Nothing
tests/testthat/_snaps/mlogitmm.md
In JointAI: Joint Analysis and Imputation of Incomplete Data
data_list remains the same
Code
lapply(models, "[[", "data_list")
Output
$m0a
$m0a$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
$m0a$M_lvlone
m1
1 3
1.1 2
1.2 1
1.3 1
2 2
2.1 2
2.2 1
3 1
3.1 2
3.2 2
4 2
4.1 1
4.2 2
4.3 3
5 2
5.1 1
5.2 2
5.3 2
6 2
7 3
7.1 2
7.2 3
8 2
8.1 1
8.2 3
8.3 2
8.4 2
8.5 2
9 3
9.1 2
9.2 3
10 3
10.1 1
11 1
11.1 1
11.2 2
11.3 3
11.4 1
12 1
13 2
13.1 3
14 1
14.1 1
14.2 1
14.3 3
15 1
15.1 1
15.2 3
15.3 2
16 2
16.1 2
16.2 1
16.3 3
16.4 2
16.5 1
17 2
17.1 3
17.2 1
17.3 1
17.4 2
18 1
19 2
19.1 3
19.2 2
19.3 3
20 2
20.1 2
20.2 1
20.3 3
20.4 2
20.5 3
21 1
21.1 2
21.2 3
22 2
22.1 2
23 2
23.1 1
24 1
25 1
25.1 3
25.2 2
25.3 2
25.4 1
25.5 1
26 2
26.1 1
26.2 1
26.3 2
27 1
27.1 3
28 1
28.1 3
28.2 1
28.3 1
29 3
29.1 3
29.2 3
29.3 2
30 1
30.1 3
30.2 3
31 1
32 3
32.1 3
32.2 2
32.3 1
33 3
33.1 1
34 1
34.1 1
34.2 2
34.3 2
35 1
35.1 1
35.2 1
36 2
36.1 3
36.2 3
36.3 3
36.4 3
37 1
37.1 3
37.2 1
38 2
39 2
39.1 3
39.2 1
39.3 2
39.4 3
39.5 3
40 3
40.1 3
40.2 1
40.3 3
41 3
41.1 3
41.2 1
41.3 1
41.4 1
42 1
42.1 1
43 3
43.1 3
43.2 2
44 2
44.1 2
44.2 1
44.3 1
45 2
45.1 3
46 3
46.1 2
46.2 3
47 1
47.1 2
47.2 2
47.3 2
47.4 2
48 3
48.1 1
49 3
50 1
51 3
52 3
52.1 2
52.2 1
52.3 3
52.4 3
52.5 3
53 1
53.1 3
53.2 2
54 3
54.1 3
54.2 3
54.3 1
54.4 1
55 1
55.1 3
55.2 2
55.3 1
55.4 1
56 2
56.1 1
56.2 3
56.3 1
56.4 2
56.5 1
57 1
57.1 1
57.2 1
57.3 1
58 3
58.1 2
58.2 1
58.3 3
58.4 3
58.5 3
59 3
59.1 1
60 3
61 1
61.1 2
61.2 2
61.3 3
61.4 2
62 2
62.1 1
62.2 3
62.3 2
63 3
63.1 1
64 3
65 3
65.1 3
65.2 2
65.3 3
66 3
66.1 3
66.2 1
67 3
68 3
68.1 1
68.2 2
68.3 3
68.4 1
69 1
70 1
70.1 2
71 3
71.1 2
71.2 2
71.3 1
71.4 2
72 1
72.1 2
72.2 1
72.3 2
72.4 2
72.5 1
73 2
74 1
75 3
76 3
76.1 3
76.2 2
77 2
78 2
79 2
79.1 2
79.2 2
80 2
80.1 1
80.2 3
81 2
81.1 3
81.2 2
81.3 1
82 1
82.1 2
82.2 3
83 2
83.1 3
83.2 3
83.3 3
84 2
84.1 3
85 1
85.1 2
85.2 3
85.3 3
85.4 2
85.5 2
86 1
86.1 2
86.2 1
86.3 1
86.4 1
86.5 2
87 3
87.1 3
87.2 2
88 3
88.1 3
88.2 3
88.3 1
89 2
90 1
90.1 2
90.2 2
90.3 2
91 3
91.1 3
91.2 3
92 2
93 2
93.1 2
93.2 2
93.3 3
93.4 2
94 2
94.1 3
94.2 3
94.3 2
94.4 3
94.5 2
95 2
95.1 3
95.2 2
96 3
96.1 2
96.2 3
96.3 2
96.4 2
96.5 3
97 3
97.1 3
98 2
98.1 3
98.2 1
99 2
99.1 1
99.2 3
100 2
100.1 1
100.2 2
100.3 2
100.4 3
$m0a$mu_reg_multinomial
[1] 0
$m0a$tau_reg_multinomial
[1] 1e-04
$m0a$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
$m0a$shape_diag_RinvD
[1] "0.01"
$m0a$rate_diag_RinvD
[1] "0.001"
$m0b
$m0b$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
$m0b$M_lvlone
m2
1 3
1.1 1
1.2 3
1.3 1
2 2
2.1 1
2.2 NA
3 3
3.1 2
3.2 1
4 1
4.1 2
4.2 3
4.3 3
5 2
5.1 3
5.2 1
5.3 1
6 2
7 2
7.1 1
7.2 3
8 2
8.1 2
8.2 1
8.3 3
8.4 NA
8.5 3
9 NA
9.1 3
9.2 1
10 1
10.1 1
11 1
11.1 1
11.2 1
11.3 NA
11.4 1
12 1
13 2
13.1 2
14 3
14.1 2
14.2 1
14.3 1
15 1
15.1 2
15.2 3
15.3 3
16 2
16.1 NA
16.2 3
16.3 2
16.4 3
16.5 1
17 1
17.1 3
17.2 NA
17.3 2
17.4 1
18 3
19 NA
19.1 1
19.2 3
19.3 3
20 2
20.1 NA
20.2 3
20.3 1
20.4 3
20.5 2
21 3
21.1 1
21.2 NA
22 3
22.1 1
23 1
23.1 2
24 2
25 2
25.1 3
25.2 3
25.3 1
25.4 3
25.5 2
26 NA
26.1 3
26.2 3
26.3 NA
27 3
27.1 3
28 3
28.1 2
28.2 2
28.3 3
29 1
29.1 NA
29.2 2
29.3 2
30 2
30.1 3
30.2 3
31 3
32 3
32.1 3
32.2 1
32.3 1
33 3
33.1 3
34 3
34.1 NA
34.2 1
34.3 NA
35 2
35.1 2
35.2 2
36 3
36.1 3
36.2 3
36.3 2
36.4 2
37 2
37.1 2
37.2 1
38 2
39 3
39.1 2
39.2 3
39.3 NA
39.4 3
39.5 3
40 3
40.1 1
40.2 3
40.3 2
41 3
41.1 3
41.2 1
41.3 2
41.4 3
42 2
42.1 NA
43 3
43.1 3
43.2 2
44 3
44.1 3
44.2 NA
44.3 1
45 3
45.1 1
46 NA
46.1 1
46.2 2
47 2
47.1 NA
47.2 NA
47.3 3
47.4 3
48 3
48.1 1
49 1
50 NA
51 1
52 2
52.1 1
52.2 1
52.3 NA
52.4 2
52.5 3
53 2
53.1 1
53.2 2
54 NA
54.1 1
54.2 NA
54.3 3
54.4 3
55 1
55.1 1
55.2 1
55.3 NA
55.4 2
56 2
56.1 3
56.2 1
56.3 1
56.4 2
56.5 NA
57 2
57.1 3
57.2 2
57.3 NA
58 1
58.1 1
58.2 NA
58.3 1
58.4 2
58.5 NA
59 1
59.1 1
60 1
61 2
61.1 1
61.2 1
61.3 2
61.4 2
62 1
62.1 1
62.2 NA
62.3 1
63 NA
63.1 3
64 3
65 NA
65.1 2
65.2 3
65.3 3
66 3
66.1 3
66.2 1
67 NA
68 1
68.1 1
68.2 1
68.3 2
68.4 3
69 NA
70 1
70.1 NA
71 1
71.1 1
71.2 NA
71.3 1
71.4 1
72 2
72.1 3
72.2 2
72.3 1
72.4 2
72.5 1
73 NA
74 1
75 NA
76 1
76.1 2
76.2 2
77 NA
78 1
79 3
79.1 3
79.2 NA
80 3
80.1 2
80.2 NA
81 1
81.1 2
81.2 1
81.3 1
82 3
82.1 1
82.2 1
83 2
83.1 3
83.2 2
83.3 3
84 1
84.1 2
85 2
85.1 1
85.2 1
85.3 NA
85.4 2
85.5 1
86 1
86.1 NA
86.2 2
86.3 1
86.4 2
86.5 2
87 NA
87.1 1
87.2 NA
88 1
88.1 2
88.2 NA
88.3 2
89 3
90 3
90.1 2
90.2 NA
90.3 2
91 3
91.1 1
91.2 3
92 2
93 2
93.1 3
93.2 NA
93.3 2
93.4 3
94 2
94.1 2
94.2 1
94.3 2
94.4 1
94.5 2
95 2
95.1 2
95.2 NA
96 1
96.1 1
96.2 2
96.3 3
96.4 2
96.5 NA
97 1
97.1 2
98 3
98.1 2
98.2 2
99 2
99.1 2
99.2 1
100 1
100.1 2
100.2 3
100.3 2
100.4 1
$m0b$mu_reg_multinomial
[1] 0
$m0b$tau_reg_multinomial
[1] 1e-04
$m0b$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
$m0b$shape_diag_RinvD
[1] "0.01"
$m0b$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
m1
1 3
1.1 2
1.2 1
1.3 1
2 2
2.1 2
2.2 1
3 1
3.1 2
3.2 2
4 2
4.1 1
4.2 2
4.3 3
5 2
5.1 1
5.2 2
5.3 2
6 2
7 3
7.1 2
7.2 3
8 2
8.1 1
8.2 3
8.3 2
8.4 2
8.5 2
9 3
9.1 2
9.2 3
10 3
10.1 1
11 1
11.1 1
11.2 2
11.3 3
11.4 1
12 1
13 2
13.1 3
14 1
14.1 1
14.2 1
14.3 3
15 1
15.1 1
15.2 3
15.3 2
16 2
16.1 2
16.2 1
16.3 3
16.4 2
16.5 1
17 2
17.1 3
17.2 1
17.3 1
17.4 2
18 1
19 2
19.1 3
19.2 2
19.3 3
20 2
20.1 2
20.2 1
20.3 3
20.4 2
20.5 3
21 1
21.1 2
21.2 3
22 2
22.1 2
23 2
23.1 1
24 1
25 1
25.1 3
25.2 2
25.3 2
25.4 1
25.5 1
26 2
26.1 1
26.2 1
26.3 2
27 1
27.1 3
28 1
28.1 3
28.2 1
28.3 1
29 3
29.1 3
29.2 3
29.3 2
30 1
30.1 3
30.2 3
31 1
32 3
32.1 3
32.2 2
32.3 1
33 3
33.1 1
34 1
34.1 1
34.2 2
34.3 2
35 1
35.1 1
35.2 1
36 2
36.1 3
36.2 3
36.3 3
36.4 3
37 1
37.1 3
37.2 1
38 2
39 2
39.1 3
39.2 1
39.3 2
39.4 3
39.5 3
40 3
40.1 3
40.2 1
40.3 3
41 3
41.1 3
41.2 1
41.3 1
41.4 1
42 1
42.1 1
43 3
43.1 3
43.2 2
44 2
44.1 2
44.2 1
44.3 1
45 2
45.1 3
46 3
46.1 2
46.2 3
47 1
47.1 2
47.2 2
47.3 2
47.4 2
48 3
48.1 1
49 3
50 1
51 3
52 3
52.1 2
52.2 1
52.3 3
52.4 3
52.5 3
53 1
53.1 3
53.2 2
54 3
54.1 3
54.2 3
54.3 1
54.4 1
55 1
55.1 3
55.2 2
55.3 1
55.4 1
56 2
56.1 1
56.2 3
56.3 1
56.4 2
56.5 1
57 1
57.1 1
57.2 1
57.3 1
58 3
58.1 2
58.2 1
58.3 3
58.4 3
58.5 3
59 3
59.1 1
60 3
61 1
61.1 2
61.2 2
61.3 3
61.4 2
62 2
62.1 1
62.2 3
62.3 2
63 3
63.1 1
64 3
65 3
65.1 3
65.2 2
65.3 3
66 3
66.1 3
66.2 1
67 3
68 3
68.1 1
68.2 2
68.3 3
68.4 1
69 1
70 1
70.1 2
71 3
71.1 2
71.2 2
71.3 1
71.4 2
72 1
72.1 2
72.2 1
72.3 2
72.4 2
72.5 1
73 2
74 1
75 3
76 3
76.1 3
76.2 2
77 2
78 2
79 2
79.1 2
79.2 2
80 2
80.1 1
80.2 3
81 2
81.1 3
81.2 2
81.3 1
82 1
82.1 2
82.2 3
83 2
83.1 3
83.2 3
83.3 3
84 2
84.1 3
85 1
85.1 2
85.2 3
85.3 3
85.4 2
85.5 2
86 1
86.1 2
86.2 1
86.3 1
86.4 1
86.5 2
87 3
87.1 3
87.2 2
88 3
88.1 3
88.2 3
88.3 1
89 2
90 1
90.1 2
90.2 2
90.3 2
91 3
91.1 3
91.2 3
92 2
93 2
93.1 2
93.2 2
93.3 3
93.4 2
94 2
94.1 3
94.2 3
94.3 2
94.4 3
94.5 2
95 2
95.1 3
95.2 2
96 3
96.1 2
96.2 3
96.3 2
96.4 2
96.5 3
97 3
97.1 3
98 2
98.1 3
98.2 1
99 2
99.1 1
99.2 3
100 2
100.1 1
100.2 2
100.3 2
100.4 3
$m1a$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1a$mu_reg_multinomial
[1] 0
$m1a$tau_reg_multinomial
[1] 1e-04
$m1a$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
m2
1 3
1.1 1
1.2 3
1.3 1
2 2
2.1 1
2.2 NA
3 3
3.1 2
3.2 1
4 1
4.1 2
4.2 3
4.3 3
5 2
5.1 3
5.2 1
5.3 1
6 2
7 2
7.1 1
7.2 3
8 2
8.1 2
8.2 1
8.3 3
8.4 NA
8.5 3
9 NA
9.1 3
9.2 1
10 1
10.1 1
11 1
11.1 1
11.2 1
11.3 NA
11.4 1
12 1
13 2
13.1 2
14 3
14.1 2
14.2 1
14.3 1
15 1
15.1 2
15.2 3
15.3 3
16 2
16.1 NA
16.2 3
16.3 2
16.4 3
16.5 1
17 1
17.1 3
17.2 NA
17.3 2
17.4 1
18 3
19 NA
19.1 1
19.2 3
19.3 3
20 2
20.1 NA
20.2 3
20.3 1
20.4 3
20.5 2
21 3
21.1 1
21.2 NA
22 3
22.1 1
23 1
23.1 2
24 2
25 2
25.1 3
25.2 3
25.3 1
25.4 3
25.5 2
26 NA
26.1 3
26.2 3
26.3 NA
27 3
27.1 3
28 3
28.1 2
28.2 2
28.3 3
29 1
29.1 NA
29.2 2
29.3 2
30 2
30.1 3
30.2 3
31 3
32 3
32.1 3
32.2 1
32.3 1
33 3
33.1 3
34 3
34.1 NA
34.2 1
34.3 NA
35 2
35.1 2
35.2 2
36 3
36.1 3
36.2 3
36.3 2
36.4 2
37 2
37.1 2
37.2 1
38 2
39 3
39.1 2
39.2 3
39.3 NA
39.4 3
39.5 3
40 3
40.1 1
40.2 3
40.3 2
41 3
41.1 3
41.2 1
41.3 2
41.4 3
42 2
42.1 NA
43 3
43.1 3
43.2 2
44 3
44.1 3
44.2 NA
44.3 1
45 3
45.1 1
46 NA
46.1 1
46.2 2
47 2
47.1 NA
47.2 NA
47.3 3
47.4 3
48 3
48.1 1
49 1
50 NA
51 1
52 2
52.1 1
52.2 1
52.3 NA
52.4 2
52.5 3
53 2
53.1 1
53.2 2
54 NA
54.1 1
54.2 NA
54.3 3
54.4 3
55 1
55.1 1
55.2 1
55.3 NA
55.4 2
56 2
56.1 3
56.2 1
56.3 1
56.4 2
56.5 NA
57 2
57.1 3
57.2 2
57.3 NA
58 1
58.1 1
58.2 NA
58.3 1
58.4 2
58.5 NA
59 1
59.1 1
60 1
61 2
61.1 1
61.2 1
61.3 2
61.4 2
62 1
62.1 1
62.2 NA
62.3 1
63 NA
63.1 3
64 3
65 NA
65.1 2
65.2 3
65.3 3
66 3
66.1 3
66.2 1
67 NA
68 1
68.1 1
68.2 1
68.3 2
68.4 3
69 NA
70 1
70.1 NA
71 1
71.1 1
71.2 NA
71.3 1
71.4 1
72 2
72.1 3
72.2 2
72.3 1
72.4 2
72.5 1
73 NA
74 1
75 NA
76 1
76.1 2
76.2 2
77 NA
78 1
79 3
79.1 3
79.2 NA
80 3
80.1 2
80.2 NA
81 1
81.1 2
81.2 1
81.3 1
82 3
82.1 1
82.2 1
83 2
83.1 3
83.2 2
83.3 3
84 1
84.1 2
85 2
85.1 1
85.2 1
85.3 NA
85.4 2
85.5 1
86 1
86.1 NA
86.2 2
86.3 1
86.4 2
86.5 2
87 NA
87.1 1
87.2 NA
88 1
88.1 2
88.2 NA
88.3 2
89 3
90 3
90.1 2
90.2 NA
90.3 2
91 3
91.1 1
91.2 3
92 2
93 2
93.1 3
93.2 NA
93.3 2
93.4 3
94 2
94.1 2
94.2 1
94.3 2
94.4 1
94.5 2
95 2
95.1 2
95.2 NA
96 1
96.1 1
96.2 2
96.3 3
96.4 2
96.5 NA
97 1
97.1 2
98 3
98.1 2
98.2 2
99 2
99.1 2
99.2 1
100 1
100.1 2
100.2 3
100.3 2
100.4 1
$m1b$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1b$mu_reg_multinomial
[1] 0
$m1b$tau_reg_multinomial
[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)
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
$m1c$M_lvlone
m1 c1
1 3 0.7592026489
1.1 2 0.9548337990
1.2 1 0.5612235156
1.3 1 1.1873391025
2 2 0.9192204198
2.1 2 -0.1870730476
2.2 1 1.2517512331
3 1 -0.0605087604
3.1 2 0.3788637747
3.2 2 0.9872578281
4 2 1.4930175328
4.1 1 -0.7692526880
4.2 2 0.9180841450
4.3 3 -0.0541170782
5 2 -0.1376784521
5.1 1 -0.2740585866
5.2 2 0.4670496929
5.3 2 0.1740288049
6 2 0.9868044683
7 3 -0.1280320918
7.1 2 0.4242971219
7.2 3 0.0777182491
8 2 -0.5791408712
8.1 1 0.3128604232
8.2 3 0.6258446356
8.3 2 -0.1040137707
8.4 2 0.0481450285
8.5 2 0.3831763675
9 3 -0.1757592269
9.1 2 -0.1791541200
9.2 3 -0.0957042935
10 3 -0.5598409704
10.1 1 -0.2318340451
11 1 0.5086859475
11.1 1 0.4951758188
11.2 2 -1.1022162541
11.3 3 -0.0611636705
11.4 1 -0.4971774316
12 1 -0.2433996286
13 2 0.8799673116
13.1 3 0.1079022586
14 1 0.9991752617
14.1 1 -0.1094019046
14.2 1 0.1518967560
14.3 3 0.3521012473
15 1 0.3464447888
15.1 1 -0.4767313971
15.2 3 0.5759767791
15.3 2 -0.1713452662
16 2 0.4564754473
16.1 2 1.0652558311
16.2 1 0.6971872493
16.3 3 0.5259331838
16.4 2 0.2046601798
16.5 1 1.0718540464
17 2 0.6048676222
17.1 3 0.2323298304
17.2 1 1.2617499032
17.3 1 -0.3913230895
17.4 2 0.9577299112
18 1 -0.0050324072
19 2 -0.4187468937
19.1 3 -0.4478828944
19.2 2 -1.1966721302
19.3 3 -0.5877091668
20 2 0.6838223064
20.1 2 0.3278571109
20.2 1 -0.8489831990
20.3 3 1.3169975191
20.4 2 0.0444804531
20.5 3 -0.4535207652
21 1 -0.4030302960
21.1 2 -0.4069674045
21.2 3 1.0650265940
22 2 -0.0673274516
22.1 2 0.9601388170
23 2 0.5556634840
23.1 1 1.4407865964
24 1 0.3856376411
25 1 0.3564400705
25.1 3 0.0982553434
25.2 2 0.1928682598
25.3 2 -0.0192488594
25.4 1 0.4466012931
25.5 1 1.1425193342
26 2 0.5341531449
26.1 1 1.2268695927
26.2 1 0.3678294939
26.3 2 0.5948516018
27 1 -0.3342844147
27.1 3 -0.4835141229
28 1 -0.7145915499
28.1 3 0.5063671955
28.2 1 -0.2067413142
28.3 1 0.1196789973
29 3 0.1392699487
29.1 3 0.7960234776
29.2 3 1.0398214352
29.3 2 0.0813246429
30 1 -0.3296323050
30.1 3 1.3635850954
30.2 3 0.7354171050
31 1 0.3708398217
32 3 -0.0474059668
32.1 3 1.2507771489
32.2 2 0.1142915519
32.3 1 0.6773270619
33 3 0.1774293842
33.1 1 0.6159606291
34 1 0.8590979166
34.1 1 0.0546216775
34.2 2 -0.0897224473
34.3 2 0.4163395571
35 1 -1.4693520528
35.1 1 -0.3031734330
35.2 1 -0.6045512101
36 2 0.9823048960
36.1 3 1.4466051416
36.2 3 1.1606752905
36.3 3 0.8373091576
36.4 3 0.2640591685
37 1 0.1177313455
37.1 3 -0.1415483779
37.2 1 0.0054610124
38 2 0.8078948077
39 2 0.9876451040
39.1 3 -0.3431222274
39.2 1 -1.7909380751
39.3 2 -0.1798746191
39.4 3 -0.1850961689
39.5 3 0.4544226146
40 3 0.5350190436
40.1 3 0.4189342752
40.2 1 0.4211994981
40.3 3 0.0916687506
41 3 -0.1035047421
41.1 3 -0.4684202411
41.2 1 0.5972615368
41.3 1 0.9885613862
41.4 1 -0.3908036794
42 1 -0.0338893961
42.1 1 -0.4498363172
43 3 0.8965546110
43.1 3 0.6199122090
43.2 2 0.1804894429
44 2 1.3221409285
44.1 2 0.3416426284
44.2 1 0.5706610068
44.3 1 1.2679497430
45 2 0.1414983160
45.1 3 0.7220892521
46 3 1.5391054233
46.1 2 0.3889107049
46.2 3 0.1248719493
47 1 0.2014101100
47.1 2 0.2982973539
47.2 2 1.1518107179
47.3 2 0.5196802157
47.4 2 0.3702301552
48 3 -0.2128602862
48.1 1 -0.5337239976
49 3 -0.5236770035
50 1 0.3897705981
51 3 -0.7213343736
52 3 0.3758235358
52.1 2 0.7138067080
52.2 1 0.8872895233
52.3 3 -0.9664587437
52.4 3 0.0254566848
52.5 3 0.4155259424
53 1 0.5675736897
53.1 3 -0.3154088781
53.2 2 0.2162315769
54 3 -0.0880802382
54.1 3 0.4129127672
54.2 3 1.0119546775
54.3 1 -0.1112901990
54.4 1 0.8587727145
55 1 -0.0116453589
55.1 3 0.5835528661
55.2 2 -1.0010857254
55.3 1 -0.4796526070
55.4 1 -0.1202746964
56 2 0.5176377612
56.1 1 -1.1136932588
56.2 3 -0.0168103281
56.3 1 0.3933023606
56.4 2 0.3714625139
56.5 1 0.7811448179
57 1 -1.0868304872
57.1 1 0.8018626997
57.2 1 -0.1159517011
57.3 1 0.6785562445
58 3 1.6476207996
58.1 2 0.3402652711
58.2 1 -0.1111300753
58.3 3 -0.5409234285
58.4 3 -0.1271327672
58.5 3 0.8713264822
59 3 0.4766421367
59.1 1 1.0028089765
60 3 0.5231452932
61 1 -0.7190130614
61.1 2 0.8353702312
61.2 2 1.0229058138
61.3 3 1.1717723589
61.4 2 -0.0629201596
62 2 -0.3979137604
62.1 1 0.6830738372
62.2 3 0.4301745954
62.3 2 -0.0333139957
63 3 0.3345678035
63.1 1 0.3643769511
64 3 0.3949911859
65 3 1.2000091513
65.1 3 0.0110122646
65.2 2 -0.5776452043
65.3 3 -0.1372183563
66 3 -0.5081302805
66.1 3 -0.1447837412
66.2 1 0.1906241379
67 3 1.6716027681
68 3 0.5691848839
68.1 1 0.1004860389
68.2 2 -0.0061241827
68.3 3 0.7443745962
68.4 1 0.8726923437
69 1 0.0381382683
70 1 0.8126204217
70.1 2 0.4691503050
71 3 -0.5529062591
71.1 2 -0.1103252087
71.2 2 1.7178492547
71.3 1 -1.0118346755
71.4 2 1.8623785017
72 1 -0.4521659275
72.1 2 0.1375317317
72.2 1 -0.4170988856
72.3 2 0.7107266765
72.4 2 0.1451969143
72.5 1 1.6298050306
73 2 -0.0307469467
74 1 0.3730017941
75 3 -0.4908003566
76 3 -0.9888876620
76.1 3 0.0003798292
76.2 2 -0.8421863763
77 2 -0.4986802480
78 2 0.0417330969
79 2 -0.3767450660
79.1 2 0.1516000028
79.2 2 -0.1888160741
80 2 -0.0041558414
80.1 1 -0.0329337062
80.2 3 0.5046816157
81 2 -0.9493950353
81.1 3 0.2443038954
81.2 2 0.6476958410
81.3 1 0.4182528210
82 1 1.1088801952
82.1 2 0.9334157763
82.2 3 0.4958140634
83 2 0.5104724530
83.1 3 -0.0513309106
83.2 3 -0.2067792494
83.3 3 -0.0534169155
84 2 -0.0255753653
84.1 3 -1.8234189877
85 1 -0.0114038622
85.1 2 -0.0577615939
85.2 3 -0.2241856342
85.3 3 -0.0520175929
85.4 2 0.2892733846
85.5 2 -0.3740417009
86 1 0.4293735089
86.1 2 -0.1363456521
86.2 1 0.1230989293
86.3 1 0.3305413955
86.4 1 2.6003411822
86.5 2 -0.1420690052
87 3 1.0457427869
87.1 3 -0.2973007190
87.2 2 0.4396872616
88 3 -0.0601928334
88.1 3 -1.0124347595
88.2 3 0.5730917016
88.3 1 -0.0029455332
89 2 1.5465903721
90 1 0.0626760573
90.1 2 1.1896872985
90.2 2 0.2597888783
90.3 2 0.6599799887
91 3 1.1213651365
91.1 3 1.2046371625
91.2 3 0.3395603754
92 2 0.4674939332
93 2 0.2677965647
93.1 2 1.6424445368
93.2 2 0.7101700066
93.3 3 1.1222322893
93.4 2 1.4628960401
94 2 -0.2904211940
94.1 3 0.0147813580
94.2 3 -0.4536774482
94.3 2 0.6793464917
94.4 3 -0.9411356550
94.5 2 0.5683867264
95 2 0.2375652188
95.1 3 0.0767152977
95.2 2 -0.6886731251
96 3 0.7813892121
96.1 2 0.3391519695
96.2 3 -0.4857246503
96.3 2 0.8771471244
96.4 2 1.9030768981
96.5 3 -0.1684332749
97 3 1.3775130083
97.1 3 -1.7323228619
98 2 -1.2648518889
98.1 3 -0.9042716241
98.2 1 -0.1560385207
99 2 0.7993356425
99.1 1 1.0355522332
99.2 3 -0.1150895843
100 2 0.0369067906
100.1 1 1.6023713093
100.2 2 0.8861545820
100.3 2 0.1277046316
100.4 3 -0.0834577654
$m1c$spM_lvlone
center scale
m1 NA NA
c1 0.2559996 0.6718095
$m1c$mu_reg_multinomial
[1] 0
$m1c$tau_reg_multinomial
[1] 1e-04
$m1c$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)
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
$m1d$M_lvlone
m2 c1
1 3 0.7592026489
1.1 1 0.9548337990
1.2 3 0.5612235156
1.3 1 1.1873391025
2 2 0.9192204198
2.1 1 -0.1870730476
2.2 NA 1.2517512331
3 3 -0.0605087604
3.1 2 0.3788637747
3.2 1 0.9872578281
4 1 1.4930175328
4.1 2 -0.7692526880
4.2 3 0.9180841450
4.3 3 -0.0541170782
5 2 -0.1376784521
5.1 3 -0.2740585866
5.2 1 0.4670496929
5.3 1 0.1740288049
6 2 0.9868044683
7 2 -0.1280320918
7.1 1 0.4242971219
7.2 3 0.0777182491
8 2 -0.5791408712
8.1 2 0.3128604232
8.2 1 0.6258446356
8.3 3 -0.1040137707
8.4 NA 0.0481450285
8.5 3 0.3831763675
9 NA -0.1757592269
9.1 3 -0.1791541200
9.2 1 -0.0957042935
10 1 -0.5598409704
10.1 1 -0.2318340451
11 1 0.5086859475
11.1 1 0.4951758188
11.2 1 -1.1022162541
11.3 NA -0.0611636705
11.4 1 -0.4971774316
12 1 -0.2433996286
13 2 0.8799673116
13.1 2 0.1079022586
14 3 0.9991752617
14.1 2 -0.1094019046
14.2 1 0.1518967560
14.3 1 0.3521012473
15 1 0.3464447888
15.1 2 -0.4767313971
15.2 3 0.5759767791
15.3 3 -0.1713452662
16 2 0.4564754473
16.1 NA 1.0652558311
16.2 3 0.6971872493
16.3 2 0.5259331838
16.4 3 0.2046601798
16.5 1 1.0718540464
17 1 0.6048676222
17.1 3 0.2323298304
17.2 NA 1.2617499032
17.3 2 -0.3913230895
17.4 1 0.9577299112
18 3 -0.0050324072
19 NA -0.4187468937
19.1 1 -0.4478828944
19.2 3 -1.1966721302
19.3 3 -0.5877091668
20 2 0.6838223064
20.1 NA 0.3278571109
20.2 3 -0.8489831990
20.3 1 1.3169975191
20.4 3 0.0444804531
20.5 2 -0.4535207652
21 3 -0.4030302960
21.1 1 -0.4069674045
21.2 NA 1.0650265940
22 3 -0.0673274516
22.1 1 0.9601388170
23 1 0.5556634840
23.1 2 1.4407865964
24 2 0.3856376411
25 2 0.3564400705
25.1 3 0.0982553434
25.2 3 0.1928682598
25.3 1 -0.0192488594
25.4 3 0.4466012931
25.5 2 1.1425193342
26 NA 0.5341531449
26.1 3 1.2268695927
26.2 3 0.3678294939
26.3 NA 0.5948516018
27 3 -0.3342844147
27.1 3 -0.4835141229
28 3 -0.7145915499
28.1 2 0.5063671955
28.2 2 -0.2067413142
28.3 3 0.1196789973
29 1 0.1392699487
29.1 NA 0.7960234776
29.2 2 1.0398214352
29.3 2 0.0813246429
30 2 -0.3296323050
30.1 3 1.3635850954
30.2 3 0.7354171050
31 3 0.3708398217
32 3 -0.0474059668
32.1 3 1.2507771489
32.2 1 0.1142915519
32.3 1 0.6773270619
33 3 0.1774293842
33.1 3 0.6159606291
34 3 0.8590979166
34.1 NA 0.0546216775
34.2 1 -0.0897224473
34.3 NA 0.4163395571
35 2 -1.4693520528
35.1 2 -0.3031734330
35.2 2 -0.6045512101
36 3 0.9823048960
36.1 3 1.4466051416
36.2 3 1.1606752905
36.3 2 0.8373091576
36.4 2 0.2640591685
37 2 0.1177313455
37.1 2 -0.1415483779
37.2 1 0.0054610124
38 2 0.8078948077
39 3 0.9876451040
39.1 2 -0.3431222274
39.2 3 -1.7909380751
39.3 NA -0.1798746191
39.4 3 -0.1850961689
39.5 3 0.4544226146
40 3 0.5350190436
40.1 1 0.4189342752
40.2 3 0.4211994981
40.3 2 0.0916687506
41 3 -0.1035047421
41.1 3 -0.4684202411
41.2 1 0.5972615368
41.3 2 0.9885613862
41.4 3 -0.3908036794
42 2 -0.0338893961
42.1 NA -0.4498363172
43 3 0.8965546110
43.1 3 0.6199122090
43.2 2 0.1804894429
44 3 1.3221409285
44.1 3 0.3416426284
44.2 NA 0.5706610068
44.3 1 1.2679497430
45 3 0.1414983160
45.1 1 0.7220892521
46 NA 1.5391054233
46.1 1 0.3889107049
46.2 2 0.1248719493
47 2 0.2014101100
47.1 NA 0.2982973539
47.2 NA 1.1518107179
47.3 3 0.5196802157
47.4 3 0.3702301552
48 3 -0.2128602862
48.1 1 -0.5337239976
49 1 -0.5236770035
50 NA 0.3897705981
51 1 -0.7213343736
52 2 0.3758235358
52.1 1 0.7138067080
52.2 1 0.8872895233
52.3 NA -0.9664587437
52.4 2 0.0254566848
52.5 3 0.4155259424
53 2 0.5675736897
53.1 1 -0.3154088781
53.2 2 0.2162315769
54 NA -0.0880802382
54.1 1 0.4129127672
54.2 NA 1.0119546775
54.3 3 -0.1112901990
54.4 3 0.8587727145
55 1 -0.0116453589
55.1 1 0.5835528661
55.2 1 -1.0010857254
55.3 NA -0.4796526070
55.4 2 -0.1202746964
56 2 0.5176377612
56.1 3 -1.1136932588
56.2 1 -0.0168103281
56.3 1 0.3933023606
56.4 2 0.3714625139
56.5 NA 0.7811448179
57 2 -1.0868304872
57.1 3 0.8018626997
57.2 2 -0.1159517011
57.3 NA 0.6785562445
58 1 1.6476207996
58.1 1 0.3402652711
58.2 NA -0.1111300753
58.3 1 -0.5409234285
58.4 2 -0.1271327672
58.5 NA 0.8713264822
59 1 0.4766421367
59.1 1 1.0028089765
60 1 0.5231452932
61 2 -0.7190130614
61.1 1 0.8353702312
61.2 1 1.0229058138
61.3 2 1.1717723589
61.4 2 -0.0629201596
62 1 -0.3979137604
62.1 1 0.6830738372
62.2 NA 0.4301745954
62.3 1 -0.0333139957
63 NA 0.3345678035
63.1 3 0.3643769511
64 3 0.3949911859
65 NA 1.2000091513
65.1 2 0.0110122646
65.2 3 -0.5776452043
65.3 3 -0.1372183563
66 3 -0.5081302805
66.1 3 -0.1447837412
66.2 1 0.1906241379
67 NA 1.6716027681
68 1 0.5691848839
68.1 1 0.1004860389
68.2 1 -0.0061241827
68.3 2 0.7443745962
68.4 3 0.8726923437
69 NA 0.0381382683
70 1 0.8126204217
70.1 NA 0.4691503050
71 1 -0.5529062591
71.1 1 -0.1103252087
71.2 NA 1.7178492547
71.3 1 -1.0118346755
71.4 1 1.8623785017
72 2 -0.4521659275
72.1 3 0.1375317317
72.2 2 -0.4170988856
72.3 1 0.7107266765
72.4 2 0.1451969143
72.5 1 1.6298050306
73 NA -0.0307469467
74 1 0.3730017941
75 NA -0.4908003566
76 1 -0.9888876620
76.1 2 0.0003798292
76.2 2 -0.8421863763
77 NA -0.4986802480
78 1 0.0417330969
79 3 -0.3767450660
79.1 3 0.1516000028
79.2 NA -0.1888160741
80 3 -0.0041558414
80.1 2 -0.0329337062
80.2 NA 0.5046816157
81 1 -0.9493950353
81.1 2 0.2443038954
81.2 1 0.6476958410
81.3 1 0.4182528210
82 3 1.1088801952
82.1 1 0.9334157763
82.2 1 0.4958140634
83 2 0.5104724530
83.1 3 -0.0513309106
83.2 2 -0.2067792494
83.3 3 -0.0534169155
84 1 -0.0255753653
84.1 2 -1.8234189877
85 2 -0.0114038622
85.1 1 -0.0577615939
85.2 1 -0.2241856342
85.3 NA -0.0520175929
85.4 2 0.2892733846
85.5 1 -0.3740417009
86 1 0.4293735089
86.1 NA -0.1363456521
86.2 2 0.1230989293
86.3 1 0.3305413955
86.4 2 2.6003411822
86.5 2 -0.1420690052
87 NA 1.0457427869
87.1 1 -0.2973007190
87.2 NA 0.4396872616
88 1 -0.0601928334
88.1 2 -1.0124347595
88.2 NA 0.5730917016
88.3 2 -0.0029455332
89 3 1.5465903721
90 3 0.0626760573
90.1 2 1.1896872985
90.2 NA 0.2597888783
90.3 2 0.6599799887
91 3 1.1213651365
91.1 1 1.2046371625
91.2 3 0.3395603754
92 2 0.4674939332
93 2 0.2677965647
93.1 3 1.6424445368
93.2 NA 0.7101700066
93.3 2 1.1222322893
93.4 3 1.4628960401
94 2 -0.2904211940
94.1 2 0.0147813580
94.2 1 -0.4536774482
94.3 2 0.6793464917
94.4 1 -0.9411356550
94.5 2 0.5683867264
95 2 0.2375652188
95.1 2 0.0767152977
95.2 NA -0.6886731251
96 1 0.7813892121
96.1 1 0.3391519695
96.2 2 -0.4857246503
96.3 3 0.8771471244
96.4 2 1.9030768981
96.5 NA -0.1684332749
97 1 1.3775130083
97.1 2 -1.7323228619
98 3 -1.2648518889
98.1 2 -0.9042716241
98.2 2 -0.1560385207
99 2 0.7993356425
99.1 2 1.0355522332
99.2 1 -0.1150895843
100 1 0.0369067906
100.1 2 1.6023713093
100.2 3 0.8861545820
100.3 2 0.1277046316
100.4 1 -0.0834577654
$m1d$spM_lvlone
center scale
m2 NA NA
c1 0.2559996 0.6718095
$m1d$mu_reg_multinomial
[1] 0
$m1d$tau_reg_multinomial
[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"
$m2a
$m2a$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
$m2a$M_lvlone
m1
1 3
1.1 2
1.2 1
1.3 1
2 2
2.1 2
2.2 1
3 1
3.1 2
3.2 2
4 2
4.1 1
4.2 2
4.3 3
5 2
5.1 1
5.2 2
5.3 2
6 2
7 3
7.1 2
7.2 3
8 2
8.1 1
8.2 3
8.3 2
8.4 2
8.5 2
9 3
9.1 2
9.2 3
10 3
10.1 1
11 1
11.1 1
11.2 2
11.3 3
11.4 1
12 1
13 2
13.1 3
14 1
14.1 1
14.2 1
14.3 3
15 1
15.1 1
15.2 3
15.3 2
16 2
16.1 2
16.2 1
16.3 3
16.4 2
16.5 1
17 2
17.1 3
17.2 1
17.3 1
17.4 2
18 1
19 2
19.1 3
19.2 2
19.3 3
20 2
20.1 2
20.2 1
20.3 3
20.4 2
20.5 3
21 1
21.1 2
21.2 3
22 2
22.1 2
23 2
23.1 1
24 1
25 1
25.1 3
25.2 2
25.3 2
25.4 1
25.5 1
26 2
26.1 1
26.2 1
26.3 2
27 1
27.1 3
28 1
28.1 3
28.2 1
28.3 1
29 3
29.1 3
29.2 3
29.3 2
30 1
30.1 3
30.2 3
31 1
32 3
32.1 3
32.2 2
32.3 1
33 3
33.1 1
34 1
34.1 1
34.2 2
34.3 2
35 1
35.1 1
35.2 1
36 2
36.1 3
36.2 3
36.3 3
36.4 3
37 1
37.1 3
37.2 1
38 2
39 2
39.1 3
39.2 1
39.3 2
39.4 3
39.5 3
40 3
40.1 3
40.2 1
40.3 3
41 3
41.1 3
41.2 1
41.3 1
41.4 1
42 1
42.1 1
43 3
43.1 3
43.2 2
44 2
44.1 2
44.2 1
44.3 1
45 2
45.1 3
46 3
46.1 2
46.2 3
47 1
47.1 2
47.2 2
47.3 2
47.4 2
48 3
48.1 1
49 3
50 1
51 3
52 3
52.1 2
52.2 1
52.3 3
52.4 3
52.5 3
53 1
53.1 3
53.2 2
54 3
54.1 3
54.2 3
54.3 1
54.4 1
55 1
55.1 3
55.2 2
55.3 1
55.4 1
56 2
56.1 1
56.2 3
56.3 1
56.4 2
56.5 1
57 1
57.1 1
57.2 1
57.3 1
58 3
58.1 2
58.2 1
58.3 3
58.4 3
58.5 3
59 3
59.1 1
60 3
61 1
61.1 2
61.2 2
61.3 3
61.4 2
62 2
62.1 1
62.2 3
62.3 2
63 3
63.1 1
64 3
65 3
65.1 3
65.2 2
65.3 3
66 3
66.1 3
66.2 1
67 3
68 3
68.1 1
68.2 2
68.3 3
68.4 1
69 1
70 1
70.1 2
71 3
71.1 2
71.2 2
71.3 1
71.4 2
72 1
72.1 2
72.2 1
72.3 2
72.4 2
72.5 1
73 2
74 1
75 3
76 3
76.1 3
76.2 2
77 2
78 2
79 2
79.1 2
79.2 2
80 2
80.1 1
80.2 3
81 2
81.1 3
81.2 2
81.3 1
82 1
82.1 2
82.2 3
83 2
83.1 3
83.2 3
83.3 3
84 2
84.1 3
85 1
85.1 2
85.2 3
85.3 3
85.4 2
85.5 2
86 1
86.1 2
86.2 1
86.3 1
86.4 1
86.5 2
87 3
87.1 3
87.2 2
88 3
88.1 3
88.2 3
88.3 1
89 2
90 1
90.1 2
90.2 2
90.3 2
91 3
91.1 3
91.2 3
92 2
93 2
93.1 2
93.2 2
93.3 3
93.4 2
94 2
94.1 3
94.2 3
94.3 2
94.4 3
94.5 2
95 2
95.1 3
95.2 2
96 3
96.1 2
96.2 3
96.3 2
96.4 2
96.5 3
97 3
97.1 3
98 2
98.1 3
98.2 1
99 2
99.1 1
99.2 3
100 2
100.1 1
100.2 2
100.3 2
100.4 3
$m2a$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m2a$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
$m2a$mu_reg_multinomial
[1] 0
$m2a$tau_reg_multinomial
[1] 1e-04
$m2a$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
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
$m2b$M_lvlone
m2
1 3
1.1 1
1.2 3
1.3 1
2 2
2.1 1
2.2 NA
3 3
3.1 2
3.2 1
4 1
4.1 2
4.2 3
4.3 3
5 2
5.1 3
5.2 1
5.3 1
6 2
7 2
7.1 1
7.2 3
8 2
8.1 2
8.2 1
8.3 3
8.4 NA
8.5 3
9 NA
9.1 3
9.2 1
10 1
10.1 1
11 1
11.1 1
11.2 1
11.3 NA
11.4 1
12 1
13 2
13.1 2
14 3
14.1 2
14.2 1
14.3 1
15 1
15.1 2
15.2 3
15.3 3
16 2
16.1 NA
16.2 3
16.3 2
16.4 3
16.5 1
17 1
17.1 3
17.2 NA
17.3 2
17.4 1
18 3
19 NA
19.1 1
19.2 3
19.3 3
20 2
20.1 NA
20.2 3
20.3 1
20.4 3
20.5 2
21 3
21.1 1
21.2 NA
22 3
22.1 1
23 1
23.1 2
24 2
25 2
25.1 3
25.2 3
25.3 1
25.4 3
25.5 2
26 NA
26.1 3
26.2 3
26.3 NA
27 3
27.1 3
28 3
28.1 2
28.2 2
28.3 3
29 1
29.1 NA
29.2 2
29.3 2
30 2
30.1 3
30.2 3
31 3
32 3
32.1 3
32.2 1
32.3 1
33 3
33.1 3
34 3
34.1 NA
34.2 1
34.3 NA
35 2
35.1 2
35.2 2
36 3
36.1 3
36.2 3
36.3 2
36.4 2
37 2
37.1 2
37.2 1
38 2
39 3
39.1 2
39.2 3
39.3 NA
39.4 3
39.5 3
40 3
40.1 1
40.2 3
40.3 2
41 3
41.1 3
41.2 1
41.3 2
41.4 3
42 2
42.1 NA
43 3
43.1 3
43.2 2
44 3
44.1 3
44.2 NA
44.3 1
45 3
45.1 1
46 NA
46.1 1
46.2 2
47 2
47.1 NA
47.2 NA
47.3 3
47.4 3
48 3
48.1 1
49 1
50 NA
51 1
52 2
52.1 1
52.2 1
52.3 NA
52.4 2
52.5 3
53 2
53.1 1
53.2 2
54 NA
54.1 1
54.2 NA
54.3 3
54.4 3
55 1
55.1 1
55.2 1
55.3 NA
55.4 2
56 2
56.1 3
56.2 1
56.3 1
56.4 2
56.5 NA
57 2
57.1 3
57.2 2
57.3 NA
58 1
58.1 1
58.2 NA
58.3 1
58.4 2
58.5 NA
59 1
59.1 1
60 1
61 2
61.1 1
61.2 1
61.3 2
61.4 2
62 1
62.1 1
62.2 NA
62.3 1
63 NA
63.1 3
64 3
65 NA
65.1 2
65.2 3
65.3 3
66 3
66.1 3
66.2 1
67 NA
68 1
68.1 1
68.2 1
68.3 2
68.4 3
69 NA
70 1
70.1 NA
71 1
71.1 1
71.2 NA
71.3 1
71.4 1
72 2
72.1 3
72.2 2
72.3 1
72.4 2
72.5 1
73 NA
74 1
75 NA
76 1
76.1 2
76.2 2
77 NA
78 1
79 3
79.1 3
79.2 NA
80 3
80.1 2
80.2 NA
81 1
81.1 2
81.2 1
81.3 1
82 3
82.1 1
82.2 1
83 2
83.1 3
83.2 2
83.3 3
84 1
84.1 2
85 2
85.1 1
85.2 1
85.3 NA
85.4 2
85.5 1
86 1
86.1 NA
86.2 2
86.3 1
86.4 2
86.5 2
87 NA
87.1 1
87.2 NA
88 1
88.1 2
88.2 NA
88.3 2
89 3
90 3
90.1 2
90.2 NA
90.3 2
91 3
91.1 1
91.2 3
92 2
93 2
93.1 3
93.2 NA
93.3 2
93.4 3
94 2
94.1 2
94.2 1
94.3 2
94.4 1
94.5 2
95 2
95.1 2
95.2 NA
96 1
96.1 1
96.2 2
96.3 3
96.4 2
96.5 NA
97 1
97.1 2
98 3
98.1 2
98.2 2
99 2
99.1 2
99.2 1
100 1
100.1 2
100.2 3
100.3 2
100.4 1
$m2b$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m2b$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_multinomial
[1] 0
$m2b$tau_reg_multinomial
[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
m1 c2
1 3 NA
1.1 2 -0.08061445
1.2 1 -0.26523782
1.3 1 -0.30260393
2 2 -0.33443795
2.1 2 -0.11819800
2.2 1 -0.31532280
3 1 -0.12920657
3.1 2 NA
3.2 2 NA
4 2 -0.31177403
4.1 1 -0.23894886
4.2 2 -0.15533613
4.3 3 -0.14644545
5 2 -0.28360457
5.1 1 -0.20135143
5.2 2 -0.28293375
5.3 2 NA
6 2 -0.08617066
7 3 -0.22243495
7.1 2 NA
7.2 3 NA
8 2 NA
8.1 1 NA
8.2 3 NA
8.3 2 -0.35148972
8.4 2 0.03661023
8.5 2 -0.08424534
9 3 NA
9.1 2 -0.43509340
9.2 3 -0.22527490
10 3 NA
10.1 1 NA
11 1 -0.08587475
11.1 1 -0.06157340
11.2 2 -0.12436018
11.3 3 -0.21377934
11.4 1 -0.32208329
12 1 NA
13 2 NA
13.1 3 -0.40300449
14 1 -0.28992072
14.1 1 NA
14.2 1 NA
14.3 3 -0.21979936
15 1 NA
15.1 1 -0.29092263
15.2 3 -0.19392239
15.3 2 -0.25718384
16 2 -0.45041108
16.1 2 -0.07599066
16.2 1 -0.32385667
16.3 3 -0.38326110
16.4 2 -0.22845856
16.5 1 -0.25497157
17 2 NA
17.1 3 -0.22105143
17.2 1 NA
17.3 1 NA
17.4 2 -0.15098046
18 1 -0.09870041
19 2 -0.26680239
19.1 3 -0.15815241
19.2 2 -0.14717437
19.3 3 -0.21271374
20 2 -0.22087628
20.1 2 NA
20.2 1 -0.30127439
20.3 3 -0.11782590
20.4 2 -0.19857957
20.5 3 -0.24338208
21 1 -0.31407992
21.1 2 -0.12424941
21.2 3 -0.27672716
22 2 -0.23790593
22.1 2 -0.15996535
23 2 -0.18236682
23.1 1 -0.20823302
24 1 -0.29026416
25 1 -0.36139273
25.1 3 -0.19571118
25.2 2 -0.21379355
25.3 2 -0.33876012
25.4 1 NA
25.5 1 -0.04068446
26 2 -0.16846716
26.1 1 -0.10440642
26.2 1 -0.26884827
26.3 2 NA
27 1 -0.19520794
27.1 3 -0.17622638
28 1 -0.32164962
28.1 3 -0.27003852
28.2 1 -0.07235801
28.3 1 -0.13462982
29 3 -0.32432030
29.1 3 -0.27034171
29.2 3 -0.10197448
29.3 2 -0.27606945
30 1 -0.06949300
30.1 3 -0.11511035
30.2 3 -0.16215882
31 1 0.05707733
32 3 -0.18446298
32.1 3 -0.14270013
32.2 2 -0.20530798
32.3 1 -0.14705649
33 3 -0.15252819
33.1 1 NA
34 1 -0.30378735
34.1 1 -0.11982431
34.2 2 -0.24278671
34.3 2 -0.19971833
35 1 NA
35.1 1 -0.24165780
35.2 1 NA
36 2 -0.49062180
36.1 3 -0.25651700
36.2 3 NA
36.3 3 -0.30401274
36.4 3 NA
37 1 -0.15276529
37.1 3 -0.30016169
37.2 1 0.06809545
38 2 -0.11218486
39 2 -0.38072211
39.1 3 -0.32094428
39.2 1 NA
39.3 2 -0.40173480
39.4 3 -0.20041848
39.5 3 -0.26027990
40 3 -0.19751956
40.1 3 -0.08399467
40.2 1 -0.20864416
40.3 3 NA
41 3 -0.26096953
41.1 3 -0.23953874
41.2 1 -0.03079344
41.3 1 NA
41.4 1 NA
42 1 -0.16084527
42.1 1 -0.13812521
43 3 -0.08864017
43.1 3 -0.12583158
43.2 2 -0.29253959
44 2 -0.22697597
44.1 2 NA
44.2 1 NA
44.3 1 -0.40544012
45 2 -0.19274788
45.1 3 -0.34860483
46 3 -0.28547861
46.1 2 -0.21977836
46.2 3 NA
47 1 -0.08597098
47.1 2 -0.35424828
47.2 2 -0.24262576
47.3 2 -0.30426315
47.4 2 NA
48 3 NA
48.1 1 NA
49 3 -0.42198781
50 1 -0.19959516
51 3 -0.16556964
52 3 -0.07438732
52.1 2 -0.37537080
52.2 1 -0.24222066
52.3 3 -0.31520603
52.4 3 -0.44619160
52.5 3 -0.11011682
53 1 -0.23278716
53.1 3 -0.28317264
53.2 2 -0.19517481
54 3 -0.10122856
54.1 3 -0.28325504
54.2 3 -0.16753120
54.3 1 -0.22217672
54.4 1 -0.34609328
55 1 -0.32428190
55.1 3 -0.24235382
55.2 2 -0.24065814
55.3 1 -0.23665476
55.4 1 NA
56 2 NA
56.1 1 -0.30357450
56.2 3 -0.51301630
56.3 1 -0.23743117
56.4 2 -0.17264917
56.5 1 -0.39188329
57 1 -0.18501692
57.1 1 -0.27274841
57.2 1 NA
57.3 1 -0.09898509
58 3 -0.29901358
58.1 2 -0.35390896
58.2 1 -0.16687336
58.3 3 -0.11784506
58.4 3 -0.05321983
58.5 3 -0.54457568
59 3 -0.27255364
59.1 1 NA
60 3 NA
61 1 -0.30550120
61.1 2 -0.35579892
61.2 2 NA
61.3 3 -0.34184391
61.4 2 -0.30891967
62 2 NA
62.1 1 -0.10504143
62.2 3 -0.20104997
62.3 2 -0.08138677
63 3 -0.12036319
63.1 1 -0.13624992
64 3 NA
65 3 -0.34450396
65.1 3 -0.32514650
65.2 2 -0.10984996
65.3 3 -0.19275692
66 3 NA
66.1 3 NA
66.2 1 -0.11687008
67 3 NA
68 3 -0.13605235
68.1 1 -0.19790827
68.2 2 -0.17750123
68.3 3 NA
68.4 1 -0.12570562
69 1 -0.32152751
70 1 -0.28190462
70.1 2 -0.11503263
71 3 -0.13029093
71.1 2 NA
71.2 2 -0.39075433
71.3 1 -0.21401028
71.4 2 -0.40219281
72 1 -0.40337108
72.1 2 -0.25978914
72.2 1 NA
72.3 2 -0.09809866
72.4 2 -0.14240019
72.5 1 -0.14794204
73 2 -0.23509343
74 1 -0.27963171
75 3 -0.12905034
76 3 0.04775562
76.1 3 -0.19399157
76.2 2 -0.02754574
77 2 -0.19053195
78 2 -0.17172929
79 2 -0.03958515
79.1 2 -0.20328809
79.2 2 -0.23901634
80 2 -0.34031873
80.1 1 -0.19526756
80.2 3 NA
81 2 -0.18401980
81.1 3 -0.16889476
81.2 2 -0.37343047
81.3 1 NA
82 1 -0.08328168
82.1 2 -0.22167084
82.2 3 -0.20971187
83 2 -0.34228255
83.1 3 -0.34075730
83.2 3 -0.32503954
83.3 3 NA
84 2 -0.20676741
84.1 3 -0.20310458
85 1 -0.12107593
85.1 2 NA
85.2 3 -0.32509207
85.3 3 NA
85.4 2 -0.30730810
85.5 2 NA
86 1 -0.10854862
86.1 2 -0.25751662
86.2 1 -0.38943076
86.3 1 -0.24454702
86.4 1 -0.12338992
86.5 2 -0.23976984
87 3 NA
87.1 3 -0.34366972
87.2 2 NA
88 3 -0.31563888
88.1 3 -0.20304028
88.2 3 -0.40311895
88.3 1 -0.12308715
89 2 -0.18527715
90 1 -0.25029126
90.1 2 -0.26974303
90.2 2 -0.28804531
90.3 2 -0.19180615
91 3 -0.26591197
91.1 3 -0.09153470
91.2 3 -0.48414390
92 2 NA
93 2 -0.11939966
93.1 2 NA
93.2 2 -0.21089379
93.3 3 NA
93.4 2 -0.23618836
94 2 NA
94.1 3 -0.10217284
94.2 3 -0.36713471
94.3 2 -0.13806763
94.4 3 -0.42353804
94.5 2 -0.15513707
95 2 -0.24149687
95.1 3 -0.21315958
95.2 2 -0.15777208
96 3 -0.16780948
96.1 2 -0.32504815
96.2 3 -0.20395970
96.3 2 -0.06221501
96.4 2 -0.14801097
96.5 3 -0.28658893
97 3 -0.34484656
97.1 3 -0.35658805
98 2 -0.36913003
98.1 3 NA
98.2 1 -0.17154225
99 2 -0.24753132
99.1 1 -0.27947829
99.2 3 -0.09033035
100 2 -0.17326698
100.1 1 NA
100.2 2 -0.12072016
100.3 2 -0.27657520
100.4 3 -0.14631556
$m2c$spM_lvlone
center scale
m1 NA NA
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_multinomial
[1] 0
$m2c$tau_reg_multinomial
[1] 1e-04
$m2c$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
m2 c2
1 3 NA
1.1 1 -0.08061445
1.2 3 -0.26523782
1.3 1 -0.30260393
2 2 -0.33443795
2.1 1 -0.11819800
2.2 NA -0.31532280
3 3 -0.12920657
3.1 2 NA
3.2 1 NA
4 1 -0.31177403
4.1 2 -0.23894886
4.2 3 -0.15533613
4.3 3 -0.14644545
5 2 -0.28360457
5.1 3 -0.20135143
5.2 1 -0.28293375
5.3 1 NA
6 2 -0.08617066
7 2 -0.22243495
7.1 1 NA
7.2 3 NA
8 2 NA
8.1 2 NA
8.2 1 NA
8.3 3 -0.35148972
8.4 NA 0.03661023
8.5 3 -0.08424534
9 NA NA
9.1 3 -0.43509340
9.2 1 -0.22527490
10 1 NA
10.1 1 NA
11 1 -0.08587475
11.1 1 -0.06157340
11.2 1 -0.12436018
11.3 NA -0.21377934
11.4 1 -0.32208329
12 1 NA
13 2 NA
13.1 2 -0.40300449
14 3 -0.28992072
14.1 2 NA
14.2 1 NA
14.3 1 -0.21979936
15 1 NA
15.1 2 -0.29092263
15.2 3 -0.19392239
15.3 3 -0.25718384
16 2 -0.45041108
16.1 NA -0.07599066
16.2 3 -0.32385667
16.3 2 -0.38326110
16.4 3 -0.22845856
16.5 1 -0.25497157
17 1 NA
17.1 3 -0.22105143
17.2 NA NA
17.3 2 NA
17.4 1 -0.15098046
18 3 -0.09870041
19 NA -0.26680239
19.1 1 -0.15815241
19.2 3 -0.14717437
19.3 3 -0.21271374
20 2 -0.22087628
20.1 NA NA
20.2 3 -0.30127439
20.3 1 -0.11782590
20.4 3 -0.19857957
20.5 2 -0.24338208
21 3 -0.31407992
21.1 1 -0.12424941
21.2 NA -0.27672716
22 3 -0.23790593
22.1 1 -0.15996535
23 1 -0.18236682
23.1 2 -0.20823302
24 2 -0.29026416
25 2 -0.36139273
25.1 3 -0.19571118
25.2 3 -0.21379355
25.3 1 -0.33876012
25.4 3 NA
25.5 2 -0.04068446
26 NA -0.16846716
26.1 3 -0.10440642
26.2 3 -0.26884827
26.3 NA NA
27 3 -0.19520794
27.1 3 -0.17622638
28 3 -0.32164962
28.1 2 -0.27003852
28.2 2 -0.07235801
28.3 3 -0.13462982
29 1 -0.32432030
29.1 NA -0.27034171
29.2 2 -0.10197448
29.3 2 -0.27606945
30 2 -0.06949300
30.1 3 -0.11511035
30.2 3 -0.16215882
31 3 0.05707733
32 3 -0.18446298
32.1 3 -0.14270013
32.2 1 -0.20530798
32.3 1 -0.14705649
33 3 -0.15252819
33.1 3 NA
34 3 -0.30378735
34.1 NA -0.11982431
34.2 1 -0.24278671
34.3 NA -0.19971833
35 2 NA
35.1 2 -0.24165780
35.2 2 NA
36 3 -0.49062180
36.1 3 -0.25651700
36.2 3 NA
36.3 2 -0.30401274
36.4 2 NA
37 2 -0.15276529
37.1 2 -0.30016169
37.2 1 0.06809545
38 2 -0.11218486
39 3 -0.38072211
39.1 2 -0.32094428
39.2 3 NA
39.3 NA -0.40173480
39.4 3 -0.20041848
39.5 3 -0.26027990
40 3 -0.19751956
40.1 1 -0.08399467
40.2 3 -0.20864416
40.3 2 NA
41 3 -0.26096953
41.1 3 -0.23953874
41.2 1 -0.03079344
41.3 2 NA
41.4 3 NA
42 2 -0.16084527
42.1 NA -0.13812521
43 3 -0.08864017
43.1 3 -0.12583158
43.2 2 -0.29253959
44 3 -0.22697597
44.1 3 NA
44.2 NA NA
44.3 1 -0.40544012
45 3 -0.19274788
45.1 1 -0.34860483
46 NA -0.28547861
46.1 1 -0.21977836
46.2 2 NA
47 2 -0.08597098
47.1 NA -0.35424828
47.2 NA -0.24262576
47.3 3 -0.30426315
47.4 3 NA
48 3 NA
48.1 1 NA
49 1 -0.42198781
50 NA -0.19959516
51 1 -0.16556964
52 2 -0.07438732
52.1 1 -0.37537080
52.2 1 -0.24222066
52.3 NA -0.31520603
52.4 2 -0.44619160
52.5 3 -0.11011682
53 2 -0.23278716
53.1 1 -0.28317264
53.2 2 -0.19517481
54 NA -0.10122856
54.1 1 -0.28325504
54.2 NA -0.16753120
54.3 3 -0.22217672
54.4 3 -0.34609328
55 1 -0.32428190
55.1 1 -0.24235382
55.2 1 -0.24065814
55.3 NA -0.23665476
55.4 2 NA
56 2 NA
56.1 3 -0.30357450
56.2 1 -0.51301630
56.3 1 -0.23743117
56.4 2 -0.17264917
56.5 NA -0.39188329
57 2 -0.18501692
57.1 3 -0.27274841
57.2 2 NA
57.3 NA -0.09898509
58 1 -0.29901358
58.1 1 -0.35390896
58.2 NA -0.16687336
58.3 1 -0.11784506
58.4 2 -0.05321983
58.5 NA -0.54457568
59 1 -0.27255364
59.1 1 NA
60 1 NA
61 2 -0.30550120
61.1 1 -0.35579892
61.2 1 NA
61.3 2 -0.34184391
61.4 2 -0.30891967
62 1 NA
62.1 1 -0.10504143
62.2 NA -0.20104997
62.3 1 -0.08138677
63 NA -0.12036319
63.1 3 -0.13624992
64 3 NA
65 NA -0.34450396
65.1 2 -0.32514650
65.2 3 -0.10984996
65.3 3 -0.19275692
66 3 NA
66.1 3 NA
66.2 1 -0.11687008
67 NA NA
68 1 -0.13605235
68.1 1 -0.19790827
68.2 1 -0.17750123
68.3 2 NA
68.4 3 -0.12570562
69 NA -0.32152751
70 1 -0.28190462
70.1 NA -0.11503263
71 1 -0.13029093
71.1 1 NA
71.2 NA -0.39075433
71.3 1 -0.21401028
71.4 1 -0.40219281
72 2 -0.40337108
72.1 3 -0.25978914
72.2 2 NA
72.3 1 -0.09809866
72.4 2 -0.14240019
72.5 1 -0.14794204
73 NA -0.23509343
74 1 -0.27963171
75 NA -0.12905034
76 1 0.04775562
76.1 2 -0.19399157
76.2 2 -0.02754574
77 NA -0.19053195
78 1 -0.17172929
79 3 -0.03958515
79.1 3 -0.20328809
79.2 NA -0.23901634
80 3 -0.34031873
80.1 2 -0.19526756
80.2 NA NA
81 1 -0.18401980
81.1 2 -0.16889476
81.2 1 -0.37343047
81.3 1 NA
82 3 -0.08328168
82.1 1 -0.22167084
82.2 1 -0.20971187
83 2 -0.34228255
83.1 3 -0.34075730
83.2 2 -0.32503954
83.3 3 NA
84 1 -0.20676741
84.1 2 -0.20310458
85 2 -0.12107593
85.1 1 NA
85.2 1 -0.32509207
85.3 NA NA
85.4 2 -0.30730810
85.5 1 NA
86 1 -0.10854862
86.1 NA -0.25751662
86.2 2 -0.38943076
86.3 1 -0.24454702
86.4 2 -0.12338992
86.5 2 -0.23976984
87 NA NA
87.1 1 -0.34366972
87.2 NA NA
88 1 -0.31563888
88.1 2 -0.20304028
88.2 NA -0.40311895
88.3 2 -0.12308715
89 3 -0.18527715
90 3 -0.25029126
90.1 2 -0.26974303
90.2 NA -0.28804531
90.3 2 -0.19180615
91 3 -0.26591197
91.1 1 -0.09153470
91.2 3 -0.48414390
92 2 NA
93 2 -0.11939966
93.1 3 NA
93.2 NA -0.21089379
93.3 2 NA
93.4 3 -0.23618836
94 2 NA
94.1 2 -0.10217284
94.2 1 -0.36713471
94.3 2 -0.13806763
94.4 1 -0.42353804
94.5 2 -0.15513707
95 2 -0.24149687
95.1 2 -0.21315958
95.2 NA -0.15777208
96 1 -0.16780948
96.1 1 -0.32504815
96.2 2 -0.20395970
96.3 3 -0.06221501
96.4 2 -0.14801097
96.5 NA -0.28658893
97 1 -0.34484656
97.1 2 -0.35658805
98 3 -0.36913003
98.1 2 NA
98.2 2 -0.17154225
99 2 -0.24753132
99.1 2 -0.27947829
99.2 1 -0.09033035
100 1 -0.17326698
100.1 2 NA
100.2 3 -0.12072016
100.3 2 -0.27657520
100.4 1 -0.14631556
$m2d$spM_lvlone
center scale
m2 NA NA
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_multinomial
[1] 0
$m2d$tau_reg_multinomial
[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"
$m3a
$m3a$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
$m3a$M_lvlone
c1 m1B m1C
1 0.7592026489 0 1
1.1 0.9548337990 1 0
1.2 0.5612235156 0 0
1.3 1.1873391025 0 0
2 0.9192204198 1 0
2.1 -0.1870730476 1 0
2.2 1.2517512331 0 0
3 -0.0605087604 0 0
3.1 0.3788637747 1 0
3.2 0.9872578281 1 0
4 1.4930175328 1 0
4.1 -0.7692526880 0 0
4.2 0.9180841450 1 0
4.3 -0.0541170782 0 1
5 -0.1376784521 1 0
5.1 -0.2740585866 0 0
5.2 0.4670496929 1 0
5.3 0.1740288049 1 0
6 0.9868044683 1 0
7 -0.1280320918 0 1
7.1 0.4242971219 1 0
7.2 0.0777182491 0 1
8 -0.5791408712 1 0
8.1 0.3128604232 0 0
8.2 0.6258446356 0 1
8.3 -0.1040137707 1 0
8.4 0.0481450285 1 0
8.5 0.3831763675 1 0
9 -0.1757592269 0 1
9.1 -0.1791541200 1 0
9.2 -0.0957042935 0 1
10 -0.5598409704 0 1
10.1 -0.2318340451 0 0
11 0.5086859475 0 0
11.1 0.4951758188 0 0
11.2 -1.1022162541 1 0
11.3 -0.0611636705 0 1
11.4 -0.4971774316 0 0
12 -0.2433996286 0 0
13 0.8799673116 1 0
13.1 0.1079022586 0 1
14 0.9991752617 0 0
14.1 -0.1094019046 0 0
14.2 0.1518967560 0 0
14.3 0.3521012473 0 1
15 0.3464447888 0 0
15.1 -0.4767313971 0 0
15.2 0.5759767791 0 1
15.3 -0.1713452662 1 0
16 0.4564754473 1 0
16.1 1.0652558311 1 0
16.2 0.6971872493 0 0
16.3 0.5259331838 0 1
16.4 0.2046601798 1 0
16.5 1.0718540464 0 0
17 0.6048676222 1 0
17.1 0.2323298304 0 1
17.2 1.2617499032 0 0
17.3 -0.3913230895 0 0
17.4 0.9577299112 1 0
18 -0.0050324072 0 0
19 -0.4187468937 1 0
19.1 -0.4478828944 0 1
19.2 -1.1966721302 1 0
19.3 -0.5877091668 0 1
20 0.6838223064 1 0
20.1 0.3278571109 1 0
20.2 -0.8489831990 0 0
20.3 1.3169975191 0 1
20.4 0.0444804531 1 0
20.5 -0.4535207652 0 1
21 -0.4030302960 0 0
21.1 -0.4069674045 1 0
21.2 1.0650265940 0 1
22 -0.0673274516 1 0
22.1 0.9601388170 1 0
23 0.5556634840 1 0
23.1 1.4407865964 0 0
24 0.3856376411 0 0
25 0.3564400705 0 0
25.1 0.0982553434 0 1
25.2 0.1928682598 1 0
25.3 -0.0192488594 1 0
25.4 0.4466012931 0 0
25.5 1.1425193342 0 0
26 0.5341531449 1 0
26.1 1.2268695927 0 0
26.2 0.3678294939 0 0
26.3 0.5948516018 1 0
27 -0.3342844147 0 0
27.1 -0.4835141229 0 1
28 -0.7145915499 0 0
28.1 0.5063671955 0 1
28.2 -0.2067413142 0 0
28.3 0.1196789973 0 0
29 0.1392699487 0 1
29.1 0.7960234776 0 1
29.2 1.0398214352 0 1
29.3 0.0813246429 1 0
30 -0.3296323050 0 0
30.1 1.3635850954 0 1
30.2 0.7354171050 0 1
31 0.3708398217 0 0
32 -0.0474059668 0 1
32.1 1.2507771489 0 1
32.2 0.1142915519 1 0
32.3 0.6773270619 0 0
33 0.1774293842 0 1
33.1 0.6159606291 0 0
34 0.8590979166 0 0
34.1 0.0546216775 0 0
34.2 -0.0897224473 1 0
34.3 0.4163395571 1 0
35 -1.4693520528 0 0
35.1 -0.3031734330 0 0
35.2 -0.6045512101 0 0
36 0.9823048960 1 0
36.1 1.4466051416 0 1
36.2 1.1606752905 0 1
36.3 0.8373091576 0 1
36.4 0.2640591685 0 1
37 0.1177313455 0 0
37.1 -0.1415483779 0 1
37.2 0.0054610124 0 0
38 0.8078948077 1 0
39 0.9876451040 1 0
39.1 -0.3431222274 0 1
39.2 -1.7909380751 0 0
39.3 -0.1798746191 1 0
39.4 -0.1850961689 0 1
39.5 0.4544226146 0 1
40 0.5350190436 0 1
40.1 0.4189342752 0 1
40.2 0.4211994981 0 0
40.3 0.0916687506 0 1
41 -0.1035047421 0 1
41.1 -0.4684202411 0 1
41.2 0.5972615368 0 0
41.3 0.9885613862 0 0
41.4 -0.3908036794 0 0
42 -0.0338893961 0 0
42.1 -0.4498363172 0 0
43 0.8965546110 0 1
43.1 0.6199122090 0 1
43.2 0.1804894429 1 0
44 1.3221409285 1 0
44.1 0.3416426284 1 0
44.2 0.5706610068 0 0
44.3 1.2679497430 0 0
45 0.1414983160 1 0
45.1 0.7220892521 0 1
46 1.5391054233 0 1
46.1 0.3889107049 1 0
46.2 0.1248719493 0 1
47 0.2014101100 0 0
47.1 0.2982973539 1 0
47.2 1.1518107179 1 0
47.3 0.5196802157 1 0
47.4 0.3702301552 1 0
48 -0.2128602862 0 1
48.1 -0.5337239976 0 0
49 -0.5236770035 0 1
50 0.3897705981 0 0
51 -0.7213343736 0 1
52 0.3758235358 0 1
52.1 0.7138067080 1 0
52.2 0.8872895233 0 0
52.3 -0.9664587437 0 1
52.4 0.0254566848 0 1
52.5 0.4155259424 0 1
53 0.5675736897 0 0
53.1 -0.3154088781 0 1
53.2 0.2162315769 1 0
54 -0.0880802382 0 1
54.1 0.4129127672 0 1
54.2 1.0119546775 0 1
54.3 -0.1112901990 0 0
54.4 0.8587727145 0 0
55 -0.0116453589 0 0
55.1 0.5835528661 0 1
55.2 -1.0010857254 1 0
55.3 -0.4796526070 0 0
55.4 -0.1202746964 0 0
56 0.5176377612 1 0
56.1 -1.1136932588 0 0
56.2 -0.0168103281 0 1
56.3 0.3933023606 0 0
56.4 0.3714625139 1 0
56.5 0.7811448179 0 0
57 -1.0868304872 0 0
57.1 0.8018626997 0 0
57.2 -0.1159517011 0 0
57.3 0.6785562445 0 0
58 1.6476207996 0 1
58.1 0.3402652711 1 0
58.2 -0.1111300753 0 0
58.3 -0.5409234285 0 1
58.4 -0.1271327672 0 1
58.5 0.8713264822 0 1
59 0.4766421367 0 1
59.1 1.0028089765 0 0
60 0.5231452932 0 1
61 -0.7190130614 0 0
61.1 0.8353702312 1 0
61.2 1.0229058138 1 0
61.3 1.1717723589 0 1
61.4 -0.0629201596 1 0
62 -0.3979137604 1 0
62.1 0.6830738372 0 0
62.2 0.4301745954 0 1
62.3 -0.0333139957 1 0
63 0.3345678035 0 1
63.1 0.3643769511 0 0
64 0.3949911859 0 1
65 1.2000091513 0 1
65.1 0.0110122646 0 1
65.2 -0.5776452043 1 0
65.3 -0.1372183563 0 1
66 -0.5081302805 0 1
66.1 -0.1447837412 0 1
66.2 0.1906241379 0 0
67 1.6716027681 0 1
68 0.5691848839 0 1
68.1 0.1004860389 0 0
68.2 -0.0061241827 1 0
68.3 0.7443745962 0 1
68.4 0.8726923437 0 0
69 0.0381382683 0 0
70 0.8126204217 0 0
70.1 0.4691503050 1 0
71 -0.5529062591 0 1
71.1 -0.1103252087 1 0
71.2 1.7178492547 1 0
71.3 -1.0118346755 0 0
71.4 1.8623785017 1 0
72 -0.4521659275 0 0
72.1 0.1375317317 1 0
72.2 -0.4170988856 0 0
72.3 0.7107266765 1 0
72.4 0.1451969143 1 0
72.5 1.6298050306 0 0
73 -0.0307469467 1 0
74 0.3730017941 0 0
75 -0.4908003566 0 1
76 -0.9888876620 0 1
76.1 0.0003798292 0 1
76.2 -0.8421863763 1 0
77 -0.4986802480 1 0
78 0.0417330969 1 0
79 -0.3767450660 1 0
79.1 0.1516000028 1 0
79.2 -0.1888160741 1 0
80 -0.0041558414 1 0
80.1 -0.0329337062 0 0
80.2 0.5046816157 0 1
81 -0.9493950353 1 0
81.1 0.2443038954 0 1
81.2 0.6476958410 1 0
81.3 0.4182528210 0 0
82 1.1088801952 0 0
82.1 0.9334157763 1 0
82.2 0.4958140634 0 1
83 0.5104724530 1 0
83.1 -0.0513309106 0 1
83.2 -0.2067792494 0 1
83.3 -0.0534169155 0 1
84 -0.0255753653 1 0
84.1 -1.8234189877 0 1
85 -0.0114038622 0 0
85.1 -0.0577615939 1 0
85.2 -0.2241856342 0 1
85.3 -0.0520175929 0 1
85.4 0.2892733846 1 0
85.5 -0.3740417009 1 0
86 0.4293735089 0 0
86.1 -0.1363456521 1 0
86.2 0.1230989293 0 0
86.3 0.3305413955 0 0
86.4 2.6003411822 0 0
86.5 -0.1420690052 1 0
87 1.0457427869 0 1
87.1 -0.2973007190 0 1
87.2 0.4396872616 1 0
88 -0.0601928334 0 1
88.1 -1.0124347595 0 1
88.2 0.5730917016 0 1
88.3 -0.0029455332 0 0
89 1.5465903721 1 0
90 0.0626760573 0 0
90.1 1.1896872985 1 0
90.2 0.2597888783 1 0
90.3 0.6599799887 1 0
91 1.1213651365 0 1
91.1 1.2046371625 0 1
91.2 0.3395603754 0 1
92 0.4674939332 1 0
93 0.2677965647 1 0
93.1 1.6424445368 1 0
93.2 0.7101700066 1 0
93.3 1.1222322893 0 1
93.4 1.4628960401 1 0
94 -0.2904211940 1 0
94.1 0.0147813580 0 1
94.2 -0.4536774482 0 1
94.3 0.6793464917 1 0
94.4 -0.9411356550 0 1
94.5 0.5683867264 1 0
95 0.2375652188 1 0
95.1 0.0767152977 0 1
95.2 -0.6886731251 1 0
96 0.7813892121 0 1
96.1 0.3391519695 1 0
96.2 -0.4857246503 0 1
96.3 0.8771471244 1 0
96.4 1.9030768981 1 0
96.5 -0.1684332749 0 1
97 1.3775130083 0 1
97.1 -1.7323228619 0 1
98 -1.2648518889 1 0
98.1 -0.9042716241 0 1
98.2 -0.1560385207 0 0
99 0.7993356425 1 0
99.1 1.0355522332 0 0
99.2 -0.1150895843 0 1
100 0.0369067906 1 0
100.1 1.6023713093 0 0
100.2 0.8861545820 1 0
100.3 0.1277046316 1 0
100.4 -0.0834577654 0 1
$m3a$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
(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
$m3b$M_lvlone
c1 m2 m2B m2C
1 0.7592026489 3 NA NA
1.1 0.9548337990 1 NA NA
1.2 0.5612235156 3 NA NA
1.3 1.1873391025 1 NA NA
2 0.9192204198 2 NA NA
2.1 -0.1870730476 1 NA NA
2.2 1.2517512331 NA NA NA
3 -0.0605087604 3 NA NA
3.1 0.3788637747 2 NA NA
3.2 0.9872578281 1 NA NA
4 1.4930175328 1 NA NA
4.1 -0.7692526880 2 NA NA
4.2 0.9180841450 3 NA NA
4.3 -0.0541170782 3 NA NA
5 -0.1376784521 2 NA NA
5.1 -0.2740585866 3 NA NA
5.2 0.4670496929 1 NA NA
5.3 0.1740288049 1 NA NA
6 0.9868044683 2 NA NA
7 -0.1280320918 2 NA NA
7.1 0.4242971219 1 NA NA
7.2 0.0777182491 3 NA NA
8 -0.5791408712 2 NA NA
8.1 0.3128604232 2 NA NA
8.2 0.6258446356 1 NA NA
8.3 -0.1040137707 3 NA NA
8.4 0.0481450285 NA NA NA
8.5 0.3831763675 3 NA NA
9 -0.1757592269 NA NA NA
9.1 -0.1791541200 3 NA NA
9.2 -0.0957042935 1 NA NA
10 -0.5598409704 1 NA NA
10.1 -0.2318340451 1 NA NA
11 0.5086859475 1 NA NA
11.1 0.4951758188 1 NA NA
11.2 -1.1022162541 1 NA NA
11.3 -0.0611636705 NA NA NA
11.4 -0.4971774316 1 NA NA
12 -0.2433996286 1 NA NA
13 0.8799673116 2 NA NA
13.1 0.1079022586 2 NA NA
14 0.9991752617 3 NA NA
14.1 -0.1094019046 2 NA NA
14.2 0.1518967560 1 NA NA
14.3 0.3521012473 1 NA NA
15 0.3464447888 1 NA NA
15.1 -0.4767313971 2 NA NA
15.2 0.5759767791 3 NA NA
15.3 -0.1713452662 3 NA NA
16 0.4564754473 2 NA NA
16.1 1.0652558311 NA NA NA
16.2 0.6971872493 3 NA NA
16.3 0.5259331838 2 NA NA
16.4 0.2046601798 3 NA NA
16.5 1.0718540464 1 NA NA
17 0.6048676222 1 NA NA
17.1 0.2323298304 3 NA NA
17.2 1.2617499032 NA NA NA
17.3 -0.3913230895 2 NA NA
17.4 0.9577299112 1 NA NA
18 -0.0050324072 3 NA NA
19 -0.4187468937 NA NA NA
19.1 -0.4478828944 1 NA NA
19.2 -1.1966721302 3 NA NA
19.3 -0.5877091668 3 NA NA
20 0.6838223064 2 NA NA
20.1 0.3278571109 NA NA NA
20.2 -0.8489831990 3 NA NA
20.3 1.3169975191 1 NA NA
20.4 0.0444804531 3 NA NA
20.5 -0.4535207652 2 NA NA
21 -0.4030302960 3 NA NA
21.1 -0.4069674045 1 NA NA
21.2 1.0650265940 NA NA NA
22 -0.0673274516 3 NA NA
22.1 0.9601388170 1 NA NA
23 0.5556634840 1 NA NA
23.1 1.4407865964 2 NA NA
24 0.3856376411 2 NA NA
25 0.3564400705 2 NA NA
25.1 0.0982553434 3 NA NA
25.2 0.1928682598 3 NA NA
25.3 -0.0192488594 1 NA NA
25.4 0.4466012931 3 NA NA
25.5 1.1425193342 2 NA NA
26 0.5341531449 NA NA NA
26.1 1.2268695927 3 NA NA
26.2 0.3678294939 3 NA NA
26.3 0.5948516018 NA NA NA
27 -0.3342844147 3 NA NA
27.1 -0.4835141229 3 NA NA
28 -0.7145915499 3 NA NA
28.1 0.5063671955 2 NA NA
28.2 -0.2067413142 2 NA NA
28.3 0.1196789973 3 NA NA
29 0.1392699487 1 NA NA
29.1 0.7960234776 NA NA NA
29.2 1.0398214352 2 NA NA
29.3 0.0813246429 2 NA NA
30 -0.3296323050 2 NA NA
30.1 1.3635850954 3 NA NA
30.2 0.7354171050 3 NA NA
31 0.3708398217 3 NA NA
32 -0.0474059668 3 NA NA
32.1 1.2507771489 3 NA NA
32.2 0.1142915519 1 NA NA
32.3 0.6773270619 1 NA NA
33 0.1774293842 3 NA NA
33.1 0.6159606291 3 NA NA
34 0.8590979166 3 NA NA
34.1 0.0546216775 NA NA NA
34.2 -0.0897224473 1 NA NA
34.3 0.4163395571 NA NA NA
35 -1.4693520528 2 NA NA
35.1 -0.3031734330 2 NA NA
35.2 -0.6045512101 2 NA NA
36 0.9823048960 3 NA NA
36.1 1.4466051416 3 NA NA
36.2 1.1606752905 3 NA NA
36.3 0.8373091576 2 NA NA
36.4 0.2640591685 2 NA NA
37 0.1177313455 2 NA NA
37.1 -0.1415483779 2 NA NA
37.2 0.0054610124 1 NA NA
38 0.8078948077 2 NA NA
39 0.9876451040 3 NA NA
39.1 -0.3431222274 2 NA NA
39.2 -1.7909380751 3 NA NA
39.3 -0.1798746191 NA NA NA
39.4 -0.1850961689 3 NA NA
39.5 0.4544226146 3 NA NA
40 0.5350190436 3 NA NA
40.1 0.4189342752 1 NA NA
40.2 0.4211994981 3 NA NA
40.3 0.0916687506 2 NA NA
41 -0.1035047421 3 NA NA
41.1 -0.4684202411 3 NA NA
41.2 0.5972615368 1 NA NA
41.3 0.9885613862 2 NA NA
41.4 -0.3908036794 3 NA NA
42 -0.0338893961 2 NA NA
42.1 -0.4498363172 NA NA NA
43 0.8965546110 3 NA NA
43.1 0.6199122090 3 NA NA
43.2 0.1804894429 2 NA NA
44 1.3221409285 3 NA NA
44.1 0.3416426284 3 NA NA
44.2 0.5706610068 NA NA NA
44.3 1.2679497430 1 NA NA
45 0.1414983160 3 NA NA
45.1 0.7220892521 1 NA NA
46 1.5391054233 NA NA NA
46.1 0.3889107049 1 NA NA
46.2 0.1248719493 2 NA NA
47 0.2014101100 2 NA NA
47.1 0.2982973539 NA NA NA
47.2 1.1518107179 NA NA NA
47.3 0.5196802157 3 NA NA
47.4 0.3702301552 3 NA NA
48 -0.2128602862 3 NA NA
48.1 -0.5337239976 1 NA NA
49 -0.5236770035 1 NA NA
50 0.3897705981 NA NA NA
51 -0.7213343736 1 NA NA
52 0.3758235358 2 NA NA
52.1 0.7138067080 1 NA NA
52.2 0.8872895233 1 NA NA
52.3 -0.9664587437 NA NA NA
52.4 0.0254566848 2 NA NA
52.5 0.4155259424 3 NA NA
53 0.5675736897 2 NA NA
53.1 -0.3154088781 1 NA NA
53.2 0.2162315769 2 NA NA
54 -0.0880802382 NA NA NA
54.1 0.4129127672 1 NA NA
54.2 1.0119546775 NA NA NA
54.3 -0.1112901990 3 NA NA
54.4 0.8587727145 3 NA NA
55 -0.0116453589 1 NA NA
55.1 0.5835528661 1 NA NA
55.2 -1.0010857254 1 NA NA
55.3 -0.4796526070 NA NA NA
55.4 -0.1202746964 2 NA NA
56 0.5176377612 2 NA NA
56.1 -1.1136932588 3 NA NA
56.2 -0.0168103281 1 NA NA
56.3 0.3933023606 1 NA NA
56.4 0.3714625139 2 NA NA
56.5 0.7811448179 NA NA NA
57 -1.0868304872 2 NA NA
57.1 0.8018626997 3 NA NA
57.2 -0.1159517011 2 NA NA
57.3 0.6785562445 NA NA NA
58 1.6476207996 1 NA NA
58.1 0.3402652711 1 NA NA
58.2 -0.1111300753 NA NA NA
58.3 -0.5409234285 1 NA NA
58.4 -0.1271327672 2 NA NA
58.5 0.8713264822 NA NA NA
59 0.4766421367 1 NA NA
59.1 1.0028089765 1 NA NA
60 0.5231452932 1 NA NA
61 -0.7190130614 2 NA NA
61.1 0.8353702312 1 NA NA
61.2 1.0229058138 1 NA NA
61.3 1.1717723589 2 NA NA
61.4 -0.0629201596 2 NA NA
62 -0.3979137604 1 NA NA
62.1 0.6830738372 1 NA NA
62.2 0.4301745954 NA NA NA
62.3 -0.0333139957 1 NA NA
63 0.3345678035 NA NA NA
63.1 0.3643769511 3 NA NA
64 0.3949911859 3 NA NA
65 1.2000091513 NA NA NA
65.1 0.0110122646 2 NA NA
65.2 -0.5776452043 3 NA NA
65.3 -0.1372183563 3 NA NA
66 -0.5081302805 3 NA NA
66.1 -0.1447837412 3 NA NA
66.2 0.1906241379 1 NA NA
67 1.6716027681 NA NA NA
68 0.5691848839 1 NA NA
68.1 0.1004860389 1 NA NA
68.2 -0.0061241827 1 NA NA
68.3 0.7443745962 2 NA NA
68.4 0.8726923437 3 NA NA
69 0.0381382683 NA NA NA
70 0.8126204217 1 NA NA
70.1 0.4691503050 NA NA NA
71 -0.5529062591 1 NA NA
71.1 -0.1103252087 1 NA NA
71.2 1.7178492547 NA NA NA
71.3 -1.0118346755 1 NA NA
71.4 1.8623785017 1 NA NA
72 -0.4521659275 2 NA NA
72.1 0.1375317317 3 NA NA
72.2 -0.4170988856 2 NA NA
72.3 0.7107266765 1 NA NA
72.4 0.1451969143 2 NA NA
72.5 1.6298050306 1 NA NA
73 -0.0307469467 NA NA NA
74 0.3730017941 1 NA NA
75 -0.4908003566 NA NA NA
76 -0.9888876620 1 NA NA
76.1 0.0003798292 2 NA NA
76.2 -0.8421863763 2 NA NA
77 -0.4986802480 NA NA NA
78 0.0417330969 1 NA NA
79 -0.3767450660 3 NA NA
79.1 0.1516000028 3 NA NA
79.2 -0.1888160741 NA NA NA
80 -0.0041558414 3 NA NA
80.1 -0.0329337062 2 NA NA
80.2 0.5046816157 NA NA NA
81 -0.9493950353 1 NA NA
81.1 0.2443038954 2 NA NA
81.2 0.6476958410 1 NA NA
81.3 0.4182528210 1 NA NA
82 1.1088801952 3 NA NA
82.1 0.9334157763 1 NA NA
82.2 0.4958140634 1 NA NA
83 0.5104724530 2 NA NA
83.1 -0.0513309106 3 NA NA
83.2 -0.2067792494 2 NA NA
83.3 -0.0534169155 3 NA NA
84 -0.0255753653 1 NA NA
84.1 -1.8234189877 2 NA NA
85 -0.0114038622 2 NA NA
85.1 -0.0577615939 1 NA NA
85.2 -0.2241856342 1 NA NA
85.3 -0.0520175929 NA NA NA
85.4 0.2892733846 2 NA NA
85.5 -0.3740417009 1 NA NA
86 0.4293735089 1 NA NA
86.1 -0.1363456521 NA NA NA
86.2 0.1230989293 2 NA NA
86.3 0.3305413955 1 NA NA
86.4 2.6003411822 2 NA NA
86.5 -0.1420690052 2 NA NA
87 1.0457427869 NA NA NA
87.1 -0.2973007190 1 NA NA
87.2 0.4396872616 NA NA NA
88 -0.0601928334 1 NA NA
88.1 -1.0124347595 2 NA NA
88.2 0.5730917016 NA NA NA
88.3 -0.0029455332 2 NA NA
89 1.5465903721 3 NA NA
90 0.0626760573 3 NA NA
90.1 1.1896872985 2 NA NA
90.2 0.2597888783 NA NA NA
90.3 0.6599799887 2 NA NA
91 1.1213651365 3 NA NA
91.1 1.2046371625 1 NA NA
91.2 0.3395603754 3 NA NA
92 0.4674939332 2 NA NA
93 0.2677965647 2 NA NA
93.1 1.6424445368 3 NA NA
93.2 0.7101700066 NA NA NA
93.3 1.1222322893 2 NA NA
93.4 1.4628960401 3 NA NA
94 -0.2904211940 2 NA NA
94.1 0.0147813580 2 NA NA
94.2 -0.4536774482 1 NA NA
94.3 0.6793464917 2 NA NA
94.4 -0.9411356550 1 NA NA
94.5 0.5683867264 2 NA NA
95 0.2375652188 2 NA NA
95.1 0.0767152977 2 NA NA
95.2 -0.6886731251 NA NA NA
96 0.7813892121 1 NA NA
96.1 0.3391519695 1 NA NA
96.2 -0.4857246503 2 NA NA
96.3 0.8771471244 3 NA NA
96.4 1.9030768981 2 NA NA
96.5 -0.1684332749 NA NA NA
97 1.3775130083 1 NA NA
97.1 -1.7323228619 2 NA NA
98 -1.2648518889 3 NA NA
98.1 -0.9042716241 2 NA NA
98.2 -0.1560385207 2 NA NA
99 0.7993356425 2 NA NA
99.1 1.0355522332 2 NA NA
99.2 -0.1150895843 1 NA NA
100 0.0369067906 1 NA NA
100.1 1.6023713093 2 NA NA
100.2 0.8861545820 3 NA NA
100.3 0.1277046316 2 NA NA
100.4 -0.0834577654 1 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_multinomial
[1] 0
$m3b$tau_reg_multinomial
[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"
$m4a
$m4a$M_id
M2 C2 (Intercept) M22 M23 M24 abs(C1 - C2) log(C1) C1
1 NA -1.381594459 1 NA NA NA NA -0.3318617 0.7175865
2 1 0.344426024 1 NA NA NA NA -0.2867266 0.7507170
3 2 NA 1 NA NA NA NA -0.3207627 0.7255954
4 2 -0.228910007 1 NA NA NA NA -0.2917769 0.7469352
5 1 NA 1 NA NA NA NA -0.3369956 0.7139120
6 NA -2.143955482 1 NA NA NA NA -0.3102679 0.7332505
7 NA -1.156567023 1 NA NA NA NA -0.3084388 0.7345929
8 2 -0.598827660 1 NA NA NA NA -0.2675411 0.7652589
9 NA NA 1 NA NA NA NA -0.3284176 0.7200622
10 NA -1.006719032 1 NA NA NA NA -0.2978834 0.7423879
11 3 0.239801450 1 NA NA NA NA -0.2960573 0.7437448
12 NA -1.064969789 1 NA NA NA NA -0.2948450 0.7446470
13 NA -0.538082688 1 NA NA NA NA -0.2836654 0.7530186
14 2 NA 1 NA NA NA NA -0.3434574 0.7093137
15 2 -1.781049276 1 NA NA NA NA -0.3104469 0.7331192
16 NA NA 1 NA NA NA NA -0.3550492 0.7011390
17 3 NA 1 NA NA NA NA -0.2967369 0.7432395
18 3 -0.014579883 1 NA NA NA NA -0.2816747 0.7545191
19 2 -2.121550136 1 NA NA NA NA -0.2838910 0.7528487
20 NA NA 1 NA NA NA NA -0.2727455 0.7612865
21 NA -0.363239698 1 NA NA NA NA -0.3213465 0.7251719
22 1 -0.121568514 1 NA NA NA NA -0.3146245 0.7300630
23 2 -0.951271111 1 NA NA NA NA -0.3442879 0.7087249
24 NA NA 1 NA NA NA NA -0.3021952 0.7391938
25 4 -0.974288621 1 NA NA NA NA -0.2458186 0.7820641
26 NA -1.130632418 1 NA NA NA NA -0.3399165 0.7118298
27 NA 0.114339868 1 NA NA NA NA -0.3242275 0.7230857
28 2 0.238334648 1 NA NA NA NA -0.2891027 0.7489353
29 4 0.840744958 1 NA NA NA NA -0.2862314 0.7510888
30 2 NA 1 NA NA NA NA -0.3146125 0.7300717
31 2 NA 1 NA NA NA NA -0.2809421 0.7550721
32 3 -1.466312154 1 NA NA NA NA -0.3117155 0.7321898
33 1 -0.637352277 1 NA NA NA NA -0.3138326 0.7306414
34 4 NA 1 NA NA NA NA -0.2974340 0.7427216
35 4 NA 1 NA NA NA NA -0.3294709 0.7193042
36 NA NA 1 NA NA NA NA -0.3129468 0.7312888
37 NA NA 1 NA NA NA NA -0.3424289 0.7100436
38 NA NA 1 NA NA NA NA -0.2652444 0.7670184
39 4 0.006728205 1 NA NA NA NA -0.3010445 0.7400449
40 NA NA 1 NA NA NA NA -0.3014695 0.7397304
41 2 -1.663281353 1 NA NA NA NA -0.2888874 0.7490966
42 NA 0.161184794 1 NA NA NA NA -0.2985038 0.7419274
43 NA 0.457939180 1 NA NA NA NA -0.2839809 0.7527810
44 3 -0.307070331 1 NA NA NA NA -0.2999821 0.7408315
45 NA NA 1 NA NA NA NA -0.3082181 0.7347550
46 NA -1.071668276 1 NA NA NA NA -0.3102825 0.7332398
47 4 -0.814751321 1 NA NA NA NA -0.3042884 0.7376481
48 3 -0.547630662 1 NA NA NA NA -0.3084048 0.7346179
49 4 NA 1 NA NA NA NA -0.3106911 0.7329402
50 3 -1.350213782 1 NA NA NA NA -0.3201451 0.7260436
51 NA 0.719054706 1 NA NA NA NA -0.3225621 0.7242910
52 1 NA 1 NA NA NA NA -0.3149755 0.7298067
53 NA -1.207130750 1 NA NA NA NA -0.3209299 0.7254741
54 NA NA 1 NA NA NA NA -0.2820889 0.7542067
55 NA -0.408600991 1 NA NA NA NA -0.3024638 0.7389952
56 NA -0.271380529 1 NA NA NA NA -0.2849341 0.7520638
57 4 -1.361925974 1 NA NA NA NA -0.3257359 0.7219958
58 1 NA 1 NA NA NA NA -0.3202560 0.7259632
59 2 NA 1 NA NA NA NA -0.2932166 0.7458606
60 3 -0.323712205 1 NA NA NA NA -0.2649529 0.7672421
61 2 NA 1 NA NA NA NA -0.3205938 0.7257179
62 3 NA 1 NA NA NA NA -0.3299089 0.7189892
63 2 -1.386906880 1 NA NA NA NA -0.3101519 0.7333356
64 NA NA 1 NA NA NA NA -0.3119416 0.7320243
65 NA NA 1 NA NA NA NA -0.2906584 0.7477711
66 2 -0.565191691 1 NA NA NA NA -0.3087049 0.7343974
67 NA -0.382899912 1 NA NA NA NA -0.2887994 0.7491624
68 3 NA 1 NA NA NA NA -0.2899866 0.7482736
69 3 -0.405642769 1 NA NA NA NA -0.3094824 0.7338267
70 4 NA 1 NA NA NA NA -0.2734187 0.7607742
71 2 -0.843748427 1 NA NA NA NA -0.2513372 0.7777600
72 NA 0.116003683 1 NA NA NA NA -0.3000053 0.7408143
73 4 -0.778634325 1 NA NA NA NA -0.3218221 0.7248271
74 NA NA 1 NA NA NA NA -0.3058575 0.7364916
75 NA NA 1 NA NA NA NA -0.2923695 0.7464926
76 4 NA 1 NA NA NA NA -0.3071463 0.7355430
77 2 -0.632974758 1 NA NA NA NA -0.3273313 0.7208449
78 2 NA 1 NA NA NA NA -0.3046827 0.7373573
79 NA -0.778064615 1 NA NA NA NA -0.2746896 0.7598079
80 1 NA 1 NA NA NA NA -0.3064688 0.7360415
81 NA NA 1 NA NA NA NA -0.3155423 0.7293932
82 3 -0.246123253 1 NA NA NA NA -0.3175491 0.7279309
83 4 -1.239659782 1 NA NA NA NA -0.3086139 0.7344643
84 3 -0.467772280 1 NA NA NA NA -0.3032222 0.7384350
85 NA NA 1 NA NA NA NA -0.3114673 0.7323716
86 3 -2.160485036 1 NA NA NA NA -0.2775210 0.7576597
87 NA -0.657675572 1 NA NA NA NA -0.2881970 0.7496139
88 NA NA 1 NA NA NA NA -0.3181084 0.7275239
89 NA -0.696710744 1 NA NA NA NA -0.3214942 0.7250648
90 4 NA 1 NA NA NA NA -0.3098919 0.7335262
91 NA -0.179395847 1 NA NA NA NA -0.3087042 0.7343980
92 2 -0.441545568 1 NA NA NA NA -0.3037539 0.7380425
93 4 -0.685799334 1 NA NA NA NA -0.3025305 0.7389460
94 NA NA 1 NA NA NA NA -0.3202120 0.7259951
95 NA 0.191929445 1 NA NA NA NA -0.3170642 0.7282840
96 NA NA 1 NA NA NA NA -0.3172240 0.7281676
97 1 -0.069760671 1 NA NA NA NA -0.3221849 0.7245642
98 NA NA 1 NA NA NA NA -0.2840967 0.7526938
99 2 NA 1 NA NA NA NA -0.3185112 0.7272309
100 NA NA 1 NA NA NA NA -0.3033427 0.7383460
$m4a$M_lvlone
m1 m2 m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2)
1 3 3 NA NA NA NA
1.1 2 1 NA NA NA NA
1.2 1 3 NA NA NA NA
1.3 1 1 NA NA NA NA
2 2 2 NA NA NA NA
2.1 2 1 NA NA NA NA
2.2 1 NA NA NA NA NA
3 1 3 NA NA NA NA
3.1 2 2 NA NA NA NA
3.2 2 1 NA NA NA NA
4 2 1 NA NA NA NA
4.1 1 2 NA NA NA NA
4.2 2 3 NA NA NA NA
4.3 3 3 NA NA NA NA
5 2 2 NA NA NA NA
5.1 1 3 NA NA NA NA
5.2 2 1 NA NA NA NA
5.3 2 1 NA NA NA NA
6 2 2 NA NA NA NA
7 3 2 NA NA NA NA
7.1 2 1 NA NA NA NA
7.2 3 3 NA NA NA NA
8 2 2 NA NA NA NA
8.1 1 2 NA NA NA NA
8.2 3 1 NA NA NA NA
8.3 2 3 NA NA NA NA
8.4 2 NA NA NA NA NA
8.5 2 3 NA NA NA NA
9 3 NA NA NA NA NA
9.1 2 3 NA NA NA NA
9.2 3 1 NA NA NA NA
10 3 1 NA NA NA NA
10.1 1 1 NA NA NA NA
11 1 1 NA NA NA NA
11.1 1 1 NA NA NA NA
11.2 2 1 NA NA NA NA
11.3 3 NA NA NA NA NA
11.4 1 1 NA NA NA NA
12 1 1 NA NA NA NA
13 2 2 NA NA NA NA
13.1 3 2 NA NA NA NA
14 1 3 NA NA NA NA
14.1 1 2 NA NA NA NA
14.2 1 1 NA NA NA NA
14.3 3 1 NA NA NA NA
15 1 1 NA NA NA NA
15.1 1 2 NA NA NA NA
15.2 3 3 NA NA NA NA
15.3 2 3 NA NA NA NA
16 2 2 NA NA NA NA
16.1 2 NA NA NA NA NA
16.2 1 3 NA NA NA NA
16.3 3 2 NA NA NA NA
16.4 2 3 NA NA NA NA
16.5 1 1 NA NA NA NA
17 2 1 NA NA NA NA
17.1 3 3 NA NA NA NA
17.2 1 NA NA NA NA NA
17.3 1 2 NA NA NA NA
17.4 2 1 NA NA NA NA
18 1 3 NA NA NA NA
19 2 NA NA NA NA NA
19.1 3 1 NA NA NA NA
19.2 2 3 NA NA NA NA
19.3 3 3 NA NA NA NA
20 2 2 NA NA NA NA
20.1 2 NA NA NA NA NA
20.2 1 3 NA NA NA NA
20.3 3 1 NA NA NA NA
20.4 2 3 NA NA NA NA
20.5 3 2 NA NA NA NA
21 1 3 NA NA NA NA
21.1 2 1 NA NA NA NA
21.2 3 NA NA NA NA NA
22 2 3 NA NA NA NA
22.1 2 1 NA NA NA NA
23 2 1 NA NA NA NA
23.1 1 2 NA NA NA NA
24 1 2 NA NA NA NA
25 1 2 NA NA NA NA
25.1 3 3 NA NA NA NA
25.2 2 3 NA NA NA NA
25.3 2 1 NA NA NA NA
25.4 1 3 NA NA NA NA
25.5 1 2 NA NA NA NA
26 2 NA NA NA NA NA
26.1 1 3 NA NA NA NA
26.2 1 3 NA NA NA NA
26.3 2 NA NA NA NA NA
27 1 3 NA NA NA NA
27.1 3 3 NA NA NA NA
28 1 3 NA NA NA NA
28.1 3 2 NA NA NA NA
28.2 1 2 NA NA NA NA
28.3 1 3 NA NA NA NA
29 3 1 NA NA NA NA
29.1 3 NA NA NA NA NA
29.2 3 2 NA NA NA NA
29.3 2 2 NA NA NA NA
30 1 2 NA NA NA NA
30.1 3 3 NA NA NA NA
30.2 3 3 NA NA NA NA
31 1 3 NA NA NA NA
32 3 3 NA NA NA NA
32.1 3 3 NA NA NA NA
32.2 2 1 NA NA NA NA
32.3 1 1 NA NA NA NA
33 3 3 NA NA NA NA
33.1 1 3 NA NA NA NA
34 1 3 NA NA NA NA
34.1 1 NA NA NA NA NA
34.2 2 1 NA NA NA NA
34.3 2 NA NA NA NA NA
35 1 2 NA NA NA NA
35.1 1 2 NA NA NA NA
35.2 1 2 NA NA NA NA
36 2 3 NA NA NA NA
36.1 3 3 NA NA NA NA
36.2 3 3 NA NA NA NA
36.3 3 2 NA NA NA NA
36.4 3 2 NA NA NA NA
37 1 2 NA NA NA NA
37.1 3 2 NA NA NA NA
37.2 1 1 NA NA NA NA
38 2 2 NA NA NA NA
39 2 3 NA NA NA NA
39.1 3 2 NA NA NA NA
39.2 1 3 NA NA NA NA
39.3 2 NA NA NA NA NA
39.4 3 3 NA NA NA NA
39.5 3 3 NA NA NA NA
40 3 3 NA NA NA NA
40.1 3 1 NA NA NA NA
40.2 1 3 NA NA NA NA
40.3 3 2 NA NA NA NA
41 3 3 NA NA NA NA
41.1 3 3 NA NA NA NA
41.2 1 1 NA NA NA NA
41.3 1 2 NA NA NA NA
41.4 1 3 NA NA NA NA
42 1 2 NA NA NA NA
42.1 1 NA NA NA NA NA
43 3 3 NA NA NA NA
43.1 3 3 NA NA NA NA
43.2 2 2 NA NA NA NA
44 2 3 NA NA NA NA
44.1 2 3 NA NA NA NA
44.2 1 NA NA NA NA NA
44.3 1 1 NA NA NA NA
45 2 3 NA NA NA NA
45.1 3 1 NA NA NA NA
46 3 NA NA NA NA NA
46.1 2 1 NA NA NA NA
46.2 3 2 NA NA NA NA
47 1 2 NA NA NA NA
47.1 2 NA NA NA NA NA
47.2 2 NA NA NA NA NA
47.3 2 3 NA NA NA NA
47.4 2 3 NA NA NA NA
48 3 3 NA NA NA NA
48.1 1 1 NA NA NA NA
49 3 1 NA NA NA NA
50 1 NA NA NA NA NA
51 3 1 NA NA NA NA
52 3 2 NA NA NA NA
52.1 2 1 NA NA NA NA
52.2 1 1 NA NA NA NA
52.3 3 NA NA NA NA NA
52.4 3 2 NA NA NA NA
52.5 3 3 NA NA NA NA
53 1 2 NA NA NA NA
53.1 3 1 NA NA NA NA
53.2 2 2 NA NA NA NA
54 3 NA NA NA NA NA
54.1 3 1 NA NA NA NA
54.2 3 NA NA NA NA NA
54.3 1 3 NA NA NA NA
54.4 1 3 NA NA NA NA
55 1 1 NA NA NA NA
55.1 3 1 NA NA NA NA
55.2 2 1 NA NA NA NA
55.3 1 NA NA NA NA NA
55.4 1 2 NA NA NA NA
56 2 2 NA NA NA NA
56.1 1 3 NA NA NA NA
56.2 3 1 NA NA NA NA
56.3 1 1 NA NA NA NA
56.4 2 2 NA NA NA NA
56.5 1 NA NA NA NA NA
57 1 2 NA NA NA NA
57.1 1 3 NA NA NA NA
57.2 1 2 NA NA NA NA
57.3 1 NA NA NA NA NA
58 3 1 NA NA NA NA
58.1 2 1 NA NA NA NA
58.2 1 NA NA NA NA NA
58.3 3 1 NA NA NA NA
58.4 3 2 NA NA NA NA
58.5 3 NA NA NA NA NA
59 3 1 NA NA NA NA
59.1 1 1 NA NA NA NA
60 3 1 NA NA NA NA
61 1 2 NA NA NA NA
61.1 2 1 NA NA NA NA
61.2 2 1 NA NA NA NA
61.3 3 2 NA NA NA NA
61.4 2 2 NA NA NA NA
62 2 1 NA NA NA NA
62.1 1 1 NA NA NA NA
62.2 3 NA NA NA NA NA
62.3 2 1 NA NA NA NA
63 3 NA NA NA NA NA
63.1 1 3 NA NA NA NA
64 3 3 NA NA NA NA
65 3 NA NA NA NA NA
65.1 3 2 NA NA NA NA
65.2 2 3 NA NA NA NA
65.3 3 3 NA NA NA NA
66 3 3 NA NA NA NA
66.1 3 3 NA NA NA NA
66.2 1 1 NA NA NA NA
67 3 NA NA NA NA NA
68 3 1 NA NA NA NA
68.1 1 1 NA NA NA NA
68.2 2 1 NA NA NA NA
68.3 3 2 NA NA NA NA
68.4 1 3 NA NA NA NA
69 1 NA NA NA NA NA
70 1 1 NA NA NA NA
70.1 2 NA NA NA NA NA
71 3 1 NA NA NA NA
71.1 2 1 NA NA NA NA
71.2 2 NA NA NA NA NA
71.3 1 1 NA NA NA NA
71.4 2 1 NA NA NA NA
72 1 2 NA NA NA NA
72.1 2 3 NA NA NA NA
72.2 1 2 NA NA NA NA
72.3 2 1 NA NA NA NA
72.4 2 2 NA NA NA NA
72.5 1 1 NA NA NA NA
73 2 NA NA NA NA NA
74 1 1 NA NA NA NA
75 3 NA NA NA NA NA
76 3 1 NA NA NA NA
76.1 3 2 NA NA NA NA
76.2 2 2 NA NA NA NA
77 2 NA NA NA NA NA
78 2 1 NA NA NA NA
79 2 3 NA NA NA NA
79.1 2 3 NA NA NA NA
79.2 2 NA NA NA NA NA
80 2 3 NA NA NA NA
80.1 1 2 NA NA NA NA
80.2 3 NA NA NA NA NA
81 2 1 NA NA NA NA
81.1 3 2 NA NA NA NA
81.2 2 1 NA NA NA NA
81.3 1 1 NA NA NA NA
82 1 3 NA NA NA NA
82.1 2 1 NA NA NA NA
82.2 3 1 NA NA NA NA
83 2 2 NA NA NA NA
83.1 3 3 NA NA NA NA
83.2 3 2 NA NA NA NA
83.3 3 3 NA NA NA NA
84 2 1 NA NA NA NA
84.1 3 2 NA NA NA NA
85 1 2 NA NA NA NA
85.1 2 1 NA NA NA NA
85.2 3 1 NA NA NA NA
85.3 3 NA NA NA NA NA
85.4 2 2 NA NA NA NA
85.5 2 1 NA NA NA NA
86 1 1 NA NA NA NA
86.1 2 NA NA NA NA NA
86.2 1 2 NA NA NA NA
86.3 1 1 NA NA NA NA
86.4 1 2 NA NA NA NA
86.5 2 2 NA NA NA NA
87 3 NA NA NA NA NA
87.1 3 1 NA NA NA NA
87.2 2 NA NA NA NA NA
88 3 1 NA NA NA NA
88.1 3 2 NA NA NA NA
88.2 3 NA NA NA NA NA
88.3 1 2 NA NA NA NA
89 2 3 NA NA NA NA
90 1 3 NA NA NA NA
90.1 2 2 NA NA NA NA
90.2 2 NA NA NA NA NA
90.3 2 2 NA NA NA NA
91 3 3 NA NA NA NA
91.1 3 1 NA NA NA NA
91.2 3 3 NA NA NA NA
92 2 2 NA NA NA NA
93 2 2 NA NA NA NA
93.1 2 3 NA NA NA NA
93.2 2 NA NA NA NA NA
93.3 3 2 NA NA NA NA
93.4 2 3 NA NA NA NA
94 2 2 NA NA NA NA
94.1 3 2 NA NA NA NA
94.2 3 1 NA NA NA NA
94.3 2 2 NA NA NA NA
94.4 3 1 NA NA NA NA
94.5 2 2 NA NA NA NA
95 2 2 NA NA NA NA
95.1 3 2 NA NA NA NA
95.2 2 NA NA NA NA NA
96 3 1 NA NA NA NA
96.1 2 1 NA NA NA NA
96.2 3 2 NA NA NA NA
96.3 2 3 NA NA NA NA
96.4 2 2 NA NA NA NA
96.5 3 NA NA NA NA NA
97 3 1 NA NA NA NA
97.1 3 2 NA NA NA NA
98 2 3 NA NA NA NA
98.1 3 2 NA NA NA NA
98.2 1 2 NA NA NA NA
99 2 2 NA NA NA NA
99.1 1 2 NA NA NA NA
99.2 3 1 NA NA NA NA
100 2 1 NA NA NA NA
100.1 1 2 NA NA NA NA
100.2 2 3 NA NA NA NA
100.3 2 2 NA NA NA NA
100.4 3 1 NA NA NA NA
$m4a$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.68571078
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
abs(C1 - C2) 1.3664060 0.67847389
log(C1) -0.3049822 0.01990873
C1 0.7372814 0.01472882
$m4a$spM_lvlone
center scale
m1 NA NA
m2 NA NA
m2B NA NA
m2C NA NA
m2B:abs(C1 - C2) 0.4042255 0.7594704
m2C:abs(C1 - C2) 0.5491518 0.8130082
$m4a$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_multinomial
[1] 0
$m4a$tau_reg_multinomial
[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
C2 (Intercept) abs(C1 - C2) log(C1) M12 M13 M14 C1 M1
1 -1.381594459 1 NA -0.3318617 0 0 0 0.7175865 1
2 0.344426024 1 NA -0.2867266 0 0 1 0.7507170 4
3 NA 1 NA -0.3207627 0 0 0 0.7255954 1
4 -0.228910007 1 NA -0.2917769 0 0 0 0.7469352 1
5 NA 1 NA -0.3369956 0 0 1 0.7139120 4
6 -2.143955482 1 NA -0.3102679 0 0 1 0.7332505 4
7 -1.156567023 1 NA -0.3084388 0 0 1 0.7345929 4
8 -0.598827660 1 NA -0.2675411 0 0 1 0.7652589 4
9 NA 1 NA -0.3284176 1 0 0 0.7200622 2
10 -1.006719032 1 NA -0.2978834 0 0 0 0.7423879 1
11 0.239801450 1 NA -0.2960573 0 1 0 0.7437448 3
12 -1.064969789 1 NA -0.2948450 0 0 0 0.7446470 1
13 -0.538082688 1 NA -0.2836654 0 1 0 0.7530186 3
14 NA 1 NA -0.3434574 0 1 0 0.7093137 3
15 -1.781049276 1 NA -0.3104469 0 0 1 0.7331192 4
16 NA 1 NA -0.3550492 0 0 0 0.7011390 1
17 NA 1 NA -0.2967369 1 0 0 0.7432395 2
18 -0.014579883 1 NA -0.2816747 0 1 0 0.7545191 3
19 -2.121550136 1 NA -0.2838910 0 0 0 0.7528487 1
20 NA 1 NA -0.2727455 1 0 0 0.7612865 2
21 -0.363239698 1 NA -0.3213465 1 0 0 0.7251719 2
22 -0.121568514 1 NA -0.3146245 0 0 1 0.7300630 4
23 -0.951271111 1 NA -0.3442879 0 0 1 0.7087249 4
24 NA 1 NA -0.3021952 0 0 0 0.7391938 1
25 -0.974288621 1 NA -0.2458186 0 0 0 0.7820641 1
26 -1.130632418 1 NA -0.3399165 0 1 0 0.7118298 3
27 0.114339868 1 NA -0.3242275 0 0 0 0.7230857 1
28 0.238334648 1 NA -0.2891027 0 0 1 0.7489353 4
29 0.840744958 1 NA -0.2862314 0 0 0 0.7510888 1
30 NA 1 NA -0.3146125 0 1 0 0.7300717 3
31 NA 1 NA -0.2809421 0 1 0 0.7550721 3
32 -1.466312154 1 NA -0.3117155 0 0 0 0.7321898 1
33 -0.637352277 1 NA -0.3138326 0 1 0 0.7306414 3
34 NA 1 NA -0.2974340 0 0 1 0.7427216 4
35 NA 1 NA -0.3294709 0 0 1 0.7193042 4
36 NA 1 NA -0.3129468 0 0 0 0.7312888 1
37 NA 1 NA -0.3424289 1 0 0 0.7100436 2
38 NA 1 NA -0.2652444 0 0 1 0.7670184 4
39 0.006728205 1 NA -0.3010445 0 1 0 0.7400449 3
40 NA 1 NA -0.3014695 1 0 0 0.7397304 2
41 -1.663281353 1 NA -0.2888874 1 0 0 0.7490966 2
42 0.161184794 1 NA -0.2985038 0 0 0 0.7419274 1
43 0.457939180 1 NA -0.2839809 0 0 0 0.7527810 1
44 -0.307070331 1 NA -0.2999821 0 1 0 0.7408315 3
45 NA 1 NA -0.3082181 1 0 0 0.7347550 2
46 -1.071668276 1 NA -0.3102825 1 0 0 0.7332398 2
47 -0.814751321 1 NA -0.3042884 0 0 0 0.7376481 1
48 -0.547630662 1 NA -0.3084048 0 0 0 0.7346179 1
49 NA 1 NA -0.3106911 0 0 0 0.7329402 1
50 -1.350213782 1 NA -0.3201451 1 0 0 0.7260436 2
51 0.719054706 1 NA -0.3225621 0 0 0 0.7242910 1
52 NA 1 NA -0.3149755 0 0 1 0.7298067 4
53 -1.207130750 1 NA -0.3209299 0 0 0 0.7254741 1
54 NA 1 NA -0.2820889 1 0 0 0.7542067 2
55 -0.408600991 1 NA -0.3024638 0 1 0 0.7389952 3
56 -0.271380529 1 NA -0.2849341 0 1 0 0.7520638 3
57 -1.361925974 1 NA -0.3257359 0 0 1 0.7219958 4
58 NA 1 NA -0.3202560 1 0 0 0.7259632 2
59 NA 1 NA -0.2932166 0 0 1 0.7458606 4
60 -0.323712205 1 NA -0.2649529 0 0 0 0.7672421 1
61 NA 1 NA -0.3205938 0 0 0 0.7257179 1
62 NA 1 NA -0.3299089 0 0 1 0.7189892 4
63 -1.386906880 1 NA -0.3101519 0 0 1 0.7333356 4
64 NA 1 NA -0.3119416 0 0 1 0.7320243 4
65 NA 1 NA -0.2906584 1 0 0 0.7477711 2
66 -0.565191691 1 NA -0.3087049 0 1 0 0.7343974 3
67 -0.382899912 1 NA -0.2887994 1 0 0 0.7491624 2
68 NA 1 NA -0.2899866 0 0 1 0.7482736 4
69 -0.405642769 1 NA -0.3094824 0 0 0 0.7338267 1
70 NA 1 NA -0.2734187 0 0 1 0.7607742 4
71 -0.843748427 1 NA -0.2513372 0 0 1 0.7777600 4
72 0.116003683 1 NA -0.3000053 0 0 1 0.7408143 4
73 -0.778634325 1 NA -0.3218221 0 0 0 0.7248271 1
74 NA 1 NA -0.3058575 0 1 0 0.7364916 3
75 NA 1 NA -0.2923695 0 0 1 0.7464926 4
76 NA 1 NA -0.3071463 1 0 0 0.7355430 2
77 -0.632974758 1 NA -0.3273313 1 0 0 0.7208449 2
78 NA 1 NA -0.3046827 0 0 0 0.7373573 1
79 -0.778064615 1 NA -0.2746896 1 0 0 0.7598079 2
80 NA 1 NA -0.3064688 0 1 0 0.7360415 3
81 NA 1 NA -0.3155423 0 0 0 0.7293932 1
82 -0.246123253 1 NA -0.3175491 0 0 0 0.7279309 1
83 -1.239659782 1 NA -0.3086139 1 0 0 0.7344643 2
84 -0.467772280 1 NA -0.3032222 0 0 1 0.7384350 4
85 NA 1 NA -0.3114673 0 1 0 0.7323716 3
86 -2.160485036 1 NA -0.2775210 1 0 0 0.7576597 2
87 -0.657675572 1 NA -0.2881970 0 0 1 0.7496139 4
88 NA 1 NA -0.3181084 0 1 0 0.7275239 3
89 -0.696710744 1 NA -0.3214942 0 0 1 0.7250648 4
90 NA 1 NA -0.3098919 1 0 0 0.7335262 2
91 -0.179395847 1 NA -0.3087042 0 0 1 0.7343980 4
92 -0.441545568 1 NA -0.3037539 1 0 0 0.7380425 2
93 -0.685799334 1 NA -0.3025305 0 0 1 0.7389460 4
94 NA 1 NA -0.3202120 0 0 1 0.7259951 4
95 0.191929445 1 NA -0.3170642 0 0 0 0.7282840 1
96 NA 1 NA -0.3172240 1 0 0 0.7281676 2
97 -0.069760671 1 NA -0.3221849 0 0 0 0.7245642 1
98 NA 1 NA -0.2840967 0 0 1 0.7526938 4
99 NA 1 NA -0.3185112 1 0 0 0.7272309 2
100 NA 1 NA -0.3033427 1 0 0 0.7383460 2
$m4b$M_lvlone
m1 m2 ifelse(as.numeric(m2) > as.numeric(M1), 1, 0)
1 3 3 NA
1.1 2 1 NA
1.2 1 3 NA
1.3 1 1 NA
2 2 2 NA
2.1 2 1 NA
2.2 1 NA NA
3 1 3 NA
3.1 2 2 NA
3.2 2 1 NA
4 2 1 NA
4.1 1 2 NA
4.2 2 3 NA
4.3 3 3 NA
5 2 2 NA
5.1 1 3 NA
5.2 2 1 NA
5.3 2 1 NA
6 2 2 NA
7 3 2 NA
7.1 2 1 NA
7.2 3 3 NA
8 2 2 NA
8.1 1 2 NA
8.2 3 1 NA
8.3 2 3 NA
8.4 2 NA NA
8.5 2 3 NA
9 3 NA NA
9.1 2 3 NA
9.2 3 1 NA
10 3 1 NA
10.1 1 1 NA
11 1 1 NA
11.1 1 1 NA
11.2 2 1 NA
11.3 3 NA NA
11.4 1 1 NA
12 1 1 NA
13 2 2 NA
13.1 3 2 NA
14 1 3 NA
14.1 1 2 NA
14.2 1 1 NA
14.3 3 1 NA
15 1 1 NA
15.1 1 2 NA
15.2 3 3 NA
15.3 2 3 NA
16 2 2 NA
16.1 2 NA NA
16.2 1 3 NA
16.3 3 2 NA
16.4 2 3 NA
16.5 1 1 NA
17 2 1 NA
17.1 3 3 NA
17.2 1 NA NA
17.3 1 2 NA
17.4 2 1 NA
18 1 3 NA
19 2 NA NA
19.1 3 1 NA
19.2 2 3 NA
19.3 3 3 NA
20 2 2 NA
20.1 2 NA NA
20.2 1 3 NA
20.3 3 1 NA
20.4 2 3 NA
20.5 3 2 NA
21 1 3 NA
21.1 2 1 NA
21.2 3 NA NA
22 2 3 NA
22.1 2 1 NA
23 2 1 NA
23.1 1 2 NA
24 1 2 NA
25 1 2 NA
25.1 3 3 NA
25.2 2 3 NA
25.3 2 1 NA
25.4 1 3 NA
25.5 1 2 NA
26 2 NA NA
26.1 1 3 NA
26.2 1 3 NA
26.3 2 NA NA
27 1 3 NA
27.1 3 3 NA
28 1 3 NA
28.1 3 2 NA
28.2 1 2 NA
28.3 1 3 NA
29 3 1 NA
29.1 3 NA NA
29.2 3 2 NA
29.3 2 2 NA
30 1 2 NA
30.1 3 3 NA
30.2 3 3 NA
31 1 3 NA
32 3 3 NA
32.1 3 3 NA
32.2 2 1 NA
32.3 1 1 NA
33 3 3 NA
33.1 1 3 NA
34 1 3 NA
34.1 1 NA NA
34.2 2 1 NA
34.3 2 NA NA
35 1 2 NA
35.1 1 2 NA
35.2 1 2 NA
36 2 3 NA
36.1 3 3 NA
36.2 3 3 NA
36.3 3 2 NA
36.4 3 2 NA
37 1 2 NA
37.1 3 2 NA
37.2 1 1 NA
38 2 2 NA
39 2 3 NA
39.1 3 2 NA
39.2 1 3 NA
39.3 2 NA NA
39.4 3 3 NA
39.5 3 3 NA
40 3 3 NA
40.1 3 1 NA
40.2 1 3 NA
40.3 3 2 NA
41 3 3 NA
41.1 3 3 NA
41.2 1 1 NA
41.3 1 2 NA
41.4 1 3 NA
42 1 2 NA
42.1 1 NA NA
43 3 3 NA
43.1 3 3 NA
43.2 2 2 NA
44 2 3 NA
44.1 2 3 NA
44.2 1 NA NA
44.3 1 1 NA
45 2 3 NA
45.1 3 1 NA
46 3 NA NA
46.1 2 1 NA
46.2 3 2 NA
47 1 2 NA
47.1 2 NA NA
47.2 2 NA NA
47.3 2 3 NA
47.4 2 3 NA
48 3 3 NA
48.1 1 1 NA
49 3 1 NA
50 1 NA NA
51 3 1 NA
52 3 2 NA
52.1 2 1 NA
52.2 1 1 NA
52.3 3 NA NA
52.4 3 2 NA
52.5 3 3 NA
53 1 2 NA
53.1 3 1 NA
53.2 2 2 NA
54 3 NA NA
54.1 3 1 NA
54.2 3 NA NA
54.3 1 3 NA
54.4 1 3 NA
55 1 1 NA
55.1 3 1 NA
55.2 2 1 NA
55.3 1 NA NA
55.4 1 2 NA
56 2 2 NA
56.1 1 3 NA
56.2 3 1 NA
56.3 1 1 NA
56.4 2 2 NA
56.5 1 NA NA
57 1 2 NA
57.1 1 3 NA
57.2 1 2 NA
57.3 1 NA NA
58 3 1 NA
58.1 2 1 NA
58.2 1 NA NA
58.3 3 1 NA
58.4 3 2 NA
58.5 3 NA NA
59 3 1 NA
59.1 1 1 NA
60 3 1 NA
61 1 2 NA
61.1 2 1 NA
61.2 2 1 NA
61.3 3 2 NA
61.4 2 2 NA
62 2 1 NA
62.1 1 1 NA
62.2 3 NA NA
62.3 2 1 NA
63 3 NA NA
63.1 1 3 NA
64 3 3 NA
65 3 NA NA
65.1 3 2 NA
65.2 2 3 NA
65.3 3 3 NA
66 3 3 NA
66.1 3 3 NA
66.2 1 1 NA
67 3 NA NA
68 3 1 NA
68.1 1 1 NA
68.2 2 1 NA
68.3 3 2 NA
68.4 1 3 NA
69 1 NA NA
70 1 1 NA
70.1 2 NA NA
71 3 1 NA
71.1 2 1 NA
71.2 2 NA NA
71.3 1 1 NA
71.4 2 1 NA
72 1 2 NA
72.1 2 3 NA
72.2 1 2 NA
72.3 2 1 NA
72.4 2 2 NA
72.5 1 1 NA
73 2 NA NA
74 1 1 NA
75 3 NA NA
76 3 1 NA
76.1 3 2 NA
76.2 2 2 NA
77 2 NA NA
78 2 1 NA
79 2 3 NA
79.1 2 3 NA
79.2 2 NA NA
80 2 3 NA
80.1 1 2 NA
80.2 3 NA NA
81 2 1 NA
81.1 3 2 NA
81.2 2 1 NA
81.3 1 1 NA
82 1 3 NA
82.1 2 1 NA
82.2 3 1 NA
83 2 2 NA
83.1 3 3 NA
83.2 3 2 NA
83.3 3 3 NA
84 2 1 NA
84.1 3 2 NA
85 1 2 NA
85.1 2 1 NA
85.2 3 1 NA
85.3 3 NA NA
85.4 2 2 NA
85.5 2 1 NA
86 1 1 NA
86.1 2 NA NA
86.2 1 2 NA
86.3 1 1 NA
86.4 1 2 NA
86.5 2 2 NA
87 3 NA NA
87.1 3 1 NA
87.2 2 NA NA
88 3 1 NA
88.1 3 2 NA
88.2 3 NA NA
88.3 1 2 NA
89 2 3 NA
90 1 3 NA
90.1 2 2 NA
90.2 2 NA NA
90.3 2 2 NA
91 3 3 NA
91.1 3 1 NA
91.2 3 3 NA
92 2 2 NA
93 2 2 NA
93.1 2 3 NA
93.2 2 NA NA
93.3 3 2 NA
93.4 2 3 NA
94 2 2 NA
94.1 3 2 NA
94.2 3 1 NA
94.3 2 2 NA
94.4 3 1 NA
94.5 2 2 NA
95 2 2 NA
95.1 3 2 NA
95.2 2 NA NA
96 3 1 NA
96.1 2 1 NA
96.2 3 2 NA
96.3 2 3 NA
96.4 2 2 NA
96.5 3 NA NA
97 3 1 NA
97.1 3 2 NA
98 2 3 NA
98.1 3 2 NA
98.2 1 2 NA
99 2 2 NA
99.1 1 2 NA
99.2 3 1 NA
100 2 1 NA
100.1 1 2 NA
100.2 2 3 NA
100.3 2 2 NA
100.4 3 1 NA
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) m2B m2C
1 NA NA NA
1.1 NA NA NA
1.2 NA NA NA
1.3 NA NA NA
2 NA NA NA
2.1 NA NA NA
2.2 NA NA NA
3 NA NA NA
3.1 NA NA NA
3.2 NA NA NA
4 NA NA NA
4.1 NA NA NA
4.2 NA NA NA
4.3 NA NA NA
5 NA NA NA
5.1 NA NA NA
5.2 NA NA NA
5.3 NA NA NA
6 NA NA NA
7 NA NA NA
7.1 NA NA NA
7.2 NA NA NA
8 NA NA NA
8.1 NA NA NA
8.2 NA NA NA
8.3 NA NA NA
8.4 NA NA NA
8.5 NA NA NA
9 NA NA NA
9.1 NA NA NA
9.2 NA NA NA
10 NA NA NA
10.1 NA NA NA
11 NA NA NA
11.1 NA NA NA
11.2 NA NA NA
11.3 NA NA NA
11.4 NA NA NA
12 NA NA NA
13 NA NA NA
13.1 NA NA NA
14 NA NA NA
14.1 NA NA NA
14.2 NA NA NA
14.3 NA NA NA
15 NA NA NA
15.1 NA NA NA
15.2 NA NA NA
15.3 NA NA NA
16 NA NA NA
16.1 NA NA NA
16.2 NA NA NA
16.3 NA NA NA
16.4 NA NA NA
16.5 NA NA NA
17 NA NA NA
17.1 NA NA NA
17.2 NA NA NA
17.3 NA NA NA
17.4 NA NA NA
18 NA NA NA
19 NA NA NA
19.1 NA NA NA
19.2 NA NA NA
19.3 NA NA NA
20 NA NA NA
20.1 NA NA NA
20.2 NA NA NA
20.3 NA NA NA
20.4 NA NA NA
20.5 NA NA NA
21 NA NA NA
21.1 NA NA NA
21.2 NA NA NA
22 NA NA NA
22.1 NA NA NA
23 NA NA NA
23.1 NA NA NA
24 NA NA NA
25 NA NA NA
25.1 NA NA NA
25.2 NA NA NA
25.3 NA NA NA
25.4 NA NA NA
25.5 NA NA NA
26 NA NA NA
26.1 NA NA NA
26.2 NA NA NA
26.3 NA NA NA
27 NA NA NA
27.1 NA NA NA
28 NA NA NA
28.1 NA NA NA
28.2 NA NA NA
28.3 NA NA NA
29 NA NA NA
29.1 NA NA NA
29.2 NA NA NA
29.3 NA NA NA
30 NA NA NA
30.1 NA NA NA
30.2 NA NA NA
31 NA NA NA
32 NA NA NA
32.1 NA NA NA
32.2 NA NA NA
32.3 NA NA NA
33 NA NA NA
33.1 NA NA NA
34 NA NA NA
34.1 NA NA NA
34.2 NA NA NA
34.3 NA NA NA
35 NA NA NA
35.1 NA NA NA
35.2 NA NA NA
36 NA NA NA
36.1 NA NA NA
36.2 NA NA NA
36.3 NA NA NA
36.4 NA NA NA
37 NA NA NA
37.1 NA NA NA
37.2 NA NA NA
38 NA NA NA
39 NA NA NA
39.1 NA NA NA
39.2 NA NA NA
39.3 NA NA NA
39.4 NA NA NA
39.5 NA NA NA
40 NA NA NA
40.1 NA NA NA
40.2 NA NA NA
40.3 NA NA NA
41 NA NA NA
41.1 NA NA NA
41.2 NA NA NA
41.3 NA NA NA
41.4 NA NA NA
42 NA NA NA
42.1 NA NA NA
43 NA NA NA
43.1 NA NA NA
43.2 NA NA NA
44 NA NA NA
44.1 NA NA NA
44.2 NA NA NA
44.3 NA NA NA
45 NA NA NA
45.1 NA NA NA
46 NA NA NA
46.1 NA NA NA
46.2 NA NA NA
47 NA NA NA
47.1 NA NA NA
47.2 NA NA NA
47.3 NA NA NA
47.4 NA NA NA
48 NA NA NA
48.1 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
52.1 NA NA NA
52.2 NA NA NA
52.3 NA NA NA
52.4 NA NA NA
52.5 NA NA NA
53 NA NA NA
53.1 NA NA NA
53.2 NA NA NA
54 NA NA NA
54.1 NA NA NA
54.2 NA NA NA
54.3 NA NA NA
54.4 NA NA NA
55 NA NA NA
55.1 NA NA NA
55.2 NA NA NA
55.3 NA NA NA
55.4 NA NA NA
56 NA NA NA
56.1 NA NA NA
56.2 NA NA NA
56.3 NA NA NA
56.4 NA NA NA
56.5 NA NA NA
57 NA NA NA
57.1 NA NA NA
57.2 NA NA NA
57.3 NA NA NA
58 NA NA NA
58.1 NA NA NA
58.2 NA NA NA
58.3 NA NA NA
58.4 NA NA NA
58.5 NA NA NA
59 NA NA NA
59.1 NA NA NA
60 NA NA NA
61 NA NA NA
61.1 NA NA NA
61.2 NA NA NA
61.3 NA NA NA
61.4 NA NA NA
62 NA NA NA
62.1 NA NA NA
62.2 NA NA NA
62.3 NA NA NA
63 NA NA NA
63.1 NA NA NA
64 NA NA NA
65 NA NA NA
65.1 NA NA NA
65.2 NA NA NA
65.3 NA NA NA
66 NA NA NA
66.1 NA NA NA
66.2 NA NA NA
67 NA NA NA
68 NA NA NA
68.1 NA NA NA
68.2 NA NA NA
68.3 NA NA NA
68.4 NA NA NA
69 NA NA NA
70 NA NA NA
70.1 NA NA NA
71 NA NA NA
71.1 NA NA NA
71.2 NA NA NA
71.3 NA NA NA
71.4 NA NA NA
72 NA NA NA
72.1 NA NA NA
72.2 NA NA NA
72.3 NA NA NA
72.4 NA NA NA
72.5 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
76.1 NA NA NA
76.2 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
79.1 NA NA NA
79.2 NA NA NA
80 NA NA NA
80.1 NA NA NA
80.2 NA NA NA
81 NA NA NA
81.1 NA NA NA
81.2 NA NA NA
81.3 NA NA NA
82 NA NA NA
82.1 NA NA NA
82.2 NA NA NA
83 NA NA NA
83.1 NA NA NA
83.2 NA NA NA
83.3 NA NA NA
84 NA NA NA
84.1 NA NA NA
85 NA NA NA
85.1 NA NA NA
85.2 NA NA NA
85.3 NA NA NA
85.4 NA NA NA
85.5 NA NA NA
86 NA NA NA
86.1 NA NA NA
86.2 NA NA NA
86.3 NA NA NA
86.4 NA NA NA
86.5 NA NA NA
87 NA NA NA
87.1 NA NA NA
87.2 NA NA NA
88 NA NA NA
88.1 NA NA NA
88.2 NA NA NA
88.3 NA NA NA
89 NA NA NA
90 NA NA NA
90.1 NA NA NA
90.2 NA NA NA
90.3 NA NA NA
91 NA NA NA
91.1 NA NA NA
91.2 NA NA NA
92 NA NA NA
93 NA NA NA
93.1 NA NA NA
93.2 NA NA NA
93.3 NA NA NA
93.4 NA NA NA
94 NA NA NA
94.1 NA NA NA
94.2 NA NA NA
94.3 NA NA NA
94.4 NA NA NA
94.5 NA NA NA
95 NA NA NA
95.1 NA NA NA
95.2 NA NA NA
96 NA NA NA
96.1 NA NA NA
96.2 NA NA NA
96.3 NA NA NA
96.4 NA NA NA
96.5 NA NA NA
97 NA NA NA
97.1 NA NA NA
98 NA NA NA
98.1 NA NA NA
98.2 NA NA NA
99 NA NA NA
99.1 NA NA NA
99.2 NA NA NA
100 NA NA NA
100.1 NA NA NA
100.2 NA NA NA
100.3 NA NA NA
100.4 NA NA NA
$m4b$spM_id
center scale
C2 -0.6240921 0.68571078
(Intercept) NA NA
abs(C1 - C2) 1.3664060 0.67847389
log(C1) -0.3049822 0.01990873
M12 NA NA
M13 NA NA
M14 NA NA
C1 0.7372814 0.01472882
M1 NA NA
$m4b$spM_lvlone
center scale
m1 NA NA
m2 NA NA
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0.2508961 0.4343078
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0.3794055 0.7450722
m2B NA NA
m2C 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_multinomial
[1] 0
$m4b$tau_reg_multinomial
[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
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
$m4c$M_lvlone
m1 time c1 c1:time
1 3 0.5090421822 0.7592026489 3.864662e-01
1.1 2 0.6666076288 0.9548337990 6.364995e-01
1.2 1 2.1304941282 0.5612235156 1.195683e+00
1.3 1 2.4954441458 1.1873391025 2.962938e+00
2 2 3.0164990982 0.9192204198 2.772828e+00
2.1 2 3.2996806887 -0.1870730476 -6.172813e-01
2.2 1 4.1747569619 1.2517512331 5.225757e+00
3 1 0.8478727890 -0.0605087604 -5.130373e-02
3.1 2 3.0654308549 0.3788637747 1.161381e+00
3.2 2 4.7381553578 0.9872578281 4.677781e+00
4 2 0.3371432109 1.4930175328 5.033607e-01
4.1 1 1.0693019140 -0.7692526880 -8.225634e-01
4.2 2 2.6148973033 0.9180841450 2.400696e+00
4.3 3 3.1336532847 -0.0541170782 -1.695842e-01
5 2 1.0762525082 -0.1376784521 -1.481768e-01
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5.3 2 2.8119940578 0.1740288049 4.893680e-01
6 2 1.7815462884 0.9868044683 1.758038e+00
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7.1 2 3.7008403614 0.4242971219 1.570256e+00
7.2 3 4.7716691741 0.0777182491 3.708458e-01
8 2 1.1246398522 -0.5791408712 -6.513249e-01
8.1 1 1.8027009873 0.3128604232 5.639938e-01
8.2 3 1.8175825174 0.6258446356 1.137524e+00
8.3 2 2.8384267003 -0.1040137707 -2.952355e-01
8.4 2 3.3630275307 0.0481450285 1.619131e-01
8.5 2 4.4360849704 0.3831763675 1.699803e+00
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13 2 0.0619085738 0.8799673116 5.447752e-02
13.1 3 3.5621061502 0.1079022586 3.843593e-01
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14.2 1 4.6359198843 0.1518967560 7.041812e-01
14.3 3 4.6886152599 0.3521012473 1.650867e+00
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15.2 3 1.5094739688 0.5759767791 8.694220e-01
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16 2 1.2417913869 0.4564754473 5.668473e-01
16.1 2 2.5675726333 1.0652558311 2.735122e+00
16.2 1 2.6524101500 0.6971872493 1.849227e+00
16.3 3 3.5585018690 0.5259331838 1.871534e+00
16.4 2 3.7612454291 0.2046601798 7.697772e-01
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17 2 1.5925356350 0.6048676222 9.632732e-01
17.1 3 2.4374032998 0.2323298304 5.662815e-01
17.2 1 3.0256489082 1.2617499032 3.817612e+00
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19 2 0.9772165376 -0.4187468937 -4.092064e-01
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20 2 1.7170160066 0.6838223064 1.174134e+00
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20.3 3 2.2609123867 1.3169975191 2.977616e+00
20.4 2 3.4913365287 0.0444804531 1.552962e-01
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21.2 3 3.7887385779 1.0650265940 4.035107e+00
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22.1 2 3.1626627257 0.9601388170 3.036595e+00
23 2 1.5414533857 0.5556634840 8.565294e-01
23.1 1 2.3369736120 1.4407865964 3.367080e+00
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25 1 0.5381704110 0.3564400705 1.918255e-01
25.1 3 1.6069735331 0.0982553434 1.578937e-01
25.2 2 1.6358226922 0.1928682598 3.154983e-01
25.3 2 3.2646870392 -0.0192488594 -6.284150e-02
25.4 1 4.0782226040 0.4466012931 1.821339e+00
25.5 1 4.1560292873 1.1425193342 4.748344e+00
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26.1 1 2.4451737676 1.2268695927 2.999909e+00
26.2 1 3.5988757887 0.3678294939 1.323773e+00
26.3 2 4.1822362854 0.5948516018 2.487810e+00
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28.3 1 4.4705521734 0.1196789973 5.350312e-01
29 3 1.1898884235 0.1392699487 1.657157e-01
29.1 3 1.7624059319 0.7960234776 1.402916e+00
29.2 3 2.0210406382 1.0398214352 2.101521e+00
29.3 2 3.4078777023 0.0813246429 2.771444e-01
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30.1 3 3.5938334477 1.3635850954 4.900498e+00
30.2 3 3.6138710892 0.7354171050 2.657703e+00
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32 3 1.6745209007 -0.0474059668 -7.938228e-02
32.1 3 2.9128167813 1.2507771489 3.643285e+00
32.2 2 2.9676558380 0.1142915519 3.391780e-01
32.3 1 4.2099863547 0.6773270619 2.851538e+00
33 3 0.0093385763 0.1774293842 1.656938e-03
33.1 1 3.4591242753 0.6159606291 2.130684e+00
34 1 1.4998774312 0.8590979166 1.288542e+00
34.1 1 3.8242761395 0.0546216775 2.088884e-01
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34.3 2 3.9582124643 0.4163395571 1.647960e+00
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35.2 1 4.5025920868 -0.6045512101 -2.722047e+00
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36.1 3 1.7972493160 1.4466051416 2.599910e+00
36.2 3 1.8262697803 1.1606752905 2.119706e+00
36.3 3 4.2840119381 0.8373091576 3.587042e+00
36.4 3 4.6194464504 0.2640591685 1.219807e+00
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37.2 1 3.9663937816 0.0054610124 2.166053e-02
38 2 0.9826511063 0.8078948077 7.938787e-01
39 2 0.6921808305 0.9876451040 6.836290e-01
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39.2 1 1.3055654289 -1.7909380751 -2.338187e+00
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39.4 3 3.1834997435 -0.1850961689 -5.892536e-01
39.5 3 4.1394166439 0.4544226146 1.881045e+00
40 3 1.1330395646 0.5350190436 6.061977e-01
40.1 3 2.6940994046 0.4189342752 1.128651e+00
40.2 1 3.0396614212 0.4211994981 1.280304e+00
40.3 3 4.6762977762 0.0916687506 4.286704e-01
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41.2 1 3.2846923557 0.5972615368 1.961820e+00
41.3 1 3.3813529415 0.9885613862 3.342675e+00
41.4 1 3.5482964432 -0.3908036794 -1.386687e+00
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43.1 3 1.0743579536 0.6199122090 6.660076e-01
43.2 2 2.6131797966 0.1804894429 4.716514e-01
44 2 0.7662644819 1.3221409285 1.013110e+00
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44.2 1 3.3371910988 0.5706610068 1.904405e+00
44.3 1 4.1154200875 1.2679497430 5.218146e+00
45 2 0.1957449992 0.1414983160 2.769759e-02
45.1 3 1.9963831536 0.7220892521 1.441567e+00
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46.1 2 2.8565793915 0.3889107049 1.110954e+00
46.2 3 4.4160729996 0.1248719493 5.514436e-01
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47.1 2 2.4097121472 0.2982973539 7.188108e-01
47.2 2 2.9975794035 1.1518107179 3.452644e+00
47.3 2 3.1829649757 0.5196802157 1.654124e+00
47.4 2 4.6201055450 0.3702301552 1.710502e+00
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52 3 2.1266646020 0.3758235358 7.992506e-01
52.1 2 3.1000545993 0.7138067080 2.212840e+00
52.2 1 3.1268477370 0.8872895233 2.774419e+00
52.3 3 3.5711459327 -0.9664587437 -3.451365e+00
52.4 3 4.7983659909 0.0254566848 1.221505e-01
52.5 3 4.9818264414 0.4155259424 2.070078e+00
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53.2 2 4.5790420019 0.2162315769 9.901335e-01
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54.1 3 1.8812377600 0.4129127672 7.767871e-01
54.2 3 2.5107589352 1.0119546775 2.540774e+00
54.3 1 2.7848406672 -0.1112901990 -3.099255e-01
54.4 1 4.0143877396 0.8587727145 3.447447e+00
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55.1 3 0.7463747414 0.5835528661 4.355491e-01
55.2 2 2.8201208171 -1.0010857254 -2.823183e+00
55.3 1 3.1326431572 -0.4796526070 -1.502580e+00
55.4 1 3.2218102901 -0.1202746964 -3.875023e-01
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56.2 3 2.5674936292 -0.0168103281 -4.316041e-02
56.3 1 2.9507164378 0.3933023606 1.160524e+00
56.4 2 3.2272730360 0.3714625139 1.198811e+00
56.5 1 3.4175522043 0.7811448179 2.669603e+00
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57.1 1 0.2481445030 0.8018626997 1.989778e-01
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57.3 1 2.1153886721 0.6785562445 1.435410e+00
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58.1 2 1.6334245703 0.3402652711 5.557977e-01
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58.4 3 2.8477731440 -0.1271327672 -3.620453e-01
58.5 3 3.5715569824 0.8713264822 3.111992e+00
59 3 1.9023998594 0.4766421367 9.067639e-01
59.1 1 4.9736620474 1.0028089765 4.987633e+00
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61.2 2 2.5077313243 1.0229058138 2.565173e+00
61.3 3 3.1731073430 1.1717723589 3.718159e+00
61.4 2 3.6022726283 -0.0629201596 -2.266556e-01
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62.2 3 3.4584309917 0.4301745954 1.487729e+00
62.3 2 4.8028772371 -0.0333139957 -1.600030e-01
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63.1 1 3.9653754211 0.3643769511 1.444891e+00
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65 3 0.7076152589 1.2000091513 8.491448e-01
65.1 3 2.0252246363 0.0110122646 2.230231e-02
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66.2 1 3.9486138616 0.1906241379 7.527011e-01
67 3 4.1728388879 1.6716027681 6.975329e+00
68 3 0.1291919907 0.5691848839 7.353413e-02
68.1 1 1.7809643946 0.1004860389 1.789621e-01
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68.3 3 2.9406870750 0.7443745962 2.188973e+00
68.4 1 4.0406670363 0.8726923437 3.526259e+00
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70.1 2 0.4829774413 0.4691503050 2.265890e-01
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71.1 2 1.4883817220 -0.1103252087 -1.642060e-01
71.2 2 4.0758526395 1.7178492547 7.001700e+00
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71.4 2 4.7242791823 1.8623785017 8.798396e+00
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72.1 2 1.1799991806 0.1375317317 1.622873e-01
72.2 1 1.8917567329 -0.4170988856 -7.890496e-01
72.3 2 3.4853593935 0.7107266765 2.477138e+00
72.4 2 3.6884259700 0.1451969143 5.355481e-01
72.5 1 4.0854155901 1.6298050306 6.658431e+00
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76 3 1.8074807397 -0.9888876620 -1.787395e+00
76.1 3 4.2685073183 0.0003798292 1.621304e-03
76.2 2 4.9688734859 -0.8421863763 -4.184718e+00
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78 2 0.8231094317 0.0417330969 3.435091e-02
79 2 0.0583819521 -0.3767450660 -2.199511e-02
79.1 2 2.4406372628 0.1516000028 3.700006e-01
79.2 2 3.2962526032 -0.1888160741 -6.223855e-01
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80.1 1 1.3434670598 -0.0329337062 -4.424535e-02
80.2 3 2.8025900386 0.5046816157 1.414416e+00
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81.1 3 0.9421709494 0.2443038954 2.301760e-01
81.2 2 3.0542453879 0.6476958410 1.978222e+00
81.3 1 3.3456630446 0.4182528210 1.399333e+00
82 1 1.3791010005 1.1088801952 1.529258e+00
82.1 2 1.7601010622 0.9334157763 1.642906e+00
82.2 3 2.6233131927 0.4958140634 1.300676e+00
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83.3 3 4.7663642222 -0.0534169155 -2.546045e-01
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85.2 3 2.6846330194 -0.2241856342 -6.018562e-01
85.3 3 3.1608762743 -0.0520175929 -1.644212e-01
85.4 2 3.9452053758 0.2892733846 1.141243e+00
85.5 2 4.5092553482 -0.3740417009 -1.686650e+00
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86.2 1 1.2511159515 0.1230989293 1.540110e-01
86.3 1 2.1870554925 0.3305413955 7.229124e-01
86.4 1 2.4532935000 2.6003411822 6.379400e+00
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87.2 2 4.5241666853 0.4396872616 1.989218e+00
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88.2 3 2.4952722900 0.5730917016 1.430020e+00
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89 2 0.6462086167 1.5465903721 9.994200e-01
90 1 0.1696030737 0.0626760573 1.063005e-02
90.1 2 2.5980385230 1.1896872985 3.090853e+00
90.2 2 2.6651392167 0.2597888783 6.923735e-01
90.3 2 3.1242690247 0.6599799887 2.061955e+00
91 3 0.6382618390 1.1213651365 7.157246e-01
91.1 3 2.6224059286 1.2046371625 3.159048e+00
91.2 3 4.7772527603 0.3395603754 1.622166e+00
92 2 0.0737052364 0.4674939332 3.445675e-02
93 2 0.2788909199 0.2677965647 7.468603e-02
93.1 2 1.0357759963 1.6424445368 1.701205e+00
93.2 2 2.4916551099 0.7101700066 1.769499e+00
93.3 3 2.8876129608 1.1222322893 3.240573e+00
93.4 2 4.4639474002 1.4628960401 6.530291e+00
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94.3 2 3.0710722448 0.6793464917 2.086322e+00
94.4 3 3.0872731935 -0.9411356550 -2.905543e+00
94.5 2 4.3805759016 0.5683867264 2.489861e+00
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96.1 2 0.1324267720 0.3391519695 4.491280e-02
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96.3 2 2.1775037691 0.8771471244 1.909991e+00
96.4 2 2.2246142488 1.9030768981 4.233612e+00
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99 2 0.3531786101 0.7993356425 2.823083e-01
99.1 1 4.6789444226 1.0355522332 4.845291e+00
99.2 3 4.9927084171 -0.1150895843 -5.746087e-01
100 2 1.0691387602 0.0369067906 3.945848e-02
100.1 1 1.5109344281 1.6023713093 2.421078e+00
100.2 2 2.1502332564 0.8861545820 1.905439e+00
100.3 2 3.8745574222 0.1277046316 4.947989e-01
100.4 3 4.6567608765 -0.0834577654 -3.886429e-01
$m4c$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m4c$spM_lvlone
center scale
m1 NA NA
time 2.5339403 1.3818094
c1 0.2559996 0.6718095
c1:time 0.6507067 1.9186258
$m4c$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_binom
[1] 0
$m4c$tau_reg_binom
[1] 1e-04
$m4c$mu_reg_multinomial
[1] 0
$m4c$tau_reg_multinomial
[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"
$m4c$RinvD_m1_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
$m4c$KinvD_m1_id
id
5
$m4d
$m4d$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
$m4d$M_lvlone
m1 b2 time I(time^2) b21 c1 C1:time b21:c1
1 3 NA 0.5090421822 2.591239e-01 NA 0.7592026489 0.3652818145 NA
1.1 2 0 0.6666076288 4.443657e-01 NA 0.9548337990 0.4783486570 NA
1.2 1 NA 2.1304941282 4.539005e+00 NA 0.5612235156 1.5288138942 NA
1.3 1 0 2.4954441458 6.227241e+00 NA 1.1873391025 1.7906971118 NA
2 2 0 3.0164990982 9.099267e+00 NA 0.9192204198 2.2645370243 NA
2.1 2 NA 3.2996806887 1.088789e+01 NA -0.1870730476 2.4771262462 NA
2.2 1 NA 4.1747569619 1.742860e+01 NA 1.2517512331 3.1340608434 NA
3 1 0 0.8478727890 7.188883e-01 NA -0.0605087604 0.6152125819 NA
3.1 2 NA 3.0654308549 9.396866e+00 NA 0.3788637747 2.2242624781 NA
3.2 2 1 4.7381553578 2.245012e+01 NA 0.9872578281 3.4379836560 NA
4 2 1 0.3371432109 1.136655e-01 NA 1.4930175328 0.2518241168 NA
4.1 1 0 1.0693019140 1.143407e+00 NA -0.7692526880 0.7986991917 NA
4.2 2 0 2.6148973033 6.837688e+00 NA 0.9180841450 1.9531587247 NA
4.3 3 0 3.1336532847 9.819783e+00 NA -0.0541170782 2.3406358046 NA
5 2 NA 1.0762525082 1.158319e+00 NA -0.1376784521 0.7683495918 NA
5.1 1 0 1.7912546196 3.208593e+00 NA -0.2740585866 1.2787981866 NA
5.2 2 NA 2.7960080339 7.817661e+00 NA 0.4670496929 1.9961037166 NA
5.3 2 NA 2.8119940578 7.907311e+00 NA 0.1740288049 2.0075163311 NA
6 2 NA 1.7815462884 3.173907e+00 NA 0.9868044683 1.3063196933 NA
7 3 NA 3.3074087673 1.093895e+01 NA -0.1280320918 2.4295989047 NA
7.1 2 NA 3.7008403614 1.369622e+01 NA 0.4242971219 2.7186109493 NA
7.2 3 0 4.7716691741 2.276883e+01 NA 0.0777182491 3.5052341620 NA
8 2 0 1.1246398522 1.264815e+00 NA -0.5791408712 0.8606406530 NA
8.1 1 0 1.8027009873 3.249731e+00 NA 0.3128604232 1.3795329695 NA
8.2 3 NA 1.8175825174 3.303606e+00 NA 0.6258446356 1.3909211928 NA
8.3 2 1 2.8384267003 8.056666e+00 NA -0.1040137707 2.1721312865 NA
8.4 2 0 3.3630275307 1.130995e+01 NA 0.0481450285 2.5735867394 NA
8.5 2 1 4.4360849704 1.967885e+01 NA 0.3831763675 3.3947534923 NA
9 3 0 0.9607803822 9.230989e-01 NA -0.1757592269 0.6918216822 NA
9.1 2 NA 2.9177753383 8.513413e+00 NA -0.1791541200 2.1009798703 NA
9.2 3 NA 4.8100892501 2.313696e+01 NA -0.0957042935 3.4635636802 NA
10 3 NA 2.2975509102 5.278740e+00 NA -0.5598409704 1.7056739807 NA
10.1 1 0 4.1734118364 1.741737e+01 NA -0.2318340451 3.0982904226 NA
11 1 0 1.1832662905 1.400119e+00 NA 0.5086859475 0.8800481723 NA
11.1 1 0 1.2346051680 1.524250e+00 NA 0.4951758188 0.9182311964 NA
11.2 2 0 1.6435316263 2.701196e+00 NA -1.1022162541 1.2223681309 NA
11.3 3 0 3.3859017969 1.146433e+01 NA -0.0611636705 2.5182469169 NA
11.4 1 0 4.8118087661 2.315350e+01 NA -0.4971774316 3.5787578367 NA
12 1 0 0.9591987054 9.200622e-01 NA -0.2433996286 0.7142644156 NA
13 2 NA 0.0619085738 3.832672e-03 NA 0.8799673116 0.0466183059 NA
13.1 3 0 3.5621061502 1.268860e+01 NA 0.1079022586 2.6823320911 NA
14 1 NA 4.0364430007 1.629287e+01 NA 0.9991752617 2.8631042655 NA
14.1 1 NA 4.4710561272 1.999034e+01 NA -0.1094019046 3.1713813046 NA
14.2 1 NA 4.6359198843 2.149175e+01 NA 0.1518967560 3.2883214239 NA
14.3 3 NA 4.6886152599 2.198311e+01 NA 0.3521012473 3.3256989750 NA
15 1 0 0.5402063532 2.918229e-01 NA 0.3464447888 0.3960356618 NA
15.1 1 0 1.1893180816 1.414477e+00 NA -0.4767313971 0.8719119477 NA
15.2 3 0 1.5094739688 2.278512e+00 NA 0.5759767791 1.1066243829 NA
15.3 2 0 4.9193474615 2.419998e+01 NA -0.1713452662 3.6064681879 NA
16 2 1 1.2417913869 1.542046e+00 NA 0.4564754473 0.8706683147 NA
16.1 2 NA 2.5675726333 6.592429e+00 NA 1.0652558311 1.8002251917 NA
16.2 1 NA 2.6524101500 7.035280e+00 NA 0.6971872493 1.8597080795 NA
16.3 3 0 3.5585018690 1.266294e+01 NA 0.5259331838 2.4950042800 NA
16.4 2 0 3.7612454291 1.414697e+01 NA 0.2046601798 2.6371556877 NA
16.5 1 NA 3.9851612889 1.588151e+01 NA 1.0718540464 2.7941518196 NA
17 2 0 1.5925356350 2.536170e+00 NA 0.6048676222 1.1836354184 NA
17.1 3 0 2.4374032998 5.940935e+00 NA 0.2323298304 1.8115744548 NA
17.2 1 0 3.0256489082 9.154551e+00 NA 1.2617499032 2.2487818375 NA
17.3 1 NA 3.3329089405 1.110828e+01 NA -0.3913230895 2.4771496359 NA
17.4 2 0 3.8693758985 1.497207e+01 NA 0.9577299112 2.8758730794 NA
18 1 0 2.4374292302 5.941061e+00 NA -0.0050324072 1.8390868081 NA
19 2 NA 0.9772165376 9.549522e-01 NA -0.4187468937 0.7356962360 NA
19.1 3 NA 1.1466335913 1.314769e+00 NA -0.4478828944 0.8632416509 NA
19.2 2 0 2.2599126538 5.107205e+00 NA -1.1966721302 1.7013723870 NA
19.3 3 1 4.2114245973 1.773610e+01 NA -0.5877091668 3.1705656887 NA
20 2 NA 1.7170160066 2.948144e+00 NA 0.6838223064 1.3071411820 NA
20.1 2 0 1.7562902288 3.084555e+00 NA 0.3278571109 1.3370401189 NA
20.2 1 1 2.2515566566 5.069507e+00 NA -0.8489831990 1.7140797861 NA
20.3 3 0 2.2609123867 5.111725e+00 NA 1.3169975191 1.7212021776 NA
20.4 2 0 3.4913365287 1.218943e+01 NA 0.0444804531 2.6579075206 NA
20.5 3 0 4.1730977828 1.741475e+01 NA -0.4535207652 3.1769231897 NA
21 1 0 1.6936582839 2.868478e+00 NA -0.4030302960 1.2281934263 NA
21.1 2 0 2.9571191233 8.744554e+00 NA -0.4069674045 2.1444197467 NA
21.2 3 NA 3.7887385779 1.435454e+01 NA 1.0650265940 2.7474868216 NA
22 2 0 2.4696226232 6.099036e+00 NA -0.0673274516 1.8029800300 NA
22.1 2 0 3.1626627257 1.000244e+01 NA 0.9601388170 2.3089429463 NA
23 2 0 1.5414533857 2.376079e+00 NA 0.5556634840 1.0924663221 NA
23.1 1 NA 2.3369736120 5.461446e+00 NA 1.4407865964 1.6562712764 NA
24 1 0 2.8283136466 7.999358e+00 NA 0.3856376411 2.0906718629 NA
25 1 0 0.5381704110 2.896274e-01 NA 0.3564400705 0.4208837547 NA
25.1 3 NA 1.6069735331 2.582364e+00 NA 0.0982553434 1.2567562995 NA
25.2 2 1 1.6358226922 2.675916e+00 NA 0.1928682598 1.2793181910 NA
25.3 2 0 3.2646870392 1.065818e+01 NA -0.0192488594 2.5531945101 NA
25.4 1 0 4.0782226040 1.663190e+01 NA 0.4466012931 3.1894314642 NA
25.5 1 NA 4.1560292873 1.727258e+01 NA 1.1425193342 3.2502812774 NA
26 2 NA 0.2412706357 5.821152e-02 NA 0.5341531449 0.1717436194 NA
26.1 1 0 2.4451737676 5.978875e+00 NA 1.2268695927 1.7405474628 NA
26.2 1 0 3.5988757887 1.295191e+01 NA 0.3678294939 2.5617868987 NA
26.3 2 0 4.1822362854 1.749110e+01 NA 0.5948516018 2.9770402626 NA
27 1 0 3.6955824879 1.365733e+01 NA -0.3342844147 2.6722228982 NA
27.1 3 0 4.2451434687 1.802124e+01 NA -0.4835141229 3.0696025919 NA
28 1 NA 0.5746519344 3.302248e-01 NA -0.7145915499 0.4303771207 NA
28.1 3 0 2.7943964268 7.808651e+00 NA 0.5063671955 2.0928221348 NA
28.2 1 0 4.2108539480 1.773129e+01 NA -0.2067413142 3.1536571778 NA
28.3 1 0 4.4705521734 1.998584e+01 NA 0.1196789973 3.3481543470 NA
29 3 0 1.1898884235 1.415834e+00 NA 0.1392699487 0.8937118818 NA
29.1 3 0 1.7624059319 3.106075e+00 NA 0.7960234776 1.3237233767 NA
29.2 3 0 2.0210406382 4.084605e+00 NA 1.0398214352 1.5179810109 NA
29.3 2 0 3.4078777023 1.161363e+01 NA 0.0813246429 2.5596188130 NA
30 1 NA 2.2635366488 5.123598e+00 NA -0.3296323050 1.6525441223 NA
30.1 3 0 3.5938334477 1.291564e+01 NA 1.3635850954 2.6237562107 NA
30.2 3 0 3.6138710892 1.306006e+01 NA 0.7354171050 2.6383851264 NA
31 1 0 4.3988140998 1.934957e+01 NA 0.3708398217 3.3214216230 NA
32 3 0 1.6745209007 2.804020e+00 NA -0.0474059668 1.2260671754 NA
32.1 3 0 2.9128167813 8.484502e+00 NA 1.2507771489 2.1327348270 NA
32.2 2 NA 2.9676558380 8.806981e+00 NA 0.1142915519 2.1728874266 NA
32.3 1 NA 4.2099863547 1.772399e+01 NA 0.6773270619 3.0825091978 NA
33 3 0 0.0093385763 8.720901e-05 NA 0.1774293842 0.0068231501 NA
33.1 1 1 3.4591242753 1.196554e+01 NA 0.6159606291 2.5273792639 NA
34 1 NA 1.4998774312 2.249632e+00 NA 0.8590979166 1.1139914202 NA
34.1 1 0 3.8242761395 1.462509e+01 NA 0.0546216775 2.8403726326 NA
34.2 2 NA 3.9072251692 1.526641e+01 NA -0.0897224473 2.9019806717 NA
34.3 2 NA 3.9582124643 1.566745e+01 NA 0.4163395571 2.9398500390 NA
35 1 0 1.3294299203 1.767384e+00 NA -1.4693520528 0.9562645676 NA
35.1 1 0 1.5276966314 2.333857e+00 NA -0.3031734330 1.0988786520 NA
35.2 1 NA 4.5025920868 2.027334e+01 NA -0.6045512101 3.2387335424 NA
36 2 NA 0.7123168337 5.073953e-01 NA 0.9823048960 0.5209093415 NA
36.1 3 NA 1.7972493160 3.230105e+00 NA 1.4466051416 1.3143083435 NA
36.2 3 0 1.8262697803 3.335261e+00 NA 1.1606752905 1.3355306848 NA
36.3 3 0 4.2840119381 1.835276e+01 NA 0.8373091576 3.1328500636 NA
36.4 3 0 4.6194464504 2.133929e+01 NA 0.2640591685 3.3781495744 NA
37 1 0 2.0018732361 4.007496e+00 NA 0.1177313455 1.4214172307 NA
37.1 3 0 3.6656836793 1.343724e+01 NA -0.1415483779 2.6027951471 NA
37.2 1 0 3.9663937816 1.573228e+01 NA 0.0054610124 2.8163124234 NA
38 2 0 0.9826511063 9.656032e-01 NA 0.8078948077 0.7537115243 NA
39 2 1 0.6921808305 4.791143e-01 NA 0.9876451040 0.5122448722 NA
39.1 3 0 0.9027792048 8.150103e-01 NA -0.3431222274 0.6680971185 NA
39.2 1 NA 1.3055654289 1.704501e+00 NA -1.7909380751 0.9661769970 NA
39.3 2 NA 1.5412842878 2.375557e+00 NA -0.1798746191 1.1406195291 NA
39.4 3 0 3.1834997435 1.013467e+01 NA -0.1850961689 2.3559326511 NA
39.5 3 1 4.1394166439 1.713477e+01 NA 0.4544226146 3.0633540486 NA
40 3 0 1.1330395646 1.283779e+00 NA 0.5350190436 0.8381437699 NA
40.1 3 1 2.6940994046 7.258172e+00 NA 0.4189342752 1.9929071342 NA
40.2 1 0 3.0396614212 9.239542e+00 NA 0.4211994981 2.2485298506 NA
40.3 3 NA 4.6762977762 2.186776e+01 NA 0.0916687506 3.4591994578 NA
41 3 0 1.9337158254 3.739257e+00 NA -0.1035047421 1.4485398595 NA
41.1 3 NA 3.1956304458 1.021205e+01 NA -0.4684202411 2.3938357519 NA
41.2 1 0 3.2846923557 1.078920e+01 NA 0.5972615368 2.4605517215 NA
41.3 1 NA 3.3813529415 1.143355e+01 NA 0.9885613862 2.5329598332 NA
41.4 1 0 3.5482964432 1.259041e+01 NA -0.3908036794 2.6580166349 NA
42 1 0 0.4859252973 2.361234e-01 NA -0.0338893961 0.3605213138 NA
42.1 1 1 4.3293134298 1.874295e+01 NA -0.4498363172 3.2120364478 NA
43 3 0 0.5616614548 3.154636e-01 NA 0.8965546110 0.4228080758 NA
43.1 3 1 1.0743579536 1.154245e+00 NA 0.6199122090 0.8087562626 NA
43.2 2 0 2.6131797966 6.828709e+00 NA 0.1804894429 1.9671521198 NA
44 2 0 0.7662644819 5.871613e-01 NA 1.3221409285 0.5676728587 NA
44.1 2 0 2.6490291790 7.017356e+00 NA 0.3416426284 1.9624842365 NA
44.2 1 0 3.3371910988 1.113684e+01 NA 0.5706610068 2.4722962576 NA
44.3 1 0 4.1154200875 1.693668e+01 NA 1.2679497430 3.0488327997 NA
45 2 NA 0.1957449992 3.831610e-02 NA 0.1414983160 0.1438246201 NA
45.1 3 1 1.9963831536 3.985546e+00 NA 0.7220892521 1.4668525369 NA
46 3 0 1.3477755385 1.816499e+00 NA 1.5391054233 0.9882426330 NA
46.1 2 0 2.8565793915 8.160046e+00 NA 0.3889107049 2.0945576311 NA
46.2 3 0 4.4160729996 1.950170e+01 NA 0.1248719493 3.2380403739 NA
47 1 0 0.6012621359 3.615162e-01 NA 0.2014101100 0.4435198614 NA
47.1 2 0 2.4097121472 5.806713e+00 NA 0.2982973539 1.7775195440 NA
47.2 2 0 2.9975794035 8.985482e+00 NA 1.1518107179 2.2111586981 NA
47.3 2 NA 3.1829649757 1.013127e+01 NA 0.5196802157 2.3479080100 NA
47.4 2 0 4.6201055450 2.134538e+01 NA 0.3702301552 3.4080119947 NA
48 3 1 2.8607365978 8.183814e+00 NA -0.2128602862 2.1015481927 NA
48.1 1 1 2.9098354396 8.467142e+00 NA -0.5337239976 2.1376170787 NA
49 3 NA 2.7179756400 7.387392e+00 NA -0.5236770035 1.9921136553 NA
50 1 0 1.1762060679 1.383461e+00 NA 0.3897705981 0.8539769445 NA
51 3 0 1.4304436720 2.046169e+00 NA -0.7213343736 1.0360574125 NA
52 3 0 2.1266646020 4.522702e+00 NA 0.3758235358 1.5520541611 NA
52.1 2 0 3.1000545993 9.610339e+00 NA 0.7138067080 2.2624407422 NA
52.2 1 0 3.1268477370 9.777177e+00 NA 0.8872895233 2.2819945547 NA
52.3 3 0 3.5711459327 1.275308e+01 NA -0.9664587437 2.6062463726 NA
52.4 3 0 4.7983659909 2.302432e+01 NA 0.0254566848 3.5018798430 NA
52.5 3 0 4.9818264414 2.481859e+01 NA 0.4155259424 3.6357705164 NA
53 1 0 0.4965799209 2.465916e-01 NA 0.5675736897 0.3602558557 NA
53.1 3 0 3.5505357443 1.260630e+01 NA -0.3154088781 2.5758216127 NA
53.2 2 NA 4.5790420019 2.096763e+01 NA 0.2162315769 3.3219762321 NA
54 3 NA 1.4034724841 1.969735e+00 NA -0.0880802382 1.0585083082 NA
54.1 3 NA 1.8812377600 3.539056e+00 NA 0.4129127672 1.4188420659 NA
54.2 3 NA 2.5107589352 6.303910e+00 NA 1.0119546775 1.8936311350 NA
54.3 1 NA 2.7848406672 7.755338e+00 NA -0.1112901990 2.1003454052 NA
54.4 1 0 4.0143877396 1.611531e+01 NA 0.8587727145 3.0276780080 NA
55 1 0 0.6118522980 3.743632e-01 NA -0.0116453589 0.4521559134 NA
55.1 3 0 0.7463747414 5.570753e-01 NA 0.5835528661 0.5515673538 NA
55.2 2 NA 2.8201208171 7.953081e+00 NA -1.0010857254 2.0840557566 NA
55.3 1 NA 3.1326431572 9.813453e+00 NA -0.4796526070 2.3150082668 NA
55.4 1 0 3.2218102901 1.038006e+01 NA -0.1202746964 2.3809023504 NA
56 2 0 1.2231332215 1.496055e+00 NA 0.5176377612 0.9198742485 NA
56.1 1 NA 2.3573202139 5.556959e+00 NA -1.1136932588 1.7728552558 NA
56.2 3 NA 2.5674936292 6.592024e+00 NA -0.0168103281 1.9309190783 NA
56.3 1 1 2.9507164378 8.706727e+00 NA 0.3933023606 2.2191270894 NA
56.4 2 0 3.2272730360 1.041529e+01 NA 0.3714625139 2.4271153024 NA
56.5 1 0 3.4175522043 1.167966e+01 NA 0.7811448179 2.5702173815 NA
57 1 0 0.2370331448 5.618471e-02 NA -1.0868304872 0.1711369455 NA
57.1 1 0 0.2481445030 6.157569e-02 NA 0.8018626997 0.1791592999 NA
57.2 1 0 1.1405586067 1.300874e+00 NA -0.1159517011 0.8234785742 NA
57.3 1 NA 2.1153886721 4.474869e+00 NA 0.6785562445 1.5273018303 NA
58 3 0 1.2210099772 1.490865e+00 NA 1.6476207996 0.8864082613 NA
58.1 2 NA 1.6334245703 2.668076e+00 NA 0.3402652711 1.1858060626 NA
58.2 1 1 1.6791862890 2.819667e+00 NA -0.1111300753 1.2190273844 NA
58.3 3 1 2.6320121693 6.927488e+00 NA -0.5409234285 1.9107438714 NA
58.4 3 0 2.8477731440 8.109812e+00 NA -0.1271327672 2.0673783904 NA
58.5 3 0 3.5715569824 1.275602e+01 NA 0.8713264822 2.5928187928 NA
59 3 NA 1.9023998594 3.619125e+00 NA 0.4766421367 1.4189250995 NA
59.1 1 1 4.9736620474 2.473731e+01 NA 1.0028089765 3.7096585560 NA
60 3 0 2.8854503250 8.325824e+00 NA 0.5231452932 2.2138389085 NA
61 1 NA 0.7213630795 5.203647e-01 NA -0.7190130614 0.5235061310 NA
61.1 2 1 2.3186947661 5.376345e+00 NA 0.8353702312 1.6827183986 NA
61.2 2 1 2.5077313243 6.288716e+00 NA 1.0229058138 1.8199056209 NA
61.3 3 0 3.1731073430 1.006861e+01 NA 1.1717723589 2.3027809373 NA
61.4 2 0 3.6022726283 1.297637e+01 NA -0.0629201596 2.6142338858 NA
62 2 NA 0.5336771999 2.848114e-01 NA -0.3979137604 0.3837081458 NA
62.1 1 1 0.6987666548 4.882748e-01 NA 0.6830738372 0.5024056819 NA
62.2 3 0 3.4584309917 1.196074e+01 NA 0.4301745954 2.4865745506 NA
62.3 2 0 4.8028772371 2.306763e+01 NA -0.0333139957 3.4532168882 NA
63 3 NA 2.8097350930 7.894611e+00 NA 0.3345678035 2.0604786922 NA
63.1 1 0 3.9653754211 1.572420e+01 NA 0.3643769511 2.9079508535 NA
64 3 0 4.1191305732 1.696724e+01 NA 0.3949911859 3.0153034804 NA
65 3 0 0.7076152589 5.007194e-01 NA 1.2000091513 0.5291342322 NA
65.1 3 0 2.0252246363 4.101535e+00 NA 0.0110122646 1.5144044302 NA
65.2 2 0 3.1127382827 9.689140e+00 NA -0.5776452043 2.3276156931 NA
65.3 3 0 3.1969087943 1.022023e+01 NA -0.1372183563 2.3905559682 NA
66 3 NA 3.4943454154 1.221045e+01 NA -0.5081302805 2.5662383121 NA
66.1 3 0 3.7677437009 1.419589e+01 NA -0.1447837412 2.7670213119 NA
66.2 1 0 3.9486138616 1.559155e+01 NA 0.1906241379 2.8998518941 NA
67 3 NA 4.1728388879 1.741258e+01 NA 1.6716027681 3.1261341693 NA
68 3 0 0.1291919907 1.669057e-02 NA 0.5691848839 0.0966709548 NA
68.1 1 0 1.7809643946 3.171834e+00 NA 0.1004860389 1.3326486232 NA
68.2 2 NA 2.0493205660 4.199715e+00 NA -0.0061241827 1.5334524594 NA
68.3 3 0 2.9406870750 8.647640e+00 NA 0.7443745962 2.2004384781 NA
68.4 1 NA 4.0406670363 1.632699e+01 NA 0.8726923437 3.0235244339 NA
69 1 0 4.1451198701 1.718202e+01 NA 0.0381382683 3.0417997050 NA
70 1 0 0.1992557163 3.970284e-02 NA 0.8126204217 0.1515886088 NA
70.1 2 0 0.4829774413 2.332672e-01 NA 0.4691503050 0.3674367781 NA
71 3 0 0.7741605386 5.993245e-01 NA -0.5529062591 0.6021111272 NA
71.1 2 1 1.4883817220 2.215280e+00 NA -0.1103252087 1.1576038195 NA
71.2 2 0 4.0758526395 1.661257e+01 NA 1.7178492547 3.1700352897 NA
71.3 1 1 4.7048238723 2.213537e+01 NA -1.0118346755 3.6592239775 NA
71.4 2 0 4.7242791823 2.231881e+01 NA 1.8623785017 3.6743555400 NA
72 1 0 0.9321196121 8.688470e-01 NA -0.4521659275 0.6905275565 NA
72.1 2 0 1.1799991806 1.392398e+00 NA 0.1375317317 0.8741602904 NA
72.2 1 NA 1.8917567329 3.578744e+00 NA -0.4170988856 1.4014404774 NA
72.3 2 0 3.4853593935 1.214773e+01 NA 0.7107266765 2.5820041485 NA
72.4 2 0 3.6884259700 1.360449e+01 NA 0.1451969143 2.7324387763 NA
72.5 1 0 4.0854155901 1.669062e+01 NA 1.6298050306 3.0265343716 NA
73 2 0 4.6019889915 2.117830e+01 NA -0.0307469467 3.3356465236 NA
74 1 0 1.4626806753 2.139435e+00 NA 0.3730017941 1.0772519613 NA
75 3 NA 3.2524286874 1.057829e+01 NA -0.4908003566 2.4279140631 NA
76 3 0 1.8074807397 3.266987e+00 NA -0.9888876620 1.3294798303 NA
76.1 3 0 4.2685073183 1.822015e+01 NA 0.0003798292 3.1396707367 NA
76.2 2 0 4.9688734859 2.468970e+01 NA -0.8421863763 3.6548201782 NA
77 2 NA 0.8459033852 7.155525e-01 NA -0.4986802480 0.6097651292 NA
78 2 0 0.8231094317 6.775091e-01 NA 0.0417330969 0.6069257516 NA
79 2 NA 0.0583819521 3.408452e-03 NA -0.3767450660 0.0443590703 NA
79.1 2 0 2.4406372628 5.956710e+00 NA 0.1516000028 1.8544155528 NA
79.2 2 NA 3.2962526032 1.086528e+01 NA -0.1888160741 2.5045188757 NA
80 2 NA 0.8985060186 8.073131e-01 NA -0.0041558414 0.6613377055 NA
80.1 1 0 1.3434670598 1.804904e+00 NA -0.0329337062 0.9888474917 NA
80.2 3 NA 2.8025900386 7.854511e+00 NA 0.5046816157 2.0628225380 NA
81 2 0 0.0101324962 1.026675e-04 NA -0.9493950353 0.0073905742 NA
81.1 3 0 0.9421709494 8.876861e-01 NA 0.2443038954 0.6872131160 NA
81.2 2 NA 3.0542453879 9.328415e+00 NA 0.6476958410 2.2277459217 NA
81.3 1 0 3.3456630446 1.119346e+01 NA 0.4182528210 2.4403039888 NA
82 1 NA 1.3791010005 1.901920e+00 NA 1.1088801952 1.0038902706 NA
82.1 2 0 1.7601010622 3.097956e+00 NA 0.9334157763 1.2812319990 NA
82.2 3 1 2.6233131927 6.881772e+00 NA 0.4958140634 1.9095908060 NA
83 2 NA 0.0537394290 2.887926e-03 NA 0.5104724530 0.0394696928 NA
83.1 3 0 2.9061570496 8.445749e+00 NA -0.0513309106 2.1344686414 NA
83.2 3 0 3.1189457362 9.727823e+00 NA -0.2067792494 2.2907543380 NA
83.3 3 NA 4.7663642222 2.271823e+01 NA -0.0534169155 3.5007244248 NA
84 2 0 2.7254060237 7.427838e+00 NA -0.0255753653 2.0125352642 NA
84.1 3 NA 3.3364784659 1.113209e+01 NA -1.8234189877 2.4637725582 NA
85 1 1 0.2977756259 8.867032e-02 NA -0.0114038622 0.2180824084 NA
85.1 2 NA 1.7394116637 3.025553e+00 NA -0.0577615939 1.2738956847 NA
85.2 3 0 2.6846330194 7.207254e+00 NA -0.2241856342 1.9661489512 NA
85.3 3 0 3.1608762743 9.991139e+00 NA -0.0520175929 2.3149359808 NA
85.4 2 0 3.9452053758 1.556465e+01 NA 0.2892733846 2.8893563314 NA
85.5 2 0 4.5092553482 2.033338e+01 NA -0.3740417009 3.3024505062 NA
86 1 0 0.8476278360 7.184729e-01 NA 0.4293735089 0.6422134099 NA
86.1 2 NA 1.0118629411 1.023867e+00 NA -0.1363456521 0.7666477221 NA
86.2 1 NA 1.2511159515 1.565291e+00 NA 0.1230989293 0.9479200743 NA
86.3 1 0 2.1870554925 4.783212e+00 NA 0.3305413955 1.6570436997 NA
86.4 1 NA 2.4532935000 6.018649e+00 NA 2.6003411822 1.8587614954 NA
86.5 2 0 3.8206058508 1.459703e+01 NA -0.1420690052 2.8947188929 NA
87 3 NA 2.7069531474 7.327595e+00 NA 1.0457427869 2.0291697589 NA
87.1 3 NA 3.4462517721 1.187665e+01 NA -0.2973007190 2.5833582987 NA
87.2 2 NA 4.5241666853 2.046808e+01 NA 0.4396872616 3.3913783218 NA
88 3 0 0.0005892443 3.472088e-07 NA -0.0601928334 0.0004286893 NA
88.1 3 NA 0.7116099866 5.063888e-01 NA -1.0124347595 0.5177132747 NA
88.2 3 0 2.4952722900 6.226384e+00 NA 0.5730917016 1.8153702347 NA
88.3 1 0 3.2995816297 1.088724e+01 NA -0.0029455332 2.4005245046 NA
89 2 0 0.6462086167 4.175856e-01 NA 1.5465903721 0.4685431339 NA
90 1 0 0.1696030737 2.876520e-02 NA 0.0626760573 0.1244083036 NA
90.1 2 0 2.5980385230 6.749804e+00 NA 1.1896872985 1.9057294087 NA
90.2 2 0 2.6651392167 7.102967e+00 NA 0.2597888783 1.9549495277 NA
90.3 2 NA 3.1242690247 9.761057e+00 NA 0.6599799887 2.2917332858 NA
91 3 0 0.6382618390 4.073782e-01 NA 1.1213651365 0.4687382105 NA
91.1 3 0 2.6224059286 6.877013e+00 NA 1.2046371625 1.9258896380 NA
91.2 3 0 4.7772527603 2.282214e+01 NA 0.3395603754 3.5084048160 NA
92 2 0 0.0737052364 5.432462e-03 NA 0.4674939332 0.0543975943 NA
93 2 NA 0.2788909199 7.778015e-02 NA 0.2677965647 0.2060853219 NA
93.1 2 0 1.0357759963 1.072832e+00 NA 1.6424445368 0.7653825006 NA
93.2 2 NA 2.4916551099 6.208345e+00 NA 0.7101700066 1.8411985077 NA
93.3 3 0 2.8876129608 8.338309e+00 NA 1.1222322893 2.1337899668 NA
93.4 2 0 4.4639474002 1.992683e+01 NA 1.4628960401 3.2986159518 NA
94 2 NA 0.8488043118 7.204688e-01 NA -0.2904211940 0.6162277526 NA
94.1 3 0 1.0552454425 1.113543e+00 NA 0.0147813580 0.7661029975 NA
94.2 3 0 1.9445500884 3.781275e+00 NA -0.4536774482 1.4117337932 NA
94.3 2 NA 3.0710722448 9.431485e+00 NA 0.6793464917 2.2295833342 NA
94.4 3 0 3.0872731935 9.531256e+00 NA -0.9411356550 2.2413451432 NA
94.5 2 1 4.3805759016 1.918945e+01 NA 0.5683867264 3.1802765437 NA
95 2 0 2.0199063048 4.080021e+00 NA 0.2375652188 1.4710654666 NA
95.1 3 NA 4.0184444457 1.614790e+01 NA 0.0767152977 2.9265688411 NA
95.2 2 0 4.5596531732 2.079044e+01 NA -0.6886731251 3.3207225043 NA
96 3 0 0.0311333477 9.692853e-04 NA 0.7813892121 0.0226702966 NA
96.1 2 0 0.1324267720 1.753685e-02 NA 0.3391519695 0.0964288912 NA
96.2 3 0 0.6701303425 4.490747e-01 NA -0.4857246503 0.4879672359 NA
96.3 2 NA 2.1775037691 4.741523e+00 NA 0.8771471244 1.5855877997 NA
96.4 2 1 2.2246142488 4.948909e+00 NA 1.9030768981 1.6198921270 NA
96.5 3 1 4.2377650598 1.795865e+01 NA -0.1684332749 3.0858034196 NA
97 3 0 1.1955102731 1.429245e+00 NA 1.3775130083 0.8662239621 NA
97.1 3 0 4.9603108643 2.460468e+01 NA -1.7323228619 3.5940637456 NA
98 2 0 0.2041732438 4.168671e-02 NA -1.2648518889 0.1536799385 NA
98.1 3 0 0.4309578973 1.857247e-01 NA -0.9042716241 0.3243793452 NA
98.2 1 1 3.5172611906 1.237113e+01 NA -0.1560385207 2.6474207553 NA
99 2 0 0.3531786101 1.247351e-01 NA 0.7993356425 0.2568424071 NA
99.1 1 0 4.6789444226 2.189252e+01 NA 1.0355522332 3.4026730772 NA
99.2 3 0 4.9927084171 2.492714e+01 NA -0.1150895843 3.6308519569 NA
100 2 NA 1.0691387602 1.143058e+00 NA 0.0369067906 0.7893943379 NA
100.1 1 NA 1.5109344281 2.282923e+00 NA 1.6023713093 1.1155924065 NA
100.2 2 0 2.1502332564 4.623503e+00 NA 0.8861545820 1.5876161457 NA
100.3 2 NA 3.8745574222 1.501220e+01 NA 0.1277046316 2.8607640137 NA
100.4 3 0 4.6567608765 2.168542e+01 NA -0.0834577654 3.4383008132 NA
$m4d$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m4d$spM_lvlone
center scale
m1 NA NA
b2 NA NA
time 2.53394028 1.3818094
I(time^2) 8.32444679 7.0900029
b21 NA NA
c1 0.25599956 0.6718095
C1:time 1.86876118 1.0180574
b21:c1 0.04082297 0.2677776
$m4d$mu_reg_binom
[1] 0
$m4d$tau_reg_binom
[1] 1e-04
$m4d$mu_reg_multinomial
[1] 0
$m4d$tau_reg_multinomial
[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"
$m4d$RinvD_m1_id
[,1] [,2]
[1,] NA 0
[2,] 0 NA
$m4d$KinvD_m1_id
id
3
$m4e
$m4e$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
$m4e$M_lvlone
m1 log(time) I(time^2) p1 time
1 3 -0.67522439 2.591239e-01 5 0.5090421822
1.1 2 -0.40555367 4.443657e-01 3 0.6666076288
1.2 1 0.75635394 4.539005e+00 8 2.1304941282
1.3 1 0.91446673 6.227241e+00 6 2.4954441458
2 2 1.10409692 9.099267e+00 5 3.0164990982
2.1 2 1.19382570 1.088789e+01 3 3.2996806887
2.2 1 1.42905614 1.742860e+01 2 4.1747569619
3 1 -0.16502467 7.188883e-01 7 0.8478727890
3.1 2 1.12018813 9.396866e+00 2 3.0654308549
3.2 2 1.55564789 2.245012e+01 8 4.7381553578
4 2 -1.08724748 1.136655e-01 2 0.3371432109
4.1 1 0.06700602 1.143407e+00 4 1.0693019140
4.2 2 0.96122482 6.837688e+00 2 2.6148973033
4.3 3 1.14219951 9.819783e+00 6 3.1336532847
5 2 0.07348511 1.158319e+00 6 1.0762525082
5.1 1 0.58291628 3.208593e+00 2 1.7912546196
5.2 2 1.02819270 7.817661e+00 3 2.7960080339
5.3 2 1.03389386 7.907311e+00 2 2.8119940578
6 2 0.57748169 3.173907e+00 4 1.7815462884
7 3 1.19616503 1.093895e+01 2 3.3074087673
7.1 2 1.30855992 1.369622e+01 6 3.7008403614
7.2 3 1.56269618 2.276883e+01 4 4.7716691741
8 2 0.11746285 1.264815e+00 2 1.1246398522
8.1 1 0.58928609 3.249731e+00 2 1.8027009873
8.2 3 0.59750733 3.303606e+00 1 1.8175825174
8.3 2 1.04324992 8.056666e+00 2 2.8384267003
8.4 2 1.21284162 1.130995e+01 2 3.3630275307
8.5 2 1.48977222 1.967885e+01 4 4.4360849704
9 3 -0.04000943 9.230989e-01 3 0.9607803822
9.1 2 1.07082146 8.513413e+00 3 2.9177753383
9.2 3 1.57071564 2.313696e+01 2 4.8100892501
10 3 0.83184373 5.278740e+00 4 2.2975509102
10.1 1 1.42873389 1.741737e+01 5 4.1734118364
11 1 0.16827866 1.400119e+00 2 1.1832662905
11.1 1 0.21075122 1.524250e+00 4 1.2346051680
11.2 2 0.49684736 2.701196e+00 6 1.6435316263
11.3 3 1.21962028 1.146433e+01 2 3.3859017969
11.4 1 1.57107306 2.315350e+01 1 4.8118087661
12 1 -0.04165702 9.200622e-01 5 0.9591987054
13 2 -2.78209660 3.832672e-03 2 0.0619085738
13.1 3 1.27035199 1.268860e+01 6 3.5621061502
14 1 1.39536386 1.629287e+01 3 4.0364430007
14.1 1 1.49762465 1.999034e+01 2 4.4710561272
14.2 1 1.53383464 2.149175e+01 4 4.6359198843
14.3 3 1.54513729 2.198311e+01 2 4.6886152599
15 1 -0.61580408 2.918229e-01 4 0.5402063532
15.1 1 0.17338010 1.414477e+00 7 1.1893180816
15.2 3 0.41176123 2.278512e+00 4 1.5094739688
15.3 2 1.59317589 2.419998e+01 3 4.9193474615
16 2 0.21655500 1.542046e+00 3 1.2417913869
16.1 2 0.94296095 6.592429e+00 2 2.5675726333
16.2 1 0.97546872 7.035280e+00 5 2.6524101500
16.3 3 1.26933963 1.266294e+01 3 3.5585018690
16.4 2 1.32475013 1.414697e+01 2 3.7612454291
16.5 1 1.38257779 1.588151e+01 6 3.9851612889
17 2 0.46532748 2.536170e+00 3 1.5925356350
17.1 3 0.89093325 5.940935e+00 1 2.4374032998
17.2 1 1.10712558 9.154551e+00 4 3.0256489082
17.3 1 1.20384548 1.110828e+01 5 3.3329089405
17.4 2 1.35309323 1.497207e+01 5 3.8693758985
18 1 0.89094389 5.941061e+00 8 2.4374292302
19 2 -0.02304702 9.549522e-01 5 0.9772165376
19.1 3 0.13683034 1.314769e+00 6 1.1466335913
19.2 2 0.81532616 5.107205e+00 4 2.2599126538
19.3 3 1.43780097 1.773610e+01 3 4.2114245973
20 2 0.54058790 2.948144e+00 5 1.7170160066
20.1 2 0.56320376 3.084555e+00 8 1.7562902288
20.2 1 0.81162182 5.069507e+00 3 2.2515566566
20.3 3 0.81576844 5.111725e+00 3 2.2609123867
20.4 2 1.25028462 1.218943e+01 3 3.4913365287
20.5 3 1.42865863 1.741475e+01 3 4.1730977828
21 1 0.52689085 2.868478e+00 3 1.6936582839
21.1 2 1.08421553 8.744554e+00 3 2.9571191233
21.2 3 1.33203313 1.435454e+01 4 3.7887385779
22 2 0.90406535 6.099036e+00 6 2.4696226232
22.1 2 1.15141431 1.000244e+01 3 3.1626627257
23 2 0.43272573 2.376079e+00 3 1.5414533857
23.1 1 0.84885676 5.461446e+00 2 2.3369736120
24 1 1.03968065 7.999358e+00 1 2.8283136466
25 1 -0.61958002 2.896274e-01 2 0.5381704110
25.1 3 0.47435262 2.582364e+00 0 1.6069735331
25.2 2 0.49214585 2.675916e+00 6 1.6358226922
25.3 2 1.18316390 1.065818e+01 6 3.2646870392
25.4 1 1.40566126 1.663190e+01 2 4.0782226040
25.5 1 1.42456012 1.727258e+01 2 4.1560292873
26 2 -1.42183601 5.821152e-02 6 0.2412706357
26.1 1 0.89411619 5.978875e+00 0 2.4451737676
26.2 1 1.28062152 1.295191e+01 1 3.5988757887
26.3 2 1.43084610 1.749110e+01 4 4.1822362854
27 1 1.30713818 1.365733e+01 2 3.6955824879
27.1 3 1.44577562 1.802124e+01 4 4.2451434687
28 1 -0.55399075 3.302248e-01 5 0.5746519344
28.1 3 1.02761614 7.808651e+00 0 2.7943964268
28.2 1 1.43766547 1.773129e+01 7 4.2108539480
28.3 1 1.49751193 1.998584e+01 3 4.4705521734
29 3 0.17385954 1.415834e+00 4 1.1898884235
29.1 3 0.56667988 3.106075e+00 1 1.7624059319
29.2 3 0.70361255 4.084605e+00 4 2.0210406382
29.3 2 1.22608972 1.161363e+01 3 3.4078777023
30 1 0.81692848 5.123598e+00 5 2.2635366488
30.1 3 1.27921945 1.291564e+01 5 3.5938334477
30.2 3 1.28477952 1.306006e+01 6 3.6138710892
31 1 1.48133498 1.934957e+01 1 4.3988140998
32 3 0.51552709 2.804020e+00 2 1.6745209007
32.1 3 1.06912058 8.484502e+00 5 2.9128167813
32.2 2 1.08777236 8.806981e+00 5 2.9676558380
32.3 1 1.43745941 1.772399e+01 6 4.2099863547
33 3 -4.67360146 8.720901e-05 4 0.0093385763
33.1 1 1.24101546 1.196554e+01 7 3.4591242753
34 1 0.40538339 2.249632e+00 2 1.4998774312
34.1 1 1.34136920 1.462509e+01 5 3.8242761395
34.2 2 1.36282745 1.526641e+01 6 3.9072251692
34.3 2 1.37579253 1.566745e+01 2 3.9582124643
35 1 0.28475022 1.767384e+00 3 1.3294299203
35.1 1 0.42376113 2.333857e+00 2 1.5276966314
35.2 1 1.50465325 2.027334e+01 3 4.5025920868
36 2 -0.33923248 5.073953e-01 3 0.7123168337
36.1 3 0.58625734 3.230105e+00 1 1.7972493160
36.2 3 0.60227552 3.335261e+00 6 1.8262697803
36.3 3 1.45488994 1.835276e+01 4 4.2840119381
36.4 3 1.53027488 2.133929e+01 1 4.6194464504
37 1 0.69408336 4.007496e+00 4 2.0018732361
37.1 3 1.29901486 1.343724e+01 6 3.6656836793
37.2 1 1.37785731 1.573228e+01 8 3.9663937816
38 2 -0.01750115 9.656032e-01 3 0.9826511063
39 2 -0.36790804 4.791143e-01 2 0.6921808305
39.1 3 -0.10227727 8.150103e-01 3 0.9027792048
39.2 1 0.26663623 1.704501e+00 6 1.3055654289
39.3 2 0.43261602 2.375557e+00 4 1.5412842878
39.4 3 1.15798114 1.013467e+01 3 3.1834997435
39.5 3 1.42055487 1.713477e+01 6 4.1394166439
40 3 0.12490390 1.283779e+00 1 1.1330395646
40.1 3 0.99106398 7.258172e+00 3 2.6940994046
40.2 1 1.11174613 9.239542e+00 0 3.0396614212
40.3 3 1.54250672 2.186776e+01 4 4.6762977762
41 3 0.65944345 3.739257e+00 1 1.9337158254
41.1 3 1.16178439 1.021205e+01 4 3.1956304458
41.2 1 1.18927300 1.078920e+01 7 3.2846923557
41.3 1 1.21827591 1.143355e+01 5 3.3813529415
41.4 1 1.26646761 1.259041e+01 2 3.5482964432
42 1 -0.72170038 2.361234e-01 1 0.4859252973
42.1 1 1.46540897 1.874295e+01 3 4.3293134298
43 3 -0.57685600 3.154636e-01 5 0.5616614548
43.1 3 0.07172323 1.154245e+00 2 1.0743579536
43.2 2 0.96056779 6.828709e+00 3 2.6131797966
44 2 -0.26622789 5.871613e-01 3 0.7662644819
44.1 2 0.97419323 7.017356e+00 3 2.6490291790
44.2 1 1.20512946 1.113684e+01 3 3.3371910988
44.3 1 1.41474092 1.693668e+01 4 4.1154200875
45 2 -1.63094249 3.831610e-02 4 0.1957449992
45.1 3 0.69133712 3.985546e+00 2 1.9963831536
46 3 0.29845548 1.816499e+00 8 1.3477755385
46.1 2 1.04962489 8.160046e+00 5 2.8565793915
46.2 3 1.48525084 1.950170e+01 5 4.4160729996
47 1 -0.50872427 3.615162e-01 3 0.6012621359
47.1 2 0.87950730 5.806713e+00 5 2.4097121472
47.2 2 1.09780510 8.985482e+00 5 2.9975794035
47.3 2 1.15781314 1.013127e+01 2 3.1829649757
47.4 2 1.53041755 2.134538e+01 5 4.6201055450
48 3 1.05107914 8.183814e+00 2 2.8607365978
48.1 1 1.06809653 8.467142e+00 5 2.9098354396
49 3 0.99988735 7.387392e+00 4 2.7179756400
50 1 0.16229406 1.383461e+00 1 1.1762060679
51 3 0.35798466 2.046169e+00 9 1.4304436720
52 3 0.75455484 4.522702e+00 3 2.1266646020
52.1 2 1.13141972 9.610339e+00 3 3.1000545993
52.2 1 1.14002538 9.777177e+00 4 3.1268477370
52.3 3 1.27288653 1.275308e+01 11 3.5711459327
52.4 3 1.56827544 2.302432e+01 3 4.7983659909
52.5 3 1.60579658 2.481859e+01 3 4.9818264414
53 1 -0.70001084 2.465916e-01 5 0.4965799209
53.1 3 1.26709851 1.260630e+01 3 3.5505357443
53.2 2 1.52148981 2.096763e+01 2 4.5790420019
54 3 0.33894951 1.969735e+00 1 1.4034724841
54.1 3 0.63192994 3.539056e+00 4 1.8812377600
54.2 3 0.92058507 6.303910e+00 2 2.5107589352
54.3 1 1.02419066 7.755338e+00 2 2.7848406672
54.4 1 1.38988484 1.611531e+01 6 4.0143877396
55 1 -0.49126437 3.743632e-01 1 0.6118522980
55.1 3 -0.29252747 5.570753e-01 2 0.7463747414
55.2 2 1.03677973 7.953081e+00 2 2.8201208171
55.3 1 1.14187711 9.813453e+00 3 3.1326431572
55.4 1 1.16994340 1.038006e+01 5 3.2218102901
56 2 0.20141578 1.496055e+00 5 1.2231332215
56.1 1 0.85752547 5.556959e+00 5 2.3573202139
56.2 3 0.94293018 6.592024e+00 2 2.5674936292
56.3 1 1.08204800 8.706727e+00 3 2.9507164378
56.4 2 1.17163752 1.041529e+01 6 3.2272730360
56.5 1 1.22892457 1.167966e+01 1 3.4175522043
57 1 -1.43955530 5.618471e-02 3 0.2370331448
57.1 1 -1.39374403 6.157569e-02 6 0.2481445030
57.2 1 0.13151815 1.300874e+00 3 1.1405586067
57.3 1 0.74923856 4.474869e+00 2 2.1153886721
58 3 0.19967837 1.490865e+00 6 1.2210099772
58.1 2 0.49067877 2.668076e+00 5 1.6334245703
58.2 1 0.51830932 2.819667e+00 2 1.6791862890
58.3 3 0.96774864 6.927488e+00 4 2.6320121693
58.4 3 1.04653734 8.109812e+00 4 2.8477731440
58.5 3 1.27300163 1.275602e+01 4 3.5715569824
59 3 0.64311617 3.619125e+00 6 1.9023998594
59.1 1 1.60415640 2.473731e+01 4 4.9736620474
60 3 1.05968098 8.325824e+00 7 2.8854503250
61 1 -0.32661269 5.203647e-01 6 0.7213630795
61.1 2 0.84100443 5.376345e+00 3 2.3186947661
61.2 2 0.91937849 6.288716e+00 2 2.5077313243
61.3 3 1.15471134 1.006861e+01 5 3.1731073430
61.4 2 1.28156493 1.297637e+01 4 3.6022726283
62 2 -0.62796412 2.848114e-01 1 0.5336771999
62.1 1 -0.35843842 4.882748e-01 1 0.6987666548
62.2 3 1.24081502 1.196074e+01 2 3.4584309917
62.3 2 1.56921516 2.306763e+01 4 4.8028772371
63 3 1.03309021 7.894611e+00 6 2.8097350930
63.1 1 1.37760053 1.572420e+01 2 3.9653754211
64 3 1.41564212 1.696724e+01 2 4.1191305732
65 3 -0.34585475 5.007194e-01 3 0.7076152589
65.1 3 0.70568063 4.101535e+00 4 2.0252246363
65.2 2 1.13550282 9.689140e+00 2 3.1127382827
65.3 3 1.16218434 1.022023e+01 2 3.1969087943
66 3 1.25114607 1.221045e+01 6 3.4943454154
66.1 3 1.32647633 1.419589e+01 0 3.7677437009
66.2 1 1.37336460 1.559155e+01 5 3.9486138616
67 3 1.42859659 1.741258e+01 8 4.1728388879
68 3 -2.04645568 1.669057e-02 5 0.1291919907
68.1 1 0.57715501 3.171834e+00 5 1.7809643946
68.2 2 0.71750831 4.199715e+00 4 2.0493205660
68.3 3 1.07864325 8.647640e+00 3 2.9406870750
68.4 1 1.39640979 1.632699e+01 1 4.0406670363
69 1 1.42193171 1.718202e+01 5 4.1451198701
70 1 -1.61316627 3.970284e-02 6 0.1992557163
70.1 2 -0.72778533 2.332672e-01 2 0.4829774413
71 3 -0.25597601 5.993245e-01 4 0.7741605386
71.1 2 0.39768944 2.215280e+00 2 1.4883817220
71.2 2 1.40507996 1.661257e+01 5 4.0758526395
71.3 1 1.54858834 2.213537e+01 10 4.7048238723
71.4 2 1.55271500 2.231881e+01 2 4.7242791823
72 1 -0.07029413 8.688470e-01 2 0.9321196121
72.1 2 0.16551374 1.392398e+00 4 1.1799991806
72.2 1 0.63750589 3.578744e+00 8 1.8917567329
72.3 2 1.24857116 1.214773e+01 6 3.4853593935
72.4 2 1.30519980 1.360449e+01 4 3.6884259700
72.5 1 1.40742346 1.669062e+01 1 4.0854155901
73 2 1.52648860 2.117830e+01 1 4.6019889915
74 1 0.38027083 2.139435e+00 1 1.4626806753
75 3 1.17940201 1.057829e+01 6 3.2524286874
76 3 0.59193402 3.266987e+00 3 1.8074807397
76.1 3 1.45126419 1.822015e+01 4 4.2685073183
76.2 2 1.60319315 2.468970e+01 5 4.9688734859
77 2 -0.16735013 7.155525e-01 1 0.8459033852
78 2 -0.19466612 6.775091e-01 2 0.8231094317
79 2 -2.84074848 3.408452e-03 2 0.0583819521
79.1 2 0.89225918 5.956710e+00 6 2.4406372628
79.2 2 1.19278625 1.086528e+01 5 3.2962526032
80 2 -0.10702187 8.073131e-01 5 0.8985060186
80.1 1 0.29525363 1.804904e+00 1 1.3434670598
80.2 3 1.03054400 7.854511e+00 4 2.8025900386
81 2 -4.59200757 1.026675e-04 4 0.0101324962
81.1 3 -0.05956855 8.876861e-01 5 0.9421709494
81.2 2 1.11653255 9.328415e+00 2 3.0542453879
81.3 1 1.20766489 1.119346e+01 5 3.3456630446
82 1 0.32143184 1.901920e+00 1 1.3791010005
82.1 2 0.56537123 3.097956e+00 2 1.7601010622
82.2 3 0.96443810 6.881772e+00 5 2.6233131927
83 2 -2.92360830 2.887926e-03 5 0.0537394290
83.1 3 1.06683161 8.445749e+00 1 2.9061570496
83.2 3 1.13749504 9.727823e+00 1 3.1189457362
83.3 3 1.56158380 2.271823e+01 4 4.7663642222
84 2 1.00261742 7.427838e+00 1 2.7254060237
84.1 3 1.20491590 1.113209e+01 5 3.3364784659
85 1 -1.21141501 8.867032e-02 6 0.2977756259
85.1 2 0.55354693 3.025553e+00 5 1.7394116637
85.2 3 0.98754404 7.207254e+00 3 2.6846330194
85.3 3 1.15084929 9.991139e+00 2 3.1608762743
85.4 2 1.37250101 1.556465e+01 2 3.9452053758
85.5 2 1.50613203 2.033338e+01 6 4.5092553482
86 1 -0.16531361 7.184729e-01 3 0.8476278360
86.1 2 0.01179313 1.023867e+00 3 1.0118629411
86.2 1 0.22403591 1.565291e+00 6 1.2511159515
86.3 1 0.78255612 4.783212e+00 5 2.1870554925
86.4 1 0.89743141 6.018649e+00 5 2.4532935000
86.5 2 1.34040901 1.459703e+01 4 3.8206058508
87 3 0.99582370 7.327595e+00 3 2.7069531474
87.1 3 1.23728720 1.187665e+01 6 3.4462517721
87.2 2 1.50943340 2.046808e+01 2 4.5241666853
88 3 -7.43666969 3.472088e-07 1 0.0005892443
88.1 3 -0.34022529 5.063888e-01 6 0.7116099866
88.2 3 0.91439786 6.226384e+00 1 2.4952722900
88.3 1 1.19379568 1.088724e+01 6 3.2995816297
89 2 -0.43663289 4.175856e-01 7 0.6462086167
90 1 -1.77429443 2.876520e-02 3 0.1696030737
90.1 2 0.95475675 6.749804e+00 8 2.5980385230
90.2 2 0.98025630 7.102967e+00 4 2.6651392167
90.3 2 1.13920034 9.761057e+00 2 3.1242690247
91 3 -0.44900667 4.073782e-01 4 0.6382618390
91.1 3 0.96409219 6.877013e+00 2 2.6224059286
91.2 3 1.56386564 2.282214e+01 5 4.7772527603
92 2 -2.60768143 5.432462e-03 3 0.0737052364
93 2 -1.27693454 7.778015e-02 3 0.2788909199
93.1 2 0.03515090 1.072832e+00 3 1.0357759963
93.2 2 0.91294719 6.208345e+00 4 2.4916551099
93.3 3 1.06043020 8.338309e+00 2 2.8876129608
93.4 2 1.49603344 1.992683e+01 6 4.4639474002
94 2 -0.16392661 7.204688e-01 2 0.8488043118
94.1 3 0.05377339 1.113543e+00 4 1.0552454425
94.2 3 0.66503063 3.781275e+00 2 1.9445500884
94.3 2 1.12202677 9.431485e+00 6 3.0710722448
94.4 3 1.12728824 9.531256e+00 5 3.0872731935
94.5 2 1.47718020 1.918945e+01 5 4.3805759016
95 2 0.70305113 4.080021e+00 8 2.0199063048
95.1 3 1.39089487 1.614790e+01 4 4.0184444457
95.2 2 1.51724656 2.079044e+01 1 4.5596531732
96 3 -3.46947576 9.692853e-04 2 0.0311333477
96.1 2 -2.02172545 1.753685e-02 3 0.1324267720
96.2 3 -0.40028304 4.490747e-01 2 0.6701303425
96.3 2 0.77817916 4.741523e+00 6 2.1775037691
96.4 2 0.79958353 4.948909e+00 6 2.2246142488
96.5 3 1.44403602 1.795865e+01 3 4.2377650598
97 3 0.17857310 1.429245e+00 2 1.1955102731
97.1 3 1.60146841 2.460468e+01 5 4.9603108643
98 2 -1.58878641 4.168671e-02 7 0.2041732438
98.1 3 -0.84174488 1.857247e-01 2 0.4309578973
98.2 1 1.25768262 1.237113e+01 6 3.5172611906
99 2 -1.04078137 1.247351e-01 3 0.3531786101
99.1 1 1.54307253 2.189252e+01 4 4.6789444226
99.2 3 1.60797853 2.492714e+01 5 4.9927084171
100 2 0.06685343 1.143058e+00 2 1.0691387602
100.1 1 0.41272829 2.282923e+00 3 1.5109344281
100.2 2 0.76557633 4.623503e+00 3 2.1502332564
100.3 2 1.35443144 1.501220e+01 7 3.8745574222
100.4 3 1.53832012 2.168542e+01 6 4.6567608765
$m4e$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m4e$spM_lvlone
center scale
m1 NA NA
log(time) 0.6318779 1.063214
I(time^2) 8.3244468 7.090003
p1 3.7264438 1.946996
time 2.5339403 1.381809
$m4e$mu_reg_multinomial
[1] 0
$m4e$tau_reg_multinomial
[1] 1e-04
$m4e$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
$m4e$shape_diag_RinvD
[1] "0.01"
$m4e$rate_diag_RinvD
[1] "0.001"
jagsmodel remains the same
Code
lapply(models, "[[", "jagsmodel")
Output
$m0a
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
beta[2] * M_id[group_id[i], 1]
}
for (ii in 1:100) {
b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
}
# Priors for the model for m1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1])
}
$m0b
model {
# Multinomial logit mixed model for m2 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3])
p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ])))
p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ])))
p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ])))
log(phi_m2[i, 1]) <- 0
log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1]
log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] +
beta[2] * M_id[group_id[i], 1]
}
for (ii in 1:100) {
b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ])
mu_b_m2_id[ii, 1] <- 0
}
# Priors for the model for m2
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1])
}
$m1a
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1] +
beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
beta[3] * M_id[group_id[i], 1] +
beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
}
# Priors for the model for m1
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1])
}
$m1b
model {
# Multinomial logit mixed model for m2 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3])
p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ])))
p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ])))
p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ])))
log(phi_m2[i, 1]) <- 0
log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1] +
beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2]
log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] +
beta[3] * M_id[group_id[i], 1] +
beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2]
}
for (ii in 1:100) {
b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ])
mu_b_m2_id[ii, 1] <- 0
}
# Priors for the model for m2
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1])
}
$m1c
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1] +
beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
beta[2] * M_id[group_id[i], 1] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
}
# Priors for the model for m1
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1])
}
$m1d
model {
# Multinomial logit mixed model for m2 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3])
p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ])))
p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ])))
p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ])))
log(phi_m2[i, 1]) <- 0
log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1] +
beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] +
beta[2] * M_id[group_id[i], 1] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ])
mu_b_m2_id[ii, 1] <- 0
}
# Priors for the model for m2
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1])
}
$m2a
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 2] +
beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
beta[3] * M_id[group_id[i], 2] +
beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
}
# Priors for the model for m1
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m1_id[1, 1] <- 1 / (invD_m1_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)
}
$m2b
model {
# Multinomial logit mixed model for m2 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3])
p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ])))
p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ])))
p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ])))
log(phi_m2[i, 1]) <- 0
log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 2] +
beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2]
log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] +
beta[3] * M_id[group_id[i], 2] +
beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2]
}
for (ii in 1:100) {
b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ])
mu_b_m2_id[ii, 1] <- 0
}
# Priors for the model for m2
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m2_id[1, 1] <- 1 / (invD_m2_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)
}
$m2c
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1] +
beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
beta[2] * M_id[group_id[i], 1] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
}
# Priors for the model for m1
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m1_id[1, 1] <- 1 / (invD_m1_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 {
# Multinomial logit mixed model for m2 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3])
p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ])))
p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ])))
p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ])))
log(phi_m2[i, 1]) <- 0
log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1] +
beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] +
beta[2] * M_id[group_id[i], 1] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ])
mu_b_m2_id[ii, 1] <- 0
}
# Priors for the model for m2
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m2_id[1, 1] <- 1 / (invD_m2_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 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, 2] +
beta[3] * M_lvlone[i, 3]
}
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 1:3) {
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])
}
$m3b
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, 3] +
beta[3] * M_lvlone[i, 4]
}
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 1:3) {
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])
# Multinomial logit mixed model for m2 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3])
p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ])))
p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ])))
p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ])))
log(phi_m2[i, 1]) <- 0
log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] +
alpha[1] * M_id[group_id[i], 1]
log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] +
alpha[2] * M_id[group_id[i], 1]
M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0)
M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0)
}
for (ii in 1:100) {
b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ])
mu_b_m2_id[ii, 1] <- 0
}
# Priors for the model for m2
for (k in 1:2) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1])
}
$m4a
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 3] +
beta[2] * M_id[group_id[i], 4] +
beta[3] * M_id[group_id[i], 5] +
beta[4] * M_id[group_id[i], 6] +
beta[5] * (M_id[group_id[i], 7] - spM_id[7, 1])/spM_id[7, 2] +
beta[6] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] +
beta[13] * M_lvlone[i, 3] + beta[14] * M_lvlone[i, 4] +
beta[15] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
beta[16] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
beta[7] * M_id[group_id[i], 3] +
beta[8] * M_id[group_id[i], 4] +
beta[9] * M_id[group_id[i], 5] +
beta[10] * M_id[group_id[i], 6] +
beta[11] * (M_id[group_id[i], 7] - spM_id[7, 1])/spM_id[7, 2] +
beta[12] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] +
beta[17] * M_lvlone[i, 3] + beta[18] * M_lvlone[i, 4] +
beta[19] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
beta[20] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
}
# Priors for the model for m1
for (k in 1:20) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1])
# Multinomial logit mixed model for m2 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3])
p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ])))
p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ])))
p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ])))
log(phi_m2[i, 1]) <- 0
log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] +
alpha[1] * M_id[group_id[i], 3] +
alpha[2] * M_id[group_id[i], 4] +
alpha[3] * M_id[group_id[i], 5] +
alpha[4] * M_id[group_id[i], 6] +
alpha[5] * (M_id[group_id[i], 9] - spM_id[9, 1])/spM_id[9, 2] +
alpha[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2]
log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] +
alpha[7] * M_id[group_id[i], 3] +
alpha[8] * M_id[group_id[i], 4] +
alpha[9] * M_id[group_id[i], 5] +
alpha[10] * M_id[group_id[i], 6] +
alpha[11] * (M_id[group_id[i], 9] - spM_id[9, 1])/spM_id[9, 2] +
alpha[12] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2]
M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0)
M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0)
}
for (ii in 1:100) {
b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ])
mu_b_m2_id[ii, 1] <- 0
}
# Priors for the model for m2
for (k in 1:12) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m2_id[1, 1] <- 1 / (invD_m2_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, 3] * alpha[13] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[14] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[15]
log(phi_M2[ii, 3]) <- M_id[ii, 3] * alpha[16] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[17] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[18]
log(phi_M2[ii, 4]) <- M_id[ii, 3] * alpha[19] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[20] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[21]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 13:21) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 3] * alpha[22] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[23]
M_id[ii, 7] <- abs(M_id[ii, 9] - M_id[ii, 2])
}
# Priors for the model for C2
for (k in 22:23) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_lvlone[i, 3] * M_id[group_id[i], 7]
M_lvlone[i, 6] <- M_lvlone[i, 4] * M_id[group_id[i], 7]
}
}
$m4b
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
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], 4] - spM_id[4, 1])/spM_id[4, 2] +
beta[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
beta[4] * M_id[group_id[i], 2] +
beta[5] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] +
beta[6] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] +
beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[10] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
}
# Priors for the model for m1
for (k in 1:10) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1])
# Multinomial logit mixed model for m2 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3])
p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ])))
p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ])))
p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ])))
log(phi_m2[i, 1]) <- 0
log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] +
alpha[1] * M_id[group_id[i], 2] +
alpha[2] * M_id[group_id[i], 5] +
alpha[3] * M_id[group_id[i], 6] +
alpha[4] * M_id[group_id[i], 7] +
alpha[5] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] +
alpha[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2]
log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] +
alpha[7] * M_id[group_id[i], 2] +
alpha[8] * M_id[group_id[i], 5] +
alpha[9] * M_id[group_id[i], 6] +
alpha[10] * M_id[group_id[i], 7] +
alpha[11] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] +
alpha[12] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 2, 1, 0)
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 3, 1, 0)
M_lvlone[i, 3] <- ifelse((M_lvlone[i, 2]) > (M_id[group_id[i], 9]), 1, 0)
}
for (ii in 1:100) {
b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ])
mu_b_m2_id[ii, 1] <- 0
}
# Priors for the model for m2
for (k in 1:12) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m2_id[1, 1] <- 1 / (invD_m2_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[13] + M_id[ii, 5] * alpha[14] +
M_id[ii, 6] * alpha[15] + M_id[ii, 7] * alpha[16] +
(M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[17]
M_id[ii, 3] <- abs(M_id[ii, 8] - M_id[ii, 1])
}
# Priors for the model for C2
for (k in 13:17) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 4] <- M_lvlone[i, 3] * M_id[group_id[i], 3]
}
}
$m4c
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_m1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
b_m1_id[group_id[i], 4] * (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], 4] +
beta[7] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_m1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
b_m1_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[4] * M_id[group_id[i], 2] +
beta[5] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] +
beta[6] * M_id[group_id[i], 4] +
beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[10] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:4] ~ dmnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
mu_b_m1_id[ii, 2] <- 0
mu_b_m1_id[ii, 3] <- 0
mu_b_m1_id[ii, 4] <- 0
}
# Priors for the model for m1
for (k in 1:10) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
for (k in 1:4) {
RinvD_m1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_m1_id[1:4, 1:4] ~ dwish(RinvD_m1_id[ , ], KinvD_m1_id)
D_m1_id[1:4, 1:4] <- inverse(invD_m1_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] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
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, 4] * alpha[3]
}
# 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 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]
}
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] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] +
M_id[ii, 4] * alpha[7]
}
# 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] - 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)
}
}
$m4d
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[1] * M_id[group_id[i], 1] +
beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[7] * M_lvlone[i, 5] +
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] +
beta[10] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[3] * M_id[group_id[i], 1] +
beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[12] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[13] * M_lvlone[i, 5] +
beta[14] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] +
beta[15] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] +
beta[16] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:2] ~ dmnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
mu_b_m1_id[ii, 2] <- 0
}
# Priors for the model for m1
for (k in 1:16) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
for (k in 1:2) {
RinvD_m1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_m1_id[1:2, 1:2] ~ dwish(RinvD_m1_id[ , ], KinvD_m1_id)
D_m1_id[1:2, 1:2] <- inverse(invD_m1_id[ , ])
# 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[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
M_lvlone[i, 5] <- 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, 1] * alpha[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2]
}
# 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])
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6]
}
}
$m4e
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1] +
beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[5] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[6] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
beta[3] * M_id[group_id[i], 1] +
beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[8] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[10] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
}
# Priors for the model for m1
for (k in 1:10) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial_ridge_beta[k])
tau_reg_multinomial_ridge_beta[k] ~ dgamma(0.01, 0.01)
}
invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1])
}
GRcrit and MCerror give same result
Code
lapply(models0, GR_crit, multivariate = FALSE)
Output
$m0a
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1C: (Intercept) NaN NaN
D_m1_id[1,1] NaN NaN
$m0b
Potential scale reduction factors:
Point est. Upper C.I.
m2B: (Intercept) NaN NaN
m2C: (Intercept) NaN NaN
D_m2_id[1,1] NaN NaN
$m1a
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1B: C1 NaN NaN
m1C: (Intercept) NaN NaN
m1C: C1 NaN NaN
D_m1_id[1,1] NaN NaN
$m1b
Potential scale reduction factors:
Point est. Upper C.I.
m2B: (Intercept) NaN NaN
m2B: C1 NaN NaN
m2C: (Intercept) NaN NaN
m2C: C1 NaN NaN
D_m2_id[1,1] NaN NaN
$m1c
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1C: (Intercept) NaN NaN
m1B: c1 NaN NaN
m1C: c1 NaN NaN
D_m1_id[1,1] NaN NaN
$m1d
Potential scale reduction factors:
Point est. Upper C.I.
m2B: (Intercept) NaN NaN
m2C: (Intercept) NaN NaN
m2B: c1 NaN NaN
m2C: c1 NaN NaN
D_m2_id[1,1] NaN NaN
$m2a
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1B: C2 NaN NaN
m1C: (Intercept) NaN NaN
m1C: C2 NaN NaN
D_m1_id[1,1] NaN NaN
$m2b
Potential scale reduction factors:
Point est. Upper C.I.
m2B: (Intercept) NaN NaN
m2B: C2 NaN NaN
m2C: (Intercept) NaN NaN
m2C: C2 NaN NaN
D_m2_id[1,1] NaN NaN
$m2c
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1C: (Intercept) NaN NaN
m1B: c2 NaN NaN
m1C: c2 NaN NaN
D_m1_id[1,1] NaN NaN
$m2d
Potential scale reduction factors:
Point est. Upper C.I.
m2B: (Intercept) NaN NaN
m2C: (Intercept) NaN NaN
m2B: c2 NaN NaN
m2C: c2 NaN NaN
D_m2_id[1,1] NaN NaN
$m3a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
m1B NaN NaN
m1C NaN NaN
sigma_c1 NaN NaN
D_c1_id[1,1] NaN NaN
$m3b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
m2B NaN NaN
m2C NaN NaN
sigma_c1 NaN NaN
D_c1_id[1,1] NaN NaN
$m4a
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1B: M22 NaN NaN
m1B: M23 NaN NaN
m1B: M24 NaN NaN
m1B: abs(C1 - C2) NaN NaN
m1B: log(C1) NaN NaN
m1C: (Intercept) NaN NaN
m1C: M22 NaN NaN
m1C: M23 NaN NaN
m1C: M24 NaN NaN
m1C: abs(C1 - C2) NaN NaN
m1C: log(C1) NaN NaN
m1B: m2B NaN NaN
m1B: m2C NaN NaN
m1B: m2B:abs(C1 - C2) NaN NaN
m1B: m2C:abs(C1 - C2) NaN NaN
m1C: m2B NaN NaN
m1C: m2C NaN NaN
m1C: m2B:abs(C1 - C2) NaN NaN
m1C: m2C:abs(C1 - C2) NaN NaN
D_m1_id[1,1] NaN NaN
$m4b
Potential scale reduction factors:
Point est.
m1B: (Intercept) NaN
m1B: abs(C1 - C2) NaN
m1B: log(C1) NaN
m1C: (Intercept) NaN
m1C: abs(C1 - C2) NaN
m1C: log(C1) NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
D_m1_id[1,1] NaN
Upper C.I.
m1B: (Intercept) NaN
m1B: abs(C1 - C2) NaN
m1B: log(C1) NaN
m1C: (Intercept) NaN
m1C: abs(C1 - C2) NaN
m1C: log(C1) NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
D_m1_id[1,1] NaN
$m4c
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1B: C1 NaN NaN
m1B: B21 NaN NaN
m1C: (Intercept) NaN NaN
m1C: C1 NaN NaN
m1C: B21 NaN NaN
m1B: time NaN NaN
m1B: c1 NaN NaN
m1C: time NaN NaN
m1C: c1 NaN NaN
D_m1_id[1,1] NaN NaN
D_m1_id[1,2] NaN NaN
D_m1_id[2,2] NaN NaN
D_m1_id[1,3] NaN NaN
D_m1_id[2,3] NaN NaN
D_m1_id[3,3] NaN NaN
D_m1_id[1,4] NaN NaN
D_m1_id[2,4] NaN NaN
D_m1_id[3,4] NaN NaN
D_m1_id[4,4] NaN NaN
$m4d
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1B: C1 NaN NaN
m1C: (Intercept) NaN NaN
m1C: C1 NaN NaN
m1B: time NaN NaN
m1B: I(time^2) NaN NaN
m1B: b21 NaN NaN
m1B: c1 NaN NaN
m1B: C1:time NaN NaN
m1B: b21:c1 NaN NaN
m1C: time NaN NaN
m1C: I(time^2) NaN NaN
m1C: b21 NaN NaN
m1C: c1 NaN NaN
m1C: C1:time NaN NaN
m1C: b21:c1 NaN NaN
D_m1_id[1,1] NaN NaN
D_m1_id[1,2] NaN NaN
D_m1_id[2,2] NaN NaN
$m4e
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1B: C1 NaN NaN
m1C: (Intercept) NaN NaN
m1C: C1 NaN NaN
m1B: log(time) NaN NaN
m1B: I(time^2) NaN NaN
m1B: p1 NaN NaN
m1C: log(time) NaN NaN
m1C: I(time^2) NaN NaN
m1C: p1 NaN NaN
D_m1_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"
$m0a
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
$m0b
est MCSE SD MCSE/SD
m2B: (Intercept) 0 0 0 NaN
m2C: (Intercept) 0 0 0 NaN
D_m2_id[1,1] 0 0 0 NaN
$m1a
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1B: C1 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
m1C: C1 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
$m1b
est MCSE SD MCSE/SD
m2B: (Intercept) 0 0 0 NaN
m2B: C1 0 0 0 NaN
m2C: (Intercept) 0 0 0 NaN
m2C: C1 0 0 0 NaN
D_m2_id[1,1] 0 0 0 NaN
$m1c
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
m1B: c1 0 0 0 NaN
m1C: c1 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
$m1d
est MCSE SD MCSE/SD
m2B: (Intercept) 0 0 0 NaN
m2C: (Intercept) 0 0 0 NaN
m2B: c1 0 0 0 NaN
m2C: c1 0 0 0 NaN
D_m2_id[1,1] 0 0 0 NaN
$m2a
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1B: C2 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
m1C: C2 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
$m2b
est MCSE SD MCSE/SD
m2B: (Intercept) 0 0 0 NaN
m2B: C2 0 0 0 NaN
m2C: (Intercept) 0 0 0 NaN
m2C: C2 0 0 0 NaN
D_m2_id[1,1] 0 0 0 NaN
$m2c
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
m1B: c2 0 0 0 NaN
m1C: c2 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
$m2d
est MCSE SD MCSE/SD
m2B: (Intercept) 0 0 0 NaN
m2C: (Intercept) 0 0 0 NaN
m2B: c2 0 0 0 NaN
m2C: c2 0 0 0 NaN
D_m2_id[1,1] 0 0 0 NaN
$m3a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
m1B 0 0 0 NaN
m1C 0 0 0 NaN
sigma_c1 0 0 0 NaN
D_c1_id[1,1] 0 0 0 NaN
$m3b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
m2B 0 0 0 NaN
m2C 0 0 0 NaN
sigma_c1 0 0 0 NaN
D_c1_id[1,1] 0 0 0 NaN
$m4a
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1B: M22 0 0 0 NaN
m1B: M23 0 0 0 NaN
m1B: M24 0 0 0 NaN
m1B: abs(C1 - C2) 0 0 0 NaN
m1B: log(C1) 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
m1C: M22 0 0 0 NaN
m1C: M23 0 0 0 NaN
m1C: M24 0 0 0 NaN
m1C: abs(C1 - C2) 0 0 0 NaN
m1C: log(C1) 0 0 0 NaN
m1B: m2B 0 0 0 NaN
m1B: m2C 0 0 0 NaN
m1B: m2B:abs(C1 - C2) 0 0 0 NaN
m1B: m2C:abs(C1 - C2) 0 0 0 NaN
m1C: m2B 0 0 0 NaN
m1C: m2C 0 0 0 NaN
m1C: m2B:abs(C1 - C2) 0 0 0 NaN
m1C: m2C:abs(C1 - C2) 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
$m4b
est MCSE SD
m1B: (Intercept) 0 0 0
m1B: abs(C1 - C2) 0 0 0
m1B: log(C1) 0 0 0
m1C: (Intercept) 0 0 0
m1C: abs(C1 - C2) 0 0 0
m1C: log(C1) 0 0 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0
D_m1_id[1,1] 0 0 0
MCSE/SD
m1B: (Intercept) NaN
m1B: abs(C1 - C2) NaN
m1B: log(C1) NaN
m1C: (Intercept) NaN
m1C: abs(C1 - C2) NaN
m1C: log(C1) NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
D_m1_id[1,1] NaN
$m4c
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1B: C1 0 0 0 NaN
m1B: B21 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
m1C: C1 0 0 0 NaN
m1C: B21 0 0 0 NaN
m1B: time 0 0 0 NaN
m1B: c1 0 0 0 NaN
m1C: time 0 0 0 NaN
m1C: c1 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
D_m1_id[1,2] 0 0 0 NaN
D_m1_id[2,2] 0 0 0 NaN
D_m1_id[1,3] 0 0 0 NaN
D_m1_id[2,3] 0 0 0 NaN
D_m1_id[3,3] 0 0 0 NaN
D_m1_id[1,4] 0 0 0 NaN
D_m1_id[2,4] 0 0 0 NaN
D_m1_id[3,4] 0 0 0 NaN
D_m1_id[4,4] 0 0 0 NaN
$m4d
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1B: C1 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
m1C: C1 0 0 0 NaN
m1B: time 0 0 0 NaN
m1B: I(time^2) 0 0 0 NaN
m1B: b21 0 0 0 NaN
m1B: c1 0 0 0 NaN
m1B: C1:time 0 0 0 NaN
m1B: b21:c1 0 0 0 NaN
m1C: time 0 0 0 NaN
m1C: I(time^2) 0 0 0 NaN
m1C: b21 0 0 0 NaN
m1C: c1 0 0 0 NaN
m1C: C1:time 0 0 0 NaN
m1C: b21:c1 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
D_m1_id[1,2] 0 0 0 NaN
D_m1_id[2,2] 0 0 0 NaN
$m4e
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1B: C1 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
m1C: C1 0 0 0 NaN
m1B: log(time) 0 0 0 NaN
m1B: I(time^2) 0 0 0 NaN
m1B: p1 0 0 0 NaN
m1C: log(time) 0 0 0 NaN
m1C: I(time^2) 0 0 0 NaN
m1C: p1 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
summary output remained the same
Code
lapply(models0, print)
Output
Call:
mlogitmm_imp(fixed = m1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) (Intercept)
0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
Call:
mlogitmm_imp(fixed = m2 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) (Intercept)
0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
Call:
mlogitmm_imp(fixed = m1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C1 (Intercept) C1
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
Call:
mlogitmm_imp(fixed = m2 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) C1 (Intercept) C1
0 0 0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
Call:
mlogitmm_imp(fixed = m1 ~ c1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) (Intercept) c1 c1
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
Call:
mlogitmm_imp(fixed = m2 ~ c1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) (Intercept) c1 c1
0 0 0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
Call:
mlogitmm_imp(fixed = m1 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C2 (Intercept) C2
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
Call:
mlogitmm_imp(fixed = m2 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) C2 (Intercept) C2
0 0 0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
Call:
mlogitmm_imp(fixed = m1 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) (Intercept) c2 c2
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
Call:
mlogitmm_imp(fixed = m2 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) (Intercept) c2 c2
0 0 0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
Call:
lme_imp(fixed = c1 ~ m1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) m1B m1C
0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
Call:
lme_imp(fixed = c1 ~ m2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) m2B m2C
0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
Call:
mlogitmm_imp(fixed = m1 ~ M2 + m2 * abs(C1 - C2) + log(C1) +
(1 | id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) M22 M23 M24
0 0 0 0
abs(C1 - C2) log(C1) (Intercept) M22
0 0 0 0
M23 M24 abs(C1 - C2) log(C1)
0 0 0 0
m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2)
0 0 0 0
m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2)
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
Call:
mlogitmm_imp(fixed = m1 ~ ifelse(as.numeric(m2) > as.numeric(M1),
1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept)
0
abs(C1 - C2)
0
log(C1)
0
(Intercept)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
Call:
mlogitmm_imp(fixed = m1 ~ time + c1 + C1 + B2 + (c1 * time |
id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C1 B21 (Intercept) C1 B21
0 0 0 0 0 0
time c1 time c1
0 0 0 0
Random effects covariance matrix:
$id
m1 m1 m1 m1
(Intercept) c1 time c1:time
m1 (Intercept) 0 0 0 0
m1 c1 0 0 0 0
m1 time 0 0 0 0
m1 c1:time 0 0 0 0
Call:
mlogitmm_imp(fixed = m1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF,
random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C1 (Intercept) C1 time I(time^2)
0 0 0 0 0 0
b21 c1 C1:time b21:c1 time I(time^2)
0 0 0 0 0 0
b21 c1 C1:time b21:c1
0 0 0 0
Random effects covariance matrix:
$id
m1 m1
(Intercept) time
m1 (Intercept) 0 0
m1 time 0 0
Call:
mlogitmm_imp(fixed = m1 ~ C1 + log(time) + I(time^2) + p1, data = longDF,
random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge",
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C1 (Intercept) C1 log(time) I(time^2)
0 0 0 0 0 0
p1 log(time) I(time^2) p1
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
$m0a
Call:
mlogitmm_imp(fixed = m1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) (Intercept)
0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
$m0b
Call:
mlogitmm_imp(fixed = m2 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) (Intercept)
0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
$m1a
Call:
mlogitmm_imp(fixed = m1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C1 (Intercept) C1
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
$m1b
Call:
mlogitmm_imp(fixed = m2 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) C1 (Intercept) C1
0 0 0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
$m1c
Call:
mlogitmm_imp(fixed = m1 ~ c1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) (Intercept) c1 c1
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
$m1d
Call:
mlogitmm_imp(fixed = m2 ~ c1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) (Intercept) c1 c1
0 0 0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
$m2a
Call:
mlogitmm_imp(fixed = m1 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C2 (Intercept) C2
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
$m2b
Call:
mlogitmm_imp(fixed = m2 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) C2 (Intercept) C2
0 0 0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
$m2c
Call:
mlogitmm_imp(fixed = m1 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) (Intercept) c2 c2
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
$m2d
Call:
mlogitmm_imp(fixed = m2 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) (Intercept) c2 c2
0 0 0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
$m3a
Call:
lme_imp(fixed = c1 ~ m1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) m1B m1C
0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
$m3b
Call:
lme_imp(fixed = c1 ~ m2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) m2B m2C
0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
$m4a
Call:
mlogitmm_imp(fixed = m1 ~ M2 + m2 * abs(C1 - C2) + log(C1) +
(1 | id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) M22 M23 M24
0 0 0 0
abs(C1 - C2) log(C1) (Intercept) M22
0 0 0 0
M23 M24 abs(C1 - C2) log(C1)
0 0 0 0
m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2)
0 0 0 0
m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2)
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
$m4b
Call:
mlogitmm_imp(fixed = m1 ~ ifelse(as.numeric(m2) > as.numeric(M1),
1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept)
0
abs(C1 - C2)
0
log(C1)
0
(Intercept)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
$m4c
Call:
mlogitmm_imp(fixed = m1 ~ time + c1 + C1 + B2 + (c1 * time |
id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C1 B21 (Intercept) C1 B21
0 0 0 0 0 0
time c1 time c1
0 0 0 0
Random effects covariance matrix:
$id
m1 m1 m1 m1
(Intercept) c1 time c1:time
m1 (Intercept) 0 0 0 0
m1 c1 0 0 0 0
m1 time 0 0 0 0
m1 c1:time 0 0 0 0
$m4d
Call:
mlogitmm_imp(fixed = m1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF,
random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C1 (Intercept) C1 time I(time^2)
0 0 0 0 0 0
b21 c1 C1:time b21:c1 time I(time^2)
0 0 0 0 0 0
b21 c1 C1:time b21:c1
0 0 0 0
Random effects covariance matrix:
$id
m1 m1
(Intercept) time
m1 (Intercept) 0 0
m1 time 0 0
$m4e
Call:
mlogitmm_imp(fixed = m1 ~ C1 + log(time) + I(time^2) + p1, data = longDF,
random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge",
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C1 (Intercept) C1 log(time) I(time^2)
0 0 0 0 0 0
p1 log(time) I(time^2) p1
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
Code
lapply(models0, coef)
Output
$m0a
$m0a$m1
(Intercept) (Intercept) D_m1_id[1,1]
0 0 0
$m0b
$m0b$m2
(Intercept) (Intercept) D_m2_id[1,1]
0 0 0
$m1a
$m1a$m1
(Intercept) C1 (Intercept) C1 D_m1_id[1,1]
0 0 0 0 0
$m1b
$m1b$m2
(Intercept) C1 (Intercept) C1 D_m2_id[1,1]
0 0 0 0 0
$m1c
$m1c$m1
(Intercept) (Intercept) c1 c1 D_m1_id[1,1]
0 0 0 0 0
$m1d
$m1d$m2
(Intercept) (Intercept) c1 c1 D_m2_id[1,1]
0 0 0 0 0
$m2a
$m2a$m1
(Intercept) C2 (Intercept) C2 D_m1_id[1,1]
0 0 0 0 0
$m2b
$m2b$m2
(Intercept) C2 (Intercept) C2 D_m2_id[1,1]
0 0 0 0 0
$m2c
$m2c$m1
(Intercept) (Intercept) c2 c2 D_m1_id[1,1]
0 0 0 0 0
$m2d
$m2d$m2
(Intercept) (Intercept) c2 c2 D_m2_id[1,1]
0 0 0 0 0
$m3a
$m3a$c1
(Intercept) m1B m1C sigma_c1 D_c1_id[1,1]
0 0 0 0 0
$m3b
$m3b$c1
(Intercept) m2B m2C sigma_c1 D_c1_id[1,1]
0 0 0 0 0
$m4a
$m4a$m1
(Intercept) M22 M23 M24
0 0 0 0
abs(C1 - C2) log(C1) (Intercept) M22
0 0 0 0
M23 M24 abs(C1 - C2) log(C1)
0 0 0 0
m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2)
0 0 0 0
m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2)
0 0 0 0
D_m1_id[1,1]
0
$m4b
$m4b$m1
(Intercept)
0
abs(C1 - C2)
0
log(C1)
0
(Intercept)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
D_m1_id[1,1]
0
$m4c
$m4c$m1
(Intercept) C1 B21 (Intercept) C1 B21
0 0 0 0 0 0
time c1 time c1 D_m1_id[1,1] D_m1_id[1,2]
0 0 0 0 0 0
D_m1_id[2,2] D_m1_id[1,3] D_m1_id[2,3] D_m1_id[3,3] D_m1_id[1,4] D_m1_id[2,4]
0 0 0 0 0 0
D_m1_id[3,4] D_m1_id[4,4]
0 0
$m4d
$m4d$m1
(Intercept) C1 (Intercept) C1 time I(time^2)
0 0 0 0 0 0
b21 c1 C1:time b21:c1 time I(time^2)
0 0 0 0 0 0
b21 c1 C1:time b21:c1 D_m1_id[1,1] D_m1_id[1,2]
0 0 0 0 0 0
D_m1_id[2,2]
0
$m4e
$m4e$m1
(Intercept) C1 (Intercept) C1 log(time) I(time^2)
0 0 0 0 0 0
p1 log(time) I(time^2) p1 D_m1_id[1,1]
0 0 0 0 0
Code
lapply(models0, confint)
Output
$m0a
$m0a$m1
2.5% 97.5%
(Intercept) 0 0
(Intercept) 0 0
D_m1_id[1,1] 0 0
$m0b
$m0b$m2
2.5% 97.5%
(Intercept) 0 0
(Intercept) 0 0
D_m2_id[1,1] 0 0
$m1a
$m1a$m1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
(Intercept) 0 0
C1 0 0
D_m1_id[1,1] 0 0
$m1b
$m1b$m2
2.5% 97.5%
(Intercept) 0 0
C1 0 0
(Intercept) 0 0
C1 0 0
D_m2_id[1,1] 0 0
$m1c
$m1c$m1
2.5% 97.5%
(Intercept) 0 0
(Intercept) 0 0
c1 0 0
c1 0 0
D_m1_id[1,1] 0 0
$m1d
$m1d$m2
2.5% 97.5%
(Intercept) 0 0
(Intercept) 0 0
c1 0 0
c1 0 0
D_m2_id[1,1] 0 0
$m2a
$m2a$m1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
(Intercept) 0 0
C2 0 0
D_m1_id[1,1] 0 0
$m2b
$m2b$m2
2.5% 97.5%
(Intercept) 0 0
C2 0 0
(Intercept) 0 0
C2 0 0
D_m2_id[1,1] 0 0
$m2c
$m2c$m1
2.5% 97.5%
(Intercept) 0 0
(Intercept) 0 0
c2 0 0
c2 0 0
D_m1_id[1,1] 0 0
$m2d
$m2d$m2
2.5% 97.5%
(Intercept) 0 0
(Intercept) 0 0
c2 0 0
c2 0 0
D_m2_id[1,1] 0 0
$m3a
$m3a$c1
2.5% 97.5%
(Intercept) 0 0
m1B 0 0
m1C 0 0
sigma_c1 0 0
D_c1_id[1,1] 0 0
$m3b
$m3b$c1
2.5% 97.5%
(Intercept) 0 0
m2B 0 0
m2C 0 0
sigma_c1 0 0
D_c1_id[1,1] 0 0
$m4a
$m4a$m1
2.5% 97.5%
(Intercept) 0 0
M22 0 0
M23 0 0
M24 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
(Intercept) 0 0
M22 0 0
M23 0 0
M24 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
m2B 0 0
m2C 0 0
m2B:abs(C1 - C2) 0 0
m2C:abs(C1 - C2) 0 0
m2B 0 0
m2C 0 0
m2B:abs(C1 - C2) 0 0
m2C:abs(C1 - C2) 0 0
D_m1_id[1,1] 0 0
$m4b
$m4b$m1
2.5% 97.5%
(Intercept) 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
(Intercept) 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0
D_m1_id[1,1] 0 0
$m4c
$m4c$m1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
(Intercept) 0 0
C1 0 0
B21 0 0
time 0 0
c1 0 0
time 0 0
c1 0 0
D_m1_id[1,1] 0 0
D_m1_id[1,2] 0 0
D_m1_id[2,2] 0 0
D_m1_id[1,3] 0 0
D_m1_id[2,3] 0 0
D_m1_id[3,3] 0 0
D_m1_id[1,4] 0 0
D_m1_id[2,4] 0 0
D_m1_id[3,4] 0 0
D_m1_id[4,4] 0 0
$m4d
$m4d$m1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
(Intercept) 0 0
C1 0 0
time 0 0
I(time^2) 0 0
b21 0 0
c1 0 0
C1:time 0 0
b21:c1 0 0
time 0 0
I(time^2) 0 0
b21 0 0
c1 0 0
C1:time 0 0
b21:c1 0 0
D_m1_id[1,1] 0 0
D_m1_id[1,2] 0 0
D_m1_id[2,2] 0 0
$m4e
$m4e$m1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
(Intercept) 0 0
C1 0 0
log(time) 0 0
I(time^2) 0 0
p1 0 0
log(time) 0 0
I(time^2) 0 0
p1 0 0
D_m1_id[1,1] 0 0
Code
lapply(models0, summary)
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"
$m0a
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ 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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1C: (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_m1_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
$m0b
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m2 ~ 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
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2C: (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_m2_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
$m1a
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ 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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C1 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: 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_m1_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
$m1b
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m2 ~ 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
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2B: C1 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
m2C: 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_m2_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
$m1c
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ 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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1B: c1 0 0 0 0 0 NaN NaN
m1C: 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_m1_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
$m1d
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m2 ~ 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
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
m2B: c1 0 0 0 0 0 NaN NaN
m2C: 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_m2_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
$m2a
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ 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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C2 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: 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_m1_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
$m2b
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m2 ~ 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
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2B: C2 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
m2C: 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_m2_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
$m2c
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ 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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1B: c2 0 0 0 0 0 NaN NaN
m1C: 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_m1_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
$m2d
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m2 ~ 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
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
m2B: c2 0 0 0 0 0 NaN NaN
m2C: 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_m2_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
$m3a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = c1 ~ m1 + (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
m1B 0 0 0 0 0 NaN NaN
m1C 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 = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
$m3b
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = c1 ~ m2 + (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
m2B 0 0 0 0 0 NaN NaN
m2C 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
$m4a
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ M2 + m2 * abs(C1 - C2) + log(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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: M22 0 0 0 0 0 NaN NaN
m1B: M23 0 0 0 0 0 NaN NaN
m1B: M24 0 0 0 0 0 NaN NaN
m1B: abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1B: log(C1) 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: M22 0 0 0 0 0 NaN NaN
m1C: M23 0 0 0 0 0 NaN NaN
m1C: M24 0 0 0 0 0 NaN NaN
m1C: abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1C: log(C1) 0 0 0 0 0 NaN NaN
m1B: m2B 0 0 0 0 0 NaN NaN
m1B: m2C 0 0 0 0 0 NaN NaN
m1B: m2B:abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1B: m2C:abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1C: m2B 0 0 0 0 0 NaN NaN
m1C: m2C 0 0 0 0 0 NaN NaN
m1C: m2B:abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1C: m2C: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_m1_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
$m4b
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ ifelse(as.numeric(m2) > as.numeric(M1),
1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5%
m1B: (Intercept) 0 0 0
m1B: abs(C1 - C2) 0 0 0
m1B: log(C1) 0 0 0
m1C: (Intercept) 0 0 0
m1C: abs(C1 - C2) 0 0 0
m1C: log(C1) 0 0 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0
97.5%
m1B: (Intercept) 0
m1B: abs(C1 - C2) 0
m1B: log(C1) 0
m1C: (Intercept) 0
m1C: abs(C1 - C2) 0
m1C: log(C1) 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0
tail-prob.
m1B: (Intercept) 0
m1B: abs(C1 - C2) 0
m1B: log(C1) 0
m1C: (Intercept) 0
m1C: abs(C1 - C2) 0
m1C: log(C1) 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0
GR-crit MCE/SD
m1B: (Intercept) NaN NaN
m1B: abs(C1 - C2) NaN NaN
m1B: log(C1) NaN NaN
m1C: (Intercept) NaN NaN
m1C: abs(C1 - C2) NaN NaN
m1C: log(C1) NaN NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_m1_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
$m4c
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ time + c1 + C1 + B2 + (c1 * 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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C1 0 0 0 0 0 NaN NaN
m1B: B21 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: C1 0 0 0 0 0 NaN NaN
m1C: B21 0 0 0 0 0 NaN NaN
m1B: time 0 0 0 0 0 NaN NaN
m1B: c1 0 0 0 0 0 NaN NaN
m1C: time 0 0 0 0 0 NaN NaN
m1C: 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_m1_id[1,1] 0 0 0 0 NaN NaN
D_m1_id[1,2] 0 0 0 0 0 NaN NaN
D_m1_id[2,2] 0 0 0 0 NaN NaN
D_m1_id[1,3] 0 0 0 0 0 NaN NaN
D_m1_id[2,3] 0 0 0 0 0 NaN NaN
D_m1_id[3,3] 0 0 0 0 NaN NaN
D_m1_id[1,4] 0 0 0 0 0 NaN NaN
D_m1_id[2,4] 0 0 0 0 0 NaN NaN
D_m1_id[3,4] 0 0 0 0 0 NaN NaN
D_m1_id[4,4] 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
$m4d
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ C1 * time + I(time^2) + b2 * c1, 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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C1 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: C1 0 0 0 0 0 NaN NaN
m1B: time 0 0 0 0 0 NaN NaN
m1B: I(time^2) 0 0 0 0 0 NaN NaN
m1B: b21 0 0 0 0 0 NaN NaN
m1B: c1 0 0 0 0 0 NaN NaN
m1B: C1:time 0 0 0 0 0 NaN NaN
m1B: b21:c1 0 0 0 0 0 NaN NaN
m1C: time 0 0 0 0 0 NaN NaN
m1C: I(time^2) 0 0 0 0 0 NaN NaN
m1C: b21 0 0 0 0 0 NaN NaN
m1C: c1 0 0 0 0 0 NaN NaN
m1C: C1:time 0 0 0 0 0 NaN NaN
m1C: 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_m1_id[1,1] 0 0 0 0 NaN NaN
D_m1_id[1,2] 0 0 0 0 0 NaN NaN
D_m1_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
$m4e
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ C1 + log(time) + I(time^2) + p1, data = longDF,
random = ~1 | id, 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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C1 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: C1 0 0 0 0 0 NaN NaN
m1B: log(time) 0 0 0 0 0 NaN NaN
m1B: I(time^2) 0 0 0 0 0 NaN NaN
m1B: p1 0 0 0 0 0 NaN NaN
m1C: log(time) 0 0 0 0 0 NaN NaN
m1C: I(time^2) 0 0 0 0 0 NaN NaN
m1C: p1 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_m1_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
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"
$m0a
$m0a$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
$m0b
$m0b$m2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
$m1a
$m1a$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C1 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: C1 0 0 0 0 0 NaN NaN
$m1b
$m1b$m2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2B: C1 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
m2C: C1 0 0 0 0 0 NaN NaN
$m1c
$m1c$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1B: c1 0 0 0 0 0 NaN NaN
m1C: c1 0 0 0 0 0 NaN NaN
$m1d
$m1d$m2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
m2B: c1 0 0 0 0 0 NaN NaN
m2C: c1 0 0 0 0 0 NaN NaN
$m2a
$m2a$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C2 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: C2 0 0 0 0 0 NaN NaN
$m2b
$m2b$m2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2B: C2 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
m2C: C2 0 0 0 0 0 NaN NaN
$m2c
$m2c$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1B: c2 0 0 0 0 0 NaN NaN
m1C: c2 0 0 0 0 0 NaN NaN
$m2d
$m2d$m2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
m2B: c2 0 0 0 0 0 NaN NaN
m2C: c2 0 0 0 0 0 NaN NaN
$m3a
$m3a$c1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
m1B 0 0 0 0 0 NaN NaN
m1C 0 0 0 0 0 NaN NaN
$m3b
$m3b$c1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
m2B 0 0 0 0 0 NaN NaN
m2C 0 0 0 0 0 NaN NaN
$m4a
$m4a$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: M22 0 0 0 0 0 NaN NaN
m1B: M23 0 0 0 0 0 NaN NaN
m1B: M24 0 0 0 0 0 NaN NaN
m1B: abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1B: log(C1) 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: M22 0 0 0 0 0 NaN NaN
m1C: M23 0 0 0 0 0 NaN NaN
m1C: M24 0 0 0 0 0 NaN NaN
m1C: abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1C: log(C1) 0 0 0 0 0 NaN NaN
m1B: m2B 0 0 0 0 0 NaN NaN
m1B: m2C 0 0 0 0 0 NaN NaN
m1B: m2B:abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1B: m2C:abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1C: m2B 0 0 0 0 0 NaN NaN
m1C: m2C 0 0 0 0 0 NaN NaN
m1C: m2B:abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1C: m2C:abs(C1 - C2) 0 0 0 0 0 NaN NaN
$m4b
$m4b$m1
Mean SD 2.5%
m1B: (Intercept) 0 0 0
m1B: abs(C1 - C2) 0 0 0
m1B: log(C1) 0 0 0
m1C: (Intercept) 0 0 0
m1C: abs(C1 - C2) 0 0 0
m1C: log(C1) 0 0 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0
97.5%
m1B: (Intercept) 0
m1B: abs(C1 - C2) 0
m1B: log(C1) 0
m1C: (Intercept) 0
m1C: abs(C1 - C2) 0
m1C: log(C1) 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0
tail-prob.
m1B: (Intercept) 0
m1B: abs(C1 - C2) 0
m1B: log(C1) 0
m1C: (Intercept) 0
m1C: abs(C1 - C2) 0
m1C: log(C1) 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0
GR-crit MCE/SD
m1B: (Intercept) NaN NaN
m1B: abs(C1 - C2) NaN NaN
m1B: log(C1) NaN NaN
m1C: (Intercept) NaN NaN
m1C: abs(C1 - C2) NaN NaN
m1C: log(C1) NaN NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN NaN
$m4c
$m4c$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C1 0 0 0 0 0 NaN NaN
m1B: B21 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: C1 0 0 0 0 0 NaN NaN
m1C: B21 0 0 0 0 0 NaN NaN
m1B: time 0 0 0 0 0 NaN NaN
m1B: c1 0 0 0 0 0 NaN NaN
m1C: time 0 0 0 0 0 NaN NaN
m1C: c1 0 0 0 0 0 NaN NaN
$m4d
$m4d$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C1 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: C1 0 0 0 0 0 NaN NaN
m1B: time 0 0 0 0 0 NaN NaN
m1B: I(time^2) 0 0 0 0 0 NaN NaN
m1B: b21 0 0 0 0 0 NaN NaN
m1B: c1 0 0 0 0 0 NaN NaN
m1B: C1:time 0 0 0 0 0 NaN NaN
m1B: b21:c1 0 0 0 0 0 NaN NaN
m1C: time 0 0 0 0 0 NaN NaN
m1C: I(time^2) 0 0 0 0 0 NaN NaN
m1C: b21 0 0 0 0 0 NaN NaN
m1C: c1 0 0 0 0 0 NaN NaN
m1C: C1:time 0 0 0 0 0 NaN NaN
m1C: b21:c1 0 0 0 0 0 NaN NaN
$m4e
$m4e$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C1 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: C1 0 0 0 0 0 NaN NaN
m1B: log(time) 0 0 0 0 0 NaN NaN
m1B: I(time^2) 0 0 0 0 0 NaN NaN
m1B: p1 0 0 0 0 0 NaN NaN
m1C: log(time) 0 0 0 0 0 NaN NaN
m1C: I(time^2) 0 0 0 0 0 NaN NaN
m1C: p1 0 0 0 0 0 NaN NaN
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data_list remains the same
Code
lapply(models, "[[", "data_list")
Output
$m0a
$m0a$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
$m0a$M_lvlone
m1
1 3
1.1 2
1.2 1
1.3 1
2 2
2.1 2
2.2 1
3 1
3.1 2
3.2 2
4 2
4.1 1
4.2 2
4.3 3
5 2
5.1 1
5.2 2
5.3 2
6 2
7 3
7.1 2
7.2 3
8 2
8.1 1
8.2 3
8.3 2
8.4 2
8.5 2
9 3
9.1 2
9.2 3
10 3
10.1 1
11 1
11.1 1
11.2 2
11.3 3
11.4 1
12 1
13 2
13.1 3
14 1
14.1 1
14.2 1
14.3 3
15 1
15.1 1
15.2 3
15.3 2
16 2
16.1 2
16.2 1
16.3 3
16.4 2
16.5 1
17 2
17.1 3
17.2 1
17.3 1
17.4 2
18 1
19 2
19.1 3
19.2 2
19.3 3
20 2
20.1 2
20.2 1
20.3 3
20.4 2
20.5 3
21 1
21.1 2
21.2 3
22 2
22.1 2
23 2
23.1 1
24 1
25 1
25.1 3
25.2 2
25.3 2
25.4 1
25.5 1
26 2
26.1 1
26.2 1
26.3 2
27 1
27.1 3
28 1
28.1 3
28.2 1
28.3 1
29 3
29.1 3
29.2 3
29.3 2
30 1
30.1 3
30.2 3
31 1
32 3
32.1 3
32.2 2
32.3 1
33 3
33.1 1
34 1
34.1 1
34.2 2
34.3 2
35 1
35.1 1
35.2 1
36 2
36.1 3
36.2 3
36.3 3
36.4 3
37 1
37.1 3
37.2 1
38 2
39 2
39.1 3
39.2 1
39.3 2
39.4 3
39.5 3
40 3
40.1 3
40.2 1
40.3 3
41 3
41.1 3
41.2 1
41.3 1
41.4 1
42 1
42.1 1
43 3
43.1 3
43.2 2
44 2
44.1 2
44.2 1
44.3 1
45 2
45.1 3
46 3
46.1 2
46.2 3
47 1
47.1 2
47.2 2
47.3 2
47.4 2
48 3
48.1 1
49 3
50 1
51 3
52 3
52.1 2
52.2 1
52.3 3
52.4 3
52.5 3
53 1
53.1 3
53.2 2
54 3
54.1 3
54.2 3
54.3 1
54.4 1
55 1
55.1 3
55.2 2
55.3 1
55.4 1
56 2
56.1 1
56.2 3
56.3 1
56.4 2
56.5 1
57 1
57.1 1
57.2 1
57.3 1
58 3
58.1 2
58.2 1
58.3 3
58.4 3
58.5 3
59 3
59.1 1
60 3
61 1
61.1 2
61.2 2
61.3 3
61.4 2
62 2
62.1 1
62.2 3
62.3 2
63 3
63.1 1
64 3
65 3
65.1 3
65.2 2
65.3 3
66 3
66.1 3
66.2 1
67 3
68 3
68.1 1
68.2 2
68.3 3
68.4 1
69 1
70 1
70.1 2
71 3
71.1 2
71.2 2
71.3 1
71.4 2
72 1
72.1 2
72.2 1
72.3 2
72.4 2
72.5 1
73 2
74 1
75 3
76 3
76.1 3
76.2 2
77 2
78 2
79 2
79.1 2
79.2 2
80 2
80.1 1
80.2 3
81 2
81.1 3
81.2 2
81.3 1
82 1
82.1 2
82.2 3
83 2
83.1 3
83.2 3
83.3 3
84 2
84.1 3
85 1
85.1 2
85.2 3
85.3 3
85.4 2
85.5 2
86 1
86.1 2
86.2 1
86.3 1
86.4 1
86.5 2
87 3
87.1 3
87.2 2
88 3
88.1 3
88.2 3
88.3 1
89 2
90 1
90.1 2
90.2 2
90.3 2
91 3
91.1 3
91.2 3
92 2
93 2
93.1 2
93.2 2
93.3 3
93.4 2
94 2
94.1 3
94.2 3
94.3 2
94.4 3
94.5 2
95 2
95.1 3
95.2 2
96 3
96.1 2
96.2 3
96.3 2
96.4 2
96.5 3
97 3
97.1 3
98 2
98.1 3
98.2 1
99 2
99.1 1
99.2 3
100 2
100.1 1
100.2 2
100.3 2
100.4 3
$m0a$mu_reg_multinomial
[1] 0
$m0a$tau_reg_multinomial
[1] 1e-04
$m0a$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
$m0a$shape_diag_RinvD
[1] "0.01"
$m0a$rate_diag_RinvD
[1] "0.001"
$m0b
$m0b$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
$m0b$M_lvlone
m2
1 3
1.1 1
1.2 3
1.3 1
2 2
2.1 1
2.2 NA
3 3
3.1 2
3.2 1
4 1
4.1 2
4.2 3
4.3 3
5 2
5.1 3
5.2 1
5.3 1
6 2
7 2
7.1 1
7.2 3
8 2
8.1 2
8.2 1
8.3 3
8.4 NA
8.5 3
9 NA
9.1 3
9.2 1
10 1
10.1 1
11 1
11.1 1
11.2 1
11.3 NA
11.4 1
12 1
13 2
13.1 2
14 3
14.1 2
14.2 1
14.3 1
15 1
15.1 2
15.2 3
15.3 3
16 2
16.1 NA
16.2 3
16.3 2
16.4 3
16.5 1
17 1
17.1 3
17.2 NA
17.3 2
17.4 1
18 3
19 NA
19.1 1
19.2 3
19.3 3
20 2
20.1 NA
20.2 3
20.3 1
20.4 3
20.5 2
21 3
21.1 1
21.2 NA
22 3
22.1 1
23 1
23.1 2
24 2
25 2
25.1 3
25.2 3
25.3 1
25.4 3
25.5 2
26 NA
26.1 3
26.2 3
26.3 NA
27 3
27.1 3
28 3
28.1 2
28.2 2
28.3 3
29 1
29.1 NA
29.2 2
29.3 2
30 2
30.1 3
30.2 3
31 3
32 3
32.1 3
32.2 1
32.3 1
33 3
33.1 3
34 3
34.1 NA
34.2 1
34.3 NA
35 2
35.1 2
35.2 2
36 3
36.1 3
36.2 3
36.3 2
36.4 2
37 2
37.1 2
37.2 1
38 2
39 3
39.1 2
39.2 3
39.3 NA
39.4 3
39.5 3
40 3
40.1 1
40.2 3
40.3 2
41 3
41.1 3
41.2 1
41.3 2
41.4 3
42 2
42.1 NA
43 3
43.1 3
43.2 2
44 3
44.1 3
44.2 NA
44.3 1
45 3
45.1 1
46 NA
46.1 1
46.2 2
47 2
47.1 NA
47.2 NA
47.3 3
47.4 3
48 3
48.1 1
49 1
50 NA
51 1
52 2
52.1 1
52.2 1
52.3 NA
52.4 2
52.5 3
53 2
53.1 1
53.2 2
54 NA
54.1 1
54.2 NA
54.3 3
54.4 3
55 1
55.1 1
55.2 1
55.3 NA
55.4 2
56 2
56.1 3
56.2 1
56.3 1
56.4 2
56.5 NA
57 2
57.1 3
57.2 2
57.3 NA
58 1
58.1 1
58.2 NA
58.3 1
58.4 2
58.5 NA
59 1
59.1 1
60 1
61 2
61.1 1
61.2 1
61.3 2
61.4 2
62 1
62.1 1
62.2 NA
62.3 1
63 NA
63.1 3
64 3
65 NA
65.1 2
65.2 3
65.3 3
66 3
66.1 3
66.2 1
67 NA
68 1
68.1 1
68.2 1
68.3 2
68.4 3
69 NA
70 1
70.1 NA
71 1
71.1 1
71.2 NA
71.3 1
71.4 1
72 2
72.1 3
72.2 2
72.3 1
72.4 2
72.5 1
73 NA
74 1
75 NA
76 1
76.1 2
76.2 2
77 NA
78 1
79 3
79.1 3
79.2 NA
80 3
80.1 2
80.2 NA
81 1
81.1 2
81.2 1
81.3 1
82 3
82.1 1
82.2 1
83 2
83.1 3
83.2 2
83.3 3
84 1
84.1 2
85 2
85.1 1
85.2 1
85.3 NA
85.4 2
85.5 1
86 1
86.1 NA
86.2 2
86.3 1
86.4 2
86.5 2
87 NA
87.1 1
87.2 NA
88 1
88.1 2
88.2 NA
88.3 2
89 3
90 3
90.1 2
90.2 NA
90.3 2
91 3
91.1 1
91.2 3
92 2
93 2
93.1 3
93.2 NA
93.3 2
93.4 3
94 2
94.1 2
94.2 1
94.3 2
94.4 1
94.5 2
95 2
95.1 2
95.2 NA
96 1
96.1 1
96.2 2
96.3 3
96.4 2
96.5 NA
97 1
97.1 2
98 3
98.1 2
98.2 2
99 2
99.1 2
99.2 1
100 1
100.1 2
100.2 3
100.3 2
100.4 1
$m0b$mu_reg_multinomial
[1] 0
$m0b$tau_reg_multinomial
[1] 1e-04
$m0b$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
$m0b$shape_diag_RinvD
[1] "0.01"
$m0b$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
m1
1 3
1.1 2
1.2 1
1.3 1
2 2
2.1 2
2.2 1
3 1
3.1 2
3.2 2
4 2
4.1 1
4.2 2
4.3 3
5 2
5.1 1
5.2 2
5.3 2
6 2
7 3
7.1 2
7.2 3
8 2
8.1 1
8.2 3
8.3 2
8.4 2
8.5 2
9 3
9.1 2
9.2 3
10 3
10.1 1
11 1
11.1 1
11.2 2
11.3 3
11.4 1
12 1
13 2
13.1 3
14 1
14.1 1
14.2 1
14.3 3
15 1
15.1 1
15.2 3
15.3 2
16 2
16.1 2
16.2 1
16.3 3
16.4 2
16.5 1
17 2
17.1 3
17.2 1
17.3 1
17.4 2
18 1
19 2
19.1 3
19.2 2
19.3 3
20 2
20.1 2
20.2 1
20.3 3
20.4 2
20.5 3
21 1
21.1 2
21.2 3
22 2
22.1 2
23 2
23.1 1
24 1
25 1
25.1 3
25.2 2
25.3 2
25.4 1
25.5 1
26 2
26.1 1
26.2 1
26.3 2
27 1
27.1 3
28 1
28.1 3
28.2 1
28.3 1
29 3
29.1 3
29.2 3
29.3 2
30 1
30.1 3
30.2 3
31 1
32 3
32.1 3
32.2 2
32.3 1
33 3
33.1 1
34 1
34.1 1
34.2 2
34.3 2
35 1
35.1 1
35.2 1
36 2
36.1 3
36.2 3
36.3 3
36.4 3
37 1
37.1 3
37.2 1
38 2
39 2
39.1 3
39.2 1
39.3 2
39.4 3
39.5 3
40 3
40.1 3
40.2 1
40.3 3
41 3
41.1 3
41.2 1
41.3 1
41.4 1
42 1
42.1 1
43 3
43.1 3
43.2 2
44 2
44.1 2
44.2 1
44.3 1
45 2
45.1 3
46 3
46.1 2
46.2 3
47 1
47.1 2
47.2 2
47.3 2
47.4 2
48 3
48.1 1
49 3
50 1
51 3
52 3
52.1 2
52.2 1
52.3 3
52.4 3
52.5 3
53 1
53.1 3
53.2 2
54 3
54.1 3
54.2 3
54.3 1
54.4 1
55 1
55.1 3
55.2 2
55.3 1
55.4 1
56 2
56.1 1
56.2 3
56.3 1
56.4 2
56.5 1
57 1
57.1 1
57.2 1
57.3 1
58 3
58.1 2
58.2 1
58.3 3
58.4 3
58.5 3
59 3
59.1 1
60 3
61 1
61.1 2
61.2 2
61.3 3
61.4 2
62 2
62.1 1
62.2 3
62.3 2
63 3
63.1 1
64 3
65 3
65.1 3
65.2 2
65.3 3
66 3
66.1 3
66.2 1
67 3
68 3
68.1 1
68.2 2
68.3 3
68.4 1
69 1
70 1
70.1 2
71 3
71.1 2
71.2 2
71.3 1
71.4 2
72 1
72.1 2
72.2 1
72.3 2
72.4 2
72.5 1
73 2
74 1
75 3
76 3
76.1 3
76.2 2
77 2
78 2
79 2
79.1 2
79.2 2
80 2
80.1 1
80.2 3
81 2
81.1 3
81.2 2
81.3 1
82 1
82.1 2
82.2 3
83 2
83.1 3
83.2 3
83.3 3
84 2
84.1 3
85 1
85.1 2
85.2 3
85.3 3
85.4 2
85.5 2
86 1
86.1 2
86.2 1
86.3 1
86.4 1
86.5 2
87 3
87.1 3
87.2 2
88 3
88.1 3
88.2 3
88.3 1
89 2
90 1
90.1 2
90.2 2
90.3 2
91 3
91.1 3
91.2 3
92 2
93 2
93.1 2
93.2 2
93.3 3
93.4 2
94 2
94.1 3
94.2 3
94.3 2
94.4 3
94.5 2
95 2
95.1 3
95.2 2
96 3
96.1 2
96.2 3
96.3 2
96.4 2
96.5 3
97 3
97.1 3
98 2
98.1 3
98.2 1
99 2
99.1 1
99.2 3
100 2
100.1 1
100.2 2
100.3 2
100.4 3
$m1a$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1a$mu_reg_multinomial
[1] 0
$m1a$tau_reg_multinomial
[1] 1e-04
$m1a$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
m2
1 3
1.1 1
1.2 3
1.3 1
2 2
2.1 1
2.2 NA
3 3
3.1 2
3.2 1
4 1
4.1 2
4.2 3
4.3 3
5 2
5.1 3
5.2 1
5.3 1
6 2
7 2
7.1 1
7.2 3
8 2
8.1 2
8.2 1
8.3 3
8.4 NA
8.5 3
9 NA
9.1 3
9.2 1
10 1
10.1 1
11 1
11.1 1
11.2 1
11.3 NA
11.4 1
12 1
13 2
13.1 2
14 3
14.1 2
14.2 1
14.3 1
15 1
15.1 2
15.2 3
15.3 3
16 2
16.1 NA
16.2 3
16.3 2
16.4 3
16.5 1
17 1
17.1 3
17.2 NA
17.3 2
17.4 1
18 3
19 NA
19.1 1
19.2 3
19.3 3
20 2
20.1 NA
20.2 3
20.3 1
20.4 3
20.5 2
21 3
21.1 1
21.2 NA
22 3
22.1 1
23 1
23.1 2
24 2
25 2
25.1 3
25.2 3
25.3 1
25.4 3
25.5 2
26 NA
26.1 3
26.2 3
26.3 NA
27 3
27.1 3
28 3
28.1 2
28.2 2
28.3 3
29 1
29.1 NA
29.2 2
29.3 2
30 2
30.1 3
30.2 3
31 3
32 3
32.1 3
32.2 1
32.3 1
33 3
33.1 3
34 3
34.1 NA
34.2 1
34.3 NA
35 2
35.1 2
35.2 2
36 3
36.1 3
36.2 3
36.3 2
36.4 2
37 2
37.1 2
37.2 1
38 2
39 3
39.1 2
39.2 3
39.3 NA
39.4 3
39.5 3
40 3
40.1 1
40.2 3
40.3 2
41 3
41.1 3
41.2 1
41.3 2
41.4 3
42 2
42.1 NA
43 3
43.1 3
43.2 2
44 3
44.1 3
44.2 NA
44.3 1
45 3
45.1 1
46 NA
46.1 1
46.2 2
47 2
47.1 NA
47.2 NA
47.3 3
47.4 3
48 3
48.1 1
49 1
50 NA
51 1
52 2
52.1 1
52.2 1
52.3 NA
52.4 2
52.5 3
53 2
53.1 1
53.2 2
54 NA
54.1 1
54.2 NA
54.3 3
54.4 3
55 1
55.1 1
55.2 1
55.3 NA
55.4 2
56 2
56.1 3
56.2 1
56.3 1
56.4 2
56.5 NA
57 2
57.1 3
57.2 2
57.3 NA
58 1
58.1 1
58.2 NA
58.3 1
58.4 2
58.5 NA
59 1
59.1 1
60 1
61 2
61.1 1
61.2 1
61.3 2
61.4 2
62 1
62.1 1
62.2 NA
62.3 1
63 NA
63.1 3
64 3
65 NA
65.1 2
65.2 3
65.3 3
66 3
66.1 3
66.2 1
67 NA
68 1
68.1 1
68.2 1
68.3 2
68.4 3
69 NA
70 1
70.1 NA
71 1
71.1 1
71.2 NA
71.3 1
71.4 1
72 2
72.1 3
72.2 2
72.3 1
72.4 2
72.5 1
73 NA
74 1
75 NA
76 1
76.1 2
76.2 2
77 NA
78 1
79 3
79.1 3
79.2 NA
80 3
80.1 2
80.2 NA
81 1
81.1 2
81.2 1
81.3 1
82 3
82.1 1
82.2 1
83 2
83.1 3
83.2 2
83.3 3
84 1
84.1 2
85 2
85.1 1
85.2 1
85.3 NA
85.4 2
85.5 1
86 1
86.1 NA
86.2 2
86.3 1
86.4 2
86.5 2
87 NA
87.1 1
87.2 NA
88 1
88.1 2
88.2 NA
88.3 2
89 3
90 3
90.1 2
90.2 NA
90.3 2
91 3
91.1 1
91.2 3
92 2
93 2
93.1 3
93.2 NA
93.3 2
93.4 3
94 2
94.1 2
94.2 1
94.3 2
94.4 1
94.5 2
95 2
95.1 2
95.2 NA
96 1
96.1 1
96.2 2
96.3 3
96.4 2
96.5 NA
97 1
97.1 2
98 3
98.1 2
98.2 2
99 2
99.1 2
99.2 1
100 1
100.1 2
100.2 3
100.3 2
100.4 1
$m1b$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1b$mu_reg_multinomial
[1] 0
$m1b$tau_reg_multinomial
[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)
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
$m1c$M_lvlone
m1 c1
1 3 0.7592026489
1.1 2 0.9548337990
1.2 1 0.5612235156
1.3 1 1.1873391025
2 2 0.9192204198
2.1 2 -0.1870730476
2.2 1 1.2517512331
3 1 -0.0605087604
3.1 2 0.3788637747
3.2 2 0.9872578281
4 2 1.4930175328
4.1 1 -0.7692526880
4.2 2 0.9180841450
4.3 3 -0.0541170782
5 2 -0.1376784521
5.1 1 -0.2740585866
5.2 2 0.4670496929
5.3 2 0.1740288049
6 2 0.9868044683
7 3 -0.1280320918
7.1 2 0.4242971219
7.2 3 0.0777182491
8 2 -0.5791408712
8.1 1 0.3128604232
8.2 3 0.6258446356
8.3 2 -0.1040137707
8.4 2 0.0481450285
8.5 2 0.3831763675
9 3 -0.1757592269
9.1 2 -0.1791541200
9.2 3 -0.0957042935
10 3 -0.5598409704
10.1 1 -0.2318340451
11 1 0.5086859475
11.1 1 0.4951758188
11.2 2 -1.1022162541
11.3 3 -0.0611636705
11.4 1 -0.4971774316
12 1 -0.2433996286
13 2 0.8799673116
13.1 3 0.1079022586
14 1 0.9991752617
14.1 1 -0.1094019046
14.2 1 0.1518967560
14.3 3 0.3521012473
15 1 0.3464447888
15.1 1 -0.4767313971
15.2 3 0.5759767791
15.3 2 -0.1713452662
16 2 0.4564754473
16.1 2 1.0652558311
16.2 1 0.6971872493
16.3 3 0.5259331838
16.4 2 0.2046601798
16.5 1 1.0718540464
17 2 0.6048676222
17.1 3 0.2323298304
17.2 1 1.2617499032
17.3 1 -0.3913230895
17.4 2 0.9577299112
18 1 -0.0050324072
19 2 -0.4187468937
19.1 3 -0.4478828944
19.2 2 -1.1966721302
19.3 3 -0.5877091668
20 2 0.6838223064
20.1 2 0.3278571109
20.2 1 -0.8489831990
20.3 3 1.3169975191
20.4 2 0.0444804531
20.5 3 -0.4535207652
21 1 -0.4030302960
21.1 2 -0.4069674045
21.2 3 1.0650265940
22 2 -0.0673274516
22.1 2 0.9601388170
23 2 0.5556634840
23.1 1 1.4407865964
24 1 0.3856376411
25 1 0.3564400705
25.1 3 0.0982553434
25.2 2 0.1928682598
25.3 2 -0.0192488594
25.4 1 0.4466012931
25.5 1 1.1425193342
26 2 0.5341531449
26.1 1 1.2268695927
26.2 1 0.3678294939
26.3 2 0.5948516018
27 1 -0.3342844147
27.1 3 -0.4835141229
28 1 -0.7145915499
28.1 3 0.5063671955
28.2 1 -0.2067413142
28.3 1 0.1196789973
29 3 0.1392699487
29.1 3 0.7960234776
29.2 3 1.0398214352
29.3 2 0.0813246429
30 1 -0.3296323050
30.1 3 1.3635850954
30.2 3 0.7354171050
31 1 0.3708398217
32 3 -0.0474059668
32.1 3 1.2507771489
32.2 2 0.1142915519
32.3 1 0.6773270619
33 3 0.1774293842
33.1 1 0.6159606291
34 1 0.8590979166
34.1 1 0.0546216775
34.2 2 -0.0897224473
34.3 2 0.4163395571
35 1 -1.4693520528
35.1 1 -0.3031734330
35.2 1 -0.6045512101
36 2 0.9823048960
36.1 3 1.4466051416
36.2 3 1.1606752905
36.3 3 0.8373091576
36.4 3 0.2640591685
37 1 0.1177313455
37.1 3 -0.1415483779
37.2 1 0.0054610124
38 2 0.8078948077
39 2 0.9876451040
39.1 3 -0.3431222274
39.2 1 -1.7909380751
39.3 2 -0.1798746191
39.4 3 -0.1850961689
39.5 3 0.4544226146
40 3 0.5350190436
40.1 3 0.4189342752
40.2 1 0.4211994981
40.3 3 0.0916687506
41 3 -0.1035047421
41.1 3 -0.4684202411
41.2 1 0.5972615368
41.3 1 0.9885613862
41.4 1 -0.3908036794
42 1 -0.0338893961
42.1 1 -0.4498363172
43 3 0.8965546110
43.1 3 0.6199122090
43.2 2 0.1804894429
44 2 1.3221409285
44.1 2 0.3416426284
44.2 1 0.5706610068
44.3 1 1.2679497430
45 2 0.1414983160
45.1 3 0.7220892521
46 3 1.5391054233
46.1 2 0.3889107049
46.2 3 0.1248719493
47 1 0.2014101100
47.1 2 0.2982973539
47.2 2 1.1518107179
47.3 2 0.5196802157
47.4 2 0.3702301552
48 3 -0.2128602862
48.1 1 -0.5337239976
49 3 -0.5236770035
50 1 0.3897705981
51 3 -0.7213343736
52 3 0.3758235358
52.1 2 0.7138067080
52.2 1 0.8872895233
52.3 3 -0.9664587437
52.4 3 0.0254566848
52.5 3 0.4155259424
53 1 0.5675736897
53.1 3 -0.3154088781
53.2 2 0.2162315769
54 3 -0.0880802382
54.1 3 0.4129127672
54.2 3 1.0119546775
54.3 1 -0.1112901990
54.4 1 0.8587727145
55 1 -0.0116453589
55.1 3 0.5835528661
55.2 2 -1.0010857254
55.3 1 -0.4796526070
55.4 1 -0.1202746964
56 2 0.5176377612
56.1 1 -1.1136932588
56.2 3 -0.0168103281
56.3 1 0.3933023606
56.4 2 0.3714625139
56.5 1 0.7811448179
57 1 -1.0868304872
57.1 1 0.8018626997
57.2 1 -0.1159517011
57.3 1 0.6785562445
58 3 1.6476207996
58.1 2 0.3402652711
58.2 1 -0.1111300753
58.3 3 -0.5409234285
58.4 3 -0.1271327672
58.5 3 0.8713264822
59 3 0.4766421367
59.1 1 1.0028089765
60 3 0.5231452932
61 1 -0.7190130614
61.1 2 0.8353702312
61.2 2 1.0229058138
61.3 3 1.1717723589
61.4 2 -0.0629201596
62 2 -0.3979137604
62.1 1 0.6830738372
62.2 3 0.4301745954
62.3 2 -0.0333139957
63 3 0.3345678035
63.1 1 0.3643769511
64 3 0.3949911859
65 3 1.2000091513
65.1 3 0.0110122646
65.2 2 -0.5776452043
65.3 3 -0.1372183563
66 3 -0.5081302805
66.1 3 -0.1447837412
66.2 1 0.1906241379
67 3 1.6716027681
68 3 0.5691848839
68.1 1 0.1004860389
68.2 2 -0.0061241827
68.3 3 0.7443745962
68.4 1 0.8726923437
69 1 0.0381382683
70 1 0.8126204217
70.1 2 0.4691503050
71 3 -0.5529062591
71.1 2 -0.1103252087
71.2 2 1.7178492547
71.3 1 -1.0118346755
71.4 2 1.8623785017
72 1 -0.4521659275
72.1 2 0.1375317317
72.2 1 -0.4170988856
72.3 2 0.7107266765
72.4 2 0.1451969143
72.5 1 1.6298050306
73 2 -0.0307469467
74 1 0.3730017941
75 3 -0.4908003566
76 3 -0.9888876620
76.1 3 0.0003798292
76.2 2 -0.8421863763
77 2 -0.4986802480
78 2 0.0417330969
79 2 -0.3767450660
79.1 2 0.1516000028
79.2 2 -0.1888160741
80 2 -0.0041558414
80.1 1 -0.0329337062
80.2 3 0.5046816157
81 2 -0.9493950353
81.1 3 0.2443038954
81.2 2 0.6476958410
81.3 1 0.4182528210
82 1 1.1088801952
82.1 2 0.9334157763
82.2 3 0.4958140634
83 2 0.5104724530
83.1 3 -0.0513309106
83.2 3 -0.2067792494
83.3 3 -0.0534169155
84 2 -0.0255753653
84.1 3 -1.8234189877
85 1 -0.0114038622
85.1 2 -0.0577615939
85.2 3 -0.2241856342
85.3 3 -0.0520175929
85.4 2 0.2892733846
85.5 2 -0.3740417009
86 1 0.4293735089
86.1 2 -0.1363456521
86.2 1 0.1230989293
86.3 1 0.3305413955
86.4 1 2.6003411822
86.5 2 -0.1420690052
87 3 1.0457427869
87.1 3 -0.2973007190
87.2 2 0.4396872616
88 3 -0.0601928334
88.1 3 -1.0124347595
88.2 3 0.5730917016
88.3 1 -0.0029455332
89 2 1.5465903721
90 1 0.0626760573
90.1 2 1.1896872985
90.2 2 0.2597888783
90.3 2 0.6599799887
91 3 1.1213651365
91.1 3 1.2046371625
91.2 3 0.3395603754
92 2 0.4674939332
93 2 0.2677965647
93.1 2 1.6424445368
93.2 2 0.7101700066
93.3 3 1.1222322893
93.4 2 1.4628960401
94 2 -0.2904211940
94.1 3 0.0147813580
94.2 3 -0.4536774482
94.3 2 0.6793464917
94.4 3 -0.9411356550
94.5 2 0.5683867264
95 2 0.2375652188
95.1 3 0.0767152977
95.2 2 -0.6886731251
96 3 0.7813892121
96.1 2 0.3391519695
96.2 3 -0.4857246503
96.3 2 0.8771471244
96.4 2 1.9030768981
96.5 3 -0.1684332749
97 3 1.3775130083
97.1 3 -1.7323228619
98 2 -1.2648518889
98.1 3 -0.9042716241
98.2 1 -0.1560385207
99 2 0.7993356425
99.1 1 1.0355522332
99.2 3 -0.1150895843
100 2 0.0369067906
100.1 1 1.6023713093
100.2 2 0.8861545820
100.3 2 0.1277046316
100.4 3 -0.0834577654
$m1c$spM_lvlone
center scale
m1 NA NA
c1 0.2559996 0.6718095
$m1c$mu_reg_multinomial
[1] 0
$m1c$tau_reg_multinomial
[1] 1e-04
$m1c$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)
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
$m1d$M_lvlone
m2 c1
1 3 0.7592026489
1.1 1 0.9548337990
1.2 3 0.5612235156
1.3 1 1.1873391025
2 2 0.9192204198
2.1 1 -0.1870730476
2.2 NA 1.2517512331
3 3 -0.0605087604
3.1 2 0.3788637747
3.2 1 0.9872578281
4 1 1.4930175328
4.1 2 -0.7692526880
4.2 3 0.9180841450
4.3 3 -0.0541170782
5 2 -0.1376784521
5.1 3 -0.2740585866
5.2 1 0.4670496929
5.3 1 0.1740288049
6 2 0.9868044683
7 2 -0.1280320918
7.1 1 0.4242971219
7.2 3 0.0777182491
8 2 -0.5791408712
8.1 2 0.3128604232
8.2 1 0.6258446356
8.3 3 -0.1040137707
8.4 NA 0.0481450285
8.5 3 0.3831763675
9 NA -0.1757592269
9.1 3 -0.1791541200
9.2 1 -0.0957042935
10 1 -0.5598409704
10.1 1 -0.2318340451
11 1 0.5086859475
11.1 1 0.4951758188
11.2 1 -1.1022162541
11.3 NA -0.0611636705
11.4 1 -0.4971774316
12 1 -0.2433996286
13 2 0.8799673116
13.1 2 0.1079022586
14 3 0.9991752617
14.1 2 -0.1094019046
14.2 1 0.1518967560
14.3 1 0.3521012473
15 1 0.3464447888
15.1 2 -0.4767313971
15.2 3 0.5759767791
15.3 3 -0.1713452662
16 2 0.4564754473
16.1 NA 1.0652558311
16.2 3 0.6971872493
16.3 2 0.5259331838
16.4 3 0.2046601798
16.5 1 1.0718540464
17 1 0.6048676222
17.1 3 0.2323298304
17.2 NA 1.2617499032
17.3 2 -0.3913230895
17.4 1 0.9577299112
18 3 -0.0050324072
19 NA -0.4187468937
19.1 1 -0.4478828944
19.2 3 -1.1966721302
19.3 3 -0.5877091668
20 2 0.6838223064
20.1 NA 0.3278571109
20.2 3 -0.8489831990
20.3 1 1.3169975191
20.4 3 0.0444804531
20.5 2 -0.4535207652
21 3 -0.4030302960
21.1 1 -0.4069674045
21.2 NA 1.0650265940
22 3 -0.0673274516
22.1 1 0.9601388170
23 1 0.5556634840
23.1 2 1.4407865964
24 2 0.3856376411
25 2 0.3564400705
25.1 3 0.0982553434
25.2 3 0.1928682598
25.3 1 -0.0192488594
25.4 3 0.4466012931
25.5 2 1.1425193342
26 NA 0.5341531449
26.1 3 1.2268695927
26.2 3 0.3678294939
26.3 NA 0.5948516018
27 3 -0.3342844147
27.1 3 -0.4835141229
28 3 -0.7145915499
28.1 2 0.5063671955
28.2 2 -0.2067413142
28.3 3 0.1196789973
29 1 0.1392699487
29.1 NA 0.7960234776
29.2 2 1.0398214352
29.3 2 0.0813246429
30 2 -0.3296323050
30.1 3 1.3635850954
30.2 3 0.7354171050
31 3 0.3708398217
32 3 -0.0474059668
32.1 3 1.2507771489
32.2 1 0.1142915519
32.3 1 0.6773270619
33 3 0.1774293842
33.1 3 0.6159606291
34 3 0.8590979166
34.1 NA 0.0546216775
34.2 1 -0.0897224473
34.3 NA 0.4163395571
35 2 -1.4693520528
35.1 2 -0.3031734330
35.2 2 -0.6045512101
36 3 0.9823048960
36.1 3 1.4466051416
36.2 3 1.1606752905
36.3 2 0.8373091576
36.4 2 0.2640591685
37 2 0.1177313455
37.1 2 -0.1415483779
37.2 1 0.0054610124
38 2 0.8078948077
39 3 0.9876451040
39.1 2 -0.3431222274
39.2 3 -1.7909380751
39.3 NA -0.1798746191
39.4 3 -0.1850961689
39.5 3 0.4544226146
40 3 0.5350190436
40.1 1 0.4189342752
40.2 3 0.4211994981
40.3 2 0.0916687506
41 3 -0.1035047421
41.1 3 -0.4684202411
41.2 1 0.5972615368
41.3 2 0.9885613862
41.4 3 -0.3908036794
42 2 -0.0338893961
42.1 NA -0.4498363172
43 3 0.8965546110
43.1 3 0.6199122090
43.2 2 0.1804894429
44 3 1.3221409285
44.1 3 0.3416426284
44.2 NA 0.5706610068
44.3 1 1.2679497430
45 3 0.1414983160
45.1 1 0.7220892521
46 NA 1.5391054233
46.1 1 0.3889107049
46.2 2 0.1248719493
47 2 0.2014101100
47.1 NA 0.2982973539
47.2 NA 1.1518107179
47.3 3 0.5196802157
47.4 3 0.3702301552
48 3 -0.2128602862
48.1 1 -0.5337239976
49 1 -0.5236770035
50 NA 0.3897705981
51 1 -0.7213343736
52 2 0.3758235358
52.1 1 0.7138067080
52.2 1 0.8872895233
52.3 NA -0.9664587437
52.4 2 0.0254566848
52.5 3 0.4155259424
53 2 0.5675736897
53.1 1 -0.3154088781
53.2 2 0.2162315769
54 NA -0.0880802382
54.1 1 0.4129127672
54.2 NA 1.0119546775
54.3 3 -0.1112901990
54.4 3 0.8587727145
55 1 -0.0116453589
55.1 1 0.5835528661
55.2 1 -1.0010857254
55.3 NA -0.4796526070
55.4 2 -0.1202746964
56 2 0.5176377612
56.1 3 -1.1136932588
56.2 1 -0.0168103281
56.3 1 0.3933023606
56.4 2 0.3714625139
56.5 NA 0.7811448179
57 2 -1.0868304872
57.1 3 0.8018626997
57.2 2 -0.1159517011
57.3 NA 0.6785562445
58 1 1.6476207996
58.1 1 0.3402652711
58.2 NA -0.1111300753
58.3 1 -0.5409234285
58.4 2 -0.1271327672
58.5 NA 0.8713264822
59 1 0.4766421367
59.1 1 1.0028089765
60 1 0.5231452932
61 2 -0.7190130614
61.1 1 0.8353702312
61.2 1 1.0229058138
61.3 2 1.1717723589
61.4 2 -0.0629201596
62 1 -0.3979137604
62.1 1 0.6830738372
62.2 NA 0.4301745954
62.3 1 -0.0333139957
63 NA 0.3345678035
63.1 3 0.3643769511
64 3 0.3949911859
65 NA 1.2000091513
65.1 2 0.0110122646
65.2 3 -0.5776452043
65.3 3 -0.1372183563
66 3 -0.5081302805
66.1 3 -0.1447837412
66.2 1 0.1906241379
67 NA 1.6716027681
68 1 0.5691848839
68.1 1 0.1004860389
68.2 1 -0.0061241827
68.3 2 0.7443745962
68.4 3 0.8726923437
69 NA 0.0381382683
70 1 0.8126204217
70.1 NA 0.4691503050
71 1 -0.5529062591
71.1 1 -0.1103252087
71.2 NA 1.7178492547
71.3 1 -1.0118346755
71.4 1 1.8623785017
72 2 -0.4521659275
72.1 3 0.1375317317
72.2 2 -0.4170988856
72.3 1 0.7107266765
72.4 2 0.1451969143
72.5 1 1.6298050306
73 NA -0.0307469467
74 1 0.3730017941
75 NA -0.4908003566
76 1 -0.9888876620
76.1 2 0.0003798292
76.2 2 -0.8421863763
77 NA -0.4986802480
78 1 0.0417330969
79 3 -0.3767450660
79.1 3 0.1516000028
79.2 NA -0.1888160741
80 3 -0.0041558414
80.1 2 -0.0329337062
80.2 NA 0.5046816157
81 1 -0.9493950353
81.1 2 0.2443038954
81.2 1 0.6476958410
81.3 1 0.4182528210
82 3 1.1088801952
82.1 1 0.9334157763
82.2 1 0.4958140634
83 2 0.5104724530
83.1 3 -0.0513309106
83.2 2 -0.2067792494
83.3 3 -0.0534169155
84 1 -0.0255753653
84.1 2 -1.8234189877
85 2 -0.0114038622
85.1 1 -0.0577615939
85.2 1 -0.2241856342
85.3 NA -0.0520175929
85.4 2 0.2892733846
85.5 1 -0.3740417009
86 1 0.4293735089
86.1 NA -0.1363456521
86.2 2 0.1230989293
86.3 1 0.3305413955
86.4 2 2.6003411822
86.5 2 -0.1420690052
87 NA 1.0457427869
87.1 1 -0.2973007190
87.2 NA 0.4396872616
88 1 -0.0601928334
88.1 2 -1.0124347595
88.2 NA 0.5730917016
88.3 2 -0.0029455332
89 3 1.5465903721
90 3 0.0626760573
90.1 2 1.1896872985
90.2 NA 0.2597888783
90.3 2 0.6599799887
91 3 1.1213651365
91.1 1 1.2046371625
91.2 3 0.3395603754
92 2 0.4674939332
93 2 0.2677965647
93.1 3 1.6424445368
93.2 NA 0.7101700066
93.3 2 1.1222322893
93.4 3 1.4628960401
94 2 -0.2904211940
94.1 2 0.0147813580
94.2 1 -0.4536774482
94.3 2 0.6793464917
94.4 1 -0.9411356550
94.5 2 0.5683867264
95 2 0.2375652188
95.1 2 0.0767152977
95.2 NA -0.6886731251
96 1 0.7813892121
96.1 1 0.3391519695
96.2 2 -0.4857246503
96.3 3 0.8771471244
96.4 2 1.9030768981
96.5 NA -0.1684332749
97 1 1.3775130083
97.1 2 -1.7323228619
98 3 -1.2648518889
98.1 2 -0.9042716241
98.2 2 -0.1560385207
99 2 0.7993356425
99.1 2 1.0355522332
99.2 1 -0.1150895843
100 1 0.0369067906
100.1 2 1.6023713093
100.2 3 0.8861545820
100.3 2 0.1277046316
100.4 1 -0.0834577654
$m1d$spM_lvlone
center scale
m2 NA NA
c1 0.2559996 0.6718095
$m1d$mu_reg_multinomial
[1] 0
$m1d$tau_reg_multinomial
[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"
$m2a
$m2a$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
$m2a$M_lvlone
m1
1 3
1.1 2
1.2 1
1.3 1
2 2
2.1 2
2.2 1
3 1
3.1 2
3.2 2
4 2
4.1 1
4.2 2
4.3 3
5 2
5.1 1
5.2 2
5.3 2
6 2
7 3
7.1 2
7.2 3
8 2
8.1 1
8.2 3
8.3 2
8.4 2
8.5 2
9 3
9.1 2
9.2 3
10 3
10.1 1
11 1
11.1 1
11.2 2
11.3 3
11.4 1
12 1
13 2
13.1 3
14 1
14.1 1
14.2 1
14.3 3
15 1
15.1 1
15.2 3
15.3 2
16 2
16.1 2
16.2 1
16.3 3
16.4 2
16.5 1
17 2
17.1 3
17.2 1
17.3 1
17.4 2
18 1
19 2
19.1 3
19.2 2
19.3 3
20 2
20.1 2
20.2 1
20.3 3
20.4 2
20.5 3
21 1
21.1 2
21.2 3
22 2
22.1 2
23 2
23.1 1
24 1
25 1
25.1 3
25.2 2
25.3 2
25.4 1
25.5 1
26 2
26.1 1
26.2 1
26.3 2
27 1
27.1 3
28 1
28.1 3
28.2 1
28.3 1
29 3
29.1 3
29.2 3
29.3 2
30 1
30.1 3
30.2 3
31 1
32 3
32.1 3
32.2 2
32.3 1
33 3
33.1 1
34 1
34.1 1
34.2 2
34.3 2
35 1
35.1 1
35.2 1
36 2
36.1 3
36.2 3
36.3 3
36.4 3
37 1
37.1 3
37.2 1
38 2
39 2
39.1 3
39.2 1
39.3 2
39.4 3
39.5 3
40 3
40.1 3
40.2 1
40.3 3
41 3
41.1 3
41.2 1
41.3 1
41.4 1
42 1
42.1 1
43 3
43.1 3
43.2 2
44 2
44.1 2
44.2 1
44.3 1
45 2
45.1 3
46 3
46.1 2
46.2 3
47 1
47.1 2
47.2 2
47.3 2
47.4 2
48 3
48.1 1
49 3
50 1
51 3
52 3
52.1 2
52.2 1
52.3 3
52.4 3
52.5 3
53 1
53.1 3
53.2 2
54 3
54.1 3
54.2 3
54.3 1
54.4 1
55 1
55.1 3
55.2 2
55.3 1
55.4 1
56 2
56.1 1
56.2 3
56.3 1
56.4 2
56.5 1
57 1
57.1 1
57.2 1
57.3 1
58 3
58.1 2
58.2 1
58.3 3
58.4 3
58.5 3
59 3
59.1 1
60 3
61 1
61.1 2
61.2 2
61.3 3
61.4 2
62 2
62.1 1
62.2 3
62.3 2
63 3
63.1 1
64 3
65 3
65.1 3
65.2 2
65.3 3
66 3
66.1 3
66.2 1
67 3
68 3
68.1 1
68.2 2
68.3 3
68.4 1
69 1
70 1
70.1 2
71 3
71.1 2
71.2 2
71.3 1
71.4 2
72 1
72.1 2
72.2 1
72.3 2
72.4 2
72.5 1
73 2
74 1
75 3
76 3
76.1 3
76.2 2
77 2
78 2
79 2
79.1 2
79.2 2
80 2
80.1 1
80.2 3
81 2
81.1 3
81.2 2
81.3 1
82 1
82.1 2
82.2 3
83 2
83.1 3
83.2 3
83.3 3
84 2
84.1 3
85 1
85.1 2
85.2 3
85.3 3
85.4 2
85.5 2
86 1
86.1 2
86.2 1
86.3 1
86.4 1
86.5 2
87 3
87.1 3
87.2 2
88 3
88.1 3
88.2 3
88.3 1
89 2
90 1
90.1 2
90.2 2
90.3 2
91 3
91.1 3
91.2 3
92 2
93 2
93.1 2
93.2 2
93.3 3
93.4 2
94 2
94.1 3
94.2 3
94.3 2
94.4 3
94.5 2
95 2
95.1 3
95.2 2
96 3
96.1 2
96.2 3
96.3 2
96.4 2
96.5 3
97 3
97.1 3
98 2
98.1 3
98.2 1
99 2
99.1 1
99.2 3
100 2
100.1 1
100.2 2
100.3 2
100.4 3
$m2a$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m2a$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
$m2a$mu_reg_multinomial
[1] 0
$m2a$tau_reg_multinomial
[1] 1e-04
$m2a$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
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
$m2b$M_lvlone
m2
1 3
1.1 1
1.2 3
1.3 1
2 2
2.1 1
2.2 NA
3 3
3.1 2
3.2 1
4 1
4.1 2
4.2 3
4.3 3
5 2
5.1 3
5.2 1
5.3 1
6 2
7 2
7.1 1
7.2 3
8 2
8.1 2
8.2 1
8.3 3
8.4 NA
8.5 3
9 NA
9.1 3
9.2 1
10 1
10.1 1
11 1
11.1 1
11.2 1
11.3 NA
11.4 1
12 1
13 2
13.1 2
14 3
14.1 2
14.2 1
14.3 1
15 1
15.1 2
15.2 3
15.3 3
16 2
16.1 NA
16.2 3
16.3 2
16.4 3
16.5 1
17 1
17.1 3
17.2 NA
17.3 2
17.4 1
18 3
19 NA
19.1 1
19.2 3
19.3 3
20 2
20.1 NA
20.2 3
20.3 1
20.4 3
20.5 2
21 3
21.1 1
21.2 NA
22 3
22.1 1
23 1
23.1 2
24 2
25 2
25.1 3
25.2 3
25.3 1
25.4 3
25.5 2
26 NA
26.1 3
26.2 3
26.3 NA
27 3
27.1 3
28 3
28.1 2
28.2 2
28.3 3
29 1
29.1 NA
29.2 2
29.3 2
30 2
30.1 3
30.2 3
31 3
32 3
32.1 3
32.2 1
32.3 1
33 3
33.1 3
34 3
34.1 NA
34.2 1
34.3 NA
35 2
35.1 2
35.2 2
36 3
36.1 3
36.2 3
36.3 2
36.4 2
37 2
37.1 2
37.2 1
38 2
39 3
39.1 2
39.2 3
39.3 NA
39.4 3
39.5 3
40 3
40.1 1
40.2 3
40.3 2
41 3
41.1 3
41.2 1
41.3 2
41.4 3
42 2
42.1 NA
43 3
43.1 3
43.2 2
44 3
44.1 3
44.2 NA
44.3 1
45 3
45.1 1
46 NA
46.1 1
46.2 2
47 2
47.1 NA
47.2 NA
47.3 3
47.4 3
48 3
48.1 1
49 1
50 NA
51 1
52 2
52.1 1
52.2 1
52.3 NA
52.4 2
52.5 3
53 2
53.1 1
53.2 2
54 NA
54.1 1
54.2 NA
54.3 3
54.4 3
55 1
55.1 1
55.2 1
55.3 NA
55.4 2
56 2
56.1 3
56.2 1
56.3 1
56.4 2
56.5 NA
57 2
57.1 3
57.2 2
57.3 NA
58 1
58.1 1
58.2 NA
58.3 1
58.4 2
58.5 NA
59 1
59.1 1
60 1
61 2
61.1 1
61.2 1
61.3 2
61.4 2
62 1
62.1 1
62.2 NA
62.3 1
63 NA
63.1 3
64 3
65 NA
65.1 2
65.2 3
65.3 3
66 3
66.1 3
66.2 1
67 NA
68 1
68.1 1
68.2 1
68.3 2
68.4 3
69 NA
70 1
70.1 NA
71 1
71.1 1
71.2 NA
71.3 1
71.4 1
72 2
72.1 3
72.2 2
72.3 1
72.4 2
72.5 1
73 NA
74 1
75 NA
76 1
76.1 2
76.2 2
77 NA
78 1
79 3
79.1 3
79.2 NA
80 3
80.1 2
80.2 NA
81 1
81.1 2
81.2 1
81.3 1
82 3
82.1 1
82.2 1
83 2
83.1 3
83.2 2
83.3 3
84 1
84.1 2
85 2
85.1 1
85.2 1
85.3 NA
85.4 2
85.5 1
86 1
86.1 NA
86.2 2
86.3 1
86.4 2
86.5 2
87 NA
87.1 1
87.2 NA
88 1
88.1 2
88.2 NA
88.3 2
89 3
90 3
90.1 2
90.2 NA
90.3 2
91 3
91.1 1
91.2 3
92 2
93 2
93.1 3
93.2 NA
93.3 2
93.4 3
94 2
94.1 2
94.2 1
94.3 2
94.4 1
94.5 2
95 2
95.1 2
95.2 NA
96 1
96.1 1
96.2 2
96.3 3
96.4 2
96.5 NA
97 1
97.1 2
98 3
98.1 2
98.2 2
99 2
99.1 2
99.2 1
100 1
100.1 2
100.2 3
100.3 2
100.4 1
$m2b$spM_id
center scale
C2 -0.6240921 0.6857108
(Intercept) NA NA
$m2b$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_multinomial
[1] 0
$m2b$tau_reg_multinomial
[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
m1 c2
1 3 NA
1.1 2 -0.08061445
1.2 1 -0.26523782
1.3 1 -0.30260393
2 2 -0.33443795
2.1 2 -0.11819800
2.2 1 -0.31532280
3 1 -0.12920657
3.1 2 NA
3.2 2 NA
4 2 -0.31177403
4.1 1 -0.23894886
4.2 2 -0.15533613
4.3 3 -0.14644545
5 2 -0.28360457
5.1 1 -0.20135143
5.2 2 -0.28293375
5.3 2 NA
6 2 -0.08617066
7 3 -0.22243495
7.1 2 NA
7.2 3 NA
8 2 NA
8.1 1 NA
8.2 3 NA
8.3 2 -0.35148972
8.4 2 0.03661023
8.5 2 -0.08424534
9 3 NA
9.1 2 -0.43509340
9.2 3 -0.22527490
10 3 NA
10.1 1 NA
11 1 -0.08587475
11.1 1 -0.06157340
11.2 2 -0.12436018
11.3 3 -0.21377934
11.4 1 -0.32208329
12 1 NA
13 2 NA
13.1 3 -0.40300449
14 1 -0.28992072
14.1 1 NA
14.2 1 NA
14.3 3 -0.21979936
15 1 NA
15.1 1 -0.29092263
15.2 3 -0.19392239
15.3 2 -0.25718384
16 2 -0.45041108
16.1 2 -0.07599066
16.2 1 -0.32385667
16.3 3 -0.38326110
16.4 2 -0.22845856
16.5 1 -0.25497157
17 2 NA
17.1 3 -0.22105143
17.2 1 NA
17.3 1 NA
17.4 2 -0.15098046
18 1 -0.09870041
19 2 -0.26680239
19.1 3 -0.15815241
19.2 2 -0.14717437
19.3 3 -0.21271374
20 2 -0.22087628
20.1 2 NA
20.2 1 -0.30127439
20.3 3 -0.11782590
20.4 2 -0.19857957
20.5 3 -0.24338208
21 1 -0.31407992
21.1 2 -0.12424941
21.2 3 -0.27672716
22 2 -0.23790593
22.1 2 -0.15996535
23 2 -0.18236682
23.1 1 -0.20823302
24 1 -0.29026416
25 1 -0.36139273
25.1 3 -0.19571118
25.2 2 -0.21379355
25.3 2 -0.33876012
25.4 1 NA
25.5 1 -0.04068446
26 2 -0.16846716
26.1 1 -0.10440642
26.2 1 -0.26884827
26.3 2 NA
27 1 -0.19520794
27.1 3 -0.17622638
28 1 -0.32164962
28.1 3 -0.27003852
28.2 1 -0.07235801
28.3 1 -0.13462982
29 3 -0.32432030
29.1 3 -0.27034171
29.2 3 -0.10197448
29.3 2 -0.27606945
30 1 -0.06949300
30.1 3 -0.11511035
30.2 3 -0.16215882
31 1 0.05707733
32 3 -0.18446298
32.1 3 -0.14270013
32.2 2 -0.20530798
32.3 1 -0.14705649
33 3 -0.15252819
33.1 1 NA
34 1 -0.30378735
34.1 1 -0.11982431
34.2 2 -0.24278671
34.3 2 -0.19971833
35 1 NA
35.1 1 -0.24165780
35.2 1 NA
36 2 -0.49062180
36.1 3 -0.25651700
36.2 3 NA
36.3 3 -0.30401274
36.4 3 NA
37 1 -0.15276529
37.1 3 -0.30016169
37.2 1 0.06809545
38 2 -0.11218486
39 2 -0.38072211
39.1 3 -0.32094428
39.2 1 NA
39.3 2 -0.40173480
39.4 3 -0.20041848
39.5 3 -0.26027990
40 3 -0.19751956
40.1 3 -0.08399467
40.2 1 -0.20864416
40.3 3 NA
41 3 -0.26096953
41.1 3 -0.23953874
41.2 1 -0.03079344
41.3 1 NA
41.4 1 NA
42 1 -0.16084527
42.1 1 -0.13812521
43 3 -0.08864017
43.1 3 -0.12583158
43.2 2 -0.29253959
44 2 -0.22697597
44.1 2 NA
44.2 1 NA
44.3 1 -0.40544012
45 2 -0.19274788
45.1 3 -0.34860483
46 3 -0.28547861
46.1 2 -0.21977836
46.2 3 NA
47 1 -0.08597098
47.1 2 -0.35424828
47.2 2 -0.24262576
47.3 2 -0.30426315
47.4 2 NA
48 3 NA
48.1 1 NA
49 3 -0.42198781
50 1 -0.19959516
51 3 -0.16556964
52 3 -0.07438732
52.1 2 -0.37537080
52.2 1 -0.24222066
52.3 3 -0.31520603
52.4 3 -0.44619160
52.5 3 -0.11011682
53 1 -0.23278716
53.1 3 -0.28317264
53.2 2 -0.19517481
54 3 -0.10122856
54.1 3 -0.28325504
54.2 3 -0.16753120
54.3 1 -0.22217672
54.4 1 -0.34609328
55 1 -0.32428190
55.1 3 -0.24235382
55.2 2 -0.24065814
55.3 1 -0.23665476
55.4 1 NA
56 2 NA
56.1 1 -0.30357450
56.2 3 -0.51301630
56.3 1 -0.23743117
56.4 2 -0.17264917
56.5 1 -0.39188329
57 1 -0.18501692
57.1 1 -0.27274841
57.2 1 NA
57.3 1 -0.09898509
58 3 -0.29901358
58.1 2 -0.35390896
58.2 1 -0.16687336
58.3 3 -0.11784506
58.4 3 -0.05321983
58.5 3 -0.54457568
59 3 -0.27255364
59.1 1 NA
60 3 NA
61 1 -0.30550120
61.1 2 -0.35579892
61.2 2 NA
61.3 3 -0.34184391
61.4 2 -0.30891967
62 2 NA
62.1 1 -0.10504143
62.2 3 -0.20104997
62.3 2 -0.08138677
63 3 -0.12036319
63.1 1 -0.13624992
64 3 NA
65 3 -0.34450396
65.1 3 -0.32514650
65.2 2 -0.10984996
65.3 3 -0.19275692
66 3 NA
66.1 3 NA
66.2 1 -0.11687008
67 3 NA
68 3 -0.13605235
68.1 1 -0.19790827
68.2 2 -0.17750123
68.3 3 NA
68.4 1 -0.12570562
69 1 -0.32152751
70 1 -0.28190462
70.1 2 -0.11503263
71 3 -0.13029093
71.1 2 NA
71.2 2 -0.39075433
71.3 1 -0.21401028
71.4 2 -0.40219281
72 1 -0.40337108
72.1 2 -0.25978914
72.2 1 NA
72.3 2 -0.09809866
72.4 2 -0.14240019
72.5 1 -0.14794204
73 2 -0.23509343
74 1 -0.27963171
75 3 -0.12905034
76 3 0.04775562
76.1 3 -0.19399157
76.2 2 -0.02754574
77 2 -0.19053195
78 2 -0.17172929
79 2 -0.03958515
79.1 2 -0.20328809
79.2 2 -0.23901634
80 2 -0.34031873
80.1 1 -0.19526756
80.2 3 NA
81 2 -0.18401980
81.1 3 -0.16889476
81.2 2 -0.37343047
81.3 1 NA
82 1 -0.08328168
82.1 2 -0.22167084
82.2 3 -0.20971187
83 2 -0.34228255
83.1 3 -0.34075730
83.2 3 -0.32503954
83.3 3 NA
84 2 -0.20676741
84.1 3 -0.20310458
85 1 -0.12107593
85.1 2 NA
85.2 3 -0.32509207
85.3 3 NA
85.4 2 -0.30730810
85.5 2 NA
86 1 -0.10854862
86.1 2 -0.25751662
86.2 1 -0.38943076
86.3 1 -0.24454702
86.4 1 -0.12338992
86.5 2 -0.23976984
87 3 NA
87.1 3 -0.34366972
87.2 2 NA
88 3 -0.31563888
88.1 3 -0.20304028
88.2 3 -0.40311895
88.3 1 -0.12308715
89 2 -0.18527715
90 1 -0.25029126
90.1 2 -0.26974303
90.2 2 -0.28804531
90.3 2 -0.19180615
91 3 -0.26591197
91.1 3 -0.09153470
91.2 3 -0.48414390
92 2 NA
93 2 -0.11939966
93.1 2 NA
93.2 2 -0.21089379
93.3 3 NA
93.4 2 -0.23618836
94 2 NA
94.1 3 -0.10217284
94.2 3 -0.36713471
94.3 2 -0.13806763
94.4 3 -0.42353804
94.5 2 -0.15513707
95 2 -0.24149687
95.1 3 -0.21315958
95.2 2 -0.15777208
96 3 -0.16780948
96.1 2 -0.32504815
96.2 3 -0.20395970
96.3 2 -0.06221501
96.4 2 -0.14801097
96.5 3 -0.28658893
97 3 -0.34484656
97.1 3 -0.35658805
98 2 -0.36913003
98.1 3 NA
98.2 1 -0.17154225
99 2 -0.24753132
99.1 1 -0.27947829
99.2 3 -0.09033035
100 2 -0.17326698
100.1 1 NA
100.2 2 -0.12072016
100.3 2 -0.27657520
100.4 3 -0.14631556
$m2c$spM_lvlone
center scale
m1 NA NA
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_multinomial
[1] 0
$m2c$tau_reg_multinomial
[1] 1e-04
$m2c$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
m2 c2
1 3 NA
1.1 1 -0.08061445
1.2 3 -0.26523782
1.3 1 -0.30260393
2 2 -0.33443795
2.1 1 -0.11819800
2.2 NA -0.31532280
3 3 -0.12920657
3.1 2 NA
3.2 1 NA
4 1 -0.31177403
4.1 2 -0.23894886
4.2 3 -0.15533613
4.3 3 -0.14644545
5 2 -0.28360457
5.1 3 -0.20135143
5.2 1 -0.28293375
5.3 1 NA
6 2 -0.08617066
7 2 -0.22243495
7.1 1 NA
7.2 3 NA
8 2 NA
8.1 2 NA
8.2 1 NA
8.3 3 -0.35148972
8.4 NA 0.03661023
8.5 3 -0.08424534
9 NA NA
9.1 3 -0.43509340
9.2 1 -0.22527490
10 1 NA
10.1 1 NA
11 1 -0.08587475
11.1 1 -0.06157340
11.2 1 -0.12436018
11.3 NA -0.21377934
11.4 1 -0.32208329
12 1 NA
13 2 NA
13.1 2 -0.40300449
14 3 -0.28992072
14.1 2 NA
14.2 1 NA
14.3 1 -0.21979936
15 1 NA
15.1 2 -0.29092263
15.2 3 -0.19392239
15.3 3 -0.25718384
16 2 -0.45041108
16.1 NA -0.07599066
16.2 3 -0.32385667
16.3 2 -0.38326110
16.4 3 -0.22845856
16.5 1 -0.25497157
17 1 NA
17.1 3 -0.22105143
17.2 NA NA
17.3 2 NA
17.4 1 -0.15098046
18 3 -0.09870041
19 NA -0.26680239
19.1 1 -0.15815241
19.2 3 -0.14717437
19.3 3 -0.21271374
20 2 -0.22087628
20.1 NA NA
20.2 3 -0.30127439
20.3 1 -0.11782590
20.4 3 -0.19857957
20.5 2 -0.24338208
21 3 -0.31407992
21.1 1 -0.12424941
21.2 NA -0.27672716
22 3 -0.23790593
22.1 1 -0.15996535
23 1 -0.18236682
23.1 2 -0.20823302
24 2 -0.29026416
25 2 -0.36139273
25.1 3 -0.19571118
25.2 3 -0.21379355
25.3 1 -0.33876012
25.4 3 NA
25.5 2 -0.04068446
26 NA -0.16846716
26.1 3 -0.10440642
26.2 3 -0.26884827
26.3 NA NA
27 3 -0.19520794
27.1 3 -0.17622638
28 3 -0.32164962
28.1 2 -0.27003852
28.2 2 -0.07235801
28.3 3 -0.13462982
29 1 -0.32432030
29.1 NA -0.27034171
29.2 2 -0.10197448
29.3 2 -0.27606945
30 2 -0.06949300
30.1 3 -0.11511035
30.2 3 -0.16215882
31 3 0.05707733
32 3 -0.18446298
32.1 3 -0.14270013
32.2 1 -0.20530798
32.3 1 -0.14705649
33 3 -0.15252819
33.1 3 NA
34 3 -0.30378735
34.1 NA -0.11982431
34.2 1 -0.24278671
34.3 NA -0.19971833
35 2 NA
35.1 2 -0.24165780
35.2 2 NA
36 3 -0.49062180
36.1 3 -0.25651700
36.2 3 NA
36.3 2 -0.30401274
36.4 2 NA
37 2 -0.15276529
37.1 2 -0.30016169
37.2 1 0.06809545
38 2 -0.11218486
39 3 -0.38072211
39.1 2 -0.32094428
39.2 3 NA
39.3 NA -0.40173480
39.4 3 -0.20041848
39.5 3 -0.26027990
40 3 -0.19751956
40.1 1 -0.08399467
40.2 3 -0.20864416
40.3 2 NA
41 3 -0.26096953
41.1 3 -0.23953874
41.2 1 -0.03079344
41.3 2 NA
41.4 3 NA
42 2 -0.16084527
42.1 NA -0.13812521
43 3 -0.08864017
43.1 3 -0.12583158
43.2 2 -0.29253959
44 3 -0.22697597
44.1 3 NA
44.2 NA NA
44.3 1 -0.40544012
45 3 -0.19274788
45.1 1 -0.34860483
46 NA -0.28547861
46.1 1 -0.21977836
46.2 2 NA
47 2 -0.08597098
47.1 NA -0.35424828
47.2 NA -0.24262576
47.3 3 -0.30426315
47.4 3 NA
48 3 NA
48.1 1 NA
49 1 -0.42198781
50 NA -0.19959516
51 1 -0.16556964
52 2 -0.07438732
52.1 1 -0.37537080
52.2 1 -0.24222066
52.3 NA -0.31520603
52.4 2 -0.44619160
52.5 3 -0.11011682
53 2 -0.23278716
53.1 1 -0.28317264
53.2 2 -0.19517481
54 NA -0.10122856
54.1 1 -0.28325504
54.2 NA -0.16753120
54.3 3 -0.22217672
54.4 3 -0.34609328
55 1 -0.32428190
55.1 1 -0.24235382
55.2 1 -0.24065814
55.3 NA -0.23665476
55.4 2 NA
56 2 NA
56.1 3 -0.30357450
56.2 1 -0.51301630
56.3 1 -0.23743117
56.4 2 -0.17264917
56.5 NA -0.39188329
57 2 -0.18501692
57.1 3 -0.27274841
57.2 2 NA
57.3 NA -0.09898509
58 1 -0.29901358
58.1 1 -0.35390896
58.2 NA -0.16687336
58.3 1 -0.11784506
58.4 2 -0.05321983
58.5 NA -0.54457568
59 1 -0.27255364
59.1 1 NA
60 1 NA
61 2 -0.30550120
61.1 1 -0.35579892
61.2 1 NA
61.3 2 -0.34184391
61.4 2 -0.30891967
62 1 NA
62.1 1 -0.10504143
62.2 NA -0.20104997
62.3 1 -0.08138677
63 NA -0.12036319
63.1 3 -0.13624992
64 3 NA
65 NA -0.34450396
65.1 2 -0.32514650
65.2 3 -0.10984996
65.3 3 -0.19275692
66 3 NA
66.1 3 NA
66.2 1 -0.11687008
67 NA NA
68 1 -0.13605235
68.1 1 -0.19790827
68.2 1 -0.17750123
68.3 2 NA
68.4 3 -0.12570562
69 NA -0.32152751
70 1 -0.28190462
70.1 NA -0.11503263
71 1 -0.13029093
71.1 1 NA
71.2 NA -0.39075433
71.3 1 -0.21401028
71.4 1 -0.40219281
72 2 -0.40337108
72.1 3 -0.25978914
72.2 2 NA
72.3 1 -0.09809866
72.4 2 -0.14240019
72.5 1 -0.14794204
73 NA -0.23509343
74 1 -0.27963171
75 NA -0.12905034
76 1 0.04775562
76.1 2 -0.19399157
76.2 2 -0.02754574
77 NA -0.19053195
78 1 -0.17172929
79 3 -0.03958515
79.1 3 -0.20328809
79.2 NA -0.23901634
80 3 -0.34031873
80.1 2 -0.19526756
80.2 NA NA
81 1 -0.18401980
81.1 2 -0.16889476
81.2 1 -0.37343047
81.3 1 NA
82 3 -0.08328168
82.1 1 -0.22167084
82.2 1 -0.20971187
83 2 -0.34228255
83.1 3 -0.34075730
83.2 2 -0.32503954
83.3 3 NA
84 1 -0.20676741
84.1 2 -0.20310458
85 2 -0.12107593
85.1 1 NA
85.2 1 -0.32509207
85.3 NA NA
85.4 2 -0.30730810
85.5 1 NA
86 1 -0.10854862
86.1 NA -0.25751662
86.2 2 -0.38943076
86.3 1 -0.24454702
86.4 2 -0.12338992
86.5 2 -0.23976984
87 NA NA
87.1 1 -0.34366972
87.2 NA NA
88 1 -0.31563888
88.1 2 -0.20304028
88.2 NA -0.40311895
88.3 2 -0.12308715
89 3 -0.18527715
90 3 -0.25029126
90.1 2 -0.26974303
90.2 NA -0.28804531
90.3 2 -0.19180615
91 3 -0.26591197
91.1 1 -0.09153470
91.2 3 -0.48414390
92 2 NA
93 2 -0.11939966
93.1 3 NA
93.2 NA -0.21089379
93.3 2 NA
93.4 3 -0.23618836
94 2 NA
94.1 2 -0.10217284
94.2 1 -0.36713471
94.3 2 -0.13806763
94.4 1 -0.42353804
94.5 2 -0.15513707
95 2 -0.24149687
95.1 2 -0.21315958
95.2 NA -0.15777208
96 1 -0.16780948
96.1 1 -0.32504815
96.2 2 -0.20395970
96.3 3 -0.06221501
96.4 2 -0.14801097
96.5 NA -0.28658893
97 1 -0.34484656
97.1 2 -0.35658805
98 3 -0.36913003
98.1 2 NA
98.2 2 -0.17154225
99 2 -0.24753132
99.1 2 -0.27947829
99.2 1 -0.09033035
100 1 -0.17326698
100.1 2 NA
100.2 3 -0.12072016
100.3 2 -0.27657520
100.4 1 -0.14631556
$m2d$spM_lvlone
center scale
m2 NA NA
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_multinomial
[1] 0
$m2d$tau_reg_multinomial
[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"
$m3a
$m3a$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
$m3a$M_lvlone
c1 m1B m1C
1 0.7592026489 0 1
1.1 0.9548337990 1 0
1.2 0.5612235156 0 0
1.3 1.1873391025 0 0
2 0.9192204198 1 0
2.1 -0.1870730476 1 0
2.2 1.2517512331 0 0
3 -0.0605087604 0 0
3.1 0.3788637747 1 0
3.2 0.9872578281 1 0
4 1.4930175328 1 0
4.1 -0.7692526880 0 0
4.2 0.9180841450 1 0
4.3 -0.0541170782 0 1
5 -0.1376784521 1 0
5.1 -0.2740585866 0 0
5.2 0.4670496929 1 0
5.3 0.1740288049 1 0
6 0.9868044683 1 0
7 -0.1280320918 0 1
7.1 0.4242971219 1 0
7.2 0.0777182491 0 1
8 -0.5791408712 1 0
8.1 0.3128604232 0 0
8.2 0.6258446356 0 1
8.3 -0.1040137707 1 0
8.4 0.0481450285 1 0
8.5 0.3831763675 1 0
9 -0.1757592269 0 1
9.1 -0.1791541200 1 0
9.2 -0.0957042935 0 1
10 -0.5598409704 0 1
10.1 -0.2318340451 0 0
11 0.5086859475 0 0
11.1 0.4951758188 0 0
11.2 -1.1022162541 1 0
11.3 -0.0611636705 0 1
11.4 -0.4971774316 0 0
12 -0.2433996286 0 0
13 0.8799673116 1 0
13.1 0.1079022586 0 1
14 0.9991752617 0 0
14.1 -0.1094019046 0 0
14.2 0.1518967560 0 0
14.3 0.3521012473 0 1
15 0.3464447888 0 0
15.1 -0.4767313971 0 0
15.2 0.5759767791 0 1
15.3 -0.1713452662 1 0
16 0.4564754473 1 0
16.1 1.0652558311 1 0
16.2 0.6971872493 0 0
16.3 0.5259331838 0 1
16.4 0.2046601798 1 0
16.5 1.0718540464 0 0
17 0.6048676222 1 0
17.1 0.2323298304 0 1
17.2 1.2617499032 0 0
17.3 -0.3913230895 0 0
17.4 0.9577299112 1 0
18 -0.0050324072 0 0
19 -0.4187468937 1 0
19.1 -0.4478828944 0 1
19.2 -1.1966721302 1 0
19.3 -0.5877091668 0 1
20 0.6838223064 1 0
20.1 0.3278571109 1 0
20.2 -0.8489831990 0 0
20.3 1.3169975191 0 1
20.4 0.0444804531 1 0
20.5 -0.4535207652 0 1
21 -0.4030302960 0 0
21.1 -0.4069674045 1 0
21.2 1.0650265940 0 1
22 -0.0673274516 1 0
22.1 0.9601388170 1 0
23 0.5556634840 1 0
23.1 1.4407865964 0 0
24 0.3856376411 0 0
25 0.3564400705 0 0
25.1 0.0982553434 0 1
25.2 0.1928682598 1 0
25.3 -0.0192488594 1 0
25.4 0.4466012931 0 0
25.5 1.1425193342 0 0
26 0.5341531449 1 0
26.1 1.2268695927 0 0
26.2 0.3678294939 0 0
26.3 0.5948516018 1 0
27 -0.3342844147 0 0
27.1 -0.4835141229 0 1
28 -0.7145915499 0 0
28.1 0.5063671955 0 1
28.2 -0.2067413142 0 0
28.3 0.1196789973 0 0
29 0.1392699487 0 1
29.1 0.7960234776 0 1
29.2 1.0398214352 0 1
29.3 0.0813246429 1 0
30 -0.3296323050 0 0
30.1 1.3635850954 0 1
30.2 0.7354171050 0 1
31 0.3708398217 0 0
32 -0.0474059668 0 1
32.1 1.2507771489 0 1
32.2 0.1142915519 1 0
32.3 0.6773270619 0 0
33 0.1774293842 0 1
33.1 0.6159606291 0 0
34 0.8590979166 0 0
34.1 0.0546216775 0 0
34.2 -0.0897224473 1 0
34.3 0.4163395571 1 0
35 -1.4693520528 0 0
35.1 -0.3031734330 0 0
35.2 -0.6045512101 0 0
36 0.9823048960 1 0
36.1 1.4466051416 0 1
36.2 1.1606752905 0 1
36.3 0.8373091576 0 1
36.4 0.2640591685 0 1
37 0.1177313455 0 0
37.1 -0.1415483779 0 1
37.2 0.0054610124 0 0
38 0.8078948077 1 0
39 0.9876451040 1 0
39.1 -0.3431222274 0 1
39.2 -1.7909380751 0 0
39.3 -0.1798746191 1 0
39.4 -0.1850961689 0 1
39.5 0.4544226146 0 1
40 0.5350190436 0 1
40.1 0.4189342752 0 1
40.2 0.4211994981 0 0
40.3 0.0916687506 0 1
41 -0.1035047421 0 1
41.1 -0.4684202411 0 1
41.2 0.5972615368 0 0
41.3 0.9885613862 0 0
41.4 -0.3908036794 0 0
42 -0.0338893961 0 0
42.1 -0.4498363172 0 0
43 0.8965546110 0 1
43.1 0.6199122090 0 1
43.2 0.1804894429 1 0
44 1.3221409285 1 0
44.1 0.3416426284 1 0
44.2 0.5706610068 0 0
44.3 1.2679497430 0 0
45 0.1414983160 1 0
45.1 0.7220892521 0 1
46 1.5391054233 0 1
46.1 0.3889107049 1 0
46.2 0.1248719493 0 1
47 0.2014101100 0 0
47.1 0.2982973539 1 0
47.2 1.1518107179 1 0
47.3 0.5196802157 1 0
47.4 0.3702301552 1 0
48 -0.2128602862 0 1
48.1 -0.5337239976 0 0
49 -0.5236770035 0 1
50 0.3897705981 0 0
51 -0.7213343736 0 1
52 0.3758235358 0 1
52.1 0.7138067080 1 0
52.2 0.8872895233 0 0
52.3 -0.9664587437 0 1
52.4 0.0254566848 0 1
52.5 0.4155259424 0 1
53 0.5675736897 0 0
53.1 -0.3154088781 0 1
53.2 0.2162315769 1 0
54 -0.0880802382 0 1
54.1 0.4129127672 0 1
54.2 1.0119546775 0 1
54.3 -0.1112901990 0 0
54.4 0.8587727145 0 0
55 -0.0116453589 0 0
55.1 0.5835528661 0 1
55.2 -1.0010857254 1 0
55.3 -0.4796526070 0 0
55.4 -0.1202746964 0 0
56 0.5176377612 1 0
56.1 -1.1136932588 0 0
56.2 -0.0168103281 0 1
56.3 0.3933023606 0 0
56.4 0.3714625139 1 0
56.5 0.7811448179 0 0
57 -1.0868304872 0 0
57.1 0.8018626997 0 0
57.2 -0.1159517011 0 0
57.3 0.6785562445 0 0
58 1.6476207996 0 1
58.1 0.3402652711 1 0
58.2 -0.1111300753 0 0
58.3 -0.5409234285 0 1
58.4 -0.1271327672 0 1
58.5 0.8713264822 0 1
59 0.4766421367 0 1
59.1 1.0028089765 0 0
60 0.5231452932 0 1
61 -0.7190130614 0 0
61.1 0.8353702312 1 0
61.2 1.0229058138 1 0
61.3 1.1717723589 0 1
61.4 -0.0629201596 1 0
62 -0.3979137604 1 0
62.1 0.6830738372 0 0
62.2 0.4301745954 0 1
62.3 -0.0333139957 1 0
63 0.3345678035 0 1
63.1 0.3643769511 0 0
64 0.3949911859 0 1
65 1.2000091513 0 1
65.1 0.0110122646 0 1
65.2 -0.5776452043 1 0
65.3 -0.1372183563 0 1
66 -0.5081302805 0 1
66.1 -0.1447837412 0 1
66.2 0.1906241379 0 0
67 1.6716027681 0 1
68 0.5691848839 0 1
68.1 0.1004860389 0 0
68.2 -0.0061241827 1 0
68.3 0.7443745962 0 1
68.4 0.8726923437 0 0
69 0.0381382683 0 0
70 0.8126204217 0 0
70.1 0.4691503050 1 0
71 -0.5529062591 0 1
71.1 -0.1103252087 1 0
71.2 1.7178492547 1 0
71.3 -1.0118346755 0 0
71.4 1.8623785017 1 0
72 -0.4521659275 0 0
72.1 0.1375317317 1 0
72.2 -0.4170988856 0 0
72.3 0.7107266765 1 0
72.4 0.1451969143 1 0
72.5 1.6298050306 0 0
73 -0.0307469467 1 0
74 0.3730017941 0 0
75 -0.4908003566 0 1
76 -0.9888876620 0 1
76.1 0.0003798292 0 1
76.2 -0.8421863763 1 0
77 -0.4986802480 1 0
78 0.0417330969 1 0
79 -0.3767450660 1 0
79.1 0.1516000028 1 0
79.2 -0.1888160741 1 0
80 -0.0041558414 1 0
80.1 -0.0329337062 0 0
80.2 0.5046816157 0 1
81 -0.9493950353 1 0
81.1 0.2443038954 0 1
81.2 0.6476958410 1 0
81.3 0.4182528210 0 0
82 1.1088801952 0 0
82.1 0.9334157763 1 0
82.2 0.4958140634 0 1
83 0.5104724530 1 0
83.1 -0.0513309106 0 1
83.2 -0.2067792494 0 1
83.3 -0.0534169155 0 1
84 -0.0255753653 1 0
84.1 -1.8234189877 0 1
85 -0.0114038622 0 0
85.1 -0.0577615939 1 0
85.2 -0.2241856342 0 1
85.3 -0.0520175929 0 1
85.4 0.2892733846 1 0
85.5 -0.3740417009 1 0
86 0.4293735089 0 0
86.1 -0.1363456521 1 0
86.2 0.1230989293 0 0
86.3 0.3305413955 0 0
86.4 2.6003411822 0 0
86.5 -0.1420690052 1 0
87 1.0457427869 0 1
87.1 -0.2973007190 0 1
87.2 0.4396872616 1 0
88 -0.0601928334 0 1
88.1 -1.0124347595 0 1
88.2 0.5730917016 0 1
88.3 -0.0029455332 0 0
89 1.5465903721 1 0
90 0.0626760573 0 0
90.1 1.1896872985 1 0
90.2 0.2597888783 1 0
90.3 0.6599799887 1 0
91 1.1213651365 0 1
91.1 1.2046371625 0 1
91.2 0.3395603754 0 1
92 0.4674939332 1 0
93 0.2677965647 1 0
93.1 1.6424445368 1 0
93.2 0.7101700066 1 0
93.3 1.1222322893 0 1
93.4 1.4628960401 1 0
94 -0.2904211940 1 0
94.1 0.0147813580 0 1
94.2 -0.4536774482 0 1
94.3 0.6793464917 1 0
94.4 -0.9411356550 0 1
94.5 0.5683867264 1 0
95 0.2375652188 1 0
95.1 0.0767152977 0 1
95.2 -0.6886731251 1 0
96 0.7813892121 0 1
96.1 0.3391519695 1 0
96.2 -0.4857246503 0 1
96.3 0.8771471244 1 0
96.4 1.9030768981 1 0
96.5 -0.1684332749 0 1
97 1.3775130083 0 1
97.1 -1.7323228619 0 1
98 -1.2648518889 1 0
98.1 -0.9042716241 0 1
98.2 -0.1560385207 0 0
99 0.7993356425 1 0
99.1 1.0355522332 0 0
99.2 -0.1150895843 0 1
100 0.0369067906 1 0
100.1 1.6023713093 0 0
100.2 0.8861545820 1 0
100.3 0.1277046316 1 0
100.4 -0.0834577654 0 1
$m3a$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
(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
$m3b$M_lvlone
c1 m2 m2B m2C
1 0.7592026489 3 NA NA
1.1 0.9548337990 1 NA NA
1.2 0.5612235156 3 NA NA
1.3 1.1873391025 1 NA NA
2 0.9192204198 2 NA NA
2.1 -0.1870730476 1 NA NA
2.2 1.2517512331 NA NA NA
3 -0.0605087604 3 NA NA
3.1 0.3788637747 2 NA NA
3.2 0.9872578281 1 NA NA
4 1.4930175328 1 NA NA
4.1 -0.7692526880 2 NA NA
4.2 0.9180841450 3 NA NA
4.3 -0.0541170782 3 NA NA
5 -0.1376784521 2 NA NA
5.1 -0.2740585866 3 NA NA
5.2 0.4670496929 1 NA NA
5.3 0.1740288049 1 NA NA
6 0.9868044683 2 NA NA
7 -0.1280320918 2 NA NA
7.1 0.4242971219 1 NA NA
7.2 0.0777182491 3 NA NA
8 -0.5791408712 2 NA NA
8.1 0.3128604232 2 NA NA
8.2 0.6258446356 1 NA NA
8.3 -0.1040137707 3 NA NA
8.4 0.0481450285 NA NA NA
8.5 0.3831763675 3 NA NA
9 -0.1757592269 NA NA NA
9.1 -0.1791541200 3 NA NA
9.2 -0.0957042935 1 NA NA
10 -0.5598409704 1 NA NA
10.1 -0.2318340451 1 NA NA
11 0.5086859475 1 NA NA
11.1 0.4951758188 1 NA NA
11.2 -1.1022162541 1 NA NA
11.3 -0.0611636705 NA NA NA
11.4 -0.4971774316 1 NA NA
12 -0.2433996286 1 NA NA
13 0.8799673116 2 NA NA
13.1 0.1079022586 2 NA NA
14 0.9991752617 3 NA NA
14.1 -0.1094019046 2 NA NA
14.2 0.1518967560 1 NA NA
14.3 0.3521012473 1 NA NA
15 0.3464447888 1 NA NA
15.1 -0.4767313971 2 NA NA
15.2 0.5759767791 3 NA NA
15.3 -0.1713452662 3 NA NA
16 0.4564754473 2 NA NA
16.1 1.0652558311 NA NA NA
16.2 0.6971872493 3 NA NA
16.3 0.5259331838 2 NA NA
16.4 0.2046601798 3 NA NA
16.5 1.0718540464 1 NA NA
17 0.6048676222 1 NA NA
17.1 0.2323298304 3 NA NA
17.2 1.2617499032 NA NA NA
17.3 -0.3913230895 2 NA NA
17.4 0.9577299112 1 NA NA
18 -0.0050324072 3 NA NA
19 -0.4187468937 NA NA NA
19.1 -0.4478828944 1 NA NA
19.2 -1.1966721302 3 NA NA
19.3 -0.5877091668 3 NA NA
20 0.6838223064 2 NA NA
20.1 0.3278571109 NA NA NA
20.2 -0.8489831990 3 NA NA
20.3 1.3169975191 1 NA NA
20.4 0.0444804531 3 NA NA
20.5 -0.4535207652 2 NA NA
21 -0.4030302960 3 NA NA
21.1 -0.4069674045 1 NA NA
21.2 1.0650265940 NA NA NA
22 -0.0673274516 3 NA NA
22.1 0.9601388170 1 NA NA
23 0.5556634840 1 NA NA
23.1 1.4407865964 2 NA NA
24 0.3856376411 2 NA NA
25 0.3564400705 2 NA NA
25.1 0.0982553434 3 NA NA
25.2 0.1928682598 3 NA NA
25.3 -0.0192488594 1 NA NA
25.4 0.4466012931 3 NA NA
25.5 1.1425193342 2 NA NA
26 0.5341531449 NA NA NA
26.1 1.2268695927 3 NA NA
26.2 0.3678294939 3 NA NA
26.3 0.5948516018 NA NA NA
27 -0.3342844147 3 NA NA
27.1 -0.4835141229 3 NA NA
28 -0.7145915499 3 NA NA
28.1 0.5063671955 2 NA NA
28.2 -0.2067413142 2 NA NA
28.3 0.1196789973 3 NA NA
29 0.1392699487 1 NA NA
29.1 0.7960234776 NA NA NA
29.2 1.0398214352 2 NA NA
29.3 0.0813246429 2 NA NA
30 -0.3296323050 2 NA NA
30.1 1.3635850954 3 NA NA
30.2 0.7354171050 3 NA NA
31 0.3708398217 3 NA NA
32 -0.0474059668 3 NA NA
32.1 1.2507771489 3 NA NA
32.2 0.1142915519 1 NA NA
32.3 0.6773270619 1 NA NA
33 0.1774293842 3 NA NA
33.1 0.6159606291 3 NA NA
34 0.8590979166 3 NA NA
34.1 0.0546216775 NA NA NA
34.2 -0.0897224473 1 NA NA
34.3 0.4163395571 NA NA NA
35 -1.4693520528 2 NA NA
35.1 -0.3031734330 2 NA NA
35.2 -0.6045512101 2 NA NA
36 0.9823048960 3 NA NA
36.1 1.4466051416 3 NA NA
36.2 1.1606752905 3 NA NA
36.3 0.8373091576 2 NA NA
36.4 0.2640591685 2 NA NA
37 0.1177313455 2 NA NA
37.1 -0.1415483779 2 NA NA
37.2 0.0054610124 1 NA NA
38 0.8078948077 2 NA NA
39 0.9876451040 3 NA NA
39.1 -0.3431222274 2 NA NA
39.2 -1.7909380751 3 NA NA
39.3 -0.1798746191 NA NA NA
39.4 -0.1850961689 3 NA NA
39.5 0.4544226146 3 NA NA
40 0.5350190436 3 NA NA
40.1 0.4189342752 1 NA NA
40.2 0.4211994981 3 NA NA
40.3 0.0916687506 2 NA NA
41 -0.1035047421 3 NA NA
41.1 -0.4684202411 3 NA NA
41.2 0.5972615368 1 NA NA
41.3 0.9885613862 2 NA NA
41.4 -0.3908036794 3 NA NA
42 -0.0338893961 2 NA NA
42.1 -0.4498363172 NA NA NA
43 0.8965546110 3 NA NA
43.1 0.6199122090 3 NA NA
43.2 0.1804894429 2 NA NA
44 1.3221409285 3 NA NA
44.1 0.3416426284 3 NA NA
44.2 0.5706610068 NA NA NA
44.3 1.2679497430 1 NA NA
45 0.1414983160 3 NA NA
45.1 0.7220892521 1 NA NA
46 1.5391054233 NA NA NA
46.1 0.3889107049 1 NA NA
46.2 0.1248719493 2 NA NA
47 0.2014101100 2 NA NA
47.1 0.2982973539 NA NA NA
47.2 1.1518107179 NA NA NA
47.3 0.5196802157 3 NA NA
47.4 0.3702301552 3 NA NA
48 -0.2128602862 3 NA NA
48.1 -0.5337239976 1 NA NA
49 -0.5236770035 1 NA NA
50 0.3897705981 NA NA NA
51 -0.7213343736 1 NA NA
52 0.3758235358 2 NA NA
52.1 0.7138067080 1 NA NA
52.2 0.8872895233 1 NA NA
52.3 -0.9664587437 NA NA NA
52.4 0.0254566848 2 NA NA
52.5 0.4155259424 3 NA NA
53 0.5675736897 2 NA NA
53.1 -0.3154088781 1 NA NA
53.2 0.2162315769 2 NA NA
54 -0.0880802382 NA NA NA
54.1 0.4129127672 1 NA NA
54.2 1.0119546775 NA NA NA
54.3 -0.1112901990 3 NA NA
54.4 0.8587727145 3 NA NA
55 -0.0116453589 1 NA NA
55.1 0.5835528661 1 NA NA
55.2 -1.0010857254 1 NA NA
55.3 -0.4796526070 NA NA NA
55.4 -0.1202746964 2 NA NA
56 0.5176377612 2 NA NA
56.1 -1.1136932588 3 NA NA
56.2 -0.0168103281 1 NA NA
56.3 0.3933023606 1 NA NA
56.4 0.3714625139 2 NA NA
56.5 0.7811448179 NA NA NA
57 -1.0868304872 2 NA NA
57.1 0.8018626997 3 NA NA
57.2 -0.1159517011 2 NA NA
57.3 0.6785562445 NA NA NA
58 1.6476207996 1 NA NA
58.1 0.3402652711 1 NA NA
58.2 -0.1111300753 NA NA NA
58.3 -0.5409234285 1 NA NA
58.4 -0.1271327672 2 NA NA
58.5 0.8713264822 NA NA NA
59 0.4766421367 1 NA NA
59.1 1.0028089765 1 NA NA
60 0.5231452932 1 NA NA
61 -0.7190130614 2 NA NA
61.1 0.8353702312 1 NA NA
61.2 1.0229058138 1 NA NA
61.3 1.1717723589 2 NA NA
61.4 -0.0629201596 2 NA NA
62 -0.3979137604 1 NA NA
62.1 0.6830738372 1 NA NA
62.2 0.4301745954 NA NA NA
62.3 -0.0333139957 1 NA NA
63 0.3345678035 NA NA NA
63.1 0.3643769511 3 NA NA
64 0.3949911859 3 NA NA
65 1.2000091513 NA NA NA
65.1 0.0110122646 2 NA NA
65.2 -0.5776452043 3 NA NA
65.3 -0.1372183563 3 NA NA
66 -0.5081302805 3 NA NA
66.1 -0.1447837412 3 NA NA
66.2 0.1906241379 1 NA NA
67 1.6716027681 NA NA NA
68 0.5691848839 1 NA NA
68.1 0.1004860389 1 NA NA
68.2 -0.0061241827 1 NA NA
68.3 0.7443745962 2 NA NA
68.4 0.8726923437 3 NA NA
69 0.0381382683 NA NA NA
70 0.8126204217 1 NA NA
70.1 0.4691503050 NA NA NA
71 -0.5529062591 1 NA NA
71.1 -0.1103252087 1 NA NA
71.2 1.7178492547 NA NA NA
71.3 -1.0118346755 1 NA NA
71.4 1.8623785017 1 NA NA
72 -0.4521659275 2 NA NA
72.1 0.1375317317 3 NA NA
72.2 -0.4170988856 2 NA NA
72.3 0.7107266765 1 NA NA
72.4 0.1451969143 2 NA NA
72.5 1.6298050306 1 NA NA
73 -0.0307469467 NA NA NA
74 0.3730017941 1 NA NA
75 -0.4908003566 NA NA NA
76 -0.9888876620 1 NA NA
76.1 0.0003798292 2 NA NA
76.2 -0.8421863763 2 NA NA
77 -0.4986802480 NA NA NA
78 0.0417330969 1 NA NA
79 -0.3767450660 3 NA NA
79.1 0.1516000028 3 NA NA
79.2 -0.1888160741 NA NA NA
80 -0.0041558414 3 NA NA
80.1 -0.0329337062 2 NA NA
80.2 0.5046816157 NA NA NA
81 -0.9493950353 1 NA NA
81.1 0.2443038954 2 NA NA
81.2 0.6476958410 1 NA NA
81.3 0.4182528210 1 NA NA
82 1.1088801952 3 NA NA
82.1 0.9334157763 1 NA NA
82.2 0.4958140634 1 NA NA
83 0.5104724530 2 NA NA
83.1 -0.0513309106 3 NA NA
83.2 -0.2067792494 2 NA NA
83.3 -0.0534169155 3 NA NA
84 -0.0255753653 1 NA NA
84.1 -1.8234189877 2 NA NA
85 -0.0114038622 2 NA NA
85.1 -0.0577615939 1 NA NA
85.2 -0.2241856342 1 NA NA
85.3 -0.0520175929 NA NA NA
85.4 0.2892733846 2 NA NA
85.5 -0.3740417009 1 NA NA
86 0.4293735089 1 NA NA
86.1 -0.1363456521 NA NA NA
86.2 0.1230989293 2 NA NA
86.3 0.3305413955 1 NA NA
86.4 2.6003411822 2 NA NA
86.5 -0.1420690052 2 NA NA
87 1.0457427869 NA NA NA
87.1 -0.2973007190 1 NA NA
87.2 0.4396872616 NA NA NA
88 -0.0601928334 1 NA NA
88.1 -1.0124347595 2 NA NA
88.2 0.5730917016 NA NA NA
88.3 -0.0029455332 2 NA NA
89 1.5465903721 3 NA NA
90 0.0626760573 3 NA NA
90.1 1.1896872985 2 NA NA
90.2 0.2597888783 NA NA NA
90.3 0.6599799887 2 NA NA
91 1.1213651365 3 NA NA
91.1 1.2046371625 1 NA NA
91.2 0.3395603754 3 NA NA
92 0.4674939332 2 NA NA
93 0.2677965647 2 NA NA
93.1 1.6424445368 3 NA NA
93.2 0.7101700066 NA NA NA
93.3 1.1222322893 2 NA NA
93.4 1.4628960401 3 NA NA
94 -0.2904211940 2 NA NA
94.1 0.0147813580 2 NA NA
94.2 -0.4536774482 1 NA NA
94.3 0.6793464917 2 NA NA
94.4 -0.9411356550 1 NA NA
94.5 0.5683867264 2 NA NA
95 0.2375652188 2 NA NA
95.1 0.0767152977 2 NA NA
95.2 -0.6886731251 NA NA NA
96 0.7813892121 1 NA NA
96.1 0.3391519695 1 NA NA
96.2 -0.4857246503 2 NA NA
96.3 0.8771471244 3 NA NA
96.4 1.9030768981 2 NA NA
96.5 -0.1684332749 NA NA NA
97 1.3775130083 1 NA NA
97.1 -1.7323228619 2 NA NA
98 -1.2648518889 3 NA NA
98.1 -0.9042716241 2 NA NA
98.2 -0.1560385207 2 NA NA
99 0.7993356425 2 NA NA
99.1 1.0355522332 2 NA NA
99.2 -0.1150895843 1 NA NA
100 0.0369067906 1 NA NA
100.1 1.6023713093 2 NA NA
100.2 0.8861545820 3 NA NA
100.3 0.1277046316 2 NA NA
100.4 -0.0834577654 1 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_multinomial
[1] 0
$m3b$tau_reg_multinomial
[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"
$m4a
$m4a$M_id
M2 C2 (Intercept) M22 M23 M24 abs(C1 - C2) log(C1) C1
1 NA -1.381594459 1 NA NA NA NA -0.3318617 0.7175865
2 1 0.344426024 1 NA NA NA NA -0.2867266 0.7507170
3 2 NA 1 NA NA NA NA -0.3207627 0.7255954
4 2 -0.228910007 1 NA NA NA NA -0.2917769 0.7469352
5 1 NA 1 NA NA NA NA -0.3369956 0.7139120
6 NA -2.143955482 1 NA NA NA NA -0.3102679 0.7332505
7 NA -1.156567023 1 NA NA NA NA -0.3084388 0.7345929
8 2 -0.598827660 1 NA NA NA NA -0.2675411 0.7652589
9 NA NA 1 NA NA NA NA -0.3284176 0.7200622
10 NA -1.006719032 1 NA NA NA NA -0.2978834 0.7423879
11 3 0.239801450 1 NA NA NA NA -0.2960573 0.7437448
12 NA -1.064969789 1 NA NA NA NA -0.2948450 0.7446470
13 NA -0.538082688 1 NA NA NA NA -0.2836654 0.7530186
14 2 NA 1 NA NA NA NA -0.3434574 0.7093137
15 2 -1.781049276 1 NA NA NA NA -0.3104469 0.7331192
16 NA NA 1 NA NA NA NA -0.3550492 0.7011390
17 3 NA 1 NA NA NA NA -0.2967369 0.7432395
18 3 -0.014579883 1 NA NA NA NA -0.2816747 0.7545191
19 2 -2.121550136 1 NA NA NA NA -0.2838910 0.7528487
20 NA NA 1 NA NA NA NA -0.2727455 0.7612865
21 NA -0.363239698 1 NA NA NA NA -0.3213465 0.7251719
22 1 -0.121568514 1 NA NA NA NA -0.3146245 0.7300630
23 2 -0.951271111 1 NA NA NA NA -0.3442879 0.7087249
24 NA NA 1 NA NA NA NA -0.3021952 0.7391938
25 4 -0.974288621 1 NA NA NA NA -0.2458186 0.7820641
26 NA -1.130632418 1 NA NA NA NA -0.3399165 0.7118298
27 NA 0.114339868 1 NA NA NA NA -0.3242275 0.7230857
28 2 0.238334648 1 NA NA NA NA -0.2891027 0.7489353
29 4 0.840744958 1 NA NA NA NA -0.2862314 0.7510888
30 2 NA 1 NA NA NA NA -0.3146125 0.7300717
31 2 NA 1 NA NA NA NA -0.2809421 0.7550721
32 3 -1.466312154 1 NA NA NA NA -0.3117155 0.7321898
33 1 -0.637352277 1 NA NA NA NA -0.3138326 0.7306414
34 4 NA 1 NA NA NA NA -0.2974340 0.7427216
35 4 NA 1 NA NA NA NA -0.3294709 0.7193042
36 NA NA 1 NA NA NA NA -0.3129468 0.7312888
37 NA NA 1 NA NA NA NA -0.3424289 0.7100436
38 NA NA 1 NA NA NA NA -0.2652444 0.7670184
39 4 0.006728205 1 NA NA NA NA -0.3010445 0.7400449
40 NA NA 1 NA NA NA NA -0.3014695 0.7397304
41 2 -1.663281353 1 NA NA NA NA -0.2888874 0.7490966
42 NA 0.161184794 1 NA NA NA NA -0.2985038 0.7419274
43 NA 0.457939180 1 NA NA NA NA -0.2839809 0.7527810
44 3 -0.307070331 1 NA NA NA NA -0.2999821 0.7408315
45 NA NA 1 NA NA NA NA -0.3082181 0.7347550
46 NA -1.071668276 1 NA NA NA NA -0.3102825 0.7332398
47 4 -0.814751321 1 NA NA NA NA -0.3042884 0.7376481
48 3 -0.547630662 1 NA NA NA NA -0.3084048 0.7346179
49 4 NA 1 NA NA NA NA -0.3106911 0.7329402
50 3 -1.350213782 1 NA NA NA NA -0.3201451 0.7260436
51 NA 0.719054706 1 NA NA NA NA -0.3225621 0.7242910
52 1 NA 1 NA NA NA NA -0.3149755 0.7298067
53 NA -1.207130750 1 NA NA NA NA -0.3209299 0.7254741
54 NA NA 1 NA NA NA NA -0.2820889 0.7542067
55 NA -0.408600991 1 NA NA NA NA -0.3024638 0.7389952
56 NA -0.271380529 1 NA NA NA NA -0.2849341 0.7520638
57 4 -1.361925974 1 NA NA NA NA -0.3257359 0.7219958
58 1 NA 1 NA NA NA NA -0.3202560 0.7259632
59 2 NA 1 NA NA NA NA -0.2932166 0.7458606
60 3 -0.323712205 1 NA NA NA NA -0.2649529 0.7672421
61 2 NA 1 NA NA NA NA -0.3205938 0.7257179
62 3 NA 1 NA NA NA NA -0.3299089 0.7189892
63 2 -1.386906880 1 NA NA NA NA -0.3101519 0.7333356
64 NA NA 1 NA NA NA NA -0.3119416 0.7320243
65 NA NA 1 NA NA NA NA -0.2906584 0.7477711
66 2 -0.565191691 1 NA NA NA NA -0.3087049 0.7343974
67 NA -0.382899912 1 NA NA NA NA -0.2887994 0.7491624
68 3 NA 1 NA NA NA NA -0.2899866 0.7482736
69 3 -0.405642769 1 NA NA NA NA -0.3094824 0.7338267
70 4 NA 1 NA NA NA NA -0.2734187 0.7607742
71 2 -0.843748427 1 NA NA NA NA -0.2513372 0.7777600
72 NA 0.116003683 1 NA NA NA NA -0.3000053 0.7408143
73 4 -0.778634325 1 NA NA NA NA -0.3218221 0.7248271
74 NA NA 1 NA NA NA NA -0.3058575 0.7364916
75 NA NA 1 NA NA NA NA -0.2923695 0.7464926
76 4 NA 1 NA NA NA NA -0.3071463 0.7355430
77 2 -0.632974758 1 NA NA NA NA -0.3273313 0.7208449
78 2 NA 1 NA NA NA NA -0.3046827 0.7373573
79 NA -0.778064615 1 NA NA NA NA -0.2746896 0.7598079
80 1 NA 1 NA NA NA NA -0.3064688 0.7360415
81 NA NA 1 NA NA NA NA -0.3155423 0.7293932
82 3 -0.246123253 1 NA NA NA NA -0.3175491 0.7279309
83 4 -1.239659782 1 NA NA NA NA -0.3086139 0.7344643
84 3 -0.467772280 1 NA NA NA NA -0.3032222 0.7384350
85 NA NA 1 NA NA NA NA -0.3114673 0.7323716
86 3 -2.160485036 1 NA NA NA NA -0.2775210 0.7576597
87 NA -0.657675572 1 NA NA NA NA -0.2881970 0.7496139
88 NA NA 1 NA NA NA NA -0.3181084 0.7275239
89 NA -0.696710744 1 NA NA NA NA -0.3214942 0.7250648
90 4 NA 1 NA NA NA NA -0.3098919 0.7335262
91 NA -0.179395847 1 NA NA NA NA -0.3087042 0.7343980
92 2 -0.441545568 1 NA NA NA NA -0.3037539 0.7380425
93 4 -0.685799334 1 NA NA NA NA -0.3025305 0.7389460
94 NA NA 1 NA NA NA NA -0.3202120 0.7259951
95 NA 0.191929445 1 NA NA NA NA -0.3170642 0.7282840
96 NA NA 1 NA NA NA NA -0.3172240 0.7281676
97 1 -0.069760671 1 NA NA NA NA -0.3221849 0.7245642
98 NA NA 1 NA NA NA NA -0.2840967 0.7526938
99 2 NA 1 NA NA NA NA -0.3185112 0.7272309
100 NA NA 1 NA NA NA NA -0.3033427 0.7383460
$m4a$M_lvlone
m1 m2 m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2)
1 3 3 NA NA NA NA
1.1 2 1 NA NA NA NA
1.2 1 3 NA NA NA NA
1.3 1 1 NA NA NA NA
2 2 2 NA NA NA NA
2.1 2 1 NA NA NA NA
2.2 1 NA NA NA NA NA
3 1 3 NA NA NA NA
3.1 2 2 NA NA NA NA
3.2 2 1 NA NA NA NA
4 2 1 NA NA NA NA
4.1 1 2 NA NA NA NA
4.2 2 3 NA NA NA NA
4.3 3 3 NA NA NA NA
5 2 2 NA NA NA NA
5.1 1 3 NA NA NA NA
5.2 2 1 NA NA NA NA
5.3 2 1 NA NA NA NA
6 2 2 NA NA NA NA
7 3 2 NA NA NA NA
7.1 2 1 NA NA NA NA
7.2 3 3 NA NA NA NA
8 2 2 NA NA NA NA
8.1 1 2 NA NA NA NA
8.2 3 1 NA NA NA NA
8.3 2 3 NA NA NA NA
8.4 2 NA NA NA NA NA
8.5 2 3 NA NA NA NA
9 3 NA NA NA NA NA
9.1 2 3 NA NA NA NA
9.2 3 1 NA NA NA NA
10 3 1 NA NA NA NA
10.1 1 1 NA NA NA NA
11 1 1 NA NA NA NA
11.1 1 1 NA NA NA NA
11.2 2 1 NA NA NA NA
11.3 3 NA NA NA NA NA
11.4 1 1 NA NA NA NA
12 1 1 NA NA NA NA
13 2 2 NA NA NA NA
13.1 3 2 NA NA NA NA
14 1 3 NA NA NA NA
14.1 1 2 NA NA NA NA
14.2 1 1 NA NA NA NA
14.3 3 1 NA NA NA NA
15 1 1 NA NA NA NA
15.1 1 2 NA NA NA NA
15.2 3 3 NA NA NA NA
15.3 2 3 NA NA NA NA
16 2 2 NA NA NA NA
16.1 2 NA NA NA NA NA
16.2 1 3 NA NA NA NA
16.3 3 2 NA NA NA NA
16.4 2 3 NA NA NA NA
16.5 1 1 NA NA NA NA
17 2 1 NA NA NA NA
17.1 3 3 NA NA NA NA
17.2 1 NA NA NA NA NA
17.3 1 2 NA NA NA NA
17.4 2 1 NA NA NA NA
18 1 3 NA NA NA NA
19 2 NA NA NA NA NA
19.1 3 1 NA NA NA NA
19.2 2 3 NA NA NA NA
19.3 3 3 NA NA NA NA
20 2 2 NA NA NA NA
20.1 2 NA NA NA NA NA
20.2 1 3 NA NA NA NA
20.3 3 1 NA NA NA NA
20.4 2 3 NA NA NA NA
20.5 3 2 NA NA NA NA
21 1 3 NA NA NA NA
21.1 2 1 NA NA NA NA
21.2 3 NA NA NA NA NA
22 2 3 NA NA NA NA
22.1 2 1 NA NA NA NA
23 2 1 NA NA NA NA
23.1 1 2 NA NA NA NA
24 1 2 NA NA NA NA
25 1 2 NA NA NA NA
25.1 3 3 NA NA NA NA
25.2 2 3 NA NA NA NA
25.3 2 1 NA NA NA NA
25.4 1 3 NA NA NA NA
25.5 1 2 NA NA NA NA
26 2 NA NA NA NA NA
26.1 1 3 NA NA NA NA
26.2 1 3 NA NA NA NA
26.3 2 NA NA NA NA NA
27 1 3 NA NA NA NA
27.1 3 3 NA NA NA NA
28 1 3 NA NA NA NA
28.1 3 2 NA NA NA NA
28.2 1 2 NA NA NA NA
28.3 1 3 NA NA NA NA
29 3 1 NA NA NA NA
29.1 3 NA NA NA NA NA
29.2 3 2 NA NA NA NA
29.3 2 2 NA NA NA NA
30 1 2 NA NA NA NA
30.1 3 3 NA NA NA NA
30.2 3 3 NA NA NA NA
31 1 3 NA NA NA NA
32 3 3 NA NA NA NA
32.1 3 3 NA NA NA NA
32.2 2 1 NA NA NA NA
32.3 1 1 NA NA NA NA
33 3 3 NA NA NA NA
33.1 1 3 NA NA NA NA
34 1 3 NA NA NA NA
34.1 1 NA NA NA NA NA
34.2 2 1 NA NA NA NA
34.3 2 NA NA NA NA NA
35 1 2 NA NA NA NA
35.1 1 2 NA NA NA NA
35.2 1 2 NA NA NA NA
36 2 3 NA NA NA NA
36.1 3 3 NA NA NA NA
36.2 3 3 NA NA NA NA
36.3 3 2 NA NA NA NA
36.4 3 2 NA NA NA NA
37 1 2 NA NA NA NA
37.1 3 2 NA NA NA NA
37.2 1 1 NA NA NA NA
38 2 2 NA NA NA NA
39 2 3 NA NA NA NA
39.1 3 2 NA NA NA NA
39.2 1 3 NA NA NA NA
39.3 2 NA NA NA NA NA
39.4 3 3 NA NA NA NA
39.5 3 3 NA NA NA NA
40 3 3 NA NA NA NA
40.1 3 1 NA NA NA NA
40.2 1 3 NA NA NA NA
40.3 3 2 NA NA NA NA
41 3 3 NA NA NA NA
41.1 3 3 NA NA NA NA
41.2 1 1 NA NA NA NA
41.3 1 2 NA NA NA NA
41.4 1 3 NA NA NA NA
42 1 2 NA NA NA NA
42.1 1 NA NA NA NA NA
43 3 3 NA NA NA NA
43.1 3 3 NA NA NA NA
43.2 2 2 NA NA NA NA
44 2 3 NA NA NA NA
44.1 2 3 NA NA NA NA
44.2 1 NA NA NA NA NA
44.3 1 1 NA NA NA NA
45 2 3 NA NA NA NA
45.1 3 1 NA NA NA NA
46 3 NA NA NA NA NA
46.1 2 1 NA NA NA NA
46.2 3 2 NA NA NA NA
47 1 2 NA NA NA NA
47.1 2 NA NA NA NA NA
47.2 2 NA NA NA NA NA
47.3 2 3 NA NA NA NA
47.4 2 3 NA NA NA NA
48 3 3 NA NA NA NA
48.1 1 1 NA NA NA NA
49 3 1 NA NA NA NA
50 1 NA NA NA NA NA
51 3 1 NA NA NA NA
52 3 2 NA NA NA NA
52.1 2 1 NA NA NA NA
52.2 1 1 NA NA NA NA
52.3 3 NA NA NA NA NA
52.4 3 2 NA NA NA NA
52.5 3 3 NA NA NA NA
53 1 2 NA NA NA NA
53.1 3 1 NA NA NA NA
53.2 2 2 NA NA NA NA
54 3 NA NA NA NA NA
54.1 3 1 NA NA NA NA
54.2 3 NA NA NA NA NA
54.3 1 3 NA NA NA NA
54.4 1 3 NA NA NA NA
55 1 1 NA NA NA NA
55.1 3 1 NA NA NA NA
55.2 2 1 NA NA NA NA
55.3 1 NA NA NA NA NA
55.4 1 2 NA NA NA NA
56 2 2 NA NA NA NA
56.1 1 3 NA NA NA NA
56.2 3 1 NA NA NA NA
56.3 1 1 NA NA NA NA
56.4 2 2 NA NA NA NA
56.5 1 NA NA NA NA NA
57 1 2 NA NA NA NA
57.1 1 3 NA NA NA NA
57.2 1 2 NA NA NA NA
57.3 1 NA NA NA NA NA
58 3 1 NA NA NA NA
58.1 2 1 NA NA NA NA
58.2 1 NA NA NA NA NA
58.3 3 1 NA NA NA NA
58.4 3 2 NA NA NA NA
58.5 3 NA NA NA NA NA
59 3 1 NA NA NA NA
59.1 1 1 NA NA NA NA
60 3 1 NA NA NA NA
61 1 2 NA NA NA NA
61.1 2 1 NA NA NA NA
61.2 2 1 NA NA NA NA
61.3 3 2 NA NA NA NA
61.4 2 2 NA NA NA NA
62 2 1 NA NA NA NA
62.1 1 1 NA NA NA NA
62.2 3 NA NA NA NA NA
62.3 2 1 NA NA NA NA
63 3 NA NA NA NA NA
63.1 1 3 NA NA NA NA
64 3 3 NA NA NA NA
65 3 NA NA NA NA NA
65.1 3 2 NA NA NA NA
65.2 2 3 NA NA NA NA
65.3 3 3 NA NA NA NA
66 3 3 NA NA NA NA
66.1 3 3 NA NA NA NA
66.2 1 1 NA NA NA NA
67 3 NA NA NA NA NA
68 3 1 NA NA NA NA
68.1 1 1 NA NA NA NA
68.2 2 1 NA NA NA NA
68.3 3 2 NA NA NA NA
68.4 1 3 NA NA NA NA
69 1 NA NA NA NA NA
70 1 1 NA NA NA NA
70.1 2 NA NA NA NA NA
71 3 1 NA NA NA NA
71.1 2 1 NA NA NA NA
71.2 2 NA NA NA NA NA
71.3 1 1 NA NA NA NA
71.4 2 1 NA NA NA NA
72 1 2 NA NA NA NA
72.1 2 3 NA NA NA NA
72.2 1 2 NA NA NA NA
72.3 2 1 NA NA NA NA
72.4 2 2 NA NA NA NA
72.5 1 1 NA NA NA NA
73 2 NA NA NA NA NA
74 1 1 NA NA NA NA
75 3 NA NA NA NA NA
76 3 1 NA NA NA NA
76.1 3 2 NA NA NA NA
76.2 2 2 NA NA NA NA
77 2 NA NA NA NA NA
78 2 1 NA NA NA NA
79 2 3 NA NA NA NA
79.1 2 3 NA NA NA NA
79.2 2 NA NA NA NA NA
80 2 3 NA NA NA NA
80.1 1 2 NA NA NA NA
80.2 3 NA NA NA NA NA
81 2 1 NA NA NA NA
81.1 3 2 NA NA NA NA
81.2 2 1 NA NA NA NA
81.3 1 1 NA NA NA NA
82 1 3 NA NA NA NA
82.1 2 1 NA NA NA NA
82.2 3 1 NA NA NA NA
83 2 2 NA NA NA NA
83.1 3 3 NA NA NA NA
83.2 3 2 NA NA NA NA
83.3 3 3 NA NA NA NA
84 2 1 NA NA NA NA
84.1 3 2 NA NA NA NA
85 1 2 NA NA NA NA
85.1 2 1 NA NA NA NA
85.2 3 1 NA NA NA NA
85.3 3 NA NA NA NA NA
85.4 2 2 NA NA NA NA
85.5 2 1 NA NA NA NA
86 1 1 NA NA NA NA
86.1 2 NA NA NA NA NA
86.2 1 2 NA NA NA NA
86.3 1 1 NA NA NA NA
86.4 1 2 NA NA NA NA
86.5 2 2 NA NA NA NA
87 3 NA NA NA NA NA
87.1 3 1 NA NA NA NA
87.2 2 NA NA NA NA NA
88 3 1 NA NA NA NA
88.1 3 2 NA NA NA NA
88.2 3 NA NA NA NA NA
88.3 1 2 NA NA NA NA
89 2 3 NA NA NA NA
90 1 3 NA NA NA NA
90.1 2 2 NA NA NA NA
90.2 2 NA NA NA NA NA
90.3 2 2 NA NA NA NA
91 3 3 NA NA NA NA
91.1 3 1 NA NA NA NA
91.2 3 3 NA NA NA NA
92 2 2 NA NA NA NA
93 2 2 NA NA NA NA
93.1 2 3 NA NA NA NA
93.2 2 NA NA NA NA NA
93.3 3 2 NA NA NA NA
93.4 2 3 NA NA NA NA
94 2 2 NA NA NA NA
94.1 3 2 NA NA NA NA
94.2 3 1 NA NA NA NA
94.3 2 2 NA NA NA NA
94.4 3 1 NA NA NA NA
94.5 2 2 NA NA NA NA
95 2 2 NA NA NA NA
95.1 3 2 NA NA NA NA
95.2 2 NA NA NA NA NA
96 3 1 NA NA NA NA
96.1 2 1 NA NA NA NA
96.2 3 2 NA NA NA NA
96.3 2 3 NA NA NA NA
96.4 2 2 NA NA NA NA
96.5 3 NA NA NA NA NA
97 3 1 NA NA NA NA
97.1 3 2 NA NA NA NA
98 2 3 NA NA NA NA
98.1 3 2 NA NA NA NA
98.2 1 2 NA NA NA NA
99 2 2 NA NA NA NA
99.1 1 2 NA NA NA NA
99.2 3 1 NA NA NA NA
100 2 1 NA NA NA NA
100.1 1 2 NA NA NA NA
100.2 2 3 NA NA NA NA
100.3 2 2 NA NA NA NA
100.4 3 1 NA NA NA NA
$m4a$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.68571078
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
abs(C1 - C2) 1.3664060 0.67847389
log(C1) -0.3049822 0.01990873
C1 0.7372814 0.01472882
$m4a$spM_lvlone
center scale
m1 NA NA
m2 NA NA
m2B NA NA
m2C NA NA
m2B:abs(C1 - C2) 0.4042255 0.7594704
m2C:abs(C1 - C2) 0.5491518 0.8130082
$m4a$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_multinomial
[1] 0
$m4a$tau_reg_multinomial
[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
C2 (Intercept) abs(C1 - C2) log(C1) M12 M13 M14 C1 M1
1 -1.381594459 1 NA -0.3318617 0 0 0 0.7175865 1
2 0.344426024 1 NA -0.2867266 0 0 1 0.7507170 4
3 NA 1 NA -0.3207627 0 0 0 0.7255954 1
4 -0.228910007 1 NA -0.2917769 0 0 0 0.7469352 1
5 NA 1 NA -0.3369956 0 0 1 0.7139120 4
6 -2.143955482 1 NA -0.3102679 0 0 1 0.7332505 4
7 -1.156567023 1 NA -0.3084388 0 0 1 0.7345929 4
8 -0.598827660 1 NA -0.2675411 0 0 1 0.7652589 4
9 NA 1 NA -0.3284176 1 0 0 0.7200622 2
10 -1.006719032 1 NA -0.2978834 0 0 0 0.7423879 1
11 0.239801450 1 NA -0.2960573 0 1 0 0.7437448 3
12 -1.064969789 1 NA -0.2948450 0 0 0 0.7446470 1
13 -0.538082688 1 NA -0.2836654 0 1 0 0.7530186 3
14 NA 1 NA -0.3434574 0 1 0 0.7093137 3
15 -1.781049276 1 NA -0.3104469 0 0 1 0.7331192 4
16 NA 1 NA -0.3550492 0 0 0 0.7011390 1
17 NA 1 NA -0.2967369 1 0 0 0.7432395 2
18 -0.014579883 1 NA -0.2816747 0 1 0 0.7545191 3
19 -2.121550136 1 NA -0.2838910 0 0 0 0.7528487 1
20 NA 1 NA -0.2727455 1 0 0 0.7612865 2
21 -0.363239698 1 NA -0.3213465 1 0 0 0.7251719 2
22 -0.121568514 1 NA -0.3146245 0 0 1 0.7300630 4
23 -0.951271111 1 NA -0.3442879 0 0 1 0.7087249 4
24 NA 1 NA -0.3021952 0 0 0 0.7391938 1
25 -0.974288621 1 NA -0.2458186 0 0 0 0.7820641 1
26 -1.130632418 1 NA -0.3399165 0 1 0 0.7118298 3
27 0.114339868 1 NA -0.3242275 0 0 0 0.7230857 1
28 0.238334648 1 NA -0.2891027 0 0 1 0.7489353 4
29 0.840744958 1 NA -0.2862314 0 0 0 0.7510888 1
30 NA 1 NA -0.3146125 0 1 0 0.7300717 3
31 NA 1 NA -0.2809421 0 1 0 0.7550721 3
32 -1.466312154 1 NA -0.3117155 0 0 0 0.7321898 1
33 -0.637352277 1 NA -0.3138326 0 1 0 0.7306414 3
34 NA 1 NA -0.2974340 0 0 1 0.7427216 4
35 NA 1 NA -0.3294709 0 0 1 0.7193042 4
36 NA 1 NA -0.3129468 0 0 0 0.7312888 1
37 NA 1 NA -0.3424289 1 0 0 0.7100436 2
38 NA 1 NA -0.2652444 0 0 1 0.7670184 4
39 0.006728205 1 NA -0.3010445 0 1 0 0.7400449 3
40 NA 1 NA -0.3014695 1 0 0 0.7397304 2
41 -1.663281353 1 NA -0.2888874 1 0 0 0.7490966 2
42 0.161184794 1 NA -0.2985038 0 0 0 0.7419274 1
43 0.457939180 1 NA -0.2839809 0 0 0 0.7527810 1
44 -0.307070331 1 NA -0.2999821 0 1 0 0.7408315 3
45 NA 1 NA -0.3082181 1 0 0 0.7347550 2
46 -1.071668276 1 NA -0.3102825 1 0 0 0.7332398 2
47 -0.814751321 1 NA -0.3042884 0 0 0 0.7376481 1
48 -0.547630662 1 NA -0.3084048 0 0 0 0.7346179 1
49 NA 1 NA -0.3106911 0 0 0 0.7329402 1
50 -1.350213782 1 NA -0.3201451 1 0 0 0.7260436 2
51 0.719054706 1 NA -0.3225621 0 0 0 0.7242910 1
52 NA 1 NA -0.3149755 0 0 1 0.7298067 4
53 -1.207130750 1 NA -0.3209299 0 0 0 0.7254741 1
54 NA 1 NA -0.2820889 1 0 0 0.7542067 2
55 -0.408600991 1 NA -0.3024638 0 1 0 0.7389952 3
56 -0.271380529 1 NA -0.2849341 0 1 0 0.7520638 3
57 -1.361925974 1 NA -0.3257359 0 0 1 0.7219958 4
58 NA 1 NA -0.3202560 1 0 0 0.7259632 2
59 NA 1 NA -0.2932166 0 0 1 0.7458606 4
60 -0.323712205 1 NA -0.2649529 0 0 0 0.7672421 1
61 NA 1 NA -0.3205938 0 0 0 0.7257179 1
62 NA 1 NA -0.3299089 0 0 1 0.7189892 4
63 -1.386906880 1 NA -0.3101519 0 0 1 0.7333356 4
64 NA 1 NA -0.3119416 0 0 1 0.7320243 4
65 NA 1 NA -0.2906584 1 0 0 0.7477711 2
66 -0.565191691 1 NA -0.3087049 0 1 0 0.7343974 3
67 -0.382899912 1 NA -0.2887994 1 0 0 0.7491624 2
68 NA 1 NA -0.2899866 0 0 1 0.7482736 4
69 -0.405642769 1 NA -0.3094824 0 0 0 0.7338267 1
70 NA 1 NA -0.2734187 0 0 1 0.7607742 4
71 -0.843748427 1 NA -0.2513372 0 0 1 0.7777600 4
72 0.116003683 1 NA -0.3000053 0 0 1 0.7408143 4
73 -0.778634325 1 NA -0.3218221 0 0 0 0.7248271 1
74 NA 1 NA -0.3058575 0 1 0 0.7364916 3
75 NA 1 NA -0.2923695 0 0 1 0.7464926 4
76 NA 1 NA -0.3071463 1 0 0 0.7355430 2
77 -0.632974758 1 NA -0.3273313 1 0 0 0.7208449 2
78 NA 1 NA -0.3046827 0 0 0 0.7373573 1
79 -0.778064615 1 NA -0.2746896 1 0 0 0.7598079 2
80 NA 1 NA -0.3064688 0 1 0 0.7360415 3
81 NA 1 NA -0.3155423 0 0 0 0.7293932 1
82 -0.246123253 1 NA -0.3175491 0 0 0 0.7279309 1
83 -1.239659782 1 NA -0.3086139 1 0 0 0.7344643 2
84 -0.467772280 1 NA -0.3032222 0 0 1 0.7384350 4
85 NA 1 NA -0.3114673 0 1 0 0.7323716 3
86 -2.160485036 1 NA -0.2775210 1 0 0 0.7576597 2
87 -0.657675572 1 NA -0.2881970 0 0 1 0.7496139 4
88 NA 1 NA -0.3181084 0 1 0 0.7275239 3
89 -0.696710744 1 NA -0.3214942 0 0 1 0.7250648 4
90 NA 1 NA -0.3098919 1 0 0 0.7335262 2
91 -0.179395847 1 NA -0.3087042 0 0 1 0.7343980 4
92 -0.441545568 1 NA -0.3037539 1 0 0 0.7380425 2
93 -0.685799334 1 NA -0.3025305 0 0 1 0.7389460 4
94 NA 1 NA -0.3202120 0 0 1 0.7259951 4
95 0.191929445 1 NA -0.3170642 0 0 0 0.7282840 1
96 NA 1 NA -0.3172240 1 0 0 0.7281676 2
97 -0.069760671 1 NA -0.3221849 0 0 0 0.7245642 1
98 NA 1 NA -0.2840967 0 0 1 0.7526938 4
99 NA 1 NA -0.3185112 1 0 0 0.7272309 2
100 NA 1 NA -0.3033427 1 0 0 0.7383460 2
$m4b$M_lvlone
m1 m2 ifelse(as.numeric(m2) > as.numeric(M1), 1, 0)
1 3 3 NA
1.1 2 1 NA
1.2 1 3 NA
1.3 1 1 NA
2 2 2 NA
2.1 2 1 NA
2.2 1 NA NA
3 1 3 NA
3.1 2 2 NA
3.2 2 1 NA
4 2 1 NA
4.1 1 2 NA
4.2 2 3 NA
4.3 3 3 NA
5 2 2 NA
5.1 1 3 NA
5.2 2 1 NA
5.3 2 1 NA
6 2 2 NA
7 3 2 NA
7.1 2 1 NA
7.2 3 3 NA
8 2 2 NA
8.1 1 2 NA
8.2 3 1 NA
8.3 2 3 NA
8.4 2 NA NA
8.5 2 3 NA
9 3 NA NA
9.1 2 3 NA
9.2 3 1 NA
10 3 1 NA
10.1 1 1 NA
11 1 1 NA
11.1 1 1 NA
11.2 2 1 NA
11.3 3 NA NA
11.4 1 1 NA
12 1 1 NA
13 2 2 NA
13.1 3 2 NA
14 1 3 NA
14.1 1 2 NA
14.2 1 1 NA
14.3 3 1 NA
15 1 1 NA
15.1 1 2 NA
15.2 3 3 NA
15.3 2 3 NA
16 2 2 NA
16.1 2 NA NA
16.2 1 3 NA
16.3 3 2 NA
16.4 2 3 NA
16.5 1 1 NA
17 2 1 NA
17.1 3 3 NA
17.2 1 NA NA
17.3 1 2 NA
17.4 2 1 NA
18 1 3 NA
19 2 NA NA
19.1 3 1 NA
19.2 2 3 NA
19.3 3 3 NA
20 2 2 NA
20.1 2 NA NA
20.2 1 3 NA
20.3 3 1 NA
20.4 2 3 NA
20.5 3 2 NA
21 1 3 NA
21.1 2 1 NA
21.2 3 NA NA
22 2 3 NA
22.1 2 1 NA
23 2 1 NA
23.1 1 2 NA
24 1 2 NA
25 1 2 NA
25.1 3 3 NA
25.2 2 3 NA
25.3 2 1 NA
25.4 1 3 NA
25.5 1 2 NA
26 2 NA NA
26.1 1 3 NA
26.2 1 3 NA
26.3 2 NA NA
27 1 3 NA
27.1 3 3 NA
28 1 3 NA
28.1 3 2 NA
28.2 1 2 NA
28.3 1 3 NA
29 3 1 NA
29.1 3 NA NA
29.2 3 2 NA
29.3 2 2 NA
30 1 2 NA
30.1 3 3 NA
30.2 3 3 NA
31 1 3 NA
32 3 3 NA
32.1 3 3 NA
32.2 2 1 NA
32.3 1 1 NA
33 3 3 NA
33.1 1 3 NA
34 1 3 NA
34.1 1 NA NA
34.2 2 1 NA
34.3 2 NA NA
35 1 2 NA
35.1 1 2 NA
35.2 1 2 NA
36 2 3 NA
36.1 3 3 NA
36.2 3 3 NA
36.3 3 2 NA
36.4 3 2 NA
37 1 2 NA
37.1 3 2 NA
37.2 1 1 NA
38 2 2 NA
39 2 3 NA
39.1 3 2 NA
39.2 1 3 NA
39.3 2 NA NA
39.4 3 3 NA
39.5 3 3 NA
40 3 3 NA
40.1 3 1 NA
40.2 1 3 NA
40.3 3 2 NA
41 3 3 NA
41.1 3 3 NA
41.2 1 1 NA
41.3 1 2 NA
41.4 1 3 NA
42 1 2 NA
42.1 1 NA NA
43 3 3 NA
43.1 3 3 NA
43.2 2 2 NA
44 2 3 NA
44.1 2 3 NA
44.2 1 NA NA
44.3 1 1 NA
45 2 3 NA
45.1 3 1 NA
46 3 NA NA
46.1 2 1 NA
46.2 3 2 NA
47 1 2 NA
47.1 2 NA NA
47.2 2 NA NA
47.3 2 3 NA
47.4 2 3 NA
48 3 3 NA
48.1 1 1 NA
49 3 1 NA
50 1 NA NA
51 3 1 NA
52 3 2 NA
52.1 2 1 NA
52.2 1 1 NA
52.3 3 NA NA
52.4 3 2 NA
52.5 3 3 NA
53 1 2 NA
53.1 3 1 NA
53.2 2 2 NA
54 3 NA NA
54.1 3 1 NA
54.2 3 NA NA
54.3 1 3 NA
54.4 1 3 NA
55 1 1 NA
55.1 3 1 NA
55.2 2 1 NA
55.3 1 NA NA
55.4 1 2 NA
56 2 2 NA
56.1 1 3 NA
56.2 3 1 NA
56.3 1 1 NA
56.4 2 2 NA
56.5 1 NA NA
57 1 2 NA
57.1 1 3 NA
57.2 1 2 NA
57.3 1 NA NA
58 3 1 NA
58.1 2 1 NA
58.2 1 NA NA
58.3 3 1 NA
58.4 3 2 NA
58.5 3 NA NA
59 3 1 NA
59.1 1 1 NA
60 3 1 NA
61 1 2 NA
61.1 2 1 NA
61.2 2 1 NA
61.3 3 2 NA
61.4 2 2 NA
62 2 1 NA
62.1 1 1 NA
62.2 3 NA NA
62.3 2 1 NA
63 3 NA NA
63.1 1 3 NA
64 3 3 NA
65 3 NA NA
65.1 3 2 NA
65.2 2 3 NA
65.3 3 3 NA
66 3 3 NA
66.1 3 3 NA
66.2 1 1 NA
67 3 NA NA
68 3 1 NA
68.1 1 1 NA
68.2 2 1 NA
68.3 3 2 NA
68.4 1 3 NA
69 1 NA NA
70 1 1 NA
70.1 2 NA NA
71 3 1 NA
71.1 2 1 NA
71.2 2 NA NA
71.3 1 1 NA
71.4 2 1 NA
72 1 2 NA
72.1 2 3 NA
72.2 1 2 NA
72.3 2 1 NA
72.4 2 2 NA
72.5 1 1 NA
73 2 NA NA
74 1 1 NA
75 3 NA NA
76 3 1 NA
76.1 3 2 NA
76.2 2 2 NA
77 2 NA NA
78 2 1 NA
79 2 3 NA
79.1 2 3 NA
79.2 2 NA NA
80 2 3 NA
80.1 1 2 NA
80.2 3 NA NA
81 2 1 NA
81.1 3 2 NA
81.2 2 1 NA
81.3 1 1 NA
82 1 3 NA
82.1 2 1 NA
82.2 3 1 NA
83 2 2 NA
83.1 3 3 NA
83.2 3 2 NA
83.3 3 3 NA
84 2 1 NA
84.1 3 2 NA
85 1 2 NA
85.1 2 1 NA
85.2 3 1 NA
85.3 3 NA NA
85.4 2 2 NA
85.5 2 1 NA
86 1 1 NA
86.1 2 NA NA
86.2 1 2 NA
86.3 1 1 NA
86.4 1 2 NA
86.5 2 2 NA
87 3 NA NA
87.1 3 1 NA
87.2 2 NA NA
88 3 1 NA
88.1 3 2 NA
88.2 3 NA NA
88.3 1 2 NA
89 2 3 NA
90 1 3 NA
90.1 2 2 NA
90.2 2 NA NA
90.3 2 2 NA
91 3 3 NA
91.1 3 1 NA
91.2 3 3 NA
92 2 2 NA
93 2 2 NA
93.1 2 3 NA
93.2 2 NA NA
93.3 3 2 NA
93.4 2 3 NA
94 2 2 NA
94.1 3 2 NA
94.2 3 1 NA
94.3 2 2 NA
94.4 3 1 NA
94.5 2 2 NA
95 2 2 NA
95.1 3 2 NA
95.2 2 NA NA
96 3 1 NA
96.1 2 1 NA
96.2 3 2 NA
96.3 2 3 NA
96.4 2 2 NA
96.5 3 NA NA
97 3 1 NA
97.1 3 2 NA
98 2 3 NA
98.1 3 2 NA
98.2 1 2 NA
99 2 2 NA
99.1 1 2 NA
99.2 3 1 NA
100 2 1 NA
100.1 1 2 NA
100.2 2 3 NA
100.3 2 2 NA
100.4 3 1 NA
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) m2B m2C
1 NA NA NA
1.1 NA NA NA
1.2 NA NA NA
1.3 NA NA NA
2 NA NA NA
2.1 NA NA NA
2.2 NA NA NA
3 NA NA NA
3.1 NA NA NA
3.2 NA NA NA
4 NA NA NA
4.1 NA NA NA
4.2 NA NA NA
4.3 NA NA NA
5 NA NA NA
5.1 NA NA NA
5.2 NA NA NA
5.3 NA NA NA
6 NA NA NA
7 NA NA NA
7.1 NA NA NA
7.2 NA NA NA
8 NA NA NA
8.1 NA NA NA
8.2 NA NA NA
8.3 NA NA NA
8.4 NA NA NA
8.5 NA NA NA
9 NA NA NA
9.1 NA NA NA
9.2 NA NA NA
10 NA NA NA
10.1 NA NA NA
11 NA NA NA
11.1 NA NA NA
11.2 NA NA NA
11.3 NA NA NA
11.4 NA NA NA
12 NA NA NA
13 NA NA NA
13.1 NA NA NA
14 NA NA NA
14.1 NA NA NA
14.2 NA NA NA
14.3 NA NA NA
15 NA NA NA
15.1 NA NA NA
15.2 NA NA NA
15.3 NA NA NA
16 NA NA NA
16.1 NA NA NA
16.2 NA NA NA
16.3 NA NA NA
16.4 NA NA NA
16.5 NA NA NA
17 NA NA NA
17.1 NA NA NA
17.2 NA NA NA
17.3 NA NA NA
17.4 NA NA NA
18 NA NA NA
19 NA NA NA
19.1 NA NA NA
19.2 NA NA NA
19.3 NA NA NA
20 NA NA NA
20.1 NA NA NA
20.2 NA NA NA
20.3 NA NA NA
20.4 NA NA NA
20.5 NA NA NA
21 NA NA NA
21.1 NA NA NA
21.2 NA NA NA
22 NA NA NA
22.1 NA NA NA
23 NA NA NA
23.1 NA NA NA
24 NA NA NA
25 NA NA NA
25.1 NA NA NA
25.2 NA NA NA
25.3 NA NA NA
25.4 NA NA NA
25.5 NA NA NA
26 NA NA NA
26.1 NA NA NA
26.2 NA NA NA
26.3 NA NA NA
27 NA NA NA
27.1 NA NA NA
28 NA NA NA
28.1 NA NA NA
28.2 NA NA NA
28.3 NA NA NA
29 NA NA NA
29.1 NA NA NA
29.2 NA NA NA
29.3 NA NA NA
30 NA NA NA
30.1 NA NA NA
30.2 NA NA NA
31 NA NA NA
32 NA NA NA
32.1 NA NA NA
32.2 NA NA NA
32.3 NA NA NA
33 NA NA NA
33.1 NA NA NA
34 NA NA NA
34.1 NA NA NA
34.2 NA NA NA
34.3 NA NA NA
35 NA NA NA
35.1 NA NA NA
35.2 NA NA NA
36 NA NA NA
36.1 NA NA NA
36.2 NA NA NA
36.3 NA NA NA
36.4 NA NA NA
37 NA NA NA
37.1 NA NA NA
37.2 NA NA NA
38 NA NA NA
39 NA NA NA
39.1 NA NA NA
39.2 NA NA NA
39.3 NA NA NA
39.4 NA NA NA
39.5 NA NA NA
40 NA NA NA
40.1 NA NA NA
40.2 NA NA NA
40.3 NA NA NA
41 NA NA NA
41.1 NA NA NA
41.2 NA NA NA
41.3 NA NA NA
41.4 NA NA NA
42 NA NA NA
42.1 NA NA NA
43 NA NA NA
43.1 NA NA NA
43.2 NA NA NA
44 NA NA NA
44.1 NA NA NA
44.2 NA NA NA
44.3 NA NA NA
45 NA NA NA
45.1 NA NA NA
46 NA NA NA
46.1 NA NA NA
46.2 NA NA NA
47 NA NA NA
47.1 NA NA NA
47.2 NA NA NA
47.3 NA NA NA
47.4 NA NA NA
48 NA NA NA
48.1 NA NA NA
49 NA NA NA
50 NA NA NA
51 NA NA NA
52 NA NA NA
52.1 NA NA NA
52.2 NA NA NA
52.3 NA NA NA
52.4 NA NA NA
52.5 NA NA NA
53 NA NA NA
53.1 NA NA NA
53.2 NA NA NA
54 NA NA NA
54.1 NA NA NA
54.2 NA NA NA
54.3 NA NA NA
54.4 NA NA NA
55 NA NA NA
55.1 NA NA NA
55.2 NA NA NA
55.3 NA NA NA
55.4 NA NA NA
56 NA NA NA
56.1 NA NA NA
56.2 NA NA NA
56.3 NA NA NA
56.4 NA NA NA
56.5 NA NA NA
57 NA NA NA
57.1 NA NA NA
57.2 NA NA NA
57.3 NA NA NA
58 NA NA NA
58.1 NA NA NA
58.2 NA NA NA
58.3 NA NA NA
58.4 NA NA NA
58.5 NA NA NA
59 NA NA NA
59.1 NA NA NA
60 NA NA NA
61 NA NA NA
61.1 NA NA NA
61.2 NA NA NA
61.3 NA NA NA
61.4 NA NA NA
62 NA NA NA
62.1 NA NA NA
62.2 NA NA NA
62.3 NA NA NA
63 NA NA NA
63.1 NA NA NA
64 NA NA NA
65 NA NA NA
65.1 NA NA NA
65.2 NA NA NA
65.3 NA NA NA
66 NA NA NA
66.1 NA NA NA
66.2 NA NA NA
67 NA NA NA
68 NA NA NA
68.1 NA NA NA
68.2 NA NA NA
68.3 NA NA NA
68.4 NA NA NA
69 NA NA NA
70 NA NA NA
70.1 NA NA NA
71 NA NA NA
71.1 NA NA NA
71.2 NA NA NA
71.3 NA NA NA
71.4 NA NA NA
72 NA NA NA
72.1 NA NA NA
72.2 NA NA NA
72.3 NA NA NA
72.4 NA NA NA
72.5 NA NA NA
73 NA NA NA
74 NA NA NA
75 NA NA NA
76 NA NA NA
76.1 NA NA NA
76.2 NA NA NA
77 NA NA NA
78 NA NA NA
79 NA NA NA
79.1 NA NA NA
79.2 NA NA NA
80 NA NA NA
80.1 NA NA NA
80.2 NA NA NA
81 NA NA NA
81.1 NA NA NA
81.2 NA NA NA
81.3 NA NA NA
82 NA NA NA
82.1 NA NA NA
82.2 NA NA NA
83 NA NA NA
83.1 NA NA NA
83.2 NA NA NA
83.3 NA NA NA
84 NA NA NA
84.1 NA NA NA
85 NA NA NA
85.1 NA NA NA
85.2 NA NA NA
85.3 NA NA NA
85.4 NA NA NA
85.5 NA NA NA
86 NA NA NA
86.1 NA NA NA
86.2 NA NA NA
86.3 NA NA NA
86.4 NA NA NA
86.5 NA NA NA
87 NA NA NA
87.1 NA NA NA
87.2 NA NA NA
88 NA NA NA
88.1 NA NA NA
88.2 NA NA NA
88.3 NA NA NA
89 NA NA NA
90 NA NA NA
90.1 NA NA NA
90.2 NA NA NA
90.3 NA NA NA
91 NA NA NA
91.1 NA NA NA
91.2 NA NA NA
92 NA NA NA
93 NA NA NA
93.1 NA NA NA
93.2 NA NA NA
93.3 NA NA NA
93.4 NA NA NA
94 NA NA NA
94.1 NA NA NA
94.2 NA NA NA
94.3 NA NA NA
94.4 NA NA NA
94.5 NA NA NA
95 NA NA NA
95.1 NA NA NA
95.2 NA NA NA
96 NA NA NA
96.1 NA NA NA
96.2 NA NA NA
96.3 NA NA NA
96.4 NA NA NA
96.5 NA NA NA
97 NA NA NA
97.1 NA NA NA
98 NA NA NA
98.1 NA NA NA
98.2 NA NA NA
99 NA NA NA
99.1 NA NA NA
99.2 NA NA NA
100 NA NA NA
100.1 NA NA NA
100.2 NA NA NA
100.3 NA NA NA
100.4 NA NA NA
$m4b$spM_id
center scale
C2 -0.6240921 0.68571078
(Intercept) NA NA
abs(C1 - C2) 1.3664060 0.67847389
log(C1) -0.3049822 0.01990873
M12 NA NA
M13 NA NA
M14 NA NA
C1 0.7372814 0.01472882
M1 NA NA
$m4b$spM_lvlone
center scale
m1 NA NA
m2 NA NA
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0.2508961 0.4343078
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0.3794055 0.7450722
m2B NA NA
m2C 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_multinomial
[1] 0
$m4b$tau_reg_multinomial
[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
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
$m4c$M_lvlone
m1 time c1 c1:time
1 3 0.5090421822 0.7592026489 3.864662e-01
1.1 2 0.6666076288 0.9548337990 6.364995e-01
1.2 1 2.1304941282 0.5612235156 1.195683e+00
1.3 1 2.4954441458 1.1873391025 2.962938e+00
2 2 3.0164990982 0.9192204198 2.772828e+00
2.1 2 3.2996806887 -0.1870730476 -6.172813e-01
2.2 1 4.1747569619 1.2517512331 5.225757e+00
3 1 0.8478727890 -0.0605087604 -5.130373e-02
3.1 2 3.0654308549 0.3788637747 1.161381e+00
3.2 2 4.7381553578 0.9872578281 4.677781e+00
4 2 0.3371432109 1.4930175328 5.033607e-01
4.1 1 1.0693019140 -0.7692526880 -8.225634e-01
4.2 2 2.6148973033 0.9180841450 2.400696e+00
4.3 3 3.1336532847 -0.0541170782 -1.695842e-01
5 2 1.0762525082 -0.1376784521 -1.481768e-01
5.1 1 1.7912546196 -0.2740585866 -4.909087e-01
5.2 2 2.7960080339 0.4670496929 1.305875e+00
5.3 2 2.8119940578 0.1740288049 4.893680e-01
6 2 1.7815462884 0.9868044683 1.758038e+00
7 3 3.3074087673 -0.1280320918 -4.234545e-01
7.1 2 3.7008403614 0.4242971219 1.570256e+00
7.2 3 4.7716691741 0.0777182491 3.708458e-01
8 2 1.1246398522 -0.5791408712 -6.513249e-01
8.1 1 1.8027009873 0.3128604232 5.639938e-01
8.2 3 1.8175825174 0.6258446356 1.137524e+00
8.3 2 2.8384267003 -0.1040137707 -2.952355e-01
8.4 2 3.3630275307 0.0481450285 1.619131e-01
8.5 2 4.4360849704 0.3831763675 1.699803e+00
9 3 0.9607803822 -0.1757592269 -1.688660e-01
9.1 2 2.9177753383 -0.1791541200 -5.227315e-01
9.2 3 4.8100892501 -0.0957042935 -4.603462e-01
10 3 2.2975509102 -0.5598409704 -1.286263e+00
10.1 1 4.1734118364 -0.2318340451 -9.675389e-01
11 1 1.1832662905 0.5086859475 6.019109e-01
11.1 1 1.2346051680 0.4951758188 6.113466e-01
11.2 2 1.6435316263 -1.1022162541 -1.811527e+00
11.3 3 3.3859017969 -0.0611636705 -2.070942e-01
11.4 1 4.8118087661 -0.4971774316 -2.392323e+00
12 1 0.9591987054 -0.2433996286 -2.334686e-01
13 2 0.0619085738 0.8799673116 5.447752e-02
13.1 3 3.5621061502 0.1079022586 3.843593e-01
14 1 4.0364430007 0.9991752617 4.033114e+00
14.1 1 4.4710561272 -0.1094019046 -4.891421e-01
14.2 1 4.6359198843 0.1518967560 7.041812e-01
14.3 3 4.6886152599 0.3521012473 1.650867e+00
15 1 0.5402063532 0.3464447888 1.871517e-01
15.1 1 1.1893180816 -0.4767313971 -5.669853e-01
15.2 3 1.5094739688 0.5759767791 8.694220e-01
15.3 2 4.9193474615 -0.1713452662 -8.429069e-01
16 2 1.2417913869 0.4564754473 5.668473e-01
16.1 2 2.5675726333 1.0652558311 2.735122e+00
16.2 1 2.6524101500 0.6971872493 1.849227e+00
16.3 3 3.5585018690 0.5259331838 1.871534e+00
16.4 2 3.7612454291 0.2046601798 7.697772e-01
16.5 1 3.9851612889 1.0718540464 4.271511e+00
17 2 1.5925356350 0.6048676222 9.632732e-01
17.1 3 2.4374032998 0.2323298304 5.662815e-01
17.2 1 3.0256489082 1.2617499032 3.817612e+00
17.3 1 3.3329089405 -0.3913230895 -1.304244e+00
17.4 2 3.8693758985 0.9577299112 3.705817e+00
18 1 2.4374292302 -0.0050324072 -1.226614e-02
19 2 0.9772165376 -0.4187468937 -4.092064e-01
19.1 3 1.1466335913 -0.4478828944 -5.135576e-01
19.2 2 2.2599126538 -1.1966721302 -2.704374e+00
19.3 3 4.2114245973 -0.5877091668 -2.475093e+00
20 2 1.7170160066 0.6838223064 1.174134e+00
20.1 2 1.7562902288 0.3278571109 5.758122e-01
20.2 1 2.2515566566 -0.8489831990 -1.911534e+00
20.3 3 2.2609123867 1.3169975191 2.977616e+00
20.4 2 3.4913365287 0.0444804531 1.552962e-01
20.5 3 4.1730977828 -0.4535207652 -1.892586e+00
21 1 1.6936582839 -0.4030302960 -6.825956e-01
21.1 2 2.9571191233 -0.4069674045 -1.203451e+00
21.2 3 3.7887385779 1.0650265940 4.035107e+00
22 2 2.4696226232 -0.0673274516 -1.662734e-01
22.1 2 3.1626627257 0.9601388170 3.036595e+00
23 2 1.5414533857 0.5556634840 8.565294e-01
23.1 1 2.3369736120 1.4407865964 3.367080e+00
24 1 2.8283136466 0.3856376411 1.090704e+00
25 1 0.5381704110 0.3564400705 1.918255e-01
25.1 3 1.6069735331 0.0982553434 1.578937e-01
25.2 2 1.6358226922 0.1928682598 3.154983e-01
25.3 2 3.2646870392 -0.0192488594 -6.284150e-02
25.4 1 4.0782226040 0.4466012931 1.821339e+00
25.5 1 4.1560292873 1.1425193342 4.748344e+00
26 2 0.2412706357 0.5341531449 1.288755e-01
26.1 1 2.4451737676 1.2268695927 2.999909e+00
26.2 1 3.5988757887 0.3678294939 1.323773e+00
26.3 2 4.1822362854 0.5948516018 2.487810e+00
27 1 3.6955824879 -0.3342844147 -1.235376e+00
27.1 3 4.2451434687 -0.4835141229 -2.052587e+00
28 1 0.5746519344 -0.7145915499 -4.106414e-01
28.1 3 2.7943964268 0.5063671955 1.414991e+00
28.2 1 4.2108539480 -0.2067413142 -8.705575e-01
28.3 1 4.4705521734 0.1196789973 5.350312e-01
29 3 1.1898884235 0.1392699487 1.657157e-01
29.1 3 1.7624059319 0.7960234776 1.402916e+00
29.2 3 2.0210406382 1.0398214352 2.101521e+00
29.3 2 3.4078777023 0.0813246429 2.771444e-01
30 1 2.2635366488 -0.3296323050 -7.461348e-01
30.1 3 3.5938334477 1.3635850954 4.900498e+00
30.2 3 3.6138710892 0.7354171050 2.657703e+00
31 1 4.3988140998 0.3708398217 1.631255e+00
32 3 1.6745209007 -0.0474059668 -7.938228e-02
32.1 3 2.9128167813 1.2507771489 3.643285e+00
32.2 2 2.9676558380 0.1142915519 3.391780e-01
32.3 1 4.2099863547 0.6773270619 2.851538e+00
33 3 0.0093385763 0.1774293842 1.656938e-03
33.1 1 3.4591242753 0.6159606291 2.130684e+00
34 1 1.4998774312 0.8590979166 1.288542e+00
34.1 1 3.8242761395 0.0546216775 2.088884e-01
34.2 2 3.9072251692 -0.0897224473 -3.505658e-01
34.3 2 3.9582124643 0.4163395571 1.647960e+00
35 1 1.3294299203 -1.4693520528 -1.953401e+00
35.1 1 1.5276966314 -0.3031734330 -4.631570e-01
35.2 1 4.5025920868 -0.6045512101 -2.722047e+00
36 2 0.7123168337 0.9823048960 6.997123e-01
36.1 3 1.7972493160 1.4466051416 2.599910e+00
36.2 3 1.8262697803 1.1606752905 2.119706e+00
36.3 3 4.2840119381 0.8373091576 3.587042e+00
36.4 3 4.6194464504 0.2640591685 1.219807e+00
37 1 2.0018732361 0.1177313455 2.356832e-01
37.1 3 3.6656836793 -0.1415483779 -5.188716e-01
37.2 1 3.9663937816 0.0054610124 2.166053e-02
38 2 0.9826511063 0.8078948077 7.938787e-01
39 2 0.6921808305 0.9876451040 6.836290e-01
39.1 3 0.9027792048 -0.3431222274 -3.097636e-01
39.2 1 1.3055654289 -1.7909380751 -2.338187e+00
39.3 2 1.5412842878 -0.1798746191 -2.772379e-01
39.4 3 3.1834997435 -0.1850961689 -5.892536e-01
39.5 3 4.1394166439 0.4544226146 1.881045e+00
40 3 1.1330395646 0.5350190436 6.061977e-01
40.1 3 2.6940994046 0.4189342752 1.128651e+00
40.2 1 3.0396614212 0.4211994981 1.280304e+00
40.3 3 4.6762977762 0.0916687506 4.286704e-01
41 3 1.9337158254 -0.1035047421 -2.001488e-01
41.1 3 3.1956304458 -0.4684202411 -1.496898e+00
41.2 1 3.2846923557 0.5972615368 1.961820e+00
41.3 1 3.3813529415 0.9885613862 3.342675e+00
41.4 1 3.5482964432 -0.3908036794 -1.386687e+00
42 1 0.4859252973 -0.0338893961 -1.646771e-02
42.1 1 4.3293134298 -0.4498363172 -1.947482e+00
43 3 0.5616614548 0.8965546110 5.035602e-01
43.1 3 1.0743579536 0.6199122090 6.660076e-01
43.2 2 2.6131797966 0.1804894429 4.716514e-01
44 2 0.7662644819 1.3221409285 1.013110e+00
44.1 2 2.6490291790 0.3416426284 9.050213e-01
44.2 1 3.3371910988 0.5706610068 1.904405e+00
44.3 1 4.1154200875 1.2679497430 5.218146e+00
45 2 0.1957449992 0.1414983160 2.769759e-02
45.1 3 1.9963831536 0.7220892521 1.441567e+00
46 3 1.3477755385 1.5391054233 2.074369e+00
46.1 2 2.8565793915 0.3889107049 1.110954e+00
46.2 3 4.4160729996 0.1248719493 5.514436e-01
47 1 0.6012621359 0.2014101100 1.211003e-01
47.1 2 2.4097121472 0.2982973539 7.188108e-01
47.2 2 2.9975794035 1.1518107179 3.452644e+00
47.3 2 3.1829649757 0.5196802157 1.654124e+00
47.4 2 4.6201055450 0.3702301552 1.710502e+00
48 3 2.8607365978 -0.2128602862 -6.089372e-01
48.1 1 2.9098354396 -0.5337239976 -1.553049e+00
49 3 2.7179756400 -0.5236770035 -1.423341e+00
50 1 1.1762060679 0.3897705981 4.584505e-01
51 3 1.4304436720 -0.7213343736 -1.031828e+00
52 3 2.1266646020 0.3758235358 7.992506e-01
52.1 2 3.1000545993 0.7138067080 2.212840e+00
52.2 1 3.1268477370 0.8872895233 2.774419e+00
52.3 3 3.5711459327 -0.9664587437 -3.451365e+00
52.4 3 4.7983659909 0.0254566848 1.221505e-01
52.5 3 4.9818264414 0.4155259424 2.070078e+00
53 1 0.4965799209 0.5675736897 2.818457e-01
53.1 3 3.5505357443 -0.3154088781 -1.119870e+00
53.2 2 4.5790420019 0.2162315769 9.901335e-01
54 3 1.4034724841 -0.0880802382 -1.236182e-01
54.1 3 1.8812377600 0.4129127672 7.767871e-01
54.2 3 2.5107589352 1.0119546775 2.540774e+00
54.3 1 2.7848406672 -0.1112901990 -3.099255e-01
54.4 1 4.0143877396 0.8587727145 3.447447e+00
55 1 0.6118522980 -0.0116453589 -7.125240e-03
55.1 3 0.7463747414 0.5835528661 4.355491e-01
55.2 2 2.8201208171 -1.0010857254 -2.823183e+00
55.3 1 3.1326431572 -0.4796526070 -1.502580e+00
55.4 1 3.2218102901 -0.1202746964 -3.875023e-01
56 2 1.2231332215 0.5176377612 6.331399e-01
56.1 1 2.3573202139 -1.1136932588 -2.625332e+00
56.2 3 2.5674936292 -0.0168103281 -4.316041e-02
56.3 1 2.9507164378 0.3933023606 1.160524e+00
56.4 2 3.2272730360 0.3714625139 1.198811e+00
56.5 1 3.4175522043 0.7811448179 2.669603e+00
57 1 0.2370331448 -1.0868304872 -2.576148e-01
57.1 1 0.2481445030 0.8018626997 1.989778e-01
57.2 1 1.1405586067 -0.1159517011 -1.322497e-01
57.3 1 2.1153886721 0.6785562445 1.435410e+00
58 3 1.2210099772 1.6476207996 2.011761e+00
58.1 2 1.6334245703 0.3402652711 5.557977e-01
58.2 1 1.6791862890 -0.1111300753 -1.866081e-01
58.3 3 2.6320121693 -0.5409234285 -1.423717e+00
58.4 3 2.8477731440 -0.1271327672 -3.620453e-01
58.5 3 3.5715569824 0.8713264822 3.111992e+00
59 3 1.9023998594 0.4766421367 9.067639e-01
59.1 1 4.9736620474 1.0028089765 4.987633e+00
60 3 2.8854503250 0.5231452932 1.509510e+00
61 1 0.7213630795 -0.7190130614 -5.186695e-01
61.1 2 2.3186947661 0.8353702312 1.936969e+00
61.2 2 2.5077313243 1.0229058138 2.565173e+00
61.3 3 3.1731073430 1.1717723589 3.718159e+00
61.4 2 3.6022726283 -0.0629201596 -2.266556e-01
62 2 0.5336771999 -0.3979137604 -2.123575e-01
62.1 1 0.6987666548 0.6830738372 4.773092e-01
62.2 3 3.4584309917 0.4301745954 1.487729e+00
62.3 2 4.8028772371 -0.0333139957 -1.600030e-01
63 3 2.8097350930 0.3345678035 9.400469e-01
63.1 1 3.9653754211 0.3643769511 1.444891e+00
64 3 4.1191305732 0.3949911859 1.627020e+00
65 3 0.7076152589 1.2000091513 8.491448e-01
65.1 3 2.0252246363 0.0110122646 2.230231e-02
65.2 2 3.1127382827 -0.5776452043 -1.798058e+00
65.3 3 3.1969087943 -0.1372183563 -4.386746e-01
66 3 3.4943454154 -0.5081302805 -1.775583e+00
66.1 3 3.7677437009 -0.1447837412 -5.455080e-01
66.2 1 3.9486138616 0.1906241379 7.527011e-01
67 3 4.1728388879 1.6716027681 6.975329e+00
68 3 0.1291919907 0.5691848839 7.353413e-02
68.1 1 1.7809643946 0.1004860389 1.789621e-01
68.2 2 2.0493205660 -0.0061241827 -1.255041e-02
68.3 3 2.9406870750 0.7443745962 2.188973e+00
68.4 1 4.0406670363 0.8726923437 3.526259e+00
69 1 4.1451198701 0.0381382683 1.580877e-01
70 1 0.1992557163 0.8126204217 1.619193e-01
70.1 2 0.4829774413 0.4691503050 2.265890e-01
71 3 0.7741605386 -0.5529062591 -4.280382e-01
71.1 2 1.4883817220 -0.1103252087 -1.642060e-01
71.2 2 4.0758526395 1.7178492547 7.001700e+00
71.3 1 4.7048238723 -1.0118346755 -4.760504e+00
71.4 2 4.7242791823 1.8623785017 8.798396e+00
72 1 0.9321196121 -0.4521659275 -4.214727e-01
72.1 2 1.1799991806 0.1375317317 1.622873e-01
72.2 1 1.8917567329 -0.4170988856 -7.890496e-01
72.3 2 3.4853593935 0.7107266765 2.477138e+00
72.4 2 3.6884259700 0.1451969143 5.355481e-01
72.5 1 4.0854155901 1.6298050306 6.658431e+00
73 2 4.6019889915 -0.0307469467 -1.414971e-01
74 1 1.4626806753 0.3730017941 5.455825e-01
75 3 3.2524286874 -0.4908003566 -1.596293e+00
76 3 1.8074807397 -0.9888876620 -1.787395e+00
76.1 3 4.2685073183 0.0003798292 1.621304e-03
76.2 2 4.9688734859 -0.8421863763 -4.184718e+00
77 2 0.8459033852 -0.4986802480 -4.218353e-01
78 2 0.8231094317 0.0417330969 3.435091e-02
79 2 0.0583819521 -0.3767450660 -2.199511e-02
79.1 2 2.4406372628 0.1516000028 3.700006e-01
79.2 2 3.2962526032 -0.1888160741 -6.223855e-01
80 2 0.8985060186 -0.0041558414 -3.734049e-03
80.1 1 1.3434670598 -0.0329337062 -4.424535e-02
80.2 3 2.8025900386 0.5046816157 1.414416e+00
81 2 0.0101324962 -0.9493950353 -9.619742e-03
81.1 3 0.9421709494 0.2443038954 2.301760e-01
81.2 2 3.0542453879 0.6476958410 1.978222e+00
81.3 1 3.3456630446 0.4182528210 1.399333e+00
82 1 1.3791010005 1.1088801952 1.529258e+00
82.1 2 1.7601010622 0.9334157763 1.642906e+00
82.2 3 2.6233131927 0.4958140634 1.300676e+00
83 2 0.0537394290 0.5104724530 2.743250e-02
83.1 3 2.9061570496 -0.0513309106 -1.491757e-01
83.2 3 3.1189457362 -0.2067792494 -6.449333e-01
83.3 3 4.7663642222 -0.0534169155 -2.546045e-01
84 2 2.7254060237 -0.0255753653 -6.970325e-02
84.1 3 3.3364784659 -1.8234189877 -6.083798e+00
85 1 0.2977756259 -0.0114038622 -3.395792e-03
85.1 2 1.7394116637 -0.0577615939 -1.004712e-01
85.2 3 2.6846330194 -0.2241856342 -6.018562e-01
85.3 3 3.1608762743 -0.0520175929 -1.644212e-01
85.4 2 3.9452053758 0.2892733846 1.141243e+00
85.5 2 4.5092553482 -0.3740417009 -1.686650e+00
86 1 0.8476278360 0.4293735089 3.639489e-01
86.1 2 1.0118629411 -0.1363456521 -1.379631e-01
86.2 1 1.2511159515 0.1230989293 1.540110e-01
86.3 1 2.1870554925 0.3305413955 7.229124e-01
86.4 1 2.4532935000 2.6003411822 6.379400e+00
86.5 2 3.8206058508 -0.1420690052 -5.427897e-01
87 3 2.7069531474 1.0457427869 2.830777e+00
87.1 3 3.4462517721 -0.2973007190 -1.024573e+00
87.2 2 4.5241666853 0.4396872616 1.989218e+00
88 3 0.0005892443 -0.0601928334 -3.546828e-05
88.1 3 0.7116099866 -1.0124347595 -7.204587e-01
88.2 3 2.4952722900 0.5730917016 1.430020e+00
88.3 1 3.2995816297 -0.0029455332 -9.719027e-03
89 2 0.6462086167 1.5465903721 9.994200e-01
90 1 0.1696030737 0.0626760573 1.063005e-02
90.1 2 2.5980385230 1.1896872985 3.090853e+00
90.2 2 2.6651392167 0.2597888783 6.923735e-01
90.3 2 3.1242690247 0.6599799887 2.061955e+00
91 3 0.6382618390 1.1213651365 7.157246e-01
91.1 3 2.6224059286 1.2046371625 3.159048e+00
91.2 3 4.7772527603 0.3395603754 1.622166e+00
92 2 0.0737052364 0.4674939332 3.445675e-02
93 2 0.2788909199 0.2677965647 7.468603e-02
93.1 2 1.0357759963 1.6424445368 1.701205e+00
93.2 2 2.4916551099 0.7101700066 1.769499e+00
93.3 3 2.8876129608 1.1222322893 3.240573e+00
93.4 2 4.4639474002 1.4628960401 6.530291e+00
94 2 0.8488043118 -0.2904211940 -2.465108e-01
94.1 3 1.0552454425 0.0147813580 1.559796e-02
94.2 3 1.9445500884 -0.4536774482 -8.821985e-01
94.3 2 3.0710722448 0.6793464917 2.086322e+00
94.4 3 3.0872731935 -0.9411356550 -2.905543e+00
94.5 2 4.3805759016 0.5683867264 2.489861e+00
95 2 2.0199063048 0.2375652188 4.798595e-01
95.1 3 4.0184444457 0.0767152977 3.082762e-01
95.2 2 4.5596531732 -0.6886731251 -3.140111e+00
96 3 0.0311333477 0.7813892121 2.432726e-02
96.1 2 0.1324267720 0.3391519695 4.491280e-02
96.2 3 0.6701303425 -0.4857246503 -3.254988e-01
96.3 2 2.1775037691 0.8771471244 1.909991e+00
96.4 2 2.2246142488 1.9030768981 4.233612e+00
96.5 3 4.2377650598 -0.1684332749 -7.137806e-01
97 3 1.1955102731 1.3775130083 1.646831e+00
97.1 3 4.9603108643 -1.7323228619 -8.592860e+00
98 2 0.2041732438 -1.2648518889 -2.582489e-01
98.1 3 0.4309578973 -0.9042716241 -3.897030e-01
98.2 1 3.5172611906 -0.1560385207 -5.488282e-01
99 2 0.3531786101 0.7993356425 2.823083e-01
99.1 1 4.6789444226 1.0355522332 4.845291e+00
99.2 3 4.9927084171 -0.1150895843 -5.746087e-01
100 2 1.0691387602 0.0369067906 3.945848e-02
100.1 1 1.5109344281 1.6023713093 2.421078e+00
100.2 2 2.1502332564 0.8861545820 1.905439e+00
100.3 2 3.8745574222 0.1277046316 4.947989e-01
100.4 3 4.6567608765 -0.0834577654 -3.886429e-01
$m4c$spM_id
center scale
B2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
B21 NA NA
$m4c$spM_lvlone
center scale
m1 NA NA
time 2.5339403 1.3818094
c1 0.2559996 0.6718095
c1:time 0.6507067 1.9186258
$m4c$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_binom
[1] 0
$m4c$tau_reg_binom
[1] 1e-04
$m4c$mu_reg_multinomial
[1] 0
$m4c$tau_reg_multinomial
[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"
$m4c$RinvD_m1_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
$m4c$KinvD_m1_id
id
5
$m4d
$m4d$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
$m4d$M_lvlone
m1 b2 time I(time^2) b21 c1 C1:time b21:c1
1 3 NA 0.5090421822 2.591239e-01 NA 0.7592026489 0.3652818145 NA
1.1 2 0 0.6666076288 4.443657e-01 NA 0.9548337990 0.4783486570 NA
1.2 1 NA 2.1304941282 4.539005e+00 NA 0.5612235156 1.5288138942 NA
1.3 1 0 2.4954441458 6.227241e+00 NA 1.1873391025 1.7906971118 NA
2 2 0 3.0164990982 9.099267e+00 NA 0.9192204198 2.2645370243 NA
2.1 2 NA 3.2996806887 1.088789e+01 NA -0.1870730476 2.4771262462 NA
2.2 1 NA 4.1747569619 1.742860e+01 NA 1.2517512331 3.1340608434 NA
3 1 0 0.8478727890 7.188883e-01 NA -0.0605087604 0.6152125819 NA
3.1 2 NA 3.0654308549 9.396866e+00 NA 0.3788637747 2.2242624781 NA
3.2 2 1 4.7381553578 2.245012e+01 NA 0.9872578281 3.4379836560 NA
4 2 1 0.3371432109 1.136655e-01 NA 1.4930175328 0.2518241168 NA
4.1 1 0 1.0693019140 1.143407e+00 NA -0.7692526880 0.7986991917 NA
4.2 2 0 2.6148973033 6.837688e+00 NA 0.9180841450 1.9531587247 NA
4.3 3 0 3.1336532847 9.819783e+00 NA -0.0541170782 2.3406358046 NA
5 2 NA 1.0762525082 1.158319e+00 NA -0.1376784521 0.7683495918 NA
5.1 1 0 1.7912546196 3.208593e+00 NA -0.2740585866 1.2787981866 NA
5.2 2 NA 2.7960080339 7.817661e+00 NA 0.4670496929 1.9961037166 NA
5.3 2 NA 2.8119940578 7.907311e+00 NA 0.1740288049 2.0075163311 NA
6 2 NA 1.7815462884 3.173907e+00 NA 0.9868044683 1.3063196933 NA
7 3 NA 3.3074087673 1.093895e+01 NA -0.1280320918 2.4295989047 NA
7.1 2 NA 3.7008403614 1.369622e+01 NA 0.4242971219 2.7186109493 NA
7.2 3 0 4.7716691741 2.276883e+01 NA 0.0777182491 3.5052341620 NA
8 2 0 1.1246398522 1.264815e+00 NA -0.5791408712 0.8606406530 NA
8.1 1 0 1.8027009873 3.249731e+00 NA 0.3128604232 1.3795329695 NA
8.2 3 NA 1.8175825174 3.303606e+00 NA 0.6258446356 1.3909211928 NA
8.3 2 1 2.8384267003 8.056666e+00 NA -0.1040137707 2.1721312865 NA
8.4 2 0 3.3630275307 1.130995e+01 NA 0.0481450285 2.5735867394 NA
8.5 2 1 4.4360849704 1.967885e+01 NA 0.3831763675 3.3947534923 NA
9 3 0 0.9607803822 9.230989e-01 NA -0.1757592269 0.6918216822 NA
9.1 2 NA 2.9177753383 8.513413e+00 NA -0.1791541200 2.1009798703 NA
9.2 3 NA 4.8100892501 2.313696e+01 NA -0.0957042935 3.4635636802 NA
10 3 NA 2.2975509102 5.278740e+00 NA -0.5598409704 1.7056739807 NA
10.1 1 0 4.1734118364 1.741737e+01 NA -0.2318340451 3.0982904226 NA
11 1 0 1.1832662905 1.400119e+00 NA 0.5086859475 0.8800481723 NA
11.1 1 0 1.2346051680 1.524250e+00 NA 0.4951758188 0.9182311964 NA
11.2 2 0 1.6435316263 2.701196e+00 NA -1.1022162541 1.2223681309 NA
11.3 3 0 3.3859017969 1.146433e+01 NA -0.0611636705 2.5182469169 NA
11.4 1 0 4.8118087661 2.315350e+01 NA -0.4971774316 3.5787578367 NA
12 1 0 0.9591987054 9.200622e-01 NA -0.2433996286 0.7142644156 NA
13 2 NA 0.0619085738 3.832672e-03 NA 0.8799673116 0.0466183059 NA
13.1 3 0 3.5621061502 1.268860e+01 NA 0.1079022586 2.6823320911 NA
14 1 NA 4.0364430007 1.629287e+01 NA 0.9991752617 2.8631042655 NA
14.1 1 NA 4.4710561272 1.999034e+01 NA -0.1094019046 3.1713813046 NA
14.2 1 NA 4.6359198843 2.149175e+01 NA 0.1518967560 3.2883214239 NA
14.3 3 NA 4.6886152599 2.198311e+01 NA 0.3521012473 3.3256989750 NA
15 1 0 0.5402063532 2.918229e-01 NA 0.3464447888 0.3960356618 NA
15.1 1 0 1.1893180816 1.414477e+00 NA -0.4767313971 0.8719119477 NA
15.2 3 0 1.5094739688 2.278512e+00 NA 0.5759767791 1.1066243829 NA
15.3 2 0 4.9193474615 2.419998e+01 NA -0.1713452662 3.6064681879 NA
16 2 1 1.2417913869 1.542046e+00 NA 0.4564754473 0.8706683147 NA
16.1 2 NA 2.5675726333 6.592429e+00 NA 1.0652558311 1.8002251917 NA
16.2 1 NA 2.6524101500 7.035280e+00 NA 0.6971872493 1.8597080795 NA
16.3 3 0 3.5585018690 1.266294e+01 NA 0.5259331838 2.4950042800 NA
16.4 2 0 3.7612454291 1.414697e+01 NA 0.2046601798 2.6371556877 NA
16.5 1 NA 3.9851612889 1.588151e+01 NA 1.0718540464 2.7941518196 NA
17 2 0 1.5925356350 2.536170e+00 NA 0.6048676222 1.1836354184 NA
17.1 3 0 2.4374032998 5.940935e+00 NA 0.2323298304 1.8115744548 NA
17.2 1 0 3.0256489082 9.154551e+00 NA 1.2617499032 2.2487818375 NA
17.3 1 NA 3.3329089405 1.110828e+01 NA -0.3913230895 2.4771496359 NA
17.4 2 0 3.8693758985 1.497207e+01 NA 0.9577299112 2.8758730794 NA
18 1 0 2.4374292302 5.941061e+00 NA -0.0050324072 1.8390868081 NA
19 2 NA 0.9772165376 9.549522e-01 NA -0.4187468937 0.7356962360 NA
19.1 3 NA 1.1466335913 1.314769e+00 NA -0.4478828944 0.8632416509 NA
19.2 2 0 2.2599126538 5.107205e+00 NA -1.1966721302 1.7013723870 NA
19.3 3 1 4.2114245973 1.773610e+01 NA -0.5877091668 3.1705656887 NA
20 2 NA 1.7170160066 2.948144e+00 NA 0.6838223064 1.3071411820 NA
20.1 2 0 1.7562902288 3.084555e+00 NA 0.3278571109 1.3370401189 NA
20.2 1 1 2.2515566566 5.069507e+00 NA -0.8489831990 1.7140797861 NA
20.3 3 0 2.2609123867 5.111725e+00 NA 1.3169975191 1.7212021776 NA
20.4 2 0 3.4913365287 1.218943e+01 NA 0.0444804531 2.6579075206 NA
20.5 3 0 4.1730977828 1.741475e+01 NA -0.4535207652 3.1769231897 NA
21 1 0 1.6936582839 2.868478e+00 NA -0.4030302960 1.2281934263 NA
21.1 2 0 2.9571191233 8.744554e+00 NA -0.4069674045 2.1444197467 NA
21.2 3 NA 3.7887385779 1.435454e+01 NA 1.0650265940 2.7474868216 NA
22 2 0 2.4696226232 6.099036e+00 NA -0.0673274516 1.8029800300 NA
22.1 2 0 3.1626627257 1.000244e+01 NA 0.9601388170 2.3089429463 NA
23 2 0 1.5414533857 2.376079e+00 NA 0.5556634840 1.0924663221 NA
23.1 1 NA 2.3369736120 5.461446e+00 NA 1.4407865964 1.6562712764 NA
24 1 0 2.8283136466 7.999358e+00 NA 0.3856376411 2.0906718629 NA
25 1 0 0.5381704110 2.896274e-01 NA 0.3564400705 0.4208837547 NA
25.1 3 NA 1.6069735331 2.582364e+00 NA 0.0982553434 1.2567562995 NA
25.2 2 1 1.6358226922 2.675916e+00 NA 0.1928682598 1.2793181910 NA
25.3 2 0 3.2646870392 1.065818e+01 NA -0.0192488594 2.5531945101 NA
25.4 1 0 4.0782226040 1.663190e+01 NA 0.4466012931 3.1894314642 NA
25.5 1 NA 4.1560292873 1.727258e+01 NA 1.1425193342 3.2502812774 NA
26 2 NA 0.2412706357 5.821152e-02 NA 0.5341531449 0.1717436194 NA
26.1 1 0 2.4451737676 5.978875e+00 NA 1.2268695927 1.7405474628 NA
26.2 1 0 3.5988757887 1.295191e+01 NA 0.3678294939 2.5617868987 NA
26.3 2 0 4.1822362854 1.749110e+01 NA 0.5948516018 2.9770402626 NA
27 1 0 3.6955824879 1.365733e+01 NA -0.3342844147 2.6722228982 NA
27.1 3 0 4.2451434687 1.802124e+01 NA -0.4835141229 3.0696025919 NA
28 1 NA 0.5746519344 3.302248e-01 NA -0.7145915499 0.4303771207 NA
28.1 3 0 2.7943964268 7.808651e+00 NA 0.5063671955 2.0928221348 NA
28.2 1 0 4.2108539480 1.773129e+01 NA -0.2067413142 3.1536571778 NA
28.3 1 0 4.4705521734 1.998584e+01 NA 0.1196789973 3.3481543470 NA
29 3 0 1.1898884235 1.415834e+00 NA 0.1392699487 0.8937118818 NA
29.1 3 0 1.7624059319 3.106075e+00 NA 0.7960234776 1.3237233767 NA
29.2 3 0 2.0210406382 4.084605e+00 NA 1.0398214352 1.5179810109 NA
29.3 2 0 3.4078777023 1.161363e+01 NA 0.0813246429 2.5596188130 NA
30 1 NA 2.2635366488 5.123598e+00 NA -0.3296323050 1.6525441223 NA
30.1 3 0 3.5938334477 1.291564e+01 NA 1.3635850954 2.6237562107 NA
30.2 3 0 3.6138710892 1.306006e+01 NA 0.7354171050 2.6383851264 NA
31 1 0 4.3988140998 1.934957e+01 NA 0.3708398217 3.3214216230 NA
32 3 0 1.6745209007 2.804020e+00 NA -0.0474059668 1.2260671754 NA
32.1 3 0 2.9128167813 8.484502e+00 NA 1.2507771489 2.1327348270 NA
32.2 2 NA 2.9676558380 8.806981e+00 NA 0.1142915519 2.1728874266 NA
32.3 1 NA 4.2099863547 1.772399e+01 NA 0.6773270619 3.0825091978 NA
33 3 0 0.0093385763 8.720901e-05 NA 0.1774293842 0.0068231501 NA
33.1 1 1 3.4591242753 1.196554e+01 NA 0.6159606291 2.5273792639 NA
34 1 NA 1.4998774312 2.249632e+00 NA 0.8590979166 1.1139914202 NA
34.1 1 0 3.8242761395 1.462509e+01 NA 0.0546216775 2.8403726326 NA
34.2 2 NA 3.9072251692 1.526641e+01 NA -0.0897224473 2.9019806717 NA
34.3 2 NA 3.9582124643 1.566745e+01 NA 0.4163395571 2.9398500390 NA
35 1 0 1.3294299203 1.767384e+00 NA -1.4693520528 0.9562645676 NA
35.1 1 0 1.5276966314 2.333857e+00 NA -0.3031734330 1.0988786520 NA
35.2 1 NA 4.5025920868 2.027334e+01 NA -0.6045512101 3.2387335424 NA
36 2 NA 0.7123168337 5.073953e-01 NA 0.9823048960 0.5209093415 NA
36.1 3 NA 1.7972493160 3.230105e+00 NA 1.4466051416 1.3143083435 NA
36.2 3 0 1.8262697803 3.335261e+00 NA 1.1606752905 1.3355306848 NA
36.3 3 0 4.2840119381 1.835276e+01 NA 0.8373091576 3.1328500636 NA
36.4 3 0 4.6194464504 2.133929e+01 NA 0.2640591685 3.3781495744 NA
37 1 0 2.0018732361 4.007496e+00 NA 0.1177313455 1.4214172307 NA
37.1 3 0 3.6656836793 1.343724e+01 NA -0.1415483779 2.6027951471 NA
37.2 1 0 3.9663937816 1.573228e+01 NA 0.0054610124 2.8163124234 NA
38 2 0 0.9826511063 9.656032e-01 NA 0.8078948077 0.7537115243 NA
39 2 1 0.6921808305 4.791143e-01 NA 0.9876451040 0.5122448722 NA
39.1 3 0 0.9027792048 8.150103e-01 NA -0.3431222274 0.6680971185 NA
39.2 1 NA 1.3055654289 1.704501e+00 NA -1.7909380751 0.9661769970 NA
39.3 2 NA 1.5412842878 2.375557e+00 NA -0.1798746191 1.1406195291 NA
39.4 3 0 3.1834997435 1.013467e+01 NA -0.1850961689 2.3559326511 NA
39.5 3 1 4.1394166439 1.713477e+01 NA 0.4544226146 3.0633540486 NA
40 3 0 1.1330395646 1.283779e+00 NA 0.5350190436 0.8381437699 NA
40.1 3 1 2.6940994046 7.258172e+00 NA 0.4189342752 1.9929071342 NA
40.2 1 0 3.0396614212 9.239542e+00 NA 0.4211994981 2.2485298506 NA
40.3 3 NA 4.6762977762 2.186776e+01 NA 0.0916687506 3.4591994578 NA
41 3 0 1.9337158254 3.739257e+00 NA -0.1035047421 1.4485398595 NA
41.1 3 NA 3.1956304458 1.021205e+01 NA -0.4684202411 2.3938357519 NA
41.2 1 0 3.2846923557 1.078920e+01 NA 0.5972615368 2.4605517215 NA
41.3 1 NA 3.3813529415 1.143355e+01 NA 0.9885613862 2.5329598332 NA
41.4 1 0 3.5482964432 1.259041e+01 NA -0.3908036794 2.6580166349 NA
42 1 0 0.4859252973 2.361234e-01 NA -0.0338893961 0.3605213138 NA
42.1 1 1 4.3293134298 1.874295e+01 NA -0.4498363172 3.2120364478 NA
43 3 0 0.5616614548 3.154636e-01 NA 0.8965546110 0.4228080758 NA
43.1 3 1 1.0743579536 1.154245e+00 NA 0.6199122090 0.8087562626 NA
43.2 2 0 2.6131797966 6.828709e+00 NA 0.1804894429 1.9671521198 NA
44 2 0 0.7662644819 5.871613e-01 NA 1.3221409285 0.5676728587 NA
44.1 2 0 2.6490291790 7.017356e+00 NA 0.3416426284 1.9624842365 NA
44.2 1 0 3.3371910988 1.113684e+01 NA 0.5706610068 2.4722962576 NA
44.3 1 0 4.1154200875 1.693668e+01 NA 1.2679497430 3.0488327997 NA
45 2 NA 0.1957449992 3.831610e-02 NA 0.1414983160 0.1438246201 NA
45.1 3 1 1.9963831536 3.985546e+00 NA 0.7220892521 1.4668525369 NA
46 3 0 1.3477755385 1.816499e+00 NA 1.5391054233 0.9882426330 NA
46.1 2 0 2.8565793915 8.160046e+00 NA 0.3889107049 2.0945576311 NA
46.2 3 0 4.4160729996 1.950170e+01 NA 0.1248719493 3.2380403739 NA
47 1 0 0.6012621359 3.615162e-01 NA 0.2014101100 0.4435198614 NA
47.1 2 0 2.4097121472 5.806713e+00 NA 0.2982973539 1.7775195440 NA
47.2 2 0 2.9975794035 8.985482e+00 NA 1.1518107179 2.2111586981 NA
47.3 2 NA 3.1829649757 1.013127e+01 NA 0.5196802157 2.3479080100 NA
47.4 2 0 4.6201055450 2.134538e+01 NA 0.3702301552 3.4080119947 NA
48 3 1 2.8607365978 8.183814e+00 NA -0.2128602862 2.1015481927 NA
48.1 1 1 2.9098354396 8.467142e+00 NA -0.5337239976 2.1376170787 NA
49 3 NA 2.7179756400 7.387392e+00 NA -0.5236770035 1.9921136553 NA
50 1 0 1.1762060679 1.383461e+00 NA 0.3897705981 0.8539769445 NA
51 3 0 1.4304436720 2.046169e+00 NA -0.7213343736 1.0360574125 NA
52 3 0 2.1266646020 4.522702e+00 NA 0.3758235358 1.5520541611 NA
52.1 2 0 3.1000545993 9.610339e+00 NA 0.7138067080 2.2624407422 NA
52.2 1 0 3.1268477370 9.777177e+00 NA 0.8872895233 2.2819945547 NA
52.3 3 0 3.5711459327 1.275308e+01 NA -0.9664587437 2.6062463726 NA
52.4 3 0 4.7983659909 2.302432e+01 NA 0.0254566848 3.5018798430 NA
52.5 3 0 4.9818264414 2.481859e+01 NA 0.4155259424 3.6357705164 NA
53 1 0 0.4965799209 2.465916e-01 NA 0.5675736897 0.3602558557 NA
53.1 3 0 3.5505357443 1.260630e+01 NA -0.3154088781 2.5758216127 NA
53.2 2 NA 4.5790420019 2.096763e+01 NA 0.2162315769 3.3219762321 NA
54 3 NA 1.4034724841 1.969735e+00 NA -0.0880802382 1.0585083082 NA
54.1 3 NA 1.8812377600 3.539056e+00 NA 0.4129127672 1.4188420659 NA
54.2 3 NA 2.5107589352 6.303910e+00 NA 1.0119546775 1.8936311350 NA
54.3 1 NA 2.7848406672 7.755338e+00 NA -0.1112901990 2.1003454052 NA
54.4 1 0 4.0143877396 1.611531e+01 NA 0.8587727145 3.0276780080 NA
55 1 0 0.6118522980 3.743632e-01 NA -0.0116453589 0.4521559134 NA
55.1 3 0 0.7463747414 5.570753e-01 NA 0.5835528661 0.5515673538 NA
55.2 2 NA 2.8201208171 7.953081e+00 NA -1.0010857254 2.0840557566 NA
55.3 1 NA 3.1326431572 9.813453e+00 NA -0.4796526070 2.3150082668 NA
55.4 1 0 3.2218102901 1.038006e+01 NA -0.1202746964 2.3809023504 NA
56 2 0 1.2231332215 1.496055e+00 NA 0.5176377612 0.9198742485 NA
56.1 1 NA 2.3573202139 5.556959e+00 NA -1.1136932588 1.7728552558 NA
56.2 3 NA 2.5674936292 6.592024e+00 NA -0.0168103281 1.9309190783 NA
56.3 1 1 2.9507164378 8.706727e+00 NA 0.3933023606 2.2191270894 NA
56.4 2 0 3.2272730360 1.041529e+01 NA 0.3714625139 2.4271153024 NA
56.5 1 0 3.4175522043 1.167966e+01 NA 0.7811448179 2.5702173815 NA
57 1 0 0.2370331448 5.618471e-02 NA -1.0868304872 0.1711369455 NA
57.1 1 0 0.2481445030 6.157569e-02 NA 0.8018626997 0.1791592999 NA
57.2 1 0 1.1405586067 1.300874e+00 NA -0.1159517011 0.8234785742 NA
57.3 1 NA 2.1153886721 4.474869e+00 NA 0.6785562445 1.5273018303 NA
58 3 0 1.2210099772 1.490865e+00 NA 1.6476207996 0.8864082613 NA
58.1 2 NA 1.6334245703 2.668076e+00 NA 0.3402652711 1.1858060626 NA
58.2 1 1 1.6791862890 2.819667e+00 NA -0.1111300753 1.2190273844 NA
58.3 3 1 2.6320121693 6.927488e+00 NA -0.5409234285 1.9107438714 NA
58.4 3 0 2.8477731440 8.109812e+00 NA -0.1271327672 2.0673783904 NA
58.5 3 0 3.5715569824 1.275602e+01 NA 0.8713264822 2.5928187928 NA
59 3 NA 1.9023998594 3.619125e+00 NA 0.4766421367 1.4189250995 NA
59.1 1 1 4.9736620474 2.473731e+01 NA 1.0028089765 3.7096585560 NA
60 3 0 2.8854503250 8.325824e+00 NA 0.5231452932 2.2138389085 NA
61 1 NA 0.7213630795 5.203647e-01 NA -0.7190130614 0.5235061310 NA
61.1 2 1 2.3186947661 5.376345e+00 NA 0.8353702312 1.6827183986 NA
61.2 2 1 2.5077313243 6.288716e+00 NA 1.0229058138 1.8199056209 NA
61.3 3 0 3.1731073430 1.006861e+01 NA 1.1717723589 2.3027809373 NA
61.4 2 0 3.6022726283 1.297637e+01 NA -0.0629201596 2.6142338858 NA
62 2 NA 0.5336771999 2.848114e-01 NA -0.3979137604 0.3837081458 NA
62.1 1 1 0.6987666548 4.882748e-01 NA 0.6830738372 0.5024056819 NA
62.2 3 0 3.4584309917 1.196074e+01 NA 0.4301745954 2.4865745506 NA
62.3 2 0 4.8028772371 2.306763e+01 NA -0.0333139957 3.4532168882 NA
63 3 NA 2.8097350930 7.894611e+00 NA 0.3345678035 2.0604786922 NA
63.1 1 0 3.9653754211 1.572420e+01 NA 0.3643769511 2.9079508535 NA
64 3 0 4.1191305732 1.696724e+01 NA 0.3949911859 3.0153034804 NA
65 3 0 0.7076152589 5.007194e-01 NA 1.2000091513 0.5291342322 NA
65.1 3 0 2.0252246363 4.101535e+00 NA 0.0110122646 1.5144044302 NA
65.2 2 0 3.1127382827 9.689140e+00 NA -0.5776452043 2.3276156931 NA
65.3 3 0 3.1969087943 1.022023e+01 NA -0.1372183563 2.3905559682 NA
66 3 NA 3.4943454154 1.221045e+01 NA -0.5081302805 2.5662383121 NA
66.1 3 0 3.7677437009 1.419589e+01 NA -0.1447837412 2.7670213119 NA
66.2 1 0 3.9486138616 1.559155e+01 NA 0.1906241379 2.8998518941 NA
67 3 NA 4.1728388879 1.741258e+01 NA 1.6716027681 3.1261341693 NA
68 3 0 0.1291919907 1.669057e-02 NA 0.5691848839 0.0966709548 NA
68.1 1 0 1.7809643946 3.171834e+00 NA 0.1004860389 1.3326486232 NA
68.2 2 NA 2.0493205660 4.199715e+00 NA -0.0061241827 1.5334524594 NA
68.3 3 0 2.9406870750 8.647640e+00 NA 0.7443745962 2.2004384781 NA
68.4 1 NA 4.0406670363 1.632699e+01 NA 0.8726923437 3.0235244339 NA
69 1 0 4.1451198701 1.718202e+01 NA 0.0381382683 3.0417997050 NA
70 1 0 0.1992557163 3.970284e-02 NA 0.8126204217 0.1515886088 NA
70.1 2 0 0.4829774413 2.332672e-01 NA 0.4691503050 0.3674367781 NA
71 3 0 0.7741605386 5.993245e-01 NA -0.5529062591 0.6021111272 NA
71.1 2 1 1.4883817220 2.215280e+00 NA -0.1103252087 1.1576038195 NA
71.2 2 0 4.0758526395 1.661257e+01 NA 1.7178492547 3.1700352897 NA
71.3 1 1 4.7048238723 2.213537e+01 NA -1.0118346755 3.6592239775 NA
71.4 2 0 4.7242791823 2.231881e+01 NA 1.8623785017 3.6743555400 NA
72 1 0 0.9321196121 8.688470e-01 NA -0.4521659275 0.6905275565 NA
72.1 2 0 1.1799991806 1.392398e+00 NA 0.1375317317 0.8741602904 NA
72.2 1 NA 1.8917567329 3.578744e+00 NA -0.4170988856 1.4014404774 NA
72.3 2 0 3.4853593935 1.214773e+01 NA 0.7107266765 2.5820041485 NA
72.4 2 0 3.6884259700 1.360449e+01 NA 0.1451969143 2.7324387763 NA
72.5 1 0 4.0854155901 1.669062e+01 NA 1.6298050306 3.0265343716 NA
73 2 0 4.6019889915 2.117830e+01 NA -0.0307469467 3.3356465236 NA
74 1 0 1.4626806753 2.139435e+00 NA 0.3730017941 1.0772519613 NA
75 3 NA 3.2524286874 1.057829e+01 NA -0.4908003566 2.4279140631 NA
76 3 0 1.8074807397 3.266987e+00 NA -0.9888876620 1.3294798303 NA
76.1 3 0 4.2685073183 1.822015e+01 NA 0.0003798292 3.1396707367 NA
76.2 2 0 4.9688734859 2.468970e+01 NA -0.8421863763 3.6548201782 NA
77 2 NA 0.8459033852 7.155525e-01 NA -0.4986802480 0.6097651292 NA
78 2 0 0.8231094317 6.775091e-01 NA 0.0417330969 0.6069257516 NA
79 2 NA 0.0583819521 3.408452e-03 NA -0.3767450660 0.0443590703 NA
79.1 2 0 2.4406372628 5.956710e+00 NA 0.1516000028 1.8544155528 NA
79.2 2 NA 3.2962526032 1.086528e+01 NA -0.1888160741 2.5045188757 NA
80 2 NA 0.8985060186 8.073131e-01 NA -0.0041558414 0.6613377055 NA
80.1 1 0 1.3434670598 1.804904e+00 NA -0.0329337062 0.9888474917 NA
80.2 3 NA 2.8025900386 7.854511e+00 NA 0.5046816157 2.0628225380 NA
81 2 0 0.0101324962 1.026675e-04 NA -0.9493950353 0.0073905742 NA
81.1 3 0 0.9421709494 8.876861e-01 NA 0.2443038954 0.6872131160 NA
81.2 2 NA 3.0542453879 9.328415e+00 NA 0.6476958410 2.2277459217 NA
81.3 1 0 3.3456630446 1.119346e+01 NA 0.4182528210 2.4403039888 NA
82 1 NA 1.3791010005 1.901920e+00 NA 1.1088801952 1.0038902706 NA
82.1 2 0 1.7601010622 3.097956e+00 NA 0.9334157763 1.2812319990 NA
82.2 3 1 2.6233131927 6.881772e+00 NA 0.4958140634 1.9095908060 NA
83 2 NA 0.0537394290 2.887926e-03 NA 0.5104724530 0.0394696928 NA
83.1 3 0 2.9061570496 8.445749e+00 NA -0.0513309106 2.1344686414 NA
83.2 3 0 3.1189457362 9.727823e+00 NA -0.2067792494 2.2907543380 NA
83.3 3 NA 4.7663642222 2.271823e+01 NA -0.0534169155 3.5007244248 NA
84 2 0 2.7254060237 7.427838e+00 NA -0.0255753653 2.0125352642 NA
84.1 3 NA 3.3364784659 1.113209e+01 NA -1.8234189877 2.4637725582 NA
85 1 1 0.2977756259 8.867032e-02 NA -0.0114038622 0.2180824084 NA
85.1 2 NA 1.7394116637 3.025553e+00 NA -0.0577615939 1.2738956847 NA
85.2 3 0 2.6846330194 7.207254e+00 NA -0.2241856342 1.9661489512 NA
85.3 3 0 3.1608762743 9.991139e+00 NA -0.0520175929 2.3149359808 NA
85.4 2 0 3.9452053758 1.556465e+01 NA 0.2892733846 2.8893563314 NA
85.5 2 0 4.5092553482 2.033338e+01 NA -0.3740417009 3.3024505062 NA
86 1 0 0.8476278360 7.184729e-01 NA 0.4293735089 0.6422134099 NA
86.1 2 NA 1.0118629411 1.023867e+00 NA -0.1363456521 0.7666477221 NA
86.2 1 NA 1.2511159515 1.565291e+00 NA 0.1230989293 0.9479200743 NA
86.3 1 0 2.1870554925 4.783212e+00 NA 0.3305413955 1.6570436997 NA
86.4 1 NA 2.4532935000 6.018649e+00 NA 2.6003411822 1.8587614954 NA
86.5 2 0 3.8206058508 1.459703e+01 NA -0.1420690052 2.8947188929 NA
87 3 NA 2.7069531474 7.327595e+00 NA 1.0457427869 2.0291697589 NA
87.1 3 NA 3.4462517721 1.187665e+01 NA -0.2973007190 2.5833582987 NA
87.2 2 NA 4.5241666853 2.046808e+01 NA 0.4396872616 3.3913783218 NA
88 3 0 0.0005892443 3.472088e-07 NA -0.0601928334 0.0004286893 NA
88.1 3 NA 0.7116099866 5.063888e-01 NA -1.0124347595 0.5177132747 NA
88.2 3 0 2.4952722900 6.226384e+00 NA 0.5730917016 1.8153702347 NA
88.3 1 0 3.2995816297 1.088724e+01 NA -0.0029455332 2.4005245046 NA
89 2 0 0.6462086167 4.175856e-01 NA 1.5465903721 0.4685431339 NA
90 1 0 0.1696030737 2.876520e-02 NA 0.0626760573 0.1244083036 NA
90.1 2 0 2.5980385230 6.749804e+00 NA 1.1896872985 1.9057294087 NA
90.2 2 0 2.6651392167 7.102967e+00 NA 0.2597888783 1.9549495277 NA
90.3 2 NA 3.1242690247 9.761057e+00 NA 0.6599799887 2.2917332858 NA
91 3 0 0.6382618390 4.073782e-01 NA 1.1213651365 0.4687382105 NA
91.1 3 0 2.6224059286 6.877013e+00 NA 1.2046371625 1.9258896380 NA
91.2 3 0 4.7772527603 2.282214e+01 NA 0.3395603754 3.5084048160 NA
92 2 0 0.0737052364 5.432462e-03 NA 0.4674939332 0.0543975943 NA
93 2 NA 0.2788909199 7.778015e-02 NA 0.2677965647 0.2060853219 NA
93.1 2 0 1.0357759963 1.072832e+00 NA 1.6424445368 0.7653825006 NA
93.2 2 NA 2.4916551099 6.208345e+00 NA 0.7101700066 1.8411985077 NA
93.3 3 0 2.8876129608 8.338309e+00 NA 1.1222322893 2.1337899668 NA
93.4 2 0 4.4639474002 1.992683e+01 NA 1.4628960401 3.2986159518 NA
94 2 NA 0.8488043118 7.204688e-01 NA -0.2904211940 0.6162277526 NA
94.1 3 0 1.0552454425 1.113543e+00 NA 0.0147813580 0.7661029975 NA
94.2 3 0 1.9445500884 3.781275e+00 NA -0.4536774482 1.4117337932 NA
94.3 2 NA 3.0710722448 9.431485e+00 NA 0.6793464917 2.2295833342 NA
94.4 3 0 3.0872731935 9.531256e+00 NA -0.9411356550 2.2413451432 NA
94.5 2 1 4.3805759016 1.918945e+01 NA 0.5683867264 3.1802765437 NA
95 2 0 2.0199063048 4.080021e+00 NA 0.2375652188 1.4710654666 NA
95.1 3 NA 4.0184444457 1.614790e+01 NA 0.0767152977 2.9265688411 NA
95.2 2 0 4.5596531732 2.079044e+01 NA -0.6886731251 3.3207225043 NA
96 3 0 0.0311333477 9.692853e-04 NA 0.7813892121 0.0226702966 NA
96.1 2 0 0.1324267720 1.753685e-02 NA 0.3391519695 0.0964288912 NA
96.2 3 0 0.6701303425 4.490747e-01 NA -0.4857246503 0.4879672359 NA
96.3 2 NA 2.1775037691 4.741523e+00 NA 0.8771471244 1.5855877997 NA
96.4 2 1 2.2246142488 4.948909e+00 NA 1.9030768981 1.6198921270 NA
96.5 3 1 4.2377650598 1.795865e+01 NA -0.1684332749 3.0858034196 NA
97 3 0 1.1955102731 1.429245e+00 NA 1.3775130083 0.8662239621 NA
97.1 3 0 4.9603108643 2.460468e+01 NA -1.7323228619 3.5940637456 NA
98 2 0 0.2041732438 4.168671e-02 NA -1.2648518889 0.1536799385 NA
98.1 3 0 0.4309578973 1.857247e-01 NA -0.9042716241 0.3243793452 NA
98.2 1 1 3.5172611906 1.237113e+01 NA -0.1560385207 2.6474207553 NA
99 2 0 0.3531786101 1.247351e-01 NA 0.7993356425 0.2568424071 NA
99.1 1 0 4.6789444226 2.189252e+01 NA 1.0355522332 3.4026730772 NA
99.2 3 0 4.9927084171 2.492714e+01 NA -0.1150895843 3.6308519569 NA
100 2 NA 1.0691387602 1.143058e+00 NA 0.0369067906 0.7893943379 NA
100.1 1 NA 1.5109344281 2.282923e+00 NA 1.6023713093 1.1155924065 NA
100.2 2 0 2.1502332564 4.623503e+00 NA 0.8861545820 1.5876161457 NA
100.3 2 NA 3.8745574222 1.501220e+01 NA 0.1277046316 2.8607640137 NA
100.4 3 0 4.6567608765 2.168542e+01 NA -0.0834577654 3.4383008132 NA
$m4d$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m4d$spM_lvlone
center scale
m1 NA NA
b2 NA NA
time 2.53394028 1.3818094
I(time^2) 8.32444679 7.0900029
b21 NA NA
c1 0.25599956 0.6718095
C1:time 1.86876118 1.0180574
b21:c1 0.04082297 0.2677776
$m4d$mu_reg_binom
[1] 0
$m4d$tau_reg_binom
[1] 1e-04
$m4d$mu_reg_multinomial
[1] 0
$m4d$tau_reg_multinomial
[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"
$m4d$RinvD_m1_id
[,1] [,2]
[1,] NA 0
[2,] 0 NA
$m4d$KinvD_m1_id
id
3
$m4e
$m4e$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
$m4e$M_lvlone
m1 log(time) I(time^2) p1 time
1 3 -0.67522439 2.591239e-01 5 0.5090421822
1.1 2 -0.40555367 4.443657e-01 3 0.6666076288
1.2 1 0.75635394 4.539005e+00 8 2.1304941282
1.3 1 0.91446673 6.227241e+00 6 2.4954441458
2 2 1.10409692 9.099267e+00 5 3.0164990982
2.1 2 1.19382570 1.088789e+01 3 3.2996806887
2.2 1 1.42905614 1.742860e+01 2 4.1747569619
3 1 -0.16502467 7.188883e-01 7 0.8478727890
3.1 2 1.12018813 9.396866e+00 2 3.0654308549
3.2 2 1.55564789 2.245012e+01 8 4.7381553578
4 2 -1.08724748 1.136655e-01 2 0.3371432109
4.1 1 0.06700602 1.143407e+00 4 1.0693019140
4.2 2 0.96122482 6.837688e+00 2 2.6148973033
4.3 3 1.14219951 9.819783e+00 6 3.1336532847
5 2 0.07348511 1.158319e+00 6 1.0762525082
5.1 1 0.58291628 3.208593e+00 2 1.7912546196
5.2 2 1.02819270 7.817661e+00 3 2.7960080339
5.3 2 1.03389386 7.907311e+00 2 2.8119940578
6 2 0.57748169 3.173907e+00 4 1.7815462884
7 3 1.19616503 1.093895e+01 2 3.3074087673
7.1 2 1.30855992 1.369622e+01 6 3.7008403614
7.2 3 1.56269618 2.276883e+01 4 4.7716691741
8 2 0.11746285 1.264815e+00 2 1.1246398522
8.1 1 0.58928609 3.249731e+00 2 1.8027009873
8.2 3 0.59750733 3.303606e+00 1 1.8175825174
8.3 2 1.04324992 8.056666e+00 2 2.8384267003
8.4 2 1.21284162 1.130995e+01 2 3.3630275307
8.5 2 1.48977222 1.967885e+01 4 4.4360849704
9 3 -0.04000943 9.230989e-01 3 0.9607803822
9.1 2 1.07082146 8.513413e+00 3 2.9177753383
9.2 3 1.57071564 2.313696e+01 2 4.8100892501
10 3 0.83184373 5.278740e+00 4 2.2975509102
10.1 1 1.42873389 1.741737e+01 5 4.1734118364
11 1 0.16827866 1.400119e+00 2 1.1832662905
11.1 1 0.21075122 1.524250e+00 4 1.2346051680
11.2 2 0.49684736 2.701196e+00 6 1.6435316263
11.3 3 1.21962028 1.146433e+01 2 3.3859017969
11.4 1 1.57107306 2.315350e+01 1 4.8118087661
12 1 -0.04165702 9.200622e-01 5 0.9591987054
13 2 -2.78209660 3.832672e-03 2 0.0619085738
13.1 3 1.27035199 1.268860e+01 6 3.5621061502
14 1 1.39536386 1.629287e+01 3 4.0364430007
14.1 1 1.49762465 1.999034e+01 2 4.4710561272
14.2 1 1.53383464 2.149175e+01 4 4.6359198843
14.3 3 1.54513729 2.198311e+01 2 4.6886152599
15 1 -0.61580408 2.918229e-01 4 0.5402063532
15.1 1 0.17338010 1.414477e+00 7 1.1893180816
15.2 3 0.41176123 2.278512e+00 4 1.5094739688
15.3 2 1.59317589 2.419998e+01 3 4.9193474615
16 2 0.21655500 1.542046e+00 3 1.2417913869
16.1 2 0.94296095 6.592429e+00 2 2.5675726333
16.2 1 0.97546872 7.035280e+00 5 2.6524101500
16.3 3 1.26933963 1.266294e+01 3 3.5585018690
16.4 2 1.32475013 1.414697e+01 2 3.7612454291
16.5 1 1.38257779 1.588151e+01 6 3.9851612889
17 2 0.46532748 2.536170e+00 3 1.5925356350
17.1 3 0.89093325 5.940935e+00 1 2.4374032998
17.2 1 1.10712558 9.154551e+00 4 3.0256489082
17.3 1 1.20384548 1.110828e+01 5 3.3329089405
17.4 2 1.35309323 1.497207e+01 5 3.8693758985
18 1 0.89094389 5.941061e+00 8 2.4374292302
19 2 -0.02304702 9.549522e-01 5 0.9772165376
19.1 3 0.13683034 1.314769e+00 6 1.1466335913
19.2 2 0.81532616 5.107205e+00 4 2.2599126538
19.3 3 1.43780097 1.773610e+01 3 4.2114245973
20 2 0.54058790 2.948144e+00 5 1.7170160066
20.1 2 0.56320376 3.084555e+00 8 1.7562902288
20.2 1 0.81162182 5.069507e+00 3 2.2515566566
20.3 3 0.81576844 5.111725e+00 3 2.2609123867
20.4 2 1.25028462 1.218943e+01 3 3.4913365287
20.5 3 1.42865863 1.741475e+01 3 4.1730977828
21 1 0.52689085 2.868478e+00 3 1.6936582839
21.1 2 1.08421553 8.744554e+00 3 2.9571191233
21.2 3 1.33203313 1.435454e+01 4 3.7887385779
22 2 0.90406535 6.099036e+00 6 2.4696226232
22.1 2 1.15141431 1.000244e+01 3 3.1626627257
23 2 0.43272573 2.376079e+00 3 1.5414533857
23.1 1 0.84885676 5.461446e+00 2 2.3369736120
24 1 1.03968065 7.999358e+00 1 2.8283136466
25 1 -0.61958002 2.896274e-01 2 0.5381704110
25.1 3 0.47435262 2.582364e+00 0 1.6069735331
25.2 2 0.49214585 2.675916e+00 6 1.6358226922
25.3 2 1.18316390 1.065818e+01 6 3.2646870392
25.4 1 1.40566126 1.663190e+01 2 4.0782226040
25.5 1 1.42456012 1.727258e+01 2 4.1560292873
26 2 -1.42183601 5.821152e-02 6 0.2412706357
26.1 1 0.89411619 5.978875e+00 0 2.4451737676
26.2 1 1.28062152 1.295191e+01 1 3.5988757887
26.3 2 1.43084610 1.749110e+01 4 4.1822362854
27 1 1.30713818 1.365733e+01 2 3.6955824879
27.1 3 1.44577562 1.802124e+01 4 4.2451434687
28 1 -0.55399075 3.302248e-01 5 0.5746519344
28.1 3 1.02761614 7.808651e+00 0 2.7943964268
28.2 1 1.43766547 1.773129e+01 7 4.2108539480
28.3 1 1.49751193 1.998584e+01 3 4.4705521734
29 3 0.17385954 1.415834e+00 4 1.1898884235
29.1 3 0.56667988 3.106075e+00 1 1.7624059319
29.2 3 0.70361255 4.084605e+00 4 2.0210406382
29.3 2 1.22608972 1.161363e+01 3 3.4078777023
30 1 0.81692848 5.123598e+00 5 2.2635366488
30.1 3 1.27921945 1.291564e+01 5 3.5938334477
30.2 3 1.28477952 1.306006e+01 6 3.6138710892
31 1 1.48133498 1.934957e+01 1 4.3988140998
32 3 0.51552709 2.804020e+00 2 1.6745209007
32.1 3 1.06912058 8.484502e+00 5 2.9128167813
32.2 2 1.08777236 8.806981e+00 5 2.9676558380
32.3 1 1.43745941 1.772399e+01 6 4.2099863547
33 3 -4.67360146 8.720901e-05 4 0.0093385763
33.1 1 1.24101546 1.196554e+01 7 3.4591242753
34 1 0.40538339 2.249632e+00 2 1.4998774312
34.1 1 1.34136920 1.462509e+01 5 3.8242761395
34.2 2 1.36282745 1.526641e+01 6 3.9072251692
34.3 2 1.37579253 1.566745e+01 2 3.9582124643
35 1 0.28475022 1.767384e+00 3 1.3294299203
35.1 1 0.42376113 2.333857e+00 2 1.5276966314
35.2 1 1.50465325 2.027334e+01 3 4.5025920868
36 2 -0.33923248 5.073953e-01 3 0.7123168337
36.1 3 0.58625734 3.230105e+00 1 1.7972493160
36.2 3 0.60227552 3.335261e+00 6 1.8262697803
36.3 3 1.45488994 1.835276e+01 4 4.2840119381
36.4 3 1.53027488 2.133929e+01 1 4.6194464504
37 1 0.69408336 4.007496e+00 4 2.0018732361
37.1 3 1.29901486 1.343724e+01 6 3.6656836793
37.2 1 1.37785731 1.573228e+01 8 3.9663937816
38 2 -0.01750115 9.656032e-01 3 0.9826511063
39 2 -0.36790804 4.791143e-01 2 0.6921808305
39.1 3 -0.10227727 8.150103e-01 3 0.9027792048
39.2 1 0.26663623 1.704501e+00 6 1.3055654289
39.3 2 0.43261602 2.375557e+00 4 1.5412842878
39.4 3 1.15798114 1.013467e+01 3 3.1834997435
39.5 3 1.42055487 1.713477e+01 6 4.1394166439
40 3 0.12490390 1.283779e+00 1 1.1330395646
40.1 3 0.99106398 7.258172e+00 3 2.6940994046
40.2 1 1.11174613 9.239542e+00 0 3.0396614212
40.3 3 1.54250672 2.186776e+01 4 4.6762977762
41 3 0.65944345 3.739257e+00 1 1.9337158254
41.1 3 1.16178439 1.021205e+01 4 3.1956304458
41.2 1 1.18927300 1.078920e+01 7 3.2846923557
41.3 1 1.21827591 1.143355e+01 5 3.3813529415
41.4 1 1.26646761 1.259041e+01 2 3.5482964432
42 1 -0.72170038 2.361234e-01 1 0.4859252973
42.1 1 1.46540897 1.874295e+01 3 4.3293134298
43 3 -0.57685600 3.154636e-01 5 0.5616614548
43.1 3 0.07172323 1.154245e+00 2 1.0743579536
43.2 2 0.96056779 6.828709e+00 3 2.6131797966
44 2 -0.26622789 5.871613e-01 3 0.7662644819
44.1 2 0.97419323 7.017356e+00 3 2.6490291790
44.2 1 1.20512946 1.113684e+01 3 3.3371910988
44.3 1 1.41474092 1.693668e+01 4 4.1154200875
45 2 -1.63094249 3.831610e-02 4 0.1957449992
45.1 3 0.69133712 3.985546e+00 2 1.9963831536
46 3 0.29845548 1.816499e+00 8 1.3477755385
46.1 2 1.04962489 8.160046e+00 5 2.8565793915
46.2 3 1.48525084 1.950170e+01 5 4.4160729996
47 1 -0.50872427 3.615162e-01 3 0.6012621359
47.1 2 0.87950730 5.806713e+00 5 2.4097121472
47.2 2 1.09780510 8.985482e+00 5 2.9975794035
47.3 2 1.15781314 1.013127e+01 2 3.1829649757
47.4 2 1.53041755 2.134538e+01 5 4.6201055450
48 3 1.05107914 8.183814e+00 2 2.8607365978
48.1 1 1.06809653 8.467142e+00 5 2.9098354396
49 3 0.99988735 7.387392e+00 4 2.7179756400
50 1 0.16229406 1.383461e+00 1 1.1762060679
51 3 0.35798466 2.046169e+00 9 1.4304436720
52 3 0.75455484 4.522702e+00 3 2.1266646020
52.1 2 1.13141972 9.610339e+00 3 3.1000545993
52.2 1 1.14002538 9.777177e+00 4 3.1268477370
52.3 3 1.27288653 1.275308e+01 11 3.5711459327
52.4 3 1.56827544 2.302432e+01 3 4.7983659909
52.5 3 1.60579658 2.481859e+01 3 4.9818264414
53 1 -0.70001084 2.465916e-01 5 0.4965799209
53.1 3 1.26709851 1.260630e+01 3 3.5505357443
53.2 2 1.52148981 2.096763e+01 2 4.5790420019
54 3 0.33894951 1.969735e+00 1 1.4034724841
54.1 3 0.63192994 3.539056e+00 4 1.8812377600
54.2 3 0.92058507 6.303910e+00 2 2.5107589352
54.3 1 1.02419066 7.755338e+00 2 2.7848406672
54.4 1 1.38988484 1.611531e+01 6 4.0143877396
55 1 -0.49126437 3.743632e-01 1 0.6118522980
55.1 3 -0.29252747 5.570753e-01 2 0.7463747414
55.2 2 1.03677973 7.953081e+00 2 2.8201208171
55.3 1 1.14187711 9.813453e+00 3 3.1326431572
55.4 1 1.16994340 1.038006e+01 5 3.2218102901
56 2 0.20141578 1.496055e+00 5 1.2231332215
56.1 1 0.85752547 5.556959e+00 5 2.3573202139
56.2 3 0.94293018 6.592024e+00 2 2.5674936292
56.3 1 1.08204800 8.706727e+00 3 2.9507164378
56.4 2 1.17163752 1.041529e+01 6 3.2272730360
56.5 1 1.22892457 1.167966e+01 1 3.4175522043
57 1 -1.43955530 5.618471e-02 3 0.2370331448
57.1 1 -1.39374403 6.157569e-02 6 0.2481445030
57.2 1 0.13151815 1.300874e+00 3 1.1405586067
57.3 1 0.74923856 4.474869e+00 2 2.1153886721
58 3 0.19967837 1.490865e+00 6 1.2210099772
58.1 2 0.49067877 2.668076e+00 5 1.6334245703
58.2 1 0.51830932 2.819667e+00 2 1.6791862890
58.3 3 0.96774864 6.927488e+00 4 2.6320121693
58.4 3 1.04653734 8.109812e+00 4 2.8477731440
58.5 3 1.27300163 1.275602e+01 4 3.5715569824
59 3 0.64311617 3.619125e+00 6 1.9023998594
59.1 1 1.60415640 2.473731e+01 4 4.9736620474
60 3 1.05968098 8.325824e+00 7 2.8854503250
61 1 -0.32661269 5.203647e-01 6 0.7213630795
61.1 2 0.84100443 5.376345e+00 3 2.3186947661
61.2 2 0.91937849 6.288716e+00 2 2.5077313243
61.3 3 1.15471134 1.006861e+01 5 3.1731073430
61.4 2 1.28156493 1.297637e+01 4 3.6022726283
62 2 -0.62796412 2.848114e-01 1 0.5336771999
62.1 1 -0.35843842 4.882748e-01 1 0.6987666548
62.2 3 1.24081502 1.196074e+01 2 3.4584309917
62.3 2 1.56921516 2.306763e+01 4 4.8028772371
63 3 1.03309021 7.894611e+00 6 2.8097350930
63.1 1 1.37760053 1.572420e+01 2 3.9653754211
64 3 1.41564212 1.696724e+01 2 4.1191305732
65 3 -0.34585475 5.007194e-01 3 0.7076152589
65.1 3 0.70568063 4.101535e+00 4 2.0252246363
65.2 2 1.13550282 9.689140e+00 2 3.1127382827
65.3 3 1.16218434 1.022023e+01 2 3.1969087943
66 3 1.25114607 1.221045e+01 6 3.4943454154
66.1 3 1.32647633 1.419589e+01 0 3.7677437009
66.2 1 1.37336460 1.559155e+01 5 3.9486138616
67 3 1.42859659 1.741258e+01 8 4.1728388879
68 3 -2.04645568 1.669057e-02 5 0.1291919907
68.1 1 0.57715501 3.171834e+00 5 1.7809643946
68.2 2 0.71750831 4.199715e+00 4 2.0493205660
68.3 3 1.07864325 8.647640e+00 3 2.9406870750
68.4 1 1.39640979 1.632699e+01 1 4.0406670363
69 1 1.42193171 1.718202e+01 5 4.1451198701
70 1 -1.61316627 3.970284e-02 6 0.1992557163
70.1 2 -0.72778533 2.332672e-01 2 0.4829774413
71 3 -0.25597601 5.993245e-01 4 0.7741605386
71.1 2 0.39768944 2.215280e+00 2 1.4883817220
71.2 2 1.40507996 1.661257e+01 5 4.0758526395
71.3 1 1.54858834 2.213537e+01 10 4.7048238723
71.4 2 1.55271500 2.231881e+01 2 4.7242791823
72 1 -0.07029413 8.688470e-01 2 0.9321196121
72.1 2 0.16551374 1.392398e+00 4 1.1799991806
72.2 1 0.63750589 3.578744e+00 8 1.8917567329
72.3 2 1.24857116 1.214773e+01 6 3.4853593935
72.4 2 1.30519980 1.360449e+01 4 3.6884259700
72.5 1 1.40742346 1.669062e+01 1 4.0854155901
73 2 1.52648860 2.117830e+01 1 4.6019889915
74 1 0.38027083 2.139435e+00 1 1.4626806753
75 3 1.17940201 1.057829e+01 6 3.2524286874
76 3 0.59193402 3.266987e+00 3 1.8074807397
76.1 3 1.45126419 1.822015e+01 4 4.2685073183
76.2 2 1.60319315 2.468970e+01 5 4.9688734859
77 2 -0.16735013 7.155525e-01 1 0.8459033852
78 2 -0.19466612 6.775091e-01 2 0.8231094317
79 2 -2.84074848 3.408452e-03 2 0.0583819521
79.1 2 0.89225918 5.956710e+00 6 2.4406372628
79.2 2 1.19278625 1.086528e+01 5 3.2962526032
80 2 -0.10702187 8.073131e-01 5 0.8985060186
80.1 1 0.29525363 1.804904e+00 1 1.3434670598
80.2 3 1.03054400 7.854511e+00 4 2.8025900386
81 2 -4.59200757 1.026675e-04 4 0.0101324962
81.1 3 -0.05956855 8.876861e-01 5 0.9421709494
81.2 2 1.11653255 9.328415e+00 2 3.0542453879
81.3 1 1.20766489 1.119346e+01 5 3.3456630446
82 1 0.32143184 1.901920e+00 1 1.3791010005
82.1 2 0.56537123 3.097956e+00 2 1.7601010622
82.2 3 0.96443810 6.881772e+00 5 2.6233131927
83 2 -2.92360830 2.887926e-03 5 0.0537394290
83.1 3 1.06683161 8.445749e+00 1 2.9061570496
83.2 3 1.13749504 9.727823e+00 1 3.1189457362
83.3 3 1.56158380 2.271823e+01 4 4.7663642222
84 2 1.00261742 7.427838e+00 1 2.7254060237
84.1 3 1.20491590 1.113209e+01 5 3.3364784659
85 1 -1.21141501 8.867032e-02 6 0.2977756259
85.1 2 0.55354693 3.025553e+00 5 1.7394116637
85.2 3 0.98754404 7.207254e+00 3 2.6846330194
85.3 3 1.15084929 9.991139e+00 2 3.1608762743
85.4 2 1.37250101 1.556465e+01 2 3.9452053758
85.5 2 1.50613203 2.033338e+01 6 4.5092553482
86 1 -0.16531361 7.184729e-01 3 0.8476278360
86.1 2 0.01179313 1.023867e+00 3 1.0118629411
86.2 1 0.22403591 1.565291e+00 6 1.2511159515
86.3 1 0.78255612 4.783212e+00 5 2.1870554925
86.4 1 0.89743141 6.018649e+00 5 2.4532935000
86.5 2 1.34040901 1.459703e+01 4 3.8206058508
87 3 0.99582370 7.327595e+00 3 2.7069531474
87.1 3 1.23728720 1.187665e+01 6 3.4462517721
87.2 2 1.50943340 2.046808e+01 2 4.5241666853
88 3 -7.43666969 3.472088e-07 1 0.0005892443
88.1 3 -0.34022529 5.063888e-01 6 0.7116099866
88.2 3 0.91439786 6.226384e+00 1 2.4952722900
88.3 1 1.19379568 1.088724e+01 6 3.2995816297
89 2 -0.43663289 4.175856e-01 7 0.6462086167
90 1 -1.77429443 2.876520e-02 3 0.1696030737
90.1 2 0.95475675 6.749804e+00 8 2.5980385230
90.2 2 0.98025630 7.102967e+00 4 2.6651392167
90.3 2 1.13920034 9.761057e+00 2 3.1242690247
91 3 -0.44900667 4.073782e-01 4 0.6382618390
91.1 3 0.96409219 6.877013e+00 2 2.6224059286
91.2 3 1.56386564 2.282214e+01 5 4.7772527603
92 2 -2.60768143 5.432462e-03 3 0.0737052364
93 2 -1.27693454 7.778015e-02 3 0.2788909199
93.1 2 0.03515090 1.072832e+00 3 1.0357759963
93.2 2 0.91294719 6.208345e+00 4 2.4916551099
93.3 3 1.06043020 8.338309e+00 2 2.8876129608
93.4 2 1.49603344 1.992683e+01 6 4.4639474002
94 2 -0.16392661 7.204688e-01 2 0.8488043118
94.1 3 0.05377339 1.113543e+00 4 1.0552454425
94.2 3 0.66503063 3.781275e+00 2 1.9445500884
94.3 2 1.12202677 9.431485e+00 6 3.0710722448
94.4 3 1.12728824 9.531256e+00 5 3.0872731935
94.5 2 1.47718020 1.918945e+01 5 4.3805759016
95 2 0.70305113 4.080021e+00 8 2.0199063048
95.1 3 1.39089487 1.614790e+01 4 4.0184444457
95.2 2 1.51724656 2.079044e+01 1 4.5596531732
96 3 -3.46947576 9.692853e-04 2 0.0311333477
96.1 2 -2.02172545 1.753685e-02 3 0.1324267720
96.2 3 -0.40028304 4.490747e-01 2 0.6701303425
96.3 2 0.77817916 4.741523e+00 6 2.1775037691
96.4 2 0.79958353 4.948909e+00 6 2.2246142488
96.5 3 1.44403602 1.795865e+01 3 4.2377650598
97 3 0.17857310 1.429245e+00 2 1.1955102731
97.1 3 1.60146841 2.460468e+01 5 4.9603108643
98 2 -1.58878641 4.168671e-02 7 0.2041732438
98.1 3 -0.84174488 1.857247e-01 2 0.4309578973
98.2 1 1.25768262 1.237113e+01 6 3.5172611906
99 2 -1.04078137 1.247351e-01 3 0.3531786101
99.1 1 1.54307253 2.189252e+01 4 4.6789444226
99.2 3 1.60797853 2.492714e+01 5 4.9927084171
100 2 0.06685343 1.143058e+00 2 1.0691387602
100.1 1 0.41272829 2.282923e+00 3 1.5109344281
100.2 2 0.76557633 4.623503e+00 3 2.1502332564
100.3 2 1.35443144 1.501220e+01 7 3.8745574222
100.4 3 1.53832012 2.168542e+01 6 4.6567608765
$m4e$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m4e$spM_lvlone
center scale
m1 NA NA
log(time) 0.6318779 1.063214
I(time^2) 8.3244468 7.090003
p1 3.7264438 1.946996
time 2.5339403 1.381809
$m4e$mu_reg_multinomial
[1] 0
$m4e$tau_reg_multinomial
[1] 1e-04
$m4e$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
$m4e$shape_diag_RinvD
[1] "0.01"
$m4e$rate_diag_RinvD
[1] "0.001"
jagsmodel remains the same
Code
lapply(models, "[[", "jagsmodel")
Output
$m0a
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
beta[2] * M_id[group_id[i], 1]
}
for (ii in 1:100) {
b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
}
# Priors for the model for m1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1])
}
$m0b
model {
# Multinomial logit mixed model for m2 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3])
p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ])))
p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ])))
p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ])))
log(phi_m2[i, 1]) <- 0
log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1]
log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] +
beta[2] * M_id[group_id[i], 1]
}
for (ii in 1:100) {
b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ])
mu_b_m2_id[ii, 1] <- 0
}
# Priors for the model for m2
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1])
}
$m1a
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1] +
beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
beta[3] * M_id[group_id[i], 1] +
beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
}
# Priors for the model for m1
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1])
}
$m1b
model {
# Multinomial logit mixed model for m2 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3])
p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ])))
p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ])))
p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ])))
log(phi_m2[i, 1]) <- 0
log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1] +
beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2]
log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] +
beta[3] * M_id[group_id[i], 1] +
beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2]
}
for (ii in 1:100) {
b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ])
mu_b_m2_id[ii, 1] <- 0
}
# Priors for the model for m2
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1])
}
$m1c
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1] +
beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
beta[2] * M_id[group_id[i], 1] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
}
# Priors for the model for m1
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1])
}
$m1d
model {
# Multinomial logit mixed model for m2 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3])
p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ])))
p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ])))
p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ])))
log(phi_m2[i, 1]) <- 0
log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1] +
beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] +
beta[2] * M_id[group_id[i], 1] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ])
mu_b_m2_id[ii, 1] <- 0
}
# Priors for the model for m2
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1])
}
$m2a
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 2] +
beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
beta[3] * M_id[group_id[i], 2] +
beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
}
# Priors for the model for m1
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m1_id[1, 1] <- 1 / (invD_m1_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)
}
$m2b
model {
# Multinomial logit mixed model for m2 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3])
p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ])))
p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ])))
p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ])))
log(phi_m2[i, 1]) <- 0
log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 2] +
beta[2] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2]
log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] +
beta[3] * M_id[group_id[i], 2] +
beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2]
}
for (ii in 1:100) {
b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ])
mu_b_m2_id[ii, 1] <- 0
}
# Priors for the model for m2
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m2_id[1, 1] <- 1 / (invD_m2_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)
}
$m2c
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1] +
beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
beta[2] * M_id[group_id[i], 1] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
}
# Priors for the model for m1
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m1_id[1, 1] <- 1 / (invD_m1_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 {
# Multinomial logit mixed model for m2 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m2[i, 1:3])
p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ])))
p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ])))
p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ])))
log(phi_m2[i, 1]) <- 0
log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1] +
beta[3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] +
beta[2] * M_id[group_id[i], 1] +
beta[4] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
}
for (ii in 1:100) {
b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ])
mu_b_m2_id[ii, 1] <- 0
}
# Priors for the model for m2
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m2_id[1, 1] <- 1 / (invD_m2_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 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, 2] +
beta[3] * M_lvlone[i, 3]
}
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 1:3) {
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])
}
$m3b
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, 3] +
beta[3] * M_lvlone[i, 4]
}
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 1:3) {
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])
# Multinomial logit mixed model for m2 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3])
p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ])))
p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ])))
p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ])))
log(phi_m2[i, 1]) <- 0
log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] +
alpha[1] * M_id[group_id[i], 1]
log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] +
alpha[2] * M_id[group_id[i], 1]
M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0)
M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0)
}
for (ii in 1:100) {
b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ])
mu_b_m2_id[ii, 1] <- 0
}
# Priors for the model for m2
for (k in 1:2) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m2_id[1, 1] <- 1 / (invD_m2_id[1, 1])
}
$m4a
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 3] +
beta[2] * M_id[group_id[i], 4] +
beta[3] * M_id[group_id[i], 5] +
beta[4] * M_id[group_id[i], 6] +
beta[5] * (M_id[group_id[i], 7] - spM_id[7, 1])/spM_id[7, 2] +
beta[6] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] +
beta[13] * M_lvlone[i, 3] + beta[14] * M_lvlone[i, 4] +
beta[15] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
beta[16] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
beta[7] * M_id[group_id[i], 3] +
beta[8] * M_id[group_id[i], 4] +
beta[9] * M_id[group_id[i], 5] +
beta[10] * M_id[group_id[i], 6] +
beta[11] * (M_id[group_id[i], 7] - spM_id[7, 1])/spM_id[7, 2] +
beta[12] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] +
beta[17] * M_lvlone[i, 3] + beta[18] * M_lvlone[i, 4] +
beta[19] * (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] +
beta[20] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
}
# Priors for the model for m1
for (k in 1:20) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1])
# Multinomial logit mixed model for m2 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3])
p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ])))
p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ])))
p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ])))
log(phi_m2[i, 1]) <- 0
log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] +
alpha[1] * M_id[group_id[i], 3] +
alpha[2] * M_id[group_id[i], 4] +
alpha[3] * M_id[group_id[i], 5] +
alpha[4] * M_id[group_id[i], 6] +
alpha[5] * (M_id[group_id[i], 9] - spM_id[9, 1])/spM_id[9, 2] +
alpha[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2]
log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] +
alpha[7] * M_id[group_id[i], 3] +
alpha[8] * M_id[group_id[i], 4] +
alpha[9] * M_id[group_id[i], 5] +
alpha[10] * M_id[group_id[i], 6] +
alpha[11] * (M_id[group_id[i], 9] - spM_id[9, 1])/spM_id[9, 2] +
alpha[12] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2]
M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0)
M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0)
}
for (ii in 1:100) {
b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ])
mu_b_m2_id[ii, 1] <- 0
}
# Priors for the model for m2
for (k in 1:12) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m2_id[1, 1] <- 1 / (invD_m2_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, 3] * alpha[13] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[14] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[15]
log(phi_M2[ii, 3]) <- M_id[ii, 3] * alpha[16] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[17] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[18]
log(phi_M2[ii, 4]) <- M_id[ii, 3] * alpha[19] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[20] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[21]
M_id[ii, 4] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 13:21) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 2] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 3] * alpha[22] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[23]
M_id[ii, 7] <- abs(M_id[ii, 9] - M_id[ii, 2])
}
# Priors for the model for C2
for (k in 22:23) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 5] <- M_lvlone[i, 3] * M_id[group_id[i], 7]
M_lvlone[i, 6] <- M_lvlone[i, 4] * M_id[group_id[i], 7]
}
}
$m4b
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
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], 4] - spM_id[4, 1])/spM_id[4, 2] +
beta[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[8] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
beta[4] * M_id[group_id[i], 2] +
beta[5] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] +
beta[6] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] +
beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[10] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
}
# Priors for the model for m1
for (k in 1:10) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1])
# Multinomial logit mixed model for m2 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dcat(p_m2[i, 1:3])
p_m2[i, 1] <- min(1-1e-7, max(1e-7, phi_m2[i, 1] / sum(phi_m2[i, ])))
p_m2[i, 2] <- min(1-1e-7, max(1e-7, phi_m2[i, 2] / sum(phi_m2[i, ])))
p_m2[i, 3] <- min(1-1e-7, max(1e-7, phi_m2[i, 3] / sum(phi_m2[i, ])))
log(phi_m2[i, 1]) <- 0
log(phi_m2[i, 2]) <- b_m2_id[group_id[i], 1] +
alpha[1] * M_id[group_id[i], 2] +
alpha[2] * M_id[group_id[i], 5] +
alpha[3] * M_id[group_id[i], 6] +
alpha[4] * M_id[group_id[i], 7] +
alpha[5] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] +
alpha[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2]
log(phi_m2[i, 3]) <- b_m2_id[group_id[i], 1] +
alpha[7] * M_id[group_id[i], 2] +
alpha[8] * M_id[group_id[i], 5] +
alpha[9] * M_id[group_id[i], 6] +
alpha[10] * M_id[group_id[i], 7] +
alpha[11] * (M_id[group_id[i], 8] - spM_id[8, 1])/spM_id[8, 2] +
alpha[12] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 2, 1, 0)
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 3, 1, 0)
M_lvlone[i, 3] <- ifelse((M_lvlone[i, 2]) > (M_id[group_id[i], 9]), 1, 0)
}
for (ii in 1:100) {
b_m2_id[ii, 1:1] ~ dnorm(mu_b_m2_id[ii, ], invD_m2_id[ , ])
mu_b_m2_id[ii, 1] <- 0
}
# Priors for the model for m2
for (k in 1:12) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
invD_m2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m2_id[1, 1] <- 1 / (invD_m2_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[13] + M_id[ii, 5] * alpha[14] +
M_id[ii, 6] * alpha[15] + M_id[ii, 7] * alpha[16] +
(M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[17]
M_id[ii, 3] <- abs(M_id[ii, 8] - M_id[ii, 1])
}
# Priors for the model for C2
for (k in 13:17) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 4] <- M_lvlone[i, 3] * M_id[group_id[i], 3]
}
}
$m4c
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_m1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
b_m1_id[group_id[i], 4] * (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], 4] +
beta[7] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_m1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
b_m1_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[4] * M_id[group_id[i], 2] +
beta[5] * (M_id[group_id[i], 3] - spM_id[3, 1])/spM_id[3, 2] +
beta[6] * M_id[group_id[i], 4] +
beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[10] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:4] ~ dmnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
mu_b_m1_id[ii, 2] <- 0
mu_b_m1_id[ii, 3] <- 0
mu_b_m1_id[ii, 4] <- 0
}
# Priors for the model for m1
for (k in 1:10) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
for (k in 1:4) {
RinvD_m1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_m1_id[1:4, 1:4] ~ dwish(RinvD_m1_id[ , ], KinvD_m1_id)
D_m1_id[1:4, 1:4] <- inverse(invD_m1_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] +
alpha[4] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
}
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, 4] * alpha[3]
}
# 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 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]
}
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] - spM_id[3, 1])/spM_id[3, 2] * alpha[6] +
M_id[ii, 4] * alpha[7]
}
# 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] - 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)
}
}
$m4d
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[1] * M_id[group_id[i], 1] +
beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[5] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[6] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[7] * M_lvlone[i, 5] +
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] +
beta[10] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
b_m1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[3] * M_id[group_id[i], 1] +
beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[12] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[13] * M_lvlone[i, 5] +
beta[14] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] +
beta[15] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] +
beta[16] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:2] ~ dmnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
mu_b_m1_id[ii, 2] <- 0
}
# Priors for the model for m1
for (k in 1:16) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
for (k in 1:2) {
RinvD_m1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_m1_id[1:2, 1:2] ~ dwish(RinvD_m1_id[ , ], KinvD_m1_id)
D_m1_id[1:2, 1:2] <- inverse(invD_m1_id[ , ])
# 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[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
M_lvlone[i, 5] <- 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, 1] * alpha[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2]
}
# 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])
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6]
}
}
$m4e
model {
# Multinomial logit mixed model for m1 ------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_m1[i, 1:3])
p_m1[i, 1] <- min(1-1e-7, max(1e-7, phi_m1[i, 1] / sum(phi_m1[i, ])))
p_m1[i, 2] <- min(1-1e-7, max(1e-7, phi_m1[i, 2] / sum(phi_m1[i, ])))
p_m1[i, 3] <- min(1-1e-7, max(1e-7, phi_m1[i, 3] / sum(phi_m1[i, ])))
log(phi_m1[i, 1]) <- 0
log(phi_m1[i, 2]) <- b_m1_id[group_id[i], 1] +
beta[1] * M_id[group_id[i], 1] +
beta[2] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[5] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[6] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[7] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
log(phi_m1[i, 3]) <- b_m1_id[group_id[i], 1] +
beta[3] * M_id[group_id[i], 1] +
beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[8] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[9] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[10] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
}
for (ii in 1:100) {
b_m1_id[ii, 1:1] ~ dnorm(mu_b_m1_id[ii, ], invD_m1_id[ , ])
mu_b_m1_id[ii, 1] <- 0
}
# Priors for the model for m1
for (k in 1:10) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial_ridge_beta[k])
tau_reg_multinomial_ridge_beta[k] ~ dgamma(0.01, 0.01)
}
invD_m1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_m1_id[1, 1] <- 1 / (invD_m1_id[1, 1])
}
GRcrit and MCerror give same result
Code
lapply(models0, GR_crit, multivariate = FALSE)
Output
$m0a
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1C: (Intercept) NaN NaN
D_m1_id[1,1] NaN NaN
$m0b
Potential scale reduction factors:
Point est. Upper C.I.
m2B: (Intercept) NaN NaN
m2C: (Intercept) NaN NaN
D_m2_id[1,1] NaN NaN
$m1a
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1B: C1 NaN NaN
m1C: (Intercept) NaN NaN
m1C: C1 NaN NaN
D_m1_id[1,1] NaN NaN
$m1b
Potential scale reduction factors:
Point est. Upper C.I.
m2B: (Intercept) NaN NaN
m2B: C1 NaN NaN
m2C: (Intercept) NaN NaN
m2C: C1 NaN NaN
D_m2_id[1,1] NaN NaN
$m1c
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1C: (Intercept) NaN NaN
m1B: c1 NaN NaN
m1C: c1 NaN NaN
D_m1_id[1,1] NaN NaN
$m1d
Potential scale reduction factors:
Point est. Upper C.I.
m2B: (Intercept) NaN NaN
m2C: (Intercept) NaN NaN
m2B: c1 NaN NaN
m2C: c1 NaN NaN
D_m2_id[1,1] NaN NaN
$m2a
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1B: C2 NaN NaN
m1C: (Intercept) NaN NaN
m1C: C2 NaN NaN
D_m1_id[1,1] NaN NaN
$m2b
Potential scale reduction factors:
Point est. Upper C.I.
m2B: (Intercept) NaN NaN
m2B: C2 NaN NaN
m2C: (Intercept) NaN NaN
m2C: C2 NaN NaN
D_m2_id[1,1] NaN NaN
$m2c
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1C: (Intercept) NaN NaN
m1B: c2 NaN NaN
m1C: c2 NaN NaN
D_m1_id[1,1] NaN NaN
$m2d
Potential scale reduction factors:
Point est. Upper C.I.
m2B: (Intercept) NaN NaN
m2C: (Intercept) NaN NaN
m2B: c2 NaN NaN
m2C: c2 NaN NaN
D_m2_id[1,1] NaN NaN
$m3a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
m1B NaN NaN
m1C NaN NaN
sigma_c1 NaN NaN
D_c1_id[1,1] NaN NaN
$m3b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
m2B NaN NaN
m2C NaN NaN
sigma_c1 NaN NaN
D_c1_id[1,1] NaN NaN
$m4a
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1B: M22 NaN NaN
m1B: M23 NaN NaN
m1B: M24 NaN NaN
m1B: abs(C1 - C2) NaN NaN
m1B: log(C1) NaN NaN
m1C: (Intercept) NaN NaN
m1C: M22 NaN NaN
m1C: M23 NaN NaN
m1C: M24 NaN NaN
m1C: abs(C1 - C2) NaN NaN
m1C: log(C1) NaN NaN
m1B: m2B NaN NaN
m1B: m2C NaN NaN
m1B: m2B:abs(C1 - C2) NaN NaN
m1B: m2C:abs(C1 - C2) NaN NaN
m1C: m2B NaN NaN
m1C: m2C NaN NaN
m1C: m2B:abs(C1 - C2) NaN NaN
m1C: m2C:abs(C1 - C2) NaN NaN
D_m1_id[1,1] NaN NaN
$m4b
Potential scale reduction factors:
Point est.
m1B: (Intercept) NaN
m1B: abs(C1 - C2) NaN
m1B: log(C1) NaN
m1C: (Intercept) NaN
m1C: abs(C1 - C2) NaN
m1C: log(C1) NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
D_m1_id[1,1] NaN
Upper C.I.
m1B: (Intercept) NaN
m1B: abs(C1 - C2) NaN
m1B: log(C1) NaN
m1C: (Intercept) NaN
m1C: abs(C1 - C2) NaN
m1C: log(C1) NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
D_m1_id[1,1] NaN
$m4c
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1B: C1 NaN NaN
m1B: B21 NaN NaN
m1C: (Intercept) NaN NaN
m1C: C1 NaN NaN
m1C: B21 NaN NaN
m1B: time NaN NaN
m1B: c1 NaN NaN
m1C: time NaN NaN
m1C: c1 NaN NaN
D_m1_id[1,1] NaN NaN
D_m1_id[1,2] NaN NaN
D_m1_id[2,2] NaN NaN
D_m1_id[1,3] NaN NaN
D_m1_id[2,3] NaN NaN
D_m1_id[3,3] NaN NaN
D_m1_id[1,4] NaN NaN
D_m1_id[2,4] NaN NaN
D_m1_id[3,4] NaN NaN
D_m1_id[4,4] NaN NaN
$m4d
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1B: C1 NaN NaN
m1C: (Intercept) NaN NaN
m1C: C1 NaN NaN
m1B: time NaN NaN
m1B: I(time^2) NaN NaN
m1B: b21 NaN NaN
m1B: c1 NaN NaN
m1B: C1:time NaN NaN
m1B: b21:c1 NaN NaN
m1C: time NaN NaN
m1C: I(time^2) NaN NaN
m1C: b21 NaN NaN
m1C: c1 NaN NaN
m1C: C1:time NaN NaN
m1C: b21:c1 NaN NaN
D_m1_id[1,1] NaN NaN
D_m1_id[1,2] NaN NaN
D_m1_id[2,2] NaN NaN
$m4e
Potential scale reduction factors:
Point est. Upper C.I.
m1B: (Intercept) NaN NaN
m1B: C1 NaN NaN
m1C: (Intercept) NaN NaN
m1C: C1 NaN NaN
m1B: log(time) NaN NaN
m1B: I(time^2) NaN NaN
m1B: p1 NaN NaN
m1C: log(time) NaN NaN
m1C: I(time^2) NaN NaN
m1C: p1 NaN NaN
D_m1_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"
$m0a
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
$m0b
est MCSE SD MCSE/SD
m2B: (Intercept) 0 0 0 NaN
m2C: (Intercept) 0 0 0 NaN
D_m2_id[1,1] 0 0 0 NaN
$m1a
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1B: C1 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
m1C: C1 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
$m1b
est MCSE SD MCSE/SD
m2B: (Intercept) 0 0 0 NaN
m2B: C1 0 0 0 NaN
m2C: (Intercept) 0 0 0 NaN
m2C: C1 0 0 0 NaN
D_m2_id[1,1] 0 0 0 NaN
$m1c
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
m1B: c1 0 0 0 NaN
m1C: c1 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
$m1d
est MCSE SD MCSE/SD
m2B: (Intercept) 0 0 0 NaN
m2C: (Intercept) 0 0 0 NaN
m2B: c1 0 0 0 NaN
m2C: c1 0 0 0 NaN
D_m2_id[1,1] 0 0 0 NaN
$m2a
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1B: C2 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
m1C: C2 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
$m2b
est MCSE SD MCSE/SD
m2B: (Intercept) 0 0 0 NaN
m2B: C2 0 0 0 NaN
m2C: (Intercept) 0 0 0 NaN
m2C: C2 0 0 0 NaN
D_m2_id[1,1] 0 0 0 NaN
$m2c
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
m1B: c2 0 0 0 NaN
m1C: c2 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
$m2d
est MCSE SD MCSE/SD
m2B: (Intercept) 0 0 0 NaN
m2C: (Intercept) 0 0 0 NaN
m2B: c2 0 0 0 NaN
m2C: c2 0 0 0 NaN
D_m2_id[1,1] 0 0 0 NaN
$m3a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
m1B 0 0 0 NaN
m1C 0 0 0 NaN
sigma_c1 0 0 0 NaN
D_c1_id[1,1] 0 0 0 NaN
$m3b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
m2B 0 0 0 NaN
m2C 0 0 0 NaN
sigma_c1 0 0 0 NaN
D_c1_id[1,1] 0 0 0 NaN
$m4a
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1B: M22 0 0 0 NaN
m1B: M23 0 0 0 NaN
m1B: M24 0 0 0 NaN
m1B: abs(C1 - C2) 0 0 0 NaN
m1B: log(C1) 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
m1C: M22 0 0 0 NaN
m1C: M23 0 0 0 NaN
m1C: M24 0 0 0 NaN
m1C: abs(C1 - C2) 0 0 0 NaN
m1C: log(C1) 0 0 0 NaN
m1B: m2B 0 0 0 NaN
m1B: m2C 0 0 0 NaN
m1B: m2B:abs(C1 - C2) 0 0 0 NaN
m1B: m2C:abs(C1 - C2) 0 0 0 NaN
m1C: m2B 0 0 0 NaN
m1C: m2C 0 0 0 NaN
m1C: m2B:abs(C1 - C2) 0 0 0 NaN
m1C: m2C:abs(C1 - C2) 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
$m4b
est MCSE SD
m1B: (Intercept) 0 0 0
m1B: abs(C1 - C2) 0 0 0
m1B: log(C1) 0 0 0
m1C: (Intercept) 0 0 0
m1C: abs(C1 - C2) 0 0 0
m1C: log(C1) 0 0 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0
D_m1_id[1,1] 0 0 0
MCSE/SD
m1B: (Intercept) NaN
m1B: abs(C1 - C2) NaN
m1B: log(C1) NaN
m1C: (Intercept) NaN
m1C: abs(C1 - C2) NaN
m1C: log(C1) NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
D_m1_id[1,1] NaN
$m4c
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1B: C1 0 0 0 NaN
m1B: B21 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
m1C: C1 0 0 0 NaN
m1C: B21 0 0 0 NaN
m1B: time 0 0 0 NaN
m1B: c1 0 0 0 NaN
m1C: time 0 0 0 NaN
m1C: c1 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
D_m1_id[1,2] 0 0 0 NaN
D_m1_id[2,2] 0 0 0 NaN
D_m1_id[1,3] 0 0 0 NaN
D_m1_id[2,3] 0 0 0 NaN
D_m1_id[3,3] 0 0 0 NaN
D_m1_id[1,4] 0 0 0 NaN
D_m1_id[2,4] 0 0 0 NaN
D_m1_id[3,4] 0 0 0 NaN
D_m1_id[4,4] 0 0 0 NaN
$m4d
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1B: C1 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
m1C: C1 0 0 0 NaN
m1B: time 0 0 0 NaN
m1B: I(time^2) 0 0 0 NaN
m1B: b21 0 0 0 NaN
m1B: c1 0 0 0 NaN
m1B: C1:time 0 0 0 NaN
m1B: b21:c1 0 0 0 NaN
m1C: time 0 0 0 NaN
m1C: I(time^2) 0 0 0 NaN
m1C: b21 0 0 0 NaN
m1C: c1 0 0 0 NaN
m1C: C1:time 0 0 0 NaN
m1C: b21:c1 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
D_m1_id[1,2] 0 0 0 NaN
D_m1_id[2,2] 0 0 0 NaN
$m4e
est MCSE SD MCSE/SD
m1B: (Intercept) 0 0 0 NaN
m1B: C1 0 0 0 NaN
m1C: (Intercept) 0 0 0 NaN
m1C: C1 0 0 0 NaN
m1B: log(time) 0 0 0 NaN
m1B: I(time^2) 0 0 0 NaN
m1B: p1 0 0 0 NaN
m1C: log(time) 0 0 0 NaN
m1C: I(time^2) 0 0 0 NaN
m1C: p1 0 0 0 NaN
D_m1_id[1,1] 0 0 0 NaN
summary output remained the same
Code
lapply(models0, print)
Output
Call:
mlogitmm_imp(fixed = m1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) (Intercept)
0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
Call:
mlogitmm_imp(fixed = m2 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) (Intercept)
0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
Call:
mlogitmm_imp(fixed = m1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C1 (Intercept) C1
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
Call:
mlogitmm_imp(fixed = m2 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) C1 (Intercept) C1
0 0 0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
Call:
mlogitmm_imp(fixed = m1 ~ c1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) (Intercept) c1 c1
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
Call:
mlogitmm_imp(fixed = m2 ~ c1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) (Intercept) c1 c1
0 0 0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
Call:
mlogitmm_imp(fixed = m1 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C2 (Intercept) C2
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
Call:
mlogitmm_imp(fixed = m2 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) C2 (Intercept) C2
0 0 0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
Call:
mlogitmm_imp(fixed = m1 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) (Intercept) c2 c2
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
Call:
mlogitmm_imp(fixed = m2 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) (Intercept) c2 c2
0 0 0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
Call:
lme_imp(fixed = c1 ~ m1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) m1B m1C
0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
Call:
lme_imp(fixed = c1 ~ m2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) m2B m2C
0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
Call:
mlogitmm_imp(fixed = m1 ~ M2 + m2 * abs(C1 - C2) + log(C1) +
(1 | id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) M22 M23 M24
0 0 0 0
abs(C1 - C2) log(C1) (Intercept) M22
0 0 0 0
M23 M24 abs(C1 - C2) log(C1)
0 0 0 0
m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2)
0 0 0 0
m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2)
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
Call:
mlogitmm_imp(fixed = m1 ~ ifelse(as.numeric(m2) > as.numeric(M1),
1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept)
0
abs(C1 - C2)
0
log(C1)
0
(Intercept)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
Call:
mlogitmm_imp(fixed = m1 ~ time + c1 + C1 + B2 + (c1 * time |
id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C1 B21 (Intercept) C1 B21
0 0 0 0 0 0
time c1 time c1
0 0 0 0
Random effects covariance matrix:
$id
m1 m1 m1 m1
(Intercept) c1 time c1:time
m1 (Intercept) 0 0 0 0
m1 c1 0 0 0 0
m1 time 0 0 0 0
m1 c1:time 0 0 0 0
Call:
mlogitmm_imp(fixed = m1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF,
random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C1 (Intercept) C1 time I(time^2)
0 0 0 0 0 0
b21 c1 C1:time b21:c1 time I(time^2)
0 0 0 0 0 0
b21 c1 C1:time b21:c1
0 0 0 0
Random effects covariance matrix:
$id
m1 m1
(Intercept) time
m1 (Intercept) 0 0
m1 time 0 0
Call:
mlogitmm_imp(fixed = m1 ~ C1 + log(time) + I(time^2) + p1, data = longDF,
random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge",
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C1 (Intercept) C1 log(time) I(time^2)
0 0 0 0 0 0
p1 log(time) I(time^2) p1
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
$m0a
Call:
mlogitmm_imp(fixed = m1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) (Intercept)
0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
$m0b
Call:
mlogitmm_imp(fixed = m2 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) (Intercept)
0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
$m1a
Call:
mlogitmm_imp(fixed = m1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C1 (Intercept) C1
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
$m1b
Call:
mlogitmm_imp(fixed = m2 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) C1 (Intercept) C1
0 0 0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
$m1c
Call:
mlogitmm_imp(fixed = m1 ~ c1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) (Intercept) c1 c1
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
$m1d
Call:
mlogitmm_imp(fixed = m2 ~ c1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) (Intercept) c1 c1
0 0 0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
$m2a
Call:
mlogitmm_imp(fixed = m1 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C2 (Intercept) C2
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
$m2b
Call:
mlogitmm_imp(fixed = m2 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) C2 (Intercept) C2
0 0 0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
$m2c
Call:
mlogitmm_imp(fixed = m1 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) (Intercept) c2 c2
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
$m2d
Call:
mlogitmm_imp(fixed = m2 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m2"
Fixed effects:
(Intercept) (Intercept) c2 c2
0 0 0 0
Random effects covariance matrix:
$id
m2
(Intercept)
m2 (Intercept) 0
$m3a
Call:
lme_imp(fixed = c1 ~ m1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) m1B m1C
0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
$m3b
Call:
lme_imp(fixed = c1 ~ m2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) m2B m2C
0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
$m4a
Call:
mlogitmm_imp(fixed = m1 ~ M2 + m2 * abs(C1 - C2) + log(C1) +
(1 | id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) M22 M23 M24
0 0 0 0
abs(C1 - C2) log(C1) (Intercept) M22
0 0 0 0
M23 M24 abs(C1 - C2) log(C1)
0 0 0 0
m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2)
0 0 0 0
m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2)
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
$m4b
Call:
mlogitmm_imp(fixed = m1 ~ ifelse(as.numeric(m2) > as.numeric(M1),
1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept)
0
abs(C1 - C2)
0
log(C1)
0
(Intercept)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
$m4c
Call:
mlogitmm_imp(fixed = m1 ~ time + c1 + C1 + B2 + (c1 * time |
id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C1 B21 (Intercept) C1 B21
0 0 0 0 0 0
time c1 time c1
0 0 0 0
Random effects covariance matrix:
$id
m1 m1 m1 m1
(Intercept) c1 time c1:time
m1 (Intercept) 0 0 0 0
m1 c1 0 0 0 0
m1 time 0 0 0 0
m1 c1:time 0 0 0 0
$m4d
Call:
mlogitmm_imp(fixed = m1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF,
random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C1 (Intercept) C1 time I(time^2)
0 0 0 0 0 0
b21 c1 C1:time b21:c1 time I(time^2)
0 0 0 0 0 0
b21 c1 C1:time b21:c1
0 0 0 0
Random effects covariance matrix:
$id
m1 m1
(Intercept) time
m1 (Intercept) 0 0
m1 time 0 0
$m4e
Call:
mlogitmm_imp(fixed = m1 ~ C1 + log(time) + I(time^2) + p1, data = longDF,
random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge",
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit mixed model for "m1"
Fixed effects:
(Intercept) C1 (Intercept) C1 log(time) I(time^2)
0 0 0 0 0 0
p1 log(time) I(time^2) p1
0 0 0 0
Random effects covariance matrix:
$id
m1
(Intercept)
m1 (Intercept) 0
Code
lapply(models0, coef)
Output
$m0a
$m0a$m1
(Intercept) (Intercept) D_m1_id[1,1]
0 0 0
$m0b
$m0b$m2
(Intercept) (Intercept) D_m2_id[1,1]
0 0 0
$m1a
$m1a$m1
(Intercept) C1 (Intercept) C1 D_m1_id[1,1]
0 0 0 0 0
$m1b
$m1b$m2
(Intercept) C1 (Intercept) C1 D_m2_id[1,1]
0 0 0 0 0
$m1c
$m1c$m1
(Intercept) (Intercept) c1 c1 D_m1_id[1,1]
0 0 0 0 0
$m1d
$m1d$m2
(Intercept) (Intercept) c1 c1 D_m2_id[1,1]
0 0 0 0 0
$m2a
$m2a$m1
(Intercept) C2 (Intercept) C2 D_m1_id[1,1]
0 0 0 0 0
$m2b
$m2b$m2
(Intercept) C2 (Intercept) C2 D_m2_id[1,1]
0 0 0 0 0
$m2c
$m2c$m1
(Intercept) (Intercept) c2 c2 D_m1_id[1,1]
0 0 0 0 0
$m2d
$m2d$m2
(Intercept) (Intercept) c2 c2 D_m2_id[1,1]
0 0 0 0 0
$m3a
$m3a$c1
(Intercept) m1B m1C sigma_c1 D_c1_id[1,1]
0 0 0 0 0
$m3b
$m3b$c1
(Intercept) m2B m2C sigma_c1 D_c1_id[1,1]
0 0 0 0 0
$m4a
$m4a$m1
(Intercept) M22 M23 M24
0 0 0 0
abs(C1 - C2) log(C1) (Intercept) M22
0 0 0 0
M23 M24 abs(C1 - C2) log(C1)
0 0 0 0
m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2)
0 0 0 0
m2B m2C m2B:abs(C1 - C2) m2C:abs(C1 - C2)
0 0 0 0
D_m1_id[1,1]
0
$m4b
$m4b$m1
(Intercept)
0
abs(C1 - C2)
0
log(C1)
0
(Intercept)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
D_m1_id[1,1]
0
$m4c
$m4c$m1
(Intercept) C1 B21 (Intercept) C1 B21
0 0 0 0 0 0
time c1 time c1 D_m1_id[1,1] D_m1_id[1,2]
0 0 0 0 0 0
D_m1_id[2,2] D_m1_id[1,3] D_m1_id[2,3] D_m1_id[3,3] D_m1_id[1,4] D_m1_id[2,4]
0 0 0 0 0 0
D_m1_id[3,4] D_m1_id[4,4]
0 0
$m4d
$m4d$m1
(Intercept) C1 (Intercept) C1 time I(time^2)
0 0 0 0 0 0
b21 c1 C1:time b21:c1 time I(time^2)
0 0 0 0 0 0
b21 c1 C1:time b21:c1 D_m1_id[1,1] D_m1_id[1,2]
0 0 0 0 0 0
D_m1_id[2,2]
0
$m4e
$m4e$m1
(Intercept) C1 (Intercept) C1 log(time) I(time^2)
0 0 0 0 0 0
p1 log(time) I(time^2) p1 D_m1_id[1,1]
0 0 0 0 0
Code
lapply(models0, confint)
Output
$m0a
$m0a$m1
2.5% 97.5%
(Intercept) 0 0
(Intercept) 0 0
D_m1_id[1,1] 0 0
$m0b
$m0b$m2
2.5% 97.5%
(Intercept) 0 0
(Intercept) 0 0
D_m2_id[1,1] 0 0
$m1a
$m1a$m1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
(Intercept) 0 0
C1 0 0
D_m1_id[1,1] 0 0
$m1b
$m1b$m2
2.5% 97.5%
(Intercept) 0 0
C1 0 0
(Intercept) 0 0
C1 0 0
D_m2_id[1,1] 0 0
$m1c
$m1c$m1
2.5% 97.5%
(Intercept) 0 0
(Intercept) 0 0
c1 0 0
c1 0 0
D_m1_id[1,1] 0 0
$m1d
$m1d$m2
2.5% 97.5%
(Intercept) 0 0
(Intercept) 0 0
c1 0 0
c1 0 0
D_m2_id[1,1] 0 0
$m2a
$m2a$m1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
(Intercept) 0 0
C2 0 0
D_m1_id[1,1] 0 0
$m2b
$m2b$m2
2.5% 97.5%
(Intercept) 0 0
C2 0 0
(Intercept) 0 0
C2 0 0
D_m2_id[1,1] 0 0
$m2c
$m2c$m1
2.5% 97.5%
(Intercept) 0 0
(Intercept) 0 0
c2 0 0
c2 0 0
D_m1_id[1,1] 0 0
$m2d
$m2d$m2
2.5% 97.5%
(Intercept) 0 0
(Intercept) 0 0
c2 0 0
c2 0 0
D_m2_id[1,1] 0 0
$m3a
$m3a$c1
2.5% 97.5%
(Intercept) 0 0
m1B 0 0
m1C 0 0
sigma_c1 0 0
D_c1_id[1,1] 0 0
$m3b
$m3b$c1
2.5% 97.5%
(Intercept) 0 0
m2B 0 0
m2C 0 0
sigma_c1 0 0
D_c1_id[1,1] 0 0
$m4a
$m4a$m1
2.5% 97.5%
(Intercept) 0 0
M22 0 0
M23 0 0
M24 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
(Intercept) 0 0
M22 0 0
M23 0 0
M24 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
m2B 0 0
m2C 0 0
m2B:abs(C1 - C2) 0 0
m2C:abs(C1 - C2) 0 0
m2B 0 0
m2C 0 0
m2B:abs(C1 - C2) 0 0
m2C:abs(C1 - C2) 0 0
D_m1_id[1,1] 0 0
$m4b
$m4b$m1
2.5% 97.5%
(Intercept) 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
(Intercept) 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0
ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0
D_m1_id[1,1] 0 0
$m4c
$m4c$m1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
B21 0 0
(Intercept) 0 0
C1 0 0
B21 0 0
time 0 0
c1 0 0
time 0 0
c1 0 0
D_m1_id[1,1] 0 0
D_m1_id[1,2] 0 0
D_m1_id[2,2] 0 0
D_m1_id[1,3] 0 0
D_m1_id[2,3] 0 0
D_m1_id[3,3] 0 0
D_m1_id[1,4] 0 0
D_m1_id[2,4] 0 0
D_m1_id[3,4] 0 0
D_m1_id[4,4] 0 0
$m4d
$m4d$m1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
(Intercept) 0 0
C1 0 0
time 0 0
I(time^2) 0 0
b21 0 0
c1 0 0
C1:time 0 0
b21:c1 0 0
time 0 0
I(time^2) 0 0
b21 0 0
c1 0 0
C1:time 0 0
b21:c1 0 0
D_m1_id[1,1] 0 0
D_m1_id[1,2] 0 0
D_m1_id[2,2] 0 0
$m4e
$m4e$m1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
(Intercept) 0 0
C1 0 0
log(time) 0 0
I(time^2) 0 0
p1 0 0
log(time) 0 0
I(time^2) 0 0
p1 0 0
D_m1_id[1,1] 0 0
Code
lapply(models0, summary)
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"
$m0a
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ 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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1C: (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_m1_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
$m0b
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m2 ~ 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
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2C: (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_m2_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
$m1a
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ 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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C1 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: 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_m1_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
$m1b
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m2 ~ 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
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2B: C1 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
m2C: 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_m2_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
$m1c
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ 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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1B: c1 0 0 0 0 0 NaN NaN
m1C: 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_m1_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
$m1d
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m2 ~ 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
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
m2B: c1 0 0 0 0 0 NaN NaN
m2C: 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_m2_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
$m2a
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ 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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C2 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: 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_m1_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
$m2b
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m2 ~ 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
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2B: C2 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
m2C: 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_m2_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
$m2c
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ 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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1B: c2 0 0 0 0 0 NaN NaN
m1C: 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_m1_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
$m2d
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m2 ~ 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
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
m2B: c2 0 0 0 0 0 NaN NaN
m2C: 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_m2_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
$m3a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = c1 ~ m1 + (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
m1B 0 0 0 0 0 NaN NaN
m1C 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 = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
$m3b
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = c1 ~ m2 + (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
m2B 0 0 0 0 0 NaN NaN
m2C 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
$m4a
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ M2 + m2 * abs(C1 - C2) + log(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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: M22 0 0 0 0 0 NaN NaN
m1B: M23 0 0 0 0 0 NaN NaN
m1B: M24 0 0 0 0 0 NaN NaN
m1B: abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1B: log(C1) 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: M22 0 0 0 0 0 NaN NaN
m1C: M23 0 0 0 0 0 NaN NaN
m1C: M24 0 0 0 0 0 NaN NaN
m1C: abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1C: log(C1) 0 0 0 0 0 NaN NaN
m1B: m2B 0 0 0 0 0 NaN NaN
m1B: m2C 0 0 0 0 0 NaN NaN
m1B: m2B:abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1B: m2C:abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1C: m2B 0 0 0 0 0 NaN NaN
m1C: m2C 0 0 0 0 0 NaN NaN
m1C: m2B:abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1C: m2C: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_m1_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
$m4b
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ ifelse(as.numeric(m2) > as.numeric(M1),
1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5%
m1B: (Intercept) 0 0 0
m1B: abs(C1 - C2) 0 0 0
m1B: log(C1) 0 0 0
m1C: (Intercept) 0 0 0
m1C: abs(C1 - C2) 0 0 0
m1C: log(C1) 0 0 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0
97.5%
m1B: (Intercept) 0
m1B: abs(C1 - C2) 0
m1B: log(C1) 0
m1C: (Intercept) 0
m1C: abs(C1 - C2) 0
m1C: log(C1) 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0
tail-prob.
m1B: (Intercept) 0
m1B: abs(C1 - C2) 0
m1B: log(C1) 0
m1C: (Intercept) 0
m1C: abs(C1 - C2) 0
m1C: log(C1) 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0
GR-crit MCE/SD
m1B: (Intercept) NaN NaN
m1B: abs(C1 - C2) NaN NaN
m1B: log(C1) NaN NaN
m1C: (Intercept) NaN NaN
m1C: abs(C1 - C2) NaN NaN
m1C: log(C1) NaN NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_m1_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
$m4c
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ time + c1 + C1 + B2 + (c1 * 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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C1 0 0 0 0 0 NaN NaN
m1B: B21 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: C1 0 0 0 0 0 NaN NaN
m1C: B21 0 0 0 0 0 NaN NaN
m1B: time 0 0 0 0 0 NaN NaN
m1B: c1 0 0 0 0 0 NaN NaN
m1C: time 0 0 0 0 0 NaN NaN
m1C: 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_m1_id[1,1] 0 0 0 0 NaN NaN
D_m1_id[1,2] 0 0 0 0 0 NaN NaN
D_m1_id[2,2] 0 0 0 0 NaN NaN
D_m1_id[1,3] 0 0 0 0 0 NaN NaN
D_m1_id[2,3] 0 0 0 0 0 NaN NaN
D_m1_id[3,3] 0 0 0 0 NaN NaN
D_m1_id[1,4] 0 0 0 0 0 NaN NaN
D_m1_id[2,4] 0 0 0 0 0 NaN NaN
D_m1_id[3,4] 0 0 0 0 0 NaN NaN
D_m1_id[4,4] 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
$m4d
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ C1 * time + I(time^2) + b2 * c1, 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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C1 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: C1 0 0 0 0 0 NaN NaN
m1B: time 0 0 0 0 0 NaN NaN
m1B: I(time^2) 0 0 0 0 0 NaN NaN
m1B: b21 0 0 0 0 0 NaN NaN
m1B: c1 0 0 0 0 0 NaN NaN
m1B: C1:time 0 0 0 0 0 NaN NaN
m1B: b21:c1 0 0 0 0 0 NaN NaN
m1C: time 0 0 0 0 0 NaN NaN
m1C: I(time^2) 0 0 0 0 0 NaN NaN
m1C: b21 0 0 0 0 0 NaN NaN
m1C: c1 0 0 0 0 0 NaN NaN
m1C: C1:time 0 0 0 0 0 NaN NaN
m1C: 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_m1_id[1,1] 0 0 0 0 NaN NaN
D_m1_id[1,2] 0 0 0 0 0 NaN NaN
D_m1_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
$m4e
Bayesian multinomial logit mixed model fitted with JointAI
Call:
mlogitmm_imp(fixed = m1 ~ C1 + log(time) + I(time^2) + p1, data = longDF,
random = ~1 | id, 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
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C1 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: C1 0 0 0 0 0 NaN NaN
m1B: log(time) 0 0 0 0 0 NaN NaN
m1B: I(time^2) 0 0 0 0 0 NaN NaN
m1B: p1 0 0 0 0 0 NaN NaN
m1C: log(time) 0 0 0 0 0 NaN NaN
m1C: I(time^2) 0 0 0 0 0 NaN NaN
m1C: p1 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_m1_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
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"
$m0a
$m0a$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
$m0b
$m0b$m2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
$m1a
$m1a$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C1 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: C1 0 0 0 0 0 NaN NaN
$m1b
$m1b$m2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2B: C1 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
m2C: C1 0 0 0 0 0 NaN NaN
$m1c
$m1c$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1B: c1 0 0 0 0 0 NaN NaN
m1C: c1 0 0 0 0 0 NaN NaN
$m1d
$m1d$m2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
m2B: c1 0 0 0 0 0 NaN NaN
m2C: c1 0 0 0 0 0 NaN NaN
$m2a
$m2a$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C2 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: C2 0 0 0 0 0 NaN NaN
$m2b
$m2b$m2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2B: C2 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
m2C: C2 0 0 0 0 0 NaN NaN
$m2c
$m2c$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1B: c2 0 0 0 0 0 NaN NaN
m1C: c2 0 0 0 0 0 NaN NaN
$m2d
$m2d$m2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m2B: (Intercept) 0 0 0 0 0 NaN NaN
m2C: (Intercept) 0 0 0 0 0 NaN NaN
m2B: c2 0 0 0 0 0 NaN NaN
m2C: c2 0 0 0 0 0 NaN NaN
$m3a
$m3a$c1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
m1B 0 0 0 0 0 NaN NaN
m1C 0 0 0 0 0 NaN NaN
$m3b
$m3b$c1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
m2B 0 0 0 0 0 NaN NaN
m2C 0 0 0 0 0 NaN NaN
$m4a
$m4a$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: M22 0 0 0 0 0 NaN NaN
m1B: M23 0 0 0 0 0 NaN NaN
m1B: M24 0 0 0 0 0 NaN NaN
m1B: abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1B: log(C1) 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: M22 0 0 0 0 0 NaN NaN
m1C: M23 0 0 0 0 0 NaN NaN
m1C: M24 0 0 0 0 0 NaN NaN
m1C: abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1C: log(C1) 0 0 0 0 0 NaN NaN
m1B: m2B 0 0 0 0 0 NaN NaN
m1B: m2C 0 0 0 0 0 NaN NaN
m1B: m2B:abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1B: m2C:abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1C: m2B 0 0 0 0 0 NaN NaN
m1C: m2C 0 0 0 0 0 NaN NaN
m1C: m2B:abs(C1 - C2) 0 0 0 0 0 NaN NaN
m1C: m2C:abs(C1 - C2) 0 0 0 0 0 NaN NaN
$m4b
$m4b$m1
Mean SD 2.5%
m1B: (Intercept) 0 0 0
m1B: abs(C1 - C2) 0 0 0
m1B: log(C1) 0 0 0
m1C: (Intercept) 0 0 0
m1C: abs(C1 - C2) 0 0 0
m1C: log(C1) 0 0 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0 0 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0
97.5%
m1B: (Intercept) 0
m1B: abs(C1 - C2) 0
m1B: log(C1) 0
m1C: (Intercept) 0
m1C: abs(C1 - C2) 0
m1C: log(C1) 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0
tail-prob.
m1B: (Intercept) 0
m1B: abs(C1 - C2) 0
m1B: log(C1) 0
m1C: (Intercept) 0
m1C: abs(C1 - C2) 0
m1C: log(C1) 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) 0
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0
GR-crit MCE/SD
m1B: (Intercept) NaN NaN
m1B: abs(C1 - C2) NaN NaN
m1B: log(C1) NaN NaN
m1C: (Intercept) NaN NaN
m1C: abs(C1 - C2) NaN NaN
m1C: log(C1) NaN NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN NaN
m1B: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0) NaN NaN
m1C: ifelse(as.numeric(m2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN NaN
$m4c
$m4c$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C1 0 0 0 0 0 NaN NaN
m1B: B21 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: C1 0 0 0 0 0 NaN NaN
m1C: B21 0 0 0 0 0 NaN NaN
m1B: time 0 0 0 0 0 NaN NaN
m1B: c1 0 0 0 0 0 NaN NaN
m1C: time 0 0 0 0 0 NaN NaN
m1C: c1 0 0 0 0 0 NaN NaN
$m4d
$m4d$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C1 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: C1 0 0 0 0 0 NaN NaN
m1B: time 0 0 0 0 0 NaN NaN
m1B: I(time^2) 0 0 0 0 0 NaN NaN
m1B: b21 0 0 0 0 0 NaN NaN
m1B: c1 0 0 0 0 0 NaN NaN
m1B: C1:time 0 0 0 0 0 NaN NaN
m1B: b21:c1 0 0 0 0 0 NaN NaN
m1C: time 0 0 0 0 0 NaN NaN
m1C: I(time^2) 0 0 0 0 0 NaN NaN
m1C: b21 0 0 0 0 0 NaN NaN
m1C: c1 0 0 0 0 0 NaN NaN
m1C: C1:time 0 0 0 0 0 NaN NaN
m1C: b21:c1 0 0 0 0 0 NaN NaN
$m4e
$m4e$m1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
m1B: (Intercept) 0 0 0 0 0 NaN NaN
m1B: C1 0 0 0 0 0 NaN NaN
m1C: (Intercept) 0 0 0 0 0 NaN NaN
m1C: C1 0 0 0 0 0 NaN NaN
m1B: log(time) 0 0 0 0 0 NaN NaN
m1B: I(time^2) 0 0 0 0 0 NaN NaN
m1B: p1 0 0 0 0 0 NaN NaN
m1C: log(time) 0 0 0 0 0 NaN NaN
m1C: I(time^2) 0 0 0 0 0 NaN NaN
m1C: p1 0 0 0 0 0 NaN NaN
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