0.11819800
2.2 NA -0.31532280
3 0 -0.12920657
3.1 NA NA
3.2 1 NA
4 1 -0.31177403
4.1 0 -0.23894886
4.2 0 -0.15533613
4.3 0 -0.14644545
5 NA -0.28360457
5.1 0 -0.20135143
5.2 NA -0.28293375
5.3 NA NA
6 NA -0.08617066
7 NA -0.22243495
7.1 NA NA
7.2 0 NA
8 0 NA
8.1 0 NA
8.2 NA NA
8.3 1 -0.35148972
8.4 0 0.03661023
8.5 1 -0.08424534
9 0 NA
9.1 NA -0.43509340
9.2 NA -0.22527490
10 NA NA
10.1 0 NA
11 0 -0.08587475
11.1 0 -0.06157340
11.2 0 -0.12436018
11.3 0 -0.21377934
11.4 0 -0.32208329
12 0 NA
13 NA NA
13.1 0 -0.40300449
14 NA -0.28992072
14.1 NA NA
14.2 NA NA
14.3 NA -0.21979936
15 0 NA
15.1 0 -0.29092263
15.2 0 -0.19392239
15.3 0 -0.25718384
16 1 -0.45041108
16.1 NA -0.07599066
16.2 NA -0.32385667
16.3 0 -0.38326110
16.4 0 -0.22845856
16.5 NA -0.25497157
17 0 NA
17.1 0 -0.22105143
17.2 0 NA
17.3 NA NA
17.4 0 -0.15098046
18 0 -0.09870041
19 NA -0.26680239
19.1 NA -0.15815241
19.2 0 -0.14717437
19.3 1 -0.21271374
20 NA -0.22087628
20.1 0 NA
20.2 1 -0.30127439
20.3 0 -0.11782590
20.4 0 -0.19857957
20.5 0 -0.24338208
21 0 -0.31407992
21.1 0 -0.12424941
21.2 NA -0.27672716
22 0 -0.23790593
22.1 0 -0.15996535
23 0 -0.18236682
23.1 NA -0.20823302
24 0 -0.29026416
25 0 -0.36139273
25.1 NA -0.19571118
25.2 1 -0.21379355
25.3 0 -0.33876012
25.4 0 NA
25.5 NA -0.04068446
26 NA -0.16846716
26.1 0 -0.10440642
26.2 0 -0.26884827
26.3 0 NA
27 0 -0.19520794
27.1 0 -0.17622638
28 NA -0.32164962
28.1 0 -0.27003852
28.2 0 -0.07235801
28.3 0 -0.13462982
29 0 -0.32432030
29.1 0 -0.27034171
29.2 0 -0.10197448
29.3 0 -0.27606945
30 NA -0.06949300
30.1 0 -0.11511035
30.2 0 -0.16215882
31 0 0.05707733
32 0 -0.18446298
32.1 0 -0.14270013
32.2 NA -0.20530798
32.3 NA -0.14705649
33 0 -0.15252819
33.1 1 NA
34 NA -0.30378735
34.1 0 -0.11982431
34.2 NA -0.24278671
34.3 NA -0.19971833
35 0 NA
35.1 0 -0.24165780
35.2 NA NA
36 NA -0.49062180
36.1 NA -0.25651700
36.2 0 NA
36.3 0 -0.30401274
36.4 0 NA
37 0 -0.15276529
37.1 0 -0.30016169
37.2 0 0.06809545
38 0 -0.11218486
39 1 -0.38072211
39.1 0 -0.32094428
39.2 NA NA
39.3 NA -0.40173480
39.4 0 -0.20041848
39.5 1 -0.26027990
40 0 -0.19751956
40.1 1 -0.08399467
40.2 0 -0.20864416
40.3 NA NA
41 0 -0.26096953
41.1 NA -0.23953874
41.2 0 -0.03079344
41.3 NA NA
41.4 0 NA
42 0 -0.16084527
42.1 1 -0.13812521
43 0 -0.08864017
43.1 1 -0.12583158
43.2 0 -0.29253959
44 0 -0.22697597
44.1 0 NA
44.2 0 NA
44.3 0 -0.40544012
45 NA -0.19274788
45.1 1 -0.34860483
46 0 -0.28547861
46.1 0 -0.21977836
46.2 0 NA
47 0 -0.08597098
47.1 0 -0.35424828
47.2 0 -0.24262576
47.3 NA -0.30426315
47.4 0 NA
48 1 NA
48.1 1 NA
49 NA -0.42198781
50 0 -0.19959516
51 0 -0.16556964
52 0 -0.07438732
52.1 0 -0.37537080
52.2 0 -0.24222066
52.3 0 -0.31520603
52.4 0 -0.44619160
52.5 0 -0.11011682
53 0 -0.23278716
53.1 0 -0.28317264
53.2 NA -0.19517481
54 NA -0.10122856
54.1 NA -0.28325504
54.2 NA -0.16753120
54.3 NA -0.22217672
54.4 0 -0.34609328
55 0 -0.32428190
55.1 0 -0.24235382
55.2 NA -0.24065814
55.3 NA -0.23665476
55.4 0 NA
56 0 NA
56.1 NA -0.30357450
56.2 NA -0.51301630
56.3 1 -0.23743117
56.4 0 -0.17264917
56.5 0 -0.39188329
57 0 -0.18501692
57.1 0 -0.27274841
57.2 0 NA
57.3 NA -0.09898509
58 0 -0.29901358
58.1 NA -0.35390896
58.2 1 -0.16687336
58.3 1 -0.11784506
58.4 0 -0.05321983
58.5 0 -0.54457568
59 NA -0.27255364
59.1 1 NA
60 0 NA
61 NA -0.30550120
61.1 1 -0.35579892
61.2 1 NA
61.3 0 -0.34184391
61.4 0 -0.30891967
62 NA NA
62.1 1 -0.10504143
62.2 0 -0.20104997
62.3 0 -0.08138677
63 NA -0.12036319
63.1 0 -0.13624992
64 0 NA
65 0 -0.34450396
65.1 0 -0.32514650
65.2 0 -0.10984996
65.3 0 -0.19275692
66 NA NA
66.1 0 NA
66.2 0 -0.11687008
67 NA NA
68 0 -0.13605235
68.1 0 -0.19790827
68.2 NA -0.17750123
68.3 0 NA
68.4 NA -0.12570562
69 0 -0.32152751
70 0 -0.28190462
70.1 0 -0.11503263
71 0 -0.13029093
71.1 1 NA
71.2 0 -0.39075433
71.3 1 -0.21401028
71.4 0 -0.40219281
72 0 -0.40337108
72.1 0 -0.25978914
72.2 NA NA
72.3 0 -0.09809866
72.4 0 -0.14240019
72.5 0 -0.14794204
73 0 -0.23509343
74 0 -0.27963171
75 NA -0.12905034
76 0 0.04775562
76.1 0 -0.19399157
76.2 0 -0.02754574
77 NA -0.19053195
78 0 -0.17172929
79 NA -0.03958515
79.1 0 -0.20328809
79.2 NA -0.23901634
80 NA -0.34031873
80.1 0 -0.19526756
80.2 NA NA
81 0 -0.18401980
81.1 0 -0.16889476
81.2 NA -0.37343047
81.3 0 NA
82 NA -0.08328168
82.1 0 -0.22167084
82.2 1 -0.20971187
83 NA -0.34228255
83.1 0 -0.34075730
83.2 0 -0.32503954
83.3 NA NA
84 0 -0.20676741
84.1 NA -0.20310458
85 1 -0.12107593
85.1 NA NA
85.2 0 -0.32509207
85.3 0 NA
85.4 0 -0.30730810
85.5 0 NA
86 0 -0.10854862
86.1 NA -0.25751662
86.2 NA -0.38943076
86.3 0 -0.24454702
86.4 NA -0.12338992
86.5 0 -0.23976984
87 NA NA
87.1 NA -0.34366972
87.2 NA NA
88 0 -0.31563888
88.1 NA -0.20304028
88.2 0 -0.40311895
88.3 0 -0.12308715
89 0 -0.18527715
90 0 -0.25029126
90.1 0 -0.26974303
90.2 0 -0.28804531
90.3 NA -0.19180615
91 0 -0.26591197
91.1 0 -0.09153470
91.2 0 -0.48414390
92 0 NA
93 NA -0.11939966
93.1 0 NA
93.2 NA -0.21089379
93.3 0 NA
93.4 0 -0.23618836
94 NA NA
94.1 0 -0.10217284
94.2 0 -0.36713471
94.3 NA -0.13806763
94.4 0 -0.42353804
94.5 1 -0.15513707
95 0 -0.24149687
95.1 NA -0.21315958
95.2 0 -0.15777208
96 0 -0.16780948
96.1 0 -0.32504815
96.2 0 -0.20395970
96.3 NA -0.06221501
96.4 1 -0.14801097
96.5 1 -0.28658893
97 0 -0.34484656
97.1 0 -0.35658805
98 0 -0.36913003
98.1 0 NA
98.2 1 -0.17154225
99 0 -0.24753132
99.1 0 -0.27947829
99.2 0 -0.09033035
100 NA -0.17326698
100.1 NA NA
100.2 0 -0.12072016
100.3 NA -0.27657520
100.4 0 -0.14631556
$m2b$spM_lvlone
center scale
b2 NA NA
c2 -0.2237158 0.1059527
$m2b$mu_reg_norm
[1] 0
$m2b$tau_reg_norm
[1] 1e-04
$m2b$shape_tau_norm
[1] 0.01
$m2b$rate_tau_norm
[1] 0.01
$m2b$mu_reg_binom
[1] 0
$m2b$tau_reg_binom
[1] 1e-04
$m2b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m2b$shape_diag_RinvD
[1] "0.01"
$m2b$rate_diag_RinvD
[1] "0.001"
$m2c
$m2c$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m2c$M_lvlone
L1mis c2
1 1.38634787 NA
1.1 0.79402906 -0.08061445
1.2 0.53603334 -0.26523782
1.3 0.24129804 -0.30260393
2 NA -0.33443795
2.1 0.31668065 -0.11819800
2.2 0.37114414 -0.31532280
3 0.54680608 -0.12920657
3.1 0.28280274 NA
3.2 0.76277262 NA
4 0.56100366 -0.31177403
4.1 0.38514140 -0.23894886
4.2 0.04026174 -0.15533613
4.3 0.16025873 -0.14644545
5 0.21080161 -0.28360457
5.1 0.36665700 -0.20135143
5.2 0.66368829 -0.28293375
5.3 0.40788895 NA
6 0.11889539 -0.08617066
7 1.04286843 -0.22243495
7.1 0.52098933 NA
7.2 0.09858876 NA
8 0.17281472 NA
8.1 0.25970093 NA
8.2 0.30550233 NA
8.3 0.88029778 -0.35148972
8.4 0.20200392 0.03661023
8.5 NA -0.08424534
9 1.12218535 NA
9.1 0.57911079 -0.43509340
9.2 0.81350994 -0.22527490
10 0.32744766 NA
10.1 0.62912282 NA
11 0.92140073 -0.08587475
11.1 0.16012129 -0.06157340
11.2 0.16166775 -0.12436018
11.3 0.14979756 -0.21377934
11.4 0.46855190 -0.32208329
12 0.76818678 NA
13 0.34264972 NA
13.1 0.14526619 -0.40300449
14 0.80630788 -0.28992072
14.1 0.35697552 NA
14.2 0.21330192 NA
14.3 NA -0.21979936
15 0.30769119 NA
15.1 0.28349746 -0.29092263
15.2 0.64618365 -0.19392239
15.3 0.51680884 -0.25718384
16 0.71265471 -0.45041108
16.1 0.38925880 -0.07599066
16.2 0.23648869 -0.32385667
16.3 0.45048730 -0.38326110
16.4 0.23181791 -0.22845856
16.5 0.13985349 -0.25497157
17 0.25995399 NA
17.1 0.03594878 -0.22105143
17.2 0.77583623 NA
17.3 0.60015197 NA
17.4 0.13998405 -0.15098046
18 0.96475839 -0.09870041
19 0.10596495 -0.26680239
19.1 0.13338947 -0.15815241
19.2 0.41662218 -0.14717437
19.3 0.53670855 -0.21271374
20 0.41688567 -0.22087628
20.1 NA NA
20.2 0.81634101 -0.30127439
20.3 0.39232496 -0.11782590
20.4 0.57925554 -0.19857957
20.5 0.74200986 -0.24338208
21 0.24759801 -0.31407992
21.1 0.34052205 -0.12424941
21.2 0.03905058 -0.27672716
22 0.48605351 -0.23790593
22.1 0.43761071 -0.15996535
23 0.47599712 -0.18236682
23.1 0.47680301 -0.20823302
24 0.51696505 -0.29026416
25 0.59392591 -0.36139273
25.1 0.74010330 -0.19571118
25.2 NA -0.21379355
25.3 0.73081722 -0.33876012
25.4 0.29274286 NA
25.5 0.74425342 -0.04068446
26 0.20974346 -0.16846716
26.1 NA -0.10440642
26.2 0.22908815 -0.26884827
26.3 0.41880799 NA
27 0.10097167 -0.19520794
27.1 NA -0.17622638
28 NA -0.32164962
28.1 0.56052750 -0.27003852
28.2 0.15301800 -0.07235801
28.3 0.27802542 -0.13462982
29 0.43556671 -0.32432030
29.1 0.27593085 -0.27034171
29.2 0.55256871 -0.10197448
29.3 0.47272109 -0.27606945
30 0.32743933 -0.06949300
30.1 0.02231535 -0.11511035
30.2 0.12833697 -0.16215882
31 0.11126366 0.05707733
32 1.11731084 -0.18446298
32.1 0.85943330 -0.14270013
32.2 1.53730925 -0.20530798
32.3 0.43831965 -0.14705649
33 0.46726055 -0.15252819
33.1 0.76818259 NA
34 NA -0.30378735
34.1 1.14350292 -0.11982431
34.2 0.19103604 -0.24278671
34.3 NA -0.19971833
35 0.66303137 NA
35.1 NA -0.24165780
35.2 NA NA
36 0.93843318 -0.49062180
36.1 NA -0.25651700
36.2 0.29886676 NA
36.3 0.22616598 -0.30401274
36.4 0.53849566 NA
37 1.68107300 -0.15276529
37.1 1.13777638 -0.30016169
37.2 0.26931933 0.06809545
38 NA -0.11218486
39 0.14395367 -0.38072211
39.1 0.36454923 -0.32094428
39.2 1.03700002 NA
39.3 0.41320585 -0.40173480
39.4 0.20901554 -0.20041848
39.5 0.51603848 -0.26027990
40 0.33912363 -0.19751956
40.1 0.21892118 -0.08399467
40.2 0.74070896 -0.20864416
40.3 0.82927399 NA
41 0.25193679 -0.26096953
41.1 0.28760510 -0.23953874
41.2 0.45553197 -0.03079344
41.3 0.79237611 NA
41.4 0.12582175 NA
42 0.50079604 -0.16084527
42.1 0.61140760 -0.13812521
43 0.29752019 -0.08864017
43.1 0.51793497 -0.12583158
43.2 0.15152473 -0.29253959
44 0.38806434 -0.22697597
44.1 1.11140786 NA
44.2 0.39132534 NA
44.3 0.40934909 -0.40544012
45 0.68587067 -0.19274788
45.1 0.34530800 -0.34860483
46 0.71312288 -0.28547861
46.1 0.62537420 -0.21977836
46.2 0.79574391 NA
47 0.48660773 -0.08597098
47.1 0.51241790 -0.35424828
47.2 0.58869379 -0.24262576
47.3 0.22171504 -0.30426315
47.4 0.11366347 NA
48 0.19677010 NA
48.1 0.17706320 NA
49 0.30752382 -0.42198781
50 0.93663423 -0.19959516
51 0.34107606 -0.16556964
52 0.19007135 -0.07438732
52.1 0.75662940 -0.37537080
52.2 1.66104719 -0.24222066
52.3 NA -0.31520603
52.4 0.18369708 -0.44619160
52.5 0.48689343 -0.11011682
53 0.31983157 -0.23278716
53.1 0.61569501 -0.28317264
53.2 NA -0.19517481
54 1.90522418 -0.10122856
54.1 0.59484889 -0.28325504
54.2 1.47174857 -0.16753120
54.3 0.27307143 -0.22217672
54.4 0.81272938 -0.34609328
55 0.22735476 -0.32428190
55.1 0.54683512 -0.24235382
55.2 1.03503777 -0.24065814
55.3 0.30169529 -0.23665476
55.4 0.36008059 NA
56 0.14193566 NA
56.1 0.65073539 -0.30357450
56.2 0.11338262 -0.51301630
56.3 0.16820103 -0.23743117
56.4 0.27419110 -0.17264917
56.5 0.57110215 -0.39188329
57 0.85104054 -0.18501692
57.1 0.34733833 -0.27274841
57.2 1.44438762 NA
57.3 0.31836125 -0.09898509
58 0.37456898 -0.29901358
58.1 0.22120158 -0.35390896
58.2 0.78885210 -0.16687336
58.3 0.10114937 -0.11784506
58.4 0.13385114 -0.05321983
58.5 NA -0.54457568
59 0.13202156 -0.27255364
59.1 0.33371896 NA
60 0.35096579 NA
61 0.36933806 -0.30550120
61.1 0.17623067 -0.35579892
61.2 0.21286227 NA
61.3 0.12689308 -0.34184391
61.4 0.77676718 -0.30891967
62 1.38018163 NA
62.1 0.43803892 -0.10504143
62.2 0.21947900 -0.20104997
62.3 0.11571160 -0.08138677
63 0.41583568 -0.12036319
63.1 0.25598960 -0.13624992
64 0.20415642 NA
65 0.07135646 -0.34450396
65.1 0.57450574 -0.32514650
65.2 0.52562984 -0.10984996
65.3 0.21921164 -0.19275692
66 0.33281730 NA
66.1 0.03412404 NA
66.2 0.92570619 -0.11687008
67 0.15291043 NA
68 0.37543648 -0.13605235
68.1 0.20901022 -0.19790827
68.2 0.12488064 -0.17750123
68.3 0.08711204 NA
68.4 0.54611735 -0.12570562
69 0.23638239 -0.32152751
70 0.49876756 -0.28190462
70.1 0.39512615 -0.11503263
71 0.45666551 -0.13029093
71.1 0.92047456 NA
71.2 0.32792986 -0.39075433
71.3 0.95108007 -0.21401028
71.4 0.36287072 -0.40219281
72 0.12870526 -0.40337108
72.1 0.45925876 -0.25978914
72.2 0.05418867 NA
72.3 0.48937486 -0.09809866
72.4 0.64173822 -0.14240019
72.5 0.57609943 -0.14794204
73 0.17393402 -0.23509343
74 0.23990575 -0.27963171
75 0.28469861 -0.12905034
76 0.71988630 0.04775562
76.1 1.12449946 -0.19399157
76.2 0.71313766 -0.02754574
77 0.02399030 -0.19053195
78 0.42708148 -0.17172929
79 0.37579286 -0.03958515
79.1 0.78660681 -0.20328809
79.2 0.67696116 -0.23901634
80 0.34207854 -0.34031873
80.1 0.60534092 -0.19526756
80.2 0.26731034 NA
81 0.17739052 -0.18401980
81.1 0.35453673 -0.16889476
81.2 0.20244235 -0.37343047
81.3 1.26402329 NA
82 0.09303938 -0.08328168
82.1 0.27254210 -0.22167084
82.2 0.49936304 -0.20971187
83 0.21138572 -0.34228255
83.1 0.26403568 -0.34075730
83.2 0.20311133 -0.32503954
83.3 1.16864671 NA
84 1.99179346 -0.20676741
84.1 1.52199460 -0.20310458
85 NA -0.12107593
85.1 0.61458995 NA
85.2 0.07871196 -0.32509207
85.3 1.42315283 NA
85.4 0.97986129 -0.30730810
85.5 0.91792195 NA
86 0.63509597 -0.10854862
86.1 0.24546597 -0.25751662
86.2 0.53102060 -0.38943076
86.3 0.09360826 -0.24454702
86.4 0.58301186 -0.12338992
86.5 0.39146055 -0.23976984
87 NA NA
87.1 0.66043624 -0.34366972
87.2 0.13267613 NA
88 0.10696344 -0.31563888
88.1 0.13689448 -0.20304028
88.2 0.48037889 -0.40311895
88.3 0.97755681 -0.12308715
89 0.70242369 -0.18527715
90 0.40042977 -0.25029126
90.1 0.63975731 -0.26974303
90.2 0.33412775 -0.28804531
90.3 0.38399003 -0.19180615
91 0.58250391 -0.26591197
91.1 0.13223217 -0.09153470
91.2 0.46613305 -0.48414390
92 0.18997862 NA
93 1.05243347 -0.11939966
93.1 0.01479757 NA
93.2 0.50955172 -0.21089379
93.3 0.78122514 NA
93.4 0.63940704 -0.23618836
94 0.45596305 NA
94.1 0.41610667 -0.10217284
94.2 0.52744298 -0.36713471
94.3 0.70890756 -0.13806763
94.4 0.84412478 -0.42353804
94.5 0.21166602 -0.15513707
95 0.57713135 -0.24149687
95.1 0.44400207 -0.21315958
95.2 0.42397776 -0.15777208
96 0.72391015 -0.16780948
96.1 0.32593738 -0.32504815
96.2 0.23249511 -0.20395970
96.3 1.01679990 -0.06221501
96.4 0.92267953 -0.14801097
96.5 0.83843412 -0.28658893
97 0.47151154 -0.34484656
97.1 0.15596614 -0.35658805
98 0.05179545 -0.36913003
98.1 0.47332096 NA
98.2 0.19706341 -0.17154225
99 0.22574556 -0.24753132
99.1 1.00732330 -0.27947829
99.2 0.09749127 -0.09033035
100 0.22857989 -0.17326698
100.1 0.39548654 NA
100.2 NA -0.12072016
100.3 0.32695372 -0.27657520
100.4 0.10043925 -0.14631556
$m2c$spM_lvlone
center scale
L1mis 0.4818481 0.3462447
c2 -0.2237158 0.1059527
$m2c$mu_reg_norm
[1] 0
$m2c$tau_reg_norm
[1] 1e-04
$m2c$shape_tau_norm
[1] 0.01
$m2c$rate_tau_norm
[1] 0.01
$m2c$mu_reg_gamma
[1] 0
$m2c$tau_reg_gamma
[1] 1e-04
$m2c$shape_tau_gamma
[1] 0.01
$m2c$rate_tau_gamma
[1] 0.01
$m2c$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m2c$shape_diag_RinvD
[1] "0.01"
$m2c$rate_diag_RinvD
[1] "0.001"
$m2d
$m2d$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m2d$M_lvlone
p2 c2
1 2 NA
1.1 2 -0.08061445
1.2 NA -0.26523782
1.3 NA -0.30260393
2 NA -0.33443795
2.1 6 -0.11819800
2.2 3 -0.31532280
3 NA -0.12920657
3.1 NA NA
3.2 NA NA
4 NA -0.31177403
4.1 4 -0.23894886
4.2 0 -0.15533613
4.3 NA -0.14644545
5 2 -0.28360457
5.1 NA -0.20135143
5.2 7 -0.28293375
5.3 NA NA
6 NA -0.08617066
7 NA -0.22243495
7.1 NA NA
7.2 NA NA
8 1 NA
8.1 6 NA
8.2 NA NA
8.3 3 -0.35148972
8.4 2 0.03661023
8.5 1 -0.08424534
9 3 NA
9.1 NA -0.43509340
9.2 3 -0.22527490
10 3 NA
10.1 NA NA
11 1 -0.08587475
11.1 6 -0.06157340
11.2 1 -0.12436018
11.3 6 -0.21377934
11.4 NA -0.32208329
12 NA NA
13 NA NA
13.1 NA -0.40300449
14 NA -0.28992072
14.1 NA NA
14.2 2 NA
14.3 NA -0.21979936
15 NA NA
15.1 NA -0.29092263
15.2 NA -0.19392239
15.3 NA -0.25718384
16 1 -0.45041108
16.1 NA -0.07599066
16.2 2 -0.32385667
16.3 NA -0.38326110
16.4 1 -0.22845856
16.5 NA -0.25497157
17 1 NA
17.1 NA -0.22105143
17.2 3 NA
17.3 2 NA
17.4 NA -0.15098046
18 2 -0.09870041
19 NA -0.26680239
19.1 NA -0.15815241
19.2 2 -0.14717437
19.3 2 -0.21271374
20 NA -0.22087628
20.1 2 NA
20.2 NA -0.30127439
20.3 NA -0.11782590
20.4 NA -0.19857957
20.5 NA -0.24338208
21 2 -0.31407992
21.1 3 -0.12424941
21.2 2 -0.27672716
22 3 -0.23790593
22.1 3 -0.15996535
23 NA -0.18236682
23.1 5 -0.20823302
24 2 -0.29026416
25 3 -0.36139273
25.1 3 -0.19571118
25.2 3 -0.21379355
25.3 4 -0.33876012
25.4 NA NA
25.5 NA -0.04068446
26 NA -0.16846716
26.1 2 -0.10440642
26.2 NA -0.26884827
26.3 NA NA
27 1 -0.19520794
27.1 NA -0.17622638
28 0 -0.32164962
28.1 NA -0.27003852
28.2 4 -0.07235801
28.3 NA -0.13462982
29 3 -0.32432030
29.1 3 -0.27034171
29.2 3 -0.10197448
29.3 2 -0.27606945
30 NA -0.06949300
30.1 NA -0.11511035
30.2 5 -0.16215882
31 8 0.05707733
32 NA -0.18446298
32.1 2 -0.14270013
32.2 1 -0.20530798
32.3 NA -0.14705649
33 0 -0.15252819
33.1 NA NA
34 3 -0.30378735
34.1 NA -0.11982431
34.2 1 -0.24278671
34.3 2 -0.19971833
35 NA NA
35.1 NA -0.24165780
35.2 NA NA
36 5 -0.49062180
36.1 NA -0.25651700
36.2 NA NA
36.3 1 -0.30401274
36.4 1 NA
37 5 -0.15276529
37.1 NA -0.30016169
37.2 NA 0.06809545
38 0 -0.11218486
39 NA -0.38072211
39.1 1 -0.32094428
39.2 NA NA
39.3 NA -0.40173480
39.4 NA -0.20041848
39.5 NA -0.26027990
40 2 -0.19751956
40.1 4 -0.08399467
40.2 NA -0.20864416
40.3 NA NA
41 NA -0.26096953
41.1 4 -0.23953874
41.2 2 -0.03079344
41.3 3 NA
41.4 NA NA
42 3 -0.16084527
42.1 5 -0.13812521
43 4 -0.08864017
43.1 3 -0.12583158
43.2 3 -0.29253959
44 1 -0.22697597
44.1 NA NA
44.2 7 NA
44.3 NA -0.40544012
45 NA -0.19274788
45.1 NA -0.34860483
46 4 -0.28547861
46.1 6 -0.21977836
46.2 NA NA
47 NA -0.08597098
47.1 4 -0.35424828
47.2 2 -0.24262576
47.3 4 -0.30426315
47.4 NA NA
48 NA NA
48.1 6 NA
49 NA -0.42198781
50 3 -0.19959516
51 2 -0.16556964
52 3 -0.07438732
52.1 1 -0.37537080
52.2 NA -0.24222066
52.3 2 -0.31520603
52.4 3 -0.44619160
52.5 1 -0.11011682
53 3 -0.23278716
53.1 NA -0.28317264
53.2 2 -0.19517481
54 3 -0.10122856
54.1 NA -0.28325504
54.2 4 -0.16753120
54.3 0 -0.22217672
54.4 NA -0.34609328
55 NA -0.32428190
55.1 4 -0.24235382
55.2 NA -0.24065814
55.3 4 -0.23665476
55.4 3 NA
56 NA NA
56.1 2 -0.30357450
56.2 3 -0.51301630
56.3 3 -0.23743117
56.4 0 -0.17264917
56.5 NA -0.39188329
57 3 -0.18501692
57.1 4 -0.27274841
57.2 1 NA
57.3 NA -0.09898509
58 NA -0.29901358
58.1 NA -0.35390896
58.2 NA -0.16687336
58.3 3 -0.11784506
58.4 NA -0.05321983
58.5 NA -0.54457568
59 NA -0.27255364
59.1 NA NA
60 NA NA
61 2 -0.30550120
61.1 4 -0.35579892
61.2 NA NA
61.3 NA -0.34184391
61.4 NA -0.30891967
62 2 NA
62.1 NA -0.10504143
62.2 NA -0.20104997
62.3 NA -0.08138677
63 NA -0.12036319
63.1 2 -0.13624992
64 4 NA
65 NA -0.34450396
65.1 5 -0.32514650
65.2 NA -0.10984996
65.3 NA -0.19275692
66 NA NA
66.1 NA NA
66.2 NA -0.11687008
67 NA NA
68 NA -0.13605235
68.1 NA -0.19790827
68.2 NA -0.17750123
68.3 2 NA
68.4 NA -0.12570562
69 NA -0.32152751
70 4 -0.28190462
70.1 4 -0.11503263
71 4 -0.13029093
71.1 NA NA
71.2 3 -0.39075433
71.3 0 -0.21401028
71.4 0 -0.40219281
72 NA -0.40337108
72.1 8 -0.25978914
72.2 NA NA
72.3 NA -0.09809866
72.4 3 -0.14240019
72.5 NA -0.14794204
73 2 -0.23509343
74 NA -0.27963171
75 NA -0.12905034
76 1 0.04775562
76.1 0 -0.19399157
76.2 0 -0.02754574
77 2 -0.19053195
78 NA -0.17172929
79 2 -0.03958515
79.1 NA -0.20328809
79.2 2 -0.23901634
80 2 -0.34031873
80.1 NA -0.19526756
80.2 NA NA
81 NA -0.18401980
81.1 2 -0.16889476
81.2 NA -0.37343047
81.3 NA NA
82 NA -0.08328168
82.1 NA -0.22167084
82.2 4 -0.20971187
83 NA -0.34228255
83.1 NA -0.34075730
83.2 4 -0.32503954
83.3 3 NA
84 NA -0.20676741
84.1 2 -0.20310458
85 3 -0.12107593
85.1 NA NA
85.2 3 -0.32509207
85.3 NA NA
85.4 2 -0.30730810
85.5 1 NA
86 2 -0.10854862
86.1 NA -0.25751662
86.2 0 -0.38943076
86.3 0 -0.24454702
86.4 NA -0.12338992
86.5 2 -0.23976984
87 NA NA
87.1 NA -0.34366972
87.2 3 NA
88 NA -0.31563888
88.1 1 -0.20304028
88.2 1 -0.40311895
88.3 4 -0.12308715
89 NA -0.18527715
90 3 -0.25029126
90.1 NA -0.26974303
90.2 NA -0.28804531
90.3 NA -0.19180615
91 NA -0.26591197
91.1 NA -0.09153470
91.2 NA -0.48414390
92 NA NA
93 2 -0.11939966
93.1 4 NA
93.2 4 -0.21089379
93.3 NA NA
93.4 3 -0.23618836
94 4 NA
94.1 2 -0.10217284
94.2 NA -0.36713471
94.3 1 -0.13806763
94.4 NA -0.42353804
94.5 2 -0.15513707
95 3 -0.24149687
95.1 5 -0.21315958
95.2 2 -0.15777208
96 NA -0.16780948
96.1 NA -0.32504815
96.2 5 -0.20395970
96.3 1 -0.06221501
96.4 0 -0.14801097
96.5 3 -0.28658893
97 4 -0.34484656
97.1 2 -0.35658805
98 3 -0.36913003
98.1 NA NA
98.2 NA -0.17154225
99 5 -0.24753132
99.1 NA -0.27947829
99.2 NA -0.09033035
100 NA -0.17326698
100.1 4 NA
100.2 NA -0.12072016
100.3 4 -0.27657520
100.4 NA -0.14631556
$m2d$spM_lvlone
center scale
p2 2.7125749 1.6247402
c2 -0.2237158 0.1059527
$m2d$mu_reg_norm
[1] 0
$m2d$tau_reg_norm
[1] 1e-04
$m2d$shape_tau_norm
[1] 0.01
$m2d$rate_tau_norm
[1] 0.01
$m2d$mu_reg_poisson
[1] 0
$m2d$tau_reg_poisson
[1] 1e-04
$m2d$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m2d$shape_diag_RinvD
[1] "0.01"
$m2d$rate_diag_RinvD
[1] "0.001"
$m2e
$m2e$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m2e$M_lvlone
L1mis c2
1 1.38634787 NA
1.1 0.79402906 -0.08061445
1.2 0.53603334 -0.26523782
1.3 0.24129804 -0.30260393
2 NA -0.33443795
2.1 0.31668065 -0.11819800
2.2 0.37114414 -0.31532280
3 0.54680608 -0.12920657
3.1 0.28280274 NA
3.2 0.76277262 NA
4 0.56100366 -0.31177403
4.1 0.38514140 -0.23894886
4.2 0.04026174 -0.15533613
4.3 0.16025873 -0.14644545
5 0.21080161 -0.28360457
5.1 0.36665700 -0.20135143
5.2 0.66368829 -0.28293375
5.3 0.40788895 NA
6 0.11889539 -0.08617066
7 1.04286843 -0.22243495
7.1 0.52098933 NA
7.2 0.09858876 NA
8 0.17281472 NA
8.1 0.25970093 NA
8.2 0.30550233 NA
8.3 0.88029778 -0.35148972
8.4 0.20200392 0.03661023
8.5 NA -0.08424534
9 1.12218535 NA
9.1 0.57911079 -0.43509340
9.2 0.81350994 -0.22527490
10 0.32744766 NA
10.1 0.62912282 NA
11 0.92140073 -0.08587475
11.1 0.16012129 -0.06157340
11.2 0.16166775 -0.12436018
11.3 0.14979756 -0.21377934
11.4 0.46855190 -0.32208329
12 0.76818678 NA
13 0.34264972 NA
13.1 0.14526619 -0.40300449
14 0.80630788 -0.28992072
14.1 0.35697552 NA
14.2 0.21330192 NA
14.3 NA -0.21979936
15 0.30769119 NA
15.1 0.28349746 -0.29092263
15.2 0.64618365 -0.19392239
15.3 0.51680884 -0.25718384
16 0.71265471 -0.45041108
16.1 0.38925880 -0.07599066
16.2 0.23648869 -0.32385667
16.3 0.45048730 -0.38326110
16.4 0.23181791 -0.22845856
16.5 0.13985349 -0.25497157
17 0.25995399 NA
17.1 0.03594878 -0.22105143
17.2 0.77583623 NA
17.3 0.60015197 NA
17.4 0.13998405 -0.15098046
18 0.96475839 -0.09870041
19 0.10596495 -0.26680239
19.1 0.13338947 -0.15815241
19.2 0.41662218 -0.14717437
19.3 0.53670855 -0.21271374
20 0.41688567 -0.22087628
20.1 NA NA
20.2 0.81634101 -0.30127439
20.3 0.39232496 -0.11782590
20.4 0.57925554 -0.19857957
20.5 0.74200986 -0.24338208
21 0.24759801 -0.31407992
21.1 0.34052205 -0.12424941
21.2 0.03905058 -0.27672716
22 0.48605351 -0.23790593
22.1 0.43761071 -0.15996535
23 0.47599712 -0.18236682
23.1 0.47680301 -0.20823302
24 0.51696505 -0.29026416
25 0.59392591 -0.36139273
25.1 0.74010330 -0.19571118
25.2 NA -0.21379355
25.3 0.73081722 -0.33876012
25.4 0.29274286 NA
25.5 0.74425342 -0.04068446
26 0.20974346 -0.16846716
26.1 NA -0.10440642
26.2 0.22908815 -0.26884827
26.3 0.41880799 NA
27 0.10097167 -0.19520794
27.1 NA -0.17622638
28 NA -0.32164962
28.1 0.56052750 -0.27003852
28.2 0.15301800 -0.07235801
28.3 0.27802542 -0.13462982
29 0.43556671 -0.32432030
29.1 0.27593085 -0.27034171
29.2 0.55256871 -0.10197448
29.3 0.47272109 -0.27606945
30 0.32743933 -0.06949300
30.1 0.02231535 -0.11511035
30.2 0.12833697 -0.16215882
31 0.11126366 0.05707733
32 1.11731084 -0.18446298
32.1 0.85943330 -0.14270013
32.2 1.53730925 -0.20530798
32.3 0.43831965 -0.14705649
33 0.46726055 -0.15252819
33.1 0.76818259 NA
34 NA -0.30378735
34.1 1.14350292 -0.11982431
34.2 0.19103604 -0.24278671
34.3 NA -0.19971833
35 0.66303137 NA
35.1 NA -0.24165780
35.2 NA NA
36 0.93843318 -0.49062180
36.1 NA -0.25651700
36.2 0.29886676 NA
36.3 0.22616598 -0.30401274
36.4 0.53849566 NA
37 1.68107300 -0.15276529
37.1 1.13777638 -0.30016169
37.2 0.26931933 0.06809545
38 NA -0.11218486
39 0.14395367 -0.38072211
39.1 0.36454923 -0.32094428
39.2 1.03700002 NA
39.3 0.41320585 -0.40173480
39.4 0.20901554 -0.20041848
39.5 0.51603848 -0.26027990
40 0.33912363 -0.19751956
40.1 0.21892118 -0.08399467
40.2 0.74070896 -0.20864416
40.3 0.82927399 NA
41 0.25193679 -0.26096953
41.1 0.28760510 -0.23953874
41.2 0.45553197 -0.03079344
41.3 0.79237611 NA
41.4 0.12582175 NA
42 0.50079604 -0.16084527
42.1 0.61140760 -0.13812521
43 0.29752019 -0.08864017
43.1 0.51793497 -0.12583158
43.2 0.15152473 -0.29253959
44 0.38806434 -0.22697597
44.1 1.11140786 NA
44.2 0.39132534 NA
44.3 0.40934909 -0.40544012
45 0.68587067 -0.19274788
45.1 0.34530800 -0.34860483
46 0.71312288 -0.28547861
46.1 0.62537420 -0.21977836
46.2 0.79574391 NA
47 0.48660773 -0.08597098
47.1 0.51241790 -0.35424828
47.2 0.58869379 -0.24262576
47.3 0.22171504 -0.30426315
47.4 0.11366347 NA
48 0.19677010 NA
48.1 0.17706320 NA
49 0.30752382 -0.42198781
50 0.93663423 -0.19959516
51 0.34107606 -0.16556964
52 0.19007135 -0.07438732
52.1 0.75662940 -0.37537080
52.2 1.66104719 -0.24222066
52.3 NA -0.31520603
52.4 0.18369708 -0.44619160
52.5 0.48689343 -0.11011682
53 0.31983157 -0.23278716
53.1 0.61569501 -0.28317264
53.2 NA -0.19517481
54 1.90522418 -0.10122856
54.1 0.59484889 -0.28325504
54.2 1.47174857 -0.16753120
54.3 0.27307143 -0.22217672
54.4 0.81272938 -0.34609328
55 0.22735476 -0.32428190
55.1 0.54683512 -0.24235382
55.2 1.03503777 -0.24065814
55.3 0.30169529 -0.23665476
55.4 0.36008059 NA
56 0.14193566 NA
56.1 0.65073539 -0.30357450
56.2 0.11338262 -0.51301630
56.3 0.16820103 -0.23743117
56.4 0.27419110 -0.17264917
56.5 0.57110215 -0.39188329
57 0.85104054 -0.18501692
57.1 0.34733833 -0.27274841
57.2 1.44438762 NA
57.3 0.31836125 -0.09898509
58 0.37456898 -0.29901358
58.1 0.22120158 -0.35390896
58.2 0.78885210 -0.16687336
58.3 0.10114937 -0.11784506
58.4 0.13385114 -0.05321983
58.5 NA -0.54457568
59 0.13202156 -0.27255364
59.1 0.33371896 NA
60 0.35096579 NA
61 0.36933806 -0.30550120
61.1 0.17623067 -0.35579892
61.2 0.21286227 NA
61.3 0.12689308 -0.34184391
61.4 0.77676718 -0.30891967
62 1.38018163 NA
62.1 0.43803892 -0.10504143
62.2 0.21947900 -0.20104997
62.3 0.11571160 -0.08138677
63 0.41583568 -0.12036319
63.1 0.25598960 -0.13624992
64 0.20415642 NA
65 0.07135646 -0.34450396
65.1 0.57450574 -0.32514650
65.2 0.52562984 -0.10984996
65.3 0.21921164 -0.19275692
66 0.33281730 NA
66.1 0.03412404 NA
66.2 0.92570619 -0.11687008
67 0.15291043 NA
68 0.37543648 -0.13605235
68.1 0.20901022 -0.19790827
68.2 0.12488064 -0.17750123
68.3 0.08711204 NA
68.4 0.54611735 -0.12570562
69 0.23638239 -0.32152751
70 0.49876756 -0.28190462
70.1 0.39512615 -0.11503263
71 0.45666551 -0.13029093
71.1 0.92047456 NA
71.2 0.32792986 -0.39075433
71.3 0.95108007 -0.21401028
71.4 0.36287072 -0.40219281
72 0.12870526 -0.40337108
72.1 0.45925876 -0.25978914
72.2 0.05418867 NA
72.3 0.48937486 -0.09809866
72.4 0.64173822 -0.14240019
72.5 0.57609943 -0.14794204
73 0.17393402 -0.23509343
74 0.23990575 -0.27963171
75 0.28469861 -0.12905034
76 0.71988630 0.04775562
76.1 1.12449946 -0.19399157
76.2 0.71313766 -0.02754574
77 0.02399030 -0.19053195
78 0.42708148 -0.17172929
79 0.37579286 -0.03958515
79.1 0.78660681 -0.20328809
79.2 0.67696116 -0.23901634
80 0.34207854 -0.34031873
80.1 0.60534092 -0.19526756
80.2 0.26731034 NA
81 0.17739052 -0.18401980
81.1 0.35453673 -0.16889476
81.2 0.20244235 -0.37343047
81.3 1.26402329 NA
82 0.09303938 -0.08328168
82.1 0.27254210 -0.22167084
82.2 0.49936304 -0.20971187
83 0.21138572 -0.34228255
83.1 0.26403568 -0.34075730
83.2 0.20311133 -0.32503954
83.3 1.16864671 NA
84 1.99179346 -0.20676741
84.1 1.52199460 -0.20310458
85 NA -0.12107593
85.1 0.61458995 NA
85.2 0.07871196 -0.32509207
85.3 1.42315283 NA
85.4 0.97986129 -0.30730810
85.5 0.91792195 NA
86 0.63509597 -0.10854862
86.1 0.24546597 -0.25751662
86.2 0.53102060 -0.38943076
86.3 0.09360826 -0.24454702
86.4 0.58301186 -0.12338992
86.5 0.39146055 -0.23976984
87 NA NA
87.1 0.66043624 -0.34366972
87.2 0.13267613 NA
88 0.10696344 -0.31563888
88.1 0.13689448 -0.20304028
88.2 0.48037889 -0.40311895
88.3 0.97755681 -0.12308715
89 0.70242369 -0.18527715
90 0.40042977 -0.25029126
90.1 0.63975731 -0.26974303
90.2 0.33412775 -0.28804531
90.3 0.38399003 -0.19180615
91 0.58250391 -0.26591197
91.1 0.13223217 -0.09153470
91.2 0.46613305 -0.48414390
92 0.18997862 NA
93 1.05243347 -0.11939966
93.1 0.01479757 NA
93.2 0.50955172 -0.21089379
93.3 0.78122514 NA
93.4 0.63940704 -0.23618836
94 0.45596305 NA
94.1 0.41610667 -0.10217284
94.2 0.52744298 -0.36713471
94.3 0.70890756 -0.13806763
94.4 0.84412478 -0.42353804
94.5 0.21166602 -0.15513707
95 0.57713135 -0.24149687
95.1 0.44400207 -0.21315958
95.2 0.42397776 -0.15777208
96 0.72391015 -0.16780948
96.1 0.32593738 -0.32504815
96.2 0.23249511 -0.20395970
96.3 1.01679990 -0.06221501
96.4 0.92267953 -0.14801097
96.5 0.83843412 -0.28658893
97 0.47151154 -0.34484656
97.1 0.15596614 -0.35658805
98 0.05179545 -0.36913003
98.1 0.47332096 NA
98.2 0.19706341 -0.17154225
99 0.22574556 -0.24753132
99.1 1.00732330 -0.27947829
99.2 0.09749127 -0.09033035
100 0.22857989 -0.17326698
100.1 0.39548654 NA
100.2 NA -0.12072016
100.3 0.32695372 -0.27657520
100.4 0.10043925 -0.14631556
$m2e$spM_lvlone
center scale
L1mis 0.4818481 0.3462447
c2 -0.2237158 0.1059527
$m2e$mu_reg_norm
[1] 0
$m2e$tau_reg_norm
[1] 1e-04
$m2e$shape_tau_norm
[1] 0.01
$m2e$rate_tau_norm
[1] 0.01
$m2e$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m2e$shape_diag_RinvD
[1] "0.01"
$m2e$rate_diag_RinvD
[1] "0.001"
$m2f
$m2f$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m2f$M_lvlone
Be2 c2
1 4.596628e-06 NA
1.1 2.296427e-04 -0.08061445
1.2 3.455922e-10 -0.26523782
1.3 9.618613e-07 -0.30260393
2 NA -0.33443795
2.1 1.065639e-07 -0.11819800
2.2 1.320730e-03 -0.31532280
3 9.707820e-06 -0.12920657
3.1 3.645271e-05 NA
3.2 NA NA
4 5.555794e-01 -0.31177403
4.1 6.853316e-06 -0.23894886
4.2 6.324951e-02 -0.15533613
4.3 4.330745e-07 -0.14644545
5 NA -0.28360457
5.1 6.556812e-04 -0.20135143
5.2 6.963312e-06 -0.28293375
5.3 1.159006e-04 NA
6 1.509745e-02 -0.08617066
7 NA -0.22243495
7.1 1.679086e-08 NA
7.2 3.972447e-06 NA
8 9.888512e-02 NA
8.1 8.790334e-05 NA
8.2 NA NA
8.3 5.411705e-04 -0.35148972
8.4 8.446731e-04 0.03661023
8.5 2.059814e-04 -0.08424534
9 4.160033e-01 NA
9.1 NA -0.43509340
9.2 1.087331e-03 -0.22527490
10 9.321715e-04 NA
10.1 8.167897e-06 NA
11 2.528529e-04 -0.08587475
11.1 NA -0.06157340
11.2 5.587553e-10 -0.12436018
11.3 5.240776e-10 -0.21377934
11.4 2.830994e-07 -0.32208329
12 1.962202e-07 NA
13 NA NA
13.1 1.330415e-06 -0.40300449
14 5.900181e-07 -0.28992072
14.1 3.694946e-05 NA
14.2 6.871447e-08 NA
14.3 NA -0.21979936
15 1.848068e-04 NA
15.1 1.714157e-10 -0.29092263
15.2 1.088807e-03 -0.19392239
15.3 2.677330e-05 -0.25718384
16 NA -0.45041108
16.1 1.411453e-04 -0.07599066
16.2 1.897147e-03 -0.32385667
16.3 5.950632e-02 -0.38326110
16.4 3.944608e-02 -0.22845856
16.5 NA -0.25497157
17 4.808238e-05 NA
17.1 6.175264e-04 -0.22105143
17.2 2.319036e-07 NA
17.3 1.393008e-09 NA
17.4 NA -0.15098046
18 2.685853e-09 -0.09870041
19 2.949370e-07 -0.26680239
19.1 1.183423e-08 -0.15815241
19.2 7.844699e-08 -0.14717437
19.3 NA -0.21271374
20 4.920475e-06 -0.22087628
20.1 6.885500e-08 NA
20.2 9.577206e-04 -0.30127439
20.3 1.325632e-03 -0.11782590
20.4 NA -0.19857957
20.5 1.011637e-06 -0.24338208
21 3.032947e-04 -0.31407992
21.1 4.370975e-06 -0.12424941
21.2 8.793700e-06 -0.27672716
22 NA -0.23790593
22.1 7.397166e-06 -0.15996535
23 4.931346e-02 -0.18236682
23.1 3.799306e-02 -0.20823302
24 1.018950e-01 -0.29026416
25 NA -0.36139273
25.1 2.264756e-02 -0.19571118
25.2 6.622343e-07 -0.21379355
25.3 2.802504e-09 -0.33876012
25.4 1.873599e-04 NA
25.5 NA -0.04068446
26 4.587570e-09 -0.16846716
26.1 2.394334e-06 -0.10440642
26.2 4.510972e-08 -0.26884827
26.3 3.657318e-11 NA
27 NA -0.19520794
27.1 8.874134e-06 -0.17622638
28 3.673907e-06 -0.32164962
28.1 4.541426e-04 -0.27003852
28.2 2.697966e-12 -0.07235801
28.3 NA -0.13462982
29 3.282475e-03 -0.32432030
29.1 2.270717e-01 -0.27034171
29.2 9.981536e-03 -0.10197448
29.3 2.343590e-02 -0.27606945
30 NA -0.06949300
30.1 1.591483e-07 -0.11511035
30.2 1.896944e-11 -0.16215882
31 5.546285e-08 0.05707733
32 9.411981e-09 -0.18446298
32.1 1.270914e-08 -0.14270013
32.2 3.910478e-09 -0.20530798
32.3 9.124048e-10 -0.14705649
33 9.056156e-01 -0.15252819
33.1 3.047254e-06 NA
34 1.040462e-04 -0.30378735
34.1 5.714390e-12 -0.11982431
34.2 7.883166e-09 -0.24278671
34.3 3.055823e-07 -0.19971833
35 1.287796e-07 NA
35.1 1.762232e-06 -0.24165780
35.2 5.355159e-08 NA
36 7.250797e-06 -0.49062180
36.1 2.370652e-06 -0.25651700
36.2 1.537090e-05 NA
36.3 6.993214e-07 -0.30401274
36.4 4.950009e-05 NA
37 2.755165e-07 -0.15276529
37.1 3.400517e-07 -0.30016169
37.2 2.489007e-09 0.06809545
38 1.302651e-01 -0.11218486
39 4.343746e-04 -0.38072211
39.1 6.653143e-05 -0.32094428
39.2 1.940204e-09 NA
39.3 8.300468e-07 -0.40173480
39.4 7.464169e-08 -0.20041848
39.5 5.765597e-10 -0.26027990
40 9.140572e-01 -0.19751956
40.1 1.883555e-03 -0.08399467
40.2 2.303001e-01 -0.20864416
40.3 2.799910e-05 NA
41 3.700067e-02 -0.26096953
41.1 5.798225e-06 -0.23953874
41.2 1.086252e-08 -0.03079344
41.3 3.088732e-07 NA
41.4 4.549537e-05 NA
42 5.220968e-03 -0.16084527
42.1 7.264286e-08 -0.13812521
43 1.498125e-07 -0.08864017
43.1 1.316763e-04 -0.12583158
43.2 8.151771e-07 -0.29253959
44 1.032476e-03 -0.22697597
44.1 3.120174e-09 NA
44.2 2.571257e-10 NA
44.3 2.227416e-09 -0.40544012
45 3.948036e-01 -0.19274788
45.1 1.066310e-03 -0.34860483
46 2.219556e-08 -0.28547861
46.1 1.434525e-08 -0.21977836
46.2 1.523026e-07 NA
47 5.404537e-03 -0.08597098
47.1 3.739267e-07 -0.35424828
47.2 7.171916e-06 -0.24262576
47.3 3.850162e-05 -0.30426315
47.4 1.767264e-08 NA
48 1.988010e-04 NA
48.1 6.074589e-09 NA
49 1.321544e-06 -0.42198781
50 4.240393e-05 -0.19959516
51 1.986093e-09 -0.16556964
52 1.632022e-02 -0.07438732
52.1 2.653038e-02 -0.37537080
52.2 2.262881e-03 -0.24222066
52.3 6.572647e-10 -0.31520603
52.4 1.393737e-04 -0.44619160
52.5 5.069462e-03 -0.11011682
53 5.848890e-05 -0.23278716
53.1 1.878509e-04 -0.28317264
53.2 1.293417e-04 -0.19517481
54 1.818441e-03 -0.10122856
54.1 2.251839e-07 -0.28325504
54.2 5.638172e-06 -0.16753120
54.3 5.320676e-03 -0.22217672
54.4 1.491367e-07 -0.34609328
55 3.183775e-03 -0.32428190
55.1 1.183380e-03 -0.24235382
55.2 1.817077e-06 -0.24065814
55.3 1.424370e-06 -0.23665476
55.4 3.119967e-07 NA
56 1.169667e-06 NA
56.1 1.182293e-06 -0.30357450
56.2 2.087533e-04 -0.51301630
56.3 5.728251e-06 -0.23743117
56.4 4.087596e-08 -0.17264917
56.5 8.040370e-07 -0.39188329
57 1.438387e-02 -0.18501692
57.1 3.202179e-05 -0.27274841
57.2 1.486318e-03 NA
57.3 1.718412e-04 -0.09898509
58 3.114123e-05 -0.29901358
58.1 1.403881e-04 -0.35390896
58.2 2.111006e-01 -0.16687336
58.3 9.586985e-06 -0.11784506
58.4 4.073162e-03 -0.05321983
58.5 9.285307e-04 -0.54457568
59 2.711478e-06 -0.27255364
59.1 1.173472e-04 NA
60 7.579680e-09 NA
61 4.545759e-03 -0.30550120
61.1 5.936674e-02 -0.35579892
61.2 3.897281e-01 NA
61.3 6.237379e-02 -0.34184391
61.4 5.103038e-01 -0.30891967
62 3.707353e-02 NA
62.1 1.901660e-03 -0.10504143
62.2 7.844369e-08 -0.20104997
62.3 1.496168e-08 -0.08138677
63 5.101070e-11 -0.12036319
63.1 1.106013e-05 -0.13624992
64 1.685171e-09 NA
65 1.684142e-01 -0.34450396
65.1 1.413479e-05 -0.32514650
65.2 2.841196e-03 -0.10984996
65.3 3.118871e-04 -0.19275692
66 1.078473e-06 NA
66.1 1.136650e-01 NA
66.2 7.007044e-08 -0.11687008
67 4.025749e-11 NA
68 2.469503e-06 -0.13605235
68.1 1.067638e-08 -0.19790827
68.2 1.508555e-06 -0.17750123
68.3 7.862972e-06 NA
68.4 1.970326e-05 -0.12570562
69 5.089430e-07 -0.32152751
70 5.575849e-07 -0.28190462
70.1 6.115107e-04 -0.11503263
71 1.867742e-05 -0.13029093
71.1 4.616167e-04 NA
71.2 5.314611e-08 -0.39075433
71.3 1.790244e-10 -0.21401028
71.4 1.924070e-03 -0.40219281
72 6.526547e-05 -0.40337108
72.1 5.540491e-11 -0.25978914
72.2 2.391191e-12 NA
72.3 2.878783e-12 -0.09809866
72.4 1.014404e-09 -0.14240019
72.5 1.281231e-05 -0.14794204
73 6.661564e-02 -0.23509343
74 3.683842e-04 -0.27963171
75 2.274469e-06 -0.12905034
76 9.155636e-04 0.04775562
76.1 1.485365e-04 -0.19399157
76.2 3.118702e-06 -0.02754574
77 4.946432e-01 -0.19053195
78 8.533933e-05 -0.17172929
79 1.980588e-01 -0.03958515
79.1 8.624235e-06 -0.20328809
79.2 2.176176e-05 -0.23901634
80 2.929029e-06 -0.34031873
80.1 1.126162e-04 -0.19526756
80.2 9.847382e-08 NA
81 4.026095e-01 -0.18401980
81.1 2.093927e-02 -0.16889476
81.2 9.224440e-01 -0.37343047
81.3 8.175654e-03 NA
82 1.228129e-01 -0.08328168
82.1 6.656575e-05 -0.22167084
82.2 2.001426e-08 -0.20971187
83 5.690020e-06 -0.34228255
83.1 5.980615e-06 -0.34075730
83.2 1.880816e-05 -0.32503954
83.3 4.048910e-09 NA
84 6.552173e-02 -0.20676741
84.1 8.829278e-06 -0.20310458
85 4.118253e-06 -0.12107593
85.1 2.311994e-06 NA
85.2 5.182892e-05 -0.32509207
85.3 1.689467e-03 NA
85.4 1.168017e-03 -0.30730810
85.5 7.945131e-07 NA
86 2.905567e-05 -0.10854862
86.1 5.331467e-06 -0.25751662
86.2 1.761451e-06 -0.38943076
86.3 2.272397e-06 -0.24454702
86.4 4.467006e-06 -0.12338992
86.5 1.693940e-08 -0.23976984
87 6.396865e-05 NA
87.1 1.264093e-10 -0.34366972
87.2 4.933807e-07 NA
88 9.223531e-02 -0.31563888
88.1 4.654325e-05 -0.20304028
88.2 1.260399e-01 -0.40311895
88.3 8.029866e-08 -0.12308715
89 7.489307e-05 -0.18527715
90 1.100491e-02 -0.25029126
90.1 2.715349e-05 -0.26974303
90.2 5.916576e-03 -0.28804531
90.3 2.920657e-02 -0.19180615
91 2.411997e-03 -0.26591197
91.1 8.870147e-06 -0.09153470
91.2 1.652965e-08 -0.48414390
92 2.613551e-03 NA
93 9.958480e-01 -0.11939966
93.1 9.915375e-01 NA
93.2 4.861680e-02 -0.21089379
93.3 9.769008e-01 NA
93.4 5.977439e-05 -0.23618836
94 7.091952e-04 NA
94.1 6.005522e-04 -0.10217284
94.2 8.134430e-03 -0.36713471
94.3 1.747604e-05 -0.13806763
94.4 9.404259e-07 -0.42353804
94.5 6.832077e-07 -0.15513707
95 3.216011e-06 -0.24149687
95.1 6.324477e-05 -0.21315958
95.2 1.762187e-01 -0.15777208
96 1.578796e-02 -0.16780948
96.1 2.610661e-02 -0.32504815
96.2 3.941700e-05 -0.20395970
96.3 1.683671e-05 -0.06221501
96.4 1.095127e-04 -0.14801097
96.5 1.479105e-05 -0.28658893
97 2.082560e-04 -0.34484656
97.1 7.903013e-10 -0.35658805
98 1.795949e-06 -0.36913003
98.1 2.776600e-02 NA
98.2 4.050457e-06 -0.17154225
99 2.316802e-05 -0.24753132
99.1 2.206426e-06 -0.27947829
99.2 2.488411e-08 -0.09033035
100 7.572193e-01 -0.17326698
100.1 9.794641e-02 NA
100.2 4.934595e-01 -0.12072016
100.3 1.502083e-07 -0.27657520
100.4 2.515993e-06 -0.14631556
$m2f$spM_lvlone
center scale
Be2 0.04274145 0.1563798
c2 -0.22371584 0.1059527
$m2f$mu_reg_norm
[1] 0
$m2f$tau_reg_norm
[1] 1e-04
$m2f$shape_tau_norm
[1] 0.01
$m2f$rate_tau_norm
[1] 0.01
$m2f$mu_reg_beta
[1] 0
$m2f$tau_reg_beta
[1] 1e-04
$m2f$shape_tau_beta
[1] 0.01
$m2f$rate_tau_beta
[1] 0.01
$m2f$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m2f$shape_diag_RinvD
[1] "0.01"
$m2f$rate_diag_RinvD
[1] "0.001"
$m3a
$m3a$M_id
C2 (Intercept)
1 -1.381594459 1
2 0.344426024 1
3 NA 1
4 -0.228910007 1
5 NA 1
6 -2.143955482 1
7 -1.156567023 1
8 -0.598827660 1
9 NA 1
10 -1.006719032 1
11 0.239801450 1
12 -1.064969789 1
13 -0.538082688 1
14 NA 1
15 -1.781049276 1
16 NA 1
17 NA 1
18 -0.014579883 1
19 -2.121550136 1
20 NA 1
21 -0.363239698 1
22 -0.121568514 1
23 -0.951271111 1
24 NA 1
25 -0.974288621 1
26 -1.130632418 1
27 0.114339868 1
28 0.238334648 1
29 0.840744958 1
30 NA 1
31 NA 1
32 -1.466312154 1
33 -0.637352277 1
34 NA 1
35 NA 1
36 NA 1
37 NA 1
38 NA 1
39 0.006728205 1
40 NA 1
41 -1.663281353 1
42 0.161184794 1
43 0.457939180 1
44 -0.307070331 1
45 NA 1
46 -1.071668276 1
47 -0.814751321 1
48 -0.547630662 1
49 NA 1
50 -1.350213782 1
51 0.719054706 1
52 NA 1
53 -1.207130750 1
54 NA 1
55 -0.408600991 1
56 -0.271380529 1
57 -1.361925974 1
58 NA 1
59 NA 1
60 -0.323712205 1
61 NA 1
62 NA 1
63 -1.386906880 1
64 NA 1
65 NA 1
66 -0.565191691 1
67 -0.382899912 1
68 NA 1
69 -0.405642769 1
70 NA 1
71 -0.843748427 1
72 0.116003683 1
73 -0.778634325 1
74 NA 1
75 NA 1
76 NA 1
77 -0.632974758 1
78 NA 1
79 -0.778064615 1
80 NA 1
81 NA 1
82 -0.246123253 1
83 -1.239659782 1
84 -0.467772280 1
85 NA 1
86 -2.160485036 1
87 -0.657675572 1
88 NA 1
89 -0.696710744 1
90 NA 1
91 -0.179395847 1
92 -0.441545568 1
93 -0.685799334 1
94 NA 1
95 0.191929445 1
96 NA 1
97 -0.069760671 1
98 NA 1
99 NA 1
100 NA 1
$m3a$M_lvlone
y
1 -13.0493856
1.1 -9.3335901
1.2 -22.3469852
1.3 -15.0417337
2 -12.0655434
2.1 -15.8674476
2.2 -7.8800006
3 -11.4820604
3.1 -10.5983220
3.2 -22.4519157
4 -1.2697775
4.1 -11.1215184
4.2 -3.6134138
4.3 -14.5982385
5 -6.8457515
5.1 -7.0551214
5.2 -12.3418980
5.3 -9.2366906
6 -5.1648211
7 -10.0599502
7.1 -18.3267285
7.2 -12.5138426
8 -1.6305331
8.1 -9.6520453
8.2 -1.5278462
8.3 -7.4172211
8.4 -7.1238609
8.5 -8.8706950
9 -0.1634429
9.1 -2.6034300
9.2 -6.7272369
10 -6.4172202
10.1 -11.4834569
11 -8.7911356
11.1 -19.6645080
11.2 -20.2030932
11.3 -21.3082176
11.4 -14.5802901
12 -15.2006287
13 0.8058816
13.1 -13.6379208
14 -15.3422873
14.1 -10.0965208
14.2 -16.6452027
14.3 -15.8389733
15 -8.9424594
15.1 -22.0101983
15.2 -7.3975599
15.3 -10.3567334
16 -1.9691302
16.1 -9.9308357
16.2 -6.9626923
16.3 -3.2862557
16.4 -3.3972355
16.5 -11.5767835
17 -10.5474144
17.1 -7.6215009
17.2 -16.5386939
17.3 -20.0004774
17.4 -18.8505475
18 -19.7302351
19 -14.6177568
19.1 -17.8043866
19.2 -15.1641705
19.3 -16.6898418
20 -12.9059229
20.1 -16.8191201
20.2 -6.1010131
20.3 -7.9415371
20.4 -9.3904458
20.5 -13.3504189
21 -7.6974718
21.1 -11.9335526
21.2 -12.7064929
22 -21.5022909
22.1 -12.7745451
23 -3.5146508
23.1 -4.6724048
24 -2.5619821
25 -6.2944970
25.1 -3.8630505
25.2 -14.4205140
25.3 -19.6735037
25.4 -9.0288933
25.5 -9.0509738
26 -19.7340685
26.1 -14.1692728
26.2 -17.2819976
26.3 -24.6265576
27 -7.3354999
27.1 -11.1488468
28 -11.7996597
28.1 -8.2030122
28.2 -26.4317815
28.3 -18.5016071
29 -5.8551395
29.1 -2.0209442
29.2 -5.6368080
29.3 -3.8110961
30 -12.7217702
30.1 -17.0170140
30.2 -25.4236089
31 -17.0783921
32 -18.4338764
32.1 -19.4317212
32.2 -19.4738978
32.3 -21.4922645
33 2.0838099
33.1 -13.3172274
34 -10.0296691
34.1 -25.9426553
34.2 -18.5688138
34.3 -15.4173859
35 -14.3958113
35.1 -12.9457541
35.2 -16.1380691
36 -12.8166968
36.1 -14.3989481
36.2 -12.2436943
36.3 -15.0104638
36.4 -10.1775457
37 -15.2223495
37.1 -14.7526195
37.2 -19.8168430
38 -2.7065118
39 -8.7288138
39.1 -9.2746473
39.2 -18.2695344
39.3 -13.8219083
39.4 -16.2254704
39.5 -21.7283648
40 1.8291916
40.1 -6.6916432
40.2 -1.6278171
40.3 -10.5749790
41 -3.1556121
41.1 -11.5895327
41.2 -18.9352091
41.3 -15.9788960
41.4 -9.6070508
42 -5.2159485
42.1 -15.9878743
43 -16.6104361
43.1 -9.5549441
43.2 -14.2003491
44 -8.1969033
44.1 -19.9270197
44.2 -22.6521171
44.3 -21.1903736
45 -0.5686627
45.1 -7.5645740
46 -19.1624789
46.1 -18.4487574
46.2 -15.8222682
47 -5.4165074
47.1 -15.0975029
47.2 -12.9971413
47.3 -10.6844521
47.4 -18.2214784
48 -8.3101471
48.1 -18.3854275
49 -13.0130319
50 -10.4579977
51 -19.3157621
52 -4.4747188
52.1 -4.3163827
52.2 -6.9761408
52.3 -20.1764756
52.4 -8.9036692
52.5 -5.6949642
53 -10.3141887
53.1 -8.2642654
53.2 -9.1691554
54 -6.2198754
54.1 -15.7192609
54.2 -13.0978998
54.3 -5.1195299
54.4 -16.5771751
55 -5.7348534
55.1 -7.3217494
55.2 -12.2171938
55.3 -12.9821266
55.4 -14.8599983
56 -14.1764282
56.1 -12.5343602
56.2 -8.4573382
56.3 -12.4633969
56.4 -17.3841863
56.5 -14.8147645
57 -3.1403293
57.1 -11.1509248
57.2 -6.3940143
57.3 -9.3473241
58 -12.0245677
58.1 -9.2112246
58.2 -1.2071742
58.3 -11.0141711
58.4 -5.3721214
58.5 -7.8523047
59 -13.2946560
59.1 -10.0530648
60 -19.2209402
61 -4.6699914
61.1 -3.5981894
61.2 -1.4713611
61.3 -3.8819786
61.4 0.1041413
62 -2.8591600
62.1 -6.9461986
62.2 -16.7910593
62.3 -17.9844596
63 -24.0335535
63.1 -11.7765300
64 -20.5963897
65 -2.7969169
65.1 -11.1778694
65.2 -5.2830399
65.3 -7.9353390
66 -13.2318328
66.1 -1.9090560
66.2 -16.6643889
67 -25.6073277
68 -13.4806759
68.1 -18.4557183
68.2 -13.3982327
68.3 -12.4977127
68.4 -11.7073990
69 -14.5290675
70 -15.2122709
70.1 -7.8681167
71 -10.3352703
71.1 -7.5699888
71.2 -18.4680702
71.3 -21.4316644
71.4 -8.1137650
72 -9.1848162
72.1 -23.7538846
72.2 -26.3421306
72.3 -27.2843801
72.4 -20.8541617
72.5 -12.8948965
73 -2.6091307
74 -8.2790175
75 -12.5029612
76 -6.0061671
76.1 -8.8149114
76.2 -11.8359043
77 0.4772521
78 -9.4105229
79 -1.0217265
79.1 -11.8125257
79.2 -10.5465186
80 -12.7366807
80.1 -9.0584783
80.2 -16.6381566
81 0.5547913
81.1 -4.0892715
81.2 1.8283303
81.3 -5.2166381
82 -3.0749381
82.1 -10.5506696
82.2 -18.2226347
83 -12.5872635
83.1 -11.9756502
83.2 -10.6744217
83.3 -19.2714012
84 -2.6320312
84.1 -9.8140094
85 -12.3886736
85.1 -12.9196365
85.2 -9.6433248
85.3 -6.3296340
85.4 -7.0405525
85.5 -13.6714939
86 -10.8756412
86.1 -12.0055331
86.2 -13.3724699
86.3 -13.3252145
86.4 -14.9191290
86.5 -17.7515546
87 -10.7027963
87.1 -22.4941954
87.2 -14.9616716
88 -2.2264493
88.1 -8.9626474
88.2 -2.5095281
88.3 -16.3345673
89 -11.0459647
90 -4.5610239
90.1 -11.7036651
90.2 -5.3838521
90.3 -4.1636999
91 -7.1462503
91.1 -12.8374475
91.2 -18.2576707
92 -6.4119222
93 5.2122168
93.1 3.1211725
93.2 -3.6841177
93.3 2.6223542
93.4 -11.1877696
94 -6.9602492
94.1 -7.4318416
94.2 -4.3498045
94.3 -11.6340088
94.4 -12.9357964
94.5 -14.7648530
95 -12.8849309
95.1 -9.7451502
95.2 -0.8535063
96 -4.9139832
96.1 -3.9582653
96.2 -9.6555492
96.3 -11.8690793
96.4 -11.0224373
96.5 -10.9530403
97 -9.8540471
97.1 -19.2262840
98 -11.9651231
98.1 -2.6515128
98.2 -12.2606382
99 -11.4720500
99.1 -14.0596866
99.2 -17.3939469
100 1.1005874
100.1 -3.8226248
100.2 -0.9123182
100.3 -15.8389474
100.4 -12.8093826
$m3a$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m3a$mu_reg_norm
[1] 0
$m3a$tau_reg_norm
[1] 1e-04
$m3a$shape_tau_norm
[1] 0.01
$m3a$rate_tau_norm
[1] 0.01
$m3a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m3a$shape_diag_RinvD
[1] "0.01"
$m3a$rate_diag_RinvD
[1] "0.001"
$m3b
$m3b$M_id
C2 (Intercept)
1 -1.381594459 1
2 0.344426024 1
3 NA 1
4 -0.228910007 1
5 NA 1
6 -2.143955482 1
7 -1.156567023 1
8 -0.598827660 1
9 NA 1
10 -1.006719032 1
11 0.239801450 1
12 -1.064969789 1
13 -0.538082688 1
14 NA 1
15 -1.781049276 1
16 NA 1
17 NA 1
18 -0.014579883 1
19 -2.121550136 1
20 NA 1
21 -0.363239698 1
22 -0.121568514 1
23 -0.951271111 1
24 NA 1
25 -0.974288621 1
26 -1.130632418 1
27 0.114339868 1
28 0.238334648 1
29 0.840744958 1
30 NA 1
31 NA 1
32 -1.466312154 1
33 -0.637352277 1
34 NA 1
35 NA 1
36 NA 1
37 NA 1
38 NA 1
39 0.006728205 1
40 NA 1
41 -1.663281353 1
42 0.161184794 1
43 0.457939180 1
44 -0.307070331 1
45 NA 1
46 -1.071668276 1
47 -0.814751321 1
48 -0.547630662 1
49 NA 1
50 -1.350213782 1
51 0.719054706 1
52 NA 1
53 -1.207130750 1
54 NA 1
55 -0.408600991 1
56 -0.271380529 1
57 -1.361925974 1
58 NA 1
59 NA 1
60 -0.323712205 1
61 NA 1
62 NA 1
63 -1.386906880 1
64 NA 1
65 NA 1
66 -0.565191691 1
67 -0.382899912 1
68 NA 1
69 -0.405642769 1
70 NA 1
71 -0.843748427 1
72 0.116003683 1
73 -0.778634325 1
74 NA 1
75 NA 1
76 NA 1
77 -0.632974758 1
78 NA 1
79 -0.778064615 1
80 NA 1
81 NA 1
82 -0.246123253 1
83 -1.239659782 1
84 -0.467772280 1
85 NA 1
86 -2.160485036 1
87 -0.657675572 1
88 NA 1
89 -0.696710744 1
90 NA 1
91 -0.179395847 1
92 -0.441545568 1
93 -0.685799334 1
94 NA 1
95 0.191929445 1
96 NA 1
97 -0.069760671 1
98 NA 1
99 NA 1
100 NA 1
$m3b$M_lvlone
b2
1 NA
1.1 0
1.2 NA
1.3 0
2 0
2.1 NA
2.2 NA
3 0
3.1 NA
3.2 1
4 1
4.1 0
4.2 0
4.3 0
5 NA
5.1 0
5.2 NA
5.3 NA
6 NA
7 NA
7.1 NA
7.2 0
8 0
8.1 0
8.2 NA
8.3 1
8.4 0
8.5 1
9 0
9.1 NA
9.2 NA
10 NA
10.1 0
11 0
11.1 0
11.2 0
11.3 0
11.4 0
12 0
13 NA
13.1 0
14 NA
14.1 NA
14.2 NA
14.3 NA
15 0
15.1 0
15.2 0
15.3 0
16 1
16.1 NA
16.2 NA
16.3 0
16.4 0
16.5 NA
17 0
17.1 0
17.2 0
17.3 NA
17.4 0
18 0
19 NA
19.1 NA
19.2 0
19.3 1
20 NA
20.1 0
20.2 1
20.3 0
20.4 0
20.5 0
21 0
21.1 0
21.2 NA
22 0
22.1 0
23 0
23.1 NA
24 0
25 0
25.1 NA
25.2 1
25.3 0
25.4 0
25.5 NA
26 NA
26.1 0
26.2 0
26.3 0
27 0
27.1 0
28 NA
28.1 0
28.2 0
28.3 0
29 0
29.1 0
29.2 0
29.3 0
30 NA
30.1 0
30.2 0
31 0
32 0
32.1 0
32.2 NA
32.3 NA
33 0
33.1 1
34 NA
34.1 0
34.2 NA
34.3 NA
35 0
35.1 0
35.2 NA
36 NA
36.1 NA
36.2 0
36.3 0
36.4 0
37 0
37.1 0
37.2 0
38 0
39 1
39.1 0
39.2 NA
39.3 NA
39.4 0
39.5 1
40 0
40.1 1
40.2 0
40.3 NA
41 0
41.1 NA
41.2 0
41.3 NA
41.4 0
42 0
42.1 1
43 0
43.1 1
43.2 0
44 0
44.1 0
44.2 0
44.3 0
45 NA
45.1 1
46 0
46.1 0
46.2 0
47 0
47.1 0
47.2 0
47.3 NA
47.4 0
48 1
48.1 1
49 NA
50 0
51 0
52 0
52.1 0
52.2 0
52.3 0
52.4 0
52.5 0
53 0
53.1 0
53.2 NA
54 NA
54.1 NA
54.2 NA
54.3 NA
54.4 0
55 0
55.1 0
55.2 NA
55.3 NA
55.4 0
56 0
56.1 NA
56.2 NA
56.3 1
56.4 0
56.5 0
57 0
57.1 0
57.2 0
57.3 NA
58 0
58.1 NA
58.2 1
58.3 1
58.4 0
58.5 0
59 NA
59.1 1
60 0
61 NA
61.1 1
61.2 1
61.3 0
61.4 0
62 NA
62.1 1
62.2 0
62.3 0
63 NA
63.1 0
64 0
65 0
65.1 0
65.2 0
65.3 0
66 NA
66.1 0
66.2 0
67 NA
68 0
68.1 0
68.2 NA
68.3 0
68.4 NA
69 0
70 0
70.1 0
71 0
71.1 1
71.2 0
71.3 1
71.4 0
72 0
72.1 0
72.2 NA
72.3 0
72.4 0
72.5 0
73 0
74 0
75 NA
76 0
76.1 0
76.2 0
77 NA
78 0
79 NA
79.1 0
79.2 NA
80 NA
80.1 0
80.2 NA
81 0
81.1 0
81.2 NA
81.3 0
82 NA
82.1 0
82.2 1
83 NA
83.1 0
83.2 0
83.3 NA
84 0
84.1 NA
85 1
85.1 NA
85.2 0
85.3 0
85.4 0
85.5 0
86 0
86.1 NA
86.2 NA
86.3 0
86.4 NA
86.5 0
87 NA
87.1 NA
87.2 NA
88 0
88.1 NA
88.2 0
88.3 0
89 0
90 0
90.1 0
90.2 0
90.3 NA
91 0
91.1 0
91.2 0
92 0
93 NA
93.1 0
93.2 NA
93.3 0
93.4 0
94 NA
94.1 0
94.2 0
94.3 NA
94.4 0
94.5 1
95 0
95.1 NA
95.2 0
96 0
96.1 0
96.2 0
96.3 NA
96.4 1
96.5 1
97 0
97.1 0
98 0
98.1 0
98.2 1
99 0
99.1 0
99.2 0
100 NA
100.1 NA
100.2 0
100.3 NA
100.4 0
$m3b$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m3b$mu_reg_norm
[1] 0
$m3b$tau_reg_norm
[1] 1e-04
$m3b$shape_tau_norm
[1] 0.01
$m3b$rate_tau_norm
[1] 0.01
$m3b$mu_reg_binom
[1] 0
$m3b$tau_reg_binom
[1] 1e-04
$m3b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m3b$shape_diag_RinvD
[1] "0.01"
$m3b$rate_diag_RinvD
[1] "0.001"
$m3c
$m3c$M_id
C2 (Intercept)
1 -1.381594459 1
2 0.344426024 1
3 NA 1
4 -0.228910007 1
5 NA 1
6 -2.143955482 1
7 -1.156567023 1
8 -0.598827660 1
9 NA 1
10 -1.006719032 1
11 0.239801450 1
12 -1.064969789 1
13 -0.538082688 1
14 NA 1
15 -1.781049276 1
16 NA 1
17 NA 1
18 -0.014579883 1
19 -2.121550136 1
20 NA 1
21 -0.363239698 1
22 -0.121568514 1
23 -0.951271111 1
24 NA 1
25 -0.974288621 1
26 -1.130632418 1
27 0.114339868 1
28 0.238334648 1
29 0.840744958 1
30 NA 1
31 NA 1
32 -1.466312154 1
33 -0.637352277 1
34 NA 1
35 NA 1
36 NA 1
37 NA 1
38 NA 1
39 0.006728205 1
40 NA 1
41 -1.663281353 1
42 0.161184794 1
43 0.457939180 1
44 -0.307070331 1
45 NA 1
46 -1.071668276 1
47 -0.814751321 1
48 -0.547630662 1
49 NA 1
50 -1.350213782 1
51 0.719054706 1
52 NA 1
53 -1.207130750 1
54 NA 1
55 -0.408600991 1
56 -0.271380529 1
57 -1.361925974 1
58 NA 1
59 NA 1
60 -0.323712205 1
61 NA 1
62 NA 1
63 -1.386906880 1
64 NA 1
65 NA 1
66 -0.565191691 1
67 -0.382899912 1
68 NA 1
69 -0.405642769 1
70 NA 1
71 -0.843748427 1
72 0.116003683 1
73 -0.778634325 1
74 NA 1
75 NA 1
76 NA 1
77 -0.632974758 1
78 NA 1
79 -0.778064615 1
80 NA 1
81 NA 1
82 -0.246123253 1
83 -1.239659782 1
84 -0.467772280 1
85 NA 1
86 -2.160485036 1
87 -0.657675572 1
88 NA 1
89 -0.696710744 1
90 NA 1
91 -0.179395847 1
92 -0.441545568 1
93 -0.685799334 1
94 NA 1
95 0.191929445 1
96 NA 1
97 -0.069760671 1
98 NA 1
99 NA 1
100 NA 1
$m3c$M_lvlone
L1mis
1 1.38634787
1.1 0.79402906
1.2 0.53603334
1.3 0.24129804
2 NA
2.1 0.31668065
2.2 0.37114414
3 0.54680608
3.1 0.28280274
3.2 0.76277262
4 0.56100366
4.1 0.38514140
4.2 0.04026174
4.3 0.16025873
5 0.21080161
5.1 0.36665700
5.2 0.66368829
5.3 0.40788895
6 0.11889539
7 1.04286843
7.1 0.52098933
7.2 0.09858876
8 0.17281472
8.1 0.25970093
8.2 0.30550233
8.3 0.88029778
8.4 0.20200392
8.5 NA
9 1.12218535
9.1 0.57911079
9.2 0.81350994
10 0.32744766
10.1 0.62912282
11 0.92140073
11.1 0.16012129
11.2 0.16166775
11.3 0.14979756
11.4 0.46855190
12 0.76818678
13 0.34264972
13.1 0.14526619
14 0.80630788
14.1 0.35697552
14.2 0.21330192
14.3 NA
15 0.30769119
15.1 0.28349746
15.2 0.64618365
15.3 0.51680884
16 0.71265471
16.1 0.38925880
16.2 0.23648869
16.3 0.45048730
16.4 0.23181791
16.5 0.13985349
17 0.25995399
17.1 0.03594878
17.2 0.77583623
17.3 0.60015197
17.4 0.13998405
18 0.96475839
19 0.10596495
19.1 0.13338947
19.2 0.41662218
19.3 0.53670855
20 0.41688567
20.1 NA
20.2 0.81634101
20.3 0.39232496
20.4 0.57925554
20.5 0.74200986
21 0.24759801
21.1 0.34052205
21.2 0.03905058
22 0.48605351
22.1 0.43761071
23 0.47599712
23.1 0.47680301
24 0.51696505
25 0.59392591
25.1 0.74010330
25.2 NA
25.3 0.73081722
25.4 0.29274286
25.5 0.74425342
26 0.20974346
26.1 NA
26.2 0.22908815
26.3 0.41880799
27 0.10097167
27.1 NA
28 NA
28.1 0.56052750
28.2 0.15301800
28.3 0.27802542
29 0.43556671
29.1 0.27593085
29.2 0.55256871
29.3 0.47272109
30 0.32743933
30.1 0.02231535
30.2 0.12833697
31 0.11126366
32 1.11731084
32.1 0.85943330
32.2 1.53730925
32.3 0.43831965
33 0.46726055
33.1 0.76818259
34 NA
34.1 1.14350292
34.2 0.19103604
34.3 NA
35 0.66303137
35.1 NA
35.2 NA
36 0.93843318
36.1 NA
36.2 0.29886676
36.3 0.22616598
36.4 0.53849566
37 1.68107300
37.1 1.13777638
37.2 0.26931933
38 NA
39 0.14395367
39.1 0.36454923
39.2 1.03700002
39.3 0.41320585
39.4 0.20901554
39.5 0.51603848
40 0.33912363
40.1 0.21892118
40.2 0.74070896
40.3 0.82927399
41 0.25193679
41.1 0.28760510
41.2 0.45553197
41.3 0.79237611
41.4 0.12582175
42 0.50079604
42.1 0.61140760
43 0.29752019
43.1 0.51793497
43.2 0.15152473
44 0.38806434
44.1 1.11140786
44.2 0.39132534
44.3 0.40934909
45 0.68587067
45.1 0.34530800
46 0.71312288
46.1 0.62537420
46.2 0.79574391
47 0.48660773
47.1 0.51241790
47.2 0.58869379
47.3 0.22171504
47.4 0.11366347
48 0.19677010
48.1 0.17706320
49 0.30752382
50 0.93663423
51 0.34107606
52 0.19007135
52.1 0.75662940
52.2 1.66104719
52.3 NA
52.4 0.18369708
52.5 0.48689343
53 0.31983157
53.1 0.61569501
53.2 NA
54 1.90522418
54.1 0.59484889
54.2 1.47174857
54.3 0.27307143
54.4 0.81272938
55 0.22735476
55.1 0.54683512
55.2 1.03503777
55.3 0.30169529
55.4 0.36008059
56 0.14193566
56.1 0.65073539
56.2 0.11338262
56.3 0.16820103
56.4 0.27419110
56.5 0.57110215
57 0.85104054
57.1 0.34733833
57.2 1.44438762
57.3 0.31836125
58 0.37456898
58.1 0.22120158
58.2 0.78885210
58.3 0.10114937
58.4 0.13385114
58.5 NA
59 0.13202156
59.1 0.33371896
60 0.35096579
61 0.36933806
61.1 0.17623067
61.2 0.21286227
61.3 0.12689308
61.4 0.77676718
62 1.38018163
62.1 0.43803892
62.2 0.21947900
62.3 0.11571160
63 0.41583568
63.1 0.25598960
64 0.20415642
65 0.07135646
65.1 0.57450574
65.2 0.52562984
65.3 0.21921164
66 0.33281730
66.1 0.03412404
66.2 0.92570619
67 0.15291043
68 0.37543648
68.1 0.20901022
68.2 0.12488064
68.3 0.08711204
68.4 0.54611735
69 0.23638239
70 0.49876756
70.1 0.39512615
71 0.45666551
71.1 0.92047456
71.2 0.32792986
71.3 0.95108007
71.4 0.36287072
72 0.12870526
72.1 0.45925876
72.2 0.05418867
72.3 0.48937486
72.4 0.64173822
72.5 0.57609943
73 0.17393402
74 0.23990575
75 0.28469861
76 0.71988630
76.1 1.12449946
76.2 0.71313766
77 0.02399030
78 0.42708148
79 0.37579286
79.1 0.78660681
79.2 0.67696116
80 0.34207854
80.1 0.60534092
80.2 0.26731034
81 0.17739052
81.1 0.35453673
81.2 0.20244235
81.3 1.26402329
82 0.09303938
82.1 0.27254210
82.2 0.49936304
83 0.21138572
83.1 0.26403568
83.2 0.20311133
83.3 1.16864671
84 1.99179346
84.1 1.52199460
85 NA
85.1 0.61458995
85.2 0.07871196
85.3 1.42315283
85.4 0.97986129
85.5 0.91792195
86 0.63509597
86.1 0.24546597
86.2 0.53102060
86.3 0.09360826
86.4 0.58301186
86.5 0.39146055
87 NA
87.1 0.66043624
87.2 0.13267613
88 0.10696344
88.1 0.13689448
88.2 0.48037889
88.3 0.97755681
89 0.70242369
90 0.40042977
90.1 0.63975731
90.2 0.33412775
90.3 0.38399003
91 0.58250391
91.1 0.13223217
91.2 0.46613305
92 0.18997862
93 1.05243347
93.1 0.01479757
93.2 0.50955172
93.3 0.78122514
93.4 0.63940704
94 0.45596305
94.1 0.41610667
94.2 0.52744298
94.3 0.70890756
94.4 0.84412478
94.5 0.21166602
95 0.57713135
95.1 0.44400207
95.2 0.42397776
96 0.72391015
96.1 0.32593738
96.2 0.23249511
96.3 1.01679990
96.4 0.92267953
96.5 0.83843412
97 0.47151154
97.1 0.15596614
98 0.05179545
98.1 0.47332096
98.2 0.19706341
99 0.22574556
99.1 1.00732330
99.2 0.09749127
100 0.22857989
100.1 0.39548654
100.2 NA
100.3 0.32695372
100.4 0.10043925
$m3c$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m3c$mu_reg_norm
[1] 0
$m3c$tau_reg_norm
[1] 1e-04
$m3c$shape_tau_norm
[1] 0.01
$m3c$rate_tau_norm
[1] 0.01
$m3c$mu_reg_gamma
[1] 0
$m3c$tau_reg_gamma
[1] 1e-04
$m3c$shape_tau_gamma
[1] 0.01
$m3c$rate_tau_gamma
[1] 0.01
$m3c$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m3c$shape_diag_RinvD
[1] "0.01"
$m3c$rate_diag_RinvD
[1] "0.001"
$m3d
$m3d$M_id
C2 (Intercept)
1 -1.381594459 1
2 0.344426024 1
3 NA 1
4 -0.228910007 1
5 NA 1
6 -2.143955482 1
7 -1.156567023 1
8 -0.598827660 1
9 NA 1
10 -1.006719032 1
11 0.239801450 1
12 -1.064969789 1
13 -0.538082688 1
14 NA 1
15 -1.781049276 1
16 NA 1
17 NA 1
18 -0.014579883 1
19 -2.121550136 1
20 NA 1
21 -0.363239698 1
22 -0.121568514 1
23 -0.951271111 1
24 NA 1
25 -0.974288621 1
26 -1.130632418 1
27 0.114339868 1
28 0.238334648 1
29 0.840744958 1
30 NA 1
31 NA 1
32 -1.466312154 1
33 -0.637352277 1
34 NA 1
35 NA 1
36 NA 1
37 NA 1
38 NA 1
39 0.006728205 1
40 NA 1
41 -1.663281353 1
42 0.161184794 1
43 0.457939180 1
44 -0.307070331 1
45 NA 1
46 -1.071668276 1
47 -0.814751321 1
48 -0.547630662 1
49 NA 1
50 -1.350213782 1
51 0.719054706 1
52 NA 1
53 -1.207130750 1
54 NA 1
55 -0.408600991 1
56 -0.271380529 1
57 -1.361925974 1
58 NA 1
59 NA 1
60 -0.323712205 1
61 NA 1
62 NA 1
63 -1.386906880 1
64 NA 1
65 NA 1
66 -0.565191691 1
67 -0.382899912 1
68 NA 1
69 -0.405642769 1
70 NA 1
71 -0.843748427 1
72 0.116003683 1
73 -0.778634325 1
74 NA 1
75 NA 1
76 NA 1
77 -0.632974758 1
78 NA 1
79 -0.778064615 1
80 NA 1
81 NA 1
82 -0.246123253 1
83 -1.239659782 1
84 -0.467772280 1
85 NA 1
86 -2.160485036 1
87 -0.657675572 1
88 NA 1
89 -0.696710744 1
90 NA 1
91 -0.179395847 1
92 -0.441545568 1
93 -0.685799334 1
94 NA 1
95 0.191929445 1
96 NA 1
97 -0.069760671 1
98 NA 1
99 NA 1
100 NA 1
$m3d$M_lvlone
p2
1 2
1.1 2
1.2 NA
1.3 NA
2 NA
2.1 6
2.2 3
3 NA
3.1 NA
3.2 NA
4 NA
4.1 4
4.2 0
4.3 NA
5 2
5.1 NA
5.2 7
5.3 NA
6 NA
7 NA
7.1 NA
7.2 NA
8 1
8.1 6
8.2 NA
8.3 3
8.4 2
8.5 1
9 3
9.1 NA
9.2 3
10 3
10.1 NA
11 1
11.1 6
11.2 1
11.3 6
11.4 NA
12 NA
13 NA
13.1 NA
14 NA
14.1 NA
14.2 2
14.3 NA
15 NA
15.1 NA
15.2 NA
15.3 NA
16 1
16.1 NA
16.2 2
16.3 NA
16.4 1
16.5 NA
17 1
17.1 NA
17.2 3
17.3 2
17.4 NA
18 2
19 NA
19.1 NA
19.2 2
19.3 2
20 NA
20.1 2
20.2 NA
20.3 NA
20.4 NA
20.5 NA
21 2
21.1 3
21.2 2
22 3
22.1 3
23 NA
23.1 5
24 2
25 3
25.1 3
25.2 3
25.3 4
25.4 NA
25.5 NA
26 NA
26.1 2
26.2 NA
26.3 NA
27 1
27.1 NA
28 0
28.1 NA
28.2 4
28.3 NA
29 3
29.1 3
29.2 3
29.3 2
30 NA
30.1 NA
30.2 5
31 8
32 NA
32.1 2
32.2 1
32.3 NA
33 0
33.1 NA
34 3
34.1 NA
34.2 1
34.3 2
35 NA
35.1 NA
35.2 NA
36 5
36.1 NA
36.2 NA
36.3 1
36.4 1
37 5
37.1 NA
37.2 NA
38 0
39 NA
39.1 1
39.2 NA
39.3 NA
39.4 NA
39.5 NA
40 2
40.1 4
40.2 NA
40.3 NA
41 NA
41.1 4
41.2 2
41.3 3
41.4 NA
42 3
42.1 5
43 4
43.1 3
43.2 3
44 1
44.1 NA
44.2 7
44.3 NA
45 NA
45.1 NA
46 4
46.1 6
46.2 NA
47 NA
47.1 4
47.2 2
47.3 4
47.4 NA
48 NA
48.1 6
49 NA
50 3
51 2
52 3
52.1 1
52.2 NA
52.3 2
52.4 3
52.5 1
53 3
53.1 NA
53.2 2
54 3
54.1 NA
54.2 4
54.3 0
54.4 NA
55 NA
55.1 4
55.2 NA
55.3 4
55.4 3
56 NA
56.1 2
56.2 3
56.3 3
56.4 0
56.5 NA
57 3
57.1 4
57.2 1
57.3 NA
58 NA
58.1 NA
58.2 NA
58.3 3
58.4 NA
58.5 NA
59 NA
59.1 NA
60 NA
61 2
61.1 4
61.2 NA
61.3 NA
61.4 NA
62 2
62.1 NA
62.2 NA
62.3 NA
63 NA
63.1 2
64 4
65 NA
65.1 5
65.2 NA
65.3 NA
66 NA
66.1 NA
66.2 NA
67 NA
68 NA
68.1 NA
68.2 NA
68.3 2
68.4 NA
69 NA
70 4
70.1 4
71 4
71.1 NA
71.2 3
71.3 0
71.4 0
72 NA
72.1 8
72.2 NA
72.3 NA
72.4 3
72.5 NA
73 2
74 NA
75 NA
76 1
76.1 0
76.2 0
77 2
78 NA
79 2
79.1 NA
79.2 2
80 2
80.1 NA
80.2 NA
81 NA
81.1 2
81.2 NA
81.3 NA
82 NA
82.1 NA
82.2 4
83 NA
83.1 NA
83.2 4
83.3 3
84 NA
84.1 2
85 3
85.1 NA
85.2 3
85.3 NA
85.4 2
85.5 1
86 2
86.1 NA
86.2 0
86.3 0
86.4 NA
86.5 2
87 NA
87.1 NA
87.2 3
88 NA
88.1 1
88.2 1
88.3 4
89 NA
90 3
90.1 NA
90.2 NA
90.3 NA
91 NA
91.1 NA
91.2 NA
92 NA
93 2
93.1 4
93.2 4
93.3 NA
93.4 3
94 4
94.1 2
94.2 NA
94.3 1
94.4 NA
94.5 2
95 3
95.1 5
95.2 2
96 NA
96.1 NA
96.2 5
96.3 1
96.4 0
96.5 3
97 4
97.1 2
98 3
98.1 NA
98.2 NA
99 5
99.1 NA
99.2 NA
100 NA
100.1 4
100.2 NA
100.3 4
100.4 NA
$m3d$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m3d$mu_reg_norm
[1] 0
$m3d$tau_reg_norm
[1] 1e-04
$m3d$shape_tau_norm
[1] 0.01
$m3d$rate_tau_norm
[1] 0.01
$m3d$mu_reg_poisson
[1] 0
$m3d$tau_reg_poisson
[1] 1e-04
$m3d$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m3d$shape_diag_RinvD
[1] "0.01"
$m3d$rate_diag_RinvD
[1] "0.001"
$m3e
$m3e$M_id
C2 (Intercept)
1 -1.381594459 1
2 0.344426024 1
3 NA 1
4 -0.228910007 1
5 NA 1
6 -2.143955482 1
7 -1.156567023 1
8 -0.598827660 1
9 NA 1
10 -1.006719032 1
11 0.239801450 1
12 -1.064969789 1
13 -0.538082688 1
14 NA 1
15 -1.781049276 1
16 NA 1
17 NA 1
18 -0.014579883 1
19 -2.121550136 1
20 NA 1
21 -0.363239698 1
22 -0.121568514 1
23 -0.951271111 1
24 NA 1
25 -0.974288621 1
26 -1.130632418 1
27 0.114339868 1
28 0.238334648 1
29 0.840744958 1
30 NA 1
31 NA 1
32 -1.466312154 1
33 -0.637352277 1
34 NA 1
35 NA 1
36 NA 1
37 NA 1
38 NA 1
39 0.006728205 1
40 NA 1
41 -1.663281353 1
42 0.161184794 1
43 0.457939180 1
44 -0.307070331 1
45 NA 1
46 -1.071668276 1
47 -0.814751321 1
48 -0.547630662 1
49 NA 1
50 -1.350213782 1
51 0.719054706 1
52 NA 1
53 -1.207130750 1
54 NA 1
55 -0.408600991 1
56 -0.271380529 1
57 -1.361925974 1
58 NA 1
59 NA 1
60 -0.323712205 1
61 NA 1
62 NA 1
63 -1.386906880 1
64 NA 1
65 NA 1
66 -0.565191691 1
67 -0.382899912 1
68 NA 1
69 -0.405642769 1
70 NA 1
71 -0.843748427 1
72 0.116003683 1
73 -0.778634325 1
74 NA 1
75 NA 1
76 NA 1
77 -0.632974758 1
78 NA 1
79 -0.778064615 1
80 NA 1
81 NA 1
82 -0.246123253 1
83 -1.239659782 1
84 -0.467772280 1
85 NA 1
86 -2.160485036 1
87 -0.657675572 1
88 NA 1
89 -0.696710744 1
90 NA 1
91 -0.179395847 1
92 -0.441545568 1
93 -0.685799334 1
94 NA 1
95 0.191929445 1
96 NA 1
97 -0.069760671 1
98 NA 1
99 NA 1
100 NA 1
$m3e$M_lvlone
L1mis
1 1.38634787
1.1 0.79402906
1.2 0.53603334
1.3 0.24129804
2 NA
2.1 0.31668065
2.2 0.37114414
3 0.54680608
3.1 0.28280274
3.2 0.76277262
4 0.56100366
4.1 0.38514140
4.2 0.04026174
4.3 0.16025873
5 0.21080161
5.1 0.36665700
5.2 0.66368829
5.3 0.40788895
6 0.11889539
7 1.04286843
7.1 0.52098933
7.2 0.09858876
8 0.17281472
8.1 0.25970093
8.2 0.30550233
8.3 0.88029778
8.4 0.20200392
8.5 NA
9 1.12218535
9.1 0.57911079
9.2 0.81350994
10 0.32744766
10.1 0.62912282
11 0.92140073
11.1 0.16012129
11.2 0.16166775
11.3 0.14979756
11.4 0.46855190
12 0.76818678
13 0.34264972
13.1 0.14526619
14 0.80630788
14.1 0.35697552
14.2 0.21330192
14.3 NA
15 0.30769119
15.1 0.28349746
15.2 0.64618365
15.3 0.51680884
16 0.71265471
16.1 0.38925880
16.2 0.23648869
16.3 0.45048730
16.4 0.23181791
16.5 0.13985349
17 0.25995399
17.1 0.03594878
17.2 0.77583623
17.3 0.60015197
17.4 0.13998405
18 0.96475839
19 0.10596495
19.1 0.13338947
19.2 0.41662218
19.3 0.53670855
20 0.41688567
20.1 NA
20.2 0.81634101
20.3 0.39232496
20.4 0.57925554
20.5 0.74200986
21 0.24759801
21.1 0.34052205
21.2 0.03905058
22 0.48605351
22.1 0.43761071
23 0.47599712
23.1 0.47680301
24 0.51696505
25 0.59392591
25.1 0.74010330
25.2 NA
25.3 0.73081722
25.4 0.29274286
25.5 0.74425342
26 0.20974346
26.1 NA
26.2 0.22908815
26.3 0.41880799
27 0.10097167
27.1 NA
28 NA
28.1 0.56052750
28.2 0.15301800
28.3 0.27802542
29 0.43556671
29.1 0.27593085
29.2 0.55256871
29.3 0.47272109
30 0.32743933
30.1 0.02231535
30.2 0.12833697
31 0.11126366
32 1.11731084
32.1 0.85943330
32.2 1.53730925
32.3 0.43831965
33 0.46726055
33.1 0.76818259
34 NA
34.1 1.14350292
34.2 0.19103604
34.3 NA
35 0.66303137
35.1 NA
35.2 NA
36 0.93843318
36.1 NA
36.2 0.29886676
36.3 0.22616598
36.4 0.53849566
37 1.68107300
37.1 1.13777638
37.2 0.26931933
38 NA
39 0.14395367
39.1 0.36454923
39.2 1.03700002
39.3 0.41320585
39.4 0.20901554
39.5 0.51603848
40 0.33912363
40.1 0.21892118
40.2 0.74070896
40.3 0.82927399
41 0.25193679
41.1 0.28760510
41.2 0.45553197
41.3 0.79237611
41.4 0.12582175
42 0.50079604
42.1 0.61140760
43 0.29752019
43.1 0.51793497
43.2 0.15152473
44 0.38806434
44.1 1.11140786
44.2 0.39132534
44.3 0.40934909
45 0.68587067
45.1 0.34530800
46 0.71312288
46.1 0.62537420
46.2 0.79574391
47 0.48660773
47.1 0.51241790
47.2 0.58869379
47.3 0.22171504
47.4 0.11366347
48 0.19677010
48.1 0.17706320
49 0.30752382
50 0.93663423
51 0.34107606
52 0.19007135
52.1 0.75662940
52.2 1.66104719
52.3 NA
52.4 0.18369708
52.5 0.48689343
53 0.31983157
53.1 0.61569501
53.2 NA
54 1.90522418
54.1 0.59484889
54.2 1.47174857
54.3 0.27307143
54.4 0.81272938
55 0.22735476
55.1 0.54683512
55.2 1.03503777
55.3 0.30169529
55.4 0.36008059
56 0.14193566
56.1 0.65073539
56.2 0.11338262
56.3 0.16820103
56.4 0.27419110
56.5 0.57110215
57 0.85104054
57.1 0.34733833
57.2 1.44438762
57.3 0.31836125
58 0.37456898
58.1 0.22120158
58.2 0.78885210
58.3 0.10114937
58.4 0.13385114
58.5 NA
59 0.13202156
59.1 0.33371896
60 0.35096579
61 0.36933806
61.1 0.17623067
61.2 0.21286227
61.3 0.12689308
61.4 0.77676718
62 1.38018163
62.1 0.43803892
62.2 0.21947900
62.3 0.11571160
63 0.41583568
63.1 0.25598960
64 0.20415642
65 0.07135646
65.1 0.57450574
65.2 0.52562984
65.3 0.21921164
66 0.33281730
66.1 0.03412404
66.2 0.92570619
67 0.15291043
68 0.37543648
68.1 0.20901022
68.2 0.12488064
68.3 0.08711204
68.4 0.54611735
69 0.23638239
70 0.49876756
70.1 0.39512615
71 0.45666551
71.1 0.92047456
71.2 0.32792986
71.3 0.95108007
71.4 0.36287072
72 0.12870526
72.1 0.45925876
72.2 0.05418867
72.3 0.48937486
72.4 0.64173822
72.5 0.57609943
73 0.17393402
74 0.23990575
75 0.28469861
76 0.71988630
76.1 1.12449946
76.2 0.71313766
77 0.02399030
78 0.42708148
79 0.37579286
79.1 0.78660681
79.2 0.67696116
80 0.34207854
80.1 0.60534092
80.2 0.26731034
81 0.17739052
81.1 0.35453673
81.2 0.20244235
81.3 1.26402329
82 0.09303938
82.1 0.27254210
82.2 0.49936304
83 0.21138572
83.1 0.26403568
83.2 0.20311133
83.3 1.16864671
84 1.99179346
84.1 1.52199460
85 NA
85.1 0.61458995
85.2 0.07871196
85.3 1.42315283
85.4 0.97986129
85.5 0.91792195
86 0.63509597
86.1 0.24546597
86.2 0.53102060
86.3 0.09360826
86.4 0.58301186
86.5 0.39146055
87 NA
87.1 0.66043624
87.2 0.13267613
88 0.10696344
88.1 0.13689448
88.2 0.48037889
88.3 0.97755681
89 0.70242369
90 0.40042977
90.1 0.63975731
90.2 0.33412775
90.3 0.38399003
91 0.58250391
91.1 0.13223217
91.2 0.46613305
92 0.18997862
93 1.05243347
93.1 0.01479757
93.2 0.50955172
93.3 0.78122514
93.4 0.63940704
94 0.45596305
94.1 0.41610667
94.2 0.52744298
94.3 0.70890756
94.4 0.84412478
94.5 0.21166602
95 0.57713135
95.1 0.44400207
95.2 0.42397776
96 0.72391015
96.1 0.32593738
96.2 0.23249511
96.3 1.01679990
96.4 0.92267953
96.5 0.83843412
97 0.47151154
97.1 0.15596614
98 0.05179545
98.1 0.47332096
98.2 0.19706341
99 0.22574556
99.1 1.00732330
99.2 0.09749127
100 0.22857989
100.1 0.39548654
100.2 NA
100.3 0.32695372
100.4 0.10043925
$m3e$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m3e$mu_reg_norm
[1] 0
$m3e$tau_reg_norm
[1] 1e-04
$m3e$shape_tau_norm
[1] 0.01
$m3e$rate_tau_norm
[1] 0.01
$m3e$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m3e$shape_diag_RinvD
[1] "0.01"
$m3e$rate_diag_RinvD
[1] "0.001"
$m3f
$m3f$M_id
C2 (Intercept)
1 -1.381594459 1
2 0.344426024 1
3 NA 1
4 -0.228910007 1
5 NA 1
6 -2.143955482 1
7 -1.156567023 1
8 -0.598827660 1
9 NA 1
10 -1.006719032 1
11 0.239801450 1
12 -1.064969789 1
13 -0.538082688 1
14 NA 1
15 -1.781049276 1
16 NA 1
17 NA 1
18 -0.014579883 1
19 -2.121550136 1
20 NA 1
21 -0.363239698 1
22 -0.121568514 1
23 -0.951271111 1
24 NA 1
25 -0.974288621 1
26 -1.130632418 1
27 0.114339868 1
28 0.238334648 1
29 0.840744958 1
30 NA 1
31 NA 1
32 -1.466312154 1
33 -0.637352277 1
34 NA 1
35 NA 1
36 NA 1
37 NA 1
38 NA 1
39 0.006728205 1
40 NA 1
41 -1.663281353 1
42 0.161184794 1
43 0.457939180 1
44 -0.307070331 1
45 NA 1
46 -1.071668276 1
47 -0.814751321 1
48 -0.547630662 1
49 NA 1
50 -1.350213782 1
51 0.719054706 1
52 NA 1
53 -1.207130750 1
54 NA 1
55 -0.408600991 1
56 -0.271380529 1
57 -1.361925974 1
58 NA 1
59 NA 1
60 -0.323712205 1
61 NA 1
62 NA 1
63 -1.386906880 1
64 NA 1
65 NA 1
66 -0.565191691 1
67 -0.382899912 1
68 NA 1
69 -0.405642769 1
70 NA 1
71 -0.843748427 1
72 0.116003683 1
73 -0.778634325 1
74 NA 1
75 NA 1
76 NA 1
77 -0.632974758 1
78 NA 1
79 -0.778064615 1
80 NA 1
81 NA 1
82 -0.246123253 1
83 -1.239659782 1
84 -0.467772280 1
85 NA 1
86 -2.160485036 1
87 -0.657675572 1
88 NA 1
89 -0.696710744 1
90 NA 1
91 -0.179395847 1
92 -0.441545568 1
93 -0.685799334 1
94 NA 1
95 0.191929445 1
96 NA 1
97 -0.069760671 1
98 NA 1
99 NA 1
100 NA 1
$m3f$M_lvlone
Be2
1 4.596628e-06
1.1 2.296427e-04
1.2 3.455922e-10
1.3 9.618613e-07
2 NA
2.1 1.065639e-07
2.2 1.320730e-03
3 9.707820e-06
3.1 3.645271e-05
3.2 NA
4 5.555794e-01
4.1 6.853316e-06
4.2 6.324951e-02
4.3 4.330745e-07
5 NA
5.1 6.556812e-04
5.2 6.963312e-06
5.3 1.159006e-04
6 1.509745e-02
7 NA
7.1 1.679086e-08
7.2 3.972447e-06
8 9.888512e-02
8.1 8.790334e-05
8.2 NA
8.3 5.411705e-04
8.4 8.446731e-04
8.5 2.059814e-04
9 4.160033e-01
9.1 NA
9.2 1.087331e-03
10 9.321715e-04
10.1 8.167897e-06
11 2.528529e-04
11.1 NA
11.2 5.587553e-10
11.3 5.240776e-10
11.4 2.830994e-07
12 1.962202e-07
13 NA
13.1 1.330415e-06
14 5.900181e-07
14.1 3.694946e-05
14.2 6.871447e-08
14.3 NA
15 1.848068e-04
15.1 1.714157e-10
15.2 1.088807e-03
15.3 2.677330e-05
16 NA
16.1 1.411453e-04
16.2 1.897147e-03
16.3 5.950632e-02
16.4 3.944608e-02
16.5 NA
17 4.808238e-05
17.1 6.175264e-04
17.2 2.319036e-07
17.3 1.393008e-09
17.4 NA
18 2.685853e-09
19 2.949370e-07
19.1 1.183423e-08
19.2 7.844699e-08
19.3 NA
20 4.920475e-06
20.1 6.885500e-08
20.2 9.577206e-04
20.3 1.325632e-03
20.4 NA
20.5 1.011637e-06
21 3.032947e-04
21.1 4.370975e-06
21.2 8.793700e-06
22 NA
22.1 7.397166e-06
23 4.931346e-02
23.1 3.799306e-02
24 1.018950e-01
25 NA
25.1 2.264756e-02
25.2 6.622343e-07
25.3 2.802504e-09
25.4 1.873599e-04
25.5 NA
26 4.587570e-09
26.1 2.394334e-06
26.2 4.510972e-08
26.3 3.657318e-11
27 NA
27.1 8.874134e-06
28 3.673907e-06
28.1 4.541426e-04
28.2 2.697966e-12
28.3 NA
29 3.282475e-03
29.1 2.270717e-01
29.2 9.981536e-03
29.3 2.343590e-02
30 NA
30.1 1.591483e-07
30.2 1.896944e-11
31 5.546285e-08
32 9.411981e-09
32.1 1.270914e-08
32.2 3.910478e-09
32.3 9.124048e-10
33 9.056156e-01
33.1 3.047254e-06
34 1.040462e-04
34.1 5.714390e-12
34.2 7.883166e-09
34.3 3.055823e-07
35 1.287796e-07
35.1 1.762232e-06
35.2 5.355159e-08
36 7.250797e-06
36.1 2.370652e-06
36.2 1.537090e-05
36.3 6.993214e-07
36.4 4.950009e-05
37 2.755165e-07
37.1 3.400517e-07
37.2 2.489007e-09
38 1.302651e-01
39 4.343746e-04
39.1 6.653143e-05
39.2 1.940204e-09
39.3 8.300468e-07
39.4 7.464169e-08
39.5 5.765597e-10
40 9.140572e-01
40.1 1.883555e-03
40.2 2.303001e-01
40.3 2.799910e-05
41 3.700067e-02
41.1 5.798225e-06
41.2 1.086252e-08
41.3 3.088732e-07
41.4 4.549537e-05
42 5.220968e-03
42.1 7.264286e-08
43 1.498125e-07
43.1 1.316763e-04
43.2 8.151771e-07
44 1.032476e-03
44.1 3.120174e-09
44.2 2.571257e-10
44.3 2.227416e-09
45 3.948036e-01
45.1 1.066310e-03
46 2.219556e-08
46.1 1.434525e-08
46.2 1.523026e-07
47 5.404537e-03
47.1 3.739267e-07
47.2 7.171916e-06
47.3 3.850162e-05
47.4 1.767264e-08
48 1.988010e-04
48.1 6.074589e-09
49 1.321544e-06
50 4.240393e-05
51 1.986093e-09
52 1.632022e-02
52.1 2.653038e-02
52.2 2.262881e-03
52.3 6.572647e-10
52.4 1.393737e-04
52.5 5.069462e-03
53 5.848890e-05
53.1 1.878509e-04
53.2 1.293417e-04
54 1.818441e-03
54.1 2.251839e-07
54.2 5.638172e-06
54.3 5.320676e-03
54.4 1.491367e-07
55 3.183775e-03
55.1 1.183380e-03
55.2 1.817077e-06
55.3 1.424370e-06
55.4 3.119967e-07
56 1.169667e-06
56.1 1.182293e-06
56.2 2.087533e-04
56.3 5.728251e-06
56.4 4.087596e-08
56.5 8.040370e-07
57 1.438387e-02
57.1 3.202179e-05
57.2 1.486318e-03
57.3 1.718412e-04
58 3.114123e-05
58.1 1.403881e-04
58.2 2.111006e-01
58.3 9.586985e-06
58.4 4.073162e-03
58.5 9.285307e-04
59 2.711478e-06
59.1 1.173472e-04
60 7.579680e-09
61 4.545759e-03
61.1 5.936674e-02
61.2 3.897281e-01
61.3 6.237379e-02
61.4 5.103038e-01
62 3.707353e-02
62.1 1.901660e-03
62.2 7.844369e-08
62.3 1.496168e-08
63 5.101070e-11
63.1 1.106013e-05
64 1.685171e-09
65 1.684142e-01
65.1 1.413479e-05
65.2 2.841196e-03
65.3 3.118871e-04
66 1.078473e-06
66.1 1.136650e-01
66.2 7.007044e-08
67 4.025749e-11
68 2.469503e-06
68.1 1.067638e-08
68.2 1.508555e-06
68.3 7.862972e-06
68.4 1.970326e-05
69 5.089430e-07
70 5.575849e-07
70.1 6.115107e-04
71 1.867742e-05
71.1 4.616167e-04
71.2 5.314611e-08
71.3 1.790244e-10
71.4 1.924070e-03
72 6.526547e-05
72.1 5.540491e-11
72.2 2.391191e-12
72.3 2.878783e-12
72.4 1.014404e-09
72.5 1.281231e-05
73 6.661564e-02
74 3.683842e-04
75 2.274469e-06
76 9.155636e-04
76.1 1.485365e-04
76.2 3.118702e-06
77 4.946432e-01
78 8.533933e-05
79 1.980588e-01
79.1 8.624235e-06
79.2 2.176176e-05
80 2.929029e-06
80.1 1.126162e-04
80.2 9.847382e-08
81 4.026095e-01
81.1 2.093927e-02
81.2 9.224440e-01
81.3 8.175654e-03
82 1.228129e-01
82.1 6.656575e-05
82.2 2.001426e-08
83 5.690020e-06
83.1 5.980615e-06
83.2 1.880816e-05
83.3 4.048910e-09
84 6.552173e-02
84.1 8.829278e-06
85 4.118253e-06
85.1 2.311994e-06
85.2 5.182892e-05
85.3 1.689467e-03
85.4 1.168017e-03
85.5 7.945131e-07
86 2.905567e-05
86.1 5.331467e-06
86.2 1.761451e-06
86.3 2.272397e-06
86.4 4.467006e-06
86.5 1.693940e-08
87 6.396865e-05
87.1 1.264093e-10
87.2 4.933807e-07
88 9.223531e-02
88.1 4.654325e-05
88.2 1.260399e-01
88.3 8.029866e-08
89 7.489307e-05
90 1.100491e-02
90.1 2.715349e-05
90.2 5.916576e-03
90.3 2.920657e-02
91 2.411997e-03
91.1 8.870147e-06
91.2 1.652965e-08
92 2.613551e-03
93 9.958480e-01
93.1 9.915375e-01
93.2 4.861680e-02
93.3 9.769008e-01
93.4 5.977439e-05
94 7.091952e-04
94.1 6.005522e-04
94.2 8.134430e-03
94.3 1.747604e-05
94.4 9.404259e-07
94.5 6.832077e-07
95 3.216011e-06
95.1 6.324477e-05
95.2 1.762187e-01
96 1.578796e-02
96.1 2.610661e-02
96.2 3.941700e-05
96.3 1.683671e-05
96.4 1.095127e-04
96.5 1.479105e-05
97 2.082560e-04
97.1 7.903013e-10
98 1.795949e-06
98.1 2.776600e-02
98.2 4.050457e-06
99 2.316802e-05
99.1 2.206426e-06
99.2 2.488411e-08
100 7.572193e-01
100.1 9.794641e-02
100.2 4.934595e-01
100.3 1.502083e-07
100.4 2.515993e-06
$m3f$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m3f$mu_reg_norm
[1] 0
$m3f$tau_reg_norm
[1] 1e-04
$m3f$shape_tau_norm
[1] 0.01
$m3f$rate_tau_norm
[1] 0.01
$m3f$mu_reg_beta
[1] 0
$m3f$tau_reg_beta
[1] 1e-04
$m3f$shape_tau_beta
[1] 0.01
$m3f$rate_tau_beta
[1] 0.01
$m3f$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m3f$shape_diag_RinvD
[1] "0.01"
$m3f$rate_diag_RinvD
[1] "0.001"
$m4a
$m4a$M_id
B2 (Intercept) B21
1 1 1 NA
2 NA 1 NA
3 NA 1 NA
4 1 1 NA
5 1 1 NA
6 1 1 NA
7 0 1 NA
8 1 1 NA
9 1 1 NA
10 0 1 NA
11 1 1 NA
12 1 1 NA
13 1 1 NA
14 1 1 NA
15 NA 1 NA
16 1 1 NA
17 1 1 NA
18 1 1 NA
19 1 1 NA
20 0 1 NA
21 1 1 NA
22 1 1 NA
23 1 1 NA
24 NA 1 NA
25 0 1 NA
26 1 1 NA
27 1 1 NA
28 0 1 NA
29 1 1 NA
30 0 1 NA
31 0 1 NA
32 1 1 NA
33 1 1 NA
34 0 1 NA
35 1 1 NA
36 0 1 NA
37 1 1 NA
38 1 1 NA
39 1 1 NA
40 1 1 NA
41 1 1 NA
42 1 1 NA
43 1 1 NA
44 NA 1 NA
45 1 1 NA
46 1 1 NA
47 1 1 NA
48 1 1 NA
49 1 1 NA
50 1 1 NA
51 0 1 NA
52 1 1 NA
53 1 1 NA
54 0 1 NA
55 1 1 NA
56 0 1 NA
57 1 1 NA
58 NA 1 NA
59 1 1 NA
60 1 1 NA
61 0 1 NA
62 0 1 NA
63 1 1 NA
64 1 1 NA
65 1 1 NA
66 1 1 NA
67 1 1 NA
68 1 1 NA
69 NA 1 NA
70 1 1 NA
71 1 1 NA
72 1 1 NA
73 1 1 NA
74 1 1 NA
75 1 1 NA
76 1 1 NA
77 1 1 NA
78 1 1 NA
79 1 1 NA
80 1 1 NA
81 1 1 NA
82 1 1 NA
83 1 1 NA
84 1 1 NA
85 1 1 NA
86 1 1 NA
87 1 1 NA
88 1 1 NA
89 1 1 NA
90 1 1 NA
91 NA 1 NA
92 1 1 NA
93 1 1 NA
94 1 1 NA
95 1 1 NA
96 NA 1 NA
97 NA 1 NA
98 1 1 NA
99 1 1 NA
100 1 1 NA
$m4a$M_lvlone
c1 p2 c2 L1mis Be2
1 0.7592026489 2 NA 1.38634787 4.596628e-06
1.1 0.9548337990 2 -0.08061445 0.79402906 2.296427e-04
1.2 0.5612235156 NA -0.26523782 0.53603334 3.455922e-10
1.3 1.1873391025 NA -0.30260393 0.24129804 9.618613e-07
2 0.9192204198 NA -0.33443795 NA NA
2.1 -0.1870730476 6 -0.11819800 0.31668065 1.065639e-07
2.2 1.2517512331 3 -0.31532280 0.37114414 1.320730e-03
3 -0.0605087604 NA -0.12920657 0.54680608 9.707820e-06
3.1 0.3788637747 NA NA 0.28280274 3.645271e-05
3.2 0.9872578281 NA NA 0.76277262 NA
4 1.4930175328 NA -0.31177403 0.56100366 5.555794e-01
4.1 -0.7692526880 4 -0.23894886 0.38514140 6.853316e-06
4.2 0.9180841450 0 -0.15533613 0.04026174 6.324951e-02
4.3 -0.0541170782 NA -0.14644545 0.16025873 4.330745e-07
5 -0.1376784521 2 -0.28360457 0.21080161 NA
5.1 -0.2740585866 NA -0.20135143 0.36665700 6.556812e-04
5.2 0.4670496929 7 -0.28293375 0.66368829 6.963312e-06
5.3 0.1740288049 NA NA 0.40788895 1.159006e-04
6 0.9868044683 NA -0.08617066 0.11889539 1.509745e-02
7 -0.1280320918 NA -0.22243495 1.04286843 NA
7.1 0.4242971219 NA NA 0.52098933 1.679086e-08
7.2 0.0777182491 NA NA 0.09858876 3.972447e-06
8 -0.5791408712 1 NA 0.17281472 9.888512e-02
8.1 0.3128604232 6 NA 0.25970093 8.790334e-05
8.2 0.6258446356 NA NA 0.30550233 NA
8.3 -0.1040137707 3 -0.35148972 0.88029778 5.411705e-04
8.4 0.0481450285 2 0.03661023 0.20200392 8.446731e-04
8.5 0.3831763675 1 -0.08424534 NA 2.059814e-04
9 -0.1757592269 3 NA 1.12218535 4.160033e-01
9.1 -0.1791541200 NA -0.43509340 0.57911079 NA
9.2 -0.0957042935 3 -0.22527490 0.81350994 1.087331e-03
10 -0.5598409704 3 NA 0.32744766 9.321715e-04
10.1 -0.2318340451 NA NA 0.62912282 8.167897e-06
11 0.5086859475 1 -0.08587475 0.92140073 2.528529e-04
11.1 0.4951758188 6 -0.06157340 0.16012129 NA
11.2 -1.1022162541 1 -0.12436018 0.16166775 5.587553e-10
11.3 -0.0611636705 6 -0.21377934 0.14979756 5.240776e-10
11.4 -0.4971774316 NA -0.32208329 0.46855190 2.830994e-07
12 -0.2433996286 NA NA 0.76818678 1.962202e-07
13 0.8799673116 NA NA 0.34264972 NA
13.1 0.1079022586 NA -0.40300449 0.14526619 1.330415e-06
14 0.9991752617 NA -0.28992072 0.80630788 5.900181e-07
14.1 -0.1094019046 NA NA 0.35697552 3.694946e-05
14.2 0.1518967560 2 NA 0.21330192 6.871447e-08
14.3 0.3521012473 NA -0.21979936 NA NA
15 0.3464447888 NA NA 0.30769119 1.848068e-04
15.1 -0.4767313971 NA -0.29092263 0.28349746 1.714157e-10
15.2 0.5759767791 NA -0.19392239 0.64618365 1.088807e-03
15.3 -0.1713452662 NA -0.25718384 0.51680884 2.677330e-05
16 0.4564754473 1 -0.45041108 0.71265471 NA
16.1 1.0652558311 NA -0.07599066 0.38925880 1.411453e-04
16.2 0.6971872493 2 -0.32385667 0.23648869 1.897147e-03
16.3 0.5259331838 NA -0.38326110 0.45048730 5.950632e-02
16.4 0.2046601798 1 -0.22845856 0.23181791 3.944608e-02
16.5 1.0718540464 NA -0.25497157 0.13985349 NA
17 0.6048676222 1 NA 0.25995399 4.808238e-05
17.1 0.2323298304 NA -0.22105143 0.03594878 6.175264e-04
17.2 1.2617499032 3 NA 0.77583623 2.319036e-07
17.3 -0.3913230895 2 NA 0.60015197 1.393008e-09
17.4 0.9577299112 NA -0.15098046 0.13998405 NA
18 -0.0050324072 2 -0.09870041 0.96475839 2.685853e-09
19 -0.4187468937 NA -0.26680239 0.10596495 2.949370e-07
19.1 -0.4478828944 NA -0.15815241 0.13338947 1.183423e-08
19.2 -1.1966721302 2 -0.14717437 0.41662218 7.844699e-08
19.3 -0.5877091668 2 -0.21271374 0.53670855 NA
20 0.6838223064 NA -0.22087628 0.41688567 4.920475e-06
20.1 0.3278571109 2 NA NA 6.885500e-08
20.2 -0.8489831990 NA -0.30127439 0.81634101 9.577206e-04
20.3 1.3169975191 NA -0.11782590 0.39232496 1.325632e-03
20.4 0.0444804531 NA -0.19857957 0.57925554 NA
20.5 -0.4535207652 NA -0.24338208 0.74200986 1.011637e-06
21 -0.4030302960 2 -0.31407992 0.24759801 3.032947e-04
21.1 -0.4069674045 3 -0.12424941 0.34052205 4.370975e-06
21.2 1.0650265940 2 -0.27672716 0.03905058 8.793700e-06
22 -0.0673274516 3 -0.23790593 0.48605351 NA
22.1 0.9601388170 3 -0.15996535 0.43761071 7.397166e-06
23 0.5556634840 NA -0.18236682 0.47599712 4.931346e-02
23.1 1.4407865964 5 -0.20823302 0.47680301 3.799306e-02
24 0.3856376411 2 -0.29026416 0.51696505 1.018950e-01
25 0.3564400705 3 -0.36139273 0.59392591 NA
25.1 0.0982553434 3 -0.19571118 0.74010330 2.264756e-02
25.2 0.1928682598 3 -0.21379355 NA 6.622343e-07
25.3 -0.0192488594 4 -0.33876012 0.73081722 2.802504e-09
25.4 0.4466012931 NA NA 0.29274286 1.873599e-04
25.5 1.1425193342 NA -0.04068446 0.74425342 NA
26 0.5341531449 NA -0.16846716 0.20974346 4.587570e-09
26.1 1.2268695927 2 -0.10440642 NA 2.394334e-06
26.2 0.3678294939 NA -0.26884827 0.22908815 4.510972e-08
26.3 0.5948516018 NA NA 0.41880799 3.657318e-11
27 -0.3342844147 1 -0.19520794 0.10097167 NA
27.1 -0.4835141229 NA -0.17622638 NA 8.874134e-06
28 -0.7145915499 0 -0.32164962 NA 3.673907e-06
28.1 0.5063671955 NA -0.27003852 0.56052750 4.541426e-04
28.2 -0.2067413142 4 -0.07235801 0.15301800 2.697966e-12
28.3 0.1196789973 NA -0.13462982 0.27802542 NA
29 0.1392699487 3 -0.32432030 0.43556671 3.282475e-03
29.1 0.7960234776 3 -0.27034171 0.27593085 2.270717e-01
29.2 1.0398214352 3 -0.10197448 0.55256871 9.981536e-03
29.3 0.0813246429 2 -0.27606945 0.47272109 2.343590e-02
30 -0.3296323050 NA -0.06949300 0.32743933 NA
30.1 1.3635850954 NA -0.11511035 0.02231535 1.591483e-07
30.2 0.7354171050 5 -0.16215882 0.12833697 1.896944e-11
31 0.3708398217 8 0.05707733 0.11126366 5.546285e-08
32 -0.0474059668 NA -0.18446298 1.11731084 9.411981e-09
32.1 1.2507771489 2 -0.14270013 0.85943330 1.270914e-08
32.2 0.1142915519 1 -0.20530798 1.53730925 3.910478e-09
32.3 0.6773270619 NA -0.14705649 0.43831965 9.124048e-10
33 0.1774293842 0 -0.15252819 0.46726055 9.056156e-01
33.1 0.6159606291 NA NA 0.76818259 3.047254e-06
34 0.8590979166 3 -0.30378735 NA 1.040462e-04
34.1 0.0546216775 NA -0.11982431 1.14350292 5.714390e-12
34.2 -0.0897224473 1 -0.24278671 0.19103604 7.883166e-09
34.3 0.4163395571 2 -0.19971833 NA 3.055823e-07
35 -1.4693520528 NA NA 0.66303137 1.287796e-07
35.1 -0.3031734330 NA -0.24165780 NA 1.762232e-06
35.2 -0.6045512101 NA NA NA 5.355159e-08
36 0.9823048960 5 -0.49062180 0.93843318 7.250797e-06
36.1 1.4466051416 NA -0.25651700 NA 2.370652e-06
36.2 1.1606752905 NA NA 0.29886676 1.537090e-05
36.3 0.8373091576 1 -0.30401274 0.22616598 6.993214e-07
36.4 0.2640591685 1 NA 0.53849566 4.950009e-05
37 0.1177313455 5 -0.15276529 1.68107300 2.755165e-07
37.1 -0.1415483779 NA -0.30016169 1.13777638 3.400517e-07
37.2 0.0054610124 NA 0.06809545 0.26931933 2.489007e-09
38 0.8078948077 0 -0.11218486 NA 1.302651e-01
39 0.9876451040 NA -0.38072211 0.14395367 4.343746e-04
39.1 -0.3431222274 1 -0.32094428 0.36454923 6.653143e-05
39.2 -1.7909380751 NA NA 1.03700002 1.940204e-09
39.3 -0.1798746191 NA -0.40173480 0.41320585 8.300468e-07
39.4 -0.1850961689 NA -0.20041848 0.20901554 7.464169e-08
39.5 0.4544226146 NA -0.26027990 0.51603848 5.765597e-10
40 0.5350190436 2 -0.19751956 0.33912363 9.140572e-01
40.1 0.4189342752 4 -0.08399467 0.21892118 1.883555e-03
40.2 0.4211994981 NA -0.20864416 0.74070896 2.303001e-01
40.3 0.0916687506 NA NA 0.82927399 2.799910e-05
41 -0.1035047421 NA -0.26096953 0.25193679 3.700067e-02
41.1 -0.4684202411 4 -0.23953874 0.28760510 5.798225e-06
41.2 0.5972615368 2 -0.03079344 0.45553197 1.086252e-08
41.3 0.9885613862 3 NA 0.79237611 3.088732e-07
41.4 -0.3908036794 NA NA 0.12582175 4.549537e-05
42 -0.0338893961 3 -0.16084527 0.50079604 5.220968e-03
42.1 -0.4498363172 5 -0.13812521 0.61140760 7.264286e-08
43 0.8965546110 4 -0.08864017 0.29752019 1.498125e-07
43.1 0.6199122090 3 -0.12583158 0.51793497 1.316763e-04
43.2 0.1804894429 3 -0.29253959 0.15152473 8.151771e-07
44 1.3221409285 1 -0.22697597 0.38806434 1.032476e-03
44.1 0.3416426284 NA NA 1.11140786 3.120174e-09
44.2 0.5706610068 7 NA 0.39132534 2.571257e-10
44.3 1.2679497430 NA -0.40544012 0.40934909 2.227416e-09
45 0.1414983160 NA -0.19274788 0.68587067 3.948036e-01
45.1 0.7220892521 NA -0.34860483 0.34530800 1.066310e-03
46 1.5391054233 4 -0.28547861 0.71312288 2.219556e-08
46.1 0.3889107049 6 -0.21977836 0.62537420 1.434525e-08
46.2 0.1248719493 NA NA 0.79574391 1.523026e-07
47 0.2014101100 NA -0.08597098 0.48660773 5.404537e-03
47.1 0.2982973539 4 -0.35424828 0.51241790 3.739267e-07
47.2 1.1518107179 2 -0.24262576 0.58869379 7.171916e-06
47.3 0.5196802157 4 -0.30426315 0.22171504 3.850162e-05
47.4 0.3702301552 NA NA 0.11366347 1.767264e-08
48 -0.2128602862 NA NA 0.19677010 1.988010e-04
48.1 -0.5337239976 6 NA 0.17706320 6.074589e-09
49 -0.5236770035 NA -0.42198781 0.30752382 1.321544e-06
50 0.3897705981 3 -0.19959516 0.93663423 4.240393e-05
51 -0.7213343736 2 -0.16556964 0.34107606 1.986093e-09
52 0.3758235358 3 -0.07438732 0.19007135 1.632022e-02
52.1 0.7138067080 1 -0.37537080 0.75662940 2.653038e-02
52.2 0.8872895233 NA -0.24222066 1.66104719 2.262881e-03
52.3 -0.9664587437 2 -0.31520603 NA 6.572647e-10
52.4 0.0254566848 3 -0.44619160 0.18369708 1.393737e-04
52.5 0.4155259424 1 -0.11011682 0.48689343 5.069462e-03
53 0.5675736897 3 -0.23278716 0.31983157 5.848890e-05
53.1 -0.3154088781 NA -0.28317264 0.61569501 1.878509e-04
53.2 0.2162315769 2 -0.19517481 NA 1.293417e-04
54 -0.0880802382 3 -0.10122856 1.90522418 1.818441e-03
54.1 0.4129127672 NA -0.28325504 0.59484889 2.251839e-07
54.2 1.0119546775 4 -0.16753120 1.47174857 5.638172e-06
54.3 -0.1112901990 0 -0.22217672 0.27307143 5.320676e-03
54.4 0.8587727145 NA -0.34609328 0.81272938 1.491367e-07
55 -0.0116453589 NA -0.32428190 0.22735476 3.183775e-03
55.1 0.5835528661 4 -0.24235382 0.54683512 1.183380e-03
55.2 -1.0010857254 NA -0.24065814 1.03503777 1.817077e-06
55.3 -0.4796526070 4 -0.23665476 0.30169529 1.424370e-06
55.4 -0.1202746964 3 NA 0.36008059 3.119967e-07
56 0.5176377612 NA NA 0.14193566 1.169667e-06
56.1 -1.1136932588 2 -0.30357450 0.65073539 1.182293e-06
56.2 -0.0168103281 3 -0.51301630 0.11338262 2.087533e-04
56.3 0.3933023606 3 -0.23743117 0.16820103 5.728251e-06
56.4 0.3714625139 0 -0.17264917 0.27419110 4.087596e-08
56.5 0.7811448179 NA -0.39188329 0.57110215 8.040370e-07
57 -1.0868304872 3 -0.18501692 0.85104054 1.438387e-02
57.1 0.8018626997 4 -0.27274841 0.34733833 3.202179e-05
57.2 -0.1159517011 1 NA 1.44438762 1.486318e-03
57.3 0.6785562445 NA -0.09898509 0.31836125 1.718412e-04
58 1.6476207996 NA -0.29901358 0.37456898 3.114123e-05
58.1 0.3402652711 NA -0.35390896 0.22120158 1.403881e-04
58.2 -0.1111300753 NA -0.16687336 0.78885210 2.111006e-01
58.3 -0.5409234285 3 -0.11784506 0.10114937 9.586985e-06
58.4 -0.1271327672 NA -0.05321983 0.13385114 4.073162e-03
58.5 0.8713264822 NA -0.54457568 NA 9.285307e-04
59 0.4766421367 NA -0.27255364 0.13202156 2.711478e-06
59.1 1.0028089765 NA NA 0.33371896 1.173472e-04
60 0.5231452932 NA NA 0.35096579 7.579680e-09
61 -0.7190130614 2 -0.30550120 0.36933806 4.545759e-03
61.1 0.8353702312 4 -0.35579892 0.17623067 5.936674e-02
61.2 1.0229058138 NA NA 0.21286227 3.897281e-01
61.3 1.1717723589 NA -0.34184391 0.12689308 6.237379e-02
61.4 -0.0629201596 NA -0.30891967 0.77676718 5.103038e-01
62 -0.3979137604 2 NA 1.38018163 3.707353e-02
62.1 0.6830738372 NA -0.10504143 0.43803892 1.901660e-03
62.2 0.4301745954 NA -0.20104997 0.21947900 7.844369e-08
62.3 -0.0333139957 NA -0.08138677 0.11571160 1.496168e-08
63 0.3345678035 NA -0.12036319 0.41583568 5.101070e-11
63.1 0.3643769511 2 -0.13624992 0.25598960 1.106013e-05
64 0.3949911859 4 NA 0.20415642 1.685171e-09
65 1.2000091513 NA -0.34450396 0.07135646 1.684142e-01
65.1 0.0110122646 5 -0.32514650 0.57450574 1.413479e-05
65.2 -0.5776452043 NA -0.10984996 0.52562984 2.841196e-03
65.3 -0.1372183563 NA -0.19275692 0.21921164 3.118871e-04
66 -0.5081302805 NA NA 0.33281730 1.078473e-06
66.1 -0.1447837412 NA NA 0.03412404 1.136650e-01
66.2 0.1906241379 NA -0.11687008 0.92570619 7.007044e-08
67 1.6716027681 NA NA 0.15291043 4.025749e-11
68 0.5691848839 NA -0.13605235 0.37543648 2.469503e-06
68.1 0.1004860389 NA -0.19790827 0.20901022 1.067638e-08
68.2 -0.0061241827 NA -0.17750123 0.12488064 1.508555e-06
68.3 0.7443745962 2 NA 0.08711204 7.862972e-06
68.4 0.8726923437 NA -0.12570562 0.54611735 1.970326e-05
69 0.0381382683 NA -0.32152751 0.23638239 5.089430e-07
70 0.8126204217 4 -0.28190462 0.49876756 5.575849e-07
70.1 0.4691503050 4 -0.11503263 0.39512615 6.115107e-04
71 -0.5529062591 4 -0.13029093 0.45666551 1.867742e-05
71.1 -0.1103252087 NA NA 0.92047456 4.616167e-04
71.2 1.7178492547 3 -0.39075433 0.32792986 5.314611e-08
71.3 -1.0118346755 0 -0.21401028 0.95108007 1.790244e-10
71.4 1.8623785017 0 -0.40219281 0.36287072 1.924070e-03
72 -0.4521659275 NA -0.40337108 0.12870526 6.526547e-05
72.1 0.1375317317 8 -0.25978914 0.45925876 5.540491e-11
72.2 -0.4170988856 NA NA 0.05418867 2.391191e-12
72.3 0.7107266765 NA -0.09809866 0.48937486 2.878783e-12
72.4 0.1451969143 3 -0.14240019 0.64173822 1.014404e-09
72.5 1.6298050306 NA -0.14794204 0.57609943 1.281231e-05
73 -0.0307469467 2 -0.23509343 0.17393402 6.661564e-02
74 0.3730017941 NA -0.27963171 0.23990575 3.683842e-04
75 -0.4908003566 NA -0.12905034 0.28469861 2.274469e-06
76 -0.9888876620 1 0.04775562 0.71988630 9.155636e-04
76.1 0.0003798292 0 -0.19399157 1.12449946 1.485365e-04
76.2 -0.8421863763 0 -0.02754574 0.71313766 3.118702e-06
77 -0.4986802480 2 -0.19053195 0.02399030 4.946432e-01
78 0.0417330969 NA -0.17172929 0.42708148 8.533933e-05
79 -0.3767450660 2 -0.03958515 0.37579286 1.980588e-01
79.1 0.1516000028 NA -0.20328809 0.78660681 8.624235e-06
79.2 -0.1888160741 2 -0.23901634 0.67696116 2.176176e-05
80 -0.0041558414 2 -0.34031873 0.34207854 2.929029e-06
80.1 -0.0329337062 NA -0.19526756 0.60534092 1.126162e-04
80.2 0.5046816157 NA NA 0.26731034 9.847382e-08
81 -0.9493950353 NA -0.18401980 0.17739052 4.026095e-01
81.1 0.2443038954 2 -0.16889476 0.35453673 2.093927e-02
81.2 0.6476958410 NA -0.37343047 0.20244235 9.224440e-01
81.3 0.4182528210 NA NA 1.26402329 8.175654e-03
82 1.1088801952 NA -0.08328168 0.09303938 1.228129e-01
82.1 0.9334157763 NA -0.22167084 0.27254210 6.656575e-05
82.2 0.4958140634 4 -0.20971187 0.49936304 2.001426e-08
83 0.5104724530 NA -0.34228255 0.21138572 5.690020e-06
83.1 -0.0513309106 NA -0.34075730 0.26403568 5.980615e-06
83.2 -0.2067792494 4 -0.32503954 0.20311133 1.880816e-05
83.3 -0.0534169155 3 NA 1.16864671 4.048910e-09
84 -0.0255753653 NA -0.20676741 1.99179346 6.552173e-02
84.1 -1.8234189877 2 -0.20310458 1.52199460 8.829278e-06
85 -0.0114038622 3 -0.12107593 NA 4.118253e-06
85.1 -0.0577615939 NA NA 0.61458995 2.311994e-06
85.2 -0.2241856342 3 -0.32509207 0.07871196 5.182892e-05
85.3 -0.0520175929 NA NA 1.42315283 1.689467e-03
85.4 0.2892733846 2 -0.30730810 0.97986129 1.168017e-03
85.5 -0.3740417009 1 NA 0.91792195 7.945131e-07
86 0.4293735089 2 -0.10854862 0.63509597 2.905567e-05
86.1 -0.1363456521 NA -0.25751662 0.24546597 5.331467e-06
86.2 0.1230989293 0 -0.38943076 0.53102060 1.761451e-06
86.3 0.3305413955 0 -0.24454702 0.09360826 2.272397e-06
86.4 2.6003411822 NA -0.12338992 0.58301186 4.467006e-06
86.5 -0.1420690052 2 -0.23976984 0.39146055 1.693940e-08
87 1.0457427869 NA NA NA 6.396865e-05
87.1 -0.2973007190 NA -0.34366972 0.66043624 1.264093e-10
87.2 0.4396872616 3 NA 0.13267613 4.933807e-07
88 -0.0601928334 NA -0.31563888 0.10696344 9.223531e-02
88.1 -1.0124347595 1 -0.20304028 0.13689448 4.654325e-05
88.2 0.5730917016 1 -0.40311895 0.48037889 1.260399e-01
88.3 -0.0029455332 4 -0.12308715 0.97755681 8.029866e-08
89 1.5465903721 NA -0.18527715 0.70242369 7.489307e-05
90 0.0626760573 3 -0.25029126 0.40042977 1.100491e-02
90.1 1.1896872985 NA -0.26974303 0.63975731 2.715349e-05
90.2 0.2597888783 NA -0.28804531 0.33412775 5.916576e-03
90.3 0.6599799887 NA -0.19180615 0.38399003 2.920657e-02
91 1.1213651365 NA -0.26591197 0.58250391 2.411997e-03
91.1 1.2046371625 NA -0.09153470 0.13223217 8.870147e-06
91.2 0.3395603754 NA -0.48414390 0.46613305 1.652965e-08
92 0.4674939332 NA NA 0.18997862 2.613551e-03
93 0.2677965647 2 -0.11939966 1.05243347 9.958480e-01
93.1 1.6424445368 4 NA 0.01479757 9.915375e-01
93.2 0.7101700066 4 -0.21089379 0.50955172 4.861680e-02
93.3 1.1222322893 NA NA 0.78122514 9.769008e-01
93.4 1.4628960401 3 -0.23618836 0.63940704 5.977439e-05
94 -0.2904211940 4 NA 0.45596305 7.091952e-04
94.1 0.0147813580 2 -0.10217284 0.41610667 6.005522e-04
94.2 -0.4536774482 NA -0.36713471 0.52744298 8.134430e-03
94.3 0.6793464917 1 -0.13806763 0.70890756 1.747604e-05
94.4 -0.9411356550 NA -0.42353804 0.84412478 9.404259e-07
94.5 0.5683867264 2 -0.15513707 0.21166602 6.832077e-07
95 0.2375652188 3 -0.24149687 0.57713135 3.216011e-06
95.1 0.0767152977 5 -0.21315958 0.44400207 6.324477e-05
95.2 -0.6886731251 2 -0.15777208 0.42397776 1.762187e-01
96 0.7813892121 NA -0.16780948 0.72391015 1.578796e-02
96.1 0.3391519695 NA -0.32504815 0.32593738 2.610661e-02
96.2 -0.4857246503 5 -0.20395970 0.23249511 3.941700e-05
96.3 0.8771471244 1 -0.06221501 1.01679990 1.683671e-05
96.4 1.9030768981 0 -0.14801097 0.92267953 1.095127e-04
96.5 -0.1684332749 3 -0.28658893 0.83843412 1.479105e-05
97 1.3775130083 4 -0.34484656 0.47151154 2.082560e-04
97.1 -1.7323228619 2 -0.35658805 0.15596614 7.903013e-10
98 -1.2648518889 3 -0.36913003 0.05179545 1.795949e-06
98.1 -0.9042716241 NA NA 0.47332096 2.776600e-02
98.2 -0.1560385207 NA -0.17154225 0.19706341 4.050457e-06
99 0.7993356425 5 -0.24753132 0.22574556 2.316802e-05
99.1 1.0355522332 NA -0.27947829 1.00732330 2.206426e-06
99.2 -0.1150895843 NA -0.09033035 0.09749127 2.488411e-08
100 0.0369067906 NA -0.17326698 0.22857989 7.572193e-01
100.1 1.6023713093 4 NA 0.39548654 9.794641e-02
100.2 0.8861545820 NA -0.12072016 NA 4.934595e-01
100.3 0.1277046316 4 -0.27657520 0.32695372 1.502083e-07
100.4 -0.0834577654 NA -0.14631556 0.10043925 2.515993e-06
$m4a$spM_lvlone
center scale
c1 0.25599956 0.6718095
p2 2.71257485 1.6247402
c2 -0.22371584 0.1059527
L1mis 0.48184811 0.3462447
Be2 0.04274145 0.1563798
$m4a$mu_reg_norm
[1] 0
$m4a$tau_reg_norm
[1] 1e-04
$m4a$shape_tau_norm
[1] 0.01
$m4a$rate_tau_norm
[1] 0.01
$m4a$mu_reg_gamma
[1] 0
$m4a$tau_reg_gamma
[1] 1e-04
$m4a$shape_tau_gamma
[1] 0.01
$m4a$rate_tau_gamma
[1] 0.01
$m4a$mu_reg_beta
[1] 0
$m4a$tau_reg_beta
[1] 1e-04
$m4a$shape_tau_beta
[1] 0.01
$m4a$rate_tau_beta
[1] 0.01
$m4a$mu_reg_binom
[1] 0
$m4a$tau_reg_binom
[1] 1e-04
$m4a$mu_reg_poisson
[1] 0
$m4a$tau_reg_poisson
[1] 1e-04
$m4a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m4a$shape_diag_RinvD
[1] "0.01"
$m4a$rate_diag_RinvD
[1] "0.001"
$m4b
$m4b$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m4b$M_lvlone
c1 p2 b2 c2 L1mis b21
1 0.7592026489 2 NA NA 1.38634787 NA
1.1 0.9548337990 2 0 -0.08061445 0.79402906 NA
1.2 0.5612235156 NA NA -0.26523782 0.53603334 NA
1.3 1.1873391025 NA 0 -0.30260393 0.24129804 NA
2 0.9192204198 NA 0 -0.33443795 NA NA
2.1 -0.1870730476 6 NA -0.11819800 0.31668065 NA
2.2 1.2517512331 3 NA -0.31532280 0.37114414 NA
3 -0.0605087604 NA 0 -0.12920657 0.54680608 NA
3.1 0.3788637747 NA NA NA 0.28280274 NA
3.2 0.9872578281 NA 1 NA 0.76277262 NA
4 1.4930175328 NA 1 -0.31177403 0.56100366 NA
4.1 -0.7692526880 4 0 -0.23894886 0.38514140 NA
4.2 0.9180841450 0 0 -0.15533613 0.04026174 NA
4.3 -0.0541170782 NA 0 -0.14644545 0.16025873 NA
5 -0.1376784521 2 NA -0.28360457 0.21080161 NA
5.1 -0.2740585866 NA 0 -0.20135143 0.36665700 NA
5.2 0.4670496929 7 NA -0.28293375 0.66368829 NA
5.3 0.1740288049 NA NA NA 0.40788895 NA
6 0.9868044683 NA NA -0.08617066 0.11889539 NA
7 -0.1280320918 NA NA -0.22243495 1.04286843 NA
7.1 0.4242971219 NA NA NA 0.52098933 NA
7.2 0.0777182491 NA 0 NA 0.09858876 NA
8 -0.5791408712 1 0 NA 0.17281472 NA
8.1 0.3128604232 6 0 NA 0.25970093 NA
8.2 0.6258446356 NA NA NA 0.30550233 NA
8.3 -0.1040137707 3 1 -0.35148972 0.88029778 NA
8.4 0.0481450285 2 0 0.03661023 0.20200392 NA
8.5 0.3831763675 1 1 -0.08424534 NA NA
9 -0.1757592269 3 0 NA 1.12218535 NA
9.1 -0.1791541200 NA NA -0.43509340 0.57911079 NA
9.2 -0.0957042935 3 NA -0.22527490 0.81350994 NA
10 -0.5598409704 3 NA NA 0.32744766 NA
10.1 -0.2318340451 NA 0 NA 0.62912282 NA
11 0.5086859475 1 0 -0.08587475 0.92140073 NA
11.1 0.4951758188 6 0 -0.06157340 0.16012129 NA
11.2 -1.1022162541 1 0 -0.12436018 0.16166775 NA
11.3 -0.0611636705 6 0 -0.21377934 0.14979756 NA
11.4 -0.4971774316 NA 0 -0.32208329 0.46855190 NA
12 -0.2433996286 NA 0 NA 0.76818678 NA
13 0.8799673116 NA NA NA 0.34264972 NA
13.1 0.1079022586 NA 0 -0.40300449 0.14526619 NA
14 0.9991752617 NA NA -0.28992072 0.80630788 NA
14.1 -0.1094019046 NA NA NA 0.35697552 NA
14.2 0.1518967560 2 NA NA 0.21330192 NA
14.3 0.3521012473 NA NA -0.21979936 NA NA
15 0.3464447888 NA 0 NA 0.30769119 NA
15.1 -0.4767313971 NA 0 -0.29092263 0.28349746 NA
15.2 0.5759767791 NA 0 -0.19392239 0.64618365 NA
15.3 -0.1713452662 NA 0 -0.25718384 0.51680884 NA
16 0.4564754473 1 1 -0.45041108 0.71265471 NA
16.1 1.0652558311 NA NA -0.07599066 0.38925880 NA
16.2 0.6971872493 2 NA -0.32385667 0.23648869 NA
16.3 0.5259331838 NA 0 -0.38326110 0.45048730 NA
16.4 0.2046601798 1 0 -0.22845856 0.23181791 NA
16.5 1.0718540464 NA NA -0.25497157 0.13985349 NA
17 0.6048676222 1 0 NA 0.25995399 NA
17.1 0.2323298304 NA 0 -0.22105143 0.03594878 NA
17.2 1.2617499032 3 0 NA 0.77583623 NA
17.3 -0.3913230895 2 NA NA 0.60015197 NA
17.4 0.9577299112 NA 0 -0.15098046 0.13998405 NA
18 -0.0050324072 2 0 -0.09870041 0.96475839 NA
19 -0.4187468937 NA NA -0.26680239 0.10596495 NA
19.1 -0.4478828944 NA NA -0.15815241 0.13338947 NA
19.2 -1.1966721302 2 0 -0.14717437 0.41662218 NA
19.3 -0.5877091668 2 1 -0.21271374 0.53670855 NA
20 0.6838223064 NA NA -0.22087628 0.41688567 NA
20.1 0.3278571109 2 0 NA NA NA
20.2 -0.8489831990 NA 1 -0.30127439 0.81634101 NA
20.3 1.3169975191 NA 0 -0.11782590 0.39232496 NA
20.4 0.0444804531 NA 0 -0.19857957 0.57925554 NA
20.5 -0.4535207652 NA 0 -0.24338208 0.74200986 NA
21 -0.4030302960 2 0 -0.31407992 0.24759801 NA
21.1 -0.4069674045 3 0 -0.12424941 0.34052205 NA
21.2 1.0650265940 2 NA -0.27672716 0.03905058 NA
22 -0.0673274516 3 0 -0.23790593 0.48605351 NA
22.1 0.9601388170 3 0 -0.15996535 0.43761071 NA
23 0.5556634840 NA 0 -0.18236682 0.47599712 NA
23.1 1.4407865964 5 NA -0.20823302 0.47680301 NA
24 0.3856376411 2 0 -0.29026416 0.51696505 NA
25 0.3564400705 3 0 -0.36139273 0.59392591 NA
25.1 0.0982553434 3 NA -0.19571118 0.74010330 NA
25.2 0.1928682598 3 1 -0.21379355 NA NA
25.3 -0.0192488594 4 0 -0.33876012 0.73081722 NA
25.4 0.4466012931 NA 0 NA 0.29274286 NA
25.5 1.1425193342 NA NA -0.04068446 0.74425342 NA
26 0.5341531449 NA NA -0.16846716 0.20974346 NA
26.1 1.2268695927 2 0 -0.10440642 NA NA
26.2 0.3678294939 NA 0 -0.26884827 0.22908815 NA
26.3 0.5948516018 NA 0 NA 0.41880799 NA
27 -0.3342844147 1 0 -0.19520794 0.10097167 NA
27.1 -0.4835141229 NA 0 -0.17622638 NA NA
28 -0.7145915499 0 NA -0.32164962 NA NA
28.1 0.5063671955 NA 0 -0.27003852 0.56052750 NA
28.2 -0.2067413142 4 0 -0.07235801 0.15301800 NA
28.3 0.1196789973 NA 0 -0.13462982 0.27802542 NA
29 0.1392699487 3 0 -0.32432030 0.43556671 NA
29.1 0.7960234776 3 0 -0.27034171 0.27593085 NA
29.2 1.0398214352 3 0 -0.10197448 0.55256871 NA
29.3 0.0813246429 2 0 -0.27606945 0.47272109 NA
30 -0.3296323050 NA NA -0.06949300 0.32743933 NA
30.1 1.3635850954 NA 0 -0.11511035 0.02231535 NA
30.2 0.7354171050 5 0 -0.16215882 0.12833697 NA
31 0.3708398217 8 0 0.05707733 0.11126366 NA
32 -0.0474059668 NA 0 -0.18446298 1.11731084 NA
32.1 1.2507771489 2 0 -0.14270013 0.85943330 NA
32.2 0.1142915519 1 NA -0.20530798 1.53730925 NA
32.3 0.6773270619 NA NA -0.14705649 0.43831965 NA
33 0.1774293842 0 0 -0.15252819 0.46726055 NA
33.1 0.6159606291 NA 1 NA 0.76818259 NA
34 0.8590979166 3 NA -0.30378735 NA NA
34.1 0.0546216775 NA 0 -0.11982431 1.14350292 NA
34.2 -0.0897224473 1 NA -0.24278671 0.19103604 NA
34.3 0.4163395571 2 NA -0.19971833 NA NA
35 -1.4693520528 NA 0 NA 0.66303137 NA
35.1 -0.3031734330 NA 0 -0.24165780 NA NA
35.2 -0.6045512101 NA NA NA NA NA
36 0.9823048960 5 NA -0.49062180 0.93843318 NA
36.1 1.4466051416 NA NA -0.25651700 NA NA
36.2 1.1606752905 NA 0 NA 0.29886676 NA
36.3 0.8373091576 1 0 -0.30401274 0.22616598 NA
36.4 0.2640591685 1 0 NA 0.53849566 NA
37 0.1177313455 5 0 -0.15276529 1.68107300 NA
37.1 -0.1415483779 NA 0 -0.30016169 1.13777638 NA
37.2 0.0054610124 NA 0 0.06809545 0.26931933 NA
38 0.8078948077 0 0 -0.11218486 NA NA
39 0.9876451040 NA 1 -0.38072211 0.14395367 NA
39.1 -0.3431222274 1 0 -0.32094428 0.36454923 NA
39.2 -1.7909380751 NA NA NA 1.03700002 NA
39.3 -0.1798746191 NA NA -0.40173480 0.41320585 NA
39.4 -0.1850961689 NA 0 -0.20041848 0.20901554 NA
39.5 0.4544226146 NA 1 -0.26027990 0.51603848 NA
40 0.5350190436 2 0 -0.19751956 0.33912363 NA
40.1 0.4189342752 4 1 -0.08399467 0.21892118 NA
40.2 0.4211994981 NA 0 -0.20864416 0.74070896 NA
40.3 0.0916687506 NA NA NA 0.82927399 NA
41 -0.1035047421 NA 0 -0.26096953 0.25193679 NA
41.1 -0.4684202411 4 NA -0.23953874 0.28760510 NA
41.2 0.5972615368 2 0 -0.03079344 0.45553197 NA
41.3 0.9885613862 3 NA NA 0.79237611 NA
41.4 -0.3908036794 NA 0 NA 0.12582175 NA
42 -0.0338893961 3 0 -0.16084527 0.50079604 NA
42.1 -0.4498363172 5 1 -0.13812521 0.61140760 NA
43 0.8965546110 4 0 -0.08864017 0.29752019 NA
43.1 0.6199122090 3 1 -0.12583158 0.51793497 NA
43.2 0.1804894429 3 0 -0.29253959 0.15152473 NA
44 1.3221409285 1 0 -0.22697597 0.38806434 NA
44.1 0.3416426284 NA 0 NA 1.11140786 NA
44.2 0.5706610068 7 0 NA 0.39132534 NA
44.3 1.2679497430 NA 0 -0.40544012 0.40934909 NA
45 0.1414983160 NA NA -0.19274788 0.68587067 NA
45.1 0.7220892521 NA 1 -0.34860483 0.34530800 NA
46 1.5391054233 4 0 -0.28547861 0.71312288 NA
46.1 0.3889107049 6 0 -0.21977836 0.62537420 NA
46.2 0.1248719493 NA 0 NA 0.79574391 NA
47 0.2014101100 NA 0 -0.08597098 0.48660773 NA
47.1 0.2982973539 4 0 -0.35424828 0.51241790 NA
47.2 1.1518107179 2 0 -0.24262576 0.58869379 NA
47.3 0.5196802157 4 NA -0.30426315 0.22171504 NA
47.4 0.3702301552 NA 0 NA 0.11366347 NA
48 -0.2128602862 NA 1 NA 0.19677010 NA
48.1 -0.5337239976 6 1 NA 0.17706320 NA
49 -0.5236770035 NA NA -0.42198781 0.30752382 NA
50 0.3897705981 3 0 -0.19959516 0.93663423 NA
51 -0.7213343736 2 0 -0.16556964 0.34107606 NA
52 0.3758235358 3 0 -0.07438732 0.19007135 NA
52.1 0.7138067080 1 0 -0.37537080 0.75662940 NA
52.2 0.8872895233 NA 0 -0.24222066 1.66104719 NA
52.3 -0.9664587437 2 0 -0.31520603 NA NA
52.4 0.0254566848 3 0 -0.44619160 0.18369708 NA
52.5 0.4155259424 1 0 -0.11011682 0.48689343 NA
53 0.5675736897 3 0 -0.23278716 0.31983157 NA
53.1 -0.3154088781 NA 0 -0.28317264 0.61569501 NA
53.2 0.2162315769 2 NA -0.19517481 NA NA
54 -0.0880802382 3 NA -0.10122856 1.90522418 NA
54.1 0.4129127672 NA NA -0.28325504 0.59484889 NA
54.2 1.0119546775 4 NA -0.16753120 1.47174857 NA
54.3 -0.1112901990 0 NA -0.22217672 0.27307143 NA
54.4 0.8587727145 NA 0 -0.34609328 0.81272938 NA
55 -0.0116453589 NA 0 -0.32428190 0.22735476 NA
55.1 0.5835528661 4 0 -0.24235382 0.54683512 NA
55.2 -1.0010857254 NA NA -0.24065814 1.03503777 NA
55.3 -0.4796526070 4 NA -0.23665476 0.30169529 NA
55.4 -0.1202746964 3 0 NA 0.36008059 NA
56 0.5176377612 NA 0 NA 0.14193566 NA
56.1 -1.1136932588 2 NA -0.30357450 0.65073539 NA
56.2 -0.0168103281 3 NA -0.51301630 0.11338262 NA
56.3 0.3933023606 3 1 -0.23743117 0.16820103 NA
56.4 0.3714625139 0 0 -0.17264917 0.27419110 NA
56.5 0.7811448179 NA 0 -0.39188329 0.57110215 NA
57 -1.0868304872 3 0 -0.18501692 0.85104054 NA
57.1 0.8018626997 4 0 -0.27274841 0.34733833 NA
57.2 -0.1159517011 1 0 NA 1.44438762 NA
57.3 0.6785562445 NA NA -0.09898509 0.31836125 NA
58 1.6476207996 NA 0 -0.29901358 0.37456898 NA
58.1 0.3402652711 NA NA -0.35390896 0.22120158 NA
58.2 -0.1111300753 NA 1 -0.16687336 0.78885210 NA
58.3 -0.5409234285 3 1 -0.11784506 0.10114937 NA
58.4 -0.1271327672 NA 0 -0.05321983 0.13385114 NA
58.5 0.8713264822 NA 0 -0.54457568 NA NA
59 0.4766421367 NA NA -0.27255364 0.13202156 NA
59.1 1.0028089765 NA 1 NA 0.33371896 NA
60 0.5231452932 NA 0 NA 0.35096579 NA
61 -0.7190130614 2 NA -0.30550120 0.36933806 NA
61.1 0.8353702312 4 1 -0.35579892 0.17623067 NA
61.2 1.0229058138 NA 1 NA 0.21286227 NA
61.3 1.1717723589 NA 0 -0.34184391 0.12689308 NA
61.4 -0.0629201596 NA 0 -0.30891967 0.77676718 NA
62 -0.3979137604 2 NA NA 1.38018163 NA
62.1 0.6830738372 NA 1 -0.10504143 0.43803892 NA
62.2 0.4301745954 NA 0 -0.20104997 0.21947900 NA
62.3 -0.0333139957 NA 0 -0.08138677 0.11571160 NA
63 0.3345678035 NA NA -0.12036319 0.41583568 NA
63.1 0.3643769511 2 0 -0.13624992 0.25598960 NA
64 0.3949911859 4 0 NA 0.20415642 NA
65 1.2000091513 NA 0 -0.34450396 0.07135646 NA
65.1 0.0110122646 5 0 -0.32514650 0.57450574 NA
65.2 -0.5776452043 NA 0 -0.10984996 0.52562984 NA
65.3 -0.1372183563 NA 0 -0.19275692 0.21921164 NA
66 -0.5081302805 NA NA NA 0.33281730 NA
66.1 -0.1447837412 NA 0 NA 0.03412404 NA
66.2 0.1906241379 NA 0 -0.11687008 0.92570619 NA
67 1.6716027681 NA NA NA 0.15291043 NA
68 0.5691848839 NA 0 -0.13605235 0.37543648 NA
68.1 0.1004860389 NA 0 -0.19790827 0.20901022 NA
68.2 -0.0061241827 NA NA -0.17750123 0.12488064 NA
68.3 0.7443745962 2 0 NA 0.08711204 NA
68.4 0.8726923437 NA NA -0.12570562 0.54611735 NA
69 0.0381382683 NA 0 -0.32152751 0.23638239 NA
70 0.8126204217 4 0 -0.28190462 0.49876756 NA
70.1 0.4691503050 4 0 -0.11503263 0.39512615 NA
71 -0.5529062591 4 0 -0.13029093 0.45666551 NA
71.1 -0.1103252087 NA 1 NA 0.92047456 NA
71.2 1.7178492547 3 0 -0.39075433 0.32792986 NA
71.3 -1.0118346755 0 1 -0.21401028 0.95108007 NA
71.4 1.8623785017 0 0 -0.40219281 0.36287072 NA
72 -0.4521659275 NA 0 -0.40337108 0.12870526 NA
72.1 0.1375317317 8 0 -0.25978914 0.45925876 NA
72.2 -0.4170988856 NA NA NA 0.05418867 NA
72.3 0.7107266765 NA 0 -0.09809866 0.48937486 NA
72.4 0.1451969143 3 0 -0.14240019 0.64173822 NA
72.5 1.6298050306 NA 0 -0.14794204 0.57609943 NA
73 -0.0307469467 2 0 -0.23509343 0.17393402 NA
74 0.3730017941 NA 0 -0.27963171 0.23990575 NA
75 -0.4908003566 NA NA -0.12905034 0.28469861 NA
76 -0.9888876620 1 0 0.04775562 0.71988630 NA
76.1 0.0003798292 0 0 -0.19399157 1.12449946 NA
76.2 -0.8421863763 0 0 -0.02754574 0.71313766 NA
77 -0.4986802480 2 NA -0.19053195 0.02399030 NA
78 0.0417330969 NA 0 -0.17172929 0.42708148 NA
79 -0.3767450660 2 NA -0.03958515 0.37579286 NA
79.1 0.1516000028 NA 0 -0.20328809 0.78660681 NA
79.2 -0.1888160741 2 NA -0.23901634 0.67696116 NA
80 -0.0041558414 2 NA -0.34031873 0.34207854 NA
80.1 -0.0329337062 NA 0 -0.19526756 0.60534092 NA
80.2 0.5046816157 NA NA NA 0.26731034 NA
81 -0.9493950353 NA 0 -0.18401980 0.17739052 NA
81.1 0.2443038954 2 0 -0.16889476 0.35453673 NA
81.2 0.6476958410 NA NA -0.37343047 0.20244235 NA
81.3 0.4182528210 NA 0 NA 1.26402329 NA
82 1.1088801952 NA NA -0.08328168 0.09303938 NA
82.1 0.9334157763 NA 0 -0.22167084 0.27254210 NA
82.2 0.4958140634 4 1 -0.20971187 0.49936304 NA
83 0.5104724530 NA NA -0.34228255 0.21138572 NA
83.1 -0.0513309106 NA 0 -0.34075730 0.26403568 NA
83.2 -0.2067792494 4 0 -0.32503954 0.20311133 NA
83.3 -0.0534169155 3 NA NA 1.16864671 NA
84 -0.0255753653 NA 0 -0.20676741 1.99179346 NA
84.1 -1.8234189877 2 NA -0.20310458 1.52199460 NA
85 -0.0114038622 3 1 -0.12107593 NA NA
85.1 -0.0577615939 NA NA NA 0.61458995 NA
85.2 -0.2241856342 3 0 -0.32509207 0.07871196 NA
85.3 -0.0520175929 NA 0 NA 1.42315283 NA
85.4 0.2892733846 2 0 -0.30730810 0.97986129 NA
85.5 -0.3740417009 1 0 NA 0.91792195 NA
86 0.4293735089 2 0 -0.10854862 0.63509597 NA
86.1 -0.1363456521 NA NA -0.25751662 0.24546597 NA
86.2 0.1230989293 0 NA -0.38943076 0.53102060 NA
86.3 0.3305413955 0 0 -0.24454702 0.09360826 NA
86.4 2.6003411822 NA NA -0.12338992 0.58301186 NA
86.5 -0.1420690052 2 0 -0.23976984 0.39146055 NA
87 1.0457427869 NA NA NA NA NA
87.1 -0.2973007190 NA NA -0.34366972 0.66043624 NA
87.2 0.4396872616 3 NA NA 0.13267613 NA
88 -0.0601928334 NA 0 -0.31563888 0.10696344 NA
88.1 -1.0124347595 1 NA -0.20304028 0.13689448 NA
88.2 0.5730917016 1 0 -0.40311895 0.48037889 NA
88.3 -0.0029455332 4 0 -0.12308715 0.97755681 NA
89 1.5465903721 NA 0 -0.18527715 0.70242369 NA
90 0.0626760573 3 0 -0.25029126 0.40042977 NA
90.1 1.1896872985 NA 0 -0.26974303 0.63975731 NA
90.2 0.2597888783 NA 0 -0.28804531 0.33412775 NA
90.3 0.6599799887 NA NA -0.19180615 0.38399003 NA
91 1.1213651365 NA 0 -0.26591197 0.58250391 NA
91.1 1.2046371625 NA 0 -0.09153470 0.13223217 NA
91.2 0.3395603754 NA 0 -0.48414390 0.46613305 NA
92 0.4674939332 NA 0 NA 0.18997862 NA
93 0.2677965647 2 NA -0.11939966 1.05243347 NA
93.1 1.6424445368 4 0 NA 0.01479757 NA
93.2 0.7101700066 4 NA -0.21089379 0.50955172 NA
93.3 1.1222322893 NA 0 NA 0.78122514 NA
93.4 1.4628960401 3 0 -0.23618836 0.63940704 NA
94 -0.2904211940 4 NA NA 0.45596305 NA
94.1 0.0147813580 2 0 -0.10217284 0.41610667 NA
94.2 -0.4536774482 NA 0 -0.36713471 0.52744298 NA
94.3 0.6793464917 1 NA -0.13806763 0.70890756 NA
94.4 -0.9411356550 NA 0 -0.42353804 0.84412478 NA
94.5 0.5683867264 2 1 -0.15513707 0.21166602 NA
95 0.2375652188 3 0 -0.24149687 0.57713135 NA
95.1 0.0767152977 5 NA -0.21315958 0.44400207 NA
95.2 -0.6886731251 2 0 -0.15777208 0.42397776 NA
96 0.7813892121 NA 0 -0.16780948 0.72391015 NA
96.1 0.3391519695 NA 0 -0.32504815 0.32593738 NA
96.2 -0.4857246503 5 0 -0.20395970 0.23249511 NA
96.3 0.8771471244 1 NA -0.06221501 1.01679990 NA
96.4 1.9030768981 0 1 -0.14801097 0.92267953 NA
96.5 -0.1684332749 3 1 -0.28658893 0.83843412 NA
97 1.3775130083 4 0 -0.34484656 0.47151154 NA
97.1 -1.7323228619 2 0 -0.35658805 0.15596614 NA
98 -1.2648518889 3 0 -0.36913003 0.05179545 NA
98.1 -0.9042716241 NA 0 NA 0.47332096 NA
98.2 -0.1560385207 NA 1 -0.17154225 0.19706341 NA
99 0.7993356425 5 0 -0.24753132 0.22574556 NA
99.1 1.0355522332 NA 0 -0.27947829 1.00732330 NA
99.2 -0.1150895843 NA 0 -0.09033035 0.09749127 NA
100 0.0369067906 NA NA -0.17326698 0.22857989 NA
100.1 1.6023713093 4 NA NA 0.39548654 NA
100.2 0.8861545820 NA 0 -0.12072016 NA NA
100.3 0.1277046316 4 NA -0.27657520 0.32695372 NA
100.4 -0.0834577654 NA 0 -0.14631556 0.10043925 NA
$m4b$spM_lvlone
center scale
c1 0.2559996 0.6718095
p2 2.7125749 1.6247402
b2 NA NA
c2 -0.2237158 0.1059527
L1mis 0.4818481 0.3462447
b21 NA NA
$m4b$mu_reg_norm
[1] 0
$m4b$tau_reg_norm
[1] 1e-04
$m4b$shape_tau_norm
[1] 0.01
$m4b$rate_tau_norm
[1] 0.01
$m4b$mu_reg_binom
[1] 0
$m4b$tau_reg_binom
[1] 1e-04
$m4b$mu_reg_poisson
[1] 0
$m4b$tau_reg_poisson
[1] 1e-04
$m4b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m4b$shape_diag_RinvD
[1] "0.01"
$m4b$rate_diag_RinvD
[1] "0.001"
$m4c
$m4c$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m4c$M_lvlone
c1 p2 b2 c2 L1mis b21
1 0.7592026489 2 NA NA 1.38634787 NA
1.1 0.9548337990 2 0 -0.08061445 0.79402906 NA
1.2 0.5612235156 NA NA -0.26523782 0.53603334 NA
1.3 1.1873391025 NA 0 -0.30260393 0.24129804 NA
2 0.9192204198 NA 0 -0.33443795 NA NA
2.1 -0.1870730476 6 NA -0.11819800 0.31668065 NA
2.2 1.2517512331 3 NA -0.31532280 0.37114414 NA
3 -0.0605087604 NA 0 -0.12920657 0.54680608 NA
3.1 0.3788637747 NA NA NA 0.28280274 NA
3.2 0.9872578281 NA 1 NA 0.76277262 NA
4 1.4930175328 NA 1 -0.31177403 0.56100366 NA
4.1 -0.7692526880 4 0 -0.23894886 0.38514140 NA
4.2 0.9180841450 0 0 -0.15533613 0.04026174 NA
4.3 -0.0541170782 NA 0 -0.14644545 0.16025873 NA
5 -0.1376784521 2 NA -0.28360457 0.21080161 NA
5.1 -0.2740585866 NA 0 -0.20135143 0.36665700 NA
5.2 0.4670496929 7 NA -0.28293375 0.66368829 NA
5.3 0.1740288049 NA NA NA 0.40788895 NA
6 0.9868044683 NA NA -0.08617066 0.11889539 NA
7 -0.1280320918 NA NA -0.22243495 1.04286843 NA
7.1 0.4242971219 NA NA NA 0.52098933 NA
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14 0.9991752617 NA NA -0.28992072 0.80630788 NA
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15 0.3464447888 NA 0 NA 0.30769119 NA
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31 0.3708398217 8 0 0.05707733 0.11126366 NA
32 -0.0474059668 NA 0 -0.18446298 1.11731084 NA
32.1 1.2507771489 2 0 -0.14270013 0.85943330 NA
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32.3 0.6773270619 NA NA -0.14705649 0.43831965 NA
33 0.1774293842 0 0 -0.15252819 0.46726055 NA
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34 0.8590979166 3 NA -0.30378735 NA NA
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35 -1.4693520528 NA 0 NA 0.66303137 NA
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35.2 -0.6045512101 NA NA NA NA NA
36 0.9823048960 5 NA -0.49062180 0.93843318 NA
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38 0.8078948077 0 0 -0.11218486 NA NA
39 0.9876451040 NA 1 -0.38072211 0.14395367 NA
39.1 -0.3431222274 1 0 -0.32094428 0.36454923 NA
39.2 -1.7909380751 NA NA NA 1.03700002 NA
39.3 -0.1798746191 NA NA -0.40173480 0.41320585 NA
39.4 -0.1850961689 NA 0 -0.20041848 0.20901554 NA
39.5 0.4544226146 NA 1 -0.26027990 0.51603848 NA
40 0.5350190436 2 0 -0.19751956 0.33912363 NA
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44.2 0.5706610068 7 0 NA 0.39132534 NA
44.3 1.2679497430 NA 0 -0.40544012 0.40934909 NA
45 0.1414983160 NA NA -0.19274788 0.68587067 NA
45.1 0.7220892521 NA 1 -0.34860483 0.34530800 NA
46 1.5391054233 4 0 -0.28547861 0.71312288 NA
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47 0.2014101100 NA 0 -0.08597098 0.48660773 NA
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48 -0.2128602862 NA 1 NA 0.19677010 NA
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50 0.3897705981 3 0 -0.19959516 0.93663423 NA
51 -0.7213343736 2 0 -0.16556964 0.34107606 NA
52 0.3758235358 3 0 -0.07438732 0.19007135 NA
52.1 0.7138067080 1 0 -0.37537080 0.75662940 NA
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52.3 -0.9664587437 2 0 -0.31520603 NA NA
52.4 0.0254566848 3 0 -0.44619160 0.18369708 NA
52.5 0.4155259424 1 0 -0.11011682 0.48689343 NA
53 0.5675736897 3 0 -0.23278716 0.31983157 NA
53.1 -0.3154088781 NA 0 -0.28317264 0.61569501 NA
53.2 0.2162315769 2 NA -0.19517481 NA NA
54 -0.0880802382 3 NA -0.10122856 1.90522418 NA
54.1 0.4129127672 NA NA -0.28325504 0.59484889 NA
54.2 1.0119546775 4 NA -0.16753120 1.47174857 NA
54.3 -0.1112901990 0 NA -0.22217672 0.27307143 NA
54.4 0.8587727145 NA 0 -0.34609328 0.81272938 NA
55 -0.0116453589 NA 0 -0.32428190 0.22735476 NA
55.1 0.5835528661 4 0 -0.24235382 0.54683512 NA
55.2 -1.0010857254 NA NA -0.24065814 1.03503777 NA
55.3 -0.4796526070 4 NA -0.23665476 0.30169529 NA
55.4 -0.1202746964 3 0 NA 0.36008059 NA
56 0.5176377612 NA 0 NA 0.14193566 NA
56.1 -1.1136932588 2 NA -0.30357450 0.65073539 NA
56.2 -0.0168103281 3 NA -0.51301630 0.11338262 NA
56.3 0.3933023606 3 1 -0.23743117 0.16820103 NA
56.4 0.3714625139 0 0 -0.17264917 0.27419110 NA
56.5 0.7811448179 NA 0 -0.39188329 0.57110215 NA
57 -1.0868304872 3 0 -0.18501692 0.85104054 NA
57.1 0.8018626997 4 0 -0.27274841 0.34733833 NA
57.2 -0.1159517011 1 0 NA 1.44438762 NA
57.3 0.6785562445 NA NA -0.09898509 0.31836125 NA
58 1.6476207996 NA 0 -0.29901358 0.37456898 NA
58.1 0.3402652711 NA NA -0.35390896 0.22120158 NA
58.2 -0.1111300753 NA 1 -0.16687336 0.78885210 NA
58.3 -0.5409234285 3 1 -0.11784506 0.10114937 NA
58.4 -0.1271327672 NA 0 -0.05321983 0.13385114 NA
58.5 0.8713264822 NA 0 -0.54457568 NA NA
59 0.4766421367 NA NA -0.27255364 0.13202156 NA
59.1 1.0028089765 NA 1 NA 0.33371896 NA
60 0.5231452932 NA 0 NA 0.35096579 NA
61 -0.7190130614 2 NA -0.30550120 0.36933806 NA
61.1 0.8353702312 4 1 -0.35579892 0.17623067 NA
61.2 1.0229058138 NA 1 NA 0.21286227 NA
61.3 1.1717723589 NA 0 -0.34184391 0.12689308 NA
61.4 -0.0629201596 NA 0 -0.30891967 0.77676718 NA
62 -0.3979137604 2 NA NA 1.38018163 NA
62.1 0.6830738372 NA 1 -0.10504143 0.43803892 NA
62.2 0.4301745954 NA 0 -0.20104997 0.21947900 NA
62.3 -0.0333139957 NA 0 -0.08138677 0.11571160 NA
63 0.3345678035 NA NA -0.12036319 0.41583568 NA
63.1 0.3643769511 2 0 -0.13624992 0.25598960 NA
64 0.3949911859 4 0 NA 0.20415642 NA
65 1.2000091513 NA 0 -0.34450396 0.07135646 NA
65.1 0.0110122646 5 0 -0.32514650 0.57450574 NA
65.2 -0.5776452043 NA 0 -0.10984996 0.52562984 NA
65.3 -0.1372183563 NA 0 -0.19275692 0.21921164 NA
66 -0.5081302805 NA NA NA 0.33281730 NA
66.1 -0.1447837412 NA 0 NA 0.03412404 NA
66.2 0.1906241379 NA 0 -0.11687008 0.92570619 NA
67 1.6716027681 NA NA NA 0.15291043 NA
68 0.5691848839 NA 0 -0.13605235 0.37543648 NA
68.1 0.1004860389 NA 0 -0.19790827 0.20901022 NA
68.2 -0.0061241827 NA NA -0.17750123 0.12488064 NA
68.3 0.7443745962 2 0 NA 0.08711204 NA
68.4 0.8726923437 NA NA -0.12570562 0.54611735 NA
69 0.0381382683 NA 0 -0.32152751 0.23638239 NA
70 0.8126204217 4 0 -0.28190462 0.49876756 NA
70.1 0.4691503050 4 0 -0.11503263 0.39512615 NA
71 -0.5529062591 4 0 -0.13029093 0.45666551 NA
71.1 -0.1103252087 NA 1 NA 0.92047456 NA
71.2 1.7178492547 3 0 -0.39075433 0.32792986 NA
71.3 -1.0118346755 0 1 -0.21401028 0.95108007 NA
71.4 1.8623785017 0 0 -0.40219281 0.36287072 NA
72 -0.4521659275 NA 0 -0.40337108 0.12870526 NA
72.1 0.1375317317 8 0 -0.25978914 0.45925876 NA
72.2 -0.4170988856 NA NA NA 0.05418867 NA
72.3 0.7107266765 NA 0 -0.09809866 0.48937486 NA
72.4 0.1451969143 3 0 -0.14240019 0.64173822 NA
72.5 1.6298050306 NA 0 -0.14794204 0.57609943 NA
73 -0.0307469467 2 0 -0.23509343 0.17393402 NA
74 0.3730017941 NA 0 -0.27963171 0.23990575 NA
75 -0.4908003566 NA NA -0.12905034 0.28469861 NA
76 -0.9888876620 1 0 0.04775562 0.71988630 NA
76.1 0.0003798292 0 0 -0.19399157 1.12449946 NA
76.2 -0.8421863763 0 0 -0.02754574 0.71313766 NA
77 -0.4986802480 2 NA -0.19053195 0.02399030 NA
78 0.0417330969 NA 0 -0.17172929 0.42708148 NA
79 -0.3767450660 2 NA -0.03958515 0.37579286 NA
79.1 0.1516000028 NA 0 -0.20328809 0.78660681 NA
79.2 -0.1888160741 2 NA -0.23901634 0.67696116 NA
80 -0.0041558414 2 NA -0.34031873 0.34207854 NA
80.1 -0.0329337062 NA 0 -0.19526756 0.60534092 NA
80.2 0.5046816157 NA NA NA 0.26731034 NA
81 -0.9493950353 NA 0 -0.18401980 0.17739052 NA
81.1 0.2443038954 2 0 -0.16889476 0.35453673 NA
81.2 0.6476958410 NA NA -0.37343047 0.20244235 NA
81.3 0.4182528210 NA 0 NA 1.26402329 NA
82 1.1088801952 NA NA -0.08328168 0.09303938 NA
82.1 0.9334157763 NA 0 -0.22167084 0.27254210 NA
82.2 0.4958140634 4 1 -0.20971187 0.49936304 NA
83 0.5104724530 NA NA -0.34228255 0.21138572 NA
83.1 -0.0513309106 NA 0 -0.34075730 0.26403568 NA
83.2 -0.2067792494 4 0 -0.32503954 0.20311133 NA
83.3 -0.0534169155 3 NA NA 1.16864671 NA
84 -0.0255753653 NA 0 -0.20676741 1.99179346 NA
84.1 -1.8234189877 2 NA -0.20310458 1.52199460 NA
85 -0.0114038622 3 1 -0.12107593 NA NA
85.1 -0.0577615939 NA NA NA 0.61458995 NA
85.2 -0.2241856342 3 0 -0.32509207 0.07871196 NA
85.3 -0.0520175929 NA 0 NA 1.42315283 NA
85.4 0.2892733846 2 0 -0.30730810 0.97986129 NA
85.5 -0.3740417009 1 0 NA 0.91792195 NA
86 0.4293735089 2 0 -0.10854862 0.63509597 NA
86.1 -0.1363456521 NA NA -0.25751662 0.24546597 NA
86.2 0.1230989293 0 NA -0.38943076 0.53102060 NA
86.3 0.3305413955 0 0 -0.24454702 0.09360826 NA
86.4 2.6003411822 NA NA -0.12338992 0.58301186 NA
86.5 -0.1420690052 2 0 -0.23976984 0.39146055 NA
87 1.0457427869 NA NA NA NA NA
87.1 -0.2973007190 NA NA -0.34366972 0.66043624 NA
87.2 0.4396872616 3 NA NA 0.13267613 NA
88 -0.0601928334 NA 0 -0.31563888 0.10696344 NA
88.1 -1.0124347595 1 NA -0.20304028 0.13689448 NA
88.2 0.5730917016 1 0 -0.40311895 0.48037889 NA
88.3 -0.0029455332 4 0 -0.12308715 0.97755681 NA
89 1.5465903721 NA 0 -0.18527715 0.70242369 NA
90 0.0626760573 3 0 -0.25029126 0.40042977 NA
90.1 1.1896872985 NA 0 -0.26974303 0.63975731 NA
90.2 0.2597888783 NA 0 -0.28804531 0.33412775 NA
90.3 0.6599799887 NA NA -0.19180615 0.38399003 NA
91 1.1213651365 NA 0 -0.26591197 0.58250391 NA
91.1 1.2046371625 NA 0 -0.09153470 0.13223217 NA
91.2 0.3395603754 NA 0 -0.48414390 0.46613305 NA
92 0.4674939332 NA 0 NA 0.18997862 NA
93 0.2677965647 2 NA -0.11939966 1.05243347 NA
93.1 1.6424445368 4 0 NA 0.01479757 NA
93.2 0.7101700066 4 NA -0.21089379 0.50955172 NA
93.3 1.1222322893 NA 0 NA 0.78122514 NA
93.4 1.4628960401 3 0 -0.23618836 0.63940704 NA
94 -0.2904211940 4 NA NA 0.45596305 NA
94.1 0.0147813580 2 0 -0.10217284 0.41610667 NA
94.2 -0.4536774482 NA 0 -0.36713471 0.52744298 NA
94.3 0.6793464917 1 NA -0.13806763 0.70890756 NA
94.4 -0.9411356550 NA 0 -0.42353804 0.84412478 NA
94.5 0.5683867264 2 1 -0.15513707 0.21166602 NA
95 0.2375652188 3 0 -0.24149687 0.57713135 NA
95.1 0.0767152977 5 NA -0.21315958 0.44400207 NA
95.2 -0.6886731251 2 0 -0.15777208 0.42397776 NA
96 0.7813892121 NA 0 -0.16780948 0.72391015 NA
96.1 0.3391519695 NA 0 -0.32504815 0.32593738 NA
96.2 -0.4857246503 5 0 -0.20395970 0.23249511 NA
96.3 0.8771471244 1 NA -0.06221501 1.01679990 NA
96.4 1.9030768981 0 1 -0.14801097 0.92267953 NA
96.5 -0.1684332749 3 1 -0.28658893 0.83843412 NA
97 1.3775130083 4 0 -0.34484656 0.47151154 NA
97.1 -1.7323228619 2 0 -0.35658805 0.15596614 NA
98 -1.2648518889 3 0 -0.36913003 0.05179545 NA
98.1 -0.9042716241 NA 0 NA 0.47332096 NA
98.2 -0.1560385207 NA 1 -0.17154225 0.19706341 NA
99 0.7993356425 5 0 -0.24753132 0.22574556 NA
99.1 1.0355522332 NA 0 -0.27947829 1.00732330 NA
99.2 -0.1150895843 NA 0 -0.09033035 0.09749127 NA
100 0.0369067906 NA NA -0.17326698 0.22857989 NA
100.1 1.6023713093 4 NA NA 0.39548654 NA
100.2 0.8861545820 NA 0 -0.12072016 NA NA
100.3 0.1277046316 4 NA -0.27657520 0.32695372 NA
100.4 -0.0834577654 NA 0 -0.14631556 0.10043925 NA
$m4c$spM_lvlone
center scale
c1 0.2559996 0.6718095
p2 2.7125749 1.6247402
b2 NA NA
c2 -0.2237158 0.1059527
L1mis 0.4818481 0.3462447
b21 NA NA
$m4c$mu_reg_norm
[1] 0
$m4c$tau_reg_norm
[1] 1e-04
$m4c$shape_tau_norm
[1] 0.01
$m4c$rate_tau_norm
[1] 0.01
$m4c$mu_reg_gamma
[1] 0
$m4c$tau_reg_gamma
[1] 1e-04
$m4c$shape_tau_gamma
[1] 0.01
$m4c$rate_tau_gamma
[1] 0.01
$m4c$mu_reg_binom
[1] 0
$m4c$tau_reg_binom
[1] 1e-04
$m4c$mu_reg_poisson
[1] 0
$m4c$tau_reg_poisson
[1] 1e-04
$m4c$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m4c$shape_diag_RinvD
[1] "0.01"
$m4c$rate_diag_RinvD
[1] "0.001"
$m4d
$m4d$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m4d$M_lvlone
c1 p2 b2 c2 L1mis Be2 b21
1 0.7592026489 2 NA NA 1.38634787 4.596628e-06 NA
1.1 0.9548337990 2 0 -0.08061445 0.79402906 2.296427e-04 NA
1.2 0.5612235156 NA NA -0.26523782 0.53603334 3.455922e-10 NA
1.3 1.1873391025 NA 0 -0.30260393 0.24129804 9.618613e-07 NA
2 0.9192204198 NA 0 -0.33443795 NA NA NA
2.1 -0.1870730476 6 NA -0.11819800 0.31668065 1.065639e-07 NA
2.2 1.2517512331 3 NA -0.31532280 0.37114414 1.320730e-03 NA
3 -0.0605087604 NA 0 -0.12920657 0.54680608 9.707820e-06 NA
3.1 0.3788637747 NA NA NA 0.28280274 3.645271e-05 NA
3.2 0.9872578281 NA 1 NA 0.76277262 NA NA
4 1.4930175328 NA 1 -0.31177403 0.56100366 5.555794e-01 NA
4.1 -0.7692526880 4 0 -0.23894886 0.38514140 6.853316e-06 NA
4.2 0.9180841450 0 0 -0.15533613 0.04026174 6.324951e-02 NA
4.3 -0.0541170782 NA 0 -0.14644545 0.16025873 4.330745e-07 NA
5 -0.1376784521 2 NA -0.28360457 0.21080161 NA NA
5.1 -0.2740585866 NA 0 -0.20135143 0.36665700 6.556812e-04 NA
5.2 0.4670496929 7 NA -0.28293375 0.66368829 6.963312e-06 NA
5.3 0.1740288049 NA NA NA 0.40788895 1.159006e-04 NA
6 0.9868044683 NA NA -0.08617066 0.11889539 1.509745e-02 NA
7 -0.1280320918 NA NA -0.22243495 1.04286843 NA NA
7.1 0.4242971219 NA NA NA 0.52098933 1.679086e-08 NA
7.2 0.0777182491 NA 0 NA 0.09858876 3.972447e-06 NA
8 -0.5791408712 1 0 NA 0.17281472 9.888512e-02 NA
8.1 0.3128604232 6 0 NA 0.25970093 8.790334e-05 NA
8.2 0.6258446356 NA NA NA 0.30550233 NA NA
8.3 -0.1040137707 3 1 -0.35148972 0.88029778 5.411705e-04 NA
8.4 0.0481450285 2 0 0.03661023 0.20200392 8.446731e-04 NA
8.5 0.3831763675 1 1 -0.08424534 NA 2.059814e-04 NA
9 -0.1757592269 3 0 NA 1.12218535 4.160033e-01 NA
9.1 -0.1791541200 NA NA -0.43509340 0.57911079 NA NA
9.2 -0.0957042935 3 NA -0.22527490 0.81350994 1.087331e-03 NA
10 -0.5598409704 3 NA NA 0.32744766 9.321715e-04 NA
10.1 -0.2318340451 NA 0 NA 0.62912282 8.167897e-06 NA
11 0.5086859475 1 0 -0.08587475 0.92140073 2.528529e-04 NA
11.1 0.4951758188 6 0 -0.06157340 0.16012129 NA NA
11.2 -1.1022162541 1 0 -0.12436018 0.16166775 5.587553e-10 NA
11.3 -0.0611636705 6 0 -0.21377934 0.14979756 5.240776e-10 NA
11.4 -0.4971774316 NA 0 -0.32208329 0.46855190 2.830994e-07 NA
12 -0.2433996286 NA 0 NA 0.76818678 1.962202e-07 NA
13 0.8799673116 NA NA NA 0.34264972 NA NA
13.1 0.1079022586 NA 0 -0.40300449 0.14526619 1.330415e-06 NA
14 0.9991752617 NA NA -0.28992072 0.80630788 5.900181e-07 NA
14.1 -0.1094019046 NA NA NA 0.35697552 3.694946e-05 NA
14.2 0.1518967560 2 NA NA 0.21330192 6.871447e-08 NA
14.3 0.3521012473 NA NA -0.21979936 NA NA NA
15 0.3464447888 NA 0 NA 0.30769119 1.848068e-04 NA
15.1 -0.4767313971 NA 0 -0.29092263 0.28349746 1.714157e-10 NA
15.2 0.5759767791 NA 0 -0.19392239 0.64618365 1.088807e-03 NA
15.3 -0.1713452662 NA 0 -0.25718384 0.51680884 2.677330e-05 NA
16 0.4564754473 1 1 -0.45041108 0.71265471 NA NA
16.1 1.0652558311 NA NA -0.07599066 0.38925880 1.411453e-04 NA
16.2 0.6971872493 2 NA -0.32385667 0.23648869 1.897147e-03 NA
16.3 0.5259331838 NA 0 -0.38326110 0.45048730 5.950632e-02 NA
16.4 0.2046601798 1 0 -0.22845856 0.23181791 3.944608e-02 NA
16.5 1.0718540464 NA NA -0.25497157 0.13985349 NA NA
17 0.6048676222 1 0 NA 0.25995399 4.808238e-05 NA
17.1 0.2323298304 NA 0 -0.22105143 0.03594878 6.175264e-04 NA
17.2 1.2617499032 3 0 NA 0.77583623 2.319036e-07 NA
17.3 -0.3913230895 2 NA NA 0.60015197 1.393008e-09 NA
17.4 0.9577299112 NA 0 -0.15098046 0.13998405 NA NA
18 -0.0050324072 2 0 -0.09870041 0.96475839 2.685853e-09 NA
19 -0.4187468937 NA NA -0.26680239 0.10596495 2.949370e-07 NA
19.1 -0.4478828944 NA NA -0.15815241 0.13338947 1.183423e-08 NA
19.2 -1.1966721302 2 0 -0.14717437 0.41662218 7.844699e-08 NA
19.3 -0.5877091668 2 1 -0.21271374 0.53670855 NA NA
20 0.6838223064 NA NA -0.22087628 0.41688567 4.920475e-06 NA
20.1 0.3278571109 2 0 NA NA 6.885500e-08 NA
20.2 -0.8489831990 NA 1 -0.30127439 0.81634101 9.577206e-04 NA
20.3 1.3169975191 NA 0 -0.11782590 0.39232496 1.325632e-03 NA
20.4 0.0444804531 NA 0 -0.19857957 0.57925554 NA NA
20.5 -0.4535207652 NA 0 -0.24338208 0.74200986 1.011637e-06 NA
21 -0.4030302960 2 0 -0.31407992 0.24759801 3.032947e-04 NA
21.1 -0.4069674045 3 0 -0.12424941 0.34052205 4.370975e-06 NA
21.2 1.0650265940 2 NA -0.27672716 0.03905058 8.793700e-06 NA
22 -0.0673274516 3 0 -0.23790593 0.48605351 NA NA
22.1 0.9601388170 3 0 -0.15996535 0.43761071 7.397166e-06 NA
23 0.5556634840 NA 0 -0.18236682 0.47599712 4.931346e-02 NA
23.1 1.4407865964 5 NA -0.20823302 0.47680301 3.799306e-02 NA
24 0.3856376411 2 0 -0.29026416 0.51696505 1.018950e-01 NA
25 0.3564400705 3 0 -0.36139273 0.59392591 NA NA
25.1 0.0982553434 3 NA -0.19571118 0.74010330 2.264756e-02 NA
25.2 0.1928682598 3 1 -0.21379355 NA 6.622343e-07 NA
25.3 -0.0192488594 4 0 -0.33876012 0.73081722 2.802504e-09 NA
25.4 0.4466012931 NA 0 NA 0.29274286 1.873599e-04 NA
25.5 1.1425193342 NA NA -0.04068446 0.74425342 NA NA
26 0.5341531449 NA NA -0.16846716 0.20974346 4.587570e-09 NA
26.1 1.2268695927 2 0 -0.10440642 NA 2.394334e-06 NA
26.2 0.3678294939 NA 0 -0.26884827 0.22908815 4.510972e-08 NA
26.3 0.5948516018 NA 0 NA 0.41880799 3.657318e-11 NA
27 -0.3342844147 1 0 -0.19520794 0.10097167 NA NA
27.1 -0.4835141229 NA 0 -0.17622638 NA 8.874134e-06 NA
28 -0.7145915499 0 NA -0.32164962 NA 3.673907e-06 NA
28.1 0.5063671955 NA 0 -0.27003852 0.56052750 4.541426e-04 NA
28.2 -0.2067413142 4 0 -0.07235801 0.15301800 2.697966e-12 NA
28.3 0.1196789973 NA 0 -0.13462982 0.27802542 NA NA
29 0.1392699487 3 0 -0.32432030 0.43556671 3.282475e-03 NA
29.1 0.7960234776 3 0 -0.27034171 0.27593085 2.270717e-01 NA
29.2 1.0398214352 3 0 -0.10197448 0.55256871 9.981536e-03 NA
29.3 0.0813246429 2 0 -0.27606945 0.47272109 2.343590e-02 NA
30 -0.3296323050 NA NA -0.06949300 0.32743933 NA NA
30.1 1.3635850954 NA 0 -0.11511035 0.02231535 1.591483e-07 NA
30.2 0.7354171050 5 0 -0.16215882 0.12833697 1.896944e-11 NA
31 0.3708398217 8 0 0.05707733 0.11126366 5.546285e-08 NA
32 -0.0474059668 NA 0 -0.18446298 1.11731084 9.411981e-09 NA
32.1 1.2507771489 2 0 -0.14270013 0.85943330 1.270914e-08 NA
32.2 0.1142915519 1 NA -0.20530798 1.53730925 3.910478e-09 NA
32.3 0.6773270619 NA NA -0.14705649 0.43831965 9.124048e-10 NA
33 0.1774293842 0 0 -0.15252819 0.46726055 9.056156e-01 NA
33.1 0.6159606291 NA 1 NA 0.76818259 3.047254e-06 NA
34 0.8590979166 3 NA -0.30378735 NA 1.040462e-04 NA
34.1 0.0546216775 NA 0 -0.11982431 1.14350292 5.714390e-12 NA
34.2 -0.0897224473 1 NA -0.24278671 0.19103604 7.883166e-09 NA
34.3 0.4163395571 2 NA -0.19971833 NA 3.055823e-07 NA
35 -1.4693520528 NA 0 NA 0.66303137 1.287796e-07 NA
35.1 -0.3031734330 NA 0 -0.24165780 NA 1.762232e-06 NA
35.2 -0.6045512101 NA NA NA NA 5.355159e-08 NA
36 0.9823048960 5 NA -0.49062180 0.93843318 7.250797e-06 NA
36.1 1.4466051416 NA NA -0.25651700 NA 2.370652e-06 NA
36.2 1.1606752905 NA 0 NA 0.29886676 1.537090e-05 NA
36.3 0.8373091576 1 0 -0.30401274 0.22616598 6.993214e-07 NA
36.4 0.2640591685 1 0 NA 0.53849566 4.950009e-05 NA
37 0.1177313455 5 0 -0.15276529 1.68107300 2.755165e-07 NA
37.1 -0.1415483779 NA 0 -0.30016169 1.13777638 3.400517e-07 NA
37.2 0.0054610124 NA 0 0.06809545 0.26931933 2.489007e-09 NA
38 0.8078948077 0 0 -0.11218486 NA 1.302651e-01 NA
39 0.9876451040 NA 1 -0.38072211 0.14395367 4.343746e-04 NA
39.1 -0.3431222274 1 0 -0.32094428 0.36454923 6.653143e-05 NA
39.2 -1.7909380751 NA NA NA 1.03700002 1.940204e-09 NA
39.3 -0.1798746191 NA NA -0.40173480 0.41320585 8.300468e-07 NA
39.4 -0.1850961689 NA 0 -0.20041848 0.20901554 7.464169e-08 NA
39.5 0.4544226146 NA 1 -0.26027990 0.51603848 5.765597e-10 NA
40 0.5350190436 2 0 -0.19751956 0.33912363 9.140572e-01 NA
40.1 0.4189342752 4 1 -0.08399467 0.21892118 1.883555e-03 NA
40.2 0.4211994981 NA 0 -0.20864416 0.74070896 2.303001e-01 NA
40.3 0.0916687506 NA NA NA 0.82927399 2.799910e-05 NA
41 -0.1035047421 NA 0 -0.26096953 0.25193679 3.700067e-02 NA
41.1 -0.4684202411 4 NA -0.23953874 0.28760510 5.798225e-06 NA
41.2 0.5972615368 2 0 -0.03079344 0.45553197 1.086252e-08 NA
41.3 0.9885613862 3 NA NA 0.79237611 3.088732e-07 NA
41.4 -0.3908036794 NA 0 NA 0.12582175 4.549537e-05 NA
42 -0.0338893961 3 0 -0.16084527 0.50079604 5.220968e-03 NA
42.1 -0.4498363172 5 1 -0.13812521 0.61140760 7.264286e-08 NA
43 0.8965546110 4 0 -0.08864017 0.29752019 1.498125e-07 NA
43.1 0.6199122090 3 1 -0.12583158 0.51793497 1.316763e-04 NA
43.2 0.1804894429 3 0 -0.29253959 0.15152473 8.151771e-07 NA
44 1.3221409285 1 0 -0.22697597 0.38806434 1.032476e-03 NA
44.1 0.3416426284 NA 0 NA 1.11140786 3.120174e-09 NA
44.2 0.5706610068 7 0 NA 0.39132534 2.571257e-10 NA
44.3 1.2679497430 NA 0 -0.40544012 0.40934909 2.227416e-09 NA
45 0.1414983160 NA NA -0.19274788 0.68587067 3.948036e-01 NA
45.1 0.7220892521 NA 1 -0.34860483 0.34530800 1.066310e-03 NA
46 1.5391054233 4 0 -0.28547861 0.71312288 2.219556e-08 NA
46.1 0.3889107049 6 0 -0.21977836 0.62537420 1.434525e-08 NA
46.2 0.1248719493 NA 0 NA 0.79574391 1.523026e-07 NA
47 0.2014101100 NA 0 -0.08597098 0.48660773 5.404537e-03 NA
47.1 0.2982973539 4 0 -0.35424828 0.51241790 3.739267e-07 NA
47.2 1.1518107179 2 0 -0.24262576 0.58869379 7.171916e-06 NA
47.3 0.5196802157 4 NA -0.30426315 0.22171504 3.850162e-05 NA
47.4 0.3702301552 NA 0 NA 0.11366347 1.767264e-08 NA
48 -0.2128602862 NA 1 NA 0.19677010 1.988010e-04 NA
48.1 -0.5337239976 6 1 NA 0.17706320 6.074589e-09 NA
49 -0.5236770035 NA NA -0.42198781 0.30752382 1.321544e-06 NA
50 0.3897705981 3 0 -0.19959516 0.93663423 4.240393e-05 NA
51 -0.7213343736 2 0 -0.16556964 0.34107606 1.986093e-09 NA
52 0.3758235358 3 0 -0.07438732 0.19007135 1.632022e-02 NA
52.1 0.7138067080 1 0 -0.37537080 0.75662940 2.653038e-02 NA
52.2 0.8872895233 NA 0 -0.24222066 1.66104719 2.262881e-03 NA
52.3 -0.9664587437 2 0 -0.31520603 NA 6.572647e-10 NA
52.4 0.0254566848 3 0 -0.44619160 0.18369708 1.393737e-04 NA
52.5 0.4155259424 1 0 -0.11011682 0.48689343 5.069462e-03 NA
53 0.5675736897 3 0 -0.23278716 0.31983157 5.848890e-05 NA
53.1 -0.3154088781 NA 0 -0.28317264 0.61569501 1.878509e-04 NA
53.2 0.2162315769 2 NA -0.19517481 NA 1.293417e-04 NA
54 -0.0880802382 3 NA -0.10122856 1.90522418 1.818441e-03 NA
54.1 0.4129127672 NA NA -0.28325504 0.59484889 2.251839e-07 NA
54.2 1.0119546775 4 NA -0.16753120 1.47174857 5.638172e-06 NA
54.3 -0.1112901990 0 NA -0.22217672 0.27307143 5.320676e-03 NA
54.4 0.8587727145 NA 0 -0.34609328 0.81272938 1.491367e-07 NA
55 -0.0116453589 NA 0 -0.32428190 0.22735476 3.183775e-03 NA
55.1 0.5835528661 4 0 -0.24235382 0.54683512 1.183380e-03 NA
55.2 -1.0010857254 NA NA -0.24065814 1.03503777 1.817077e-06 NA
55.3 -0.4796526070 4 NA -0.23665476 0.30169529 1.424370e-06 NA
55.4 -0.1202746964 3 0 NA 0.36008059 3.119967e-07 NA
56 0.5176377612 NA 0 NA 0.14193566 1.169667e-06 NA
56.1 -1.1136932588 2 NA -0.30357450 0.65073539 1.182293e-06 NA
56.2 -0.0168103281 3 NA -0.51301630 0.11338262 2.087533e-04 NA
56.3 0.3933023606 3 1 -0.23743117 0.16820103 5.728251e-06 NA
56.4 0.3714625139 0 0 -0.17264917 0.27419110 4.087596e-08 NA
56.5 0.7811448179 NA 0 -0.39188329 0.57110215 8.040370e-07 NA
57 -1.0868304872 3 0 -0.18501692 0.85104054 1.438387e-02 NA
57.1 0.8018626997 4 0 -0.27274841 0.34733833 3.202179e-05 NA
57.2 -0.1159517011 1 0 NA 1.44438762 1.486318e-03 NA
57.3 0.6785562445 NA NA -0.09898509 0.31836125 1.718412e-04 NA
58 1.6476207996 NA 0 -0.29901358 0.37456898 3.114123e-05 NA
58.1 0.3402652711 NA NA -0.35390896 0.22120158 1.403881e-04 NA
58.2 -0.1111300753 NA 1 -0.16687336 0.78885210 2.111006e-01 NA
58.3 -0.5409234285 3 1 -0.11784506 0.10114937 9.586985e-06 NA
58.4 -0.1271327672 NA 0 -0.05321983 0.13385114 4.073162e-03 NA
58.5 0.8713264822 NA 0 -0.54457568 NA 9.285307e-04 NA
59 0.4766421367 NA NA -0.27255364 0.13202156 2.711478e-06 NA
59.1 1.0028089765 NA 1 NA 0.33371896 1.173472e-04 NA
60 0.5231452932 NA 0 NA 0.35096579 7.579680e-09 NA
61 -0.7190130614 2 NA -0.30550120 0.36933806 4.545759e-03 NA
61.1 0.8353702312 4 1 -0.35579892 0.17623067 5.936674e-02 NA
61.2 1.0229058138 NA 1 NA 0.21286227 3.897281e-01 NA
61.3 1.1717723589 NA 0 -0.34184391 0.12689308 6.237379e-02 NA
61.4 -0.0629201596 NA 0 -0.30891967 0.77676718 5.103038e-01 NA
62 -0.3979137604 2 NA NA 1.38018163 3.707353e-02 NA
62.1 0.6830738372 NA 1 -0.10504143 0.43803892 1.901660e-03 NA
62.2 0.4301745954 NA 0 -0.20104997 0.21947900 7.844369e-08 NA
62.3 -0.0333139957 NA 0 -0.08138677 0.11571160 1.496168e-08 NA
63 0.3345678035 NA NA -0.12036319 0.41583568 5.101070e-11 NA
63.1 0.3643769511 2 0 -0.13624992 0.25598960 1.106013e-05 NA
64 0.3949911859 4 0 NA 0.20415642 1.685171e-09 NA
65 1.2000091513 NA 0 -0.34450396 0.07135646 1.684142e-01 NA
65.1 0.0110122646 5 0 -0.32514650 0.57450574 1.413479e-05 NA
65.2 -0.5776452043 NA 0 -0.10984996 0.52562984 2.841196e-03 NA
65.3 -0.1372183563 NA 0 -0.19275692 0.21921164 3.118871e-04 NA
66 -0.5081302805 NA NA NA 0.33281730 1.078473e-06 NA
66.1 -0.1447837412 NA 0 NA 0.03412404 1.136650e-01 NA
66.2 0.1906241379 NA 0 -0.11687008 0.92570619 7.007044e-08 NA
67 1.6716027681 NA NA NA 0.15291043 4.025749e-11 NA
68 0.5691848839 NA 0 -0.13605235 0.37543648 2.469503e-06 NA
68.1 0.1004860389 NA 0 -0.19790827 0.20901022 1.067638e-08 NA
68.2 -0.0061241827 NA NA -0.17750123 0.12488064 1.508555e-06 NA
68.3 0.7443745962 2 0 NA 0.08711204 7.862972e-06 NA
68.4 0.8726923437 NA NA -0.12570562 0.54611735 1.970326e-05 NA
69 0.0381382683 NA 0 -0.32152751 0.23638239 5.089430e-07 NA
70 0.8126204217 4 0 -0.28190462 0.49876756 5.575849e-07 NA
70.1 0.4691503050 4 0 -0.11503263 0.39512615 6.115107e-04 NA
71 -0.5529062591 4 0 -0.13029093 0.45666551 1.867742e-05 NA
71.1 -0.1103252087 NA 1 NA 0.92047456 4.616167e-04 NA
71.2 1.7178492547 3 0 -0.39075433 0.32792986 5.314611e-08 NA
71.3 -1.0118346755 0 1 -0.21401028 0.95108007 1.790244e-10 NA
71.4 1.8623785017 0 0 -0.40219281 0.36287072 1.924070e-03 NA
72 -0.4521659275 NA 0 -0.40337108 0.12870526 6.526547e-05 NA
72.1 0.1375317317 8 0 -0.25978914 0.45925876 5.540491e-11 NA
72.2 -0.4170988856 NA NA NA 0.05418867 2.391191e-12 NA
72.3 0.7107266765 NA 0 -0.09809866 0.48937486 2.878783e-12 NA
72.4 0.1451969143 3 0 -0.14240019 0.64173822 1.014404e-09 NA
72.5 1.6298050306 NA 0 -0.14794204 0.57609943 1.281231e-05 NA
73 -0.0307469467 2 0 -0.23509343 0.17393402 6.661564e-02 NA
74 0.3730017941 NA 0 -0.27963171 0.23990575 3.683842e-04 NA
75 -0.4908003566 NA NA -0.12905034 0.28469861 2.274469e-06 NA
76 -0.9888876620 1 0 0.04775562 0.71988630 9.155636e-04 NA
76.1 0.0003798292 0 0 -0.19399157 1.12449946 1.485365e-04 NA
76.2 -0.8421863763 0 0 -0.02754574 0.71313766 3.118702e-06 NA
77 -0.4986802480 2 NA -0.19053195 0.02399030 4.946432e-01 NA
78 0.0417330969 NA 0 -0.17172929 0.42708148 8.533933e-05 NA
79 -0.3767450660 2 NA -0.03958515 0.37579286 1.980588e-01 NA
79.1 0.1516000028 NA 0 -0.20328809 0.78660681 8.624235e-06 NA
79.2 -0.1888160741 2 NA -0.23901634 0.67696116 2.176176e-05 NA
80 -0.0041558414 2 NA -0.34031873 0.34207854 2.929029e-06 NA
80.1 -0.0329337062 NA 0 -0.19526756 0.60534092 1.126162e-04 NA
80.2 0.5046816157 NA NA NA 0.26731034 9.847382e-08 NA
81 -0.9493950353 NA 0 -0.18401980 0.17739052 4.026095e-01 NA
81.1 0.2443038954 2 0 -0.16889476 0.35453673 2.093927e-02 NA
81.2 0.6476958410 NA NA -0.37343047 0.20244235 9.224440e-01 NA
81.3 0.4182528210 NA 0 NA 1.26402329 8.175654e-03 NA
82 1.1088801952 NA NA -0.08328168 0.09303938 1.228129e-01 NA
82.1 0.9334157763 NA 0 -0.22167084 0.27254210 6.656575e-05 NA
82.2 0.4958140634 4 1 -0.20971187 0.49936304 2.001426e-08 NA
83 0.5104724530 NA NA -0.34228255 0.21138572 5.690020e-06 NA
83.1 -0.0513309106 NA 0 -0.34075730 0.26403568 5.980615e-06 NA
83.2 -0.2067792494 4 0 -0.32503954 0.20311133 1.880816e-05 NA
83.3 -0.0534169155 3 NA NA 1.16864671 4.048910e-09 NA
84 -0.0255753653 NA 0 -0.20676741 1.99179346 6.552173e-02 NA
84.1 -1.8234189877 2 NA -0.20310458 1.52199460 8.829278e-06 NA
85 -0.0114038622 3 1 -0.12107593 NA 4.118253e-06 NA
85.1 -0.0577615939 NA NA NA 0.61458995 2.311994e-06 NA
85.2 -0.2241856342 3 0 -0.32509207 0.07871196 5.182892e-05 NA
85.3 -0.0520175929 NA 0 NA 1.42315283 1.689467e-03 NA
85.4 0.2892733846 2 0 -0.30730810 0.97986129 1.168017e-03 NA
85.5 -0.3740417009 1 0 NA 0.91792195 7.945131e-07 NA
86 0.4293735089 2 0 -0.10854862 0.63509597 2.905567e-05 NA
86.1 -0.1363456521 NA NA -0.25751662 0.24546597 5.331467e-06 NA
86.2 0.1230989293 0 NA -0.38943076 0.53102060 1.761451e-06 NA
86.3 0.3305413955 0 0 -0.24454702 0.09360826 2.272397e-06 NA
86.4 2.6003411822 NA NA -0.12338992 0.58301186 4.467006e-06 NA
86.5 -0.1420690052 2 0 -0.23976984 0.39146055 1.693940e-08 NA
87 1.0457427869 NA NA NA NA 6.396865e-05 NA
87.1 -0.2973007190 NA NA -0.34366972 0.66043624 1.264093e-10 NA
87.2 0.4396872616 3 NA NA 0.13267613 4.933807e-07 NA
88 -0.0601928334 NA 0 -0.31563888 0.10696344 9.223531e-02 NA
88.1 -1.0124347595 1 NA -0.20304028 0.13689448 4.654325e-05 NA
88.2 0.5730917016 1 0 -0.40311895 0.48037889 1.260399e-01 NA
88.3 -0.0029455332 4 0 -0.12308715 0.97755681 8.029866e-08 NA
89 1.5465903721 NA 0 -0.18527715 0.70242369 7.489307e-05 NA
90 0.0626760573 3 0 -0.25029126 0.40042977 1.100491e-02 NA
90.1 1.1896872985 NA 0 -0.26974303 0.63975731 2.715349e-05 NA
90.2 0.2597888783 NA 0 -0.28804531 0.33412775 5.916576e-03 NA
90.3 0.6599799887 NA NA -0.19180615 0.38399003 2.920657e-02 NA
91 1.1213651365 NA 0 -0.26591197 0.58250391 2.411997e-03 NA
91.1 1.2046371625 NA 0 -0.09153470 0.13223217 8.870147e-06 NA
91.2 0.3395603754 NA 0 -0.48414390 0.46613305 1.652965e-08 NA
92 0.4674939332 NA 0 NA 0.18997862 2.613551e-03 NA
93 0.2677965647 2 NA -0.11939966 1.05243347 9.958480e-01 NA
93.1 1.6424445368 4 0 NA 0.01479757 9.915375e-01 NA
93.2 0.7101700066 4 NA -0.21089379 0.50955172 4.861680e-02 NA
93.3 1.1222322893 NA 0 NA 0.78122514 9.769008e-01 NA
93.4 1.4628960401 3 0 -0.23618836 0.63940704 5.977439e-05 NA
94 -0.2904211940 4 NA NA 0.45596305 7.091952e-04 NA
94.1 0.0147813580 2 0 -0.10217284 0.41610667 6.005522e-04 NA
94.2 -0.4536774482 NA 0 -0.36713471 0.52744298 8.134430e-03 NA
94.3 0.6793464917 1 NA -0.13806763 0.70890756 1.747604e-05 NA
94.4 -0.9411356550 NA 0 -0.42353804 0.84412478 9.404259e-07 NA
94.5 0.5683867264 2 1 -0.15513707 0.21166602 6.832077e-07 NA
95 0.2375652188 3 0 -0.24149687 0.57713135 3.216011e-06 NA
95.1 0.0767152977 5 NA -0.21315958 0.44400207 6.324477e-05 NA
95.2 -0.6886731251 2 0 -0.15777208 0.42397776 1.762187e-01 NA
96 0.7813892121 NA 0 -0.16780948 0.72391015 1.578796e-02 NA
96.1 0.3391519695 NA 0 -0.32504815 0.32593738 2.610661e-02 NA
96.2 -0.4857246503 5 0 -0.20395970 0.23249511 3.941700e-05 NA
96.3 0.8771471244 1 NA -0.06221501 1.01679990 1.683671e-05 NA
96.4 1.9030768981 0 1 -0.14801097 0.92267953 1.095127e-04 NA
96.5 -0.1684332749 3 1 -0.28658893 0.83843412 1.479105e-05 NA
97 1.3775130083 4 0 -0.34484656 0.47151154 2.082560e-04 NA
97.1 -1.7323228619 2 0 -0.35658805 0.15596614 7.903013e-10 NA
98 -1.2648518889 3 0 -0.36913003 0.05179545 1.795949e-06 NA
98.1 -0.9042716241 NA 0 NA 0.47332096 2.776600e-02 NA
98.2 -0.1560385207 NA 1 -0.17154225 0.19706341 4.050457e-06 NA
99 0.7993356425 5 0 -0.24753132 0.22574556 2.316802e-05 NA
99.1 1.0355522332 NA 0 -0.27947829 1.00732330 2.206426e-06 NA
99.2 -0.1150895843 NA 0 -0.09033035 0.09749127 2.488411e-08 NA
100 0.0369067906 NA NA -0.17326698 0.22857989 7.572193e-01 NA
100.1 1.6023713093 4 NA NA 0.39548654 9.794641e-02 NA
100.2 0.8861545820 NA 0 -0.12072016 NA 4.934595e-01 NA
100.3 0.1277046316 4 NA -0.27657520 0.32695372 1.502083e-07 NA
100.4 -0.0834577654 NA 0 -0.14631556 0.10043925 2.515993e-06 NA
$m4d$spM_lvlone
center scale
c1 0.25599956 0.6718095
p2 2.71257485 1.6247402
b2 NA NA
c2 -0.22371584 0.1059527
L1mis 0.48184811 0.3462447
Be2 0.04274145 0.1563798
b21 NA NA
$m4d$mu_reg_norm
[1] 0
$m4d$tau_reg_norm
[1] 1e-04
$m4d$shape_tau_norm
[1] 0.01
$m4d$rate_tau_norm
[1] 0.01
$m4d$mu_reg_gamma
[1] 0
$m4d$tau_reg_gamma
[1] 1e-04
$m4d$shape_tau_gamma
[1] 0.01
$m4d$rate_tau_gamma
[1] 0.01
$m4d$mu_reg_binom
[1] 0
$m4d$tau_reg_binom
[1] 1e-04
$m4d$mu_reg_poisson
[1] 0
$m4d$tau_reg_poisson
[1] 1e-04
$m4d$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m4d$shape_diag_RinvD
[1] "0.01"
$m4d$rate_diag_RinvD
[1] "0.001"
$m5a
$m5a$M_id
M2 (Intercept) M22 M23 M24 log(C1) C1
1 NA 1 NA NA NA -0.3318617 0.7175865
2 1 1 NA NA NA -0.2867266 0.7507170
3 2 1 NA NA NA -0.3207627 0.7255954
4 2 1 NA NA NA -0.2917769 0.7469352
5 1 1 NA NA NA -0.3369956 0.7139120
6 NA 1 NA NA NA -0.3102679 0.7332505
7 NA 1 NA NA NA -0.3084388 0.7345929
8 2 1 NA NA NA -0.2675411 0.7652589
9 NA 1 NA NA NA -0.3284176 0.7200622
10 NA 1 NA NA NA -0.2978834 0.7423879
11 3 1 NA NA NA -0.2960573 0.7437448
12 NA 1 NA NA NA -0.2948450 0.7446470
13 NA 1 NA NA NA -0.2836654 0.7530186
14 2 1 NA NA NA -0.3434574 0.7093137
15 2 1 NA NA NA -0.3104469 0.7331192
16 NA 1 NA NA NA -0.3550492 0.7011390
17 3 1 NA NA NA -0.2967369 0.7432395
18 3 1 NA NA NA -0.2816747 0.7545191
19 2 1 NA NA NA -0.2838910 0.7528487
20 NA 1 NA NA NA -0.2727455 0.7612865
21 NA 1 NA NA NA -0.3213465 0.7251719
22 1 1 NA NA NA -0.3146245 0.7300630
23 2 1 NA NA NA -0.3442879 0.7087249
24 NA 1 NA NA NA -0.3021952 0.7391938
25 4 1 NA NA NA -0.2458186 0.7820641
26 NA 1 NA NA NA -0.3399165 0.7118298
27 NA 1 NA NA NA -0.3242275 0.7230857
28 2 1 NA NA NA -0.2891027 0.7489353
29 4 1 NA NA NA -0.2862314 0.7510888
30 2 1 NA NA NA -0.3146125 0.7300717
31 2 1 NA NA NA -0.2809421 0.7550721
32 3 1 NA NA NA -0.3117155 0.7321898
33 1 1 NA NA NA -0.3138326 0.7306414
34 4 1 NA NA NA -0.2974340 0.7427216
35 4 1 NA NA NA -0.3294709 0.7193042
36 NA 1 NA NA NA -0.3129468 0.7312888
37 NA 1 NA NA NA -0.3424289 0.7100436
38 NA 1 NA NA NA -0.2652444 0.7670184
39 4 1 NA NA NA -0.3010445 0.7400449
40 NA 1 NA NA NA -0.3014695 0.7397304
41 2 1 NA NA NA -0.2888874 0.7490966
42 NA 1 NA NA NA -0.2985038 0.7419274
43 NA 1 NA NA NA -0.2839809 0.7527810
44 3 1 NA NA NA -0.2999821 0.7408315
45 NA 1 NA NA NA -0.3082181 0.7347550
46 NA 1 NA NA NA -0.3102825 0.7332398
47 4 1 NA NA NA -0.3042884 0.7376481
48 3 1 NA NA NA -0.3084048 0.7346179
49 4 1 NA NA NA -0.3106911 0.7329402
50 3 1 NA NA NA -0.3201451 0.7260436
51 NA 1 NA NA NA -0.3225621 0.7242910
52 1 1 NA NA NA -0.3149755 0.7298067
53 NA 1 NA NA NA -0.3209299 0.7254741
54 NA 1 NA NA NA -0.2820889 0.7542067
55 NA 1 NA NA NA -0.3024638 0.7389952
56 NA 1 NA NA NA -0.2849341 0.7520638
57 4 1 NA NA NA -0.3257359 0.7219958
58 1 1 NA NA NA -0.3202560 0.7259632
59 2 1 NA NA NA -0.2932166 0.7458606
60 3 1 NA NA NA -0.2649529 0.7672421
61 2 1 NA NA NA -0.3205938 0.7257179
62 3 1 NA NA NA -0.3299089 0.7189892
63 2 1 NA NA NA -0.3101519 0.7333356
64 NA 1 NA NA NA -0.3119416 0.7320243
65 NA 1 NA NA NA -0.2906584 0.7477711
66 2 1 NA NA NA -0.3087049 0.7343974
67 NA 1 NA NA NA -0.2887994 0.7491624
68 3 1 NA NA NA -0.2899866 0.7482736
69 3 1 NA NA NA -0.3094824 0.7338267
70 4 1 NA NA NA -0.2734187 0.7607742
71 2 1 NA NA NA -0.2513372 0.7777600
72 NA 1 NA NA NA -0.3000053 0.7408143
73 4 1 NA NA NA -0.3218221 0.7248271
74 NA 1 NA NA NA -0.3058575 0.7364916
75 NA 1 NA NA NA -0.2923695 0.7464926
76 4 1 NA NA NA -0.3071463 0.7355430
77 2 1 NA NA NA -0.3273313 0.7208449
78 2 1 NA NA NA -0.3046827 0.7373573
79 NA 1 NA NA NA -0.2746896 0.7598079
80 1 1 NA NA NA -0.3064688 0.7360415
81 NA 1 NA NA NA -0.3155423 0.7293932
82 3 1 NA NA NA -0.3175491 0.7279309
83 4 1 NA NA NA -0.3086139 0.7344643
84 3 1 NA NA NA -0.3032222 0.7384350
85 NA 1 NA NA NA -0.3114673 0.7323716
86 3 1 NA NA NA -0.2775210 0.7576597
87 NA 1 NA NA NA -0.2881970 0.7496139
88 NA 1 NA NA NA -0.3181084 0.7275239
89 NA 1 NA NA NA -0.3214942 0.7250648
90 4 1 NA NA NA -0.3098919 0.7335262
91 NA 1 NA NA NA -0.3087042 0.7343980
92 2 1 NA NA NA -0.3037539 0.7380425
93 4 1 NA NA NA -0.3025305 0.7389460
94 NA 1 NA NA NA -0.3202120 0.7259951
95 NA 1 NA NA NA -0.3170642 0.7282840
96 NA 1 NA NA NA -0.3172240 0.7281676
97 1 1 NA NA NA -0.3221849 0.7245642
98 NA 1 NA NA NA -0.2840967 0.7526938
99 2 1 NA NA NA -0.3185112 0.7272309
100 NA 1 NA NA NA -0.3033427 0.7383460
$m5a$M_lvlone
y c2 o2 time o22 o23 o24 abs(C1 - c2)
1 -13.0493856 NA 1 0.5090421822 NA NA NA NA
1.1 -9.3335901 -0.08061445 1 0.6666076288 NA NA NA NA
1.2 -22.3469852 -0.26523782 3 2.1304941282 NA NA NA NA
1.3 -15.0417337 -0.30260393 1 2.4954441458 NA NA NA NA
2 -12.0655434 -0.33443795 4 3.0164990982 NA NA NA NA
2.1 -15.8674476 -0.11819800 4 3.2996806887 NA NA NA NA
2.2 -7.8800006 -0.31532280 2 4.1747569619 NA NA NA NA
3 -11.4820604 -0.12920657 2 0.8478727890 NA NA NA NA
3.1 -10.5983220 NA 4 3.0654308549 NA NA NA NA
3.2 -22.4519157 NA 2 4.7381553578 NA NA NA NA
4 -1.2697775 -0.31177403 4 0.3371432109 NA NA NA NA
4.1 -11.1215184 -0.23894886 3 1.0693019140 NA NA NA NA
4.2 -3.6134138 -0.15533613 NA 2.6148973033 NA NA NA NA
4.3 -14.5982385 -0.14644545 2 3.1336532847 NA NA NA NA
5 -6.8457515 -0.28360457 2 1.0762525082 NA NA NA NA
5.1 -7.0551214 -0.20135143 4 1.7912546196 NA NA NA NA
5.2 -12.3418980 -0.28293375 2 2.7960080339 NA NA NA NA
5.3 -9.2366906 NA 4 2.8119940578 NA NA NA NA
6 -5.1648211 -0.08617066 3 1.7815462884 NA NA NA NA
7 -10.0599502 -0.22243495 1 3.3074087673 NA NA NA NA
7.1 -18.3267285 NA NA 3.7008403614 NA NA NA NA
7.2 -12.5138426 NA 4 4.7716691741 NA NA NA NA
8 -1.6305331 NA 1 1.1246398522 NA NA NA NA
8.1 -9.6520453 NA 3 1.8027009873 NA NA NA NA
8.2 -1.5278462 NA 1 1.8175825174 NA NA NA NA
8.3 -7.4172211 -0.35148972 4 2.8384267003 NA NA NA NA
8.4 -7.1238609 0.03661023 3 3.3630275307 NA NA NA NA
8.5 -8.8706950 -0.08424534 3 4.4360849704 NA NA NA NA
9 -0.1634429 NA 2 0.9607803822 NA NA NA NA
9.1 -2.6034300 -0.43509340 2 2.9177753383 NA NA NA NA
9.2 -6.7272369 -0.22527490 4 4.8100892501 NA NA NA NA
10 -6.4172202 NA 1 2.2975509102 NA NA NA NA
10.1 -11.4834569 NA 4 4.1734118364 NA NA NA NA
11 -8.7911356 -0.08587475 3 1.1832662905 NA NA NA NA
11.1 -19.6645080 -0.06157340 1 1.2346051680 NA NA NA NA
11.2 -20.2030932 -0.12436018 4 1.6435316263 NA NA NA NA
11.3 -21.3082176 -0.21377934 3 3.3859017969 NA NA NA NA
11.4 -14.5802901 -0.32208329 3 4.8118087661 NA NA NA NA
12 -15.2006287 NA 3 0.9591987054 NA NA NA NA
13 0.8058816 NA NA 0.0619085738 NA NA NA NA
13.1 -13.6379208 -0.40300449 1 3.5621061502 NA NA NA NA
14 -15.3422873 -0.28992072 1 4.0364430007 NA NA NA NA
14.1 -10.0965208 NA 4 4.4710561272 NA NA NA NA
14.2 -16.6452027 NA 3 4.6359198843 NA NA NA NA
14.3 -15.8389733 -0.21979936 4 4.6886152599 NA NA NA NA
15 -8.9424594 NA 1 0.5402063532 NA NA NA NA
15.1 -22.0101983 -0.29092263 4 1.1893180816 NA NA NA NA
15.2 -7.3975599 -0.19392239 NA 1.5094739688 NA NA NA NA
15.3 -10.3567334 -0.25718384 2 4.9193474615 NA NA NA NA
16 -1.9691302 -0.45041108 NA 1.2417913869 NA NA NA NA
16.1 -9.9308357 -0.07599066 NA 2.5675726333 NA NA NA NA
16.2 -6.9626923 -0.32385667 1 2.6524101500 NA NA NA NA
16.3 -3.2862557 -0.38326110 3 3.5585018690 NA NA NA NA
16.4 -3.3972355 -0.22845856 3 3.7612454291 NA NA NA NA
16.5 -11.5767835 -0.25497157 1 3.9851612889 NA NA NA NA
17 -10.5474144 NA 3 1.5925356350 NA NA NA NA
17.1 -7.6215009 -0.22105143 2 2.4374032998 NA NA NA NA
17.2 -16.5386939 NA 2 3.0256489082 NA NA NA NA
17.3 -20.0004774 NA 3 3.3329089405 NA NA NA NA
17.4 -18.8505475 -0.15098046 1 3.8693758985 NA NA NA NA
18 -19.7302351 -0.09870041 4 2.4374292302 NA NA NA NA
19 -14.6177568 -0.26680239 1 0.9772165376 NA NA NA NA
19.1 -17.8043866 -0.15815241 NA 1.1466335913 NA NA NA NA
19.2 -15.1641705 -0.14717437 NA 2.2599126538 NA NA NA NA
19.3 -16.6898418 -0.21271374 2 4.2114245973 NA NA NA NA
20 -12.9059229 -0.22087628 1 1.7170160066 NA NA NA NA
20.1 -16.8191201 NA 4 1.7562902288 NA NA NA NA
20.2 -6.1010131 -0.30127439 3 2.2515566566 NA NA NA NA
20.3 -7.9415371 -0.11782590 3 2.2609123867 NA NA NA NA
20.4 -9.3904458 -0.19857957 1 3.4913365287 NA NA NA NA
20.5 -13.3504189 -0.24338208 3 4.1730977828 NA NA NA NA
21 -7.6974718 -0.31407992 3 1.6936582839 NA NA NA NA
21.1 -11.9335526 -0.12424941 1 2.9571191233 NA NA NA NA
21.2 -12.7064929 -0.27672716 2 3.7887385779 NA NA NA NA
22 -21.5022909 -0.23790593 4 2.4696226232 NA NA NA NA
22.1 -12.7745451 -0.15996535 NA 3.1626627257 NA NA NA NA
23 -3.5146508 -0.18236682 4 1.5414533857 NA NA NA NA
23.1 -4.6724048 -0.20823302 NA 2.3369736120 NA NA NA NA
24 -2.5619821 -0.29026416 3 2.8283136466 NA NA NA NA
25 -6.2944970 -0.36139273 1 0.5381704110 NA NA NA NA
25.1 -3.8630505 -0.19571118 3 1.6069735331 NA NA NA NA
25.2 -14.4205140 -0.21379355 2 1.6358226922 NA NA NA NA
25.3 -19.6735037 -0.33876012 1 3.2646870392 NA NA NA NA
25.4 -9.0288933 NA 1 4.0782226040 NA NA NA NA
25.5 -9.0509738 -0.04068446 NA 4.1560292873 NA NA NA NA
26 -19.7340685 -0.16846716 3 0.2412706357 NA NA NA NA
26.1 -14.1692728 -0.10440642 4 2.4451737676 NA NA NA NA
26.2 -17.2819976 -0.26884827 3 3.5988757887 NA NA NA NA
26.3 -24.6265576 NA 1 4.1822362854 NA NA NA NA
27 -7.3354999 -0.19520794 4 3.6955824879 NA NA NA NA
27.1 -11.1488468 -0.17622638 4 4.2451434687 NA NA NA NA
28 -11.7996597 -0.32164962 1 0.5746519344 NA NA NA NA
28.1 -8.2030122 -0.27003852 2 2.7943964268 NA NA NA NA
28.2 -26.4317815 -0.07235801 3 4.2108539480 NA NA NA NA
28.3 -18.5016071 -0.13462982 3 4.4705521734 NA NA NA NA
29 -5.8551395 -0.32432030 4 1.1898884235 NA NA NA NA
29.1 -2.0209442 -0.27034171 4 1.7624059319 NA NA NA NA
29.2 -5.6368080 -0.10197448 3 2.0210406382 NA NA NA NA
29.3 -3.8110961 -0.27606945 2 3.4078777023 NA NA NA NA
30 -12.7217702 -0.06949300 3 2.2635366488 NA NA NA NA
30.1 -17.0170140 -0.11511035 4 3.5938334477 NA NA NA NA
30.2 -25.4236089 -0.16215882 4 3.6138710892 NA NA NA NA
31 -17.0783921 0.05707733 2 4.3988140998 NA NA NA NA
32 -18.4338764 -0.18446298 NA 1.6745209007 NA NA NA NA
32.1 -19.4317212 -0.14270013 2 2.9128167813 NA NA NA NA
32.2 -19.4738978 -0.20530798 4 2.9676558380 NA NA NA NA
32.3 -21.4922645 -0.14705649 3 4.2099863547 NA NA NA NA
33 2.0838099 -0.15252819 4 0.0093385763 NA NA NA NA
33.1 -13.3172274 NA 4 3.4591242753 NA NA NA NA
34 -10.0296691 -0.30378735 NA 1.4998774312 NA NA NA NA
34.1 -25.9426553 -0.11982431 NA 3.8242761395 NA NA NA NA
34.2 -18.5688138 -0.24278671 NA 3.9072251692 NA NA NA NA
34.3 -15.4173859 -0.19971833 NA 3.9582124643 NA NA NA NA
35 -14.3958113 NA 4 1.3294299203 NA NA NA NA
35.1 -12.9457541 -0.24165780 1 1.5276966314 NA NA NA NA
35.2 -16.1380691 NA NA 4.5025920868 NA NA NA NA
36 -12.8166968 -0.49062180 1 0.7123168337 NA NA NA NA
36.1 -14.3989481 -0.25651700 1 1.7972493160 NA NA NA NA
36.2 -12.2436943 NA 2 1.8262697803 NA NA NA NA
36.3 -15.0104638 -0.30401274 2 4.2840119381 NA NA NA NA
36.4 -10.1775457 NA 1 4.6194464504 NA NA NA NA
37 -15.2223495 -0.15276529 4 2.0018732361 NA NA NA NA
37.1 -14.7526195 -0.30016169 2 3.6656836793 NA NA NA NA
37.2 -19.8168430 0.06809545 2 3.9663937816 NA NA NA NA
38 -2.7065118 -0.11218486 NA 0.9826511063 NA NA NA NA
39 -8.7288138 -0.38072211 NA 0.6921808305 NA NA NA NA
39.1 -9.2746473 -0.32094428 2 0.9027792048 NA NA NA NA
39.2 -18.2695344 NA 2 1.3055654289 NA NA NA NA
39.3 -13.8219083 -0.40173480 3 1.5412842878 NA NA NA NA
39.4 -16.2254704 -0.20041848 3 3.1834997435 NA NA NA NA
39.5 -21.7283648 -0.26027990 1 4.1394166439 NA NA NA NA
40 1.8291916 -0.19751956 1 1.1330395646 NA NA NA NA
40.1 -6.6916432 -0.08399467 2 2.6940994046 NA NA NA NA
40.2 -1.6278171 -0.20864416 NA 3.0396614212 NA NA NA NA
40.3 -10.5749790 NA 2 4.6762977762 NA NA NA NA
41 -3.1556121 -0.26096953 4 1.9337158254 NA NA NA NA
41.1 -11.5895327 -0.23953874 3 3.1956304458 NA NA NA NA
41.2 -18.9352091 -0.03079344 4 3.2846923557 NA NA NA NA
41.3 -15.9788960 NA NA 3.3813529415 NA NA NA NA
41.4 -9.6070508 NA 4 3.5482964432 NA NA NA NA
42 -5.2159485 -0.16084527 4 0.4859252973 NA NA NA NA
42.1 -15.9878743 -0.13812521 4 4.3293134298 NA NA NA NA
43 -16.6104361 -0.08864017 NA 0.5616614548 NA NA NA NA
43.1 -9.5549441 -0.12583158 NA 1.0743579536 NA NA NA NA
43.2 -14.2003491 -0.29253959 3 2.6131797966 NA NA NA NA
44 -8.1969033 -0.22697597 2 0.7662644819 NA NA NA NA
44.1 -19.9270197 NA 4 2.6490291790 NA NA NA NA
44.2 -22.6521171 NA NA 3.3371910988 NA NA NA NA
44.3 -21.1903736 -0.40544012 1 4.1154200875 NA NA NA NA
45 -0.5686627 -0.19274788 3 0.1957449992 NA NA NA NA
45.1 -7.5645740 -0.34860483 4 1.9963831536 NA NA NA NA
46 -19.1624789 -0.28547861 NA 1.3477755385 NA NA NA NA
46.1 -18.4487574 -0.21977836 4 2.8565793915 NA NA NA NA
46.2 -15.8222682 NA 4 4.4160729996 NA NA NA NA
47 -5.4165074 -0.08597098 3 0.6012621359 NA NA NA NA
47.1 -15.0975029 -0.35424828 2 2.4097121472 NA NA NA NA
47.2 -12.9971413 -0.24262576 1 2.9975794035 NA NA NA NA
47.3 -10.6844521 -0.30426315 2 3.1829649757 NA NA NA NA
47.4 -18.2214784 NA 1 4.6201055450 NA NA NA NA
48 -8.3101471 NA 4 2.8607365978 NA NA NA NA
48.1 -18.3854275 NA NA 2.9098354396 NA NA NA NA
49 -13.0130319 -0.42198781 4 2.7179756400 NA NA NA NA
50 -10.4579977 -0.19959516 4 1.1762060679 NA NA NA NA
51 -19.3157621 -0.16556964 4 1.4304436720 NA NA NA NA
52 -4.4747188 -0.07438732 NA 2.1266646020 NA NA NA NA
52.1 -4.3163827 -0.37537080 NA 3.1000545993 NA NA NA NA
52.2 -6.9761408 -0.24222066 3 3.1268477370 NA NA NA NA
52.3 -20.1764756 -0.31520603 NA 3.5711459327 NA NA NA NA
52.4 -8.9036692 -0.44619160 4 4.7983659909 NA NA NA NA
52.5 -5.6949642 -0.11011682 1 4.9818264414 NA NA NA NA
53 -10.3141887 -0.23278716 2 0.4965799209 NA NA NA NA
53.1 -8.2642654 -0.28317264 1 3.5505357443 NA NA NA NA
53.2 -9.1691554 -0.19517481 3 4.5790420019 NA NA NA NA
54 -6.2198754 -0.10122856 3 1.4034724841 NA NA NA NA
54.1 -15.7192609 -0.28325504 4 1.8812377600 NA NA NA NA
54.2 -13.0978998 -0.16753120 4 2.5107589352 NA NA NA NA
54.3 -5.1195299 -0.22217672 3 2.7848406672 NA NA NA NA
54.4 -16.5771751 -0.34609328 NA 4.0143877396 NA NA NA NA
55 -5.7348534 -0.32428190 4 0.6118522980 NA NA NA NA
55.1 -7.3217494 -0.24235382 1 0.7463747414 NA NA NA NA
55.2 -12.2171938 -0.24065814 4 2.8201208171 NA NA NA NA
55.3 -12.9821266 -0.23665476 NA 3.1326431572 NA NA NA NA
55.4 -14.8599983 NA 1 3.2218102901 NA NA NA NA
56 -14.1764282 NA 1 1.2231332215 NA NA NA NA
56.1 -12.5343602 -0.30357450 2 2.3573202139 NA NA NA NA
56.2 -8.4573382 -0.51301630 2 2.5674936292 NA NA NA NA
56.3 -12.4633969 -0.23743117 3 2.9507164378 NA NA NA NA
56.4 -17.3841863 -0.17264917 4 3.2272730360 NA NA NA NA
56.5 -14.8147645 -0.39188329 4 3.4175522043 NA NA NA NA
57 -3.1403293 -0.18501692 2 0.2370331448 NA NA NA NA
57.1 -11.1509248 -0.27274841 2 0.2481445030 NA NA NA NA
57.2 -6.3940143 NA 4 1.1405586067 NA NA NA NA
57.3 -9.3473241 -0.09898509 NA 2.1153886721 NA NA NA NA
58 -12.0245677 -0.29901358 1 1.2210099772 NA NA NA NA
58.1 -9.2112246 -0.35390896 2 1.6334245703 NA NA NA NA
58.2 -1.2071742 -0.16687336 2 1.6791862890 NA NA NA NA
58.3 -11.0141711 -0.11784506 4 2.6320121693 NA NA NA NA
58.4 -5.3721214 -0.05321983 1 2.8477731440 NA NA NA NA
58.5 -7.8523047 -0.54457568 NA 3.5715569824 NA NA NA NA
59 -13.2946560 -0.27255364 4 1.9023998594 NA NA NA NA
59.1 -10.0530648 NA 1 4.9736620474 NA NA NA NA
60 -19.2209402 NA 1 2.8854503250 NA NA NA NA
61 -4.6699914 -0.30550120 1 0.7213630795 NA NA NA NA
61.1 -3.5981894 -0.35579892 1 2.3186947661 NA NA NA NA
61.2 -1.4713611 NA NA 2.5077313243 NA NA NA NA
61.3 -3.8819786 -0.34184391 1 3.1731073430 NA NA NA NA
61.4 0.1041413 -0.30891967 3 3.6022726283 NA NA NA NA
62 -2.8591600 NA NA 0.5336771999 NA NA NA NA
62.1 -6.9461986 -0.10504143 NA 0.6987666548 NA NA NA NA
62.2 -16.7910593 -0.20104997 NA 3.4584309917 NA NA NA NA
62.3 -17.9844596 -0.08138677 3 4.8028772371 NA NA NA NA
63 -24.0335535 -0.12036319 4 2.8097350930 NA NA NA NA
63.1 -11.7765300 -0.13624992 3 3.9653754211 NA NA NA NA
64 -20.5963897 NA 4 4.1191305732 NA NA NA NA
65 -2.7969169 -0.34450396 2 0.7076152589 NA NA NA NA
65.1 -11.1778694 -0.32514650 1 2.0252246363 NA NA NA NA
65.2 -5.2830399 -0.10984996 3 3.1127382827 NA NA NA NA
65.3 -7.9353390 -0.19275692 NA 3.1969087943 NA NA NA NA
66 -13.2318328 NA 1 3.4943454154 NA NA NA NA
66.1 -1.9090560 NA 3 3.7677437009 NA NA NA NA
66.2 -16.6643889 -0.11687008 2 3.9486138616 NA NA NA NA
67 -25.6073277 NA 3 4.1728388879 NA NA NA NA
68 -13.4806759 -0.13605235 3 0.1291919907 NA NA NA NA
68.1 -18.4557183 -0.19790827 4 1.7809643946 NA NA NA NA
68.2 -13.3982327 -0.17750123 3 2.0493205660 NA NA NA NA
68.3 -12.4977127 NA 1 2.9406870750 NA NA NA NA
68.4 -11.7073990 -0.12570562 4 4.0406670363 NA NA NA NA
69 -14.5290675 -0.32152751 4 4.1451198701 NA NA NA NA
70 -15.2122709 -0.28190462 4 0.1992557163 NA NA NA NA
70.1 -7.8681167 -0.11503263 4 0.4829774413 NA NA NA NA
71 -10.3352703 -0.13029093 2 0.7741605386 NA NA NA NA
71.1 -7.5699888 NA NA 1.4883817220 NA NA NA NA
71.2 -18.4680702 -0.39075433 4 4.0758526395 NA NA NA NA
71.3 -21.4316644 -0.21401028 3 4.7048238723 NA NA NA NA
71.4 -8.1137650 -0.40219281 1 4.7242791823 NA NA NA NA
72 -9.1848162 -0.40337108 1 0.9321196121 NA NA NA NA
72.1 -23.7538846 -0.25978914 NA 1.1799991806 NA NA NA NA
72.2 -26.3421306 NA 4 1.8917567329 NA NA NA NA
72.3 -27.2843801 -0.09809866 1 3.4853593935 NA NA NA NA
72.4 -20.8541617 -0.14240019 3 3.6884259700 NA NA NA NA
72.5 -12.8948965 -0.14794204 1 4.0854155901 NA NA NA NA
73 -2.6091307 -0.23509343 2 4.6019889915 NA NA NA NA
74 -8.2790175 -0.27963171 4 1.4626806753 NA NA NA NA
75 -12.5029612 -0.12905034 1 3.2524286874 NA NA NA NA
76 -6.0061671 0.04775562 2 1.8074807397 NA NA NA NA
76.1 -8.8149114 -0.19399157 1 4.2685073183 NA NA NA NA
76.2 -11.8359043 -0.02754574 1 4.9688734859 NA NA NA NA
77 0.4772521 -0.19053195 3 0.8459033852 NA NA NA NA
78 -9.4105229 -0.17172929 3 0.8231094317 NA NA NA NA
79 -1.0217265 -0.03958515 NA 0.0583819521 NA NA NA NA
79.1 -11.8125257 -0.20328809 3 2.4406372628 NA NA NA NA
79.2 -10.5465186 -0.23901634 NA 3.2962526032 NA NA NA NA
80 -12.7366807 -0.34031873 2 0.8985060186 NA NA NA NA
80.1 -9.0584783 -0.19526756 3 1.3434670598 NA NA NA NA
80.2 -16.6381566 NA 1 2.8025900386 NA NA NA NA
81 0.5547913 -0.18401980 3 0.0101324962 NA NA NA NA
81.1 -4.0892715 -0.16889476 NA 0.9421709494 NA NA NA NA
81.2 1.8283303 -0.37343047 3 3.0542453879 NA NA NA NA
81.3 -5.2166381 NA 2 3.3456630446 NA NA NA NA
82 -3.0749381 -0.08328168 NA 1.3791010005 NA NA NA NA
82.1 -10.5506696 -0.22167084 3 1.7601010622 NA NA NA NA
82.2 -18.2226347 -0.20971187 1 2.6233131927 NA NA NA NA
83 -12.5872635 -0.34228255 4 0.0537394290 NA NA NA NA
83.1 -11.9756502 -0.34075730 NA 2.9061570496 NA NA NA NA
83.2 -10.6744217 -0.32503954 2 3.1189457362 NA NA NA NA
83.3 -19.2714012 NA NA 4.7663642222 NA NA NA NA
84 -2.6320312 -0.20676741 2 2.7254060237 NA NA NA NA
84.1 -9.8140094 -0.20310458 1 3.3364784659 NA NA NA NA
85 -12.3886736 -0.12107593 1 0.2977756259 NA NA NA NA
85.1 -12.9196365 NA 4 1.7394116637 NA NA NA NA
85.2 -9.6433248 -0.32509207 3 2.6846330194 NA NA NA NA
85.3 -6.3296340 NA 3 3.1608762743 NA NA NA NA
85.4 -7.0405525 -0.30730810 NA 3.9452053758 NA NA NA NA
85.5 -13.6714939 NA 2 4.5092553482 NA NA NA NA
86 -10.8756412 -0.10854862 1 0.8476278360 NA NA NA NA
86.1 -12.0055331 -0.25751662 3 1.0118629411 NA NA NA NA
86.2 -13.3724699 -0.38943076 1 1.2511159515 NA NA NA NA
86.3 -13.3252145 -0.24454702 2 2.1870554925 NA NA NA NA
86.4 -14.9191290 -0.12338992 3 2.4532935000 NA NA NA NA
86.5 -17.7515546 -0.23976984 4 3.8206058508 NA NA NA NA
87 -10.7027963 NA NA 2.7069531474 NA NA NA NA
87.1 -22.4941954 -0.34366972 3 3.4462517721 NA NA NA NA
87.2 -14.9616716 NA 3 4.5241666853 NA NA NA NA
88 -2.2264493 -0.31563888 NA 0.0005892443 NA NA NA NA
88.1 -8.9626474 -0.20304028 1 0.7116099866 NA NA NA NA
88.2 -2.5095281 -0.40311895 2 2.4952722900 NA NA NA NA
88.3 -16.3345673 -0.12308715 NA 3.2995816297 NA NA NA NA
89 -11.0459647 -0.18527715 3 0.6462086167 NA NA NA NA
90 -4.5610239 -0.25029126 2 0.1696030737 NA NA NA NA
90.1 -11.7036651 -0.26974303 2 2.5980385230 NA NA NA NA
90.2 -5.3838521 -0.28804531 2 2.6651392167 NA NA NA NA
90.3 -4.1636999 -0.19180615 4 3.1242690247 NA NA NA NA
91 -7.1462503 -0.26591197 2 0.6382618390 NA NA NA NA
91.1 -12.8374475 -0.09153470 NA 2.6224059286 NA NA NA NA
91.2 -18.2576707 -0.48414390 3 4.7772527603 NA NA NA NA
92 -6.4119222 NA 2 0.0737052364 NA NA NA NA
93 5.2122168 -0.11939966 3 0.2788909199 NA NA NA NA
93.1 3.1211725 NA 2 1.0357759963 NA NA NA NA
93.2 -3.6841177 -0.21089379 3 2.4916551099 NA NA NA NA
93.3 2.6223542 NA 2 2.8876129608 NA NA NA NA
93.4 -11.1877696 -0.23618836 4 4.4639474002 NA NA NA NA
94 -6.9602492 NA NA 0.8488043118 NA NA NA NA
94.1 -7.4318416 -0.10217284 2 1.0552454425 NA NA NA NA
94.2 -4.3498045 -0.36713471 NA 1.9445500884 NA NA NA NA
94.3 -11.6340088 -0.13806763 3 3.0710722448 NA NA NA NA
94.4 -12.9357964 -0.42353804 4 3.0872731935 NA NA NA NA
94.5 -14.7648530 -0.15513707 3 4.3805759016 NA NA NA NA
95 -12.8849309 -0.24149687 NA 2.0199063048 NA NA NA NA
95.1 -9.7451502 -0.21315958 2 4.0184444457 NA NA NA NA
95.2 -0.8535063 -0.15777208 3 4.5596531732 NA NA NA NA
96 -4.9139832 -0.16780948 3 0.0311333477 NA NA NA NA
96.1 -3.9582653 -0.32504815 NA 0.1324267720 NA NA NA NA
96.2 -9.6555492 -0.20395970 4 0.6701303425 NA NA NA NA
96.3 -11.8690793 -0.06221501 3 2.1775037691 NA NA NA NA
96.4 -11.0224373 -0.14801097 NA 2.2246142488 NA NA NA NA
96.5 -10.9530403 -0.28658893 1 4.2377650598 NA NA NA NA
97 -9.8540471 -0.34484656 2 1.1955102731 NA NA NA NA
97.1 -19.2262840 -0.35658805 1 4.9603108643 NA NA NA NA
98 -11.9651231 -0.36913003 2 0.2041732438 NA NA NA NA
98.1 -2.6515128 NA 1 0.4309578973 NA NA NA NA
98.2 -12.2606382 -0.17154225 3 3.5172611906 NA NA NA NA
99 -11.4720500 -0.24753132 NA 0.3531786101 NA NA NA NA
99.1 -14.0596866 -0.27947829 NA 4.6789444226 NA NA NA NA
99.2 -17.3939469 -0.09033035 4 4.9927084171 NA NA NA NA
100 1.1005874 -0.17326698 1 1.0691387602 NA NA NA NA
100.1 -3.8226248 NA NA 1.5109344281 NA NA NA NA
100.2 -0.9123182 -0.12072016 1 2.1502332564 NA NA NA NA
100.3 -15.8389474 -0.27657520 4 3.8745574222 NA NA NA NA
100.4 -12.8093826 -0.14631556 1 4.6567608765 NA NA NA NA
I(time^2) o22:abs(C1 - c2) o23:abs(C1 - c2) o24:abs(C1 - c2)
1 2.591239e-01 NA NA NA
1.1 4.443657e-01 NA NA NA
1.2 4.539005e+00 NA NA NA
1.3 6.227241e+00 NA NA NA
2 9.099267e+00 NA NA NA
2.1 1.088789e+01 NA NA NA
2.2 1.742860e+01 NA NA NA
3 7.188883e-01 NA NA NA
3.1 9.396866e+00 NA NA NA
3.2 2.245012e+01 NA NA NA
4 1.136655e-01 NA NA NA
4.1 1.143407e+00 NA NA NA
4.2 6.837688e+00 NA NA NA
4.3 9.819783e+00 NA NA NA
5 1.158319e+00 NA NA NA
5.1 3.208593e+00 NA NA NA
5.2 7.817661e+00 NA NA NA
5.3 7.907311e+00 NA NA NA
6 3.173907e+00 NA NA NA
7 1.093895e+01 NA NA NA
7.1 1.369622e+01 NA NA NA
7.2 2.276883e+01 NA NA NA
8 1.264815e+00 NA NA NA
8.1 3.249731e+00 NA NA NA
8.2 3.303606e+00 NA NA NA
8.3 8.056666e+00 NA NA NA
8.4 1.130995e+01 NA NA NA
8.5 1.967885e+01 NA NA NA
9 9.230989e-01 NA NA NA
9.1 8.513413e+00 NA NA NA
9.2 2.313696e+01 NA NA NA
10 5.278740e+00 NA NA NA
10.1 1.741737e+01 NA NA NA
11 1.400119e+00 NA NA NA
11.1 1.524250e+00 NA NA NA
11.2 2.701196e+00 NA NA NA
11.3 1.146433e+01 NA NA NA
11.4 2.315350e+01 NA NA NA
12 9.200622e-01 NA NA NA
13 3.832672e-03 NA NA NA
13.1 1.268860e+01 NA NA NA
14 1.629287e+01 NA NA NA
14.1 1.999034e+01 NA NA NA
14.2 2.149175e+01 NA NA NA
14.3 2.198311e+01 NA NA NA
15 2.918229e-01 NA NA NA
15.1 1.414477e+00 NA NA NA
15.2 2.278512e+00 NA NA NA
15.3 2.419998e+01 NA NA NA
16 1.542046e+00 NA NA NA
16.1 6.592429e+00 NA NA NA
16.2 7.035280e+00 NA NA NA
16.3 1.266294e+01 NA NA NA
16.4 1.414697e+01 NA NA NA
16.5 1.588151e+01 NA NA NA
17 2.536170e+00 NA NA NA
17.1 5.940935e+00 NA NA NA
17.2 9.154551e+00 NA NA NA
17.3 1.110828e+01 NA NA NA
17.4 1.497207e+01 NA NA NA
18 5.941061e+00 NA NA NA
19 9.549522e-01 NA NA NA
19.1 1.314769e+00 NA NA NA
19.2 5.107205e+00 NA NA NA
19.3 1.773610e+01 NA NA NA
20 2.948144e+00 NA NA NA
20.1 3.084555e+00 NA NA NA
20.2 5.069507e+00 NA NA NA
20.3 5.111725e+00 NA NA NA
20.4 1.218943e+01 NA NA NA
20.5 1.741475e+01 NA NA NA
21 2.868478e+00 NA NA NA
21.1 8.744554e+00 NA NA NA
21.2 1.435454e+01 NA NA NA
22 6.099036e+00 NA NA NA
22.1 1.000244e+01 NA NA NA
23 2.376079e+00 NA NA NA
23.1 5.461446e+00 NA NA NA
24 7.999358e+00 NA NA NA
25 2.896274e-01 NA NA NA
25.1 2.582364e+00 NA NA NA
25.2 2.675916e+00 NA NA NA
25.3 1.065818e+01 NA NA NA
25.4 1.663190e+01 NA NA NA
25.5 1.727258e+01 NA NA NA
26 5.821152e-02 NA NA NA
26.1 5.978875e+00 NA NA NA
26.2 1.295191e+01 NA NA NA
26.3 1.749110e+01 NA NA NA
27 1.365733e+01 NA NA NA
27.1 1.802124e+01 NA NA NA
28 3.302248e-01 NA NA NA
28.1 7.808651e+00 NA NA NA
28.2 1.773129e+01 NA NA NA
28.3 1.998584e+01 NA NA NA
29 1.415834e+00 NA NA NA
29.1 3.106075e+00 NA NA NA
29.2 4.084605e+00 NA NA NA
29.3 1.161363e+01 NA NA NA
30 5.123598e+00 NA NA NA
30.1 1.291564e+01 NA NA NA
30.2 1.306006e+01 NA NA NA
31 1.934957e+01 NA NA NA
32 2.804020e+00 NA NA NA
32.1 8.484502e+00 NA NA NA
32.2 8.806981e+00 NA NA NA
32.3 1.772399e+01 NA NA NA
33 8.720901e-05 NA NA NA
33.1 1.196554e+01 NA NA NA
34 2.249632e+00 NA NA NA
34.1 1.462509e+01 NA NA NA
34.2 1.526641e+01 NA NA NA
34.3 1.566745e+01 NA NA NA
35 1.767384e+00 NA NA NA
35.1 2.333857e+00 NA NA NA
35.2 2.027334e+01 NA NA NA
36 5.073953e-01 NA NA NA
36.1 3.230105e+00 NA NA NA
36.2 3.335261e+00 NA NA NA
36.3 1.835276e+01 NA NA NA
36.4 2.133929e+01 NA NA NA
37 4.007496e+00 NA NA NA
37.1 1.343724e+01 NA NA NA
37.2 1.573228e+01 NA NA NA
38 9.656032e-01 NA NA NA
39 4.791143e-01 NA NA NA
39.1 8.150103e-01 NA NA NA
39.2 1.704501e+00 NA NA NA
39.3 2.375557e+00 NA NA NA
39.4 1.013467e+01 NA NA NA
39.5 1.713477e+01 NA NA NA
40 1.283779e+00 NA NA NA
40.1 7.258172e+00 NA NA NA
40.2 9.239542e+00 NA NA NA
40.3 2.186776e+01 NA NA NA
41 3.739257e+00 NA NA NA
41.1 1.021205e+01 NA NA NA
41.2 1.078920e+01 NA NA NA
41.3 1.143355e+01 NA NA NA
41.4 1.259041e+01 NA NA NA
42 2.361234e-01 NA NA NA
42.1 1.874295e+01 NA NA NA
43 3.154636e-01 NA NA NA
43.1 1.154245e+00 NA NA NA
43.2 6.828709e+00 NA NA NA
44 5.871613e-01 NA NA NA
44.1 7.017356e+00 NA NA NA
44.2 1.113684e+01 NA NA NA
44.3 1.693668e+01 NA NA NA
45 3.831610e-02 NA NA NA
45.1 3.985546e+00 NA NA NA
46 1.816499e+00 NA NA NA
46.1 8.160046e+00 NA NA NA
46.2 1.950170e+01 NA NA NA
47 3.615162e-01 NA NA NA
47.1 5.806713e+00 NA NA NA
47.2 8.985482e+00 NA NA NA
47.3 1.013127e+01 NA NA NA
47.4 2.134538e+01 NA NA NA
48 8.183814e+00 NA NA NA
48.1 8.467142e+00 NA NA NA
49 7.387392e+00 NA NA NA
50 1.383461e+00 NA NA NA
51 2.046169e+00 NA NA NA
52 4.522702e+00 NA NA NA
52.1 9.610339e+00 NA NA NA
52.2 9.777177e+00 NA NA NA
52.3 1.275308e+01 NA NA NA
52.4 2.302432e+01 NA NA NA
52.5 2.481859e+01 NA NA NA
53 2.465916e-01 NA NA NA
53.1 1.260630e+01 NA NA NA
53.2 2.096763e+01 NA NA NA
54 1.969735e+00 NA NA NA
54.1 3.539056e+00 NA NA NA
54.2 6.303910e+00 NA NA NA
54.3 7.755338e+00 NA NA NA
54.4 1.611531e+01 NA NA NA
55 3.743632e-01 NA NA NA
55.1 5.570753e-01 NA NA NA
55.2 7.953081e+00 NA NA NA
55.3 9.813453e+00 NA NA NA
55.4 1.038006e+01 NA NA NA
56 1.496055e+00 NA NA NA
56.1 5.556959e+00 NA NA NA
56.2 6.592024e+00 NA NA NA
56.3 8.706727e+00 NA NA NA
56.4 1.041529e+01 NA NA NA
56.5 1.167966e+01 NA NA NA
57 5.618471e-02 NA NA NA
57.1 6.157569e-02 NA NA NA
57.2 1.300874e+00 NA NA NA
57.3 4.474869e+00 NA NA NA
58 1.490865e+00 NA NA NA
58.1 2.668076e+00 NA NA NA
58.2 2.819667e+00 NA NA NA
58.3 6.927488e+00 NA NA NA
58.4 8.109812e+00 NA NA NA
58.5 1.275602e+01 NA NA NA
59 3.619125e+00 NA NA NA
59.1 2.473731e+01 NA NA NA
60 8.325824e+00 NA NA NA
61 5.203647e-01 NA NA NA
61.1 5.376345e+00 NA NA NA
61.2 6.288716e+00 NA NA NA
61.3 1.006861e+01 NA NA NA
61.4 1.297637e+01 NA NA NA
62 2.848114e-01 NA NA NA
62.1 4.882748e-01 NA NA NA
62.2 1.196074e+01 NA NA NA
62.3 2.306763e+01 NA NA NA
63 7.894611e+00 NA NA NA
63.1 1.572420e+01 NA NA NA
64 1.696724e+01 NA NA NA
65 5.007194e-01 NA NA NA
65.1 4.101535e+00 NA NA NA
65.2 9.689140e+00 NA NA NA
65.3 1.022023e+01 NA NA NA
66 1.221045e+01 NA NA NA
66.1 1.419589e+01 NA NA NA
66.2 1.559155e+01 NA NA NA
67 1.741258e+01 NA NA NA
68 1.669057e-02 NA NA NA
68.1 3.171834e+00 NA NA NA
68.2 4.199715e+00 NA NA NA
68.3 8.647640e+00 NA NA NA
68.4 1.632699e+01 NA NA NA
69 1.718202e+01 NA NA NA
70 3.970284e-02 NA NA NA
70.1 2.332672e-01 NA NA NA
71 5.993245e-01 NA NA NA
71.1 2.215280e+00 NA NA NA
71.2 1.661257e+01 NA NA NA
71.3 2.213537e+01 NA NA NA
71.4 2.231881e+01 NA NA NA
72 8.688470e-01 NA NA NA
72.1 1.392398e+00 NA NA NA
72.2 3.578744e+00 NA NA NA
72.3 1.214773e+01 NA NA NA
72.4 1.360449e+01 NA NA NA
72.5 1.669062e+01 NA NA NA
73 2.117830e+01 NA NA NA
74 2.139435e+00 NA NA NA
75 1.057829e+01 NA NA NA
76 3.266987e+00 NA NA NA
76.1 1.822015e+01 NA NA NA
76.2 2.468970e+01 NA NA NA
77 7.155525e-01 NA NA NA
78 6.775091e-01 NA NA NA
79 3.408452e-03 NA NA NA
79.1 5.956710e+00 NA NA NA
79.2 1.086528e+01 NA NA NA
80 8.073131e-01 NA NA NA
80.1 1.804904e+00 NA NA NA
80.2 7.854511e+00 NA NA NA
81 1.026675e-04 NA NA NA
81.1 8.876861e-01 NA NA NA
81.2 9.328415e+00 NA NA NA
81.3 1.119346e+01 NA NA NA
82 1.901920e+00 NA NA NA
82.1 3.097956e+00 NA NA NA
82.2 6.881772e+00 NA NA NA
83 2.887926e-03 NA NA NA
83.1 8.445749e+00 NA NA NA
83.2 9.727823e+00 NA NA NA
83.3 2.271823e+01 NA NA NA
84 7.427838e+00 NA NA NA
84.1 1.113209e+01 NA NA NA
85 8.867032e-02 NA NA NA
85.1 3.025553e+00 NA NA NA
85.2 7.207254e+00 NA NA NA
85.3 9.991139e+00 NA NA NA
85.4 1.556465e+01 NA NA NA
85.5 2.033338e+01 NA NA NA
86 7.184729e-01 NA NA NA
86.1 1.023867e+00 NA NA NA
86.2 1.565291e+00 NA NA NA
86.3 4.783212e+00 NA NA NA
86.4 6.018649e+00 NA NA NA
86.5 1.459703e+01 NA NA NA
87 7.327595e+00 NA NA NA
87.1 1.187665e+01 NA NA NA
87.2 2.046808e+01 NA NA NA
88 3.472088e-07 NA NA NA
88.1 5.063888e-01 NA NA NA
88.2 6.226384e+00 NA NA NA
88.3 1.088724e+01 NA NA NA
89 4.175856e-01 NA NA NA
90 2.876520e-02 NA NA NA
90.1 6.749804e+00 NA NA NA
90.2 7.102967e+00 NA NA NA
90.3 9.761057e+00 NA NA NA
91 4.073782e-01 NA NA NA
91.1 6.877013e+00 NA NA NA
91.2 2.282214e+01 NA NA NA
92 5.432462e-03 NA NA NA
93 7.778015e-02 NA NA NA
93.1 1.072832e+00 NA NA NA
93.2 6.208345e+00 NA NA NA
93.3 8.338309e+00 NA NA NA
93.4 1.992683e+01 NA NA NA
94 7.204688e-01 NA NA NA
94.1 1.113543e+00 NA NA NA
94.2 3.781275e+00 NA NA NA
94.3 9.431485e+00 NA NA NA
94.4 9.531256e+00 NA NA NA
94.5 1.918945e+01 NA NA NA
95 4.080021e+00 NA NA NA
95.1 1.614790e+01 NA NA NA
95.2 2.079044e+01 NA NA NA
96 9.692853e-04 NA NA NA
96.1 1.753685e-02 NA NA NA
96.2 4.490747e-01 NA NA NA
96.3 4.741523e+00 NA NA NA
96.4 4.948909e+00 NA NA NA
96.5 1.795865e+01 NA NA NA
97 1.429245e+00 NA NA NA
97.1 2.460468e+01 NA NA NA
98 4.168671e-02 NA NA NA
98.1 1.857247e-01 NA NA NA
98.2 1.237113e+01 NA NA NA
99 1.247351e-01 NA NA NA
99.1 2.189252e+01 NA NA NA
99.2 2.492714e+01 NA NA NA
100 1.143058e+00 NA NA NA
100.1 2.282923e+00 NA NA NA
100.2 4.623503e+00 NA NA NA
100.3 1.501220e+01 NA NA NA
100.4 2.168542e+01 NA NA NA
$m5a$spM_id
center scale
M2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
log(C1) -0.3049822 0.01990873
C1 0.7372814 0.01472882
$m5a$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
o2 NA NA
time 2.5339403 1.3818094
o22 NA NA
o23 NA NA
o24 NA NA
abs(C1 - c2) 0.9613865 0.1064886
I(time^2) 8.3244468 7.0900029
o22:abs(C1 - c2) 0.2166402 0.4111132
o23:abs(C1 - c2) 0.2721613 0.4294402
o24:abs(C1 - c2) 0.2492394 0.4265852
$m5a$mu_reg_norm
[1] 0
$m5a$tau_reg_norm
[1] 1e-04
$m5a$shape_tau_norm
[1] 0.01
$m5a$rate_tau_norm
[1] 0.01
$m5a$mu_reg_multinomial
[1] 0
$m5a$tau_reg_multinomial
[1] 1e-04
$m5a$mu_reg_ordinal
[1] 0
$m5a$tau_reg_ordinal
[1] 1e-04
$m5a$mu_delta_ordinal
[1] 0
$m5a$tau_delta_ordinal
[1] 1e-04
$m5a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m5a$shape_diag_RinvD
[1] "0.01"
$m5a$rate_diag_RinvD
[1] "0.001"
$m5a$RinvD_y_id
[,1] [,2]
[1,] NA 0
[2,] 0 NA
$m5a$KinvD_y_id
id
3
$m5b
$m5b$M_id
C2 (Intercept)
1 -1.381594459 1
2 0.344426024 1
3 NA 1
4 -0.228910007 1
5 NA 1
6 -2.143955482 1
7 -1.156567023 1
8 -0.598827660 1
9 NA 1
10 -1.006719032 1
11 0.239801450 1
12 -1.064969789 1
13 -0.538082688 1
14 NA 1
15 -1.781049276 1
16 NA 1
17 NA 1
18 -0.014579883 1
19 -2.121550136 1
20 NA 1
21 -0.363239698 1
22 -0.121568514 1
23 -0.951271111 1
24 NA 1
25 -0.974288621 1
26 -1.130632418 1
27 0.114339868 1
28 0.238334648 1
29 0.840744958 1
30 NA 1
31 NA 1
32 -1.466312154 1
33 -0.637352277 1
34 NA 1
35 NA 1
36 NA 1
37 NA 1
38 NA 1
39 0.006728205 1
40 NA 1
41 -1.663281353 1
42 0.161184794 1
43 0.457939180 1
44 -0.307070331 1
45 NA 1
46 -1.071668276 1
47 -0.814751321 1
48 -0.547630662 1
49 NA 1
50 -1.350213782 1
51 0.719054706 1
52 NA 1
53 -1.207130750 1
54 NA 1
55 -0.408600991 1
56 -0.271380529 1
57 -1.361925974 1
58 NA 1
59 NA 1
60 -0.323712205 1
61 NA 1
62 NA 1
63 -1.386906880 1
64 NA 1
65 NA 1
66 -0.565191691 1
67 -0.382899912 1
68 NA 1
69 -0.405642769 1
70 NA 1
71 -0.843748427 1
72 0.116003683 1
73 -0.778634325 1
74 NA 1
75 NA 1
76 NA 1
77 -0.632974758 1
78 NA 1
79 -0.778064615 1
80 NA 1
81 NA 1
82 -0.246123253 1
83 -1.239659782 1
84 -0.467772280 1
85 NA 1
86 -2.160485036 1
87 -0.657675572 1
88 NA 1
89 -0.696710744 1
90 NA 1
91 -0.179395847 1
92 -0.441545568 1
93 -0.685799334 1
94 NA 1
95 0.191929445 1
96 NA 1
97 -0.069760671 1
98 NA 1
99 NA 1
100 NA 1
$m5b$M_lvlone
b1 L1mis Be2 c1 time abs(c1 - C2)
1 0 1.38634787 4.596628e-06 0.7592026489 0.5090421822 NA
1.1 1 0.79402906 2.296427e-04 0.9548337990 0.6666076288 NA
1.2 1 0.53603334 3.455922e-10 0.5612235156 2.1304941282 NA
1.3 0 0.24129804 9.618613e-07 1.1873391025 2.4954441458 NA
2 1 NA NA 0.9192204198 3.0164990982 NA
2.1 1 0.31668065 1.065639e-07 -0.1870730476 3.2996806887 NA
2.2 1 0.37114414 1.320730e-03 1.2517512331 4.1747569619 NA
3 1 0.54680608 9.707820e-06 -0.0605087604 0.8478727890 NA
3.1 0 0.28280274 3.645271e-05 0.3788637747 3.0654308549 NA
3.2 0 0.76277262 NA 0.9872578281 4.7381553578 NA
4 1 0.56100366 5.555794e-01 1.4930175328 0.3371432109 NA
4.1 1 0.38514140 6.853316e-06 -0.7692526880 1.0693019140 NA
4.2 0 0.04026174 6.324951e-02 0.9180841450 2.6148973033 NA
4.3 1 0.16025873 4.330745e-07 -0.0541170782 3.1336532847 NA
5 0 0.21080161 NA -0.1376784521 1.0762525082 NA
5.1 1 0.36665700 6.556812e-04 -0.2740585866 1.7912546196 NA
5.2 1 0.66368829 6.963312e-06 0.4670496929 2.7960080339 NA
5.3 1 0.40788895 1.159006e-04 0.1740288049 2.8119940578 NA
6 0 0.11889539 1.509745e-02 0.9868044683 1.7815462884 NA
7 1 1.04286843 NA -0.1280320918 3.3074087673 NA
7.1 0 0.52098933 1.679086e-08 0.4242971219 3.7008403614 NA
7.2 1 0.09858876 3.972447e-06 0.0777182491 4.7716691741 NA
8 0 0.17281472 9.888512e-02 -0.5791408712 1.1246398522 NA
8.1 1 0.25970093 8.790334e-05 0.3128604232 1.8027009873 NA
8.2 1 0.30550233 NA 0.6258446356 1.8175825174 NA
8.3 0 0.88029778 5.411705e-04 -0.1040137707 2.8384267003 NA
8.4 0 0.20200392 8.446731e-04 0.0481450285 3.3630275307 NA
8.5 1 NA 2.059814e-04 0.3831763675 4.4360849704 NA
9 1 1.12218535 4.160033e-01 -0.1757592269 0.9607803822 NA
9.1 1 0.57911079 NA -0.1791541200 2.9177753383 NA
9.2 0 0.81350994 1.087331e-03 -0.0957042935 4.8100892501 NA
10 1 0.32744766 9.321715e-04 -0.5598409704 2.2975509102 NA
10.1 1 0.62912282 8.167897e-06 -0.2318340451 4.1734118364 NA
11 1 0.92140073 2.528529e-04 0.5086859475 1.1832662905 NA
11.1 1 0.16012129 NA 0.4951758188 1.2346051680 NA
11.2 1 0.16166775 5.587553e-10 -1.1022162541 1.6435316263 NA
11.3 1 0.14979756 5.240776e-10 -0.0611636705 3.3859017969 NA
11.4 1 0.46855190 2.830994e-07 -0.4971774316 4.8118087661 NA
12 1 0.76818678 1.962202e-07 -0.2433996286 0.9591987054 NA
13 0 0.34264972 NA 0.8799673116 0.0619085738 NA
13.1 1 0.14526619 1.330415e-06 0.1079022586 3.5621061502 NA
14 0 0.80630788 5.900181e-07 0.9991752617 4.0364430007 NA
14.1 1 0.35697552 3.694946e-05 -0.1094019046 4.4710561272 NA
14.2 0 0.21330192 6.871447e-08 0.1518967560 4.6359198843 NA
14.3 0 NA NA 0.3521012473 4.6886152599 NA
15 0 0.30769119 1.848068e-04 0.3464447888 0.5402063532 NA
15.1 0 0.28349746 1.714157e-10 -0.4767313971 1.1893180816 NA
15.2 0 0.64618365 1.088807e-03 0.5759767791 1.5094739688 NA
15.3 1 0.51680884 2.677330e-05 -0.1713452662 4.9193474615 NA
16 1 0.71265471 NA 0.4564754473 1.2417913869 NA
16.1 0 0.38925880 1.411453e-04 1.0652558311 2.5675726333 NA
16.2 1 0.23648869 1.897147e-03 0.6971872493 2.6524101500 NA
16.3 1 0.45048730 5.950632e-02 0.5259331838 3.5585018690 NA
16.4 1 0.23181791 3.944608e-02 0.2046601798 3.7612454291 NA
16.5 0 0.13985349 NA 1.0718540464 3.9851612889 NA
17 0 0.25995399 4.808238e-05 0.6048676222 1.5925356350 NA
17.1 0 0.03594878 6.175264e-04 0.2323298304 2.4374032998 NA
17.2 1 0.77583623 2.319036e-07 1.2617499032 3.0256489082 NA
17.3 0 0.60015197 1.393008e-09 -0.3913230895 3.3329089405 NA
17.4 1 0.13998405 NA 0.9577299112 3.8693758985 NA
18 1 0.96475839 2.685853e-09 -0.0050324072 2.4374292302 NA
19 1 0.10596495 2.949370e-07 -0.4187468937 0.9772165376 NA
19.1 1 0.13338947 1.183423e-08 -0.4478828944 1.1466335913 NA
19.2 1 0.41662218 7.844699e-08 -1.1966721302 2.2599126538 NA
19.3 1 0.53670855 NA -0.5877091668 4.2114245973 NA
20 0 0.41688567 4.920475e-06 0.6838223064 1.7170160066 NA
20.1 1 NA 6.885500e-08 0.3278571109 1.7562902288 NA
20.2 0 0.81634101 9.577206e-04 -0.8489831990 2.2515566566 NA
20.3 0 0.39232496 1.325632e-03 1.3169975191 2.2609123867 NA
20.4 0 0.57925554 NA 0.0444804531 3.4913365287 NA
20.5 0 0.74200986 1.011637e-06 -0.4535207652 4.1730977828 NA
21 1 0.24759801 3.032947e-04 -0.4030302960 1.6936582839 NA
21.1 1 0.34052205 4.370975e-06 -0.4069674045 2.9571191233 NA
21.2 0 0.03905058 8.793700e-06 1.0650265940 3.7887385779 NA
22 0 0.48605351 NA -0.0673274516 2.4696226232 NA
22.1 1 0.43761071 7.397166e-06 0.9601388170 3.1626627257 NA
23 1 0.47599712 4.931346e-02 0.5556634840 1.5414533857 NA
23.1 1 0.47680301 3.799306e-02 1.4407865964 2.3369736120 NA
24 0 0.51696505 1.018950e-01 0.3856376411 2.8283136466 NA
25 0 0.59392591 NA 0.3564400705 0.5381704110 NA
25.1 1 0.74010330 2.264756e-02 0.0982553434 1.6069735331 NA
25.2 1 NA 6.622343e-07 0.1928682598 1.6358226922 NA
25.3 0 0.73081722 2.802504e-09 -0.0192488594 3.2646870392 NA
25.4 0 0.29274286 1.873599e-04 0.4466012931 4.0782226040 NA
25.5 0 0.74425342 NA 1.1425193342 4.1560292873 NA
26 1 0.20974346 4.587570e-09 0.5341531449 0.2412706357 NA
26.1 1 NA 2.394334e-06 1.2268695927 2.4451737676 NA
26.2 1 0.22908815 4.510972e-08 0.3678294939 3.5988757887 NA
26.3 0 0.41880799 3.657318e-11 0.5948516018 4.1822362854 NA
27 1 0.10097167 NA -0.3342844147 3.6955824879 NA
27.1 1 NA 8.874134e-06 -0.4835141229 4.2451434687 NA
28 1 NA 3.673907e-06 -0.7145915499 0.5746519344 NA
28.1 0 0.56052750 4.541426e-04 0.5063671955 2.7943964268 NA
28.2 1 0.15301800 2.697966e-12 -0.2067413142 4.2108539480 NA
28.3 1 0.27802542 NA 0.1196789973 4.4705521734 NA
29 1 0.43556671 3.282475e-03 0.1392699487 1.1898884235 NA
29.1 0 0.27593085 2.270717e-01 0.7960234776 1.7624059319 NA
29.2 0 0.55256871 9.981536e-03 1.0398214352 2.0210406382 NA
29.3 1 0.47272109 2.343590e-02 0.0813246429 3.4078777023 NA
30 1 0.32743933 NA -0.3296323050 2.2635366488 NA
30.1 1 0.02231535 1.591483e-07 1.3635850954 3.5938334477 NA
30.2 1 0.12833697 1.896944e-11 0.7354171050 3.6138710892 NA
31 0 0.11126366 5.546285e-08 0.3708398217 4.3988140998 NA
32 1 1.11731084 9.411981e-09 -0.0474059668 1.6745209007 NA
32.1 1 0.85943330 1.270914e-08 1.2507771489 2.9128167813 NA
32.2 1 1.53730925 3.910478e-09 0.1142915519 2.9676558380 NA
32.3 1 0.43831965 9.124048e-10 0.6773270619 4.2099863547 NA
33 0 0.46726055 9.056156e-01 0.1774293842 0.0093385763 NA
33.1 0 0.76818259 3.047254e-06 0.6159606291 3.4591242753 NA
34 1 NA 1.040462e-04 0.8590979166 1.4998774312 NA
34.1 0 1.14350292 5.714390e-12 0.0546216775 3.8242761395 NA
34.2 1 0.19103604 7.883166e-09 -0.0897224473 3.9072251692 NA
34.3 1 NA 3.055823e-07 0.4163395571 3.9582124643 NA
35 1 0.66303137 1.287796e-07 -1.4693520528 1.3294299203 NA
35.1 0 NA 1.762232e-06 -0.3031734330 1.5276966314 NA
35.2 1 NA 5.355159e-08 -0.6045512101 4.5025920868 NA
36 0 0.93843318 7.250797e-06 0.9823048960 0.7123168337 NA
36.1 0 NA 2.370652e-06 1.4466051416 1.7972493160 NA
36.2 1 0.29886676 1.537090e-05 1.1606752905 1.8262697803 NA
36.3 0 0.22616598 6.993214e-07 0.8373091576 4.2840119381 NA
36.4 1 0.53849566 4.950009e-05 0.2640591685 4.6194464504 NA
37 1 1.68107300 2.755165e-07 0.1177313455 2.0018732361 NA
37.1 0 1.13777638 3.400517e-07 -0.1415483779 3.6656836793 NA
37.2 0 0.26931933 2.489007e-09 0.0054610124 3.9663937816 NA
38 1 NA 1.302651e-01 0.8078948077 0.9826511063 NA
39 1 0.14395367 4.343746e-04 0.9876451040 0.6921808305 NA
39.1 0 0.36454923 6.653143e-05 -0.3431222274 0.9027792048 NA
39.2 0 1.03700002 1.940204e-09 -1.7909380751 1.3055654289 NA
39.3 0 0.41320585 8.300468e-07 -0.1798746191 1.5412842878 NA
39.4 1 0.20901554 7.464169e-08 -0.1850961689 3.1834997435 NA
39.5 1 0.51603848 5.765597e-10 0.4544226146 4.1394166439 NA
40 0 0.33912363 9.140572e-01 0.5350190436 1.1330395646 NA
40.1 0 0.21892118 1.883555e-03 0.4189342752 2.6940994046 NA
40.2 0 0.74070896 2.303001e-01 0.4211994981 3.0396614212 NA
40.3 1 0.82927399 2.799910e-05 0.0916687506 4.6762977762 NA
41 1 0.25193679 3.700067e-02 -0.1035047421 1.9337158254 NA
41.1 1 0.28760510 5.798225e-06 -0.4684202411 3.1956304458 NA
41.2 0 0.45553197 1.086252e-08 0.5972615368 3.2846923557 NA
41.3 1 0.79237611 3.088732e-07 0.9885613862 3.3813529415 NA
41.4 1 0.12582175 4.549537e-05 -0.3908036794 3.5482964432 NA
42 1 0.50079604 5.220968e-03 -0.0338893961 0.4859252973 NA
42.1 1 0.61140760 7.264286e-08 -0.4498363172 4.3293134298 NA
43 0 0.29752019 1.498125e-07 0.8965546110 0.5616614548 NA
43.1 0 0.51793497 1.316763e-04 0.6199122090 1.0743579536 NA
43.2 1 0.15152473 8.151771e-07 0.1804894429 2.6131797966 NA
44 1 0.38806434 1.032476e-03 1.3221409285 0.7662644819 NA
44.1 0 1.11140786 3.120174e-09 0.3416426284 2.6490291790 NA
44.2 0 0.39132534 2.571257e-10 0.5706610068 3.3371910988 NA
44.3 1 0.40934909 2.227416e-09 1.2679497430 4.1154200875 NA
45 1 0.68587067 3.948036e-01 0.1414983160 0.1957449992 NA
45.1 0 0.34530800 1.066310e-03 0.7220892521 1.9963831536 NA
46 1 0.71312288 2.219556e-08 1.5391054233 1.3477755385 NA
46.1 0 0.62537420 1.434525e-08 0.3889107049 2.8565793915 NA
46.2 1 0.79574391 1.523026e-07 0.1248719493 4.4160729996 NA
47 0 0.48660773 5.404537e-03 0.2014101100 0.6012621359 NA
47.1 0 0.51241790 3.739267e-07 0.2982973539 2.4097121472 NA
47.2 1 0.58869379 7.171916e-06 1.1518107179 2.9975794035 NA
47.3 0 0.22171504 3.850162e-05 0.5196802157 3.1829649757 NA
47.4 0 0.11366347 1.767264e-08 0.3702301552 4.6201055450 NA
48 0 0.19677010 1.988010e-04 -0.2128602862 2.8607365978 NA
48.1 1 0.17706320 6.074589e-09 -0.5337239976 2.9098354396 NA
49 0 0.30752382 1.321544e-06 -0.5236770035 2.7179756400 NA
50 1 0.93663423 4.240393e-05 0.3897705981 1.1762060679 NA
51 1 0.34107606 1.986093e-09 -0.7213343736 1.4304436720 NA
52 1 0.19007135 1.632022e-02 0.3758235358 2.1266646020 NA
52.1 1 0.75662940 2.653038e-02 0.7138067080 3.1000545993 NA
52.2 0 1.66104719 2.262881e-03 0.8872895233 3.1268477370 NA
52.3 0 NA 6.572647e-10 -0.9664587437 3.5711459327 NA
52.4 1 0.18369708 1.393737e-04 0.0254566848 4.7983659909 NA
52.5 1 0.48689343 5.069462e-03 0.4155259424 4.9818264414 NA
53 1 0.31983157 5.848890e-05 0.5675736897 0.4965799209 NA
53.1 1 0.61569501 1.878509e-04 -0.3154088781 3.5505357443 NA
53.2 1 NA 1.293417e-04 0.2162315769 4.5790420019 NA
54 0 1.90522418 1.818441e-03 -0.0880802382 1.4034724841 NA
54.1 1 0.59484889 2.251839e-07 0.4129127672 1.8812377600 NA
54.2 0 1.47174857 5.638172e-06 1.0119546775 2.5107589352 NA
54.3 1 0.27307143 5.320676e-03 -0.1112901990 2.7848406672 NA
54.4 0 0.81272938 1.491367e-07 0.8587727145 4.0143877396 NA
55 1 0.22735476 3.183775e-03 -0.0116453589 0.6118522980 NA
55.1 1 0.54683512 1.183380e-03 0.5835528661 0.7463747414 NA
55.2 1 1.03503777 1.817077e-06 -1.0010857254 2.8201208171 NA
55.3 0 0.30169529 1.424370e-06 -0.4796526070 3.1326431572 NA
55.4 1 0.36008059 3.119967e-07 -0.1202746964 3.2218102901 NA
56 0 0.14193566 1.169667e-06 0.5176377612 1.2231332215 NA
56.1 1 0.65073539 1.182293e-06 -1.1136932588 2.3573202139 NA
56.2 1 0.11338262 2.087533e-04 -0.0168103281 2.5674936292 NA
56.3 0 0.16820103 5.728251e-06 0.3933023606 2.9507164378 NA
56.4 0 0.27419110 4.087596e-08 0.3714625139 3.2272730360 NA
56.5 1 0.57110215 8.040370e-07 0.7811448179 3.4175522043 NA
57 1 0.85104054 1.438387e-02 -1.0868304872 0.2370331448 NA
57.1 1 0.34733833 3.202179e-05 0.8018626997 0.2481445030 NA
57.2 0 1.44438762 1.486318e-03 -0.1159517011 1.1405586067 NA
57.3 0 0.31836125 1.718412e-04 0.6785562445 2.1153886721 NA
58 1 0.37456898 3.114123e-05 1.6476207996 1.2210099772 NA
58.1 1 0.22120158 1.403881e-04 0.3402652711 1.6334245703 NA
58.2 1 0.78885210 2.111006e-01 -0.1111300753 1.6791862890 NA
58.3 1 0.10114937 9.586985e-06 -0.5409234285 2.6320121693 NA
58.4 1 0.13385114 4.073162e-03 -0.1271327672 2.8477731440 NA
58.5 1 NA 9.285307e-04 0.8713264822 3.5715569824 NA
59 0 0.13202156 2.711478e-06 0.4766421367 1.9023998594 NA
59.1 1 0.33371896 1.173472e-04 1.0028089765 4.9736620474 NA
60 0 0.35096579 7.579680e-09 0.5231452932 2.8854503250 NA
61 1 0.36933806 4.545759e-03 -0.7190130614 0.7213630795 NA
61.1 1 0.17623067 5.936674e-02 0.8353702312 2.3186947661 NA
61.2 1 0.21286227 3.897281e-01 1.0229058138 2.5077313243 NA
61.3 0 0.12689308 6.237379e-02 1.1717723589 3.1731073430 NA
61.4 1 0.77676718 5.103038e-01 -0.0629201596 3.6022726283 NA
62 1 1.38018163 3.707353e-02 -0.3979137604 0.5336771999 NA
62.1 0 0.43803892 1.901660e-03 0.6830738372 0.6987666548 NA
62.2 0 0.21947900 7.844369e-08 0.4301745954 3.4584309917 NA
62.3 1 0.11571160 1.496168e-08 -0.0333139957 4.8028772371 NA
63 0 0.41583568 5.101070e-11 0.3345678035 2.8097350930 NA
63.1 1 0.25598960 1.106013e-05 0.3643769511 3.9653754211 NA
64 1 0.20415642 1.685171e-09 0.3949911859 4.1191305732 NA
65 1 0.07135646 1.684142e-01 1.2000091513 0.7076152589 NA
65.1 1 0.57450574 1.413479e-05 0.0110122646 2.0252246363 NA
65.2 0 0.52562984 2.841196e-03 -0.5776452043 3.1127382827 NA
65.3 0 0.21921164 3.118871e-04 -0.1372183563 3.1969087943 NA
66 1 0.33281730 1.078473e-06 -0.5081302805 3.4943454154 NA
66.1 0 0.03412404 1.136650e-01 -0.1447837412 3.7677437009 NA
66.2 0 0.92570619 7.007044e-08 0.1906241379 3.9486138616 NA
67 0 0.15291043 4.025749e-11 1.6716027681 4.1728388879 NA
68 0 0.37543648 2.469503e-06 0.5691848839 0.1291919907 NA
68.1 0 0.20901022 1.067638e-08 0.1004860389 1.7809643946 NA
68.2 0 0.12488064 1.508555e-06 -0.0061241827 2.0493205660 NA
68.3 0 0.08711204 7.862972e-06 0.7443745962 2.9406870750 NA
68.4 1 0.54611735 1.970326e-05 0.8726923437 4.0406670363 NA
69 1 0.23638239 5.089430e-07 0.0381382683 4.1451198701 NA
70 1 0.49876756 5.575849e-07 0.8126204217 0.1992557163 NA
70.1 1 0.39512615 6.115107e-04 0.4691503050 0.4829774413 NA
71 1 0.45666551 1.867742e-05 -0.5529062591 0.7741605386 NA
71.1 1 0.92047456 4.616167e-04 -0.1103252087 1.4883817220 NA
71.2 0 0.32792986 5.314611e-08 1.7178492547 4.0758526395 NA
71.3 0 0.95108007 1.790244e-10 -1.0118346755 4.7048238723 NA
71.4 0 0.36287072 1.924070e-03 1.8623785017 4.7242791823 NA
72 1 0.12870526 6.526547e-05 -0.4521659275 0.9321196121 NA
72.1 1 0.45925876 5.540491e-11 0.1375317317 1.1799991806 NA
72.2 1 0.05418867 2.391191e-12 -0.4170988856 1.8917567329 NA
72.3 0 0.48937486 2.878783e-12 0.7107266765 3.4853593935 NA
72.4 0 0.64173822 1.014404e-09 0.1451969143 3.6884259700 NA
72.5 1 0.57609943 1.281231e-05 1.6298050306 4.0854155901 NA
73 1 0.17393402 6.661564e-02 -0.0307469467 4.6019889915 NA
74 1 0.23990575 3.683842e-04 0.3730017941 1.4626806753 NA
75 0 0.28469861 2.274469e-06 -0.4908003566 3.2524286874 NA
76 1 0.71988630 9.155636e-04 -0.9888876620 1.8074807397 NA
76.1 1 1.12449946 1.485365e-04 0.0003798292 4.2685073183 NA
76.2 1 0.71313766 3.118702e-06 -0.8421863763 4.9688734859 NA
77 1 0.02399030 4.946432e-01 -0.4986802480 0.8459033852 NA
78 1 0.42708148 8.533933e-05 0.0417330969 0.8231094317 NA
79 0 0.37579286 1.980588e-01 -0.3767450660 0.0583819521 NA
79.1 1 0.78660681 8.624235e-06 0.1516000028 2.4406372628 NA
79.2 0 0.67696116 2.176176e-05 -0.1888160741 3.2962526032 NA
80 1 0.34207854 2.929029e-06 -0.0041558414 0.8985060186 NA
80.1 0 0.60534092 1.126162e-04 -0.0329337062 1.3434670598 NA
80.2 1 0.26731034 9.847382e-08 0.5046816157 2.8025900386 NA
81 1 0.17739052 4.026095e-01 -0.9493950353 0.0101324962 NA
81.1 1 0.35453673 2.093927e-02 0.2443038954 0.9421709494 NA
81.2 1 0.20244235 9.224440e-01 0.6476958410 3.0542453879 NA
81.3 1 1.26402329 8.175654e-03 0.4182528210 3.3456630446 NA
82 1 0.09303938 1.228129e-01 1.1088801952 1.3791010005 NA
82.1 1 0.27254210 6.656575e-05 0.9334157763 1.7601010622 NA
82.2 0 0.49936304 2.001426e-08 0.4958140634 2.6233131927 NA
83 1 0.21138572 5.690020e-06 0.5104724530 0.0537394290 NA
83.1 0 0.26403568 5.980615e-06 -0.0513309106 2.9061570496 NA
83.2 0 0.20311133 1.880816e-05 -0.2067792494 3.1189457362 NA
83.3 1 1.16864671 4.048910e-09 -0.0534169155 4.7663642222 NA
84 1 1.99179346 6.552173e-02 -0.0255753653 2.7254060237 NA
84.1 0 1.52199460 8.829278e-06 -1.8234189877 3.3364784659 NA
85 0 NA 4.118253e-06 -0.0114038622 0.2977756259 NA
85.1 0 0.61458995 2.311994e-06 -0.0577615939 1.7394116637 NA
85.2 1 0.07871196 5.182892e-05 -0.2241856342 2.6846330194 NA
85.3 1 1.42315283 1.689467e-03 -0.0520175929 3.1608762743 NA
85.4 1 0.97986129 1.168017e-03 0.2892733846 3.9452053758 NA
85.5 1 0.91792195 7.945131e-07 -0.3740417009 4.5092553482 NA
86 0 0.63509597 2.905567e-05 0.4293735089 0.8476278360 NA
86.1 1 0.24546597 5.331467e-06 -0.1363456521 1.0118629411 NA
86.2 1 0.53102060 1.761451e-06 0.1230989293 1.2511159515 NA
86.3 0 0.09360826 2.272397e-06 0.3305413955 2.1870554925 NA
86.4 1 0.58301186 4.467006e-06 2.6003411822 2.4532935000 NA
86.5 0 0.39146055 1.693940e-08 -0.1420690052 3.8206058508 NA
87 0 NA 6.396865e-05 1.0457427869 2.7069531474 NA
87.1 1 0.66043624 1.264093e-10 -0.2973007190 3.4462517721 NA
87.2 0 0.13267613 4.933807e-07 0.4396872616 4.5241666853 NA
88 0 0.10696344 9.223531e-02 -0.0601928334 0.0005892443 NA
88.1 0 0.13689448 4.654325e-05 -1.0124347595 0.7116099866 NA
88.2 0 0.48037889 1.260399e-01 0.5730917016 2.4952722900 NA
88.3 0 0.97755681 8.029866e-08 -0.0029455332 3.2995816297 NA
89 1 0.70242369 7.489307e-05 1.5465903721 0.6462086167 NA
90 0 0.40042977 1.100491e-02 0.0626760573 0.1696030737 NA
90.1 1 0.63975731 2.715349e-05 1.1896872985 2.5980385230 NA
90.2 1 0.33412775 5.916576e-03 0.2597888783 2.6651392167 NA
90.3 0 0.38399003 2.920657e-02 0.6599799887 3.1242690247 NA
91 0 0.58250391 2.411997e-03 1.1213651365 0.6382618390 NA
91.1 0 0.13223217 8.870147e-06 1.2046371625 2.6224059286 NA
91.2 1 0.46613305 1.652965e-08 0.3395603754 4.7772527603 NA
92 1 0.18997862 2.613551e-03 0.4674939332 0.0737052364 NA
93 0 1.05243347 9.958480e-01 0.2677965647 0.2788909199 NA
93.1 1 0.01479757 9.915375e-01 1.6424445368 1.0357759963 NA
93.2 0 0.50955172 4.861680e-02 0.7101700066 2.4916551099 NA
93.3 1 0.78122514 9.769008e-01 1.1222322893 2.8876129608 NA
93.4 0 0.63940704 5.977439e-05 1.4628960401 4.4639474002 NA
94 1 0.45596305 7.091952e-04 -0.2904211940 0.8488043118 NA
94.1 0 0.41610667 6.005522e-04 0.0147813580 1.0552454425 NA
94.2 1 0.52744298 8.134430e-03 -0.4536774482 1.9445500884 NA
94.3 0 0.70890756 1.747604e-05 0.6793464917 3.0710722448 NA
94.4 0 0.84412478 9.404259e-07 -0.9411356550 3.0872731935 NA
94.5 0 0.21166602 6.832077e-07 0.5683867264 4.3805759016 NA
95 1 0.57713135 3.216011e-06 0.2375652188 2.0199063048 NA
95.1 1 0.44400207 6.324477e-05 0.0767152977 4.0184444457 NA
95.2 0 0.42397776 1.762187e-01 -0.6886731251 4.5596531732 NA
96 1 0.72391015 1.578796e-02 0.7813892121 0.0311333477 NA
96.1 0 0.32593738 2.610661e-02 0.3391519695 0.1324267720 NA
96.2 0 0.23249511 3.941700e-05 -0.4857246503 0.6701303425 NA
96.3 0 1.01679990 1.683671e-05 0.8771471244 2.1775037691 NA
96.4 0 0.92267953 1.095127e-04 1.9030768981 2.2246142488 NA
96.5 1 0.83843412 1.479105e-05 -0.1684332749 4.2377650598 NA
97 0 0.47151154 2.082560e-04 1.3775130083 1.1955102731 NA
97.1 0 0.15596614 7.903013e-10 -1.7323228619 4.9603108643 NA
98 0 0.05179545 1.795949e-06 -1.2648518889 0.2041732438 NA
98.1 0 0.47332096 2.776600e-02 -0.9042716241 0.4309578973 NA
98.2 0 0.19706341 4.050457e-06 -0.1560385207 3.5172611906 NA
99 1 0.22574556 2.316802e-05 0.7993356425 0.3531786101 NA
99.1 1 1.00732330 2.206426e-06 1.0355522332 4.6789444226 NA
99.2 1 0.09749127 2.488411e-08 -0.1150895843 4.9927084171 NA
100 0 0.22857989 7.572193e-01 0.0369067906 1.0691387602 NA
100.1 0 0.39548654 9.794641e-02 1.6023713093 1.5109344281 NA
100.2 1 NA 4.934595e-01 0.8861545820 2.1502332564 NA
100.3 1 0.32695372 1.502083e-07 0.1277046316 3.8745574222 NA
100.4 1 0.10043925 2.515993e-06 -0.0834577654 4.6567608765 NA
log(Be2) I(time^2)
1 NA 2.591239e-01
1.1 NA 4.443657e-01
1.2 NA 4.539005e+00
1.3 NA 6.227241e+00
2 NA 9.099267e+00
2.1 NA 1.088789e+01
2.2 NA 1.742860e+01
3 NA 7.188883e-01
3.1 NA 9.396866e+00
3.2 NA 2.245012e+01
4 NA 1.136655e-01
4.1 NA 1.143407e+00
4.2 NA 6.837688e+00
4.3 NA 9.819783e+00
5 NA 1.158319e+00
5.1 NA 3.208593e+00
5.2 NA 7.817661e+00
5.3 NA 7.907311e+00
6 NA 3.173907e+00
7 NA 1.093895e+01
7.1 NA 1.369622e+01
7.2 NA 2.276883e+01
8 NA 1.264815e+00
8.1 NA 3.249731e+00
8.2 NA 3.303606e+00
8.3 NA 8.056666e+00
8.4 NA 1.130995e+01
8.5 NA 1.967885e+01
9 NA 9.230989e-01
9.1 NA 8.513413e+00
9.2 NA 2.313696e+01
10 NA 5.278740e+00
10.1 NA 1.741737e+01
11 NA 1.400119e+00
11.1 NA 1.524250e+00
11.2 NA 2.701196e+00
11.3 NA 1.146433e+01
11.4 NA 2.315350e+01
12 NA 9.200622e-01
13 NA 3.832672e-03
13.1 NA 1.268860e+01
14 NA 1.629287e+01
14.1 NA 1.999034e+01
14.2 NA 2.149175e+01
14.3 NA 2.198311e+01
15 NA 2.918229e-01
15.1 NA 1.414477e+00
15.2 NA 2.278512e+00
15.3 NA 2.419998e+01
16 NA 1.542046e+00
16.1 NA 6.592429e+00
16.2 NA 7.035280e+00
16.3 NA 1.266294e+01
16.4 NA 1.414697e+01
16.5 NA 1.588151e+01
17 NA 2.536170e+00
17.1 NA 5.940935e+00
17.2 NA 9.154551e+00
17.3 NA 1.110828e+01
17.4 NA 1.497207e+01
18 NA 5.941061e+00
19 NA 9.549522e-01
19.1 NA 1.314769e+00
19.2 NA 5.107205e+00
19.3 NA 1.773610e+01
20 NA 2.948144e+00
20.1 NA 3.084555e+00
20.2 NA 5.069507e+00
20.3 NA 5.111725e+00
20.4 NA 1.218943e+01
20.5 NA 1.741475e+01
21 NA 2.868478e+00
21.1 NA 8.744554e+00
21.2 NA 1.435454e+01
22 NA 6.099036e+00
22.1 NA 1.000244e+01
23 NA 2.376079e+00
23.1 NA 5.461446e+00
24 NA 7.999358e+00
25 NA 2.896274e-01
25.1 NA 2.582364e+00
25.2 NA 2.675916e+00
25.3 NA 1.065818e+01
25.4 NA 1.663190e+01
25.5 NA 1.727258e+01
26 NA 5.821152e-02
26.1 NA 5.978875e+00
26.2 NA 1.295191e+01
26.3 NA 1.749110e+01
27 NA 1.365733e+01
27.1 NA 1.802124e+01
28 NA 3.302248e-01
28.1 NA 7.808651e+00
28.2 NA 1.773129e+01
28.3 NA 1.998584e+01
29 NA 1.415834e+00
29.1 NA 3.106075e+00
29.2 NA 4.084605e+00
29.3 NA 1.161363e+01
30 NA 5.123598e+00
30.1 NA 1.291564e+01
30.2 NA 1.306006e+01
31 NA 1.934957e+01
32 NA 2.804020e+00
32.1 NA 8.484502e+00
32.2 NA 8.806981e+00
32.3 NA 1.772399e+01
33 NA 8.720901e-05
33.1 NA 1.196554e+01
34 NA 2.249632e+00
34.1 NA 1.462509e+01
34.2 NA 1.526641e+01
34.3 NA 1.566745e+01
35 NA 1.767384e+00
35.1 NA 2.333857e+00
35.2 NA 2.027334e+01
36 NA 5.073953e-01
36.1 NA 3.230105e+00
36.2 NA 3.335261e+00
36.3 NA 1.835276e+01
36.4 NA 2.133929e+01
37 NA 4.007496e+00
37.1 NA 1.343724e+01
37.2 NA 1.573228e+01
38 NA 9.656032e-01
39 NA 4.791143e-01
39.1 NA 8.150103e-01
39.2 NA 1.704501e+00
39.3 NA 2.375557e+00
39.4 NA 1.013467e+01
39.5 NA 1.713477e+01
40 NA 1.283779e+00
40.1 NA 7.258172e+00
40.2 NA 9.239542e+00
40.3 NA 2.186776e+01
41 NA 3.739257e+00
41.1 NA 1.021205e+01
41.2 NA 1.078920e+01
41.3 NA 1.143355e+01
41.4 NA 1.259041e+01
42 NA 2.361234e-01
42.1 NA 1.874295e+01
43 NA 3.154636e-01
43.1 NA 1.154245e+00
43.2 NA 6.828709e+00
44 NA 5.871613e-01
44.1 NA 7.017356e+00
44.2 NA 1.113684e+01
44.3 NA 1.693668e+01
45 NA 3.831610e-02
45.1 NA 3.985546e+00
46 NA 1.816499e+00
46.1 NA 8.160046e+00
46.2 NA 1.950170e+01
47 NA 3.615162e-01
47.1 NA 5.806713e+00
47.2 NA 8.985482e+00
47.3 NA 1.013127e+01
47.4 NA 2.134538e+01
48 NA 8.183814e+00
48.1 NA 8.467142e+00
49 NA 7.387392e+00
50 NA 1.383461e+00
51 NA 2.046169e+00
52 NA 4.522702e+00
52.1 NA 9.610339e+00
52.2 NA 9.777177e+00
52.3 NA 1.275308e+01
52.4 NA 2.302432e+01
52.5 NA 2.481859e+01
53 NA 2.465916e-01
53.1 NA 1.260630e+01
53.2 NA 2.096763e+01
54 NA 1.969735e+00
54.1 NA 3.539056e+00
54.2 NA 6.303910e+00
54.3 NA 7.755338e+00
54.4 NA 1.611531e+01
55 NA 3.743632e-01
55.1 NA 5.570753e-01
55.2 NA 7.953081e+00
55.3 NA 9.813453e+00
55.4 NA 1.038006e+01
56 NA 1.496055e+00
56.1 NA 5.556959e+00
56.2 NA 6.592024e+00
56.3 NA 8.706727e+00
56.4 NA 1.041529e+01
56.5 NA 1.167966e+01
57 NA 5.618471e-02
57.1 NA 6.157569e-02
57.2 NA 1.300874e+00
57.3 NA 4.474869e+00
58 NA 1.490865e+00
58.1 NA 2.668076e+00
58.2 NA 2.819667e+00
58.3 NA 6.927488e+00
58.4 NA 8.109812e+00
58.5 NA 1.275602e+01
59 NA 3.619125e+00
59.1 NA 2.473731e+01
60 NA 8.325824e+00
61 NA 5.203647e-01
61.1 NA 5.376345e+00
61.2 NA 6.288716e+00
61.3 NA 1.006861e+01
61.4 NA 1.297637e+01
62 NA 2.848114e-01
62.1 NA 4.882748e-01
62.2 NA 1.196074e+01
62.3 NA 2.306763e+01
63 NA 7.894611e+00
63.1 NA 1.572420e+01
64 NA 1.696724e+01
65 NA 5.007194e-01
65.1 NA 4.101535e+00
65.2 NA 9.689140e+00
65.3 NA 1.022023e+01
66 NA 1.221045e+01
66.1 NA 1.419589e+01
66.2 NA 1.559155e+01
67 NA 1.741258e+01
68 NA 1.669057e-02
68.1 NA 3.171834e+00
68.2 NA 4.199715e+00
68.3 NA 8.647640e+00
68.4 NA 1.632699e+01
69 NA 1.718202e+01
70 NA 3.970284e-02
70.1 NA 2.332672e-01
71 NA 5.993245e-01
71.1 NA 2.215280e+00
71.2 NA 1.661257e+01
71.3 NA 2.213537e+01
71.4 NA 2.231881e+01
72 NA 8.688470e-01
72.1 NA 1.392398e+00
72.2 NA 3.578744e+00
72.3 NA 1.214773e+01
72.4 NA 1.360449e+01
72.5 NA 1.669062e+01
73 NA 2.117830e+01
74 NA 2.139435e+00
75 NA 1.057829e+01
76 NA 3.266987e+00
76.1 NA 1.822015e+01
76.2 NA 2.468970e+01
77 NA 7.155525e-01
78 NA 6.775091e-01
79 NA 3.408452e-03
79.1 NA 5.956710e+00
79.2 NA 1.086528e+01
80 NA 8.073131e-01
80.1 NA 1.804904e+00
80.2 NA 7.854511e+00
81 NA 1.026675e-04
81.1 NA 8.876861e-01
81.2 NA 9.328415e+00
81.3 NA 1.119346e+01
82 NA 1.901920e+00
82.1 NA 3.097956e+00
82.2 NA 6.881772e+00
83 NA 2.887926e-03
83.1 NA 8.445749e+00
83.2 NA 9.727823e+00
83.3 NA 2.271823e+01
84 NA 7.427838e+00
84.1 NA 1.113209e+01
85 NA 8.867032e-02
85.1 NA 3.025553e+00
85.2 NA 7.207254e+00
85.3 NA 9.991139e+00
85.4 NA 1.556465e+01
85.5 NA 2.033338e+01
86 NA 7.184729e-01
86.1 NA 1.023867e+00
86.2 NA 1.565291e+00
86.3 NA 4.783212e+00
86.4 NA 6.018649e+00
86.5 NA 1.459703e+01
87 NA 7.327595e+00
87.1 NA 1.187665e+01
87.2 NA 2.046808e+01
88 NA 3.472088e-07
88.1 NA 5.063888e-01
88.2 NA 6.226384e+00
88.3 NA 1.088724e+01
89 NA 4.175856e-01
90 NA 2.876520e-02
90.1 NA 6.749804e+00
90.2 NA 7.102967e+00
90.3 NA 9.761057e+00
91 NA 4.073782e-01
91.1 NA 6.877013e+00
91.2 NA 2.282214e+01
92 NA 5.432462e-03
93 NA 7.778015e-02
93.1 NA 1.072832e+00
93.2 NA 6.208345e+00
93.3 NA 8.338309e+00
93.4 NA 1.992683e+01
94 NA 7.204688e-01
94.1 NA 1.113543e+00
94.2 NA 3.781275e+00
94.3 NA 9.431485e+00
94.4 NA 9.531256e+00
94.5 NA 1.918945e+01
95 NA 4.080021e+00
95.1 NA 1.614790e+01
95.2 NA 2.079044e+01
96 NA 9.692853e-04
96.1 NA 1.753685e-02
96.2 NA 4.490747e-01
96.3 NA 4.741523e+00
96.4 NA 4.948909e+00
96.5 NA 1.795865e+01
97 NA 1.429245e+00
97.1 NA 2.460468e+01
98 NA 4.168671e-02
98.1 NA 1.857247e-01
98.2 NA 1.237113e+01
99 NA 1.247351e-01
99.1 NA 2.189252e+01
99.2 NA 2.492714e+01
100 NA 1.143058e+00
100.1 NA 2.282923e+00
100.2 NA 4.623503e+00
100.3 NA 1.501220e+01
100.4 NA 2.168542e+01
$m5b$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m5b$spM_lvlone
center scale
b1 NA NA
L1mis 0.48184811 0.3462447
Be2 0.04274145 0.1563798
c1 0.25599956 0.6718095
time 2.53394028 1.3818094
abs(c1 - C2) 1.12675664 0.7813693
log(Be2) -11.02063958 6.0744935
I(time^2) 8.32444679 7.0900029
$m5b$mu_reg_norm
[1] 0
$m5b$tau_reg_norm
[1] 1e-04
$m5b$shape_tau_norm
[1] 0.01
$m5b$rate_tau_norm
[1] 0.01
$m5b$mu_reg_gamma
[1] 0
$m5b$tau_reg_gamma
[1] 1e-04
$m5b$shape_tau_gamma
[1] 0.01
$m5b$rate_tau_gamma
[1] 0.01
$m5b$mu_reg_beta
[1] 0
$m5b$tau_reg_beta
[1] 1e-04
$m5b$shape_tau_beta
[1] 0.01
$m5b$rate_tau_beta
[1] 0.01
$m5b$mu_reg_binom
[1] 0
$m5b$tau_reg_binom
[1] 1e-04
$m5b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m5b$shape_diag_RinvD
[1] "0.01"
$m5b$rate_diag_RinvD
[1] "0.001"
$m5b$RinvD_b1_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m5b$KinvD_b1_id
id
4
$m6a
$m6a$M_id
C2 (Intercept) C1
1 -1.381594459 1 0.7175865
2 0.344426024 1 0.7507170
3 NA 1 0.7255954
4 -0.228910007 1 0.7469352
5 NA 1 0.7139120
6 -2.143955482 1 0.7332505
7 -1.156567023 1 0.7345929
8 -0.598827660 1 0.7652589
9 NA 1 0.7200622
10 -1.006719032 1 0.7423879
11 0.239801450 1 0.7437448
12 -1.064969789 1 0.7446470
13 -0.538082688 1 0.7530186
14 NA 1 0.7093137
15 -1.781049276 1 0.7331192
16 NA 1 0.7011390
17 NA 1 0.7432395
18 -0.014579883 1 0.7545191
19 -2.121550136 1 0.7528487
20 NA 1 0.7612865
21 -0.363239698 1 0.7251719
22 -0.121568514 1 0.7300630
23 -0.951271111 1 0.7087249
24 NA 1 0.7391938
25 -0.974288621 1 0.7820641
26 -1.130632418 1 0.7118298
27 0.114339868 1 0.7230857
28 0.238334648 1 0.7489353
29 0.840744958 1 0.7510888
30 NA 1 0.7300717
31 NA 1 0.7550721
32 -1.466312154 1 0.7321898
33 -0.637352277 1 0.7306414
34 NA 1 0.7427216
35 NA 1 0.7193042
36 NA 1 0.7312888
37 NA 1 0.7100436
38 NA 1 0.7670184
39 0.006728205 1 0.7400449
40 NA 1 0.7397304
41 -1.663281353 1 0.7490966
42 0.161184794 1 0.7419274
43 0.457939180 1 0.7527810
44 -0.307070331 1 0.7408315
45 NA 1 0.7347550
46 -1.071668276 1 0.7332398
47 -0.814751321 1 0.7376481
48 -0.547630662 1 0.7346179
49 NA 1 0.7329402
50 -1.350213782 1 0.7260436
51 0.719054706 1 0.7242910
52 NA 1 0.7298067
53 -1.207130750 1 0.7254741
54 NA 1 0.7542067
55 -0.408600991 1 0.7389952
56 -0.271380529 1 0.7520638
57 -1.361925974 1 0.7219958
58 NA 1 0.7259632
59 NA 1 0.7458606
60 -0.323712205 1 0.7672421
61 NA 1 0.7257179
62 NA 1 0.7189892
63 -1.386906880 1 0.7333356
64 NA 1 0.7320243
65 NA 1 0.7477711
66 -0.565191691 1 0.7343974
67 -0.382899912 1 0.7491624
68 NA 1 0.7482736
69 -0.405642769 1 0.7338267
70 NA 1 0.7607742
71 -0.843748427 1 0.7777600
72 0.116003683 1 0.7408143
73 -0.778634325 1 0.7248271
74 NA 1 0.7364916
75 NA 1 0.7464926
76 NA 1 0.7355430
77 -0.632974758 1 0.7208449
78 NA 1 0.7373573
79 -0.778064615 1 0.7598079
80 NA 1 0.7360415
81 NA 1 0.7293932
82 -0.246123253 1 0.7279309
83 -1.239659782 1 0.7344643
84 -0.467772280 1 0.7384350
85 NA 1 0.7323716
86 -2.160485036 1 0.7576597
87 -0.657675572 1 0.7496139
88 NA 1 0.7275239
89 -0.696710744 1 0.7250648
90 NA 1 0.7335262
91 -0.179395847 1 0.7343980
92 -0.441545568 1 0.7380425
93 -0.685799334 1 0.7389460
94 NA 1 0.7259951
95 0.191929445 1 0.7282840
96 NA 1 0.7281676
97 -0.069760671 1 0.7245642
98 NA 1 0.7526938
99 NA 1 0.7272309
100 NA 1 0.7383460
$m6a$M_lvlone
y b2 b21 time
1 -13.0493856 NA NA 0.5090421822
1.1 -9.3335901 0 NA 0.6666076288
1.2 -22.3469852 NA NA 2.1304941282
1.3 -15.0417337 0 NA 2.4954441458
2 -12.0655434 0 NA 3.0164990982
2.1 -15.8674476 NA NA 3.2996806887
2.2 -7.8800006 NA NA 4.1747569619
3 -11.4820604 0 NA 0.8478727890
3.1 -10.5983220 NA NA 3.0654308549
3.2 -22.4519157 1 NA 4.7381553578
4 -1.2697775 1 NA 0.3371432109
4.1 -11.1215184 0 NA 1.0693019140
4.2 -3.6134138 0 NA 2.6148973033
4.3 -14.5982385 0 NA 3.1336532847
5 -6.8457515 NA NA 1.0762525082
5.1 -7.0551214 0 NA 1.7912546196
5.2 -12.3418980 NA NA 2.7960080339
5.3 -9.2366906 NA NA 2.8119940578
6 -5.1648211 NA NA 1.7815462884
7 -10.0599502 NA NA 3.3074087673
7.1 -18.3267285 NA NA 3.7008403614
7.2 -12.5138426 0 NA 4.7716691741
8 -1.6305331 0 NA 1.1246398522
8.1 -9.6520453 0 NA 1.8027009873
8.2 -1.5278462 NA NA 1.8175825174
8.3 -7.4172211 1 NA 2.8384267003
8.4 -7.1238609 0 NA 3.3630275307
8.5 -8.8706950 1 NA 4.4360849704
9 -0.1634429 0 NA 0.9607803822
9.1 -2.6034300 NA NA 2.9177753383
9.2 -6.7272369 NA NA 4.8100892501
10 -6.4172202 NA NA 2.2975509102
10.1 -11.4834569 0 NA 4.1734118364
11 -8.7911356 0 NA 1.1832662905
11.1 -19.6645080 0 NA 1.2346051680
11.2 -20.2030932 0 NA 1.6435316263
11.3 -21.3082176 0 NA 3.3859017969
11.4 -14.5802901 0 NA 4.8118087661
12 -15.2006287 0 NA 0.9591987054
13 0.8058816 NA NA 0.0619085738
13.1 -13.6379208 0 NA 3.5621061502
14 -15.3422873 NA NA 4.0364430007
14.1 -10.0965208 NA NA 4.4710561272
14.2 -16.6452027 NA NA 4.6359198843
14.3 -15.8389733 NA NA 4.6886152599
15 -8.9424594 0 NA 0.5402063532
15.1 -22.0101983 0 NA 1.1893180816
15.2 -7.3975599 0 NA 1.5094739688
15.3 -10.3567334 0 NA 4.9193474615
16 -1.9691302 1 NA 1.2417913869
16.1 -9.9308357 NA NA 2.5675726333
16.2 -6.9626923 NA NA 2.6524101500
16.3 -3.2862557 0 NA 3.5585018690
16.4 -3.3972355 0 NA 3.7612454291
16.5 -11.5767835 NA NA 3.9851612889
17 -10.5474144 0 NA 1.5925356350
17.1 -7.6215009 0 NA 2.4374032998
17.2 -16.5386939 0 NA 3.0256489082
17.3 -20.0004774 NA NA 3.3329089405
17.4 -18.8505475 0 NA 3.8693758985
18 -19.7302351 0 NA 2.4374292302
19 -14.6177568 NA NA 0.9772165376
19.1 -17.8043866 NA NA 1.1466335913
19.2 -15.1641705 0 NA 2.2599126538
19.3 -16.6898418 1 NA 4.2114245973
20 -12.9059229 NA NA 1.7170160066
20.1 -16.8191201 0 NA 1.7562902288
20.2 -6.1010131 1 NA 2.2515566566
20.3 -7.9415371 0 NA 2.2609123867
20.4 -9.3904458 0 NA 3.4913365287
20.5 -13.3504189 0 NA 4.1730977828
21 -7.6974718 0 NA 1.6936582839
21.1 -11.9335526 0 NA 2.9571191233
21.2 -12.7064929 NA NA 3.7887385779
22 -21.5022909 0 NA 2.4696226232
22.1 -12.7745451 0 NA 3.1626627257
23 -3.5146508 0 NA 1.5414533857
23.1 -4.6724048 NA NA 2.3369736120
24 -2.5619821 0 NA 2.8283136466
25 -6.2944970 0 NA 0.5381704110
25.1 -3.8630505 NA NA 1.6069735331
25.2 -14.4205140 1 NA 1.6358226922
25.3 -19.6735037 0 NA 3.2646870392
25.4 -9.0288933 0 NA 4.0782226040
25.5 -9.0509738 NA NA 4.1560292873
26 -19.7340685 NA NA 0.2412706357
26.1 -14.1692728 0 NA 2.4451737676
26.2 -17.2819976 0 NA 3.5988757887
26.3 -24.6265576 0 NA 4.1822362854
27 -7.3354999 0 NA 3.6955824879
27.1 -11.1488468 0 NA 4.2451434687
28 -11.7996597 NA NA 0.5746519344
28.1 -8.2030122 0 NA 2.7943964268
28.2 -26.4317815 0 NA 4.2108539480
28.3 -18.5016071 0 NA 4.4705521734
29 -5.8551395 0 NA 1.1898884235
29.1 -2.0209442 0 NA 1.7624059319
29.2 -5.6368080 0 NA 2.0210406382
29.3 -3.8110961 0 NA 3.4078777023
30 -12.7217702 NA NA 2.2635366488
30.1 -17.0170140 0 NA 3.5938334477
30.2 -25.4236089 0 NA 3.6138710892
31 -17.0783921 0 NA 4.3988140998
32 -18.4338764 0 NA 1.6745209007
32.1 -19.4317212 0 NA 2.9128167813
32.2 -19.4738978 NA NA 2.9676558380
32.3 -21.4922645 NA NA 4.2099863547
33 2.0838099 0 NA 0.0093385763
33.1 -13.3172274 1 NA 3.4591242753
34 -10.0296691 NA NA 1.4998774312
34.1 -25.9426553 0 NA 3.8242761395
34.2 -18.5688138 NA NA 3.9072251692
34.3 -15.4173859 NA NA 3.9582124643
35 -14.3958113 0 NA 1.3294299203
35.1 -12.9457541 0 NA 1.5276966314
35.2 -16.1380691 NA NA 4.5025920868
36 -12.8166968 NA NA 0.7123168337
36.1 -14.3989481 NA NA 1.7972493160
36.2 -12.2436943 0 NA 1.8262697803
36.3 -15.0104638 0 NA 4.2840119381
36.4 -10.1775457 0 NA 4.6194464504
37 -15.2223495 0 NA 2.0018732361
37.1 -14.7526195 0 NA 3.6656836793
37.2 -19.8168430 0 NA 3.9663937816
38 -2.7065118 0 NA 0.9826511063
39 -8.7288138 1 NA 0.6921808305
39.1 -9.2746473 0 NA 0.9027792048
39.2 -18.2695344 NA NA 1.3055654289
39.3 -13.8219083 NA NA 1.5412842878
39.4 -16.2254704 0 NA 3.1834997435
39.5 -21.7283648 1 NA 4.1394166439
40 1.8291916 0 NA 1.1330395646
40.1 -6.6916432 1 NA 2.6940994046
40.2 -1.6278171 0 NA 3.0396614212
40.3 -10.5749790 NA NA 4.6762977762
41 -3.1556121 0 NA 1.9337158254
41.1 -11.5895327 NA NA 3.1956304458
41.2 -18.9352091 0 NA 3.2846923557
41.3 -15.9788960 NA NA 3.3813529415
41.4 -9.6070508 0 NA 3.5482964432
42 -5.2159485 0 NA 0.4859252973
42.1 -15.9878743 1 NA 4.3293134298
43 -16.6104361 0 NA 0.5616614548
43.1 -9.5549441 1 NA 1.0743579536
43.2 -14.2003491 0 NA 2.6131797966
44 -8.1969033 0 NA 0.7662644819
44.1 -19.9270197 0 NA 2.6490291790
44.2 -22.6521171 0 NA 3.3371910988
44.3 -21.1903736 0 NA 4.1154200875
45 -0.5686627 NA NA 0.1957449992
45.1 -7.5645740 1 NA 1.9963831536
46 -19.1624789 0 NA 1.3477755385
46.1 -18.4487574 0 NA 2.8565793915
46.2 -15.8222682 0 NA 4.4160729996
47 -5.4165074 0 NA 0.6012621359
47.1 -15.0975029 0 NA 2.4097121472
47.2 -12.9971413 0 NA 2.9975794035
47.3 -10.6844521 NA NA 3.1829649757
47.4 -18.2214784 0 NA 4.6201055450
48 -8.3101471 1 NA 2.8607365978
48.1 -18.3854275 1 NA 2.9098354396
49 -13.0130319 NA NA 2.7179756400
50 -10.4579977 0 NA 1.1762060679
51 -19.3157621 0 NA 1.4304436720
52 -4.4747188 0 NA 2.1266646020
52.1 -4.3163827 0 NA 3.1000545993
52.2 -6.9761408 0 NA 3.1268477370
52.3 -20.1764756 0 NA 3.5711459327
52.4 -8.9036692 0 NA 4.7983659909
52.5 -5.6949642 0 NA 4.9818264414
53 -10.3141887 0 NA 0.4965799209
53.1 -8.2642654 0 NA 3.5505357443
53.2 -9.1691554 NA NA 4.5790420019
54 -6.2198754 NA NA 1.4034724841
54.1 -15.7192609 NA NA 1.8812377600
54.2 -13.0978998 NA NA 2.5107589352
54.3 -5.1195299 NA NA 2.7848406672
54.4 -16.5771751 0 NA 4.0143877396
55 -5.7348534 0 NA 0.6118522980
55.1 -7.3217494 0 NA 0.7463747414
55.2 -12.2171938 NA NA 2.8201208171
55.3 -12.9821266 NA NA 3.1326431572
55.4 -14.8599983 0 NA 3.2218102901
56 -14.1764282 0 NA 1.2231332215
56.1 -12.5343602 NA NA 2.3573202139
56.2 -8.4573382 NA NA 2.5674936292
56.3 -12.4633969 1 NA 2.9507164378
56.4 -17.3841863 0 NA 3.2272730360
56.5 -14.8147645 0 NA 3.4175522043
57 -3.1403293 0 NA 0.2370331448
57.1 -11.1509248 0 NA 0.2481445030
57.2 -6.3940143 0 NA 1.1405586067
57.3 -9.3473241 NA NA 2.1153886721
58 -12.0245677 0 NA 1.2210099772
58.1 -9.2112246 NA NA 1.6334245703
58.2 -1.2071742 1 NA 1.6791862890
58.3 -11.0141711 1 NA 2.6320121693
58.4 -5.3721214 0 NA 2.8477731440
58.5 -7.8523047 0 NA 3.5715569824
59 -13.2946560 NA NA 1.9023998594
59.1 -10.0530648 1 NA 4.9736620474
60 -19.2209402 0 NA 2.8854503250
61 -4.6699914 NA NA 0.7213630795
61.1 -3.5981894 1 NA 2.3186947661
61.2 -1.4713611 1 NA 2.5077313243
61.3 -3.8819786 0 NA 3.1731073430
61.4 0.1041413 0 NA 3.6022726283
62 -2.8591600 NA NA 0.5336771999
62.1 -6.9461986 1 NA 0.6987666548
62.2 -16.7910593 0 NA 3.4584309917
62.3 -17.9844596 0 NA 4.8028772371
63 -24.0335535 NA NA 2.8097350930
63.1 -11.7765300 0 NA 3.9653754211
64 -20.5963897 0 NA 4.1191305732
65 -2.7969169 0 NA 0.7076152589
65.1 -11.1778694 0 NA 2.0252246363
65.2 -5.2830399 0 NA 3.1127382827
65.3 -7.9353390 0 NA 3.1969087943
66 -13.2318328 NA NA 3.4943454154
66.1 -1.9090560 0 NA 3.7677437009
66.2 -16.6643889 0 NA 3.9486138616
67 -25.6073277 NA NA 4.1728388879
68 -13.4806759 0 NA 0.1291919907
68.1 -18.4557183 0 NA 1.7809643946
68.2 -13.3982327 NA NA 2.0493205660
68.3 -12.4977127 0 NA 2.9406870750
68.4 -11.7073990 NA NA 4.0406670363
69 -14.5290675 0 NA 4.1451198701
70 -15.2122709 0 NA 0.1992557163
70.1 -7.8681167 0 NA 0.4829774413
71 -10.3352703 0 NA 0.7741605386
71.1 -7.5699888 1 NA 1.4883817220
71.2 -18.4680702 0 NA 4.0758526395
71.3 -21.4316644 1 NA 4.7048238723
71.4 -8.1137650 0 NA 4.7242791823
72 -9.1848162 0 NA 0.9321196121
72.1 -23.7538846 0 NA 1.1799991806
72.2 -26.3421306 NA NA 1.8917567329
72.3 -27.2843801 0 NA 3.4853593935
72.4 -20.8541617 0 NA 3.6884259700
72.5 -12.8948965 0 NA 4.0854155901
73 -2.6091307 0 NA 4.6019889915
74 -8.2790175 0 NA 1.4626806753
75 -12.5029612 NA NA 3.2524286874
76 -6.0061671 0 NA 1.8074807397
76.1 -8.8149114 0 NA 4.2685073183
76.2 -11.8359043 0 NA 4.9688734859
77 0.4772521 NA NA 0.8459033852
78 -9.4105229 0 NA 0.8231094317
79 -1.0217265 NA NA 0.0583819521
79.1 -11.8125257 0 NA 2.4406372628
79.2 -10.5465186 NA NA 3.2962526032
80 -12.7366807 NA NA 0.8985060186
80.1 -9.0584783 0 NA 1.3434670598
80.2 -16.6381566 NA NA 2.8025900386
81 0.5547913 0 NA 0.0101324962
81.1 -4.0892715 0 NA 0.9421709494
81.2 1.8283303 NA NA 3.0542453879
81.3 -5.2166381 0 NA 3.3456630446
82 -3.0749381 NA NA 1.3791010005
82.1 -10.5506696 0 NA 1.7601010622
82.2 -18.2226347 1 NA 2.6233131927
83 -12.5872635 NA NA 0.0537394290
83.1 -11.9756502 0 NA 2.9061570496
83.2 -10.6744217 0 NA 3.1189457362
83.3 -19.2714012 NA NA 4.7663642222
84 -2.6320312 0 NA 2.7254060237
84.1 -9.8140094 NA NA 3.3364784659
85 -12.3886736 1 NA 0.2977756259
85.1 -12.9196365 NA NA 1.7394116637
85.2 -9.6433248 0 NA 2.6846330194
85.3 -6.3296340 0 NA 3.1608762743
85.4 -7.0405525 0 NA 3.9452053758
85.5 -13.6714939 0 NA 4.5092553482
86 -10.8756412 0 NA 0.8476278360
86.1 -12.0055331 NA NA 1.0118629411
86.2 -13.3724699 NA NA 1.2511159515
86.3 -13.3252145 0 NA 2.1870554925
86.4 -14.9191290 NA NA 2.4532935000
86.5 -17.7515546 0 NA 3.8206058508
87 -10.7027963 NA NA 2.7069531474
87.1 -22.4941954 NA NA 3.4462517721
87.2 -14.9616716 NA NA 4.5241666853
88 -2.2264493 0 NA 0.0005892443
88.1 -8.9626474 NA NA 0.7116099866
88.2 -2.5095281 0 NA 2.4952722900
88.3 -16.3345673 0 NA 3.2995816297
89 -11.0459647 0 NA 0.6462086167
90 -4.5610239 0 NA 0.1696030737
90.1 -11.7036651 0 NA 2.5980385230
90.2 -5.3838521 0 NA 2.6651392167
90.3 -4.1636999 NA NA 3.1242690247
91 -7.1462503 0 NA 0.6382618390
91.1 -12.8374475 0 NA 2.6224059286
91.2 -18.2576707 0 NA 4.7772527603
92 -6.4119222 0 NA 0.0737052364
93 5.2122168 NA NA 0.2788909199
93.1 3.1211725 0 NA 1.0357759963
93.2 -3.6841177 NA NA 2.4916551099
93.3 2.6223542 0 NA 2.8876129608
93.4 -11.1877696 0 NA 4.4639474002
94 -6.9602492 NA NA 0.8488043118
94.1 -7.4318416 0 NA 1.0552454425
94.2 -4.3498045 0 NA 1.9445500884
94.3 -11.6340088 NA NA 3.0710722448
94.4 -12.9357964 0 NA 3.0872731935
94.5 -14.7648530 1 NA 4.3805759016
95 -12.8849309 0 NA 2.0199063048
95.1 -9.7451502 NA NA 4.0184444457
95.2 -0.8535063 0 NA 4.5596531732
96 -4.9139832 0 NA 0.0311333477
96.1 -3.9582653 0 NA 0.1324267720
96.2 -9.6555492 0 NA 0.6701303425
96.3 -11.8690793 NA NA 2.1775037691
96.4 -11.0224373 1 NA 2.2246142488
96.5 -10.9530403 1 NA 4.2377650598
97 -9.8540471 0 NA 1.1955102731
97.1 -19.2262840 0 NA 4.9603108643
98 -11.9651231 0 NA 0.2041732438
98.1 -2.6515128 0 NA 0.4309578973
98.2 -12.2606382 1 NA 3.5172611906
99 -11.4720500 0 NA 0.3531786101
99.1 -14.0596866 0 NA 4.6789444226
99.2 -17.3939469 0 NA 4.9927084171
100 1.1005874 NA NA 1.0691387602
100.1 -3.8226248 NA NA 1.5109344281
100.2 -0.9123182 0 NA 2.1502332564
100.3 -15.8389474 NA NA 3.8745574222
100.4 -12.8093826 0 NA 4.6567608765
$m6a$spM_id
center scale
C2 -0.6240921 0.68571078
(Intercept) NA NA
C1 0.7372814 0.01472882
$m6a$spM_lvlone
center scale
y -11.17337 6.249662
b2 NA NA
b21 NA NA
time 2.53394 1.381809
$m6a$mu_reg_norm
[1] 0
$m6a$tau_reg_norm
[1] 1e-04
$m6a$shape_tau_norm
[1] 0.01
$m6a$rate_tau_norm
[1] 0.01
$m6a$mu_reg_binom
[1] 0
$m6a$tau_reg_binom
[1] 1e-04
$m6a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m6a$shape_diag_RinvD
[1] "0.01"
$m6a$rate_diag_RinvD
[1] "0.001"
$m6b
$m6b$M_id
C2 (Intercept) B11
1 -1.381594459 1 1
2 0.344426024 1 1
3 NA 1 1
4 -0.228910007 1 0
5 NA 1 1
6 -2.143955482 1 1
7 -1.156567023 1 1
8 -0.598827660 1 0
9 NA 1 1
10 -1.006719032 1 1
11 0.239801450 1 0
12 -1.064969789 1 1
13 -0.538082688 1 1
14 NA 1 1
15 -1.781049276 1 1
16 NA 1 1
17 NA 1 1
18 -0.014579883 1 1
19 -2.121550136 1 1
20 NA 1 0
21 -0.363239698 1 1
22 -0.121568514 1 1
23 -0.951271111 1 1
24 NA 1 0
25 -0.974288621 1 1
26 -1.130632418 1 1
27 0.114339868 1 0
28 0.238334648 1 1
29 0.840744958 1 1
30 NA 1 1
31 NA 1 1
32 -1.466312154 1 1
33 -0.637352277 1 1
34 NA 1 1
35 NA 1 1
36 NA 1 0
37 NA 1 0
38 NA 1 1
39 0.006728205 1 1
40 NA 1 1
41 -1.663281353 1 1
42 0.161184794 1 1
43 0.457939180 1 1
44 -0.307070331 1 1
45 NA 1 0
46 -1.071668276 1 1
47 -0.814751321 1 0
48 -0.547630662 1 0
49 NA 1 1
50 -1.350213782 1 1
51 0.719054706 1 1
52 NA 1 0
53 -1.207130750 1 1
54 NA 1 1
55 -0.408600991 1 1
56 -0.271380529 1 1
57 -1.361925974 1 1
58 NA 1 1
59 NA 1 1
60 -0.323712205 1 1
61 NA 1 0
62 NA 1 1
63 -1.386906880 1 1
64 NA 1 1
65 NA 1 1
66 -0.565191691 1 0
67 -0.382899912 1 0
68 NA 1 1
69 -0.405642769 1 1
70 NA 1 1
71 -0.843748427 1 1
72 0.116003683 1 1
73 -0.778634325 1 1
74 NA 1 0
75 NA 1 1
76 NA 1 1
77 -0.632974758 1 1
78 NA 1 1
79 -0.778064615 1 1
80 NA 1 1
81 NA 1 1
82 -0.246123253 1 1
83 -1.239659782 1 0
84 -0.467772280 1 0
85 NA 1 1
86 -2.160485036 1 1
87 -0.657675572 1 1
88 NA 1 1
89 -0.696710744 1 1
90 NA 1 0
91 -0.179395847 1 1
92 -0.441545568 1 1
93 -0.685799334 1 0
94 NA 1 1
95 0.191929445 1 0
96 NA 1 0
97 -0.069760671 1 1
98 NA 1 1
99 NA 1 1
100 NA 1 1
$m6b$M_lvlone
b1 c1 time I(time^2)
1 0 0.7592026489 0.5090421822 2.591239e-01
1.1 1 0.9548337990 0.6666076288 4.443657e-01
1.2 1 0.5612235156 2.1304941282 4.539005e+00
1.3 0 1.1873391025 2.4954441458 6.227241e+00
2 1 0.9192204198 3.0164990982 9.099267e+00
2.1 1 -0.1870730476 3.2996806887 1.088789e+01
2.2 1 1.2517512331 4.1747569619 1.742860e+01
3 1 -0.0605087604 0.8478727890 7.188883e-01
3.1 0 0.3788637747 3.0654308549 9.396866e+00
3.2 0 0.9872578281 4.7381553578 2.245012e+01
4 1 1.4930175328 0.3371432109 1.136655e-01
4.1 1 -0.7692526880 1.0693019140 1.143407e+00
4.2 0 0.9180841450 2.6148973033 6.837688e+00
4.3 1 -0.0541170782 3.1336532847 9.819783e+00
5 0 -0.1376784521 1.0762525082 1.158319e+00
5.1 1 -0.2740585866 1.7912546196 3.208593e+00
5.2 1 0.4670496929 2.7960080339 7.817661e+00
5.3 1 0.1740288049 2.8119940578 7.907311e+00
6 0 0.9868044683 1.7815462884 3.173907e+00
7 1 -0.1280320918 3.3074087673 1.093895e+01
7.1 0 0.4242971219 3.7008403614 1.369622e+01
7.2 1 0.0777182491 4.7716691741 2.276883e+01
8 0 -0.5791408712 1.1246398522 1.264815e+00
8.1 1 0.3128604232 1.8027009873 3.249731e+00
8.2 1 0.6258446356 1.8175825174 3.303606e+00
8.3 0 -0.1040137707 2.8384267003 8.056666e+00
8.4 0 0.0481450285 3.3630275307 1.130995e+01
8.5 1 0.3831763675 4.4360849704 1.967885e+01
9 1 -0.1757592269 0.9607803822 9.230989e-01
9.1 1 -0.1791541200 2.9177753383 8.513413e+00
9.2 0 -0.0957042935 4.8100892501 2.313696e+01
10 1 -0.5598409704 2.2975509102 5.278740e+00
10.1 1 -0.2318340451 4.1734118364 1.741737e+01
11 1 0.5086859475 1.1832662905 1.400119e+00
11.1 1 0.4951758188 1.2346051680 1.524250e+00
11.2 1 -1.1022162541 1.6435316263 2.701196e+00
11.3 1 -0.0611636705 3.3859017969 1.146433e+01
11.4 1 -0.4971774316 4.8118087661 2.315350e+01
12 1 -0.2433996286 0.9591987054 9.200622e-01
13 0 0.8799673116 0.0619085738 3.832672e-03
13.1 1 0.1079022586 3.5621061502 1.268860e+01
14 0 0.9991752617 4.0364430007 1.629287e+01
14.1 1 -0.1094019046 4.4710561272 1.999034e+01
14.2 0 0.1518967560 4.6359198843 2.149175e+01
14.3 0 0.3521012473 4.6886152599 2.198311e+01
15 0 0.3464447888 0.5402063532 2.918229e-01
15.1 0 -0.4767313971 1.1893180816 1.414477e+00
15.2 0 0.5759767791 1.5094739688 2.278512e+00
15.3 1 -0.1713452662 4.9193474615 2.419998e+01
16 1 0.4564754473 1.2417913869 1.542046e+00
16.1 0 1.0652558311 2.5675726333 6.592429e+00
16.2 1 0.6971872493 2.6524101500 7.035280e+00
16.3 1 0.5259331838 3.5585018690 1.266294e+01
16.4 1 0.2046601798 3.7612454291 1.414697e+01
16.5 0 1.0718540464 3.9851612889 1.588151e+01
17 0 0.6048676222 1.5925356350 2.536170e+00
17.1 0 0.2323298304 2.4374032998 5.940935e+00
17.2 1 1.2617499032 3.0256489082 9.154551e+00
17.3 0 -0.3913230895 3.3329089405 1.110828e+01
17.4 1 0.9577299112 3.8693758985 1.497207e+01
18 1 -0.0050324072 2.4374292302 5.941061e+00
19 1 -0.4187468937 0.9772165376 9.549522e-01
19.1 1 -0.4478828944 1.1466335913 1.314769e+00
19.2 1 -1.1966721302 2.2599126538 5.107205e+00
19.3 1 -0.5877091668 4.2114245973 1.773610e+01
20 0 0.6838223064 1.7170160066 2.948144e+00
20.1 1 0.3278571109 1.7562902288 3.084555e+00
20.2 0 -0.8489831990 2.2515566566 5.069507e+00
20.3 0 1.3169975191 2.2609123867 5.111725e+00
20.4 0 0.0444804531 3.4913365287 1.218943e+01
20.5 0 -0.4535207652 4.1730977828 1.741475e+01
21 1 -0.4030302960 1.6936582839 2.868478e+00
21.1 1 -0.4069674045 2.9571191233 8.744554e+00
21.2 0 1.0650265940 3.7887385779 1.435454e+01
22 0 -0.0673274516 2.4696226232 6.099036e+00
22.1 1 0.9601388170 3.1626627257 1.000244e+01
23 1 0.5556634840 1.5414533857 2.376079e+00
23.1 1 1.4407865964 2.3369736120 5.461446e+00
24 0 0.3856376411 2.8283136466 7.999358e+00
25 0 0.3564400705 0.5381704110 2.896274e-01
25.1 1 0.0982553434 1.6069735331 2.582364e+00
25.2 1 0.1928682598 1.6358226922 2.675916e+00
25.3 0 -0.0192488594 3.2646870392 1.065818e+01
25.4 0 0.4466012931 4.0782226040 1.663190e+01
25.5 0 1.1425193342 4.1560292873 1.727258e+01
26 1 0.5341531449 0.2412706357 5.821152e-02
26.1 1 1.2268695927 2.4451737676 5.978875e+00
26.2 1 0.3678294939 3.5988757887 1.295191e+01
26.3 0 0.5948516018 4.1822362854 1.749110e+01
27 1 -0.3342844147 3.6955824879 1.365733e+01
27.1 1 -0.4835141229 4.2451434687 1.802124e+01
28 1 -0.7145915499 0.5746519344 3.302248e-01
28.1 0 0.5063671955 2.7943964268 7.808651e+00
28.2 1 -0.2067413142 4.2108539480 1.773129e+01
28.3 1 0.1196789973 4.4705521734 1.998584e+01
29 1 0.1392699487 1.1898884235 1.415834e+00
29.1 0 0.7960234776 1.7624059319 3.106075e+00
29.2 0 1.0398214352 2.0210406382 4.084605e+00
29.3 1 0.0813246429 3.4078777023 1.161363e+01
30 1 -0.3296323050 2.2635366488 5.123598e+00
30.1 1 1.3635850954 3.5938334477 1.291564e+01
30.2 1 0.7354171050 3.6138710892 1.306006e+01
31 0 0.3708398217 4.3988140998 1.934957e+01
32 1 -0.0474059668 1.6745209007 2.804020e+00
32.1 1 1.2507771489 2.9128167813 8.484502e+00
32.2 1 0.1142915519 2.9676558380 8.806981e+00
32.3 1 0.6773270619 4.2099863547 1.772399e+01
33 0 0.1774293842 0.0093385763 8.720901e-05
33.1 0 0.6159606291 3.4591242753 1.196554e+01
34 1 0.8590979166 1.4998774312 2.249632e+00
34.1 0 0.0546216775 3.8242761395 1.462509e+01
34.2 1 -0.0897224473 3.9072251692 1.526641e+01
34.3 1 0.4163395571 3.9582124643 1.566745e+01
35 1 -1.4693520528 1.3294299203 1.767384e+00
35.1 0 -0.3031734330 1.5276966314 2.333857e+00
35.2 1 -0.6045512101 4.5025920868 2.027334e+01
36 0 0.9823048960 0.7123168337 5.073953e-01
36.1 0 1.4466051416 1.7972493160 3.230105e+00
36.2 1 1.1606752905 1.8262697803 3.335261e+00
36.3 0 0.8373091576 4.2840119381 1.835276e+01
36.4 1 0.2640591685 4.6194464504 2.133929e+01
37 1 0.1177313455 2.0018732361 4.007496e+00
37.1 0 -0.1415483779 3.6656836793 1.343724e+01
37.2 0 0.0054610124 3.9663937816 1.573228e+01
38 1 0.8078948077 0.9826511063 9.656032e-01
39 1 0.9876451040 0.6921808305 4.791143e-01
39.1 0 -0.3431222274 0.9027792048 8.150103e-01
39.2 0 -1.7909380751 1.3055654289 1.704501e+00
39.3 0 -0.1798746191 1.5412842878 2.375557e+00
39.4 1 -0.1850961689 3.1834997435 1.013467e+01
39.5 1 0.4544226146 4.1394166439 1.713477e+01
40 0 0.5350190436 1.1330395646 1.283779e+00
40.1 0 0.4189342752 2.6940994046 7.258172e+00
40.2 0 0.4211994981 3.0396614212 9.239542e+00
40.3 1 0.0916687506 4.6762977762 2.186776e+01
41 1 -0.1035047421 1.9337158254 3.739257e+00
41.1 1 -0.4684202411 3.1956304458 1.021205e+01
41.2 0 0.5972615368 3.2846923557 1.078920e+01
41.3 1 0.9885613862 3.3813529415 1.143355e+01
41.4 1 -0.3908036794 3.5482964432 1.259041e+01
42 1 -0.0338893961 0.4859252973 2.361234e-01
42.1 1 -0.4498363172 4.3293134298 1.874295e+01
43 0 0.8965546110 0.5616614548 3.154636e-01
43.1 0 0.6199122090 1.0743579536 1.154245e+00
43.2 1 0.1804894429 2.6131797966 6.828709e+00
44 1 1.3221409285 0.7662644819 5.871613e-01
44.1 0 0.3416426284 2.6490291790 7.017356e+00
44.2 0 0.5706610068 3.3371910988 1.113684e+01
44.3 1 1.2679497430 4.1154200875 1.693668e+01
45 1 0.1414983160 0.1957449992 3.831610e-02
45.1 0 0.7220892521 1.9963831536 3.985546e+00
46 1 1.5391054233 1.3477755385 1.816499e+00
46.1 0 0.3889107049 2.8565793915 8.160046e+00
46.2 1 0.1248719493 4.4160729996 1.950170e+01
47 0 0.2014101100 0.6012621359 3.615162e-01
47.1 0 0.2982973539 2.4097121472 5.806713e+00
47.2 1 1.1518107179 2.9975794035 8.985482e+00
47.3 0 0.5196802157 3.1829649757 1.013127e+01
47.4 0 0.3702301552 4.6201055450 2.134538e+01
48 0 -0.2128602862 2.8607365978 8.183814e+00
48.1 1 -0.5337239976 2.9098354396 8.467142e+00
49 0 -0.5236770035 2.7179756400 7.387392e+00
50 1 0.3897705981 1.1762060679 1.383461e+00
51 1 -0.7213343736 1.4304436720 2.046169e+00
52 1 0.3758235358 2.1266646020 4.522702e+00
52.1 1 0.7138067080 3.1000545993 9.610339e+00
52.2 0 0.8872895233 3.1268477370 9.777177e+00
52.3 0 -0.9664587437 3.5711459327 1.275308e+01
52.4 1 0.0254566848 4.7983659909 2.302432e+01
52.5 1 0.4155259424 4.9818264414 2.481859e+01
53 1 0.5675736897 0.4965799209 2.465916e-01
53.1 1 -0.3154088781 3.5505357443 1.260630e+01
53.2 1 0.2162315769 4.5790420019 2.096763e+01
54 0 -0.0880802382 1.4034724841 1.969735e+00
54.1 1 0.4129127672 1.8812377600 3.539056e+00
54.2 0 1.0119546775 2.5107589352 6.303910e+00
54.3 1 -0.1112901990 2.7848406672 7.755338e+00
54.4 0 0.8587727145 4.0143877396 1.611531e+01
55 1 -0.0116453589 0.6118522980 3.743632e-01
55.1 1 0.5835528661 0.7463747414 5.570753e-01
55.2 1 -1.0010857254 2.8201208171 7.953081e+00
55.3 0 -0.4796526070 3.1326431572 9.813453e+00
55.4 1 -0.1202746964 3.2218102901 1.038006e+01
56 0 0.5176377612 1.2231332215 1.496055e+00
56.1 1 -1.1136932588 2.3573202139 5.556959e+00
56.2 1 -0.0168103281 2.5674936292 6.592024e+00
56.3 0 0.3933023606 2.9507164378 8.706727e+00
56.4 0 0.3714625139 3.2272730360 1.041529e+01
56.5 1 0.7811448179 3.4175522043 1.167966e+01
57 1 -1.0868304872 0.2370331448 5.618471e-02
57.1 1 0.8018626997 0.2481445030 6.157569e-02
57.2 0 -0.1159517011 1.1405586067 1.300874e+00
57.3 0 0.6785562445 2.1153886721 4.474869e+00
58 1 1.6476207996 1.2210099772 1.490865e+00
58.1 1 0.3402652711 1.6334245703 2.668076e+00
58.2 1 -0.1111300753 1.6791862890 2.819667e+00
58.3 1 -0.5409234285 2.6320121693 6.927488e+00
58.4 1 -0.1271327672 2.8477731440 8.109812e+00
58.5 1 0.8713264822 3.5715569824 1.275602e+01
59 0 0.4766421367 1.9023998594 3.619125e+00
59.1 1 1.0028089765 4.9736620474 2.473731e+01
60 0 0.5231452932 2.8854503250 8.325824e+00
61 1 -0.7190130614 0.7213630795 5.203647e-01
61.1 1 0.8353702312 2.3186947661 5.376345e+00
61.2 1 1.0229058138 2.5077313243 6.288716e+00
61.3 0 1.1717723589 3.1731073430 1.006861e+01
61.4 1 -0.0629201596 3.6022726283 1.297637e+01
62 1 -0.3979137604 0.5336771999 2.848114e-01
62.1 0 0.6830738372 0.6987666548 4.882748e-01
62.2 0 0.4301745954 3.4584309917 1.196074e+01
62.3 1 -0.0333139957 4.8028772371 2.306763e+01
63 0 0.3345678035 2.8097350930 7.894611e+00
63.1 1 0.3643769511 3.9653754211 1.572420e+01
64 1 0.3949911859 4.1191305732 1.696724e+01
65 1 1.2000091513 0.7076152589 5.007194e-01
65.1 1 0.0110122646 2.0252246363 4.101535e+00
65.2 0 -0.5776452043 3.1127382827 9.689140e+00
65.3 0 -0.1372183563 3.1969087943 1.022023e+01
66 1 -0.5081302805 3.4943454154 1.221045e+01
66.1 0 -0.1447837412 3.7677437009 1.419589e+01
66.2 0 0.1906241379 3.9486138616 1.559155e+01
67 0 1.6716027681 4.1728388879 1.741258e+01
68 0 0.5691848839 0.1291919907 1.669057e-02
68.1 0 0.1004860389 1.7809643946 3.171834e+00
68.2 0 -0.0061241827 2.0493205660 4.199715e+00
68.3 0 0.7443745962 2.9406870750 8.647640e+00
68.4 1 0.8726923437 4.0406670363 1.632699e+01
69 1 0.0381382683 4.1451198701 1.718202e+01
70 1 0.8126204217 0.1992557163 3.970284e-02
70.1 1 0.4691503050 0.4829774413 2.332672e-01
71 1 -0.5529062591 0.7741605386 5.993245e-01
71.1 1 -0.1103252087 1.4883817220 2.215280e+00
71.2 0 1.7178492547 4.0758526395 1.661257e+01
71.3 0 -1.0118346755 4.7048238723 2.213537e+01
71.4 0 1.8623785017 4.7242791823 2.231881e+01
72 1 -0.4521659275 0.9321196121 8.688470e-01
72.1 1 0.1375317317 1.1799991806 1.392398e+00
72.2 1 -0.4170988856 1.8917567329 3.578744e+00
72.3 0 0.7107266765 3.4853593935 1.214773e+01
72.4 0 0.1451969143 3.6884259700 1.360449e+01
72.5 1 1.6298050306 4.0854155901 1.669062e+01
73 1 -0.0307469467 4.6019889915 2.117830e+01
74 1 0.3730017941 1.4626806753 2.139435e+00
75 0 -0.4908003566 3.2524286874 1.057829e+01
76 1 -0.9888876620 1.8074807397 3.266987e+00
76.1 1 0.0003798292 4.2685073183 1.822015e+01
76.2 1 -0.8421863763 4.9688734859 2.468970e+01
77 1 -0.4986802480 0.8459033852 7.155525e-01
78 1 0.0417330969 0.8231094317 6.775091e-01
79 0 -0.3767450660 0.0583819521 3.408452e-03
79.1 1 0.1516000028 2.4406372628 5.956710e+00
79.2 0 -0.1888160741 3.2962526032 1.086528e+01
80 1 -0.0041558414 0.8985060186 8.073131e-01
80.1 0 -0.0329337062 1.3434670598 1.804904e+00
80.2 1 0.5046816157 2.8025900386 7.854511e+00
81 1 -0.9493950353 0.0101324962 1.026675e-04
81.1 1 0.2443038954 0.9421709494 8.876861e-01
81.2 1 0.6476958410 3.0542453879 9.328415e+00
81.3 1 0.4182528210 3.3456630446 1.119346e+01
82 1 1.1088801952 1.3791010005 1.901920e+00
82.1 1 0.9334157763 1.7601010622 3.097956e+00
82.2 0 0.4958140634 2.6233131927 6.881772e+00
83 1 0.5104724530 0.0537394290 2.887926e-03
83.1 0 -0.0513309106 2.9061570496 8.445749e+00
83.2 0 -0.2067792494 3.1189457362 9.727823e+00
83.3 1 -0.0534169155 4.7663642222 2.271823e+01
84 1 -0.0255753653 2.7254060237 7.427838e+00
84.1 0 -1.8234189877 3.3364784659 1.113209e+01
85 0 -0.0114038622 0.2977756259 8.867032e-02
85.1 0 -0.0577615939 1.7394116637 3.025553e+00
85.2 1 -0.2241856342 2.6846330194 7.207254e+00
85.3 1 -0.0520175929 3.1608762743 9.991139e+00
85.4 1 0.2892733846 3.9452053758 1.556465e+01
85.5 1 -0.3740417009 4.5092553482 2.033338e+01
86 0 0.4293735089 0.8476278360 7.184729e-01
86.1 1 -0.1363456521 1.0118629411 1.023867e+00
86.2 1 0.1230989293 1.2511159515 1.565291e+00
86.3 0 0.3305413955 2.1870554925 4.783212e+00
86.4 1 2.6003411822 2.4532935000 6.018649e+00
86.5 0 -0.1420690052 3.8206058508 1.459703e+01
87 0 1.0457427869 2.7069531474 7.327595e+00
87.1 1 -0.2973007190 3.4462517721 1.187665e+01
87.2 0 0.4396872616 4.5241666853 2.046808e+01
88 0 -0.0601928334 0.0005892443 3.472088e-07
88.1 0 -1.0124347595 0.7116099866 5.063888e-01
88.2 0 0.5730917016 2.4952722900 6.226384e+00
88.3 0 -0.0029455332 3.2995816297 1.088724e+01
89 1 1.5465903721 0.6462086167 4.175856e-01
90 0 0.0626760573 0.1696030737 2.876520e-02
90.1 1 1.1896872985 2.5980385230 6.749804e+00
90.2 1 0.2597888783 2.6651392167 7.102967e+00
90.3 0 0.6599799887 3.1242690247 9.761057e+00
91 0 1.1213651365 0.6382618390 4.073782e-01
91.1 0 1.2046371625 2.6224059286 6.877013e+00
91.2 1 0.3395603754 4.7772527603 2.282214e+01
92 1 0.4674939332 0.0737052364 5.432462e-03
93 0 0.2677965647 0.2788909199 7.778015e-02
93.1 1 1.6424445368 1.0357759963 1.072832e+00
93.2 0 0.7101700066 2.4916551099 6.208345e+00
93.3 1 1.1222322893 2.8876129608 8.338309e+00
93.4 0 1.4628960401 4.4639474002 1.992683e+01
94 1 -0.2904211940 0.8488043118 7.204688e-01
94.1 0 0.0147813580 1.0552454425 1.113543e+00
94.2 1 -0.4536774482 1.9445500884 3.781275e+00
94.3 0 0.6793464917 3.0710722448 9.431485e+00
94.4 0 -0.9411356550 3.0872731935 9.531256e+00
94.5 0 0.5683867264 4.3805759016 1.918945e+01
95 1 0.2375652188 2.0199063048 4.080021e+00
95.1 1 0.0767152977 4.0184444457 1.614790e+01
95.2 0 -0.6886731251 4.5596531732 2.079044e+01
96 1 0.7813892121 0.0311333477 9.692853e-04
96.1 0 0.3391519695 0.1324267720 1.753685e-02
96.2 0 -0.4857246503 0.6701303425 4.490747e-01
96.3 0 0.8771471244 2.1775037691 4.741523e+00
96.4 0 1.9030768981 2.2246142488 4.948909e+00
96.5 1 -0.1684332749 4.2377650598 1.795865e+01
97 0 1.3775130083 1.1955102731 1.429245e+00
97.1 0 -1.7323228619 4.9603108643 2.460468e+01
98 0 -1.2648518889 0.2041732438 4.168671e-02
98.1 0 -0.9042716241 0.4309578973 1.857247e-01
98.2 0 -0.1560385207 3.5172611906 1.237113e+01
99 1 0.7993356425 0.3531786101 1.247351e-01
99.1 1 1.0355522332 4.6789444226 2.189252e+01
99.2 1 -0.1150895843 4.9927084171 2.492714e+01
100 0 0.0369067906 1.0691387602 1.143058e+00
100.1 0 1.6023713093 1.5109344281 2.282923e+00
100.2 1 0.8861545820 2.1502332564 4.623503e+00
100.3 1 0.1277046316 3.8745574222 1.501220e+01
100.4 1 -0.0834577654 4.6567608765 2.168542e+01
$m6b$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
B11 NA NA
$m6b$spM_lvlone
center scale
b1 NA NA
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
I(time^2) 8.3244468 7.0900029
$m6b$mu_reg_norm
[1] 0
$m6b$tau_reg_norm
[1] 1e-04
$m6b$shape_tau_norm
[1] 0.01
$m6b$rate_tau_norm
[1] 0.01
$m6b$mu_reg_binom
[1] 0
$m6b$tau_reg_binom
[1] 1e-04
$m6b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m6b$shape_diag_RinvD
[1] "0.01"
$m6b$rate_diag_RinvD
[1] "0.001"
$m6b$RinvD_b1_id
[,1] [,2]
[1,] NA 0
[2,] 0 NA
$m6b$KinvD_b1_id
id
3
$m7a
$m7a$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m7a$M_lvlone
y ns(time, df = 2)1 ns(time, df = 2)2 time
1 -13.0493856 0.149679884 -0.100552161 0.5090421822
1.1 -9.3335901 0.194627180 -0.129464178 0.6666076288
1.2 -22.3469852 0.520751993 -0.255001297 2.1304941282
1.3 -15.0417337 0.560875996 -0.221882653 2.4954441458
2 -12.0655434 0.578228925 -0.112131092 3.0164990982
2.1 -15.8674476 0.569154825 -0.023537063 3.2996806887
2.2 -7.8800006 0.481017405 0.344239525 4.1747569619
3 -11.4820604 0.244887044 -0.160459809 0.8478727890
3.1 -10.5983220 0.577508632 -0.098148611 3.0654308549
3.2 -22.4519157 0.394656259 0.627350944 4.7381553578
4 -1.2697775 0.099645844 -0.067449012 0.3371432109
4.1 -11.1215184 0.303598405 -0.194123505 1.0693019140
4.2 -3.6134138 0.569124392 -0.203401494 2.6148973033
4.3 -14.5982385 0.575895782 -0.077701736 3.1336532847
5 -6.8457515 0.305385958 -0.195093572 1.0762525082
5.1 -7.0551214 0.465582593 -0.257830256 1.7912546196
5.2 -12.3418980 0.576686171 -0.167647096 2.7960080339
5.3 -9.2366906 0.577072492 -0.164051419 2.8119940578
6 -5.1648211 0.463778516 -0.257558879 1.7815462884
7 -10.0599502 0.568748124 -0.020870495 3.3074087673
7.1 -18.3267285 0.538303487 0.130118265 3.7008403614
7.2 -12.5138426 0.389177122 0.644726730 4.7716691741
8 -1.6305331 0.317728152 -0.201687154 1.1246398522
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94.2 -4.3498045 0.492461920 -0.259601249 1.9445500884
94.3 -11.6340088 0.577401892 -0.096499489 3.0710722448
94.4 -12.9357964 0.577068448 -0.091721448 3.0872731935
94.5 -14.7648530 0.451106514 0.445102199 4.3805759016
95 -12.8849309 0.504518977 -0.258663317 2.0199063048
95.1 -9.7451502 0.501935320 0.270450881 4.0184444457
95.2 -0.8535063 0.423384987 0.535514358 4.5596531732
96 -4.9139832 0.009083415 -0.006183955 0.0311333477
96.1 -3.9582653 0.039181462 -0.026652251 0.1324267720
96.2 -9.6555492 0.195619870 -0.130091480 0.6701303425
96.3 -11.8690793 0.527105916 -0.252589672 2.1775037691
96.4 -11.0224373 0.533135775 -0.249644636 2.2246142488
96.5 -10.9530403 0.472120477 0.374710585 4.2377650598
97 -9.8540471 0.335470540 -0.210821063 1.1955102731
97.1 -19.2262840 0.358054995 0.742971132 4.9603108643
98 -11.9651231 0.060447025 -0.041067134 0.2041732438
98.1 -2.6515128 0.127061111 -0.085685639 0.4309578973
98.2 -12.2606382 0.554775889 0.056119803 3.5172611906
99 -11.4720500 0.104348118 -0.070591602 0.3531786101
99.1 -14.0596866 0.404279323 0.596741645 4.6789444226
99.2 -17.3939469 0.352686062 0.759881258 4.9927084171
100 1.1005874 0.303556402 -0.194100667 1.0691387602
100.1 -3.8226248 0.408995749 -0.242962199 1.5109344281
100.2 -0.9123182 0.523460505 -0.254052065 2.1502332564
100.3 -15.8389474 0.519529301 0.205126241 3.8745574222
100.4 -12.8093826 0.407863056 0.585307533 4.6567608765
$m7a$spM_lvlone
center scale
y -11.173370994 6.2496619
ns(time, df = 2)1 0.430938966 0.1552899
ns(time, df = 2)2 -0.008514259 0.2716805
time 2.533940277 1.3818094
$m7a$mu_reg_norm
[1] 0
$m7a$tau_reg_norm
[1] 1e-04
$m7a$shape_tau_norm
[1] 0.01
$m7a$rate_tau_norm
[1] 0.01
$m7a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m7a$shape_diag_RinvD
[1] "0.01"
$m7a$rate_diag_RinvD
[1] "0.001"
$m7a$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m7a$KinvD_y_id
id
4
$m7b
$m7b$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m7b$M_lvlone
y bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3
1 -13.0493856 2.464812e-01 2.795125e-02 1.056568e-03
1.1 -9.3335901 3.005702e-01 4.627383e-02 2.374673e-03
1.2 -22.3469852 4.207566e-01 3.131043e-01 7.766508e-02
1.3 -15.0417337 3.751809e-01 3.748189e-01 1.248191e-01
2 -12.0655434 2.840211e-01 4.334471e-01 2.204958e-01
2.1 -15.8674476 2.280284e-01 4.443439e-01 2.886212e-01
2.2 -7.8800006 6.734283e-02 3.436638e-01 5.845947e-01
3 -11.4820604 3.510022e-01 7.175155e-02 4.889129e-03
3.1 -10.5983220 2.745130e-01 4.365427e-01 2.314032e-01
3.2 -22.4519157 7.402499e-03 1.377702e-01 8.546947e-01
4 -1.2697775 1.759000e-01 1.271593e-02 3.064145e-04
4.1 -11.1215184 3.966925e-01 1.080567e-01 9.811334e-03
4.2 -3.6134138 3.564330e-01 3.918838e-01 1.436202e-01
4.3 -14.5982385 2.611080e-01 4.400451e-01 2.472026e-01
5 -6.8457515 3.978591e-01 1.092729e-01 1.000401e-02
5.1 -7.0551214 4.425632e-01 2.475384e-01 4.615178e-02
5.2 -12.3418980 3.252782e-01 4.139339e-01 1.755844e-01
5.3 -9.2366906 3.223943e-01 4.156349e-01 1.786139e-01
6 -5.1648211 4.428374e-01 2.456041e-01 4.540519e-02
7 -10.0599502 2.264807e-01 4.443903e-01 2.906542e-01
7.1 -18.3267285 1.489135e-01 4.265274e-01 4.072291e-01
7.2 -12.5138426 5.621107e-03 1.213303e-01 8.729618e-01
8 -1.6305331 4.055465e-01 1.178507e-01 1.141571e-02
8.1 -9.6520453 4.422130e-01 2.498167e-01 4.704249e-02
8.2 -1.5278462 4.417145e-01 2.527749e-01 4.821755e-02
8.3 -7.4172211 3.175842e-01 4.183540e-01 1.836994e-01
8.4 -7.1238609 2.153406e-01 4.443014e-01 3.055682e-01
8.5 -8.8706950 3.313840e-02 2.640659e-01 7.014095e-01
9 -0.1634429 3.764000e-01 8.963850e-02 7.115710e-03
9.1 -2.6034300 3.028578e-01 4.257933e-01 1.995435e-01
9.2 -6.7272369 3.867756e-03 1.018621e-01 8.942212e-01
10 -6.4172202 4.023356e-01 3.428926e-01 9.741064e-02
10.1 -11.4834569 6.754273e-02 3.440071e-01 5.840297e-01
11 -8.7911356 4.138618e-01 1.284873e-01 1.329669e-02
11.1 -19.6645080 4.202663e-01 1.379992e-01 1.510454e-02
11.2 -20.2030932 4.443906e-01 2.179963e-01 3.564611e-02
11.3 -21.3082176 2.107621e-01 4.440456e-01 3.118469e-01
11.4 -14.5802901 3.796619e-03 1.009751e-01 8.951806e-01
12 -15.2006287 3.760749e-01 8.937848e-02 7.080604e-03
13 0.8058816 3.594997e-02 4.470732e-04 1.853264e-06
13.1 -13.6379208 1.757678e-01 4.375779e-01 3.631200e-01
14 -15.3422873 8.899359e-02 3.755915e-01 5.283862e-01
14.1 -10.0965208 2.933469e-02 2.513931e-01 7.181312e-01
14.2 -16.6452027 1.422882e-02 1.848582e-01 8.005479e-01
14.3 -15.8389733 1.045369e-02 1.611585e-01 8.281618e-01
15 -8.9424594 2.579648e-01 3.126382e-02 1.262997e-03
15.1 -22.0101983 4.146589e-01 1.295994e-01 1.350186e-02
15.2 -7.3975599 4.414565e-01 1.912323e-01 2.761298e-02
15.3 -10.3567334 6.383397e-04 4.279986e-02 9.565586e-01
16 -1.9691302 4.210987e-01 1.393442e-01 1.536996e-02
16.1 -9.9308357 3.640505e-01 3.853440e-01 1.359610e-01
16.2 -6.9626923 3.502297e-01 3.968497e-01 1.498918e-01
16.3 -3.2862557 1.764758e-01 4.377929e-01 3.620187e-01
16.4 -3.3972355 1.375222e-01 4.199669e-01 4.274999e-01
16.5 -11.5767835 9.753912e-02 3.857404e-01 5.084991e-01
17 -10.5474144 4.438097e-01 2.077898e-01 3.242877e-02
17.1 -7.6215009 3.836846e-01 3.658929e-01 1.163087e-01
17.2 -16.5386939 2.822509e-01 4.340620e-01 2.225087e-01
17.3 -20.0004774 2.213728e-01 4.444423e-01 2.974303e-01
17.4 -18.8505475 1.177221e-01 4.054379e-01 4.654462e-01
18 -19.7302351 3.836809e-01 3.658970e-01 1.163125e-01
19 -14.6177568 3.797281e-01 9.235553e-02 7.487412e-03
19.1 -17.8043866 4.087929e-01 1.218111e-01 1.209900e-02
19.2 -15.1641705 4.068733e-01 3.363802e-01 9.270014e-02
19.3 -16.6898418 6.198011e-02 3.340502e-01 6.001364e-01
20 -12.9059229 4.441175e-01 2.327127e-01 4.064630e-02
20.1 -16.8191201 4.434516e-01 2.405648e-01 4.350076e-02
20.2 -6.1010131 4.078512e-01 3.349176e-01 9.167540e-02
20.3 -7.9415371 4.067556e-01 3.365548e-01 9.282325e-02
20.4 -9.3904458 1.897411e-01 4.411553e-01 3.419010e-01
20.5 -13.3504189 6.758944e-02 3.440872e-01 5.838978e-01
21 -7.6974718 4.443436e-01 2.280367e-01 3.900939e-02
21.1 -11.9335526 2.954126e-01 4.290631e-01 2.077265e-01
21.2 -12.7064929 1.324112e-01 4.166163e-01 4.369447e-01
22 -21.5022909 3.790159e-01 3.708962e-01 1.209835e-01
22.1 -12.7745451 2.553655e-01 4.412374e-01 2.541330e-01
23 -3.5146508 4.425730e-01 1.975933e-01 2.940615e-02
23.1 -4.6724048 3.973563e-01 3.495745e-01 1.025128e-01
24 -2.5619821 3.194306e-01 4.173276e-01 1.817425e-01
25 -6.2944970 2.572266e-01 3.104254e-02 1.248755e-03
25.1 -3.8630505 4.440397e-01 2.106776e-01 3.331912e-02
25.2 -14.4205140 4.443440e-01 2.164524e-01 3.514669e-02
25.3 -19.6735037 2.350324e-01 4.439580e-01 2.795340e-01
25.4 -9.0288933 8.222967e-02 3.666568e-01 5.449664e-01
25.5 -9.0509738 7.014574e-02 3.483850e-01 5.767615e-01
26 -19.7340685 1.310265e-01 6.637071e-03 1.120657e-04
26.1 -14.1692728 3.825707e-01 3.671104e-01 1.174249e-01
26.2 -17.2819976 1.685712e-01 4.351796e-01 3.744833e-01
26.3 -24.6265576 6.623537e-02 3.417427e-01 5.877428e-01
27 -7.3354999 1.499147e-01 4.270472e-01 4.054956e-01
27.1 -11.1488468 5.720004e-02 3.247727e-01 6.146692e-01
28 -11.7996597 2.702018e-01 3.510883e-02 1.520629e-03
28.1 -8.2030122 3.255678e-01 4.137601e-01 1.752808e-01
28.2 -26.4317815 6.206227e-02 3.342036e-01 5.998924e-01
28.3 -18.5016071 2.938809e-02 2.515792e-01 7.178884e-01
29 -5.8551395 4.147335e-01 1.297043e-01 1.352130e-02
29.1 -2.0209442 4.433161e-01 2.417859e-01 4.395693e-02
29.2 -5.6368080 4.302445e-01 2.925253e-01 6.629647e-02
29.3 -3.8110961 2.063674e-01 4.436772e-01 3.179595e-01
30 -12.7217702 4.064458e-01 3.370127e-01 9.314693e-02
30.1 -17.0170140 1.695551e-01 4.355307e-01 3.729111e-01
30.2 -25.4236089 1.656511e-01 4.340933e-01 3.791846e-01
31 -17.0783921 3.740780e-02 2.770323e-01 6.838761e-01
32 -18.4338764 4.444327e-01 2.242037e-01 3.770147e-02
32.1 -19.4317212 3.037898e-01 4.253611e-01 1.985277e-01
32.2 -19.4738978 2.934042e-01 4.298900e-01 2.099554e-01
32.3 -21.4922645 6.218727e-02 3.344366e-01 5.995216e-01
33 2.0838099 5.239472e-03 9.198973e-06 5.383564e-09
33.1 -13.3172274 1.961435e-01 4.423422e-01 3.325230e-01
34 -10.0296691 4.410692e-01 1.893277e-01 2.708947e-02
34.1 -25.9426553 1.258797e-01 4.119406e-01 4.493576e-01
34.2 -18.5688138 1.109981e-01 3.994804e-01 4.792410e-01
34.3 -15.4173859 1.021312e-01 3.907186e-01 4.982514e-01
35 -14.3958113 4.300106e-01 1.559848e-01 1.886096e-02
35.1 -12.9457541 4.421254e-01 1.948544e-01 2.862555e-02
35.2 -16.1380691 2.607777e-02 2.395395e-01 7.334364e-01
36 -12.8166968 3.144468e-01 5.228505e-02 2.897921e-03
36.1 -14.3989481 4.423834e-01 2.487319e-01 4.661685e-02
36.2 -12.2436943 4.414010e-01 2.544996e-01 4.891246e-02
36.3 -15.0104638 5.187736e-02 3.135513e-01 6.317103e-01
36.4 -10.1775457 1.551774e-02 1.920212e-01 7.920430e-01
37 -15.2223495 4.316782e-01 2.888526e-01 6.442751e-02
37.1 -14.7526195 1.556358e-01 4.298489e-01 3.957315e-01
37.2 -19.8168430 1.007300e-01 3.892329e-01 5.013478e-01
38 -2.7065118 3.808083e-01 9.325985e-02 7.613103e-03
39 -8.7288138 3.084321e-01 4.960067e-02 2.658853e-03
39.1 -9.2746473 3.639117e-01 8.027461e-02 5.902543e-03
39.2 -18.2695344 4.278081e-01 1.514124e-01 1.786294e-02
39.3 -13.8219083 4.425678e-01 1.975596e-01 2.939647e-02
39.4 -16.2254704 2.512281e-01 4.419813e-01 2.591901e-01
39.5 -21.7283648 7.266727e-02 3.524671e-01 5.698717e-01
40 1.8291916 4.068044e-01 1.193589e-01 1.167354e-02
40.1 -6.6916432 3.431747e-01 4.021321e-01 1.570728e-01
40.2 -1.6278171 2.795328e-01 4.349718e-01 2.256151e-01
40.3 -10.5749790 1.128797e-02 1.668063e-01 8.216511e-01
41 -3.1556121 4.361979e-01 2.756547e-01 5.806655e-02
41.1 -11.5895327 2.488151e-01 4.423706e-01 2.621649e-01
41.2 -18.9352091 2.310293e-01 4.442137e-01 2.847053e-01
41.3 -15.9788960 2.116723e-01 4.441068e-01 3.105915e-01
41.4 -9.6070508 1.784828e-01 4.383823e-01 3.589124e-01
42 -5.2159485 2.377072e-01 2.559872e-02 9.189097e-04
42.1 -15.9878743 4.593785e-02 2.997495e-01 6.519659e-01
43 -16.6104361 2.656427e-01 3.363646e-02 1.419716e-03
43.1 -9.5549441 3.975426e-01 1.089410e-01 9.951245e-03
43.2 -14.2003491 3.567136e-01 3.916517e-01 1.433373e-01
44 -8.1969033 3.298080e-01 5.974901e-02 3.608103e-03
44.1 -19.9270197 3.507946e-01 3.964102e-01 1.493192e-01
44.2 -22.6521171 2.205151e-01 4.444357e-01 2.985784e-01
44.3 -21.1903736 7.636654e-02 3.581894e-01 5.600169e-01
45 -0.5686627 1.082881e-01 4.405504e-03 5.974331e-05
45.1 -7.5645740 4.320759e-01 2.877973e-01 6.389873e-02
46 -19.1624789 4.315918e-01 1.595186e-01 1.965297e-02
46.1 -18.4487574 3.142520e-01 4.201528e-01 1.872472e-01
46.2 -15.8222682 3.540359e-02 2.710968e-01 6.919585e-01
47 -5.4165074 2.793313e-01 3.820763e-02 1.742045e-03
47.1 -15.0975029 3.875903e-01 3.614998e-01 1.123885e-01
47.2 -12.9971413 2.876694e-01 4.321237e-01 2.163720e-01
47.3 -10.6844521 2.513344e-01 4.419634e-01 2.590595e-01
47.4 -18.2214784 1.546519e-02 1.917369e-01 7.923821e-01
48 -8.3101471 3.134857e-01 4.205568e-01 1.880660e-01
48.1 -18.3854275 3.043494e-01 4.250991e-01 1.979186e-01
49 -13.0130319 3.390616e-01 4.050416e-01 1.612870e-01
50 -10.4579977 4.129175e-01 1.271931e-01 1.305998e-02
51 -19.3157621 4.375328e-01 1.756209e-01 2.349744e-02
52 -4.4747188 4.211247e-01 3.123968e-01 7.724691e-02
52.1 -4.3163827 2.677291e-01 4.384410e-01 2.393346e-01
52.2 -6.9761408 2.624519e-01 4.397394e-01 2.455952e-01
52.3 -20.1764756 1.739940e-01 4.370229e-01 3.658920e-01
52.4 -8.9036692 4.369600e-03 1.078733e-01 8.876981e-01
52.5 -5.6949642 1.422392e-05 6.511014e-03 9.934748e-01
53 -10.3141887 2.417783e-01 2.667179e-02 9.807667e-04
53.1 -8.2642654 1.780421e-01 4.382554e-01 3.595925e-01
53.2 -9.1691554 1.889234e-02 2.091001e-01 7.714386e-01
54 -6.2198754 4.358048e-01 1.703380e-01 2.219268e-02
54.1 -15.7192609 4.390414e-01 2.653673e-01 5.346482e-02
54.2 -13.0978998 3.728682e-01 3.771078e-01 1.271319e-01
54.3 -5.1195299 3.272811e-01 4.127208e-01 1.734884e-01
54.4 -16.5771751 9.263699e-02 3.800658e-01 5.197708e-01
55 -5.7348534 2.828867e-01 3.947132e-02 1.835818e-03
55.1 -7.3217494 3.242714e-01 5.695192e-02 3.334164e-03
55.2 -12.2171938 3.209209e-01 4.164833e-01 1.801673e-01
55.3 -12.9821266 2.613076e-01 4.400004e-01 2.469635e-01
55.4 -14.8599983 2.435982e-01 4.430993e-01 2.686623e-01
56 -14.1764282 4.189052e-01 1.358588e-01 1.468719e-02
56.1 -12.5343602 3.946986e-01 3.529645e-01 1.052144e-01
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96.1 0.1324267720
96.2 0.6701303425
96.3 2.1775037691
96.4 2.2246142488
96.5 4.2377650598
97 1.1955102731
97.1 4.9603108643
98 0.2041732438
98.1 0.4309578973
98.2 3.5172611906
99 0.3531786101
99.1 4.6789444226
99.2 4.9927084171
100 1.0691387602
100.1 1.5109344281
100.2 2.1502332564
100.3 3.8745574222
100.4 4.6567608765
$m7b$spM_lvlone
center scale
y -11.1733710 6.2496619
bs(time, df = 3)1 0.2549546 0.1475335
bs(time, df = 3)2 0.2657250 0.1531363
bs(time, df = 3)3 0.2453352 0.2691884
time 2.5339403 1.3818094
$m7b$mu_reg_norm
[1] 0
$m7b$tau_reg_norm
[1] 1e-04
$m7b$shape_tau_norm
[1] 0.01
$m7b$rate_tau_norm
[1] 0.01
$m7b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m7b$shape_diag_RinvD
[1] "0.01"
$m7b$rate_diag_RinvD
[1] "0.001"
$m7b$RinvD_y_id
[,1] [,2] [,3] [,4]
[1,] NA 0 0 0
[2,] 0 NA 0 0
[3,] 0 0 NA 0
[4,] 0 0 0 NA
$m7b$KinvD_y_id
id
5
$m7c
$m7c$M_id
(Intercept) C1
1 1 0.7175865
2 1 0.7507170
3 1 0.7255954
4 1 0.7469352
5 1 0.7139120
6 1 0.7332505
7 1 0.7345929
8 1 0.7652589
9 1 0.7200622
10 1 0.7423879
11 1 0.7437448
12 1 0.7446470
13 1 0.7530186
14 1 0.7093137
15 1 0.7331192
16 1 0.7011390
17 1 0.7432395
18 1 0.7545191
19 1 0.7528487
20 1 0.7612865
21 1 0.7251719
22 1 0.7300630
23 1 0.7087249
24 1 0.7391938
25 1 0.7820641
26 1 0.7118298
27 1 0.7230857
28 1 0.7489353
29 1 0.7510888
30 1 0.7300717
31 1 0.7550721
32 1 0.7321898
33 1 0.7306414
34 1 0.7427216
35 1 0.7193042
36 1 0.7312888
37 1 0.7100436
38 1 0.7670184
39 1 0.7400449
40 1 0.7397304
41 1 0.7490966
42 1 0.7419274
43 1 0.7527810
44 1 0.7408315
45 1 0.7347550
46 1 0.7332398
47 1 0.7376481
48 1 0.7346179
49 1 0.7329402
50 1 0.7260436
51 1 0.7242910
52 1 0.7298067
53 1 0.7254741
54 1 0.7542067
55 1 0.7389952
56 1 0.7520638
57 1 0.7219958
58 1 0.7259632
59 1 0.7458606
60 1 0.7672421
61 1 0.7257179
62 1 0.7189892
63 1 0.7333356
64 1 0.7320243
65 1 0.7477711
66 1 0.7343974
67 1 0.7491624
68 1 0.7482736
69 1 0.7338267
70 1 0.7607742
71 1 0.7777600
72 1 0.7408143
73 1 0.7248271
74 1 0.7364916
75 1 0.7464926
76 1 0.7355430
77 1 0.7208449
78 1 0.7373573
79 1 0.7598079
80 1 0.7360415
81 1 0.7293932
82 1 0.7279309
83 1 0.7344643
84 1 0.7384350
85 1 0.7323716
86 1 0.7576597
87 1 0.7496139
88 1 0.7275239
89 1 0.7250648
90 1 0.7335262
91 1 0.7343980
92 1 0.7380425
93 1 0.7389460
94 1 0.7259951
95 1 0.7282840
96 1 0.7281676
97 1 0.7245642
98 1 0.7526938
99 1 0.7272309
100 1 0.7383460
$m7c$M_lvlone
y c1 ns(time, df = 3)1 ns(time, df = 3)2
1 -13.0493856 0.7592026489 -0.0731022196 0.222983368
1.1 -9.3335901 0.9548337990 -0.0896372079 0.286659651
1.2 -22.3469852 0.5612235156 0.1374616725 0.538466292
1.3 -15.0417337 1.1873391025 0.3061500570 0.485312041
2 -12.0655434 0.9192204198 0.5064248381 0.388851338
2.1 -15.8674476 -0.1870730476 0.5543647993 0.348347565
2.2 -7.8800006 1.2517512331 0.3402753582 0.338334366
3 -11.4820604 -0.0605087604 -0.1024946971 0.354448579
3.1 -10.5983220 0.3788637747 0.5187768948 0.380711276
3.2 -22.4519157 0.9872578281 0.0174998856 0.391617016
4 -1.2697775 1.4930175328 -0.0508146200 0.149748191
4.1 -11.1215184 -0.7692526880 -0.1067711172 0.427124949
4.2 -3.6134138 0.9180841450 0.3598480506 0.463409709
4.3 -14.5982385 -0.0541170782 0.5333652385 0.370048079
5 -6.8457515 -0.1376784521 -0.1066695938 0.429197233
5.1 -7.0551214 -0.2740585866 0.0046159078 0.552972700
5.2 -12.3418980 0.4670496929 0.4342731827 0.428967354
5.3 -9.2366906 0.1740288049 0.4402846745 0.425938437
6 -5.1648211 0.9868044683 0.0015253168 0.552692434
7 -10.0599502 -0.1280320918 0.5547950298 0.347509550
7.1 -18.3267285 0.4242971219 0.5158768825 0.324489976
7.2 -12.5138426 0.0777182491 -0.0038356900 0.395476156
8 -1.6305331 -0.5791408712 -0.1055283475 0.443238494
8.1 -9.6520453 0.3128604232 0.0083228157 0.553246856
8.2 -1.5278462 0.6258446356 0.0132420073 0.553513388
8.3 -7.4172211 -0.1040137707 0.4499876083 0.420954925
8.4 -7.1238609 0.0481450285 0.5564354493 0.341948600
8.5 -8.8706950 0.3831763675 0.2011700353 0.359621900
9 -0.1634429 -0.1757592269 -0.1064248362 0.393057590
9.1 -2.6034300 -0.1791541200 0.4771840649 0.406264856
9.2 -6.7272369 -0.0957042935 -0.0284447928 0.399948734
10 -6.4172202 -0.5598409704 0.2139341504 0.517577740
10.1 -11.4834569 -0.2318340451 0.3409274088 0.338245451
11 -8.7911356 0.5086859475 -0.1030921083 0.459317031
11.1 -19.6645080 0.4951758188 -0.0999663372 0.472517168
11.2 -20.2030932 -1.1022162541 -0.0371358422 0.544030949
11.3 -21.3082176 -0.0611636705 0.5563809266 0.339897056
11.4 -14.5802901 -0.4971774316 -0.0295495187 0.400149982
12 -15.2006287 -0.2433996286 -0.1063939605 0.392538168
13 0.8058816 0.8799673116 -0.0095916688 0.027579300
13.1 -13.6379208 0.1079022586 0.5424520669 0.328456601
14 -15.3422873 0.9991752617 0.4032689358 0.330500975
14.1 -10.0965208 -0.1094019046 0.1809335363 0.362994245
14.2 -16.6452027 0.1518967560 0.0816318440 0.380152387
14.3 -15.8389733 0.3521012473 0.0487734088 0.385997930
15 -8.9424594 0.3464447888 -0.0767078114 0.235874789
15.1 -22.0101983 -0.4767313971 -0.1027727004 0.460916543
15.2 -7.3975599 0.5759767791 -0.0657566366 0.527699447
15.3 -10.3567334 -0.1713452662 -0.0990627407 0.412872874
16 -1.9691302 0.4564754473 -0.0994524289 0.474297164
16.1 -9.9308357 1.0652558311 0.3388926675 0.472228980
16.2 -6.9626923 0.6971872493 0.3760850293 0.456327895
16.3 -3.2862557 0.5259331838 0.5429665219 0.328616483
16.4 -3.3972355 0.2046601798 0.5003814501 0.324030412
16.5 -11.5767835 1.0718540464 0.4243928946 0.328317423
17 -10.5474144 0.6048676222 -0.0490208458 0.538702746
17.1 -7.6215009 0.2323298304 0.2793033691 0.495414246
17.2 -16.5386939 1.2617499032 0.5088484549 0.387301236
17.3 -20.0004774 -0.3913230895 0.5558624020 0.344858135
17.4 -18.8505475 0.9577299112 0.4671596304 0.324978786
18 -19.7302351 -0.0050324072 0.2793154325 0.495409834
19 -14.6177568 -0.4187468937 -0.1067031159 0.398417477
19.1 -17.8043866 -0.4478828944 -0.1047524970 0.449392800
19.2 -15.1641705 -1.1966721302 0.1964187590 0.522886360
19.3 -16.6898418 -0.5877091668 0.3222225327 0.340847993
20 -12.9059229 0.6838223064 -0.0177612481 0.549715454
20.1 -16.8191201 0.3278571109 -0.0062817988 0.551756621
20.2 -6.1010131 -0.8489831990 0.1925456183 0.524021244
20.3 -7.9415371 1.3169975191 0.1968825793 0.522749488
20.4 -9.3904458 0.0444804531 0.5508408484 0.332148944
20.5 -13.3504189 -0.4535207652 0.3410795409 0.338224725
21 -7.6974718 -0.4030302960 -0.0242133322 0.548168935
21.1 -11.9335526 -0.4069674045 0.4894906279 0.399187414
21.2 -12.7064929 1.0650265940 0.4925855178 0.324061287
22 -21.5022909 -0.0673274516 0.2942488077 0.489859980
22.1 -12.7745451 0.9601388170 0.5385758261 0.365790145
23 -3.5146508 0.5556634840 -0.0596896592 0.532269527
23.1 -4.6724048 1.4407865964 0.2323619738 0.511693737
24 -2.5619821 0.3856376411 0.4463109938 0.422857517
25 -6.2944970 0.3564400705 -0.0764769279 0.235036737
25.1 -3.8630505 0.0982553434 -0.0457828435 0.540323722
25.2 -14.4205140 0.1928682598 -0.0390131484 0.543297023
25.3 -19.6735037 -0.0192488594 0.5517873130 0.352345378
25.4 -9.0288933 0.4466012931 0.3851356619 0.332578168
25.5 -9.0509738 1.1425193342 0.3492871599 0.337117898
26 -19.7340685 0.5341531449 -0.0370005446 0.107676329
26.1 -14.1692728 1.2268695927 0.2829160492 0.494087736
26.2 -17.2819976 0.3678294939 0.5366821080 0.326994020
26.3 -24.6265576 0.5948516018 0.3366364198 0.338833066
27 -7.3354999 -0.3342844147 0.5171168233 0.324565129
27.1 -11.1488468 -0.4835141229 0.3051619029 0.343307911
28 -11.7996597 -0.7145915499 -0.0805110543 0.249962259
28.1 -8.2030122 0.5063671955 0.4336613409 0.429273232
28.2 -26.4317815 -0.2067413142 0.3225075315 0.340807567
28.3 -18.5016071 0.1196789973 0.1812274493 0.362944907
29 -5.8551395 0.1392699487 -0.1027419307 0.461066695
29.1 -2.0209442 0.7960234776 -0.0044221127 0.552010523
29.2 -5.6368080 1.0398214352 0.0902449711 0.547810299
29.3 -3.8110961 0.0813246429 0.5559364351 0.338052723
30 -12.7217702 -0.3296323050 0.1981005081 0.522389104
30.1 -17.0170140 1.3635850954 0.5375291120 0.327176583
30.2 -25.4236089 0.7354171050 0.5340597966 0.326484504
31 -17.0783921 0.3708398217 0.2223657818 0.356147774
32 -18.4338764 -0.0474059668 -0.0292943991 0.546719898
32.1 -19.4317212 1.2507771489 0.4755751076 0.407167901
32.2 -19.4738978 0.1142915519 0.4926434114 0.397320853
32.3 -21.4922645 0.6773270619 0.3229405911 0.340746183
33 2.0838099 0.1774293842 -0.0013701847 0.003936563
33.1 -13.3172274 0.6159606291 0.5534368438 0.334224366
34 -10.0296691 0.8590979166 -0.0674871387 0.526248154
34.1 -25.9426553 0.0546216775 0.4818424185 0.324316326
34.2 -18.5688138 -0.0897224473 0.4539624195 0.325817315
34.3 -15.4173859 0.4163395571 0.4349720689 0.327338410
35 -14.3958113 -1.4693520528 -0.0916157074 0.494613531
35.1 -12.9457541 -0.3031734330 -0.0623564831 0.530354088
35.2 -16.1380691 -0.6045512101 0.1624134259 0.366122990
36 -12.8166968 0.9823048960 -0.0935638890 0.304360595
36.1 -14.3989481 1.4466051416 0.0065488640 0.553123835
36.2 -12.2436943 1.1606752905 0.0161646973 0.553622593
36.3 -15.0104638 0.8373091576 0.2849748388 0.346311748
36.4 -10.1775457 0.2640591685 0.0918066953 0.378356377
37 -15.2223495 0.1177313455 0.0823235483 0.549032516
37.1 -14.7526195 -0.1415483779 0.5238280708 0.325102184
37.2 -19.8168430 0.0054610124 0.4317992421 0.327623070
38 -2.7065118 0.8078948077 -0.1067779048 0.400174439
39 -8.7288138 0.9876451040 -0.0918869929 0.296609802
39.1 -9.2746473 -0.3431222274 -0.1048343071 0.373603743
39.2 -18.2695344 -1.7909380751 -0.0940424764 0.489340823
39.3 -13.8219083 -0.1798746191 -0.0597229655 0.532246449
39.4 -16.2254704 -0.1850961689 0.5419341500 0.362843629
39.5 -21.7283648 0.4544226146 0.3571597096 0.336078053
40 1.8291916 0.5350190436 -0.1052513278 0.445606008
40.1 -6.6916432 0.4189342752 0.3936778108 0.448397811
40.2 -1.6278171 0.4211994981 0.5124598453 0.384951582
40.3 -10.5749790 0.0916687506 0.0564941376 0.384618587
41 -3.1556121 -0.1035047421 0.0551736455 0.552281804
41.1 -11.5895327 -0.4684202411 0.5437374685 0.361173390
41.2 -18.9352091 0.5972615368 0.5533874833 0.350019018
41.3 -15.9788960 0.9885613862 0.5564251145 0.340294264
41.4 -9.6070508 -0.3908036794 0.5443730748 0.329085370
42 -5.2159485 -0.0338893961 -0.0703321485 0.213336117
42.1 -15.9878743 -0.4498363172 0.2607797560 0.350028134
43 -16.6104361 0.8965546110 -0.0790999084 0.244669988
43.1 -9.5549441 0.6199122090 -0.1066987969 0.428633738
43.2 -14.2003491 0.1804894429 0.3590962645 0.463732274
44 -8.1969033 1.3221409285 -0.0976262102 0.324744715
44.1 -19.9270197 0.3416426284 0.3746366123 0.456968690
44.2 -22.6521171 0.5706610068 0.5559890192 0.344429887
44.3 -21.1903736 1.2679497430 0.3683249841 0.334642692
45 -0.5686627 0.1414983160 -0.0301940136 0.087479438
45.1 -7.5645740 0.7220892521 0.0800764791 0.549358350
46 -19.1624789 1.5391054233 -0.0895971055 0.498531201
46.1 -18.4487574 0.3889107049 0.4564725410 0.417554417
46.2 -15.8222682 0.1248719493 0.2125999596 0.357740629
47 -5.4165074 0.2014101100 -0.0833108913 0.260722648
47.1 -15.0975029 0.2982973539 0.2663955892 0.500069201
47.2 -12.9971413 1.1518107179 0.5012527278 0.392094432
47.3 -10.6844521 0.5196802157 0.5418520608 0.362918036
47.4 -18.2214784 0.3702301552 0.0914005525 0.378427928
48 -8.3101471 -0.2128602862 0.4579365172 0.416778543
48.1 -18.3854275 -0.5337239976 0.4746016472 0.407711957
49 -13.0130319 -0.5236770035 0.4035144992 0.443841107
50 -10.4579977 0.3897705981 -0.1034484387 0.457436535
51 -19.3157621 -0.7213343736 -0.0787988153 0.514675808
52 -4.4747188 0.3758235358 0.1357604672 0.538856976
52.1 -4.3163827 0.7138067080 0.5265788733 0.375191670
52.2 -6.9761408 0.8872895233 0.5320547699 0.371072093
52.3 -20.1764756 -0.9664587437 0.5411213524 0.328068662
52.4 -8.9036692 0.0254566848 -0.0209202834 0.398579012
52.5 -5.6949642 0.4155259424 -0.1396816763 0.420339814
53 -10.3141887 0.5675736897 -0.0716187009 0.217791262
53.1 -8.2642654 -0.3154088781 0.5440708053 0.328980432
53.2 -9.1691554 0.2162315769 0.1165457070 0.374021344
54 -6.2198754 -0.0880802382 -0.0826335785 0.509684846
54.1 -15.7192609 0.4129127672 0.0354880963 0.553543641
54.2 -13.0978998 1.0119546775 0.3131694667 0.482577881
54.3 -5.1195299 -0.1112901990 0.4300121383 0.431088614
54.4 -16.5771751 0.8587727145 0.4125104256 0.329511331
55 -5.7348534 -0.0116453589 -0.0843902223 0.264974033
55.1 -7.3217494 0.5835528661 -0.0962032547 0.317295658
55.2 -12.2171938 -1.0010857254 0.4432998504 0.424402666
55.3 -12.9821266 -0.4796526070 0.5331728179 0.370199481
55.4 -14.8599983 -0.1202746964 0.5472394872 0.357686883
56 -14.1764282 0.5176377612 -0.1007474372 0.469640780
56.1 -12.5343602 -1.1136932588 0.2418874691 0.508537162
56.2 -8.4573382 -0.0168103281 0.3388572935 0.472243570
56.3 -12.4633969 0.3933023606 0.4875447544 0.400327890
56.4 -17.3841863 0.3714625139 0.5479018829 0.356980364
56.5 -14.8147645 0.7811448179 0.5556203170 0.337279686
57 -3.1403293 -1.0868304872 -0.0363728373 0.105801595
57.1 -11.1509248 0.8018626997 -0.0380160354 0.110715003
57.2 -6.3940143 -0.1159517011 -0.1049831697 0.447707406
57.3 -9.3473241 0.6785562445 0.1307695676 0.539981816
58 -12.0245677 1.6476207996 -0.1008867350 0.469103743
58.1 -9.2112246 0.3402652711 -0.0395912379 0.543063473
58.2 -1.2071742 -0.1111300753 -0.0280726286 0.547088143
58.3 -11.0141711 -0.5409234285 0.3673004631 0.460186824
58.4 -5.3721214 -0.1271327672 0.4533451791 0.419201600
58.5 -7.8523047 0.8713264822 0.5410594734 0.328051465
59 -13.2946560 0.4766421367 0.0432887218 0.553170063
59.1 -10.0530648 1.0028089765 -0.1343700478 0.419362856
60 -19.2209402 0.5231452932 0.4664716194 0.412191135
61 -4.6699914 -0.7190130614 -0.0942894088 0.307818016
61.1 -3.5981894 0.8353702312 0.2238107295 0.514461229
61.2 -1.4713611 1.0229058138 0.3117843207 0.483120456
61.3 -3.8819786 1.1717723589 0.5403000211 0.364301147
61.4 0.1041413 -0.0629201596 0.5361016178 0.326874230
62 -2.8591600 -0.3979137604 -0.0759650358 0.233185122
62.1 -6.9461986 0.6830738372 -0.0924447607 0.299153085
62.2 -16.7910593 0.4301745954 0.5534841147 0.334271811
62.3 -17.9844596 -0.0333139957 -0.0238142803 0.399105605
63 -24.0335535 0.3345678035 0.4394416153 0.426365839
63.1 -11.7765300 0.3643769511 0.4321960350 0.327587037
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80.2 -16.6381566 0.5046816157 0.4367610654 0.427719060
81 0.5547913 -0.9493950353 -0.0014945092 0.004293764
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81.2 1.8283303 0.6476958410 0.5160872572 0.382538581
81.3 -5.2166381 0.4182528210 0.5561952783 0.343596919
82 -3.0749381 1.1088801952 -0.0858371096 0.504943034
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88 -2.2264493 -0.0601928334 0.0000000000 0.000000000
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92 -6.4119222 0.4674939332 -0.0114311653 0.032879925
93 5.2122168 0.2677965647 -0.0425142500 0.124267794
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94 -6.9602492 -0.2904211940 -0.1025408741 0.354779307
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100 1.1005874 0.0369067906 -0.1067733151 0.427076142
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ns(time, df = 3)3 time
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1.1 -0.186881442 0.6666076288
1.2 -0.349241050 2.1304941282
1.3 -0.300999737 2.4954441458
2 -0.173896710 3.0164990982
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3 -0.231074940 0.8478727890
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4.3 -0.136774026 3.1336532847
5 -0.279805678 1.0762525082
5.1 -0.360498322 1.7912546196
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5.3 -0.231426488 2.8119940578
6 -0.360315653 1.7815462884
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12 -0.255906608 0.9591987054
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27 0.078928640 3.6955824879
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85.3 -0.127729570 3.1608762743
85.4 0.191335442 3.9452053758
85.5 0.470626547 4.5092553482
86 -0.231018222 0.8476278360
86.1 -0.266955474 1.0118629411
86.2 -0.310696855 1.2511159515
86.3 -0.344135221 2.1870554925
86.4 -0.308378023 2.4532935000
86.5 0.134159804 3.8206058508
87 -0.257217600 2.7069531474
87.1 -0.023912615 3.4462517721
87.2 0.478346358 4.5241666853
88 0.000000000 0.0005892443
88.1 -0.198244638 0.7116099866
88.2 -0.301030746 2.4952722900
88.3 -0.079274225 3.2995816297
89 -0.181653221 0.6462086167
90 -0.049437014 0.1696030737
90.1 -0.281151299 2.5980385230
90.2 -0.266749122 2.6651392167
90.3 -0.139855690 3.1242690247
91 -0.179603880 0.6382618390
91.1 -0.276049539 2.6224059286
91.2 0.610915809 4.7772527603
92 -0.021435342 0.0737052364
93 -0.081013650 0.2788909199
93.1 -0.271814501 1.0357759963
93.2 -0.301681670 2.4916551099
93.3 -0.211259675 2.8876129608
93.4 0.447248720 4.4639474002
94 -0.231290551 0.8488043118
94.1 -0.275695453 1.0552454425
94.2 -0.359604210 1.9445500884
94.3 -0.156971195 3.0710722448
94.4 -0.151822700 3.0872731935
94.5 0.404570621 4.3805759016
95 -0.356607715 2.0199063048
95.1 0.225844939 4.0184444457
95.2 0.496766490 4.5596531732
96 -0.008958452 0.0311333477
96.1 -0.038605151 0.1324267720
96.2 -0.187779516 0.6701303425
96.3 -0.345060176 2.1775037691
96.4 -0.340252993 2.2246142488
96.5 0.332658824 4.2377650598
97 -0.301543584 1.1955102731
97.1 0.707918494 4.9603108643
98 -0.059473644 0.2041732438
98.1 -0.123949646 0.4309578973
98.2 0.004310743 3.5172611906
99 -0.102164665 0.3531786101
99.1 0.559124871 4.6789444226
99.2 0.725119709 4.9927084171
100 -0.278422879 1.0691387602
100.1 -0.344163920 1.5109344281
100.2 -0.347560939 2.1502332564
100.3 0.158668905 3.8745574222
100.4 0.547483173 4.6567608765
$m7c$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m7c$spM_lvlone
center scale
y -11.17337099 6.2496619
c1 0.25599956 0.6718095
ns(time, df = 3)1 0.19883694 0.2502686
ns(time, df = 3)2 0.38513689 0.1171115
ns(time, df = 3)3 -0.07137294 0.2891059
time 2.53394028 1.3818094
$m7c$mu_reg_norm
[1] 0
$m7c$tau_reg_norm
[1] 1e-04
$m7c$shape_tau_norm
[1] 0.01
$m7c$rate_tau_norm
[1] 0.01
$m7c$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m7c$shape_diag_RinvD
[1] "0.01"
$m7c$rate_diag_RinvD
[1] "0.001"
$m7c$RinvD_y_id
[,1] [,2] [,3] [,4]
[1,] NA 0 0 0
[2,] 0 NA 0 0
[3,] 0 0 NA 0
[4,] 0 0 0 NA
$m7c$KinvD_y_id
id
5
$m7d
$m7d$M_id
C2 (Intercept) C1
1 -1.381594459 1 0.7175865
2 0.344426024 1 0.7507170
3 NA 1 0.7255954
4 -0.228910007 1 0.7469352
5 NA 1 0.7139120
6 -2.143955482 1 0.7332505
7 -1.156567023 1 0.7345929
8 -0.598827660 1 0.7652589
9 NA 1 0.7200622
10 -1.006719032 1 0.7423879
11 0.239801450 1 0.7437448
12 -1.064969789 1 0.7446470
13 -0.538082688 1 0.7530186
14 NA 1 0.7093137
15 -1.781049276 1 0.7331192
16 NA 1 0.7011390
17 NA 1 0.7432395
18 -0.014579883 1 0.7545191
19 -2.121550136 1 0.7528487
20 NA 1 0.7612865
21 -0.363239698 1 0.7251719
22 -0.121568514 1 0.7300630
23 -0.951271111 1 0.7087249
24 NA 1 0.7391938
25 -0.974288621 1 0.7820641
26 -1.130632418 1 0.7118298
27 0.114339868 1 0.7230857
28 0.238334648 1 0.7489353
29 0.840744958 1 0.7510888
30 NA 1 0.7300717
31 NA 1 0.7550721
32 -1.466312154 1 0.7321898
33 -0.637352277 1 0.7306414
34 NA 1 0.7427216
35 NA 1 0.7193042
36 NA 1 0.7312888
37 NA 1 0.7100436
38 NA 1 0.7670184
39 0.006728205 1 0.7400449
40 NA 1 0.7397304
41 -1.663281353 1 0.7490966
42 0.161184794 1 0.7419274
43 0.457939180 1 0.7527810
44 -0.307070331 1 0.7408315
45 NA 1 0.7347550
46 -1.071668276 1 0.7332398
47 -0.814751321 1 0.7376481
48 -0.547630662 1 0.7346179
49 NA 1 0.7329402
50 -1.350213782 1 0.7260436
51 0.719054706 1 0.7242910
52 NA 1 0.7298067
53 -1.207130750 1 0.7254741
54 NA 1 0.7542067
55 -0.408600991 1 0.7389952
56 -0.271380529 1 0.7520638
57 -1.361925974 1 0.7219958
58 NA 1 0.7259632
59 NA 1 0.7458606
60 -0.323712205 1 0.7672421
61 NA 1 0.7257179
62 NA 1 0.7189892
63 -1.386906880 1 0.7333356
64 NA 1 0.7320243
65 NA 1 0.7477711
66 -0.565191691 1 0.7343974
67 -0.382899912 1 0.7491624
68 NA 1 0.7482736
69 -0.405642769 1 0.7338267
70 NA 1 0.7607742
71 -0.843748427 1 0.7777600
72 0.116003683 1 0.7408143
73 -0.778634325 1 0.7248271
74 NA 1 0.7364916
75 NA 1 0.7464926
76 NA 1 0.7355430
77 -0.632974758 1 0.7208449
78 NA 1 0.7373573
79 -0.778064615 1 0.7598079
80 NA 1 0.7360415
81 NA 1 0.7293932
82 -0.246123253 1 0.7279309
83 -1.239659782 1 0.7344643
84 -0.467772280 1 0.7384350
85 NA 1 0.7323716
86 -2.160485036 1 0.7576597
87 -0.657675572 1 0.7496139
88 NA 1 0.7275239
89 -0.696710744 1 0.7250648
90 NA 1 0.7335262
91 -0.179395847 1 0.7343980
92 -0.441545568 1 0.7380425
93 -0.685799334 1 0.7389460
94 NA 1 0.7259951
95 0.191929445 1 0.7282840
96 NA 1 0.7281676
97 -0.069760671 1 0.7245642
98 NA 1 0.7526938
99 NA 1 0.7272309
100 NA 1 0.7383460
$m7d$M_lvlone
y c1 time ns(time, df = 3)1
1 -13.0493856 0.7592026489 0.5090421822 -0.0731022196
1.1 -9.3335901 0.9548337990 0.6666076288 -0.0896372079
1.2 -22.3469852 0.5612235156 2.1304941282 0.1374616725
1.3 -15.0417337 1.1873391025 2.4954441458 0.3061500570
2 -12.0655434 0.9192204198 3.0164990982 0.5064248381
2.1 -15.8674476 -0.1870730476 3.2996806887 0.5543647993
2.2 -7.8800006 1.2517512331 4.1747569619 0.3402753582
3 -11.4820604 -0.0605087604 0.8478727890 -0.1024946971
3.1 -10.5983220 0.3788637747 3.0654308549 0.5187768948
3.2 -22.4519157 0.9872578281 4.7381553578 0.0174998856
4 -1.2697775 1.4930175328 0.3371432109 -0.0508146200
4.1 -11.1215184 -0.7692526880 1.0693019140 -0.1067711172
4.2 -3.6134138 0.9180841450 2.6148973033 0.3598480506
4.3 -14.5982385 -0.0541170782 3.1336532847 0.5333652385
5 -6.8457515 -0.1376784521 1.0762525082 -0.1066695938
5.1 -7.0551214 -0.2740585866 1.7912546196 0.0046159078
5.2 -12.3418980 0.4670496929 2.7960080339 0.4342731827
5.3 -9.2366906 0.1740288049 2.8119940578 0.4402846745
6 -5.1648211 0.9868044683 1.7815462884 0.0015253168
7 -10.0599502 -0.1280320918 3.3074087673 0.5547950298
7.1 -18.3267285 0.4242971219 3.7008403614 0.5158768825
7.2 -12.5138426 0.0777182491 4.7716691741 -0.0038356900
8 -1.6305331 -0.5791408712 1.1246398522 -0.1055283475
8.1 -9.6520453 0.3128604232 1.8027009873 0.0083228157
8.2 -1.5278462 0.6258446356 1.8175825174 0.0132420073
8.3 -7.4172211 -0.1040137707 2.8384267003 0.4499876083
8.4 -7.1238609 0.0481450285 3.3630275307 0.5564354493
8.5 -8.8706950 0.3831763675 4.4360849704 0.2011700353
9 -0.1634429 -0.1757592269 0.9607803822 -0.1064248362
9.1 -2.6034300 -0.1791541200 2.9177753383 0.4771840649
9.2 -6.7272369 -0.0957042935 4.8100892501 -0.0284447928
10 -6.4172202 -0.5598409704 2.2975509102 0.2139341504
10.1 -11.4834569 -0.2318340451 4.1734118364 0.3409274088
11 -8.7911356 0.5086859475 1.1832662905 -0.1030921083
11.1 -19.6645080 0.4951758188 1.2346051680 -0.0999663372
11.2 -20.2030932 -1.1022162541 1.6435316263 -0.0371358422
11.3 -21.3082176 -0.0611636705 3.3859017969 0.5563809266
11.4 -14.5802901 -0.4971774316 4.8118087661 -0.0295495187
12 -15.2006287 -0.2433996286 0.9591987054 -0.1063939605
13 0.8058816 0.8799673116 0.0619085738 -0.0095916688
13.1 -13.6379208 0.1079022586 3.5621061502 0.5424520669
14 -15.3422873 0.9991752617 4.0364430007 0.4032689358
14.1 -10.0965208 -0.1094019046 4.4710561272 0.1809335363
14.2 -16.6452027 0.1518967560 4.6359198843 0.0816318440
14.3 -15.8389733 0.3521012473 4.6886152599 0.0487734088
15 -8.9424594 0.3464447888 0.5402063532 -0.0767078114
15.1 -22.0101983 -0.4767313971 1.1893180816 -0.1027727004
15.2 -7.3975599 0.5759767791 1.5094739688 -0.0657566366
15.3 -10.3567334 -0.1713452662 4.9193474615 -0.0990627407
16 -1.9691302 0.4564754473 1.2417913869 -0.0994524289
16.1 -9.9308357 1.0652558311 2.5675726333 0.3388926675
16.2 -6.9626923 0.6971872493 2.6524101500 0.3760850293
16.3 -3.2862557 0.5259331838 3.5585018690 0.5429665219
16.4 -3.3972355 0.2046601798 3.7612454291 0.5003814501
16.5 -11.5767835 1.0718540464 3.9851612889 0.4243928946
17 -10.5474144 0.6048676222 1.5925356350 -0.0490208458
17.1 -7.6215009 0.2323298304 2.4374032998 0.2793033691
17.2 -16.5386939 1.2617499032 3.0256489082 0.5088484549
17.3 -20.0004774 -0.3913230895 3.3329089405 0.5558624020
17.4 -18.8505475 0.9577299112 3.8693758985 0.4671596304
18 -19.7302351 -0.0050324072 2.4374292302 0.2793154325
19 -14.6177568 -0.4187468937 0.9772165376 -0.1067031159
19.1 -17.8043866 -0.4478828944 1.1466335913 -0.1047524970
19.2 -15.1641705 -1.1966721302 2.2599126538 0.1964187590
19.3 -16.6898418 -0.5877091668 4.2114245973 0.3222225327
20 -12.9059229 0.6838223064 1.7170160066 -0.0177612481
20.1 -16.8191201 0.3278571109 1.7562902288 -0.0062817988
20.2 -6.1010131 -0.8489831990 2.2515566566 0.1925456183
20.3 -7.9415371 1.3169975191 2.2609123867 0.1968825793
20.4 -9.3904458 0.0444804531 3.4913365287 0.5508408484
20.5 -13.3504189 -0.4535207652 4.1730977828 0.3410795409
21 -7.6974718 -0.4030302960 1.6936582839 -0.0242133322
21.1 -11.9335526 -0.4069674045 2.9571191233 0.4894906279
21.2 -12.7064929 1.0650265940 3.7887385779 0.4925855178
22 -21.5022909 -0.0673274516 2.4696226232 0.2942488077
22.1 -12.7745451 0.9601388170 3.1626627257 0.5385758261
23 -3.5146508 0.5556634840 1.5414533857 -0.0596896592
23.1 -4.6724048 1.4407865964 2.3369736120 0.2323619738
24 -2.5619821 0.3856376411 2.8283136466 0.4463109938
25 -6.2944970 0.3564400705 0.5381704110 -0.0764769279
25.1 -3.8630505 0.0982553434 1.6069735331 -0.0457828435
25.2 -14.4205140 0.1928682598 1.6358226922 -0.0390131484
25.3 -19.6735037 -0.0192488594 3.2646870392 0.5517873130
25.4 -9.0288933 0.4466012931 4.0782226040 0.3851356619
25.5 -9.0509738 1.1425193342 4.1560292873 0.3492871599
26 -19.7340685 0.5341531449 0.2412706357 -0.0370005446
26.1 -14.1692728 1.2268695927 2.4451737676 0.2829160492
26.2 -17.2819976 0.3678294939 3.5988757887 0.5366821080
26.3 -24.6265576 0.5948516018 4.1822362854 0.3366364198
27 -7.3354999 -0.3342844147 3.6955824879 0.5171168233
27.1 -11.1488468 -0.4835141229 4.2451434687 0.3051619029
28 -11.7996597 -0.7145915499 0.5746519344 -0.0805110543
28.1 -8.2030122 0.5063671955 2.7943964268 0.4336613409
28.2 -26.4317815 -0.2067413142 4.2108539480 0.3225075315
28.3 -18.5016071 0.1196789973 4.4705521734 0.1812274493
29 -5.8551395 0.1392699487 1.1898884235 -0.1027419307
29.1 -2.0209442 0.7960234776 1.7624059319 -0.0044221127
29.2 -5.6368080 1.0398214352 2.0210406382 0.0902449711
29.3 -3.8110961 0.0813246429 3.4078777023 0.5559364351
30 -12.7217702 -0.3296323050 2.2635366488 0.1981005081
30.1 -17.0170140 1.3635850954 3.5938334477 0.5375291120
30.2 -25.4236089 0.7354171050 3.6138710892 0.5340597966
31 -17.0783921 0.3708398217 4.3988140998 0.2223657818
32 -18.4338764 -0.0474059668 1.6745209007 -0.0292943991
32.1 -19.4317212 1.2507771489 2.9128167813 0.4755751076
32.2 -19.4738978 0.1142915519 2.9676558380 0.4926434114
32.3 -21.4922645 0.6773270619 4.2099863547 0.3229405911
33 2.0838099 0.1774293842 0.0093385763 -0.0013701847
33.1 -13.3172274 0.6159606291 3.4591242753 0.5534368438
34 -10.0296691 0.8590979166 1.4998774312 -0.0674871387
34.1 -25.9426553 0.0546216775 3.8242761395 0.4818424185
34.2 -18.5688138 -0.0897224473 3.9072251692 0.4539624195
34.3 -15.4173859 0.4163395571 3.9582124643 0.4349720689
35 -14.3958113 -1.4693520528 1.3294299203 -0.0916157074
35.1 -12.9457541 -0.3031734330 1.5276966314 -0.0623564831
35.2 -16.1380691 -0.6045512101 4.5025920868 0.1624134259
36 -12.8166968 0.9823048960 0.7123168337 -0.0935638890
36.1 -14.3989481 1.4466051416 1.7972493160 0.0065488640
36.2 -12.2436943 1.1606752905 1.8262697803 0.0161646973
36.3 -15.0104638 0.8373091576 4.2840119381 0.2849748388
36.4 -10.1775457 0.2640591685 4.6194464504 0.0918066953
37 -15.2223495 0.1177313455 2.0018732361 0.0823235483
37.1 -14.7526195 -0.1415483779 3.6656836793 0.5238280708
37.2 -19.8168430 0.0054610124 3.9663937816 0.4317992421
38 -2.7065118 0.8078948077 0.9826511063 -0.1067779048
39 -8.7288138 0.9876451040 0.6921808305 -0.0918869929
39.1 -9.2746473 -0.3431222274 0.9027792048 -0.1048343071
39.2 -18.2695344 -1.7909380751 1.3055654289 -0.0940424764
39.3 -13.8219083 -0.1798746191 1.5412842878 -0.0597229655
39.4 -16.2254704 -0.1850961689 3.1834997435 0.5419341500
39.5 -21.7283648 0.4544226146 4.1394166439 0.3571597096
40 1.8291916 0.5350190436 1.1330395646 -0.1052513278
40.1 -6.6916432 0.4189342752 2.6940994046 0.3936778108
40.2 -1.6278171 0.4211994981 3.0396614212 0.5124598453
40.3 -10.5749790 0.0916687506 4.6762977762 0.0564941376
41 -3.1556121 -0.1035047421 1.9337158254 0.0551736455
41.1 -11.5895327 -0.4684202411 3.1956304458 0.5437374685
41.2 -18.9352091 0.5972615368 3.2846923557 0.5533874833
41.3 -15.9788960 0.9885613862 3.3813529415 0.5564251145
41.4 -9.6070508 -0.3908036794 3.5482964432 0.5443730748
42 -5.2159485 -0.0338893961 0.4859252973 -0.0703321485
42.1 -15.9878743 -0.4498363172 4.3293134298 0.2607797560
43 -16.6104361 0.8965546110 0.5616614548 -0.0790999084
43.1 -9.5549441 0.6199122090 1.0743579536 -0.1066987969
43.2 -14.2003491 0.1804894429 2.6131797966 0.3590962645
44 -8.1969033 1.3221409285 0.7662644819 -0.0976262102
44.1 -19.9270197 0.3416426284 2.6490291790 0.3746366123
44.2 -22.6521171 0.5706610068 3.3371910988 0.5559890192
44.3 -21.1903736 1.2679497430 4.1154200875 0.3683249841
45 -0.5686627 0.1414983160 0.1957449992 -0.0301940136
45.1 -7.5645740 0.7220892521 1.9963831536 0.0800764791
46 -19.1624789 1.5391054233 1.3477755385 -0.0895971055
46.1 -18.4487574 0.3889107049 2.8565793915 0.4564725410
46.2 -15.8222682 0.1248719493 4.4160729996 0.2125999596
47 -5.4165074 0.2014101100 0.6012621359 -0.0833108913
47.1 -15.0975029 0.2982973539 2.4097121472 0.2663955892
47.2 -12.9971413 1.1518107179 2.9975794035 0.5012527278
47.3 -10.6844521 0.5196802157 3.1829649757 0.5418520608
47.4 -18.2214784 0.3702301552 4.6201055450 0.0914005525
48 -8.3101471 -0.2128602862 2.8607365978 0.4579365172
48.1 -18.3854275 -0.5337239976 2.9098354396 0.4746016472
49 -13.0130319 -0.5236770035 2.7179756400 0.4035144992
50 -10.4579977 0.3897705981 1.1762060679 -0.1034484387
51 -19.3157621 -0.7213343736 1.4304436720 -0.0787988153
52 -4.4747188 0.3758235358 2.1266646020 0.1357604672
52.1 -4.3163827 0.7138067080 3.1000545993 0.5265788733
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80.2 -16.6381566 0.5046816157 2.8025900386 0.4367610654
81 0.5547913 -0.9493950353 0.0101324962 -0.0014945092
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81.2 1.8283303 0.6476958410 3.0542453879 0.5160872572
81.3 -5.2166381 0.4182528210 3.3456630446 0.5561952783
82 -3.0749381 1.1088801952 1.3791010005 -0.0858371096
82.1 -10.5506696 0.9334157763 1.7601010622 -0.0051252873
82.2 -18.2226347 0.4958140634 2.6233131927 0.3635216183
83 -12.5872635 0.5104724530 0.0537394290 -0.0083163054
83.1 -11.9756502 -0.0513309106 2.9061570496 0.4733943484
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84 -2.6320312 -0.0255753653 2.7254060237 0.4065375903
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85 -12.3886736 -0.0114038622 0.2977756259 -0.0452387941
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85.3 -6.3296340 -0.0520175929 3.1608762743 0.5382727768
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88 -2.2264493 -0.0601928334 0.0005892443 0.0000000000
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91 -7.1462503 1.1213651365 0.6382618390 -0.0869921115
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92 -6.4119222 0.4674939332 0.0737052364 -0.0114311653
93 5.2122168 0.2677965647 0.2788909199 -0.0425142500
93.1 3.1211725 1.6424445368 1.0357759963 -0.1070466070
93.2 -3.6841177 0.7101700066 2.4916551099 0.3044086294
93.3 2.6223542 1.1222322893 2.8876129608 0.4672045475
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94 -6.9602492 -0.2904211940 0.8488043118 -0.1025408741
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100 1.1005874 0.0369067906 1.0691387602 -0.1067733151
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ns(time, df = 3)2 ns(time, df = 3)3
1 0.222983368 -0.145369093
1.1 0.286659651 -0.186881442
1.2 0.538466292 -0.349241050
1.3 0.485312041 -0.300999737
2 0.388851338 -0.173896710
2.1 0.348347565 -0.079238230
2.2 0.338334366 0.301483601
3 0.354448579 -0.231074940
3.1 0.380711276 -0.158750730
3.2 0.391617016 0.590283099
4 0.149748191 -0.097625033
4.1 0.427124949 -0.278454698
4.2 0.463409709 -0.277637284
4.3 0.370048079 -0.136774026
5 0.429197233 -0.279805678
5.1 0.552972700 -0.360498322
5.2 0.428967354 -0.235519853
5.3 0.425938437 -0.231426488
6 0.552692434 -0.360315653
7 0.347509550 -0.076424127
7.1 0.324489976 0.081205236
7.2 0.395476156 0.607966686
8 0.443238494 -0.288959568
8.1 0.553246856 -0.360676673
8.2 0.553513388 -0.360848815
8.3 0.420954925 -0.224527396
8.4 0.341948600 -0.055827230
8.5 0.359621900 0.432934699
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10 0.517577740 -0.331608701
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11.3 0.339897056 -0.047184145
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12 0.392538168 -0.255906608
13 0.027579300 -0.017979717
13.1 0.328456601 0.022585629
14 0.330500975 0.234420981
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14.2 0.380152387 0.536563622
14.3 0.385997930 0.564205754
15 0.235874789 -0.153773371
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15.3 0.412872874 0.686175505
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16.4 0.324030412 0.107655226
16.5 0.328317423 0.210083745
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18 0.495409834 -0.311036128
19 0.398417477 -0.259739494
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20 0.549715454 -0.358374876
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20.3 0.522749488 -0.336132246
20.4 0.332148944 -0.006096291
20.5 0.338224725 0.300667673
21 0.548168935 -0.357366657
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21.2 0.324061287 0.119869388
22 0.489859980 -0.305573977
22.1 0.365790145 -0.127130621
23 0.532269527 -0.347001388
23.1 0.511693737 -0.326336775
24 0.422857517 -0.227186155
25 0.235036737 -0.153227021
25.1 0.540323722 -0.352252143
25.2 0.543297023 -0.354190522
25.3 0.352345378 -0.091832063
25.4 0.332578168 0.254466815
25.5 0.337117898 0.292289382
26 0.107676329 -0.070197210
26.1 0.494087736 -0.309746667
26.2 0.326994020 0.037821198
26.3 0.338833066 0.305164939
27 0.324565129 0.078928640
27.1 0.343307911 0.336333009
28 0.249962259 -0.162957386
28.1 0.429273232 -0.235929197
28.2 0.340807567 0.319299132
28.3 0.362944907 0.450648982
29 0.461066695 -0.300582271
29.1 0.552010523 -0.359871096
29.2 0.547810299 -0.356549969
29.3 0.338052723 -0.038787896
30 0.522389104 -0.335820366
30.1 0.327176583 0.035718727
30.2 0.326484504 0.044098116
31 0.356147774 0.413866643
32 0.546719898 -0.356421989
32.1 0.407167901 -0.204246665
32.2 0.397320853 -0.188492301
32.3 0.340746183 0.318869516
33 0.003936563 -0.002566356
33.1 0.334224366 -0.018862759
34 0.526248154 -0.343075887
34.1 0.324316326 0.135814702
34.2 0.325817315 0.173695359
34.3 0.327338410 0.197417584
35 0.494613531 -0.322452391
35.1 0.530354088 -0.345752660
35.2 0.366122990 0.467180923
36 0.304360595 -0.198421183
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36.2 0.553622593 -0.360918172
36.3 0.346311748 0.355765584
36.4 0.378356377 0.527945205
37 0.549032516 -0.357475622
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37.2 0.327623070 0.201253686
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39.2 0.489340823 -0.319014966
39.3 0.532246449 -0.346986343
39.4 0.362843629 -0.120095446
39.5 0.336078053 0.284162218
40 0.445606008 -0.290503017
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40.2 0.384951582 -0.166792072
40.3 0.384618587 0.557734965
41 0.552281804 -0.359898022
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41.2 0.350019018 -0.084662333
41.3 0.340294264 -0.048910814
41.4 0.329085370 0.016921661
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44.2 0.344429887 -0.065469588
44.3 0.334642692 0.272471485
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45.1 0.549358350 -0.357721087
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46.1 0.417554417 -0.219695608
46.2 0.357740629 0.422684799
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47.2 0.392094432 -0.179612843
47.3 0.362918036 -0.120277127
47.4 0.378427928 0.528289800
48 0.416778543 -0.218578367
48.1 0.407711957 -0.205083750
49 0.443841107 -0.254634636
50 0.457436535 -0.298215668
51 0.514675808 -0.335531551
52 0.538856976 -0.349554303
52.1 0.375191670 -0.147721246
52.2 0.371072093 -0.139010728
52.3 0.328068662 0.026310537
52.4 0.398579012 0.622074267
52.5 0.420339814 0.719341816
53 0.217791262 -0.141984214
53.1 0.328980432 0.017837917
53.2 0.374021344 0.506858010
54 0.509684846 -0.332277803
54.1 0.553543641 -0.360828251
54.2 0.482577881 -0.298206049
54.3 0.431088614 -0.238343800
54.4 0.329511331 0.223917052
55 0.264974033 -0.172743982
55.1 0.317295658 -0.206853912
55.2 0.424402666 -0.229322660
55.3 0.370199481 -0.137106630
55.4 0.357686883 -0.106926420
56 0.469640780 -0.306171957
56.1 0.508537162 -0.323453138
56.2 0.472243570 -0.287337760
56.3 0.400327890 -0.193430676
56.4 0.356980364 -0.105024091
56.5 0.337279686 -0.035063150
57 0.105801595 -0.068975018
57.1 0.110715003 -0.072178206
57.2 0.447707406 -0.291872977
57.3 0.539981816 -0.350452633
58 0.469103743 -0.305821847
58.1 0.543063473 -0.354038263
58.2 0.547088143 -0.356662058
58.3 0.460186824 -0.273997945
58.4 0.419201600 -0.222049115
58.5 0.328051465 0.026480236
59 0.553170063 -0.360551776
59.1 0.419362856 0.715006940
60 0.412191135 -0.211854697
61 0.307818016 -0.200675172
61.1 0.514461229 -0.328833062
61.2 0.483120456 -0.298763087
61.3 0.364301147 -0.123615504
61.4 0.326874230 0.039239893
62 0.233185122 -0.152019902
62.1 0.299153085 -0.195026261
62.2 0.334271811 -0.019135492
62.3 0.399105605 0.624459836
63 0.426365839 -0.232008543
63.1 0.327587037 0.200775752
64 0.334859412 0.274275354
65 0.302557595 -0.197245756
65.1 0.547526242 -0.356333749
65.2 0.373224291 -0.143616734
65.3 0.360999362 -0.115520552
66 0.331967886 -0.004894436
66.1 0.324024401 0.110532429
66.2 0.327018558 0.192927263
67 0.338207650 0.300540381
68 0.057768712 -0.037661039
68.1 0.552674233 -0.360303787
68.2 0.545772611 -0.354990134
68.3 0.402123497 -0.196324326
68.4 0.330699051 0.236438945
69 0.336430831 0.286949248
70 0.089041044 -0.058048346
70.1 0.212100911 -0.138274515
71 0.327679863 -0.213623666
71.1 0.524461669 -0.341911227
71.2 0.332453400 0.253324633
71.3 0.387824204 0.572728871
71.4 0.390032203 0.582970861
72 0.383548046 -0.250045696
72.1 0.458448763 -0.298875568
72.2 0.553381188 -0.360707545
72.3 0.332515066 -0.008479051
72.4 0.324676638 0.075836695
72.5 0.332961867 0.257937078
73 0.376470777 0.518824914
74 0.520280412 -0.339185349
75 0.353824007 -0.096185550
76 0.553343477 -0.360739315
76.1 0.345092469 0.347998542
76.2 0.418789942 0.712464524
77 0.353748723 -0.230618684
78 0.345586078 -0.225297228
79 0.025994217 -0.016946357
79.1 0.494863212 -0.310503883
79.2 0.348724471 -0.080482751
80 0.372138169 -0.242607278
80.1 0.497621832 -0.324413587
80.2 0.427719060 -0.233841717
81 0.004293764 -0.002799225
81.1 0.386906369 -0.252235081
81.2 0.382538581 -0.162258804
81.3 0.343596919 -0.062321933
82 0.504943034 -0.329186483
82.1 0.551916873 -0.359810043
82.2 0.461826800 -0.275856749
83 0.023907290 -0.015585831
83.1 0.408384331 -0.206113773
83.2 0.372272641 -0.141595515
83.3 0.394862487 0.605165478
84 0.442422222 -0.252876961
84.1 0.344500819 -0.065733726
85 0.132558080 -0.086418319
85.1 0.550966098 -0.359190206
85.2 0.450202261 -0.262358242
85.3 0.366047208 -0.127729570
85.4 0.326908671 0.191335442
85.5 0.366794208 0.470626547
86 0.354361579 -0.231018222
86.1 0.409486154 -0.266955474
86.2 0.476581575 -0.310696855
86.3 0.532204554 -0.344135221
86.4 0.492692574 -0.308378023
86.5 0.324278938 0.134159804
87 0.445945432 -0.257217600
87.1 0.335124592 -0.023912615
87.2 0.368308652 0.478346358
88 0.000000000 0.000000000
88.1 0.304089792 -0.198244638
88.2 0.485342573 -0.301030746
88.3 0.348358411 -0.079274225
89 0.278640020 -0.181653221
90 0.075832019 -0.049437014
90.1 0.466568464 -0.281151299
90.2 0.453911731 -0.266749122
90.3 0.371462522 -0.139855690
91 0.275496513 -0.179603880
91.1 0.461997624 -0.276049539
91.2 0.396123130 0.610915809
92 0.032879925 -0.021435342
93 0.124267794 -0.081013650
93.1 0.416939473 -0.271814501
93.2 0.485984408 -0.301681670
93.3 0.411791889 -0.211259675
93.4 0.362300252 0.447248720
94 0.354779307 -0.231290551
94.1 0.422892510 -0.275695453
94.2 0.551883683 -0.359604210
94.3 0.379797627 -0.156971195
94.4 0.377204420 -0.151822700
94.5 0.354495295 0.404570621
95 0.547886254 -0.356607715
95.1 0.329687700 0.225844939
95.2 0.371978790 0.496766490
96 0.013741475 -0.008958452
96.1 0.059216896 -0.038605151
96.2 0.288037217 -0.187779516
96.3 0.533324704 -0.345060176
96.4 0.527566661 -0.340252993
96.5 0.342757800 0.332658824
97 0.462541264 -0.301543584
97.1 0.417765721 0.707918494
98 0.091227325 -0.059473644
98.1 0.190127826 -0.123949646
98.2 0.330659794 0.004310743
99 0.156711586 -0.102164665
99.1 0.384914328 0.559124871
99.2 0.421642124 0.725119709
100 0.427076142 -0.278422879
100.1 0.527917102 -0.344163920
100.2 0.536384181 -0.347560939
100.3 0.325078572 0.158668905
100.4 0.382446660 0.547483173
$m7d$spM_id
center scale
C2 -0.6240921 0.68571078
(Intercept) NA NA
C1 0.7372814 0.01472882
$m7d$spM_lvlone
center scale
y -11.17337099 6.2496619
c1 0.25599956 0.6718095
time 2.53394028 1.3818094
ns(time, df = 3)1 0.19883694 0.2502686
ns(time, df = 3)2 0.38513689 0.1171115
ns(time, df = 3)3 -0.07137294 0.2891059
$m7d$mu_reg_norm
[1] 0
$m7d$tau_reg_norm
[1] 1e-04
$m7d$shape_tau_norm
[1] 0.01
$m7d$rate_tau_norm
[1] 0.01
$m7d$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m7d$shape_diag_RinvD
[1] "0.01"
$m7d$rate_diag_RinvD
[1] "0.001"
$m7d$RinvD_y_id
[,1] [,2]
[1,] NA 0
[2,] 0 NA
$m7d$KinvD_y_id
id
3
$m7e
$m7e$M_id
C2 (Intercept) C1
1 -1.381594459 1 0.7175865
2 0.344426024 1 0.7507170
3 NA 1 0.7255954
4 -0.228910007 1 0.7469352
5 NA 1 0.7139120
6 -2.143955482 1 0.7332505
7 -1.156567023 1 0.7345929
8 -0.598827660 1 0.7652589
9 NA 1 0.7200622
10 -1.006719032 1 0.7423879
11 0.239801450 1 0.7437448
12 -1.064969789 1 0.7446470
13 -0.538082688 1 0.7530186
14 NA 1 0.7093137
15 -1.781049276 1 0.7331192
16 NA 1 0.7011390
17 NA 1 0.7432395
18 -0.014579883 1 0.7545191
19 -2.121550136 1 0.7528487
20 NA 1 0.7612865
21 -0.363239698 1 0.7251719
22 -0.121568514 1 0.7300630
23 -0.951271111 1 0.7087249
24 NA 1 0.7391938
25 -0.974288621 1 0.7820641
26 -1.130632418 1 0.7118298
27 0.114339868 1 0.7230857
28 0.238334648 1 0.7489353
29 0.840744958 1 0.7510888
30 NA 1 0.7300717
31 NA 1 0.7550721
32 -1.466312154 1 0.7321898
33 -0.637352277 1 0.7306414
34 NA 1 0.7427216
35 NA 1 0.7193042
36 NA 1 0.7312888
37 NA 1 0.7100436
38 NA 1 0.7670184
39 0.006728205 1 0.7400449
40 NA 1 0.7397304
41 -1.663281353 1 0.7490966
42 0.161184794 1 0.7419274
43 0.457939180 1 0.7527810
44 -0.307070331 1 0.7408315
45 NA 1 0.7347550
46 -1.071668276 1 0.7332398
47 -0.814751321 1 0.7376481
48 -0.547630662 1 0.7346179
49 NA 1 0.7329402
50 -1.350213782 1 0.7260436
51 0.719054706 1 0.7242910
52 NA 1 0.7298067
53 -1.207130750 1 0.7254741
54 NA 1 0.7542067
55 -0.408600991 1 0.7389952
56 -0.271380529 1 0.7520638
57 -1.361925974 1 0.7219958
58 NA 1 0.7259632
59 NA 1 0.7458606
60 -0.323712205 1 0.7672421
61 NA 1 0.7257179
62 NA 1 0.7189892
63 -1.386906880 1 0.7333356
64 NA 1 0.7320243
65 NA 1 0.7477711
66 -0.565191691 1 0.7343974
67 -0.382899912 1 0.7491624
68 NA 1 0.7482736
69 -0.405642769 1 0.7338267
70 NA 1 0.7607742
71 -0.843748427 1 0.7777600
72 0.116003683 1 0.7408143
73 -0.778634325 1 0.7248271
74 NA 1 0.7364916
75 NA 1 0.7464926
76 NA 1 0.7355430
77 -0.632974758 1 0.7208449
78 NA 1 0.7373573
79 -0.778064615 1 0.7598079
80 NA 1 0.7360415
81 NA 1 0.7293932
82 -0.246123253 1 0.7279309
83 -1.239659782 1 0.7344643
84 -0.467772280 1 0.7384350
85 NA 1 0.7323716
86 -2.160485036 1 0.7576597
87 -0.657675572 1 0.7496139
88 NA 1 0.7275239
89 -0.696710744 1 0.7250648
90 NA 1 0.7335262
91 -0.179395847 1 0.7343980
92 -0.441545568 1 0.7380425
93 -0.685799334 1 0.7389460
94 NA 1 0.7259951
95 0.191929445 1 0.7282840
96 NA 1 0.7281676
97 -0.069760671 1 0.7245642
98 NA 1 0.7526938
99 NA 1 0.7272309
100 NA 1 0.7383460
$m7e$M_lvlone
y c1 ns(time, df = 3)1 ns(time, df = 3)2
1 -13.0493856 0.7592026489 -0.0731022196 0.222983368
1.1 -9.3335901 0.9548337990 -0.0896372079 0.286659651
1.2 -22.3469852 0.5612235156 0.1374616725 0.538466292
1.3 -15.0417337 1.1873391025 0.3061500570 0.485312041
2 -12.0655434 0.9192204198 0.5064248381 0.388851338
2.1 -15.8674476 -0.1870730476 0.5543647993 0.348347565
2.2 -7.8800006 1.2517512331 0.3402753582 0.338334366
3 -11.4820604 -0.0605087604 -0.1024946971 0.354448579
3.1 -10.5983220 0.3788637747 0.5187768948 0.380711276
3.2 -22.4519157 0.9872578281 0.0174998856 0.391617016
4 -1.2697775 1.4930175328 -0.0508146200 0.149748191
4.1 -11.1215184 -0.7692526880 -0.1067711172 0.427124949
4.2 -3.6134138 0.9180841450 0.3598480506 0.463409709
4.3 -14.5982385 -0.0541170782 0.5333652385 0.370048079
5 -6.8457515 -0.1376784521 -0.1066695938 0.429197233
5.1 -7.0551214 -0.2740585866 0.0046159078 0.552972700
5.2 -12.3418980 0.4670496929 0.4342731827 0.428967354
5.3 -9.2366906 0.1740288049 0.4402846745 0.425938437
6 -5.1648211 0.9868044683 0.0015253168 0.552692434
7 -10.0599502 -0.1280320918 0.5547950298 0.347509550
7.1 -18.3267285 0.4242971219 0.5158768825 0.324489976
7.2 -12.5138426 0.0777182491 -0.0038356900 0.395476156
8 -1.6305331 -0.5791408712 -0.1055283475 0.443238494
8.1 -9.6520453 0.3128604232 0.0083228157 0.553246856
8.2 -1.5278462 0.6258446356 0.0132420073 0.553513388
8.3 -7.4172211 -0.1040137707 0.4499876083 0.420954925
8.4 -7.1238609 0.0481450285 0.5564354493 0.341948600
8.5 -8.8706950 0.3831763675 0.2011700353 0.359621900
9 -0.1634429 -0.1757592269 -0.1064248362 0.393057590
9.1 -2.6034300 -0.1791541200 0.4771840649 0.406264856
9.2 -6.7272369 -0.0957042935 -0.0284447928 0.399948734
10 -6.4172202 -0.5598409704 0.2139341504 0.517577740
10.1 -11.4834569 -0.2318340451 0.3409274088 0.338245451
11 -8.7911356 0.5086859475 -0.1030921083 0.459317031
11.1 -19.6645080 0.4951758188 -0.0999663372 0.472517168
11.2 -20.2030932 -1.1022162541 -0.0371358422 0.544030949
11.3 -21.3082176 -0.0611636705 0.5563809266 0.339897056
11.4 -14.5802901 -0.4971774316 -0.0295495187 0.400149982
12 -15.2006287 -0.2433996286 -0.1063939605 0.392538168
13 0.8058816 0.8799673116 -0.0095916688 0.027579300
13.1 -13.6379208 0.1079022586 0.5424520669 0.328456601
14 -15.3422873 0.9991752617 0.4032689358 0.330500975
14.1 -10.0965208 -0.1094019046 0.1809335363 0.362994245
14.2 -16.6452027 0.1518967560 0.0816318440 0.380152387
14.3 -15.8389733 0.3521012473 0.0487734088 0.385997930
15 -8.9424594 0.3464447888 -0.0767078114 0.235874789
15.1 -22.0101983 -0.4767313971 -0.1027727004 0.460916543
15.2 -7.3975599 0.5759767791 -0.0657566366 0.527699447
15.3 -10.3567334 -0.1713452662 -0.0990627407 0.412872874
16 -1.9691302 0.4564754473 -0.0994524289 0.474297164
16.1 -9.9308357 1.0652558311 0.3388926675 0.472228980
16.2 -6.9626923 0.6971872493 0.3760850293 0.456327895
16.3 -3.2862557 0.5259331838 0.5429665219 0.328616483
16.4 -3.3972355 0.2046601798 0.5003814501 0.324030412
16.5 -11.5767835 1.0718540464 0.4243928946 0.328317423
17 -10.5474144 0.6048676222 -0.0490208458 0.538702746
17.1 -7.6215009 0.2323298304 0.2793033691 0.495414246
17.2 -16.5386939 1.2617499032 0.5088484549 0.387301236
17.3 -20.0004774 -0.3913230895 0.5558624020 0.344858135
17.4 -18.8505475 0.9577299112 0.4671596304 0.324978786
18 -19.7302351 -0.0050324072 0.2793154325 0.495409834
19 -14.6177568 -0.4187468937 -0.1067031159 0.398417477
19.1 -17.8043866 -0.4478828944 -0.1047524970 0.449392800
19.2 -15.1641705 -1.1966721302 0.1964187590 0.522886360
19.3 -16.6898418 -0.5877091668 0.3222225327 0.340847993
20 -12.9059229 0.6838223064 -0.0177612481 0.549715454
20.1 -16.8191201 0.3278571109 -0.0062817988 0.551756621
20.2 -6.1010131 -0.8489831990 0.1925456183 0.524021244
20.3 -7.9415371 1.3169975191 0.1968825793 0.522749488
20.4 -9.3904458 0.0444804531 0.5508408484 0.332148944
20.5 -13.3504189 -0.4535207652 0.3410795409 0.338224725
21 -7.6974718 -0.4030302960 -0.0242133322 0.548168935
21.1 -11.9335526 -0.4069674045 0.4894906279 0.399187414
21.2 -12.7064929 1.0650265940 0.4925855178 0.324061287
22 -21.5022909 -0.0673274516 0.2942488077 0.489859980
22.1 -12.7745451 0.9601388170 0.5385758261 0.365790145
23 -3.5146508 0.5556634840 -0.0596896592 0.532269527
23.1 -4.6724048 1.4407865964 0.2323619738 0.511693737
24 -2.5619821 0.3856376411 0.4463109938 0.422857517
25 -6.2944970 0.3564400705 -0.0764769279 0.235036737
25.1 -3.8630505 0.0982553434 -0.0457828435 0.540323722
25.2 -14.4205140 0.1928682598 -0.0390131484 0.543297023
25.3 -19.6735037 -0.0192488594 0.5517873130 0.352345378
25.4 -9.0288933 0.4466012931 0.3851356619 0.332578168
25.5 -9.0509738 1.1425193342 0.3492871599 0.337117898
26 -19.7340685 0.5341531449 -0.0370005446 0.107676329
26.1 -14.1692728 1.2268695927 0.2829160492 0.494087736
26.2 -17.2819976 0.3678294939 0.5366821080 0.326994020
26.3 -24.6265576 0.5948516018 0.3366364198 0.338833066
27 -7.3354999 -0.3342844147 0.5171168233 0.324565129
27.1 -11.1488468 -0.4835141229 0.3051619029 0.343307911
28 -11.7996597 -0.7145915499 -0.0805110543 0.249962259
28.1 -8.2030122 0.5063671955 0.4336613409 0.429273232
28.2 -26.4317815 -0.2067413142 0.3225075315 0.340807567
28.3 -18.5016071 0.1196789973 0.1812274493 0.362944907
29 -5.8551395 0.1392699487 -0.1027419307 0.461066695
29.1 -2.0209442 0.7960234776 -0.0044221127 0.552010523
29.2 -5.6368080 1.0398214352 0.0902449711 0.547810299
29.3 -3.8110961 0.0813246429 0.5559364351 0.338052723
30 -12.7217702 -0.3296323050 0.1981005081 0.522389104
30.1 -17.0170140 1.3635850954 0.5375291120 0.327176583
30.2 -25.4236089 0.7354171050 0.5340597966 0.326484504
31 -17.0783921 0.3708398217 0.2223657818 0.356147774
32 -18.4338764 -0.0474059668 -0.0292943991 0.546719898
32.1 -19.4317212 1.2507771489 0.4755751076 0.407167901
32.2 -19.4738978 0.1142915519 0.4926434114 0.397320853
32.3 -21.4922645 0.6773270619 0.3229405911 0.340746183
33 2.0838099 0.1774293842 -0.0013701847 0.003936563
33.1 -13.3172274 0.6159606291 0.5534368438 0.334224366
34 -10.0296691 0.8590979166 -0.0674871387 0.526248154
34.1 -25.9426553 0.0546216775 0.4818424185 0.324316326
34.2 -18.5688138 -0.0897224473 0.4539624195 0.325817315
34.3 -15.4173859 0.4163395571 0.4349720689 0.327338410
35 -14.3958113 -1.4693520528 -0.0916157074 0.494613531
35.1 -12.9457541 -0.3031734330 -0.0623564831 0.530354088
35.2 -16.1380691 -0.6045512101 0.1624134259 0.366122990
36 -12.8166968 0.9823048960 -0.0935638890 0.304360595
36.1 -14.3989481 1.4466051416 0.0065488640 0.553123835
36.2 -12.2436943 1.1606752905 0.0161646973 0.553622593
36.3 -15.0104638 0.8373091576 0.2849748388 0.346311748
36.4 -10.1775457 0.2640591685 0.0918066953 0.378356377
37 -15.2223495 0.1177313455 0.0823235483 0.549032516
37.1 -14.7526195 -0.1415483779 0.5238280708 0.325102184
37.2 -19.8168430 0.0054610124 0.4317992421 0.327623070
38 -2.7065118 0.8078948077 -0.1067779048 0.400174439
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57.3 -9.3473241 0.6785562445 0.1307695676 0.539981816
58 -12.0245677 1.6476207996 -0.1008867350 0.469103743
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59 -13.2946560 0.4766421367 0.0432887218 0.553170063
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60 -19.2209402 0.5231452932 0.4664716194 0.412191135
61 -4.6699914 -0.7190130614 -0.0942894088 0.307818016
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63 -24.0335535 0.3345678035 0.4394416153 0.426365839
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67 -25.6073277 1.6716027681 0.3412049232 0.338207650
68 -13.4806759 0.5691848839 -0.0200346649 0.057768712
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69 -14.5290675 0.0381382683 0.3544700410 0.336430831
70 -15.2122709 0.8126204217 -0.0307235467 0.089041044
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71 -10.3352703 -0.5529062591 -0.0981662227 0.327679863
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71.3 -21.4316644 -1.0118346755 0.0385790226 0.387824204
71.4 -8.1137650 1.8623785017 0.0262933692 0.390032203
72 -9.1848162 -0.4521659275 -0.1057555291 0.383548046
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72.5 -12.8948965 1.6298050306 0.3819329983 0.332961867
73 -2.6091307 -0.0307469467 0.1025345515 0.376470777
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76 -6.0061671 -0.9888876620 0.0098906162 0.553343477
76.1 -8.8149114 0.0003798292 0.2930926278 0.345092469
76.2 -11.8359043 -0.8421863763 -0.1312549589 0.418789942
77 0.4772521 -0.4986802480 -0.1023963499 0.353748723
78 -9.4105229 0.0417330969 -0.1011875966 0.345586078
79 -1.0217265 -0.3767450660 -0.0090412342 0.025994217
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80 -12.7366807 -0.0041558414 -0.1046806598 0.372138169
80.1 -9.0584783 -0.0329337062 -0.0900832419 0.497621832
80.2 -16.6381566 0.5046816157 0.4367610654 0.427719060
81 0.5547913 -0.9493950353 -0.0014945092 0.004293764
81.1 -4.0892715 0.2443038954 -0.1060165656 0.386906369
81.2 1.8283303 0.6476958410 0.5160872572 0.382538581
81.3 -5.2166381 0.4182528210 0.5561952783 0.343596919
82 -3.0749381 1.1088801952 -0.0858371096 0.504943034
82.1 -10.5506696 0.9334157763 -0.0051252873 0.551916873
82.2 -18.2226347 0.4958140634 0.3635216183 0.461826800
83 -12.5872635 0.5104724530 -0.0083163054 0.023907290
83.1 -11.9756502 -0.0513309106 0.4733943484 0.408384331
83.2 -10.6744217 -0.2067792494 0.5304919537 0.372272641
83.3 -19.2714012 -0.0534169155 -0.0004497824 0.394862487
84 -2.6320312 -0.0255753653 0.4065375903 0.442422222
84.1 -9.8140094 -1.8234189877 0.5559689912 0.344500819
85 -12.3886736 -0.0114038622 -0.0452387941 0.132558080
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85.2 -9.6433248 -0.2241856342 0.3897280300 0.450202261
85.3 -6.3296340 -0.0520175929 0.5382727768 0.366047208
85.4 -7.0405525 0.2892733846 0.4399457097 0.326908671
85.5 -13.6714939 -0.3740417009 0.1584688915 0.366794208
86 -10.8756412 0.4293735089 -0.1024825180 0.354361579
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86.4 -14.9191290 2.6003411822 0.2866858473 0.492692574
86.5 -17.7515546 -0.1420690052 0.4829861649 0.324278938
87 -10.7027963 1.0457427869 0.3989962007 0.445945432
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87.2 -14.9616716 0.4396872616 0.1496033589 0.368308652
88 -2.2264493 -0.0601928334 0.0000000000 0.000000000
88.1 -8.9626474 -1.0124347595 -0.0935064654 0.304089792
88.2 -2.5095281 0.5730917016 0.3060711120 0.485342573
88.3 -16.3345673 -0.0029455332 0.5543589602 0.348358411
89 -11.0459647 1.5465903721 -0.0877493552 0.278640020
90 -4.5610239 0.0626760573 -0.0262292802 0.075832019
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91 -7.1462503 1.1213651365 -0.0869921115 0.275496513
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91.2 -18.2576707 0.3395603754 -0.0074027623 0.396123130
92 -6.4119222 0.4674939332 -0.0114311653 0.032879925
93 5.2122168 0.2677965647 -0.0425142500 0.124267794
93.1 3.1211725 1.6424445368 -0.1070466070 0.416939473
93.2 -3.6841177 0.7101700066 0.3044086294 0.485984408
93.3 2.6223542 1.1222322893 0.4672045475 0.411791889
93.4 -11.1877696 1.4628960401 0.1850733549 0.362300252
94 -6.9602492 -0.2904211940 -0.1025408741 0.354779307
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94.3 -11.6340088 0.6793464917 0.5201024995 0.379797627
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95 -12.8849309 0.2375652188 0.0897729510 0.547886254
95.1 -9.7451502 0.0767152977 0.4108281293 0.329687700
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96 -4.9139832 0.7813892121 -0.0047820450 0.013741475
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96.3 -11.8690793 0.8771471244 0.1585782261 0.533324704
96.4 -11.0224373 1.9030768981 0.1801061786 0.527566661
96.5 -10.9530403 -0.1684332749 0.3089317576 0.342757800
97 -9.8540471 1.3775130083 -0.1024324589 0.462541264
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99 -11.4720500 0.7993356425 -0.0530422907 0.156711586
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99.2 -17.3939469 -0.1150895843 -0.1467618333 0.421642124
100 1.1005874 0.0369067906 -0.1067733151 0.427076142
100.1 -3.8226248 1.6023713093 -0.0654896559 0.527917102
100.2 -0.9123182 0.8861545820 0.1462778204 0.536384181
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100.4 -12.8093826 -0.0834577654 0.0686909568 0.382446660
ns(time, df = 3)3 time
1 -0.145369093 0.5090421822
1.1 -0.186881442 0.6666076288
1.2 -0.349241050 2.1304941282
1.3 -0.300999737 2.4954441458
2 -0.173896710 3.0164990982
2.1 -0.079238230 3.2996806887
2.2 0.301483601 4.1747569619
3 -0.231074940 0.8478727890
3.1 -0.158750730 3.0654308549
3.2 0.590283099 4.7381553578
4 -0.097625033 0.3371432109
4.1 -0.278454698 1.0693019140
4.2 -0.277637284 2.6148973033
4.3 -0.136774026 3.1336532847
5 -0.279805678 1.0762525082
5.1 -0.360498322 1.7912546196
5.2 -0.235519853 2.7960080339
5.3 -0.231426488 2.8119940578
6 -0.360315653 1.7815462884
7 -0.076424127 3.3074087673
7.1 0.081205236 3.7008403614
7.2 0.607966686 4.7716691741
8 -0.288959568 1.1246398522
8.1 -0.360676673 1.8027009873
8.2 -0.360848815 1.8175825174
8.3 -0.224527396 2.8384267003
8.4 -0.055827230 3.3630275307
8.5 0.432934699 4.4360849704
9 -0.256245233 0.9607803822
9.1 -0.202849973 2.9177753383
9.2 0.628274517 4.8100892501
10 -0.331608701 2.2975509102
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11 -0.299441616 1.1832662905
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11.2 -0.354668988 1.6435316263
11.3 -0.047184145 3.3859017969
11.4 0.629184194 4.8118087661
12 -0.255906608 0.9591987054
13 -0.017979717 0.0619085738
13.1 0.022585629 3.5621061502
14 0.234420981 4.0364430007
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14.2 0.536563622 4.6359198843
14.3 0.564205754 4.6886152599
15 -0.153773371 0.5402063532
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15.3 0.686175505 4.9193474615
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16.3 0.021104265 3.5585018690
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16.5 0.210083745 3.9851612889
17 -0.351195384 1.5925356350
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17.4 0.156298232 3.8693758985
18 -0.311036128 2.4374292302
19 -0.259739494 0.9772165376
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19.3 0.319581745 4.2114245973
20 -0.358374876 1.7170160066
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20.2 -0.337228841 2.2515566566
20.3 -0.336132246 2.2609123867
20.4 -0.006096291 3.4913365287
20.5 0.300667673 4.1730977828
21 -0.357366657 1.6936582839
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21.2 0.119869388 3.7887385779
22 -0.305573977 2.4696226232
22.1 -0.127130621 3.1626627257
23 -0.347001388 1.5414533857
23.1 -0.326336775 2.3369736120
24 -0.227186155 2.8283136466
25 -0.153227021 0.5381704110
25.1 -0.352252143 1.6069735331
25.2 -0.354190522 1.6358226922
25.3 -0.091832063 3.2646870392
25.4 0.254466815 4.0782226040
25.5 0.292289382 4.1560292873
26 -0.070197210 0.2412706357
26.1 -0.309746667 2.4451737676
26.2 0.037821198 3.5988757887
26.3 0.305164939 4.1822362854
27 0.078928640 3.6955824879
27.1 0.336333009 4.2451434687
28 -0.162957386 0.5746519344
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28.2 0.319299132 4.2108539480
28.3 0.450648982 4.4705521734
29 -0.300582271 1.1898884235
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29.2 -0.356549969 2.0210406382
29.3 -0.038787896 3.4078777023
30 -0.335820366 2.2635366488
30.1 0.035718727 3.5938334477
30.2 0.044098116 3.6138710892
31 0.413866643 4.3988140998
32 -0.356421989 1.6745209007
32.1 -0.204246665 2.9128167813
32.2 -0.188492301 2.9676558380
32.3 0.318869516 4.2099863547
33 -0.002566356 0.0093385763
33.1 -0.018862759 3.4591242753
34 -0.343075887 1.4998774312
34.1 0.135814702 3.8242761395
34.2 0.173695359 3.9072251692
34.3 0.197417584 3.9582124643
35 -0.322452391 1.3294299203
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35.2 0.467180923 4.5025920868
36 -0.198421183 0.7123168337
36.1 -0.360596719 1.7972493160
36.2 -0.360918172 1.8262697803
36.3 0.355765584 4.2840119381
36.4 0.527945205 4.6194464504
37 -0.357475622 2.0018732361
37.1 0.066063038 3.6656836793
37.2 0.201253686 3.9663937816
38 -0.260884907 0.9826511063
39 -0.193368224 0.6921808305
39.1 -0.243562727 0.9027792048
39.2 -0.319014966 1.3055654289
39.3 -0.346986343 1.5412842878
39.4 -0.120095446 3.1834997435
39.5 0.284162218 4.1394166439
40 -0.290503017 1.1330395646
40.1 -0.260192709 2.6940994046
40.2 -0.166792072 3.0396614212
40.3 0.557734965 4.6762977762
41 -0.359898022 1.9337158254
41.1 -0.115958300 3.1956304458
41.2 -0.084662333 3.2846923557
41.3 -0.048910814 3.3813529415
41.4 0.016921661 3.5482964432
42 -0.139079780 0.4859252973
42.1 0.378571910 4.3293134298
43 -0.159507207 0.5616614548
43.1 -0.279438320 1.0743579536
43.2 -0.277998502 2.6131797966
44 -0.211710161 0.7662644819
44.1 -0.270307896 2.6490291790
44.2 -0.065469588 3.3371910988
44.3 0.272471485 4.1154200875
45 -0.057030292 0.1957449992
45.1 -0.357721087 1.9963831536
46 -0.325006430 1.3477755385
46.1 -0.219695608 2.8565793915
46.2 0.422684799 4.4160729996
47 -0.169972385 0.6012621359
47.1 -0.315522977 2.4097121472
47.2 -0.179612843 2.9975794035
47.3 -0.120277127 3.1829649757
47.4 0.528289800 4.6201055450
48 -0.218578367 2.8607365978
48.1 -0.205083750 2.9098354396
49 -0.254634636 2.7179756400
50 -0.298215668 1.1762060679
51 -0.335531551 1.4304436720
52 -0.349554303 2.1266646020
52.1 -0.147721246 3.1000545993
52.2 -0.139010728 3.1268477370
52.3 0.026310537 3.5711459327
52.4 0.622074267 4.7983659909
52.5 0.719341816 4.9818264414
53 -0.141984214 0.4965799209
53.1 0.017837917 3.5505357443
53.2 0.506858010 4.5790420019
54 -0.332277803 1.4034724841
54.1 -0.360828251 1.8812377600
54.2 -0.298206049 2.5107589352
54.3 -0.238343800 2.7848406672
54.4 0.223917052 4.0143877396
55 -0.172743982 0.6118522980
55.1 -0.206853912 0.7463747414
55.2 -0.229322660 2.8201208171
55.3 -0.137106630 3.1326431572
55.4 -0.106926420 3.2218102901
56 -0.306171957 1.2231332215
56.1 -0.323453138 2.3573202139
56.2 -0.287337760 2.5674936292
56.3 -0.193430676 2.9507164378
56.4 -0.105024091 3.2272730360
56.5 -0.035063150 3.4175522043
57 -0.068975018 0.2370331448
57.1 -0.072178206 0.2481445030
57.2 -0.291872977 1.1405586067
57.3 -0.350452633 2.1153886721
58 -0.305821847 1.2210099772
58.1 -0.354038263 1.6334245703
58.2 -0.356662058 1.6791862890
58.3 -0.273997945 2.6320121693
58.4 -0.222049115 2.8477731440
58.5 0.026480236 3.5715569824
59 -0.360551776 1.9023998594
59.1 0.715006940 4.9736620474
60 -0.211854697 2.8854503250
61 -0.200675172 0.7213630795
61.1 -0.328833062 2.3186947661
61.2 -0.298763087 2.5077313243
61.3 -0.123615504 3.1731073430
61.4 0.039239893 3.6022726283
62 -0.152019902 0.5336771999
62.1 -0.195026261 0.6987666548
62.2 -0.019135492 3.4584309917
62.3 0.624459836 4.8028772371
63 -0.232008543 2.8097350930
63.1 0.200775752 3.9653754211
64 0.274275354 4.1191305732
65 -0.197245756 0.7076152589
65.1 -0.356333749 2.0252246363
65.2 -0.143616734 3.1127382827
65.3 -0.115520552 3.1969087943
66 -0.004894436 3.4943454154
66.1 0.110532429 3.7677437009
66.2 0.192927263 3.9486138616
67 0.300540381 4.1728388879
68 -0.037661039 0.1291919907
68.1 -0.360303787 1.7809643946
68.2 -0.354990134 2.0493205660
68.3 -0.196324326 2.9406870750
68.4 0.236438945 4.0406670363
69 0.286949248 4.1451198701
70 -0.058048346 0.1992557163
70.1 -0.138274515 0.4829774413
71 -0.213623666 0.7741605386
71.1 -0.341911227 1.4883817220
71.2 0.253324633 4.0758526395
71.3 0.572728871 4.7048238723
71.4 0.582970861 4.7242791823
72 -0.250045696 0.9321196121
72.1 -0.298875568 1.1799991806
72.2 -0.360707545 1.8917567329
72.3 -0.008479051 3.4853593935
72.4 0.075836695 3.6884259700
72.5 0.257937078 4.0854155901
73 0.518824914 4.6019889915
74 -0.339185349 1.4626806753
75 -0.096185550 3.2524286874
76 -0.360739315 1.8074807397
76.1 0.347998542 4.2685073183
76.2 0.712464524 4.9688734859
77 -0.230618684 0.8459033852
78 -0.225297228 0.8231094317
79 -0.016946357 0.0583819521
79.1 -0.310503883 2.4406372628
79.2 -0.080482751 3.2962526032
80 -0.242607278 0.8985060186
80.1 -0.324413587 1.3434670598
80.2 -0.233841717 2.8025900386
81 -0.002799225 0.0101324962
81.1 -0.252235081 0.9421709494
81.2 -0.162258804 3.0542453879
81.3 -0.062321933 3.3456630446
82 -0.329186483 1.3791010005
82.1 -0.359810043 1.7601010622
82.2 -0.275856749 2.6233131927
83 -0.015585831 0.0537394290
83.1 -0.206113773 2.9061570496
83.2 -0.141595515 3.1189457362
83.3 0.605165478 4.7663642222
84 -0.252876961 2.7254060237
84.1 -0.065733726 3.3364784659
85 -0.086418319 0.2977756259
85.1 -0.359190206 1.7394116637
85.2 -0.262358242 2.6846330194
85.3 -0.127729570 3.1608762743
85.4 0.191335442 3.9452053758
85.5 0.470626547 4.5092553482
86 -0.231018222 0.8476278360
86.1 -0.266955474 1.0118629411
86.2 -0.310696855 1.2511159515
86.3 -0.344135221 2.1870554925
86.4 -0.308378023 2.4532935000
86.5 0.134159804 3.8206058508
87 -0.257217600 2.7069531474
87.1 -0.023912615 3.4462517721
87.2 0.478346358 4.5241666853
88 0.000000000 0.0005892443
88.1 -0.198244638 0.7116099866
88.2 -0.301030746 2.4952722900
88.3 -0.079274225 3.2995816297
89 -0.181653221 0.6462086167
90 -0.049437014 0.1696030737
90.1 -0.281151299 2.5980385230
90.2 -0.266749122 2.6651392167
90.3 -0.139855690 3.1242690247
91 -0.179603880 0.6382618390
91.1 -0.276049539 2.6224059286
91.2 0.610915809 4.7772527603
92 -0.021435342 0.0737052364
93 -0.081013650 0.2788909199
93.1 -0.271814501 1.0357759963
93.2 -0.301681670 2.4916551099
93.3 -0.211259675 2.8876129608
93.4 0.447248720 4.4639474002
94 -0.231290551 0.8488043118
94.1 -0.275695453 1.0552454425
94.2 -0.359604210 1.9445500884
94.3 -0.156971195 3.0710722448
94.4 -0.151822700 3.0872731935
94.5 0.404570621 4.3805759016
95 -0.356607715 2.0199063048
95.1 0.225844939 4.0184444457
95.2 0.496766490 4.5596531732
96 -0.008958452 0.0311333477
96.1 -0.038605151 0.1324267720
96.2 -0.187779516 0.6701303425
96.3 -0.345060176 2.1775037691
96.4 -0.340252993 2.2246142488
96.5 0.332658824 4.2377650598
97 -0.301543584 1.1955102731
97.1 0.707918494 4.9603108643
98 -0.059473644 0.2041732438
98.1 -0.123949646 0.4309578973
98.2 0.004310743 3.5172611906
99 -0.102164665 0.3531786101
99.1 0.559124871 4.6789444226
99.2 0.725119709 4.9927084171
100 -0.278422879 1.0691387602
100.1 -0.344163920 1.5109344281
100.2 -0.347560939 2.1502332564
100.3 0.158668905 3.8745574222
100.4 0.547483173 4.6567608765
$m7e$spM_id
center scale
C2 -0.6240921 0.68571078
(Intercept) NA NA
C1 0.7372814 0.01472882
$m7e$spM_lvlone
center scale
y -11.17337099 6.2496619
c1 0.25599956 0.6718095
ns(time, df = 3)1 0.19883694 0.2502686
ns(time, df = 3)2 0.38513689 0.1171115
ns(time, df = 3)3 -0.07137294 0.2891059
time 2.53394028 1.3818094
$m7e$mu_reg_norm
[1] 0
$m7e$tau_reg_norm
[1] 1e-04
$m7e$shape_tau_norm
[1] 0.01
$m7e$rate_tau_norm
[1] 0.01
$m7e$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m7e$shape_diag_RinvD
[1] "0.01"
$m7e$rate_diag_RinvD
[1] "0.001"
$m7e$RinvD_y_id
[,1] [,2] [,3] [,4]
[1,] NA 0 0 0
[2,] 0 NA 0 0
[3,] 0 0 NA 0
[4,] 0 0 0 NA
$m7e$KinvD_y_id
id
5
$m7f
$m7f$M_id
C2 (Intercept) C1
1 -1.381594459 1 0.7175865
2 0.344426024 1 0.7507170
3 NA 1 0.7255954
4 -0.228910007 1 0.7469352
5 NA 1 0.7139120
6 -2.143955482 1 0.7332505
7 -1.156567023 1 0.7345929
8 -0.598827660 1 0.7652589
9 NA 1 0.7200622
10 -1.006719032 1 0.7423879
11 0.239801450 1 0.7437448
12 -1.064969789 1 0.7446470
13 -0.538082688 1 0.7530186
14 NA 1 0.7093137
15 -1.781049276 1 0.7331192
16 NA 1 0.7011390
17 NA 1 0.7432395
18 -0.014579883 1 0.7545191
19 -2.121550136 1 0.7528487
20 NA 1 0.7612865
21 -0.363239698 1 0.7251719
22 -0.121568514 1 0.7300630
23 -0.951271111 1 0.7087249
24 NA 1 0.7391938
25 -0.974288621 1 0.7820641
26 -1.130632418 1 0.7118298
27 0.114339868 1 0.7230857
28 0.238334648 1 0.7489353
29 0.840744958 1 0.7510888
30 NA 1 0.7300717
31 NA 1 0.7550721
32 -1.466312154 1 0.7321898
33 -0.637352277 1 0.7306414
34 NA 1 0.7427216
35 NA 1 0.7193042
36 NA 1 0.7312888
37 NA 1 0.7100436
38 NA 1 0.7670184
39 0.006728205 1 0.7400449
40 NA 1 0.7397304
41 -1.663281353 1 0.7490966
42 0.161184794 1 0.7419274
43 0.457939180 1 0.7527810
44 -0.307070331 1 0.7408315
45 NA 1 0.7347550
46 -1.071668276 1 0.7332398
47 -0.814751321 1 0.7376481
48 -0.547630662 1 0.7346179
49 NA 1 0.7329402
50 -1.350213782 1 0.7260436
51 0.719054706 1 0.7242910
52 NA 1 0.7298067
53 -1.207130750 1 0.7254741
54 NA 1 0.7542067
55 -0.408600991 1 0.7389952
56 -0.271380529 1 0.7520638
57 -1.361925974 1 0.7219958
58 NA 1 0.7259632
59 NA 1 0.7458606
60 -0.323712205 1 0.7672421
61 NA 1 0.7257179
62 NA 1 0.7189892
63 -1.386906880 1 0.7333356
64 NA 1 0.7320243
65 NA 1 0.7477711
66 -0.565191691 1 0.7343974
67 -0.382899912 1 0.7491624
68 NA 1 0.7482736
69 -0.405642769 1 0.7338267
70 NA 1 0.7607742
71 -0.843748427 1 0.7777600
72 0.116003683 1 0.7408143
73 -0.778634325 1 0.7248271
74 NA 1 0.7364916
75 NA 1 0.7464926
76 NA 1 0.7355430
77 -0.632974758 1 0.7208449
78 NA 1 0.7373573
79 -0.778064615 1 0.7598079
80 NA 1 0.7360415
81 NA 1 0.7293932
82 -0.246123253 1 0.7279309
83 -1.239659782 1 0.7344643
84 -0.467772280 1 0.7384350
85 NA 1 0.7323716
86 -2.160485036 1 0.7576597
87 -0.657675572 1 0.7496139
88 NA 1 0.7275239
89 -0.696710744 1 0.7250648
90 NA 1 0.7335262
91 -0.179395847 1 0.7343980
92 -0.441545568 1 0.7380425
93 -0.685799334 1 0.7389460
94 NA 1 0.7259951
95 0.191929445 1 0.7282840
96 NA 1 0.7281676
97 -0.069760671 1 0.7245642
98 NA 1 0.7526938
99 NA 1 0.7272309
100 NA 1 0.7383460
$m7f$M_lvlone
y c1 time ns(time, df = 3)1
1 -13.0493856 0.7592026489 0.5090421822 -0.0731022196
1.1 -9.3335901 0.9548337990 0.6666076288 -0.0896372079
1.2 -22.3469852 0.5612235156 2.1304941282 0.1374616725
1.3 -15.0417337 1.1873391025 2.4954441458 0.3061500570
2 -12.0655434 0.9192204198 3.0164990982 0.5064248381
2.1 -15.8674476 -0.1870730476 3.2996806887 0.5543647993
2.2 -7.8800006 1.2517512331 4.1747569619 0.3402753582
3 -11.4820604 -0.0605087604 0.8478727890 -0.1024946971
3.1 -10.5983220 0.3788637747 3.0654308549 0.5187768948
3.2 -22.4519157 0.9872578281 4.7381553578 0.0174998856
4 -1.2697775 1.4930175328 0.3371432109 -0.0508146200
4.1 -11.1215184 -0.7692526880 1.0693019140 -0.1067711172
4.2 -3.6134138 0.9180841450 2.6148973033 0.3598480506
4.3 -14.5982385 -0.0541170782 3.1336532847 0.5333652385
5 -6.8457515 -0.1376784521 1.0762525082 -0.1066695938
5.1 -7.0551214 -0.2740585866 1.7912546196 0.0046159078
5.2 -12.3418980 0.4670496929 2.7960080339 0.4342731827
5.3 -9.2366906 0.1740288049 2.8119940578 0.4402846745
6 -5.1648211 0.9868044683 1.7815462884 0.0015253168
7 -10.0599502 -0.1280320918 3.3074087673 0.5547950298
7.1 -18.3267285 0.4242971219 3.7008403614 0.5158768825
7.2 -12.5138426 0.0777182491 4.7716691741 -0.0038356900
8 -1.6305331 -0.5791408712 1.1246398522 -0.1055283475
8.1 -9.6520453 0.3128604232 1.8027009873 0.0083228157
8.2 -1.5278462 0.6258446356 1.8175825174 0.0132420073
8.3 -7.4172211 -0.1040137707 2.8384267003 0.4499876083
8.4 -7.1238609 0.0481450285 3.3630275307 0.5564354493
8.5 -8.8706950 0.3831763675 4.4360849704 0.2011700353
9 -0.1634429 -0.1757592269 0.9607803822 -0.1064248362
9.1 -2.6034300 -0.1791541200 2.9177753383 0.4771840649
9.2 -6.7272369 -0.0957042935 4.8100892501 -0.0284447928
10 -6.4172202 -0.5598409704 2.2975509102 0.2139341504
10.1 -11.4834569 -0.2318340451 4.1734118364 0.3409274088
11 -8.7911356 0.5086859475 1.1832662905 -0.1030921083
11.1 -19.6645080 0.4951758188 1.2346051680 -0.0999663372
11.2 -20.2030932 -1.1022162541 1.6435316263 -0.0371358422
11.3 -21.3082176 -0.0611636705 3.3859017969 0.5563809266
11.4 -14.5802901 -0.4971774316 4.8118087661 -0.0295495187
12 -15.2006287 -0.2433996286 0.9591987054 -0.1063939605
13 0.8058816 0.8799673116 0.0619085738 -0.0095916688
13.1 -13.6379208 0.1079022586 3.5621061502 0.5424520669
14 -15.3422873 0.9991752617 4.0364430007 0.4032689358
14.1 -10.0965208 -0.1094019046 4.4710561272 0.1809335363
14.2 -16.6452027 0.1518967560 4.6359198843 0.0816318440
14.3 -15.8389733 0.3521012473 4.6886152599 0.0487734088
15 -8.9424594 0.3464447888 0.5402063532 -0.0767078114
15.1 -22.0101983 -0.4767313971 1.1893180816 -0.1027727004
15.2 -7.3975599 0.5759767791 1.5094739688 -0.0657566366
15.3 -10.3567334 -0.1713452662 4.9193474615 -0.0990627407
16 -1.9691302 0.4564754473 1.2417913869 -0.0994524289
16.1 -9.9308357 1.0652558311 2.5675726333 0.3388926675
16.2 -6.9626923 0.6971872493 2.6524101500 0.3760850293
16.3 -3.2862557 0.5259331838 3.5585018690 0.5429665219
16.4 -3.3972355 0.2046601798 3.7612454291 0.5003814501
16.5 -11.5767835 1.0718540464 3.9851612889 0.4243928946
17 -10.5474144 0.6048676222 1.5925356350 -0.0490208458
17.1 -7.6215009 0.2323298304 2.4374032998 0.2793033691
17.2 -16.5386939 1.2617499032 3.0256489082 0.5088484549
17.3 -20.0004774 -0.3913230895 3.3329089405 0.5558624020
17.4 -18.8505475 0.9577299112 3.8693758985 0.4671596304
18 -19.7302351 -0.0050324072 2.4374292302 0.2793154325
19 -14.6177568 -0.4187468937 0.9772165376 -0.1067031159
19.1 -17.8043866 -0.4478828944 1.1466335913 -0.1047524970
19.2 -15.1641705 -1.1966721302 2.2599126538 0.1964187590
19.3 -16.6898418 -0.5877091668 4.2114245973 0.3222225327
20 -12.9059229 0.6838223064 1.7170160066 -0.0177612481
20.1 -16.8191201 0.3278571109 1.7562902288 -0.0062817988
20.2 -6.1010131 -0.8489831990 2.2515566566 0.1925456183
20.3 -7.9415371 1.3169975191 2.2609123867 0.1968825793
20.4 -9.3904458 0.0444804531 3.4913365287 0.5508408484
20.5 -13.3504189 -0.4535207652 4.1730977828 0.3410795409
21 -7.6974718 -0.4030302960 1.6936582839 -0.0242133322
21.1 -11.9335526 -0.4069674045 2.9571191233 0.4894906279
21.2 -12.7064929 1.0650265940 3.7887385779 0.4925855178
22 -21.5022909 -0.0673274516 2.4696226232 0.2942488077
22.1 -12.7745451 0.9601388170 3.1626627257 0.5385758261
23 -3.5146508 0.5556634840 1.5414533857 -0.0596896592
23.1 -4.6724048 1.4407865964 2.3369736120 0.2323619738
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62 -2.8591600 -0.3979137604 0.5336771999 -0.0759650358
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68 -13.4806759 0.5691848839 0.1291919907 -0.0200346649
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71 -10.3352703 -0.5529062591 0.7741605386 -0.0981662227
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77 0.4772521 -0.4986802480 0.8459033852 -0.1023963499
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79 -1.0217265 -0.3767450660 0.0583819521 -0.0090412342
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80 -12.7366807 -0.0041558414 0.8985060186 -0.1046806598
80.1 -9.0584783 -0.0329337062 1.3434670598 -0.0900832419
80.2 -16.6381566 0.5046816157 2.8025900386 0.4367610654
81 0.5547913 -0.9493950353 0.0101324962 -0.0014945092
81.1 -4.0892715 0.2443038954 0.9421709494 -0.1060165656
81.2 1.8283303 0.6476958410 3.0542453879 0.5160872572
81.3 -5.2166381 0.4182528210 3.3456630446 0.5561952783
82 -3.0749381 1.1088801952 1.3791010005 -0.0858371096
82.1 -10.5506696 0.9334157763 1.7601010622 -0.0051252873
82.2 -18.2226347 0.4958140634 2.6233131927 0.3635216183
83 -12.5872635 0.5104724530 0.0537394290 -0.0083163054
83.1 -11.9756502 -0.0513309106 2.9061570496 0.4733943484
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83.3 -19.2714012 -0.0534169155 4.7663642222 -0.0004497824
84 -2.6320312 -0.0255753653 2.7254060237 0.4065375903
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85 -12.3886736 -0.0114038622 0.2977756259 -0.0452387941
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85.2 -9.6433248 -0.2241856342 2.6846330194 0.3897280300
85.3 -6.3296340 -0.0520175929 3.1608762743 0.5382727768
85.4 -7.0405525 0.2892733846 3.9452053758 0.4399457097
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86 -10.8756412 0.4293735089 0.8476278360 -0.1024825180
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87 -10.7027963 1.0457427869 2.7069531474 0.3989962007
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88 -2.2264493 -0.0601928334 0.0005892443 0.0000000000
88.1 -8.9626474 -1.0124347595 0.7116099866 -0.0935064654
88.2 -2.5095281 0.5730917016 2.4952722900 0.3060711120
88.3 -16.3345673 -0.0029455332 3.2995816297 0.5543589602
89 -11.0459647 1.5465903721 0.6462086167 -0.0877493552
90 -4.5610239 0.0626760573 0.1696030737 -0.0262292802
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91 -7.1462503 1.1213651365 0.6382618390 -0.0869921115
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92 -6.4119222 0.4674939332 0.0737052364 -0.0114311653
93 5.2122168 0.2677965647 0.2788909199 -0.0425142500
93.1 3.1211725 1.6424445368 1.0357759963 -0.1070466070
93.2 -3.6841177 0.7101700066 2.4916551099 0.3044086294
93.3 2.6223542 1.1222322893 2.8876129608 0.4672045475
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94 -6.9602492 -0.2904211940 0.8488043118 -0.1025408741
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96 -4.9139832 0.7813892121 0.0311333477 -0.0047820450
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96.3 -11.8690793 0.8771471244 2.1775037691 0.1585782261
96.4 -11.0224373 1.9030768981 2.2246142488 0.1801061786
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97 -9.8540471 1.3775130083 1.1955102731 -0.1024324589
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98 -11.9651231 -1.2648518889 0.2041732438 -0.0314640445
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99 -11.4720500 0.7993356425 0.3531786101 -0.0530422907
99.1 -14.0596866 1.0355522332 4.6789444226 0.0548371648
99.2 -17.3939469 -0.1150895843 4.9927084171 -0.1467618333
100 1.1005874 0.0369067906 1.0691387602 -0.1067733151
100.1 -3.8226248 1.6023713093 1.5109344281 -0.0654896559
100.2 -0.9123182 0.8861545820 2.1502332564 0.1462778204
100.3 -15.8389474 0.1277046316 3.8745574222 0.4653994149
100.4 -12.8093826 -0.0834577654 4.6567608765 0.0686909568
ns(time, df = 3)2 ns(time, df = 3)3
1 0.222983368 -0.145369093
1.1 0.286659651 -0.186881442
1.2 0.538466292 -0.349241050
1.3 0.485312041 -0.300999737
2 0.388851338 -0.173896710
2.1 0.348347565 -0.079238230
2.2 0.338334366 0.301483601
3 0.354448579 -0.231074940
3.1 0.380711276 -0.158750730
3.2 0.391617016 0.590283099
4 0.149748191 -0.097625033
4.1 0.427124949 -0.278454698
4.2 0.463409709 -0.277637284
4.3 0.370048079 -0.136774026
5 0.429197233 -0.279805678
5.1 0.552972700 -0.360498322
5.2 0.428967354 -0.235519853
5.3 0.425938437 -0.231426488
6 0.552692434 -0.360315653
7 0.347509550 -0.076424127
7.1 0.324489976 0.081205236
7.2 0.395476156 0.607966686
8 0.443238494 -0.288959568
8.1 0.553246856 -0.360676673
8.2 0.553513388 -0.360848815
8.3 0.420954925 -0.224527396
8.4 0.341948600 -0.055827230
8.5 0.359621900 0.432934699
9 0.393057590 -0.256245233
9.1 0.406264856 -0.202849973
9.2 0.399948734 0.628274517
10 0.517577740 -0.331608701
10.1 0.338245451 0.300822094
11 0.459317031 -0.299441616
11.1 0.472517168 -0.308047155
11.2 0.544030949 -0.354668988
11.3 0.339897056 -0.047184145
11.4 0.400149982 0.629184194
12 0.392538168 -0.255906608
13 0.027579300 -0.017979717
13.1 0.328456601 0.022585629
14 0.330500975 0.234420981
14.1 0.362994245 0.450908537
14.2 0.380152387 0.536563622
14.3 0.385997930 0.564205754
15 0.235874789 -0.153773371
15.1 0.460916543 -0.300484383
15.2 0.527699447 -0.344022025
15.3 0.412872874 0.686175505
16 0.474297164 -0.309207584
16.1 0.472228980 -0.287322060
16.2 0.456327895 -0.269566289
16.3 0.328616483 0.021104265
16.4 0.324030412 0.107655226
16.5 0.328317423 0.210083745
17 0.538702746 -0.351195384
17.1 0.495414246 -0.311040420
17.2 0.387301236 -0.171104158
17.3 0.344858135 -0.067055294
17.4 0.324978786 0.156298232
18 0.495409834 -0.311036128
19 0.398417477 -0.259739494
19.1 0.449392800 -0.292971732
19.2 0.522886360 -0.336250565
19.3 0.340847993 0.319581745
20 0.549715454 -0.358374876
20.1 0.551756621 -0.359705570
20.2 0.524021244 -0.337228841
20.3 0.522749488 -0.336132246
20.4 0.332148944 -0.006096291
20.5 0.338224725 0.300667673
21 0.548168935 -0.357366657
21.1 0.399187414 -0.191571616
21.2 0.324061287 0.119869388
22 0.489859980 -0.305573977
22.1 0.365790145 -0.127130621
23 0.532269527 -0.347001388
23.1 0.511693737 -0.326336775
24 0.422857517 -0.227186155
25 0.235036737 -0.153227021
25.1 0.540323722 -0.352252143
25.2 0.543297023 -0.354190522
25.3 0.352345378 -0.091832063
25.4 0.332578168 0.254466815
25.5 0.337117898 0.292289382
26 0.107676329 -0.070197210
26.1 0.494087736 -0.309746667
26.2 0.326994020 0.037821198
26.3 0.338833066 0.305164939
27 0.324565129 0.078928640
27.1 0.343307911 0.336333009
28 0.249962259 -0.162957386
28.1 0.429273232 -0.235929197
28.2 0.340807567 0.319299132
28.3 0.362944907 0.450648982
29 0.461066695 -0.300582271
29.1 0.552010523 -0.359871096
29.2 0.547810299 -0.356549969
29.3 0.338052723 -0.038787896
30 0.522389104 -0.335820366
30.1 0.327176583 0.035718727
30.2 0.326484504 0.044098116
31 0.356147774 0.413866643
32 0.546719898 -0.356421989
32.1 0.407167901 -0.204246665
32.2 0.397320853 -0.188492301
32.3 0.340746183 0.318869516
33 0.003936563 -0.002566356
33.1 0.334224366 -0.018862759
34 0.526248154 -0.343075887
34.1 0.324316326 0.135814702
34.2 0.325817315 0.173695359
34.3 0.327338410 0.197417584
35 0.494613531 -0.322452391
35.1 0.530354088 -0.345752660
35.2 0.366122990 0.467180923
36 0.304360595 -0.198421183
36.1 0.553123835 -0.360596719
36.2 0.553622593 -0.360918172
36.3 0.346311748 0.355765584
36.4 0.378356377 0.527945205
37 0.549032516 -0.357475622
37.1 0.325102184 0.066063038
37.2 0.327623070 0.201253686
38 0.400174439 -0.260884907
39 0.296609802 -0.193368224
39.1 0.373603743 -0.243562727
39.2 0.489340823 -0.319014966
39.3 0.532246449 -0.346986343
39.4 0.362843629 -0.120095446
39.5 0.336078053 0.284162218
40 0.445606008 -0.290503017
40.1 0.448397811 -0.260192709
40.2 0.384951582 -0.166792072
40.3 0.384618587 0.557734965
41 0.552281804 -0.359898022
41.1 0.361173390 -0.115958300
41.2 0.350019018 -0.084662333
41.3 0.340294264 -0.048910814
41.4 0.329085370 0.016921661
42 0.213336117 -0.139079780
42.1 0.350028134 0.378571910
43 0.244669988 -0.159507207
43.1 0.428633738 -0.279438320
43.2 0.463732274 -0.277998502
44 0.324744715 -0.211710161
44.1 0.456968690 -0.270307896
44.2 0.344429887 -0.065469588
44.3 0.334642692 0.272471485
45 0.087479438 -0.057030292
45.1 0.549358350 -0.357721087
46 0.498531201 -0.325006430
46.1 0.417554417 -0.219695608
46.2 0.357740629 0.422684799
47 0.260722648 -0.169972385
47.1 0.500069201 -0.315522977
47.2 0.392094432 -0.179612843
47.3 0.362918036 -0.120277127
47.4 0.378427928 0.528289800
48 0.416778543 -0.218578367
48.1 0.407711957 -0.205083750
49 0.443841107 -0.254634636
50 0.457436535 -0.298215668
51 0.514675808 -0.335531551
52 0.538856976 -0.349554303
52.1 0.375191670 -0.147721246
52.2 0.371072093 -0.139010728
52.3 0.328068662 0.026310537
52.4 0.398579012 0.622074267
52.5 0.420339814 0.719341816
53 0.217791262 -0.141984214
53.1 0.328980432 0.017837917
53.2 0.374021344 0.506858010
54 0.509684846 -0.332277803
54.1 0.553543641 -0.360828251
54.2 0.482577881 -0.298206049
54.3 0.431088614 -0.238343800
54.4 0.329511331 0.223917052
55 0.264974033 -0.172743982
55.1 0.317295658 -0.206853912
55.2 0.424402666 -0.229322660
55.3 0.370199481 -0.137106630
55.4 0.357686883 -0.106926420
56 0.469640780 -0.306171957
56.1 0.508537162 -0.323453138
56.2 0.472243570 -0.287337760
56.3 0.400327890 -0.193430676
56.4 0.356980364 -0.105024091
56.5 0.337279686 -0.035063150
57 0.105801595 -0.068975018
57.1 0.110715003 -0.072178206
57.2 0.447707406 -0.291872977
57.3 0.539981816 -0.350452633
58 0.469103743 -0.305821847
58.1 0.543063473 -0.354038263
58.2 0.547088143 -0.356662058
58.3 0.460186824 -0.273997945
58.4 0.419201600 -0.222049115
58.5 0.328051465 0.026480236
59 0.553170063 -0.360551776
59.1 0.419362856 0.715006940
60 0.412191135 -0.211854697
61 0.307818016 -0.200675172
61.1 0.514461229 -0.328833062
61.2 0.483120456 -0.298763087
61.3 0.364301147 -0.123615504
61.4 0.326874230 0.039239893
62 0.233185122 -0.152019902
62.1 0.299153085 -0.195026261
62.2 0.334271811 -0.019135492
62.3 0.399105605 0.624459836
63 0.426365839 -0.232008543
63.1 0.327587037 0.200775752
64 0.334859412 0.274275354
65 0.302557595 -0.197245756
65.1 0.547526242 -0.356333749
65.2 0.373224291 -0.143616734
65.3 0.360999362 -0.115520552
66 0.331967886 -0.004894436
66.1 0.324024401 0.110532429
66.2 0.327018558 0.192927263
67 0.338207650 0.300540381
68 0.057768712 -0.037661039
68.1 0.552674233 -0.360303787
68.2 0.545772611 -0.354990134
68.3 0.402123497 -0.196324326
68.4 0.330699051 0.236438945
69 0.336430831 0.286949248
70 0.089041044 -0.058048346
70.1 0.212100911 -0.138274515
71 0.327679863 -0.213623666
71.1 0.524461669 -0.341911227
71.2 0.332453400 0.253324633
71.3 0.387824204 0.572728871
71.4 0.390032203 0.582970861
72 0.383548046 -0.250045696
72.1 0.458448763 -0.298875568
72.2 0.553381188 -0.360707545
72.3 0.332515066 -0.008479051
72.4 0.324676638 0.075836695
72.5 0.332961867 0.257937078
73 0.376470777 0.518824914
74 0.520280412 -0.339185349
75 0.353824007 -0.096185550
76 0.553343477 -0.360739315
76.1 0.345092469 0.347998542
76.2 0.418789942 0.712464524
77 0.353748723 -0.230618684
78 0.345586078 -0.225297228
79 0.025994217 -0.016946357
79.1 0.494863212 -0.310503883
79.2 0.348724471 -0.080482751
80 0.372138169 -0.242607278
80.1 0.497621832 -0.324413587
80.2 0.427719060 -0.233841717
81 0.004293764 -0.002799225
81.1 0.386906369 -0.252235081
81.2 0.382538581 -0.162258804
81.3 0.343596919 -0.062321933
82 0.504943034 -0.329186483
82.1 0.551916873 -0.359810043
82.2 0.461826800 -0.275856749
83 0.023907290 -0.015585831
83.1 0.408384331 -0.206113773
83.2 0.372272641 -0.141595515
83.3 0.394862487 0.605165478
84 0.442422222 -0.252876961
84.1 0.344500819 -0.065733726
85 0.132558080 -0.086418319
85.1 0.550966098 -0.359190206
85.2 0.450202261 -0.262358242
85.3 0.366047208 -0.127729570
85.4 0.326908671 0.191335442
85.5 0.366794208 0.470626547
86 0.354361579 -0.231018222
86.1 0.409486154 -0.266955474
86.2 0.476581575 -0.310696855
86.3 0.532204554 -0.344135221
86.4 0.492692574 -0.308378023
86.5 0.324278938 0.134159804
87 0.445945432 -0.257217600
87.1 0.335124592 -0.023912615
87.2 0.368308652 0.478346358
88 0.000000000 0.000000000
88.1 0.304089792 -0.198244638
88.2 0.485342573 -0.301030746
88.3 0.348358411 -0.079274225
89 0.278640020 -0.181653221
90 0.075832019 -0.049437014
90.1 0.466568464 -0.281151299
90.2 0.453911731 -0.266749122
90.3 0.371462522 -0.139855690
91 0.275496513 -0.179603880
91.1 0.461997624 -0.276049539
91.2 0.396123130 0.610915809
92 0.032879925 -0.021435342
93 0.124267794 -0.081013650
93.1 0.416939473 -0.271814501
93.2 0.485984408 -0.301681670
93.3 0.411791889 -0.211259675
93.4 0.362300252 0.447248720
94 0.354779307 -0.231290551
94.1 0.422892510 -0.275695453
94.2 0.551883683 -0.359604210
94.3 0.379797627 -0.156971195
94.4 0.377204420 -0.151822700
94.5 0.354495295 0.404570621
95 0.547886254 -0.356607715
95.1 0.329687700 0.225844939
95.2 0.371978790 0.496766490
96 0.013741475 -0.008958452
96.1 0.059216896 -0.038605151
96.2 0.288037217 -0.187779516
96.3 0.533324704 -0.345060176
96.4 0.527566661 -0.340252993
96.5 0.342757800 0.332658824
97 0.462541264 -0.301543584
97.1 0.417765721 0.707918494
98 0.091227325 -0.059473644
98.1 0.190127826 -0.123949646
98.2 0.330659794 0.004310743
99 0.156711586 -0.102164665
99.1 0.384914328 0.559124871
99.2 0.421642124 0.725119709
100 0.427076142 -0.278422879
100.1 0.527917102 -0.344163920
100.2 0.536384181 -0.347560939
100.3 0.325078572 0.158668905
100.4 0.382446660 0.547483173
$m7f$spM_id
center scale
C2 -0.6240921 0.68571078
(Intercept) NA NA
C1 0.7372814 0.01472882
$m7f$spM_lvlone
center scale
y -11.17337099 6.2496619
c1 0.25599956 0.6718095
time 2.53394028 1.3818094
ns(time, df = 3)1 0.19883694 0.2502686
ns(time, df = 3)2 0.38513689 0.1171115
ns(time, df = 3)3 -0.07137294 0.2891059
$m7f$mu_reg_norm
[1] 0
$m7f$tau_reg_norm
[1] 1e-04
$m7f$shape_tau_norm
[1] 0.01
$m7f$rate_tau_norm
[1] 0.01
$m7f$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m7f$shape_diag_RinvD
[1] "0.01"
$m7f$rate_diag_RinvD
[1] "0.001"
$m7f$RinvD_y_id
[,1] [,2]
[1,] NA 0
[2,] 0 NA
$m7f$KinvD_y_id
id
3
$m8a
$m8a$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m8a$M_lvlone
y c2 c1 time
1 -13.0493856 NA 0.7592026489 0.5090421822
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890
3.1 -10.5983220 NA 0.3788637747 3.0654308549
3.2 -22.4519157 NA 0.9872578281 4.7381553578
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339
5.3 -9.2366906 NA 0.1740288049 2.8119940578
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673
7.1 -18.3267285 NA 0.4242971219 3.7008403614
7.2 -12.5138426 NA 0.0777182491 4.7716691741
8 -1.6305331 NA -0.5791408712 1.1246398522
8.1 -9.6520453 NA 0.3128604232 1.8027009873
8.2 -1.5278462 NA 0.6258446356 1.8175825174
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704
9 -0.1634429 NA -0.1757592269 0.9607803822
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501
10 -6.4172202 NA -0.5598409704 2.2975509102
10.1 -11.4834569 NA -0.2318340451 4.1734118364
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661
12 -15.2006287 NA -0.2433996286 0.9591987054
13 0.8058816 NA 0.8799673116 0.0619085738
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007
14.1 -10.0965208 NA -0.1094019046 4.4710561272
14.2 -16.6452027 NA 0.1518967560 4.6359198843
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599
15 -8.9424594 NA 0.3464447888 0.5402063532
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869
16.1 -9.9308357 -0.07599066 1.0652558311 2.5675726333
16.2 -6.9626923 -0.32385667 0.6971872493 2.6524101500
16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690
16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291
16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889
17 -10.5474144 NA 0.6048676222 1.5925356350
17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998
17.2 -16.5386939 NA 1.2617499032 3.0256489082
17.3 -20.0004774 NA -0.3913230895 3.3329089405
17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985
18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302
19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376
19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913
19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538
19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973
20 -12.9059229 -0.22087628 0.6838223064 1.7170160066
20.1 -16.8191201 NA 0.3278571109 1.7562902288
20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566
20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867
20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287
20.5 -13.3504189 -0.24338208 -0.4535207652 4.1730977828
21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839
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22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232
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23 -3.5146508 -0.18236682 0.5556634840 1.5414533857
23.1 -4.6724048 -0.20823302 1.4407865964 2.3369736120
24 -2.5619821 -0.29026416 0.3856376411 2.8283136466
25 -6.2944970 -0.36139273 0.3564400705 0.5381704110
25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331
25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922
25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392
25.4 -9.0288933 NA 0.4466012931 4.0782226040
25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873
26 -19.7340685 -0.16846716 0.5341531449 0.2412706357
26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676
26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887
26.3 -24.6265576 NA 0.5948516018 4.1822362854
27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879
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28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344
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29 -5.8551395 -0.32432030 0.1392699487 1.1898884235
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30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488
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32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007
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32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547
33 2.0838099 -0.15252819 0.1774293842 0.0093385763
33.1 -13.3172274 NA 0.6159606291 3.4591242753
34 -10.0296691 -0.30378735 0.8590979166 1.4998774312
34.1 -25.9426553 -0.11982431 0.0546216775 3.8242761395
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35 -14.3958113 NA -1.4693520528 1.3294299203
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36 -12.8166968 -0.49062180 0.9823048960 0.7123168337
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39 -8.7288138 -0.38072211 0.9876451040 0.6921808305
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40 1.8291916 -0.19751956 0.5350190436 1.1330395646
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41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254
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41.4 -9.6070508 NA -0.3908036794 3.5482964432
42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973
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43 -16.6104361 -0.08864017 0.8965546110 0.5616614548
43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536
43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966
44 -8.1969033 -0.22697597 1.3221409285 0.7662644819
44.1 -19.9270197 NA 0.3416426284 2.6490291790
44.2 -22.6521171 NA 0.5706610068 3.3371910988
44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875
45 -0.5686627 -0.19274788 0.1414983160 0.1957449992
45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536
46 -19.1624789 -0.28547861 1.5391054233 1.3477755385
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46.2 -15.8222682 NA 0.1248719493 4.4160729996
47 -5.4165074 -0.08597098 0.2014101100 0.6012621359
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47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035
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47.4 -18.2214784 NA 0.3702301552 4.6201055450
48 -8.3101471 NA -0.2128602862 2.8607365978
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49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400
50 -10.4579977 -0.19959516 0.3897705981 1.1762060679
51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720
52 -4.4747188 -0.07438732 0.3758235358 2.1266646020
52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993
52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370
52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327
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53 -10.3141887 -0.23278716 0.5675736897 0.4965799209
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54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841
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54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352
54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672
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55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980
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55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171
55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572
55.4 -14.8599983 NA -0.1202746964 3.2218102901
56 -14.1764282 NA 0.5176377612 1.2231332215
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56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292
56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378
56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360
56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043
57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448
57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030
57.2 -6.3940143 NA -0.1159517011 1.1405586067
57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721
58 -12.0245677 -0.29901358 1.6476207996 1.2210099772
58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703
58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890
58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693
58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440
58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824
59 -13.2946560 -0.27255364 0.4766421367 1.9023998594
59.1 -10.0530648 NA 1.0028089765 4.9736620474
60 -19.2209402 NA 0.5231452932 2.8854503250
61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795
61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661
61.2 -1.4713611 NA 1.0229058138 2.5077313243
61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430
61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283
62 -2.8591600 NA -0.3979137604 0.5336771999
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62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371
63 -24.0335535 -0.12036319 0.3345678035 2.8097350930
63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211
64 -20.5963897 NA 0.3949911859 4.1191305732
65 -2.7969169 -0.34450396 1.2000091513 0.7076152589
65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363
65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827
65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943
66 -13.2318328 NA -0.5081302805 3.4943454154
66.1 -1.9090560 NA -0.1447837412 3.7677437009
66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616
67 -25.6073277 NA 1.6716027681 4.1728388879
68 -13.4806759 -0.13605235 0.5691848839 0.1291919907
68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946
68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660
68.3 -12.4977127 NA 0.7443745962 2.9406870750
68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363
69 -14.5290675 -0.32152751 0.0381382683 4.1451198701
70 -15.2122709 -0.28190462 0.8126204217 0.1992557163
70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413
71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386
71.1 -7.5699888 NA -0.1103252087 1.4883817220
71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395
71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723
71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823
72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121
72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806
72.2 -26.3421306 NA -0.4170988856 1.8917567329
72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935
72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700
72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901
73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915
74 -8.2790175 -0.27963171 0.3730017941 1.4626806753
75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874
76 -6.0061671 0.04775562 -0.9888876620 1.8074807397
76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183
76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859
77 0.4772521 -0.19053195 -0.4986802480 0.8459033852
78 -9.4105229 -0.17172929 0.0417330969 0.8231094317
79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521
79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628
79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032
80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186
80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598
80.2 -16.6381566 NA 0.5046816157 2.8025900386
81 0.5547913 -0.18401980 -0.9493950353 0.0101324962
81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494
81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879
81.3 -5.2166381 NA 0.4182528210 3.3456630446
82 -3.0749381 -0.08328168 1.1088801952 1.3791010005
82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622
82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927
83 -12.5872635 -0.34228255 0.5104724530 0.0537394290
83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496
83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362
83.3 -19.2714012 NA -0.0534169155 4.7663642222
84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237
84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659
85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259
85.1 -12.9196365 NA -0.0577615939 1.7394116637
85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194
85.3 -6.3296340 NA -0.0520175929 3.1608762743
85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758
85.5 -13.6714939 NA -0.3740417009 4.5092553482
86 -10.8756412 -0.10854862 0.4293735089 0.8476278360
86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411
86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515
86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925
86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000
86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508
87 -10.7027963 NA 1.0457427869 2.7069531474
87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721
87.2 -14.9616716 NA 0.4396872616 4.5241666853
88 -2.2264493 -0.31563888 -0.0601928334 0.0005892443
88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603
92 -6.4119222 NA 0.4674939332 0.0737052364
93 5.2122168 -0.11939966 0.2677965647 0.2788909199
93.1 3.1211725 NA 1.6424445368 1.0357759963
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099
93.3 2.6223542 NA 1.1222322893 2.8876129608
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002
94 -6.9602492 NA -0.2904211940 0.8488043118
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438
98.1 -2.6515128 NA -0.9042716241 0.4309578973
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171
100 1.1005874 -0.17326698 0.0369067906 1.0691387602
100.1 -3.8226248 NA 1.6023713093 1.5109344281
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765
$m8a$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
$m8a$mu_reg_norm
[1] 0
$m8a$tau_reg_norm
[1] 1e-04
$m8a$shape_tau_norm
[1] 0.01
$m8a$rate_tau_norm
[1] 0.01
$m8a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8a$shape_diag_RinvD
[1] "0.01"
$m8a$rate_diag_RinvD
[1] "0.001"
$m8a$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8a$KinvD_y_id
id
4
$m8b
$m8b$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m8b$M_lvlone
y c2 c1 time
1 -13.0493856 NA 0.7592026489 0.5090421822
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890
3.1 -10.5983220 NA 0.3788637747 3.0654308549
3.2 -22.4519157 NA 0.9872578281 4.7381553578
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339
5.3 -9.2366906 NA 0.1740288049 2.8119940578
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673
7.1 -18.3267285 NA 0.4242971219 3.7008403614
7.2 -12.5138426 NA 0.0777182491 4.7716691741
8 -1.6305331 NA -0.5791408712 1.1246398522
8.1 -9.6520453 NA 0.3128604232 1.8027009873
8.2 -1.5278462 NA 0.6258446356 1.8175825174
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704
9 -0.1634429 NA -0.1757592269 0.9607803822
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501
10 -6.4172202 NA -0.5598409704 2.2975509102
10.1 -11.4834569 NA -0.2318340451 4.1734118364
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661
12 -15.2006287 NA -0.2433996286 0.9591987054
13 0.8058816 NA 0.8799673116 0.0619085738
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007
14.1 -10.0965208 NA -0.1094019046 4.4710561272
14.2 -16.6452027 NA 0.1518967560 4.6359198843
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599
15 -8.9424594 NA 0.3464447888 0.5402063532
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869
16.1 -9.9308357 -0.07599066 1.0652558311 2.5675726333
16.2 -6.9626923 -0.32385667 0.6971872493 2.6524101500
16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690
16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291
16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889
17 -10.5474144 NA 0.6048676222 1.5925356350
17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998
17.2 -16.5386939 NA 1.2617499032 3.0256489082
17.3 -20.0004774 NA -0.3913230895 3.3329089405
17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985
18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302
19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376
19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913
19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538
19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973
20 -12.9059229 -0.22087628 0.6838223064 1.7170160066
20.1 -16.8191201 NA 0.3278571109 1.7562902288
20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566
20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867
20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287
20.5 -13.3504189 -0.24338208 -0.4535207652 4.1730977828
21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839
21.1 -11.9335526 -0.12424941 -0.4069674045 2.9571191233
21.2 -12.7064929 -0.27672716 1.0650265940 3.7887385779
22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232
22.1 -12.7745451 -0.15996535 0.9601388170 3.1626627257
23 -3.5146508 -0.18236682 0.5556634840 1.5414533857
23.1 -4.6724048 -0.20823302 1.4407865964 2.3369736120
24 -2.5619821 -0.29026416 0.3856376411 2.8283136466
25 -6.2944970 -0.36139273 0.3564400705 0.5381704110
25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331
25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922
25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392
25.4 -9.0288933 NA 0.4466012931 4.0782226040
25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873
26 -19.7340685 -0.16846716 0.5341531449 0.2412706357
26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676
26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887
26.3 -24.6265576 NA 0.5948516018 4.1822362854
27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879
27.1 -11.1488468 -0.17622638 -0.4835141229 4.2451434687
28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344
28.1 -8.2030122 -0.27003852 0.5063671955 2.7943964268
28.2 -26.4317815 -0.07235801 -0.2067413142 4.2108539480
28.3 -18.5016071 -0.13462982 0.1196789973 4.4705521734
29 -5.8551395 -0.32432030 0.1392699487 1.1898884235
29.1 -2.0209442 -0.27034171 0.7960234776 1.7624059319
29.2 -5.6368080 -0.10197448 1.0398214352 2.0210406382
29.3 -3.8110961 -0.27606945 0.0813246429 3.4078777023
30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488
30.1 -17.0170140 -0.11511035 1.3635850954 3.5938334477
30.2 -25.4236089 -0.16215882 0.7354171050 3.6138710892
31 -17.0783921 0.05707733 0.3708398217 4.3988140998
32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007
32.1 -19.4317212 -0.14270013 1.2507771489 2.9128167813
32.2 -19.4738978 -0.20530798 0.1142915519 2.9676558380
32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547
33 2.0838099 -0.15252819 0.1774293842 0.0093385763
33.1 -13.3172274 NA 0.6159606291 3.4591242753
34 -10.0296691 -0.30378735 0.8590979166 1.4998774312
34.1 -25.9426553 -0.11982431 0.0546216775 3.8242761395
34.2 -18.5688138 -0.24278671 -0.0897224473 3.9072251692
34.3 -15.4173859 -0.19971833 0.4163395571 3.9582124643
35 -14.3958113 NA -1.4693520528 1.3294299203
35.1 -12.9457541 -0.24165780 -0.3031734330 1.5276966314
35.2 -16.1380691 NA -0.6045512101 4.5025920868
36 -12.8166968 -0.49062180 0.9823048960 0.7123168337
36.1 -14.3989481 -0.25651700 1.4466051416 1.7972493160
36.2 -12.2436943 NA 1.1606752905 1.8262697803
36.3 -15.0104638 -0.30401274 0.8373091576 4.2840119381
36.4 -10.1775457 NA 0.2640591685 4.6194464504
37 -15.2223495 -0.15276529 0.1177313455 2.0018732361
37.1 -14.7526195 -0.30016169 -0.1415483779 3.6656836793
37.2 -19.8168430 0.06809545 0.0054610124 3.9663937816
38 -2.7065118 -0.11218486 0.8078948077 0.9826511063
39 -8.7288138 -0.38072211 0.9876451040 0.6921808305
39.1 -9.2746473 -0.32094428 -0.3431222274 0.9027792048
39.2 -18.2695344 NA -1.7909380751 1.3055654289
39.3 -13.8219083 -0.40173480 -0.1798746191 1.5412842878
39.4 -16.2254704 -0.20041848 -0.1850961689 3.1834997435
39.5 -21.7283648 -0.26027990 0.4544226146 4.1394166439
40 1.8291916 -0.19751956 0.5350190436 1.1330395646
40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046
40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212
40.3 -10.5749790 NA 0.0916687506 4.6762977762
41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254
41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458
41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557
41.3 -15.9788960 NA 0.9885613862 3.3813529415
41.4 -9.6070508 NA -0.3908036794 3.5482964432
42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973
42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298
43 -16.6104361 -0.08864017 0.8965546110 0.5616614548
43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536
43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966
44 -8.1969033 -0.22697597 1.3221409285 0.7662644819
44.1 -19.9270197 NA 0.3416426284 2.6490291790
44.2 -22.6521171 NA 0.5706610068 3.3371910988
44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875
45 -0.5686627 -0.19274788 0.1414983160 0.1957449992
45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536
46 -19.1624789 -0.28547861 1.5391054233 1.3477755385
46.1 -18.4487574 -0.21977836 0.3889107049 2.8565793915
46.2 -15.8222682 NA 0.1248719493 4.4160729996
47 -5.4165074 -0.08597098 0.2014101100 0.6012621359
47.1 -15.0975029 -0.35424828 0.2982973539 2.4097121472
47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035
47.3 -10.6844521 -0.30426315 0.5196802157 3.1829649757
47.4 -18.2214784 NA 0.3702301552 4.6201055450
48 -8.3101471 NA -0.2128602862 2.8607365978
48.1 -18.3854275 NA -0.5337239976 2.9098354396
49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400
50 -10.4579977 -0.19959516 0.3897705981 1.1762060679
51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720
52 -4.4747188 -0.07438732 0.3758235358 2.1266646020
52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993
52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370
52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327
52.4 -8.9036692 -0.44619160 0.0254566848 4.7983659909
52.5 -5.6949642 -0.11011682 0.4155259424 4.9818264414
53 -10.3141887 -0.23278716 0.5675736897 0.4965799209
53.1 -8.2642654 -0.28317264 -0.3154088781 3.5505357443
53.2 -9.1691554 -0.19517481 0.2162315769 4.5790420019
54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841
54.1 -15.7192609 -0.28325504 0.4129127672 1.8812377600
54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352
54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672
54.4 -16.5771751 -0.34609328 0.8587727145 4.0143877396
55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980
55.1 -7.3217494 -0.24235382 0.5835528661 0.7463747414
55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171
55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572
55.4 -14.8599983 NA -0.1202746964 3.2218102901
56 -14.1764282 NA 0.5176377612 1.2231332215
56.1 -12.5343602 -0.30357450 -1.1136932588 2.3573202139
56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292
56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378
56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360
56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043
57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448
57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030
57.2 -6.3940143 NA -0.1159517011 1.1405586067
57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721
58 -12.0245677 -0.29901358 1.6476207996 1.2210099772
58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703
58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890
58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693
58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440
58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824
59 -13.2946560 -0.27255364 0.4766421367 1.9023998594
59.1 -10.0530648 NA 1.0028089765 4.9736620474
60 -19.2209402 NA 0.5231452932 2.8854503250
61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795
61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661
61.2 -1.4713611 NA 1.0229058138 2.5077313243
61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430
61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283
62 -2.8591600 NA -0.3979137604 0.5336771999
62.1 -6.9461986 -0.10504143 0.6830738372 0.6987666548
62.2 -16.7910593 -0.20104997 0.4301745954 3.4584309917
62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371
63 -24.0335535 -0.12036319 0.3345678035 2.8097350930
63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211
64 -20.5963897 NA 0.3949911859 4.1191305732
65 -2.7969169 -0.34450396 1.2000091513 0.7076152589
65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363
65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827
65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943
66 -13.2318328 NA -0.5081302805 3.4943454154
66.1 -1.9090560 NA -0.1447837412 3.7677437009
66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616
67 -25.6073277 NA 1.6716027681 4.1728388879
68 -13.4806759 -0.13605235 0.5691848839 0.1291919907
68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946
68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660
68.3 -12.4977127 NA 0.7443745962 2.9406870750
68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363
69 -14.5290675 -0.32152751 0.0381382683 4.1451198701
70 -15.2122709 -0.28190462 0.8126204217 0.1992557163
70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413
71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386
71.1 -7.5699888 NA -0.1103252087 1.4883817220
71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395
71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723
71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823
72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121
72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806
72.2 -26.3421306 NA -0.4170988856 1.8917567329
72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935
72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700
72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901
73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915
74 -8.2790175 -0.27963171 0.3730017941 1.4626806753
75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874
76 -6.0061671 0.04775562 -0.9888876620 1.8074807397
76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183
76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859
77 0.4772521 -0.19053195 -0.4986802480 0.8459033852
78 -9.4105229 -0.17172929 0.0417330969 0.8231094317
79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521
79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628
79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032
80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186
80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598
80.2 -16.6381566 NA 0.5046816157 2.8025900386
81 0.5547913 -0.18401980 -0.9493950353 0.0101324962
81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494
81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879
81.3 -5.2166381 NA 0.4182528210 3.3456630446
82 -3.0749381 -0.08328168 1.1088801952 1.3791010005
82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622
82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927
83 -12.5872635 -0.34228255 0.5104724530 0.0537394290
83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496
83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362
83.3 -19.2714012 NA -0.0534169155 4.7663642222
84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237
84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659
85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259
85.1 -12.9196365 NA -0.0577615939 1.7394116637
85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194
85.3 -6.3296340 NA -0.0520175929 3.1608762743
85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758
85.5 -13.6714939 NA -0.3740417009 4.5092553482
86 -10.8756412 -0.10854862 0.4293735089 0.8476278360
86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411
86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515
86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925
86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000
86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508
87 -10.7027963 NA 1.0457427869 2.7069531474
87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721
87.2 -14.9616716 NA 0.4396872616 4.5241666853
88 -2.2264493 -0.31563888 -0.0601928334 0.0005892443
88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603
92 -6.4119222 NA 0.4674939332 0.0737052364
93 5.2122168 -0.11939966 0.2677965647 0.2788909199
93.1 3.1211725 NA 1.6424445368 1.0357759963
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099
93.3 2.6223542 NA 1.1222322893 2.8876129608
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002
94 -6.9602492 NA -0.2904211940 0.8488043118
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438
98.1 -2.6515128 NA -0.9042716241 0.4309578973
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171
100 1.1005874 -0.17326698 0.0369067906 1.0691387602
100.1 -3.8226248 NA 1.6023713093 1.5109344281
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765
$m8b$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
$m8b$mu_reg_norm
[1] 0
$m8b$tau_reg_norm
[1] 1e-04
$m8b$shape_tau_norm
[1] 0.01
$m8b$rate_tau_norm
[1] 0.01
$m8b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8b$shape_diag_RinvD
[1] "0.01"
$m8b$rate_diag_RinvD
[1] "0.001"
$m8b$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8b$KinvD_y_id
id
4
$m8c
$m8c$M_id
B2 (Intercept) B21
1 1 1 NA
2 NA 1 NA
3 NA 1 NA
4 1 1 NA
5 1 1 NA
6 1 1 NA
7 0 1 NA
8 1 1 NA
9 1 1 NA
10 0 1 NA
11 1 1 NA
12 1 1 NA
13 1 1 NA
14 1 1 NA
15 NA 1 NA
16 1 1 NA
17 1 1 NA
18 1 1 NA
19 1 1 NA
20 0 1 NA
21 1 1 NA
22 1 1 NA
23 1 1 NA
24 NA 1 NA
25 0 1 NA
26 1 1 NA
27 1 1 NA
28 0 1 NA
29 1 1 NA
30 0 1 NA
31 0 1 NA
32 1 1 NA
33 1 1 NA
34 0 1 NA
35 1 1 NA
36 0 1 NA
37 1 1 NA
38 1 1 NA
39 1 1 NA
40 1 1 NA
41 1 1 NA
42 1 1 NA
43 1 1 NA
44 NA 1 NA
45 1 1 NA
46 1 1 NA
47 1 1 NA
48 1 1 NA
49 1 1 NA
50 1 1 NA
51 0 1 NA
52 1 1 NA
53 1 1 NA
54 0 1 NA
55 1 1 NA
56 0 1 NA
57 1 1 NA
58 NA 1 NA
59 1 1 NA
60 1 1 NA
61 0 1 NA
62 0 1 NA
63 1 1 NA
64 1 1 NA
65 1 1 NA
66 1 1 NA
67 1 1 NA
68 1 1 NA
69 NA 1 NA
70 1 1 NA
71 1 1 NA
72 1 1 NA
73 1 1 NA
74 1 1 NA
75 1 1 NA
76 1 1 NA
77 1 1 NA
78 1 1 NA
79 1 1 NA
80 1 1 NA
81 1 1 NA
82 1 1 NA
83 1 1 NA
84 1 1 NA
85 1 1 NA
86 1 1 NA
87 1 1 NA
88 1 1 NA
89 1 1 NA
90 1 1 NA
91 NA 1 NA
92 1 1 NA
93 1 1 NA
94 1 1 NA
95 1 1 NA
96 NA 1 NA
97 NA 1 NA
98 1 1 NA
99 1 1 NA
100 1 1 NA
$m8c$M_lvlone
y c2 c1 time B21:c1
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 NA
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847 NA
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082 NA
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196 NA
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339 NA
5.3 -9.2366906 NA 0.1740288049 2.8119940578 NA
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884 NA
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673 NA
7.1 -18.3267285 NA 0.4242971219 3.7008403614 NA
7.2 -12.5138426 NA 0.0777182491 4.7716691741 NA
8 -1.6305331 NA -0.5791408712 1.1246398522 NA
8.1 -9.6520453 NA 0.3128604232 1.8027009873 NA
8.2 -1.5278462 NA 0.6258446356 1.8175825174 NA
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003 NA
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307 NA
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704 NA
9 -0.1634429 NA -0.1757592269 0.9607803822 NA
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383 NA
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501 NA
10 -6.4172202 NA -0.5598409704 2.2975509102 NA
10.1 -11.4834569 NA -0.2318340451 4.1734118364 NA
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905 NA
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680 NA
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263 NA
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969 NA
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661 NA
12 -15.2006287 NA -0.2433996286 0.9591987054 NA
13 0.8058816 NA 0.8799673116 0.0619085738 NA
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502 NA
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007 NA
14.1 -10.0965208 NA -0.1094019046 4.4710561272 NA
14.2 -16.6452027 NA 0.1518967560 4.6359198843 NA
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599 NA
15 -8.9424594 NA 0.3464447888 0.5402063532 NA
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816 NA
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688 NA
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615 NA
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869 NA
16.1 -9.9308357 -0.07599066 1.0652558311 2.5675726333 NA
16.2 -6.9626923 -0.32385667 0.6971872493 2.6524101500 NA
16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690 NA
16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291 NA
16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889 NA
17 -10.5474144 NA 0.6048676222 1.5925356350 NA
17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998 NA
17.2 -16.5386939 NA 1.2617499032 3.0256489082 NA
17.3 -20.0004774 NA -0.3913230895 3.3329089405 NA
17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985 NA
18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302 NA
19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376 NA
19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913 NA
19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538 NA
19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973 NA
20 -12.9059229 -0.22087628 0.6838223064 1.7170160066 NA
20.1 -16.8191201 NA 0.3278571109 1.7562902288 NA
20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566 NA
20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867 NA
20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287 NA
20.5 -13.3504189 -0.24338208 -0.4535207652 4.1730977828 NA
21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839 NA
21.1 -11.9335526 -0.12424941 -0.4069674045 2.9571191233 NA
21.2 -12.7064929 -0.27672716 1.0650265940 3.7887385779 NA
22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232 NA
22.1 -12.7745451 -0.15996535 0.9601388170 3.1626627257 NA
23 -3.5146508 -0.18236682 0.5556634840 1.5414533857 NA
23.1 -4.6724048 -0.20823302 1.4407865964 2.3369736120 NA
24 -2.5619821 -0.29026416 0.3856376411 2.8283136466 NA
25 -6.2944970 -0.36139273 0.3564400705 0.5381704110 NA
25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331 NA
25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922 NA
25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392 NA
25.4 -9.0288933 NA 0.4466012931 4.0782226040 NA
25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873 NA
26 -19.7340685 -0.16846716 0.5341531449 0.2412706357 NA
26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676 NA
26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887 NA
26.3 -24.6265576 NA 0.5948516018 4.1822362854 NA
27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879 NA
27.1 -11.1488468 -0.17622638 -0.4835141229 4.2451434687 NA
28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344 NA
28.1 -8.2030122 -0.27003852 0.5063671955 2.7943964268 NA
28.2 -26.4317815 -0.07235801 -0.2067413142 4.2108539480 NA
28.3 -18.5016071 -0.13462982 0.1196789973 4.4705521734 NA
29 -5.8551395 -0.32432030 0.1392699487 1.1898884235 NA
29.1 -2.0209442 -0.27034171 0.7960234776 1.7624059319 NA
29.2 -5.6368080 -0.10197448 1.0398214352 2.0210406382 NA
29.3 -3.8110961 -0.27606945 0.0813246429 3.4078777023 NA
30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488 NA
30.1 -17.0170140 -0.11511035 1.3635850954 3.5938334477 NA
30.2 -25.4236089 -0.16215882 0.7354171050 3.6138710892 NA
31 -17.0783921 0.05707733 0.3708398217 4.3988140998 NA
32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007 NA
32.1 -19.4317212 -0.14270013 1.2507771489 2.9128167813 NA
32.2 -19.4738978 -0.20530798 0.1142915519 2.9676558380 NA
32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547 NA
33 2.0838099 -0.15252819 0.1774293842 0.0093385763 NA
33.1 -13.3172274 NA 0.6159606291 3.4591242753 NA
34 -10.0296691 -0.30378735 0.8590979166 1.4998774312 NA
34.1 -25.9426553 -0.11982431 0.0546216775 3.8242761395 NA
34.2 -18.5688138 -0.24278671 -0.0897224473 3.9072251692 NA
34.3 -15.4173859 -0.19971833 0.4163395571 3.9582124643 NA
35 -14.3958113 NA -1.4693520528 1.3294299203 NA
35.1 -12.9457541 -0.24165780 -0.3031734330 1.5276966314 NA
35.2 -16.1380691 NA -0.6045512101 4.5025920868 NA
36 -12.8166968 -0.49062180 0.9823048960 0.7123168337 NA
36.1 -14.3989481 -0.25651700 1.4466051416 1.7972493160 NA
36.2 -12.2436943 NA 1.1606752905 1.8262697803 NA
36.3 -15.0104638 -0.30401274 0.8373091576 4.2840119381 NA
36.4 -10.1775457 NA 0.2640591685 4.6194464504 NA
37 -15.2223495 -0.15276529 0.1177313455 2.0018732361 NA
37.1 -14.7526195 -0.30016169 -0.1415483779 3.6656836793 NA
37.2 -19.8168430 0.06809545 0.0054610124 3.9663937816 NA
38 -2.7065118 -0.11218486 0.8078948077 0.9826511063 NA
39 -8.7288138 -0.38072211 0.9876451040 0.6921808305 NA
39.1 -9.2746473 -0.32094428 -0.3431222274 0.9027792048 NA
39.2 -18.2695344 NA -1.7909380751 1.3055654289 NA
39.3 -13.8219083 -0.40173480 -0.1798746191 1.5412842878 NA
39.4 -16.2254704 -0.20041848 -0.1850961689 3.1834997435 NA
39.5 -21.7283648 -0.26027990 0.4544226146 4.1394166439 NA
40 1.8291916 -0.19751956 0.5350190436 1.1330395646 NA
40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046 NA
40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212 NA
40.3 -10.5749790 NA 0.0916687506 4.6762977762 NA
41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254 NA
41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458 NA
41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557 NA
41.3 -15.9788960 NA 0.9885613862 3.3813529415 NA
41.4 -9.6070508 NA -0.3908036794 3.5482964432 NA
42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973 NA
42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298 NA
43 -16.6104361 -0.08864017 0.8965546110 0.5616614548 NA
43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536 NA
43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966 NA
44 -8.1969033 -0.22697597 1.3221409285 0.7662644819 NA
44.1 -19.9270197 NA 0.3416426284 2.6490291790 NA
44.2 -22.6521171 NA 0.5706610068 3.3371910988 NA
44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875 NA
45 -0.5686627 -0.19274788 0.1414983160 0.1957449992 NA
45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536 NA
46 -19.1624789 -0.28547861 1.5391054233 1.3477755385 NA
46.1 -18.4487574 -0.21977836 0.3889107049 2.8565793915 NA
46.2 -15.8222682 NA 0.1248719493 4.4160729996 NA
47 -5.4165074 -0.08597098 0.2014101100 0.6012621359 NA
47.1 -15.0975029 -0.35424828 0.2982973539 2.4097121472 NA
47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035 NA
47.3 -10.6844521 -0.30426315 0.5196802157 3.1829649757 NA
47.4 -18.2214784 NA 0.3702301552 4.6201055450 NA
48 -8.3101471 NA -0.2128602862 2.8607365978 NA
48.1 -18.3854275 NA -0.5337239976 2.9098354396 NA
49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400 NA
50 -10.4579977 -0.19959516 0.3897705981 1.1762060679 NA
51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720 NA
52 -4.4747188 -0.07438732 0.3758235358 2.1266646020 NA
52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993 NA
52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370 NA
52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327 NA
52.4 -8.9036692 -0.44619160 0.0254566848 4.7983659909 NA
52.5 -5.6949642 -0.11011682 0.4155259424 4.9818264414 NA
53 -10.3141887 -0.23278716 0.5675736897 0.4965799209 NA
53.1 -8.2642654 -0.28317264 -0.3154088781 3.5505357443 NA
53.2 -9.1691554 -0.19517481 0.2162315769 4.5790420019 NA
54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841 NA
54.1 -15.7192609 -0.28325504 0.4129127672 1.8812377600 NA
54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352 NA
54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672 NA
54.4 -16.5771751 -0.34609328 0.8587727145 4.0143877396 NA
55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980 NA
55.1 -7.3217494 -0.24235382 0.5835528661 0.7463747414 NA
55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171 NA
55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572 NA
55.4 -14.8599983 NA -0.1202746964 3.2218102901 NA
56 -14.1764282 NA 0.5176377612 1.2231332215 NA
56.1 -12.5343602 -0.30357450 -1.1136932588 2.3573202139 NA
56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292 NA
56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378 NA
56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360 NA
56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043 NA
57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448 NA
57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030 NA
57.2 -6.3940143 NA -0.1159517011 1.1405586067 NA
57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721 NA
58 -12.0245677 -0.29901358 1.6476207996 1.2210099772 NA
58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703 NA
58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890 NA
58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693 NA
58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440 NA
58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824 NA
59 -13.2946560 -0.27255364 0.4766421367 1.9023998594 NA
59.1 -10.0530648 NA 1.0028089765 4.9736620474 NA
60 -19.2209402 NA 0.5231452932 2.8854503250 NA
61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795 NA
61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661 NA
61.2 -1.4713611 NA 1.0229058138 2.5077313243 NA
61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430 NA
61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283 NA
62 -2.8591600 NA -0.3979137604 0.5336771999 NA
62.1 -6.9461986 -0.10504143 0.6830738372 0.6987666548 NA
62.2 -16.7910593 -0.20104997 0.4301745954 3.4584309917 NA
62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371 NA
63 -24.0335535 -0.12036319 0.3345678035 2.8097350930 NA
63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211 NA
64 -20.5963897 NA 0.3949911859 4.1191305732 NA
65 -2.7969169 -0.34450396 1.2000091513 0.7076152589 NA
65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363 NA
65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827 NA
65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943 NA
66 -13.2318328 NA -0.5081302805 3.4943454154 NA
66.1 -1.9090560 NA -0.1447837412 3.7677437009 NA
66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616 NA
67 -25.6073277 NA 1.6716027681 4.1728388879 NA
68 -13.4806759 -0.13605235 0.5691848839 0.1291919907 NA
68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946 NA
68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660 NA
68.3 -12.4977127 NA 0.7443745962 2.9406870750 NA
68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363 NA
69 -14.5290675 -0.32152751 0.0381382683 4.1451198701 NA
70 -15.2122709 -0.28190462 0.8126204217 0.1992557163 NA
70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413 NA
71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386 NA
71.1 -7.5699888 NA -0.1103252087 1.4883817220 NA
71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395 NA
71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723 NA
71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823 NA
72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121 NA
72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806 NA
72.2 -26.3421306 NA -0.4170988856 1.8917567329 NA
72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935 NA
72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700 NA
72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901 NA
73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915 NA
74 -8.2790175 -0.27963171 0.3730017941 1.4626806753 NA
75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874 NA
76 -6.0061671 0.04775562 -0.9888876620 1.8074807397 NA
76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183 NA
76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859 NA
77 0.4772521 -0.19053195 -0.4986802480 0.8459033852 NA
78 -9.4105229 -0.17172929 0.0417330969 0.8231094317 NA
79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521 NA
79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628 NA
79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032 NA
80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186 NA
80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598 NA
80.2 -16.6381566 NA 0.5046816157 2.8025900386 NA
81 0.5547913 -0.18401980 -0.9493950353 0.0101324962 NA
81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494 NA
81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879 NA
81.3 -5.2166381 NA 0.4182528210 3.3456630446 NA
82 -3.0749381 -0.08328168 1.1088801952 1.3791010005 NA
82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622 NA
82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927 NA
83 -12.5872635 -0.34228255 0.5104724530 0.0537394290 NA
83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496 NA
83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362 NA
83.3 -19.2714012 NA -0.0534169155 4.7663642222 NA
84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237 NA
84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659 NA
85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259 NA
85.1 -12.9196365 NA -0.0577615939 1.7394116637 NA
85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194 NA
85.3 -6.3296340 NA -0.0520175929 3.1608762743 NA
85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758 NA
85.5 -13.6714939 NA -0.3740417009 4.5092553482 NA
86 -10.8756412 -0.10854862 0.4293735089 0.8476278360 NA
86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411 NA
86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515 NA
86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925 NA
86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000 NA
86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508 NA
87 -10.7027963 NA 1.0457427869 2.7069531474 NA
87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721 NA
87.2 -14.9616716 NA 0.4396872616 4.5241666853 NA
88 -2.2264493 -0.31563888 -0.0601928334 0.0005892443 NA
88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866 NA
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900 NA
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297 NA
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167 NA
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737 NA
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230 NA
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167 NA
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247 NA
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390 NA
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286 NA
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603 NA
92 -6.4119222 NA 0.4674939332 0.0737052364 NA
93 5.2122168 -0.11939966 0.2677965647 0.2788909199 NA
93.1 3.1211725 NA 1.6424445368 1.0357759963 NA
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099 NA
93.3 2.6223542 NA 1.1222322893 2.8876129608 NA
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002 NA
94 -6.9602492 NA -0.2904211940 0.8488043118 NA
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425 NA
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884 NA
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448 NA
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935 NA
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016 NA
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048 NA
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457 NA
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732 NA
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477 NA
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720 NA
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425 NA
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691 NA
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488 NA
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598 NA
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731 NA
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643 NA
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438 NA
98.1 -2.6515128 NA -0.9042716241 0.4309578973 NA
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906 NA
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8c$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c1 0.1798099 0.6117459
$m8c$mu_reg_norm
[1] 0
$m8c$tau_reg_norm
[1] 1e-04
$m8c$shape_tau_norm
[1] 0.01
$m8c$rate_tau_norm
[1] 0.01
$m8c$mu_reg_binom
[1] 0
$m8c$tau_reg_binom
[1] 1e-04
$m8c$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8c$shape_diag_RinvD
[1] "0.01"
$m8c$rate_diag_RinvD
[1] "0.001"
$m8c$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8c$KinvD_y_id
id
4
$m8d
$m8d$M_id
B2 (Intercept) B21
1 1 1 NA
2 NA 1 NA
3 NA 1 NA
4 1 1 NA
5 1 1 NA
6 1 1 NA
7 0 1 NA
8 1 1 NA
9 1 1 NA
10 0 1 NA
11 1 1 NA
12 1 1 NA
13 1 1 NA
14 1 1 NA
15 NA 1 NA
16 1 1 NA
17 1 1 NA
18 1 1 NA
19 1 1 NA
20 0 1 NA
21 1 1 NA
22 1 1 NA
23 1 1 NA
24 NA 1 NA
25 0 1 NA
26 1 1 NA
27 1 1 NA
28 0 1 NA
29 1 1 NA
30 0 1 NA
31 0 1 NA
32 1 1 NA
33 1 1 NA
34 0 1 NA
35 1 1 NA
36 0 1 NA
37 1 1 NA
38 1 1 NA
39 1 1 NA
40 1 1 NA
41 1 1 NA
42 1 1 NA
43 1 1 NA
44 NA 1 NA
45 1 1 NA
46 1 1 NA
47 1 1 NA
48 1 1 NA
49 1 1 NA
50 1 1 NA
51 0 1 NA
52 1 1 NA
53 1 1 NA
54 0 1 NA
55 1 1 NA
56 0 1 NA
57 1 1 NA
58 NA 1 NA
59 1 1 NA
60 1 1 NA
61 0 1 NA
62 0 1 NA
63 1 1 NA
64 1 1 NA
65 1 1 NA
66 1 1 NA
67 1 1 NA
68 1 1 NA
69 NA 1 NA
70 1 1 NA
71 1 1 NA
72 1 1 NA
73 1 1 NA
74 1 1 NA
75 1 1 NA
76 1 1 NA
77 1 1 NA
78 1 1 NA
79 1 1 NA
80 1 1 NA
81 1 1 NA
82 1 1 NA
83 1 1 NA
84 1 1 NA
85 1 1 NA
86 1 1 NA
87 1 1 NA
88 1 1 NA
89 1 1 NA
90 1 1 NA
91 NA 1 NA
92 1 1 NA
93 1 1 NA
94 1 1 NA
95 1 1 NA
96 NA 1 NA
97 NA 1 NA
98 1 1 NA
99 1 1 NA
100 1 1 NA
$m8d$M_lvlone
y c2 c1 time B21:c1
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 NA
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847 NA
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082 NA
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196 NA
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339 NA
5.3 -9.2366906 NA 0.1740288049 2.8119940578 NA
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884 NA
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673 NA
7.1 -18.3267285 NA 0.4242971219 3.7008403614 NA
7.2 -12.5138426 NA 0.0777182491 4.7716691741 NA
8 -1.6305331 NA -0.5791408712 1.1246398522 NA
8.1 -9.6520453 NA 0.3128604232 1.8027009873 NA
8.2 -1.5278462 NA 0.6258446356 1.8175825174 NA
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003 NA
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307 NA
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704 NA
9 -0.1634429 NA -0.1757592269 0.9607803822 NA
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383 NA
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501 NA
10 -6.4172202 NA -0.5598409704 2.2975509102 NA
10.1 -11.4834569 NA -0.2318340451 4.1734118364 NA
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905 NA
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680 NA
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263 NA
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969 NA
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661 NA
12 -15.2006287 NA -0.2433996286 0.9591987054 NA
13 0.8058816 NA 0.8799673116 0.0619085738 NA
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502 NA
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007 NA
14.1 -10.0965208 NA -0.1094019046 4.4710561272 NA
14.2 -16.6452027 NA 0.1518967560 4.6359198843 NA
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599 NA
15 -8.9424594 NA 0.3464447888 0.5402063532 NA
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816 NA
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688 NA
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615 NA
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869 NA
16.1 -9.9308357 -0.07599066 1.0652558311 2.5675726333 NA
16.2 -6.9626923 -0.32385667 0.6971872493 2.6524101500 NA
16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690 NA
16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291 NA
16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889 NA
17 -10.5474144 NA 0.6048676222 1.5925356350 NA
17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998 NA
17.2 -16.5386939 NA 1.2617499032 3.0256489082 NA
17.3 -20.0004774 NA -0.3913230895 3.3329089405 NA
17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985 NA
18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302 NA
19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376 NA
19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913 NA
19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538 NA
19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973 NA
20 -12.9059229 -0.22087628 0.6838223064 1.7170160066 NA
20.1 -16.8191201 NA 0.3278571109 1.7562902288 NA
20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566 NA
20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867 NA
20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287 NA
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21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839 NA
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22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232 NA
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23 -3.5146508 -0.18236682 0.5556634840 1.5414533857 NA
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24 -2.5619821 -0.29026416 0.3856376411 2.8283136466 NA
25 -6.2944970 -0.36139273 0.3564400705 0.5381704110 NA
25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331 NA
25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922 NA
25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392 NA
25.4 -9.0288933 NA 0.4466012931 4.0782226040 NA
25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873 NA
26 -19.7340685 -0.16846716 0.5341531449 0.2412706357 NA
26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676 NA
26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887 NA
26.3 -24.6265576 NA 0.5948516018 4.1822362854 NA
27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879 NA
27.1 -11.1488468 -0.17622638 -0.4835141229 4.2451434687 NA
28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344 NA
28.1 -8.2030122 -0.27003852 0.5063671955 2.7943964268 NA
28.2 -26.4317815 -0.07235801 -0.2067413142 4.2108539480 NA
28.3 -18.5016071 -0.13462982 0.1196789973 4.4705521734 NA
29 -5.8551395 -0.32432030 0.1392699487 1.1898884235 NA
29.1 -2.0209442 -0.27034171 0.7960234776 1.7624059319 NA
29.2 -5.6368080 -0.10197448 1.0398214352 2.0210406382 NA
29.3 -3.8110961 -0.27606945 0.0813246429 3.4078777023 NA
30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488 NA
30.1 -17.0170140 -0.11511035 1.3635850954 3.5938334477 NA
30.2 -25.4236089 -0.16215882 0.7354171050 3.6138710892 NA
31 -17.0783921 0.05707733 0.3708398217 4.3988140998 NA
32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007 NA
32.1 -19.4317212 -0.14270013 1.2507771489 2.9128167813 NA
32.2 -19.4738978 -0.20530798 0.1142915519 2.9676558380 NA
32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547 NA
33 2.0838099 -0.15252819 0.1774293842 0.0093385763 NA
33.1 -13.3172274 NA 0.6159606291 3.4591242753 NA
34 -10.0296691 -0.30378735 0.8590979166 1.4998774312 NA
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34.2 -18.5688138 -0.24278671 -0.0897224473 3.9072251692 NA
34.3 -15.4173859 -0.19971833 0.4163395571 3.9582124643 NA
35 -14.3958113 NA -1.4693520528 1.3294299203 NA
35.1 -12.9457541 -0.24165780 -0.3031734330 1.5276966314 NA
35.2 -16.1380691 NA -0.6045512101 4.5025920868 NA
36 -12.8166968 -0.49062180 0.9823048960 0.7123168337 NA
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36.2 -12.2436943 NA 1.1606752905 1.8262697803 NA
36.3 -15.0104638 -0.30401274 0.8373091576 4.2840119381 NA
36.4 -10.1775457 NA 0.2640591685 4.6194464504 NA
37 -15.2223495 -0.15276529 0.1177313455 2.0018732361 NA
37.1 -14.7526195 -0.30016169 -0.1415483779 3.6656836793 NA
37.2 -19.8168430 0.06809545 0.0054610124 3.9663937816 NA
38 -2.7065118 -0.11218486 0.8078948077 0.9826511063 NA
39 -8.7288138 -0.38072211 0.9876451040 0.6921808305 NA
39.1 -9.2746473 -0.32094428 -0.3431222274 0.9027792048 NA
39.2 -18.2695344 NA -1.7909380751 1.3055654289 NA
39.3 -13.8219083 -0.40173480 -0.1798746191 1.5412842878 NA
39.4 -16.2254704 -0.20041848 -0.1850961689 3.1834997435 NA
39.5 -21.7283648 -0.26027990 0.4544226146 4.1394166439 NA
40 1.8291916 -0.19751956 0.5350190436 1.1330395646 NA
40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046 NA
40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212 NA
40.3 -10.5749790 NA 0.0916687506 4.6762977762 NA
41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254 NA
41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458 NA
41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557 NA
41.3 -15.9788960 NA 0.9885613862 3.3813529415 NA
41.4 -9.6070508 NA -0.3908036794 3.5482964432 NA
42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973 NA
42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298 NA
43 -16.6104361 -0.08864017 0.8965546110 0.5616614548 NA
43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536 NA
43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966 NA
44 -8.1969033 -0.22697597 1.3221409285 0.7662644819 NA
44.1 -19.9270197 NA 0.3416426284 2.6490291790 NA
44.2 -22.6521171 NA 0.5706610068 3.3371910988 NA
44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875 NA
45 -0.5686627 -0.19274788 0.1414983160 0.1957449992 NA
45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536 NA
46 -19.1624789 -0.28547861 1.5391054233 1.3477755385 NA
46.1 -18.4487574 -0.21977836 0.3889107049 2.8565793915 NA
46.2 -15.8222682 NA 0.1248719493 4.4160729996 NA
47 -5.4165074 -0.08597098 0.2014101100 0.6012621359 NA
47.1 -15.0975029 -0.35424828 0.2982973539 2.4097121472 NA
47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035 NA
47.3 -10.6844521 -0.30426315 0.5196802157 3.1829649757 NA
47.4 -18.2214784 NA 0.3702301552 4.6201055450 NA
48 -8.3101471 NA -0.2128602862 2.8607365978 NA
48.1 -18.3854275 NA -0.5337239976 2.9098354396 NA
49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400 NA
50 -10.4579977 -0.19959516 0.3897705981 1.1762060679 NA
51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720 NA
52 -4.4747188 -0.07438732 0.3758235358 2.1266646020 NA
52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993 NA
52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370 NA
52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327 NA
52.4 -8.9036692 -0.44619160 0.0254566848 4.7983659909 NA
52.5 -5.6949642 -0.11011682 0.4155259424 4.9818264414 NA
53 -10.3141887 -0.23278716 0.5675736897 0.4965799209 NA
53.1 -8.2642654 -0.28317264 -0.3154088781 3.5505357443 NA
53.2 -9.1691554 -0.19517481 0.2162315769 4.5790420019 NA
54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841 NA
54.1 -15.7192609 -0.28325504 0.4129127672 1.8812377600 NA
54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352 NA
54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672 NA
54.4 -16.5771751 -0.34609328 0.8587727145 4.0143877396 NA
55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980 NA
55.1 -7.3217494 -0.24235382 0.5835528661 0.7463747414 NA
55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171 NA
55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572 NA
55.4 -14.8599983 NA -0.1202746964 3.2218102901 NA
56 -14.1764282 NA 0.5176377612 1.2231332215 NA
56.1 -12.5343602 -0.30357450 -1.1136932588 2.3573202139 NA
56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292 NA
56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378 NA
56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360 NA
56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043 NA
57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448 NA
57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030 NA
57.2 -6.3940143 NA -0.1159517011 1.1405586067 NA
57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721 NA
58 -12.0245677 -0.29901358 1.6476207996 1.2210099772 NA
58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703 NA
58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890 NA
58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693 NA
58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440 NA
58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824 NA
59 -13.2946560 -0.27255364 0.4766421367 1.9023998594 NA
59.1 -10.0530648 NA 1.0028089765 4.9736620474 NA
60 -19.2209402 NA 0.5231452932 2.8854503250 NA
61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795 NA
61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661 NA
61.2 -1.4713611 NA 1.0229058138 2.5077313243 NA
61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430 NA
61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283 NA
62 -2.8591600 NA -0.3979137604 0.5336771999 NA
62.1 -6.9461986 -0.10504143 0.6830738372 0.6987666548 NA
62.2 -16.7910593 -0.20104997 0.4301745954 3.4584309917 NA
62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371 NA
63 -24.0335535 -0.12036319 0.3345678035 2.8097350930 NA
63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211 NA
64 -20.5963897 NA 0.3949911859 4.1191305732 NA
65 -2.7969169 -0.34450396 1.2000091513 0.7076152589 NA
65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363 NA
65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827 NA
65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943 NA
66 -13.2318328 NA -0.5081302805 3.4943454154 NA
66.1 -1.9090560 NA -0.1447837412 3.7677437009 NA
66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616 NA
67 -25.6073277 NA 1.6716027681 4.1728388879 NA
68 -13.4806759 -0.13605235 0.5691848839 0.1291919907 NA
68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946 NA
68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660 NA
68.3 -12.4977127 NA 0.7443745962 2.9406870750 NA
68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363 NA
69 -14.5290675 -0.32152751 0.0381382683 4.1451198701 NA
70 -15.2122709 -0.28190462 0.8126204217 0.1992557163 NA
70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413 NA
71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386 NA
71.1 -7.5699888 NA -0.1103252087 1.4883817220 NA
71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395 NA
71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723 NA
71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823 NA
72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121 NA
72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806 NA
72.2 -26.3421306 NA -0.4170988856 1.8917567329 NA
72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935 NA
72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700 NA
72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901 NA
73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915 NA
74 -8.2790175 -0.27963171 0.3730017941 1.4626806753 NA
75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874 NA
76 -6.0061671 0.04775562 -0.9888876620 1.8074807397 NA
76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183 NA
76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859 NA
77 0.4772521 -0.19053195 -0.4986802480 0.8459033852 NA
78 -9.4105229 -0.17172929 0.0417330969 0.8231094317 NA
79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521 NA
79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628 NA
79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032 NA
80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186 NA
80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598 NA
80.2 -16.6381566 NA 0.5046816157 2.8025900386 NA
81 0.5547913 -0.18401980 -0.9493950353 0.0101324962 NA
81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494 NA
81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879 NA
81.3 -5.2166381 NA 0.4182528210 3.3456630446 NA
82 -3.0749381 -0.08328168 1.1088801952 1.3791010005 NA
82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622 NA
82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927 NA
83 -12.5872635 -0.34228255 0.5104724530 0.0537394290 NA
83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496 NA
83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362 NA
83.3 -19.2714012 NA -0.0534169155 4.7663642222 NA
84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237 NA
84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659 NA
85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259 NA
85.1 -12.9196365 NA -0.0577615939 1.7394116637 NA
85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194 NA
85.3 -6.3296340 NA -0.0520175929 3.1608762743 NA
85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758 NA
85.5 -13.6714939 NA -0.3740417009 4.5092553482 NA
86 -10.8756412 -0.10854862 0.4293735089 0.8476278360 NA
86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411 NA
86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515 NA
86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925 NA
86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000 NA
86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508 NA
87 -10.7027963 NA 1.0457427869 2.7069531474 NA
87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721 NA
87.2 -14.9616716 NA 0.4396872616 4.5241666853 NA
88 -2.2264493 -0.31563888 -0.0601928334 0.0005892443 NA
88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866 NA
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900 NA
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297 NA
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167 NA
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737 NA
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230 NA
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167 NA
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247 NA
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390 NA
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286 NA
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603 NA
92 -6.4119222 NA 0.4674939332 0.0737052364 NA
93 5.2122168 -0.11939966 0.2677965647 0.2788909199 NA
93.1 3.1211725 NA 1.6424445368 1.0357759963 NA
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099 NA
93.3 2.6223542 NA 1.1222322893 2.8876129608 NA
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002 NA
94 -6.9602492 NA -0.2904211940 0.8488043118 NA
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425 NA
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884 NA
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448 NA
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935 NA
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016 NA
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048 NA
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457 NA
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732 NA
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477 NA
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720 NA
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425 NA
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691 NA
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488 NA
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598 NA
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731 NA
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643 NA
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438 NA
98.1 -2.6515128 NA -0.9042716241 0.4309578973 NA
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906 NA
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8d$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c1 0.1798099 0.6117459
$m8d$mu_reg_norm
[1] 0
$m8d$tau_reg_norm
[1] 1e-04
$m8d$shape_tau_norm
[1] 0.01
$m8d$rate_tau_norm
[1] 0.01
$m8d$mu_reg_binom
[1] 0
$m8d$tau_reg_binom
[1] 1e-04
$m8d$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8d$shape_diag_RinvD
[1] "0.01"
$m8d$rate_diag_RinvD
[1] "0.001"
$m8d$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8d$KinvD_y_id
id
4
$m8e
$m8e$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8e$M_lvlone
y c2 c1 time B21:c1
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 NA
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847 NA
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082 NA
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196 NA
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339 NA
5.3 -9.2366906 NA 0.1740288049 2.8119940578 NA
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884 NA
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673 NA
7.1 -18.3267285 NA 0.4242971219 3.7008403614 NA
7.2 -12.5138426 NA 0.0777182491 4.7716691741 NA
8 -1.6305331 NA -0.5791408712 1.1246398522 NA
8.1 -9.6520453 NA 0.3128604232 1.8027009873 NA
8.2 -1.5278462 NA 0.6258446356 1.8175825174 NA
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003 NA
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307 NA
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704 NA
9 -0.1634429 NA -0.1757592269 0.9607803822 NA
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383 NA
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501 NA
10 -6.4172202 NA -0.5598409704 2.2975509102 NA
10.1 -11.4834569 NA -0.2318340451 4.1734118364 NA
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905 NA
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680 NA
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263 NA
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969 NA
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661 NA
12 -15.2006287 NA -0.2433996286 0.9591987054 NA
13 0.8058816 NA 0.8799673116 0.0619085738 NA
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502 NA
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007 NA
14.1 -10.0965208 NA -0.1094019046 4.4710561272 NA
14.2 -16.6452027 NA 0.1518967560 4.6359198843 NA
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599 NA
15 -8.9424594 NA 0.3464447888 0.5402063532 NA
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816 NA
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688 NA
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615 NA
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869 NA
16.1 -9.9308357 -0.07599066 1.0652558311 2.5675726333 NA
16.2 -6.9626923 -0.32385667 0.6971872493 2.6524101500 NA
16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690 NA
16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291 NA
16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889 NA
17 -10.5474144 NA 0.6048676222 1.5925356350 NA
17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998 NA
17.2 -16.5386939 NA 1.2617499032 3.0256489082 NA
17.3 -20.0004774 NA -0.3913230895 3.3329089405 NA
17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985 NA
18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302 NA
19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376 NA
19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913 NA
19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538 NA
19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973 NA
20 -12.9059229 -0.22087628 0.6838223064 1.7170160066 NA
20.1 -16.8191201 NA 0.3278571109 1.7562902288 NA
20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566 NA
20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867 NA
20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287 NA
20.5 -13.3504189 -0.24338208 -0.4535207652 4.1730977828 NA
21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839 NA
21.1 -11.9335526 -0.12424941 -0.4069674045 2.9571191233 NA
21.2 -12.7064929 -0.27672716 1.0650265940 3.7887385779 NA
22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232 NA
22.1 -12.7745451 -0.15996535 0.9601388170 3.1626627257 NA
23 -3.5146508 -0.18236682 0.5556634840 1.5414533857 NA
23.1 -4.6724048 -0.20823302 1.4407865964 2.3369736120 NA
24 -2.5619821 -0.29026416 0.3856376411 2.8283136466 NA
25 -6.2944970 -0.36139273 0.3564400705 0.5381704110 NA
25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331 NA
25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922 NA
25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392 NA
25.4 -9.0288933 NA 0.4466012931 4.0782226040 NA
25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873 NA
26 -19.7340685 -0.16846716 0.5341531449 0.2412706357 NA
26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676 NA
26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887 NA
26.3 -24.6265576 NA 0.5948516018 4.1822362854 NA
27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879 NA
27.1 -11.1488468 -0.17622638 -0.4835141229 4.2451434687 NA
28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344 NA
28.1 -8.2030122 -0.27003852 0.5063671955 2.7943964268 NA
28.2 -26.4317815 -0.07235801 -0.2067413142 4.2108539480 NA
28.3 -18.5016071 -0.13462982 0.1196789973 4.4705521734 NA
29 -5.8551395 -0.32432030 0.1392699487 1.1898884235 NA
29.1 -2.0209442 -0.27034171 0.7960234776 1.7624059319 NA
29.2 -5.6368080 -0.10197448 1.0398214352 2.0210406382 NA
29.3 -3.8110961 -0.27606945 0.0813246429 3.4078777023 NA
30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488 NA
30.1 -17.0170140 -0.11511035 1.3635850954 3.5938334477 NA
30.2 -25.4236089 -0.16215882 0.7354171050 3.6138710892 NA
31 -17.0783921 0.05707733 0.3708398217 4.3988140998 NA
32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007 NA
32.1 -19.4317212 -0.14270013 1.2507771489 2.9128167813 NA
32.2 -19.4738978 -0.20530798 0.1142915519 2.9676558380 NA
32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547 NA
33 2.0838099 -0.15252819 0.1774293842 0.0093385763 NA
33.1 -13.3172274 NA 0.6159606291 3.4591242753 NA
34 -10.0296691 -0.30378735 0.8590979166 1.4998774312 NA
34.1 -25.9426553 -0.11982431 0.0546216775 3.8242761395 NA
34.2 -18.5688138 -0.24278671 -0.0897224473 3.9072251692 NA
34.3 -15.4173859 -0.19971833 0.4163395571 3.9582124643 NA
35 -14.3958113 NA -1.4693520528 1.3294299203 NA
35.1 -12.9457541 -0.24165780 -0.3031734330 1.5276966314 NA
35.2 -16.1380691 NA -0.6045512101 4.5025920868 NA
36 -12.8166968 -0.49062180 0.9823048960 0.7123168337 NA
36.1 -14.3989481 -0.25651700 1.4466051416 1.7972493160 NA
36.2 -12.2436943 NA 1.1606752905 1.8262697803 NA
36.3 -15.0104638 -0.30401274 0.8373091576 4.2840119381 NA
36.4 -10.1775457 NA 0.2640591685 4.6194464504 NA
37 -15.2223495 -0.15276529 0.1177313455 2.0018732361 NA
37.1 -14.7526195 -0.30016169 -0.1415483779 3.6656836793 NA
37.2 -19.8168430 0.06809545 0.0054610124 3.9663937816 NA
38 -2.7065118 -0.11218486 0.8078948077 0.9826511063 NA
39 -8.7288138 -0.38072211 0.9876451040 0.6921808305 NA
39.1 -9.2746473 -0.32094428 -0.3431222274 0.9027792048 NA
39.2 -18.2695344 NA -1.7909380751 1.3055654289 NA
39.3 -13.8219083 -0.40173480 -0.1798746191 1.5412842878 NA
39.4 -16.2254704 -0.20041848 -0.1850961689 3.1834997435 NA
39.5 -21.7283648 -0.26027990 0.4544226146 4.1394166439 NA
40 1.8291916 -0.19751956 0.5350190436 1.1330395646 NA
40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046 NA
40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212 NA
40.3 -10.5749790 NA 0.0916687506 4.6762977762 NA
41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254 NA
41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458 NA
41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557 NA
41.3 -15.9788960 NA 0.9885613862 3.3813529415 NA
41.4 -9.6070508 NA -0.3908036794 3.5482964432 NA
42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973 NA
42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298 NA
43 -16.6104361 -0.08864017 0.8965546110 0.5616614548 NA
43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536 NA
43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966 NA
44 -8.1969033 -0.22697597 1.3221409285 0.7662644819 NA
44.1 -19.9270197 NA 0.3416426284 2.6490291790 NA
44.2 -22.6521171 NA 0.5706610068 3.3371910988 NA
44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875 NA
45 -0.5686627 -0.19274788 0.1414983160 0.1957449992 NA
45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536 NA
46 -19.1624789 -0.28547861 1.5391054233 1.3477755385 NA
46.1 -18.4487574 -0.21977836 0.3889107049 2.8565793915 NA
46.2 -15.8222682 NA 0.1248719493 4.4160729996 NA
47 -5.4165074 -0.08597098 0.2014101100 0.6012621359 NA
47.1 -15.0975029 -0.35424828 0.2982973539 2.4097121472 NA
47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035 NA
47.3 -10.6844521 -0.30426315 0.5196802157 3.1829649757 NA
47.4 -18.2214784 NA 0.3702301552 4.6201055450 NA
48 -8.3101471 NA -0.2128602862 2.8607365978 NA
48.1 -18.3854275 NA -0.5337239976 2.9098354396 NA
49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400 NA
50 -10.4579977 -0.19959516 0.3897705981 1.1762060679 NA
51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720 NA
52 -4.4747188 -0.07438732 0.3758235358 2.1266646020 NA
52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993 NA
52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370 NA
52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327 NA
52.4 -8.9036692 -0.44619160 0.0254566848 4.7983659909 NA
52.5 -5.6949642 -0.11011682 0.4155259424 4.9818264414 NA
53 -10.3141887 -0.23278716 0.5675736897 0.4965799209 NA
53.1 -8.2642654 -0.28317264 -0.3154088781 3.5505357443 NA
53.2 -9.1691554 -0.19517481 0.2162315769 4.5790420019 NA
54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841 NA
54.1 -15.7192609 -0.28325504 0.4129127672 1.8812377600 NA
54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352 NA
54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672 NA
54.4 -16.5771751 -0.34609328 0.8587727145 4.0143877396 NA
55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980 NA
55.1 -7.3217494 -0.24235382 0.5835528661 0.7463747414 NA
55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171 NA
55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572 NA
55.4 -14.8599983 NA -0.1202746964 3.2218102901 NA
56 -14.1764282 NA 0.5176377612 1.2231332215 NA
56.1 -12.5343602 -0.30357450 -1.1136932588 2.3573202139 NA
56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292 NA
56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378 NA
56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360 NA
56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043 NA
57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448 NA
57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030 NA
57.2 -6.3940143 NA -0.1159517011 1.1405586067 NA
57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721 NA
58 -12.0245677 -0.29901358 1.6476207996 1.2210099772 NA
58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703 NA
58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890 NA
58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693 NA
58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440 NA
58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824 NA
59 -13.2946560 -0.27255364 0.4766421367 1.9023998594 NA
59.1 -10.0530648 NA 1.0028089765 4.9736620474 NA
60 -19.2209402 NA 0.5231452932 2.8854503250 NA
61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795 NA
61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661 NA
61.2 -1.4713611 NA 1.0229058138 2.5077313243 NA
61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430 NA
61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283 NA
62 -2.8591600 NA -0.3979137604 0.5336771999 NA
62.1 -6.9461986 -0.10504143 0.6830738372 0.6987666548 NA
62.2 -16.7910593 -0.20104997 0.4301745954 3.4584309917 NA
62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371 NA
63 -24.0335535 -0.12036319 0.3345678035 2.8097350930 NA
63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211 NA
64 -20.5963897 NA 0.3949911859 4.1191305732 NA
65 -2.7969169 -0.34450396 1.2000091513 0.7076152589 NA
65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363 NA
65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827 NA
65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943 NA
66 -13.2318328 NA -0.5081302805 3.4943454154 NA
66.1 -1.9090560 NA -0.1447837412 3.7677437009 NA
66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616 NA
67 -25.6073277 NA 1.6716027681 4.1728388879 NA
68 -13.4806759 -0.13605235 0.5691848839 0.1291919907 NA
68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946 NA
68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660 NA
68.3 -12.4977127 NA 0.7443745962 2.9406870750 NA
68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363 NA
69 -14.5290675 -0.32152751 0.0381382683 4.1451198701 NA
70 -15.2122709 -0.28190462 0.8126204217 0.1992557163 NA
70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413 NA
71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386 NA
71.1 -7.5699888 NA -0.1103252087 1.4883817220 NA
71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395 NA
71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723 NA
71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823 NA
72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121 NA
72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806 NA
72.2 -26.3421306 NA -0.4170988856 1.8917567329 NA
72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935 NA
72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700 NA
72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901 NA
73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915 NA
74 -8.2790175 -0.27963171 0.3730017941 1.4626806753 NA
75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874 NA
76 -6.0061671 0.04775562 -0.9888876620 1.8074807397 NA
76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183 NA
76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859 NA
77 0.4772521 -0.19053195 -0.4986802480 0.8459033852 NA
78 -9.4105229 -0.17172929 0.0417330969 0.8231094317 NA
79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521 NA
79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628 NA
79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032 NA
80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186 NA
80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598 NA
80.2 -16.6381566 NA 0.5046816157 2.8025900386 NA
81 0.5547913 -0.18401980 -0.9493950353 0.0101324962 NA
81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494 NA
81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879 NA
81.3 -5.2166381 NA 0.4182528210 3.3456630446 NA
82 -3.0749381 -0.08328168 1.1088801952 1.3791010005 NA
82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622 NA
82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927 NA
83 -12.5872635 -0.34228255 0.5104724530 0.0537394290 NA
83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496 NA
83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362 NA
83.3 -19.2714012 NA -0.0534169155 4.7663642222 NA
84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237 NA
84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659 NA
85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259 NA
85.1 -12.9196365 NA -0.0577615939 1.7394116637 NA
85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194 NA
85.3 -6.3296340 NA -0.0520175929 3.1608762743 NA
85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758 NA
85.5 -13.6714939 NA -0.3740417009 4.5092553482 NA
86 -10.8756412 -0.10854862 0.4293735089 0.8476278360 NA
86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411 NA
86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515 NA
86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925 NA
86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000 NA
86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508 NA
87 -10.7027963 NA 1.0457427869 2.7069531474 NA
87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721 NA
87.2 -14.9616716 NA 0.4396872616 4.5241666853 NA
88 -2.2264493 -0.31563888 -0.0601928334 0.0005892443 NA
88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866 NA
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900 NA
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297 NA
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167 NA
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737 NA
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230 NA
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167 NA
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247 NA
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390 NA
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286 NA
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603 NA
92 -6.4119222 NA 0.4674939332 0.0737052364 NA
93 5.2122168 -0.11939966 0.2677965647 0.2788909199 NA
93.1 3.1211725 NA 1.6424445368 1.0357759963 NA
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099 NA
93.3 2.6223542 NA 1.1222322893 2.8876129608 NA
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002 NA
94 -6.9602492 NA -0.2904211940 0.8488043118 NA
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425 NA
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884 NA
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448 NA
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935 NA
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016 NA
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048 NA
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457 NA
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732 NA
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477 NA
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720 NA
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425 NA
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691 NA
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488 NA
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598 NA
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731 NA
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643 NA
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438 NA
98.1 -2.6515128 NA -0.9042716241 0.4309578973 NA
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906 NA
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8e$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8e$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c1 0.1798099 0.6117459
$m8e$mu_reg_norm
[1] 0
$m8e$tau_reg_norm
[1] 1e-04
$m8e$shape_tau_norm
[1] 0.01
$m8e$rate_tau_norm
[1] 0.01
$m8e$mu_reg_binom
[1] 0
$m8e$tau_reg_binom
[1] 1e-04
$m8e$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8e$shape_diag_RinvD
[1] "0.01"
$m8e$rate_diag_RinvD
[1] "0.001"
$m8e$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8e$KinvD_y_id
id
4
$m8f
$m8f$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8f$M_lvlone
y c2 c1 time B21:c1
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 NA
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847 NA
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082 NA
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196 NA
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339 NA
5.3 -9.2366906 NA 0.1740288049 2.8119940578 NA
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884 NA
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673 NA
7.1 -18.3267285 NA 0.4242971219 3.7008403614 NA
7.2 -12.5138426 NA 0.0777182491 4.7716691741 NA
8 -1.6305331 NA -0.5791408712 1.1246398522 NA
8.1 -9.6520453 NA 0.3128604232 1.8027009873 NA
8.2 -1.5278462 NA 0.6258446356 1.8175825174 NA
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003 NA
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307 NA
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704 NA
9 -0.1634429 NA -0.1757592269 0.9607803822 NA
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383 NA
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501 NA
10 -6.4172202 NA -0.5598409704 2.2975509102 NA
10.1 -11.4834569 NA -0.2318340451 4.1734118364 NA
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905 NA
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680 NA
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263 NA
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969 NA
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661 NA
12 -15.2006287 NA -0.2433996286 0.9591987054 NA
13 0.8058816 NA 0.8799673116 0.0619085738 NA
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502 NA
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007 NA
14.1 -10.0965208 NA -0.1094019046 4.4710561272 NA
14.2 -16.6452027 NA 0.1518967560 4.6359198843 NA
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599 NA
15 -8.9424594 NA 0.3464447888 0.5402063532 NA
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816 NA
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688 NA
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615 NA
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869 NA
16.1 -9.9308357 -0.07599066 1.0652558311 2.5675726333 NA
16.2 -6.9626923 -0.32385667 0.6971872493 2.6524101500 NA
16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690 NA
16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291 NA
16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889 NA
17 -10.5474144 NA 0.6048676222 1.5925356350 NA
17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998 NA
17.2 -16.5386939 NA 1.2617499032 3.0256489082 NA
17.3 -20.0004774 NA -0.3913230895 3.3329089405 NA
17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985 NA
18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302 NA
19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376 NA
19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913 NA
19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538 NA
19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973 NA
20 -12.9059229 -0.22087628 0.6838223064 1.7170160066 NA
20.1 -16.8191201 NA 0.3278571109 1.7562902288 NA
20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566 NA
20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867 NA
20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287 NA
20.5 -13.3504189 -0.24338208 -0.4535207652 4.1730977828 NA
21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839 NA
21.1 -11.9335526 -0.12424941 -0.4069674045 2.9571191233 NA
21.2 -12.7064929 -0.27672716 1.0650265940 3.7887385779 NA
22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232 NA
22.1 -12.7745451 -0.15996535 0.9601388170 3.1626627257 NA
23 -3.5146508 -0.18236682 0.5556634840 1.5414533857 NA
23.1 -4.6724048 -0.20823302 1.4407865964 2.3369736120 NA
24 -2.5619821 -0.29026416 0.3856376411 2.8283136466 NA
25 -6.2944970 -0.36139273 0.3564400705 0.5381704110 NA
25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331 NA
25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922 NA
25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392 NA
25.4 -9.0288933 NA 0.4466012931 4.0782226040 NA
25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873 NA
26 -19.7340685 -0.16846716 0.5341531449 0.2412706357 NA
26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676 NA
26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887 NA
26.3 -24.6265576 NA 0.5948516018 4.1822362854 NA
27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879 NA
27.1 -11.1488468 -0.17622638 -0.4835141229 4.2451434687 NA
28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344 NA
28.1 -8.2030122 -0.27003852 0.5063671955 2.7943964268 NA
28.2 -26.4317815 -0.07235801 -0.2067413142 4.2108539480 NA
28.3 -18.5016071 -0.13462982 0.1196789973 4.4705521734 NA
29 -5.8551395 -0.32432030 0.1392699487 1.1898884235 NA
29.1 -2.0209442 -0.27034171 0.7960234776 1.7624059319 NA
29.2 -5.6368080 -0.10197448 1.0398214352 2.0210406382 NA
29.3 -3.8110961 -0.27606945 0.0813246429 3.4078777023 NA
30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488 NA
30.1 -17.0170140 -0.11511035 1.3635850954 3.5938334477 NA
30.2 -25.4236089 -0.16215882 0.7354171050 3.6138710892 NA
31 -17.0783921 0.05707733 0.3708398217 4.3988140998 NA
32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007 NA
32.1 -19.4317212 -0.14270013 1.2507771489 2.9128167813 NA
32.2 -19.4738978 -0.20530798 0.1142915519 2.9676558380 NA
32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547 NA
33 2.0838099 -0.15252819 0.1774293842 0.0093385763 NA
33.1 -13.3172274 NA 0.6159606291 3.4591242753 NA
34 -10.0296691 -0.30378735 0.8590979166 1.4998774312 NA
34.1 -25.9426553 -0.11982431 0.0546216775 3.8242761395 NA
34.2 -18.5688138 -0.24278671 -0.0897224473 3.9072251692 NA
34.3 -15.4173859 -0.19971833 0.4163395571 3.9582124643 NA
35 -14.3958113 NA -1.4693520528 1.3294299203 NA
35.1 -12.9457541 -0.24165780 -0.3031734330 1.5276966314 NA
35.2 -16.1380691 NA -0.6045512101 4.5025920868 NA
36 -12.8166968 -0.49062180 0.9823048960 0.7123168337 NA
36.1 -14.3989481 -0.25651700 1.4466051416 1.7972493160 NA
36.2 -12.2436943 NA 1.1606752905 1.8262697803 NA
36.3 -15.0104638 -0.30401274 0.8373091576 4.2840119381 NA
36.4 -10.1775457 NA 0.2640591685 4.6194464504 NA
37 -15.2223495 -0.15276529 0.1177313455 2.0018732361 NA
37.1 -14.7526195 -0.30016169 -0.1415483779 3.6656836793 NA
37.2 -19.8168430 0.06809545 0.0054610124 3.9663937816 NA
38 -2.7065118 -0.11218486 0.8078948077 0.9826511063 NA
39 -8.7288138 -0.38072211 0.9876451040 0.6921808305 NA
39.1 -9.2746473 -0.32094428 -0.3431222274 0.9027792048 NA
39.2 -18.2695344 NA -1.7909380751 1.3055654289 NA
39.3 -13.8219083 -0.40173480 -0.1798746191 1.5412842878 NA
39.4 -16.2254704 -0.20041848 -0.1850961689 3.1834997435 NA
39.5 -21.7283648 -0.26027990 0.4544226146 4.1394166439 NA
40 1.8291916 -0.19751956 0.5350190436 1.1330395646 NA
40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046 NA
40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212 NA
40.3 -10.5749790 NA 0.0916687506 4.6762977762 NA
41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254 NA
41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458 NA
41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557 NA
41.3 -15.9788960 NA 0.9885613862 3.3813529415 NA
41.4 -9.6070508 NA -0.3908036794 3.5482964432 NA
42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973 NA
42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298 NA
43 -16.6104361 -0.08864017 0.8965546110 0.5616614548 NA
43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536 NA
43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966 NA
44 -8.1969033 -0.22697597 1.3221409285 0.7662644819 NA
44.1 -19.9270197 NA 0.3416426284 2.6490291790 NA
44.2 -22.6521171 NA 0.5706610068 3.3371910988 NA
44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875 NA
45 -0.5686627 -0.19274788 0.1414983160 0.1957449992 NA
45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536 NA
46 -19.1624789 -0.28547861 1.5391054233 1.3477755385 NA
46.1 -18.4487574 -0.21977836 0.3889107049 2.8565793915 NA
46.2 -15.8222682 NA 0.1248719493 4.4160729996 NA
47 -5.4165074 -0.08597098 0.2014101100 0.6012621359 NA
47.1 -15.0975029 -0.35424828 0.2982973539 2.4097121472 NA
47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035 NA
47.3 -10.6844521 -0.30426315 0.5196802157 3.1829649757 NA
47.4 -18.2214784 NA 0.3702301552 4.6201055450 NA
48 -8.3101471 NA -0.2128602862 2.8607365978 NA
48.1 -18.3854275 NA -0.5337239976 2.9098354396 NA
49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400 NA
50 -10.4579977 -0.19959516 0.3897705981 1.1762060679 NA
51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720 NA
52 -4.4747188 -0.07438732 0.3758235358 2.1266646020 NA
52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993 NA
52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370 NA
52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327 NA
52.4 -8.9036692 -0.44619160 0.0254566848 4.7983659909 NA
52.5 -5.6949642 -0.11011682 0.4155259424 4.9818264414 NA
53 -10.3141887 -0.23278716 0.5675736897 0.4965799209 NA
53.1 -8.2642654 -0.28317264 -0.3154088781 3.5505357443 NA
53.2 -9.1691554 -0.19517481 0.2162315769 4.5790420019 NA
54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841 NA
54.1 -15.7192609 -0.28325504 0.4129127672 1.8812377600 NA
54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352 NA
54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672 NA
54.4 -16.5771751 -0.34609328 0.8587727145 4.0143877396 NA
55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980 NA
55.1 -7.3217494 -0.24235382 0.5835528661 0.7463747414 NA
55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171 NA
55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572 NA
55.4 -14.8599983 NA -0.1202746964 3.2218102901 NA
56 -14.1764282 NA 0.5176377612 1.2231332215 NA
56.1 -12.5343602 -0.30357450 -1.1136932588 2.3573202139 NA
56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292 NA
56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378 NA
56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360 NA
56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043 NA
57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448 NA
57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030 NA
57.2 -6.3940143 NA -0.1159517011 1.1405586067 NA
57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721 NA
58 -12.0245677 -0.29901358 1.6476207996 1.2210099772 NA
58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703 NA
58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890 NA
58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693 NA
58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440 NA
58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824 NA
59 -13.2946560 -0.27255364 0.4766421367 1.9023998594 NA
59.1 -10.0530648 NA 1.0028089765 4.9736620474 NA
60 -19.2209402 NA 0.5231452932 2.8854503250 NA
61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795 NA
61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661 NA
61.2 -1.4713611 NA 1.0229058138 2.5077313243 NA
61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430 NA
61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283 NA
62 -2.8591600 NA -0.3979137604 0.5336771999 NA
62.1 -6.9461986 -0.10504143 0.6830738372 0.6987666548 NA
62.2 -16.7910593 -0.20104997 0.4301745954 3.4584309917 NA
62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371 NA
63 -24.0335535 -0.12036319 0.3345678035 2.8097350930 NA
63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211 NA
64 -20.5963897 NA 0.3949911859 4.1191305732 NA
65 -2.7969169 -0.34450396 1.2000091513 0.7076152589 NA
65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363 NA
65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827 NA
65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943 NA
66 -13.2318328 NA -0.5081302805 3.4943454154 NA
66.1 -1.9090560 NA -0.1447837412 3.7677437009 NA
66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616 NA
67 -25.6073277 NA 1.6716027681 4.1728388879 NA
68 -13.4806759 -0.13605235 0.5691848839 0.1291919907 NA
68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946 NA
68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660 NA
68.3 -12.4977127 NA 0.7443745962 2.9406870750 NA
68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363 NA
69 -14.5290675 -0.32152751 0.0381382683 4.1451198701 NA
70 -15.2122709 -0.28190462 0.8126204217 0.1992557163 NA
70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413 NA
71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386 NA
71.1 -7.5699888 NA -0.1103252087 1.4883817220 NA
71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395 NA
71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723 NA
71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823 NA
72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121 NA
72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806 NA
72.2 -26.3421306 NA -0.4170988856 1.8917567329 NA
72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935 NA
72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700 NA
72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901 NA
73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915 NA
74 -8.2790175 -0.27963171 0.3730017941 1.4626806753 NA
75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874 NA
76 -6.0061671 0.04775562 -0.9888876620 1.8074807397 NA
76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183 NA
76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859 NA
77 0.4772521 -0.19053195 -0.4986802480 0.8459033852 NA
78 -9.4105229 -0.17172929 0.0417330969 0.8231094317 NA
79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521 NA
79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628 NA
79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032 NA
80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186 NA
80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598 NA
80.2 -16.6381566 NA 0.5046816157 2.8025900386 NA
81 0.5547913 -0.18401980 -0.9493950353 0.0101324962 NA
81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494 NA
81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879 NA
81.3 -5.2166381 NA 0.4182528210 3.3456630446 NA
82 -3.0749381 -0.08328168 1.1088801952 1.3791010005 NA
82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622 NA
82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927 NA
83 -12.5872635 -0.34228255 0.5104724530 0.0537394290 NA
83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496 NA
83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362 NA
83.3 -19.2714012 NA -0.0534169155 4.7663642222 NA
84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237 NA
84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659 NA
85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259 NA
85.1 -12.9196365 NA -0.0577615939 1.7394116637 NA
85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194 NA
85.3 -6.3296340 NA -0.0520175929 3.1608762743 NA
85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758 NA
85.5 -13.6714939 NA -0.3740417009 4.5092553482 NA
86 -10.8756412 -0.10854862 0.4293735089 0.8476278360 NA
86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411 NA
86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515 NA
86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925 NA
86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000 NA
86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508 NA
87 -10.7027963 NA 1.0457427869 2.7069531474 NA
87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721 NA
87.2 -14.9616716 NA 0.4396872616 4.5241666853 NA
88 -2.2264493 -0.31563888 -0.0601928334 0.0005892443 NA
88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866 NA
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900 NA
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297 NA
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167 NA
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737 NA
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230 NA
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167 NA
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247 NA
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390 NA
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286 NA
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603 NA
92 -6.4119222 NA 0.4674939332 0.0737052364 NA
93 5.2122168 -0.11939966 0.2677965647 0.2788909199 NA
93.1 3.1211725 NA 1.6424445368 1.0357759963 NA
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099 NA
93.3 2.6223542 NA 1.1222322893 2.8876129608 NA
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002 NA
94 -6.9602492 NA -0.2904211940 0.8488043118 NA
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425 NA
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884 NA
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448 NA
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935 NA
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016 NA
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048 NA
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457 NA
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732 NA
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477 NA
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720 NA
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425 NA
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691 NA
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488 NA
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598 NA
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731 NA
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643 NA
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438 NA
98.1 -2.6515128 NA -0.9042716241 0.4309578973 NA
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906 NA
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8f$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8f$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c1 0.1798099 0.6117459
$m8f$mu_reg_norm
[1] 0
$m8f$tau_reg_norm
[1] 1e-04
$m8f$shape_tau_norm
[1] 0.01
$m8f$rate_tau_norm
[1] 0.01
$m8f$mu_reg_binom
[1] 0
$m8f$tau_reg_binom
[1] 1e-04
$m8f$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8f$shape_diag_RinvD
[1] "0.01"
$m8f$rate_diag_RinvD
[1] "0.001"
$m8f$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8f$KinvD_y_id
id
4
$m8g
$m8g$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8g$M_lvlone
y c2 c1 time B21:c1
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 NA
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847 NA
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082 NA
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196 NA
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339 NA
5.3 -9.2366906 NA 0.1740288049 2.8119940578 NA
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884 NA
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673 NA
7.1 -18.3267285 NA 0.4242971219 3.7008403614 NA
7.2 -12.5138426 NA 0.0777182491 4.7716691741 NA
8 -1.6305331 NA -0.5791408712 1.1246398522 NA
8.1 -9.6520453 NA 0.3128604232 1.8027009873 NA
8.2 -1.5278462 NA 0.6258446356 1.8175825174 NA
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003 NA
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307 NA
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704 NA
9 -0.1634429 NA -0.1757592269 0.9607803822 NA
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383 NA
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501 NA
10 -6.4172202 NA -0.5598409704 2.2975509102 NA
10.1 -11.4834569 NA -0.2318340451 4.1734118364 NA
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905 NA
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680 NA
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263 NA
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969 NA
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661 NA
12 -15.2006287 NA -0.2433996286 0.9591987054 NA
13 0.8058816 NA 0.8799673116 0.0619085738 NA
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502 NA
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007 NA
14.1 -10.0965208 NA -0.1094019046 4.4710561272 NA
14.2 -16.6452027 NA 0.1518967560 4.6359198843 NA
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599 NA
15 -8.9424594 NA 0.3464447888 0.5402063532 NA
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816 NA
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688 NA
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615 NA
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869 NA
16.1 -9.9308357 -0.07599066 1.0652558311 2.5675726333 NA
16.2 -6.9626923 -0.32385667 0.6971872493 2.6524101500 NA
16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690 NA
16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291 NA
16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889 NA
17 -10.5474144 NA 0.6048676222 1.5925356350 NA
17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998 NA
17.2 -16.5386939 NA 1.2617499032 3.0256489082 NA
17.3 -20.0004774 NA -0.3913230895 3.3329089405 NA
17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985 NA
18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302 NA
19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376 NA
19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913 NA
19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538 NA
19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973 NA
20 -12.9059229 -0.22087628 0.6838223064 1.7170160066 NA
20.1 -16.8191201 NA 0.3278571109 1.7562902288 NA
20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566 NA
20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867 NA
20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287 NA
20.5 -13.3504189 -0.24338208 -0.4535207652 4.1730977828 NA
21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839 NA
21.1 -11.9335526 -0.12424941 -0.4069674045 2.9571191233 NA
21.2 -12.7064929 -0.27672716 1.0650265940 3.7887385779 NA
22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232 NA
22.1 -12.7745451 -0.15996535 0.9601388170 3.1626627257 NA
23 -3.5146508 -0.18236682 0.5556634840 1.5414533857 NA
23.1 -4.6724048 -0.20823302 1.4407865964 2.3369736120 NA
24 -2.5619821 -0.29026416 0.3856376411 2.8283136466 NA
25 -6.2944970 -0.36139273 0.3564400705 0.5381704110 NA
25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331 NA
25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922 NA
25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392 NA
25.4 -9.0288933 NA 0.4466012931 4.0782226040 NA
25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873 NA
26 -19.7340685 -0.16846716 0.5341531449 0.2412706357 NA
26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676 NA
26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887 NA
26.3 -24.6265576 NA 0.5948516018 4.1822362854 NA
27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879 NA
27.1 -11.1488468 -0.17622638 -0.4835141229 4.2451434687 NA
28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344 NA
28.1 -8.2030122 -0.27003852 0.5063671955 2.7943964268 NA
28.2 -26.4317815 -0.07235801 -0.2067413142 4.2108539480 NA
28.3 -18.5016071 -0.13462982 0.1196789973 4.4705521734 NA
29 -5.8551395 -0.32432030 0.1392699487 1.1898884235 NA
29.1 -2.0209442 -0.27034171 0.7960234776 1.7624059319 NA
29.2 -5.6368080 -0.10197448 1.0398214352 2.0210406382 NA
29.3 -3.8110961 -0.27606945 0.0813246429 3.4078777023 NA
30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488 NA
30.1 -17.0170140 -0.11511035 1.3635850954 3.5938334477 NA
30.2 -25.4236089 -0.16215882 0.7354171050 3.6138710892 NA
31 -17.0783921 0.05707733 0.3708398217 4.3988140998 NA
32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007 NA
32.1 -19.4317212 -0.14270013 1.2507771489 2.9128167813 NA
32.2 -19.4738978 -0.20530798 0.1142915519 2.9676558380 NA
32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547 NA
33 2.0838099 -0.15252819 0.1774293842 0.0093385763 NA
33.1 -13.3172274 NA 0.6159606291 3.4591242753 NA
34 -10.0296691 -0.30378735 0.8590979166 1.4998774312 NA
34.1 -25.9426553 -0.11982431 0.0546216775 3.8242761395 NA
34.2 -18.5688138 -0.24278671 -0.0897224473 3.9072251692 NA
34.3 -15.4173859 -0.19971833 0.4163395571 3.9582124643 NA
35 -14.3958113 NA -1.4693520528 1.3294299203 NA
35.1 -12.9457541 -0.24165780 -0.3031734330 1.5276966314 NA
35.2 -16.1380691 NA -0.6045512101 4.5025920868 NA
36 -12.8166968 -0.49062180 0.9823048960 0.7123168337 NA
36.1 -14.3989481 -0.25651700 1.4466051416 1.7972493160 NA
36.2 -12.2436943 NA 1.1606752905 1.8262697803 NA
36.3 -15.0104638 -0.30401274 0.8373091576 4.2840119381 NA
36.4 -10.1775457 NA 0.2640591685 4.6194464504 NA
37 -15.2223495 -0.15276529 0.1177313455 2.0018732361 NA
37.1 -14.7526195 -0.30016169 -0.1415483779 3.6656836793 NA
37.2 -19.8168430 0.06809545 0.0054610124 3.9663937816 NA
38 -2.7065118 -0.11218486 0.8078948077 0.9826511063 NA
39 -8.7288138 -0.38072211 0.9876451040 0.6921808305 NA
39.1 -9.2746473 -0.32094428 -0.3431222274 0.9027792048 NA
39.2 -18.2695344 NA -1.7909380751 1.3055654289 NA
39.3 -13.8219083 -0.40173480 -0.1798746191 1.5412842878 NA
39.4 -16.2254704 -0.20041848 -0.1850961689 3.1834997435 NA
39.5 -21.7283648 -0.26027990 0.4544226146 4.1394166439 NA
40 1.8291916 -0.19751956 0.5350190436 1.1330395646 NA
40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046 NA
40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212 NA
40.3 -10.5749790 NA 0.0916687506 4.6762977762 NA
41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254 NA
41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458 NA
41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557 NA
41.3 -15.9788960 NA 0.9885613862 3.3813529415 NA
41.4 -9.6070508 NA -0.3908036794 3.5482964432 NA
42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973 NA
42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298 NA
43 -16.6104361 -0.08864017 0.8965546110 0.5616614548 NA
43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536 NA
43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966 NA
44 -8.1969033 -0.22697597 1.3221409285 0.7662644819 NA
44.1 -19.9270197 NA 0.3416426284 2.6490291790 NA
44.2 -22.6521171 NA 0.5706610068 3.3371910988 NA
44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875 NA
45 -0.5686627 -0.19274788 0.1414983160 0.1957449992 NA
45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536 NA
46 -19.1624789 -0.28547861 1.5391054233 1.3477755385 NA
46.1 -18.4487574 -0.21977836 0.3889107049 2.8565793915 NA
46.2 -15.8222682 NA 0.1248719493 4.4160729996 NA
47 -5.4165074 -0.08597098 0.2014101100 0.6012621359 NA
47.1 -15.0975029 -0.35424828 0.2982973539 2.4097121472 NA
47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035 NA
47.3 -10.6844521 -0.30426315 0.5196802157 3.1829649757 NA
47.4 -18.2214784 NA 0.3702301552 4.6201055450 NA
48 -8.3101471 NA -0.2128602862 2.8607365978 NA
48.1 -18.3854275 NA -0.5337239976 2.9098354396 NA
49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400 NA
50 -10.4579977 -0.19959516 0.3897705981 1.1762060679 NA
51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720 NA
52 -4.4747188 -0.07438732 0.3758235358 2.1266646020 NA
52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993 NA
52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370 NA
52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327 NA
52.4 -8.9036692 -0.44619160 0.0254566848 4.7983659909 NA
52.5 -5.6949642 -0.11011682 0.4155259424 4.9818264414 NA
53 -10.3141887 -0.23278716 0.5675736897 0.4965799209 NA
53.1 -8.2642654 -0.28317264 -0.3154088781 3.5505357443 NA
53.2 -9.1691554 -0.19517481 0.2162315769 4.5790420019 NA
54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841 NA
54.1 -15.7192609 -0.28325504 0.4129127672 1.8812377600 NA
54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352 NA
54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672 NA
54.4 -16.5771751 -0.34609328 0.8587727145 4.0143877396 NA
55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980 NA
55.1 -7.3217494 -0.24235382 0.5835528661 0.7463747414 NA
55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171 NA
55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572 NA
55.4 -14.8599983 NA -0.1202746964 3.2218102901 NA
56 -14.1764282 NA 0.5176377612 1.2231332215 NA
56.1 -12.5343602 -0.30357450 -1.1136932588 2.3573202139 NA
56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292 NA
56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378 NA
56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360 NA
56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043 NA
57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448 NA
57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030 NA
57.2 -6.3940143 NA -0.1159517011 1.1405586067 NA
57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721 NA
58 -12.0245677 -0.29901358 1.6476207996 1.2210099772 NA
58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703 NA
58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890 NA
58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693 NA
58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440 NA
58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824 NA
59 -13.2946560 -0.27255364 0.4766421367 1.9023998594 NA
59.1 -10.0530648 NA 1.0028089765 4.9736620474 NA
60 -19.2209402 NA 0.5231452932 2.8854503250 NA
61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795 NA
61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661 NA
61.2 -1.4713611 NA 1.0229058138 2.5077313243 NA
61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430 NA
61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283 NA
62 -2.8591600 NA -0.3979137604 0.5336771999 NA
62.1 -6.9461986 -0.10504143 0.6830738372 0.6987666548 NA
62.2 -16.7910593 -0.20104997 0.4301745954 3.4584309917 NA
62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371 NA
63 -24.0335535 -0.12036319 0.3345678035 2.8097350930 NA
63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211 NA
64 -20.5963897 NA 0.3949911859 4.1191305732 NA
65 -2.7969169 -0.34450396 1.2000091513 0.7076152589 NA
65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363 NA
65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827 NA
65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943 NA
66 -13.2318328 NA -0.5081302805 3.4943454154 NA
66.1 -1.9090560 NA -0.1447837412 3.7677437009 NA
66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616 NA
67 -25.6073277 NA 1.6716027681 4.1728388879 NA
68 -13.4806759 -0.13605235 0.5691848839 0.1291919907 NA
68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946 NA
68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660 NA
68.3 -12.4977127 NA 0.7443745962 2.9406870750 NA
68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363 NA
69 -14.5290675 -0.32152751 0.0381382683 4.1451198701 NA
70 -15.2122709 -0.28190462 0.8126204217 0.1992557163 NA
70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413 NA
71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386 NA
71.1 -7.5699888 NA -0.1103252087 1.4883817220 NA
71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395 NA
71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723 NA
71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823 NA
72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121 NA
72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806 NA
72.2 -26.3421306 NA -0.4170988856 1.8917567329 NA
72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935 NA
72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700 NA
72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901 NA
73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915 NA
74 -8.2790175 -0.27963171 0.3730017941 1.4626806753 NA
75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874 NA
76 -6.0061671 0.04775562 -0.9888876620 1.8074807397 NA
76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183 NA
76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859 NA
77 0.4772521 -0.19053195 -0.4986802480 0.8459033852 NA
78 -9.4105229 -0.17172929 0.0417330969 0.8231094317 NA
79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521 NA
79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628 NA
79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032 NA
80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186 NA
80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598 NA
80.2 -16.6381566 NA 0.5046816157 2.8025900386 NA
81 0.5547913 -0.18401980 -0.9493950353 0.0101324962 NA
81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494 NA
81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879 NA
81.3 -5.2166381 NA 0.4182528210 3.3456630446 NA
82 -3.0749381 -0.08328168 1.1088801952 1.3791010005 NA
82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622 NA
82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927 NA
83 -12.5872635 -0.34228255 0.5104724530 0.0537394290 NA
83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496 NA
83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362 NA
83.3 -19.2714012 NA -0.0534169155 4.7663642222 NA
84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237 NA
84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659 NA
85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259 NA
85.1 -12.9196365 NA -0.0577615939 1.7394116637 NA
85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194 NA
85.3 -6.3296340 NA -0.0520175929 3.1608762743 NA
85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758 NA
85.5 -13.6714939 NA -0.3740417009 4.5092553482 NA
86 -10.8756412 -0.10854862 0.4293735089 0.8476278360 NA
86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411 NA
86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515 NA
86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925 NA
86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000 NA
86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508 NA
87 -10.7027963 NA 1.0457427869 2.7069531474 NA
87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721 NA
87.2 -14.9616716 NA 0.4396872616 4.5241666853 NA
88 -2.2264493 -0.31563888 -0.0601928334 0.0005892443 NA
88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866 NA
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900 NA
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297 NA
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167 NA
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737 NA
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230 NA
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167 NA
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247 NA
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390 NA
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286 NA
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603 NA
92 -6.4119222 NA 0.4674939332 0.0737052364 NA
93 5.2122168 -0.11939966 0.2677965647 0.2788909199 NA
93.1 3.1211725 NA 1.6424445368 1.0357759963 NA
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099 NA
93.3 2.6223542 NA 1.1222322893 2.8876129608 NA
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002 NA
94 -6.9602492 NA -0.2904211940 0.8488043118 NA
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425 NA
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884 NA
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448 NA
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935 NA
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016 NA
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048 NA
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457 NA
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732 NA
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477 NA
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720 NA
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425 NA
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691 NA
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488 NA
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598 NA
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731 NA
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643 NA
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438 NA
98.1 -2.6515128 NA -0.9042716241 0.4309578973 NA
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906 NA
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8g$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8g$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c1 0.1798099 0.6117459
$m8g$mu_reg_norm
[1] 0
$m8g$tau_reg_norm
[1] 1e-04
$m8g$shape_tau_norm
[1] 0.01
$m8g$rate_tau_norm
[1] 0.01
$m8g$mu_reg_binom
[1] 0
$m8g$tau_reg_binom
[1] 1e-04
$m8g$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8g$shape_diag_RinvD
[1] "0.01"
$m8g$rate_diag_RinvD
[1] "0.001"
$m8g$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8g$KinvD_y_id
id
4
$m8h
$m8h$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8h$M_lvlone
y c2 c1 time B21:c2
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 NA
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847 NA
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082 NA
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196 NA
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339 NA
5.3 -9.2366906 NA 0.1740288049 2.8119940578 NA
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884 NA
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673 NA
7.1 -18.3267285 NA 0.4242971219 3.7008403614 NA
7.2 -12.5138426 NA 0.0777182491 4.7716691741 NA
8 -1.6305331 NA -0.5791408712 1.1246398522 NA
8.1 -9.6520453 NA 0.3128604232 1.8027009873 NA
8.2 -1.5278462 NA 0.6258446356 1.8175825174 NA
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003 NA
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307 NA
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704 NA
9 -0.1634429 NA -0.1757592269 0.9607803822 NA
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383 NA
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501 NA
10 -6.4172202 NA -0.5598409704 2.2975509102 NA
10.1 -11.4834569 NA -0.2318340451 4.1734118364 NA
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905 NA
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680 NA
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263 NA
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969 NA
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661 NA
12 -15.2006287 NA -0.2433996286 0.9591987054 NA
13 0.8058816 NA 0.8799673116 0.0619085738 NA
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502 NA
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007 NA
14.1 -10.0965208 NA -0.1094019046 4.4710561272 NA
14.2 -16.6452027 NA 0.1518967560 4.6359198843 NA
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599 NA
15 -8.9424594 NA 0.3464447888 0.5402063532 NA
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816 NA
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688 NA
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615 NA
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869 NA
16.1 -9.9308357 -0.07599066 1.0652558311 2.5675726333 NA
16.2 -6.9626923 -0.32385667 0.6971872493 2.6524101500 NA
16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690 NA
16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291 NA
16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889 NA
17 -10.5474144 NA 0.6048676222 1.5925356350 NA
17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998 NA
17.2 -16.5386939 NA 1.2617499032 3.0256489082 NA
17.3 -20.0004774 NA -0.3913230895 3.3329089405 NA
17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985 NA
18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302 NA
19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376 NA
19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913 NA
19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538 NA
19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973 NA
20 -12.9059229 -0.22087628 0.6838223064 1.7170160066 NA
20.1 -16.8191201 NA 0.3278571109 1.7562902288 NA
20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566 NA
20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867 NA
20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287 NA
20.5 -13.3504189 -0.24338208 -0.4535207652 4.1730977828 NA
21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839 NA
21.1 -11.9335526 -0.12424941 -0.4069674045 2.9571191233 NA
21.2 -12.7064929 -0.27672716 1.0650265940 3.7887385779 NA
22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232 NA
22.1 -12.7745451 -0.15996535 0.9601388170 3.1626627257 NA
23 -3.5146508 -0.18236682 0.5556634840 1.5414533857 NA
23.1 -4.6724048 -0.20823302 1.4407865964 2.3369736120 NA
24 -2.5619821 -0.29026416 0.3856376411 2.8283136466 NA
25 -6.2944970 -0.36139273 0.3564400705 0.5381704110 NA
25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331 NA
25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922 NA
25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392 NA
25.4 -9.0288933 NA 0.4466012931 4.0782226040 NA
25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873 NA
26 -19.7340685 -0.16846716 0.5341531449 0.2412706357 NA
26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676 NA
26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887 NA
26.3 -24.6265576 NA 0.5948516018 4.1822362854 NA
27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879 NA
27.1 -11.1488468 -0.17622638 -0.4835141229 4.2451434687 NA
28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344 NA
28.1 -8.2030122 -0.27003852 0.5063671955 2.7943964268 NA
28.2 -26.4317815 -0.07235801 -0.2067413142 4.2108539480 NA
28.3 -18.5016071 -0.13462982 0.1196789973 4.4705521734 NA
29 -5.8551395 -0.32432030 0.1392699487 1.1898884235 NA
29.1 -2.0209442 -0.27034171 0.7960234776 1.7624059319 NA
29.2 -5.6368080 -0.10197448 1.0398214352 2.0210406382 NA
29.3 -3.8110961 -0.27606945 0.0813246429 3.4078777023 NA
30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488 NA
30.1 -17.0170140 -0.11511035 1.3635850954 3.5938334477 NA
30.2 -25.4236089 -0.16215882 0.7354171050 3.6138710892 NA
31 -17.0783921 0.05707733 0.3708398217 4.3988140998 NA
32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007 NA
32.1 -19.4317212 -0.14270013 1.2507771489 2.9128167813 NA
32.2 -19.4738978 -0.20530798 0.1142915519 2.9676558380 NA
32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547 NA
33 2.0838099 -0.15252819 0.1774293842 0.0093385763 NA
33.1 -13.3172274 NA 0.6159606291 3.4591242753 NA
34 -10.0296691 -0.30378735 0.8590979166 1.4998774312 NA
34.1 -25.9426553 -0.11982431 0.0546216775 3.8242761395 NA
34.2 -18.5688138 -0.24278671 -0.0897224473 3.9072251692 NA
34.3 -15.4173859 -0.19971833 0.4163395571 3.9582124643 NA
35 -14.3958113 NA -1.4693520528 1.3294299203 NA
35.1 -12.9457541 -0.24165780 -0.3031734330 1.5276966314 NA
35.2 -16.1380691 NA -0.6045512101 4.5025920868 NA
36 -12.8166968 -0.49062180 0.9823048960 0.7123168337 NA
36.1 -14.3989481 -0.25651700 1.4466051416 1.7972493160 NA
36.2 -12.2436943 NA 1.1606752905 1.8262697803 NA
36.3 -15.0104638 -0.30401274 0.8373091576 4.2840119381 NA
36.4 -10.1775457 NA 0.2640591685 4.6194464504 NA
37 -15.2223495 -0.15276529 0.1177313455 2.0018732361 NA
37.1 -14.7526195 -0.30016169 -0.1415483779 3.6656836793 NA
37.2 -19.8168430 0.06809545 0.0054610124 3.9663937816 NA
38 -2.7065118 -0.11218486 0.8078948077 0.9826511063 NA
39 -8.7288138 -0.38072211 0.9876451040 0.6921808305 NA
39.1 -9.2746473 -0.32094428 -0.3431222274 0.9027792048 NA
39.2 -18.2695344 NA -1.7909380751 1.3055654289 NA
39.3 -13.8219083 -0.40173480 -0.1798746191 1.5412842878 NA
39.4 -16.2254704 -0.20041848 -0.1850961689 3.1834997435 NA
39.5 -21.7283648 -0.26027990 0.4544226146 4.1394166439 NA
40 1.8291916 -0.19751956 0.5350190436 1.1330395646 NA
40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046 NA
40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212 NA
40.3 -10.5749790 NA 0.0916687506 4.6762977762 NA
41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254 NA
41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458 NA
41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557 NA
41.3 -15.9788960 NA 0.9885613862 3.3813529415 NA
41.4 -9.6070508 NA -0.3908036794 3.5482964432 NA
42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973 NA
42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298 NA
43 -16.6104361 -0.08864017 0.8965546110 0.5616614548 NA
43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536 NA
43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966 NA
44 -8.1969033 -0.22697597 1.3221409285 0.7662644819 NA
44.1 -19.9270197 NA 0.3416426284 2.6490291790 NA
44.2 -22.6521171 NA 0.5706610068 3.3371910988 NA
44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875 NA
45 -0.5686627 -0.19274788 0.1414983160 0.1957449992 NA
45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536 NA
46 -19.1624789 -0.28547861 1.5391054233 1.3477755385 NA
46.1 -18.4487574 -0.21977836 0.3889107049 2.8565793915 NA
46.2 -15.8222682 NA 0.1248719493 4.4160729996 NA
47 -5.4165074 -0.08597098 0.2014101100 0.6012621359 NA
47.1 -15.0975029 -0.35424828 0.2982973539 2.4097121472 NA
47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035 NA
47.3 -10.6844521 -0.30426315 0.5196802157 3.1829649757 NA
47.4 -18.2214784 NA 0.3702301552 4.6201055450 NA
48 -8.3101471 NA -0.2128602862 2.8607365978 NA
48.1 -18.3854275 NA -0.5337239976 2.9098354396 NA
49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400 NA
50 -10.4579977 -0.19959516 0.3897705981 1.1762060679 NA
51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720 NA
52 -4.4747188 -0.07438732 0.3758235358 2.1266646020 NA
52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993 NA
52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370 NA
52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327 NA
52.4 -8.9036692 -0.44619160 0.0254566848 4.7983659909 NA
52.5 -5.6949642 -0.11011682 0.4155259424 4.9818264414 NA
53 -10.3141887 -0.23278716 0.5675736897 0.4965799209 NA
53.1 -8.2642654 -0.28317264 -0.3154088781 3.5505357443 NA
53.2 -9.1691554 -0.19517481 0.2162315769 4.5790420019 NA
54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841 NA
54.1 -15.7192609 -0.28325504 0.4129127672 1.8812377600 NA
54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352 NA
54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672 NA
54.4 -16.5771751 -0.34609328 0.8587727145 4.0143877396 NA
55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980 NA
55.1 -7.3217494 -0.24235382 0.5835528661 0.7463747414 NA
55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171 NA
55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572 NA
55.4 -14.8599983 NA -0.1202746964 3.2218102901 NA
56 -14.1764282 NA 0.5176377612 1.2231332215 NA
56.1 -12.5343602 -0.30357450 -1.1136932588 2.3573202139 NA
56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292 NA
56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378 NA
56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360 NA
56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043 NA
57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448 NA
57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030 NA
57.2 -6.3940143 NA -0.1159517011 1.1405586067 NA
57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721 NA
58 -12.0245677 -0.29901358 1.6476207996 1.2210099772 NA
58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703 NA
58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890 NA
58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693 NA
58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440 NA
58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824 NA
59 -13.2946560 -0.27255364 0.4766421367 1.9023998594 NA
59.1 -10.0530648 NA 1.0028089765 4.9736620474 NA
60 -19.2209402 NA 0.5231452932 2.8854503250 NA
61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795 NA
61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661 NA
61.2 -1.4713611 NA 1.0229058138 2.5077313243 NA
61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430 NA
61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283 NA
62 -2.8591600 NA -0.3979137604 0.5336771999 NA
62.1 -6.9461986 -0.10504143 0.6830738372 0.6987666548 NA
62.2 -16.7910593 -0.20104997 0.4301745954 3.4584309917 NA
62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371 NA
63 -24.0335535 -0.12036319 0.3345678035 2.8097350930 NA
63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211 NA
64 -20.5963897 NA 0.3949911859 4.1191305732 NA
65 -2.7969169 -0.34450396 1.2000091513 0.7076152589 NA
65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363 NA
65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827 NA
65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943 NA
66 -13.2318328 NA -0.5081302805 3.4943454154 NA
66.1 -1.9090560 NA -0.1447837412 3.7677437009 NA
66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616 NA
67 -25.6073277 NA 1.6716027681 4.1728388879 NA
68 -13.4806759 -0.13605235 0.5691848839 0.1291919907 NA
68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946 NA
68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660 NA
68.3 -12.4977127 NA 0.7443745962 2.9406870750 NA
68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363 NA
69 -14.5290675 -0.32152751 0.0381382683 4.1451198701 NA
70 -15.2122709 -0.28190462 0.8126204217 0.1992557163 NA
70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413 NA
71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386 NA
71.1 -7.5699888 NA -0.1103252087 1.4883817220 NA
71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395 NA
71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723 NA
71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823 NA
72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121 NA
72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806 NA
72.2 -26.3421306 NA -0.4170988856 1.8917567329 NA
72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935 NA
72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700 NA
72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901 NA
73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915 NA
74 -8.2790175 -0.27963171 0.3730017941 1.4626806753 NA
75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874 NA
76 -6.0061671 0.04775562 -0.9888876620 1.8074807397 NA
76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183 NA
76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859 NA
77 0.4772521 -0.19053195 -0.4986802480 0.8459033852 NA
78 -9.4105229 -0.17172929 0.0417330969 0.8231094317 NA
79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521 NA
79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628 NA
79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032 NA
80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186 NA
80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598 NA
80.2 -16.6381566 NA 0.5046816157 2.8025900386 NA
81 0.5547913 -0.18401980 -0.9493950353 0.0101324962 NA
81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494 NA
81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879 NA
81.3 -5.2166381 NA 0.4182528210 3.3456630446 NA
82 -3.0749381 -0.08328168 1.1088801952 1.3791010005 NA
82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622 NA
82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927 NA
83 -12.5872635 -0.34228255 0.5104724530 0.0537394290 NA
83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496 NA
83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362 NA
83.3 -19.2714012 NA -0.0534169155 4.7663642222 NA
84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237 NA
84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659 NA
85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259 NA
85.1 -12.9196365 NA -0.0577615939 1.7394116637 NA
85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194 NA
85.3 -6.3296340 NA -0.0520175929 3.1608762743 NA
85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758 NA
85.5 -13.6714939 NA -0.3740417009 4.5092553482 NA
86 -10.8756412 -0.10854862 0.4293735089 0.8476278360 NA
86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411 NA
86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515 NA
86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925 NA
86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000 NA
86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508 NA
87 -10.7027963 NA 1.0457427869 2.7069531474 NA
87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721 NA
87.2 -14.9616716 NA 0.4396872616 4.5241666853 NA
88 -2.2264493 -0.31563888 -0.0601928334 0.0005892443 NA
88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866 NA
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900 NA
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297 NA
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167 NA
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737 NA
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230 NA
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167 NA
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247 NA
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390 NA
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286 NA
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603 NA
92 -6.4119222 NA 0.4674939332 0.0737052364 NA
93 5.2122168 -0.11939966 0.2677965647 0.2788909199 NA
93.1 3.1211725 NA 1.6424445368 1.0357759963 NA
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099 NA
93.3 2.6223542 NA 1.1222322893 2.8876129608 NA
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002 NA
94 -6.9602492 NA -0.2904211940 0.8488043118 NA
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425 NA
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884 NA
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448 NA
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935 NA
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016 NA
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048 NA
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457 NA
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732 NA
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477 NA
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720 NA
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425 NA
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691 NA
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488 NA
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598 NA
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731 NA
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643 NA
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438 NA
98.1 -2.6515128 NA -0.9042716241 0.4309578973 NA
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906 NA
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8h$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8h$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c2 -0.1770956 0.1243159
$m8h$mu_reg_norm
[1] 0
$m8h$tau_reg_norm
[1] 1e-04
$m8h$shape_tau_norm
[1] 0.01
$m8h$rate_tau_norm
[1] 0.01
$m8h$mu_reg_binom
[1] 0
$m8h$tau_reg_binom
[1] 1e-04
$m8h$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8h$shape_diag_RinvD
[1] "0.01"
$m8h$rate_diag_RinvD
[1] "0.001"
$m8h$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8h$KinvD_y_id
id
4
$m8i
$m8i$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8i$M_lvlone
y c2 c1 time B21:c2
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 NA
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88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900 NA
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297 NA
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167 NA
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737 NA
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230 NA
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167 NA
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247 NA
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390 NA
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286 NA
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603 NA
92 -6.4119222 NA 0.4674939332 0.0737052364 NA
93 5.2122168 -0.11939966 0.2677965647 0.2788909199 NA
93.1 3.1211725 NA 1.6424445368 1.0357759963 NA
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099 NA
93.3 2.6223542 NA 1.1222322893 2.8876129608 NA
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002 NA
94 -6.9602492 NA -0.2904211940 0.8488043118 NA
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425 NA
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884 NA
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448 NA
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935 NA
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016 NA
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048 NA
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457 NA
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732 NA
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477 NA
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720 NA
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425 NA
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691 NA
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488 NA
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598 NA
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731 NA
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643 NA
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438 NA
98.1 -2.6515128 NA -0.9042716241 0.4309578973 NA
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906 NA
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8i$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8i$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c2 -0.1770956 0.1243159
$m8i$mu_reg_norm
[1] 0
$m8i$tau_reg_norm
[1] 1e-04
$m8i$shape_tau_norm
[1] 0.01
$m8i$rate_tau_norm
[1] 0.01
$m8i$mu_reg_binom
[1] 0
$m8i$tau_reg_binom
[1] 1e-04
$m8i$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8i$shape_diag_RinvD
[1] "0.01"
$m8i$rate_diag_RinvD
[1] "0.001"
$m8i$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8i$KinvD_y_id
id
4
$m8j
$m8j$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8j$M_lvlone
y c2 c1 time B21:c2
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 NA
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847 NA
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082 NA
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196 NA
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339 NA
5.3 -9.2366906 NA 0.1740288049 2.8119940578 NA
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884 NA
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673 NA
7.1 -18.3267285 NA 0.4242971219 3.7008403614 NA
7.2 -12.5138426 NA 0.0777182491 4.7716691741 NA
8 -1.6305331 NA -0.5791408712 1.1246398522 NA
8.1 -9.6520453 NA 0.3128604232 1.8027009873 NA
8.2 -1.5278462 NA 0.6258446356 1.8175825174 NA
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003 NA
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307 NA
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704 NA
9 -0.1634429 NA -0.1757592269 0.9607803822 NA
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383 NA
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501 NA
10 -6.4172202 NA -0.5598409704 2.2975509102 NA
10.1 -11.4834569 NA -0.2318340451 4.1734118364 NA
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905 NA
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680 NA
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263 NA
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969 NA
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661 NA
12 -15.2006287 NA -0.2433996286 0.9591987054 NA
13 0.8058816 NA 0.8799673116 0.0619085738 NA
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502 NA
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007 NA
14.1 -10.0965208 NA -0.1094019046 4.4710561272 NA
14.2 -16.6452027 NA 0.1518967560 4.6359198843 NA
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599 NA
15 -8.9424594 NA 0.3464447888 0.5402063532 NA
15.1 -22.0101983 -0.29092263 -0.4767313971 1.1893180816 NA
15.2 -7.3975599 -0.19392239 0.5759767791 1.5094739688 NA
15.3 -10.3567334 -0.25718384 -0.1713452662 4.9193474615 NA
16 -1.9691302 -0.45041108 0.4564754473 1.2417913869 NA
16.1 -9.9308357 -0.07599066 1.0652558311 2.5675726333 NA
16.2 -6.9626923 -0.32385667 0.6971872493 2.6524101500 NA
16.3 -3.2862557 -0.38326110 0.5259331838 3.5585018690 NA
16.4 -3.3972355 -0.22845856 0.2046601798 3.7612454291 NA
16.5 -11.5767835 -0.25497157 1.0718540464 3.9851612889 NA
17 -10.5474144 NA 0.6048676222 1.5925356350 NA
17.1 -7.6215009 -0.22105143 0.2323298304 2.4374032998 NA
17.2 -16.5386939 NA 1.2617499032 3.0256489082 NA
17.3 -20.0004774 NA -0.3913230895 3.3329089405 NA
17.4 -18.8505475 -0.15098046 0.9577299112 3.8693758985 NA
18 -19.7302351 -0.09870041 -0.0050324072 2.4374292302 NA
19 -14.6177568 -0.26680239 -0.4187468937 0.9772165376 NA
19.1 -17.8043866 -0.15815241 -0.4478828944 1.1466335913 NA
19.2 -15.1641705 -0.14717437 -1.1966721302 2.2599126538 NA
19.3 -16.6898418 -0.21271374 -0.5877091668 4.2114245973 NA
20 -12.9059229 -0.22087628 0.6838223064 1.7170160066 NA
20.1 -16.8191201 NA 0.3278571109 1.7562902288 NA
20.2 -6.1010131 -0.30127439 -0.8489831990 2.2515566566 NA
20.3 -7.9415371 -0.11782590 1.3169975191 2.2609123867 NA
20.4 -9.3904458 -0.19857957 0.0444804531 3.4913365287 NA
20.5 -13.3504189 -0.24338208 -0.4535207652 4.1730977828 NA
21 -7.6974718 -0.31407992 -0.4030302960 1.6936582839 NA
21.1 -11.9335526 -0.12424941 -0.4069674045 2.9571191233 NA
21.2 -12.7064929 -0.27672716 1.0650265940 3.7887385779 NA
22 -21.5022909 -0.23790593 -0.0673274516 2.4696226232 NA
22.1 -12.7745451 -0.15996535 0.9601388170 3.1626627257 NA
23 -3.5146508 -0.18236682 0.5556634840 1.5414533857 NA
23.1 -4.6724048 -0.20823302 1.4407865964 2.3369736120 NA
24 -2.5619821 -0.29026416 0.3856376411 2.8283136466 NA
25 -6.2944970 -0.36139273 0.3564400705 0.5381704110 NA
25.1 -3.8630505 -0.19571118 0.0982553434 1.6069735331 NA
25.2 -14.4205140 -0.21379355 0.1928682598 1.6358226922 NA
25.3 -19.6735037 -0.33876012 -0.0192488594 3.2646870392 NA
25.4 -9.0288933 NA 0.4466012931 4.0782226040 NA
25.5 -9.0509738 -0.04068446 1.1425193342 4.1560292873 NA
26 -19.7340685 -0.16846716 0.5341531449 0.2412706357 NA
26.1 -14.1692728 -0.10440642 1.2268695927 2.4451737676 NA
26.2 -17.2819976 -0.26884827 0.3678294939 3.5988757887 NA
26.3 -24.6265576 NA 0.5948516018 4.1822362854 NA
27 -7.3354999 -0.19520794 -0.3342844147 3.6955824879 NA
27.1 -11.1488468 -0.17622638 -0.4835141229 4.2451434687 NA
28 -11.7996597 -0.32164962 -0.7145915499 0.5746519344 NA
28.1 -8.2030122 -0.27003852 0.5063671955 2.7943964268 NA
28.2 -26.4317815 -0.07235801 -0.2067413142 4.2108539480 NA
28.3 -18.5016071 -0.13462982 0.1196789973 4.4705521734 NA
29 -5.8551395 -0.32432030 0.1392699487 1.1898884235 NA
29.1 -2.0209442 -0.27034171 0.7960234776 1.7624059319 NA
29.2 -5.6368080 -0.10197448 1.0398214352 2.0210406382 NA
29.3 -3.8110961 -0.27606945 0.0813246429 3.4078777023 NA
30 -12.7217702 -0.06949300 -0.3296323050 2.2635366488 NA
30.1 -17.0170140 -0.11511035 1.3635850954 3.5938334477 NA
30.2 -25.4236089 -0.16215882 0.7354171050 3.6138710892 NA
31 -17.0783921 0.05707733 0.3708398217 4.3988140998 NA
32 -18.4338764 -0.18446298 -0.0474059668 1.6745209007 NA
32.1 -19.4317212 -0.14270013 1.2507771489 2.9128167813 NA
32.2 -19.4738978 -0.20530798 0.1142915519 2.9676558380 NA
32.3 -21.4922645 -0.14705649 0.6773270619 4.2099863547 NA
33 2.0838099 -0.15252819 0.1774293842 0.0093385763 NA
33.1 -13.3172274 NA 0.6159606291 3.4591242753 NA
34 -10.0296691 -0.30378735 0.8590979166 1.4998774312 NA
34.1 -25.9426553 -0.11982431 0.0546216775 3.8242761395 NA
34.2 -18.5688138 -0.24278671 -0.0897224473 3.9072251692 NA
34.3 -15.4173859 -0.19971833 0.4163395571 3.9582124643 NA
35 -14.3958113 NA -1.4693520528 1.3294299203 NA
35.1 -12.9457541 -0.24165780 -0.3031734330 1.5276966314 NA
35.2 -16.1380691 NA -0.6045512101 4.5025920868 NA
36 -12.8166968 -0.49062180 0.9823048960 0.7123168337 NA
36.1 -14.3989481 -0.25651700 1.4466051416 1.7972493160 NA
36.2 -12.2436943 NA 1.1606752905 1.8262697803 NA
36.3 -15.0104638 -0.30401274 0.8373091576 4.2840119381 NA
36.4 -10.1775457 NA 0.2640591685 4.6194464504 NA
37 -15.2223495 -0.15276529 0.1177313455 2.0018732361 NA
37.1 -14.7526195 -0.30016169 -0.1415483779 3.6656836793 NA
37.2 -19.8168430 0.06809545 0.0054610124 3.9663937816 NA
38 -2.7065118 -0.11218486 0.8078948077 0.9826511063 NA
39 -8.7288138 -0.38072211 0.9876451040 0.6921808305 NA
39.1 -9.2746473 -0.32094428 -0.3431222274 0.9027792048 NA
39.2 -18.2695344 NA -1.7909380751 1.3055654289 NA
39.3 -13.8219083 -0.40173480 -0.1798746191 1.5412842878 NA
39.4 -16.2254704 -0.20041848 -0.1850961689 3.1834997435 NA
39.5 -21.7283648 -0.26027990 0.4544226146 4.1394166439 NA
40 1.8291916 -0.19751956 0.5350190436 1.1330395646 NA
40.1 -6.6916432 -0.08399467 0.4189342752 2.6940994046 NA
40.2 -1.6278171 -0.20864416 0.4211994981 3.0396614212 NA
40.3 -10.5749790 NA 0.0916687506 4.6762977762 NA
41 -3.1556121 -0.26096953 -0.1035047421 1.9337158254 NA
41.1 -11.5895327 -0.23953874 -0.4684202411 3.1956304458 NA
41.2 -18.9352091 -0.03079344 0.5972615368 3.2846923557 NA
41.3 -15.9788960 NA 0.9885613862 3.3813529415 NA
41.4 -9.6070508 NA -0.3908036794 3.5482964432 NA
42 -5.2159485 -0.16084527 -0.0338893961 0.4859252973 NA
42.1 -15.9878743 -0.13812521 -0.4498363172 4.3293134298 NA
43 -16.6104361 -0.08864017 0.8965546110 0.5616614548 NA
43.1 -9.5549441 -0.12583158 0.6199122090 1.0743579536 NA
43.2 -14.2003491 -0.29253959 0.1804894429 2.6131797966 NA
44 -8.1969033 -0.22697597 1.3221409285 0.7662644819 NA
44.1 -19.9270197 NA 0.3416426284 2.6490291790 NA
44.2 -22.6521171 NA 0.5706610068 3.3371910988 NA
44.3 -21.1903736 -0.40544012 1.2679497430 4.1154200875 NA
45 -0.5686627 -0.19274788 0.1414983160 0.1957449992 NA
45.1 -7.5645740 -0.34860483 0.7220892521 1.9963831536 NA
46 -19.1624789 -0.28547861 1.5391054233 1.3477755385 NA
46.1 -18.4487574 -0.21977836 0.3889107049 2.8565793915 NA
46.2 -15.8222682 NA 0.1248719493 4.4160729996 NA
47 -5.4165074 -0.08597098 0.2014101100 0.6012621359 NA
47.1 -15.0975029 -0.35424828 0.2982973539 2.4097121472 NA
47.2 -12.9971413 -0.24262576 1.1518107179 2.9975794035 NA
47.3 -10.6844521 -0.30426315 0.5196802157 3.1829649757 NA
47.4 -18.2214784 NA 0.3702301552 4.6201055450 NA
48 -8.3101471 NA -0.2128602862 2.8607365978 NA
48.1 -18.3854275 NA -0.5337239976 2.9098354396 NA
49 -13.0130319 -0.42198781 -0.5236770035 2.7179756400 NA
50 -10.4579977 -0.19959516 0.3897705981 1.1762060679 NA
51 -19.3157621 -0.16556964 -0.7213343736 1.4304436720 NA
52 -4.4747188 -0.07438732 0.3758235358 2.1266646020 NA
52.1 -4.3163827 -0.37537080 0.7138067080 3.1000545993 NA
52.2 -6.9761408 -0.24222066 0.8872895233 3.1268477370 NA
52.3 -20.1764756 -0.31520603 -0.9664587437 3.5711459327 NA
52.4 -8.9036692 -0.44619160 0.0254566848 4.7983659909 NA
52.5 -5.6949642 -0.11011682 0.4155259424 4.9818264414 NA
53 -10.3141887 -0.23278716 0.5675736897 0.4965799209 NA
53.1 -8.2642654 -0.28317264 -0.3154088781 3.5505357443 NA
53.2 -9.1691554 -0.19517481 0.2162315769 4.5790420019 NA
54 -6.2198754 -0.10122856 -0.0880802382 1.4034724841 NA
54.1 -15.7192609 -0.28325504 0.4129127672 1.8812377600 NA
54.2 -13.0978998 -0.16753120 1.0119546775 2.5107589352 NA
54.3 -5.1195299 -0.22217672 -0.1112901990 2.7848406672 NA
54.4 -16.5771751 -0.34609328 0.8587727145 4.0143877396 NA
55 -5.7348534 -0.32428190 -0.0116453589 0.6118522980 NA
55.1 -7.3217494 -0.24235382 0.5835528661 0.7463747414 NA
55.2 -12.2171938 -0.24065814 -1.0010857254 2.8201208171 NA
55.3 -12.9821266 -0.23665476 -0.4796526070 3.1326431572 NA
55.4 -14.8599983 NA -0.1202746964 3.2218102901 NA
56 -14.1764282 NA 0.5176377612 1.2231332215 NA
56.1 -12.5343602 -0.30357450 -1.1136932588 2.3573202139 NA
56.2 -8.4573382 -0.51301630 -0.0168103281 2.5674936292 NA
56.3 -12.4633969 -0.23743117 0.3933023606 2.9507164378 NA
56.4 -17.3841863 -0.17264917 0.3714625139 3.2272730360 NA
56.5 -14.8147645 -0.39188329 0.7811448179 3.4175522043 NA
57 -3.1403293 -0.18501692 -1.0868304872 0.2370331448 NA
57.1 -11.1509248 -0.27274841 0.8018626997 0.2481445030 NA
57.2 -6.3940143 NA -0.1159517011 1.1405586067 NA
57.3 -9.3473241 -0.09898509 0.6785562445 2.1153886721 NA
58 -12.0245677 -0.29901358 1.6476207996 1.2210099772 NA
58.1 -9.2112246 -0.35390896 0.3402652711 1.6334245703 NA
58.2 -1.2071742 -0.16687336 -0.1111300753 1.6791862890 NA
58.3 -11.0141711 -0.11784506 -0.5409234285 2.6320121693 NA
58.4 -5.3721214 -0.05321983 -0.1271327672 2.8477731440 NA
58.5 -7.8523047 -0.54457568 0.8713264822 3.5715569824 NA
59 -13.2946560 -0.27255364 0.4766421367 1.9023998594 NA
59.1 -10.0530648 NA 1.0028089765 4.9736620474 NA
60 -19.2209402 NA 0.5231452932 2.8854503250 NA
61 -4.6699914 -0.30550120 -0.7190130614 0.7213630795 NA
61.1 -3.5981894 -0.35579892 0.8353702312 2.3186947661 NA
61.2 -1.4713611 NA 1.0229058138 2.5077313243 NA
61.3 -3.8819786 -0.34184391 1.1717723589 3.1731073430 NA
61.4 0.1041413 -0.30891967 -0.0629201596 3.6022726283 NA
62 -2.8591600 NA -0.3979137604 0.5336771999 NA
62.1 -6.9461986 -0.10504143 0.6830738372 0.6987666548 NA
62.2 -16.7910593 -0.20104997 0.4301745954 3.4584309917 NA
62.3 -17.9844596 -0.08138677 -0.0333139957 4.8028772371 NA
63 -24.0335535 -0.12036319 0.3345678035 2.8097350930 NA
63.1 -11.7765300 -0.13624992 0.3643769511 3.9653754211 NA
64 -20.5963897 NA 0.3949911859 4.1191305732 NA
65 -2.7969169 -0.34450396 1.2000091513 0.7076152589 NA
65.1 -11.1778694 -0.32514650 0.0110122646 2.0252246363 NA
65.2 -5.2830399 -0.10984996 -0.5776452043 3.1127382827 NA
65.3 -7.9353390 -0.19275692 -0.1372183563 3.1969087943 NA
66 -13.2318328 NA -0.5081302805 3.4943454154 NA
66.1 -1.9090560 NA -0.1447837412 3.7677437009 NA
66.2 -16.6643889 -0.11687008 0.1906241379 3.9486138616 NA
67 -25.6073277 NA 1.6716027681 4.1728388879 NA
68 -13.4806759 -0.13605235 0.5691848839 0.1291919907 NA
68.1 -18.4557183 -0.19790827 0.1004860389 1.7809643946 NA
68.2 -13.3982327 -0.17750123 -0.0061241827 2.0493205660 NA
68.3 -12.4977127 NA 0.7443745962 2.9406870750 NA
68.4 -11.7073990 -0.12570562 0.8726923437 4.0406670363 NA
69 -14.5290675 -0.32152751 0.0381382683 4.1451198701 NA
70 -15.2122709 -0.28190462 0.8126204217 0.1992557163 NA
70.1 -7.8681167 -0.11503263 0.4691503050 0.4829774413 NA
71 -10.3352703 -0.13029093 -0.5529062591 0.7741605386 NA
71.1 -7.5699888 NA -0.1103252087 1.4883817220 NA
71.2 -18.4680702 -0.39075433 1.7178492547 4.0758526395 NA
71.3 -21.4316644 -0.21401028 -1.0118346755 4.7048238723 NA
71.4 -8.1137650 -0.40219281 1.8623785017 4.7242791823 NA
72 -9.1848162 -0.40337108 -0.4521659275 0.9321196121 NA
72.1 -23.7538846 -0.25978914 0.1375317317 1.1799991806 NA
72.2 -26.3421306 NA -0.4170988856 1.8917567329 NA
72.3 -27.2843801 -0.09809866 0.7107266765 3.4853593935 NA
72.4 -20.8541617 -0.14240019 0.1451969143 3.6884259700 NA
72.5 -12.8948965 -0.14794204 1.6298050306 4.0854155901 NA
73 -2.6091307 -0.23509343 -0.0307469467 4.6019889915 NA
74 -8.2790175 -0.27963171 0.3730017941 1.4626806753 NA
75 -12.5029612 -0.12905034 -0.4908003566 3.2524286874 NA
76 -6.0061671 0.04775562 -0.9888876620 1.8074807397 NA
76.1 -8.8149114 -0.19399157 0.0003798292 4.2685073183 NA
76.2 -11.8359043 -0.02754574 -0.8421863763 4.9688734859 NA
77 0.4772521 -0.19053195 -0.4986802480 0.8459033852 NA
78 -9.4105229 -0.17172929 0.0417330969 0.8231094317 NA
79 -1.0217265 -0.03958515 -0.3767450660 0.0583819521 NA
79.1 -11.8125257 -0.20328809 0.1516000028 2.4406372628 NA
79.2 -10.5465186 -0.23901634 -0.1888160741 3.2962526032 NA
80 -12.7366807 -0.34031873 -0.0041558414 0.8985060186 NA
80.1 -9.0584783 -0.19526756 -0.0329337062 1.3434670598 NA
80.2 -16.6381566 NA 0.5046816157 2.8025900386 NA
81 0.5547913 -0.18401980 -0.9493950353 0.0101324962 NA
81.1 -4.0892715 -0.16889476 0.2443038954 0.9421709494 NA
81.2 1.8283303 -0.37343047 0.6476958410 3.0542453879 NA
81.3 -5.2166381 NA 0.4182528210 3.3456630446 NA
82 -3.0749381 -0.08328168 1.1088801952 1.3791010005 NA
82.1 -10.5506696 -0.22167084 0.9334157763 1.7601010622 NA
82.2 -18.2226347 -0.20971187 0.4958140634 2.6233131927 NA
83 -12.5872635 -0.34228255 0.5104724530 0.0537394290 NA
83.1 -11.9756502 -0.34075730 -0.0513309106 2.9061570496 NA
83.2 -10.6744217 -0.32503954 -0.2067792494 3.1189457362 NA
83.3 -19.2714012 NA -0.0534169155 4.7663642222 NA
84 -2.6320312 -0.20676741 -0.0255753653 2.7254060237 NA
84.1 -9.8140094 -0.20310458 -1.8234189877 3.3364784659 NA
85 -12.3886736 -0.12107593 -0.0114038622 0.2977756259 NA
85.1 -12.9196365 NA -0.0577615939 1.7394116637 NA
85.2 -9.6433248 -0.32509207 -0.2241856342 2.6846330194 NA
85.3 -6.3296340 NA -0.0520175929 3.1608762743 NA
85.4 -7.0405525 -0.30730810 0.2892733846 3.9452053758 NA
85.5 -13.6714939 NA -0.3740417009 4.5092553482 NA
86 -10.8756412 -0.10854862 0.4293735089 0.8476278360 NA
86.1 -12.0055331 -0.25751662 -0.1363456521 1.0118629411 NA
86.2 -13.3724699 -0.38943076 0.1230989293 1.2511159515 NA
86.3 -13.3252145 -0.24454702 0.3305413955 2.1870554925 NA
86.4 -14.9191290 -0.12338992 2.6003411822 2.4532935000 NA
86.5 -17.7515546 -0.23976984 -0.1420690052 3.8206058508 NA
87 -10.7027963 NA 1.0457427869 2.7069531474 NA
87.1 -22.4941954 -0.34366972 -0.2973007190 3.4462517721 NA
87.2 -14.9616716 NA 0.4396872616 4.5241666853 NA
88 -2.2264493 -0.31563888 -0.0601928334 0.0005892443 NA
88.1 -8.9626474 -0.20304028 -1.0124347595 0.7116099866 NA
88.2 -2.5095281 -0.40311895 0.5730917016 2.4952722900 NA
88.3 -16.3345673 -0.12308715 -0.0029455332 3.2995816297 NA
89 -11.0459647 -0.18527715 1.5465903721 0.6462086167 NA
90 -4.5610239 -0.25029126 0.0626760573 0.1696030737 NA
90.1 -11.7036651 -0.26974303 1.1896872985 2.5980385230 NA
90.2 -5.3838521 -0.28804531 0.2597888783 2.6651392167 NA
90.3 -4.1636999 -0.19180615 0.6599799887 3.1242690247 NA
91 -7.1462503 -0.26591197 1.1213651365 0.6382618390 NA
91.1 -12.8374475 -0.09153470 1.2046371625 2.6224059286 NA
91.2 -18.2576707 -0.48414390 0.3395603754 4.7772527603 NA
92 -6.4119222 NA 0.4674939332 0.0737052364 NA
93 5.2122168 -0.11939966 0.2677965647 0.2788909199 NA
93.1 3.1211725 NA 1.6424445368 1.0357759963 NA
93.2 -3.6841177 -0.21089379 0.7101700066 2.4916551099 NA
93.3 2.6223542 NA 1.1222322893 2.8876129608 NA
93.4 -11.1877696 -0.23618836 1.4628960401 4.4639474002 NA
94 -6.9602492 NA -0.2904211940 0.8488043118 NA
94.1 -7.4318416 -0.10217284 0.0147813580 1.0552454425 NA
94.2 -4.3498045 -0.36713471 -0.4536774482 1.9445500884 NA
94.3 -11.6340088 -0.13806763 0.6793464917 3.0710722448 NA
94.4 -12.9357964 -0.42353804 -0.9411356550 3.0872731935 NA
94.5 -14.7648530 -0.15513707 0.5683867264 4.3805759016 NA
95 -12.8849309 -0.24149687 0.2375652188 2.0199063048 NA
95.1 -9.7451502 -0.21315958 0.0767152977 4.0184444457 NA
95.2 -0.8535063 -0.15777208 -0.6886731251 4.5596531732 NA
96 -4.9139832 -0.16780948 0.7813892121 0.0311333477 NA
96.1 -3.9582653 -0.32504815 0.3391519695 0.1324267720 NA
96.2 -9.6555492 -0.20395970 -0.4857246503 0.6701303425 NA
96.3 -11.8690793 -0.06221501 0.8771471244 2.1775037691 NA
96.4 -11.0224373 -0.14801097 1.9030768981 2.2246142488 NA
96.5 -10.9530403 -0.28658893 -0.1684332749 4.2377650598 NA
97 -9.8540471 -0.34484656 1.3775130083 1.1955102731 NA
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643 NA
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438 NA
98.1 -2.6515128 NA -0.9042716241 0.4309578973 NA
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906 NA
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8j$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8j$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c2 -0.1770956 0.1243159
$m8j$mu_reg_norm
[1] 0
$m8j$tau_reg_norm
[1] 1e-04
$m8j$shape_tau_norm
[1] 0.01
$m8j$rate_tau_norm
[1] 0.01
$m8j$mu_reg_binom
[1] 0
$m8j$tau_reg_binom
[1] 1e-04
$m8j$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8j$shape_diag_RinvD
[1] "0.01"
$m8j$rate_diag_RinvD
[1] "0.001"
$m8j$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8j$KinvD_y_id
id
4
$m8k
$m8k$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8k$M_lvlone
y c2 c1 time B21:c2
1 -13.0493856 NA 0.7592026489 0.5090421822 NA
1.1 -9.3335901 -0.08061445 0.9548337990 0.6666076288 NA
1.2 -22.3469852 -0.26523782 0.5612235156 2.1304941282 NA
1.3 -15.0417337 -0.30260393 1.1873391025 2.4954441458 NA
2 -12.0655434 -0.33443795 0.9192204198 3.0164990982 NA
2.1 -15.8674476 -0.11819800 -0.1870730476 3.2996806887 NA
2.2 -7.8800006 -0.31532280 1.2517512331 4.1747569619 NA
3 -11.4820604 -0.12920657 -0.0605087604 0.8478727890 NA
3.1 -10.5983220 NA 0.3788637747 3.0654308549 NA
3.2 -22.4519157 NA 0.9872578281 4.7381553578 NA
4 -1.2697775 -0.31177403 1.4930175328 0.3371432109 NA
4.1 -11.1215184 -0.23894886 -0.7692526880 1.0693019140 NA
4.2 -3.6134138 -0.15533613 0.9180841450 2.6148973033 NA
4.3 -14.5982385 -0.14644545 -0.0541170782 3.1336532847 NA
5 -6.8457515 -0.28360457 -0.1376784521 1.0762525082 NA
5.1 -7.0551214 -0.20135143 -0.2740585866 1.7912546196 NA
5.2 -12.3418980 -0.28293375 0.4670496929 2.7960080339 NA
5.3 -9.2366906 NA 0.1740288049 2.8119940578 NA
6 -5.1648211 -0.08617066 0.9868044683 1.7815462884 NA
7 -10.0599502 -0.22243495 -0.1280320918 3.3074087673 NA
7.1 -18.3267285 NA 0.4242971219 3.7008403614 NA
7.2 -12.5138426 NA 0.0777182491 4.7716691741 NA
8 -1.6305331 NA -0.5791408712 1.1246398522 NA
8.1 -9.6520453 NA 0.3128604232 1.8027009873 NA
8.2 -1.5278462 NA 0.6258446356 1.8175825174 NA
8.3 -7.4172211 -0.35148972 -0.1040137707 2.8384267003 NA
8.4 -7.1238609 0.03661023 0.0481450285 3.3630275307 NA
8.5 -8.8706950 -0.08424534 0.3831763675 4.4360849704 NA
9 -0.1634429 NA -0.1757592269 0.9607803822 NA
9.1 -2.6034300 -0.43509340 -0.1791541200 2.9177753383 NA
9.2 -6.7272369 -0.22527490 -0.0957042935 4.8100892501 NA
10 -6.4172202 NA -0.5598409704 2.2975509102 NA
10.1 -11.4834569 NA -0.2318340451 4.1734118364 NA
11 -8.7911356 -0.08587475 0.5086859475 1.1832662905 NA
11.1 -19.6645080 -0.06157340 0.4951758188 1.2346051680 NA
11.2 -20.2030932 -0.12436018 -1.1022162541 1.6435316263 NA
11.3 -21.3082176 -0.21377934 -0.0611636705 3.3859017969 NA
11.4 -14.5802901 -0.32208329 -0.4971774316 4.8118087661 NA
12 -15.2006287 NA -0.2433996286 0.9591987054 NA
13 0.8058816 NA 0.8799673116 0.0619085738 NA
13.1 -13.6379208 -0.40300449 0.1079022586 3.5621061502 NA
14 -15.3422873 -0.28992072 0.9991752617 4.0364430007 NA
14.1 -10.0965208 NA -0.1094019046 4.4710561272 NA
14.2 -16.6452027 NA 0.1518967560 4.6359198843 NA
14.3 -15.8389733 -0.21979936 0.3521012473 4.6886152599 NA
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97 -9.8540471 -0.34484656 1.3775130083 1.1955102731 NA
97.1 -19.2262840 -0.35658805 -1.7323228619 4.9603108643 NA
98 -11.9651231 -0.36913003 -1.2648518889 0.2041732438 NA
98.1 -2.6515128 NA -0.9042716241 0.4309578973 NA
98.2 -12.2606382 -0.17154225 -0.1560385207 3.5172611906 NA
99 -11.4720500 -0.24753132 0.7993356425 0.3531786101 NA
99.1 -14.0596866 -0.27947829 1.0355522332 4.6789444226 NA
99.2 -17.3939469 -0.09033035 -0.1150895843 4.9927084171 NA
100 1.1005874 -0.17326698 0.0369067906 1.0691387602 NA
100.1 -3.8226248 NA 1.6023713093 1.5109344281 NA
100.2 -0.9123182 -0.12072016 0.8861545820 2.1502332564 NA
100.3 -15.8389474 -0.27657520 0.1277046316 3.8745574222 NA
100.4 -12.8093826 -0.14631556 -0.0834577654 4.6567608765 NA
$m8k$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8k$spM_lvlone
center scale
y -11.1733710 6.2496619
c2 -0.2237158 0.1059527
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c2 -0.1770956 0.1243159
$m8k$mu_reg_norm
[1] 0
$m8k$tau_reg_norm
[1] 1e-04
$m8k$shape_tau_norm
[1] 0.01
$m8k$rate_tau_norm
[1] 0.01
$m8k$mu_reg_binom
[1] 0
$m8k$tau_reg_binom
[1] 1e-04
$m8k$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8k$shape_diag_RinvD
[1] "0.01"
$m8k$rate_diag_RinvD
[1] "0.001"
$m8k$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8k$KinvD_y_id
id
4
$m8l
$m8l$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8l$M_lvlone
y c1 time B21:c1 B21:time c1:time
1 -13.0493856 0.7592026489 0.5090421822 NA NA 3.864662e-01
1.1 -9.3335901 0.9548337990 0.6666076288 NA NA 6.364995e-01
1.2 -22.3469852 0.5612235156 2.1304941282 NA NA 1.195683e+00
1.3 -15.0417337 1.1873391025 2.4954441458 NA NA 2.962938e+00
2 -12.0655434 0.9192204198 3.0164990982 NA NA 2.772828e+00
2.1 -15.8674476 -0.1870730476 3.2996806887 NA NA -6.172813e-01
2.2 -7.8800006 1.2517512331 4.1747569619 NA NA 5.225757e+00
3 -11.4820604 -0.0605087604 0.8478727890 NA NA -5.130373e-02
3.1 -10.5983220 0.3788637747 3.0654308549 NA NA 1.161381e+00
3.2 -22.4519157 0.9872578281 4.7381553578 NA NA 4.677781e+00
4 -1.2697775 1.4930175328 0.3371432109 NA NA 5.033607e-01
4.1 -11.1215184 -0.7692526880 1.0693019140 NA NA -8.225634e-01
4.2 -3.6134138 0.9180841450 2.6148973033 NA NA 2.400696e+00
4.3 -14.5982385 -0.0541170782 3.1336532847 NA NA -1.695842e-01
5 -6.8457515 -0.1376784521 1.0762525082 NA NA -1.481768e-01
5.1 -7.0551214 -0.2740585866 1.7912546196 NA NA -4.909087e-01
5.2 -12.3418980 0.4670496929 2.7960080339 NA NA 1.305875e+00
5.3 -9.2366906 0.1740288049 2.8119940578 NA NA 4.893680e-01
6 -5.1648211 0.9868044683 1.7815462884 NA NA 1.758038e+00
7 -10.0599502 -0.1280320918 3.3074087673 NA NA -4.234545e-01
7.1 -18.3267285 0.4242971219 3.7008403614 NA NA 1.570256e+00
7.2 -12.5138426 0.0777182491 4.7716691741 NA NA 3.708458e-01
8 -1.6305331 -0.5791408712 1.1246398522 NA NA -6.513249e-01
8.1 -9.6520453 0.3128604232 1.8027009873 NA NA 5.639938e-01
8.2 -1.5278462 0.6258446356 1.8175825174 NA NA 1.137524e+00
8.3 -7.4172211 -0.1040137707 2.8384267003 NA NA -2.952355e-01
8.4 -7.1238609 0.0481450285 3.3630275307 NA NA 1.619131e-01
8.5 -8.8706950 0.3831763675 4.4360849704 NA NA 1.699803e+00
9 -0.1634429 -0.1757592269 0.9607803822 NA NA -1.688660e-01
9.1 -2.6034300 -0.1791541200 2.9177753383 NA NA -5.227315e-01
9.2 -6.7272369 -0.0957042935 4.8100892501 NA NA -4.603462e-01
10 -6.4172202 -0.5598409704 2.2975509102 NA NA -1.286263e+00
10.1 -11.4834569 -0.2318340451 4.1734118364 NA NA -9.675389e-01
11 -8.7911356 0.5086859475 1.1832662905 NA NA 6.019109e-01
11.1 -19.6645080 0.4951758188 1.2346051680 NA NA 6.113466e-01
11.2 -20.2030932 -1.1022162541 1.6435316263 NA NA -1.811527e+00
11.3 -21.3082176 -0.0611636705 3.3859017969 NA NA -2.070942e-01
11.4 -14.5802901 -0.4971774316 4.8118087661 NA NA -2.392323e+00
12 -15.2006287 -0.2433996286 0.9591987054 NA NA -2.334686e-01
13 0.8058816 0.8799673116 0.0619085738 NA NA 5.447752e-02
13.1 -13.6379208 0.1079022586 3.5621061502 NA NA 3.843593e-01
14 -15.3422873 0.9991752617 4.0364430007 NA NA 4.033114e+00
14.1 -10.0965208 -0.1094019046 4.4710561272 NA NA -4.891421e-01
14.2 -16.6452027 0.1518967560 4.6359198843 NA NA 7.041812e-01
14.3 -15.8389733 0.3521012473 4.6886152599 NA NA 1.650867e+00
15 -8.9424594 0.3464447888 0.5402063532 NA NA 1.871517e-01
15.1 -22.0101983 -0.4767313971 1.1893180816 NA NA -5.669853e-01
15.2 -7.3975599 0.5759767791 1.5094739688 NA NA 8.694220e-01
15.3 -10.3567334 -0.1713452662 4.9193474615 NA NA -8.429069e-01
16 -1.9691302 0.4564754473 1.2417913869 NA NA 5.668473e-01
16.1 -9.9308357 1.0652558311 2.5675726333 NA NA 2.735122e+00
16.2 -6.9626923 0.6971872493 2.6524101500 NA NA 1.849227e+00
16.3 -3.2862557 0.5259331838 3.5585018690 NA NA 1.871534e+00
16.4 -3.3972355 0.2046601798 3.7612454291 NA NA 7.697772e-01
16.5 -11.5767835 1.0718540464 3.9851612889 NA NA 4.271511e+00
17 -10.5474144 0.6048676222 1.5925356350 NA NA 9.632732e-01
17.1 -7.6215009 0.2323298304 2.4374032998 NA NA 5.662815e-01
17.2 -16.5386939 1.2617499032 3.0256489082 NA NA 3.817612e+00
17.3 -20.0004774 -0.3913230895 3.3329089405 NA NA -1.304244e+00
17.4 -18.8505475 0.9577299112 3.8693758985 NA NA 3.705817e+00
18 -19.7302351 -0.0050324072 2.4374292302 NA NA -1.226614e-02
19 -14.6177568 -0.4187468937 0.9772165376 NA NA -4.092064e-01
19.1 -17.8043866 -0.4478828944 1.1466335913 NA NA -5.135576e-01
19.2 -15.1641705 -1.1966721302 2.2599126538 NA NA -2.704374e+00
19.3 -16.6898418 -0.5877091668 4.2114245973 NA NA -2.475093e+00
20 -12.9059229 0.6838223064 1.7170160066 NA NA 1.174134e+00
20.1 -16.8191201 0.3278571109 1.7562902288 NA NA 5.758122e-01
20.2 -6.1010131 -0.8489831990 2.2515566566 NA NA -1.911534e+00
20.3 -7.9415371 1.3169975191 2.2609123867 NA NA 2.977616e+00
20.4 -9.3904458 0.0444804531 3.4913365287 NA NA 1.552962e-01
20.5 -13.3504189 -0.4535207652 4.1730977828 NA NA -1.892586e+00
21 -7.6974718 -0.4030302960 1.6936582839 NA NA -6.825956e-01
21.1 -11.9335526 -0.4069674045 2.9571191233 NA NA -1.203451e+00
21.2 -12.7064929 1.0650265940 3.7887385779 NA NA 4.035107e+00
22 -21.5022909 -0.0673274516 2.4696226232 NA NA -1.662734e-01
22.1 -12.7745451 0.9601388170 3.1626627257 NA NA 3.036595e+00
23 -3.5146508 0.5556634840 1.5414533857 NA NA 8.565294e-01
23.1 -4.6724048 1.4407865964 2.3369736120 NA NA 3.367080e+00
24 -2.5619821 0.3856376411 2.8283136466 NA NA 1.090704e+00
25 -6.2944970 0.3564400705 0.5381704110 NA NA 1.918255e-01
25.1 -3.8630505 0.0982553434 1.6069735331 NA NA 1.578937e-01
25.2 -14.4205140 0.1928682598 1.6358226922 NA NA 3.154983e-01
25.3 -19.6735037 -0.0192488594 3.2646870392 NA NA -6.284150e-02
25.4 -9.0288933 0.4466012931 4.0782226040 NA NA 1.821339e+00
25.5 -9.0509738 1.1425193342 4.1560292873 NA NA 4.748344e+00
26 -19.7340685 0.5341531449 0.2412706357 NA NA 1.288755e-01
26.1 -14.1692728 1.2268695927 2.4451737676 NA NA 2.999909e+00
26.2 -17.2819976 0.3678294939 3.5988757887 NA NA 1.323773e+00
26.3 -24.6265576 0.5948516018 4.1822362854 NA NA 2.487810e+00
27 -7.3354999 -0.3342844147 3.6955824879 NA NA -1.235376e+00
27.1 -11.1488468 -0.4835141229 4.2451434687 NA NA -2.052587e+00
28 -11.7996597 -0.7145915499 0.5746519344 NA NA -4.106414e-01
28.1 -8.2030122 0.5063671955 2.7943964268 NA NA 1.414991e+00
28.2 -26.4317815 -0.2067413142 4.2108539480 NA NA -8.705575e-01
28.3 -18.5016071 0.1196789973 4.4705521734 NA NA 5.350312e-01
29 -5.8551395 0.1392699487 1.1898884235 NA NA 1.657157e-01
29.1 -2.0209442 0.7960234776 1.7624059319 NA NA 1.402916e+00
29.2 -5.6368080 1.0398214352 2.0210406382 NA NA 2.101521e+00
29.3 -3.8110961 0.0813246429 3.4078777023 NA NA 2.771444e-01
30 -12.7217702 -0.3296323050 2.2635366488 NA NA -7.461348e-01
30.1 -17.0170140 1.3635850954 3.5938334477 NA NA 4.900498e+00
30.2 -25.4236089 0.7354171050 3.6138710892 NA NA 2.657703e+00
31 -17.0783921 0.3708398217 4.3988140998 NA NA 1.631255e+00
32 -18.4338764 -0.0474059668 1.6745209007 NA NA -7.938228e-02
32.1 -19.4317212 1.2507771489 2.9128167813 NA NA 3.643285e+00
32.2 -19.4738978 0.1142915519 2.9676558380 NA NA 3.391780e-01
32.3 -21.4922645 0.6773270619 4.2099863547 NA NA 2.851538e+00
33 2.0838099 0.1774293842 0.0093385763 NA NA 1.656938e-03
33.1 -13.3172274 0.6159606291 3.4591242753 NA NA 2.130684e+00
34 -10.0296691 0.8590979166 1.4998774312 NA NA 1.288542e+00
34.1 -25.9426553 0.0546216775 3.8242761395 NA NA 2.088884e-01
34.2 -18.5688138 -0.0897224473 3.9072251692 NA NA -3.505658e-01
34.3 -15.4173859 0.4163395571 3.9582124643 NA NA 1.647960e+00
35 -14.3958113 -1.4693520528 1.3294299203 NA NA -1.953401e+00
35.1 -12.9457541 -0.3031734330 1.5276966314 NA NA -4.631570e-01
35.2 -16.1380691 -0.6045512101 4.5025920868 NA NA -2.722047e+00
36 -12.8166968 0.9823048960 0.7123168337 NA NA 6.997123e-01
36.1 -14.3989481 1.4466051416 1.7972493160 NA NA 2.599910e+00
36.2 -12.2436943 1.1606752905 1.8262697803 NA NA 2.119706e+00
36.3 -15.0104638 0.8373091576 4.2840119381 NA NA 3.587042e+00
36.4 -10.1775457 0.2640591685 4.6194464504 NA NA 1.219807e+00
37 -15.2223495 0.1177313455 2.0018732361 NA NA 2.356832e-01
37.1 -14.7526195 -0.1415483779 3.6656836793 NA NA -5.188716e-01
37.2 -19.8168430 0.0054610124 3.9663937816 NA NA 2.166053e-02
38 -2.7065118 0.8078948077 0.9826511063 NA NA 7.938787e-01
39 -8.7288138 0.9876451040 0.6921808305 NA NA 6.836290e-01
39.1 -9.2746473 -0.3431222274 0.9027792048 NA NA -3.097636e-01
39.2 -18.2695344 -1.7909380751 1.3055654289 NA NA -2.338187e+00
39.3 -13.8219083 -0.1798746191 1.5412842878 NA NA -2.772379e-01
39.4 -16.2254704 -0.1850961689 3.1834997435 NA NA -5.892536e-01
39.5 -21.7283648 0.4544226146 4.1394166439 NA NA 1.881045e+00
40 1.8291916 0.5350190436 1.1330395646 NA NA 6.061977e-01
40.1 -6.6916432 0.4189342752 2.6940994046 NA NA 1.128651e+00
40.2 -1.6278171 0.4211994981 3.0396614212 NA NA 1.280304e+00
40.3 -10.5749790 0.0916687506 4.6762977762 NA NA 4.286704e-01
41 -3.1556121 -0.1035047421 1.9337158254 NA NA -2.001488e-01
41.1 -11.5895327 -0.4684202411 3.1956304458 NA NA -1.496898e+00
41.2 -18.9352091 0.5972615368 3.2846923557 NA NA 1.961820e+00
41.3 -15.9788960 0.9885613862 3.3813529415 NA NA 3.342675e+00
41.4 -9.6070508 -0.3908036794 3.5482964432 NA NA -1.386687e+00
42 -5.2159485 -0.0338893961 0.4859252973 NA NA -1.646771e-02
42.1 -15.9878743 -0.4498363172 4.3293134298 NA NA -1.947482e+00
43 -16.6104361 0.8965546110 0.5616614548 NA NA 5.035602e-01
43.1 -9.5549441 0.6199122090 1.0743579536 NA NA 6.660076e-01
43.2 -14.2003491 0.1804894429 2.6131797966 NA NA 4.716514e-01
44 -8.1969033 1.3221409285 0.7662644819 NA NA 1.013110e+00
44.1 -19.9270197 0.3416426284 2.6490291790 NA NA 9.050213e-01
44.2 -22.6521171 0.5706610068 3.3371910988 NA NA 1.904405e+00
44.3 -21.1903736 1.2679497430 4.1154200875 NA NA 5.218146e+00
45 -0.5686627 0.1414983160 0.1957449992 NA NA 2.769759e-02
45.1 -7.5645740 0.7220892521 1.9963831536 NA NA 1.441567e+00
46 -19.1624789 1.5391054233 1.3477755385 NA NA 2.074369e+00
46.1 -18.4487574 0.3889107049 2.8565793915 NA NA 1.110954e+00
46.2 -15.8222682 0.1248719493 4.4160729996 NA NA 5.514436e-01
47 -5.4165074 0.2014101100 0.6012621359 NA NA 1.211003e-01
47.1 -15.0975029 0.2982973539 2.4097121472 NA NA 7.188108e-01
47.2 -12.9971413 1.1518107179 2.9975794035 NA NA 3.452644e+00
47.3 -10.6844521 0.5196802157 3.1829649757 NA NA 1.654124e+00
47.4 -18.2214784 0.3702301552 4.6201055450 NA NA 1.710502e+00
48 -8.3101471 -0.2128602862 2.8607365978 NA NA -6.089372e-01
48.1 -18.3854275 -0.5337239976 2.9098354396 NA NA -1.553049e+00
49 -13.0130319 -0.5236770035 2.7179756400 NA NA -1.423341e+00
50 -10.4579977 0.3897705981 1.1762060679 NA NA 4.584505e-01
51 -19.3157621 -0.7213343736 1.4304436720 NA NA -1.031828e+00
52 -4.4747188 0.3758235358 2.1266646020 NA NA 7.992506e-01
52.1 -4.3163827 0.7138067080 3.1000545993 NA NA 2.212840e+00
52.2 -6.9761408 0.8872895233 3.1268477370 NA NA 2.774419e+00
52.3 -20.1764756 -0.9664587437 3.5711459327 NA NA -3.451365e+00
52.4 -8.9036692 0.0254566848 4.7983659909 NA NA 1.221505e-01
52.5 -5.6949642 0.4155259424 4.9818264414 NA NA 2.070078e+00
53 -10.3141887 0.5675736897 0.4965799209 NA NA 2.818457e-01
53.1 -8.2642654 -0.3154088781 3.5505357443 NA NA -1.119870e+00
53.2 -9.1691554 0.2162315769 4.5790420019 NA NA 9.901335e-01
54 -6.2198754 -0.0880802382 1.4034724841 NA NA -1.236182e-01
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57.3 -9.3473241 0.6785562445 2.1153886721 NA NA 1.435410e+00
58 -12.0245677 1.6476207996 1.2210099772 NA NA 2.011761e+00
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58.5 -7.8523047 0.8713264822 3.5715569824 NA NA 3.111992e+00
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72.3 -27.2843801 0.7107266765 3.4853593935 NA NA 2.477138e+00
72.4 -20.8541617 0.1451969143 3.6884259700 NA NA 5.355481e-01
72.5 -12.8948965 1.6298050306 4.0854155901 NA NA 6.658431e+00
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79 -1.0217265 -0.3767450660 0.0583819521 NA NA -2.199511e-02
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81.1 -4.0892715 0.2443038954 0.9421709494 NA NA 2.301760e-01
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81.3 -5.2166381 0.4182528210 3.3456630446 NA NA 1.399333e+00
82 -3.0749381 1.1088801952 1.3791010005 NA NA 1.529258e+00
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91 -7.1462503 1.1213651365 0.6382618390 NA NA 7.157246e-01
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92 -6.4119222 0.4674939332 0.0737052364 NA NA 3.445675e-02
93 5.2122168 0.2677965647 0.2788909199 NA NA 7.468603e-02
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100.4 -12.8093826 -0.0834577654 4.6567608765 NA NA -3.886429e-01
B21:c1:time I(time^2)
1 NA 2.591239e-01
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1.2 NA 4.539005e+00
1.3 NA 6.227241e+00
2 NA 9.099267e+00
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3 NA 7.188883e-01
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3.2 NA 2.245012e+01
4 NA 1.136655e-01
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4.3 NA 9.819783e+00
5 NA 1.158319e+00
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5.2 NA 7.817661e+00
5.3 NA 7.907311e+00
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7.1 NA 1.369622e+01
7.2 NA 2.276883e+01
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8.2 NA 3.303606e+00
8.3 NA 8.056666e+00
8.4 NA 1.130995e+01
8.5 NA 1.967885e+01
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28.2 NA 1.773129e+01
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29.3 NA 1.161363e+01
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31 NA 1.934957e+01
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52.5 NA 2.481859e+01
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53.2 NA 2.096763e+01
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54.4 NA 1.611531e+01
55 NA 3.743632e-01
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55.2 NA 7.953081e+00
55.3 NA 9.813453e+00
55.4 NA 1.038006e+01
56 NA 1.496055e+00
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56.2 NA 6.592024e+00
56.3 NA 8.706727e+00
56.4 NA 1.041529e+01
56.5 NA 1.167966e+01
57 NA 5.618471e-02
57.1 NA 6.157569e-02
57.2 NA 1.300874e+00
57.3 NA 4.474869e+00
58 NA 1.490865e+00
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58.2 NA 2.819667e+00
58.3 NA 6.927488e+00
58.4 NA 8.109812e+00
58.5 NA 1.275602e+01
59 NA 3.619125e+00
59.1 NA 2.473731e+01
60 NA 8.325824e+00
61 NA 5.203647e-01
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61.2 NA 6.288716e+00
61.3 NA 1.006861e+01
61.4 NA 1.297637e+01
62 NA 2.848114e-01
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62.2 NA 1.196074e+01
62.3 NA 2.306763e+01
63 NA 7.894611e+00
63.1 NA 1.572420e+01
64 NA 1.696724e+01
65 NA 5.007194e-01
65.1 NA 4.101535e+00
65.2 NA 9.689140e+00
65.3 NA 1.022023e+01
66 NA 1.221045e+01
66.1 NA 1.419589e+01
66.2 NA 1.559155e+01
67 NA 1.741258e+01
68 NA 1.669057e-02
68.1 NA 3.171834e+00
68.2 NA 4.199715e+00
68.3 NA 8.647640e+00
68.4 NA 1.632699e+01
69 NA 1.718202e+01
70 NA 3.970284e-02
70.1 NA 2.332672e-01
71 NA 5.993245e-01
71.1 NA 2.215280e+00
71.2 NA 1.661257e+01
71.3 NA 2.213537e+01
71.4 NA 2.231881e+01
72 NA 8.688470e-01
72.1 NA 1.392398e+00
72.2 NA 3.578744e+00
72.3 NA 1.214773e+01
72.4 NA 1.360449e+01
72.5 NA 1.669062e+01
73 NA 2.117830e+01
74 NA 2.139435e+00
75 NA 1.057829e+01
76 NA 3.266987e+00
76.1 NA 1.822015e+01
76.2 NA 2.468970e+01
77 NA 7.155525e-01
78 NA 6.775091e-01
79 NA 3.408452e-03
79.1 NA 5.956710e+00
79.2 NA 1.086528e+01
80 NA 8.073131e-01
80.1 NA 1.804904e+00
80.2 NA 7.854511e+00
81 NA 1.026675e-04
81.1 NA 8.876861e-01
81.2 NA 9.328415e+00
81.3 NA 1.119346e+01
82 NA 1.901920e+00
82.1 NA 3.097956e+00
82.2 NA 6.881772e+00
83 NA 2.887926e-03
83.1 NA 8.445749e+00
83.2 NA 9.727823e+00
83.3 NA 2.271823e+01
84 NA 7.427838e+00
84.1 NA 1.113209e+01
85 NA 8.867032e-02
85.1 NA 3.025553e+00
85.2 NA 7.207254e+00
85.3 NA 9.991139e+00
85.4 NA 1.556465e+01
85.5 NA 2.033338e+01
86 NA 7.184729e-01
86.1 NA 1.023867e+00
86.2 NA 1.565291e+00
86.3 NA 4.783212e+00
86.4 NA 6.018649e+00
86.5 NA 1.459703e+01
87 NA 7.327595e+00
87.1 NA 1.187665e+01
87.2 NA 2.046808e+01
88 NA 3.472088e-07
88.1 NA 5.063888e-01
88.2 NA 6.226384e+00
88.3 NA 1.088724e+01
89 NA 4.175856e-01
90 NA 2.876520e-02
90.1 NA 6.749804e+00
90.2 NA 7.102967e+00
90.3 NA 9.761057e+00
91 NA 4.073782e-01
91.1 NA 6.877013e+00
91.2 NA 2.282214e+01
92 NA 5.432462e-03
93 NA 7.778015e-02
93.1 NA 1.072832e+00
93.2 NA 6.208345e+00
93.3 NA 8.338309e+00
93.4 NA 1.992683e+01
94 NA 7.204688e-01
94.1 NA 1.113543e+00
94.2 NA 3.781275e+00
94.3 NA 9.431485e+00
94.4 NA 9.531256e+00
94.5 NA 1.918945e+01
95 NA 4.080021e+00
95.1 NA 1.614790e+01
95.2 NA 2.079044e+01
96 NA 9.692853e-04
96.1 NA 1.753685e-02
96.2 NA 4.490747e-01
96.3 NA 4.741523e+00
96.4 NA 4.948909e+00
96.5 NA 1.795865e+01
97 NA 1.429245e+00
97.1 NA 2.460468e+01
98 NA 4.168671e-02
98.1 NA 1.857247e-01
98.2 NA 1.237113e+01
99 NA 1.247351e-01
99.1 NA 2.189252e+01
99.2 NA 2.492714e+01
100 NA 1.143058e+00
100.1 NA 2.282923e+00
100.2 NA 4.623503e+00
100.3 NA 1.501220e+01
100.4 NA 2.168542e+01
$m8l$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8l$spM_lvlone
center scale
y -11.1733710 6.2496619
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
B21:c1 0.1798099 0.6117459
B21:time 2.0207975 1.5857637
c1:time 0.6507067 1.9186258
B21:c1:time 0.4612732 1.7267423
I(time^2) 8.3244468 7.0900029
$m8l$mu_reg_norm
[1] 0
$m8l$tau_reg_norm
[1] 1e-04
$m8l$shape_tau_norm
[1] 0.01
$m8l$rate_tau_norm
[1] 0.01
$m8l$mu_reg_binom
[1] 0
$m8l$tau_reg_binom
[1] 1e-04
$m8l$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8l$shape_diag_RinvD
[1] "0.01"
$m8l$rate_diag_RinvD
[1] "0.001"
$m8l$RinvD_y_id
[,1] [,2] [,3]
[1,] NA 0 0
[2,] 0 NA 0
[3,] 0 0 NA
$m8l$KinvD_y_id
id
4
$m8m
$m8m$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m8m$M_lvlone
y c1 b11 o1.L o1.Q c1:b11 b1
1 -13.0493856 0.7592026489 0 -7.850462e-17 -0.8164966 0.0000000000 1
1.1 -9.3335901 0.9548337990 1 -7.071068e-01 0.4082483 0.9548337990 2
1.2 -22.3469852 0.5612235156 1 -7.071068e-01 0.4082483 0.5612235156 2
1.3 -15.0417337 1.1873391025 0 -7.850462e-17 -0.8164966 0.0000000000 1
2 -12.0655434 0.9192204198 1 7.071068e-01 0.4082483 0.9192204198 2
2.1 -15.8674476 -0.1870730476 1 -7.071068e-01 0.4082483 -0.1870730476 2
2.2 -7.8800006 1.2517512331 1 -7.850462e-17 -0.8164966 1.2517512331 2
3 -11.4820604 -0.0605087604 1 -7.071068e-01 0.4082483 -0.0605087604 2
3.1 -10.5983220 0.3788637747 0 7.071068e-01 0.4082483 0.0000000000 1
3.2 -22.4519157 0.9872578281 0 -7.850462e-17 -0.8164966 0.0000000000 1
4 -1.2697775 1.4930175328 1 7.071068e-01 0.4082483 1.4930175328 2
4.1 -11.1215184 -0.7692526880 1 -7.850462e-17 -0.8164966 -0.7692526880 2
4.2 -3.6134138 0.9180841450 0 -7.071068e-01 0.4082483 0.0000000000 1
4.3 -14.5982385 -0.0541170782 1 -7.850462e-17 -0.8164966 -0.0541170782 2
5 -6.8457515 -0.1376784521 0 -7.850462e-17 -0.8164966 0.0000000000 1
5.1 -7.0551214 -0.2740585866 1 -7.850462e-17 -0.8164966 -0.2740585866 2
5.2 -12.3418980 0.4670496929 1 7.071068e-01 0.4082483 0.4670496929 2
5.3 -9.2366906 0.1740288049 1 -7.850462e-17 -0.8164966 0.1740288049 2
6 -5.1648211 0.9868044683 0 7.071068e-01 0.4082483 0.0000000000 1
7 -10.0599502 -0.1280320918 1 7.071068e-01 0.4082483 -0.1280320918 2
7.1 -18.3267285 0.4242971219 0 -7.071068e-01 0.4082483 0.0000000000 1
7.2 -12.5138426 0.0777182491 1 -7.071068e-01 0.4082483 0.0777182491 2
8 -1.6305331 -0.5791408712 0 -7.850462e-17 -0.8164966 0.0000000000 1
8.1 -9.6520453 0.3128604232 1 -7.850462e-17 -0.8164966 0.3128604232 2
8.2 -1.5278462 0.6258446356 1 7.071068e-01 0.4082483 0.6258446356 2
8.3 -7.4172211 -0.1040137707 0 7.071068e-01 0.4082483 0.0000000000 1
8.4 -7.1238609 0.0481450285 0 -7.850462e-17 -0.8164966 0.0000000000 1
8.5 -8.8706950 0.3831763675 1 -7.850462e-17 -0.8164966 0.3831763675 2
9 -0.1634429 -0.1757592269 1 -7.850462e-17 -0.8164966 -0.1757592269 2
9.1 -2.6034300 -0.1791541200 1 -7.850462e-17 -0.8164966 -0.1791541200 2
9.2 -6.7272369 -0.0957042935 0 7.071068e-01 0.4082483 0.0000000000 1
10 -6.4172202 -0.5598409704 1 -7.850462e-17 -0.8164966 -0.5598409704 2
10.1 -11.4834569 -0.2318340451 1 -7.071068e-01 0.4082483 -0.2318340451 2
11 -8.7911356 0.5086859475 1 -7.850462e-17 -0.8164966 0.5086859475 2
11.1 -19.6645080 0.4951758188 1 7.071068e-01 0.4082483 0.4951758188 2
11.2 -20.2030932 -1.1022162541 1 7.071068e-01 0.4082483 -1.1022162541 2
11.3 -21.3082176 -0.0611636705 1 -7.071068e-01 0.4082483 -0.0611636705 2
11.4 -14.5802901 -0.4971774316 1 7.071068e-01 0.4082483 -0.4971774316 2
12 -15.2006287 -0.2433996286 1 -7.071068e-01 0.4082483 -0.2433996286 2
13 0.8058816 0.8799673116 0 -7.850462e-17 -0.8164966 0.0000000000 1
13.1 -13.6379208 0.1079022586 1 -7.071068e-01 0.4082483 0.1079022586 2
14 -15.3422873 0.9991752617 0 7.071068e-01 0.4082483 0.0000000000 1
14.1 -10.0965208 -0.1094019046 1 7.071068e-01 0.4082483 -0.1094019046 2
14.2 -16.6452027 0.1518967560 0 -7.850462e-17 -0.8164966 0.0000000000 1
14.3 -15.8389733 0.3521012473 0 -7.071068e-01 0.4082483 0.0000000000 1
15 -8.9424594 0.3464447888 0 7.071068e-01 0.4082483 0.0000000000 1
15.1 -22.0101983 -0.4767313971 0 7.071068e-01 0.4082483 0.0000000000 1
15.2 -7.3975599 0.5759767791 0 -7.850462e-17 -0.8164966 0.0000000000 1
15.3 -10.3567334 -0.1713452662 1 -7.850462e-17 -0.8164966 -0.1713452662 2
16 -1.9691302 0.4564754473 1 7.071068e-01 0.4082483 0.4564754473 2
16.1 -9.9308357 1.0652558311 0 -7.071068e-01 0.4082483 0.0000000000 1
16.2 -6.9626923 0.6971872493 1 7.071068e-01 0.4082483 0.6971872493 2
16.3 -3.2862557 0.5259331838 1 -7.071068e-01 0.4082483 0.5259331838 2
16.4 -3.3972355 0.2046601798 1 -7.071068e-01 0.4082483 0.2046601798 2
16.5 -11.5767835 1.0718540464 0 -7.850462e-17 -0.8164966 0.0000000000 1
17 -10.5474144 0.6048676222 0 -7.850462e-17 -0.8164966 0.0000000000 1
17.1 -7.6215009 0.2323298304 0 -7.850462e-17 -0.8164966 0.0000000000 1
17.2 -16.5386939 1.2617499032 1 7.071068e-01 0.4082483 1.2617499032 2
17.3 -20.0004774 -0.3913230895 0 -7.850462e-17 -0.8164966 0.0000000000 1
17.4 -18.8505475 0.9577299112 1 7.071068e-01 0.4082483 0.9577299112 2
18 -19.7302351 -0.0050324072 1 -7.850462e-17 -0.8164966 -0.0050324072 2
19 -14.6177568 -0.4187468937 1 7.071068e-01 0.4082483 -0.4187468937 2
19.1 -17.8043866 -0.4478828944 1 -7.850462e-17 -0.8164966 -0.4478828944 2
19.2 -15.1641705 -1.1966721302 1 -7.850462e-17 -0.8164966 -1.1966721302 2
19.3 -16.6898418 -0.5877091668 1 -7.850462e-17 -0.8164966 -0.5877091668 2
20 -12.9059229 0.6838223064 0 -7.071068e-01 0.4082483 0.0000000000 1
20.1 -16.8191201 0.3278571109 1 -7.850462e-17 -0.8164966 0.3278571109 2
20.2 -6.1010131 -0.8489831990 0 -7.850462e-17 -0.8164966 0.0000000000 1
20.3 -7.9415371 1.3169975191 0 -7.071068e-01 0.4082483 0.0000000000 1
20.4 -9.3904458 0.0444804531 0 -7.071068e-01 0.4082483 0.0000000000 1
20.5 -13.3504189 -0.4535207652 0 -7.850462e-17 -0.8164966 0.0000000000 1
21 -7.6974718 -0.4030302960 1 -7.850462e-17 -0.8164966 -0.4030302960 2
21.1 -11.9335526 -0.4069674045 1 -7.850462e-17 -0.8164966 -0.4069674045 2
21.2 -12.7064929 1.0650265940 0 -7.850462e-17 -0.8164966 0.0000000000 1
22 -21.5022909 -0.0673274516 0 -7.071068e-01 0.4082483 0.0000000000 1
22.1 -12.7745451 0.9601388170 1 -7.071068e-01 0.4082483 0.9601388170 2
23 -3.5146508 0.5556634840 1 -7.071068e-01 0.4082483 0.5556634840 2
23.1 -4.6724048 1.4407865964 1 -7.850462e-17 -0.8164966 1.4407865964 2
24 -2.5619821 0.3856376411 0 -7.071068e-01 0.4082483 0.0000000000 1
25 -6.2944970 0.3564400705 0 -7.850462e-17 -0.8164966 0.0000000000 1
25.1 -3.8630505 0.0982553434 1 -7.071068e-01 0.4082483 0.0982553434 2
25.2 -14.4205140 0.1928682598 1 -7.071068e-01 0.4082483 0.1928682598 2
25.3 -19.6735037 -0.0192488594 0 -7.071068e-01 0.4082483 0.0000000000 1
25.4 -9.0288933 0.4466012931 0 -7.850462e-17 -0.8164966 0.0000000000 1
25.5 -9.0509738 1.1425193342 0 -7.850462e-17 -0.8164966 0.0000000000 1
26 -19.7340685 0.5341531449 1 -7.850462e-17 -0.8164966 0.5341531449 2
26.1 -14.1692728 1.2268695927 1 7.071068e-01 0.4082483 1.2268695927 2
26.2 -17.2819976 0.3678294939 1 -7.850462e-17 -0.8164966 0.3678294939 2
26.3 -24.6265576 0.5948516018 0 -7.071068e-01 0.4082483 0.0000000000 1
27 -7.3354999 -0.3342844147 1 -7.850462e-17 -0.8164966 -0.3342844147 2
27.1 -11.1488468 -0.4835141229 1 -7.850462e-17 -0.8164966 -0.4835141229 2
28 -11.7996597 -0.7145915499 1 -7.071068e-01 0.4082483 -0.7145915499 2
28.1 -8.2030122 0.5063671955 0 -7.071068e-01 0.4082483 0.0000000000 1
28.2 -26.4317815 -0.2067413142 1 -7.071068e-01 0.4082483 -0.2067413142 2
28.3 -18.5016071 0.1196789973 1 -7.071068e-01 0.4082483 0.1196789973 2
29 -5.8551395 0.1392699487 1 -7.850462e-17 -0.8164966 0.1392699487 2
29.1 -2.0209442 0.7960234776 0 -7.850462e-17 -0.8164966 0.0000000000 1
29.2 -5.6368080 1.0398214352 0 -7.071068e-01 0.4082483 0.0000000000 1
29.3 -3.8110961 0.0813246429 1 -7.850462e-17 -0.8164966 0.0813246429 2
30 -12.7217702 -0.3296323050 1 -7.850462e-17 -0.8164966 -0.3296323050 2
30.1 -17.0170140 1.3635850954 1 -7.850462e-17 -0.8164966 1.3635850954 2
30.2 -25.4236089 0.7354171050 1 -7.850462e-17 -0.8164966 0.7354171050 2
31 -17.0783921 0.3708398217 0 -7.850462e-17 -0.8164966 0.0000000000 1
32 -18.4338764 -0.0474059668 1 7.071068e-01 0.4082483 -0.0474059668 2
32.1 -19.4317212 1.2507771489 1 -7.071068e-01 0.4082483 1.2507771489 2
32.2 -19.4738978 0.1142915519 1 7.071068e-01 0.4082483 0.1142915519 2
32.3 -21.4922645 0.6773270619 1 7.071068e-01 0.4082483 0.6773270619 2
33 2.0838099 0.1774293842 0 -7.850462e-17 -0.8164966 0.0000000000 1
33.1 -13.3172274 0.6159606291 0 -7.850462e-17 -0.8164966 0.0000000000 1
34 -10.0296691 0.8590979166 1 7.071068e-01 0.4082483 0.8590979166 2
34.1 -25.9426553 0.0546216775 0 -7.850462e-17 -0.8164966 0.0000000000 1
34.2 -18.5688138 -0.0897224473 1 -7.850462e-17 -0.8164966 -0.0897224473 2
34.3 -15.4173859 0.4163395571 1 -7.071068e-01 0.4082483 0.4163395571 2
35 -14.3958113 -1.4693520528 1 -7.850462e-17 -0.8164966 -1.4693520528 2
35.1 -12.9457541 -0.3031734330 0 7.071068e-01 0.4082483 0.0000000000 1
35.2 -16.1380691 -0.6045512101 1 -7.850462e-17 -0.8164966 -0.6045512101 2
36 -12.8166968 0.9823048960 0 7.071068e-01 0.4082483 0.0000000000 1
36.1 -14.3989481 1.4466051416 0 -7.850462e-17 -0.8164966 0.0000000000 1
36.2 -12.2436943 1.1606752905 1 7.071068e-01 0.4082483 1.1606752905 2
36.3 -15.0104638 0.8373091576 0 7.071068e-01 0.4082483 0.0000000000 1
36.4 -10.1775457 0.2640591685 1 7.071068e-01 0.4082483 0.2640591685 2
37 -15.2223495 0.1177313455 1 7.071068e-01 0.4082483 0.1177313455 2
37.1 -14.7526195 -0.1415483779 0 7.071068e-01 0.4082483 0.0000000000 1
37.2 -19.8168430 0.0054610124 0 7.071068e-01 0.4082483 0.0000000000 1
38 -2.7065118 0.8078948077 1 -7.850462e-17 -0.8164966 0.8078948077 2
39 -8.7288138 0.9876451040 1 -7.071068e-01 0.4082483 0.9876451040 2
39.1 -9.2746473 -0.3431222274 0 -7.850462e-17 -0.8164966 0.0000000000 1
39.2 -18.2695344 -1.7909380751 0 -7.850462e-17 -0.8164966 0.0000000000 1
39.3 -13.8219083 -0.1798746191 0 -7.071068e-01 0.4082483 0.0000000000 1
39.4 -16.2254704 -0.1850961689 1 -7.071068e-01 0.4082483 -0.1850961689 2
39.5 -21.7283648 0.4544226146 1 -7.071068e-01 0.4082483 0.4544226146 2
40 1.8291916 0.5350190436 0 7.071068e-01 0.4082483 0.0000000000 1
40.1 -6.6916432 0.4189342752 0 -7.071068e-01 0.4082483 0.0000000000 1
40.2 -1.6278171 0.4211994981 0 -7.071068e-01 0.4082483 0.0000000000 1
40.3 -10.5749790 0.0916687506 1 -7.850462e-17 -0.8164966 0.0916687506 2
41 -3.1556121 -0.1035047421 1 7.071068e-01 0.4082483 -0.1035047421 2
41.1 -11.5895327 -0.4684202411 1 -7.850462e-17 -0.8164966 -0.4684202411 2
41.2 -18.9352091 0.5972615368 0 -7.071068e-01 0.4082483 0.0000000000 1
41.3 -15.9788960 0.9885613862 1 -7.071068e-01 0.4082483 0.9885613862 2
41.4 -9.6070508 -0.3908036794 1 7.071068e-01 0.4082483 -0.3908036794 2
42 -5.2159485 -0.0338893961 1 -7.850462e-17 -0.8164966 -0.0338893961 2
42.1 -15.9878743 -0.4498363172 1 -7.071068e-01 0.4082483 -0.4498363172 2
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91 -7.1462503 1.1213651365 0 -7.071068e-01 0.4082483 0.0000000000 1
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91.2 -18.2576707 0.3395603754 1 -7.071068e-01 0.4082483 0.3395603754 2
92 -6.4119222 0.4674939332 1 -7.850462e-17 -0.8164966 0.4674939332 2
93 5.2122168 0.2677965647 0 -7.071068e-01 0.4082483 0.0000000000 1
93.1 3.1211725 1.6424445368 1 7.071068e-01 0.4082483 1.6424445368 2
93.2 -3.6841177 0.7101700066 0 -7.071068e-01 0.4082483 0.0000000000 1
93.3 2.6223542 1.1222322893 1 7.071068e-01 0.4082483 1.1222322893 2
93.4 -11.1877696 1.4628960401 0 -7.071068e-01 0.4082483 0.0000000000 1
94 -6.9602492 -0.2904211940 1 -7.850462e-17 -0.8164966 -0.2904211940 2
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94.2 -4.3498045 -0.4536774482 1 -7.071068e-01 0.4082483 -0.4536774482 2
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94.4 -12.9357964 -0.9411356550 0 -7.850462e-17 -0.8164966 0.0000000000 1
94.5 -14.7648530 0.5683867264 0 7.071068e-01 0.4082483 0.0000000000 1
95 -12.8849309 0.2375652188 1 7.071068e-01 0.4082483 0.2375652188 2
95.1 -9.7451502 0.0767152977 1 -7.071068e-01 0.4082483 0.0767152977 2
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96 -4.9139832 0.7813892121 1 -7.071068e-01 0.4082483 0.7813892121 2
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96.2 -9.6555492 -0.4857246503 0 -7.071068e-01 0.4082483 0.0000000000 1
96.3 -11.8690793 0.8771471244 0 -7.071068e-01 0.4082483 0.0000000000 1
96.4 -11.0224373 1.9030768981 0 -7.850462e-17 -0.8164966 0.0000000000 1
96.5 -10.9530403 -0.1684332749 1 7.071068e-01 0.4082483 -0.1684332749 2
97 -9.8540471 1.3775130083 0 7.071068e-01 0.4082483 0.0000000000 1
97.1 -19.2262840 -1.7323228619 0 7.071068e-01 0.4082483 0.0000000000 1
98 -11.9651231 -1.2648518889 0 7.071068e-01 0.4082483 0.0000000000 1
98.1 -2.6515128 -0.9042716241 0 -7.071068e-01 0.4082483 0.0000000000 1
98.2 -12.2606382 -0.1560385207 0 -7.071068e-01 0.4082483 0.0000000000 1
99 -11.4720500 0.7993356425 1 -7.850462e-17 -0.8164966 0.7993356425 2
99.1 -14.0596866 1.0355522332 1 -7.850462e-17 -0.8164966 1.0355522332 2
99.2 -17.3939469 -0.1150895843 1 -7.071068e-01 0.4082483 -0.1150895843 2
100 1.1005874 0.0369067906 0 -7.071068e-01 0.4082483 0.0000000000 1
100.1 -3.8226248 1.6023713093 0 -7.850462e-17 -0.8164966 0.0000000000 1
100.2 -0.9123182 0.8861545820 1 -7.850462e-17 -0.8164966 0.8861545820 2
100.3 -15.8389474 0.1277046316 1 -7.071068e-01 0.4082483 0.1277046316 2
100.4 -12.8093826 -0.0834577654 1 -7.071068e-01 0.4082483 -0.0834577654 2
$m8m$spM_lvlone
center scale
y -11.1733710 6.2496619
c1 0.2559996 0.6718095
b11 NA NA
o1.L NA NA
o1.Q NA NA
c1:b11 0.1247936 0.5064162
b1 NA NA
$m8m$mu_reg_norm
[1] 0
$m8m$tau_reg_norm
[1] 1e-04
$m8m$shape_tau_norm
[1] 0.01
$m8m$rate_tau_norm
[1] 0.01
$m8m$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8m$shape_diag_RinvD
[1] "0.01"
$m8m$rate_diag_RinvD
[1] "0.001"
$m8m$RinvD_y_id
[,1] [,2]
[1,] NA 0
[2,] 0 NA
$m8m$KinvD_y_id
id
3
$m8n
$m8n$M_id
B2 (Intercept) C1 B21
1 1 1 0.7175865 NA
2 NA 1 0.7507170 NA
3 NA 1 0.7255954 NA
4 1 1 0.7469352 NA
5 1 1 0.7139120 NA
6 1 1 0.7332505 NA
7 0 1 0.7345929 NA
8 1 1 0.7652589 NA
9 1 1 0.7200622 NA
10 0 1 0.7423879 NA
11 1 1 0.7437448 NA
12 1 1 0.7446470 NA
13 1 1 0.7530186 NA
14 1 1 0.7093137 NA
15 NA 1 0.7331192 NA
16 1 1 0.7011390 NA
17 1 1 0.7432395 NA
18 1 1 0.7545191 NA
19 1 1 0.7528487 NA
20 0 1 0.7612865 NA
21 1 1 0.7251719 NA
22 1 1 0.7300630 NA
23 1 1 0.7087249 NA
24 NA 1 0.7391938 NA
25 0 1 0.7820641 NA
26 1 1 0.7118298 NA
27 1 1 0.7230857 NA
28 0 1 0.7489353 NA
29 1 1 0.7510888 NA
30 0 1 0.7300717 NA
31 0 1 0.7550721 NA
32 1 1 0.7321898 NA
33 1 1 0.7306414 NA
34 0 1 0.7427216 NA
35 1 1 0.7193042 NA
36 0 1 0.7312888 NA
37 1 1 0.7100436 NA
38 1 1 0.7670184 NA
39 1 1 0.7400449 NA
40 1 1 0.7397304 NA
41 1 1 0.7490966 NA
42 1 1 0.7419274 NA
43 1 1 0.7527810 NA
44 NA 1 0.7408315 NA
45 1 1 0.7347550 NA
46 1 1 0.7332398 NA
47 1 1 0.7376481 NA
48 1 1 0.7346179 NA
49 1 1 0.7329402 NA
50 1 1 0.7260436 NA
51 0 1 0.7242910 NA
52 1 1 0.7298067 NA
53 1 1 0.7254741 NA
54 0 1 0.7542067 NA
55 1 1 0.7389952 NA
56 0 1 0.7520638 NA
57 1 1 0.7219958 NA
58 NA 1 0.7259632 NA
59 1 1 0.7458606 NA
60 1 1 0.7672421 NA
61 0 1 0.7257179 NA
62 0 1 0.7189892 NA
63 1 1 0.7333356 NA
64 1 1 0.7320243 NA
65 1 1 0.7477711 NA
66 1 1 0.7343974 NA
67 1 1 0.7491624 NA
68 1 1 0.7482736 NA
69 NA 1 0.7338267 NA
70 1 1 0.7607742 NA
71 1 1 0.7777600 NA
72 1 1 0.7408143 NA
73 1 1 0.7248271 NA
74 1 1 0.7364916 NA
75 1 1 0.7464926 NA
76 1 1 0.7355430 NA
77 1 1 0.7208449 NA
78 1 1 0.7373573 NA
79 1 1 0.7598079 NA
80 1 1 0.7360415 NA
81 1 1 0.7293932 NA
82 1 1 0.7279309 NA
83 1 1 0.7344643 NA
84 1 1 0.7384350 NA
85 1 1 0.7323716 NA
86 1 1 0.7576597 NA
87 1 1 0.7496139 NA
88 1 1 0.7275239 NA
89 1 1 0.7250648 NA
90 1 1 0.7335262 NA
91 NA 1 0.7343980 NA
92 1 1 0.7380425 NA
93 1 1 0.7389460 NA
94 1 1 0.7259951 NA
95 1 1 0.7282840 NA
96 NA 1 0.7281676 NA
97 NA 1 0.7245642 NA
98 1 1 0.7526938 NA
99 1 1 0.7272309 NA
100 1 1 0.7383460 NA
$m8n$M_lvlone
y c1 time b1 b11 C1:time
1 -13.0493856 0.7592026489 0.5090421822 0 0 0.3652818145
1.1 -9.3335901 0.9548337990 0.6666076288 1 1 0.4783486570
1.2 -22.3469852 0.5612235156 2.1304941282 1 1 1.5288138942
1.3 -15.0417337 1.1873391025 2.4954441458 0 0 1.7906971118
2 -12.0655434 0.9192204198 3.0164990982 1 1 2.2645370243
2.1 -15.8674476 -0.1870730476 3.2996806887 1 1 2.4771262462
2.2 -7.8800006 1.2517512331 4.1747569619 1 1 3.1340608434
3 -11.4820604 -0.0605087604 0.8478727890 1 1 0.6152125819
3.1 -10.5983220 0.3788637747 3.0654308549 0 0 2.2242624781
3.2 -22.4519157 0.9872578281 4.7381553578 0 0 3.4379836560
4 -1.2697775 1.4930175328 0.3371432109 1 1 0.2518241168
4.1 -11.1215184 -0.7692526880 1.0693019140 1 1 0.7986991917
4.2 -3.6134138 0.9180841450 2.6148973033 0 0 1.9531587247
4.3 -14.5982385 -0.0541170782 3.1336532847 1 1 2.3406358046
5 -6.8457515 -0.1376784521 1.0762525082 0 0 0.7683495918
5.1 -7.0551214 -0.2740585866 1.7912546196 1 1 1.2787981866
5.2 -12.3418980 0.4670496929 2.7960080339 1 1 1.9961037166
5.3 -9.2366906 0.1740288049 2.8119940578 1 1 2.0075163311
6 -5.1648211 0.9868044683 1.7815462884 0 0 1.3063196933
7 -10.0599502 -0.1280320918 3.3074087673 1 1 2.4295989047
7.1 -18.3267285 0.4242971219 3.7008403614 0 0 2.7186109493
7.2 -12.5138426 0.0777182491 4.7716691741 1 1 3.5052341620
8 -1.6305331 -0.5791408712 1.1246398522 0 0 0.8606406530
8.1 -9.6520453 0.3128604232 1.8027009873 1 1 1.3795329695
8.2 -1.5278462 0.6258446356 1.8175825174 1 1 1.3909211928
8.3 -7.4172211 -0.1040137707 2.8384267003 0 0 2.1721312865
8.4 -7.1238609 0.0481450285 3.3630275307 0 0 2.5735867394
8.5 -8.8706950 0.3831763675 4.4360849704 1 1 3.3947534923
9 -0.1634429 -0.1757592269 0.9607803822 1 1 0.6918216822
9.1 -2.6034300 -0.1791541200 2.9177753383 1 1 2.1009798703
9.2 -6.7272369 -0.0957042935 4.8100892501 0 0 3.4635636802
10 -6.4172202 -0.5598409704 2.2975509102 1 1 1.7056739807
10.1 -11.4834569 -0.2318340451 4.1734118364 1 1 3.0982904226
11 -8.7911356 0.5086859475 1.1832662905 1 1 0.8800481723
11.1 -19.6645080 0.4951758188 1.2346051680 1 1 0.9182311964
11.2 -20.2030932 -1.1022162541 1.6435316263 1 1 1.2223681309
11.3 -21.3082176 -0.0611636705 3.3859017969 1 1 2.5182469169
11.4 -14.5802901 -0.4971774316 4.8118087661 1 1 3.5787578367
12 -15.2006287 -0.2433996286 0.9591987054 1 1 0.7142644156
13 0.8058816 0.8799673116 0.0619085738 0 0 0.0466183059
13.1 -13.6379208 0.1079022586 3.5621061502 1 1 2.6823320911
14 -15.3422873 0.9991752617 4.0364430007 0 0 2.8631042655
14.1 -10.0965208 -0.1094019046 4.4710561272 1 1 3.1713813046
14.2 -16.6452027 0.1518967560 4.6359198843 0 0 3.2883214239
14.3 -15.8389733 0.3521012473 4.6886152599 0 0 3.3256989750
15 -8.9424594 0.3464447888 0.5402063532 0 0 0.3960356618
15.1 -22.0101983 -0.4767313971 1.1893180816 0 0 0.8719119477
15.2 -7.3975599 0.5759767791 1.5094739688 0 0 1.1066243829
15.3 -10.3567334 -0.1713452662 4.9193474615 1 1 3.6064681879
16 -1.9691302 0.4564754473 1.2417913869 1 1 0.8706683147
16.1 -9.9308357 1.0652558311 2.5675726333 0 0 1.8002251917
16.2 -6.9626923 0.6971872493 2.6524101500 1 1 1.8597080795
16.3 -3.2862557 0.5259331838 3.5585018690 1 1 2.4950042800
16.4 -3.3972355 0.2046601798 3.7612454291 1 1 2.6371556877
16.5 -11.5767835 1.0718540464 3.9851612889 0 0 2.7941518196
17 -10.5474144 0.6048676222 1.5925356350 0 0 1.1836354184
17.1 -7.6215009 0.2323298304 2.4374032998 0 0 1.8115744548
17.2 -16.5386939 1.2617499032 3.0256489082 1 1 2.2487818375
17.3 -20.0004774 -0.3913230895 3.3329089405 0 0 2.4771496359
17.4 -18.8505475 0.9577299112 3.8693758985 1 1 2.8758730794
18 -19.7302351 -0.0050324072 2.4374292302 1 1 1.8390868081
19 -14.6177568 -0.4187468937 0.9772165376 1 1 0.7356962360
19.1 -17.8043866 -0.4478828944 1.1466335913 1 1 0.8632416509
19.2 -15.1641705 -1.1966721302 2.2599126538 1 1 1.7013723870
19.3 -16.6898418 -0.5877091668 4.2114245973 1 1 3.1705656887
20 -12.9059229 0.6838223064 1.7170160066 0 0 1.3071411820
20.1 -16.8191201 0.3278571109 1.7562902288 1 1 1.3370401189
20.2 -6.1010131 -0.8489831990 2.2515566566 0 0 1.7140797861
20.3 -7.9415371 1.3169975191 2.2609123867 0 0 1.7212021776
20.4 -9.3904458 0.0444804531 3.4913365287 0 0 2.6579075206
20.5 -13.3504189 -0.4535207652 4.1730977828 0 0 3.1769231897
21 -7.6974718 -0.4030302960 1.6936582839 1 1 1.2281934263
21.1 -11.9335526 -0.4069674045 2.9571191233 1 1 2.1444197467
21.2 -12.7064929 1.0650265940 3.7887385779 0 0 2.7474868216
22 -21.5022909 -0.0673274516 2.4696226232 0 0 1.8029800300
22.1 -12.7745451 0.9601388170 3.1626627257 1 1 2.3089429463
23 -3.5146508 0.5556634840 1.5414533857 1 1 1.0924663221
23.1 -4.6724048 1.4407865964 2.3369736120 1 1 1.6562712764
24 -2.5619821 0.3856376411 2.8283136466 0 0 2.0906718629
25 -6.2944970 0.3564400705 0.5381704110 0 0 0.4208837547
25.1 -3.8630505 0.0982553434 1.6069735331 1 1 1.2567562995
25.2 -14.4205140 0.1928682598 1.6358226922 1 1 1.2793181910
25.3 -19.6735037 -0.0192488594 3.2646870392 0 0 2.5531945101
25.4 -9.0288933 0.4466012931 4.0782226040 0 0 3.1894314642
25.5 -9.0509738 1.1425193342 4.1560292873 0 0 3.2502812774
26 -19.7340685 0.5341531449 0.2412706357 1 1 0.1717436194
26.1 -14.1692728 1.2268695927 2.4451737676 1 1 1.7405474628
26.2 -17.2819976 0.3678294939 3.5988757887 1 1 2.5617868987
26.3 -24.6265576 0.5948516018 4.1822362854 0 0 2.9770402626
27 -7.3354999 -0.3342844147 3.6955824879 1 1 2.6722228982
27.1 -11.1488468 -0.4835141229 4.2451434687 1 1 3.0696025919
28 -11.7996597 -0.7145915499 0.5746519344 1 1 0.4303771207
28.1 -8.2030122 0.5063671955 2.7943964268 0 0 2.0928221348
28.2 -26.4317815 -0.2067413142 4.2108539480 1 1 3.1536571778
28.3 -18.5016071 0.1196789973 4.4705521734 1 1 3.3481543470
29 -5.8551395 0.1392699487 1.1898884235 1 1 0.8937118818
29.1 -2.0209442 0.7960234776 1.7624059319 0 0 1.3237233767
29.2 -5.6368080 1.0398214352 2.0210406382 0 0 1.5179810109
29.3 -3.8110961 0.0813246429 3.4078777023 1 1 2.5596188130
30 -12.7217702 -0.3296323050 2.2635366488 1 1 1.6525441223
30.1 -17.0170140 1.3635850954 3.5938334477 1 1 2.6237562107
30.2 -25.4236089 0.7354171050 3.6138710892 1 1 2.6383851264
31 -17.0783921 0.3708398217 4.3988140998 0 0 3.3214216230
32 -18.4338764 -0.0474059668 1.6745209007 1 1 1.2260671754
32.1 -19.4317212 1.2507771489 2.9128167813 1 1 2.1327348270
32.2 -19.4738978 0.1142915519 2.9676558380 1 1 2.1728874266
32.3 -21.4922645 0.6773270619 4.2099863547 1 1 3.0825091978
33 2.0838099 0.1774293842 0.0093385763 0 0 0.0068231501
33.1 -13.3172274 0.6159606291 3.4591242753 0 0 2.5273792639
34 -10.0296691 0.8590979166 1.4998774312 1 1 1.1139914202
34.1 -25.9426553 0.0546216775 3.8242761395 0 0 2.8403726326
34.2 -18.5688138 -0.0897224473 3.9072251692 1 1 2.9019806717
34.3 -15.4173859 0.4163395571 3.9582124643 1 1 2.9398500390
35 -14.3958113 -1.4693520528 1.3294299203 1 1 0.9562645676
35.1 -12.9457541 -0.3031734330 1.5276966314 0 0 1.0988786520
35.2 -16.1380691 -0.6045512101 4.5025920868 1 1 3.2387335424
36 -12.8166968 0.9823048960 0.7123168337 0 0 0.5209093415
36.1 -14.3989481 1.4466051416 1.7972493160 0 0 1.3143083435
36.2 -12.2436943 1.1606752905 1.8262697803 1 1 1.3355306848
36.3 -15.0104638 0.8373091576 4.2840119381 0 0 3.1328500636
36.4 -10.1775457 0.2640591685 4.6194464504 1 1 3.3781495744
37 -15.2223495 0.1177313455 2.0018732361 1 1 1.4214172307
37.1 -14.7526195 -0.1415483779 3.6656836793 0 0 2.6027951471
37.2 -19.8168430 0.0054610124 3.9663937816 0 0 2.8163124234
38 -2.7065118 0.8078948077 0.9826511063 1 1 0.7537115243
39 -8.7288138 0.9876451040 0.6921808305 1 1 0.5122448722
39.1 -9.2746473 -0.3431222274 0.9027792048 0 0 0.6680971185
39.2 -18.2695344 -1.7909380751 1.3055654289 0 0 0.9661769970
39.3 -13.8219083 -0.1798746191 1.5412842878 0 0 1.1406195291
39.4 -16.2254704 -0.1850961689 3.1834997435 1 1 2.3559326511
39.5 -21.7283648 0.4544226146 4.1394166439 1 1 3.0633540486
40 1.8291916 0.5350190436 1.1330395646 0 0 0.8381437699
40.1 -6.6916432 0.4189342752 2.6940994046 0 0 1.9929071342
40.2 -1.6278171 0.4211994981 3.0396614212 0 0 2.2485298506
40.3 -10.5749790 0.0916687506 4.6762977762 1 1 3.4591994578
41 -3.1556121 -0.1035047421 1.9337158254 1 1 1.4485398595
41.1 -11.5895327 -0.4684202411 3.1956304458 1 1 2.3938357519
41.2 -18.9352091 0.5972615368 3.2846923557 0 0 2.4605517215
41.3 -15.9788960 0.9885613862 3.3813529415 1 1 2.5329598332
41.4 -9.6070508 -0.3908036794 3.5482964432 1 1 2.6580166349
42 -5.2159485 -0.0338893961 0.4859252973 1 1 0.3605213138
42.1 -15.9878743 -0.4498363172 4.3293134298 1 1 3.2120364478
43 -16.6104361 0.8965546110 0.5616614548 0 0 0.4228080758
43.1 -9.5549441 0.6199122090 1.0743579536 0 0 0.8087562626
43.2 -14.2003491 0.1804894429 2.6131797966 1 1 1.9671521198
44 -8.1969033 1.3221409285 0.7662644819 1 1 0.5676728587
44.1 -19.9270197 0.3416426284 2.6490291790 0 0 1.9624842365
44.2 -22.6521171 0.5706610068 3.3371910988 0 0 2.4722962576
44.3 -21.1903736 1.2679497430 4.1154200875 1 1 3.0488327997
45 -0.5686627 0.1414983160 0.1957449992 1 1 0.1438246201
45.1 -7.5645740 0.7220892521 1.9963831536 0 0 1.4668525369
46 -19.1624789 1.5391054233 1.3477755385 1 1 0.9882426330
46.1 -18.4487574 0.3889107049 2.8565793915 0 0 2.0945576311
46.2 -15.8222682 0.1248719493 4.4160729996 1 1 3.2380403739
47 -5.4165074 0.2014101100 0.6012621359 0 0 0.4435198614
47.1 -15.0975029 0.2982973539 2.4097121472 0 0 1.7775195440
47.2 -12.9971413 1.1518107179 2.9975794035 1 1 2.2111586981
47.3 -10.6844521 0.5196802157 3.1829649757 0 0 2.3479080100
47.4 -18.2214784 0.3702301552 4.6201055450 0 0 3.4080119947
48 -8.3101471 -0.2128602862 2.8607365978 0 0 2.1015481927
48.1 -18.3854275 -0.5337239976 2.9098354396 1 1 2.1376170787
49 -13.0130319 -0.5236770035 2.7179756400 0 0 1.9921136553
50 -10.4579977 0.3897705981 1.1762060679 1 1 0.8539769445
51 -19.3157621 -0.7213343736 1.4304436720 1 1 1.0360574125
52 -4.4747188 0.3758235358 2.1266646020 1 1 1.5520541611
52.1 -4.3163827 0.7138067080 3.1000545993 1 1 2.2624407422
52.2 -6.9761408 0.8872895233 3.1268477370 0 0 2.2819945547
52.3 -20.1764756 -0.9664587437 3.5711459327 0 0 2.6062463726
52.4 -8.9036692 0.0254566848 4.7983659909 1 1 3.5018798430
52.5 -5.6949642 0.4155259424 4.9818264414 1 1 3.6357705164
53 -10.3141887 0.5675736897 0.4965799209 1 1 0.3602558557
53.1 -8.2642654 -0.3154088781 3.5505357443 1 1 2.5758216127
53.2 -9.1691554 0.2162315769 4.5790420019 1 1 3.3219762321
54 -6.2198754 -0.0880802382 1.4034724841 0 0 1.0585083082
54.1 -15.7192609 0.4129127672 1.8812377600 1 1 1.4188420659
54.2 -13.0978998 1.0119546775 2.5107589352 0 0 1.8936311350
54.3 -5.1195299 -0.1112901990 2.7848406672 1 1 2.1003454052
54.4 -16.5771751 0.8587727145 4.0143877396 0 0 3.0276780080
55 -5.7348534 -0.0116453589 0.6118522980 1 1 0.4521559134
55.1 -7.3217494 0.5835528661 0.7463747414 1 1 0.5515673538
55.2 -12.2171938 -1.0010857254 2.8201208171 1 1 2.0840557566
55.3 -12.9821266 -0.4796526070 3.1326431572 0 0 2.3150082668
55.4 -14.8599983 -0.1202746964 3.2218102901 1 1 2.3809023504
56 -14.1764282 0.5176377612 1.2231332215 0 0 0.9198742485
56.1 -12.5343602 -1.1136932588 2.3573202139 1 1 1.7728552558
56.2 -8.4573382 -0.0168103281 2.5674936292 1 1 1.9309190783
56.3 -12.4633969 0.3933023606 2.9507164378 0 0 2.2191270894
56.4 -17.3841863 0.3714625139 3.2272730360 0 0 2.4271153024
56.5 -14.8147645 0.7811448179 3.4175522043 1 1 2.5702173815
57 -3.1403293 -1.0868304872 0.2370331448 1 1 0.1711369455
57.1 -11.1509248 0.8018626997 0.2481445030 1 1 0.1791592999
57.2 -6.3940143 -0.1159517011 1.1405586067 0 0 0.8234785742
57.3 -9.3473241 0.6785562445 2.1153886721 0 0 1.5273018303
58 -12.0245677 1.6476207996 1.2210099772 1 1 0.8864082613
58.1 -9.2112246 0.3402652711 1.6334245703 1 1 1.1858060626
58.2 -1.2071742 -0.1111300753 1.6791862890 1 1 1.2190273844
58.3 -11.0141711 -0.5409234285 2.6320121693 1 1 1.9107438714
58.4 -5.3721214 -0.1271327672 2.8477731440 1 1 2.0673783904
58.5 -7.8523047 0.8713264822 3.5715569824 1 1 2.5928187928
59 -13.2946560 0.4766421367 1.9023998594 0 0 1.4189250995
59.1 -10.0530648 1.0028089765 4.9736620474 1 1 3.7096585560
60 -19.2209402 0.5231452932 2.8854503250 0 0 2.2138389085
61 -4.6699914 -0.7190130614 0.7213630795 1 1 0.5235061310
61.1 -3.5981894 0.8353702312 2.3186947661 1 1 1.6827183986
61.2 -1.4713611 1.0229058138 2.5077313243 1 1 1.8199056209
61.3 -3.8819786 1.1717723589 3.1731073430 0 0 2.3027809373
61.4 0.1041413 -0.0629201596 3.6022726283 1 1 2.6142338858
62 -2.8591600 -0.3979137604 0.5336771999 1 1 0.3837081458
62.1 -6.9461986 0.6830738372 0.6987666548 0 0 0.5024056819
62.2 -16.7910593 0.4301745954 3.4584309917 0 0 2.4865745506
62.3 -17.9844596 -0.0333139957 4.8028772371 1 1 3.4532168882
63 -24.0335535 0.3345678035 2.8097350930 0 0 2.0604786922
63.1 -11.7765300 0.3643769511 3.9653754211 1 1 2.9079508535
64 -20.5963897 0.3949911859 4.1191305732 1 1 3.0153034804
65 -2.7969169 1.2000091513 0.7076152589 1 1 0.5291342322
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66 -13.2318328 -0.5081302805 3.4943454154 1 1 2.5662383121
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67 -25.6073277 1.6716027681 4.1728388879 0 0 3.1261341693
68 -13.4806759 0.5691848839 0.1291919907 0 0 0.0966709548
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69 -14.5290675 0.0381382683 4.1451198701 1 1 3.0417997050
70 -15.2122709 0.8126204217 0.1992557163 1 1 0.1515886088
70.1 -7.8681167 0.4691503050 0.4829774413 1 1 0.3674367781
71 -10.3352703 -0.5529062591 0.7741605386 1 1 0.6021111272
71.1 -7.5699888 -0.1103252087 1.4883817220 1 1 1.1576038195
71.2 -18.4680702 1.7178492547 4.0758526395 0 0 3.1700352897
71.3 -21.4316644 -1.0118346755 4.7048238723 0 0 3.6592239775
71.4 -8.1137650 1.8623785017 4.7242791823 0 0 3.6743555400
72 -9.1848162 -0.4521659275 0.9321196121 1 1 0.6905275565
72.1 -23.7538846 0.1375317317 1.1799991806 1 1 0.8741602904
72.2 -26.3421306 -0.4170988856 1.8917567329 1 1 1.4014404774
72.3 -27.2843801 0.7107266765 3.4853593935 0 0 2.5820041485
72.4 -20.8541617 0.1451969143 3.6884259700 0 0 2.7324387763
72.5 -12.8948965 1.6298050306 4.0854155901 1 1 3.0265343716
73 -2.6091307 -0.0307469467 4.6019889915 1 1 3.3356465236
74 -8.2790175 0.3730017941 1.4626806753 1 1 1.0772519613
75 -12.5029612 -0.4908003566 3.2524286874 0 0 2.4279140631
76 -6.0061671 -0.9888876620 1.8074807397 1 1 1.3294798303
76.1 -8.8149114 0.0003798292 4.2685073183 1 1 3.1396707367
76.2 -11.8359043 -0.8421863763 4.9688734859 1 1 3.6548201782
77 0.4772521 -0.4986802480 0.8459033852 1 1 0.6097651292
78 -9.4105229 0.0417330969 0.8231094317 1 1 0.6069257516
79 -1.0217265 -0.3767450660 0.0583819521 0 0 0.0443590703
79.1 -11.8125257 0.1516000028 2.4406372628 1 1 1.8544155528
79.2 -10.5465186 -0.1888160741 3.2962526032 0 0 2.5045188757
80 -12.7366807 -0.0041558414 0.8985060186 1 1 0.6613377055
80.1 -9.0584783 -0.0329337062 1.3434670598 0 0 0.9888474917
80.2 -16.6381566 0.5046816157 2.8025900386 1 1 2.0628225380
81 0.5547913 -0.9493950353 0.0101324962 1 1 0.0073905742
81.1 -4.0892715 0.2443038954 0.9421709494 1 1 0.6872131160
81.2 1.8283303 0.6476958410 3.0542453879 1 1 2.2277459217
81.3 -5.2166381 0.4182528210 3.3456630446 1 1 2.4403039888
82 -3.0749381 1.1088801952 1.3791010005 1 1 1.0038902706
82.1 -10.5506696 0.9334157763 1.7601010622 1 1 1.2812319990
82.2 -18.2226347 0.4958140634 2.6233131927 0 0 1.9095908060
83 -12.5872635 0.5104724530 0.0537394290 1 1 0.0394696928
83.1 -11.9756502 -0.0513309106 2.9061570496 0 0 2.1344686414
83.2 -10.6744217 -0.2067792494 3.1189457362 0 0 2.2907543380
83.3 -19.2714012 -0.0534169155 4.7663642222 1 1 3.5007244248
84 -2.6320312 -0.0255753653 2.7254060237 1 1 2.0125352642
84.1 -9.8140094 -1.8234189877 3.3364784659 0 0 2.4637725582
85 -12.3886736 -0.0114038622 0.2977756259 0 0 0.2180824084
85.1 -12.9196365 -0.0577615939 1.7394116637 0 0 1.2738956847
85.2 -9.6433248 -0.2241856342 2.6846330194 1 1 1.9661489512
85.3 -6.3296340 -0.0520175929 3.1608762743 1 1 2.3149359808
85.4 -7.0405525 0.2892733846 3.9452053758 1 1 2.8893563314
85.5 -13.6714939 -0.3740417009 4.5092553482 1 1 3.3024505062
86 -10.8756412 0.4293735089 0.8476278360 0 0 0.6422134099
86.1 -12.0055331 -0.1363456521 1.0118629411 1 1 0.7666477221
86.2 -13.3724699 0.1230989293 1.2511159515 1 1 0.9479200743
86.3 -13.3252145 0.3305413955 2.1870554925 0 0 1.6570436997
86.4 -14.9191290 2.6003411822 2.4532935000 1 1 1.8587614954
86.5 -17.7515546 -0.1420690052 3.8206058508 0 0 2.8947188929
87 -10.7027963 1.0457427869 2.7069531474 0 0 2.0291697589
87.1 -22.4941954 -0.2973007190 3.4462517721 1 1 2.5833582987
87.2 -14.9616716 0.4396872616 4.5241666853 0 0 3.3913783218
88 -2.2264493 -0.0601928334 0.0005892443 0 0 0.0004286893
88.1 -8.9626474 -1.0124347595 0.7116099866 0 0 0.5177132747
88.2 -2.5095281 0.5730917016 2.4952722900 0 0 1.8153702347
88.3 -16.3345673 -0.0029455332 3.2995816297 0 0 2.4005245046
89 -11.0459647 1.5465903721 0.6462086167 1 1 0.4685431339
90 -4.5610239 0.0626760573 0.1696030737 0 0 0.1244083036
90.1 -11.7036651 1.1896872985 2.5980385230 1 1 1.9057294087
90.2 -5.3838521 0.2597888783 2.6651392167 1 1 1.9549495277
90.3 -4.1636999 0.6599799887 3.1242690247 0 0 2.2917332858
91 -7.1462503 1.1213651365 0.6382618390 0 0 0.4687382105
91.1 -12.8374475 1.2046371625 2.6224059286 0 0 1.9258896380
91.2 -18.2576707 0.3395603754 4.7772527603 1 1 3.5084048160
92 -6.4119222 0.4674939332 0.0737052364 1 1 0.0543975943
93 5.2122168 0.2677965647 0.2788909199 0 0 0.2060853219
93.1 3.1211725 1.6424445368 1.0357759963 1 1 0.7653825006
93.2 -3.6841177 0.7101700066 2.4916551099 0 0 1.8411985077
93.3 2.6223542 1.1222322893 2.8876129608 1 1 2.1337899668
93.4 -11.1877696 1.4628960401 4.4639474002 0 0 3.2986159518
94 -6.9602492 -0.2904211940 0.8488043118 1 1 0.6162277526
94.1 -7.4318416 0.0147813580 1.0552454425 0 0 0.7661029975
94.2 -4.3498045 -0.4536774482 1.9445500884 1 1 1.4117337932
94.3 -11.6340088 0.6793464917 3.0710722448 0 0 2.2295833342
94.4 -12.9357964 -0.9411356550 3.0872731935 0 0 2.2413451432
94.5 -14.7648530 0.5683867264 4.3805759016 0 0 3.1802765437
95 -12.8849309 0.2375652188 2.0199063048 1 1 1.4710654666
95.1 -9.7451502 0.0767152977 4.0184444457 1 1 2.9265688411
95.2 -0.8535063 -0.6886731251 4.5596531732 0 0 3.3207225043
96 -4.9139832 0.7813892121 0.0311333477 1 1 0.0226702966
96.1 -3.9582653 0.3391519695 0.1324267720 0 0 0.0964288912
96.2 -9.6555492 -0.4857246503 0.6701303425 0 0 0.4879672359
96.3 -11.8690793 0.8771471244 2.1775037691 0 0 1.5855877997
96.4 -11.0224373 1.9030768981 2.2246142488 0 0 1.6198921270
96.5 -10.9530403 -0.1684332749 4.2377650598 1 1 3.0858034196
97 -9.8540471 1.3775130083 1.1955102731 0 0 0.8662239621
97.1 -19.2262840 -1.7323228619 4.9603108643 0 0 3.5940637456
98 -11.9651231 -1.2648518889 0.2041732438 0 0 0.1536799385
98.1 -2.6515128 -0.9042716241 0.4309578973 0 0 0.3243793452
98.2 -12.2606382 -0.1560385207 3.5172611906 0 0 2.6474207553
99 -11.4720500 0.7993356425 0.3531786101 1 1 0.2568424071
99.1 -14.0596866 1.0355522332 4.6789444226 1 1 3.4026730772
99.2 -17.3939469 -0.1150895843 4.9927084171 1 1 3.6308519569
100 1.1005874 0.0369067906 1.0691387602 0 0 0.7893943379
100.1 -3.8226248 1.6023713093 1.5109344281 0 0 1.1155924065
100.2 -0.9123182 0.8861545820 2.1502332564 1 1 1.5876161457
100.3 -15.8389474 0.1277046316 3.8745574222 1 1 2.8607640137
100.4 -12.8093826 -0.0834577654 4.6567608765 1 1 3.4383008132
$m8n$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m8n$spM_lvlone
center scale
y -11.1733710 6.2496619
c1 0.2559996 0.6718095
time 2.5339403 1.3818094
b1 NA NA
b11 NA NA
C1:time 1.8687612 1.0180574
$m8n$mu_reg_norm
[1] 0
$m8n$tau_reg_norm
[1] 1e-04
$m8n$shape_tau_norm
[1] 0.01
$m8n$rate_tau_norm
[1] 0.01
$m8n$mu_reg_binom
[1] 0
$m8n$tau_reg_binom
[1] 1e-04
$m8n$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m8n$shape_diag_RinvD
[1] "0.01"
$m8n$rate_diag_RinvD
[1] "0.001"
$m8n$RinvD_y_id
[,1] [,2] [,3] [,4]
[1,] NA 0 0 0
[2,] 0 NA 0 0
[3,] 0 0 NA 0
[4,] 0 0 0 NA
$m8n$KinvD_y_id
id
5
$m9a
$m9a$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m9a$M_lvlone
y c1 b11 time
1 -13.0493856 0.7592026489 0 0.5090421822
1.1 -9.3335901 0.9548337990 1 0.6666076288
1.2 -22.3469852 0.5612235156 1 2.1304941282
1.3 -15.0417337 1.1873391025 0 2.4954441458
2 -12.0655434 0.9192204198 1 3.0164990982
2.1 -15.8674476 -0.1870730476 1 3.2996806887
2.2 -7.8800006 1.2517512331 1 4.1747569619
3 -11.4820604 -0.0605087604 1 0.8478727890
3.1 -10.5983220 0.3788637747 0 3.0654308549
3.2 -22.4519157 0.9872578281 0 4.7381553578
4 -1.2697775 1.4930175328 1 0.3371432109
4.1 -11.1215184 -0.7692526880 1 1.0693019140
4.2 -3.6134138 0.9180841450 0 2.6148973033
4.3 -14.5982385 -0.0541170782 1 3.1336532847
5 -6.8457515 -0.1376784521 0 1.0762525082
5.1 -7.0551214 -0.2740585866 1 1.7912546196
5.2 -12.3418980 0.4670496929 1 2.7960080339
5.3 -9.2366906 0.1740288049 1 2.8119940578
6 -5.1648211 0.9868044683 0 1.7815462884
7 -10.0599502 -0.1280320918 1 3.3074087673
7.1 -18.3267285 0.4242971219 0 3.7008403614
7.2 -12.5138426 0.0777182491 1 4.7716691741
8 -1.6305331 -0.5791408712 0 1.1246398522
8.1 -9.6520453 0.3128604232 1 1.8027009873
8.2 -1.5278462 0.6258446356 1 1.8175825174
8.3 -7.4172211 -0.1040137707 0 2.8384267003
8.4 -7.1238609 0.0481450285 0 3.3630275307
8.5 -8.8706950 0.3831763675 1 4.4360849704
9 -0.1634429 -0.1757592269 1 0.9607803822
9.1 -2.6034300 -0.1791541200 1 2.9177753383
9.2 -6.7272369 -0.0957042935 0 4.8100892501
10 -6.4172202 -0.5598409704 1 2.2975509102
10.1 -11.4834569 -0.2318340451 1 4.1734118364
11 -8.7911356 0.5086859475 1 1.1832662905
11.1 -19.6645080 0.4951758188 1 1.2346051680
11.2 -20.2030932 -1.1022162541 1 1.6435316263
11.3 -21.3082176 -0.0611636705 1 3.3859017969
11.4 -14.5802901 -0.4971774316 1 4.8118087661
12 -15.2006287 -0.2433996286 1 0.9591987054
13 0.8058816 0.8799673116 0 0.0619085738
13.1 -13.6379208 0.1079022586 1 3.5621061502
14 -15.3422873 0.9991752617 0 4.0364430007
14.1 -10.0965208 -0.1094019046 1 4.4710561272
14.2 -16.6452027 0.1518967560 0 4.6359198843
14.3 -15.8389733 0.3521012473 0 4.6886152599
15 -8.9424594 0.3464447888 0 0.5402063532
15.1 -22.0101983 -0.4767313971 0 1.1893180816
15.2 -7.3975599 0.5759767791 0 1.5094739688
15.3 -10.3567334 -0.1713452662 1 4.9193474615
16 -1.9691302 0.4564754473 1 1.2417913869
16.1 -9.9308357 1.0652558311 0 2.5675726333
16.2 -6.9626923 0.6971872493 1 2.6524101500
16.3 -3.2862557 0.5259331838 1 3.5585018690
16.4 -3.3972355 0.2046601798 1 3.7612454291
16.5 -11.5767835 1.0718540464 0 3.9851612889
17 -10.5474144 0.6048676222 0 1.5925356350
17.1 -7.6215009 0.2323298304 0 2.4374032998
17.2 -16.5386939 1.2617499032 1 3.0256489082
17.3 -20.0004774 -0.3913230895 0 3.3329089405
17.4 -18.8505475 0.9577299112 1 3.8693758985
18 -19.7302351 -0.0050324072 1 2.4374292302
19 -14.6177568 -0.4187468937 1 0.9772165376
19.1 -17.8043866 -0.4478828944 1 1.1466335913
19.2 -15.1641705 -1.1966721302 1 2.2599126538
19.3 -16.6898418 -0.5877091668 1 4.2114245973
20 -12.9059229 0.6838223064 0 1.7170160066
20.1 -16.8191201 0.3278571109 1 1.7562902288
20.2 -6.1010131 -0.8489831990 0 2.2515566566
20.3 -7.9415371 1.3169975191 0 2.2609123867
20.4 -9.3904458 0.0444804531 0 3.4913365287
20.5 -13.3504189 -0.4535207652 0 4.1730977828
21 -7.6974718 -0.4030302960 1 1.6936582839
21.1 -11.9335526 -0.4069674045 1 2.9571191233
21.2 -12.7064929 1.0650265940 0 3.7887385779
22 -21.5022909 -0.0673274516 0 2.4696226232
22.1 -12.7745451 0.9601388170 1 3.1626627257
23 -3.5146508 0.5556634840 1 1.5414533857
23.1 -4.6724048 1.4407865964 1 2.3369736120
24 -2.5619821 0.3856376411 0 2.8283136466
25 -6.2944970 0.3564400705 0 0.5381704110
25.1 -3.8630505 0.0982553434 1 1.6069735331
25.2 -14.4205140 0.1928682598 1 1.6358226922
25.3 -19.6735037 -0.0192488594 0 3.2646870392
25.4 -9.0288933 0.4466012931 0 4.0782226040
25.5 -9.0509738 1.1425193342 0 4.1560292873
26 -19.7340685 0.5341531449 1 0.2412706357
26.1 -14.1692728 1.2268695927 1 2.4451737676
26.2 -17.2819976 0.3678294939 1 3.5988757887
26.3 -24.6265576 0.5948516018 0 4.1822362854
27 -7.3354999 -0.3342844147 1 3.6955824879
27.1 -11.1488468 -0.4835141229 1 4.2451434687
28 -11.7996597 -0.7145915499 1 0.5746519344
28.1 -8.2030122 0.5063671955 0 2.7943964268
28.2 -26.4317815 -0.2067413142 1 4.2108539480
28.3 -18.5016071 0.1196789973 1 4.4705521734
29 -5.8551395 0.1392699487 1 1.1898884235
29.1 -2.0209442 0.7960234776 0 1.7624059319
29.2 -5.6368080 1.0398214352 0 2.0210406382
29.3 -3.8110961 0.0813246429 1 3.4078777023
30 -12.7217702 -0.3296323050 1 2.2635366488
30.1 -17.0170140 1.3635850954 1 3.5938334477
30.2 -25.4236089 0.7354171050 1 3.6138710892
31 -17.0783921 0.3708398217 0 4.3988140998
32 -18.4338764 -0.0474059668 1 1.6745209007
32.1 -19.4317212 1.2507771489 1 2.9128167813
32.2 -19.4738978 0.1142915519 1 2.9676558380
32.3 -21.4922645 0.6773270619 1 4.2099863547
33 2.0838099 0.1774293842 0 0.0093385763
33.1 -13.3172274 0.6159606291 0 3.4591242753
34 -10.0296691 0.8590979166 1 1.4998774312
34.1 -25.9426553 0.0546216775 0 3.8242761395
34.2 -18.5688138 -0.0897224473 1 3.9072251692
34.3 -15.4173859 0.4163395571 1 3.9582124643
35 -14.3958113 -1.4693520528 1 1.3294299203
35.1 -12.9457541 -0.3031734330 0 1.5276966314
35.2 -16.1380691 -0.6045512101 1 4.5025920868
36 -12.8166968 0.9823048960 0 0.7123168337
36.1 -14.3989481 1.4466051416 0 1.7972493160
36.2 -12.2436943 1.1606752905 1 1.8262697803
36.3 -15.0104638 0.8373091576 0 4.2840119381
36.4 -10.1775457 0.2640591685 1 4.6194464504
37 -15.2223495 0.1177313455 1 2.0018732361
37.1 -14.7526195 -0.1415483779 0 3.6656836793
37.2 -19.8168430 0.0054610124 0 3.9663937816
38 -2.7065118 0.8078948077 1 0.9826511063
39 -8.7288138 0.9876451040 1 0.6921808305
39.1 -9.2746473 -0.3431222274 0 0.9027792048
39.2 -18.2695344 -1.7909380751 0 1.3055654289
39.3 -13.8219083 -0.1798746191 0 1.5412842878
39.4 -16.2254704 -0.1850961689 1 3.1834997435
39.5 -21.7283648 0.4544226146 1 4.1394166439
40 1.8291916 0.5350190436 0 1.1330395646
40.1 -6.6916432 0.4189342752 0 2.6940994046
40.2 -1.6278171 0.4211994981 0 3.0396614212
40.3 -10.5749790 0.0916687506 1 4.6762977762
41 -3.1556121 -0.1035047421 1 1.9337158254
41.1 -11.5895327 -0.4684202411 1 3.1956304458
41.2 -18.9352091 0.5972615368 0 3.2846923557
41.3 -15.9788960 0.9885613862 1 3.3813529415
41.4 -9.6070508 -0.3908036794 1 3.5482964432
42 -5.2159485 -0.0338893961 1 0.4859252973
42.1 -15.9878743 -0.4498363172 1 4.3293134298
43 -16.6104361 0.8965546110 0 0.5616614548
43.1 -9.5549441 0.6199122090 0 1.0743579536
43.2 -14.2003491 0.1804894429 1 2.6131797966
44 -8.1969033 1.3221409285 1 0.7662644819
44.1 -19.9270197 0.3416426284 0 2.6490291790
44.2 -22.6521171 0.5706610068 0 3.3371910988
44.3 -21.1903736 1.2679497430 1 4.1154200875
45 -0.5686627 0.1414983160 1 0.1957449992
45.1 -7.5645740 0.7220892521 0 1.9963831536
46 -19.1624789 1.5391054233 1 1.3477755385
46.1 -18.4487574 0.3889107049 0 2.8565793915
46.2 -15.8222682 0.1248719493 1 4.4160729996
47 -5.4165074 0.2014101100 0 0.6012621359
47.1 -15.0975029 0.2982973539 0 2.4097121472
47.2 -12.9971413 1.1518107179 1 2.9975794035
47.3 -10.6844521 0.5196802157 0 3.1829649757
47.4 -18.2214784 0.3702301552 0 4.6201055450
48 -8.3101471 -0.2128602862 0 2.8607365978
48.1 -18.3854275 -0.5337239976 1 2.9098354396
49 -13.0130319 -0.5236770035 0 2.7179756400
50 -10.4579977 0.3897705981 1 1.1762060679
51 -19.3157621 -0.7213343736 1 1.4304436720
52 -4.4747188 0.3758235358 1 2.1266646020
52.1 -4.3163827 0.7138067080 1 3.1000545993
52.2 -6.9761408 0.8872895233 0 3.1268477370
52.3 -20.1764756 -0.9664587437 0 3.5711459327
52.4 -8.9036692 0.0254566848 1 4.7983659909
52.5 -5.6949642 0.4155259424 1 4.9818264414
53 -10.3141887 0.5675736897 1 0.4965799209
53.1 -8.2642654 -0.3154088781 1 3.5505357443
53.2 -9.1691554 0.2162315769 1 4.5790420019
54 -6.2198754 -0.0880802382 0 1.4034724841
54.1 -15.7192609 0.4129127672 1 1.8812377600
54.2 -13.0978998 1.0119546775 0 2.5107589352
54.3 -5.1195299 -0.1112901990 1 2.7848406672
54.4 -16.5771751 0.8587727145 0 4.0143877396
55 -5.7348534 -0.0116453589 1 0.6118522980
55.1 -7.3217494 0.5835528661 1 0.7463747414
55.2 -12.2171938 -1.0010857254 1 2.8201208171
55.3 -12.9821266 -0.4796526070 0 3.1326431572
55.4 -14.8599983 -0.1202746964 1 3.2218102901
56 -14.1764282 0.5176377612 0 1.2231332215
56.1 -12.5343602 -1.1136932588 1 2.3573202139
56.2 -8.4573382 -0.0168103281 1 2.5674936292
56.3 -12.4633969 0.3933023606 0 2.9507164378
56.4 -17.3841863 0.3714625139 0 3.2272730360
56.5 -14.8147645 0.7811448179 1 3.4175522043
57 -3.1403293 -1.0868304872 1 0.2370331448
57.1 -11.1509248 0.8018626997 1 0.2481445030
57.2 -6.3940143 -0.1159517011 0 1.1405586067
57.3 -9.3473241 0.6785562445 0 2.1153886721
58 -12.0245677 1.6476207996 1 1.2210099772
58.1 -9.2112246 0.3402652711 1 1.6334245703
58.2 -1.2071742 -0.1111300753 1 1.6791862890
58.3 -11.0141711 -0.5409234285 1 2.6320121693
58.4 -5.3721214 -0.1271327672 1 2.8477731440
58.5 -7.8523047 0.8713264822 1 3.5715569824
59 -13.2946560 0.4766421367 0 1.9023998594
59.1 -10.0530648 1.0028089765 1 4.9736620474
60 -19.2209402 0.5231452932 0 2.8854503250
61 -4.6699914 -0.7190130614 1 0.7213630795
61.1 -3.5981894 0.8353702312 1 2.3186947661
61.2 -1.4713611 1.0229058138 1 2.5077313243
61.3 -3.8819786 1.1717723589 0 3.1731073430
61.4 0.1041413 -0.0629201596 1 3.6022726283
62 -2.8591600 -0.3979137604 1 0.5336771999
62.1 -6.9461986 0.6830738372 0 0.6987666548
62.2 -16.7910593 0.4301745954 0 3.4584309917
62.3 -17.9844596 -0.0333139957 1 4.8028772371
63 -24.0335535 0.3345678035 0 2.8097350930
63.1 -11.7765300 0.3643769511 1 3.9653754211
64 -20.5963897 0.3949911859 1 4.1191305732
65 -2.7969169 1.2000091513 1 0.7076152589
65.1 -11.1778694 0.0110122646 1 2.0252246363
65.2 -5.2830399 -0.5776452043 0 3.1127382827
65.3 -7.9353390 -0.1372183563 0 3.1969087943
66 -13.2318328 -0.5081302805 1 3.4943454154
66.1 -1.9090560 -0.1447837412 0 3.7677437009
66.2 -16.6643889 0.1906241379 0 3.9486138616
67 -25.6073277 1.6716027681 0 4.1728388879
68 -13.4806759 0.5691848839 0 0.1291919907
68.1 -18.4557183 0.1004860389 0 1.7809643946
68.2 -13.3982327 -0.0061241827 0 2.0493205660
68.3 -12.4977127 0.7443745962 0 2.9406870750
68.4 -11.7073990 0.8726923437 1 4.0406670363
69 -14.5290675 0.0381382683 1 4.1451198701
70 -15.2122709 0.8126204217 1 0.1992557163
70.1 -7.8681167 0.4691503050 1 0.4829774413
71 -10.3352703 -0.5529062591 1 0.7741605386
71.1 -7.5699888 -0.1103252087 1 1.4883817220
71.2 -18.4680702 1.7178492547 0 4.0758526395
71.3 -21.4316644 -1.0118346755 0 4.7048238723
71.4 -8.1137650 1.8623785017 0 4.7242791823
72 -9.1848162 -0.4521659275 1 0.9321196121
72.1 -23.7538846 0.1375317317 1 1.1799991806
72.2 -26.3421306 -0.4170988856 1 1.8917567329
72.3 -27.2843801 0.7107266765 0 3.4853593935
72.4 -20.8541617 0.1451969143 0 3.6884259700
72.5 -12.8948965 1.6298050306 1 4.0854155901
73 -2.6091307 -0.0307469467 1 4.6019889915
74 -8.2790175 0.3730017941 1 1.4626806753
75 -12.5029612 -0.4908003566 0 3.2524286874
76 -6.0061671 -0.9888876620 1 1.8074807397
76.1 -8.8149114 0.0003798292 1 4.2685073183
76.2 -11.8359043 -0.8421863763 1 4.9688734859
77 0.4772521 -0.4986802480 1 0.8459033852
78 -9.4105229 0.0417330969 1 0.8231094317
79 -1.0217265 -0.3767450660 0 0.0583819521
79.1 -11.8125257 0.1516000028 1 2.4406372628
79.2 -10.5465186 -0.1888160741 0 3.2962526032
80 -12.7366807 -0.0041558414 1 0.8985060186
80.1 -9.0584783 -0.0329337062 0 1.3434670598
80.2 -16.6381566 0.5046816157 1 2.8025900386
81 0.5547913 -0.9493950353 1 0.0101324962
81.1 -4.0892715 0.2443038954 1 0.9421709494
81.2 1.8283303 0.6476958410 1 3.0542453879
81.3 -5.2166381 0.4182528210 1 3.3456630446
82 -3.0749381 1.1088801952 1 1.3791010005
82.1 -10.5506696 0.9334157763 1 1.7601010622
82.2 -18.2226347 0.4958140634 0 2.6233131927
83 -12.5872635 0.5104724530 1 0.0537394290
83.1 -11.9756502 -0.0513309106 0 2.9061570496
83.2 -10.6744217 -0.2067792494 0 3.1189457362
83.3 -19.2714012 -0.0534169155 1 4.7663642222
84 -2.6320312 -0.0255753653 1 2.7254060237
84.1 -9.8140094 -1.8234189877 0 3.3364784659
85 -12.3886736 -0.0114038622 0 0.2977756259
85.1 -12.9196365 -0.0577615939 0 1.7394116637
85.2 -9.6433248 -0.2241856342 1 2.6846330194
85.3 -6.3296340 -0.0520175929 1 3.1608762743
85.4 -7.0405525 0.2892733846 1 3.9452053758
85.5 -13.6714939 -0.3740417009 1 4.5092553482
86 -10.8756412 0.4293735089 0 0.8476278360
86.1 -12.0055331 -0.1363456521 1 1.0118629411
86.2 -13.3724699 0.1230989293 1 1.2511159515
86.3 -13.3252145 0.3305413955 0 2.1870554925
86.4 -14.9191290 2.6003411822 1 2.4532935000
86.5 -17.7515546 -0.1420690052 0 3.8206058508
87 -10.7027963 1.0457427869 0 2.7069531474
87.1 -22.4941954 -0.2973007190 1 3.4462517721
87.2 -14.9616716 0.4396872616 0 4.5241666853
88 -2.2264493 -0.0601928334 0 0.0005892443
88.1 -8.9626474 -1.0124347595 0 0.7116099866
88.2 -2.5095281 0.5730917016 0 2.4952722900
88.3 -16.3345673 -0.0029455332 0 3.2995816297
89 -11.0459647 1.5465903721 1 0.6462086167
90 -4.5610239 0.0626760573 0 0.1696030737
90.1 -11.7036651 1.1896872985 1 2.5980385230
90.2 -5.3838521 0.2597888783 1 2.6651392167
90.3 -4.1636999 0.6599799887 0 3.1242690247
91 -7.1462503 1.1213651365 0 0.6382618390
91.1 -12.8374475 1.2046371625 0 2.6224059286
91.2 -18.2576707 0.3395603754 1 4.7772527603
92 -6.4119222 0.4674939332 1 0.0737052364
93 5.2122168 0.2677965647 0 0.2788909199
93.1 3.1211725 1.6424445368 1 1.0357759963
93.2 -3.6841177 0.7101700066 0 2.4916551099
93.3 2.6223542 1.1222322893 1 2.8876129608
93.4 -11.1877696 1.4628960401 0 4.4639474002
94 -6.9602492 -0.2904211940 1 0.8488043118
94.1 -7.4318416 0.0147813580 0 1.0552454425
94.2 -4.3498045 -0.4536774482 1 1.9445500884
94.3 -11.6340088 0.6793464917 0 3.0710722448
94.4 -12.9357964 -0.9411356550 0 3.0872731935
94.5 -14.7648530 0.5683867264 0 4.3805759016
95 -12.8849309 0.2375652188 1 2.0199063048
95.1 -9.7451502 0.0767152977 1 4.0184444457
95.2 -0.8535063 -0.6886731251 0 4.5596531732
96 -4.9139832 0.7813892121 1 0.0311333477
96.1 -3.9582653 0.3391519695 0 0.1324267720
96.2 -9.6555492 -0.4857246503 0 0.6701303425
96.3 -11.8690793 0.8771471244 0 2.1775037691
96.4 -11.0224373 1.9030768981 0 2.2246142488
96.5 -10.9530403 -0.1684332749 1 4.2377650598
97 -9.8540471 1.3775130083 0 1.1955102731
97.1 -19.2262840 -1.7323228619 0 4.9603108643
98 -11.9651231 -1.2648518889 0 0.2041732438
98.1 -2.6515128 -0.9042716241 0 0.4309578973
98.2 -12.2606382 -0.1560385207 0 3.5172611906
99 -11.4720500 0.7993356425 1 0.3531786101
99.1 -14.0596866 1.0355522332 1 4.6789444226
99.2 -17.3939469 -0.1150895843 1 4.9927084171
100 1.1005874 0.0369067906 0 1.0691387602
100.1 -3.8226248 1.6023713093 0 1.5109344281
100.2 -0.9123182 0.8861545820 1 2.1502332564
100.3 -15.8389474 0.1277046316 1 3.8745574222
100.4 -12.8093826 -0.0834577654 1 4.6567608765
$m9a$spM_lvlone
center scale
y -11.1733710 6.2496619
c1 0.2559996 0.6718095
b11 NA NA
time 2.5339403 1.3818094
$m9a$mu_reg_norm
[1] 0
$m9a$tau_reg_norm
[1] 1e-04
$m9a$shape_tau_norm
[1] 0.01
$m9a$rate_tau_norm
[1] 0.01
$m9a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m9a$group_o1
[1] 1 2 2 1 3 2 1 2 3 1 3 1 2 1 1 1 3 1 3 3 2 2 1 1 3 3 1 1 1 1 3 1 2 1 3 3 2
[38] 3 2 1 2 3 3 1 2 3 3 1 1 3 2 3 2 2 1 1 1 3 1 3 1 3 1 1 1 2 1 1 2 2 1 1 1 1
[75] 2 2 2 1 2 1 2 2 2 1 1 1 3 1 2 1 1 2 2 2 2 1 1 2 1 1 1 1 1 3 2 3 3 1 1 3 1
[112] 1 2 1 3 1 3 1 3 3 3 3 3 3 1 2 1 1 2 2 2 3 2 2 1 3 1 2 2 3 1 2 1 1 2 3 1 2
[149] 2 2 2 2 1 3 2 2 2 3 1 3 1 2 1 1 1 3 1 2 3 1 1 2 2 2 1 3 2 1 2 2 1 2 2 3 1
[186] 3 3 2 3 1 1 1 1 3 1 3 3 1 3 3 2 3 2 1 1 2 1 1 3 2 1 2 3 1 2 1 2 2 2 2 3 3
[223] 2 1 1 1 1 1 2 3 3 3 3 1 1 2 2 1 1 2 2 2 2 3 3 3 2 1 1 2 1 2 1 1 3 3 1 3 3
[260] 3 1 1 3 3 3 1 3 3 3 1 1 1 2 3 1 3 2 2 2 3 2 2 2 3 2 2 2 2 1 2 3 2 2 1 2 1
[297] 2 3 2 3 2 1 3 2 3 1 3 3 2 1 2 3 2 2 1 3 3 3 3 2 2 1 1 2 2 1 1 2 2
$m9a$shape_diag_RinvD
[1] "0.01"
$m9a$rate_diag_RinvD
[1] "0.001"
$m9b
$m9b$M_id
C2 (Intercept) C1 B11
1 -1.381594459 1 0.7175865 1
2 0.344426024 1 0.7507170 1
3 NA 1 0.7255954 1
4 -0.228910007 1 0.7469352 0
5 NA 1 0.7139120 1
6 -2.143955482 1 0.7332505 1
7 -1.156567023 1 0.7345929 1
8 -0.598827660 1 0.7652589 0
9 NA 1 0.7200622 1
10 -1.006719032 1 0.7423879 1
11 0.239801450 1 0.7437448 0
12 -1.064969789 1 0.7446470 1
13 -0.538082688 1 0.7530186 1
14 NA 1 0.7093137 1
15 -1.781049276 1 0.7331192 1
16 NA 1 0.7011390 1
17 NA 1 0.7432395 1
18 -0.014579883 1 0.7545191 1
19 -2.121550136 1 0.7528487 1
20 NA 1 0.7612865 0
21 -0.363239698 1 0.7251719 1
22 -0.121568514 1 0.7300630 1
23 -0.951271111 1 0.7087249 1
24 NA 1 0.7391938 0
25 -0.974288621 1 0.7820641 1
26 -1.130632418 1 0.7118298 1
27 0.114339868 1 0.7230857 0
28 0.238334648 1 0.7489353 1
29 0.840744958 1 0.7510888 1
30 NA 1 0.7300717 1
31 NA 1 0.7550721 1
32 -1.466312154 1 0.7321898 1
33 -0.637352277 1 0.7306414 1
34 NA 1 0.7427216 1
35 NA 1 0.7193042 1
36 NA 1 0.7312888 0
37 NA 1 0.7100436 0
38 NA 1 0.7670184 1
39 0.006728205 1 0.7400449 1
40 NA 1 0.7397304 1
41 -1.663281353 1 0.7490966 1
42 0.161184794 1 0.7419274 1
43 0.457939180 1 0.7527810 1
44 -0.307070331 1 0.7408315 1
45 NA 1 0.7347550 0
46 -1.071668276 1 0.7332398 1
47 -0.814751321 1 0.7376481 0
48 -0.547630662 1 0.7346179 0
49 NA 1 0.7329402 1
50 -1.350213782 1 0.7260436 1
51 0.719054706 1 0.7242910 1
52 NA 1 0.7298067 0
53 -1.207130750 1 0.7254741 1
54 NA 1 0.7542067 1
55 -0.408600991 1 0.7389952 1
56 -0.271380529 1 0.7520638 1
57 -1.361925974 1 0.7219958 1
58 NA 1 0.7259632 1
59 NA 1 0.7458606 1
60 -0.323712205 1 0.7672421 1
61 NA 1 0.7257179 0
62 NA 1 0.7189892 1
63 -1.386906880 1 0.7333356 1
64 NA 1 0.7320243 1
65 NA 1 0.7477711 1
66 -0.565191691 1 0.7343974 0
67 -0.382899912 1 0.7491624 0
68 NA 1 0.7482736 1
69 -0.405642769 1 0.7338267 1
70 NA 1 0.7607742 1
71 -0.843748427 1 0.7777600 1
72 0.116003683 1 0.7408143 1
73 -0.778634325 1 0.7248271 1
74 NA 1 0.7364916 0
75 NA 1 0.7464926 1
76 NA 1 0.7355430 1
77 -0.632974758 1 0.7208449 1
78 NA 1 0.7373573 1
79 -0.778064615 1 0.7598079 1
80 NA 1 0.7360415 1
81 NA 1 0.7293932 1
82 -0.246123253 1 0.7279309 1
83 -1.239659782 1 0.7344643 0
84 -0.467772280 1 0.7384350 0
85 NA 1 0.7323716 1
86 -2.160485036 1 0.7576597 1
87 -0.657675572 1 0.7496139 1
88 NA 1 0.7275239 1
89 -0.696710744 1 0.7250648 1
90 NA 1 0.7335262 0
91 -0.179395847 1 0.7343980 1
92 -0.441545568 1 0.7380425 1
93 -0.685799334 1 0.7389460 0
94 NA 1 0.7259951 1
95 0.191929445 1 0.7282840 0
96 NA 1 0.7281676 0
97 -0.069760671 1 0.7245642 1
98 NA 1 0.7526938 1
99 NA 1 0.7272309 1
100 NA 1 0.7383460 1
$m9b$M_lvlone
y time
1 -13.0493856 0.5090421822
1.1 -9.3335901 0.6666076288
1.2 -22.3469852 2.1304941282
1.3 -15.0417337 2.4954441458
2 -12.0655434 3.0164990982
2.1 -15.8674476 3.2996806887
2.2 -7.8800006 4.1747569619
3 -11.4820604 0.8478727890
3.1 -10.5983220 3.0654308549
3.2 -22.4519157 4.7381553578
4 -1.2697775 0.3371432109
4.1 -11.1215184 1.0693019140
4.2 -3.6134138 2.6148973033
4.3 -14.5982385 3.1336532847
5 -6.8457515 1.0762525082
5.1 -7.0551214 1.7912546196
5.2 -12.3418980 2.7960080339
5.3 -9.2366906 2.8119940578
6 -5.1648211 1.7815462884
7 -10.0599502 3.3074087673
7.1 -18.3267285 3.7008403614
7.2 -12.5138426 4.7716691741
8 -1.6305331 1.1246398522
8.1 -9.6520453 1.8027009873
8.2 -1.5278462 1.8175825174
8.3 -7.4172211 2.8384267003
8.4 -7.1238609 3.3630275307
8.5 -8.8706950 4.4360849704
9 -0.1634429 0.9607803822
9.1 -2.6034300 2.9177753383
9.2 -6.7272369 4.8100892501
10 -6.4172202 2.2975509102
10.1 -11.4834569 4.1734118364
11 -8.7911356 1.1832662905
11.1 -19.6645080 1.2346051680
11.2 -20.2030932 1.6435316263
11.3 -21.3082176 3.3859017969
11.4 -14.5802901 4.8118087661
12 -15.2006287 0.9591987054
13 0.8058816 0.0619085738
13.1 -13.6379208 3.5621061502
14 -15.3422873 4.0364430007
14.1 -10.0965208 4.4710561272
14.2 -16.6452027 4.6359198843
14.3 -15.8389733 4.6886152599
15 -8.9424594 0.5402063532
15.1 -22.0101983 1.1893180816
15.2 -7.3975599 1.5094739688
15.3 -10.3567334 4.9193474615
16 -1.9691302 1.2417913869
16.1 -9.9308357 2.5675726333
16.2 -6.9626923 2.6524101500
16.3 -3.2862557 3.5585018690
16.4 -3.3972355 3.7612454291
16.5 -11.5767835 3.9851612889
17 -10.5474144 1.5925356350
17.1 -7.6215009 2.4374032998
17.2 -16.5386939 3.0256489082
17.3 -20.0004774 3.3329089405
17.4 -18.8505475 3.8693758985
18 -19.7302351 2.4374292302
19 -14.6177568 0.9772165376
19.1 -17.8043866 1.1466335913
19.2 -15.1641705 2.2599126538
19.3 -16.6898418 4.2114245973
20 -12.9059229 1.7170160066
20.1 -16.8191201 1.7562902288
20.2 -6.1010131 2.2515566566
20.3 -7.9415371 2.2609123867
20.4 -9.3904458 3.4913365287
20.5 -13.3504189 4.1730977828
21 -7.6974718 1.6936582839
21.1 -11.9335526 2.9571191233
21.2 -12.7064929 3.7887385779
22 -21.5022909 2.4696226232
22.1 -12.7745451 3.1626627257
23 -3.5146508 1.5414533857
23.1 -4.6724048 2.3369736120
24 -2.5619821 2.8283136466
25 -6.2944970 0.5381704110
25.1 -3.8630505 1.6069735331
25.2 -14.4205140 1.6358226922
25.3 -19.6735037 3.2646870392
25.4 -9.0288933 4.0782226040
25.5 -9.0509738 4.1560292873
26 -19.7340685 0.2412706357
26.1 -14.1692728 2.4451737676
26.2 -17.2819976 3.5988757887
26.3 -24.6265576 4.1822362854
27 -7.3354999 3.6955824879
27.1 -11.1488468 4.2451434687
28 -11.7996597 0.5746519344
28.1 -8.2030122 2.7943964268
28.2 -26.4317815 4.2108539480
28.3 -18.5016071 4.4705521734
29 -5.8551395 1.1898884235
29.1 -2.0209442 1.7624059319
29.2 -5.6368080 2.0210406382
29.3 -3.8110961 3.4078777023
30 -12.7217702 2.2635366488
30.1 -17.0170140 3.5938334477
30.2 -25.4236089 3.6138710892
31 -17.0783921 4.3988140998
32 -18.4338764 1.6745209007
32.1 -19.4317212 2.9128167813
32.2 -19.4738978 2.9676558380
32.3 -21.4922645 4.2099863547
33 2.0838099 0.0093385763
33.1 -13.3172274 3.4591242753
34 -10.0296691 1.4998774312
34.1 -25.9426553 3.8242761395
34.2 -18.5688138 3.9072251692
34.3 -15.4173859 3.9582124643
35 -14.3958113 1.3294299203
35.1 -12.9457541 1.5276966314
35.2 -16.1380691 4.5025920868
36 -12.8166968 0.7123168337
36.1 -14.3989481 1.7972493160
36.2 -12.2436943 1.8262697803
36.3 -15.0104638 4.2840119381
36.4 -10.1775457 4.6194464504
37 -15.2223495 2.0018732361
37.1 -14.7526195 3.6656836793
37.2 -19.8168430 3.9663937816
38 -2.7065118 0.9826511063
39 -8.7288138 0.6921808305
39.1 -9.2746473 0.9027792048
39.2 -18.2695344 1.3055654289
39.3 -13.8219083 1.5412842878
39.4 -16.2254704 3.1834997435
39.5 -21.7283648 4.1394166439
40 1.8291916 1.1330395646
40.1 -6.6916432 2.6940994046
40.2 -1.6278171 3.0396614212
40.3 -10.5749790 4.6762977762
41 -3.1556121 1.9337158254
41.1 -11.5895327 3.1956304458
41.2 -18.9352091 3.2846923557
41.3 -15.9788960 3.3813529415
41.4 -9.6070508 3.5482964432
42 -5.2159485 0.4859252973
42.1 -15.9878743 4.3293134298
43 -16.6104361 0.5616614548
43.1 -9.5549441 1.0743579536
43.2 -14.2003491 2.6131797966
44 -8.1969033 0.7662644819
44.1 -19.9270197 2.6490291790
44.2 -22.6521171 3.3371910988
44.3 -21.1903736 4.1154200875
45 -0.5686627 0.1957449992
45.1 -7.5645740 1.9963831536
46 -19.1624789 1.3477755385
46.1 -18.4487574 2.8565793915
46.2 -15.8222682 4.4160729996
47 -5.4165074 0.6012621359
47.1 -15.0975029 2.4097121472
47.2 -12.9971413 2.9975794035
47.3 -10.6844521 3.1829649757
47.4 -18.2214784 4.6201055450
48 -8.3101471 2.8607365978
48.1 -18.3854275 2.9098354396
49 -13.0130319 2.7179756400
50 -10.4579977 1.1762060679
51 -19.3157621 1.4304436720
52 -4.4747188 2.1266646020
52.1 -4.3163827 3.1000545993
52.2 -6.9761408 3.1268477370
52.3 -20.1764756 3.5711459327
52.4 -8.9036692 4.7983659909
52.5 -5.6949642 4.9818264414
53 -10.3141887 0.4965799209
53.1 -8.2642654 3.5505357443
53.2 -9.1691554 4.5790420019
54 -6.2198754 1.4034724841
54.1 -15.7192609 1.8812377600
54.2 -13.0978998 2.5107589352
54.3 -5.1195299 2.7848406672
54.4 -16.5771751 4.0143877396
55 -5.7348534 0.6118522980
55.1 -7.3217494 0.7463747414
55.2 -12.2171938 2.8201208171
55.3 -12.9821266 3.1326431572
55.4 -14.8599983 3.2218102901
56 -14.1764282 1.2231332215
56.1 -12.5343602 2.3573202139
56.2 -8.4573382 2.5674936292
56.3 -12.4633969 2.9507164378
56.4 -17.3841863 3.2272730360
56.5 -14.8147645 3.4175522043
57 -3.1403293 0.2370331448
57.1 -11.1509248 0.2481445030
57.2 -6.3940143 1.1405586067
57.3 -9.3473241 2.1153886721
58 -12.0245677 1.2210099772
58.1 -9.2112246 1.6334245703
58.2 -1.2071742 1.6791862890
58.3 -11.0141711 2.6320121693
58.4 -5.3721214 2.8477731440
58.5 -7.8523047 3.5715569824
59 -13.2946560 1.9023998594
59.1 -10.0530648 4.9736620474
60 -19.2209402 2.8854503250
61 -4.6699914 0.7213630795
61.1 -3.5981894 2.3186947661
61.2 -1.4713611 2.5077313243
61.3 -3.8819786 3.1731073430
61.4 0.1041413 3.6022726283
62 -2.8591600 0.5336771999
62.1 -6.9461986 0.6987666548
62.2 -16.7910593 3.4584309917
62.3 -17.9844596 4.8028772371
63 -24.0335535 2.8097350930
63.1 -11.7765300 3.9653754211
64 -20.5963897 4.1191305732
65 -2.7969169 0.7076152589
65.1 -11.1778694 2.0252246363
65.2 -5.2830399 3.1127382827
65.3 -7.9353390 3.1969087943
66 -13.2318328 3.4943454154
66.1 -1.9090560 3.7677437009
66.2 -16.6643889 3.9486138616
67 -25.6073277 4.1728388879
68 -13.4806759 0.1291919907
68.1 -18.4557183 1.7809643946
68.2 -13.3982327 2.0493205660
68.3 -12.4977127 2.9406870750
68.4 -11.7073990 4.0406670363
69 -14.5290675 4.1451198701
70 -15.2122709 0.1992557163
70.1 -7.8681167 0.4829774413
71 -10.3352703 0.7741605386
71.1 -7.5699888 1.4883817220
71.2 -18.4680702 4.0758526395
71.3 -21.4316644 4.7048238723
71.4 -8.1137650 4.7242791823
72 -9.1848162 0.9321196121
72.1 -23.7538846 1.1799991806
72.2 -26.3421306 1.8917567329
72.3 -27.2843801 3.4853593935
72.4 -20.8541617 3.6884259700
72.5 -12.8948965 4.0854155901
73 -2.6091307 4.6019889915
74 -8.2790175 1.4626806753
75 -12.5029612 3.2524286874
76 -6.0061671 1.8074807397
76.1 -8.8149114 4.2685073183
76.2 -11.8359043 4.9688734859
77 0.4772521 0.8459033852
78 -9.4105229 0.8231094317
79 -1.0217265 0.0583819521
79.1 -11.8125257 2.4406372628
79.2 -10.5465186 3.2962526032
80 -12.7366807 0.8985060186
80.1 -9.0584783 1.3434670598
80.2 -16.6381566 2.8025900386
81 0.5547913 0.0101324962
81.1 -4.0892715 0.9421709494
81.2 1.8283303 3.0542453879
81.3 -5.2166381 3.3456630446
82 -3.0749381 1.3791010005
82.1 -10.5506696 1.7601010622
82.2 -18.2226347 2.6233131927
83 -12.5872635 0.0537394290
83.1 -11.9756502 2.9061570496
83.2 -10.6744217 3.1189457362
83.3 -19.2714012 4.7663642222
84 -2.6320312 2.7254060237
84.1 -9.8140094 3.3364784659
85 -12.3886736 0.2977756259
85.1 -12.9196365 1.7394116637
85.2 -9.6433248 2.6846330194
85.3 -6.3296340 3.1608762743
85.4 -7.0405525 3.9452053758
85.5 -13.6714939 4.5092553482
86 -10.8756412 0.8476278360
86.1 -12.0055331 1.0118629411
86.2 -13.3724699 1.2511159515
86.3 -13.3252145 2.1870554925
86.4 -14.9191290 2.4532935000
86.5 -17.7515546 3.8206058508
87 -10.7027963 2.7069531474
87.1 -22.4941954 3.4462517721
87.2 -14.9616716 4.5241666853
88 -2.2264493 0.0005892443
88.1 -8.9626474 0.7116099866
88.2 -2.5095281 2.4952722900
88.3 -16.3345673 3.2995816297
89 -11.0459647 0.6462086167
90 -4.5610239 0.1696030737
90.1 -11.7036651 2.5980385230
90.2 -5.3838521 2.6651392167
90.3 -4.1636999 3.1242690247
91 -7.1462503 0.6382618390
91.1 -12.8374475 2.6224059286
91.2 -18.2576707 4.7772527603
92 -6.4119222 0.0737052364
93 5.2122168 0.2788909199
93.1 3.1211725 1.0357759963
93.2 -3.6841177 2.4916551099
93.3 2.6223542 2.8876129608
93.4 -11.1877696 4.4639474002
94 -6.9602492 0.8488043118
94.1 -7.4318416 1.0552454425
94.2 -4.3498045 1.9445500884
94.3 -11.6340088 3.0710722448
94.4 -12.9357964 3.0872731935
94.5 -14.7648530 4.3805759016
95 -12.8849309 2.0199063048
95.1 -9.7451502 4.0184444457
95.2 -0.8535063 4.5596531732
96 -4.9139832 0.0311333477
96.1 -3.9582653 0.1324267720
96.2 -9.6555492 0.6701303425
96.3 -11.8690793 2.1775037691
96.4 -11.0224373 2.2246142488
96.5 -10.9530403 4.2377650598
97 -9.8540471 1.1955102731
97.1 -19.2262840 4.9603108643
98 -11.9651231 0.2041732438
98.1 -2.6515128 0.4309578973
98.2 -12.2606382 3.5172611906
99 -11.4720500 0.3531786101
99.1 -14.0596866 4.6789444226
99.2 -17.3939469 4.9927084171
100 1.1005874 1.0691387602
100.1 -3.8226248 1.5109344281
100.2 -0.9123182 2.1502332564
100.3 -15.8389474 3.8745574222
100.4 -12.8093826 4.6567608765
$m9b$spM_id
center scale
C2 -0.6240921 0.68571078
(Intercept) NA NA
C1 0.7372814 0.01472882
B11 NA NA
$m9b$spM_lvlone
center scale
y -11.17337 6.249662
time 2.53394 1.381809
$m9b$mu_reg_norm
[1] 0
$m9b$tau_reg_norm
[1] 1e-04
$m9b$shape_tau_norm
[1] 0.01
$m9b$rate_tau_norm
[1] 0.01
$m9b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m9b$shape_diag_RinvD
[1] "0.01"
$m9b$rate_diag_RinvD
[1] "0.001"
$m9b$RinvD_y_id
[,1] [,2]
[1,] NA 0
[2,] 0 NA
$m9b$KinvD_y_id
id
3
$m9c
$m9c$M_id
C2 (Intercept) C1 B11
1 -1.381594459 1 0.7175865 1
2 0.344426024 1 0.7507170 1
3 NA 1 0.7255954 1
4 -0.228910007 1 0.7469352 0
5 NA 1 0.7139120 1
6 -2.143955482 1 0.7332505 1
7 -1.156567023 1 0.7345929 1
8 -0.598827660 1 0.7652589 0
9 NA 1 0.7200622 1
10 -1.006719032 1 0.7423879 1
11 0.239801450 1 0.7437448 0
12 -1.064969789 1 0.7446470 1
13 -0.538082688 1 0.7530186 1
14 NA 1 0.7093137 1
15 -1.781049276 1 0.7331192 1
16 NA 1 0.7011390 1
17 NA 1 0.7432395 1
18 -0.014579883 1 0.7545191 1
19 -2.121550136 1 0.7528487 1
20 NA 1 0.7612865 0
21 -0.363239698 1 0.7251719 1
22 -0.121568514 1 0.7300630 1
23 -0.951271111 1 0.7087249 1
24 NA 1 0.7391938 0
25 -0.974288621 1 0.7820641 1
26 -1.130632418 1 0.7118298 1
27 0.114339868 1 0.7230857 0
28 0.238334648 1 0.7489353 1
29 0.840744958 1 0.7510888 1
30 NA 1 0.7300717 1
31 NA 1 0.7550721 1
32 -1.466312154 1 0.7321898 1
33 -0.637352277 1 0.7306414 1
34 NA 1 0.7427216 1
35 NA 1 0.7193042 1
36 NA 1 0.7312888 0
37 NA 1 0.7100436 0
38 NA 1 0.7670184 1
39 0.006728205 1 0.7400449 1
40 NA 1 0.7397304 1
41 -1.663281353 1 0.7490966 1
42 0.161184794 1 0.7419274 1
43 0.457939180 1 0.7527810 1
44 -0.307070331 1 0.7408315 1
45 NA 1 0.7347550 0
46 -1.071668276 1 0.7332398 1
47 -0.814751321 1 0.7376481 0
48 -0.547630662 1 0.7346179 0
49 NA 1 0.7329402 1
50 -1.350213782 1 0.7260436 1
51 0.719054706 1 0.7242910 1
52 NA 1 0.7298067 0
53 -1.207130750 1 0.7254741 1
54 NA 1 0.7542067 1
55 -0.408600991 1 0.7389952 1
56 -0.271380529 1 0.7520638 1
57 -1.361925974 1 0.7219958 1
58 NA 1 0.7259632 1
59 NA 1 0.7458606 1
60 -0.323712205 1 0.7672421 1
61 NA 1 0.7257179 0
62 NA 1 0.7189892 1
63 -1.386906880 1 0.7333356 1
64 NA 1 0.7320243 1
65 NA 1 0.7477711 1
66 -0.565191691 1 0.7343974 0
67 -0.382899912 1 0.7491624 0
68 NA 1 0.7482736 1
69 -0.405642769 1 0.7338267 1
70 NA 1 0.7607742 1
71 -0.843748427 1 0.7777600 1
72 0.116003683 1 0.7408143 1
73 -0.778634325 1 0.7248271 1
74 NA 1 0.7364916 0
75 NA 1 0.7464926 1
76 NA 1 0.7355430 1
77 -0.632974758 1 0.7208449 1
78 NA 1 0.7373573 1
79 -0.778064615 1 0.7598079 1
80 NA 1 0.7360415 1
81 NA 1 0.7293932 1
82 -0.246123253 1 0.7279309 1
83 -1.239659782 1 0.7344643 0
84 -0.467772280 1 0.7384350 0
85 NA 1 0.7323716 1
86 -2.160485036 1 0.7576597 1
87 -0.657675572 1 0.7496139 1
88 NA 1 0.7275239 1
89 -0.696710744 1 0.7250648 1
90 NA 1 0.7335262 0
91 -0.179395847 1 0.7343980 1
92 -0.441545568 1 0.7380425 1
93 -0.685799334 1 0.7389460 0
94 NA 1 0.7259951 1
95 0.191929445 1 0.7282840 0
96 NA 1 0.7281676 0
97 -0.069760671 1 0.7245642 1
98 NA 1 0.7526938 1
99 NA 1 0.7272309 1
100 NA 1 0.7383460 1
$m9c$M_lvlone
y
1 -13.0493856
1.1 -9.3335901
1.2 -22.3469852
1.3 -15.0417337
2 -12.0655434
2.1 -15.8674476
2.2 -7.8800006
3 -11.4820604
3.1 -10.5983220
3.2 -22.4519157
4 -1.2697775
4.1 -11.1215184
4.2 -3.6134138
4.3 -14.5982385
5 -6.8457515
5.1 -7.0551214
5.2 -12.3418980
5.3 -9.2366906
6 -5.1648211
7 -10.0599502
7.1 -18.3267285
7.2 -12.5138426
8 -1.6305331
8.1 -9.6520453
8.2 -1.5278462
8.3 -7.4172211
8.4 -7.1238609
8.5 -8.8706950
9 -0.1634429
9.1 -2.6034300
9.2 -6.7272369
10 -6.4172202
10.1 -11.4834569
11 -8.7911356
11.1 -19.6645080
11.2 -20.2030932
11.3 -21.3082176
11.4 -14.5802901
12 -15.2006287
13 0.8058816
13.1 -13.6379208
14 -15.3422873
14.1 -10.0965208
14.2 -16.6452027
14.3 -15.8389733
15 -8.9424594
15.1 -22.0101983
15.2 -7.3975599
15.3 -10.3567334
16 -1.9691302
16.1 -9.9308357
16.2 -6.9626923
16.3 -3.2862557
16.4 -3.3972355
16.5 -11.5767835
17 -10.5474144
17.1 -7.6215009
17.2 -16.5386939
17.3 -20.0004774
17.4 -18.8505475
18 -19.7302351
19 -14.6177568
19.1 -17.8043866
19.2 -15.1641705
19.3 -16.6898418
20 -12.9059229
20.1 -16.8191201
20.2 -6.1010131
20.3 -7.9415371
20.4 -9.3904458
20.5 -13.3504189
21 -7.6974718
21.1 -11.9335526
21.2 -12.7064929
22 -21.5022909
22.1 -12.7745451
23 -3.5146508
23.1 -4.6724048
24 -2.5619821
25 -6.2944970
25.1 -3.8630505
25.2 -14.4205140
25.3 -19.6735037
25.4 -9.0288933
25.5 -9.0509738
26 -19.7340685
26.1 -14.1692728
26.2 -17.2819976
26.3 -24.6265576
27 -7.3354999
27.1 -11.1488468
28 -11.7996597
28.1 -8.2030122
28.2 -26.4317815
28.3 -18.5016071
29 -5.8551395
29.1 -2.0209442
29.2 -5.6368080
29.3 -3.8110961
30 -12.7217702
30.1 -17.0170140
30.2 -25.4236089
31 -17.0783921
32 -18.4338764
32.1 -19.4317212
32.2 -19.4738978
32.3 -21.4922645
33 2.0838099
33.1 -13.3172274
34 -10.0296691
34.1 -25.9426553
34.2 -18.5688138
34.3 -15.4173859
35 -14.3958113
35.1 -12.9457541
35.2 -16.1380691
36 -12.8166968
36.1 -14.3989481
36.2 -12.2436943
36.3 -15.0104638
36.4 -10.1775457
37 -15.2223495
37.1 -14.7526195
37.2 -19.8168430
38 -2.7065118
39 -8.7288138
39.1 -9.2746473
39.2 -18.2695344
39.3 -13.8219083
39.4 -16.2254704
39.5 -21.7283648
40 1.8291916
40.1 -6.6916432
40.2 -1.6278171
40.3 -10.5749790
41 -3.1556121
41.1 -11.5895327
41.2 -18.9352091
41.3 -15.9788960
41.4 -9.6070508
42 -5.2159485
42.1 -15.9878743
43 -16.6104361
43.1 -9.5549441
43.2 -14.2003491
44 -8.1969033
44.1 -19.9270197
44.2 -22.6521171
44.3 -21.1903736
45 -0.5686627
45.1 -7.5645740
46 -19.1624789
46.1 -18.4487574
46.2 -15.8222682
47 -5.4165074
47.1 -15.0975029
47.2 -12.9971413
47.3 -10.6844521
47.4 -18.2214784
48 -8.3101471
48.1 -18.3854275
49 -13.0130319
50 -10.4579977
51 -19.3157621
52 -4.4747188
52.1 -4.3163827
52.2 -6.9761408
52.3 -20.1764756
52.4 -8.9036692
52.5 -5.6949642
53 -10.3141887
53.1 -8.2642654
53.2 -9.1691554
54 -6.2198754
54.1 -15.7192609
54.2 -13.0978998
54.3 -5.1195299
54.4 -16.5771751
55 -5.7348534
55.1 -7.3217494
55.2 -12.2171938
55.3 -12.9821266
55.4 -14.8599983
56 -14.1764282
56.1 -12.5343602
56.2 -8.4573382
56.3 -12.4633969
56.4 -17.3841863
56.5 -14.8147645
57 -3.1403293
57.1 -11.1509248
57.2 -6.3940143
57.3 -9.3473241
58 -12.0245677
58.1 -9.2112246
58.2 -1.2071742
58.3 -11.0141711
58.4 -5.3721214
58.5 -7.8523047
59 -13.2946560
59.1 -10.0530648
60 -19.2209402
61 -4.6699914
61.1 -3.5981894
61.2 -1.4713611
61.3 -3.8819786
61.4 0.1041413
62 -2.8591600
62.1 -6.9461986
62.2 -16.7910593
62.3 -17.9844596
63 -24.0335535
63.1 -11.7765300
64 -20.5963897
65 -2.7969169
65.1 -11.1778694
65.2 -5.2830399
65.3 -7.9353390
66 -13.2318328
66.1 -1.9090560
66.2 -16.6643889
67 -25.6073277
68 -13.4806759
68.1 -18.4557183
68.2 -13.3982327
68.3 -12.4977127
68.4 -11.7073990
69 -14.5290675
70 -15.2122709
70.1 -7.8681167
71 -10.3352703
71.1 -7.5699888
71.2 -18.4680702
71.3 -21.4316644
71.4 -8.1137650
72 -9.1848162
72.1 -23.7538846
72.2 -26.3421306
72.3 -27.2843801
72.4 -20.8541617
72.5 -12.8948965
73 -2.6091307
74 -8.2790175
75 -12.5029612
76 -6.0061671
76.1 -8.8149114
76.2 -11.8359043
77 0.4772521
78 -9.4105229
79 -1.0217265
79.1 -11.8125257
79.2 -10.5465186
80 -12.7366807
80.1 -9.0584783
80.2 -16.6381566
81 0.5547913
81.1 -4.0892715
81.2 1.8283303
81.3 -5.2166381
82 -3.0749381
82.1 -10.5506696
82.2 -18.2226347
83 -12.5872635
83.1 -11.9756502
83.2 -10.6744217
83.3 -19.2714012
84 -2.6320312
84.1 -9.8140094
85 -12.3886736
85.1 -12.9196365
85.2 -9.6433248
85.3 -6.3296340
85.4 -7.0405525
85.5 -13.6714939
86 -10.8756412
86.1 -12.0055331
86.2 -13.3724699
86.3 -13.3252145
86.4 -14.9191290
86.5 -17.7515546
87 -10.7027963
87.1 -22.4941954
87.2 -14.9616716
88 -2.2264493
88.1 -8.9626474
88.2 -2.5095281
88.3 -16.3345673
89 -11.0459647
90 -4.5610239
90.1 -11.7036651
90.2 -5.3838521
90.3 -4.1636999
91 -7.1462503
91.1 -12.8374475
91.2 -18.2576707
92 -6.4119222
93 5.2122168
93.1 3.1211725
93.2 -3.6841177
93.3 2.6223542
93.4 -11.1877696
94 -6.9602492
94.1 -7.4318416
94.2 -4.3498045
94.3 -11.6340088
94.4 -12.9357964
94.5 -14.7648530
95 -12.8849309
95.1 -9.7451502
95.2 -0.8535063
96 -4.9139832
96.1 -3.9582653
96.2 -9.6555492
96.3 -11.8690793
96.4 -11.0224373
96.5 -10.9530403
97 -9.8540471
97.1 -19.2262840
98 -11.9651231
98.1 -2.6515128
98.2 -12.2606382
99 -11.4720500
99.1 -14.0596866
99.2 -17.3939469
100 1.1005874
100.1 -3.8226248
100.2 -0.9123182
100.3 -15.8389474
100.4 -12.8093826
$m9c$spM_id
center scale
C2 -0.6240921 0.68571078
(Intercept) NA NA
C1 0.7372814 0.01472882
B11 NA NA
$m9c$mu_reg_norm
[1] 0
$m9c$tau_reg_norm
[1] 1e-04
$m9c$shape_tau_norm
[1] 0.01
$m9c$rate_tau_norm
[1] 0.01
$m9c$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m9c$shape_diag_RinvD
[1] "0.01"
$m9c$rate_diag_RinvD
[1] "0.001"
jagsmodel remains the same
Code
lapply(models, "[[", "jagsmodel")
Output
$m0a1
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
}
$m0a2
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
}
$m0a3
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
log(mu_y[i]) <- b_y_id[group_id[i], 1]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
}
$m0a4
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- 1/max(1e-10, inv_mu_y[i])
inv_mu_y[i] <- b_y_id[group_id[i], 1]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
}
$m0b1
model {
# Binomial mixed effects model for b1 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i])))
logit(mu_b1[i]) <- b_b1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ])
mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for b1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1])
}
$m0b2
model {
# Binomial mixed effects model for b1 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i])))
probit(mu_b1[i]) <- b_b1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ])
mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for b1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1])
}
$m0b3
model {
# Binomial mixed effects model for b1 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i])))
log(mu_b1[i]) <- b_b1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ])
mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for b1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1])
}
$m0b4
model {
# Binomial mixed effects model for b1 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i])))
log(mu_b1[i]) <- b_b1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ])
mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for b1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1])
}
$m0c1
model {
# Gamma mixed effects model for L1 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i])
shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2)
rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2)
mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i])
inv_mu_L1[i] <- b_L1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ])
mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for L1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1 <- sqrt(1/tau_L1)
invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1])
}
$m0c2
model {
# Gamma mixed effects model for L1 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i])
shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2)
rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2)
log(mu_L1[i]) <- b_L1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ])
mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for L1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1 <- sqrt(1/tau_L1)
invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1])
}
$m0d1
model {
# Poisson mixed effects model for p1 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i]))
log(mu_p1[i]) <- b_p1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ])
mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for p1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1])
}
$m0d2
model {
# Poisson mixed effects model for p1 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i]))
mu_p1[i] <- b_p1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ])
mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for p1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1])
}
$m0e1
model {
# Log-normal mixed effects model for L1 -----------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1)
mu_L1[i] <- b_L1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ])
mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for L1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1 <- sqrt(1/tau_L1)
invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1])
}
$m0f1
model {
# Beta mixed effects model for Be1 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15)
shape1_Be1[i] <- mu_Be1[i] * tau_Be1
shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1
logit(mu_Be1[i]) <- b_Be1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_Be1_id[ii, 1:1] ~ dnorm(mu_b_Be1_id[ii, ], invD_Be1_id[ , ])
mu_b_Be1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for Be1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta)
invD_Be1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Be1_id[1, 1] <- 1 / (invD_Be1_id[1, 1])
}
$m1a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2]
}
# Priors for the model for y
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
}
$m1b
model {
# Binomial mixed effects model for b1 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i])))
logit(mu_b1[i]) <- b_b1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ])
mu_b_b1_id[ii, 1] <- M_id[ii, 1] * beta[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2]
}
# Priors for the model for b1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1])
}
$m1c
model {
# Gamma mixed effects model for L1 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i])
shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2)
rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2)
mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i])
inv_mu_L1[i] <- b_L1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ])
mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2]
}
# Priors for the model for L1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1 <- sqrt(1/tau_L1)
invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1])
}
$m1d
model {
# Poisson mixed effects model for p1 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p1[i]))
log(mu_p1[i]) <- b_p1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_p1_id[ii, 1:1] ~ dnorm(mu_b_p1_id[ii, ], invD_p1_id[ , ])
mu_b_p1_id[ii, 1] <- M_id[ii, 1] * beta[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2]
}
# Priors for the model for p1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
invD_p1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p1_id[1, 1] <- 1 / (invD_p1_id[1, 1])
}
$m1e
model {
# Log-normal mixed effects model for L1 -----------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1)
mu_L1[i] <- b_L1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1_id[ii, 1:1] ~ dnorm(mu_b_L1_id[ii, ], invD_L1_id[ , ])
mu_b_L1_id[ii, 1] <- M_id[ii, 1] * beta[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2]
}
# Priors for the model for L1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1 <- sqrt(1/tau_L1)
invD_L1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1_id[1, 1] <- 1 / (invD_L1_id[1, 1])
}
$m1f
model {
# Beta mixed effects model for Be1 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15)
shape1_Be1[i] <- mu_Be1[i] * tau_Be1
shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1
logit(mu_Be1[i]) <- b_Be1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_Be1_id[ii, 1:1] ~ dnorm(mu_b_Be1_id[ii, ], invD_Be1_id[ , ])
mu_b_Be1_id[ii, 1] <- M_id[ii, 1] * beta[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2]
}
# Priors for the model for Be1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta)
invD_Be1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Be1_id[1, 1] <- 1 / (invD_Be1_id[1, 1])
}
$m2a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for y
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for c2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
}
$m2b
model {
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ])
mu_b_b2_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for b2
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for c2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
}
$m2c
model {
# Gamma mixed effects model for L1mis -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i])
inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for L1mis
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for c2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
}
$m2d
model {
# Poisson mixed effects model for p2 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p2[i]))
log(mu_p2[i]) <- b_p2_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ])
mu_b_p2_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for p2
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for c2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
}
$m2e
model {
# Log-normal mixed effects model for L1mis --------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dlnorm(mu_L1mis[i], tau_L1mis)
mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for L1mis
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for c2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
}
$m2f
model {
# Beta mixed effects model for Be2 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15)
shape1_Be2[i] <- mu_Be2[i] * tau_Be2
shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2
logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ])
mu_b_Be2_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for Be2
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta)
invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for c2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
}
$m3a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[1]
}
# Priors for the model for C2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m3b
model {
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
logit(mu_b2[i]) <- b_b2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ])
mu_b_b2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1]
}
# Priors for the model for b2
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[1]
}
# Priors for the model for C2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m3c
model {
# Gamma mixed effects model for L1mis -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i])
inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1]
}
# Priors for the model for L1mis
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[1]
}
# Priors for the model for C2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m3d
model {
# Poisson mixed effects model for p2 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_p2[i]))
log(mu_p2[i]) <- b_p2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ])
mu_b_p2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1]
}
# Priors for the model for p2
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[1]
}
# Priors for the model for C2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m3e
model {
# Log-normal mixed effects model for L1mis --------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dlnorm(mu_L1mis[i], tau_L1mis)
mu_L1mis[i] <- b_L1mis_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1]
}
# Priors for the model for L1mis
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[1]
}
# Priors for the model for C2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m3f
model {
# Beta mixed effects model for Be2 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15)
shape1_Be2[i] <- mu_Be2[i] * tau_Be2
shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2
logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ])
mu_b_Be2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1]
}
# Priors for the model for Be2
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta)
invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[1]
}
# Priors for the model for C2
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m4a
model {
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2]
}
# Priors for the model for c1
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Poisson mixed effects model for p2 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i]))
log(mu_p2[i]) <- b_p2_id[group_id[i], 1] +
alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ])
mu_b_p2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2]
}
# Priors for the model for p2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[6] + M_id[ii, 3] * alpha[7]
}
# Priors for the model for c2
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Gamma mixed effects model for L1mis -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i])
inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] +
alpha[12] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- M_id[ii, 2] * alpha[10] + M_id[ii, 3] * alpha[11]
}
# Priors for the model for L1mis
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
# Beta mixed effects model for Be2 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 5] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15)
shape1_Be2[i] <- mu_Be2[i] * tau_Be2
shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2
logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ])
mu_b_Be2_id[ii, 1] <- M_id[ii, 2] * alpha[13] + M_id[ii, 3] * alpha[14]
}
# Priors for the model for Be2
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta)
invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[15]
M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 15:15) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
}
$m4b
model {
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[3] * M_lvlone[i, 6] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for c1
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Poisson mixed effects model for p2 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i]))
mu_p2[i] <- b_p2_id[group_id[i], 1] +
alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[3] * M_lvlone[i, 6] +
alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ])
mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for p2
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1])
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
probit(mu_b2[i]) <- b_b2_id[group_id[i], 1] +
alpha[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 1, 1, 0)
}
for (ii in 1:100) {
b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ])
mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[5]
}
# Priors for the model for b2
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- 1/max(1e-10, inv_mu_c2[i])
inv_mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8]
}
# Priors for the model for c2
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Log-normal mixed effects model for L1mis --------------------------------------
for (i in 1:329) {
M_lvlone[i, 5] ~ dlnorm(mu_L1mis[i], tau_L1mis)
mu_L1mis[i] <- b_L1mis_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[10]
}
# Priors for the model for L1mis
for (k in 10:10) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
}
$m4c
model {
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[3] * M_lvlone[i, 6] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for c1
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Poisson mixed effects model for p2 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i]))
mu_p2[i] <- b_p2_id[group_id[i], 1] +
alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[3] * M_lvlone[i, 6] +
alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ])
mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for p2
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1])
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
log(mu_b2[i]) <- b_b2_id[group_id[i], 1] +
alpha[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 1, 1, 0)
}
for (ii in 1:100) {
b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ])
mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[5]
}
# Priors for the model for b2
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2)
log(mu_c2[i]) <- b_c2_id[group_id[i], 1] +
alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8]
}
# Priors for the model for c2
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Gamma mixed effects model for L1mis -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 5] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
log(mu_L1mis[i]) <- b_L1mis_id[group_id[i], 1]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[10]
}
# Priors for the model for L1mis
for (k in 10:10) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
}
$m4d
model {
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[3] * M_lvlone[i, 7] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
beta[6] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for c1
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_beta[k])
tau_reg_norm_ridge_beta[k] ~ dgamma(0.01, 0.01)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Poisson mixed effects model for p2 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dpois(max(1e-10, mu_p2[i]))
mu_p2[i] <- b_p2_id[group_id[i], 1] +
alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[3] * M_lvlone[i, 7] +
alpha[4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
alpha[5] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_p2_id[ii, 1:1] ~ dnorm(mu_b_p2_id[ii, ], invD_p2_id[ , ])
mu_b_p2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for p2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson_ridge_alpha[k])
tau_reg_poisson_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
invD_p2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_p2_id[1, 1] <- 1 / (invD_p2_id[1, 1])
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
log(mu_b2[i]) <- b_b2_id[group_id[i], 1] +
alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[8] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
alpha[9] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 1, 1, 0)
}
for (ii in 1:100) {
b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ])
mu_b_b2_id[ii, 1] <- M_id[ii, 1] * alpha[6]
}
# Priors for the model for b2
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_alpha[k])
tau_reg_binom_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_c2[i], tau_c2)
log(mu_c2[i]) <- b_c2_id[group_id[i], 1] +
alpha[11] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
alpha[12] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[10]
}
# Priors for the model for c2
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k])
tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Gamma mixed effects model for L1mis -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 5] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
log(mu_L1mis[i]) <- b_L1mis_id[group_id[i], 1] +
alpha[14] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- M_id[ii, 1] * alpha[13]
}
# Priors for the model for L1mis
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma_ridge_alpha[k])
tau_reg_gamma_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
# Normal mixed effects model for Be2 --------------------------------------------
for (i in 1:329) {
M_lvlone[i, 6] ~ dnorm(mu_Be2[i], tau_Be2)T(0, 1)
mu_Be2[i] <- b_Be2_id[group_id[i], 1]
}
for (ii in 1:100) {
b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ])
mu_b_Be2_id[ii, 1] <- M_id[ii, 1] * alpha[15]
}
# Priors for the model for Be2
for (k in 15:15) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k])
tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_Be2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_Be2 <- sqrt(1/tau_Be2)
invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1])
}
$m5a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[6] * M_lvlone[i, 5] + beta[7] * M_lvlone[i, 6] +
beta[8] * M_lvlone[i, 7] +
beta[9] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] +
beta[11] * (M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] +
beta[12] * (M_lvlone[i, 10] - spM_lvlone[10, 1])/spM_lvlone[10, 2] +
beta[13] * (M_lvlone[i, 11] - spM_lvlone[11, 1])/spM_lvlone[11, 2] +
beta[14] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2] +
M_id[ii, 4] * beta[3] + M_id[ii, 5] * beta[4] +
(M_id[ii, 6] - spM_id[6, 1])/spM_id[6, 2] * beta[5]
mu_b_y_id[ii, 2] <- beta[10]
}
# Priors for the model for y
for (k in 1:14) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:2) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] + alpha[6] * M_lvlone[i, 5] +
alpha[7] * M_lvlone[i, 6] + alpha[8] * M_lvlone[i, 7] +
alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
M_lvlone[i, 8] <- abs(M_id[group_id[i], 7] - M_lvlone[i, 2])
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2] +
M_id[ii, 4] * alpha[3] + M_id[ii, 5] * alpha[4] +
(M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[5]
}
# Priors for the model for c2
for (k in 1:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1] +
alpha[14] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- M_id[ii, 3] * alpha[10] + M_id[ii, 4] * alpha[11] +
M_id[ii, 5] * alpha[12] +
(M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[13]
}
# Priors for the model for o2
for (k in 10:14) {
alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[15] + M_id[ii, 3] * alpha[16] +
M_id[ii, 4] * alpha[17] + M_id[ii, 5] * alpha[18] +
(M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[19]
}
# Priors for the model for time
for (k in 15:19) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Multinomial logit model for M2 ------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dcat(p_M2[ii, 1:4])
p_M2[ii, 1] <- min(1-1e-7, max(1e-7, phi_M2[ii, 1] / sum(phi_M2[ii, ])))
p_M2[ii, 2] <- min(1-1e-7, max(1e-7, phi_M2[ii, 2] / sum(phi_M2[ii, ])))
p_M2[ii, 3] <- min(1-1e-7, max(1e-7, phi_M2[ii, 3] / sum(phi_M2[ii, ])))
p_M2[ii, 4] <- min(1-1e-7, max(1e-7, phi_M2[ii, 4] / sum(phi_M2[ii, ])))
log(phi_M2[ii, 1]) <- 0
log(phi_M2[ii, 2]) <- M_id[ii, 2] * alpha[20] +
(M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[21]
log(phi_M2[ii, 3]) <- M_id[ii, 2] * alpha[22] +
(M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[23]
log(phi_M2[ii, 4]) <- M_id[ii, 2] * alpha[24] +
(M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * alpha[25]
M_id[ii, 3] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 20:25) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 10] <- M_lvlone[i, 5] * M_lvlone[i, 8]
M_lvlone[i, 11] <- M_lvlone[i, 6] * M_lvlone[i, 8]
M_lvlone[i, 12] <- M_lvlone[i, 7] * M_lvlone[i, 8]
}
}
$m5b
model {
# Binomial mixed effects model for b1 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i])))
logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] +
b_b1_id[group_id[i], 2] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
b_b1_id[group_id[i], 3] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[3] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] +
beta[4] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2]
}
for (ii in 1:100) {
b_b1_id[ii, 1:3] ~ dmnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ])
mu_b_b1_id[ii, 1] <- M_id[ii, 2] * beta[1]
mu_b_b1_id[ii, 2] <- beta[5]
mu_b_b1_id[ii, 3] <- 0
}
# Priors for the model for b1
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_beta[k])
tau_reg_binom_ridge_beta[k] ~ dgamma(0.01, 0.01)
}
for (k in 1:3) {
RinvD_b1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_b1_id[1:3, 1:3] ~ dwish(RinvD_b1_id[ , ], KinvD_b1_id)
D_b1_id[1:3, 1:3] <- inverse(invD_b1_id[ , ])
# Gamma mixed effects model for L1mis -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i])
inv_mu_L1mis[i] <- b_L1mis_id[group_id[i], 1] +
alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_L1mis_id[ii, 1:1] ~ dnorm(mu_b_L1mis_id[ii, ], invD_L1mis_id[ , ])
mu_b_L1mis_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[2]
}
# Priors for the model for L1mis
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma_ridge_alpha[k])
tau_reg_gamma_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
invD_L1mis_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_L1mis_id[1, 1] <- 1 / (invD_L1mis_id[1, 1])
# Beta mixed effects model for Be2 ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15)
shape1_Be2[i] <- mu_Be2[i] * tau_Be2
shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2
logit(mu_Be2[i]) <- b_Be2_id[group_id[i], 1] +
alpha[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[9] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
M_lvlone[i, 7] <- log(M_lvlone[i, 3])
}
for (ii in 1:100) {
b_Be2_id[ii, 1:1] ~ dnorm(mu_b_Be2_id[ii, ], invD_Be2_id[ , ])
mu_b_Be2_id[ii, 1] <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7]
}
# Priors for the model for Be2
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta_ridge_alpha[k])
tau_reg_beta_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta)
invD_Be2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Be2_id[1, 1] <- 1 / (invD_Be2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[12] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
M_lvlone[i, 6] <- abs(M_lvlone[i, 4] - M_id[group_id[i], 1])
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[10] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[11]
}
# Priors for the model for c1
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k])
tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 5] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[13] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[14]
}
# Priors for the model for time
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k])
tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
log(mu_C2[ii]) <- M_id[ii, 2] * alpha[15]
}
# Priors for the model for C2
for (k in 15:15) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k])
tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m6a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[1] * M_id[group_id[i], 2] +
beta[2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] +
beta[3] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] +
beta[4] * M_lvlone[i, 3]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- beta[5]
}
# Priors for the model for y
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
for (ii in 1:100) {
b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ])
mu_b_b2_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3]
}
# Priors for the model for b2
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[5] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6]
}
# Priors for the model for C2
for (k in 5:6) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m6b
model {
# Binomial mixed effects model for b1 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i])))
logit(mu_b1[i]) <- b_b1_id[group_id[i], 1] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_b1_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[1] * M_id[group_id[i], 2] +
beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] +
beta[3] * M_id[group_id[i], 3] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_b1_id[ii, 1:2] ~ dmnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ])
mu_b_b1_id[ii, 1] <- beta[5]
mu_b_b1_id[ii, 2] <- 0
}
# Priors for the model for b1
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom_ridge_beta[k])
tau_reg_binom_ridge_beta[k] ~ dgamma(0.01, 0.01)
}
for (k in 1:2) {
RinvD_b1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_b1_id[1:2, 1:2] ~ dwish(RinvD_b1_id[ , ], KinvD_b1_id)
D_b1_id[1:2, 1:2] <- inverse(invD_b1_id[ , ])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[2] +
M_id[ii, 3] * alpha[3]
}
# Priors for the model for c1
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k])
tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[6] +
M_id[ii, 3] * alpha[7]
}
# Priors for the model for time
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k])
tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[8] + M_id[ii, 3] * alpha[9]
}
# Priors for the model for C2
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm_ridge_alpha[k])
tau_reg_norm_ridge_alpha[k] ~ dgamma(0.01, 0.01)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m7a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
mu_b_y_id[ii, 2] <- beta[2]
mu_b_y_id[ii, 3] <- beta[3]
}
# Priors for the model for y
for (k in 1:3) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
}
$m7b
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_y_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
mu_b_y_id[ii, 2] <- beta[2]
mu_b_y_id[ii, 3] <- beta[3]
mu_b_y_id[ii, 4] <- beta[4]
}
# Priors for the model for y
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:4) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ])
}
$m7c
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2]
mu_b_y_id[ii, 2] <- beta[4]
mu_b_y_id[ii, 3] <- beta[5]
mu_b_y_id[ii, 4] <- beta[6]
}
# Priors for the model for y
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:4) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ])
}
$m7d
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
beta[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3]
mu_b_y_id[ii, 2] <- 0
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:2) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3]
}
# Priors for the model for c1
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7]
}
# Priors for the model for time
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[8] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9]
}
# Priors for the model for C2
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m7e
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 4] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3]
mu_b_y_id[ii, 2] <- beta[5]
mu_b_y_id[ii, 3] <- beta[6]
mu_b_y_id[ii, 4] <- beta[7]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:4) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3]
}
# Priors for the model for c1
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[5] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6]
}
# Priors for the model for C2
for (k in 5:6) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m7f
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[6] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
beta[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3]
mu_b_y_id[ii, 2] <- 0
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:2) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3]
}
# Priors for the model for c1
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[7]
}
# Priors for the model for time
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[8] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9]
}
# Priors for the model for C2
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m8a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
mu_b_y_id[ii, 2] <- beta[4]
mu_b_y_id[ii, 3] <- beta[3]
}
# Priors for the model for y
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for c2
for (k in 1:3) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
}
$m8b
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
mu_b_y_id[ii, 2] <- beta[4]
mu_b_y_id[ii, 3] <- beta[3]
}
# Priors for the model for y
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[1]
}
# Priors for the model for c2
for (k in 1:3) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
}
$m8c
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2]
mu_b_y_id[ii, 2] <- beta[5]
mu_b_y_id[ii, 3] <- beta[3] + M_id[ii, 3] * beta[6]
}
# Priors for the model for y
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2]
}
# Priors for the model for c2
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + M_id[ii, 3] * alpha[6]
}
# Priors for the model for c1
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8]
M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 8:8) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 3] * M_lvlone[i, 3]
}
}
$m8d
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 3] * beta[2]
mu_b_y_id[ii, 2] <- beta[5]
mu_b_y_id[ii, 3] <- beta[3] + M_id[ii, 3] * beta[6]
}
# Priors for the model for y
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] + M_id[ii, 3] * alpha[2]
}
# Priors for the model for c2
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[5] + M_id[ii, 3] * alpha[6]
}
# Priors for the model for c1
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[8] + M_id[ii, 3] * alpha[9]
}
# Priors for the model for time
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10]
M_id[ii, 3] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 10:10) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 3] * M_lvlone[i, 3]
}
}
$m8e
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[6]
mu_b_y_id[ii, 3] <- beta[5]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] +
M_id[ii, 4] * alpha[8]
}
# Priors for the model for c1
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] +
M_id[ii, 4] * alpha[12]
}
# Priors for the model for time
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3]
}
}
$m8f
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[6]
mu_b_y_id[ii, 3] <- beta[5]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] +
M_id[ii, 4] * alpha[8]
}
# Priors for the model for c1
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 10:11) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3]
}
}
$m8g
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[6]
mu_b_y_id[ii, 3] <- beta[5]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 6:7) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3]
}
}
$m8h
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[6]
mu_b_y_id[ii, 3] <- beta[5]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] +
M_id[ii, 4] * alpha[8]
}
# Priors for the model for c1
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] +
M_id[ii, 4] * alpha[12]
}
# Priors for the model for time
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2]
}
}
$m8i
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[7] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[6]
mu_b_y_id[ii, 3] <- beta[5]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] +
M_id[ii, 4] * alpha[8]
}
# Priors for the model for c1
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[10] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 10:11) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2]
}
}
$m8j
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[6]
mu_b_y_id[ii, 3] <- beta[4] + M_id[ii, 4] * beta[7]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] +
M_id[ii, 4] * alpha[8]
}
# Priors for the model for c1
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] +
M_id[ii, 4] * alpha[12]
}
# Priors for the model for time
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2]
}
}
$m8k
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[6]
mu_b_y_id[ii, 3] <- beta[4] + M_id[ii, 4] * beta[7]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[9] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] +
M_id[ii, 4] * alpha[8]
}
# Priors for the model for c1
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[10] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] +
M_id[ii, 4] * alpha[12]
}
# Priors for the model for time
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 2]
}
}
$m8l
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[8] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] +
beta[9] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:3] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[5] + M_id[ii, 4] * beta[7]
mu_b_y_id[ii, 3] <- 0
}
# Priors for the model for y
for (k in 1:9) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:3) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:3, 1:3] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:3, 1:3] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c1
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[5] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] +
M_id[ii, 4] * alpha[7]
}
# Priors for the model for time
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[8] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[9]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 4] <- M_id[group_id[i], 4] * M_lvlone[i, 2]
M_lvlone[i, 5] <- M_id[group_id[i], 4] * M_lvlone[i, 3]
M_lvlone[i, 7] <- M_id[group_id[i], 4] * M_lvlone[i, 2] * M_lvlone[i, 3]
}
}
$m8m
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] + b_y_id[group_id[i], 2] * M_lvlone[i, 3] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[4] * M_lvlone[i, 4] + beta[5] * M_lvlone[i, 5] +
beta[6] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
mu_b_y_id[ii, 2] <- beta[3]
}
# Priors for the model for y
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:2) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ])
}
$m8n
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] +
b_y_id[group_id[i], 3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_y_id[group_id[i], 4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[6] * M_lvlone[i, 5]
}
for (ii in 1:100) {
b_y_id[ii, 1:4] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] + M_id[ii, 4] * beta[3]
mu_b_y_id[ii, 2] <- beta[2]
mu_b_y_id[ii, 3] <- beta[5]
mu_b_y_id[ii, 4] <- beta[7]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:4) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:4, 1:4] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:4, 1:4] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[5] * M_lvlone[i, 5]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for c1
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c1 <- sqrt(1/tau_c1)
invD_c1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c1_id[1, 1] <- 1 / (invD_c1_id[1, 1])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1] + alpha[9] * M_lvlone[i, 5]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[6] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[7] +
M_id[ii, 4] * alpha[8]
}
# Priors for the model for time
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Binomial mixed effects model for b1 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b1[i])))
logit(mu_b1[i]) <- b_b1_id[group_id[i], 1]
}
for (ii in 1:100) {
b_b1_id[ii, 1:1] ~ dnorm(mu_b_b1_id[ii, ], invD_b1_id[ , ])
mu_b_b1_id[ii, 1] <- M_id[ii, 2] * alpha[10] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[11] +
M_id[ii, 4] * alpha[12]
}
# Priors for the model for b1
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b1_id[1, 1] <- 1 / (invD_b1_id[1, 1])
# Binomial model for B2 ---------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[ii])))
logit(mu_B2[ii]) <- M_id[ii, 2] * alpha[13] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[14]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
}
$m9a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] + b_y_o1[group_o1[i], 1] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[3] * M_lvlone[i, 3] +
beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
for (iii in 1:3) {
b_y_o1[iii, 1:1] ~ dnorm(mu_b_y_o1[iii, ], invD_y_o1[ , ])
mu_b_y_o1[iii, 1] <- 0
}
# Priors for the model for y
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
invD_y_o1[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_o1[1, 1] <- 1 / (invD_y_o1[1, 1])
}
$m9b
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] +
b_y_id[group_id[i], 2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:2] ~ dmnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] +
M_id[ii, 4] * beta[4]
mu_b_y_id[ii, 2] <- beta[5]
}
# Priors for the model for y
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
for (k in 1:2) {
RinvD_y_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_y_id[1:2, 1:2] ~ dwish(RinvD_y_id[ , ], KinvD_y_id)
D_y_id[1:2, 1:2] <- inverse(invD_y_id[ , ])
# Normal mixed effects model for time -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_time[i], tau_time)
mu_time[i] <- b_time_id[group_id[i], 1]
}
for (ii in 1:100) {
b_time_id[ii, 1:1] ~ dnorm(mu_b_time_id[ii, ], invD_time_id[ , ])
mu_b_time_id[ii, 1] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] +
M_id[ii, 4] * alpha[4]
}
# Priors for the model for time
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_time ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_time <- sqrt(1/tau_time)
invD_time_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_time_id[1, 1] <- 1 / (invD_time_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[5] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] +
M_id[ii, 4] * alpha[7]
}
# Priors for the model for C2
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m9c
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 2] * beta[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[3] +
M_id[ii, 4] * beta[4]
}
# Priors for the model for y
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 2] * alpha[1] +
(M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * alpha[2] +
M_id[ii, 4] * alpha[3]
}
# Priors for the model for C2
for (k in 1:3) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
GRcrit and MCerror give same result
Code
lapply(models0, GR_crit, multivariate = FALSE)
Output
$m0a1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m0a2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m0a3
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m0a4
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m0b1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
D_b1_id[1,1] NaN NaN
$m0b2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
D_b1_id[1,1] NaN NaN
$m0b3
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
D_b1_id[1,1] NaN NaN
$m0b4
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
D_b1_id[1,1] NaN NaN
$m0c1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_L1 NaN NaN
D_L1_id[1,1] NaN NaN
$m0c2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_L1 NaN NaN
D_L1_id[1,1] NaN NaN
$m0d1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
D_p1_id[1,1] NaN NaN
$m0d2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
D_p1_id[1,1] NaN NaN
$m0e1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_L1 NaN NaN
D_L1_id[1,1] NaN NaN
$m0f1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
tau_Be1 NaN NaN
D_Be1_id[1,1] NaN NaN
$m1a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m1b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
D_b1_id[1,1] NaN NaN
$m1c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
sigma_L1 NaN NaN
D_L1_id[1,1] NaN NaN
$m1d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
D_p1_id[1,1] NaN NaN
$m1e
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
sigma_L1 NaN NaN
D_L1_id[1,1] NaN NaN
$m1f
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
tau_Be1 NaN NaN
D_Be1_id[1,1] NaN NaN
$m2a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m2b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
D_b2_id[1,1] NaN NaN
$m2c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
sigma_L1mis NaN NaN
D_L1mis_id[1,1] NaN NaN
$m2d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
D_p2_id[1,1] NaN NaN
$m2e
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
sigma_L1mis NaN NaN
D_L1mis_id[1,1] NaN NaN
$m2f
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
tau_Be2 NaN NaN
D_Be2_id[1,1] NaN NaN
$m3a
Potential scale reduction factors:
Point est. Upper C.I.
C2 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m3b
Potential scale reduction factors:
Point est. Upper C.I.
C2 NaN NaN
D_b2_id[1,1] NaN NaN
$m3c
Potential scale reduction factors:
Point est. Upper C.I.
C2 NaN NaN
sigma_L1mis NaN NaN
D_L1mis_id[1,1] NaN NaN
$m3d
Potential scale reduction factors:
Point est. Upper C.I.
C2 NaN NaN
D_p2_id[1,1] NaN NaN
$m3e
Potential scale reduction factors:
Point est. Upper C.I.
C2 NaN NaN
sigma_L1mis NaN NaN
D_L1mis_id[1,1] NaN NaN
$m3f
Potential scale reduction factors:
Point est. Upper C.I.
C2 NaN NaN
tau_Be2 NaN NaN
D_Be2_id[1,1] NaN NaN
$m4a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
B21 NaN NaN
c2 NaN NaN
p2 NaN NaN
L1mis NaN NaN
Be2 NaN NaN
sigma_c1 NaN NaN
D_c1_id[1,1] NaN NaN
$m4b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
b21 NaN NaN
p2 NaN NaN
L1mis NaN NaN
sigma_c1 NaN NaN
D_c1_id[1,1] NaN NaN
$m4c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
b21 NaN NaN
p2 NaN NaN
L1mis NaN NaN
sigma_c1 NaN NaN
D_c1_id[1,1] NaN NaN
$m4d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c2 NaN NaN
b21 NaN NaN
p2 NaN NaN
L1mis NaN NaN
Be2 NaN NaN
sigma_c1 NaN NaN
D_c1_id[1,1] NaN NaN
$m5a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
log(C1) NaN NaN
o22 NaN NaN
o23 NaN NaN
o24 NaN NaN
abs(C1 - c2) NaN NaN
time NaN NaN
I(time^2) NaN NaN
o22:abs(C1 - c2) NaN NaN
o23:abs(C1 - c2) NaN NaN
o24:abs(C1 - c2) NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
$m5b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
L1mis NaN NaN
abs(c1 - C2) NaN NaN
log(Be2) NaN NaN
time NaN NaN
D_b1_id[1,1] NaN NaN
D_b1_id[1,2] NaN NaN
D_b1_id[2,2] NaN NaN
D_b1_id[1,3] NaN NaN
D_b1_id[2,3] NaN NaN
D_b1_id[3,3] NaN NaN
$m6a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
C2 NaN NaN
b21 NaN NaN
time NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m6b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B11 NaN NaN
c1 NaN NaN
time NaN NaN
D_b1_id[1,1] NaN NaN
D_b1_id[1,2] NaN NaN
D_b1_id[2,2] NaN NaN
$m7a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
ns(time, df = 2)1 NaN NaN
ns(time, df = 2)2 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m7b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
bs(time, df = 3)1 NaN NaN
bs(time, df = 3)2 NaN NaN
bs(time, df = 3)3 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
D_y_id[1,4] NaN NaN
D_y_id[2,4] NaN NaN
D_y_id[3,4] NaN NaN
D_y_id[4,4] NaN NaN
$m7c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
c1 NaN NaN
ns(time, df = 3)1 NaN NaN
ns(time, df = 3)2 NaN NaN
ns(time, df = 3)3 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
D_y_id[1,4] NaN NaN
D_y_id[2,4] NaN NaN
D_y_id[3,4] NaN NaN
D_y_id[4,4] NaN NaN
$m7d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
C2 NaN NaN
c1 NaN NaN
ns(time, df = 3)1 NaN NaN
ns(time, df = 3)2 NaN NaN
ns(time, df = 3)3 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
$m7e
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
C2 NaN NaN
c1 NaN NaN
ns(time, df = 3)1 NaN NaN
ns(time, df = 3)2 NaN NaN
ns(time, df = 3)3 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
D_y_id[1,4] NaN NaN
D_y_id[2,4] NaN NaN
D_y_id[3,4] NaN NaN
D_y_id[4,4] NaN NaN
$m7f
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
C2 NaN NaN
c1 NaN NaN
ns(time, df = 3)1 NaN NaN
ns(time, df = 3)2 NaN NaN
ns(time, df = 3)3 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
$m8a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c1 NaN NaN
c2 NaN NaN
time NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c1 NaN NaN
c2 NaN NaN
time NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
B21 NaN NaN
c1 NaN NaN
c2 NaN NaN
time NaN NaN
B21:c1 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
B21 NaN NaN
c1 NaN NaN
c2 NaN NaN
time NaN NaN
B21:c1 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8e
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c1 NaN NaN
c2 NaN NaN
time NaN NaN
B21:c1 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8f
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c1 NaN NaN
c2 NaN NaN
time NaN NaN
B21:c1 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8g
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c1 NaN NaN
c2 NaN NaN
time NaN NaN
B21:c1 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8h
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c2 NaN NaN
c1 NaN NaN
time NaN NaN
B21:c2 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8i
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c2 NaN NaN
c1 NaN NaN
time NaN NaN
B21:c2 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8j
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c2 NaN NaN
c1 NaN NaN
time NaN NaN
B21:c2 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8k
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c2 NaN NaN
c1 NaN NaN
time NaN NaN
B21:c2 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8l
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c1 NaN NaN
time NaN NaN
B21:c1 NaN NaN
B21:time NaN NaN
c1:time NaN NaN
B21:c1:time NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
$m8m
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c1 NaN NaN
b11 NaN NaN
o1.L NaN NaN
o1.Q NaN NaN
c1:b11 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
$m8n
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
B21 NaN NaN
c1 NaN NaN
time NaN NaN
b11 NaN NaN
C1:time NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
D_y_id[1,3] NaN NaN
D_y_id[2,3] NaN NaN
D_y_id[3,3] NaN NaN
D_y_id[1,4] NaN NaN
D_y_id[2,4] NaN NaN
D_y_id[3,4] NaN NaN
D_y_id[4,4] NaN NaN
$m9a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
c1 NaN NaN
b11 NaN NaN
time NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_o1[1,1] NaN NaN
$m9b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
C2 NaN NaN
B11 NaN NaN
time NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
D_y_id[1,2] NaN NaN
D_y_id[2,2] NaN NaN
$m9c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
C2 NaN NaN
B11 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
Code
lapply(models0, MC_error)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m0a2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m0a3
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m0a4
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m0b1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
D_b1_id[1,1] 0 0 0 NaN
$m0b2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
D_b1_id[1,1] 0 0 0 NaN
$m0b3
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
D_b1_id[1,1] 0 0 0 NaN
$m0b4
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
D_b1_id[1,1] 0 0 0 NaN
$m0c1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_L1 0 0 0 NaN
D_L1_id[1,1] 0 0 0 NaN
$m0c2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_L1 0 0 0 NaN
D_L1_id[1,1] 0 0 0 NaN
$m0d1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
D_p1_id[1,1] 0 0 0 NaN
$m0d2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
D_p1_id[1,1] 0 0 0 NaN
$m0e1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_L1 0 0 0 NaN
D_L1_id[1,1] 0 0 0 NaN
$m0f1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
tau_Be1 0 0 0 NaN
D_Be1_id[1,1] 0 0 0 NaN
$m1a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m1b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
D_b1_id[1,1] 0 0 0 NaN
$m1c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
sigma_L1 0 0 0 NaN
D_L1_id[1,1] 0 0 0 NaN
$m1d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
D_p1_id[1,1] 0 0 0 NaN
$m1e
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
sigma_L1 0 0 0 NaN
D_L1_id[1,1] 0 0 0 NaN
$m1f
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
tau_Be1 0 0 0 NaN
D_Be1_id[1,1] 0 0 0 NaN
$m2a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m2b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
D_b2_id[1,1] 0 0 0 NaN
$m2c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
sigma_L1mis 0 0 0 NaN
D_L1mis_id[1,1] 0 0 0 NaN
$m2d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
D_p2_id[1,1] 0 0 0 NaN
$m2e
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
sigma_L1mis 0 0 0 NaN
D_L1mis_id[1,1] 0 0 0 NaN
$m2f
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
tau_Be2 0 0 0 NaN
D_Be2_id[1,1] 0 0 0 NaN
$m3a
est MCSE SD MCSE/SD
C2 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m3b
est MCSE SD MCSE/SD
C2 0 0 0 NaN
D_b2_id[1,1] 0 0 0 NaN
$m3c
est MCSE SD MCSE/SD
C2 0 0 0 NaN
sigma_L1mis 0 0 0 NaN
D_L1mis_id[1,1] 0 0 0 NaN
$m3d
est MCSE SD MCSE/SD
C2 0 0 0 NaN
D_p2_id[1,1] 0 0 0 NaN
$m3e
est MCSE SD MCSE/SD
C2 0 0 0 NaN
sigma_L1mis 0 0 0 NaN
D_L1mis_id[1,1] 0 0 0 NaN
$m3f
est MCSE SD MCSE/SD
C2 0 0 0 NaN
tau_Be2 0 0 0 NaN
D_Be2_id[1,1] 0 0 0 NaN
$m4a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
B21 0 0 0 NaN
c2 0 0 0 NaN
p2 0 0 0 NaN
L1mis 0 0 0 NaN
Be2 0 0 0 NaN
sigma_c1 0 0 0 NaN
D_c1_id[1,1] 0 0 0 NaN
$m4b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
b21 0 0 0 NaN
p2 0 0 0 NaN
L1mis 0 0 0 NaN
sigma_c1 0 0 0 NaN
D_c1_id[1,1] 0 0 0 NaN
$m4c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
b21 0 0 0 NaN
p2 0 0 0 NaN
L1mis 0 0 0 NaN
sigma_c1 0 0 0 NaN
D_c1_id[1,1] 0 0 0 NaN
$m4d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c2 0 0 0 NaN
b21 0 0 0 NaN
p2 0 0 0 NaN
L1mis 0 0 0 NaN
Be2 0 0 0 NaN
sigma_c1 0 0 0 NaN
D_c1_id[1,1] 0 0 0 NaN
$m5a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
log(C1) 0 0 0 NaN
o22 0 0 0 NaN
o23 0 0 0 NaN
o24 0 0 0 NaN
abs(C1 - c2) 0 0 0 NaN
time 0 0 0 NaN
I(time^2) 0 0 0 NaN
o22:abs(C1 - c2) 0 0 0 NaN
o23:abs(C1 - c2) 0 0 0 NaN
o24:abs(C1 - c2) 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
$m5b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
L1mis 0 0 0 NaN
abs(c1 - C2) 0 0 0 NaN
log(Be2) 0 0 0 NaN
time 0 0 0 NaN
D_b1_id[1,1] 0 0 0 NaN
D_b1_id[1,2] 0 0 0 NaN
D_b1_id[2,2] 0 0 0 NaN
D_b1_id[1,3] 0 0 0 NaN
D_b1_id[2,3] 0 0 0 NaN
D_b1_id[3,3] 0 0 0 NaN
$m6a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
C2 0 0 0 NaN
b21 0 0 0 NaN
time 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m6b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B11 0 0 0 NaN
c1 0 0 0 NaN
time 0 0 0 NaN
D_b1_id[1,1] 0 0 0 NaN
D_b1_id[1,2] 0 0 0 NaN
D_b1_id[2,2] 0 0 0 NaN
$m7a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
ns(time, df = 2)1 0 0 0 NaN
ns(time, df = 2)2 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m7b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
bs(time, df = 3)1 0 0 0 NaN
bs(time, df = 3)2 0 0 0 NaN
bs(time, df = 3)3 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
D_y_id[1,4] 0 0 0 NaN
D_y_id[2,4] 0 0 0 NaN
D_y_id[3,4] 0 0 0 NaN
D_y_id[4,4] 0 0 0 NaN
$m7c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
c1 0 0 0 NaN
ns(time, df = 3)1 0 0 0 NaN
ns(time, df = 3)2 0 0 0 NaN
ns(time, df = 3)3 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
D_y_id[1,4] 0 0 0 NaN
D_y_id[2,4] 0 0 0 NaN
D_y_id[3,4] 0 0 0 NaN
D_y_id[4,4] 0 0 0 NaN
$m7d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
C2 0 0 0 NaN
c1 0 0 0 NaN
ns(time, df = 3)1 0 0 0 NaN
ns(time, df = 3)2 0 0 0 NaN
ns(time, df = 3)3 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
$m7e
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
C2 0 0 0 NaN
c1 0 0 0 NaN
ns(time, df = 3)1 0 0 0 NaN
ns(time, df = 3)2 0 0 0 NaN
ns(time, df = 3)3 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
D_y_id[1,4] 0 0 0 NaN
D_y_id[2,4] 0 0 0 NaN
D_y_id[3,4] 0 0 0 NaN
D_y_id[4,4] 0 0 0 NaN
$m7f
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
C2 0 0 0 NaN
c1 0 0 0 NaN
ns(time, df = 3)1 0 0 0 NaN
ns(time, df = 3)2 0 0 0 NaN
ns(time, df = 3)3 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
$m8a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c1 0 0 0 NaN
c2 0 0 0 NaN
time 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c1 0 0 0 NaN
c2 0 0 0 NaN
time 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
B21 0 0 0 NaN
c1 0 0 0 NaN
c2 0 0 0 NaN
time 0 0 0 NaN
B21:c1 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
B21 0 0 0 NaN
c1 0 0 0 NaN
c2 0 0 0 NaN
time 0 0 0 NaN
B21:c1 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8e
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c1 0 0 0 NaN
c2 0 0 0 NaN
time 0 0 0 NaN
B21:c1 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8f
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c1 0 0 0 NaN
c2 0 0 0 NaN
time 0 0 0 NaN
B21:c1 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8g
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c1 0 0 0 NaN
c2 0 0 0 NaN
time 0 0 0 NaN
B21:c1 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8h
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c2 0 0 0 NaN
c1 0 0 0 NaN
time 0 0 0 NaN
B21:c2 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8i
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c2 0 0 0 NaN
c1 0 0 0 NaN
time 0 0 0 NaN
B21:c2 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8j
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c2 0 0 0 NaN
c1 0 0 0 NaN
time 0 0 0 NaN
B21:c2 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8k
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c2 0 0 0 NaN
c1 0 0 0 NaN
time 0 0 0 NaN
B21:c2 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8l
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c1 0 0 0 NaN
time 0 0 0 NaN
B21:c1 0 0 0 NaN
B21:time 0 0 0 NaN
c1:time 0 0 0 NaN
B21:c1:time 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
$m8m
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c1 0 0 0 NaN
b11 0 0 0 NaN
o1.L 0 0 0 NaN
o1.Q 0 0 0 NaN
c1:b11 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
$m8n
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
B21 0 0 0 NaN
c1 0 0 0 NaN
time 0 0 0 NaN
b11 0 0 0 NaN
C1:time 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
D_y_id[1,3] 0 0 0 NaN
D_y_id[2,3] 0 0 0 NaN
D_y_id[3,3] 0 0 0 NaN
D_y_id[1,4] 0 0 0 NaN
D_y_id[2,4] 0 0 0 NaN
D_y_id[3,4] 0 0 0 NaN
D_y_id[4,4] 0 0 0 NaN
$m9a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
c1 0 0 0 NaN
b11 0 0 0 NaN
time 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_o1[1,1] 0 0 0 NaN
$m9b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
C2 0 0 0 NaN
B11 0 0 0 NaN
time 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
D_y_id[1,2] 0 0 0 NaN
D_y_id[2,2] 0 0 0 NaN
$m9c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
C2 0 0 0 NaN
B11 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
summary output remained the same
Code
lapply(models0, print)
Output
Call:
lme_imp(fixed = y ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "identity"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "inverse"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "logit"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "probit"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "log"),
n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "cloglog"),
n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "inverse"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
p1
(Intercept)
p1 (Intercept) 0
Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "identity"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
p1
(Intercept)
p1 (Intercept) 0
Call:
lognormmm_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal mixed model for "L1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
Call:
betamm_imp(fixed = Be1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta mixed model for "Be1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
Be1
(Intercept)
Be1 (Intercept) 0
Call:
lme_imp(fixed = y ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = b1 ~ C1 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
Call:
glme_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
Call:
glme_imp(fixed = p1 ~ C1 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
p1
(Intercept)
p1 (Intercept) 0
Call:
lognormmm_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal mixed model for "L1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
Call:
betamm_imp(fixed = Be1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta mixed model for "Be1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
Be1
(Intercept)
Be1 (Intercept) 0
Call:
lme_imp(fixed = y ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = b2 ~ c2 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b2"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
b2
(Intercept)
b2 (Intercept) 0
Call:
glme_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1mis"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
L1mis
(Intercept)
L1mis (Intercept) 0
Residual standard deviation:
sigma_L1mis
0
Call:
glme_imp(fixed = p2 ~ c2 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p2"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
p2
(Intercept)
p2 (Intercept) 0
Call:
lognormmm_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal mixed model for "L1mis"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
L1mis
(Intercept)
L1mis (Intercept) 0
Residual standard deviation:
sigma_L1mis
0
Call:
betamm_imp(fixed = Be2 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta mixed model for "Be2"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
Be2
(Intercept)
Be2 (Intercept) 0
Call:
lme_imp(fixed = y ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = b2 ~ 0 + C2 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b2"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
b2
(Intercept)
b2 (Intercept) 0
Call:
glme_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1mis"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
L1mis
(Intercept)
L1mis (Intercept) 0
Residual standard deviation:
sigma_L1mis
0
Call:
glme_imp(fixed = p2 ~ 0 + C2 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p2"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
p2
(Intercept)
p2 (Intercept) 0
Call:
lognormmm_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal mixed model for "L1mis"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
L1mis
(Intercept)
L1mis (Intercept) 0
Residual standard deviation:
sigma_L1mis
0
Call:
betamm_imp(fixed = Be2 ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta mixed model for "Be2"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
Be2
(Intercept)
Be2 (Intercept) 0
Call:
lme_imp(fixed = c1 ~ c2 + B2 + p2 + L1mis + Be2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(p2 = "glmm_poisson_log",
L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) B21 c2 p2 L1mis Be2
0 0 0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_inverse",
p2 = "glmm_poisson_identity", b2 = "glmm_binomial_probit",
L1mis = "glmm_lognorm"), seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) c2 b21 p2 L1mis
0 0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log",
p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log",
b2 = "glmm_binomial_log"), no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) c2 b21 p2 L1mis
0 0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + Be2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log",
p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log",
b2 = "glmm_binomial_log"), shrinkage = "ridge", seed = 2020,
warn = FALSE, mess = FALSE, trunc = list(Be2 = c(0, 1)))
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) c2 b21 p2 L1mis Be2
0 0 0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
Call:
lme_imp(fixed = y ~ M2 + o2 * abs(C1 - c2) + log(C1) + time +
I(time^2) + (time | id), data = longDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) M22 M23 M24
0 0 0 0
log(C1) o22 o23 o24
0 0 0 0
abs(C1 - c2) time I(time^2) o22:abs(C1 - c2)
0 0 0 0
o23:abs(C1 - c2) o24:abs(C1 - c2)
0 0
Random effects covariance matrix:
$id
y y
(Intercept) time
y (Intercept) 0 0
y time 0 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = b1 ~ L1mis + abs(c1 - C2) + log(Be2) + time +
(time + I(time^2) | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), shrinkage = "ridge",
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept) L1mis abs(c1 - C2) log(Be2) time
0 0 0 0 0
Random effects covariance matrix:
$id
b1 b1 b1
(Intercept) time I(time^2)
b1 (Intercept) 0 0 0
b1 time 0 0 0
b1 I(time^2) 0 0 0
Call:
lme_imp(fixed = y ~ b2 + C1 + C2 + time + (0 + time | id), data = longDF,
n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 b21 time
0 0 0 0 0
Random effects covariance matrix:
$id
y
time
y time 0
Residual standard deviation:
sigma_y
0
Call:
glme_imp(fixed = b1 ~ c1 + C2 + B1 + time + (0 + time + I(time^2) |
id), data = longDF, family = binomial(), n.adapt = 5, n.iter = 10,
shrinkage = "ridge", seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept) C2 B11 c1 time
0 0 0 0 0
Random effects covariance matrix:
$id
b1 b1
time I(time^2)
b1 time 0 0
b1 I(time^2) 0 0
Call:
lme_imp(fixed = y ~ ns(time, df = 2), data = longDF, random = ~ns(time,
df = 2) | id, n.iter = 10, seed = 2020, adapt = 5)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) ns(time, df = 2)1 ns(time, df = 2)2
0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) ns(time, df = 2)1 ns(time, df = 2)2
y (Intercept) 0 0 0
y ns(time, df = 2)1 0 0 0
y ns(time, df = 2)2 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ bs(time, df = 3), data = longDF, random = ~bs(time,
df = 3) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3
0 0 0 0
Random effects covariance matrix:
$id
y y y y
(Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3
y (Intercept) 0 0 0 0
y bs(time, df = 3)1 0 0 0 0
y bs(time, df = 3)2 0 0 0 0
y bs(time, df = 3)3 0 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + c1 + ns(time, df = 3), data = longDF,
random = ~ns(time, df = 3) | id, n.iter = 10, seed = 2020,
nadapt = 5)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 c1 ns(time, df = 3)1
0 0 0 0
ns(time, df = 3)2 ns(time, df = 3)3
0 0
Random effects covariance matrix:
$id
y y y y
(Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
y (Intercept) 0 0 0 0
y ns(time, df = 3)1 0 0 0 0
y ns(time, df = 3)2 0 0 0 0
y ns(time, df = 3)3 0 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
0 0 0
Random effects covariance matrix:
$id
y y
(Intercept) time
y (Intercept) 0 0
y time 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~ns(time, df = 3) | id, n.adapt = 5, n.iter = 10,
no_model = "time", seed = 2020)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
0 0 0
Random effects covariance matrix:
$id
y y y y
(Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
y (Intercept) 0 0 0 0
y ns(time, df = 3)1 0 0 0 0
y ns(time, df = 3)2 0 0 0 0
y ns(time, df = 3)3 0 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
0 0 0
Random effects covariance matrix:
$id
y y
(Intercept) time
y (Intercept) 0 0
y time 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time +
c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c1 c2 time
0 0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time +
c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c1 c2 time
0 0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time +
c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) B21 c1 c2 time B21:c1
0 0 0 0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time c1
y (Intercept) 0 0 0
y time 0 0 0
y c1 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time +
c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) B21 c1 c2 time B21:c1
0 0 0 0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time c1
y (Intercept) 0 0 0
y time 0 0 0
y c1 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time",
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = c("time",
"c1"), seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c1
y (Intercept) 0 0 0
y time 0 0 0
y c1 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time",
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c1
y (Intercept) 0 0 0
y time 0 0 0
y c1 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 * time, data = longDF, random = ~time +
I(time^2) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 time B21:c1
0 0 0 0 0 0
B21:time c1:time B21:c1:time
0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time I(time^2)
y (Intercept) 0 0 0
y time 0 0 0
y I(time^2) 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ c1 * b1 + o1, data = longDF, random = ~b1 |
id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c1 b11 o1.L o1.Q c1:b11
0 0 0 0 0 0
Random effects covariance matrix:
$id
y y
(Intercept) b11
y (Intercept) 0 0
y b11 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ c1 + C1 * time + b1 + B2, data = longDF,
random = ~C1 * time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 time b11
0 0 0 0 0 0
C1:time
0
Random effects covariance matrix:
$id
y y y y
(Intercept) C1 time C1:time
y (Intercept) 0 0 0 0
y C1 0 0 0 0
y time 0 0 0 0
y C1:time 0 0 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ c1 + b1 + time + (1 | id) + (1 | o1), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c1 b11 time
0 0 0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
$o1
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + time + (time | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = c(analysis_random = TRUE),
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 B11 time
0 0 0 0 0
Random effects covariance matrix:
$id
y y
(Intercept) time
y (Intercept) 0 0
y time 0 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, monitor_params = c(analysis_random = TRUE),
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 B11
0 0 0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m0a1
Call:
lme_imp(fixed = y ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m0a2
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "identity"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m0a3
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m0a4
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "inverse"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m0b1
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "logit"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
$m0b2
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "probit"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
$m0b3
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "log"),
n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
$m0b4
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "cloglog"),
n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
$m0c1
Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "inverse"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
$m0c2
Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
$m0d1
Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
p1
(Intercept)
p1 (Intercept) 0
$m0d2
Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "identity"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
p1
(Intercept)
p1 (Intercept) 0
$m0e1
Call:
lognormmm_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal mixed model for "L1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
$m0f1
Call:
betamm_imp(fixed = Be1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta mixed model for "Be1"
Fixed effects:
(Intercept)
0
Random effects covariance matrix:
$id
Be1
(Intercept)
Be1 (Intercept) 0
$m1a
Call:
lme_imp(fixed = y ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m1b
Call:
glme_imp(fixed = b1 ~ C1 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
b1
(Intercept)
b1 (Intercept) 0
$m1c
Call:
glme_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
$m1d
Call:
glme_imp(fixed = p1 ~ C1 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
p1
(Intercept)
p1 (Intercept) 0
$m1e
Call:
lognormmm_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal mixed model for "L1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
L1
(Intercept)
L1 (Intercept) 0
Residual standard deviation:
sigma_L1
0
$m1f
Call:
betamm_imp(fixed = Be1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta mixed model for "Be1"
Fixed effects:
(Intercept) C1
0 0
Random effects covariance matrix:
$id
Be1
(Intercept)
Be1 (Intercept) 0
$m2a
Call:
lme_imp(fixed = y ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m2b
Call:
glme_imp(fixed = b2 ~ c2 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b2"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
b2
(Intercept)
b2 (Intercept) 0
$m2c
Call:
glme_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1mis"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
L1mis
(Intercept)
L1mis (Intercept) 0
Residual standard deviation:
sigma_L1mis
0
$m2d
Call:
glme_imp(fixed = p2 ~ c2 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p2"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
p2
(Intercept)
p2 (Intercept) 0
$m2e
Call:
lognormmm_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal mixed model for "L1mis"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
L1mis
(Intercept)
L1mis (Intercept) 0
Residual standard deviation:
sigma_L1mis
0
$m2f
Call:
betamm_imp(fixed = Be2 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta mixed model for "Be2"
Fixed effects:
(Intercept) c2
0 0
Random effects covariance matrix:
$id
Be2
(Intercept)
Be2 (Intercept) 0
$m3a
Call:
lme_imp(fixed = y ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m3b
Call:
glme_imp(fixed = b2 ~ 0 + C2 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b2"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
b2
(Intercept)
b2 (Intercept) 0
$m3c
Call:
glme_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma mixed model for "L1mis"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
L1mis
(Intercept)
L1mis (Intercept) 0
Residual standard deviation:
sigma_L1mis
0
$m3d
Call:
glme_imp(fixed = p2 ~ 0 + C2 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson mixed model for "p2"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
p2
(Intercept)
p2 (Intercept) 0
$m3e
Call:
lognormmm_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal mixed model for "L1mis"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
L1mis
(Intercept)
L1mis (Intercept) 0
Residual standard deviation:
sigma_L1mis
0
$m3f
Call:
betamm_imp(fixed = Be2 ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta mixed model for "Be2"
Fixed effects:
C2
0
Random effects covariance matrix:
$id
Be2
(Intercept)
Be2 (Intercept) 0
$m4a
Call:
lme_imp(fixed = c1 ~ c2 + B2 + p2 + L1mis + Be2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(p2 = "glmm_poisson_log",
L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) B21 c2 p2 L1mis Be2
0 0 0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
$m4b
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_inverse",
p2 = "glmm_poisson_identity", b2 = "glmm_binomial_probit",
L1mis = "glmm_lognorm"), seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) c2 b21 p2 L1mis
0 0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
$m4c
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log",
p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log",
b2 = "glmm_binomial_log"), no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) c2 b21 p2 L1mis
0 0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
$m4d
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + Be2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log",
p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log",
b2 = "glmm_binomial_log"), shrinkage = "ridge", seed = 2020,
warn = FALSE, mess = FALSE, trunc = list(Be2 = c(0, 1)))
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) c2 b21 p2 L1mis Be2
0 0 0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
$m5a
Call:
lme_imp(fixed = y ~ M2 + o2 * abs(C1 - c2) + log(C1) + time +
I(time^2) + (time | id), data = longDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) M22 M23 M24
0 0 0 0
log(C1) o22 o23 o24
0 0 0 0
abs(C1 - c2) time I(time^2) o22:abs(C1 - c2)
0 0 0 0
o23:abs(C1 - c2) o24:abs(C1 - c2)
0 0
Random effects covariance matrix:
$id
y y
(Intercept) time
y (Intercept) 0 0
y time 0 0
Residual standard deviation:
sigma_y
0
$m5b
Call:
glme_imp(fixed = b1 ~ L1mis + abs(c1 - C2) + log(Be2) + time +
(time + I(time^2) | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), shrinkage = "ridge",
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept) L1mis abs(c1 - C2) log(Be2) time
0 0 0 0 0
Random effects covariance matrix:
$id
b1 b1 b1
(Intercept) time I(time^2)
b1 (Intercept) 0 0 0
b1 time 0 0 0
b1 I(time^2) 0 0 0
$m6a
Call:
lme_imp(fixed = y ~ b2 + C1 + C2 + time + (0 + time | id), data = longDF,
n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 b21 time
0 0 0 0 0
Random effects covariance matrix:
$id
y
time
y time 0
Residual standard deviation:
sigma_y
0
$m6b
Call:
glme_imp(fixed = b1 ~ c1 + C2 + B1 + time + (0 + time + I(time^2) |
id), data = longDF, family = binomial(), n.adapt = 5, n.iter = 10,
shrinkage = "ridge", seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial mixed model for "b1"
Fixed effects:
(Intercept) C2 B11 c1 time
0 0 0 0 0
Random effects covariance matrix:
$id
b1 b1
time I(time^2)
b1 time 0 0
b1 I(time^2) 0 0
$m7a
Call:
lme_imp(fixed = y ~ ns(time, df = 2), data = longDF, random = ~ns(time,
df = 2) | id, n.iter = 10, seed = 2020, adapt = 5)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) ns(time, df = 2)1 ns(time, df = 2)2
0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) ns(time, df = 2)1 ns(time, df = 2)2
y (Intercept) 0 0 0
y ns(time, df = 2)1 0 0 0
y ns(time, df = 2)2 0 0 0
Residual standard deviation:
sigma_y
0
$m7b
Call:
lme_imp(fixed = y ~ bs(time, df = 3), data = longDF, random = ~bs(time,
df = 3) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3
0 0 0 0
Random effects covariance matrix:
$id
y y y y
(Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3
y (Intercept) 0 0 0 0
y bs(time, df = 3)1 0 0 0 0
y bs(time, df = 3)2 0 0 0 0
y bs(time, df = 3)3 0 0 0 0
Residual standard deviation:
sigma_y
0
$m7c
Call:
lme_imp(fixed = y ~ C1 + c1 + ns(time, df = 3), data = longDF,
random = ~ns(time, df = 3) | id, n.iter = 10, seed = 2020,
nadapt = 5)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 c1 ns(time, df = 3)1
0 0 0 0
ns(time, df = 3)2 ns(time, df = 3)3
0 0
Random effects covariance matrix:
$id
y y y y
(Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
y (Intercept) 0 0 0 0
y ns(time, df = 3)1 0 0 0 0
y ns(time, df = 3)2 0 0 0 0
y ns(time, df = 3)3 0 0 0 0
Residual standard deviation:
sigma_y
0
$m7d
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
0 0 0
Random effects covariance matrix:
$id
y y
(Intercept) time
y (Intercept) 0 0
y time 0 0
Residual standard deviation:
sigma_y
0
$m7e
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~ns(time, df = 3) | id, n.adapt = 5, n.iter = 10,
no_model = "time", seed = 2020)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
0 0 0
Random effects covariance matrix:
$id
y y y y
(Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
y (Intercept) 0 0 0 0
y ns(time, df = 3)1 0 0 0 0
y ns(time, df = 3)2 0 0 0 0
y ns(time, df = 3)3 0 0 0 0
Residual standard deviation:
sigma_y
0
$m7f
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
0 0 0
Random effects covariance matrix:
$id
y y
(Intercept) time
y (Intercept) 0 0
y time 0 0
Residual standard deviation:
sigma_y
0
$m8a
Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time +
c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c1 c2 time
0 0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
$m8b
Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time +
c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c1 c2 time
0 0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
$m8c
Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time +
c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) B21 c1 c2 time B21:c1
0 0 0 0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time c1
y (Intercept) 0 0 0
y time 0 0 0
y c1 0 0 0
Residual standard deviation:
sigma_y
0
$m8d
Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time +
c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) B21 c1 c2 time B21:c1
0 0 0 0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time c1
y (Intercept) 0 0 0
y time 0 0 0
y c1 0 0 0
Residual standard deviation:
sigma_y
0
$m8e
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
$m8f
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time",
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
$m8g
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = c("time",
"c1"), seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
$m8h
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c1
y (Intercept) 0 0 0
y time 0 0 0
y c1 0 0 0
Residual standard deviation:
sigma_y
0
$m8i
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time",
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c1
y (Intercept) 0 0 0
y time 0 0 0
y c1 0 0 0
Residual standard deviation:
sigma_y
0
$m8j
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
$m8k
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2
0
Random effects covariance matrix:
$id
y y y
(Intercept) time c2
y (Intercept) 0 0 0
y time 0 0 0
y c2 0 0 0
Residual standard deviation:
sigma_y
0
$m8l
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 * time, data = longDF, random = ~time +
I(time^2) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 time B21:c1
0 0 0 0 0 0
B21:time c1:time B21:c1:time
0 0 0
Random effects covariance matrix:
$id
y y y
(Intercept) time I(time^2)
y (Intercept) 0 0 0
y time 0 0 0
y I(time^2) 0 0 0
Residual standard deviation:
sigma_y
0
$m8m
Call:
lme_imp(fixed = y ~ c1 * b1 + o1, data = longDF, random = ~b1 |
id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c1 b11 o1.L o1.Q c1:b11
0 0 0 0 0 0
Random effects covariance matrix:
$id
y y
(Intercept) b11
y (Intercept) 0 0
y b11 0 0
Residual standard deviation:
sigma_y
0
$m8n
Call:
lme_imp(fixed = y ~ c1 + C1 * time + b1 + B2, data = longDF,
random = ~C1 * time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 B21 c1 time b11
0 0 0 0 0 0
C1:time
0
Random effects covariance matrix:
$id
y y y y
(Intercept) C1 time C1:time
y (Intercept) 0 0 0 0
y C1 0 0 0 0
y time 0 0 0 0
y C1:time 0 0 0 0
Residual standard deviation:
sigma_y
0
$m9a
Call:
lme_imp(fixed = y ~ c1 + b1 + time + (1 | id) + (1 | o1), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) c1 b11 time
0 0 0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
$o1
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m9b
Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + time + (time | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = c(analysis_random = TRUE),
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 B11 time
0 0 0 0 0
Random effects covariance matrix:
$id
y y
(Intercept) time
y (Intercept) 0 0
y time 0 0
Residual standard deviation:
sigma_y
0
$m9c
Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, monitor_params = c(analysis_random = TRUE),
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 C2 B11
0 0 0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Code
lapply(models0, coef)
Output
$m0a1
$m0a1$y
(Intercept) sigma_y D_y_id[1,1]
0 0 0
$m0a2
$m0a2$y
(Intercept) sigma_y D_y_id[1,1]
0 0 0
$m0a3
$m0a3$y
(Intercept) sigma_y D_y_id[1,1]
0 0 0
$m0a4
$m0a4$y
(Intercept) sigma_y D_y_id[1,1]
0 0 0
$m0b1
$m0b1$b1
(Intercept) D_b1_id[1,1]
0 0
$m0b2
$m0b2$b1
(Intercept) D_b1_id[1,1]
0 0
$m0b3
$m0b3$b1
(Intercept) D_b1_id[1,1]
0 0
$m0b4
$m0b4$b1
(Intercept) D_b1_id[1,1]
0 0
$m0c1
$m0c1$L1
(Intercept) sigma_L1 D_L1_id[1,1]
0 0 0
$m0c2
$m0c2$L1
(Intercept) sigma_L1 D_L1_id[1,1]
0 0 0
$m0d1
$m0d1$p1
(Intercept) D_p1_id[1,1]
0 0
$m0d2
$m0d2$p1
(Intercept) D_p1_id[1,1]
0 0
$m0e1
$m0e1$L1
(Intercept) sigma_L1 D_L1_id[1,1]
0 0 0
$m0f1
$m0f1$Be1
(Intercept) tau_Be1 D_Be1_id[1,1]
0 0 0
$m1a
$m1a$y
(Intercept) C1 sigma_y D_y_id[1,1]
0 0 0 0
$m1b
$m1b$b1
(Intercept) C1 D_b1_id[1,1]
0 0 0
$m1c
$m1c$L1
(Intercept) C1 sigma_L1 D_L1_id[1,1]
0 0 0 0
$m1d
$m1d$p1
(Intercept) C1 D_p1_id[1,1]
0 0 0
$m1e
$m1e$L1
(Intercept) C1 sigma_L1 D_L1_id[1,1]
0 0 0 0
$m1f
$m1f$Be1
(Intercept) C1 tau_Be1 D_Be1_id[1,1]
0 0 0 0
$m2a
$m2a$y
(Intercept) c2 sigma_y D_y_id[1,1]
0 0 0 0
$m2b
$m2b$b2
(Intercept) c2 D_b2_id[1,1]
0 0 0
$m2c
$m2c$L1mis
(Intercept) c2 sigma_L1mis D_L1mis_id[1,1]
0 0 0 0
$m2d
$m2d$p2
(Intercept) c2 D_p2_id[1,1]
0 0 0
$m2e
$m2e$L1mis
(Intercept) c2 sigma_L1mis D_L1mis_id[1,1]
0 0 0 0
$m2f
$m2f$Be2
(Intercept) c2 tau_Be2 D_Be2_id[1,1]
0 0 0 0
$m3a
$m3a$y
C2 sigma_y D_y_id[1,1]
0 0 0
$m3b
$m3b$b2
C2 D_b2_id[1,1]
0 0
$m3c
$m3c$L1mis
C2 sigma_L1mis D_L1mis_id[1,1]
0 0 0
$m3d
$m3d$p2
C2 D_p2_id[1,1]
0 0
$m3e
$m3e$L1mis
C2 sigma_L1mis D_L1mis_id[1,1]
0 0 0
$m3f
$m3f$Be2
C2 tau_Be2 D_Be2_id[1,1]
0 0 0
$m4a
$m4a$c1
(Intercept) B21 c2 p2 L1mis Be2
0 0 0 0 0 0
sigma_c1 D_c1_id[1,1]
0 0
$m4b
$m4b$c1
(Intercept) c2 b21 p2 L1mis sigma_c1
0 0 0 0 0 0
D_c1_id[1,1]
0
$m4c
$m4c$c1
(Intercept) c2 b21 p2 L1mis sigma_c1
0 0 0 0 0 0
D_c1_id[1,1]
0
$m4d
$m4d$c1
(Intercept) c2 b21 p2 L1mis Be2
0 0 0 0 0 0
sigma_c1 D_c1_id[1,1]
0 0
$m5a
$m5a$y
(Intercept) M22 M23 M24
0 0 0 0
log(C1) o22 o23 o24
0 0 0 0
abs(C1 - c2) time I(time^2) o22:abs(C1 - c2)
0 0 0 0
o23:abs(C1 - c2) o24:abs(C1 - c2) sigma_y D_y_id[1,1]
0 0 0 0
D_y_id[1,2] D_y_id[2,2]
0 0
$m5b
$m5b$b1
(Intercept) L1mis abs(c1 - C2) log(Be2) time D_b1_id[1,1]
0 0 0 0 0 0
D_b1_id[1,2] D_b1_id[2,2] D_b1_id[1,3] D_b1_id[2,3] D_b1_id[3,3]
0 0 0 0 0
$m6a
$m6a$y
(Intercept) C1 C2 b21 time sigma_y
0 0 0 0 0 0
D_y_id[1,1]
0
$m6b
$m6b$b1
(Intercept) C2 B11 c1 time D_b1_id[1,1]
0 0 0 0 0 0
D_b1_id[1,2] D_b1_id[2,2]
0 0
$m7a
$m7a$y
(Intercept) ns(time, df = 2)1 ns(time, df = 2)2 sigma_y
0 0 0 0
D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0
D_y_id[2,3] D_y_id[3,3]
0 0
$m7b
$m7b$y
(Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3
0 0 0 0
sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2]
0 0 0 0
D_y_id[1,3] D_y_id[2,3] D_y_id[3,3] D_y_id[1,4]
0 0 0 0
D_y_id[2,4] D_y_id[3,4] D_y_id[4,4]
0 0 0
$m7c
$m7c$y
(Intercept) C1 c1 ns(time, df = 3)1
0 0 0 0
ns(time, df = 3)2 ns(time, df = 3)3 sigma_y D_y_id[1,1]
0 0 0 0
D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3]
0 0 0 0
D_y_id[3,3] D_y_id[1,4] D_y_id[2,4] D_y_id[3,4]
0 0 0 0
D_y_id[4,4]
0
$m7d
$m7d$y
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 sigma_y
0 0 0 0
D_y_id[1,1] D_y_id[1,2] D_y_id[2,2]
0 0 0
$m7e
$m7e$y
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 sigma_y
0 0 0 0
D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0
D_y_id[2,3] D_y_id[3,3] D_y_id[1,4] D_y_id[2,4]
0 0 0 0
D_y_id[3,4] D_y_id[4,4]
0 0
$m7f
$m7f$y
(Intercept) C1 C2 c1
0 0 0 0
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 sigma_y
0 0 0 0
D_y_id[1,1] D_y_id[1,2] D_y_id[2,2]
0 0 0
$m8a
$m8a$y
(Intercept) c1 c2 time sigma_y D_y_id[1,1]
0 0 0 0 0 0
D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] D_y_id[3,3]
0 0 0 0 0
$m8b
$m8b$y
(Intercept) c1 c2 time sigma_y D_y_id[1,1]
0 0 0 0 0 0
D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] D_y_id[3,3]
0 0 0 0 0
$m8c
$m8c$y
(Intercept) B21 c1 c2 time B21:c1
0 0 0 0 0 0
sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3]
0 0 0 0 0 0
D_y_id[3,3]
0
$m8d
$m8d$y
(Intercept) B21 c1 c2 time B21:c1
0 0 0 0 0 0
sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3] D_y_id[2,3]
0 0 0 0 0 0
D_y_id[3,3]
0
$m8e
$m8e$y
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0 0 0
D_y_id[2,3] D_y_id[3,3]
0 0
$m8f
$m8f$y
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0 0 0
D_y_id[2,3] D_y_id[3,3]
0 0
$m8g
$m8g$y
(Intercept) C1 B21 c1 c2 time
0 0 0 0 0 0
B21:c1 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0 0 0
D_y_id[2,3] D_y_id[3,3]
0 0
$m8h
$m8h$y
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0 0 0
D_y_id[2,3] D_y_id[3,3]
0 0
$m8i
$m8i$y
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0 0 0
D_y_id[2,3] D_y_id[3,3]
0 0
$m8j
$m8j$y
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0 0 0
D_y_id[2,3] D_y_id[3,3]
0 0
$m8k
$m8k$y
(Intercept) C1 B21 c2 c1 time
0 0 0 0 0 0
B21:c2 sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0 0 0
D_y_id[2,3] D_y_id[3,3]
0 0
$m8l
$m8l$y
(Intercept) C1 B21 c1 time B21:c1
0 0 0 0 0 0
B21:time c1:time B21:c1:time sigma_y D_y_id[1,1] D_y_id[1,2]
0 0 0 0 0 0
D_y_id[2,2] D_y_id[1,3] D_y_id[2,3] D_y_id[3,3]
0 0 0 0
$m8m
$m8m$y
(Intercept) c1 b11 o1.L o1.Q c1:b11
0 0 0 0 0 0
sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2]
0 0 0 0
$m8n
$m8n$y
(Intercept) C1 B21 c1 time b11
0 0 0 0 0 0
C1:time sigma_y D_y_id[1,1] D_y_id[1,2] D_y_id[2,2] D_y_id[1,3]
0 0 0 0 0 0
D_y_id[2,3] D_y_id[3,3] D_y_id[1,4] D_y_id[2,4] D_y_id[3,4] D_y_id[4,4]
0 0 0 0 0 0
$m9a
$m9a$y
(Intercept) c1 b11 time sigma_y D_y_id[1,1]
0 0 0 0 0 0
D_y_o1[1,1]
0
$m9b
$m9b$y
(Intercept) C1 C2 B11 time sigma_y
0 0 0 0 0 0
D_y_id[1,1] D_y_id[1,2] D_y_id[2,2]
0 0 0
$m9c
$m9c$y
(Intercept) C1 C2 B11 sigma_y D_y_id[1,1]
0 0 0 0 0 0
Code
lapply(models0, confint)
Output
$m0a1
$m0a1$y
2.5% 97.5%
(Intercept) 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m0a2
$m0a2$y
2.5% 97.5%
(Intercept) 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m0a3
$m0a3$y
2.5% 97.5%
(Intercept) 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m0a4
$m0a4$y
2.5% 97.5%
(Intercept) 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m0b1
$m0b1$b1
2.5% 97.5%
(Intercept) 0 0
D_b1_id[1,1] 0 0
$m0b2
$m0b2$b1
2.5% 97.5%
(Intercept) 0 0
D_b1_id[1,1] 0 0
$m0b3
$m0b3$b1
2.5% 97.5%
(Intercept) 0 0
D_b1_id[1,1] 0 0
$m0b4
$m0b4$b1
2.5% 97.5%
(Intercept) 0 0
D_b1_id[1,1] 0 0
$m0c1
$m0c1$L1
2.5% 97.5%
(Intercept) 0 0
sigma_L1 0 0
D_L1_id[1,1] 0 0
$m0c2
$m0c2$L1
2.5% 97.5%
(Intercept) 0 0
sigma_L1 0 0
D_L1_id[1,1] 0 0
$m0d1
$m0d1$p1
2.5% 97.5%
(Intercept) 0 0
D_p1_id[1,1] 0 0
$m0d2
$m0d2$p1
2.5% 97.5%
(Intercept) 0 0
D_p1_id[1,1] 0 0
$m0e1
$m0e1$L1
2.5% 97.5%
(Intercept) 0 0
sigma_L1 0 0
D_L1_id[1,1] 0 0
$m0f1
$m0f1$Be1
2.5% 97.5%
(Intercept) 0 0
tau_Be1 0 0
D_Be1_id[1,1] 0 0
$m1a
$m1a$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m1b
$m1b$b1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
D_b1_id[1,1] 0 0
$m1c
$m1c$L1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
sigma_L1 0 0
D_L1_id[1,1] 0 0
$m1d
$m1d$p1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
D_p1_id[1,1] 0 0
$m1e
$m1e$L1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
sigma_L1 0 0
D_L1_id[1,1] 0 0
$m1f
$m1f$Be1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
tau_Be1 0 0
D_Be1_id[1,1] 0 0
$m2a
$m2a$y
2.5% 97.5%
(Intercept) 0 0
c2 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m2b
$m2b$b2
2.5% 97.5%
(Intercept) 0 0
c2 0 0
D_b2_id[1,1] 0 0
$m2c
$m2c$L1mis
2.5% 97.5%
(Intercept) 0 0
c2 0 0
sigma_L1mis 0 0
D_L1mis_id[1,1] 0 0
$m2d
$m2d$p2
2.5% 97.5%
(Intercept) 0 0
c2 0 0
D_p2_id[1,1] 0 0
$m2e
$m2e$L1mis
2.5% 97.5%
(Intercept) 0 0
c2 0 0
sigma_L1mis 0 0
D_L1mis_id[1,1] 0 0
$m2f
$m2f$Be2
2.5% 97.5%
(Intercept) 0 0
c2 0 0
tau_Be2 0 0
D_Be2_id[1,1] 0 0
$m3a
$m3a$y
2.5% 97.5%
C2 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m3b
$m3b$b2
2.5% 97.5%
C2 0 0
D_b2_id[1,1] 0 0
$m3c
$m3c$L1mis
2.5% 97.5%
C2 0 0
sigma_L1mis 0 0
D_L1mis_id[1,1] 0 0
$m3d
$m3d$p2
2.5% 97.5%
C2 0 0
D_p2_id[1,1] 0 0
$m3e
$m3e$L1mis
2.5% 97.5%
C2 0 0
sigma_L1mis 0 0
D_L1mis_id[1,1] 0 0
$m3f
$m3f$Be2
2.5% 97.5%
C2 0 0
tau_Be2 0 0
D_Be2_id[1,1] 0 0
$m4a
$m4a$c1
2.5% 97.5%
(Intercept) 0 0
B21 0 0
c2 0 0
p2 0 0
L1mis 0 0
Be2 0 0
sigma_c1 0 0
D_c1_id[1,1] 0 0
$m4b
$m4b$c1
2.5% 97.5%
(Intercept) 0 0
c2 0 0
b21 0 0
p2 0 0
L1mis 0 0
sigma_c1 0 0
D_c1_id[1,1] 0 0
$m4c
$m4c$c1
2.5% 97.5%
(Intercept) 0 0
c2 0 0
b21 0 0
p2 0 0
L1mis 0 0
sigma_c1 0 0
D_c1_id[1,1] 0 0
$m4d
$m4d$c1
2.5% 97.5%
(Intercept) 0 0
c2 0 0
b21 0 0
p2 0 0
L1mis 0 0
Be2 0 0
sigma_c1 0 0
D_c1_id[1,1] 0 0
$m5a
$m5a$y
2.5% 97.5%
(Intercept) 0 0
M22 0 0
M23 0 0
M24 0 0
log(C1) 0 0
o22 0 0
o23 0 0
o24 0 0
abs(C1 - c2) 0 0
time 0 0
I(time^2) 0 0
o22:abs(C1 - c2) 0 0
o23:abs(C1 - c2) 0 0
o24:abs(C1 - c2) 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
$m5b
$m5b$b1
2.5% 97.5%
(Intercept) 0 0
L1mis 0 0
abs(c1 - C2) 0 0
log(Be2) 0 0
time 0 0
D_b1_id[1,1] 0 0
D_b1_id[1,2] 0 0
D_b1_id[2,2] 0 0
D_b1_id[1,3] 0 0
D_b1_id[2,3] 0 0
D_b1_id[3,3] 0 0
$m6a
$m6a$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
C2 0 0
b21 0 0
time 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m6b
$m6b$b1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B11 0 0
c1 0 0
time 0 0
D_b1_id[1,1] 0 0
D_b1_id[1,2] 0 0
D_b1_id[2,2] 0 0
$m7a
$m7a$y
2.5% 97.5%
(Intercept) 0 0
ns(time, df = 2)1 0 0
ns(time, df = 2)2 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m7b
$m7b$y
2.5% 97.5%
(Intercept) 0 0
bs(time, df = 3)1 0 0
bs(time, df = 3)2 0 0
bs(time, df = 3)3 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
D_y_id[1,4] 0 0
D_y_id[2,4] 0 0
D_y_id[3,4] 0 0
D_y_id[4,4] 0 0
$m7c
$m7c$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
c1 0 0
ns(time, df = 3)1 0 0
ns(time, df = 3)2 0 0
ns(time, df = 3)3 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
D_y_id[1,4] 0 0
D_y_id[2,4] 0 0
D_y_id[3,4] 0 0
D_y_id[4,4] 0 0
$m7d
$m7d$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
C2 0 0
c1 0 0
ns(time, df = 3)1 0 0
ns(time, df = 3)2 0 0
ns(time, df = 3)3 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
$m7e
$m7e$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
C2 0 0
c1 0 0
ns(time, df = 3)1 0 0
ns(time, df = 3)2 0 0
ns(time, df = 3)3 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
D_y_id[1,4] 0 0
D_y_id[2,4] 0 0
D_y_id[3,4] 0 0
D_y_id[4,4] 0 0
$m7f
$m7f$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
C2 0 0
c1 0 0
ns(time, df = 3)1 0 0
ns(time, df = 3)2 0 0
ns(time, df = 3)3 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
$m8a
$m8a$y
2.5% 97.5%
(Intercept) 0 0
c1 0 0
c2 0 0
time 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8b
$m8b$y
2.5% 97.5%
(Intercept) 0 0
c1 0 0
c2 0 0
time 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8c
$m8c$y
2.5% 97.5%
(Intercept) 0 0
B21 0 0
c1 0 0
c2 0 0
time 0 0
B21:c1 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8d
$m8d$y
2.5% 97.5%
(Intercept) 0 0
B21 0 0
c1 0 0
c2 0 0
time 0 0
B21:c1 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8e
$m8e$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c1 0 0
c2 0 0
time 0 0
B21:c1 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8f
$m8f$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c1 0 0
c2 0 0
time 0 0
B21:c1 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8g
$m8g$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c1 0 0
c2 0 0
time 0 0
B21:c1 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8h
$m8h$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c2 0 0
c1 0 0
time 0 0
B21:c2 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8i
$m8i$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c2 0 0
c1 0 0
time 0 0
B21:c2 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8j
$m8j$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c2 0 0
c1 0 0
time 0 0
B21:c2 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8k
$m8k$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c2 0 0
c1 0 0
time 0 0
B21:c2 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8l
$m8l$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c1 0 0
time 0 0
B21:c1 0 0
B21:time 0 0
c1:time 0 0
B21:c1:time 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
$m8m
$m8m$y
2.5% 97.5%
(Intercept) 0 0
c1 0 0
b11 0 0
o1.L 0 0
o1.Q 0 0
c1:b11 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
$m8n
$m8n$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
c1 0 0
time 0 0
b11 0 0
C1:time 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
D_y_id[1,3] 0 0
D_y_id[2,3] 0 0
D_y_id[3,3] 0 0
D_y_id[1,4] 0 0
D_y_id[2,4] 0 0
D_y_id[3,4] 0 0
D_y_id[4,4] 0 0
$m9a
$m9a$y
2.5% 97.5%
(Intercept) 0 0
c1 0 0
b11 0 0
time 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_o1[1,1] 0 0
$m9b
$m9b$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
C2 0 0
B11 0 0
time 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
D_y_id[1,2] 0 0
D_y_id[2,2] 0 0
$m9c
$m9c$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
C2 0 0
B11 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
Code
lapply(models0, summary, missinfo = TRUE)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a1
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
level # NA % NA
id id 0 0
$m0a2
Bayesian linear mixed model fitted with JointAI
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "identity"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
level # NA % NA
id id 0 0
$m0a3
Bayesian linear mixed model fitted with JointAI
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
level # NA % NA
id id 0 0
$m0a4
Bayesian linear mixed model fitted with JointAI
Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "inverse"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
level # NA % NA
id id 0 0
$m0b1
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "logit"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
b1 lvlone 0 0
level # NA % NA
id id 0 0
$m0b2
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "probit"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
b1 lvlone 0 0
level # NA % NA
id id 0 0
$m0b3
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "log"),
n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 51:60
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
b1 lvlone 0 0
level # NA % NA
id id 0 0
$m0b4
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "cloglog"),
n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 51:60
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
b1 lvlone 0 0
level # NA % NA
id id 0 0
$m0c1
Bayesian Gamma mixed model fitted with JointAI
Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "inverse"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
L1 lvlone 0 0
level # NA % NA
id id 0 0
$m0c2
Bayesian Gamma mixed model fitted with JointAI
Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
L1 lvlone 0 0
level # NA % NA
id id 0 0
$m0d1
Bayesian poisson mixed model fitted with JointAI
Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "log"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_p1_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
p1 lvlone 0 0
level # NA % NA
id id 0 0
$m0d2
Bayesian poisson mixed model fitted with JointAI
Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "identity"),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_p1_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
p1 lvlone 0 0
level # NA % NA
id id 0 0
$m0e1
Bayesian log-normal mixed model fitted with JointAI
Call:
lognormmm_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
L1 lvlone 0 0
level # NA % NA
id id 0 0
$m0f1
Bayesian beta mixed model fitted with JointAI
Call:
betamm_imp(fixed = Be1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Be1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be1 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
Be1 lvlone 0 0
level # NA % NA
id id 0 0
$m1a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
$m1b
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b1 ~ C1 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
b1 lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
$m1c
Bayesian Gamma mixed model fitted with JointAI
Call:
glme_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
L1 lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
$m1d
Bayesian poisson mixed model fitted with JointAI
Call:
glme_imp(fixed = p1 ~ C1 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_p1_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
p1 lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
$m1e
Bayesian log-normal mixed model fitted with JointAI
Call:
lognormmm_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
L1 lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
$m1f
Bayesian beta mixed model fitted with JointAI
Call:
betamm_imp(fixed = Be1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Be1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be1 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
Be1 lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
$m2a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
id id 0 0
$m2b
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b2 ~ c2 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b2_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 189 57.4
Number and proportion of missing values:
level # NA % NA
c2 lvlone 66 20.1
b2 lvlone 99 30.1
level # NA % NA
id id 0 0
$m2c
Bayesian Gamma mixed model fitted with JointAI
Call:
glme_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1mis_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1mis 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 246 74.8
Number and proportion of missing values:
level # NA % NA
L1mis lvlone 20 6.08
c2 lvlone 66 20.06
level # NA % NA
id id 0 0
$m2d
Bayesian poisson mixed model fitted with JointAI
Call:
glme_imp(fixed = p2 ~ c2 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_p2_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 142 43.2
Number and proportion of missing values:
level # NA % NA
c2 lvlone 66 20.1
p2 lvlone 162 49.2
level # NA % NA
id id 0 0
$m2e
Bayesian log-normal mixed model fitted with JointAI
Call:
lognormmm_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1mis_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1mis 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 246 74.8
Number and proportion of missing values:
level # NA % NA
L1mis lvlone 20 6.08
c2 lvlone 66 20.06
level # NA % NA
id id 0 0
$m2f
Bayesian beta mixed model fitted with JointAI
Call:
betamm_imp(fixed = Be2 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Be2_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be2 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 246 74.8
Number and proportion of missing values:
level # NA % NA
Be2 lvlone 20 6.08
c2 lvlone 66 20.06
level # NA % NA
id id 0 0
$m3a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
level # NA % NA
id id 0 0
C2 id 42 42
$m3b
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b2 ~ 0 + C2 + (1 | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b2_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58.0
lvlone lvlone 230 69.9
Number and proportion of missing values:
level # NA % NA
b2 lvlone 99 30.1
level # NA % NA
id id 0 0
C2 id 42 42
$m3c
Bayesian Gamma mixed model fitted with JointAI
Call:
glme_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, family = Gamma(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1mis_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1mis 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58.0
lvlone lvlone 309 93.9
Number and proportion of missing values:
level # NA % NA
L1mis lvlone 20 6.08
level # NA % NA
id id 0 0
C2 id 42 42
$m3d
Bayesian poisson mixed model fitted with JointAI
Call:
glme_imp(fixed = p2 ~ 0 + C2 + (1 | id), data = longDF, family = poisson(),
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_p2_id[1,1] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58.0
lvlone lvlone 167 50.8
Number and proportion of missing values:
level # NA % NA
p2 lvlone 162 49.2
level # NA % NA
id id 0 0
C2 id 42 42
$m3e
Bayesian log-normal mixed model fitted with JointAI
Call:
lognormmm_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_L1mis_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1mis 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58.0
lvlone lvlone 309 93.9
Number and proportion of missing values:
level # NA % NA
L1mis lvlone 20 6.08
level # NA % NA
id id 0 0
C2 id 42 42
$m3f
Bayesian beta mixed model fitted with JointAI
Call:
betamm_imp(fixed = Be2 ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Be2_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be2 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58.0
lvlone lvlone 309 93.9
Number and proportion of missing values:
level # NA % NA
Be2 lvlone 20 6.08
level # NA % NA
id id 0 0
C2 id 42 42
$m4a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = c1 ~ c2 + B2 + p2 + L1mis + Be2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(p2 = "glmm_poisson_log",
L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
p2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Be2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_c1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_c1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90
lvlone lvlone 125 38
Number and proportion of missing values:
level # NA % NA
c1 lvlone 0 0.00
L1mis lvlone 20 6.08
Be2 lvlone 20 6.08
c2 lvlone 66 20.06
p2 lvlone 162 49.24
level # NA % NA
id id 0 0
B2 id 10 10
$m4b
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_inverse",
p2 = "glmm_poisson_identity", b2 = "glmm_binomial_probit",
L1mis = "glmm_lognorm"), seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
p2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_c1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_c1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 98 29.8
Number and proportion of missing values:
level # NA % NA
c1 lvlone 0 0.00
L1mis lvlone 20 6.08
c2 lvlone 66 20.06
b2 lvlone 99 30.09
p2 lvlone 162 49.24
level # NA % NA
id id 0 0
$m4c
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log",
p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log",
b2 = "glmm_binomial_log"), no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
p2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_c1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_c1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 98 29.8
Number and proportion of missing values:
level # NA % NA
c1 lvlone 0 0.00
L1mis lvlone 20 6.08
c2 lvlone 66 20.06
b2 lvlone 99 30.09
p2 lvlone 162 49.24
level # NA % NA
id id 0 0
$m4d
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + Be2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log",
p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log",
b2 = "glmm_binomial_log"), shrinkage = "ridge", seed = 2020,
warn = FALSE, mess = FALSE, trunc = list(Be2 = c(0, 1)))
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
p2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Be2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_c1_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_c1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 92 28
Number and proportion of missing values:
level # NA % NA
c1 lvlone 0 0.00
L1mis lvlone 20 6.08
Be2 lvlone 20 6.08
c2 lvlone 66 20.06
b2 lvlone 99 30.09
p2 lvlone 162 49.24
level # NA % NA
id id 0 0
$m5a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ M2 + o2 * abs(C1 - c2) + log(C1) + time +
I(time^2) + (time | id), data = longDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
abs(C1 - c2) 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
I(time^2) 0 0 0 0 0 NaN NaN
o22:abs(C1 - c2) 0 0 0 0 0 NaN NaN
o23:abs(C1 - c2) 0 0 0 0 0 NaN NaN
o24:abs(C1 - c2) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 56 56
lvlone lvlone 217 66
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
time lvlone 0 0.0
o2 lvlone 59 17.9
c2 lvlone 66 20.1
level # NA % NA
C1 id 0 0
id id 0 0
M2 id 44 44
$m5b
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b1 ~ L1mis + abs(c1 - C2) + log(Be2) + time +
(time + I(time^2) | id), data = longDF, family = binomial(),
n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), shrinkage = "ridge",
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
abs(c1 - C2) 0 0 0 0 0 NaN NaN
log(Be2) 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1] 0 0 0 0 NaN NaN
D_b1_id[1,2] 0 0 0 0 0 NaN NaN
D_b1_id[2,2] 0 0 0 0 NaN NaN
D_b1_id[1,3] 0 0 0 0 0 NaN NaN
D_b1_id[2,3] 0 0 0 0 0 NaN NaN
D_b1_id[3,3] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58.0
lvlone lvlone 291 88.4
Number and proportion of missing values:
level # NA % NA
b1 lvlone 0 0.00
c1 lvlone 0 0.00
time lvlone 0 0.00
L1mis lvlone 20 6.08
Be2 lvlone 20 6.08
level # NA % NA
id id 0 0
C2 id 42 42
$m6a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ b2 + C1 + C2 + time + (0 + time | id), data = longDF,
n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58.0
lvlone lvlone 230 69.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
time lvlone 0 0.0
b2 lvlone 99 30.1
level # NA % NA
C1 id 0 0
id id 0 0
C2 id 42 42
$m6b
Bayesian binomial mixed model fitted with JointAI
Call:
glme_imp(fixed = b1 ~ c1 + C2 + B1 + time + (0 + time + I(time^2) |
id), data = longDF, family = binomial(), n.adapt = 5, n.iter = 10,
shrinkage = "ridge", seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_b1_id[1,1] 0 0 0 0 NaN NaN
D_b1_id[1,2] 0 0 0 0 0 NaN NaN
D_b1_id[2,2] 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
b1 lvlone 0 0
c1 lvlone 0 0
time lvlone 0 0
level # NA % NA
B1 id 0 0
id id 0 0
C2 id 42 42
$m7a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ ns(time, df = 2), data = longDF, random = ~ns(time,
df = 2) | id, n.iter = 10, seed = 2020, adapt = 5)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
ns(time, df = 2)1 0 0 0 0 0 NaN NaN
ns(time, df = 2)2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 101:110
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
time lvlone 0 0
level # NA % NA
id id 0 0
$m7b
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ bs(time, df = 3), data = longDF, random = ~bs(time,
df = 3) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
bs(time, df = 3)1 0 0 0 0 0 NaN NaN
bs(time, df = 3)2 0 0 0 0 0 NaN NaN
bs(time, df = 3)3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
D_y_id[1,4] 0 0 0 0 0 NaN NaN
D_y_id[2,4] 0 0 0 0 0 NaN NaN
D_y_id[3,4] 0 0 0 0 0 NaN NaN
D_y_id[4,4] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
time lvlone 0 0
level # NA % NA
id id 0 0
$m7c
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + c1 + ns(time, df = 3), data = longDF,
random = ~ns(time, df = 3) | id, n.iter = 10, seed = 2020,
nadapt = 5)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
ns(time, df = 3)1 0 0 0 0 0 NaN NaN
ns(time, df = 3)2 0 0 0 0 0 NaN NaN
ns(time, df = 3)3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
D_y_id[1,4] 0 0 0 0 0 NaN NaN
D_y_id[2,4] 0 0 0 0 0 NaN NaN
D_y_id[3,4] 0 0 0 0 0 NaN NaN
D_y_id[4,4] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 101:110
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
c1 lvlone 0 0
time lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
$m7d
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
ns(time, df = 3)1 0 0 0 0 0 NaN NaN
ns(time, df = 3)2 0 0 0 0 0 NaN NaN
ns(time, df = 3)3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
c1 lvlone 0 0
time lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
C2 id 42 42
$m7e
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~ns(time, df = 3) | id, n.adapt = 5, n.iter = 10,
no_model = "time", seed = 2020)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
ns(time, df = 3)1 0 0 0 0 0 NaN NaN
ns(time, df = 3)2 0 0 0 0 0 NaN NaN
ns(time, df = 3)3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
D_y_id[1,4] 0 0 0 0 0 NaN NaN
D_y_id[2,4] 0 0 0 0 0 NaN NaN
D_y_id[3,4] 0 0 0 0 0 NaN NaN
D_y_id[4,4] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
c1 lvlone 0 0
time lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
C2 id 42 42
$m7f
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF,
random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
ns(time, df = 3)1 0 0 0 0 0 NaN NaN
ns(time, df = 3)2 0 0 0 0 0 NaN NaN
ns(time, df = 3)3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
c1 lvlone 0 0
time lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
C2 id 42 42
$m8a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time +
c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Warning <simpleWarning>
There are missing values in a variable for which a random effect is
specified ("c2"). It will not be possible to re-scale the random
effects "b_y_id" and their variance covariance matrix "D_y_id" back to
the original scale of the data. If you are not interested in the
estimated random effects or their (co)variances this is not a problem.
The fixed effects estimates are not affected by this. If you are
interested in the random effects or the (co)variances you need to
specify that "time" and "c2" are not scaled (using the argument
"scale_params").
Output
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
id id 0 0
$m8b
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time +
c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Warning <simpleWarning>
There are missing values in a variable for which a random effect is
specified ("c2"). It will not be possible to re-scale the random
effects "b_y_id" and their variance covariance matrix "D_y_id" back to
the original scale of the data. If you are not interested in the
estimated random effects or their (co)variances this is not a problem.
The fixed effects estimates are not affected by this. If you are
interested in the random effects or the (co)variances you need to
specify that "time" and "c2" are not scaled (using the argument
"scale_params").
Output
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
id id 0 0
$m8c
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time +
c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
id id 0 0
B2 id 10 10
$m8d
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time +
c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
id id 0 0
B2 id 10 10
$m8e
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Warning <simpleWarning>
There are missing values in a variable for which a random effect is
specified ("c2"). It will not be possible to re-scale the random
effects "b_y_id" and their variance covariance matrix "D_y_id" back to
the original scale of the data. If you are not interested in the
estimated random effects or their (co)variances this is not a problem.
The fixed effects estimates are not affected by this. If you are
interested in the random effects or the (co)variances you need to
specify that "time" and "c2" are not scaled (using the argument
"scale_params").
Output
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m8f
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time",
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Warning <simpleWarning>
There are missing values in a variable for which a random effect is
specified ("c2"). It will not be possible to re-scale the random
effects "b_y_id" and their variance covariance matrix "D_y_id" back to
the original scale of the data. If you are not interested in the
estimated random effects or their (co)variances this is not a problem.
The fixed effects estimates are not affected by this. If you are
interested in the random effects or the (co)variances you need to
specify that "time" and "c2" are not scaled (using the argument
"scale_params").
Output
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m8g
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = c("time",
"c1"), seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Warning <simpleWarning>
There are missing values in a variable for which a random effect is
specified ("c2"). It will not be possible to re-scale the random
effects "b_y_id" and their variance covariance matrix "D_y_id" back to
the original scale of the data. If you are not interested in the
estimated random effects or their (co)variances this is not a problem.
The fixed effects estimates are not affected by this. If you are
interested in the random effects or the (co)variances you need to
specify that "time" and "c2" are not scaled (using the argument
"scale_params").
Output
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m8h
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m8i
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time",
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m8j
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Warning <simpleWarning>
There are missing values in a variable for which a random effect is
specified ("c2"). It will not be possible to re-scale the random
effects "b_y_id" and their variance covariance matrix "D_y_id" back to
the original scale of the data. If you are not interested in the
estimated random effects or their (co)variances this is not a problem.
The fixed effects estimates are not affected by this. If you are
interested in the random effects or the (co)variances you need to
specify that "time" and "c2" are not scaled (using the argument
"scale_params").
Output
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m8k
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF,
random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Warning <simpleWarning>
There are missing values in a variable for which a random effect is
specified ("c2"). It will not be possible to re-scale the random
effects "b_y_id" and their variance covariance matrix "D_y_id" back to
the original scale of the data. If you are not interested in the
estimated random effects or their (co)variances this is not a problem.
The fixed effects estimates are not affected by this. If you are
interested in the random effects or the (co)variances you need to
specify that "time" and "c2" are not scaled (using the argument
"scale_params").
Output
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90.0
lvlone lvlone 263 79.9
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0.0
c1 lvlone 0 0.0
time lvlone 0 0.0
c2 lvlone 66 20.1
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m8l
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + B2 * c1 * time, data = longDF, random = ~time +
I(time^2) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
B21:time 0 0 0 0 0 NaN NaN
c1:time 0 0 0 0 0 NaN NaN
B21:c1:time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
c1 lvlone 0 0
time lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m8m
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ c1 * b1 + o1, data = longDF, random = ~b1 |
id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
b11 0 0 0 0 0 NaN NaN
o1.L 0 0 0 0 0 NaN NaN
o1.Q 0 0 0 0 0 NaN NaN
c1:b11 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 100 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
c1 lvlone 0 0
b1 lvlone 0 0
o1 lvlone 0 0
level # NA % NA
id id 0 0
$m8n
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ c1 + C1 * time + b1 + B2, data = longDF,
random = ~C1 * time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
b11 0 0 0 0 0 NaN NaN
C1:time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
D_y_id[1,3] 0 0 0 0 0 NaN NaN
D_y_id[2,3] 0 0 0 0 0 NaN NaN
D_y_id[3,3] 0 0 0 0 NaN NaN
D_y_id[1,4] 0 0 0 0 0 NaN NaN
D_y_id[2,4] 0 0 0 0 0 NaN NaN
D_y_id[3,4] 0 0 0 0 0 NaN NaN
D_y_id[4,4] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 90 90
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
c1 lvlone 0 0
time lvlone 0 0
b1 lvlone 0 0
level # NA % NA
C1 id 0 0
id id 0 0
B2 id 10 10
$m9a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ c1 + b1 + time + (1 | id) + (1 | o1), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
b11 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
* For level "id":
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
* For level "o1":
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_o1[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
- o1: 3
Number and proportion of complete cases:
level # %
id id 100 100
o1 o1 3 100
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
c1 lvlone 0 0
b1 lvlone 0 0
time lvlone 0 0
level # NA % NA
id id 0 0
level # NA % NA
o1 o1 0 0
$m9b
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + time + (time | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = c(analysis_random = TRUE),
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
D_y_id[1,2] 0 0 0 0 0 NaN NaN
D_y_id[2,2] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
time lvlone 0 0
level # NA % NA
C1 id 0 0
B1 id 0 0
id id 0 0
C2 id 42 42
$m9c
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, monitor_params = c(analysis_random = TRUE),
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Number and proportion of complete cases:
level # %
id id 58 58
lvlone lvlone 329 100
Number and proportion of missing values:
level # NA % NA
y lvlone 0 0
level # NA % NA
C1 id 0 0
B1 id 0 0
id id 0 0
C2 id 42 42
Code
lapply(models0, function(x) coef(summary(x)))
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a1
$m0a1$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0a2
$m0a2$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0a3
$m0a3$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0a4
$m0a4$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0b1
$m0b1$b1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0b2
$m0b2$b1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0b3
$m0b3$b1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0b4
$m0b4$b1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0c1
$m0c1$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0c2
$m0c2$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0d1
$m0d1$p1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0d2
$m0d2$p1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0e1
$m0e1$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0f1
$m0f1$Be1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m1a
$m1a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1b
$m1b$b1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1c
$m1c$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1d
$m1d$p1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1e
$m1e$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1f
$m1f$Be1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m2a
$m2a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
$m2b
$m2b$b2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
$m2c
$m2c$L1mis
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
$m2d
$m2d$p2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
$m2e
$m2e$L1mis
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
$m2f
$m2f$Be2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
$m3a
$m3a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
$m3b
$m3b$b2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
$m3c
$m3c$L1mis
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
$m3d
$m3d$p2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
$m3e
$m3e$L1mis
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
$m3f
$m3f$Be2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
$m4a
$m4a$c1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
p2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Be2 0 0 0 0 0 NaN NaN
$m4b
$m4b$c1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
p2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
$m4c
$m4c$c1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
p2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
$m4d
$m4d$c1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
p2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Be2 0 0 0 0 0 NaN NaN
$m5a
$m5a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
abs(C1 - c2) 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
I(time^2) 0 0 0 0 0 NaN NaN
o22:abs(C1 - c2) 0 0 0 0 0 NaN NaN
o23:abs(C1 - c2) 0 0 0 0 0 NaN NaN
o24:abs(C1 - c2) 0 0 0 0 0 NaN NaN
$m5b
$m5b$b1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
abs(c1 - C2) 0 0 0 0 0 NaN NaN
log(Be2) 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
$m6a
$m6a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
$m6b
$m6b$b1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
$m7a
$m7a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
ns(time, df = 2)1 0 0 0 0 0 NaN NaN
ns(time, df = 2)2 0 0 0 0 0 NaN NaN
$m7b
$m7b$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
bs(time, df = 3)1 0 0 0 0 0 NaN NaN
bs(time, df = 3)2 0 0 0 0 0 NaN NaN
bs(time, df = 3)3 0 0 0 0 0 NaN NaN
$m7c
$m7c$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
ns(time, df = 3)1 0 0 0 0 0 NaN NaN
ns(time, df = 3)2 0 0 0 0 0 NaN NaN
ns(time, df = 3)3 0 0 0 0 0 NaN NaN
$m7d
$m7d$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
ns(time, df = 3)1 0 0 0 0 0 NaN NaN
ns(time, df = 3)2 0 0 0 0 0 NaN NaN
ns(time, df = 3)3 0 0 0 0 0 NaN NaN
$m7e
$m7e$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
ns(time, df = 3)1 0 0 0 0 0 NaN NaN
ns(time, df = 3)2 0 0 0 0 0 NaN NaN
ns(time, df = 3)3 0 0 0 0 0 NaN NaN
$m7f
$m7f$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
ns(time, df = 3)1 0 0 0 0 0 NaN NaN
ns(time, df = 3)2 0 0 0 0 0 NaN NaN
ns(time, df = 3)3 0 0 0 0 0 NaN NaN
$m8a
$m8a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
$m8b
$m8b$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
$m8c
$m8c$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
$m8d
$m8d$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
$m8e
$m8e$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
$m8f
$m8f$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
$m8g
$m8g$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
$m8h
$m8h$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c2 0 0 0 0 0 NaN NaN
$m8i
$m8i$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c2 0 0 0 0 0 NaN NaN
$m8j
$m8j$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c2 0 0 0 0 0 NaN NaN
$m8k
$m8k$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c2 0 0 0 0 0 NaN NaN
$m8l
$m8l$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
B21:c1 0 0 0 0 0 NaN NaN
B21:time 0 0 0 0 0 NaN NaN
c1:time 0 0 0 0 0 NaN NaN
B21:c1:time 0 0 0 0 0 NaN NaN
$m8m
$m8m$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
b11 0 0 0 0 0 NaN NaN
o1.L 0 0 0 0 0 NaN NaN
o1.Q 0 0 0 0 0 NaN NaN
c1:b11 0 0 0 0 0 NaN NaN
$m8n
$m8n$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
b11 0 0 0 0 0 NaN NaN
C1:time 0 0 0 0 0 NaN NaN
$m9a
$m9a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
b11 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
$m9b
$m9b$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
$m9c
$m9c$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
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