Nothing
tests/testthat/_snaps/clmm.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
o1
1 2
1.1 1
1.2 1
1.3 2
2 3
2.1 1
2.2 2
3 1
3.1 3
3.2 2
4 3
4.1 2
4.2 1
4.3 2
5 2
5.1 2
5.2 3
5.3 2
6 3
7 3
7.1 1
7.2 1
8 2
8.1 2
8.2 3
8.3 3
8.4 2
8.5 2
9 2
9.1 2
9.2 3
10 2
10.1 1
11 2
11.1 3
11.2 3
11.3 1
11.4 3
12 1
13 2
13.1 1
14 3
14.1 3
14.2 2
14.3 1
15 3
15.1 3
15.2 2
15.3 2
16 3
16.1 1
16.2 3
16.3 1
16.4 1
16.5 2
17 2
17.1 2
17.2 3
17.3 2
17.4 3
18 2
19 3
19.1 2
19.2 2
19.3 2
20 1
20.1 2
20.2 2
20.3 1
20.4 1
20.5 2
21 2
21.1 2
21.2 2
22 1
22.1 1
23 1
23.1 2
24 1
25 2
25.1 1
25.2 1
25.3 1
25.4 2
25.5 2
26 2
26.1 3
26.2 2
26.3 1
27 2
27.1 2
28 1
28.1 1
28.2 1
28.3 1
29 2
29.1 2
29.2 1
29.3 2
30 2
30.1 2
30.2 2
31 2
32 3
32.1 1
32.2 3
32.3 3
33 2
33.1 2
34 3
34.1 2
34.2 2
34.3 1
35 2
35.1 3
35.2 2
36 3
36.1 2
36.2 3
36.3 3
36.4 3
37 3
37.1 3
37.2 3
38 2
39 1
39.1 2
39.2 2
39.3 1
39.4 1
39.5 1
40 3
40.1 1
40.2 1
40.3 2
41 3
41.1 2
41.2 1
41.3 1
41.4 3
42 2
42.1 1
43 2
43.1 2
43.2 1
44 3
44.1 2
44.2 1
44.3 1
45 1
45.1 1
46 1
46.1 2
46.2 3
47 1
47.1 1
47.2 1
47.3 3
47.4 2
48 3
48.1 2
49 1
50 2
51 2
52 2
52.1 3
52.2 2
52.3 1
52.4 3
52.5 2
53 2
53.1 1
53.2 1
54 1
54.1 2
54.2 3
54.3 1
54.4 2
55 1
55.1 1
55.2 2
55.3 1
55.4 1
56 3
56.1 2
56.2 3
56.3 3
56.4 1
56.5 3
57 2
57.1 2
57.2 2
57.3 2
58 3
58.1 2
58.2 3
58.3 3
58.4 2
58.5 3
59 3
59.1 1
60 3
61 1
61.1 2
61.2 2
61.3 1
61.4 2
62 2
62.1 3
62.2 1
62.3 2
63 1
63.1 3
64 2
65 1
65.1 2
65.2 1
65.3 1
66 1
66.1 1
66.2 3
67 3
68 1
68.1 2
68.2 2
68.3 2
68.4 2
69 2
70 1
70.1 3
71 3
71.1 3
71.2 3
71.3 2
71.4 2
72 1
72.1 1
72.2 2
72.3 2
72.4 1
72.5 1
73 1
74 1
75 3
76 3
76.1 3
76.2 1
77 2
78 2
79 1
79.1 2
79.2 1
80 2
80.1 2
80.2 3
81 3
81.1 2
81.2 3
81.3 3
82 3
82.1 2
82.2 2
83 3
83.1 3
83.2 3
83.3 2
84 3
84.1 3
85 3
85.1 2
85.2 2
85.3 2
85.4 1
85.5 3
86 2
86.1 3
86.2 1
86.3 1
86.4 1
86.5 3
87 1
87.1 1
87.2 1
88 3
88.1 1
88.2 1
88.3 1
89 1
90 2
90.1 1
90.2 3
90.3 1
91 1
91.1 2
91.2 1
92 2
93 1
93.1 3
93.2 1
93.3 3
93.4 1
94 2
94.1 3
94.2 1
94.3 3
94.4 2
94.5 3
95 3
95.1 1
95.2 2
96 1
96.1 3
96.2 1
96.3 1
96.4 2
96.5 3
97 3
97.1 3
98 3
98.1 1
98.2 1
99 2
99.1 2
99.2 1
100 1
100.1 2
100.2 2
100.3 1
100.4 1
$m0a$mu_delta_ordinal
[1] 0
$m0a$tau_delta_ordinal
[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
o2
1 1
1.1 1
1.2 3
1.3 1
2 4
2.1 4
2.2 2
3 2
3.1 4
3.2 2
4 4
4.1 3
4.2 NA
4.3 2
5 2
5.1 4
5.2 2
5.3 4
6 3
7 1
7.1 NA
7.2 4
8 1
8.1 3
8.2 1
8.3 4
8.4 3
8.5 3
9 2
9.1 2
9.2 4
10 1
10.1 4
11 3
11.1 1
11.2 4
11.3 3
11.4 3
12 3
13 NA
13.1 1
14 1
14.1 4
14.2 3
14.3 4
15 1
15.1 4
15.2 NA
15.3 2
16 NA
16.1 NA
16.2 1
16.3 3
16.4 3
16.5 1
17 3
17.1 2
17.2 2
17.3 3
17.4 1
18 4
19 1
19.1 NA
19.2 NA
19.3 2
20 1
20.1 4
20.2 3
20.3 3
20.4 1
20.5 3
21 3
21.1 1
21.2 2
22 4
22.1 NA
23 4
23.1 NA
24 3
25 1
25.1 3
25.2 2
25.3 1
25.4 1
25.5 NA
26 3
26.1 4
26.2 3
26.3 1
27 4
27.1 4
28 1
28.1 2
28.2 3
28.3 3
29 4
29.1 4
29.2 3
29.3 2
30 3
30.1 4
30.2 4
31 2
32 NA
32.1 2
32.2 4
32.3 3
33 4
33.1 4
34 NA
34.1 NA
34.2 NA
34.3 NA
35 4
35.1 1
35.2 NA
36 1
36.1 1
36.2 2
36.3 2
36.4 1
37 4
37.1 2
37.2 2
38 NA
39 NA
39.1 2
39.2 2
39.3 3
39.4 3
39.5 1
40 1
40.1 2
40.2 NA
40.3 2
41 4
41.1 3
41.2 4
41.3 NA
41.4 4
42 4
42.1 4
43 NA
43.1 NA
43.2 3
44 2
44.1 4
44.2 NA
44.3 1
45 3
45.1 4
46 NA
46.1 4
46.2 4
47 3
47.1 2
47.2 1
47.3 2
47.4 1
48 4
48.1 NA
49 4
50 4
51 4
52 NA
52.1 NA
52.2 3
52.3 NA
52.4 4
52.5 1
53 2
53.1 1
53.2 3
54 3
54.1 4
54.2 4
54.3 3
54.4 NA
55 4
55.1 1
55.2 4
55.3 NA
55.4 1
56 1
56.1 2
56.2 2
56.3 3
56.4 4
56.5 4
57 2
57.1 2
57.2 4
57.3 NA
58 1
58.1 2
58.2 2
58.3 4
58.4 1
58.5 NA
59 4
59.1 1
60 1
61 1
61.1 1
61.2 NA
61.3 1
61.4 3
62 NA
62.1 NA
62.2 NA
62.3 3
63 4
63.1 3
64 4
65 2
65.1 1
65.2 3
65.3 NA
66 1
66.1 3
66.2 2
67 3
68 3
68.1 4
68.2 3
68.3 1
68.4 4
69 4
70 4
70.1 4
71 2
71.1 NA
71.2 4
71.3 3
71.4 1
72 1
72.1 NA
72.2 4
72.3 1
72.4 3
72.5 1
73 2
74 4
75 1
76 2
76.1 1
76.2 1
77 3
78 3
79 NA
79.1 3
79.2 NA
80 2
80.1 3
80.2 1
81 3
81.1 NA
81.2 3
81.3 2
82 NA
82.1 3
82.2 1
83 4
83.1 NA
83.2 2
83.3 NA
84 2
84.1 1
85 1
85.1 4
85.2 3
85.3 3
85.4 NA
85.5 2
86 1
86.1 3
86.2 1
86.3 2
86.4 3
86.5 4
87 NA
87.1 3
87.2 3
88 NA
88.1 1
88.2 2
88.3 NA
89 3
90 2
90.1 2
90.2 2
90.3 4
91 2
91.1 NA
91.2 3
92 2
93 3
93.1 2
93.2 3
93.3 2
93.4 4
94 NA
94.1 2
94.2 NA
94.3 3
94.4 4
94.5 3
95 NA
95.1 2
95.2 3
96 3
96.1 NA
96.2 4
96.3 3
96.4 NA
96.5 1
97 2
97.1 1
98 2
98.1 1
98.2 3
99 NA
99.1 NA
99.2 4
100 1
100.1 NA
100.2 1
100.3 4
100.4 1
$m0b$mu_delta_ordinal
[1] 0
$m0b$tau_delta_ordinal
[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
o1
1 2
1.1 1
1.2 1
1.3 2
2 3
2.1 1
2.2 2
3 1
3.1 3
3.2 2
4 3
4.1 2
4.2 1
4.3 2
5 2
5.1 2
5.2 3
5.3 2
6 3
7 3
7.1 1
7.2 1
8 2
8.1 2
8.2 3
8.3 3
8.4 2
8.5 2
9 2
9.1 2
9.2 3
10 2
10.1 1
11 2
11.1 3
11.2 3
11.3 1
11.4 3
12 1
13 2
13.1 1
14 3
14.1 3
14.2 2
14.3 1
15 3
15.1 3
15.2 2
15.3 2
16 3
16.1 1
16.2 3
16.3 1
16.4 1
16.5 2
17 2
17.1 2
17.2 3
17.3 2
17.4 3
18 2
19 3
19.1 2
19.2 2
19.3 2
20 1
20.1 2
20.2 2
20.3 1
20.4 1
20.5 2
21 2
21.1 2
21.2 2
22 1
22.1 1
23 1
23.1 2
24 1
25 2
25.1 1
25.2 1
25.3 1
25.4 2
25.5 2
26 2
26.1 3
26.2 2
26.3 1
27 2
27.1 2
28 1
28.1 1
28.2 1
28.3 1
29 2
29.1 2
29.2 1
29.3 2
30 2
30.1 2
30.2 2
31 2
32 3
32.1 1
32.2 3
32.3 3
33 2
33.1 2
34 3
34.1 2
34.2 2
34.3 1
35 2
35.1 3
35.2 2
36 3
36.1 2
36.2 3
36.3 3
36.4 3
37 3
37.1 3
37.2 3
38 2
39 1
39.1 2
39.2 2
39.3 1
39.4 1
39.5 1
40 3
40.1 1
40.2 1
40.3 2
41 3
41.1 2
41.2 1
41.3 1
41.4 3
42 2
42.1 1
43 2
43.1 2
43.2 1
44 3
44.1 2
44.2 1
44.3 1
45 1
45.1 1
46 1
46.1 2
46.2 3
47 1
47.1 1
47.2 1
47.3 3
47.4 2
48 3
48.1 2
49 1
50 2
51 2
52 2
52.1 3
52.2 2
52.3 1
52.4 3
52.5 2
53 2
53.1 1
53.2 1
54 1
54.1 2
54.2 3
54.3 1
54.4 2
55 1
55.1 1
55.2 2
55.3 1
55.4 1
56 3
56.1 2
56.2 3
56.3 3
56.4 1
56.5 3
57 2
57.1 2
57.2 2
57.3 2
58 3
58.1 2
58.2 3
58.3 3
58.4 2
58.5 3
59 3
59.1 1
60 3
61 1
61.1 2
61.2 2
61.3 1
61.4 2
62 2
62.1 3
62.2 1
62.3 2
63 1
63.1 3
64 2
65 1
65.1 2
65.2 1
65.3 1
66 1
66.1 1
66.2 3
67 3
68 1
68.1 2
68.2 2
68.3 2
68.4 2
69 2
70 1
70.1 3
71 3
71.1 3
71.2 3
71.3 2
71.4 2
72 1
72.1 1
72.2 2
72.3 2
72.4 1
72.5 1
73 1
74 1
75 3
76 3
76.1 3
76.2 1
77 2
78 2
79 1
79.1 2
79.2 1
80 2
80.1 2
80.2 3
81 3
81.1 2
81.2 3
81.3 3
82 3
82.1 2
82.2 2
83 3
83.1 3
83.2 3
83.3 2
84 3
84.1 3
85 3
85.1 2
85.2 2
85.3 2
85.4 1
85.5 3
86 2
86.1 3
86.2 1
86.3 1
86.4 1
86.5 3
87 1
87.1 1
87.2 1
88 3
88.1 1
88.2 1
88.3 1
89 1
90 2
90.1 1
90.2 3
90.3 1
91 1
91.1 2
91.2 1
92 2
93 1
93.1 3
93.2 1
93.3 3
93.4 1
94 2
94.1 3
94.2 1
94.3 3
94.4 2
94.5 3
95 3
95.1 1
95.2 2
96 1
96.1 3
96.2 1
96.3 1
96.4 2
96.5 3
97 3
97.1 3
98 3
98.1 1
98.2 1
99 2
99.1 2
99.2 1
100 1
100.1 2
100.2 2
100.3 1
100.4 1
$m1a$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1a$mu_reg_ordinal
[1] 0
$m1a$tau_reg_ordinal
[1] 1e-04
$m1a$mu_delta_ordinal
[1] 0
$m1a$tau_delta_ordinal
[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
o2
1 1
1.1 1
1.2 3
1.3 1
2 4
2.1 4
2.2 2
3 2
3.1 4
3.2 2
4 4
4.1 3
4.2 NA
4.3 2
5 2
5.1 4
5.2 2
5.3 4
6 3
7 1
7.1 NA
7.2 4
8 1
8.1 3
8.2 1
8.3 4
8.4 3
8.5 3
9 2
9.1 2
9.2 4
10 1
10.1 4
11 3
11.1 1
11.2 4
11.3 3
11.4 3
12 3
13 NA
13.1 1
14 1
14.1 4
14.2 3
14.3 4
15 1
15.1 4
15.2 NA
15.3 2
16 NA
16.1 NA
16.2 1
16.3 3
16.4 3
16.5 1
17 3
17.1 2
17.2 2
17.3 3
17.4 1
18 4
19 1
19.1 NA
19.2 NA
19.3 2
20 1
20.1 4
20.2 3
20.3 3
20.4 1
20.5 3
21 3
21.1 1
21.2 2
22 4
22.1 NA
23 4
23.1 NA
24 3
25 1
25.1 3
25.2 2
25.3 1
25.4 1
25.5 NA
26 3
26.1 4
26.2 3
26.3 1
27 4
27.1 4
28 1
28.1 2
28.2 3
28.3 3
29 4
29.1 4
29.2 3
29.3 2
30 3
30.1 4
30.2 4
31 2
32 NA
32.1 2
32.2 4
32.3 3
33 4
33.1 4
34 NA
34.1 NA
34.2 NA
34.3 NA
35 4
35.1 1
35.2 NA
36 1
36.1 1
36.2 2
36.3 2
36.4 1
37 4
37.1 2
37.2 2
38 NA
39 NA
39.1 2
39.2 2
39.3 3
39.4 3
39.5 1
40 1
40.1 2
40.2 NA
40.3 2
41 4
41.1 3
41.2 4
41.3 NA
41.4 4
42 4
42.1 4
43 NA
43.1 NA
43.2 3
44 2
44.1 4
44.2 NA
44.3 1
45 3
45.1 4
46 NA
46.1 4
46.2 4
47 3
47.1 2
47.2 1
47.3 2
47.4 1
48 4
48.1 NA
49 4
50 4
51 4
52 NA
52.1 NA
52.2 3
52.3 NA
52.4 4
52.5 1
53 2
53.1 1
53.2 3
54 3
54.1 4
54.2 4
54.3 3
54.4 NA
55 4
55.1 1
55.2 4
55.3 NA
55.4 1
56 1
56.1 2
56.2 2
56.3 3
56.4 4
56.5 4
57 2
57.1 2
57.2 4
57.3 NA
58 1
58.1 2
58.2 2
58.3 4
58.4 1
58.5 NA
59 4
59.1 1
60 1
61 1
61.1 1
61.2 NA
61.3 1
61.4 3
62 NA
62.1 NA
62.2 NA
62.3 3
63 4
63.1 3
64 4
65 2
65.1 1
65.2 3
65.3 NA
66 1
66.1 3
66.2 2
67 3
68 3
68.1 4
68.2 3
68.3 1
68.4 4
69 4
70 4
70.1 4
71 2
71.1 NA
71.2 4
71.3 3
71.4 1
72 1
72.1 NA
72.2 4
72.3 1
72.4 3
72.5 1
73 2
74 4
75 1
76 2
76.1 1
76.2 1
77 3
78 3
79 NA
79.1 3
79.2 NA
80 2
80.1 3
80.2 1
81 3
81.1 NA
81.2 3
81.3 2
82 NA
82.1 3
82.2 1
83 4
83.1 NA
83.2 2
83.3 NA
84 2
84.1 1
85 1
85.1 4
85.2 3
85.3 3
85.4 NA
85.5 2
86 1
86.1 3
86.2 1
86.3 2
86.4 3
86.5 4
87 NA
87.1 3
87.2 3
88 NA
88.1 1
88.2 2
88.3 NA
89 3
90 2
90.1 2
90.2 2
90.3 4
91 2
91.1 NA
91.2 3
92 2
93 3
93.1 2
93.2 3
93.3 2
93.4 4
94 NA
94.1 2
94.2 NA
94.3 3
94.4 4
94.5 3
95 NA
95.1 2
95.2 3
96 3
96.1 NA
96.2 4
96.3 3
96.4 NA
96.5 1
97 2
97.1 1
98 2
98.1 1
98.2 3
99 NA
99.1 NA
99.2 4
100 1
100.1 NA
100.2 1
100.3 4
100.4 1
$m1b$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1b$mu_reg_ordinal
[1] 0
$m1b$tau_reg_ordinal
[1] 1e-04
$m1b$mu_delta_ordinal
[1] 0
$m1b$tau_delta_ordinal
[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
o1 c1
1 2 0.7592026489
1.1 1 0.9548337990
1.2 1 0.5612235156
1.3 2 1.1873391025
2 3 0.9192204198
2.1 1 -0.1870730476
2.2 2 1.2517512331
3 1 -0.0605087604
3.1 3 0.3788637747
3.2 2 0.9872578281
4 3 1.4930175328
4.1 2 -0.7692526880
4.2 1 0.9180841450
4.3 2 -0.0541170782
5 2 -0.1376784521
5.1 2 -0.2740585866
5.2 3 0.4670496929
5.3 2 0.1740288049
6 3 0.9868044683
7 3 -0.1280320918
7.1 1 0.4242971219
7.2 1 0.0777182491
8 2 -0.5791408712
8.1 2 0.3128604232
8.2 3 0.6258446356
8.3 3 -0.1040137707
8.4 2 0.0481450285
8.5 2 0.3831763675
9 2 -0.1757592269
9.1 2 -0.1791541200
9.2 3 -0.0957042935
10 2 -0.5598409704
10.1 1 -0.2318340451
11 2 0.5086859475
11.1 3 0.4951758188
11.2 3 -1.1022162541
11.3 1 -0.0611636705
11.4 3 -0.4971774316
12 1 -0.2433996286
13 2 0.8799673116
13.1 1 0.1079022586
14 3 0.9991752617
14.1 3 -0.1094019046
14.2 2 0.1518967560
14.3 1 0.3521012473
15 3 0.3464447888
15.1 3 -0.4767313971
15.2 2 0.5759767791
15.3 2 -0.1713452662
16 3 0.4564754473
16.1 1 1.0652558311
16.2 3 0.6971872493
16.3 1 0.5259331838
16.4 1 0.2046601798
16.5 2 1.0718540464
17 2 0.6048676222
17.1 2 0.2323298304
17.2 3 1.2617499032
17.3 2 -0.3913230895
17.4 3 0.9577299112
18 2 -0.0050324072
19 3 -0.4187468937
19.1 2 -0.4478828944
19.2 2 -1.1966721302
19.3 2 -0.5877091668
20 1 0.6838223064
20.1 2 0.3278571109
20.2 2 -0.8489831990
20.3 1 1.3169975191
20.4 1 0.0444804531
20.5 2 -0.4535207652
21 2 -0.4030302960
21.1 2 -0.4069674045
21.2 2 1.0650265940
22 1 -0.0673274516
22.1 1 0.9601388170
23 1 0.5556634840
23.1 2 1.4407865964
24 1 0.3856376411
25 2 0.3564400705
25.1 1 0.0982553434
25.2 1 0.1928682598
25.3 1 -0.0192488594
25.4 2 0.4466012931
25.5 2 1.1425193342
26 2 0.5341531449
26.1 3 1.2268695927
26.2 2 0.3678294939
26.3 1 0.5948516018
27 2 -0.3342844147
27.1 2 -0.4835141229
28 1 -0.7145915499
28.1 1 0.5063671955
28.2 1 -0.2067413142
28.3 1 0.1196789973
29 2 0.1392699487
29.1 2 0.7960234776
29.2 1 1.0398214352
29.3 2 0.0813246429
30 2 -0.3296323050
30.1 2 1.3635850954
30.2 2 0.7354171050
31 2 0.3708398217
32 3 -0.0474059668
32.1 1 1.2507771489
32.2 3 0.1142915519
32.3 3 0.6773270619
33 2 0.1774293842
33.1 2 0.6159606291
34 3 0.8590979166
34.1 2 0.0546216775
34.2 2 -0.0897224473
34.3 1 0.4163395571
35 2 -1.4693520528
35.1 3 -0.3031734330
35.2 2 -0.6045512101
36 3 0.9823048960
36.1 2 1.4466051416
36.2 3 1.1606752905
36.3 3 0.8373091576
36.4 3 0.2640591685
37 3 0.1177313455
37.1 3 -0.1415483779
37.2 3 0.0054610124
38 2 0.8078948077
39 1 0.9876451040
39.1 2 -0.3431222274
39.2 2 -1.7909380751
39.3 1 -0.1798746191
39.4 1 -0.1850961689
39.5 1 0.4544226146
40 3 0.5350190436
40.1 1 0.4189342752
40.2 1 0.4211994981
40.3 2 0.0916687506
41 3 -0.1035047421
41.1 2 -0.4684202411
41.2 1 0.5972615368
41.3 1 0.9885613862
41.4 3 -0.3908036794
42 2 -0.0338893961
42.1 1 -0.4498363172
43 2 0.8965546110
43.1 2 0.6199122090
43.2 1 0.1804894429
44 3 1.3221409285
44.1 2 0.3416426284
44.2 1 0.5706610068
44.3 1 1.2679497430
45 1 0.1414983160
45.1 1 0.7220892521
46 1 1.5391054233
46.1 2 0.3889107049
46.2 3 0.1248719493
47 1 0.2014101100
47.1 1 0.2982973539
47.2 1 1.1518107179
47.3 3 0.5196802157
47.4 2 0.3702301552
48 3 -0.2128602862
48.1 2 -0.5337239976
49 1 -0.5236770035
50 2 0.3897705981
51 2 -0.7213343736
52 2 0.3758235358
52.1 3 0.7138067080
52.2 2 0.8872895233
52.3 1 -0.9664587437
52.4 3 0.0254566848
52.5 2 0.4155259424
53 2 0.5675736897
53.1 1 -0.3154088781
53.2 1 0.2162315769
54 1 -0.0880802382
54.1 2 0.4129127672
54.2 3 1.0119546775
54.3 1 -0.1112901990
54.4 2 0.8587727145
55 1 -0.0116453589
55.1 1 0.5835528661
55.2 2 -1.0010857254
55.3 1 -0.4796526070
55.4 1 -0.1202746964
56 3 0.5176377612
56.1 2 -1.1136932588
56.2 3 -0.0168103281
56.3 3 0.3933023606
56.4 1 0.3714625139
56.5 3 0.7811448179
57 2 -1.0868304872
57.1 2 0.8018626997
57.2 2 -0.1159517011
57.3 2 0.6785562445
58 3 1.6476207996
58.1 2 0.3402652711
58.2 3 -0.1111300753
58.3 3 -0.5409234285
58.4 2 -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 1 1.1717723589
61.4 2 -0.0629201596
62 2 -0.3979137604
62.1 3 0.6830738372
62.2 1 0.4301745954
62.3 2 -0.0333139957
63 1 0.3345678035
63.1 3 0.3643769511
64 2 0.3949911859
65 1 1.2000091513
65.1 2 0.0110122646
65.2 1 -0.5776452043
65.3 1 -0.1372183563
66 1 -0.5081302805
66.1 1 -0.1447837412
66.2 3 0.1906241379
67 3 1.6716027681
68 1 0.5691848839
68.1 2 0.1004860389
68.2 2 -0.0061241827
68.3 2 0.7443745962
68.4 2 0.8726923437
69 2 0.0381382683
70 1 0.8126204217
70.1 3 0.4691503050
71 3 -0.5529062591
71.1 3 -0.1103252087
71.2 3 1.7178492547
71.3 2 -1.0118346755
71.4 2 1.8623785017
72 1 -0.4521659275
72.1 1 0.1375317317
72.2 2 -0.4170988856
72.3 2 0.7107266765
72.4 1 0.1451969143
72.5 1 1.6298050306
73 1 -0.0307469467
74 1 0.3730017941
75 3 -0.4908003566
76 3 -0.9888876620
76.1 3 0.0003798292
76.2 1 -0.8421863763
77 2 -0.4986802480
78 2 0.0417330969
79 1 -0.3767450660
79.1 2 0.1516000028
79.2 1 -0.1888160741
80 2 -0.0041558414
80.1 2 -0.0329337062
80.2 3 0.5046816157
81 3 -0.9493950353
81.1 2 0.2443038954
81.2 3 0.6476958410
81.3 3 0.4182528210
82 3 1.1088801952
82.1 2 0.9334157763
82.2 2 0.4958140634
83 3 0.5104724530
83.1 3 -0.0513309106
83.2 3 -0.2067792494
83.3 2 -0.0534169155
84 3 -0.0255753653
84.1 3 -1.8234189877
85 3 -0.0114038622
85.1 2 -0.0577615939
85.2 2 -0.2241856342
85.3 2 -0.0520175929
85.4 1 0.2892733846
85.5 3 -0.3740417009
86 2 0.4293735089
86.1 3 -0.1363456521
86.2 1 0.1230989293
86.3 1 0.3305413955
86.4 1 2.6003411822
86.5 3 -0.1420690052
87 1 1.0457427869
87.1 1 -0.2973007190
87.2 1 0.4396872616
88 3 -0.0601928334
88.1 1 -1.0124347595
88.2 1 0.5730917016
88.3 1 -0.0029455332
89 1 1.5465903721
90 2 0.0626760573
90.1 1 1.1896872985
90.2 3 0.2597888783
90.3 1 0.6599799887
91 1 1.1213651365
91.1 2 1.2046371625
91.2 1 0.3395603754
92 2 0.4674939332
93 1 0.2677965647
93.1 3 1.6424445368
93.2 1 0.7101700066
93.3 3 1.1222322893
93.4 1 1.4628960401
94 2 -0.2904211940
94.1 3 0.0147813580
94.2 1 -0.4536774482
94.3 3 0.6793464917
94.4 2 -0.9411356550
94.5 3 0.5683867264
95 3 0.2375652188
95.1 1 0.0767152977
95.2 2 -0.6886731251
96 1 0.7813892121
96.1 3 0.3391519695
96.2 1 -0.4857246503
96.3 1 0.8771471244
96.4 2 1.9030768981
96.5 3 -0.1684332749
97 3 1.3775130083
97.1 3 -1.7323228619
98 3 -1.2648518889
98.1 1 -0.9042716241
98.2 1 -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 2 0.8861545820
100.3 1 0.1277046316
100.4 1 -0.0834577654
$m1c$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
$m1c$mu_reg_ordinal
[1] 0
$m1c$tau_reg_ordinal
[1] 1e-04
$m1c$mu_delta_ordinal
[1] 0
$m1c$tau_delta_ordinal
[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
o2 c1
1 1 0.7592026489
1.1 1 0.9548337990
1.2 3 0.5612235156
1.3 1 1.1873391025
2 4 0.9192204198
2.1 4 -0.1870730476
2.2 2 1.2517512331
3 2 -0.0605087604
3.1 4 0.3788637747
3.2 2 0.9872578281
4 4 1.4930175328
4.1 3 -0.7692526880
4.2 NA 0.9180841450
4.3 2 -0.0541170782
5 2 -0.1376784521
5.1 4 -0.2740585866
5.2 2 0.4670496929
5.3 4 0.1740288049
6 3 0.9868044683
7 1 -0.1280320918
7.1 NA 0.4242971219
7.2 4 0.0777182491
8 1 -0.5791408712
8.1 3 0.3128604232
8.2 1 0.6258446356
8.3 4 -0.1040137707
8.4 3 0.0481450285
8.5 3 0.3831763675
9 2 -0.1757592269
9.1 2 -0.1791541200
9.2 4 -0.0957042935
10 1 -0.5598409704
10.1 4 -0.2318340451
11 3 0.5086859475
11.1 1 0.4951758188
11.2 4 -1.1022162541
11.3 3 -0.0611636705
11.4 3 -0.4971774316
12 3 -0.2433996286
13 NA 0.8799673116
13.1 1 0.1079022586
14 1 0.9991752617
14.1 4 -0.1094019046
14.2 3 0.1518967560
14.3 4 0.3521012473
15 1 0.3464447888
15.1 4 -0.4767313971
15.2 NA 0.5759767791
15.3 2 -0.1713452662
16 NA 0.4564754473
16.1 NA 1.0652558311
16.2 1 0.6971872493
16.3 3 0.5259331838
16.4 3 0.2046601798
16.5 1 1.0718540464
17 3 0.6048676222
17.1 2 0.2323298304
17.2 2 1.2617499032
17.3 3 -0.3913230895
17.4 1 0.9577299112
18 4 -0.0050324072
19 1 -0.4187468937
19.1 NA -0.4478828944
19.2 NA -1.1966721302
19.3 2 -0.5877091668
20 1 0.6838223064
20.1 4 0.3278571109
20.2 3 -0.8489831990
20.3 3 1.3169975191
20.4 1 0.0444804531
20.5 3 -0.4535207652
21 3 -0.4030302960
21.1 1 -0.4069674045
21.2 2 1.0650265940
22 4 -0.0673274516
22.1 NA 0.9601388170
23 4 0.5556634840
23.1 NA 1.4407865964
24 3 0.3856376411
25 1 0.3564400705
25.1 3 0.0982553434
25.2 2 0.1928682598
25.3 1 -0.0192488594
25.4 1 0.4466012931
25.5 NA 1.1425193342
26 3 0.5341531449
26.1 4 1.2268695927
26.2 3 0.3678294939
26.3 1 0.5948516018
27 4 -0.3342844147
27.1 4 -0.4835141229
28 1 -0.7145915499
28.1 2 0.5063671955
28.2 3 -0.2067413142
28.3 3 0.1196789973
29 4 0.1392699487
29.1 4 0.7960234776
29.2 3 1.0398214352
29.3 2 0.0813246429
30 3 -0.3296323050
30.1 4 1.3635850954
30.2 4 0.7354171050
31 2 0.3708398217
32 NA -0.0474059668
32.1 2 1.2507771489
32.2 4 0.1142915519
32.3 3 0.6773270619
33 4 0.1774293842
33.1 4 0.6159606291
34 NA 0.8590979166
34.1 NA 0.0546216775
34.2 NA -0.0897224473
34.3 NA 0.4163395571
35 4 -1.4693520528
35.1 1 -0.3031734330
35.2 NA -0.6045512101
36 1 0.9823048960
36.1 1 1.4466051416
36.2 2 1.1606752905
36.3 2 0.8373091576
36.4 1 0.2640591685
37 4 0.1177313455
37.1 2 -0.1415483779
37.2 2 0.0054610124
38 NA 0.8078948077
39 NA 0.9876451040
39.1 2 -0.3431222274
39.2 2 -1.7909380751
39.3 3 -0.1798746191
39.4 3 -0.1850961689
39.5 1 0.4544226146
40 1 0.5350190436
40.1 2 0.4189342752
40.2 NA 0.4211994981
40.3 2 0.0916687506
41 4 -0.1035047421
41.1 3 -0.4684202411
41.2 4 0.5972615368
41.3 NA 0.9885613862
41.4 4 -0.3908036794
42 4 -0.0338893961
42.1 4 -0.4498363172
43 NA 0.8965546110
43.1 NA 0.6199122090
43.2 3 0.1804894429
44 2 1.3221409285
44.1 4 0.3416426284
44.2 NA 0.5706610068
44.3 1 1.2679497430
45 3 0.1414983160
45.1 4 0.7220892521
46 NA 1.5391054233
46.1 4 0.3889107049
46.2 4 0.1248719493
47 3 0.2014101100
47.1 2 0.2982973539
47.2 1 1.1518107179
47.3 2 0.5196802157
47.4 1 0.3702301552
48 4 -0.2128602862
48.1 NA -0.5337239976
49 4 -0.5236770035
50 4 0.3897705981
51 4 -0.7213343736
52 NA 0.3758235358
52.1 NA 0.7138067080
52.2 3 0.8872895233
52.3 NA -0.9664587437
52.4 4 0.0254566848
52.5 1 0.4155259424
53 2 0.5675736897
53.1 1 -0.3154088781
53.2 3 0.2162315769
54 3 -0.0880802382
54.1 4 0.4129127672
54.2 4 1.0119546775
54.3 3 -0.1112901990
54.4 NA 0.8587727145
55 4 -0.0116453589
55.1 1 0.5835528661
55.2 4 -1.0010857254
55.3 NA -0.4796526070
55.4 1 -0.1202746964
56 1 0.5176377612
56.1 2 -1.1136932588
56.2 2 -0.0168103281
56.3 3 0.3933023606
56.4 4 0.3714625139
56.5 4 0.7811448179
57 2 -1.0868304872
57.1 2 0.8018626997
57.2 4 -0.1159517011
57.3 NA 0.6785562445
58 1 1.6476207996
58.1 2 0.3402652711
58.2 2 -0.1111300753
58.3 4 -0.5409234285
58.4 1 -0.1271327672
58.5 NA 0.8713264822
59 4 0.4766421367
59.1 1 1.0028089765
60 1 0.5231452932
61 1 -0.7190130614
61.1 1 0.8353702312
61.2 NA 1.0229058138
61.3 1 1.1717723589
61.4 3 -0.0629201596
62 NA -0.3979137604
62.1 NA 0.6830738372
62.2 NA 0.4301745954
62.3 3 -0.0333139957
63 4 0.3345678035
63.1 3 0.3643769511
64 4 0.3949911859
65 2 1.2000091513
65.1 1 0.0110122646
65.2 3 -0.5776452043
65.3 NA -0.1372183563
66 1 -0.5081302805
66.1 3 -0.1447837412
66.2 2 0.1906241379
67 3 1.6716027681
68 3 0.5691848839
68.1 4 0.1004860389
68.2 3 -0.0061241827
68.3 1 0.7443745962
68.4 4 0.8726923437
69 4 0.0381382683
70 4 0.8126204217
70.1 4 0.4691503050
71 2 -0.5529062591
71.1 NA -0.1103252087
71.2 4 1.7178492547
71.3 3 -1.0118346755
71.4 1 1.8623785017
72 1 -0.4521659275
72.1 NA 0.1375317317
72.2 4 -0.4170988856
72.3 1 0.7107266765
72.4 3 0.1451969143
72.5 1 1.6298050306
73 2 -0.0307469467
74 4 0.3730017941
75 1 -0.4908003566
76 2 -0.9888876620
76.1 1 0.0003798292
76.2 1 -0.8421863763
77 3 -0.4986802480
78 3 0.0417330969
79 NA -0.3767450660
79.1 3 0.1516000028
79.2 NA -0.1888160741
80 2 -0.0041558414
80.1 3 -0.0329337062
80.2 1 0.5046816157
81 3 -0.9493950353
81.1 NA 0.2443038954
81.2 3 0.6476958410
81.3 2 0.4182528210
82 NA 1.1088801952
82.1 3 0.9334157763
82.2 1 0.4958140634
83 4 0.5104724530
83.1 NA -0.0513309106
83.2 2 -0.2067792494
83.3 NA -0.0534169155
84 2 -0.0255753653
84.1 1 -1.8234189877
85 1 -0.0114038622
85.1 4 -0.0577615939
85.2 3 -0.2241856342
85.3 3 -0.0520175929
85.4 NA 0.2892733846
85.5 2 -0.3740417009
86 1 0.4293735089
86.1 3 -0.1363456521
86.2 1 0.1230989293
86.3 2 0.3305413955
86.4 3 2.6003411822
86.5 4 -0.1420690052
87 NA 1.0457427869
87.1 3 -0.2973007190
87.2 3 0.4396872616
88 NA -0.0601928334
88.1 1 -1.0124347595
88.2 2 0.5730917016
88.3 NA -0.0029455332
89 3 1.5465903721
90 2 0.0626760573
90.1 2 1.1896872985
90.2 2 0.2597888783
90.3 4 0.6599799887
91 2 1.1213651365
91.1 NA 1.2046371625
91.2 3 0.3395603754
92 2 0.4674939332
93 3 0.2677965647
93.1 2 1.6424445368
93.2 3 0.7101700066
93.3 2 1.1222322893
93.4 4 1.4628960401
94 NA -0.2904211940
94.1 2 0.0147813580
94.2 NA -0.4536774482
94.3 3 0.6793464917
94.4 4 -0.9411356550
94.5 3 0.5683867264
95 NA 0.2375652188
95.1 2 0.0767152977
95.2 3 -0.6886731251
96 3 0.7813892121
96.1 NA 0.3391519695
96.2 4 -0.4857246503
96.3 3 0.8771471244
96.4 NA 1.9030768981
96.5 1 -0.1684332749
97 2 1.3775130083
97.1 1 -1.7323228619
98 2 -1.2648518889
98.1 1 -0.9042716241
98.2 3 -0.1560385207
99 NA 0.7993356425
99.1 NA 1.0355522332
99.2 4 -0.1150895843
100 1 0.0369067906
100.1 NA 1.6023713093
100.2 1 0.8861545820
100.3 4 0.1277046316
100.4 1 -0.0834577654
$m1d$spM_lvlone
center scale
o2 NA NA
c1 0.2559996 0.6718095
$m1d$mu_reg_ordinal
[1] 0
$m1d$tau_reg_ordinal
[1] 1e-04
$m1d$mu_delta_ordinal
[1] 0
$m1d$tau_delta_ordinal
[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
o1
1 2
1.1 1
1.2 1
1.3 2
2 3
2.1 1
2.2 2
3 1
3.1 3
3.2 2
4 3
4.1 2
4.2 1
4.3 2
5 2
5.1 2
5.2 3
5.3 2
6 3
7 3
7.1 1
7.2 1
8 2
8.1 2
8.2 3
8.3 3
8.4 2
8.5 2
9 2
9.1 2
9.2 3
10 2
10.1 1
11 2
11.1 3
11.2 3
11.3 1
11.4 3
12 1
13 2
13.1 1
14 3
14.1 3
14.2 2
14.3 1
15 3
15.1 3
15.2 2
15.3 2
16 3
16.1 1
16.2 3
16.3 1
16.4 1
16.5 2
17 2
17.1 2
17.2 3
17.3 2
17.4 3
18 2
19 3
19.1 2
19.2 2
19.3 2
20 1
20.1 2
20.2 2
20.3 1
20.4 1
20.5 2
21 2
21.1 2
21.2 2
22 1
22.1 1
23 1
23.1 2
24 1
25 2
25.1 1
25.2 1
25.3 1
25.4 2
25.5 2
26 2
26.1 3
26.2 2
26.3 1
27 2
27.1 2
28 1
28.1 1
28.2 1
28.3 1
29 2
29.1 2
29.2 1
29.3 2
30 2
30.1 2
30.2 2
31 2
32 3
32.1 1
32.2 3
32.3 3
33 2
33.1 2
34 3
34.1 2
34.2 2
34.3 1
35 2
35.1 3
35.2 2
36 3
36.1 2
36.2 3
36.3 3
36.4 3
37 3
37.1 3
37.2 3
38 2
39 1
39.1 2
39.2 2
39.3 1
39.4 1
39.5 1
40 3
40.1 1
40.2 1
40.3 2
41 3
41.1 2
41.2 1
41.3 1
41.4 3
42 2
42.1 1
43 2
43.1 2
43.2 1
44 3
44.1 2
44.2 1
44.3 1
45 1
45.1 1
46 1
46.1 2
46.2 3
47 1
47.1 1
47.2 1
47.3 3
47.4 2
48 3
48.1 2
49 1
50 2
51 2
52 2
52.1 3
52.2 2
52.3 1
52.4 3
52.5 2
53 2
53.1 1
53.2 1
54 1
54.1 2
54.2 3
54.3 1
54.4 2
55 1
55.1 1
55.2 2
55.3 1
55.4 1
56 3
56.1 2
56.2 3
56.3 3
56.4 1
56.5 3
57 2
57.1 2
57.2 2
57.3 2
58 3
58.1 2
58.2 3
58.3 3
58.4 2
58.5 3
59 3
59.1 1
60 3
61 1
61.1 2
61.2 2
61.3 1
61.4 2
62 2
62.1 3
62.2 1
62.3 2
63 1
63.1 3
64 2
65 1
65.1 2
65.2 1
65.3 1
66 1
66.1 1
66.2 3
67 3
68 1
68.1 2
68.2 2
68.3 2
68.4 2
69 2
70 1
70.1 3
71 3
71.1 3
71.2 3
71.3 2
71.4 2
72 1
72.1 1
72.2 2
72.3 2
72.4 1
72.5 1
73 1
74 1
75 3
76 3
76.1 3
76.2 1
77 2
78 2
79 1
79.1 2
79.2 1
80 2
80.1 2
80.2 3
81 3
81.1 2
81.2 3
81.3 3
82 3
82.1 2
82.2 2
83 3
83.1 3
83.2 3
83.3 2
84 3
84.1 3
85 3
85.1 2
85.2 2
85.3 2
85.4 1
85.5 3
86 2
86.1 3
86.2 1
86.3 1
86.4 1
86.5 3
87 1
87.1 1
87.2 1
88 3
88.1 1
88.2 1
88.3 1
89 1
90 2
90.1 1
90.2 3
90.3 1
91 1
91.1 2
91.2 1
92 2
93 1
93.1 3
93.2 1
93.3 3
93.4 1
94 2
94.1 3
94.2 1
94.3 3
94.4 2
94.5 3
95 3
95.1 1
95.2 2
96 1
96.1 3
96.2 1
96.3 1
96.4 2
96.5 3
97 3
97.1 3
98 3
98.1 1
98.2 1
99 2
99.1 2
99.2 1
100 1
100.1 2
100.2 2
100.3 1
100.4 1
$m2a$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_ordinal
[1] 0
$m2a$tau_reg_ordinal
[1] 1e-04
$m2a$mu_delta_ordinal
[1] 0
$m2a$tau_delta_ordinal
[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
o2
1 1
1.1 1
1.2 3
1.3 1
2 4
2.1 4
2.2 2
3 2
3.1 4
3.2 2
4 4
4.1 3
4.2 NA
4.3 2
5 2
5.1 4
5.2 2
5.3 4
6 3
7 1
7.1 NA
7.2 4
8 1
8.1 3
8.2 1
8.3 4
8.4 3
8.5 3
9 2
9.1 2
9.2 4
10 1
10.1 4
11 3
11.1 1
11.2 4
11.3 3
11.4 3
12 3
13 NA
13.1 1
14 1
14.1 4
14.2 3
14.3 4
15 1
15.1 4
15.2 NA
15.3 2
16 NA
16.1 NA
16.2 1
16.3 3
16.4 3
16.5 1
17 3
17.1 2
17.2 2
17.3 3
17.4 1
18 4
19 1
19.1 NA
19.2 NA
19.3 2
20 1
20.1 4
20.2 3
20.3 3
20.4 1
20.5 3
21 3
21.1 1
21.2 2
22 4
22.1 NA
23 4
23.1 NA
24 3
25 1
25.1 3
25.2 2
25.3 1
25.4 1
25.5 NA
26 3
26.1 4
26.2 3
26.3 1
27 4
27.1 4
28 1
28.1 2
28.2 3
28.3 3
29 4
29.1 4
29.2 3
29.3 2
30 3
30.1 4
30.2 4
31 2
32 NA
32.1 2
32.2 4
32.3 3
33 4
33.1 4
34 NA
34.1 NA
34.2 NA
34.3 NA
35 4
35.1 1
35.2 NA
36 1
36.1 1
36.2 2
36.3 2
36.4 1
37 4
37.1 2
37.2 2
38 NA
39 NA
39.1 2
39.2 2
39.3 3
39.4 3
39.5 1
40 1
40.1 2
40.2 NA
40.3 2
41 4
41.1 3
41.2 4
41.3 NA
41.4 4
42 4
42.1 4
43 NA
43.1 NA
43.2 3
44 2
44.1 4
44.2 NA
44.3 1
45 3
45.1 4
46 NA
46.1 4
46.2 4
47 3
47.1 2
47.2 1
47.3 2
47.4 1
48 4
48.1 NA
49 4
50 4
51 4
52 NA
52.1 NA
52.2 3
52.3 NA
52.4 4
52.5 1
53 2
53.1 1
53.2 3
54 3
54.1 4
54.2 4
54.3 3
54.4 NA
55 4
55.1 1
55.2 4
55.3 NA
55.4 1
56 1
56.1 2
56.2 2
56.3 3
56.4 4
56.5 4
57 2
57.1 2
57.2 4
57.3 NA
58 1
58.1 2
58.2 2
58.3 4
58.4 1
58.5 NA
59 4
59.1 1
60 1
61 1
61.1 1
61.2 NA
61.3 1
61.4 3
62 NA
62.1 NA
62.2 NA
62.3 3
63 4
63.1 3
64 4
65 2
65.1 1
65.2 3
65.3 NA
66 1
66.1 3
66.2 2
67 3
68 3
68.1 4
68.2 3
68.3 1
68.4 4
69 4
70 4
70.1 4
71 2
71.1 NA
71.2 4
71.3 3
71.4 1
72 1
72.1 NA
72.2 4
72.3 1
72.4 3
72.5 1
73 2
74 4
75 1
76 2
76.1 1
76.2 1
77 3
78 3
79 NA
79.1 3
79.2 NA
80 2
80.1 3
80.2 1
81 3
81.1 NA
81.2 3
81.3 2
82 NA
82.1 3
82.2 1
83 4
83.1 NA
83.2 2
83.3 NA
84 2
84.1 1
85 1
85.1 4
85.2 3
85.3 3
85.4 NA
85.5 2
86 1
86.1 3
86.2 1
86.3 2
86.4 3
86.5 4
87 NA
87.1 3
87.2 3
88 NA
88.1 1
88.2 2
88.3 NA
89 3
90 2
90.1 2
90.2 2
90.3 4
91 2
91.1 NA
91.2 3
92 2
93 3
93.1 2
93.2 3
93.3 2
93.4 4
94 NA
94.1 2
94.2 NA
94.3 3
94.4 4
94.5 3
95 NA
95.1 2
95.2 3
96 3
96.1 NA
96.2 4
96.3 3
96.4 NA
96.5 1
97 2
97.1 1
98 2
98.1 1
98.2 3
99 NA
99.1 NA
99.2 4
100 1
100.1 NA
100.2 1
100.3 4
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_ordinal
[1] 0
$m2b$tau_reg_ordinal
[1] 1e-04
$m2b$mu_delta_ordinal
[1] 0
$m2b$tau_delta_ordinal
[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
o1 c2
1 2 NA
1.1 1 -0.08061445
1.2 1 -0.26523782
1.3 2 -0.30260393
2 3 -0.33443795
2.1 1 -0.11819800
2.2 2 -0.31532280
3 1 -0.12920657
3.1 3 NA
3.2 2 NA
4 3 -0.31177403
4.1 2 -0.23894886
4.2 1 -0.15533613
4.3 2 -0.14644545
5 2 -0.28360457
5.1 2 -0.20135143
5.2 3 -0.28293375
5.3 2 NA
6 3 -0.08617066
7 3 -0.22243495
7.1 1 NA
7.2 1 NA
8 2 NA
8.1 2 NA
8.2 3 NA
8.3 3 -0.35148972
8.4 2 0.03661023
8.5 2 -0.08424534
9 2 NA
9.1 2 -0.43509340
9.2 3 -0.22527490
10 2 NA
10.1 1 NA
11 2 -0.08587475
11.1 3 -0.06157340
11.2 3 -0.12436018
11.3 1 -0.21377934
11.4 3 -0.32208329
12 1 NA
13 2 NA
13.1 1 -0.40300449
14 3 -0.28992072
14.1 3 NA
14.2 2 NA
14.3 1 -0.21979936
15 3 NA
15.1 3 -0.29092263
15.2 2 -0.19392239
15.3 2 -0.25718384
16 3 -0.45041108
16.1 1 -0.07599066
16.2 3 -0.32385667
16.3 1 -0.38326110
16.4 1 -0.22845856
16.5 2 -0.25497157
17 2 NA
17.1 2 -0.22105143
17.2 3 NA
17.3 2 NA
17.4 3 -0.15098046
18 2 -0.09870041
19 3 -0.26680239
19.1 2 -0.15815241
19.2 2 -0.14717437
19.3 2 -0.21271374
20 1 -0.22087628
20.1 2 NA
20.2 2 -0.30127439
20.3 1 -0.11782590
20.4 1 -0.19857957
20.5 2 -0.24338208
21 2 -0.31407992
21.1 2 -0.12424941
21.2 2 -0.27672716
22 1 -0.23790593
22.1 1 -0.15996535
23 1 -0.18236682
23.1 2 -0.20823302
24 1 -0.29026416
25 2 -0.36139273
25.1 1 -0.19571118
25.2 1 -0.21379355
25.3 1 -0.33876012
25.4 2 NA
25.5 2 -0.04068446
26 2 -0.16846716
26.1 3 -0.10440642
26.2 2 -0.26884827
26.3 1 NA
27 2 -0.19520794
27.1 2 -0.17622638
28 1 -0.32164962
28.1 1 -0.27003852
28.2 1 -0.07235801
28.3 1 -0.13462982
29 2 -0.32432030
29.1 2 -0.27034171
29.2 1 -0.10197448
29.3 2 -0.27606945
30 2 -0.06949300
30.1 2 -0.11511035
30.2 2 -0.16215882
31 2 0.05707733
32 3 -0.18446298
32.1 1 -0.14270013
32.2 3 -0.20530798
32.3 3 -0.14705649
33 2 -0.15252819
33.1 2 NA
34 3 -0.30378735
34.1 2 -0.11982431
34.2 2 -0.24278671
34.3 1 -0.19971833
35 2 NA
35.1 3 -0.24165780
35.2 2 NA
36 3 -0.49062180
36.1 2 -0.25651700
36.2 3 NA
36.3 3 -0.30401274
36.4 3 NA
37 3 -0.15276529
37.1 3 -0.30016169
37.2 3 0.06809545
38 2 -0.11218486
39 1 -0.38072211
39.1 2 -0.32094428
39.2 2 NA
39.3 1 -0.40173480
39.4 1 -0.20041848
39.5 1 -0.26027990
40 3 -0.19751956
40.1 1 -0.08399467
40.2 1 -0.20864416
40.3 2 NA
41 3 -0.26096953
41.1 2 -0.23953874
41.2 1 -0.03079344
41.3 1 NA
41.4 3 NA
42 2 -0.16084527
42.1 1 -0.13812521
43 2 -0.08864017
43.1 2 -0.12583158
43.2 1 -0.29253959
44 3 -0.22697597
44.1 2 NA
44.2 1 NA
44.3 1 -0.40544012
45 1 -0.19274788
45.1 1 -0.34860483
46 1 -0.28547861
46.1 2 -0.21977836
46.2 3 NA
47 1 -0.08597098
47.1 1 -0.35424828
47.2 1 -0.24262576
47.3 3 -0.30426315
47.4 2 NA
48 3 NA
48.1 2 NA
49 1 -0.42198781
50 2 -0.19959516
51 2 -0.16556964
52 2 -0.07438732
52.1 3 -0.37537080
52.2 2 -0.24222066
52.3 1 -0.31520603
52.4 3 -0.44619160
52.5 2 -0.11011682
53 2 -0.23278716
53.1 1 -0.28317264
53.2 1 -0.19517481
54 1 -0.10122856
54.1 2 -0.28325504
54.2 3 -0.16753120
54.3 1 -0.22217672
54.4 2 -0.34609328
55 1 -0.32428190
55.1 1 -0.24235382
55.2 2 -0.24065814
55.3 1 -0.23665476
55.4 1 NA
56 3 NA
56.1 2 -0.30357450
56.2 3 -0.51301630
56.3 3 -0.23743117
56.4 1 -0.17264917
56.5 3 -0.39188329
57 2 -0.18501692
57.1 2 -0.27274841
57.2 2 NA
57.3 2 -0.09898509
58 3 -0.29901358
58.1 2 -0.35390896
58.2 3 -0.16687336
58.3 3 -0.11784506
58.4 2 -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 1 -0.34184391
61.4 2 -0.30891967
62 2 NA
62.1 3 -0.10504143
62.2 1 -0.20104997
62.3 2 -0.08138677
63 1 -0.12036319
63.1 3 -0.13624992
64 2 NA
65 1 -0.34450396
65.1 2 -0.32514650
65.2 1 -0.10984996
65.3 1 -0.19275692
66 1 NA
66.1 1 NA
66.2 3 -0.11687008
67 3 NA
68 1 -0.13605235
68.1 2 -0.19790827
68.2 2 -0.17750123
68.3 2 NA
68.4 2 -0.12570562
69 2 -0.32152751
70 1 -0.28190462
70.1 3 -0.11503263
71 3 -0.13029093
71.1 3 NA
71.2 3 -0.39075433
71.3 2 -0.21401028
71.4 2 -0.40219281
72 1 -0.40337108
72.1 1 -0.25978914
72.2 2 NA
72.3 2 -0.09809866
72.4 1 -0.14240019
72.5 1 -0.14794204
73 1 -0.23509343
74 1 -0.27963171
75 3 -0.12905034
76 3 0.04775562
76.1 3 -0.19399157
76.2 1 -0.02754574
77 2 -0.19053195
78 2 -0.17172929
79 1 -0.03958515
79.1 2 -0.20328809
79.2 1 -0.23901634
80 2 -0.34031873
80.1 2 -0.19526756
80.2 3 NA
81 3 -0.18401980
81.1 2 -0.16889476
81.2 3 -0.37343047
81.3 3 NA
82 3 -0.08328168
82.1 2 -0.22167084
82.2 2 -0.20971187
83 3 -0.34228255
83.1 3 -0.34075730
83.2 3 -0.32503954
83.3 2 NA
84 3 -0.20676741
84.1 3 -0.20310458
85 3 -0.12107593
85.1 2 NA
85.2 2 -0.32509207
85.3 2 NA
85.4 1 -0.30730810
85.5 3 NA
86 2 -0.10854862
86.1 3 -0.25751662
86.2 1 -0.38943076
86.3 1 -0.24454702
86.4 1 -0.12338992
86.5 3 -0.23976984
87 1 NA
87.1 1 -0.34366972
87.2 1 NA
88 3 -0.31563888
88.1 1 -0.20304028
88.2 1 -0.40311895
88.3 1 -0.12308715
89 1 -0.18527715
90 2 -0.25029126
90.1 1 -0.26974303
90.2 3 -0.28804531
90.3 1 -0.19180615
91 1 -0.26591197
91.1 2 -0.09153470
91.2 1 -0.48414390
92 2 NA
93 1 -0.11939966
93.1 3 NA
93.2 1 -0.21089379
93.3 3 NA
93.4 1 -0.23618836
94 2 NA
94.1 3 -0.10217284
94.2 1 -0.36713471
94.3 3 -0.13806763
94.4 2 -0.42353804
94.5 3 -0.15513707
95 3 -0.24149687
95.1 1 -0.21315958
95.2 2 -0.15777208
96 1 -0.16780948
96.1 3 -0.32504815
96.2 1 -0.20395970
96.3 1 -0.06221501
96.4 2 -0.14801097
96.5 3 -0.28658893
97 3 -0.34484656
97.1 3 -0.35658805
98 3 -0.36913003
98.1 1 NA
98.2 1 -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 2 -0.12072016
100.3 1 -0.27657520
100.4 1 -0.14631556
$m2c$spM_lvlone
center scale
o1 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_ordinal
[1] 0
$m2c$tau_reg_ordinal
[1] 1e-04
$m2c$mu_delta_ordinal
[1] 0
$m2c$tau_delta_ordinal
[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
o2 c2
1 1 NA
1.1 1 -0.08061445
1.2 3 -0.26523782
1.3 1 -0.30260393
2 4 -0.33443795
2.1 4 -0.11819800
2.2 2 -0.31532280
3 2 -0.12920657
3.1 4 NA
3.2 2 NA
4 4 -0.31177403
4.1 3 -0.23894886
4.2 NA -0.15533613
4.3 2 -0.14644545
5 2 -0.28360457
5.1 4 -0.20135143
5.2 2 -0.28293375
5.3 4 NA
6 3 -0.08617066
7 1 -0.22243495
7.1 NA NA
7.2 4 NA
8 1 NA
8.1 3 NA
8.2 1 NA
8.3 4 -0.35148972
8.4 3 0.03661023
8.5 3 -0.08424534
9 2 NA
9.1 2 -0.43509340
9.2 4 -0.22527490
10 1 NA
10.1 4 NA
11 3 -0.08587475
11.1 1 -0.06157340
11.2 4 -0.12436018
11.3 3 -0.21377934
11.4 3 -0.32208329
12 3 NA
13 NA NA
13.1 1 -0.40300449
14 1 -0.28992072
14.1 4 NA
14.2 3 NA
14.3 4 -0.21979936
15 1 NA
15.1 4 -0.29092263
15.2 NA -0.19392239
15.3 2 -0.25718384
16 NA -0.45041108
16.1 NA -0.07599066
16.2 1 -0.32385667
16.3 3 -0.38326110
16.4 3 -0.22845856
16.5 1 -0.25497157
17 3 NA
17.1 2 -0.22105143
17.2 2 NA
17.3 3 NA
17.4 1 -0.15098046
18 4 -0.09870041
19 1 -0.26680239
19.1 NA -0.15815241
19.2 NA -0.14717437
19.3 2 -0.21271374
20 1 -0.22087628
20.1 4 NA
20.2 3 -0.30127439
20.3 3 -0.11782590
20.4 1 -0.19857957
20.5 3 -0.24338208
21 3 -0.31407992
21.1 1 -0.12424941
21.2 2 -0.27672716
22 4 -0.23790593
22.1 NA -0.15996535
23 4 -0.18236682
23.1 NA -0.20823302
24 3 -0.29026416
25 1 -0.36139273
25.1 3 -0.19571118
25.2 2 -0.21379355
25.3 1 -0.33876012
25.4 1 NA
25.5 NA -0.04068446
26 3 -0.16846716
26.1 4 -0.10440642
26.2 3 -0.26884827
26.3 1 NA
27 4 -0.19520794
27.1 4 -0.17622638
28 1 -0.32164962
28.1 2 -0.27003852
28.2 3 -0.07235801
28.3 3 -0.13462982
29 4 -0.32432030
29.1 4 -0.27034171
29.2 3 -0.10197448
29.3 2 -0.27606945
30 3 -0.06949300
30.1 4 -0.11511035
30.2 4 -0.16215882
31 2 0.05707733
32 NA -0.18446298
32.1 2 -0.14270013
32.2 4 -0.20530798
32.3 3 -0.14705649
33 4 -0.15252819
33.1 4 NA
34 NA -0.30378735
34.1 NA -0.11982431
34.2 NA -0.24278671
34.3 NA -0.19971833
35 4 NA
35.1 1 -0.24165780
35.2 NA NA
36 1 -0.49062180
36.1 1 -0.25651700
36.2 2 NA
36.3 2 -0.30401274
36.4 1 NA
37 4 -0.15276529
37.1 2 -0.30016169
37.2 2 0.06809545
38 NA -0.11218486
39 NA -0.38072211
39.1 2 -0.32094428
39.2 2 NA
39.3 3 -0.40173480
39.4 3 -0.20041848
39.5 1 -0.26027990
40 1 -0.19751956
40.1 2 -0.08399467
40.2 NA -0.20864416
40.3 2 NA
41 4 -0.26096953
41.1 3 -0.23953874
41.2 4 -0.03079344
41.3 NA NA
41.4 4 NA
42 4 -0.16084527
42.1 4 -0.13812521
43 NA -0.08864017
43.1 NA -0.12583158
43.2 3 -0.29253959
44 2 -0.22697597
44.1 4 NA
44.2 NA NA
44.3 1 -0.40544012
45 3 -0.19274788
45.1 4 -0.34860483
46 NA -0.28547861
46.1 4 -0.21977836
46.2 4 NA
47 3 -0.08597098
47.1 2 -0.35424828
47.2 1 -0.24262576
47.3 2 -0.30426315
47.4 1 NA
48 4 NA
48.1 NA NA
49 4 -0.42198781
50 4 -0.19959516
51 4 -0.16556964
52 NA -0.07438732
52.1 NA -0.37537080
52.2 3 -0.24222066
52.3 NA -0.31520603
52.4 4 -0.44619160
52.5 1 -0.11011682
53 2 -0.23278716
53.1 1 -0.28317264
53.2 3 -0.19517481
54 3 -0.10122856
54.1 4 -0.28325504
54.2 4 -0.16753120
54.3 3 -0.22217672
54.4 NA -0.34609328
55 4 -0.32428190
55.1 1 -0.24235382
55.2 4 -0.24065814
55.3 NA -0.23665476
55.4 1 NA
56 1 NA
56.1 2 -0.30357450
56.2 2 -0.51301630
56.3 3 -0.23743117
56.4 4 -0.17264917
56.5 4 -0.39188329
57 2 -0.18501692
57.1 2 -0.27274841
57.2 4 NA
57.3 NA -0.09898509
58 1 -0.29901358
58.1 2 -0.35390896
58.2 2 -0.16687336
58.3 4 -0.11784506
58.4 1 -0.05321983
58.5 NA -0.54457568
59 4 -0.27255364
59.1 1 NA
60 1 NA
61 1 -0.30550120
61.1 1 -0.35579892
61.2 NA NA
61.3 1 -0.34184391
61.4 3 -0.30891967
62 NA NA
62.1 NA -0.10504143
62.2 NA -0.20104997
62.3 3 -0.08138677
63 4 -0.12036319
63.1 3 -0.13624992
64 4 NA
65 2 -0.34450396
65.1 1 -0.32514650
65.2 3 -0.10984996
65.3 NA -0.19275692
66 1 NA
66.1 3 NA
66.2 2 -0.11687008
67 3 NA
68 3 -0.13605235
68.1 4 -0.19790827
68.2 3 -0.17750123
68.3 1 NA
68.4 4 -0.12570562
69 4 -0.32152751
70 4 -0.28190462
70.1 4 -0.11503263
71 2 -0.13029093
71.1 NA NA
71.2 4 -0.39075433
71.3 3 -0.21401028
71.4 1 -0.40219281
72 1 -0.40337108
72.1 NA -0.25978914
72.2 4 NA
72.3 1 -0.09809866
72.4 3 -0.14240019
72.5 1 -0.14794204
73 2 -0.23509343
74 4 -0.27963171
75 1 -0.12905034
76 2 0.04775562
76.1 1 -0.19399157
76.2 1 -0.02754574
77 3 -0.19053195
78 3 -0.17172929
79 NA -0.03958515
79.1 3 -0.20328809
79.2 NA -0.23901634
80 2 -0.34031873
80.1 3 -0.19526756
80.2 1 NA
81 3 -0.18401980
81.1 NA -0.16889476
81.2 3 -0.37343047
81.3 2 NA
82 NA -0.08328168
82.1 3 -0.22167084
82.2 1 -0.20971187
83 4 -0.34228255
83.1 NA -0.34075730
83.2 2 -0.32503954
83.3 NA NA
84 2 -0.20676741
84.1 1 -0.20310458
85 1 -0.12107593
85.1 4 NA
85.2 3 -0.32509207
85.3 3 NA
85.4 NA -0.30730810
85.5 2 NA
86 1 -0.10854862
86.1 3 -0.25751662
86.2 1 -0.38943076
86.3 2 -0.24454702
86.4 3 -0.12338992
86.5 4 -0.23976984
87 NA NA
87.1 3 -0.34366972
87.2 3 NA
88 NA -0.31563888
88.1 1 -0.20304028
88.2 2 -0.40311895
88.3 NA -0.12308715
89 3 -0.18527715
90 2 -0.25029126
90.1 2 -0.26974303
90.2 2 -0.28804531
90.3 4 -0.19180615
91 2 -0.26591197
91.1 NA -0.09153470
91.2 3 -0.48414390
92 2 NA
93 3 -0.11939966
93.1 2 NA
93.2 3 -0.21089379
93.3 2 NA
93.4 4 -0.23618836
94 NA NA
94.1 2 -0.10217284
94.2 NA -0.36713471
94.3 3 -0.13806763
94.4 4 -0.42353804
94.5 3 -0.15513707
95 NA -0.24149687
95.1 2 -0.21315958
95.2 3 -0.15777208
96 3 -0.16780948
96.1 NA -0.32504815
96.2 4 -0.20395970
96.3 3 -0.06221501
96.4 NA -0.14801097
96.5 1 -0.28658893
97 2 -0.34484656
97.1 1 -0.35658805
98 2 -0.36913003
98.1 1 NA
98.2 3 -0.17154225
99 NA -0.24753132
99.1 NA -0.27947829
99.2 4 -0.09033035
100 1 -0.17326698
100.1 NA NA
100.2 1 -0.12072016
100.3 4 -0.27657520
100.4 1 -0.14631556
$m2d$spM_lvlone
center scale
o2 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_ordinal
[1] 0
$m2d$tau_reg_ordinal
[1] 1e-04
$m2d$mu_delta_ordinal
[1] 0
$m2d$tau_delta_ordinal
[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 o1.L o1.Q
1 0.7592026489 -7.850462e-17 -0.8164966
1.1 0.9548337990 -7.071068e-01 0.4082483
1.2 0.5612235156 -7.071068e-01 0.4082483
1.3 1.1873391025 -7.850462e-17 -0.8164966
2 0.9192204198 7.071068e-01 0.4082483
2.1 -0.1870730476 -7.071068e-01 0.4082483
2.2 1.2517512331 -7.850462e-17 -0.8164966
3 -0.0605087604 -7.071068e-01 0.4082483
3.1 0.3788637747 7.071068e-01 0.4082483
3.2 0.9872578281 -7.850462e-17 -0.8164966
4 1.4930175328 7.071068e-01 0.4082483
4.1 -0.7692526880 -7.850462e-17 -0.8164966
4.2 0.9180841450 -7.071068e-01 0.4082483
4.3 -0.0541170782 -7.850462e-17 -0.8164966
5 -0.1376784521 -7.850462e-17 -0.8164966
5.1 -0.2740585866 -7.850462e-17 -0.8164966
5.2 0.4670496929 7.071068e-01 0.4082483
5.3 0.1740288049 -7.850462e-17 -0.8164966
6 0.9868044683 7.071068e-01 0.4082483
7 -0.1280320918 7.071068e-01 0.4082483
7.1 0.4242971219 -7.071068e-01 0.4082483
7.2 0.0777182491 -7.071068e-01 0.4082483
8 -0.5791408712 -7.850462e-17 -0.8164966
8.1 0.3128604232 -7.850462e-17 -0.8164966
8.2 0.6258446356 7.071068e-01 0.4082483
8.3 -0.1040137707 7.071068e-01 0.4082483
8.4 0.0481450285 -7.850462e-17 -0.8164966
8.5 0.3831763675 -7.850462e-17 -0.8164966
9 -0.1757592269 -7.850462e-17 -0.8164966
9.1 -0.1791541200 -7.850462e-17 -0.8164966
9.2 -0.0957042935 7.071068e-01 0.4082483
10 -0.5598409704 -7.850462e-17 -0.8164966
10.1 -0.2318340451 -7.071068e-01 0.4082483
11 0.5086859475 -7.850462e-17 -0.8164966
11.1 0.4951758188 7.071068e-01 0.4082483
11.2 -1.1022162541 7.071068e-01 0.4082483
11.3 -0.0611636705 -7.071068e-01 0.4082483
11.4 -0.4971774316 7.071068e-01 0.4082483
12 -0.2433996286 -7.071068e-01 0.4082483
13 0.8799673116 -7.850462e-17 -0.8164966
13.1 0.1079022586 -7.071068e-01 0.4082483
14 0.9991752617 7.071068e-01 0.4082483
14.1 -0.1094019046 7.071068e-01 0.4082483
14.2 0.1518967560 -7.850462e-17 -0.8164966
14.3 0.3521012473 -7.071068e-01 0.4082483
15 0.3464447888 7.071068e-01 0.4082483
15.1 -0.4767313971 7.071068e-01 0.4082483
15.2 0.5759767791 -7.850462e-17 -0.8164966
15.3 -0.1713452662 -7.850462e-17 -0.8164966
16 0.4564754473 7.071068e-01 0.4082483
16.1 1.0652558311 -7.071068e-01 0.4082483
16.2 0.6971872493 7.071068e-01 0.4082483
16.3 0.5259331838 -7.071068e-01 0.4082483
16.4 0.2046601798 -7.071068e-01 0.4082483
16.5 1.0718540464 -7.850462e-17 -0.8164966
17 0.6048676222 -7.850462e-17 -0.8164966
17.1 0.2323298304 -7.850462e-17 -0.8164966
17.2 1.2617499032 7.071068e-01 0.4082483
17.3 -0.3913230895 -7.850462e-17 -0.8164966
17.4 0.9577299112 7.071068e-01 0.4082483
18 -0.0050324072 -7.850462e-17 -0.8164966
19 -0.4187468937 7.071068e-01 0.4082483
19.1 -0.4478828944 -7.850462e-17 -0.8164966
19.2 -1.1966721302 -7.850462e-17 -0.8164966
19.3 -0.5877091668 -7.850462e-17 -0.8164966
20 0.6838223064 -7.071068e-01 0.4082483
20.1 0.3278571109 -7.850462e-17 -0.8164966
20.2 -0.8489831990 -7.850462e-17 -0.8164966
20.3 1.3169975191 -7.071068e-01 0.4082483
20.4 0.0444804531 -7.071068e-01 0.4082483
20.5 -0.4535207652 -7.850462e-17 -0.8164966
21 -0.4030302960 -7.850462e-17 -0.8164966
21.1 -0.4069674045 -7.850462e-17 -0.8164966
21.2 1.0650265940 -7.850462e-17 -0.8164966
22 -0.0673274516 -7.071068e-01 0.4082483
22.1 0.9601388170 -7.071068e-01 0.4082483
23 0.5556634840 -7.071068e-01 0.4082483
23.1 1.4407865964 -7.850462e-17 -0.8164966
24 0.3856376411 -7.071068e-01 0.4082483
25 0.3564400705 -7.850462e-17 -0.8164966
25.1 0.0982553434 -7.071068e-01 0.4082483
25.2 0.1928682598 -7.071068e-01 0.4082483
25.3 -0.0192488594 -7.071068e-01 0.4082483
25.4 0.4466012931 -7.850462e-17 -0.8164966
25.5 1.1425193342 -7.850462e-17 -0.8164966
26 0.5341531449 -7.850462e-17 -0.8164966
26.1 1.2268695927 7.071068e-01 0.4082483
26.2 0.3678294939 -7.850462e-17 -0.8164966
26.3 0.5948516018 -7.071068e-01 0.4082483
27 -0.3342844147 -7.850462e-17 -0.8164966
27.1 -0.4835141229 -7.850462e-17 -0.8164966
28 -0.7145915499 -7.071068e-01 0.4082483
28.1 0.5063671955 -7.071068e-01 0.4082483
28.2 -0.2067413142 -7.071068e-01 0.4082483
28.3 0.1196789973 -7.071068e-01 0.4082483
29 0.1392699487 -7.850462e-17 -0.8164966
29.1 0.7960234776 -7.850462e-17 -0.8164966
29.2 1.0398214352 -7.071068e-01 0.4082483
29.3 0.0813246429 -7.850462e-17 -0.8164966
30 -0.3296323050 -7.850462e-17 -0.8164966
30.1 1.3635850954 -7.850462e-17 -0.8164966
30.2 0.7354171050 -7.850462e-17 -0.8164966
31 0.3708398217 -7.850462e-17 -0.8164966
32 -0.0474059668 7.071068e-01 0.4082483
32.1 1.2507771489 -7.071068e-01 0.4082483
32.2 0.1142915519 7.071068e-01 0.4082483
32.3 0.6773270619 7.071068e-01 0.4082483
33 0.1774293842 -7.850462e-17 -0.8164966
33.1 0.6159606291 -7.850462e-17 -0.8164966
34 0.8590979166 7.071068e-01 0.4082483
34.1 0.0546216775 -7.850462e-17 -0.8164966
34.2 -0.0897224473 -7.850462e-17 -0.8164966
34.3 0.4163395571 -7.071068e-01 0.4082483
35 -1.4693520528 -7.850462e-17 -0.8164966
35.1 -0.3031734330 7.071068e-01 0.4082483
35.2 -0.6045512101 -7.850462e-17 -0.8164966
36 0.9823048960 7.071068e-01 0.4082483
36.1 1.4466051416 -7.850462e-17 -0.8164966
36.2 1.1606752905 7.071068e-01 0.4082483
36.3 0.8373091576 7.071068e-01 0.4082483
36.4 0.2640591685 7.071068e-01 0.4082483
37 0.1177313455 7.071068e-01 0.4082483
37.1 -0.1415483779 7.071068e-01 0.4082483
37.2 0.0054610124 7.071068e-01 0.4082483
38 0.8078948077 -7.850462e-17 -0.8164966
39 0.9876451040 -7.071068e-01 0.4082483
39.1 -0.3431222274 -7.850462e-17 -0.8164966
39.2 -1.7909380751 -7.850462e-17 -0.8164966
39.3 -0.1798746191 -7.071068e-01 0.4082483
39.4 -0.1850961689 -7.071068e-01 0.4082483
39.5 0.4544226146 -7.071068e-01 0.4082483
40 0.5350190436 7.071068e-01 0.4082483
40.1 0.4189342752 -7.071068e-01 0.4082483
40.2 0.4211994981 -7.071068e-01 0.4082483
40.3 0.0916687506 -7.850462e-17 -0.8164966
41 -0.1035047421 7.071068e-01 0.4082483
41.1 -0.4684202411 -7.850462e-17 -0.8164966
41.2 0.5972615368 -7.071068e-01 0.4082483
41.3 0.9885613862 -7.071068e-01 0.4082483
41.4 -0.3908036794 7.071068e-01 0.4082483
42 -0.0338893961 -7.850462e-17 -0.8164966
42.1 -0.4498363172 -7.071068e-01 0.4082483
43 0.8965546110 -7.850462e-17 -0.8164966
43.1 0.6199122090 -7.850462e-17 -0.8164966
43.2 0.1804894429 -7.071068e-01 0.4082483
44 1.3221409285 7.071068e-01 0.4082483
44.1 0.3416426284 -7.850462e-17 -0.8164966
44.2 0.5706610068 -7.071068e-01 0.4082483
44.3 1.2679497430 -7.071068e-01 0.4082483
45 0.1414983160 -7.071068e-01 0.4082483
45.1 0.7220892521 -7.071068e-01 0.4082483
46 1.5391054233 -7.071068e-01 0.4082483
46.1 0.3889107049 -7.850462e-17 -0.8164966
46.2 0.1248719493 7.071068e-01 0.4082483
47 0.2014101100 -7.071068e-01 0.4082483
47.1 0.2982973539 -7.071068e-01 0.4082483
47.2 1.1518107179 -7.071068e-01 0.4082483
47.3 0.5196802157 7.071068e-01 0.4082483
47.4 0.3702301552 -7.850462e-17 -0.8164966
48 -0.2128602862 7.071068e-01 0.4082483
48.1 -0.5337239976 -7.850462e-17 -0.8164966
49 -0.5236770035 -7.071068e-01 0.4082483
50 0.3897705981 -7.850462e-17 -0.8164966
51 -0.7213343736 -7.850462e-17 -0.8164966
52 0.3758235358 -7.850462e-17 -0.8164966
52.1 0.7138067080 7.071068e-01 0.4082483
52.2 0.8872895233 -7.850462e-17 -0.8164966
52.3 -0.9664587437 -7.071068e-01 0.4082483
52.4 0.0254566848 7.071068e-01 0.4082483
52.5 0.4155259424 -7.850462e-17 -0.8164966
53 0.5675736897 -7.850462e-17 -0.8164966
53.1 -0.3154088781 -7.071068e-01 0.4082483
53.2 0.2162315769 -7.071068e-01 0.4082483
54 -0.0880802382 -7.071068e-01 0.4082483
54.1 0.4129127672 -7.850462e-17 -0.8164966
54.2 1.0119546775 7.071068e-01 0.4082483
54.3 -0.1112901990 -7.071068e-01 0.4082483
54.4 0.8587727145 -7.850462e-17 -0.8164966
55 -0.0116453589 -7.071068e-01 0.4082483
55.1 0.5835528661 -7.071068e-01 0.4082483
55.2 -1.0010857254 -7.850462e-17 -0.8164966
55.3 -0.4796526070 -7.071068e-01 0.4082483
55.4 -0.1202746964 -7.071068e-01 0.4082483
56 0.5176377612 7.071068e-01 0.4082483
56.1 -1.1136932588 -7.850462e-17 -0.8164966
56.2 -0.0168103281 7.071068e-01 0.4082483
56.3 0.3933023606 7.071068e-01 0.4082483
56.4 0.3714625139 -7.071068e-01 0.4082483
56.5 0.7811448179 7.071068e-01 0.4082483
57 -1.0868304872 -7.850462e-17 -0.8164966
57.1 0.8018626997 -7.850462e-17 -0.8164966
57.2 -0.1159517011 -7.850462e-17 -0.8164966
57.3 0.6785562445 -7.850462e-17 -0.8164966
58 1.6476207996 7.071068e-01 0.4082483
58.1 0.3402652711 -7.850462e-17 -0.8164966
58.2 -0.1111300753 7.071068e-01 0.4082483
58.3 -0.5409234285 7.071068e-01 0.4082483
58.4 -0.1271327672 -7.850462e-17 -0.8164966
58.5 0.8713264822 7.071068e-01 0.4082483
59 0.4766421367 7.071068e-01 0.4082483
59.1 1.0028089765 -7.071068e-01 0.4082483
60 0.5231452932 7.071068e-01 0.4082483
61 -0.7190130614 -7.071068e-01 0.4082483
61.1 0.8353702312 -7.850462e-17 -0.8164966
61.2 1.0229058138 -7.850462e-17 -0.8164966
61.3 1.1717723589 -7.071068e-01 0.4082483
61.4 -0.0629201596 -7.850462e-17 -0.8164966
62 -0.3979137604 -7.850462e-17 -0.8164966
62.1 0.6830738372 7.071068e-01 0.4082483
62.2 0.4301745954 -7.071068e-01 0.4082483
62.3 -0.0333139957 -7.850462e-17 -0.8164966
63 0.3345678035 -7.071068e-01 0.4082483
63.1 0.3643769511 7.071068e-01 0.4082483
64 0.3949911859 -7.850462e-17 -0.8164966
65 1.2000091513 -7.071068e-01 0.4082483
65.1 0.0110122646 -7.850462e-17 -0.8164966
65.2 -0.5776452043 -7.071068e-01 0.4082483
65.3 -0.1372183563 -7.071068e-01 0.4082483
66 -0.5081302805 -7.071068e-01 0.4082483
66.1 -0.1447837412 -7.071068e-01 0.4082483
66.2 0.1906241379 7.071068e-01 0.4082483
67 1.6716027681 7.071068e-01 0.4082483
68 0.5691848839 -7.071068e-01 0.4082483
68.1 0.1004860389 -7.850462e-17 -0.8164966
68.2 -0.0061241827 -7.850462e-17 -0.8164966
68.3 0.7443745962 -7.850462e-17 -0.8164966
68.4 0.8726923437 -7.850462e-17 -0.8164966
69 0.0381382683 -7.850462e-17 -0.8164966
70 0.8126204217 -7.071068e-01 0.4082483
70.1 0.4691503050 7.071068e-01 0.4082483
71 -0.5529062591 7.071068e-01 0.4082483
71.1 -0.1103252087 7.071068e-01 0.4082483
71.2 1.7178492547 7.071068e-01 0.4082483
71.3 -1.0118346755 -7.850462e-17 -0.8164966
71.4 1.8623785017 -7.850462e-17 -0.8164966
72 -0.4521659275 -7.071068e-01 0.4082483
72.1 0.1375317317 -7.071068e-01 0.4082483
72.2 -0.4170988856 -7.850462e-17 -0.8164966
72.3 0.7107266765 -7.850462e-17 -0.8164966
72.4 0.1451969143 -7.071068e-01 0.4082483
72.5 1.6298050306 -7.071068e-01 0.4082483
73 -0.0307469467 -7.071068e-01 0.4082483
74 0.3730017941 -7.071068e-01 0.4082483
75 -0.4908003566 7.071068e-01 0.4082483
76 -0.9888876620 7.071068e-01 0.4082483
76.1 0.0003798292 7.071068e-01 0.4082483
76.2 -0.8421863763 -7.071068e-01 0.4082483
77 -0.4986802480 -7.850462e-17 -0.8164966
78 0.0417330969 -7.850462e-17 -0.8164966
79 -0.3767450660 -7.071068e-01 0.4082483
79.1 0.1516000028 -7.850462e-17 -0.8164966
79.2 -0.1888160741 -7.071068e-01 0.4082483
80 -0.0041558414 -7.850462e-17 -0.8164966
80.1 -0.0329337062 -7.850462e-17 -0.8164966
80.2 0.5046816157 7.071068e-01 0.4082483
81 -0.9493950353 7.071068e-01 0.4082483
81.1 0.2443038954 -7.850462e-17 -0.8164966
81.2 0.6476958410 7.071068e-01 0.4082483
81.3 0.4182528210 7.071068e-01 0.4082483
82 1.1088801952 7.071068e-01 0.4082483
82.1 0.9334157763 -7.850462e-17 -0.8164966
82.2 0.4958140634 -7.850462e-17 -0.8164966
83 0.5104724530 7.071068e-01 0.4082483
83.1 -0.0513309106 7.071068e-01 0.4082483
83.2 -0.2067792494 7.071068e-01 0.4082483
83.3 -0.0534169155 -7.850462e-17 -0.8164966
84 -0.0255753653 7.071068e-01 0.4082483
84.1 -1.8234189877 7.071068e-01 0.4082483
85 -0.0114038622 7.071068e-01 0.4082483
85.1 -0.0577615939 -7.850462e-17 -0.8164966
85.2 -0.2241856342 -7.850462e-17 -0.8164966
85.3 -0.0520175929 -7.850462e-17 -0.8164966
85.4 0.2892733846 -7.071068e-01 0.4082483
85.5 -0.3740417009 7.071068e-01 0.4082483
86 0.4293735089 -7.850462e-17 -0.8164966
86.1 -0.1363456521 7.071068e-01 0.4082483
86.2 0.1230989293 -7.071068e-01 0.4082483
86.3 0.3305413955 -7.071068e-01 0.4082483
86.4 2.6003411822 -7.071068e-01 0.4082483
86.5 -0.1420690052 7.071068e-01 0.4082483
87 1.0457427869 -7.071068e-01 0.4082483
87.1 -0.2973007190 -7.071068e-01 0.4082483
87.2 0.4396872616 -7.071068e-01 0.4082483
88 -0.0601928334 7.071068e-01 0.4082483
88.1 -1.0124347595 -7.071068e-01 0.4082483
88.2 0.5730917016 -7.071068e-01 0.4082483
88.3 -0.0029455332 -7.071068e-01 0.4082483
89 1.5465903721 -7.071068e-01 0.4082483
90 0.0626760573 -7.850462e-17 -0.8164966
90.1 1.1896872985 -7.071068e-01 0.4082483
90.2 0.2597888783 7.071068e-01 0.4082483
90.3 0.6599799887 -7.071068e-01 0.4082483
91 1.1213651365 -7.071068e-01 0.4082483
91.1 1.2046371625 -7.850462e-17 -0.8164966
91.2 0.3395603754 -7.071068e-01 0.4082483
92 0.4674939332 -7.850462e-17 -0.8164966
93 0.2677965647 -7.071068e-01 0.4082483
93.1 1.6424445368 7.071068e-01 0.4082483
93.2 0.7101700066 -7.071068e-01 0.4082483
93.3 1.1222322893 7.071068e-01 0.4082483
93.4 1.4628960401 -7.071068e-01 0.4082483
94 -0.2904211940 -7.850462e-17 -0.8164966
94.1 0.0147813580 7.071068e-01 0.4082483
94.2 -0.4536774482 -7.071068e-01 0.4082483
94.3 0.6793464917 7.071068e-01 0.4082483
94.4 -0.9411356550 -7.850462e-17 -0.8164966
94.5 0.5683867264 7.071068e-01 0.4082483
95 0.2375652188 7.071068e-01 0.4082483
95.1 0.0767152977 -7.071068e-01 0.4082483
95.2 -0.6886731251 -7.850462e-17 -0.8164966
96 0.7813892121 -7.071068e-01 0.4082483
96.1 0.3391519695 7.071068e-01 0.4082483
96.2 -0.4857246503 -7.071068e-01 0.4082483
96.3 0.8771471244 -7.071068e-01 0.4082483
96.4 1.9030768981 -7.850462e-17 -0.8164966
96.5 -0.1684332749 7.071068e-01 0.4082483
97 1.3775130083 7.071068e-01 0.4082483
97.1 -1.7323228619 7.071068e-01 0.4082483
98 -1.2648518889 7.071068e-01 0.4082483
98.1 -0.9042716241 -7.071068e-01 0.4082483
98.2 -0.1560385207 -7.071068e-01 0.4082483
99 0.7993356425 -7.850462e-17 -0.8164966
99.1 1.0355522332 -7.850462e-17 -0.8164966
99.2 -0.1150895843 -7.071068e-01 0.4082483
100 0.0369067906 -7.071068e-01 0.4082483
100.1 1.6023713093 -7.850462e-17 -0.8164966
100.2 0.8861545820 -7.850462e-17 -0.8164966
100.3 0.1277046316 -7.071068e-01 0.4082483
100.4 -0.0834577654 -7.071068e-01 0.4082483
$m3a$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 o2 o22 o23 o24
1 0.7592026489 1 NA NA NA
1.1 0.9548337990 1 NA NA NA
1.2 0.5612235156 3 NA NA NA
1.3 1.1873391025 1 NA NA NA
2 0.9192204198 4 NA NA NA
2.1 -0.1870730476 4 NA NA NA
2.2 1.2517512331 2 NA NA NA
3 -0.0605087604 2 NA NA NA
3.1 0.3788637747 4 NA NA NA
3.2 0.9872578281 2 NA NA NA
4 1.4930175328 4 NA NA NA
4.1 -0.7692526880 3 NA NA NA
4.2 0.9180841450 NA NA NA NA
4.3 -0.0541170782 2 NA NA NA
5 -0.1376784521 2 NA NA NA
5.1 -0.2740585866 4 NA NA NA
5.2 0.4670496929 2 NA NA NA
5.3 0.1740288049 4 NA NA NA
6 0.9868044683 3 NA NA NA
7 -0.1280320918 1 NA NA NA
7.1 0.4242971219 NA NA NA NA
7.2 0.0777182491 4 NA NA NA
8 -0.5791408712 1 NA NA NA
8.1 0.3128604232 3 NA NA NA
8.2 0.6258446356 1 NA NA NA
8.3 -0.1040137707 4 NA NA NA
8.4 0.0481450285 3 NA NA NA
8.5 0.3831763675 3 NA NA NA
9 -0.1757592269 2 NA NA NA
9.1 -0.1791541200 2 NA NA NA
9.2 -0.0957042935 4 NA NA NA
10 -0.5598409704 1 NA NA NA
10.1 -0.2318340451 4 NA NA NA
11 0.5086859475 3 NA NA NA
11.1 0.4951758188 1 NA NA NA
11.2 -1.1022162541 4 NA NA NA
11.3 -0.0611636705 3 NA NA NA
11.4 -0.4971774316 3 NA NA NA
12 -0.2433996286 3 NA NA NA
13 0.8799673116 NA NA NA NA
13.1 0.1079022586 1 NA NA NA
14 0.9991752617 1 NA NA NA
14.1 -0.1094019046 4 NA NA NA
14.2 0.1518967560 3 NA NA NA
14.3 0.3521012473 4 NA NA NA
15 0.3464447888 1 NA NA NA
15.1 -0.4767313971 4 NA NA NA
15.2 0.5759767791 NA NA NA NA
15.3 -0.1713452662 2 NA NA NA
16 0.4564754473 NA NA NA NA
16.1 1.0652558311 NA NA NA NA
16.2 0.6971872493 1 NA NA NA
16.3 0.5259331838 3 NA NA NA
16.4 0.2046601798 3 NA NA NA
16.5 1.0718540464 1 NA NA NA
17 0.6048676222 3 NA NA NA
17.1 0.2323298304 2 NA NA NA
17.2 1.2617499032 2 NA NA NA
17.3 -0.3913230895 3 NA NA NA
17.4 0.9577299112 1 NA NA NA
18 -0.0050324072 4 NA NA NA
19 -0.4187468937 1 NA NA NA
19.1 -0.4478828944 NA NA NA NA
19.2 -1.1966721302 NA NA NA NA
19.3 -0.5877091668 2 NA NA NA
20 0.6838223064 1 NA NA NA
20.1 0.3278571109 4 NA NA NA
20.2 -0.8489831990 3 NA NA NA
20.3 1.3169975191 3 NA NA NA
20.4 0.0444804531 1 NA NA NA
20.5 -0.4535207652 3 NA NA NA
21 -0.4030302960 3 NA NA NA
21.1 -0.4069674045 1 NA NA NA
21.2 1.0650265940 2 NA NA NA
22 -0.0673274516 4 NA NA NA
22.1 0.9601388170 NA NA NA NA
23 0.5556634840 4 NA NA NA
23.1 1.4407865964 NA NA NA NA
24 0.3856376411 3 NA NA NA
25 0.3564400705 1 NA NA NA
25.1 0.0982553434 3 NA NA NA
25.2 0.1928682598 2 NA NA NA
25.3 -0.0192488594 1 NA NA NA
25.4 0.4466012931 1 NA NA NA
25.5 1.1425193342 NA NA NA NA
26 0.5341531449 3 NA NA NA
26.1 1.2268695927 4 NA NA NA
26.2 0.3678294939 3 NA NA NA
26.3 0.5948516018 1 NA NA NA
27 -0.3342844147 4 NA NA NA
27.1 -0.4835141229 4 NA NA NA
28 -0.7145915499 1 NA NA NA
28.1 0.5063671955 2 NA NA NA
28.2 -0.2067413142 3 NA NA NA
28.3 0.1196789973 3 NA NA NA
29 0.1392699487 4 NA NA NA
29.1 0.7960234776 4 NA NA NA
29.2 1.0398214352 3 NA NA NA
29.3 0.0813246429 2 NA NA NA
30 -0.3296323050 3 NA NA NA
30.1 1.3635850954 4 NA NA NA
30.2 0.7354171050 4 NA NA NA
31 0.3708398217 2 NA NA NA
32 -0.0474059668 NA NA NA NA
32.1 1.2507771489 2 NA NA NA
32.2 0.1142915519 4 NA NA NA
32.3 0.6773270619 3 NA NA NA
33 0.1774293842 4 NA NA NA
33.1 0.6159606291 4 NA NA NA
34 0.8590979166 NA NA NA NA
34.1 0.0546216775 NA NA NA NA
34.2 -0.0897224473 NA NA NA NA
34.3 0.4163395571 NA NA NA NA
35 -1.4693520528 4 NA NA NA
35.1 -0.3031734330 1 NA NA NA
35.2 -0.6045512101 NA NA NA NA
36 0.9823048960 1 NA NA NA
36.1 1.4466051416 1 NA NA NA
36.2 1.1606752905 2 NA NA NA
36.3 0.8373091576 2 NA NA NA
36.4 0.2640591685 1 NA NA NA
37 0.1177313455 4 NA NA NA
37.1 -0.1415483779 2 NA NA NA
37.2 0.0054610124 2 NA NA NA
38 0.8078948077 NA NA NA NA
39 0.9876451040 NA NA NA NA
39.1 -0.3431222274 2 NA NA NA
39.2 -1.7909380751 2 NA NA NA
39.3 -0.1798746191 3 NA NA NA
39.4 -0.1850961689 3 NA NA NA
39.5 0.4544226146 1 NA NA NA
40 0.5350190436 1 NA NA NA
40.1 0.4189342752 2 NA NA NA
40.2 0.4211994981 NA NA NA NA
40.3 0.0916687506 2 NA NA NA
41 -0.1035047421 4 NA NA NA
41.1 -0.4684202411 3 NA NA NA
41.2 0.5972615368 4 NA NA NA
41.3 0.9885613862 NA NA NA NA
41.4 -0.3908036794 4 NA NA NA
42 -0.0338893961 4 NA NA NA
42.1 -0.4498363172 4 NA NA NA
43 0.8965546110 NA NA NA NA
43.1 0.6199122090 NA NA NA NA
43.2 0.1804894429 3 NA NA NA
44 1.3221409285 2 NA NA NA
44.1 0.3416426284 4 NA NA NA
44.2 0.5706610068 NA NA NA NA
44.3 1.2679497430 1 NA NA NA
45 0.1414983160 3 NA NA NA
45.1 0.7220892521 4 NA NA NA
46 1.5391054233 NA NA NA NA
46.1 0.3889107049 4 NA NA NA
46.2 0.1248719493 4 NA NA NA
47 0.2014101100 3 NA NA NA
47.1 0.2982973539 2 NA NA NA
47.2 1.1518107179 1 NA NA NA
47.3 0.5196802157 2 NA NA NA
47.4 0.3702301552 1 NA NA NA
48 -0.2128602862 4 NA NA NA
48.1 -0.5337239976 NA NA NA NA
49 -0.5236770035 4 NA NA NA
50 0.3897705981 4 NA NA NA
51 -0.7213343736 4 NA NA NA
52 0.3758235358 NA NA NA NA
52.1 0.7138067080 NA NA NA NA
52.2 0.8872895233 3 NA NA NA
52.3 -0.9664587437 NA NA NA NA
52.4 0.0254566848 4 NA NA NA
52.5 0.4155259424 1 NA NA NA
53 0.5675736897 2 NA NA NA
53.1 -0.3154088781 1 NA NA NA
53.2 0.2162315769 3 NA NA NA
54 -0.0880802382 3 NA NA NA
54.1 0.4129127672 4 NA NA NA
54.2 1.0119546775 4 NA NA NA
54.3 -0.1112901990 3 NA NA NA
54.4 0.8587727145 NA NA NA NA
55 -0.0116453589 4 NA NA NA
55.1 0.5835528661 1 NA NA NA
55.2 -1.0010857254 4 NA NA NA
55.3 -0.4796526070 NA NA NA NA
55.4 -0.1202746964 1 NA NA NA
56 0.5176377612 1 NA NA NA
56.1 -1.1136932588 2 NA NA NA
56.2 -0.0168103281 2 NA NA NA
56.3 0.3933023606 3 NA NA NA
56.4 0.3714625139 4 NA NA NA
56.5 0.7811448179 4 NA NA NA
57 -1.0868304872 2 NA NA NA
57.1 0.8018626997 2 NA NA NA
57.2 -0.1159517011 4 NA NA NA
57.3 0.6785562445 NA NA NA NA
58 1.6476207996 1 NA NA NA
58.1 0.3402652711 2 NA NA NA
58.2 -0.1111300753 2 NA NA NA
58.3 -0.5409234285 4 NA NA NA
58.4 -0.1271327672 1 NA NA NA
58.5 0.8713264822 NA NA NA NA
59 0.4766421367 4 NA NA NA
59.1 1.0028089765 1 NA NA NA
60 0.5231452932 1 NA NA NA
61 -0.7190130614 1 NA NA NA
61.1 0.8353702312 1 NA NA NA
61.2 1.0229058138 NA NA NA NA
61.3 1.1717723589 1 NA NA NA
61.4 -0.0629201596 3 NA NA NA
62 -0.3979137604 NA NA NA NA
62.1 0.6830738372 NA NA NA NA
62.2 0.4301745954 NA NA NA NA
62.3 -0.0333139957 3 NA NA NA
63 0.3345678035 4 NA NA NA
63.1 0.3643769511 3 NA NA NA
64 0.3949911859 4 NA NA NA
65 1.2000091513 2 NA NA NA
65.1 0.0110122646 1 NA NA NA
65.2 -0.5776452043 3 NA NA NA
65.3 -0.1372183563 NA NA NA NA
66 -0.5081302805 1 NA NA NA
66.1 -0.1447837412 3 NA NA NA
66.2 0.1906241379 2 NA NA NA
67 1.6716027681 3 NA NA NA
68 0.5691848839 3 NA NA NA
68.1 0.1004860389 4 NA NA NA
68.2 -0.0061241827 3 NA NA NA
68.3 0.7443745962 1 NA NA NA
68.4 0.8726923437 4 NA NA NA
69 0.0381382683 4 NA NA NA
70 0.8126204217 4 NA NA NA
70.1 0.4691503050 4 NA NA NA
71 -0.5529062591 2 NA NA NA
71.1 -0.1103252087 NA NA NA NA
71.2 1.7178492547 4 NA NA NA
71.3 -1.0118346755 3 NA NA NA
71.4 1.8623785017 1 NA NA NA
72 -0.4521659275 1 NA NA NA
72.1 0.1375317317 NA NA NA NA
72.2 -0.4170988856 4 NA NA NA
72.3 0.7107266765 1 NA NA NA
72.4 0.1451969143 3 NA NA NA
72.5 1.6298050306 1 NA NA NA
73 -0.0307469467 2 NA NA NA
74 0.3730017941 4 NA NA NA
75 -0.4908003566 1 NA NA NA
76 -0.9888876620 2 NA NA NA
76.1 0.0003798292 1 NA NA NA
76.2 -0.8421863763 1 NA NA NA
77 -0.4986802480 3 NA NA NA
78 0.0417330969 3 NA NA NA
79 -0.3767450660 NA NA NA NA
79.1 0.1516000028 3 NA NA NA
79.2 -0.1888160741 NA NA NA NA
80 -0.0041558414 2 NA NA NA
80.1 -0.0329337062 3 NA NA NA
80.2 0.5046816157 1 NA NA NA
81 -0.9493950353 3 NA NA NA
81.1 0.2443038954 NA NA NA NA
81.2 0.6476958410 3 NA NA NA
81.3 0.4182528210 2 NA NA NA
82 1.1088801952 NA NA NA NA
82.1 0.9334157763 3 NA NA NA
82.2 0.4958140634 1 NA NA NA
83 0.5104724530 4 NA NA NA
83.1 -0.0513309106 NA NA NA NA
83.2 -0.2067792494 2 NA NA NA
83.3 -0.0534169155 NA NA NA NA
84 -0.0255753653 2 NA NA NA
84.1 -1.8234189877 1 NA NA NA
85 -0.0114038622 1 NA NA NA
85.1 -0.0577615939 4 NA NA NA
85.2 -0.2241856342 3 NA NA NA
85.3 -0.0520175929 3 NA NA NA
85.4 0.2892733846 NA NA NA NA
85.5 -0.3740417009 2 NA NA NA
86 0.4293735089 1 NA NA NA
86.1 -0.1363456521 3 NA NA NA
86.2 0.1230989293 1 NA NA NA
86.3 0.3305413955 2 NA NA NA
86.4 2.6003411822 3 NA NA NA
86.5 -0.1420690052 4 NA NA NA
87 1.0457427869 NA NA NA NA
87.1 -0.2973007190 3 NA NA NA
87.2 0.4396872616 3 NA NA NA
88 -0.0601928334 NA NA NA NA
88.1 -1.0124347595 1 NA NA NA
88.2 0.5730917016 2 NA NA NA
88.3 -0.0029455332 NA NA NA NA
89 1.5465903721 3 NA NA NA
90 0.0626760573 2 NA NA NA
90.1 1.1896872985 2 NA NA NA
90.2 0.2597888783 2 NA NA NA
90.3 0.6599799887 4 NA NA NA
91 1.1213651365 2 NA NA NA
91.1 1.2046371625 NA NA NA NA
91.2 0.3395603754 3 NA NA NA
92 0.4674939332 2 NA NA NA
93 0.2677965647 3 NA NA NA
93.1 1.6424445368 2 NA NA NA
93.2 0.7101700066 3 NA NA NA
93.3 1.1222322893 2 NA NA NA
93.4 1.4628960401 4 NA NA NA
94 -0.2904211940 NA NA NA NA
94.1 0.0147813580 2 NA NA NA
94.2 -0.4536774482 NA NA NA NA
94.3 0.6793464917 3 NA NA NA
94.4 -0.9411356550 4 NA NA NA
94.5 0.5683867264 3 NA NA NA
95 0.2375652188 NA NA NA NA
95.1 0.0767152977 2 NA NA NA
95.2 -0.6886731251 3 NA NA NA
96 0.7813892121 3 NA NA NA
96.1 0.3391519695 NA NA NA NA
96.2 -0.4857246503 4 NA NA NA
96.3 0.8771471244 3 NA NA NA
96.4 1.9030768981 NA NA NA NA
96.5 -0.1684332749 1 NA NA NA
97 1.3775130083 2 NA NA NA
97.1 -1.7323228619 1 NA NA NA
98 -1.2648518889 2 NA NA NA
98.1 -0.9042716241 1 NA NA NA
98.2 -0.1560385207 3 NA NA NA
99 0.7993356425 NA NA NA NA
99.1 1.0355522332 NA NA NA NA
99.2 -0.1150895843 4 NA NA NA
100 0.0369067906 1 NA NA NA
100.1 1.6023713093 NA NA NA NA
100.2 0.8861545820 1 NA NA NA
100.3 0.1277046316 4 NA NA NA
100.4 -0.0834577654 1 NA 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_delta_ordinal
[1] 0
$m3b$tau_delta_ordinal
[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
o1 o2 o22 o23 o24 o22:abs(C1 - C2) o23:abs(C1 - C2) o24:abs(C1 - C2)
1 2 1 NA NA NA NA NA NA
1.1 1 1 NA NA NA NA NA NA
1.2 1 3 NA NA NA NA NA NA
1.3 2 1 NA NA NA NA NA NA
2 3 4 NA NA NA NA NA NA
2.1 1 4 NA NA NA NA NA NA
2.2 2 2 NA NA NA NA NA NA
3 1 2 NA NA NA NA NA NA
3.1 3 4 NA NA NA NA NA NA
3.2 2 2 NA NA NA NA NA NA
4 3 4 NA NA NA NA NA NA
4.1 2 3 NA NA NA NA NA NA
4.2 1 NA NA NA NA NA NA NA
4.3 2 2 NA NA NA NA NA NA
5 2 2 NA NA NA NA NA NA
5.1 2 4 NA NA NA NA NA NA
5.2 3 2 NA NA NA NA NA NA
5.3 2 4 NA NA NA NA NA NA
6 3 3 NA NA NA NA NA NA
7 3 1 NA NA NA NA NA NA
7.1 1 NA NA NA NA NA NA NA
7.2 1 4 NA NA NA NA NA NA
8 2 1 NA NA NA NA NA NA
8.1 2 3 NA NA NA NA NA NA
8.2 3 1 NA NA NA NA NA NA
8.3 3 4 NA NA NA NA NA NA
8.4 2 3 NA NA NA NA NA NA
8.5 2 3 NA NA NA NA NA NA
9 2 2 NA NA NA NA NA NA
9.1 2 2 NA NA NA NA NA NA
9.2 3 4 NA NA NA NA NA NA
10 2 1 NA NA NA NA NA NA
10.1 1 4 NA NA NA NA NA NA
11 2 3 NA NA NA NA NA NA
11.1 3 1 NA NA NA NA NA NA
11.2 3 4 NA NA NA NA NA NA
11.3 1 3 NA NA NA NA NA NA
11.4 3 3 NA NA NA NA NA NA
12 1 3 NA NA NA NA NA NA
13 2 NA NA NA NA NA NA NA
13.1 1 1 NA NA NA NA NA NA
14 3 1 NA NA NA NA NA NA
14.1 3 4 NA NA NA NA NA NA
14.2 2 3 NA NA NA NA NA NA
14.3 1 4 NA NA NA NA NA NA
15 3 1 NA NA NA NA NA NA
15.1 3 4 NA NA NA NA NA NA
15.2 2 NA NA NA NA NA NA NA
15.3 2 2 NA NA NA NA NA NA
16 3 NA NA NA NA NA NA NA
16.1 1 NA NA NA NA NA NA NA
16.2 3 1 NA NA NA NA NA NA
16.3 1 3 NA NA NA NA NA NA
16.4 1 3 NA NA NA NA NA NA
16.5 2 1 NA NA NA NA NA NA
17 2 3 NA NA NA NA NA NA
17.1 2 2 NA NA NA NA NA NA
17.2 3 2 NA NA NA NA NA NA
17.3 2 3 NA NA NA NA NA NA
17.4 3 1 NA NA NA NA NA NA
18 2 4 NA NA NA NA NA NA
19 3 1 NA NA NA NA NA NA
19.1 2 NA NA NA NA NA NA NA
19.2 2 NA NA NA NA NA NA NA
19.3 2 2 NA NA NA NA NA NA
20 1 1 NA NA NA NA NA NA
20.1 2 4 NA NA NA NA NA NA
20.2 2 3 NA NA NA NA NA NA
20.3 1 3 NA NA NA NA NA NA
20.4 1 1 NA NA NA NA NA NA
20.5 2 3 NA NA NA NA NA NA
21 2 3 NA NA NA NA NA NA
21.1 2 1 NA NA NA NA NA NA
21.2 2 2 NA NA NA NA NA NA
22 1 4 NA NA NA NA NA NA
22.1 1 NA NA NA NA NA NA NA
23 1 4 NA NA NA NA NA NA
23.1 2 NA NA NA NA NA NA NA
24 1 3 NA NA NA NA NA NA
25 2 1 NA NA NA NA NA NA
25.1 1 3 NA NA NA NA NA NA
25.2 1 2 NA NA NA NA NA NA
25.3 1 1 NA NA NA NA NA NA
25.4 2 1 NA NA NA NA NA NA
25.5 2 NA NA NA NA NA NA NA
26 2 3 NA NA NA NA NA NA
26.1 3 4 NA NA NA NA NA NA
26.2 2 3 NA NA NA NA NA NA
26.3 1 1 NA NA NA NA NA NA
27 2 4 NA NA NA NA NA NA
27.1 2 4 NA NA NA NA NA NA
28 1 1 NA NA NA NA NA NA
28.1 1 2 NA NA NA NA NA NA
28.2 1 3 NA NA NA NA NA NA
28.3 1 3 NA NA NA NA NA NA
29 2 4 NA NA NA NA NA NA
29.1 2 4 NA NA NA NA NA NA
29.2 1 3 NA NA NA NA NA NA
29.3 2 2 NA NA NA NA NA NA
30 2 3 NA NA NA NA NA NA
30.1 2 4 NA NA NA NA NA NA
30.2 2 4 NA NA NA NA NA NA
31 2 2 NA NA NA NA NA NA
32 3 NA NA NA NA NA NA NA
32.1 1 2 NA NA NA NA NA NA
32.2 3 4 NA NA NA NA NA NA
32.3 3 3 NA NA NA NA NA NA
33 2 4 NA NA NA NA NA NA
33.1 2 4 NA NA NA NA NA NA
34 3 NA NA NA NA NA NA NA
34.1 2 NA NA NA NA NA NA NA
34.2 2 NA NA NA NA NA NA NA
34.3 1 NA NA NA NA NA NA NA
35 2 4 NA NA NA NA NA NA
35.1 3 1 NA NA NA NA NA NA
35.2 2 NA NA NA NA NA NA NA
36 3 1 NA NA NA NA NA NA
36.1 2 1 NA NA NA NA NA NA
36.2 3 2 NA NA NA NA NA NA
36.3 3 2 NA NA NA NA NA NA
36.4 3 1 NA NA NA NA NA NA
37 3 4 NA NA NA NA NA NA
37.1 3 2 NA NA NA NA NA NA
37.2 3 2 NA NA NA NA NA NA
38 2 NA NA NA NA NA NA NA
39 1 NA NA NA NA NA NA NA
39.1 2 2 NA NA NA NA NA NA
39.2 2 2 NA NA NA NA NA NA
39.3 1 3 NA NA NA NA NA NA
39.4 1 3 NA NA NA NA NA NA
39.5 1 1 NA NA NA NA NA NA
40 3 1 NA NA NA NA NA NA
40.1 1 2 NA NA NA NA NA NA
40.2 1 NA NA NA NA NA NA NA
40.3 2 2 NA NA NA NA NA NA
41 3 4 NA NA NA NA NA NA
41.1 2 3 NA NA NA NA NA NA
41.2 1 4 NA NA NA NA NA NA
41.3 1 NA NA NA NA NA NA NA
41.4 3 4 NA NA NA NA NA NA
42 2 4 NA NA NA NA NA NA
42.1 1 4 NA NA NA NA NA NA
43 2 NA NA NA NA NA NA NA
43.1 2 NA NA NA NA NA NA NA
43.2 1 3 NA NA NA NA NA NA
44 3 2 NA NA NA NA NA NA
44.1 2 4 NA NA NA NA NA NA
44.2 1 NA NA NA NA NA NA NA
44.3 1 1 NA NA NA NA NA NA
45 1 3 NA NA NA NA NA NA
45.1 1 4 NA NA NA NA NA NA
46 1 NA NA NA NA NA NA NA
46.1 2 4 NA NA NA NA NA NA
46.2 3 4 NA NA NA NA NA NA
47 1 3 NA NA NA NA NA NA
47.1 1 2 NA NA NA NA NA NA
47.2 1 1 NA NA NA NA NA NA
47.3 3 2 NA NA NA NA NA NA
47.4 2 1 NA NA NA NA NA NA
48 3 4 NA NA NA NA NA NA
48.1 2 NA NA NA NA NA NA NA
49 1 4 NA NA NA NA NA NA
50 2 4 NA NA NA NA NA NA
51 2 4 NA NA NA NA NA NA
52 2 NA NA NA NA NA NA NA
52.1 3 NA NA NA NA NA NA NA
52.2 2 3 NA NA NA NA NA NA
52.3 1 NA NA NA NA NA NA NA
52.4 3 4 NA NA NA NA NA NA
52.5 2 1 NA NA NA NA NA NA
53 2 2 NA NA NA NA NA NA
53.1 1 1 NA NA NA NA NA NA
53.2 1 3 NA NA NA NA NA NA
54 1 3 NA NA NA NA NA NA
54.1 2 4 NA NA NA NA NA NA
54.2 3 4 NA NA NA NA NA NA
54.3 1 3 NA NA NA NA NA NA
54.4 2 NA NA NA NA NA NA NA
55 1 4 NA NA NA NA NA NA
55.1 1 1 NA NA NA NA NA NA
55.2 2 4 NA NA NA NA NA NA
55.3 1 NA NA NA NA NA NA NA
55.4 1 1 NA NA NA NA NA NA
56 3 1 NA NA NA NA NA NA
56.1 2 2 NA NA NA NA NA NA
56.2 3 2 NA NA NA NA NA NA
56.3 3 3 NA NA NA NA NA NA
56.4 1 4 NA NA NA NA NA NA
56.5 3 4 NA NA NA NA NA NA
57 2 2 NA NA NA NA NA NA
57.1 2 2 NA NA NA NA NA NA
57.2 2 4 NA NA NA NA NA NA
57.3 2 NA NA NA NA NA NA NA
58 3 1 NA NA NA NA NA NA
58.1 2 2 NA NA NA NA NA NA
58.2 3 2 NA NA NA NA NA NA
58.3 3 4 NA NA NA NA NA NA
58.4 2 1 NA NA NA NA NA NA
58.5 3 NA NA NA NA NA NA NA
59 3 4 NA NA NA NA NA NA
59.1 1 1 NA NA NA NA NA NA
60 3 1 NA NA NA NA NA NA
61 1 1 NA NA NA NA NA NA
61.1 2 1 NA NA NA NA NA NA
61.2 2 NA NA NA NA NA NA NA
61.3 1 1 NA NA NA NA NA NA
61.4 2 3 NA NA NA NA NA NA
62 2 NA NA NA NA NA NA NA
62.1 3 NA NA NA NA NA NA NA
62.2 1 NA NA NA NA NA NA NA
62.3 2 3 NA NA NA NA NA NA
63 1 4 NA NA NA NA NA NA
63.1 3 3 NA NA NA NA NA NA
64 2 4 NA NA NA NA NA NA
65 1 2 NA NA NA NA NA NA
65.1 2 1 NA NA NA NA NA NA
65.2 1 3 NA NA NA NA NA NA
65.3 1 NA NA NA NA NA NA NA
66 1 1 NA NA NA NA NA NA
66.1 1 3 NA NA NA NA NA NA
66.2 3 2 NA NA NA NA NA NA
67 3 3 NA NA NA NA NA NA
68 1 3 NA NA NA NA NA NA
68.1 2 4 NA NA NA NA NA NA
68.2 2 3 NA NA NA NA NA NA
68.3 2 1 NA NA NA NA NA NA
68.4 2 4 NA NA NA NA NA NA
69 2 4 NA NA NA NA NA NA
70 1 4 NA NA NA NA NA NA
70.1 3 4 NA NA NA NA NA NA
71 3 2 NA NA NA NA NA NA
71.1 3 NA NA NA NA NA NA NA
71.2 3 4 NA NA NA NA NA NA
71.3 2 3 NA NA NA NA NA NA
71.4 2 1 NA NA NA NA NA NA
72 1 1 NA NA NA NA NA NA
72.1 1 NA NA NA NA NA NA NA
72.2 2 4 NA NA NA NA NA NA
72.3 2 1 NA NA NA NA NA NA
72.4 1 3 NA NA NA NA NA NA
72.5 1 1 NA NA NA NA NA NA
73 1 2 NA NA NA NA NA NA
74 1 4 NA NA NA NA NA NA
75 3 1 NA NA NA NA NA NA
76 3 2 NA NA NA NA NA NA
76.1 3 1 NA NA NA NA NA NA
76.2 1 1 NA NA NA NA NA NA
77 2 3 NA NA NA NA NA NA
78 2 3 NA NA NA NA NA NA
79 1 NA NA NA NA NA NA NA
79.1 2 3 NA NA NA NA NA NA
79.2 1 NA NA NA NA NA NA NA
80 2 2 NA NA NA NA NA NA
80.1 2 3 NA NA NA NA NA NA
80.2 3 1 NA NA NA NA NA NA
81 3 3 NA NA NA NA NA NA
81.1 2 NA NA NA NA NA NA NA
81.2 3 3 NA NA NA NA NA NA
81.3 3 2 NA NA NA NA NA NA
82 3 NA NA NA NA NA NA NA
82.1 2 3 NA NA NA NA NA NA
82.2 2 1 NA NA NA NA NA NA
83 3 4 NA NA NA NA NA NA
83.1 3 NA NA NA NA NA NA NA
83.2 3 2 NA NA NA NA NA NA
83.3 2 NA NA NA NA NA NA NA
84 3 2 NA NA NA NA NA NA
84.1 3 1 NA NA NA NA NA NA
85 3 1 NA NA NA NA NA NA
85.1 2 4 NA NA NA NA NA NA
85.2 2 3 NA NA NA NA NA NA
85.3 2 3 NA NA NA NA NA NA
85.4 1 NA NA NA NA NA NA NA
85.5 3 2 NA NA NA NA NA NA
86 2 1 NA NA NA NA NA NA
86.1 3 3 NA NA NA NA NA NA
86.2 1 1 NA NA NA NA NA NA
86.3 1 2 NA NA NA NA NA NA
86.4 1 3 NA NA NA NA NA NA
86.5 3 4 NA NA NA NA NA NA
87 1 NA NA NA NA NA NA NA
87.1 1 3 NA NA NA NA NA NA
87.2 1 3 NA NA NA NA NA NA
88 3 NA NA NA NA NA NA NA
88.1 1 1 NA NA NA NA NA NA
88.2 1 2 NA NA NA NA NA NA
88.3 1 NA NA NA NA NA NA NA
89 1 3 NA NA NA NA NA NA
90 2 2 NA NA NA NA NA NA
90.1 1 2 NA NA NA NA NA NA
90.2 3 2 NA NA NA NA NA NA
90.3 1 4 NA NA NA NA NA NA
91 1 2 NA NA NA NA NA NA
91.1 2 NA NA NA NA NA NA NA
91.2 1 3 NA NA NA NA NA NA
92 2 2 NA NA NA NA NA NA
93 1 3 NA NA NA NA NA NA
93.1 3 2 NA NA NA NA NA NA
93.2 1 3 NA NA NA NA NA NA
93.3 3 2 NA NA NA NA NA NA
93.4 1 4 NA NA NA NA NA NA
94 2 NA NA NA NA NA NA NA
94.1 3 2 NA NA NA NA NA NA
94.2 1 NA NA NA NA NA NA NA
94.3 3 3 NA NA NA NA NA NA
94.4 2 4 NA NA NA NA NA NA
94.5 3 3 NA NA NA NA NA NA
95 3 NA NA NA NA NA NA NA
95.1 1 2 NA NA NA NA NA NA
95.2 2 3 NA NA NA NA NA NA
96 1 3 NA NA NA NA NA NA
96.1 3 NA NA NA NA NA NA NA
96.2 1 4 NA NA NA NA NA NA
96.3 1 3 NA NA NA NA NA NA
96.4 2 NA NA NA NA NA NA NA
96.5 3 1 NA NA NA NA NA NA
97 3 2 NA NA NA NA NA NA
97.1 3 1 NA NA NA NA NA NA
98 3 2 NA NA NA NA NA NA
98.1 1 1 NA NA NA NA NA NA
98.2 1 3 NA NA NA NA NA NA
99 2 NA NA NA NA NA NA NA
99.1 2 NA NA NA NA NA NA NA
99.2 1 4 NA NA NA NA NA NA
100 1 1 NA NA NA NA NA NA
100.1 2 NA NA NA NA NA NA NA
100.2 2 1 NA NA NA NA NA NA
100.3 1 4 NA NA NA NA NA NA
100.4 1 1 NA NA 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
o1 NA NA
o2 NA NA
o22 NA NA
o23 NA NA
o24 NA NA
o22:abs(C1 - C2) 0.2846191 0.6429094
o23:abs(C1 - C2) 0.3769770 0.7128409
o24:abs(C1 - C2) 0.3704272 0.7139761
$m4a$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
$m4a$mu_reg_ordinal
[1] 0
$m4a$tau_reg_ordinal
[1] 1e-04
$m4a$mu_delta_ordinal
[1] 0
$m4a$tau_delta_ordinal
[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
o1 o2 ifelse(as.numeric(o2) > as.numeric(M1), 1, 0)
1 2 1 NA
1.1 1 1 NA
1.2 1 3 NA
1.3 2 1 NA
2 3 4 NA
2.1 1 4 NA
2.2 2 2 NA
3 1 2 NA
3.1 3 4 NA
3.2 2 2 NA
4 3 4 NA
4.1 2 3 NA
4.2 1 NA NA
4.3 2 2 NA
5 2 2 NA
5.1 2 4 NA
5.2 3 2 NA
5.3 2 4 NA
6 3 3 NA
7 3 1 NA
7.1 1 NA NA
7.2 1 4 NA
8 2 1 NA
8.1 2 3 NA
8.2 3 1 NA
8.3 3 4 NA
8.4 2 3 NA
8.5 2 3 NA
9 2 2 NA
9.1 2 2 NA
9.2 3 4 NA
10 2 1 NA
10.1 1 4 NA
11 2 3 NA
11.1 3 1 NA
11.2 3 4 NA
11.3 1 3 NA
11.4 3 3 NA
12 1 3 NA
13 2 NA NA
13.1 1 1 NA
14 3 1 NA
14.1 3 4 NA
14.2 2 3 NA
14.3 1 4 NA
15 3 1 NA
15.1 3 4 NA
15.2 2 NA NA
15.3 2 2 NA
16 3 NA NA
16.1 1 NA NA
16.2 3 1 NA
16.3 1 3 NA
16.4 1 3 NA
16.5 2 1 NA
17 2 3 NA
17.1 2 2 NA
17.2 3 2 NA
17.3 2 3 NA
17.4 3 1 NA
18 2 4 NA
19 3 1 NA
19.1 2 NA NA
19.2 2 NA NA
19.3 2 2 NA
20 1 1 NA
20.1 2 4 NA
20.2 2 3 NA
20.3 1 3 NA
20.4 1 1 NA
20.5 2 3 NA
21 2 3 NA
21.1 2 1 NA
21.2 2 2 NA
22 1 4 NA
22.1 1 NA NA
23 1 4 NA
23.1 2 NA NA
24 1 3 NA
25 2 1 NA
25.1 1 3 NA
25.2 1 2 NA
25.3 1 1 NA
25.4 2 1 NA
25.5 2 NA NA
26 2 3 NA
26.1 3 4 NA
26.2 2 3 NA
26.3 1 1 NA
27 2 4 NA
27.1 2 4 NA
28 1 1 NA
28.1 1 2 NA
28.2 1 3 NA
28.3 1 3 NA
29 2 4 NA
29.1 2 4 NA
29.2 1 3 NA
29.3 2 2 NA
30 2 3 NA
30.1 2 4 NA
30.2 2 4 NA
31 2 2 NA
32 3 NA NA
32.1 1 2 NA
32.2 3 4 NA
32.3 3 3 NA
33 2 4 NA
33.1 2 4 NA
34 3 NA NA
34.1 2 NA NA
34.2 2 NA NA
34.3 1 NA NA
35 2 4 NA
35.1 3 1 NA
35.2 2 NA NA
36 3 1 NA
36.1 2 1 NA
36.2 3 2 NA
36.3 3 2 NA
36.4 3 1 NA
37 3 4 NA
37.1 3 2 NA
37.2 3 2 NA
38 2 NA NA
39 1 NA NA
39.1 2 2 NA
39.2 2 2 NA
39.3 1 3 NA
39.4 1 3 NA
39.5 1 1 NA
40 3 1 NA
40.1 1 2 NA
40.2 1 NA NA
40.3 2 2 NA
41 3 4 NA
41.1 2 3 NA
41.2 1 4 NA
41.3 1 NA NA
41.4 3 4 NA
42 2 4 NA
42.1 1 4 NA
43 2 NA NA
43.1 2 NA NA
43.2 1 3 NA
44 3 2 NA
44.1 2 4 NA
44.2 1 NA NA
44.3 1 1 NA
45 1 3 NA
45.1 1 4 NA
46 1 NA NA
46.1 2 4 NA
46.2 3 4 NA
47 1 3 NA
47.1 1 2 NA
47.2 1 1 NA
47.3 3 2 NA
47.4 2 1 NA
48 3 4 NA
48.1 2 NA NA
49 1 4 NA
50 2 4 NA
51 2 4 NA
52 2 NA NA
52.1 3 NA NA
52.2 2 3 NA
52.3 1 NA NA
52.4 3 4 NA
52.5 2 1 NA
53 2 2 NA
53.1 1 1 NA
53.2 1 3 NA
54 1 3 NA
54.1 2 4 NA
54.2 3 4 NA
54.3 1 3 NA
54.4 2 NA NA
55 1 4 NA
55.1 1 1 NA
55.2 2 4 NA
55.3 1 NA NA
55.4 1 1 NA
56 3 1 NA
56.1 2 2 NA
56.2 3 2 NA
56.3 3 3 NA
56.4 1 4 NA
56.5 3 4 NA
57 2 2 NA
57.1 2 2 NA
57.2 2 4 NA
57.3 2 NA NA
58 3 1 NA
58.1 2 2 NA
58.2 3 2 NA
58.3 3 4 NA
58.4 2 1 NA
58.5 3 NA NA
59 3 4 NA
59.1 1 1 NA
60 3 1 NA
61 1 1 NA
61.1 2 1 NA
61.2 2 NA NA
61.3 1 1 NA
61.4 2 3 NA
62 2 NA NA
62.1 3 NA NA
62.2 1 NA NA
62.3 2 3 NA
63 1 4 NA
63.1 3 3 NA
64 2 4 NA
65 1 2 NA
65.1 2 1 NA
65.2 1 3 NA
65.3 1 NA NA
66 1 1 NA
66.1 1 3 NA
66.2 3 2 NA
67 3 3 NA
68 1 3 NA
68.1 2 4 NA
68.2 2 3 NA
68.3 2 1 NA
68.4 2 4 NA
69 2 4 NA
70 1 4 NA
70.1 3 4 NA
71 3 2 NA
71.1 3 NA NA
71.2 3 4 NA
71.3 2 3 NA
71.4 2 1 NA
72 1 1 NA
72.1 1 NA NA
72.2 2 4 NA
72.3 2 1 NA
72.4 1 3 NA
72.5 1 1 NA
73 1 2 NA
74 1 4 NA
75 3 1 NA
76 3 2 NA
76.1 3 1 NA
76.2 1 1 NA
77 2 3 NA
78 2 3 NA
79 1 NA NA
79.1 2 3 NA
79.2 1 NA NA
80 2 2 NA
80.1 2 3 NA
80.2 3 1 NA
81 3 3 NA
81.1 2 NA NA
81.2 3 3 NA
81.3 3 2 NA
82 3 NA NA
82.1 2 3 NA
82.2 2 1 NA
83 3 4 NA
83.1 3 NA NA
83.2 3 2 NA
83.3 2 NA NA
84 3 2 NA
84.1 3 1 NA
85 3 1 NA
85.1 2 4 NA
85.2 2 3 NA
85.3 2 3 NA
85.4 1 NA NA
85.5 3 2 NA
86 2 1 NA
86.1 3 3 NA
86.2 1 1 NA
86.3 1 2 NA
86.4 1 3 NA
86.5 3 4 NA
87 1 NA NA
87.1 1 3 NA
87.2 1 3 NA
88 3 NA NA
88.1 1 1 NA
88.2 1 2 NA
88.3 1 NA NA
89 1 3 NA
90 2 2 NA
90.1 1 2 NA
90.2 3 2 NA
90.3 1 4 NA
91 1 2 NA
91.1 2 NA NA
91.2 1 3 NA
92 2 2 NA
93 1 3 NA
93.1 3 2 NA
93.2 1 3 NA
93.3 3 2 NA
93.4 1 4 NA
94 2 NA NA
94.1 3 2 NA
94.2 1 NA NA
94.3 3 3 NA
94.4 2 4 NA
94.5 3 3 NA
95 3 NA NA
95.1 1 2 NA
95.2 2 3 NA
96 1 3 NA
96.1 3 NA NA
96.2 1 4 NA
96.3 1 3 NA
96.4 2 NA NA
96.5 3 1 NA
97 3 2 NA
97.1 3 1 NA
98 3 2 NA
98.1 1 1 NA
98.2 1 3 NA
99 2 NA NA
99.1 2 NA NA
99.2 1 4 NA
100 1 1 NA
100.1 2 NA NA
100.2 2 1 NA
100.3 1 4 NA
100.4 1 1 NA
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) o22 o23 o24
1 NA NA NA NA
1.1 NA NA NA NA
1.2 NA NA NA NA
1.3 NA NA NA NA
2 NA NA NA NA
2.1 NA NA NA NA
2.2 NA NA NA NA
3 NA NA NA NA
3.1 NA NA NA NA
3.2 NA NA NA NA
4 NA NA NA NA
4.1 NA NA NA NA
4.2 NA NA NA NA
4.3 NA NA NA NA
5 NA NA NA NA
5.1 NA NA NA NA
5.2 NA NA NA NA
5.3 NA NA NA NA
6 NA NA NA NA
7 NA NA NA NA
7.1 NA NA NA NA
7.2 NA NA NA NA
8 NA NA NA NA
8.1 NA NA NA NA
8.2 NA NA NA NA
8.3 NA NA NA NA
8.4 NA NA NA NA
8.5 NA NA NA NA
9 NA NA NA NA
9.1 NA NA NA NA
9.2 NA NA NA NA
10 NA NA NA NA
10.1 NA NA NA NA
11 NA NA NA NA
11.1 NA NA NA NA
11.2 NA NA NA NA
11.3 NA NA NA NA
11.4 NA NA NA NA
12 NA NA NA NA
13 NA NA NA NA
13.1 NA NA NA NA
14 NA NA NA NA
14.1 NA NA NA NA
14.2 NA NA NA NA
14.3 NA NA NA NA
15 NA NA NA NA
15.1 NA NA NA NA
15.2 NA NA NA NA
15.3 NA NA NA NA
16 NA NA NA NA
16.1 NA NA NA NA
16.2 NA NA NA NA
16.3 NA NA NA NA
16.4 NA NA NA NA
16.5 NA NA NA NA
17 NA NA NA NA
17.1 NA NA NA NA
17.2 NA NA NA NA
17.3 NA NA NA NA
17.4 NA NA NA NA
18 NA NA NA NA
19 NA NA NA NA
19.1 NA NA NA NA
19.2 NA NA NA NA
19.3 NA NA NA NA
20 NA NA NA NA
20.1 NA NA NA NA
20.2 NA NA NA NA
20.3 NA NA NA NA
20.4 NA NA NA NA
20.5 NA NA NA NA
21 NA NA NA NA
21.1 NA NA NA NA
21.2 NA NA NA NA
22 NA NA NA NA
22.1 NA NA NA NA
23 NA NA NA NA
23.1 NA NA NA NA
24 NA NA NA NA
25 NA NA NA NA
25.1 NA NA NA NA
25.2 NA NA NA NA
25.3 NA NA NA NA
25.4 NA NA NA NA
25.5 NA NA NA NA
26 NA NA NA NA
26.1 NA NA NA NA
26.2 NA NA NA NA
26.3 NA NA NA NA
27 NA NA NA NA
27.1 NA NA NA NA
28 NA NA NA NA
28.1 NA NA NA NA
28.2 NA NA NA NA
28.3 NA NA NA NA
29 NA NA NA NA
29.1 NA NA NA NA
29.2 NA NA NA NA
29.3 NA NA NA NA
30 NA NA NA NA
30.1 NA NA NA NA
30.2 NA NA NA NA
31 NA NA NA NA
32 NA NA NA NA
32.1 NA NA NA NA
32.2 NA NA NA NA
32.3 NA NA NA NA
33 NA NA NA NA
33.1 NA NA NA NA
34 NA NA NA NA
34.1 NA NA NA NA
34.2 NA NA NA NA
34.3 NA NA NA NA
35 NA NA NA NA
35.1 NA NA NA NA
35.2 NA NA NA NA
36 NA NA NA NA
36.1 NA NA NA NA
36.2 NA NA NA NA
36.3 NA NA NA NA
36.4 NA NA NA NA
37 NA NA NA NA
37.1 NA NA NA NA
37.2 NA NA NA NA
38 NA NA NA NA
39 NA NA NA NA
39.1 NA NA NA NA
39.2 NA NA NA NA
39.3 NA NA NA NA
39.4 NA NA NA NA
39.5 NA NA NA NA
40 NA NA NA NA
40.1 NA NA NA NA
40.2 NA NA NA NA
40.3 NA NA NA NA
41 NA NA NA NA
41.1 NA NA NA NA
41.2 NA NA NA NA
41.3 NA NA NA NA
41.4 NA NA NA NA
42 NA NA NA NA
42.1 NA NA NA NA
43 NA NA NA NA
43.1 NA NA NA NA
43.2 NA NA NA NA
44 NA NA NA NA
44.1 NA NA NA NA
44.2 NA NA NA NA
44.3 NA NA NA NA
45 NA NA NA NA
45.1 NA NA NA NA
46 NA NA NA NA
46.1 NA NA NA NA
46.2 NA NA NA NA
47 NA NA NA NA
47.1 NA NA NA NA
47.2 NA NA NA NA
47.3 NA NA NA NA
47.4 NA NA NA NA
48 NA NA NA NA
48.1 NA NA NA NA
49 NA NA NA NA
50 NA NA NA NA
51 NA NA NA NA
52 NA NA NA NA
52.1 NA NA NA NA
52.2 NA NA NA NA
52.3 NA NA NA NA
52.4 NA NA NA NA
52.5 NA NA NA NA
53 NA NA NA NA
53.1 NA NA NA NA
53.2 NA NA NA NA
54 NA NA NA NA
54.1 NA NA NA NA
54.2 NA NA NA NA
54.3 NA NA NA NA
54.4 NA NA NA NA
55 NA NA NA NA
55.1 NA NA NA NA
55.2 NA NA NA NA
55.3 NA NA NA NA
55.4 NA NA NA NA
56 NA NA NA NA
56.1 NA NA NA NA
56.2 NA NA NA NA
56.3 NA NA NA NA
56.4 NA NA NA NA
56.5 NA NA NA NA
57 NA NA NA NA
57.1 NA NA NA NA
57.2 NA NA NA NA
57.3 NA NA NA NA
58 NA NA NA NA
58.1 NA NA NA NA
58.2 NA NA NA NA
58.3 NA NA NA NA
58.4 NA NA NA NA
58.5 NA NA NA NA
59 NA NA NA NA
59.1 NA NA NA NA
60 NA NA NA NA
61 NA NA NA NA
61.1 NA NA NA NA
61.2 NA NA NA NA
61.3 NA NA NA NA
61.4 NA NA NA NA
62 NA NA NA NA
62.1 NA NA NA NA
62.2 NA NA NA NA
62.3 NA NA NA NA
63 NA NA NA NA
63.1 NA NA NA NA
64 NA NA NA NA
65 NA NA NA NA
65.1 NA NA NA NA
65.2 NA NA NA NA
65.3 NA NA NA NA
66 NA NA NA NA
66.1 NA NA NA NA
66.2 NA NA NA NA
67 NA NA NA NA
68 NA NA NA NA
68.1 NA NA NA NA
68.2 NA NA NA NA
68.3 NA NA NA NA
68.4 NA NA NA NA
69 NA NA NA NA
70 NA NA NA NA
70.1 NA NA NA NA
71 NA NA NA NA
71.1 NA NA NA NA
71.2 NA NA NA NA
71.3 NA NA NA NA
71.4 NA NA NA NA
72 NA NA NA NA
72.1 NA NA NA NA
72.2 NA NA NA NA
72.3 NA NA NA NA
72.4 NA NA NA NA
72.5 NA NA NA NA
73 NA NA NA NA
74 NA NA NA NA
75 NA NA NA NA
76 NA NA NA NA
76.1 NA NA NA NA
76.2 NA NA NA NA
77 NA NA NA NA
78 NA NA NA NA
79 NA NA NA NA
79.1 NA NA NA NA
79.2 NA NA NA NA
80 NA NA NA NA
80.1 NA NA NA NA
80.2 NA NA NA NA
81 NA NA NA NA
81.1 NA NA NA NA
81.2 NA NA NA NA
81.3 NA NA NA NA
82 NA NA NA NA
82.1 NA NA NA NA
82.2 NA NA NA NA
83 NA NA NA NA
83.1 NA NA NA NA
83.2 NA NA NA NA
83.3 NA NA NA NA
84 NA NA NA NA
84.1 NA NA NA NA
85 NA NA NA NA
85.1 NA NA NA NA
85.2 NA NA NA NA
85.3 NA NA NA NA
85.4 NA NA NA NA
85.5 NA NA NA NA
86 NA NA NA NA
86.1 NA NA NA NA
86.2 NA NA NA NA
86.3 NA NA NA NA
86.4 NA NA NA NA
86.5 NA NA NA NA
87 NA NA NA NA
87.1 NA NA NA NA
87.2 NA NA NA NA
88 NA NA NA NA
88.1 NA NA NA NA
88.2 NA NA NA NA
88.3 NA NA NA NA
89 NA NA NA NA
90 NA NA NA NA
90.1 NA NA NA NA
90.2 NA NA NA NA
90.3 NA NA NA NA
91 NA NA NA NA
91.1 NA NA NA NA
91.2 NA NA NA NA
92 NA NA NA NA
93 NA NA NA NA
93.1 NA NA NA NA
93.2 NA NA NA NA
93.3 NA NA NA NA
93.4 NA NA NA NA
94 NA NA NA NA
94.1 NA NA NA NA
94.2 NA NA NA NA
94.3 NA NA NA NA
94.4 NA NA NA NA
94.5 NA NA NA NA
95 NA NA NA NA
95.1 NA NA NA NA
95.2 NA NA NA NA
96 NA NA NA NA
96.1 NA NA NA NA
96.2 NA NA NA NA
96.3 NA NA NA NA
96.4 NA NA NA NA
96.5 NA NA NA NA
97 NA NA NA NA
97.1 NA NA NA NA
98 NA NA NA NA
98.1 NA NA NA NA
98.2 NA NA NA NA
99 NA NA NA NA
99.1 NA NA NA NA
99.2 NA NA NA NA
100 NA NA NA NA
100.1 NA NA NA NA
100.2 NA NA NA NA
100.3 NA NA NA NA
100.4 NA 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
o1 NA NA
o2 NA NA
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0.3740741 0.4847815
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0.5451138 0.8377542
o22 NA NA
o23 NA NA
o24 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_ordinal
[1] 0
$m4b$tau_reg_ordinal
[1] 1e-04
$m4b$mu_delta_ordinal
[1] 0
$m4b$tau_delta_ordinal
[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
o1 time c1 c1:time
1 2 0.5090421822 0.7592026489 3.864662e-01
1.1 1 0.6666076288 0.9548337990 6.364995e-01
1.2 1 2.1304941282 0.5612235156 1.195683e+00
1.3 2 2.4954441458 1.1873391025 2.962938e+00
2 3 3.0164990982 0.9192204198 2.772828e+00
2.1 1 3.2996806887 -0.1870730476 -6.172813e-01
2.2 2 4.1747569619 1.2517512331 5.225757e+00
3 1 0.8478727890 -0.0605087604 -5.130373e-02
3.1 3 3.0654308549 0.3788637747 1.161381e+00
3.2 2 4.7381553578 0.9872578281 4.677781e+00
4 3 0.3371432109 1.4930175328 5.033607e-01
4.1 2 1.0693019140 -0.7692526880 -8.225634e-01
4.2 1 2.6148973033 0.9180841450 2.400696e+00
4.3 2 3.1336532847 -0.0541170782 -1.695842e-01
5 2 1.0762525082 -0.1376784521 -1.481768e-01
5.1 2 1.7912546196 -0.2740585866 -4.909087e-01
5.2 3 2.7960080339 0.4670496929 1.305875e+00
5.3 2 2.8119940578 0.1740288049 4.893680e-01
6 3 1.7815462884 0.9868044683 1.758038e+00
7 3 3.3074087673 -0.1280320918 -4.234545e-01
7.1 1 3.7008403614 0.4242971219 1.570256e+00
7.2 1 4.7716691741 0.0777182491 3.708458e-01
8 2 1.1246398522 -0.5791408712 -6.513249e-01
8.1 2 1.8027009873 0.3128604232 5.639938e-01
8.2 3 1.8175825174 0.6258446356 1.137524e+00
8.3 3 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 2 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 2 2.2975509102 -0.5598409704 -1.286263e+00
10.1 1 4.1734118364 -0.2318340451 -9.675389e-01
11 2 1.1832662905 0.5086859475 6.019109e-01
11.1 3 1.2346051680 0.4951758188 6.113466e-01
11.2 3 1.6435316263 -1.1022162541 -1.811527e+00
11.3 1 3.3859017969 -0.0611636705 -2.070942e-01
11.4 3 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 1 3.5621061502 0.1079022586 3.843593e-01
14 3 4.0364430007 0.9991752617 4.033114e+00
14.1 3 4.4710561272 -0.1094019046 -4.891421e-01
14.2 2 4.6359198843 0.1518967560 7.041812e-01
14.3 1 4.6886152599 0.3521012473 1.650867e+00
15 3 0.5402063532 0.3464447888 1.871517e-01
15.1 3 1.1893180816 -0.4767313971 -5.669853e-01
15.2 2 1.5094739688 0.5759767791 8.694220e-01
15.3 2 4.9193474615 -0.1713452662 -8.429069e-01
16 3 1.2417913869 0.4564754473 5.668473e-01
16.1 1 2.5675726333 1.0652558311 2.735122e+00
16.2 3 2.6524101500 0.6971872493 1.849227e+00
16.3 1 3.5585018690 0.5259331838 1.871534e+00
16.4 1 3.7612454291 0.2046601798 7.697772e-01
16.5 2 3.9851612889 1.0718540464 4.271511e+00
17 2 1.5925356350 0.6048676222 9.632732e-01
17.1 2 2.4374032998 0.2323298304 5.662815e-01
17.2 3 3.0256489082 1.2617499032 3.817612e+00
17.3 2 3.3329089405 -0.3913230895 -1.304244e+00
17.4 3 3.8693758985 0.9577299112 3.705817e+00
18 2 2.4374292302 -0.0050324072 -1.226614e-02
19 3 0.9772165376 -0.4187468937 -4.092064e-01
19.1 2 1.1466335913 -0.4478828944 -5.135576e-01
19.2 2 2.2599126538 -1.1966721302 -2.704374e+00
19.3 2 4.2114245973 -0.5877091668 -2.475093e+00
20 1 1.7170160066 0.6838223064 1.174134e+00
20.1 2 1.7562902288 0.3278571109 5.758122e-01
20.2 2 2.2515566566 -0.8489831990 -1.911534e+00
20.3 1 2.2609123867 1.3169975191 2.977616e+00
20.4 1 3.4913365287 0.0444804531 1.552962e-01
20.5 2 4.1730977828 -0.4535207652 -1.892586e+00
21 2 1.6936582839 -0.4030302960 -6.825956e-01
21.1 2 2.9571191233 -0.4069674045 -1.203451e+00
21.2 2 3.7887385779 1.0650265940 4.035107e+00
22 1 2.4696226232 -0.0673274516 -1.662734e-01
22.1 1 3.1626627257 0.9601388170 3.036595e+00
23 1 1.5414533857 0.5556634840 8.565294e-01
23.1 2 2.3369736120 1.4407865964 3.367080e+00
24 1 2.8283136466 0.3856376411 1.090704e+00
25 2 0.5381704110 0.3564400705 1.918255e-01
25.1 1 1.6069735331 0.0982553434 1.578937e-01
25.2 1 1.6358226922 0.1928682598 3.154983e-01
25.3 1 3.2646870392 -0.0192488594 -6.284150e-02
25.4 2 4.0782226040 0.4466012931 1.821339e+00
25.5 2 4.1560292873 1.1425193342 4.748344e+00
26 2 0.2412706357 0.5341531449 1.288755e-01
26.1 3 2.4451737676 1.2268695927 2.999909e+00
26.2 2 3.5988757887 0.3678294939 1.323773e+00
26.3 1 4.1822362854 0.5948516018 2.487810e+00
27 2 3.6955824879 -0.3342844147 -1.235376e+00
27.1 2 4.2451434687 -0.4835141229 -2.052587e+00
28 1 0.5746519344 -0.7145915499 -4.106414e-01
28.1 1 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 2 1.1898884235 0.1392699487 1.657157e-01
29.1 2 1.7624059319 0.7960234776 1.402916e+00
29.2 1 2.0210406382 1.0398214352 2.101521e+00
29.3 2 3.4078777023 0.0813246429 2.771444e-01
30 2 2.2635366488 -0.3296323050 -7.461348e-01
30.1 2 3.5938334477 1.3635850954 4.900498e+00
30.2 2 3.6138710892 0.7354171050 2.657703e+00
31 2 4.3988140998 0.3708398217 1.631255e+00
32 3 1.6745209007 -0.0474059668 -7.938228e-02
32.1 1 2.9128167813 1.2507771489 3.643285e+00
32.2 3 2.9676558380 0.1142915519 3.391780e-01
32.3 3 4.2099863547 0.6773270619 2.851538e+00
33 2 0.0093385763 0.1774293842 1.656938e-03
33.1 2 3.4591242753 0.6159606291 2.130684e+00
34 3 1.4998774312 0.8590979166 1.288542e+00
34.1 2 3.8242761395 0.0546216775 2.088884e-01
34.2 2 3.9072251692 -0.0897224473 -3.505658e-01
34.3 1 3.9582124643 0.4163395571 1.647960e+00
35 2 1.3294299203 -1.4693520528 -1.953401e+00
35.1 3 1.5276966314 -0.3031734330 -4.631570e-01
35.2 2 4.5025920868 -0.6045512101 -2.722047e+00
36 3 0.7123168337 0.9823048960 6.997123e-01
36.1 2 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 3 2.0018732361 0.1177313455 2.356832e-01
37.1 3 3.6656836793 -0.1415483779 -5.188716e-01
37.2 3 3.9663937816 0.0054610124 2.166053e-02
38 2 0.9826511063 0.8078948077 7.938787e-01
39 1 0.6921808305 0.9876451040 6.836290e-01
39.1 2 0.9027792048 -0.3431222274 -3.097636e-01
39.2 2 1.3055654289 -1.7909380751 -2.338187e+00
39.3 1 1.5412842878 -0.1798746191 -2.772379e-01
39.4 1 3.1834997435 -0.1850961689 -5.892536e-01
39.5 1 4.1394166439 0.4544226146 1.881045e+00
40 3 1.1330395646 0.5350190436 6.061977e-01
40.1 1 2.6940994046 0.4189342752 1.128651e+00
40.2 1 3.0396614212 0.4211994981 1.280304e+00
40.3 2 4.6762977762 0.0916687506 4.286704e-01
41 3 1.9337158254 -0.1035047421 -2.001488e-01
41.1 2 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 3 3.5482964432 -0.3908036794 -1.386687e+00
42 2 0.4859252973 -0.0338893961 -1.646771e-02
42.1 1 4.3293134298 -0.4498363172 -1.947482e+00
43 2 0.5616614548 0.8965546110 5.035602e-01
43.1 2 1.0743579536 0.6199122090 6.660076e-01
43.2 1 2.6131797966 0.1804894429 4.716514e-01
44 3 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 1 0.1957449992 0.1414983160 2.769759e-02
45.1 1 1.9963831536 0.7220892521 1.441567e+00
46 1 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 1 2.4097121472 0.2982973539 7.188108e-01
47.2 1 2.9975794035 1.1518107179 3.452644e+00
47.3 3 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 2 2.9098354396 -0.5337239976 -1.553049e+00
49 1 2.7179756400 -0.5236770035 -1.423341e+00
50 2 1.1762060679 0.3897705981 4.584505e-01
51 2 1.4304436720 -0.7213343736 -1.031828e+00
52 2 2.1266646020 0.3758235358 7.992506e-01
52.1 3 3.1000545993 0.7138067080 2.212840e+00
52.2 2 3.1268477370 0.8872895233 2.774419e+00
52.3 1 3.5711459327 -0.9664587437 -3.451365e+00
52.4 3 4.7983659909 0.0254566848 1.221505e-01
52.5 2 4.9818264414 0.4155259424 2.070078e+00
53 2 0.4965799209 0.5675736897 2.818457e-01
53.1 1 3.5505357443 -0.3154088781 -1.119870e+00
53.2 1 4.5790420019 0.2162315769 9.901335e-01
54 1 1.4034724841 -0.0880802382 -1.236182e-01
54.1 2 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 2 4.0143877396 0.8587727145 3.447447e+00
55 1 0.6118522980 -0.0116453589 -7.125240e-03
55.1 1 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 3 1.2231332215 0.5176377612 6.331399e-01
56.1 2 2.3573202139 -1.1136932588 -2.625332e+00
56.2 3 2.5674936292 -0.0168103281 -4.316041e-02
56.3 3 2.9507164378 0.3933023606 1.160524e+00
56.4 1 3.2272730360 0.3714625139 1.198811e+00
56.5 3 3.4175522043 0.7811448179 2.669603e+00
57 2 0.2370331448 -1.0868304872 -2.576148e-01
57.1 2 0.2481445030 0.8018626997 1.989778e-01
57.2 2 1.1405586067 -0.1159517011 -1.322497e-01
57.3 2 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 3 1.6791862890 -0.1111300753 -1.866081e-01
58.3 3 2.6320121693 -0.5409234285 -1.423717e+00
58.4 2 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 1 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 3 0.6987666548 0.6830738372 4.773092e-01
62.2 1 3.4584309917 0.4301745954 1.487729e+00
62.3 2 4.8028772371 -0.0333139957 -1.600030e-01
63 1 2.8097350930 0.3345678035 9.400469e-01
63.1 3 3.9653754211 0.3643769511 1.444891e+00
64 2 4.1191305732 0.3949911859 1.627020e+00
65 1 0.7076152589 1.2000091513 8.491448e-01
65.1 2 2.0252246363 0.0110122646 2.230231e-02
65.2 1 3.1127382827 -0.5776452043 -1.798058e+00
65.3 1 3.1969087943 -0.1372183563 -4.386746e-01
66 1 3.4943454154 -0.5081302805 -1.775583e+00
66.1 1 3.7677437009 -0.1447837412 -5.455080e-01
66.2 3 3.9486138616 0.1906241379 7.527011e-01
67 3 4.1728388879 1.6716027681 6.975329e+00
68 1 0.1291919907 0.5691848839 7.353413e-02
68.1 2 1.7809643946 0.1004860389 1.789621e-01
68.2 2 2.0493205660 -0.0061241827 -1.255041e-02
68.3 2 2.9406870750 0.7443745962 2.188973e+00
68.4 2 4.0406670363 0.8726923437 3.526259e+00
69 2 4.1451198701 0.0381382683 1.580877e-01
70 1 0.1992557163 0.8126204217 1.619193e-01
70.1 3 0.4829774413 0.4691503050 2.265890e-01
71 3 0.7741605386 -0.5529062591 -4.280382e-01
71.1 3 1.4883817220 -0.1103252087 -1.642060e-01
71.2 3 4.0758526395 1.7178492547 7.001700e+00
71.3 2 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 1 1.1799991806 0.1375317317 1.622873e-01
72.2 2 1.8917567329 -0.4170988856 -7.890496e-01
72.3 2 3.4853593935 0.7107266765 2.477138e+00
72.4 1 3.6884259700 0.1451969143 5.355481e-01
72.5 1 4.0854155901 1.6298050306 6.658431e+00
73 1 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 1 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 1 0.0583819521 -0.3767450660 -2.199511e-02
79.1 2 2.4406372628 0.1516000028 3.700006e-01
79.2 1 3.2962526032 -0.1888160741 -6.223855e-01
80 2 0.8985060186 -0.0041558414 -3.734049e-03
80.1 2 1.3434670598 -0.0329337062 -4.424535e-02
80.2 3 2.8025900386 0.5046816157 1.414416e+00
81 3 0.0101324962 -0.9493950353 -9.619742e-03
81.1 2 0.9421709494 0.2443038954 2.301760e-01
81.2 3 3.0542453879 0.6476958410 1.978222e+00
81.3 3 3.3456630446 0.4182528210 1.399333e+00
82 3 1.3791010005 1.1088801952 1.529258e+00
82.1 2 1.7601010622 0.9334157763 1.642906e+00
82.2 2 2.6233131927 0.4958140634 1.300676e+00
83 3 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 2 4.7663642222 -0.0534169155 -2.546045e-01
84 3 2.7254060237 -0.0255753653 -6.970325e-02
84.1 3 3.3364784659 -1.8234189877 -6.083798e+00
85 3 0.2977756259 -0.0114038622 -3.395792e-03
85.1 2 1.7394116637 -0.0577615939 -1.004712e-01
85.2 2 2.6846330194 -0.2241856342 -6.018562e-01
85.3 2 3.1608762743 -0.0520175929 -1.644212e-01
85.4 1 3.9452053758 0.2892733846 1.141243e+00
85.5 3 4.5092553482 -0.3740417009 -1.686650e+00
86 2 0.8476278360 0.4293735089 3.639489e-01
86.1 3 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 3 3.8206058508 -0.1420690052 -5.427897e-01
87 1 2.7069531474 1.0457427869 2.830777e+00
87.1 1 3.4462517721 -0.2973007190 -1.024573e+00
87.2 1 4.5241666853 0.4396872616 1.989218e+00
88 3 0.0005892443 -0.0601928334 -3.546828e-05
88.1 1 0.7116099866 -1.0124347595 -7.204587e-01
88.2 1 2.4952722900 0.5730917016 1.430020e+00
88.3 1 3.2995816297 -0.0029455332 -9.719027e-03
89 1 0.6462086167 1.5465903721 9.994200e-01
90 2 0.1696030737 0.0626760573 1.063005e-02
90.1 1 2.5980385230 1.1896872985 3.090853e+00
90.2 3 2.6651392167 0.2597888783 6.923735e-01
90.3 1 3.1242690247 0.6599799887 2.061955e+00
91 1 0.6382618390 1.1213651365 7.157246e-01
91.1 2 2.6224059286 1.2046371625 3.159048e+00
91.2 1 4.7772527603 0.3395603754 1.622166e+00
92 2 0.0737052364 0.4674939332 3.445675e-02
93 1 0.2788909199 0.2677965647 7.468603e-02
93.1 3 1.0357759963 1.6424445368 1.701205e+00
93.2 1 2.4916551099 0.7101700066 1.769499e+00
93.3 3 2.8876129608 1.1222322893 3.240573e+00
93.4 1 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 1 1.9445500884 -0.4536774482 -8.821985e-01
94.3 3 3.0710722448 0.6793464917 2.086322e+00
94.4 2 3.0872731935 -0.9411356550 -2.905543e+00
94.5 3 4.3805759016 0.5683867264 2.489861e+00
95 3 2.0199063048 0.2375652188 4.798595e-01
95.1 1 4.0184444457 0.0767152977 3.082762e-01
95.2 2 4.5596531732 -0.6886731251 -3.140111e+00
96 1 0.0311333477 0.7813892121 2.432726e-02
96.1 3 0.1324267720 0.3391519695 4.491280e-02
96.2 1 0.6701303425 -0.4857246503 -3.254988e-01
96.3 1 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 3 0.2041732438 -1.2648518889 -2.582489e-01
98.1 1 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 2 4.6789444226 1.0355522332 4.845291e+00
99.2 1 4.9927084171 -0.1150895843 -5.746087e-01
100 1 1.0691387602 0.0369067906 3.945848e-02
100.1 2 1.5109344281 1.6023713093 2.421078e+00
100.2 2 2.1502332564 0.8861545820 1.905439e+00
100.3 1 3.8745574222 0.1277046316 4.947989e-01
100.4 1 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
o1 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_ordinal
[1] 0
$m4c$tau_reg_ordinal
[1] 1e-04
$m4c$mu_delta_ordinal
[1] 0
$m4c$tau_delta_ordinal
[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_o1_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_o1_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
o1 b2 time I(time^2) b21 c1 C1:time b21:c1
1 2 NA 0.5090421822 2.591239e-01 NA 0.7592026489 0.3652818145 NA
1.1 1 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 2 0 2.4954441458 6.227241e+00 NA 1.1873391025 1.7906971118 NA
2 3 0 3.0164990982 9.099267e+00 NA 0.9192204198 2.2645370243 NA
2.1 1 NA 3.2996806887 1.088789e+01 NA -0.1870730476 2.4771262462 NA
2.2 2 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 3 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 3 1 0.3371432109 1.136655e-01 NA 1.4930175328 0.2518241168 NA
4.1 2 0 1.0693019140 1.143407e+00 NA -0.7692526880 0.7986991917 NA
4.2 1 0 2.6148973033 6.837688e+00 NA 0.9180841450 1.9531587247 NA
4.3 2 0 3.1336532847 9.819783e+00 NA -0.0541170782 2.3406358046 NA
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5.2 3 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 3 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 1 NA 3.7008403614 1.369622e+01 NA 0.4242971219 2.7186109493 NA
7.2 1 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 2 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
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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 2 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 2 NA 2.2975509102 5.278740e+00 NA -0.5598409704 1.7056739807 NA
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11 2 0 1.1832662905 1.400119e+00 NA 0.5086859475 0.8800481723 NA
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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 1 0 3.5621061502 1.268860e+01 NA 0.1079022586 2.6823320911 NA
14 3 NA 4.0364430007 1.629287e+01 NA 0.9991752617 2.8631042655 NA
14.1 3 NA 4.4710561272 1.999034e+01 NA -0.1094019046 3.1713813046 NA
14.2 2 NA 4.6359198843 2.149175e+01 NA 0.1518967560 3.2883214239 NA
14.3 1 NA 4.6886152599 2.198311e+01 NA 0.3521012473 3.3256989750 NA
15 3 0 0.5402063532 2.918229e-01 NA 0.3464447888 0.3960356618 NA
15.1 3 0 1.1893180816 1.414477e+00 NA -0.4767313971 0.8719119477 NA
15.2 2 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 3 1 1.2417913869 1.542046e+00 NA 0.4564754473 0.8706683147 NA
16.1 1 NA 2.5675726333 6.592429e+00 NA 1.0652558311 1.8002251917 NA
16.2 3 NA 2.6524101500 7.035280e+00 NA 0.6971872493 1.8597080795 NA
16.3 1 0 3.5585018690 1.266294e+01 NA 0.5259331838 2.4950042800 NA
16.4 1 0 3.7612454291 1.414697e+01 NA 0.2046601798 2.6371556877 NA
16.5 2 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 2 0 2.4374032998 5.940935e+00 NA 0.2323298304 1.8115744548 NA
17.2 3 0 3.0256489082 9.154551e+00 NA 1.2617499032 2.2487818375 NA
17.3 2 NA 3.3329089405 1.110828e+01 NA -0.3913230895 2.4771496359 NA
17.4 3 0 3.8693758985 1.497207e+01 NA 0.9577299112 2.8758730794 NA
18 2 0 2.4374292302 5.941061e+00 NA -0.0050324072 1.8390868081 NA
19 3 NA 0.9772165376 9.549522e-01 NA -0.4187468937 0.7356962360 NA
19.1 2 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 2 1 4.2114245973 1.773610e+01 NA -0.5877091668 3.1705656887 NA
20 1 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 2 1 2.2515566566 5.069507e+00 NA -0.8489831990 1.7140797861 NA
20.3 1 0 2.2609123867 5.111725e+00 NA 1.3169975191 1.7212021776 NA
20.4 1 0 3.4913365287 1.218943e+01 NA 0.0444804531 2.6579075206 NA
20.5 2 0 4.1730977828 1.741475e+01 NA -0.4535207652 3.1769231897 NA
21 2 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 2 NA 3.7887385779 1.435454e+01 NA 1.0650265940 2.7474868216 NA
22 1 0 2.4696226232 6.099036e+00 NA -0.0673274516 1.8029800300 NA
22.1 1 0 3.1626627257 1.000244e+01 NA 0.9601388170 2.3089429463 NA
23 1 0 1.5414533857 2.376079e+00 NA 0.5556634840 1.0924663221 NA
23.1 2 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 2 0 0.5381704110 2.896274e-01 NA 0.3564400705 0.4208837547 NA
25.1 1 NA 1.6069735331 2.582364e+00 NA 0.0982553434 1.2567562995 NA
25.2 1 1 1.6358226922 2.675916e+00 NA 0.1928682598 1.2793181910 NA
25.3 1 0 3.2646870392 1.065818e+01 NA -0.0192488594 2.5531945101 NA
25.4 2 0 4.0782226040 1.663190e+01 NA 0.4466012931 3.1894314642 NA
25.5 2 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 3 0 2.4451737676 5.978875e+00 NA 1.2268695927 1.7405474628 NA
26.2 2 0 3.5988757887 1.295191e+01 NA 0.3678294939 2.5617868987 NA
26.3 1 0 4.1822362854 1.749110e+01 NA 0.5948516018 2.9770402626 NA
27 2 0 3.6955824879 1.365733e+01 NA -0.3342844147 2.6722228982 NA
27.1 2 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 1 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 2 0 1.1898884235 1.415834e+00 NA 0.1392699487 0.8937118818 NA
29.1 2 0 1.7624059319 3.106075e+00 NA 0.7960234776 1.3237233767 NA
29.2 1 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 2 NA 2.2635366488 5.123598e+00 NA -0.3296323050 1.6525441223 NA
30.1 2 0 3.5938334477 1.291564e+01 NA 1.3635850954 2.6237562107 NA
30.2 2 0 3.6138710892 1.306006e+01 NA 0.7354171050 2.6383851264 NA
31 2 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 1 0 2.9128167813 8.484502e+00 NA 1.2507771489 2.1327348270 NA
32.2 3 NA 2.9676558380 8.806981e+00 NA 0.1142915519 2.1728874266 NA
32.3 3 NA 4.2099863547 1.772399e+01 NA 0.6773270619 3.0825091978 NA
33 2 0 0.0093385763 8.720901e-05 NA 0.1774293842 0.0068231501 NA
33.1 2 1 3.4591242753 1.196554e+01 NA 0.6159606291 2.5273792639 NA
34 3 NA 1.4998774312 2.249632e+00 NA 0.8590979166 1.1139914202 NA
34.1 2 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 1 NA 3.9582124643 1.566745e+01 NA 0.4163395571 2.9398500390 NA
35 2 0 1.3294299203 1.767384e+00 NA -1.4693520528 0.9562645676 NA
35.1 3 0 1.5276966314 2.333857e+00 NA -0.3031734330 1.0988786520 NA
35.2 2 NA 4.5025920868 2.027334e+01 NA -0.6045512101 3.2387335424 NA
36 3 NA 0.7123168337 5.073953e-01 NA 0.9823048960 0.5209093415 NA
36.1 2 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 3 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 3 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 1 1 0.6921808305 4.791143e-01 NA 0.9876451040 0.5122448722 NA
39.1 2 0 0.9027792048 8.150103e-01 NA -0.3431222274 0.6680971185 NA
39.2 2 NA 1.3055654289 1.704501e+00 NA -1.7909380751 0.9661769970 NA
39.3 1 NA 1.5412842878 2.375557e+00 NA -0.1798746191 1.1406195291 NA
39.4 1 0 3.1834997435 1.013467e+01 NA -0.1850961689 2.3559326511 NA
39.5 1 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 1 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 2 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 2 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 3 0 3.5482964432 1.259041e+01 NA -0.3908036794 2.6580166349 NA
42 2 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 2 0 0.5616614548 3.154636e-01 NA 0.8965546110 0.4228080758 NA
43.1 2 1 1.0743579536 1.154245e+00 NA 0.6199122090 0.8087562626 NA
43.2 1 0 2.6131797966 6.828709e+00 NA 0.1804894429 1.9671521198 NA
44 3 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 1 NA 0.1957449992 3.831610e-02 NA 0.1414983160 0.1438246201 NA
45.1 1 1 1.9963831536 3.985546e+00 NA 0.7220892521 1.4668525369 NA
46 1 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 1 0 2.4097121472 5.806713e+00 NA 0.2982973539 1.7775195440 NA
47.2 1 0 2.9975794035 8.985482e+00 NA 1.1518107179 2.2111586981 NA
47.3 3 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 2 1 2.9098354396 8.467142e+00 NA -0.5337239976 2.1376170787 NA
49 1 NA 2.7179756400 7.387392e+00 NA -0.5236770035 1.9921136553 NA
50 2 0 1.1762060679 1.383461e+00 NA 0.3897705981 0.8539769445 NA
51 2 0 1.4304436720 2.046169e+00 NA -0.7213343736 1.0360574125 NA
52 2 0 2.1266646020 4.522702e+00 NA 0.3758235358 1.5520541611 NA
52.1 3 0 3.1000545993 9.610339e+00 NA 0.7138067080 2.2624407422 NA
52.2 2 0 3.1268477370 9.777177e+00 NA 0.8872895233 2.2819945547 NA
52.3 1 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 2 0 4.9818264414 2.481859e+01 NA 0.4155259424 3.6357705164 NA
53 2 0 0.4965799209 2.465916e-01 NA 0.5675736897 0.3602558557 NA
53.1 1 0 3.5505357443 1.260630e+01 NA -0.3154088781 2.5758216127 NA
53.2 1 NA 4.5790420019 2.096763e+01 NA 0.2162315769 3.3219762321 NA
54 1 NA 1.4034724841 1.969735e+00 NA -0.0880802382 1.0585083082 NA
54.1 2 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 2 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 1 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 3 0 1.2231332215 1.496055e+00 NA 0.5176377612 0.9198742485 NA
56.1 2 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 3 1 2.9507164378 8.706727e+00 NA 0.3933023606 2.2191270894 NA
56.4 1 0 3.2272730360 1.041529e+01 NA 0.3714625139 2.4271153024 NA
56.5 3 0 3.4175522043 1.167966e+01 NA 0.7811448179 2.5702173815 NA
57 2 0 0.2370331448 5.618471e-02 NA -1.0868304872 0.1711369455 NA
57.1 2 0 0.2481445030 6.157569e-02 NA 0.8018626997 0.1791592999 NA
57.2 2 0 1.1405586067 1.300874e+00 NA -0.1159517011 0.8234785742 NA
57.3 2 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 3 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 2 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 1 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 3 1 0.6987666548 4.882748e-01 NA 0.6830738372 0.5024056819 NA
62.2 1 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 1 NA 2.8097350930 7.894611e+00 NA 0.3345678035 2.0604786922 NA
63.1 3 0 3.9653754211 1.572420e+01 NA 0.3643769511 2.9079508535 NA
64 2 0 4.1191305732 1.696724e+01 NA 0.3949911859 3.0153034804 NA
65 1 0 0.7076152589 5.007194e-01 NA 1.2000091513 0.5291342322 NA
65.1 2 0 2.0252246363 4.101535e+00 NA 0.0110122646 1.5144044302 NA
65.2 1 0 3.1127382827 9.689140e+00 NA -0.5776452043 2.3276156931 NA
65.3 1 0 3.1969087943 1.022023e+01 NA -0.1372183563 2.3905559682 NA
66 1 NA 3.4943454154 1.221045e+01 NA -0.5081302805 2.5662383121 NA
66.1 1 0 3.7677437009 1.419589e+01 NA -0.1447837412 2.7670213119 NA
66.2 3 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 1 0 0.1291919907 1.669057e-02 NA 0.5691848839 0.0966709548 NA
68.1 2 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 2 0 2.9406870750 8.647640e+00 NA 0.7443745962 2.2004384781 NA
68.4 2 NA 4.0406670363 1.632699e+01 NA 0.8726923437 3.0235244339 NA
69 2 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 3 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 3 1 1.4883817220 2.215280e+00 NA -0.1103252087 1.1576038195 NA
71.2 3 0 4.0758526395 1.661257e+01 NA 1.7178492547 3.1700352897 NA
71.3 2 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 1 0 1.1799991806 1.392398e+00 NA 0.1375317317 0.8741602904 NA
72.2 2 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 1 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 1 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 1 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 1 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 1 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 2 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 3 0 0.0101324962 1.026675e-04 NA -0.9493950353 0.0073905742 NA
81.1 2 0 0.9421709494 8.876861e-01 NA 0.2443038954 0.6872131160 NA
81.2 3 NA 3.0542453879 9.328415e+00 NA 0.6476958410 2.2277459217 NA
81.3 3 0 3.3456630446 1.119346e+01 NA 0.4182528210 2.4403039888 NA
82 3 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 2 1 2.6233131927 6.881772e+00 NA 0.4958140634 1.9095908060 NA
83 3 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 2 NA 4.7663642222 2.271823e+01 NA -0.0534169155 3.5007244248 NA
84 3 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 3 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 2 0 2.6846330194 7.207254e+00 NA -0.2241856342 1.9661489512 NA
85.3 2 0 3.1608762743 9.991139e+00 NA -0.0520175929 2.3149359808 NA
85.4 1 0 3.9452053758 1.556465e+01 NA 0.2892733846 2.8893563314 NA
85.5 3 0 4.5092553482 2.033338e+01 NA -0.3740417009 3.3024505062 NA
86 2 0 0.8476278360 7.184729e-01 NA 0.4293735089 0.6422134099 NA
86.1 3 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 3 0 3.8206058508 1.459703e+01 NA -0.1420690052 2.8947188929 NA
87 1 NA 2.7069531474 7.327595e+00 NA 1.0457427869 2.0291697589 NA
87.1 1 NA 3.4462517721 1.187665e+01 NA -0.2973007190 2.5833582987 NA
87.2 1 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 1 NA 0.7116099866 5.063888e-01 NA -1.0124347595 0.5177132747 NA
88.2 1 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 1 0 0.6462086167 4.175856e-01 NA 1.5465903721 0.4685431339 NA
90 2 0 0.1696030737 2.876520e-02 NA 0.0626760573 0.1244083036 NA
90.1 1 0 2.5980385230 6.749804e+00 NA 1.1896872985 1.9057294087 NA
90.2 3 0 2.6651392167 7.102967e+00 NA 0.2597888783 1.9549495277 NA
90.3 1 NA 3.1242690247 9.761057e+00 NA 0.6599799887 2.2917332858 NA
91 1 0 0.6382618390 4.073782e-01 NA 1.1213651365 0.4687382105 NA
91.1 2 0 2.6224059286 6.877013e+00 NA 1.2046371625 1.9258896380 NA
91.2 1 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 1 NA 0.2788909199 7.778015e-02 NA 0.2677965647 0.2060853219 NA
93.1 3 0 1.0357759963 1.072832e+00 NA 1.6424445368 0.7653825006 NA
93.2 1 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 1 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 1 0 1.9445500884 3.781275e+00 NA -0.4536774482 1.4117337932 NA
94.3 3 NA 3.0710722448 9.431485e+00 NA 0.6793464917 2.2295833342 NA
94.4 2 0 3.0872731935 9.531256e+00 NA -0.9411356550 2.2413451432 NA
94.5 3 1 4.3805759016 1.918945e+01 NA 0.5683867264 3.1802765437 NA
95 3 0 2.0199063048 4.080021e+00 NA 0.2375652188 1.4710654666 NA
95.1 1 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 1 0 0.0311333477 9.692853e-04 NA 0.7813892121 0.0226702966 NA
96.1 3 0 0.1324267720 1.753685e-02 NA 0.3391519695 0.0964288912 NA
96.2 1 0 0.6701303425 4.490747e-01 NA -0.4857246503 0.4879672359 NA
96.3 1 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 3 0 0.2041732438 4.168671e-02 NA -1.2648518889 0.1536799385 NA
98.1 1 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 2 0 4.6789444226 2.189252e+01 NA 1.0355522332 3.4026730772 NA
99.2 1 0 4.9927084171 2.492714e+01 NA -0.1150895843 3.6308519569 NA
100 1 NA 1.0691387602 1.143058e+00 NA 0.0369067906 0.7893943379 NA
100.1 2 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 1 NA 3.8745574222 1.501220e+01 NA 0.1277046316 2.8607640137 NA
100.4 1 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
o1 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_ordinal
[1] 0
$m4d$tau_reg_ordinal
[1] 1e-04
$m4d$mu_delta_ordinal
[1] 0
$m4d$tau_delta_ordinal
[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_o1_id
[,1] [,2]
[1,] NA 0
[2,] 0 NA
$m4d$KinvD_o1_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
o1 log(time) I(time^2) p1 time
1 2 -0.67522439 2.591239e-01 5 0.5090421822
1.1 1 -0.40555367 4.443657e-01 3 0.6666076288
1.2 1 0.75635394 4.539005e+00 8 2.1304941282
1.3 2 0.91446673 6.227241e+00 6 2.4954441458
2 3 1.10409692 9.099267e+00 5 3.0164990982
2.1 1 1.19382570 1.088789e+01 3 3.2996806887
2.2 2 1.42905614 1.742860e+01 2 4.1747569619
3 1 -0.16502467 7.188883e-01 7 0.8478727890
3.1 3 1.12018813 9.396866e+00 2 3.0654308549
3.2 2 1.55564789 2.245012e+01 8 4.7381553578
4 3 -1.08724748 1.136655e-01 2 0.3371432109
4.1 2 0.06700602 1.143407e+00 4 1.0693019140
4.2 1 0.96122482 6.837688e+00 2 2.6148973033
4.3 2 1.14219951 9.819783e+00 6 3.1336532847
5 2 0.07348511 1.158319e+00 6 1.0762525082
5.1 2 0.58291628 3.208593e+00 2 1.7912546196
5.2 3 1.02819270 7.817661e+00 3 2.7960080339
5.3 2 1.03389386 7.907311e+00 2 2.8119940578
6 3 0.57748169 3.173907e+00 4 1.7815462884
7 3 1.19616503 1.093895e+01 2 3.3074087673
7.1 1 1.30855992 1.369622e+01 6 3.7008403614
7.2 1 1.56269618 2.276883e+01 4 4.7716691741
8 2 0.11746285 1.264815e+00 2 1.1246398522
8.1 2 0.58928609 3.249731e+00 2 1.8027009873
8.2 3 0.59750733 3.303606e+00 1 1.8175825174
8.3 3 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 2 -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 2 0.83184373 5.278740e+00 4 2.2975509102
10.1 1 1.42873389 1.741737e+01 5 4.1734118364
11 2 0.16827866 1.400119e+00 2 1.1832662905
11.1 3 0.21075122 1.524250e+00 4 1.2346051680
11.2 3 0.49684736 2.701196e+00 6 1.6435316263
11.3 1 1.21962028 1.146433e+01 2 3.3859017969
11.4 3 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 1 1.27035199 1.268860e+01 6 3.5621061502
14 3 1.39536386 1.629287e+01 3 4.0364430007
14.1 3 1.49762465 1.999034e+01 2 4.4710561272
14.2 2 1.53383464 2.149175e+01 4 4.6359198843
14.3 1 1.54513729 2.198311e+01 2 4.6886152599
15 3 -0.61580408 2.918229e-01 4 0.5402063532
15.1 3 0.17338010 1.414477e+00 7 1.1893180816
15.2 2 0.41176123 2.278512e+00 4 1.5094739688
15.3 2 1.59317589 2.419998e+01 3 4.9193474615
16 3 0.21655500 1.542046e+00 3 1.2417913869
16.1 1 0.94296095 6.592429e+00 2 2.5675726333
16.2 3 0.97546872 7.035280e+00 5 2.6524101500
16.3 1 1.26933963 1.266294e+01 3 3.5585018690
16.4 1 1.32475013 1.414697e+01 2 3.7612454291
16.5 2 1.38257779 1.588151e+01 6 3.9851612889
17 2 0.46532748 2.536170e+00 3 1.5925356350
17.1 2 0.89093325 5.940935e+00 1 2.4374032998
17.2 3 1.10712558 9.154551e+00 4 3.0256489082
17.3 2 1.20384548 1.110828e+01 5 3.3329089405
17.4 3 1.35309323 1.497207e+01 5 3.8693758985
18 2 0.89094389 5.941061e+00 8 2.4374292302
19 3 -0.02304702 9.549522e-01 5 0.9772165376
19.1 2 0.13683034 1.314769e+00 6 1.1466335913
19.2 2 0.81532616 5.107205e+00 4 2.2599126538
19.3 2 1.43780097 1.773610e+01 3 4.2114245973
20 1 0.54058790 2.948144e+00 5 1.7170160066
20.1 2 0.56320376 3.084555e+00 8 1.7562902288
20.2 2 0.81162182 5.069507e+00 3 2.2515566566
20.3 1 0.81576844 5.111725e+00 3 2.2609123867
20.4 1 1.25028462 1.218943e+01 3 3.4913365287
20.5 2 1.42865863 1.741475e+01 3 4.1730977828
21 2 0.52689085 2.868478e+00 3 1.6936582839
21.1 2 1.08421553 8.744554e+00 3 2.9571191233
21.2 2 1.33203313 1.435454e+01 4 3.7887385779
22 1 0.90406535 6.099036e+00 6 2.4696226232
22.1 1 1.15141431 1.000244e+01 3 3.1626627257
23 1 0.43272573 2.376079e+00 3 1.5414533857
23.1 2 0.84885676 5.461446e+00 2 2.3369736120
24 1 1.03968065 7.999358e+00 1 2.8283136466
25 2 -0.61958002 2.896274e-01 2 0.5381704110
25.1 1 0.47435262 2.582364e+00 0 1.6069735331
25.2 1 0.49214585 2.675916e+00 6 1.6358226922
25.3 1 1.18316390 1.065818e+01 6 3.2646870392
25.4 2 1.40566126 1.663190e+01 2 4.0782226040
25.5 2 1.42456012 1.727258e+01 2 4.1560292873
26 2 -1.42183601 5.821152e-02 6 0.2412706357
26.1 3 0.89411619 5.978875e+00 0 2.4451737676
26.2 2 1.28062152 1.295191e+01 1 3.5988757887
26.3 1 1.43084610 1.749110e+01 4 4.1822362854
27 2 1.30713818 1.365733e+01 2 3.6955824879
27.1 2 1.44577562 1.802124e+01 4 4.2451434687
28 1 -0.55399075 3.302248e-01 5 0.5746519344
28.1 1 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 2 0.17385954 1.415834e+00 4 1.1898884235
29.1 2 0.56667988 3.106075e+00 1 1.7624059319
29.2 1 0.70361255 4.084605e+00 4 2.0210406382
29.3 2 1.22608972 1.161363e+01 3 3.4078777023
30 2 0.81692848 5.123598e+00 5 2.2635366488
30.1 2 1.27921945 1.291564e+01 5 3.5938334477
30.2 2 1.28477952 1.306006e+01 6 3.6138710892
31 2 1.48133498 1.934957e+01 1 4.3988140998
32 3 0.51552709 2.804020e+00 2 1.6745209007
32.1 1 1.06912058 8.484502e+00 5 2.9128167813
32.2 3 1.08777236 8.806981e+00 5 2.9676558380
32.3 3 1.43745941 1.772399e+01 6 4.2099863547
33 2 -4.67360146 8.720901e-05 4 0.0093385763
33.1 2 1.24101546 1.196554e+01 7 3.4591242753
34 3 0.40538339 2.249632e+00 2 1.4998774312
34.1 2 1.34136920 1.462509e+01 5 3.8242761395
34.2 2 1.36282745 1.526641e+01 6 3.9072251692
34.3 1 1.37579253 1.566745e+01 2 3.9582124643
35 2 0.28475022 1.767384e+00 3 1.3294299203
35.1 3 0.42376113 2.333857e+00 2 1.5276966314
35.2 2 1.50465325 2.027334e+01 3 4.5025920868
36 3 -0.33923248 5.073953e-01 3 0.7123168337
36.1 2 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 3 0.69408336 4.007496e+00 4 2.0018732361
37.1 3 1.29901486 1.343724e+01 6 3.6656836793
37.2 3 1.37785731 1.573228e+01 8 3.9663937816
38 2 -0.01750115 9.656032e-01 3 0.9826511063
39 1 -0.36790804 4.791143e-01 2 0.6921808305
39.1 2 -0.10227727 8.150103e-01 3 0.9027792048
39.2 2 0.26663623 1.704501e+00 6 1.3055654289
39.3 1 0.43261602 2.375557e+00 4 1.5412842878
39.4 1 1.15798114 1.013467e+01 3 3.1834997435
39.5 1 1.42055487 1.713477e+01 6 4.1394166439
40 3 0.12490390 1.283779e+00 1 1.1330395646
40.1 1 0.99106398 7.258172e+00 3 2.6940994046
40.2 1 1.11174613 9.239542e+00 0 3.0396614212
40.3 2 1.54250672 2.186776e+01 4 4.6762977762
41 3 0.65944345 3.739257e+00 1 1.9337158254
41.1 2 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 3 1.26646761 1.259041e+01 2 3.5482964432
42 2 -0.72170038 2.361234e-01 1 0.4859252973
42.1 1 1.46540897 1.874295e+01 3 4.3293134298
43 2 -0.57685600 3.154636e-01 5 0.5616614548
43.1 2 0.07172323 1.154245e+00 2 1.0743579536
43.2 1 0.96056779 6.828709e+00 3 2.6131797966
44 3 -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 1 -1.63094249 3.831610e-02 4 0.1957449992
45.1 1 0.69133712 3.985546e+00 2 1.9963831536
46 1 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 1 0.87950730 5.806713e+00 5 2.4097121472
47.2 1 1.09780510 8.985482e+00 5 2.9975794035
47.3 3 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 2 1.06809653 8.467142e+00 5 2.9098354396
49 1 0.99988735 7.387392e+00 4 2.7179756400
50 2 0.16229406 1.383461e+00 1 1.1762060679
51 2 0.35798466 2.046169e+00 9 1.4304436720
52 2 0.75455484 4.522702e+00 3 2.1266646020
52.1 3 1.13141972 9.610339e+00 3 3.1000545993
52.2 2 1.14002538 9.777177e+00 4 3.1268477370
52.3 1 1.27288653 1.275308e+01 11 3.5711459327
52.4 3 1.56827544 2.302432e+01 3 4.7983659909
52.5 2 1.60579658 2.481859e+01 3 4.9818264414
53 2 -0.70001084 2.465916e-01 5 0.4965799209
53.1 1 1.26709851 1.260630e+01 3 3.5505357443
53.2 1 1.52148981 2.096763e+01 2 4.5790420019
54 1 0.33894951 1.969735e+00 1 1.4034724841
54.1 2 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 2 1.38988484 1.611531e+01 6 4.0143877396
55 1 -0.49126437 3.743632e-01 1 0.6118522980
55.1 1 -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 3 0.20141578 1.496055e+00 5 1.2231332215
56.1 2 0.85752547 5.556959e+00 5 2.3573202139
56.2 3 0.94293018 6.592024e+00 2 2.5674936292
56.3 3 1.08204800 8.706727e+00 3 2.9507164378
56.4 1 1.17163752 1.041529e+01 6 3.2272730360
56.5 3 1.22892457 1.167966e+01 1 3.4175522043
57 2 -1.43955530 5.618471e-02 3 0.2370331448
57.1 2 -1.39374403 6.157569e-02 6 0.2481445030
57.2 2 0.13151815 1.300874e+00 3 1.1405586067
57.3 2 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 3 0.51830932 2.819667e+00 2 1.6791862890
58.3 3 0.96774864 6.927488e+00 4 2.6320121693
58.4 2 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 1 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 3 -0.35843842 4.882748e-01 1 0.6987666548
62.2 1 1.24081502 1.196074e+01 2 3.4584309917
62.3 2 1.56921516 2.306763e+01 4 4.8028772371
63 1 1.03309021 7.894611e+00 6 2.8097350930
63.1 3 1.37760053 1.572420e+01 2 3.9653754211
64 2 1.41564212 1.696724e+01 2 4.1191305732
65 1 -0.34585475 5.007194e-01 3 0.7076152589
65.1 2 0.70568063 4.101535e+00 4 2.0252246363
65.2 1 1.13550282 9.689140e+00 2 3.1127382827
65.3 1 1.16218434 1.022023e+01 2 3.1969087943
66 1 1.25114607 1.221045e+01 6 3.4943454154
66.1 1 1.32647633 1.419589e+01 0 3.7677437009
66.2 3 1.37336460 1.559155e+01 5 3.9486138616
67 3 1.42859659 1.741258e+01 8 4.1728388879
68 1 -2.04645568 1.669057e-02 5 0.1291919907
68.1 2 0.57715501 3.171834e+00 5 1.7809643946
68.2 2 0.71750831 4.199715e+00 4 2.0493205660
68.3 2 1.07864325 8.647640e+00 3 2.9406870750
68.4 2 1.39640979 1.632699e+01 1 4.0406670363
69 2 1.42193171 1.718202e+01 5 4.1451198701
70 1 -1.61316627 3.970284e-02 6 0.1992557163
70.1 3 -0.72778533 2.332672e-01 2 0.4829774413
71 3 -0.25597601 5.993245e-01 4 0.7741605386
71.1 3 0.39768944 2.215280e+00 2 1.4883817220
71.2 3 1.40507996 1.661257e+01 5 4.0758526395
71.3 2 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 1 0.16551374 1.392398e+00 4 1.1799991806
72.2 2 0.63750589 3.578744e+00 8 1.8917567329
72.3 2 1.24857116 1.214773e+01 6 3.4853593935
72.4 1 1.30519980 1.360449e+01 4 3.6884259700
72.5 1 1.40742346 1.669062e+01 1 4.0854155901
73 1 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 1 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 1 -2.84074848 3.408452e-03 2 0.0583819521
79.1 2 0.89225918 5.956710e+00 6 2.4406372628
79.2 1 1.19278625 1.086528e+01 5 3.2962526032
80 2 -0.10702187 8.073131e-01 5 0.8985060186
80.1 2 0.29525363 1.804904e+00 1 1.3434670598
80.2 3 1.03054400 7.854511e+00 4 2.8025900386
81 3 -4.59200757 1.026675e-04 4 0.0101324962
81.1 2 -0.05956855 8.876861e-01 5 0.9421709494
81.2 3 1.11653255 9.328415e+00 2 3.0542453879
81.3 3 1.20766489 1.119346e+01 5 3.3456630446
82 3 0.32143184 1.901920e+00 1 1.3791010005
82.1 2 0.56537123 3.097956e+00 2 1.7601010622
82.2 2 0.96443810 6.881772e+00 5 2.6233131927
83 3 -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 2 1.56158380 2.271823e+01 4 4.7663642222
84 3 1.00261742 7.427838e+00 1 2.7254060237
84.1 3 1.20491590 1.113209e+01 5 3.3364784659
85 3 -1.21141501 8.867032e-02 6 0.2977756259
85.1 2 0.55354693 3.025553e+00 5 1.7394116637
85.2 2 0.98754404 7.207254e+00 3 2.6846330194
85.3 2 1.15084929 9.991139e+00 2 3.1608762743
85.4 1 1.37250101 1.556465e+01 2 3.9452053758
85.5 3 1.50613203 2.033338e+01 6 4.5092553482
86 2 -0.16531361 7.184729e-01 3 0.8476278360
86.1 3 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 3 1.34040901 1.459703e+01 4 3.8206058508
87 1 0.99582370 7.327595e+00 3 2.7069531474
87.1 1 1.23728720 1.187665e+01 6 3.4462517721
87.2 1 1.50943340 2.046808e+01 2 4.5241666853
88 3 -7.43666969 3.472088e-07 1 0.0005892443
88.1 1 -0.34022529 5.063888e-01 6 0.7116099866
88.2 1 0.91439786 6.226384e+00 1 2.4952722900
88.3 1 1.19379568 1.088724e+01 6 3.2995816297
89 1 -0.43663289 4.175856e-01 7 0.6462086167
90 2 -1.77429443 2.876520e-02 3 0.1696030737
90.1 1 0.95475675 6.749804e+00 8 2.5980385230
90.2 3 0.98025630 7.102967e+00 4 2.6651392167
90.3 1 1.13920034 9.761057e+00 2 3.1242690247
91 1 -0.44900667 4.073782e-01 4 0.6382618390
91.1 2 0.96409219 6.877013e+00 2 2.6224059286
91.2 1 1.56386564 2.282214e+01 5 4.7772527603
92 2 -2.60768143 5.432462e-03 3 0.0737052364
93 1 -1.27693454 7.778015e-02 3 0.2788909199
93.1 3 0.03515090 1.072832e+00 3 1.0357759963
93.2 1 0.91294719 6.208345e+00 4 2.4916551099
93.3 3 1.06043020 8.338309e+00 2 2.8876129608
93.4 1 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 1 0.66503063 3.781275e+00 2 1.9445500884
94.3 3 1.12202677 9.431485e+00 6 3.0710722448
94.4 2 1.12728824 9.531256e+00 5 3.0872731935
94.5 3 1.47718020 1.918945e+01 5 4.3805759016
95 3 0.70305113 4.080021e+00 8 2.0199063048
95.1 1 1.39089487 1.614790e+01 4 4.0184444457
95.2 2 1.51724656 2.079044e+01 1 4.5596531732
96 1 -3.46947576 9.692853e-04 2 0.0311333477
96.1 3 -2.02172545 1.753685e-02 3 0.1324267720
96.2 1 -0.40028304 4.490747e-01 2 0.6701303425
96.3 1 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 3 -1.58878641 4.168671e-02 7 0.2041732438
98.1 1 -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 2 1.54307253 2.189252e+01 4 4.6789444226
99.2 1 1.60797853 2.492714e+01 5 4.9927084171
100 1 0.06685343 1.143058e+00 2 1.0691387602
100.1 2 0.41272829 2.282923e+00 3 1.5109344281
100.2 2 0.76557633 4.623503e+00 3 2.1502332564
100.3 1 1.35443144 1.501220e+01 7 3.8745574222
100.4 1 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
o1 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_ordinal
[1] 0
$m4e$tau_reg_ordinal
[1] 1e-04
$m4e$mu_delta_ordinal
[1] 0
$m4e$tau_delta_ordinal
[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"
$m5a
$m5a$M_id
C2 O2 (Intercept) C1 O22 O23
1 -1.381594459 3 1 0.7175865 NA NA
2 0.344426024 2 1 0.7507170 NA NA
3 NA 2 1 0.7255954 NA NA
4 -0.228910007 3 1 0.7469352 NA NA
5 NA 1 1 0.7139120 NA NA
6 -2.143955482 3 1 0.7332505 NA NA
7 -1.156567023 NA 1 0.7345929 NA NA
8 -0.598827660 1 1 0.7652589 NA NA
9 NA 1 1 0.7200622 NA NA
10 -1.006719032 3 1 0.7423879 NA NA
11 0.239801450 3 1 0.7437448 NA NA
12 -1.064969789 NA 1 0.7446470 NA NA
13 -0.538082688 3 1 0.7530186 NA NA
14 NA 2 1 0.7093137 NA NA
15 -1.781049276 2 1 0.7331192 NA NA
16 NA 3 1 0.7011390 NA NA
17 NA 3 1 0.7432395 NA NA
18 -0.014579883 NA 1 0.7545191 NA NA
19 -2.121550136 2 1 0.7528487 NA NA
20 NA 1 1 0.7612865 NA NA
21 -0.363239698 1 1 0.7251719 NA NA
22 -0.121568514 3 1 0.7300630 NA NA
23 -0.951271111 3 1 0.7087249 NA NA
24 NA 3 1 0.7391938 NA NA
25 -0.974288621 2 1 0.7820641 NA NA
26 -1.130632418 1 1 0.7118298 NA NA
27 0.114339868 3 1 0.7230857 NA NA
28 0.238334648 3 1 0.7489353 NA NA
29 0.840744958 2 1 0.7510888 NA NA
30 NA 1 1 0.7300717 NA NA
31 NA 2 1 0.7550721 NA NA
32 -1.466312154 3 1 0.7321898 NA NA
33 -0.637352277 2 1 0.7306414 NA NA
34 NA 2 1 0.7427216 NA NA
35 NA 3 1 0.7193042 NA NA
36 NA 1 1 0.7312888 NA NA
37 NA 3 1 0.7100436 NA NA
38 NA 2 1 0.7670184 NA NA
39 0.006728205 3 1 0.7400449 NA NA
40 NA 2 1 0.7397304 NA NA
41 -1.663281353 2 1 0.7490966 NA NA
42 0.161184794 2 1 0.7419274 NA NA
43 0.457939180 3 1 0.7527810 NA NA
44 -0.307070331 2 1 0.7408315 NA NA
45 NA 1 1 0.7347550 NA NA
46 -1.071668276 NA 1 0.7332398 NA NA
47 -0.814751321 1 1 0.7376481 NA NA
48 -0.547630662 2 1 0.7346179 NA NA
49 NA 1 1 0.7329402 NA NA
50 -1.350213782 2 1 0.7260436 NA NA
51 0.719054706 2 1 0.7242910 NA NA
52 NA 3 1 0.7298067 NA NA
53 -1.207130750 3 1 0.7254741 NA NA
54 NA 1 1 0.7542067 NA NA
55 -0.408600991 2 1 0.7389952 NA NA
56 -0.271380529 NA 1 0.7520638 NA NA
57 -1.361925974 3 1 0.7219958 NA NA
58 NA 1 1 0.7259632 NA NA
59 NA 2 1 0.7458606 NA NA
60 -0.323712205 2 1 0.7672421 NA NA
61 NA 3 1 0.7257179 NA NA
62 NA 2 1 0.7189892 NA NA
63 -1.386906880 1 1 0.7333356 NA NA
64 NA 3 1 0.7320243 NA NA
65 NA 1 1 0.7477711 NA NA
66 -0.565191691 3 1 0.7343974 NA NA
67 -0.382899912 3 1 0.7491624 NA NA
68 NA 2 1 0.7482736 NA NA
69 -0.405642769 NA 1 0.7338267 NA NA
70 NA 2 1 0.7607742 NA NA
71 -0.843748427 NA 1 0.7777600 NA NA
72 0.116003683 NA 1 0.7408143 NA NA
73 -0.778634325 1 1 0.7248271 NA NA
74 NA 2 1 0.7364916 NA NA
75 NA 2 1 0.7464926 NA NA
76 NA 1 1 0.7355430 NA NA
77 -0.632974758 3 1 0.7208449 NA NA
78 NA 2 1 0.7373573 NA NA
79 -0.778064615 NA 1 0.7598079 NA NA
80 NA 1 1 0.7360415 NA NA
81 NA 1 1 0.7293932 NA NA
82 -0.246123253 3 1 0.7279309 NA NA
83 -1.239659782 NA 1 0.7344643 NA NA
84 -0.467772280 3 1 0.7384350 NA NA
85 NA 3 1 0.7323716 NA NA
86 -2.160485036 2 1 0.7576597 NA NA
87 -0.657675572 1 1 0.7496139 NA NA
88 NA 1 1 0.7275239 NA NA
89 -0.696710744 3 1 0.7250648 NA NA
90 NA 1 1 0.7335262 NA NA
91 -0.179395847 2 1 0.7343980 NA NA
92 -0.441545568 NA 1 0.7380425 NA NA
93 -0.685799334 1 1 0.7389460 NA NA
94 NA 3 1 0.7259951 NA NA
95 0.191929445 2 1 0.7282840 NA NA
96 NA 2 1 0.7281676 NA NA
97 -0.069760671 3 1 0.7245642 NA NA
98 NA 2 1 0.7526938 NA NA
99 NA 2 1 0.7272309 NA NA
100 NA 3 1 0.7383460 NA NA
$m5a$M_lvlone
o1 b2 b21
1 2 NA NA
1.1 1 0 NA
1.2 1 NA NA
1.3 2 0 NA
2 3 0 NA
2.1 1 NA NA
2.2 2 NA NA
3 1 0 NA
3.1 3 NA NA
3.2 2 1 NA
4 3 1 NA
4.1 2 0 NA
4.2 1 0 NA
4.3 2 0 NA
5 2 NA NA
5.1 2 0 NA
5.2 3 NA NA
5.3 2 NA NA
6 3 NA NA
7 3 NA NA
7.1 1 NA NA
7.2 1 0 NA
8 2 0 NA
8.1 2 0 NA
8.2 3 NA NA
8.3 3 1 NA
8.4 2 0 NA
8.5 2 1 NA
9 2 0 NA
9.1 2 NA NA
9.2 3 NA NA
10 2 NA NA
10.1 1 0 NA
11 2 0 NA
11.1 3 0 NA
11.2 3 0 NA
11.3 1 0 NA
11.4 3 0 NA
12 1 0 NA
13 2 NA NA
13.1 1 0 NA
14 3 NA NA
14.1 3 NA NA
14.2 2 NA NA
14.3 1 NA NA
15 3 0 NA
15.1 3 0 NA
15.2 2 0 NA
15.3 2 0 NA
16 3 1 NA
16.1 1 NA NA
16.2 3 NA NA
16.3 1 0 NA
16.4 1 0 NA
16.5 2 NA NA
17 2 0 NA
17.1 2 0 NA
17.2 3 0 NA
17.3 2 NA NA
17.4 3 0 NA
18 2 0 NA
19 3 NA NA
19.1 2 NA NA
19.2 2 0 NA
19.3 2 1 NA
20 1 NA NA
20.1 2 0 NA
20.2 2 1 NA
20.3 1 0 NA
20.4 1 0 NA
20.5 2 0 NA
21 2 0 NA
21.1 2 0 NA
21.2 2 NA NA
22 1 0 NA
22.1 1 0 NA
23 1 0 NA
23.1 2 NA NA
24 1 0 NA
25 2 0 NA
25.1 1 NA NA
25.2 1 1 NA
25.3 1 0 NA
25.4 2 0 NA
25.5 2 NA NA
26 2 NA NA
26.1 3 0 NA
26.2 2 0 NA
26.3 1 0 NA
27 2 0 NA
27.1 2 0 NA
28 1 NA NA
28.1 1 0 NA
28.2 1 0 NA
28.3 1 0 NA
29 2 0 NA
29.1 2 0 NA
29.2 1 0 NA
29.3 2 0 NA
30 2 NA NA
30.1 2 0 NA
30.2 2 0 NA
31 2 0 NA
32 3 0 NA
32.1 1 0 NA
32.2 3 NA NA
32.3 3 NA NA
33 2 0 NA
33.1 2 1 NA
34 3 NA NA
34.1 2 0 NA
34.2 2 NA NA
34.3 1 NA NA
35 2 0 NA
35.1 3 0 NA
35.2 2 NA NA
36 3 NA NA
36.1 2 NA NA
36.2 3 0 NA
36.3 3 0 NA
36.4 3 0 NA
37 3 0 NA
37.1 3 0 NA
37.2 3 0 NA
38 2 0 NA
39 1 1 NA
39.1 2 0 NA
39.2 2 NA NA
39.3 1 NA NA
39.4 1 0 NA
39.5 1 1 NA
40 3 0 NA
40.1 1 1 NA
40.2 1 0 NA
40.3 2 NA NA
41 3 0 NA
41.1 2 NA NA
41.2 1 0 NA
41.3 1 NA NA
41.4 3 0 NA
42 2 0 NA
42.1 1 1 NA
43 2 0 NA
43.1 2 1 NA
43.2 1 0 NA
44 3 0 NA
44.1 2 0 NA
44.2 1 0 NA
44.3 1 0 NA
45 1 NA NA
45.1 1 1 NA
46 1 0 NA
46.1 2 0 NA
46.2 3 0 NA
47 1 0 NA
47.1 1 0 NA
47.2 1 0 NA
47.3 3 NA NA
47.4 2 0 NA
48 3 1 NA
48.1 2 1 NA
49 1 NA NA
50 2 0 NA
51 2 0 NA
52 2 0 NA
52.1 3 0 NA
52.2 2 0 NA
52.3 1 0 NA
52.4 3 0 NA
52.5 2 0 NA
53 2 0 NA
53.1 1 0 NA
53.2 1 NA NA
54 1 NA NA
54.1 2 NA NA
54.2 3 NA NA
54.3 1 NA NA
54.4 2 0 NA
55 1 0 NA
55.1 1 0 NA
55.2 2 NA NA
55.3 1 NA NA
55.4 1 0 NA
56 3 0 NA
56.1 2 NA NA
56.2 3 NA NA
56.3 3 1 NA
56.4 1 0 NA
56.5 3 0 NA
57 2 0 NA
57.1 2 0 NA
57.2 2 0 NA
57.3 2 NA NA
58 3 0 NA
58.1 2 NA NA
58.2 3 1 NA
58.3 3 1 NA
58.4 2 0 NA
58.5 3 0 NA
59 3 NA NA
59.1 1 1 NA
60 3 0 NA
61 1 NA NA
61.1 2 1 NA
61.2 2 1 NA
61.3 1 0 NA
61.4 2 0 NA
62 2 NA NA
62.1 3 1 NA
62.2 1 0 NA
62.3 2 0 NA
63 1 NA NA
63.1 3 0 NA
64 2 0 NA
65 1 0 NA
65.1 2 0 NA
65.2 1 0 NA
65.3 1 0 NA
66 1 NA NA
66.1 1 0 NA
66.2 3 0 NA
67 3 NA NA
68 1 0 NA
68.1 2 0 NA
68.2 2 NA NA
68.3 2 0 NA
68.4 2 NA NA
69 2 0 NA
70 1 0 NA
70.1 3 0 NA
71 3 0 NA
71.1 3 1 NA
71.2 3 0 NA
71.3 2 1 NA
71.4 2 0 NA
72 1 0 NA
72.1 1 0 NA
72.2 2 NA NA
72.3 2 0 NA
72.4 1 0 NA
72.5 1 0 NA
73 1 0 NA
74 1 0 NA
75 3 NA NA
76 3 0 NA
76.1 3 0 NA
76.2 1 0 NA
77 2 NA NA
78 2 0 NA
79 1 NA NA
79.1 2 0 NA
79.2 1 NA NA
80 2 NA NA
80.1 2 0 NA
80.2 3 NA NA
81 3 0 NA
81.1 2 0 NA
81.2 3 NA NA
81.3 3 0 NA
82 3 NA NA
82.1 2 0 NA
82.2 2 1 NA
83 3 NA NA
83.1 3 0 NA
83.2 3 0 NA
83.3 2 NA NA
84 3 0 NA
84.1 3 NA NA
85 3 1 NA
85.1 2 NA NA
85.2 2 0 NA
85.3 2 0 NA
85.4 1 0 NA
85.5 3 0 NA
86 2 0 NA
86.1 3 NA NA
86.2 1 NA NA
86.3 1 0 NA
86.4 1 NA NA
86.5 3 0 NA
87 1 NA NA
87.1 1 NA NA
87.2 1 NA NA
88 3 0 NA
88.1 1 NA NA
88.2 1 0 NA
88.3 1 0 NA
89 1 0 NA
90 2 0 NA
90.1 1 0 NA
90.2 3 0 NA
90.3 1 NA NA
91 1 0 NA
91.1 2 0 NA
91.2 1 0 NA
92 2 0 NA
93 1 NA NA
93.1 3 0 NA
93.2 1 NA NA
93.3 3 0 NA
93.4 1 0 NA
94 2 NA NA
94.1 3 0 NA
94.2 1 0 NA
94.3 3 NA NA
94.4 2 0 NA
94.5 3 1 NA
95 3 0 NA
95.1 1 NA NA
95.2 2 0 NA
96 1 0 NA
96.1 3 0 NA
96.2 1 0 NA
96.3 1 NA NA
96.4 2 1 NA
96.5 3 1 NA
97 3 0 NA
97.1 3 0 NA
98 3 0 NA
98.1 1 0 NA
98.2 1 1 NA
99 2 0 NA
99.1 2 0 NA
99.2 1 0 NA
100 1 NA NA
100.1 2 NA NA
100.2 2 0 NA
100.3 1 NA NA
100.4 1 0 NA
$m5a$spM_id
center scale
C2 -0.6240921 0.68571078
O2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
O22 NA NA
O23 NA NA
$m5a$mu_reg_norm
[1] 0
$m5a$tau_reg_norm
[1] 1e-04
$m5a$shape_tau_norm
[1] 0.01
$m5a$rate_tau_norm
[1] 0.01
$m5a$mu_reg_binom
[1] 0
$m5a$tau_reg_binom
[1] 1e-04
$m5a$mu_reg_ordinal
[1] 0
$m5a$tau_reg_ordinal
[1] 1e-04
$m5a$mu_delta_ordinal
[1] 0
$m5a$tau_delta_ordinal
[1] 1e-04
$m5a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m5a$shape_diag_RinvD
[1] "0.01"
$m5a$rate_diag_RinvD
[1] "0.001"
$m5b
$m5b$M_id
M2 C2 O2 (Intercept) M22 M23 M24 O22 O23
1 NA -1.381594459 3 1 NA NA NA NA NA
2 1 0.344426024 2 1 NA NA NA NA NA
3 2 NA 2 1 NA NA NA NA NA
4 2 -0.228910007 3 1 NA NA NA NA NA
5 1 NA 1 1 NA NA NA NA NA
6 NA -2.143955482 3 1 NA NA NA NA NA
7 NA -1.156567023 NA 1 NA NA NA NA NA
8 2 -0.598827660 1 1 NA NA NA NA NA
9 NA NA 1 1 NA NA NA NA NA
10 NA -1.006719032 3 1 NA NA NA NA NA
11 3 0.239801450 3 1 NA NA NA NA NA
12 NA -1.064969789 NA 1 NA NA NA NA NA
13 NA -0.538082688 3 1 NA NA NA NA NA
14 2 NA 2 1 NA NA NA NA NA
15 2 -1.781049276 2 1 NA NA NA NA NA
16 NA NA 3 1 NA NA NA NA NA
17 3 NA 3 1 NA NA NA NA NA
18 3 -0.014579883 NA 1 NA NA NA NA NA
19 2 -2.121550136 2 1 NA NA NA NA NA
20 NA NA 1 1 NA NA NA NA NA
21 NA -0.363239698 1 1 NA NA NA NA NA
22 1 -0.121568514 3 1 NA NA NA NA NA
23 2 -0.951271111 3 1 NA NA NA NA NA
24 NA NA 3 1 NA NA NA NA NA
25 4 -0.974288621 2 1 NA NA NA NA NA
26 NA -1.130632418 1 1 NA NA NA NA NA
27 NA 0.114339868 3 1 NA NA NA NA NA
28 2 0.238334648 3 1 NA NA NA NA NA
29 4 0.840744958 2 1 NA NA NA NA NA
30 2 NA 1 1 NA NA NA NA NA
31 2 NA 2 1 NA NA NA NA NA
32 3 -1.466312154 3 1 NA NA NA NA NA
33 1 -0.637352277 2 1 NA NA NA NA NA
34 4 NA 2 1 NA NA NA NA NA
35 4 NA 3 1 NA NA NA NA NA
36 NA NA 1 1 NA NA NA NA NA
37 NA NA 3 1 NA NA NA NA NA
38 NA NA 2 1 NA NA NA NA NA
39 4 0.006728205 3 1 NA NA NA NA NA
40 NA NA 2 1 NA NA NA NA NA
41 2 -1.663281353 2 1 NA NA NA NA NA
42 NA 0.161184794 2 1 NA NA NA NA NA
43 NA 0.457939180 3 1 NA NA NA NA NA
44 3 -0.307070331 2 1 NA NA NA NA NA
45 NA NA 1 1 NA NA NA NA NA
46 NA -1.071668276 NA 1 NA NA NA NA NA
47 4 -0.814751321 1 1 NA NA NA NA NA
48 3 -0.547630662 2 1 NA NA NA NA NA
49 4 NA 1 1 NA NA NA NA NA
50 3 -1.350213782 2 1 NA NA NA NA NA
51 NA 0.719054706 2 1 NA NA NA NA NA
52 1 NA 3 1 NA NA NA NA NA
53 NA -1.207130750 3 1 NA NA NA NA NA
54 NA NA 1 1 NA NA NA NA NA
55 NA -0.408600991 2 1 NA NA NA NA NA
56 NA -0.271380529 NA 1 NA NA NA NA NA
57 4 -1.361925974 3 1 NA NA NA NA NA
58 1 NA 1 1 NA NA NA NA NA
59 2 NA 2 1 NA NA NA NA NA
60 3 -0.323712205 2 1 NA NA NA NA NA
61 2 NA 3 1 NA NA NA NA NA
62 3 NA 2 1 NA NA NA NA NA
63 2 -1.386906880 1 1 NA NA NA NA NA
64 NA NA 3 1 NA NA NA NA NA
65 NA NA 1 1 NA NA NA NA NA
66 2 -0.565191691 3 1 NA NA NA NA NA
67 NA -0.382899912 3 1 NA NA NA NA NA
68 3 NA 2 1 NA NA NA NA NA
69 3 -0.405642769 NA 1 NA NA NA NA NA
70 4 NA 2 1 NA NA NA NA NA
71 2 -0.843748427 NA 1 NA NA NA NA NA
72 NA 0.116003683 NA 1 NA NA NA NA NA
73 4 -0.778634325 1 1 NA NA NA NA NA
74 NA NA 2 1 NA NA NA NA NA
75 NA NA 2 1 NA NA NA NA NA
76 4 NA 1 1 NA NA NA NA NA
77 2 -0.632974758 3 1 NA NA NA NA NA
78 2 NA 2 1 NA NA NA NA NA
79 NA -0.778064615 NA 1 NA NA NA NA NA
80 1 NA 1 1 NA NA NA NA NA
81 NA NA 1 1 NA NA NA NA NA
82 3 -0.246123253 3 1 NA NA NA NA NA
83 4 -1.239659782 NA 1 NA NA NA NA NA
84 3 -0.467772280 3 1 NA NA NA NA NA
85 NA NA 3 1 NA NA NA NA NA
86 3 -2.160485036 2 1 NA NA NA NA NA
87 NA -0.657675572 1 1 NA NA NA NA NA
88 NA NA 1 1 NA NA NA NA NA
89 NA -0.696710744 3 1 NA NA NA NA NA
90 4 NA 1 1 NA NA NA NA NA
91 NA -0.179395847 2 1 NA NA NA NA NA
92 2 -0.441545568 NA 1 NA NA NA NA NA
93 4 -0.685799334 1 1 NA NA NA NA NA
94 NA NA 3 1 NA NA NA NA NA
95 NA 0.191929445 2 1 NA NA NA NA NA
96 NA NA 2 1 NA NA NA NA NA
97 1 -0.069760671 3 1 NA NA NA NA NA
98 NA NA 2 1 NA NA NA NA NA
99 2 NA 2 1 NA NA NA NA NA
100 NA NA 3 1 NA NA NA NA NA
$m5b$M_lvlone
o1 c1 c1:C2
1 2 0.7592026489 NA
1.1 1 0.9548337990 NA
1.2 1 0.5612235156 NA
1.3 2 1.1873391025 NA
2 3 0.9192204198 NA
2.1 1 -0.1870730476 NA
2.2 2 1.2517512331 NA
3 1 -0.0605087604 NA
3.1 3 0.3788637747 NA
3.2 2 0.9872578281 NA
4 3 1.4930175328 NA
4.1 2 -0.7692526880 NA
4.2 1 0.9180841450 NA
4.3 2 -0.0541170782 NA
5 2 -0.1376784521 NA
5.1 2 -0.2740585866 NA
5.2 3 0.4670496929 NA
5.3 2 0.1740288049 NA
6 3 0.9868044683 NA
7 3 -0.1280320918 NA
7.1 1 0.4242971219 NA
7.2 1 0.0777182491 NA
8 2 -0.5791408712 NA
8.1 2 0.3128604232 NA
8.2 3 0.6258446356 NA
8.3 3 -0.1040137707 NA
8.4 2 0.0481450285 NA
8.5 2 0.3831763675 NA
9 2 -0.1757592269 NA
9.1 2 -0.1791541200 NA
9.2 3 -0.0957042935 NA
10 2 -0.5598409704 NA
10.1 1 -0.2318340451 NA
11 2 0.5086859475 NA
11.1 3 0.4951758188 NA
11.2 3 -1.1022162541 NA
11.3 1 -0.0611636705 NA
11.4 3 -0.4971774316 NA
12 1 -0.2433996286 NA
13 2 0.8799673116 NA
13.1 1 0.1079022586 NA
14 3 0.9991752617 NA
14.1 3 -0.1094019046 NA
14.2 2 0.1518967560 NA
14.3 1 0.3521012473 NA
15 3 0.3464447888 NA
15.1 3 -0.4767313971 NA
15.2 2 0.5759767791 NA
15.3 2 -0.1713452662 NA
16 3 0.4564754473 NA
16.1 1 1.0652558311 NA
16.2 3 0.6971872493 NA
16.3 1 0.5259331838 NA
16.4 1 0.2046601798 NA
16.5 2 1.0718540464 NA
17 2 0.6048676222 NA
17.1 2 0.2323298304 NA
17.2 3 1.2617499032 NA
17.3 2 -0.3913230895 NA
17.4 3 0.9577299112 NA
18 2 -0.0050324072 NA
19 3 -0.4187468937 NA
19.1 2 -0.4478828944 NA
19.2 2 -1.1966721302 NA
19.3 2 -0.5877091668 NA
20 1 0.6838223064 NA
20.1 2 0.3278571109 NA
20.2 2 -0.8489831990 NA
20.3 1 1.3169975191 NA
20.4 1 0.0444804531 NA
20.5 2 -0.4535207652 NA
21 2 -0.4030302960 NA
21.1 2 -0.4069674045 NA
21.2 2 1.0650265940 NA
22 1 -0.0673274516 NA
22.1 1 0.9601388170 NA
23 1 0.5556634840 NA
23.1 2 1.4407865964 NA
24 1 0.3856376411 NA
25 2 0.3564400705 NA
25.1 1 0.0982553434 NA
25.2 1 0.1928682598 NA
25.3 1 -0.0192488594 NA
25.4 2 0.4466012931 NA
25.5 2 1.1425193342 NA
26 2 0.5341531449 NA
26.1 3 1.2268695927 NA
26.2 2 0.3678294939 NA
26.3 1 0.5948516018 NA
27 2 -0.3342844147 NA
27.1 2 -0.4835141229 NA
28 1 -0.7145915499 NA
28.1 1 0.5063671955 NA
28.2 1 -0.2067413142 NA
28.3 1 0.1196789973 NA
29 2 0.1392699487 NA
29.1 2 0.7960234776 NA
29.2 1 1.0398214352 NA
29.3 2 0.0813246429 NA
30 2 -0.3296323050 NA
30.1 2 1.3635850954 NA
30.2 2 0.7354171050 NA
31 2 0.3708398217 NA
32 3 -0.0474059668 NA
32.1 1 1.2507771489 NA
32.2 3 0.1142915519 NA
32.3 3 0.6773270619 NA
33 2 0.1774293842 NA
33.1 2 0.6159606291 NA
34 3 0.8590979166 NA
34.1 2 0.0546216775 NA
34.2 2 -0.0897224473 NA
34.3 1 0.4163395571 NA
35 2 -1.4693520528 NA
35.1 3 -0.3031734330 NA
35.2 2 -0.6045512101 NA
36 3 0.9823048960 NA
36.1 2 1.4466051416 NA
36.2 3 1.1606752905 NA
36.3 3 0.8373091576 NA
36.4 3 0.2640591685 NA
37 3 0.1177313455 NA
37.1 3 -0.1415483779 NA
37.2 3 0.0054610124 NA
38 2 0.8078948077 NA
39 1 0.9876451040 NA
39.1 2 -0.3431222274 NA
39.2 2 -1.7909380751 NA
39.3 1 -0.1798746191 NA
39.4 1 -0.1850961689 NA
39.5 1 0.4544226146 NA
40 3 0.5350190436 NA
40.1 1 0.4189342752 NA
40.2 1 0.4211994981 NA
40.3 2 0.0916687506 NA
41 3 -0.1035047421 NA
41.1 2 -0.4684202411 NA
41.2 1 0.5972615368 NA
41.3 1 0.9885613862 NA
41.4 3 -0.3908036794 NA
42 2 -0.0338893961 NA
42.1 1 -0.4498363172 NA
43 2 0.8965546110 NA
43.1 2 0.6199122090 NA
43.2 1 0.1804894429 NA
44 3 1.3221409285 NA
44.1 2 0.3416426284 NA
44.2 1 0.5706610068 NA
44.3 1 1.2679497430 NA
45 1 0.1414983160 NA
45.1 1 0.7220892521 NA
46 1 1.5391054233 NA
46.1 2 0.3889107049 NA
46.2 3 0.1248719493 NA
47 1 0.2014101100 NA
47.1 1 0.2982973539 NA
47.2 1 1.1518107179 NA
47.3 3 0.5196802157 NA
47.4 2 0.3702301552 NA
48 3 -0.2128602862 NA
48.1 2 -0.5337239976 NA
49 1 -0.5236770035 NA
50 2 0.3897705981 NA
51 2 -0.7213343736 NA
52 2 0.3758235358 NA
52.1 3 0.7138067080 NA
52.2 2 0.8872895233 NA
52.3 1 -0.9664587437 NA
52.4 3 0.0254566848 NA
52.5 2 0.4155259424 NA
53 2 0.5675736897 NA
53.1 1 -0.3154088781 NA
53.2 1 0.2162315769 NA
54 1 -0.0880802382 NA
54.1 2 0.4129127672 NA
54.2 3 1.0119546775 NA
54.3 1 -0.1112901990 NA
54.4 2 0.8587727145 NA
55 1 -0.0116453589 NA
55.1 1 0.5835528661 NA
55.2 2 -1.0010857254 NA
55.3 1 -0.4796526070 NA
55.4 1 -0.1202746964 NA
56 3 0.5176377612 NA
56.1 2 -1.1136932588 NA
56.2 3 -0.0168103281 NA
56.3 3 0.3933023606 NA
56.4 1 0.3714625139 NA
56.5 3 0.7811448179 NA
57 2 -1.0868304872 NA
57.1 2 0.8018626997 NA
57.2 2 -0.1159517011 NA
57.3 2 0.6785562445 NA
58 3 1.6476207996 NA
58.1 2 0.3402652711 NA
58.2 3 -0.1111300753 NA
58.3 3 -0.5409234285 NA
58.4 2 -0.1271327672 NA
58.5 3 0.8713264822 NA
59 3 0.4766421367 NA
59.1 1 1.0028089765 NA
60 3 0.5231452932 NA
61 1 -0.7190130614 NA
61.1 2 0.8353702312 NA
61.2 2 1.0229058138 NA
61.3 1 1.1717723589 NA
61.4 2 -0.0629201596 NA
62 2 -0.3979137604 NA
62.1 3 0.6830738372 NA
62.2 1 0.4301745954 NA
62.3 2 -0.0333139957 NA
63 1 0.3345678035 NA
63.1 3 0.3643769511 NA
64 2 0.3949911859 NA
65 1 1.2000091513 NA
65.1 2 0.0110122646 NA
65.2 1 -0.5776452043 NA
65.3 1 -0.1372183563 NA
66 1 -0.5081302805 NA
66.1 1 -0.1447837412 NA
66.2 3 0.1906241379 NA
67 3 1.6716027681 NA
68 1 0.5691848839 NA
68.1 2 0.1004860389 NA
68.2 2 -0.0061241827 NA
68.3 2 0.7443745962 NA
68.4 2 0.8726923437 NA
69 2 0.0381382683 NA
70 1 0.8126204217 NA
70.1 3 0.4691503050 NA
71 3 -0.5529062591 NA
71.1 3 -0.1103252087 NA
71.2 3 1.7178492547 NA
71.3 2 -1.0118346755 NA
71.4 2 1.8623785017 NA
72 1 -0.4521659275 NA
72.1 1 0.1375317317 NA
72.2 2 -0.4170988856 NA
72.3 2 0.7107266765 NA
72.4 1 0.1451969143 NA
72.5 1 1.6298050306 NA
73 1 -0.0307469467 NA
74 1 0.3730017941 NA
75 3 -0.4908003566 NA
76 3 -0.9888876620 NA
76.1 3 0.0003798292 NA
76.2 1 -0.8421863763 NA
77 2 -0.4986802480 NA
78 2 0.0417330969 NA
79 1 -0.3767450660 NA
79.1 2 0.1516000028 NA
79.2 1 -0.1888160741 NA
80 2 -0.0041558414 NA
80.1 2 -0.0329337062 NA
80.2 3 0.5046816157 NA
81 3 -0.9493950353 NA
81.1 2 0.2443038954 NA
81.2 3 0.6476958410 NA
81.3 3 0.4182528210 NA
82 3 1.1088801952 NA
82.1 2 0.9334157763 NA
82.2 2 0.4958140634 NA
83 3 0.5104724530 NA
83.1 3 -0.0513309106 NA
83.2 3 -0.2067792494 NA
83.3 2 -0.0534169155 NA
84 3 -0.0255753653 NA
84.1 3 -1.8234189877 NA
85 3 -0.0114038622 NA
85.1 2 -0.0577615939 NA
85.2 2 -0.2241856342 NA
85.3 2 -0.0520175929 NA
85.4 1 0.2892733846 NA
85.5 3 -0.3740417009 NA
86 2 0.4293735089 NA
86.1 3 -0.1363456521 NA
86.2 1 0.1230989293 NA
86.3 1 0.3305413955 NA
86.4 1 2.6003411822 NA
86.5 3 -0.1420690052 NA
87 1 1.0457427869 NA
87.1 1 -0.2973007190 NA
87.2 1 0.4396872616 NA
88 3 -0.0601928334 NA
88.1 1 -1.0124347595 NA
88.2 1 0.5730917016 NA
88.3 1 -0.0029455332 NA
89 1 1.5465903721 NA
90 2 0.0626760573 NA
90.1 1 1.1896872985 NA
90.2 3 0.2597888783 NA
90.3 1 0.6599799887 NA
91 1 1.1213651365 NA
91.1 2 1.2046371625 NA
91.2 1 0.3395603754 NA
92 2 0.4674939332 NA
93 1 0.2677965647 NA
93.1 3 1.6424445368 NA
93.2 1 0.7101700066 NA
93.3 3 1.1222322893 NA
93.4 1 1.4628960401 NA
94 2 -0.2904211940 NA
94.1 3 0.0147813580 NA
94.2 1 -0.4536774482 NA
94.3 3 0.6793464917 NA
94.4 2 -0.9411356550 NA
94.5 3 0.5683867264 NA
95 3 0.2375652188 NA
95.1 1 0.0767152977 NA
95.2 2 -0.6886731251 NA
96 1 0.7813892121 NA
96.1 3 0.3391519695 NA
96.2 1 -0.4857246503 NA
96.3 1 0.8771471244 NA
96.4 2 1.9030768981 NA
96.5 3 -0.1684332749 NA
97 3 1.3775130083 NA
97.1 3 -1.7323228619 NA
98 3 -1.2648518889 NA
98.1 1 -0.9042716241 NA
98.2 1 -0.1560385207 NA
99 2 0.7993356425 NA
99.1 2 1.0355522332 NA
99.2 1 -0.1150895843 NA
100 1 0.0369067906 NA
100.1 2 1.6023713093 NA
100.2 2 0.8861545820 NA
100.3 1 0.1277046316 NA
100.4 1 -0.0834577654 NA
$m5b$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.6857108
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
$m5b$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
c1:C2 -0.1866456 0.7201206
$m5b$mu_reg_norm
[1] 0
$m5b$tau_reg_norm
[1] 1e-04
$m5b$shape_tau_norm
[1] 0.01
$m5b$rate_tau_norm
[1] 0.01
$m5b$mu_reg_multinomial
[1] 0
$m5b$tau_reg_multinomial
[1] 1e-04
$m5b$mu_reg_ordinal
[1] 0
$m5b$tau_reg_ordinal
[1] 1e-04
$m5b$mu_delta_ordinal
[1] 0
$m5b$tau_delta_ordinal
[1] 1e-04
$m5b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m5b$shape_diag_RinvD
[1] "0.01"
$m5b$rate_diag_RinvD
[1] "0.001"
$m5c
$m5c$M_id
M2 C2 O2 (Intercept) M22 M23 M24 O22 O23
1 NA -1.381594459 3 1 NA NA NA NA NA
2 1 0.344426024 2 1 NA NA NA NA NA
3 2 NA 2 1 NA NA NA NA NA
4 2 -0.228910007 3 1 NA NA NA NA NA
5 1 NA 1 1 NA NA NA NA NA
6 NA -2.143955482 3 1 NA NA NA NA NA
7 NA -1.156567023 NA 1 NA NA NA NA NA
8 2 -0.598827660 1 1 NA NA NA NA NA
9 NA NA 1 1 NA NA NA NA NA
10 NA -1.006719032 3 1 NA NA NA NA NA
11 3 0.239801450 3 1 NA NA NA NA NA
12 NA -1.064969789 NA 1 NA NA NA NA NA
13 NA -0.538082688 3 1 NA NA NA NA NA
14 2 NA 2 1 NA NA NA NA NA
15 2 -1.781049276 2 1 NA NA NA NA NA
16 NA NA 3 1 NA NA NA NA NA
17 3 NA 3 1 NA NA NA NA NA
18 3 -0.014579883 NA 1 NA NA NA NA NA
19 2 -2.121550136 2 1 NA NA NA NA NA
20 NA NA 1 1 NA NA NA NA NA
21 NA -0.363239698 1 1 NA NA NA NA NA
22 1 -0.121568514 3 1 NA NA NA NA NA
23 2 -0.951271111 3 1 NA NA NA NA NA
24 NA NA 3 1 NA NA NA NA NA
25 4 -0.974288621 2 1 NA NA NA NA NA
26 NA -1.130632418 1 1 NA NA NA NA NA
27 NA 0.114339868 3 1 NA NA NA NA NA
28 2 0.238334648 3 1 NA NA NA NA NA
29 4 0.840744958 2 1 NA NA NA NA NA
30 2 NA 1 1 NA NA NA NA NA
31 2 NA 2 1 NA NA NA NA NA
32 3 -1.466312154 3 1 NA NA NA NA NA
33 1 -0.637352277 2 1 NA NA NA NA NA
34 4 NA 2 1 NA NA NA NA NA
35 4 NA 3 1 NA NA NA NA NA
36 NA NA 1 1 NA NA NA NA NA
37 NA NA 3 1 NA NA NA NA NA
38 NA NA 2 1 NA NA NA NA NA
39 4 0.006728205 3 1 NA NA NA NA NA
40 NA NA 2 1 NA NA NA NA NA
41 2 -1.663281353 2 1 NA NA NA NA NA
42 NA 0.161184794 2 1 NA NA NA NA NA
43 NA 0.457939180 3 1 NA NA NA NA NA
44 3 -0.307070331 2 1 NA NA NA NA NA
45 NA NA 1 1 NA NA NA NA NA
46 NA -1.071668276 NA 1 NA NA NA NA NA
47 4 -0.814751321 1 1 NA NA NA NA NA
48 3 -0.547630662 2 1 NA NA NA NA NA
49 4 NA 1 1 NA NA NA NA NA
50 3 -1.350213782 2 1 NA NA NA NA NA
51 NA 0.719054706 2 1 NA NA NA NA NA
52 1 NA 3 1 NA NA NA NA NA
53 NA -1.207130750 3 1 NA NA NA NA NA
54 NA NA 1 1 NA NA NA NA NA
55 NA -0.408600991 2 1 NA NA NA NA NA
56 NA -0.271380529 NA 1 NA NA NA NA NA
57 4 -1.361925974 3 1 NA NA NA NA NA
58 1 NA 1 1 NA NA NA NA NA
59 2 NA 2 1 NA NA NA NA NA
60 3 -0.323712205 2 1 NA NA NA NA NA
61 2 NA 3 1 NA NA NA NA NA
62 3 NA 2 1 NA NA NA NA NA
63 2 -1.386906880 1 1 NA NA NA NA NA
64 NA NA 3 1 NA NA NA NA NA
65 NA NA 1 1 NA NA NA NA NA
66 2 -0.565191691 3 1 NA NA NA NA NA
67 NA -0.382899912 3 1 NA NA NA NA NA
68 3 NA 2 1 NA NA NA NA NA
69 3 -0.405642769 NA 1 NA NA NA NA NA
70 4 NA 2 1 NA NA NA NA NA
71 2 -0.843748427 NA 1 NA NA NA NA NA
72 NA 0.116003683 NA 1 NA NA NA NA NA
73 4 -0.778634325 1 1 NA NA NA NA NA
74 NA NA 2 1 NA NA NA NA NA
75 NA NA 2 1 NA NA NA NA NA
76 4 NA 1 1 NA NA NA NA NA
77 2 -0.632974758 3 1 NA NA NA NA NA
78 2 NA 2 1 NA NA NA NA NA
79 NA -0.778064615 NA 1 NA NA NA NA NA
80 1 NA 1 1 NA NA NA NA NA
81 NA NA 1 1 NA NA NA NA NA
82 3 -0.246123253 3 1 NA NA NA NA NA
83 4 -1.239659782 NA 1 NA NA NA NA NA
84 3 -0.467772280 3 1 NA NA NA NA NA
85 NA NA 3 1 NA NA NA NA NA
86 3 -2.160485036 2 1 NA NA NA NA NA
87 NA -0.657675572 1 1 NA NA NA NA NA
88 NA NA 1 1 NA NA NA NA NA
89 NA -0.696710744 3 1 NA NA NA NA NA
90 4 NA 1 1 NA NA NA NA NA
91 NA -0.179395847 2 1 NA NA NA NA NA
92 2 -0.441545568 NA 1 NA NA NA NA NA
93 4 -0.685799334 1 1 NA NA NA NA NA
94 NA NA 3 1 NA NA NA NA NA
95 NA 0.191929445 2 1 NA NA NA NA NA
96 NA NA 2 1 NA NA NA NA NA
97 1 -0.069760671 3 1 NA NA NA NA NA
98 NA NA 2 1 NA NA NA NA NA
99 2 NA 2 1 NA NA NA NA NA
100 NA NA 3 1 NA NA NA NA NA
$m5c$M_lvlone
o1 c1 c1:C2
1 2 0.7592026489 NA
1.1 1 0.9548337990 NA
1.2 1 0.5612235156 NA
1.3 2 1.1873391025 NA
2 3 0.9192204198 NA
2.1 1 -0.1870730476 NA
2.2 2 1.2517512331 NA
3 1 -0.0605087604 NA
3.1 3 0.3788637747 NA
3.2 2 0.9872578281 NA
4 3 1.4930175328 NA
4.1 2 -0.7692526880 NA
4.2 1 0.9180841450 NA
4.3 2 -0.0541170782 NA
5 2 -0.1376784521 NA
5.1 2 -0.2740585866 NA
5.2 3 0.4670496929 NA
5.3 2 0.1740288049 NA
6 3 0.9868044683 NA
7 3 -0.1280320918 NA
7.1 1 0.4242971219 NA
7.2 1 0.0777182491 NA
8 2 -0.5791408712 NA
8.1 2 0.3128604232 NA
8.2 3 0.6258446356 NA
8.3 3 -0.1040137707 NA
8.4 2 0.0481450285 NA
8.5 2 0.3831763675 NA
9 2 -0.1757592269 NA
9.1 2 -0.1791541200 NA
9.2 3 -0.0957042935 NA
10 2 -0.5598409704 NA
10.1 1 -0.2318340451 NA
11 2 0.5086859475 NA
11.1 3 0.4951758188 NA
11.2 3 -1.1022162541 NA
11.3 1 -0.0611636705 NA
11.4 3 -0.4971774316 NA
12 1 -0.2433996286 NA
13 2 0.8799673116 NA
13.1 1 0.1079022586 NA
14 3 0.9991752617 NA
14.1 3 -0.1094019046 NA
14.2 2 0.1518967560 NA
14.3 1 0.3521012473 NA
15 3 0.3464447888 NA
15.1 3 -0.4767313971 NA
15.2 2 0.5759767791 NA
15.3 2 -0.1713452662 NA
16 3 0.4564754473 NA
16.1 1 1.0652558311 NA
16.2 3 0.6971872493 NA
16.3 1 0.5259331838 NA
16.4 1 0.2046601798 NA
16.5 2 1.0718540464 NA
17 2 0.6048676222 NA
17.1 2 0.2323298304 NA
17.2 3 1.2617499032 NA
17.3 2 -0.3913230895 NA
17.4 3 0.9577299112 NA
18 2 -0.0050324072 NA
19 3 -0.4187468937 NA
19.1 2 -0.4478828944 NA
19.2 2 -1.1966721302 NA
19.3 2 -0.5877091668 NA
20 1 0.6838223064 NA
20.1 2 0.3278571109 NA
20.2 2 -0.8489831990 NA
20.3 1 1.3169975191 NA
20.4 1 0.0444804531 NA
20.5 2 -0.4535207652 NA
21 2 -0.4030302960 NA
21.1 2 -0.4069674045 NA
21.2 2 1.0650265940 NA
22 1 -0.0673274516 NA
22.1 1 0.9601388170 NA
23 1 0.5556634840 NA
23.1 2 1.4407865964 NA
24 1 0.3856376411 NA
25 2 0.3564400705 NA
25.1 1 0.0982553434 NA
25.2 1 0.1928682598 NA
25.3 1 -0.0192488594 NA
25.4 2 0.4466012931 NA
25.5 2 1.1425193342 NA
26 2 0.5341531449 NA
26.1 3 1.2268695927 NA
26.2 2 0.3678294939 NA
26.3 1 0.5948516018 NA
27 2 -0.3342844147 NA
27.1 2 -0.4835141229 NA
28 1 -0.7145915499 NA
28.1 1 0.5063671955 NA
28.2 1 -0.2067413142 NA
28.3 1 0.1196789973 NA
29 2 0.1392699487 NA
29.1 2 0.7960234776 NA
29.2 1 1.0398214352 NA
29.3 2 0.0813246429 NA
30 2 -0.3296323050 NA
30.1 2 1.3635850954 NA
30.2 2 0.7354171050 NA
31 2 0.3708398217 NA
32 3 -0.0474059668 NA
32.1 1 1.2507771489 NA
32.2 3 0.1142915519 NA
32.3 3 0.6773270619 NA
33 2 0.1774293842 NA
33.1 2 0.6159606291 NA
34 3 0.8590979166 NA
34.1 2 0.0546216775 NA
34.2 2 -0.0897224473 NA
34.3 1 0.4163395571 NA
35 2 -1.4693520528 NA
35.1 3 -0.3031734330 NA
35.2 2 -0.6045512101 NA
36 3 0.9823048960 NA
36.1 2 1.4466051416 NA
36.2 3 1.1606752905 NA
36.3 3 0.8373091576 NA
36.4 3 0.2640591685 NA
37 3 0.1177313455 NA
37.1 3 -0.1415483779 NA
37.2 3 0.0054610124 NA
38 2 0.8078948077 NA
39 1 0.9876451040 NA
39.1 2 -0.3431222274 NA
39.2 2 -1.7909380751 NA
39.3 1 -0.1798746191 NA
39.4 1 -0.1850961689 NA
39.5 1 0.4544226146 NA
40 3 0.5350190436 NA
40.1 1 0.4189342752 NA
40.2 1 0.4211994981 NA
40.3 2 0.0916687506 NA
41 3 -0.1035047421 NA
41.1 2 -0.4684202411 NA
41.2 1 0.5972615368 NA
41.3 1 0.9885613862 NA
41.4 3 -0.3908036794 NA
42 2 -0.0338893961 NA
42.1 1 -0.4498363172 NA
43 2 0.8965546110 NA
43.1 2 0.6199122090 NA
43.2 1 0.1804894429 NA
44 3 1.3221409285 NA
44.1 2 0.3416426284 NA
44.2 1 0.5706610068 NA
44.3 1 1.2679497430 NA
45 1 0.1414983160 NA
45.1 1 0.7220892521 NA
46 1 1.5391054233 NA
46.1 2 0.3889107049 NA
46.2 3 0.1248719493 NA
47 1 0.2014101100 NA
47.1 1 0.2982973539 NA
47.2 1 1.1518107179 NA
47.3 3 0.5196802157 NA
47.4 2 0.3702301552 NA
48 3 -0.2128602862 NA
48.1 2 -0.5337239976 NA
49 1 -0.5236770035 NA
50 2 0.3897705981 NA
51 2 -0.7213343736 NA
52 2 0.3758235358 NA
52.1 3 0.7138067080 NA
52.2 2 0.8872895233 NA
52.3 1 -0.9664587437 NA
52.4 3 0.0254566848 NA
52.5 2 0.4155259424 NA
53 2 0.5675736897 NA
53.1 1 -0.3154088781 NA
53.2 1 0.2162315769 NA
54 1 -0.0880802382 NA
54.1 2 0.4129127672 NA
54.2 3 1.0119546775 NA
54.3 1 -0.1112901990 NA
54.4 2 0.8587727145 NA
55 1 -0.0116453589 NA
55.1 1 0.5835528661 NA
55.2 2 -1.0010857254 NA
55.3 1 -0.4796526070 NA
55.4 1 -0.1202746964 NA
56 3 0.5176377612 NA
56.1 2 -1.1136932588 NA
56.2 3 -0.0168103281 NA
56.3 3 0.3933023606 NA
56.4 1 0.3714625139 NA
56.5 3 0.7811448179 NA
57 2 -1.0868304872 NA
57.1 2 0.8018626997 NA
57.2 2 -0.1159517011 NA
57.3 2 0.6785562445 NA
58 3 1.6476207996 NA
58.1 2 0.3402652711 NA
58.2 3 -0.1111300753 NA
58.3 3 -0.5409234285 NA
58.4 2 -0.1271327672 NA
58.5 3 0.8713264822 NA
59 3 0.4766421367 NA
59.1 1 1.0028089765 NA
60 3 0.5231452932 NA
61 1 -0.7190130614 NA
61.1 2 0.8353702312 NA
61.2 2 1.0229058138 NA
61.3 1 1.1717723589 NA
61.4 2 -0.0629201596 NA
62 2 -0.3979137604 NA
62.1 3 0.6830738372 NA
62.2 1 0.4301745954 NA
62.3 2 -0.0333139957 NA
63 1 0.3345678035 NA
63.1 3 0.3643769511 NA
64 2 0.3949911859 NA
65 1 1.2000091513 NA
65.1 2 0.0110122646 NA
65.2 1 -0.5776452043 NA
65.3 1 -0.1372183563 NA
66 1 -0.5081302805 NA
66.1 1 -0.1447837412 NA
66.2 3 0.1906241379 NA
67 3 1.6716027681 NA
68 1 0.5691848839 NA
68.1 2 0.1004860389 NA
68.2 2 -0.0061241827 NA
68.3 2 0.7443745962 NA
68.4 2 0.8726923437 NA
69 2 0.0381382683 NA
70 1 0.8126204217 NA
70.1 3 0.4691503050 NA
71 3 -0.5529062591 NA
71.1 3 -0.1103252087 NA
71.2 3 1.7178492547 NA
71.3 2 -1.0118346755 NA
71.4 2 1.8623785017 NA
72 1 -0.4521659275 NA
72.1 1 0.1375317317 NA
72.2 2 -0.4170988856 NA
72.3 2 0.7107266765 NA
72.4 1 0.1451969143 NA
72.5 1 1.6298050306 NA
73 1 -0.0307469467 NA
74 1 0.3730017941 NA
75 3 -0.4908003566 NA
76 3 -0.9888876620 NA
76.1 3 0.0003798292 NA
76.2 1 -0.8421863763 NA
77 2 -0.4986802480 NA
78 2 0.0417330969 NA
79 1 -0.3767450660 NA
79.1 2 0.1516000028 NA
79.2 1 -0.1888160741 NA
80 2 -0.0041558414 NA
80.1 2 -0.0329337062 NA
80.2 3 0.5046816157 NA
81 3 -0.9493950353 NA
81.1 2 0.2443038954 NA
81.2 3 0.6476958410 NA
81.3 3 0.4182528210 NA
82 3 1.1088801952 NA
82.1 2 0.9334157763 NA
82.2 2 0.4958140634 NA
83 3 0.5104724530 NA
83.1 3 -0.0513309106 NA
83.2 3 -0.2067792494 NA
83.3 2 -0.0534169155 NA
84 3 -0.0255753653 NA
84.1 3 -1.8234189877 NA
85 3 -0.0114038622 NA
85.1 2 -0.0577615939 NA
85.2 2 -0.2241856342 NA
85.3 2 -0.0520175929 NA
85.4 1 0.2892733846 NA
85.5 3 -0.3740417009 NA
86 2 0.4293735089 NA
86.1 3 -0.1363456521 NA
86.2 1 0.1230989293 NA
86.3 1 0.3305413955 NA
86.4 1 2.6003411822 NA
86.5 3 -0.1420690052 NA
87 1 1.0457427869 NA
87.1 1 -0.2973007190 NA
87.2 1 0.4396872616 NA
88 3 -0.0601928334 NA
88.1 1 -1.0124347595 NA
88.2 1 0.5730917016 NA
88.3 1 -0.0029455332 NA
89 1 1.5465903721 NA
90 2 0.0626760573 NA
90.1 1 1.1896872985 NA
90.2 3 0.2597888783 NA
90.3 1 0.6599799887 NA
91 1 1.1213651365 NA
91.1 2 1.2046371625 NA
91.2 1 0.3395603754 NA
92 2 0.4674939332 NA
93 1 0.2677965647 NA
93.1 3 1.6424445368 NA
93.2 1 0.7101700066 NA
93.3 3 1.1222322893 NA
93.4 1 1.4628960401 NA
94 2 -0.2904211940 NA
94.1 3 0.0147813580 NA
94.2 1 -0.4536774482 NA
94.3 3 0.6793464917 NA
94.4 2 -0.9411356550 NA
94.5 3 0.5683867264 NA
95 3 0.2375652188 NA
95.1 1 0.0767152977 NA
95.2 2 -0.6886731251 NA
96 1 0.7813892121 NA
96.1 3 0.3391519695 NA
96.2 1 -0.4857246503 NA
96.3 1 0.8771471244 NA
96.4 2 1.9030768981 NA
96.5 3 -0.1684332749 NA
97 3 1.3775130083 NA
97.1 3 -1.7323228619 NA
98 3 -1.2648518889 NA
98.1 1 -0.9042716241 NA
98.2 1 -0.1560385207 NA
99 2 0.7993356425 NA
99.1 2 1.0355522332 NA
99.2 1 -0.1150895843 NA
100 1 0.0369067906 NA
100.1 2 1.6023713093 NA
100.2 2 0.8861545820 NA
100.3 1 0.1277046316 NA
100.4 1 -0.0834577654 NA
$m5c$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.6857108
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
$m5c$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
c1:C2 -0.1866456 0.7201206
$m5c$mu_reg_norm
[1] 0
$m5c$tau_reg_norm
[1] 1e-04
$m5c$shape_tau_norm
[1] 0.01
$m5c$rate_tau_norm
[1] 0.01
$m5c$mu_reg_multinomial
[1] 0
$m5c$tau_reg_multinomial
[1] 1e-04
$m5c$mu_reg_ordinal
[1] 0
$m5c$tau_reg_ordinal
[1] 1e-04
$m5c$mu_delta_ordinal
[1] 0
$m5c$tau_delta_ordinal
[1] 1e-04
$m5c$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
$m5c$shape_diag_RinvD
[1] "0.01"
$m5c$rate_diag_RinvD
[1] "0.001"
$m5d
$m5d$M_id
M2 C2 O2 (Intercept) M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2
1 NA -1.381594459 3 1 NA NA NA NA NA NA NA NA
2 1 0.344426024 2 1 NA NA NA NA NA NA NA NA
3 2 NA 2 1 NA NA NA NA NA NA NA NA
4 2 -0.228910007 3 1 NA NA NA NA NA NA NA NA
5 1 NA 1 1 NA NA NA NA NA NA NA NA
6 NA -2.143955482 3 1 NA NA NA NA NA NA NA NA
7 NA -1.156567023 NA 1 NA NA NA NA NA NA NA NA
8 2 -0.598827660 1 1 NA NA NA NA NA NA NA NA
9 NA NA 1 1 NA NA NA NA NA NA NA NA
10 NA -1.006719032 3 1 NA NA NA NA NA NA NA NA
11 3 0.239801450 3 1 NA NA NA NA NA NA NA NA
12 NA -1.064969789 NA 1 NA NA NA NA NA NA NA NA
13 NA -0.538082688 3 1 NA NA NA NA NA NA NA NA
14 2 NA 2 1 NA NA NA NA NA NA NA NA
15 2 -1.781049276 2 1 NA NA NA NA NA NA NA NA
16 NA NA 3 1 NA NA NA NA NA NA NA NA
17 3 NA 3 1 NA NA NA NA NA NA NA NA
18 3 -0.014579883 NA 1 NA NA NA NA NA NA NA NA
19 2 -2.121550136 2 1 NA NA NA NA NA NA NA NA
20 NA NA 1 1 NA NA NA NA NA NA NA NA
21 NA -0.363239698 1 1 NA NA NA NA NA NA NA NA
22 1 -0.121568514 3 1 NA NA NA NA NA NA NA NA
23 2 -0.951271111 3 1 NA NA NA NA NA NA NA NA
24 NA NA 3 1 NA NA NA NA NA NA NA NA
25 4 -0.974288621 2 1 NA NA NA NA NA NA NA NA
26 NA -1.130632418 1 1 NA NA NA NA NA NA NA NA
27 NA 0.114339868 3 1 NA NA NA NA NA NA NA NA
28 2 0.238334648 3 1 NA NA NA NA NA NA NA NA
29 4 0.840744958 2 1 NA NA NA NA NA NA NA NA
30 2 NA 1 1 NA NA NA NA NA NA NA NA
31 2 NA 2 1 NA NA NA NA NA NA NA NA
32 3 -1.466312154 3 1 NA NA NA NA NA NA NA NA
33 1 -0.637352277 2 1 NA NA NA NA NA NA NA NA
34 4 NA 2 1 NA NA NA NA NA NA NA NA
35 4 NA 3 1 NA NA NA NA NA NA NA NA
36 NA NA 1 1 NA NA NA NA NA NA NA NA
37 NA NA 3 1 NA NA NA NA NA NA NA NA
38 NA NA 2 1 NA NA NA NA NA NA NA NA
39 4 0.006728205 3 1 NA NA NA NA NA NA NA NA
40 NA NA 2 1 NA NA NA NA NA NA NA NA
41 2 -1.663281353 2 1 NA NA NA NA NA NA NA NA
42 NA 0.161184794 2 1 NA NA NA NA NA NA NA NA
43 NA 0.457939180 3 1 NA NA NA NA NA NA NA NA
44 3 -0.307070331 2 1 NA NA NA NA NA NA NA NA
45 NA NA 1 1 NA NA NA NA NA NA NA NA
46 NA -1.071668276 NA 1 NA NA NA NA NA NA NA NA
47 4 -0.814751321 1 1 NA NA NA NA NA NA NA NA
48 3 -0.547630662 2 1 NA NA NA NA NA NA NA NA
49 4 NA 1 1 NA NA NA NA NA NA NA NA
50 3 -1.350213782 2 1 NA NA NA NA NA NA NA NA
51 NA 0.719054706 2 1 NA NA NA NA NA NA NA NA
52 1 NA 3 1 NA NA NA NA NA NA NA NA
53 NA -1.207130750 3 1 NA NA NA NA NA NA NA NA
54 NA NA 1 1 NA NA NA NA NA NA NA NA
55 NA -0.408600991 2 1 NA NA NA NA NA NA NA NA
56 NA -0.271380529 NA 1 NA NA NA NA NA NA NA NA
57 4 -1.361925974 3 1 NA NA NA NA NA NA NA NA
58 1 NA 1 1 NA NA NA NA NA NA NA NA
59 2 NA 2 1 NA NA NA NA NA NA NA NA
60 3 -0.323712205 2 1 NA NA NA NA NA NA NA NA
61 2 NA 3 1 NA NA NA NA NA NA NA NA
62 3 NA 2 1 NA NA NA NA NA NA NA NA
63 2 -1.386906880 1 1 NA NA NA NA NA NA NA NA
64 NA NA 3 1 NA NA NA NA NA NA NA NA
65 NA NA 1 1 NA NA NA NA NA NA NA NA
66 2 -0.565191691 3 1 NA NA NA NA NA NA NA NA
67 NA -0.382899912 3 1 NA NA NA NA NA NA NA NA
68 3 NA 2 1 NA NA NA NA NA NA NA NA
69 3 -0.405642769 NA 1 NA NA NA NA NA NA NA NA
70 4 NA 2 1 NA NA NA NA NA NA NA NA
71 2 -0.843748427 NA 1 NA NA NA NA NA NA NA NA
72 NA 0.116003683 NA 1 NA NA NA NA NA NA NA NA
73 4 -0.778634325 1 1 NA NA NA NA NA NA NA NA
74 NA NA 2 1 NA NA NA NA NA NA NA NA
75 NA NA 2 1 NA NA NA NA NA NA NA NA
76 4 NA 1 1 NA NA NA NA NA NA NA NA
77 2 -0.632974758 3 1 NA NA NA NA NA NA NA NA
78 2 NA 2 1 NA NA NA NA NA NA NA NA
79 NA -0.778064615 NA 1 NA NA NA NA NA NA NA NA
80 1 NA 1 1 NA NA NA NA NA NA NA NA
81 NA NA 1 1 NA NA NA NA NA NA NA NA
82 3 -0.246123253 3 1 NA NA NA NA NA NA NA NA
83 4 -1.239659782 NA 1 NA NA NA NA NA NA NA NA
84 3 -0.467772280 3 1 NA NA NA NA NA NA NA NA
85 NA NA 3 1 NA NA NA NA NA NA NA NA
86 3 -2.160485036 2 1 NA NA NA NA NA NA NA NA
87 NA -0.657675572 1 1 NA NA NA NA NA NA NA NA
88 NA NA 1 1 NA NA NA NA NA NA NA NA
89 NA -0.696710744 3 1 NA NA NA NA NA NA NA NA
90 4 NA 1 1 NA NA NA NA NA NA NA NA
91 NA -0.179395847 2 1 NA NA NA NA NA NA NA NA
92 2 -0.441545568 NA 1 NA NA NA NA NA NA NA NA
93 4 -0.685799334 1 1 NA NA NA NA NA NA NA NA
94 NA NA 3 1 NA NA NA NA NA NA NA NA
95 NA 0.191929445 2 1 NA NA NA NA NA NA NA NA
96 NA NA 2 1 NA NA NA NA NA NA NA NA
97 1 -0.069760671 3 1 NA NA NA NA NA NA NA NA
98 NA NA 2 1 NA NA NA NA NA NA NA NA
99 2 NA 2 1 NA NA NA NA NA NA NA NA
100 NA NA 3 1 NA NA NA NA NA NA NA NA
$m5d$M_lvlone
o1 c1
1 2 0.7592026489
1.1 1 0.9548337990
1.2 1 0.5612235156
1.3 2 1.1873391025
2 3 0.9192204198
2.1 1 -0.1870730476
2.2 2 1.2517512331
3 1 -0.0605087604
3.1 3 0.3788637747
3.2 2 0.9872578281
4 3 1.4930175328
4.1 2 -0.7692526880
4.2 1 0.9180841450
4.3 2 -0.0541170782
5 2 -0.1376784521
5.1 2 -0.2740585866
5.2 3 0.4670496929
5.3 2 0.1740288049
6 3 0.9868044683
7 3 -0.1280320918
7.1 1 0.4242971219
7.2 1 0.0777182491
8 2 -0.5791408712
8.1 2 0.3128604232
8.2 3 0.6258446356
8.3 3 -0.1040137707
8.4 2 0.0481450285
8.5 2 0.3831763675
9 2 -0.1757592269
9.1 2 -0.1791541200
9.2 3 -0.0957042935
10 2 -0.5598409704
10.1 1 -0.2318340451
11 2 0.5086859475
11.1 3 0.4951758188
11.2 3 -1.1022162541
11.3 1 -0.0611636705
11.4 3 -0.4971774316
12 1 -0.2433996286
13 2 0.8799673116
13.1 1 0.1079022586
14 3 0.9991752617
14.1 3 -0.1094019046
14.2 2 0.1518967560
14.3 1 0.3521012473
15 3 0.3464447888
15.1 3 -0.4767313971
15.2 2 0.5759767791
15.3 2 -0.1713452662
16 3 0.4564754473
16.1 1 1.0652558311
16.2 3 0.6971872493
16.3 1 0.5259331838
16.4 1 0.2046601798
16.5 2 1.0718540464
17 2 0.6048676222
17.1 2 0.2323298304
17.2 3 1.2617499032
17.3 2 -0.3913230895
17.4 3 0.9577299112
18 2 -0.0050324072
19 3 -0.4187468937
19.1 2 -0.4478828944
19.2 2 -1.1966721302
19.3 2 -0.5877091668
20 1 0.6838223064
20.1 2 0.3278571109
20.2 2 -0.8489831990
20.3 1 1.3169975191
20.4 1 0.0444804531
20.5 2 -0.4535207652
21 2 -0.4030302960
21.1 2 -0.4069674045
21.2 2 1.0650265940
22 1 -0.0673274516
22.1 1 0.9601388170
23 1 0.5556634840
23.1 2 1.4407865964
24 1 0.3856376411
25 2 0.3564400705
25.1 1 0.0982553434
25.2 1 0.1928682598
25.3 1 -0.0192488594
25.4 2 0.4466012931
25.5 2 1.1425193342
26 2 0.5341531449
26.1 3 1.2268695927
26.2 2 0.3678294939
26.3 1 0.5948516018
27 2 -0.3342844147
27.1 2 -0.4835141229
28 1 -0.7145915499
28.1 1 0.5063671955
28.2 1 -0.2067413142
28.3 1 0.1196789973
29 2 0.1392699487
29.1 2 0.7960234776
29.2 1 1.0398214352
29.3 2 0.0813246429
30 2 -0.3296323050
30.1 2 1.3635850954
30.2 2 0.7354171050
31 2 0.3708398217
32 3 -0.0474059668
32.1 1 1.2507771489
32.2 3 0.1142915519
32.3 3 0.6773270619
33 2 0.1774293842
33.1 2 0.6159606291
34 3 0.8590979166
34.1 2 0.0546216775
34.2 2 -0.0897224473
34.3 1 0.4163395571
35 2 -1.4693520528
35.1 3 -0.3031734330
35.2 2 -0.6045512101
36 3 0.9823048960
36.1 2 1.4466051416
36.2 3 1.1606752905
36.3 3 0.8373091576
36.4 3 0.2640591685
37 3 0.1177313455
37.1 3 -0.1415483779
37.2 3 0.0054610124
38 2 0.8078948077
39 1 0.9876451040
39.1 2 -0.3431222274
39.2 2 -1.7909380751
39.3 1 -0.1798746191
39.4 1 -0.1850961689
39.5 1 0.4544226146
40 3 0.5350190436
40.1 1 0.4189342752
40.2 1 0.4211994981
40.3 2 0.0916687506
41 3 -0.1035047421
41.1 2 -0.4684202411
41.2 1 0.5972615368
41.3 1 0.9885613862
41.4 3 -0.3908036794
42 2 -0.0338893961
42.1 1 -0.4498363172
43 2 0.8965546110
43.1 2 0.6199122090
43.2 1 0.1804894429
44 3 1.3221409285
44.1 2 0.3416426284
44.2 1 0.5706610068
44.3 1 1.2679497430
45 1 0.1414983160
45.1 1 0.7220892521
46 1 1.5391054233
46.1 2 0.3889107049
46.2 3 0.1248719493
47 1 0.2014101100
47.1 1 0.2982973539
47.2 1 1.1518107179
47.3 3 0.5196802157
47.4 2 0.3702301552
48 3 -0.2128602862
48.1 2 -0.5337239976
49 1 -0.5236770035
50 2 0.3897705981
51 2 -0.7213343736
52 2 0.3758235358
52.1 3 0.7138067080
52.2 2 0.8872895233
52.3 1 -0.9664587437
52.4 3 0.0254566848
52.5 2 0.4155259424
53 2 0.5675736897
53.1 1 -0.3154088781
53.2 1 0.2162315769
54 1 -0.0880802382
54.1 2 0.4129127672
54.2 3 1.0119546775
54.3 1 -0.1112901990
54.4 2 0.8587727145
55 1 -0.0116453589
55.1 1 0.5835528661
55.2 2 -1.0010857254
55.3 1 -0.4796526070
55.4 1 -0.1202746964
56 3 0.5176377612
56.1 2 -1.1136932588
56.2 3 -0.0168103281
56.3 3 0.3933023606
56.4 1 0.3714625139
56.5 3 0.7811448179
57 2 -1.0868304872
57.1 2 0.8018626997
57.2 2 -0.1159517011
57.3 2 0.6785562445
58 3 1.6476207996
58.1 2 0.3402652711
58.2 3 -0.1111300753
58.3 3 -0.5409234285
58.4 2 -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 1 1.1717723589
61.4 2 -0.0629201596
62 2 -0.3979137604
62.1 3 0.6830738372
62.2 1 0.4301745954
62.3 2 -0.0333139957
63 1 0.3345678035
63.1 3 0.3643769511
64 2 0.3949911859
65 1 1.2000091513
65.1 2 0.0110122646
65.2 1 -0.5776452043
65.3 1 -0.1372183563
66 1 -0.5081302805
66.1 1 -0.1447837412
66.2 3 0.1906241379
67 3 1.6716027681
68 1 0.5691848839
68.1 2 0.1004860389
68.2 2 -0.0061241827
68.3 2 0.7443745962
68.4 2 0.8726923437
69 2 0.0381382683
70 1 0.8126204217
70.1 3 0.4691503050
71 3 -0.5529062591
71.1 3 -0.1103252087
71.2 3 1.7178492547
71.3 2 -1.0118346755
71.4 2 1.8623785017
72 1 -0.4521659275
72.1 1 0.1375317317
72.2 2 -0.4170988856
72.3 2 0.7107266765
72.4 1 0.1451969143
72.5 1 1.6298050306
73 1 -0.0307469467
74 1 0.3730017941
75 3 -0.4908003566
76 3 -0.9888876620
76.1 3 0.0003798292
76.2 1 -0.8421863763
77 2 -0.4986802480
78 2 0.0417330969
79 1 -0.3767450660
79.1 2 0.1516000028
79.2 1 -0.1888160741
80 2 -0.0041558414
80.1 2 -0.0329337062
80.2 3 0.5046816157
81 3 -0.9493950353
81.1 2 0.2443038954
81.2 3 0.6476958410
81.3 3 0.4182528210
82 3 1.1088801952
82.1 2 0.9334157763
82.2 2 0.4958140634
83 3 0.5104724530
83.1 3 -0.0513309106
83.2 3 -0.2067792494
83.3 2 -0.0534169155
84 3 -0.0255753653
84.1 3 -1.8234189877
85 3 -0.0114038622
85.1 2 -0.0577615939
85.2 2 -0.2241856342
85.3 2 -0.0520175929
85.4 1 0.2892733846
85.5 3 -0.3740417009
86 2 0.4293735089
86.1 3 -0.1363456521
86.2 1 0.1230989293
86.3 1 0.3305413955
86.4 1 2.6003411822
86.5 3 -0.1420690052
87 1 1.0457427869
87.1 1 -0.2973007190
87.2 1 0.4396872616
88 3 -0.0601928334
88.1 1 -1.0124347595
88.2 1 0.5730917016
88.3 1 -0.0029455332
89 1 1.5465903721
90 2 0.0626760573
90.1 1 1.1896872985
90.2 3 0.2597888783
90.3 1 0.6599799887
91 1 1.1213651365
91.1 2 1.2046371625
91.2 1 0.3395603754
92 2 0.4674939332
93 1 0.2677965647
93.1 3 1.6424445368
93.2 1 0.7101700066
93.3 3 1.1222322893
93.4 1 1.4628960401
94 2 -0.2904211940
94.1 3 0.0147813580
94.2 1 -0.4536774482
94.3 3 0.6793464917
94.4 2 -0.9411356550
94.5 3 0.5683867264
95 3 0.2375652188
95.1 1 0.0767152977
95.2 2 -0.6886731251
96 1 0.7813892121
96.1 3 0.3391519695
96.2 1 -0.4857246503
96.3 1 0.8771471244
96.4 2 1.9030768981
96.5 3 -0.1684332749
97 3 1.3775130083
97.1 3 -1.7323228619
98 3 -1.2648518889
98.1 1 -0.9042716241
98.2 1 -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 2 0.8861545820
100.3 1 0.1277046316
100.4 1 -0.0834577654
$m5d$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.6857108
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
M22:C2 -0.3136263 0.5921089
M23:C2 -0.2014212 0.4917409
M24:C2 -0.1430739 0.4225863
$m5d$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
$m5d$mu_reg_norm
[1] 0
$m5d$tau_reg_norm
[1] 1e-04
$m5d$shape_tau_norm
[1] 0.01
$m5d$rate_tau_norm
[1] 0.01
$m5d$mu_reg_multinomial
[1] 0
$m5d$tau_reg_multinomial
[1] 1e-04
$m5d$mu_reg_ordinal
[1] 0
$m5d$tau_reg_ordinal
[1] 1e-04
$m5d$mu_delta_ordinal
[1] 0
$m5d$tau_delta_ordinal
[1] 1e-04
$m5d$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
$m5d$shape_diag_RinvD
[1] "0.01"
$m5d$rate_diag_RinvD
[1] "0.001"
$m5e
$m5e$M_id
M2 C2 O2 (Intercept) M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2
1 NA -1.381594459 3 1 NA NA NA NA NA NA NA NA
2 1 0.344426024 2 1 NA NA NA NA NA NA NA NA
3 2 NA 2 1 NA NA NA NA NA NA NA NA
4 2 -0.228910007 3 1 NA NA NA NA NA NA NA NA
5 1 NA 1 1 NA NA NA NA NA NA NA NA
6 NA -2.143955482 3 1 NA NA NA NA NA NA NA NA
7 NA -1.156567023 NA 1 NA NA NA NA NA NA NA NA
8 2 -0.598827660 1 1 NA NA NA NA NA NA NA NA
9 NA NA 1 1 NA NA NA NA NA NA NA NA
10 NA -1.006719032 3 1 NA NA NA NA NA NA NA NA
11 3 0.239801450 3 1 NA NA NA NA NA NA NA NA
12 NA -1.064969789 NA 1 NA NA NA NA NA NA NA NA
13 NA -0.538082688 3 1 NA NA NA NA NA NA NA NA
14 2 NA 2 1 NA NA NA NA NA NA NA NA
15 2 -1.781049276 2 1 NA NA NA NA NA NA NA NA
16 NA NA 3 1 NA NA NA NA NA NA NA NA
17 3 NA 3 1 NA NA NA NA NA NA NA NA
18 3 -0.014579883 NA 1 NA NA NA NA NA NA NA NA
19 2 -2.121550136 2 1 NA NA NA NA NA NA NA NA
20 NA NA 1 1 NA NA NA NA NA NA NA NA
21 NA -0.363239698 1 1 NA NA NA NA NA NA NA NA
22 1 -0.121568514 3 1 NA NA NA NA NA NA NA NA
23 2 -0.951271111 3 1 NA NA NA NA NA NA NA NA
24 NA NA 3 1 NA NA NA NA NA NA NA NA
25 4 -0.974288621 2 1 NA NA NA NA NA NA NA NA
26 NA -1.130632418 1 1 NA NA NA NA NA NA NA NA
27 NA 0.114339868 3 1 NA NA NA NA NA NA NA NA
28 2 0.238334648 3 1 NA NA NA NA NA NA NA NA
29 4 0.840744958 2 1 NA NA NA NA NA NA NA NA
30 2 NA 1 1 NA NA NA NA NA NA NA NA
31 2 NA 2 1 NA NA NA NA NA NA NA NA
32 3 -1.466312154 3 1 NA NA NA NA NA NA NA NA
33 1 -0.637352277 2 1 NA NA NA NA NA NA NA NA
34 4 NA 2 1 NA NA NA NA NA NA NA NA
35 4 NA 3 1 NA NA NA NA NA NA NA NA
36 NA NA 1 1 NA NA NA NA NA NA NA NA
37 NA NA 3 1 NA NA NA NA NA NA NA NA
38 NA NA 2 1 NA NA NA NA NA NA NA NA
39 4 0.006728205 3 1 NA NA NA NA NA NA NA NA
40 NA NA 2 1 NA NA NA NA NA NA NA NA
41 2 -1.663281353 2 1 NA NA NA NA NA NA NA NA
42 NA 0.161184794 2 1 NA NA NA NA NA NA NA NA
43 NA 0.457939180 3 1 NA NA NA NA NA NA NA NA
44 3 -0.307070331 2 1 NA NA NA NA NA NA NA NA
45 NA NA 1 1 NA NA NA NA NA NA NA NA
46 NA -1.071668276 NA 1 NA NA NA NA NA NA NA NA
47 4 -0.814751321 1 1 NA NA NA NA NA NA NA NA
48 3 -0.547630662 2 1 NA NA NA NA NA NA NA NA
49 4 NA 1 1 NA NA NA NA NA NA NA NA
50 3 -1.350213782 2 1 NA NA NA NA NA NA NA NA
51 NA 0.719054706 2 1 NA NA NA NA NA NA NA NA
52 1 NA 3 1 NA NA NA NA NA NA NA NA
53 NA -1.207130750 3 1 NA NA NA NA NA NA NA NA
54 NA NA 1 1 NA NA NA NA NA NA NA NA
55 NA -0.408600991 2 1 NA NA NA NA NA NA NA NA
56 NA -0.271380529 NA 1 NA NA NA NA NA NA NA NA
57 4 -1.361925974 3 1 NA NA NA NA NA NA NA NA
58 1 NA 1 1 NA NA NA NA NA NA NA NA
59 2 NA 2 1 NA NA NA NA NA NA NA NA
60 3 -0.323712205 2 1 NA NA NA NA NA NA NA NA
61 2 NA 3 1 NA NA NA NA NA NA NA NA
62 3 NA 2 1 NA NA NA NA NA NA NA NA
63 2 -1.386906880 1 1 NA NA NA NA NA NA NA NA
64 NA NA 3 1 NA NA NA NA NA NA NA NA
65 NA NA 1 1 NA NA NA NA NA NA NA NA
66 2 -0.565191691 3 1 NA NA NA NA NA NA NA NA
67 NA -0.382899912 3 1 NA NA NA NA NA NA NA NA
68 3 NA 2 1 NA NA NA NA NA NA NA NA
69 3 -0.405642769 NA 1 NA NA NA NA NA NA NA NA
70 4 NA 2 1 NA NA NA NA NA NA NA NA
71 2 -0.843748427 NA 1 NA NA NA NA NA NA NA NA
72 NA 0.116003683 NA 1 NA NA NA NA NA NA NA NA
73 4 -0.778634325 1 1 NA NA NA NA NA NA NA NA
74 NA NA 2 1 NA NA NA NA NA NA NA NA
75 NA NA 2 1 NA NA NA NA NA NA NA NA
76 4 NA 1 1 NA NA NA NA NA NA NA NA
77 2 -0.632974758 3 1 NA NA NA NA NA NA NA NA
78 2 NA 2 1 NA NA NA NA NA NA NA NA
79 NA -0.778064615 NA 1 NA NA NA NA NA NA NA NA
80 1 NA 1 1 NA NA NA NA NA NA NA NA
81 NA NA 1 1 NA NA NA NA NA NA NA NA
82 3 -0.246123253 3 1 NA NA NA NA NA NA NA NA
83 4 -1.239659782 NA 1 NA NA NA NA NA NA NA NA
84 3 -0.467772280 3 1 NA NA NA NA NA NA NA NA
85 NA NA 3 1 NA NA NA NA NA NA NA NA
86 3 -2.160485036 2 1 NA NA NA NA NA NA NA NA
87 NA -0.657675572 1 1 NA NA NA NA NA NA NA NA
88 NA NA 1 1 NA NA NA NA NA NA NA NA
89 NA -0.696710744 3 1 NA NA NA NA NA NA NA NA
90 4 NA 1 1 NA NA NA NA NA NA NA NA
91 NA -0.179395847 2 1 NA NA NA NA NA NA NA NA
92 2 -0.441545568 NA 1 NA NA NA NA NA NA NA NA
93 4 -0.685799334 1 1 NA NA NA NA NA NA NA NA
94 NA NA 3 1 NA NA NA NA NA NA NA NA
95 NA 0.191929445 2 1 NA NA NA NA NA NA NA NA
96 NA NA 2 1 NA NA NA NA NA NA NA NA
97 1 -0.069760671 3 1 NA NA NA NA NA NA NA NA
98 NA NA 2 1 NA NA NA NA NA NA NA NA
99 2 NA 2 1 NA NA NA NA NA NA NA NA
100 NA NA 3 1 NA NA NA NA NA NA NA NA
$m5e$M_lvlone
o1 c1
1 2 0.7592026489
1.1 1 0.9548337990
1.2 1 0.5612235156
1.3 2 1.1873391025
2 3 0.9192204198
2.1 1 -0.1870730476
2.2 2 1.2517512331
3 1 -0.0605087604
3.1 3 0.3788637747
3.2 2 0.9872578281
4 3 1.4930175328
4.1 2 -0.7692526880
4.2 1 0.9180841450
4.3 2 -0.0541170782
5 2 -0.1376784521
5.1 2 -0.2740585866
5.2 3 0.4670496929
5.3 2 0.1740288049
6 3 0.9868044683
7 3 -0.1280320918
7.1 1 0.4242971219
7.2 1 0.0777182491
8 2 -0.5791408712
8.1 2 0.3128604232
8.2 3 0.6258446356
8.3 3 -0.1040137707
8.4 2 0.0481450285
8.5 2 0.3831763675
9 2 -0.1757592269
9.1 2 -0.1791541200
9.2 3 -0.0957042935
10 2 -0.5598409704
10.1 1 -0.2318340451
11 2 0.5086859475
11.1 3 0.4951758188
11.2 3 -1.1022162541
11.3 1 -0.0611636705
11.4 3 -0.4971774316
12 1 -0.2433996286
13 2 0.8799673116
13.1 1 0.1079022586
14 3 0.9991752617
14.1 3 -0.1094019046
14.2 2 0.1518967560
14.3 1 0.3521012473
15 3 0.3464447888
15.1 3 -0.4767313971
15.2 2 0.5759767791
15.3 2 -0.1713452662
16 3 0.4564754473
16.1 1 1.0652558311
16.2 3 0.6971872493
16.3 1 0.5259331838
16.4 1 0.2046601798
16.5 2 1.0718540464
17 2 0.6048676222
17.1 2 0.2323298304
17.2 3 1.2617499032
17.3 2 -0.3913230895
17.4 3 0.9577299112
18 2 -0.0050324072
19 3 -0.4187468937
19.1 2 -0.4478828944
19.2 2 -1.1966721302
19.3 2 -0.5877091668
20 1 0.6838223064
20.1 2 0.3278571109
20.2 2 -0.8489831990
20.3 1 1.3169975191
20.4 1 0.0444804531
20.5 2 -0.4535207652
21 2 -0.4030302960
21.1 2 -0.4069674045
21.2 2 1.0650265940
22 1 -0.0673274516
22.1 1 0.9601388170
23 1 0.5556634840
23.1 2 1.4407865964
24 1 0.3856376411
25 2 0.3564400705
25.1 1 0.0982553434
25.2 1 0.1928682598
25.3 1 -0.0192488594
25.4 2 0.4466012931
25.5 2 1.1425193342
26 2 0.5341531449
26.1 3 1.2268695927
26.2 2 0.3678294939
26.3 1 0.5948516018
27 2 -0.3342844147
27.1 2 -0.4835141229
28 1 -0.7145915499
28.1 1 0.5063671955
28.2 1 -0.2067413142
28.3 1 0.1196789973
29 2 0.1392699487
29.1 2 0.7960234776
29.2 1 1.0398214352
29.3 2 0.0813246429
30 2 -0.3296323050
30.1 2 1.3635850954
30.2 2 0.7354171050
31 2 0.3708398217
32 3 -0.0474059668
32.1 1 1.2507771489
32.2 3 0.1142915519
32.3 3 0.6773270619
33 2 0.1774293842
33.1 2 0.6159606291
34 3 0.8590979166
34.1 2 0.0546216775
34.2 2 -0.0897224473
34.3 1 0.4163395571
35 2 -1.4693520528
35.1 3 -0.3031734330
35.2 2 -0.6045512101
36 3 0.9823048960
36.1 2 1.4466051416
36.2 3 1.1606752905
36.3 3 0.8373091576
36.4 3 0.2640591685
37 3 0.1177313455
37.1 3 -0.1415483779
37.2 3 0.0054610124
38 2 0.8078948077
39 1 0.9876451040
39.1 2 -0.3431222274
39.2 2 -1.7909380751
39.3 1 -0.1798746191
39.4 1 -0.1850961689
39.5 1 0.4544226146
40 3 0.5350190436
40.1 1 0.4189342752
40.2 1 0.4211994981
40.3 2 0.0916687506
41 3 -0.1035047421
41.1 2 -0.4684202411
41.2 1 0.5972615368
41.3 1 0.9885613862
41.4 3 -0.3908036794
42 2 -0.0338893961
42.1 1 -0.4498363172
43 2 0.8965546110
43.1 2 0.6199122090
43.2 1 0.1804894429
44 3 1.3221409285
44.1 2 0.3416426284
44.2 1 0.5706610068
44.3 1 1.2679497430
45 1 0.1414983160
45.1 1 0.7220892521
46 1 1.5391054233
46.1 2 0.3889107049
46.2 3 0.1248719493
47 1 0.2014101100
47.1 1 0.2982973539
47.2 1 1.1518107179
47.3 3 0.5196802157
47.4 2 0.3702301552
48 3 -0.2128602862
48.1 2 -0.5337239976
49 1 -0.5236770035
50 2 0.3897705981
51 2 -0.7213343736
52 2 0.3758235358
52.1 3 0.7138067080
52.2 2 0.8872895233
52.3 1 -0.9664587437
52.4 3 0.0254566848
52.5 2 0.4155259424
53 2 0.5675736897
53.1 1 -0.3154088781
53.2 1 0.2162315769
54 1 -0.0880802382
54.1 2 0.4129127672
54.2 3 1.0119546775
54.3 1 -0.1112901990
54.4 2 0.8587727145
55 1 -0.0116453589
55.1 1 0.5835528661
55.2 2 -1.0010857254
55.3 1 -0.4796526070
55.4 1 -0.1202746964
56 3 0.5176377612
56.1 2 -1.1136932588
56.2 3 -0.0168103281
56.3 3 0.3933023606
56.4 1 0.3714625139
56.5 3 0.7811448179
57 2 -1.0868304872
57.1 2 0.8018626997
57.2 2 -0.1159517011
57.3 2 0.6785562445
58 3 1.6476207996
58.1 2 0.3402652711
58.2 3 -0.1111300753
58.3 3 -0.5409234285
58.4 2 -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 1 1.1717723589
61.4 2 -0.0629201596
62 2 -0.3979137604
62.1 3 0.6830738372
62.2 1 0.4301745954
62.3 2 -0.0333139957
63 1 0.3345678035
63.1 3 0.3643769511
64 2 0.3949911859
65 1 1.2000091513
65.1 2 0.0110122646
65.2 1 -0.5776452043
65.3 1 -0.1372183563
66 1 -0.5081302805
66.1 1 -0.1447837412
66.2 3 0.1906241379
67 3 1.6716027681
68 1 0.5691848839
68.1 2 0.1004860389
68.2 2 -0.0061241827
68.3 2 0.7443745962
68.4 2 0.8726923437
69 2 0.0381382683
70 1 0.8126204217
70.1 3 0.4691503050
71 3 -0.5529062591
71.1 3 -0.1103252087
71.2 3 1.7178492547
71.3 2 -1.0118346755
71.4 2 1.8623785017
72 1 -0.4521659275
72.1 1 0.1375317317
72.2 2 -0.4170988856
72.3 2 0.7107266765
72.4 1 0.1451969143
72.5 1 1.6298050306
73 1 -0.0307469467
74 1 0.3730017941
75 3 -0.4908003566
76 3 -0.9888876620
76.1 3 0.0003798292
76.2 1 -0.8421863763
77 2 -0.4986802480
78 2 0.0417330969
79 1 -0.3767450660
79.1 2 0.1516000028
79.2 1 -0.1888160741
80 2 -0.0041558414
80.1 2 -0.0329337062
80.2 3 0.5046816157
81 3 -0.9493950353
81.1 2 0.2443038954
81.2 3 0.6476958410
81.3 3 0.4182528210
82 3 1.1088801952
82.1 2 0.9334157763
82.2 2 0.4958140634
83 3 0.5104724530
83.1 3 -0.0513309106
83.2 3 -0.2067792494
83.3 2 -0.0534169155
84 3 -0.0255753653
84.1 3 -1.8234189877
85 3 -0.0114038622
85.1 2 -0.0577615939
85.2 2 -0.2241856342
85.3 2 -0.0520175929
85.4 1 0.2892733846
85.5 3 -0.3740417009
86 2 0.4293735089
86.1 3 -0.1363456521
86.2 1 0.1230989293
86.3 1 0.3305413955
86.4 1 2.6003411822
86.5 3 -0.1420690052
87 1 1.0457427869
87.1 1 -0.2973007190
87.2 1 0.4396872616
88 3 -0.0601928334
88.1 1 -1.0124347595
88.2 1 0.5730917016
88.3 1 -0.0029455332
89 1 1.5465903721
90 2 0.0626760573
90.1 1 1.1896872985
90.2 3 0.2597888783
90.3 1 0.6599799887
91 1 1.1213651365
91.1 2 1.2046371625
91.2 1 0.3395603754
92 2 0.4674939332
93 1 0.2677965647
93.1 3 1.6424445368
93.2 1 0.7101700066
93.3 3 1.1222322893
93.4 1 1.4628960401
94 2 -0.2904211940
94.1 3 0.0147813580
94.2 1 -0.4536774482
94.3 3 0.6793464917
94.4 2 -0.9411356550
94.5 3 0.5683867264
95 3 0.2375652188
95.1 1 0.0767152977
95.2 2 -0.6886731251
96 1 0.7813892121
96.1 3 0.3391519695
96.2 1 -0.4857246503
96.3 1 0.8771471244
96.4 2 1.9030768981
96.5 3 -0.1684332749
97 3 1.3775130083
97.1 3 -1.7323228619
98 3 -1.2648518889
98.1 1 -0.9042716241
98.2 1 -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 2 0.8861545820
100.3 1 0.1277046316
100.4 1 -0.0834577654
$m5e$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.6857108
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
M22:C2 -0.3136263 0.5921089
M23:C2 -0.2014212 0.4917409
M24:C2 -0.1430739 0.4225863
$m5e$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
$m5e$mu_reg_norm
[1] 0
$m5e$tau_reg_norm
[1] 1e-04
$m5e$shape_tau_norm
[1] 0.01
$m5e$rate_tau_norm
[1] 0.01
$m5e$mu_reg_multinomial
[1] 0
$m5e$tau_reg_multinomial
[1] 1e-04
$m5e$mu_reg_ordinal
[1] 0
$m5e$tau_reg_ordinal
[1] 1e-04
$m5e$mu_delta_ordinal
[1] 0
$m5e$tau_delta_ordinal
[1] 1e-04
$m5e$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
$m5e$shape_diag_RinvD
[1] "0.01"
$m5e$rate_diag_RinvD
[1] "0.001"
$m6a
$m6a$M_id
C2 O2 (Intercept) C1 O22 O23
1 -1.381594459 3 1 0.7175865 NA NA
2 0.344426024 2 1 0.7507170 NA NA
3 NA 2 1 0.7255954 NA NA
4 -0.228910007 3 1 0.7469352 NA NA
5 NA 1 1 0.7139120 NA NA
6 -2.143955482 3 1 0.7332505 NA NA
7 -1.156567023 NA 1 0.7345929 NA NA
8 -0.598827660 1 1 0.7652589 NA NA
9 NA 1 1 0.7200622 NA NA
10 -1.006719032 3 1 0.7423879 NA NA
11 0.239801450 3 1 0.7437448 NA NA
12 -1.064969789 NA 1 0.7446470 NA NA
13 -0.538082688 3 1 0.7530186 NA NA
14 NA 2 1 0.7093137 NA NA
15 -1.781049276 2 1 0.7331192 NA NA
16 NA 3 1 0.7011390 NA NA
17 NA 3 1 0.7432395 NA NA
18 -0.014579883 NA 1 0.7545191 NA NA
19 -2.121550136 2 1 0.7528487 NA NA
20 NA 1 1 0.7612865 NA NA
21 -0.363239698 1 1 0.7251719 NA NA
22 -0.121568514 3 1 0.7300630 NA NA
23 -0.951271111 3 1 0.7087249 NA NA
24 NA 3 1 0.7391938 NA NA
25 -0.974288621 2 1 0.7820641 NA NA
26 -1.130632418 1 1 0.7118298 NA NA
27 0.114339868 3 1 0.7230857 NA NA
28 0.238334648 3 1 0.7489353 NA NA
29 0.840744958 2 1 0.7510888 NA NA
30 NA 1 1 0.7300717 NA NA
31 NA 2 1 0.7550721 NA NA
32 -1.466312154 3 1 0.7321898 NA NA
33 -0.637352277 2 1 0.7306414 NA NA
34 NA 2 1 0.7427216 NA NA
35 NA 3 1 0.7193042 NA NA
36 NA 1 1 0.7312888 NA NA
37 NA 3 1 0.7100436 NA NA
38 NA 2 1 0.7670184 NA NA
39 0.006728205 3 1 0.7400449 NA NA
40 NA 2 1 0.7397304 NA NA
41 -1.663281353 2 1 0.7490966 NA NA
42 0.161184794 2 1 0.7419274 NA NA
43 0.457939180 3 1 0.7527810 NA NA
44 -0.307070331 2 1 0.7408315 NA NA
45 NA 1 1 0.7347550 NA NA
46 -1.071668276 NA 1 0.7332398 NA NA
47 -0.814751321 1 1 0.7376481 NA NA
48 -0.547630662 2 1 0.7346179 NA NA
49 NA 1 1 0.7329402 NA NA
50 -1.350213782 2 1 0.7260436 NA NA
51 0.719054706 2 1 0.7242910 NA NA
52 NA 3 1 0.7298067 NA NA
53 -1.207130750 3 1 0.7254741 NA NA
54 NA 1 1 0.7542067 NA NA
55 -0.408600991 2 1 0.7389952 NA NA
56 -0.271380529 NA 1 0.7520638 NA NA
57 -1.361925974 3 1 0.7219958 NA NA
58 NA 1 1 0.7259632 NA NA
59 NA 2 1 0.7458606 NA NA
60 -0.323712205 2 1 0.7672421 NA NA
61 NA 3 1 0.7257179 NA NA
62 NA 2 1 0.7189892 NA NA
63 -1.386906880 1 1 0.7333356 NA NA
64 NA 3 1 0.7320243 NA NA
65 NA 1 1 0.7477711 NA NA
66 -0.565191691 3 1 0.7343974 NA NA
67 -0.382899912 3 1 0.7491624 NA NA
68 NA 2 1 0.7482736 NA NA
69 -0.405642769 NA 1 0.7338267 NA NA
70 NA 2 1 0.7607742 NA NA
71 -0.843748427 NA 1 0.7777600 NA NA
72 0.116003683 NA 1 0.7408143 NA NA
73 -0.778634325 1 1 0.7248271 NA NA
74 NA 2 1 0.7364916 NA NA
75 NA 2 1 0.7464926 NA NA
76 NA 1 1 0.7355430 NA NA
77 -0.632974758 3 1 0.7208449 NA NA
78 NA 2 1 0.7373573 NA NA
79 -0.778064615 NA 1 0.7598079 NA NA
80 NA 1 1 0.7360415 NA NA
81 NA 1 1 0.7293932 NA NA
82 -0.246123253 3 1 0.7279309 NA NA
83 -1.239659782 NA 1 0.7344643 NA NA
84 -0.467772280 3 1 0.7384350 NA NA
85 NA 3 1 0.7323716 NA NA
86 -2.160485036 2 1 0.7576597 NA NA
87 -0.657675572 1 1 0.7496139 NA NA
88 NA 1 1 0.7275239 NA NA
89 -0.696710744 3 1 0.7250648 NA NA
90 NA 1 1 0.7335262 NA NA
91 -0.179395847 2 1 0.7343980 NA NA
92 -0.441545568 NA 1 0.7380425 NA NA
93 -0.685799334 1 1 0.7389460 NA NA
94 NA 3 1 0.7259951 NA NA
95 0.191929445 2 1 0.7282840 NA NA
96 NA 2 1 0.7281676 NA NA
97 -0.069760671 3 1 0.7245642 NA NA
98 NA 2 1 0.7526938 NA NA
99 NA 2 1 0.7272309 NA NA
100 NA 3 1 0.7383460 NA NA
$m6a$M_lvlone
o1 b2 b21
1 2 NA NA
1.1 1 0 NA
1.2 1 NA NA
1.3 2 0 NA
2 3 0 NA
2.1 1 NA NA
2.2 2 NA NA
3 1 0 NA
3.1 3 NA NA
3.2 2 1 NA
4 3 1 NA
4.1 2 0 NA
4.2 1 0 NA
4.3 2 0 NA
5 2 NA NA
5.1 2 0 NA
5.2 3 NA NA
5.3 2 NA NA
6 3 NA NA
7 3 NA NA
7.1 1 NA NA
7.2 1 0 NA
8 2 0 NA
8.1 2 0 NA
8.2 3 NA NA
8.3 3 1 NA
8.4 2 0 NA
8.5 2 1 NA
9 2 0 NA
9.1 2 NA NA
9.2 3 NA NA
10 2 NA NA
10.1 1 0 NA
11 2 0 NA
11.1 3 0 NA
11.2 3 0 NA
11.3 1 0 NA
11.4 3 0 NA
12 1 0 NA
13 2 NA NA
13.1 1 0 NA
14 3 NA NA
14.1 3 NA NA
14.2 2 NA NA
14.3 1 NA NA
15 3 0 NA
15.1 3 0 NA
15.2 2 0 NA
15.3 2 0 NA
16 3 1 NA
16.1 1 NA NA
16.2 3 NA NA
16.3 1 0 NA
16.4 1 0 NA
16.5 2 NA NA
17 2 0 NA
17.1 2 0 NA
17.2 3 0 NA
17.3 2 NA NA
17.4 3 0 NA
18 2 0 NA
19 3 NA NA
19.1 2 NA NA
19.2 2 0 NA
19.3 2 1 NA
20 1 NA NA
20.1 2 0 NA
20.2 2 1 NA
20.3 1 0 NA
20.4 1 0 NA
20.5 2 0 NA
21 2 0 NA
21.1 2 0 NA
21.2 2 NA NA
22 1 0 NA
22.1 1 0 NA
23 1 0 NA
23.1 2 NA NA
24 1 0 NA
25 2 0 NA
25.1 1 NA NA
25.2 1 1 NA
25.3 1 0 NA
25.4 2 0 NA
25.5 2 NA NA
26 2 NA NA
26.1 3 0 NA
26.2 2 0 NA
26.3 1 0 NA
27 2 0 NA
27.1 2 0 NA
28 1 NA NA
28.1 1 0 NA
28.2 1 0 NA
28.3 1 0 NA
29 2 0 NA
29.1 2 0 NA
29.2 1 0 NA
29.3 2 0 NA
30 2 NA NA
30.1 2 0 NA
30.2 2 0 NA
31 2 0 NA
32 3 0 NA
32.1 1 0 NA
32.2 3 NA NA
32.3 3 NA NA
33 2 0 NA
33.1 2 1 NA
34 3 NA NA
34.1 2 0 NA
34.2 2 NA NA
34.3 1 NA NA
35 2 0 NA
35.1 3 0 NA
35.2 2 NA NA
36 3 NA NA
36.1 2 NA NA
36.2 3 0 NA
36.3 3 0 NA
36.4 3 0 NA
37 3 0 NA
37.1 3 0 NA
37.2 3 0 NA
38 2 0 NA
39 1 1 NA
39.1 2 0 NA
39.2 2 NA NA
39.3 1 NA NA
39.4 1 0 NA
39.5 1 1 NA
40 3 0 NA
40.1 1 1 NA
40.2 1 0 NA
40.3 2 NA NA
41 3 0 NA
41.1 2 NA NA
41.2 1 0 NA
41.3 1 NA NA
41.4 3 0 NA
42 2 0 NA
42.1 1 1 NA
43 2 0 NA
43.1 2 1 NA
43.2 1 0 NA
44 3 0 NA
44.1 2 0 NA
44.2 1 0 NA
44.3 1 0 NA
45 1 NA NA
45.1 1 1 NA
46 1 0 NA
46.1 2 0 NA
46.2 3 0 NA
47 1 0 NA
47.1 1 0 NA
47.2 1 0 NA
47.3 3 NA NA
47.4 2 0 NA
48 3 1 NA
48.1 2 1 NA
49 1 NA NA
50 2 0 NA
51 2 0 NA
52 2 0 NA
52.1 3 0 NA
52.2 2 0 NA
52.3 1 0 NA
52.4 3 0 NA
52.5 2 0 NA
53 2 0 NA
53.1 1 0 NA
53.2 1 NA NA
54 1 NA NA
54.1 2 NA NA
54.2 3 NA NA
54.3 1 NA NA
54.4 2 0 NA
55 1 0 NA
55.1 1 0 NA
55.2 2 NA NA
55.3 1 NA NA
55.4 1 0 NA
56 3 0 NA
56.1 2 NA NA
56.2 3 NA NA
56.3 3 1 NA
56.4 1 0 NA
56.5 3 0 NA
57 2 0 NA
57.1 2 0 NA
57.2 2 0 NA
57.3 2 NA NA
58 3 0 NA
58.1 2 NA NA
58.2 3 1 NA
58.3 3 1 NA
58.4 2 0 NA
58.5 3 0 NA
59 3 NA NA
59.1 1 1 NA
60 3 0 NA
61 1 NA NA
61.1 2 1 NA
61.2 2 1 NA
61.3 1 0 NA
61.4 2 0 NA
62 2 NA NA
62.1 3 1 NA
62.2 1 0 NA
62.3 2 0 NA
63 1 NA NA
63.1 3 0 NA
64 2 0 NA
65 1 0 NA
65.1 2 0 NA
65.2 1 0 NA
65.3 1 0 NA
66 1 NA NA
66.1 1 0 NA
66.2 3 0 NA
67 3 NA NA
68 1 0 NA
68.1 2 0 NA
68.2 2 NA NA
68.3 2 0 NA
68.4 2 NA NA
69 2 0 NA
70 1 0 NA
70.1 3 0 NA
71 3 0 NA
71.1 3 1 NA
71.2 3 0 NA
71.3 2 1 NA
71.4 2 0 NA
72 1 0 NA
72.1 1 0 NA
72.2 2 NA NA
72.3 2 0 NA
72.4 1 0 NA
72.5 1 0 NA
73 1 0 NA
74 1 0 NA
75 3 NA NA
76 3 0 NA
76.1 3 0 NA
76.2 1 0 NA
77 2 NA NA
78 2 0 NA
79 1 NA NA
79.1 2 0 NA
79.2 1 NA NA
80 2 NA NA
80.1 2 0 NA
80.2 3 NA NA
81 3 0 NA
81.1 2 0 NA
81.2 3 NA NA
81.3 3 0 NA
82 3 NA NA
82.1 2 0 NA
82.2 2 1 NA
83 3 NA NA
83.1 3 0 NA
83.2 3 0 NA
83.3 2 NA NA
84 3 0 NA
84.1 3 NA NA
85 3 1 NA
85.1 2 NA NA
85.2 2 0 NA
85.3 2 0 NA
85.4 1 0 NA
85.5 3 0 NA
86 2 0 NA
86.1 3 NA NA
86.2 1 NA NA
86.3 1 0 NA
86.4 1 NA NA
86.5 3 0 NA
87 1 NA NA
87.1 1 NA NA
87.2 1 NA NA
88 3 0 NA
88.1 1 NA NA
88.2 1 0 NA
88.3 1 0 NA
89 1 0 NA
90 2 0 NA
90.1 1 0 NA
90.2 3 0 NA
90.3 1 NA NA
91 1 0 NA
91.1 2 0 NA
91.2 1 0 NA
92 2 0 NA
93 1 NA NA
93.1 3 0 NA
93.2 1 NA NA
93.3 3 0 NA
93.4 1 0 NA
94 2 NA NA
94.1 3 0 NA
94.2 1 0 NA
94.3 3 NA NA
94.4 2 0 NA
94.5 3 1 NA
95 3 0 NA
95.1 1 NA NA
95.2 2 0 NA
96 1 0 NA
96.1 3 0 NA
96.2 1 0 NA
96.3 1 NA NA
96.4 2 1 NA
96.5 3 1 NA
97 3 0 NA
97.1 3 0 NA
98 3 0 NA
98.1 1 0 NA
98.2 1 1 NA
99 2 0 NA
99.1 2 0 NA
99.2 1 0 NA
100 1 NA NA
100.1 2 NA NA
100.2 2 0 NA
100.3 1 NA NA
100.4 1 0 NA
$m6a$spM_id
center scale
C2 -0.6240921 0.68571078
O2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
O22 NA NA
O23 NA NA
$m6a$mu_reg_norm
[1] 0
$m6a$tau_reg_norm
[1] 1e-04
$m6a$shape_tau_norm
[1] 0.01
$m6a$rate_tau_norm
[1] 0.01
$m6a$mu_reg_binom
[1] 0
$m6a$tau_reg_binom
[1] 1e-04
$m6a$mu_reg_ordinal
[1] 0
$m6a$tau_reg_ordinal
[1] 1e-04
$m6a$mu_delta_ordinal
[1] 0
$m6a$tau_delta_ordinal
[1] 1e-04
$m6a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m6a$shape_diag_RinvD
[1] "0.01"
$m6a$rate_diag_RinvD
[1] "0.001"
$m6b
$m6b$M_id
M2 C2 O2 (Intercept) M22 M23 M24 O22 O23
1 NA -1.381594459 3 1 NA NA NA NA NA
2 1 0.344426024 2 1 NA NA NA NA NA
3 2 NA 2 1 NA NA NA NA NA
4 2 -0.228910007 3 1 NA NA NA NA NA
5 1 NA 1 1 NA NA NA NA NA
6 NA -2.143955482 3 1 NA NA NA NA NA
7 NA -1.156567023 NA 1 NA NA NA NA NA
8 2 -0.598827660 1 1 NA NA NA NA NA
9 NA NA 1 1 NA NA NA NA NA
10 NA -1.006719032 3 1 NA NA NA NA NA
11 3 0.239801450 3 1 NA NA NA NA NA
12 NA -1.064969789 NA 1 NA NA NA NA NA
13 NA -0.538082688 3 1 NA NA NA NA NA
14 2 NA 2 1 NA NA NA NA NA
15 2 -1.781049276 2 1 NA NA NA NA NA
16 NA NA 3 1 NA NA NA NA NA
17 3 NA 3 1 NA NA NA NA NA
18 3 -0.014579883 NA 1 NA NA NA NA NA
19 2 -2.121550136 2 1 NA NA NA NA NA
20 NA NA 1 1 NA NA NA NA NA
21 NA -0.363239698 1 1 NA NA NA NA NA
22 1 -0.121568514 3 1 NA NA NA NA NA
23 2 -0.951271111 3 1 NA NA NA NA NA
24 NA NA 3 1 NA NA NA NA NA
25 4 -0.974288621 2 1 NA NA NA NA NA
26 NA -1.130632418 1 1 NA NA NA NA NA
27 NA 0.114339868 3 1 NA NA NA NA NA
28 2 0.238334648 3 1 NA NA NA NA NA
29 4 0.840744958 2 1 NA NA NA NA NA
30 2 NA 1 1 NA NA NA NA NA
31 2 NA 2 1 NA NA NA NA NA
32 3 -1.466312154 3 1 NA NA NA NA NA
33 1 -0.637352277 2 1 NA NA NA NA NA
34 4 NA 2 1 NA NA NA NA NA
35 4 NA 3 1 NA NA NA NA NA
36 NA NA 1 1 NA NA NA NA NA
37 NA NA 3 1 NA NA NA NA NA
38 NA NA 2 1 NA NA NA NA NA
39 4 0.006728205 3 1 NA NA NA NA NA
40 NA NA 2 1 NA NA NA NA NA
41 2 -1.663281353 2 1 NA NA NA NA NA
42 NA 0.161184794 2 1 NA NA NA NA NA
43 NA 0.457939180 3 1 NA NA NA NA NA
44 3 -0.307070331 2 1 NA NA NA NA NA
45 NA NA 1 1 NA NA NA NA NA
46 NA -1.071668276 NA 1 NA NA NA NA NA
47 4 -0.814751321 1 1 NA NA NA NA NA
48 3 -0.547630662 2 1 NA NA NA NA NA
49 4 NA 1 1 NA NA NA NA NA
50 3 -1.350213782 2 1 NA NA NA NA NA
51 NA 0.719054706 2 1 NA NA NA NA NA
52 1 NA 3 1 NA NA NA NA NA
53 NA -1.207130750 3 1 NA NA NA NA NA
54 NA NA 1 1 NA NA NA NA NA
55 NA -0.408600991 2 1 NA NA NA NA NA
56 NA -0.271380529 NA 1 NA NA NA NA NA
57 4 -1.361925974 3 1 NA NA NA NA NA
58 1 NA 1 1 NA NA NA NA NA
59 2 NA 2 1 NA NA NA NA NA
60 3 -0.323712205 2 1 NA NA NA NA NA
61 2 NA 3 1 NA NA NA NA NA
62 3 NA 2 1 NA NA NA NA NA
63 2 -1.386906880 1 1 NA NA NA NA NA
64 NA NA 3 1 NA NA NA NA NA
65 NA NA 1 1 NA NA NA NA NA
66 2 -0.565191691 3 1 NA NA NA NA NA
67 NA -0.382899912 3 1 NA NA NA NA NA
68 3 NA 2 1 NA NA NA NA NA
69 3 -0.405642769 NA 1 NA NA NA NA NA
70 4 NA 2 1 NA NA NA NA NA
71 2 -0.843748427 NA 1 NA NA NA NA NA
72 NA 0.116003683 NA 1 NA NA NA NA NA
73 4 -0.778634325 1 1 NA NA NA NA NA
74 NA NA 2 1 NA NA NA NA NA
75 NA NA 2 1 NA NA NA NA NA
76 4 NA 1 1 NA NA NA NA NA
77 2 -0.632974758 3 1 NA NA NA NA NA
78 2 NA 2 1 NA NA NA NA NA
79 NA -0.778064615 NA 1 NA NA NA NA NA
80 1 NA 1 1 NA NA NA NA NA
81 NA NA 1 1 NA NA NA NA NA
82 3 -0.246123253 3 1 NA NA NA NA NA
83 4 -1.239659782 NA 1 NA NA NA NA NA
84 3 -0.467772280 3 1 NA NA NA NA NA
85 NA NA 3 1 NA NA NA NA NA
86 3 -2.160485036 2 1 NA NA NA NA NA
87 NA -0.657675572 1 1 NA NA NA NA NA
88 NA NA 1 1 NA NA NA NA NA
89 NA -0.696710744 3 1 NA NA NA NA NA
90 4 NA 1 1 NA NA NA NA NA
91 NA -0.179395847 2 1 NA NA NA NA NA
92 2 -0.441545568 NA 1 NA NA NA NA NA
93 4 -0.685799334 1 1 NA NA NA NA NA
94 NA NA 3 1 NA NA NA NA NA
95 NA 0.191929445 2 1 NA NA NA NA NA
96 NA NA 2 1 NA NA NA NA NA
97 1 -0.069760671 3 1 NA NA NA NA NA
98 NA NA 2 1 NA NA NA NA NA
99 2 NA 2 1 NA NA NA NA NA
100 NA NA 3 1 NA NA NA NA NA
$m6b$M_lvlone
o1 c1 c1:C2
1 2 0.7592026489 NA
1.1 1 0.9548337990 NA
1.2 1 0.5612235156 NA
1.3 2 1.1873391025 NA
2 3 0.9192204198 NA
2.1 1 -0.1870730476 NA
2.2 2 1.2517512331 NA
3 1 -0.0605087604 NA
3.1 3 0.3788637747 NA
3.2 2 0.9872578281 NA
4 3 1.4930175328 NA
4.1 2 -0.7692526880 NA
4.2 1 0.9180841450 NA
4.3 2 -0.0541170782 NA
5 2 -0.1376784521 NA
5.1 2 -0.2740585866 NA
5.2 3 0.4670496929 NA
5.3 2 0.1740288049 NA
6 3 0.9868044683 NA
7 3 -0.1280320918 NA
7.1 1 0.4242971219 NA
7.2 1 0.0777182491 NA
8 2 -0.5791408712 NA
8.1 2 0.3128604232 NA
8.2 3 0.6258446356 NA
8.3 3 -0.1040137707 NA
8.4 2 0.0481450285 NA
8.5 2 0.3831763675 NA
9 2 -0.1757592269 NA
9.1 2 -0.1791541200 NA
9.2 3 -0.0957042935 NA
10 2 -0.5598409704 NA
10.1 1 -0.2318340451 NA
11 2 0.5086859475 NA
11.1 3 0.4951758188 NA
11.2 3 -1.1022162541 NA
11.3 1 -0.0611636705 NA
11.4 3 -0.4971774316 NA
12 1 -0.2433996286 NA
13 2 0.8799673116 NA
13.1 1 0.1079022586 NA
14 3 0.9991752617 NA
14.1 3 -0.1094019046 NA
14.2 2 0.1518967560 NA
14.3 1 0.3521012473 NA
15 3 0.3464447888 NA
15.1 3 -0.4767313971 NA
15.2 2 0.5759767791 NA
15.3 2 -0.1713452662 NA
16 3 0.4564754473 NA
16.1 1 1.0652558311 NA
16.2 3 0.6971872493 NA
16.3 1 0.5259331838 NA
16.4 1 0.2046601798 NA
16.5 2 1.0718540464 NA
17 2 0.6048676222 NA
17.1 2 0.2323298304 NA
17.2 3 1.2617499032 NA
17.3 2 -0.3913230895 NA
17.4 3 0.9577299112 NA
18 2 -0.0050324072 NA
19 3 -0.4187468937 NA
19.1 2 -0.4478828944 NA
19.2 2 -1.1966721302 NA
19.3 2 -0.5877091668 NA
20 1 0.6838223064 NA
20.1 2 0.3278571109 NA
20.2 2 -0.8489831990 NA
20.3 1 1.3169975191 NA
20.4 1 0.0444804531 NA
20.5 2 -0.4535207652 NA
21 2 -0.4030302960 NA
21.1 2 -0.4069674045 NA
21.2 2 1.0650265940 NA
22 1 -0.0673274516 NA
22.1 1 0.9601388170 NA
23 1 0.5556634840 NA
23.1 2 1.4407865964 NA
24 1 0.3856376411 NA
25 2 0.3564400705 NA
25.1 1 0.0982553434 NA
25.2 1 0.1928682598 NA
25.3 1 -0.0192488594 NA
25.4 2 0.4466012931 NA
25.5 2 1.1425193342 NA
26 2 0.5341531449 NA
26.1 3 1.2268695927 NA
26.2 2 0.3678294939 NA
26.3 1 0.5948516018 NA
27 2 -0.3342844147 NA
27.1 2 -0.4835141229 NA
28 1 -0.7145915499 NA
28.1 1 0.5063671955 NA
28.2 1 -0.2067413142 NA
28.3 1 0.1196789973 NA
29 2 0.1392699487 NA
29.1 2 0.7960234776 NA
29.2 1 1.0398214352 NA
29.3 2 0.0813246429 NA
30 2 -0.3296323050 NA
30.1 2 1.3635850954 NA
30.2 2 0.7354171050 NA
31 2 0.3708398217 NA
32 3 -0.0474059668 NA
32.1 1 1.2507771489 NA
32.2 3 0.1142915519 NA
32.3 3 0.6773270619 NA
33 2 0.1774293842 NA
33.1 2 0.6159606291 NA
34 3 0.8590979166 NA
34.1 2 0.0546216775 NA
34.2 2 -0.0897224473 NA
34.3 1 0.4163395571 NA
35 2 -1.4693520528 NA
35.1 3 -0.3031734330 NA
35.2 2 -0.6045512101 NA
36 3 0.9823048960 NA
36.1 2 1.4466051416 NA
36.2 3 1.1606752905 NA
36.3 3 0.8373091576 NA
36.4 3 0.2640591685 NA
37 3 0.1177313455 NA
37.1 3 -0.1415483779 NA
37.2 3 0.0054610124 NA
38 2 0.8078948077 NA
39 1 0.9876451040 NA
39.1 2 -0.3431222274 NA
39.2 2 -1.7909380751 NA
39.3 1 -0.1798746191 NA
39.4 1 -0.1850961689 NA
39.5 1 0.4544226146 NA
40 3 0.5350190436 NA
40.1 1 0.4189342752 NA
40.2 1 0.4211994981 NA
40.3 2 0.0916687506 NA
41 3 -0.1035047421 NA
41.1 2 -0.4684202411 NA
41.2 1 0.5972615368 NA
41.3 1 0.9885613862 NA
41.4 3 -0.3908036794 NA
42 2 -0.0338893961 NA
42.1 1 -0.4498363172 NA
43 2 0.8965546110 NA
43.1 2 0.6199122090 NA
43.2 1 0.1804894429 NA
44 3 1.3221409285 NA
44.1 2 0.3416426284 NA
44.2 1 0.5706610068 NA
44.3 1 1.2679497430 NA
45 1 0.1414983160 NA
45.1 1 0.7220892521 NA
46 1 1.5391054233 NA
46.1 2 0.3889107049 NA
46.2 3 0.1248719493 NA
47 1 0.2014101100 NA
47.1 1 0.2982973539 NA
47.2 1 1.1518107179 NA
47.3 3 0.5196802157 NA
47.4 2 0.3702301552 NA
48 3 -0.2128602862 NA
48.1 2 -0.5337239976 NA
49 1 -0.5236770035 NA
50 2 0.3897705981 NA
51 2 -0.7213343736 NA
52 2 0.3758235358 NA
52.1 3 0.7138067080 NA
52.2 2 0.8872895233 NA
52.3 1 -0.9664587437 NA
52.4 3 0.0254566848 NA
52.5 2 0.4155259424 NA
53 2 0.5675736897 NA
53.1 1 -0.3154088781 NA
53.2 1 0.2162315769 NA
54 1 -0.0880802382 NA
54.1 2 0.4129127672 NA
54.2 3 1.0119546775 NA
54.3 1 -0.1112901990 NA
54.4 2 0.8587727145 NA
55 1 -0.0116453589 NA
55.1 1 0.5835528661 NA
55.2 2 -1.0010857254 NA
55.3 1 -0.4796526070 NA
55.4 1 -0.1202746964 NA
56 3 0.5176377612 NA
56.1 2 -1.1136932588 NA
56.2 3 -0.0168103281 NA
56.3 3 0.3933023606 NA
56.4 1 0.3714625139 NA
56.5 3 0.7811448179 NA
57 2 -1.0868304872 NA
57.1 2 0.8018626997 NA
57.2 2 -0.1159517011 NA
57.3 2 0.6785562445 NA
58 3 1.6476207996 NA
58.1 2 0.3402652711 NA
58.2 3 -0.1111300753 NA
58.3 3 -0.5409234285 NA
58.4 2 -0.1271327672 NA
58.5 3 0.8713264822 NA
59 3 0.4766421367 NA
59.1 1 1.0028089765 NA
60 3 0.5231452932 NA
61 1 -0.7190130614 NA
61.1 2 0.8353702312 NA
61.2 2 1.0229058138 NA
61.3 1 1.1717723589 NA
61.4 2 -0.0629201596 NA
62 2 -0.3979137604 NA
62.1 3 0.6830738372 NA
62.2 1 0.4301745954 NA
62.3 2 -0.0333139957 NA
63 1 0.3345678035 NA
63.1 3 0.3643769511 NA
64 2 0.3949911859 NA
65 1 1.2000091513 NA
65.1 2 0.0110122646 NA
65.2 1 -0.5776452043 NA
65.3 1 -0.1372183563 NA
66 1 -0.5081302805 NA
66.1 1 -0.1447837412 NA
66.2 3 0.1906241379 NA
67 3 1.6716027681 NA
68 1 0.5691848839 NA
68.1 2 0.1004860389 NA
68.2 2 -0.0061241827 NA
68.3 2 0.7443745962 NA
68.4 2 0.8726923437 NA
69 2 0.0381382683 NA
70 1 0.8126204217 NA
70.1 3 0.4691503050 NA
71 3 -0.5529062591 NA
71.1 3 -0.1103252087 NA
71.2 3 1.7178492547 NA
71.3 2 -1.0118346755 NA
71.4 2 1.8623785017 NA
72 1 -0.4521659275 NA
72.1 1 0.1375317317 NA
72.2 2 -0.4170988856 NA
72.3 2 0.7107266765 NA
72.4 1 0.1451969143 NA
72.5 1 1.6298050306 NA
73 1 -0.0307469467 NA
74 1 0.3730017941 NA
75 3 -0.4908003566 NA
76 3 -0.9888876620 NA
76.1 3 0.0003798292 NA
76.2 1 -0.8421863763 NA
77 2 -0.4986802480 NA
78 2 0.0417330969 NA
79 1 -0.3767450660 NA
79.1 2 0.1516000028 NA
79.2 1 -0.1888160741 NA
80 2 -0.0041558414 NA
80.1 2 -0.0329337062 NA
80.2 3 0.5046816157 NA
81 3 -0.9493950353 NA
81.1 2 0.2443038954 NA
81.2 3 0.6476958410 NA
81.3 3 0.4182528210 NA
82 3 1.1088801952 NA
82.1 2 0.9334157763 NA
82.2 2 0.4958140634 NA
83 3 0.5104724530 NA
83.1 3 -0.0513309106 NA
83.2 3 -0.2067792494 NA
83.3 2 -0.0534169155 NA
84 3 -0.0255753653 NA
84.1 3 -1.8234189877 NA
85 3 -0.0114038622 NA
85.1 2 -0.0577615939 NA
85.2 2 -0.2241856342 NA
85.3 2 -0.0520175929 NA
85.4 1 0.2892733846 NA
85.5 3 -0.3740417009 NA
86 2 0.4293735089 NA
86.1 3 -0.1363456521 NA
86.2 1 0.1230989293 NA
86.3 1 0.3305413955 NA
86.4 1 2.6003411822 NA
86.5 3 -0.1420690052 NA
87 1 1.0457427869 NA
87.1 1 -0.2973007190 NA
87.2 1 0.4396872616 NA
88 3 -0.0601928334 NA
88.1 1 -1.0124347595 NA
88.2 1 0.5730917016 NA
88.3 1 -0.0029455332 NA
89 1 1.5465903721 NA
90 2 0.0626760573 NA
90.1 1 1.1896872985 NA
90.2 3 0.2597888783 NA
90.3 1 0.6599799887 NA
91 1 1.1213651365 NA
91.1 2 1.2046371625 NA
91.2 1 0.3395603754 NA
92 2 0.4674939332 NA
93 1 0.2677965647 NA
93.1 3 1.6424445368 NA
93.2 1 0.7101700066 NA
93.3 3 1.1222322893 NA
93.4 1 1.4628960401 NA
94 2 -0.2904211940 NA
94.1 3 0.0147813580 NA
94.2 1 -0.4536774482 NA
94.3 3 0.6793464917 NA
94.4 2 -0.9411356550 NA
94.5 3 0.5683867264 NA
95 3 0.2375652188 NA
95.1 1 0.0767152977 NA
95.2 2 -0.6886731251 NA
96 1 0.7813892121 NA
96.1 3 0.3391519695 NA
96.2 1 -0.4857246503 NA
96.3 1 0.8771471244 NA
96.4 2 1.9030768981 NA
96.5 3 -0.1684332749 NA
97 3 1.3775130083 NA
97.1 3 -1.7323228619 NA
98 3 -1.2648518889 NA
98.1 1 -0.9042716241 NA
98.2 1 -0.1560385207 NA
99 2 0.7993356425 NA
99.1 2 1.0355522332 NA
99.2 1 -0.1150895843 NA
100 1 0.0369067906 NA
100.1 2 1.6023713093 NA
100.2 2 0.8861545820 NA
100.3 1 0.1277046316 NA
100.4 1 -0.0834577654 NA
$m6b$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.6857108
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
$m6b$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
c1:C2 -0.1866456 0.7201206
$m6b$mu_reg_norm
[1] 0
$m6b$tau_reg_norm
[1] 1e-04
$m6b$shape_tau_norm
[1] 0.01
$m6b$rate_tau_norm
[1] 0.01
$m6b$mu_reg_multinomial
[1] 0
$m6b$tau_reg_multinomial
[1] 1e-04
$m6b$mu_reg_ordinal
[1] 0
$m6b$tau_reg_ordinal
[1] 1e-04
$m6b$mu_delta_ordinal
[1] 0
$m6b$tau_delta_ordinal
[1] 1e-04
$m6b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m6b$shape_diag_RinvD
[1] "0.01"
$m6b$rate_diag_RinvD
[1] "0.001"
$m6c
$m6c$M_id
M2 C2 O2 (Intercept) M22 M23 M24 O22 O23
1 NA -1.381594459 3 1 NA NA NA NA NA
2 1 0.344426024 2 1 NA NA NA NA NA
3 2 NA 2 1 NA NA NA NA NA
4 2 -0.228910007 3 1 NA NA NA NA NA
5 1 NA 1 1 NA NA NA NA NA
6 NA -2.143955482 3 1 NA NA NA NA NA
7 NA -1.156567023 NA 1 NA NA NA NA NA
8 2 -0.598827660 1 1 NA NA NA NA NA
9 NA NA 1 1 NA NA NA NA NA
10 NA -1.006719032 3 1 NA NA NA NA NA
11 3 0.239801450 3 1 NA NA NA NA NA
12 NA -1.064969789 NA 1 NA NA NA NA NA
13 NA -0.538082688 3 1 NA NA NA NA NA
14 2 NA 2 1 NA NA NA NA NA
15 2 -1.781049276 2 1 NA NA NA NA NA
16 NA NA 3 1 NA NA NA NA NA
17 3 NA 3 1 NA NA NA NA NA
18 3 -0.014579883 NA 1 NA NA NA NA NA
19 2 -2.121550136 2 1 NA NA NA NA NA
20 NA NA 1 1 NA NA NA NA NA
21 NA -0.363239698 1 1 NA NA NA NA NA
22 1 -0.121568514 3 1 NA NA NA NA NA
23 2 -0.951271111 3 1 NA NA NA NA NA
24 NA NA 3 1 NA NA NA NA NA
25 4 -0.974288621 2 1 NA NA NA NA NA
26 NA -1.130632418 1 1 NA NA NA NA NA
27 NA 0.114339868 3 1 NA NA NA NA NA
28 2 0.238334648 3 1 NA NA NA NA NA
29 4 0.840744958 2 1 NA NA NA NA NA
30 2 NA 1 1 NA NA NA NA NA
31 2 NA 2 1 NA NA NA NA NA
32 3 -1.466312154 3 1 NA NA NA NA NA
33 1 -0.637352277 2 1 NA NA NA NA NA
34 4 NA 2 1 NA NA NA NA NA
35 4 NA 3 1 NA NA NA NA NA
36 NA NA 1 1 NA NA NA NA NA
37 NA NA 3 1 NA NA NA NA NA
38 NA NA 2 1 NA NA NA NA NA
39 4 0.006728205 3 1 NA NA NA NA NA
40 NA NA 2 1 NA NA NA NA NA
41 2 -1.663281353 2 1 NA NA NA NA NA
42 NA 0.161184794 2 1 NA NA NA NA NA
43 NA 0.457939180 3 1 NA NA NA NA NA
44 3 -0.307070331 2 1 NA NA NA NA NA
45 NA NA 1 1 NA NA NA NA NA
46 NA -1.071668276 NA 1 NA NA NA NA NA
47 4 -0.814751321 1 1 NA NA NA NA NA
48 3 -0.547630662 2 1 NA NA NA NA NA
49 4 NA 1 1 NA NA NA NA NA
50 3 -1.350213782 2 1 NA NA NA NA NA
51 NA 0.719054706 2 1 NA NA NA NA NA
52 1 NA 3 1 NA NA NA NA NA
53 NA -1.207130750 3 1 NA NA NA NA NA
54 NA NA 1 1 NA NA NA NA NA
55 NA -0.408600991 2 1 NA NA NA NA NA
56 NA -0.271380529 NA 1 NA NA NA NA NA
57 4 -1.361925974 3 1 NA NA NA NA NA
58 1 NA 1 1 NA NA NA NA NA
59 2 NA 2 1 NA NA NA NA NA
60 3 -0.323712205 2 1 NA NA NA NA NA
61 2 NA 3 1 NA NA NA NA NA
62 3 NA 2 1 NA NA NA NA NA
63 2 -1.386906880 1 1 NA NA NA NA NA
64 NA NA 3 1 NA NA NA NA NA
65 NA NA 1 1 NA NA NA NA NA
66 2 -0.565191691 3 1 NA NA NA NA NA
67 NA -0.382899912 3 1 NA NA NA NA NA
68 3 NA 2 1 NA NA NA NA NA
69 3 -0.405642769 NA 1 NA NA NA NA NA
70 4 NA 2 1 NA NA NA NA NA
71 2 -0.843748427 NA 1 NA NA NA NA NA
72 NA 0.116003683 NA 1 NA NA NA NA NA
73 4 -0.778634325 1 1 NA NA NA NA NA
74 NA NA 2 1 NA NA NA NA NA
75 NA NA 2 1 NA NA NA NA NA
76 4 NA 1 1 NA NA NA NA NA
77 2 -0.632974758 3 1 NA NA NA NA NA
78 2 NA 2 1 NA NA NA NA NA
79 NA -0.778064615 NA 1 NA NA NA NA NA
80 1 NA 1 1 NA NA NA NA NA
81 NA NA 1 1 NA NA NA NA NA
82 3 -0.246123253 3 1 NA NA NA NA NA
83 4 -1.239659782 NA 1 NA NA NA NA NA
84 3 -0.467772280 3 1 NA NA NA NA NA
85 NA NA 3 1 NA NA NA NA NA
86 3 -2.160485036 2 1 NA NA NA NA NA
87 NA -0.657675572 1 1 NA NA NA NA NA
88 NA NA 1 1 NA NA NA NA NA
89 NA -0.696710744 3 1 NA NA NA NA NA
90 4 NA 1 1 NA NA NA NA NA
91 NA -0.179395847 2 1 NA NA NA NA NA
92 2 -0.441545568 NA 1 NA NA NA NA NA
93 4 -0.685799334 1 1 NA NA NA NA NA
94 NA NA 3 1 NA NA NA NA NA
95 NA 0.191929445 2 1 NA NA NA NA NA
96 NA NA 2 1 NA NA NA NA NA
97 1 -0.069760671 3 1 NA NA NA NA NA
98 NA NA 2 1 NA NA NA NA NA
99 2 NA 2 1 NA NA NA NA NA
100 NA NA 3 1 NA NA NA NA NA
$m6c$M_lvlone
o1 c1 c1:C2
1 2 0.7592026489 NA
1.1 1 0.9548337990 NA
1.2 1 0.5612235156 NA
1.3 2 1.1873391025 NA
2 3 0.9192204198 NA
2.1 1 -0.1870730476 NA
2.2 2 1.2517512331 NA
3 1 -0.0605087604 NA
3.1 3 0.3788637747 NA
3.2 2 0.9872578281 NA
4 3 1.4930175328 NA
4.1 2 -0.7692526880 NA
4.2 1 0.9180841450 NA
4.3 2 -0.0541170782 NA
5 2 -0.1376784521 NA
5.1 2 -0.2740585866 NA
5.2 3 0.4670496929 NA
5.3 2 0.1740288049 NA
6 3 0.9868044683 NA
7 3 -0.1280320918 NA
7.1 1 0.4242971219 NA
7.2 1 0.0777182491 NA
8 2 -0.5791408712 NA
8.1 2 0.3128604232 NA
8.2 3 0.6258446356 NA
8.3 3 -0.1040137707 NA
8.4 2 0.0481450285 NA
8.5 2 0.3831763675 NA
9 2 -0.1757592269 NA
9.1 2 -0.1791541200 NA
9.2 3 -0.0957042935 NA
10 2 -0.5598409704 NA
10.1 1 -0.2318340451 NA
11 2 0.5086859475 NA
11.1 3 0.4951758188 NA
11.2 3 -1.1022162541 NA
11.3 1 -0.0611636705 NA
11.4 3 -0.4971774316 NA
12 1 -0.2433996286 NA
13 2 0.8799673116 NA
13.1 1 0.1079022586 NA
14 3 0.9991752617 NA
14.1 3 -0.1094019046 NA
14.2 2 0.1518967560 NA
14.3 1 0.3521012473 NA
15 3 0.3464447888 NA
15.1 3 -0.4767313971 NA
15.2 2 0.5759767791 NA
15.3 2 -0.1713452662 NA
16 3 0.4564754473 NA
16.1 1 1.0652558311 NA
16.2 3 0.6971872493 NA
16.3 1 0.5259331838 NA
16.4 1 0.2046601798 NA
16.5 2 1.0718540464 NA
17 2 0.6048676222 NA
17.1 2 0.2323298304 NA
17.2 3 1.2617499032 NA
17.3 2 -0.3913230895 NA
17.4 3 0.9577299112 NA
18 2 -0.0050324072 NA
19 3 -0.4187468937 NA
19.1 2 -0.4478828944 NA
19.2 2 -1.1966721302 NA
19.3 2 -0.5877091668 NA
20 1 0.6838223064 NA
20.1 2 0.3278571109 NA
20.2 2 -0.8489831990 NA
20.3 1 1.3169975191 NA
20.4 1 0.0444804531 NA
20.5 2 -0.4535207652 NA
21 2 -0.4030302960 NA
21.1 2 -0.4069674045 NA
21.2 2 1.0650265940 NA
22 1 -0.0673274516 NA
22.1 1 0.9601388170 NA
23 1 0.5556634840 NA
23.1 2 1.4407865964 NA
24 1 0.3856376411 NA
25 2 0.3564400705 NA
25.1 1 0.0982553434 NA
25.2 1 0.1928682598 NA
25.3 1 -0.0192488594 NA
25.4 2 0.4466012931 NA
25.5 2 1.1425193342 NA
26 2 0.5341531449 NA
26.1 3 1.2268695927 NA
26.2 2 0.3678294939 NA
26.3 1 0.5948516018 NA
27 2 -0.3342844147 NA
27.1 2 -0.4835141229 NA
28 1 -0.7145915499 NA
28.1 1 0.5063671955 NA
28.2 1 -0.2067413142 NA
28.3 1 0.1196789973 NA
29 2 0.1392699487 NA
29.1 2 0.7960234776 NA
29.2 1 1.0398214352 NA
29.3 2 0.0813246429 NA
30 2 -0.3296323050 NA
30.1 2 1.3635850954 NA
30.2 2 0.7354171050 NA
31 2 0.3708398217 NA
32 3 -0.0474059668 NA
32.1 1 1.2507771489 NA
32.2 3 0.1142915519 NA
32.3 3 0.6773270619 NA
33 2 0.1774293842 NA
33.1 2 0.6159606291 NA
34 3 0.8590979166 NA
34.1 2 0.0546216775 NA
34.2 2 -0.0897224473 NA
34.3 1 0.4163395571 NA
35 2 -1.4693520528 NA
35.1 3 -0.3031734330 NA
35.2 2 -0.6045512101 NA
36 3 0.9823048960 NA
36.1 2 1.4466051416 NA
36.2 3 1.1606752905 NA
36.3 3 0.8373091576 NA
36.4 3 0.2640591685 NA
37 3 0.1177313455 NA
37.1 3 -0.1415483779 NA
37.2 3 0.0054610124 NA
38 2 0.8078948077 NA
39 1 0.9876451040 NA
39.1 2 -0.3431222274 NA
39.2 2 -1.7909380751 NA
39.3 1 -0.1798746191 NA
39.4 1 -0.1850961689 NA
39.5 1 0.4544226146 NA
40 3 0.5350190436 NA
40.1 1 0.4189342752 NA
40.2 1 0.4211994981 NA
40.3 2 0.0916687506 NA
41 3 -0.1035047421 NA
41.1 2 -0.4684202411 NA
41.2 1 0.5972615368 NA
41.3 1 0.9885613862 NA
41.4 3 -0.3908036794 NA
42 2 -0.0338893961 NA
42.1 1 -0.4498363172 NA
43 2 0.8965546110 NA
43.1 2 0.6199122090 NA
43.2 1 0.1804894429 NA
44 3 1.3221409285 NA
44.1 2 0.3416426284 NA
44.2 1 0.5706610068 NA
44.3 1 1.2679497430 NA
45 1 0.1414983160 NA
45.1 1 0.7220892521 NA
46 1 1.5391054233 NA
46.1 2 0.3889107049 NA
46.2 3 0.1248719493 NA
47 1 0.2014101100 NA
47.1 1 0.2982973539 NA
47.2 1 1.1518107179 NA
47.3 3 0.5196802157 NA
47.4 2 0.3702301552 NA
48 3 -0.2128602862 NA
48.1 2 -0.5337239976 NA
49 1 -0.5236770035 NA
50 2 0.3897705981 NA
51 2 -0.7213343736 NA
52 2 0.3758235358 NA
52.1 3 0.7138067080 NA
52.2 2 0.8872895233 NA
52.3 1 -0.9664587437 NA
52.4 3 0.0254566848 NA
52.5 2 0.4155259424 NA
53 2 0.5675736897 NA
53.1 1 -0.3154088781 NA
53.2 1 0.2162315769 NA
54 1 -0.0880802382 NA
54.1 2 0.4129127672 NA
54.2 3 1.0119546775 NA
54.3 1 -0.1112901990 NA
54.4 2 0.8587727145 NA
55 1 -0.0116453589 NA
55.1 1 0.5835528661 NA
55.2 2 -1.0010857254 NA
55.3 1 -0.4796526070 NA
55.4 1 -0.1202746964 NA
56 3 0.5176377612 NA
56.1 2 -1.1136932588 NA
56.2 3 -0.0168103281 NA
56.3 3 0.3933023606 NA
56.4 1 0.3714625139 NA
56.5 3 0.7811448179 NA
57 2 -1.0868304872 NA
57.1 2 0.8018626997 NA
57.2 2 -0.1159517011 NA
57.3 2 0.6785562445 NA
58 3 1.6476207996 NA
58.1 2 0.3402652711 NA
58.2 3 -0.1111300753 NA
58.3 3 -0.5409234285 NA
58.4 2 -0.1271327672 NA
58.5 3 0.8713264822 NA
59 3 0.4766421367 NA
59.1 1 1.0028089765 NA
60 3 0.5231452932 NA
61 1 -0.7190130614 NA
61.1 2 0.8353702312 NA
61.2 2 1.0229058138 NA
61.3 1 1.1717723589 NA
61.4 2 -0.0629201596 NA
62 2 -0.3979137604 NA
62.1 3 0.6830738372 NA
62.2 1 0.4301745954 NA
62.3 2 -0.0333139957 NA
63 1 0.3345678035 NA
63.1 3 0.3643769511 NA
64 2 0.3949911859 NA
65 1 1.2000091513 NA
65.1 2 0.0110122646 NA
65.2 1 -0.5776452043 NA
65.3 1 -0.1372183563 NA
66 1 -0.5081302805 NA
66.1 1 -0.1447837412 NA
66.2 3 0.1906241379 NA
67 3 1.6716027681 NA
68 1 0.5691848839 NA
68.1 2 0.1004860389 NA
68.2 2 -0.0061241827 NA
68.3 2 0.7443745962 NA
68.4 2 0.8726923437 NA
69 2 0.0381382683 NA
70 1 0.8126204217 NA
70.1 3 0.4691503050 NA
71 3 -0.5529062591 NA
71.1 3 -0.1103252087 NA
71.2 3 1.7178492547 NA
71.3 2 -1.0118346755 NA
71.4 2 1.8623785017 NA
72 1 -0.4521659275 NA
72.1 1 0.1375317317 NA
72.2 2 -0.4170988856 NA
72.3 2 0.7107266765 NA
72.4 1 0.1451969143 NA
72.5 1 1.6298050306 NA
73 1 -0.0307469467 NA
74 1 0.3730017941 NA
75 3 -0.4908003566 NA
76 3 -0.9888876620 NA
76.1 3 0.0003798292 NA
76.2 1 -0.8421863763 NA
77 2 -0.4986802480 NA
78 2 0.0417330969 NA
79 1 -0.3767450660 NA
79.1 2 0.1516000028 NA
79.2 1 -0.1888160741 NA
80 2 -0.0041558414 NA
80.1 2 -0.0329337062 NA
80.2 3 0.5046816157 NA
81 3 -0.9493950353 NA
81.1 2 0.2443038954 NA
81.2 3 0.6476958410 NA
81.3 3 0.4182528210 NA
82 3 1.1088801952 NA
82.1 2 0.9334157763 NA
82.2 2 0.4958140634 NA
83 3 0.5104724530 NA
83.1 3 -0.0513309106 NA
83.2 3 -0.2067792494 NA
83.3 2 -0.0534169155 NA
84 3 -0.0255753653 NA
84.1 3 -1.8234189877 NA
85 3 -0.0114038622 NA
85.1 2 -0.0577615939 NA
85.2 2 -0.2241856342 NA
85.3 2 -0.0520175929 NA
85.4 1 0.2892733846 NA
85.5 3 -0.3740417009 NA
86 2 0.4293735089 NA
86.1 3 -0.1363456521 NA
86.2 1 0.1230989293 NA
86.3 1 0.3305413955 NA
86.4 1 2.6003411822 NA
86.5 3 -0.1420690052 NA
87 1 1.0457427869 NA
87.1 1 -0.2973007190 NA
87.2 1 0.4396872616 NA
88 3 -0.0601928334 NA
88.1 1 -1.0124347595 NA
88.2 1 0.5730917016 NA
88.3 1 -0.0029455332 NA
89 1 1.5465903721 NA
90 2 0.0626760573 NA
90.1 1 1.1896872985 NA
90.2 3 0.2597888783 NA
90.3 1 0.6599799887 NA
91 1 1.1213651365 NA
91.1 2 1.2046371625 NA
91.2 1 0.3395603754 NA
92 2 0.4674939332 NA
93 1 0.2677965647 NA
93.1 3 1.6424445368 NA
93.2 1 0.7101700066 NA
93.3 3 1.1222322893 NA
93.4 1 1.4628960401 NA
94 2 -0.2904211940 NA
94.1 3 0.0147813580 NA
94.2 1 -0.4536774482 NA
94.3 3 0.6793464917 NA
94.4 2 -0.9411356550 NA
94.5 3 0.5683867264 NA
95 3 0.2375652188 NA
95.1 1 0.0767152977 NA
95.2 2 -0.6886731251 NA
96 1 0.7813892121 NA
96.1 3 0.3391519695 NA
96.2 1 -0.4857246503 NA
96.3 1 0.8771471244 NA
96.4 2 1.9030768981 NA
96.5 3 -0.1684332749 NA
97 3 1.3775130083 NA
97.1 3 -1.7323228619 NA
98 3 -1.2648518889 NA
98.1 1 -0.9042716241 NA
98.2 1 -0.1560385207 NA
99 2 0.7993356425 NA
99.1 2 1.0355522332 NA
99.2 1 -0.1150895843 NA
100 1 0.0369067906 NA
100.1 2 1.6023713093 NA
100.2 2 0.8861545820 NA
100.3 1 0.1277046316 NA
100.4 1 -0.0834577654 NA
$m6c$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.6857108
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
$m6c$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
c1:C2 -0.1866456 0.7201206
$m6c$mu_reg_norm
[1] 0
$m6c$tau_reg_norm
[1] 1e-04
$m6c$shape_tau_norm
[1] 0.01
$m6c$rate_tau_norm
[1] 0.01
$m6c$mu_reg_multinomial
[1] 0
$m6c$tau_reg_multinomial
[1] 1e-04
$m6c$mu_reg_ordinal
[1] 0
$m6c$tau_reg_ordinal
[1] 1e-04
$m6c$mu_delta_ordinal
[1] 0
$m6c$tau_delta_ordinal
[1] 1e-04
$m6c$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
$m6c$shape_diag_RinvD
[1] "0.01"
$m6c$rate_diag_RinvD
[1] "0.001"
$m6d
$m6d$M_id
M2 C2 O2 (Intercept) M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2
1 NA -1.381594459 3 1 NA NA NA NA NA NA NA NA
2 1 0.344426024 2 1 NA NA NA NA NA NA NA NA
3 2 NA 2 1 NA NA NA NA NA NA NA NA
4 2 -0.228910007 3 1 NA NA NA NA NA NA NA NA
5 1 NA 1 1 NA NA NA NA NA NA NA NA
6 NA -2.143955482 3 1 NA NA NA NA NA NA NA NA
7 NA -1.156567023 NA 1 NA NA NA NA NA NA NA NA
8 2 -0.598827660 1 1 NA NA NA NA NA NA NA NA
9 NA NA 1 1 NA NA NA NA NA NA NA NA
10 NA -1.006719032 3 1 NA NA NA NA NA NA NA NA
11 3 0.239801450 3 1 NA NA NA NA NA NA NA NA
12 NA -1.064969789 NA 1 NA NA NA NA NA NA NA NA
13 NA -0.538082688 3 1 NA NA NA NA NA NA NA NA
14 2 NA 2 1 NA NA NA NA NA NA NA NA
15 2 -1.781049276 2 1 NA NA NA NA NA NA NA NA
16 NA NA 3 1 NA NA NA NA NA NA NA NA
17 3 NA 3 1 NA NA NA NA NA NA NA NA
18 3 -0.014579883 NA 1 NA NA NA NA NA NA NA NA
19 2 -2.121550136 2 1 NA NA NA NA NA NA NA NA
20 NA NA 1 1 NA NA NA NA NA NA NA NA
21 NA -0.363239698 1 1 NA NA NA NA NA NA NA NA
22 1 -0.121568514 3 1 NA NA NA NA NA NA NA NA
23 2 -0.951271111 3 1 NA NA NA NA NA NA NA NA
24 NA NA 3 1 NA NA NA NA NA NA NA NA
25 4 -0.974288621 2 1 NA NA NA NA NA NA NA NA
26 NA -1.130632418 1 1 NA NA NA NA NA NA NA NA
27 NA 0.114339868 3 1 NA NA NA NA NA NA NA NA
28 2 0.238334648 3 1 NA NA NA NA NA NA NA NA
29 4 0.840744958 2 1 NA NA NA NA NA NA NA NA
30 2 NA 1 1 NA NA NA NA NA NA NA NA
31 2 NA 2 1 NA NA NA NA NA NA NA NA
32 3 -1.466312154 3 1 NA NA NA NA NA NA NA NA
33 1 -0.637352277 2 1 NA NA NA NA NA NA NA NA
34 4 NA 2 1 NA NA NA NA NA NA NA NA
35 4 NA 3 1 NA NA NA NA NA NA NA NA
36 NA NA 1 1 NA NA NA NA NA NA NA NA
37 NA NA 3 1 NA NA NA NA NA NA NA NA
38 NA NA 2 1 NA NA NA NA NA NA NA NA
39 4 0.006728205 3 1 NA NA NA NA NA NA NA NA
40 NA NA 2 1 NA NA NA NA NA NA NA NA
41 2 -1.663281353 2 1 NA NA NA NA NA NA NA NA
42 NA 0.161184794 2 1 NA NA NA NA NA NA NA NA
43 NA 0.457939180 3 1 NA NA NA NA NA NA NA NA
44 3 -0.307070331 2 1 NA NA NA NA NA NA NA NA
45 NA NA 1 1 NA NA NA NA NA NA NA NA
46 NA -1.071668276 NA 1 NA NA NA NA NA NA NA NA
47 4 -0.814751321 1 1 NA NA NA NA NA NA NA NA
48 3 -0.547630662 2 1 NA NA NA NA NA NA NA NA
49 4 NA 1 1 NA NA NA NA NA NA NA NA
50 3 -1.350213782 2 1 NA NA NA NA NA NA NA NA
51 NA 0.719054706 2 1 NA NA NA NA NA NA NA NA
52 1 NA 3 1 NA NA NA NA NA NA NA NA
53 NA -1.207130750 3 1 NA NA NA NA NA NA NA NA
54 NA NA 1 1 NA NA NA NA NA NA NA NA
55 NA -0.408600991 2 1 NA NA NA NA NA NA NA NA
56 NA -0.271380529 NA 1 NA NA NA NA NA NA NA NA
57 4 -1.361925974 3 1 NA NA NA NA NA NA NA NA
58 1 NA 1 1 NA NA NA NA NA NA NA NA
59 2 NA 2 1 NA NA NA NA NA NA NA NA
60 3 -0.323712205 2 1 NA NA NA NA NA NA NA NA
61 2 NA 3 1 NA NA NA NA NA NA NA NA
62 3 NA 2 1 NA NA NA NA NA NA NA NA
63 2 -1.386906880 1 1 NA NA NA NA NA NA NA NA
64 NA NA 3 1 NA NA NA NA NA NA NA NA
65 NA NA 1 1 NA NA NA NA NA NA NA NA
66 2 -0.565191691 3 1 NA NA NA NA NA NA NA NA
67 NA -0.382899912 3 1 NA NA NA NA NA NA NA NA
68 3 NA 2 1 NA NA NA NA NA NA NA NA
69 3 -0.405642769 NA 1 NA NA NA NA NA NA NA NA
70 4 NA 2 1 NA NA NA NA NA NA NA NA
71 2 -0.843748427 NA 1 NA NA NA NA NA NA NA NA
72 NA 0.116003683 NA 1 NA NA NA NA NA NA NA NA
73 4 -0.778634325 1 1 NA NA NA NA NA NA NA NA
74 NA NA 2 1 NA NA NA NA NA NA NA NA
75 NA NA 2 1 NA NA NA NA NA NA NA NA
76 4 NA 1 1 NA NA NA NA NA NA NA NA
77 2 -0.632974758 3 1 NA NA NA NA NA NA NA NA
78 2 NA 2 1 NA NA NA NA NA NA NA NA
79 NA -0.778064615 NA 1 NA NA NA NA NA NA NA NA
80 1 NA 1 1 NA NA NA NA NA NA NA NA
81 NA NA 1 1 NA NA NA NA NA NA NA NA
82 3 -0.246123253 3 1 NA NA NA NA NA NA NA NA
83 4 -1.239659782 NA 1 NA NA NA NA NA NA NA NA
84 3 -0.467772280 3 1 NA NA NA NA NA NA NA NA
85 NA NA 3 1 NA NA NA NA NA NA NA NA
86 3 -2.160485036 2 1 NA NA NA NA NA NA NA NA
87 NA -0.657675572 1 1 NA NA NA NA NA NA NA NA
88 NA NA 1 1 NA NA NA NA NA NA NA NA
89 NA -0.696710744 3 1 NA NA NA NA NA NA NA NA
90 4 NA 1 1 NA NA NA NA NA NA NA NA
91 NA -0.179395847 2 1 NA NA NA NA NA NA NA NA
92 2 -0.441545568 NA 1 NA NA NA NA NA NA NA NA
93 4 -0.685799334 1 1 NA NA NA NA NA NA NA NA
94 NA NA 3 1 NA NA NA NA NA NA NA NA
95 NA 0.191929445 2 1 NA NA NA NA NA NA NA NA
96 NA NA 2 1 NA NA NA NA NA NA NA NA
97 1 -0.069760671 3 1 NA NA NA NA NA NA NA NA
98 NA NA 2 1 NA NA NA NA NA NA NA NA
99 2 NA 2 1 NA NA NA NA NA NA NA NA
100 NA NA 3 1 NA NA NA NA NA NA NA NA
$m6d$M_lvlone
o1 c1
1 2 0.7592026489
1.1 1 0.9548337990
1.2 1 0.5612235156
1.3 2 1.1873391025
2 3 0.9192204198
2.1 1 -0.1870730476
2.2 2 1.2517512331
3 1 -0.0605087604
3.1 3 0.3788637747
3.2 2 0.9872578281
4 3 1.4930175328
4.1 2 -0.7692526880
4.2 1 0.9180841450
4.3 2 -0.0541170782
5 2 -0.1376784521
5.1 2 -0.2740585866
5.2 3 0.4670496929
5.3 2 0.1740288049
6 3 0.9868044683
7 3 -0.1280320918
7.1 1 0.4242971219
7.2 1 0.0777182491
8 2 -0.5791408712
8.1 2 0.3128604232
8.2 3 0.6258446356
8.3 3 -0.1040137707
8.4 2 0.0481450285
8.5 2 0.3831763675
9 2 -0.1757592269
9.1 2 -0.1791541200
9.2 3 -0.0957042935
10 2 -0.5598409704
10.1 1 -0.2318340451
11 2 0.5086859475
11.1 3 0.4951758188
11.2 3 -1.1022162541
11.3 1 -0.0611636705
11.4 3 -0.4971774316
12 1 -0.2433996286
13 2 0.8799673116
13.1 1 0.1079022586
14 3 0.9991752617
14.1 3 -0.1094019046
14.2 2 0.1518967560
14.3 1 0.3521012473
15 3 0.3464447888
15.1 3 -0.4767313971
15.2 2 0.5759767791
15.3 2 -0.1713452662
16 3 0.4564754473
16.1 1 1.0652558311
16.2 3 0.6971872493
16.3 1 0.5259331838
16.4 1 0.2046601798
16.5 2 1.0718540464
17 2 0.6048676222
17.1 2 0.2323298304
17.2 3 1.2617499032
17.3 2 -0.3913230895
17.4 3 0.9577299112
18 2 -0.0050324072
19 3 -0.4187468937
19.1 2 -0.4478828944
19.2 2 -1.1966721302
19.3 2 -0.5877091668
20 1 0.6838223064
20.1 2 0.3278571109
20.2 2 -0.8489831990
20.3 1 1.3169975191
20.4 1 0.0444804531
20.5 2 -0.4535207652
21 2 -0.4030302960
21.1 2 -0.4069674045
21.2 2 1.0650265940
22 1 -0.0673274516
22.1 1 0.9601388170
23 1 0.5556634840
23.1 2 1.4407865964
24 1 0.3856376411
25 2 0.3564400705
25.1 1 0.0982553434
25.2 1 0.1928682598
25.3 1 -0.0192488594
25.4 2 0.4466012931
25.5 2 1.1425193342
26 2 0.5341531449
26.1 3 1.2268695927
26.2 2 0.3678294939
26.3 1 0.5948516018
27 2 -0.3342844147
27.1 2 -0.4835141229
28 1 -0.7145915499
28.1 1 0.5063671955
28.2 1 -0.2067413142
28.3 1 0.1196789973
29 2 0.1392699487
29.1 2 0.7960234776
29.2 1 1.0398214352
29.3 2 0.0813246429
30 2 -0.3296323050
30.1 2 1.3635850954
30.2 2 0.7354171050
31 2 0.3708398217
32 3 -0.0474059668
32.1 1 1.2507771489
32.2 3 0.1142915519
32.3 3 0.6773270619
33 2 0.1774293842
33.1 2 0.6159606291
34 3 0.8590979166
34.1 2 0.0546216775
34.2 2 -0.0897224473
34.3 1 0.4163395571
35 2 -1.4693520528
35.1 3 -0.3031734330
35.2 2 -0.6045512101
36 3 0.9823048960
36.1 2 1.4466051416
36.2 3 1.1606752905
36.3 3 0.8373091576
36.4 3 0.2640591685
37 3 0.1177313455
37.1 3 -0.1415483779
37.2 3 0.0054610124
38 2 0.8078948077
39 1 0.9876451040
39.1 2 -0.3431222274
39.2 2 -1.7909380751
39.3 1 -0.1798746191
39.4 1 -0.1850961689
39.5 1 0.4544226146
40 3 0.5350190436
40.1 1 0.4189342752
40.2 1 0.4211994981
40.3 2 0.0916687506
41 3 -0.1035047421
41.1 2 -0.4684202411
41.2 1 0.5972615368
41.3 1 0.9885613862
41.4 3 -0.3908036794
42 2 -0.0338893961
42.1 1 -0.4498363172
43 2 0.8965546110
43.1 2 0.6199122090
43.2 1 0.1804894429
44 3 1.3221409285
44.1 2 0.3416426284
44.2 1 0.5706610068
44.3 1 1.2679497430
45 1 0.1414983160
45.1 1 0.7220892521
46 1 1.5391054233
46.1 2 0.3889107049
46.2 3 0.1248719493
47 1 0.2014101100
47.1 1 0.2982973539
47.2 1 1.1518107179
47.3 3 0.5196802157
47.4 2 0.3702301552
48 3 -0.2128602862
48.1 2 -0.5337239976
49 1 -0.5236770035
50 2 0.3897705981
51 2 -0.7213343736
52 2 0.3758235358
52.1 3 0.7138067080
52.2 2 0.8872895233
52.3 1 -0.9664587437
52.4 3 0.0254566848
52.5 2 0.4155259424
53 2 0.5675736897
53.1 1 -0.3154088781
53.2 1 0.2162315769
54 1 -0.0880802382
54.1 2 0.4129127672
54.2 3 1.0119546775
54.3 1 -0.1112901990
54.4 2 0.8587727145
55 1 -0.0116453589
55.1 1 0.5835528661
55.2 2 -1.0010857254
55.3 1 -0.4796526070
55.4 1 -0.1202746964
56 3 0.5176377612
56.1 2 -1.1136932588
56.2 3 -0.0168103281
56.3 3 0.3933023606
56.4 1 0.3714625139
56.5 3 0.7811448179
57 2 -1.0868304872
57.1 2 0.8018626997
57.2 2 -0.1159517011
57.3 2 0.6785562445
58 3 1.6476207996
58.1 2 0.3402652711
58.2 3 -0.1111300753
58.3 3 -0.5409234285
58.4 2 -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 1 1.1717723589
61.4 2 -0.0629201596
62 2 -0.3979137604
62.1 3 0.6830738372
62.2 1 0.4301745954
62.3 2 -0.0333139957
63 1 0.3345678035
63.1 3 0.3643769511
64 2 0.3949911859
65 1 1.2000091513
65.1 2 0.0110122646
65.2 1 -0.5776452043
65.3 1 -0.1372183563
66 1 -0.5081302805
66.1 1 -0.1447837412
66.2 3 0.1906241379
67 3 1.6716027681
68 1 0.5691848839
68.1 2 0.1004860389
68.2 2 -0.0061241827
68.3 2 0.7443745962
68.4 2 0.8726923437
69 2 0.0381382683
70 1 0.8126204217
70.1 3 0.4691503050
71 3 -0.5529062591
71.1 3 -0.1103252087
71.2 3 1.7178492547
71.3 2 -1.0118346755
71.4 2 1.8623785017
72 1 -0.4521659275
72.1 1 0.1375317317
72.2 2 -0.4170988856
72.3 2 0.7107266765
72.4 1 0.1451969143
72.5 1 1.6298050306
73 1 -0.0307469467
74 1 0.3730017941
75 3 -0.4908003566
76 3 -0.9888876620
76.1 3 0.0003798292
76.2 1 -0.8421863763
77 2 -0.4986802480
78 2 0.0417330969
79 1 -0.3767450660
79.1 2 0.1516000028
79.2 1 -0.1888160741
80 2 -0.0041558414
80.1 2 -0.0329337062
80.2 3 0.5046816157
81 3 -0.9493950353
81.1 2 0.2443038954
81.2 3 0.6476958410
81.3 3 0.4182528210
82 3 1.1088801952
82.1 2 0.9334157763
82.2 2 0.4958140634
83 3 0.5104724530
83.1 3 -0.0513309106
83.2 3 -0.2067792494
83.3 2 -0.0534169155
84 3 -0.0255753653
84.1 3 -1.8234189877
85 3 -0.0114038622
85.1 2 -0.0577615939
85.2 2 -0.2241856342
85.3 2 -0.0520175929
85.4 1 0.2892733846
85.5 3 -0.3740417009
86 2 0.4293735089
86.1 3 -0.1363456521
86.2 1 0.1230989293
86.3 1 0.3305413955
86.4 1 2.6003411822
86.5 3 -0.1420690052
87 1 1.0457427869
87.1 1 -0.2973007190
87.2 1 0.4396872616
88 3 -0.0601928334
88.1 1 -1.0124347595
88.2 1 0.5730917016
88.3 1 -0.0029455332
89 1 1.5465903721
90 2 0.0626760573
90.1 1 1.1896872985
90.2 3 0.2597888783
90.3 1 0.6599799887
91 1 1.1213651365
91.1 2 1.2046371625
91.2 1 0.3395603754
92 2 0.4674939332
93 1 0.2677965647
93.1 3 1.6424445368
93.2 1 0.7101700066
93.3 3 1.1222322893
93.4 1 1.4628960401
94 2 -0.2904211940
94.1 3 0.0147813580
94.2 1 -0.4536774482
94.3 3 0.6793464917
94.4 2 -0.9411356550
94.5 3 0.5683867264
95 3 0.2375652188
95.1 1 0.0767152977
95.2 2 -0.6886731251
96 1 0.7813892121
96.1 3 0.3391519695
96.2 1 -0.4857246503
96.3 1 0.8771471244
96.4 2 1.9030768981
96.5 3 -0.1684332749
97 3 1.3775130083
97.1 3 -1.7323228619
98 3 -1.2648518889
98.1 1 -0.9042716241
98.2 1 -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 2 0.8861545820
100.3 1 0.1277046316
100.4 1 -0.0834577654
$m6d$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.6857108
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
M22:C2 -0.3136263 0.5921089
M23:C2 -0.2014212 0.4917409
M24:C2 -0.1430739 0.4225863
$m6d$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
$m6d$mu_reg_norm
[1] 0
$m6d$tau_reg_norm
[1] 1e-04
$m6d$shape_tau_norm
[1] 0.01
$m6d$rate_tau_norm
[1] 0.01
$m6d$mu_reg_multinomial
[1] 0
$m6d$tau_reg_multinomial
[1] 1e-04
$m6d$mu_reg_ordinal
[1] 0
$m6d$tau_reg_ordinal
[1] 1e-04
$m6d$mu_delta_ordinal
[1] 0
$m6d$tau_delta_ordinal
[1] 1e-04
$m6d$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
$m6d$shape_diag_RinvD
[1] "0.01"
$m6d$rate_diag_RinvD
[1] "0.001"
$m6e
$m6e$M_id
M2 C2 O2 (Intercept) M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2
1 NA -1.381594459 3 1 NA NA NA NA NA NA NA NA
2 1 0.344426024 2 1 NA NA NA NA NA NA NA NA
3 2 NA 2 1 NA NA NA NA NA NA NA NA
4 2 -0.228910007 3 1 NA NA NA NA NA NA NA NA
5 1 NA 1 1 NA NA NA NA NA NA NA NA
6 NA -2.143955482 3 1 NA NA NA NA NA NA NA NA
7 NA -1.156567023 NA 1 NA NA NA NA NA NA NA NA
8 2 -0.598827660 1 1 NA NA NA NA NA NA NA NA
9 NA NA 1 1 NA NA NA NA NA NA NA NA
10 NA -1.006719032 3 1 NA NA NA NA NA NA NA NA
11 3 0.239801450 3 1 NA NA NA NA NA NA NA NA
12 NA -1.064969789 NA 1 NA NA NA NA NA NA NA NA
13 NA -0.538082688 3 1 NA NA NA NA NA NA NA NA
14 2 NA 2 1 NA NA NA NA NA NA NA NA
15 2 -1.781049276 2 1 NA NA NA NA NA NA NA NA
16 NA NA 3 1 NA NA NA NA NA NA NA NA
17 3 NA 3 1 NA NA NA NA NA NA NA NA
18 3 -0.014579883 NA 1 NA NA NA NA NA NA NA NA
19 2 -2.121550136 2 1 NA NA NA NA NA NA NA NA
20 NA NA 1 1 NA NA NA NA NA NA NA NA
21 NA -0.363239698 1 1 NA NA NA NA NA NA NA NA
22 1 -0.121568514 3 1 NA NA NA NA NA NA NA NA
23 2 -0.951271111 3 1 NA NA NA NA NA NA NA NA
24 NA NA 3 1 NA NA NA NA NA NA NA NA
25 4 -0.974288621 2 1 NA NA NA NA NA NA NA NA
26 NA -1.130632418 1 1 NA NA NA NA NA NA NA NA
27 NA 0.114339868 3 1 NA NA NA NA NA NA NA NA
28 2 0.238334648 3 1 NA NA NA NA NA NA NA NA
29 4 0.840744958 2 1 NA NA NA NA NA NA NA NA
30 2 NA 1 1 NA NA NA NA NA NA NA NA
31 2 NA 2 1 NA NA NA NA NA NA NA NA
32 3 -1.466312154 3 1 NA NA NA NA NA NA NA NA
33 1 -0.637352277 2 1 NA NA NA NA NA NA NA NA
34 4 NA 2 1 NA NA NA NA NA NA NA NA
35 4 NA 3 1 NA NA NA NA NA NA NA NA
36 NA NA 1 1 NA NA NA NA NA NA NA NA
37 NA NA 3 1 NA NA NA NA NA NA NA NA
38 NA NA 2 1 NA NA NA NA NA NA NA NA
39 4 0.006728205 3 1 NA NA NA NA NA NA NA NA
40 NA NA 2 1 NA NA NA NA NA NA NA NA
41 2 -1.663281353 2 1 NA NA NA NA NA NA NA NA
42 NA 0.161184794 2 1 NA NA NA NA NA NA NA NA
43 NA 0.457939180 3 1 NA NA NA NA NA NA NA NA
44 3 -0.307070331 2 1 NA NA NA NA NA NA NA NA
45 NA NA 1 1 NA NA NA NA NA NA NA NA
46 NA -1.071668276 NA 1 NA NA NA NA NA NA NA NA
47 4 -0.814751321 1 1 NA NA NA NA NA NA NA NA
48 3 -0.547630662 2 1 NA NA NA NA NA NA NA NA
49 4 NA 1 1 NA NA NA NA NA NA NA NA
50 3 -1.350213782 2 1 NA NA NA NA NA NA NA NA
51 NA 0.719054706 2 1 NA NA NA NA NA NA NA NA
52 1 NA 3 1 NA NA NA NA NA NA NA NA
53 NA -1.207130750 3 1 NA NA NA NA NA NA NA NA
54 NA NA 1 1 NA NA NA NA NA NA NA NA
55 NA -0.408600991 2 1 NA NA NA NA NA NA NA NA
56 NA -0.271380529 NA 1 NA NA NA NA NA NA NA NA
57 4 -1.361925974 3 1 NA NA NA NA NA NA NA NA
58 1 NA 1 1 NA NA NA NA NA NA NA NA
59 2 NA 2 1 NA NA NA NA NA NA NA NA
60 3 -0.323712205 2 1 NA NA NA NA NA NA NA NA
61 2 NA 3 1 NA NA NA NA NA NA NA NA
62 3 NA 2 1 NA NA NA NA NA NA NA NA
63 2 -1.386906880 1 1 NA NA NA NA NA NA NA NA
64 NA NA 3 1 NA NA NA NA NA NA NA NA
65 NA NA 1 1 NA NA NA NA NA NA NA NA
66 2 -0.565191691 3 1 NA NA NA NA NA NA NA NA
67 NA -0.382899912 3 1 NA NA NA NA NA NA NA NA
68 3 NA 2 1 NA NA NA NA NA NA NA NA
69 3 -0.405642769 NA 1 NA NA NA NA NA NA NA NA
70 4 NA 2 1 NA NA NA NA NA NA NA NA
71 2 -0.843748427 NA 1 NA NA NA NA NA NA NA NA
72 NA 0.116003683 NA 1 NA NA NA NA NA NA NA NA
73 4 -0.778634325 1 1 NA NA NA NA NA NA NA NA
74 NA NA 2 1 NA NA NA NA NA NA NA NA
75 NA NA 2 1 NA NA NA NA NA NA NA NA
76 4 NA 1 1 NA NA NA NA NA NA NA NA
77 2 -0.632974758 3 1 NA NA NA NA NA NA NA NA
78 2 NA 2 1 NA NA NA NA NA NA NA NA
79 NA -0.778064615 NA 1 NA NA NA NA NA NA NA NA
80 1 NA 1 1 NA NA NA NA NA NA NA NA
81 NA NA 1 1 NA NA NA NA NA NA NA NA
82 3 -0.246123253 3 1 NA NA NA NA NA NA NA NA
83 4 -1.239659782 NA 1 NA NA NA NA NA NA NA NA
84 3 -0.467772280 3 1 NA NA NA NA NA NA NA NA
85 NA NA 3 1 NA NA NA NA NA NA NA NA
86 3 -2.160485036 2 1 NA NA NA NA NA NA NA NA
87 NA -0.657675572 1 1 NA NA NA NA NA NA NA NA
88 NA NA 1 1 NA NA NA NA NA NA NA NA
89 NA -0.696710744 3 1 NA NA NA NA NA NA NA NA
90 4 NA 1 1 NA NA NA NA NA NA NA NA
91 NA -0.179395847 2 1 NA NA NA NA NA NA NA NA
92 2 -0.441545568 NA 1 NA NA NA NA NA NA NA NA
93 4 -0.685799334 1 1 NA NA NA NA NA NA NA NA
94 NA NA 3 1 NA NA NA NA NA NA NA NA
95 NA 0.191929445 2 1 NA NA NA NA NA NA NA NA
96 NA NA 2 1 NA NA NA NA NA NA NA NA
97 1 -0.069760671 3 1 NA NA NA NA NA NA NA NA
98 NA NA 2 1 NA NA NA NA NA NA NA NA
99 2 NA 2 1 NA NA NA NA NA NA NA NA
100 NA NA 3 1 NA NA NA NA NA NA NA NA
$m6e$M_lvlone
o1 c1
1 2 0.7592026489
1.1 1 0.9548337990
1.2 1 0.5612235156
1.3 2 1.1873391025
2 3 0.9192204198
2.1 1 -0.1870730476
2.2 2 1.2517512331
3 1 -0.0605087604
3.1 3 0.3788637747
3.2 2 0.9872578281
4 3 1.4930175328
4.1 2 -0.7692526880
4.2 1 0.9180841450
4.3 2 -0.0541170782
5 2 -0.1376784521
5.1 2 -0.2740585866
5.2 3 0.4670496929
5.3 2 0.1740288049
6 3 0.9868044683
7 3 -0.1280320918
7.1 1 0.4242971219
7.2 1 0.0777182491
8 2 -0.5791408712
8.1 2 0.3128604232
8.2 3 0.6258446356
8.3 3 -0.1040137707
8.4 2 0.0481450285
8.5 2 0.3831763675
9 2 -0.1757592269
9.1 2 -0.1791541200
9.2 3 -0.0957042935
10 2 -0.5598409704
10.1 1 -0.2318340451
11 2 0.5086859475
11.1 3 0.4951758188
11.2 3 -1.1022162541
11.3 1 -0.0611636705
11.4 3 -0.4971774316
12 1 -0.2433996286
13 2 0.8799673116
13.1 1 0.1079022586
14 3 0.9991752617
14.1 3 -0.1094019046
14.2 2 0.1518967560
14.3 1 0.3521012473
15 3 0.3464447888
15.1 3 -0.4767313971
15.2 2 0.5759767791
15.3 2 -0.1713452662
16 3 0.4564754473
16.1 1 1.0652558311
16.2 3 0.6971872493
16.3 1 0.5259331838
16.4 1 0.2046601798
16.5 2 1.0718540464
17 2 0.6048676222
17.1 2 0.2323298304
17.2 3 1.2617499032
17.3 2 -0.3913230895
17.4 3 0.9577299112
18 2 -0.0050324072
19 3 -0.4187468937
19.1 2 -0.4478828944
19.2 2 -1.1966721302
19.3 2 -0.5877091668
20 1 0.6838223064
20.1 2 0.3278571109
20.2 2 -0.8489831990
20.3 1 1.3169975191
20.4 1 0.0444804531
20.5 2 -0.4535207652
21 2 -0.4030302960
21.1 2 -0.4069674045
21.2 2 1.0650265940
22 1 -0.0673274516
22.1 1 0.9601388170
23 1 0.5556634840
23.1 2 1.4407865964
24 1 0.3856376411
25 2 0.3564400705
25.1 1 0.0982553434
25.2 1 0.1928682598
25.3 1 -0.0192488594
25.4 2 0.4466012931
25.5 2 1.1425193342
26 2 0.5341531449
26.1 3 1.2268695927
26.2 2 0.3678294939
26.3 1 0.5948516018
27 2 -0.3342844147
27.1 2 -0.4835141229
28 1 -0.7145915499
28.1 1 0.5063671955
28.2 1 -0.2067413142
28.3 1 0.1196789973
29 2 0.1392699487
29.1 2 0.7960234776
29.2 1 1.0398214352
29.3 2 0.0813246429
30 2 -0.3296323050
30.1 2 1.3635850954
30.2 2 0.7354171050
31 2 0.3708398217
32 3 -0.0474059668
32.1 1 1.2507771489
32.2 3 0.1142915519
32.3 3 0.6773270619
33 2 0.1774293842
33.1 2 0.6159606291
34 3 0.8590979166
34.1 2 0.0546216775
34.2 2 -0.0897224473
34.3 1 0.4163395571
35 2 -1.4693520528
35.1 3 -0.3031734330
35.2 2 -0.6045512101
36 3 0.9823048960
36.1 2 1.4466051416
36.2 3 1.1606752905
36.3 3 0.8373091576
36.4 3 0.2640591685
37 3 0.1177313455
37.1 3 -0.1415483779
37.2 3 0.0054610124
38 2 0.8078948077
39 1 0.9876451040
39.1 2 -0.3431222274
39.2 2 -1.7909380751
39.3 1 -0.1798746191
39.4 1 -0.1850961689
39.5 1 0.4544226146
40 3 0.5350190436
40.1 1 0.4189342752
40.2 1 0.4211994981
40.3 2 0.0916687506
41 3 -0.1035047421
41.1 2 -0.4684202411
41.2 1 0.5972615368
41.3 1 0.9885613862
41.4 3 -0.3908036794
42 2 -0.0338893961
42.1 1 -0.4498363172
43 2 0.8965546110
43.1 2 0.6199122090
43.2 1 0.1804894429
44 3 1.3221409285
44.1 2 0.3416426284
44.2 1 0.5706610068
44.3 1 1.2679497430
45 1 0.1414983160
45.1 1 0.7220892521
46 1 1.5391054233
46.1 2 0.3889107049
46.2 3 0.1248719493
47 1 0.2014101100
47.1 1 0.2982973539
47.2 1 1.1518107179
47.3 3 0.5196802157
47.4 2 0.3702301552
48 3 -0.2128602862
48.1 2 -0.5337239976
49 1 -0.5236770035
50 2 0.3897705981
51 2 -0.7213343736
52 2 0.3758235358
52.1 3 0.7138067080
52.2 2 0.8872895233
52.3 1 -0.9664587437
52.4 3 0.0254566848
52.5 2 0.4155259424
53 2 0.5675736897
53.1 1 -0.3154088781
53.2 1 0.2162315769
54 1 -0.0880802382
54.1 2 0.4129127672
54.2 3 1.0119546775
54.3 1 -0.1112901990
54.4 2 0.8587727145
55 1 -0.0116453589
55.1 1 0.5835528661
55.2 2 -1.0010857254
55.3 1 -0.4796526070
55.4 1 -0.1202746964
56 3 0.5176377612
56.1 2 -1.1136932588
56.2 3 -0.0168103281
56.3 3 0.3933023606
56.4 1 0.3714625139
56.5 3 0.7811448179
57 2 -1.0868304872
57.1 2 0.8018626997
57.2 2 -0.1159517011
57.3 2 0.6785562445
58 3 1.6476207996
58.1 2 0.3402652711
58.2 3 -0.1111300753
58.3 3 -0.5409234285
58.4 2 -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 1 1.1717723589
61.4 2 -0.0629201596
62 2 -0.3979137604
62.1 3 0.6830738372
62.2 1 0.4301745954
62.3 2 -0.0333139957
63 1 0.3345678035
63.1 3 0.3643769511
64 2 0.3949911859
65 1 1.2000091513
65.1 2 0.0110122646
65.2 1 -0.5776452043
65.3 1 -0.1372183563
66 1 -0.5081302805
66.1 1 -0.1447837412
66.2 3 0.1906241379
67 3 1.6716027681
68 1 0.5691848839
68.1 2 0.1004860389
68.2 2 -0.0061241827
68.3 2 0.7443745962
68.4 2 0.8726923437
69 2 0.0381382683
70 1 0.8126204217
70.1 3 0.4691503050
71 3 -0.5529062591
71.1 3 -0.1103252087
71.2 3 1.7178492547
71.3 2 -1.0118346755
71.4 2 1.8623785017
72 1 -0.4521659275
72.1 1 0.1375317317
72.2 2 -0.4170988856
72.3 2 0.7107266765
72.4 1 0.1451969143
72.5 1 1.6298050306
73 1 -0.0307469467
74 1 0.3730017941
75 3 -0.4908003566
76 3 -0.9888876620
76.1 3 0.0003798292
76.2 1 -0.8421863763
77 2 -0.4986802480
78 2 0.0417330969
79 1 -0.3767450660
79.1 2 0.1516000028
79.2 1 -0.1888160741
80 2 -0.0041558414
80.1 2 -0.0329337062
80.2 3 0.5046816157
81 3 -0.9493950353
81.1 2 0.2443038954
81.2 3 0.6476958410
81.3 3 0.4182528210
82 3 1.1088801952
82.1 2 0.9334157763
82.2 2 0.4958140634
83 3 0.5104724530
83.1 3 -0.0513309106
83.2 3 -0.2067792494
83.3 2 -0.0534169155
84 3 -0.0255753653
84.1 3 -1.8234189877
85 3 -0.0114038622
85.1 2 -0.0577615939
85.2 2 -0.2241856342
85.3 2 -0.0520175929
85.4 1 0.2892733846
85.5 3 -0.3740417009
86 2 0.4293735089
86.1 3 -0.1363456521
86.2 1 0.1230989293
86.3 1 0.3305413955
86.4 1 2.6003411822
86.5 3 -0.1420690052
87 1 1.0457427869
87.1 1 -0.2973007190
87.2 1 0.4396872616
88 3 -0.0601928334
88.1 1 -1.0124347595
88.2 1 0.5730917016
88.3 1 -0.0029455332
89 1 1.5465903721
90 2 0.0626760573
90.1 1 1.1896872985
90.2 3 0.2597888783
90.3 1 0.6599799887
91 1 1.1213651365
91.1 2 1.2046371625
91.2 1 0.3395603754
92 2 0.4674939332
93 1 0.2677965647
93.1 3 1.6424445368
93.2 1 0.7101700066
93.3 3 1.1222322893
93.4 1 1.4628960401
94 2 -0.2904211940
94.1 3 0.0147813580
94.2 1 -0.4536774482
94.3 3 0.6793464917
94.4 2 -0.9411356550
94.5 3 0.5683867264
95 3 0.2375652188
95.1 1 0.0767152977
95.2 2 -0.6886731251
96 1 0.7813892121
96.1 3 0.3391519695
96.2 1 -0.4857246503
96.3 1 0.8771471244
96.4 2 1.9030768981
96.5 3 -0.1684332749
97 3 1.3775130083
97.1 3 -1.7323228619
98 3 -1.2648518889
98.1 1 -0.9042716241
98.2 1 -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 2 0.8861545820
100.3 1 0.1277046316
100.4 1 -0.0834577654
$m6e$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.6857108
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
M22:C2 -0.3136263 0.5921089
M23:C2 -0.2014212 0.4917409
M24:C2 -0.1430739 0.4225863
$m6e$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
$m6e$mu_reg_norm
[1] 0
$m6e$tau_reg_norm
[1] 1e-04
$m6e$shape_tau_norm
[1] 0.01
$m6e$rate_tau_norm
[1] 0.01
$m6e$mu_reg_multinomial
[1] 0
$m6e$tau_reg_multinomial
[1] 1e-04
$m6e$mu_reg_ordinal
[1] 0
$m6e$tau_reg_ordinal
[1] 1e-04
$m6e$mu_delta_ordinal
[1] 0
$m6e$tau_delta_ordinal
[1] 1e-04
$m6e$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
$m6e$shape_diag_RinvD
[1] "0.01"
$m6e$rate_diag_RinvD
[1] "0.001"
$m7a
$m7a$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
$m7a$M_lvlone
y o2 x o1.L o1.Q o22 o23 o24 x2 x3 x4
1 -13.0493856 1 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
1.1 -9.3335901 1 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
1.2 -22.3469852 3 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
1.3 -15.0417337 1 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
2 -12.0655434 4 1 7.071068e-01 0.4082483 NA NA NA NA NA NA
2.1 -15.8674476 4 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
2.2 -7.8800006 2 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
3 -11.4820604 2 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
3.1 -10.5983220 4 1 7.071068e-01 0.4082483 NA NA NA NA NA NA
3.2 -22.4519157 2 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
4 -1.2697775 4 4 7.071068e-01 0.4082483 NA NA NA NA NA NA
4.1 -11.1215184 3 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
4.2 -3.6134138 NA 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
4.3 -14.5982385 2 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
5 -6.8457515 2 NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA
5.1 -7.0551214 4 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
5.2 -12.3418980 2 2 7.071068e-01 0.4082483 NA NA NA NA NA NA
5.3 -9.2366906 4 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
6 -5.1648211 3 2 7.071068e-01 0.4082483 NA NA NA NA NA NA
7 -10.0599502 1 4 7.071068e-01 0.4082483 NA NA NA NA NA NA
7.1 -18.3267285 NA 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
7.2 -12.5138426 4 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
8 -1.6305331 1 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
8.1 -9.6520453 3 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
8.2 -1.5278462 1 4 7.071068e-01 0.4082483 NA NA NA NA NA NA
8.3 -7.4172211 4 4 7.071068e-01 0.4082483 NA NA NA NA NA NA
8.4 -7.1238609 3 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
8.5 -8.8706950 3 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
9 -0.1634429 2 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
9.1 -2.6034300 2 NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA
9.2 -6.7272369 4 3 7.071068e-01 0.4082483 NA NA NA NA NA NA
10 -6.4172202 1 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
10.1 -11.4834569 4 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA
11 -8.7911356 3 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
11.1 -19.6645080 1 1 7.071068e-01 0.4082483 NA NA NA NA NA NA
11.2 -20.2030932 4 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA
11.3 -21.3082176 3 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
11.4 -14.5802901 3 2 7.071068e-01 0.4082483 NA NA NA NA NA NA
12 -15.2006287 3 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
13 0.8058816 NA 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
13.1 -13.6379208 1 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
14 -15.3422873 1 3 7.071068e-01 0.4082483 NA NA NA NA NA NA
14.1 -10.0965208 4 4 7.071068e-01 0.4082483 NA NA NA NA NA NA
14.2 -16.6452027 3 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
14.3 -15.8389733 4 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
15 -8.9424594 1 3 7.071068e-01 0.4082483 NA NA NA NA NA NA
15.1 -22.0101983 4 3 7.071068e-01 0.4082483 NA NA NA NA NA NA
15.2 -7.3975599 NA 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
15.3 -10.3567334 2 NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA
16 -1.9691302 NA 2 7.071068e-01 0.4082483 NA NA NA NA NA NA
16.1 -9.9308357 NA 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
16.2 -6.9626923 1 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA
16.3 -3.2862557 3 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
16.4 -3.3972355 3 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
16.5 -11.5767835 1 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
17 -10.5474144 3 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
17.1 -7.6215009 2 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
17.2 -16.5386939 2 3 7.071068e-01 0.4082483 NA NA NA NA NA NA
17.3 -20.0004774 3 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
17.4 -18.8505475 1 1 7.071068e-01 0.4082483 NA NA NA NA NA NA
18 -19.7302351 4 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
19 -14.6177568 1 4 7.071068e-01 0.4082483 NA NA NA NA NA NA
19.1 -17.8043866 NA 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
19.2 -15.1641705 NA 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
19.3 -16.6898418 2 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
20 -12.9059229 1 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
20.1 -16.8191201 4 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
20.2 -6.1010131 3 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
20.3 -7.9415371 3 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
20.4 -9.3904458 1 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
20.5 -13.3504189 3 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
21 -7.6974718 3 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
21.1 -11.9335526 1 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
21.2 -12.7064929 2 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
22 -21.5022909 4 NA -7.071068e-01 0.4082483 NA NA NA NA NA NA
22.1 -12.7745451 NA 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
23 -3.5146508 4 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
23.1 -4.6724048 NA 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
24 -2.5619821 3 NA -7.071068e-01 0.4082483 NA NA NA NA NA NA
25 -6.2944970 1 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
25.1 -3.8630505 3 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
25.2 -14.4205140 2 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
25.3 -19.6735037 1 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA
25.4 -9.0288933 1 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
25.5 -9.0509738 NA 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
26 -19.7340685 3 NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA
26.1 -14.1692728 4 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA
26.2 -17.2819976 3 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
26.3 -24.6265576 1 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
27 -7.3354999 4 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
27.1 -11.1488468 4 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
28 -11.7996597 1 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA
28.1 -8.2030122 2 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA
28.2 -26.4317815 3 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
28.3 -18.5016071 3 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
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52.3 -20.1764756 NA 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
52.4 -8.9036692 4 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA
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55.3 -12.9821266 NA 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA
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56.3 -12.4633969 3 3 7.071068e-01 0.4082483 NA NA NA NA NA NA
56.4 -17.3841863 4 NA -7.071068e-01 0.4082483 NA NA NA NA NA NA
56.5 -14.8147645 4 3 7.071068e-01 0.4082483 NA NA NA NA NA NA
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57.2 -6.3940143 4 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
57.3 -9.3473241 NA 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
58 -12.0245677 1 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA
58.1 -9.2112246 2 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
58.2 -1.2071742 2 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA
58.3 -11.0141711 4 4 7.071068e-01 0.4082483 NA NA NA NA NA NA
58.4 -5.3721214 1 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
58.5 -7.8523047 NA 1 7.071068e-01 0.4082483 NA NA NA NA NA NA
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61.3 -3.8819786 1 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
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62.3 -17.9844596 3 NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA
63 -24.0335535 4 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA
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72 -9.1848162 1 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
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72.2 -26.3421306 4 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
72.3 -27.2843801 1 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
72.4 -20.8541617 3 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
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80 -12.7366807 2 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
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81.2 1.8283303 3 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA
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89 -11.0459647 3 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
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91 -7.1462503 2 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
91.1 -12.8374475 NA NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA
91.2 -18.2576707 3 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA
92 -6.4119222 2 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
93 5.2122168 3 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
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93.2 -3.6841177 3 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
93.3 2.6223542 2 1 7.071068e-01 0.4082483 NA NA NA NA NA NA
93.4 -11.1877696 4 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA
94 -6.9602492 NA 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
94.1 -7.4318416 2 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA
94.2 -4.3498045 NA 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
94.3 -11.6340088 3 2 7.071068e-01 0.4082483 NA NA NA NA NA NA
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99 -11.4720500 NA 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
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99.2 -17.3939469 4 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
100 1.1005874 1 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
100.1 -3.8226248 NA 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
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100.3 -15.8389474 4 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
100.4 -12.8093826 1 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
time
1 0.5090421822
1.1 0.6666076288
1.2 2.1304941282
1.3 2.4954441458
2 3.0164990982
2.1 3.2996806887
2.2 4.1747569619
3 0.8478727890
3.1 3.0654308549
3.2 4.7381553578
4 0.3371432109
4.1 1.0693019140
4.2 2.6148973033
4.3 3.1336532847
5 1.0762525082
5.1 1.7912546196
5.2 2.7960080339
5.3 2.8119940578
6 1.7815462884
7 3.3074087673
7.1 3.7008403614
7.2 4.7716691741
8 1.1246398522
8.1 1.8027009873
8.2 1.8175825174
8.3 2.8384267003
8.4 3.3630275307
8.5 4.4360849704
9 0.9607803822
9.1 2.9177753383
9.2 4.8100892501
10 2.2975509102
10.1 4.1734118364
11 1.1832662905
11.1 1.2346051680
11.2 1.6435316263
11.3 3.3859017969
11.4 4.8118087661
12 0.9591987054
13 0.0619085738
13.1 3.5621061502
14 4.0364430007
14.1 4.4710561272
14.2 4.6359198843
14.3 4.6886152599
15 0.5402063532
15.1 1.1893180816
15.2 1.5094739688
15.3 4.9193474615
16 1.2417913869
16.1 2.5675726333
16.2 2.6524101500
16.3 3.5585018690
16.4 3.7612454291
16.5 3.9851612889
17 1.5925356350
17.1 2.4374032998
17.2 3.0256489082
17.3 3.3329089405
17.4 3.8693758985
18 2.4374292302
19 0.9772165376
19.1 1.1466335913
19.2 2.2599126538
19.3 4.2114245973
20 1.7170160066
20.1 1.7562902288
20.2 2.2515566566
20.3 2.2609123867
20.4 3.4913365287
20.5 4.1730977828
21 1.6936582839
21.1 2.9571191233
21.2 3.7887385779
22 2.4696226232
22.1 3.1626627257
23 1.5414533857
23.1 2.3369736120
24 2.8283136466
25 0.5381704110
25.1 1.6069735331
25.2 1.6358226922
25.3 3.2646870392
25.4 4.0782226040
25.5 4.1560292873
26 0.2412706357
26.1 2.4451737676
26.2 3.5988757887
26.3 4.1822362854
27 3.6955824879
27.1 4.2451434687
28 0.5746519344
28.1 2.7943964268
28.2 4.2108539480
28.3 4.4705521734
29 1.1898884235
29.1 1.7624059319
29.2 2.0210406382
29.3 3.4078777023
30 2.2635366488
30.1 3.5938334477
30.2 3.6138710892
31 4.3988140998
32 1.6745209007
32.1 2.9128167813
32.2 2.9676558380
32.3 4.2099863547
33 0.0093385763
33.1 3.4591242753
34 1.4998774312
34.1 3.8242761395
34.2 3.9072251692
34.3 3.9582124643
35 1.3294299203
35.1 1.5276966314
35.2 4.5025920868
36 0.7123168337
36.1 1.7972493160
36.2 1.8262697803
36.3 4.2840119381
36.4 4.6194464504
37 2.0018732361
37.1 3.6656836793
37.2 3.9663937816
38 0.9826511063
39 0.6921808305
39.1 0.9027792048
39.2 1.3055654289
39.3 1.5412842878
39.4 3.1834997435
39.5 4.1394166439
40 1.1330395646
40.1 2.6940994046
40.2 3.0396614212
40.3 4.6762977762
41 1.9337158254
41.1 3.1956304458
41.2 3.2846923557
41.3 3.3813529415
41.4 3.5482964432
42 0.4859252973
42.1 4.3293134298
43 0.5616614548
43.1 1.0743579536
43.2 2.6131797966
44 0.7662644819
44.1 2.6490291790
44.2 3.3371910988
44.3 4.1154200875
45 0.1957449992
45.1 1.9963831536
46 1.3477755385
46.1 2.8565793915
46.2 4.4160729996
47 0.6012621359
47.1 2.4097121472
47.2 2.9975794035
47.3 3.1829649757
47.4 4.6201055450
48 2.8607365978
48.1 2.9098354396
49 2.7179756400
50 1.1762060679
51 1.4304436720
52 2.1266646020
52.1 3.1000545993
52.2 3.1268477370
52.3 3.5711459327
52.4 4.7983659909
52.5 4.9818264414
53 0.4965799209
53.1 3.5505357443
53.2 4.5790420019
54 1.4034724841
54.1 1.8812377600
54.2 2.5107589352
54.3 2.7848406672
54.4 4.0143877396
55 0.6118522980
55.1 0.7463747414
55.2 2.8201208171
55.3 3.1326431572
55.4 3.2218102901
56 1.2231332215
56.1 2.3573202139
56.2 2.5674936292
56.3 2.9507164378
56.4 3.2272730360
56.5 3.4175522043
57 0.2370331448
57.1 0.2481445030
57.2 1.1405586067
57.3 2.1153886721
58 1.2210099772
58.1 1.6334245703
58.2 1.6791862890
58.3 2.6320121693
58.4 2.8477731440
58.5 3.5715569824
59 1.9023998594
59.1 4.9736620474
60 2.8854503250
61 0.7213630795
61.1 2.3186947661
61.2 2.5077313243
61.3 3.1731073430
61.4 3.6022726283
62 0.5336771999
62.1 0.6987666548
62.2 3.4584309917
62.3 4.8028772371
63 2.8097350930
63.1 3.9653754211
64 4.1191305732
65 0.7076152589
65.1 2.0252246363
65.2 3.1127382827
65.3 3.1969087943
66 3.4943454154
66.1 3.7677437009
66.2 3.9486138616
67 4.1728388879
68 0.1291919907
68.1 1.7809643946
68.2 2.0493205660
68.3 2.9406870750
68.4 4.0406670363
69 4.1451198701
70 0.1992557163
70.1 0.4829774413
71 0.7741605386
71.1 1.4883817220
71.2 4.0758526395
71.3 4.7048238723
71.4 4.7242791823
72 0.9321196121
72.1 1.1799991806
72.2 1.8917567329
72.3 3.4853593935
72.4 3.6884259700
72.5 4.0854155901
73 4.6019889915
74 1.4626806753
75 3.2524286874
76 1.8074807397
76.1 4.2685073183
76.2 4.9688734859
77 0.8459033852
78 0.8231094317
79 0.0583819521
79.1 2.4406372628
79.2 3.2962526032
80 0.8985060186
80.1 1.3434670598
80.2 2.8025900386
81 0.0101324962
81.1 0.9421709494
81.2 3.0542453879
81.3 3.3456630446
82 1.3791010005
82.1 1.7601010622
82.2 2.6233131927
83 0.0537394290
83.1 2.9061570496
83.2 3.1189457362
83.3 4.7663642222
84 2.7254060237
84.1 3.3364784659
85 0.2977756259
85.1 1.7394116637
85.2 2.6846330194
85.3 3.1608762743
85.4 3.9452053758
85.5 4.5092553482
86 0.8476278360
86.1 1.0118629411
86.2 1.2511159515
86.3 2.1870554925
86.4 2.4532935000
86.5 3.8206058508
87 2.7069531474
87.1 3.4462517721
87.2 4.5241666853
88 0.0005892443
88.1 0.7116099866
88.2 2.4952722900
88.3 3.2995816297
89 0.6462086167
90 0.1696030737
90.1 2.5980385230
90.2 2.6651392167
90.3 3.1242690247
91 0.6382618390
91.1 2.6224059286
91.2 4.7772527603
92 0.0737052364
93 0.2788909199
93.1 1.0357759963
93.2 2.4916551099
93.3 2.8876129608
93.4 4.4639474002
94 0.8488043118
94.1 1.0552454425
94.2 1.9445500884
94.3 3.0710722448
94.4 3.0872731935
94.5 4.3805759016
95 2.0199063048
95.1 4.0184444457
95.2 4.5596531732
96 0.0311333477
96.1 0.1324267720
96.2 0.6701303425
96.3 2.1775037691
96.4 2.2246142488
96.5 4.2377650598
97 1.1955102731
97.1 4.9603108643
98 0.2041732438
98.1 0.4309578973
98.2 3.5172611906
99 0.3531786101
99.1 4.6789444226
99.2 4.9927084171
100 1.0691387602
100.1 1.5109344281
100.2 2.1502332564
100.3 3.8745574222
100.4 4.6567608765
$m7a$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m7a$spM_lvlone
center scale
y -11.17337 6.249662
o2 NA NA
x NA NA
o1.L NA NA
o1.Q NA NA
o22 NA NA
o23 NA NA
o24 NA NA
x2 NA NA
x3 NA NA
x4 NA NA
time 2.53394 1.381809
$m7a$mu_reg_norm
[1] 0
$m7a$tau_reg_norm
[1] 1e-04
$m7a$shape_tau_norm
[1] 0.01
$m7a$rate_tau_norm
[1] 0.01
$m7a$mu_reg_ordinal
[1] 0
$m7a$tau_reg_ordinal
[1] 1e-04
$m7a$mu_delta_ordinal
[1] 0
$m7a$tau_delta_ordinal
[1] 1e-04
$m7a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m7a$shape_diag_RinvD
[1] "0.01"
$m7a$rate_diag_RinvD
[1] "0.001"
$m7b
$m7b$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m7b$M_lvlone
y b2 c2 o2 o22 o23 o24 o1.L o1.Q b21
1 -13.0493856 NA NA 1 NA NA NA -7.850462e-17 -0.8164966 NA
1.1 -9.3335901 0 -0.08061445 1 NA NA NA -7.071068e-01 0.4082483 NA
1.2 -22.3469852 NA -0.26523782 3 NA NA NA -7.071068e-01 0.4082483 NA
1.3 -15.0417337 0 -0.30260393 1 NA NA NA -7.850462e-17 -0.8164966 NA
2 -12.0655434 0 -0.33443795 4 NA NA NA 7.071068e-01 0.4082483 NA
2.1 -15.8674476 NA -0.11819800 4 NA NA NA -7.071068e-01 0.4082483 NA
2.2 -7.8800006 NA -0.31532280 2 NA NA NA -7.850462e-17 -0.8164966 NA
3 -11.4820604 0 -0.12920657 2 NA NA NA -7.071068e-01 0.4082483 NA
3.1 -10.5983220 NA NA 4 NA NA NA 7.071068e-01 0.4082483 NA
3.2 -22.4519157 1 NA 2 NA NA NA -7.850462e-17 -0.8164966 NA
4 -1.2697775 1 -0.31177403 4 NA NA NA 7.071068e-01 0.4082483 NA
4.1 -11.1215184 0 -0.23894886 3 NA NA NA -7.850462e-17 -0.8164966 NA
4.2 -3.6134138 0 -0.15533613 NA NA NA NA -7.071068e-01 0.4082483 NA
4.3 -14.5982385 0 -0.14644545 2 NA NA NA -7.850462e-17 -0.8164966 NA
5 -6.8457515 NA -0.28360457 2 NA NA NA -7.850462e-17 -0.8164966 NA
5.1 -7.0551214 0 -0.20135143 4 NA NA NA -7.850462e-17 -0.8164966 NA
5.2 -12.3418980 NA -0.28293375 2 NA NA NA 7.071068e-01 0.4082483 NA
5.3 -9.2366906 NA NA 4 NA NA NA -7.850462e-17 -0.8164966 NA
6 -5.1648211 NA -0.08617066 3 NA NA NA 7.071068e-01 0.4082483 NA
7 -10.0599502 NA -0.22243495 1 NA NA NA 7.071068e-01 0.4082483 NA
7.1 -18.3267285 NA NA NA NA NA NA -7.071068e-01 0.4082483 NA
7.2 -12.5138426 0 NA 4 NA NA NA -7.071068e-01 0.4082483 NA
8 -1.6305331 0 NA 1 NA NA NA -7.850462e-17 -0.8164966 NA
8.1 -9.6520453 0 NA 3 NA NA NA -7.850462e-17 -0.8164966 NA
8.2 -1.5278462 NA NA 1 NA NA NA 7.071068e-01 0.4082483 NA
8.3 -7.4172211 1 -0.35148972 4 NA NA NA 7.071068e-01 0.4082483 NA
8.4 -7.1238609 0 0.03661023 3 NA NA NA -7.850462e-17 -0.8164966 NA
8.5 -8.8706950 1 -0.08424534 3 NA NA NA -7.850462e-17 -0.8164966 NA
9 -0.1634429 0 NA 2 NA NA NA -7.850462e-17 -0.8164966 NA
9.1 -2.6034300 NA -0.43509340 2 NA NA NA -7.850462e-17 -0.8164966 NA
9.2 -6.7272369 NA -0.22527490 4 NA NA NA 7.071068e-01 0.4082483 NA
10 -6.4172202 NA NA 1 NA NA NA -7.850462e-17 -0.8164966 NA
10.1 -11.4834569 0 NA 4 NA NA NA -7.071068e-01 0.4082483 NA
11 -8.7911356 0 -0.08587475 3 NA NA NA -7.850462e-17 -0.8164966 NA
11.1 -19.6645080 0 -0.06157340 1 NA NA NA 7.071068e-01 0.4082483 NA
11.2 -20.2030932 0 -0.12436018 4 NA NA NA 7.071068e-01 0.4082483 NA
11.3 -21.3082176 0 -0.21377934 3 NA NA NA -7.071068e-01 0.4082483 NA
11.4 -14.5802901 0 -0.32208329 3 NA NA NA 7.071068e-01 0.4082483 NA
12 -15.2006287 0 NA 3 NA NA NA -7.071068e-01 0.4082483 NA
13 0.8058816 NA NA NA NA NA NA -7.850462e-17 -0.8164966 NA
13.1 -13.6379208 0 -0.40300449 1 NA NA NA -7.071068e-01 0.4082483 NA
14 -15.3422873 NA -0.28992072 1 NA NA NA 7.071068e-01 0.4082483 NA
14.1 -10.0965208 NA NA 4 NA NA NA 7.071068e-01 0.4082483 NA
14.2 -16.6452027 NA NA 3 NA NA NA -7.850462e-17 -0.8164966 NA
14.3 -15.8389733 NA -0.21979936 4 NA NA NA -7.071068e-01 0.4082483 NA
15 -8.9424594 0 NA 1 NA NA NA 7.071068e-01 0.4082483 NA
15.1 -22.0101983 0 -0.29092263 4 NA NA NA 7.071068e-01 0.4082483 NA
15.2 -7.3975599 0 -0.19392239 NA NA NA NA -7.850462e-17 -0.8164966 NA
15.3 -10.3567334 0 -0.25718384 2 NA NA NA -7.850462e-17 -0.8164966 NA
16 -1.9691302 1 -0.45041108 NA NA NA NA 7.071068e-01 0.4082483 NA
16.1 -9.9308357 NA -0.07599066 NA NA NA NA -7.071068e-01 0.4082483 NA
16.2 -6.9626923 NA -0.32385667 1 NA NA NA 7.071068e-01 0.4082483 NA
16.3 -3.2862557 0 -0.38326110 3 NA NA NA -7.071068e-01 0.4082483 NA
16.4 -3.3972355 0 -0.22845856 3 NA NA NA -7.071068e-01 0.4082483 NA
16.5 -11.5767835 NA -0.25497157 1 NA NA NA -7.850462e-17 -0.8164966 NA
17 -10.5474144 0 NA 3 NA NA NA -7.850462e-17 -0.8164966 NA
17.1 -7.6215009 0 -0.22105143 2 NA NA NA -7.850462e-17 -0.8164966 NA
17.2 -16.5386939 0 NA 2 NA NA NA 7.071068e-01 0.4082483 NA
17.3 -20.0004774 NA NA 3 NA NA NA -7.850462e-17 -0.8164966 NA
17.4 -18.8505475 0 -0.15098046 1 NA NA NA 7.071068e-01 0.4082483 NA
18 -19.7302351 0 -0.09870041 4 NA NA NA -7.850462e-17 -0.8164966 NA
19 -14.6177568 NA -0.26680239 1 NA NA NA 7.071068e-01 0.4082483 NA
19.1 -17.8043866 NA -0.15815241 NA NA NA NA -7.850462e-17 -0.8164966 NA
19.2 -15.1641705 0 -0.14717437 NA NA NA NA -7.850462e-17 -0.8164966 NA
19.3 -16.6898418 1 -0.21271374 2 NA NA NA -7.850462e-17 -0.8164966 NA
20 -12.9059229 NA -0.22087628 1 NA NA NA -7.071068e-01 0.4082483 NA
20.1 -16.8191201 0 NA 4 NA NA NA -7.850462e-17 -0.8164966 NA
20.2 -6.1010131 1 -0.30127439 3 NA NA NA -7.850462e-17 -0.8164966 NA
20.3 -7.9415371 0 -0.11782590 3 NA NA NA -7.071068e-01 0.4082483 NA
20.4 -9.3904458 0 -0.19857957 1 NA NA NA -7.071068e-01 0.4082483 NA
20.5 -13.3504189 0 -0.24338208 3 NA NA NA -7.850462e-17 -0.8164966 NA
21 -7.6974718 0 -0.31407992 3 NA NA NA -7.850462e-17 -0.8164966 NA
21.1 -11.9335526 0 -0.12424941 1 NA NA NA -7.850462e-17 -0.8164966 NA
21.2 -12.7064929 NA -0.27672716 2 NA NA NA -7.850462e-17 -0.8164966 NA
22 -21.5022909 0 -0.23790593 4 NA NA NA -7.071068e-01 0.4082483 NA
22.1 -12.7745451 0 -0.15996535 NA NA NA NA -7.071068e-01 0.4082483 NA
23 -3.5146508 0 -0.18236682 4 NA NA NA -7.071068e-01 0.4082483 NA
23.1 -4.6724048 NA -0.20823302 NA NA NA NA -7.850462e-17 -0.8164966 NA
24 -2.5619821 0 -0.29026416 3 NA NA NA -7.071068e-01 0.4082483 NA
25 -6.2944970 0 -0.36139273 1 NA NA NA -7.850462e-17 -0.8164966 NA
25.1 -3.8630505 NA -0.19571118 3 NA NA NA -7.071068e-01 0.4082483 NA
25.2 -14.4205140 1 -0.21379355 2 NA NA NA -7.071068e-01 0.4082483 NA
25.3 -19.6735037 0 -0.33876012 1 NA NA NA -7.071068e-01 0.4082483 NA
25.4 -9.0288933 0 NA 1 NA NA NA -7.850462e-17 -0.8164966 NA
25.5 -9.0509738 NA -0.04068446 NA NA NA NA -7.850462e-17 -0.8164966 NA
26 -19.7340685 NA -0.16846716 3 NA NA NA -7.850462e-17 -0.8164966 NA
26.1 -14.1692728 0 -0.10440642 4 NA NA NA 7.071068e-01 0.4082483 NA
26.2 -17.2819976 0 -0.26884827 3 NA NA NA -7.850462e-17 -0.8164966 NA
26.3 -24.6265576 0 NA 1 NA NA NA -7.071068e-01 0.4082483 NA
27 -7.3354999 0 -0.19520794 4 NA NA NA -7.850462e-17 -0.8164966 NA
27.1 -11.1488468 0 -0.17622638 4 NA NA NA -7.850462e-17 -0.8164966 NA
28 -11.7996597 NA -0.32164962 1 NA NA NA -7.071068e-01 0.4082483 NA
28.1 -8.2030122 0 -0.27003852 2 NA NA NA -7.071068e-01 0.4082483 NA
28.2 -26.4317815 0 -0.07235801 3 NA NA NA -7.071068e-01 0.4082483 NA
28.3 -18.5016071 0 -0.13462982 3 NA NA NA -7.071068e-01 0.4082483 NA
29 -5.8551395 0 -0.32432030 4 NA NA NA -7.850462e-17 -0.8164966 NA
29.1 -2.0209442 0 -0.27034171 4 NA NA NA -7.850462e-17 -0.8164966 NA
29.2 -5.6368080 0 -0.10197448 3 NA NA NA -7.071068e-01 0.4082483 NA
29.3 -3.8110961 0 -0.27606945 2 NA NA NA -7.850462e-17 -0.8164966 NA
30 -12.7217702 NA -0.06949300 3 NA NA NA -7.850462e-17 -0.8164966 NA
30.1 -17.0170140 0 -0.11511035 4 NA NA NA -7.850462e-17 -0.8164966 NA
30.2 -25.4236089 0 -0.16215882 4 NA NA NA -7.850462e-17 -0.8164966 NA
31 -17.0783921 0 0.05707733 2 NA NA NA -7.850462e-17 -0.8164966 NA
32 -18.4338764 0 -0.18446298 NA NA NA NA 7.071068e-01 0.4082483 NA
32.1 -19.4317212 0 -0.14270013 2 NA NA NA -7.071068e-01 0.4082483 NA
32.2 -19.4738978 NA -0.20530798 4 NA NA NA 7.071068e-01 0.4082483 NA
32.3 -21.4922645 NA -0.14705649 3 NA NA NA 7.071068e-01 0.4082483 NA
33 2.0838099 0 -0.15252819 4 NA NA NA -7.850462e-17 -0.8164966 NA
33.1 -13.3172274 1 NA 4 NA NA NA -7.850462e-17 -0.8164966 NA
34 -10.0296691 NA -0.30378735 NA NA NA NA 7.071068e-01 0.4082483 NA
34.1 -25.9426553 0 -0.11982431 NA NA NA NA -7.850462e-17 -0.8164966 NA
34.2 -18.5688138 NA -0.24278671 NA NA NA NA -7.850462e-17 -0.8164966 NA
34.3 -15.4173859 NA -0.19971833 NA NA NA NA -7.071068e-01 0.4082483 NA
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81 0.5547913 0 -0.18401980 3 NA NA NA 7.071068e-01 0.4082483 NA
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82 -3.0749381 NA -0.08328168 NA NA NA NA 7.071068e-01 0.4082483 NA
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85.2 -9.6433248 0 -0.32509207 3 NA NA NA -7.850462e-17 -0.8164966 NA
85.3 -6.3296340 0 NA 3 NA NA NA -7.850462e-17 -0.8164966 NA
85.4 -7.0405525 0 -0.30730810 NA NA NA NA -7.071068e-01 0.4082483 NA
85.5 -13.6714939 0 NA 2 NA NA NA 7.071068e-01 0.4082483 NA
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87 -10.7027963 NA NA NA NA NA NA -7.071068e-01 0.4082483 NA
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88.2 -2.5095281 0 -0.40311895 2 NA NA NA -7.071068e-01 0.4082483 NA
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89 -11.0459647 0 -0.18527715 3 NA NA NA -7.071068e-01 0.4082483 NA
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90.2 -5.3838521 0 -0.28804531 2 NA NA NA 7.071068e-01 0.4082483 NA
90.3 -4.1636999 NA -0.19180615 4 NA NA NA -7.071068e-01 0.4082483 NA
91 -7.1462503 0 -0.26591197 2 NA NA NA -7.071068e-01 0.4082483 NA
91.1 -12.8374475 0 -0.09153470 NA NA NA NA -7.850462e-17 -0.8164966 NA
91.2 -18.2576707 0 -0.48414390 3 NA NA NA -7.071068e-01 0.4082483 NA
92 -6.4119222 0 NA 2 NA NA NA -7.850462e-17 -0.8164966 NA
93 5.2122168 NA -0.11939966 3 NA NA NA -7.071068e-01 0.4082483 NA
93.1 3.1211725 0 NA 2 NA NA NA 7.071068e-01 0.4082483 NA
93.2 -3.6841177 NA -0.21089379 3 NA NA NA -7.071068e-01 0.4082483 NA
93.3 2.6223542 0 NA 2 NA NA NA 7.071068e-01 0.4082483 NA
93.4 -11.1877696 0 -0.23618836 4 NA NA NA -7.071068e-01 0.4082483 NA
94 -6.9602492 NA NA NA NA NA NA -7.850462e-17 -0.8164966 NA
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94.2 -4.3498045 0 -0.36713471 NA NA NA NA -7.071068e-01 0.4082483 NA
94.3 -11.6340088 NA -0.13806763 3 NA NA NA 7.071068e-01 0.4082483 NA
94.4 -12.9357964 0 -0.42353804 4 NA NA NA -7.850462e-17 -0.8164966 NA
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95 -12.8849309 0 -0.24149687 NA NA NA NA 7.071068e-01 0.4082483 NA
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96 -4.9139832 0 -0.16780948 3 NA NA NA -7.071068e-01 0.4082483 NA
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96.2 -9.6555492 0 -0.20395970 4 NA NA NA -7.071068e-01 0.4082483 NA
96.3 -11.8690793 NA -0.06221501 3 NA NA NA -7.071068e-01 0.4082483 NA
96.4 -11.0224373 1 -0.14801097 NA NA NA NA -7.850462e-17 -0.8164966 NA
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97 -9.8540471 0 -0.34484656 2 NA NA NA 7.071068e-01 0.4082483 NA
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98 -11.9651231 0 -0.36913003 2 NA NA NA 7.071068e-01 0.4082483 NA
98.1 -2.6515128 0 NA 1 NA NA NA -7.071068e-01 0.4082483 NA
98.2 -12.2606382 1 -0.17154225 3 NA NA NA -7.071068e-01 0.4082483 NA
99 -11.4720500 0 -0.24753132 NA NA NA NA -7.850462e-17 -0.8164966 NA
99.1 -14.0596866 0 -0.27947829 NA NA NA NA -7.850462e-17 -0.8164966 NA
99.2 -17.3939469 0 -0.09033035 4 NA NA NA -7.071068e-01 0.4082483 NA
100 1.1005874 NA -0.17326698 1 NA NA NA -7.071068e-01 0.4082483 NA
100.1 -3.8226248 NA NA NA NA NA NA -7.850462e-17 -0.8164966 NA
100.2 -0.9123182 0 -0.12072016 1 NA NA NA -7.850462e-17 -0.8164966 NA
100.3 -15.8389474 NA -0.27657520 4 NA NA NA -7.071068e-01 0.4082483 NA
100.4 -12.8093826 0 -0.14631556 1 NA NA NA -7.071068e-01 0.4082483 NA
$m7b$spM_lvlone
center scale
y -11.1733710 6.2496619
b2 NA NA
c2 -0.2237158 0.1059527
o2 NA NA
o22 NA NA
o23 NA NA
o24 NA NA
o1.L NA NA
o1.Q NA NA
b21 NA NA
$m7b$mu_reg_norm
[1] 0
$m7b$tau_reg_norm
[1] 1e-04
$m7b$shape_tau_norm
[1] 0.01
$m7b$rate_tau_norm
[1] 0.01
$m7b$mu_reg_binom
[1] 0
$m7b$tau_reg_binom
[1] 1e-04
$m7b$mu_delta_ordinal
[1] 0
$m7b$tau_delta_ordinal
[1] 1e-04
$m7b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m7b$shape_diag_RinvD
[1] "0.01"
$m7b$rate_diag_RinvD
[1] "0.001"
jagsmodel remains the same
Code
lapply(models, "[[", "jagsmodel")
Output
$m0a
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- 0
}
# Priors for the model for o1
delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
}
$m0b
model {
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- 0
}
# Priors for the model for o2
delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
}
$m1a
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1]
}
# Priors for the model for o1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
}
$m1b
model {
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1]
}
# Priors for the model for o2
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
}
$m1c
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] +
beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- 0
}
# Priors for the model for o1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
}
$m1d
model {
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1] +
beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- 0
}
# Priors for the model for o2
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
}
$m2a
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1]
}
# Priors for the model for o1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_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 {
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1]
}
# Priors for the model for o2
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
# Normal 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 {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] +
beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- 0
}
# Priors for the model for o1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_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 {
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1] +
beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- 0
}
# Priors for the model for o2
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
# Normal mixed effects model for 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] + beta[4] * M_lvlone[i, 5]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for c1
for (k in 1:4) {
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])
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0)
M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0)
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 4, 1, 0)
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- 0
}
# Priors for the model for o2
delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
}
$m4a
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] + beta[6] * M_lvlone[i, 3] +
beta[7] * M_lvlone[i, 4] + beta[8] * M_lvlone[i, 5] +
beta[9] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] +
beta[10] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] +
beta[11] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 4] * beta[1] + M_id[ii, 5] * beta[2] +
M_id[ii, 6] * beta[3] +
(M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * beta[4] +
(M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * beta[5]
}
# Priors for the model for o1
for (k in 1:11) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0)
M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0)
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 4, 1, 0)
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] +
M_id[ii, 6] * alpha[3] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[4] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5]
}
# Priors for the model for o2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
# 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[6] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[7] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[8]
log(phi_M2[ii, 3]) <- M_id[ii, 3] * alpha[9] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[10] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[11]
log(phi_M2[ii, 4]) <- M_id[ii, 3] * alpha[12] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[13] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[14]
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 6:14) {
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[15] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[16]
M_id[ii, 7] <- abs(M_id[ii, 9] - M_id[ii, 2])
}
# Priors for the model for C2
for (k in 15:16) {
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, 6] <- M_lvlone[i, 3] * M_id[group_id[i], 7]
M_lvlone[i, 7] <- M_lvlone[i, 4] * M_id[group_id[i], 7]
M_lvlone[i, 8] <- M_lvlone[i, 5] * M_id[group_id[i], 7]
}
}
$m4b
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] +
beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[1] +
(M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * beta[2]
}
# Priors for the model for o1
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 2, 1, 0)
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 3, 1, 0)
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 4, 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_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- M_id[ii, 5] * alpha[1] + M_id[ii, 6] * alpha[2] +
M_id[ii, 7] * alpha[3] +
(M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[4] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[5]
}
# Priors for the model for o2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
# Normal 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[6] + M_id[ii, 5] * alpha[7] + M_id[ii, 6] * alpha[8] +
M_id[ii, 7] * alpha[9] +
(M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[10]
M_id[ii, 3] <- abs(M_id[ii, 8] - M_id[ii, 1])
}
# Priors for the model for C2
for (k in 6:10) {
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 {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] +
b_o1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_o1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
b_o1_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:4] ~ dmnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[1] +
M_id[ii, 4] * beta[2]
mu_b_o1_id[ii, 2] <- beta[4]
mu_b_o1_id[ii, 3] <- beta[3]
mu_b_o1_id[ii, 4] <- 0
}
# Priors for the model for o1
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
for (k in 1:4) {
RinvD_o1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_o1_id[1:4, 1:4] ~ dwish(RinvD_o1_id[ , ], KinvD_o1_id)
D_o1_id[1:4, 1:4] <- inverse(invD_o1_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 {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] +
b_o1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[4] * M_lvlone[i, 5] +
beta[5] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] +
beta[7] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:2] ~ dmnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1]
mu_b_o1_id[ii, 2] <- beta[2] + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[6]
}
# Priors for the model for o1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
for (k in 1:2) {
RinvD_o1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_o1_id[1:2, 1:2] ~ dwish(RinvD_o1_id[ , ], KinvD_o1_id)
D_o1_id[1:2, 1:2] <- inverse(invD_o1_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 {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1]
}
# Priors for the model for o1
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal_ridge_beta[k])
tau_reg_ordinal_ridge_beta[k] ~ dgamma(0.01, 0.01)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
}
$m5a
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
eta_o1_1[i] <- beta[3] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] +
beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] +
beta[7] * M_lvlone[i, 3]
eta_o1_2[i] <- beta[5] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] +
beta[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] +
beta[8] * M_lvlone[i, 3]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2]
}
# Priors for the model for o1
for (k in 1:8) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
logit(mu_b2[i]) <- b_b2_id[group_id[i], 1]
M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
for (ii in 1:100) {
b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ])
mu_b_b2_id[ii, 1] <- M_id[ii, 3] * alpha[1] +
(M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] +
M_id[ii, 5] * alpha[4] + M_id[ii, 6] * alpha[5]
}
# Priors for the model for b2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 3] * alpha[6] +
(M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[7] +
M_id[ii, 5] * alpha[8] + M_id[ii, 6] * alpha[9]
}
# Priors for the model for C2
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 2] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[10]
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 5] <- ifelse(M_id[ii, 2] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 2] == 3, 1, 0)
}
# Priors for the model for O2
for (k in 10:10) {
alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
}
$m5b
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] +
beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] +
M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] +
M_id[ii, 9] * beta[5]
}
# Priors for the model for o1
for (k in 1:10) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1]
}
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, 4] * alpha[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] +
M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] +
M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] +
M_id[ii, 9] * alpha[7]
}
# Priors for the model for c1
for (k in 1:7) {
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])
# 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, 4] * alpha[8] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] +
M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11]
log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] +
M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15]
log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] +
M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19]
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 8:19) {
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, 4] * alpha[20] + M_id[ii, 8] * alpha[21] +
M_id[ii, 9] * alpha[22]
}
# Priors for the model for C2
for (k in 20:22) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 3] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- 0
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0)
M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0)
}
# Priors for the model for O2
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2]
}
}
$m5c
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
eta_o1_1[i] <- beta[6] * (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]
eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] +
M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] +
M_id[ii, 9] * beta[5]
}
# Priors for the model for o1
for (k in 1:11) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1]
}
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, 4] * alpha[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] +
M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] +
M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] +
M_id[ii, 9] * alpha[7]
}
# Priors for the model for c1
for (k in 1:7) {
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])
# 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, 4] * alpha[8] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] +
M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11]
log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] +
M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15]
log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] +
M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19]
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 8:19) {
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, 4] * alpha[20] + M_id[ii, 8] * alpha[21] +
M_id[ii, 9] * alpha[22]
}
# Priors for the model for C2
for (k in 20:22) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 3] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- 0
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0)
M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0)
}
# Priors for the model for O2
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2]
}
}
$m5d
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
eta_o1_1[i] <- beta[9] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[11] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
eta_o1_2[i] <- beta[10] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[12] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] +
M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] +
M_id[ii, 9] * beta[5] +
(M_id[ii, 10] - spM_id[10, 1])/spM_id[10, 2] * beta[6] +
(M_id[ii, 11] - spM_id[11, 1])/spM_id[11, 2] * beta[7] +
(M_id[ii, 12] - spM_id[12, 1])/spM_id[12, 2] * beta[8]
}
# Priors for the model for o1
for (k in 1:12) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1]
}
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, 4] * alpha[1] + M_id[ii, 5] * alpha[2] +
M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] +
M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7]
}
# Priors for the model for c1
for (k in 1:7) {
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])
# 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, 4] * alpha[8] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] +
M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11]
log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] +
M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15]
log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] +
M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19]
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 8:19) {
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, 4] * alpha[20] + M_id[ii, 8] * alpha[21] +
M_id[ii, 9] * alpha[22]
}
# Priors for the model for C2
for (k in 20:22) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 3] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- 0
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0)
M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0)
}
# Priors for the model for O2
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
# Re-calculate interaction terms
for (ii in 1:100) {
M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2]
M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2]
M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2]
}
}
$m5e
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
eta_o1_1[i] <- beta[1] * M_id[group_id[i], 5] + beta[2] * M_id[group_id[i], 6] +
beta[3] * M_id[group_id[i], 7] +
beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[5] * M_id[group_id[i], 8] + beta[6] * M_id[group_id[i], 9] +
beta[7] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] +
beta[8] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] +
beta[9] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] +
beta[19] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
eta_o1_2[i] <- beta[10] * M_id[group_id[i], 5] + beta[11] * M_id[group_id[i], 6] +
beta[12] * M_id[group_id[i], 7] +
beta[13] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[14] * M_id[group_id[i], 8] + beta[15] * M_id[group_id[i], 9] +
beta[16] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] +
beta[17] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] +
beta[18] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] +
beta[20] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- 0
}
# Priors for the model for o1
for (k in 1:20) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1]
}
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, 4] * alpha[1] + M_id[ii, 5] * alpha[2] +
M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] +
M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7]
}
# Priors for the model for c1
for (k in 1:7) {
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])
# 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, 4] * alpha[8] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] +
M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11]
log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] +
M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15]
log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] +
M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19]
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 8:19) {
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, 4] * alpha[20] + M_id[ii, 8] * alpha[21] +
M_id[ii, 9] * alpha[22]
}
# Priors for the model for C2
for (k in 20:22) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 3] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- 0
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0)
M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0)
}
# Priors for the model for O2
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
# Re-calculate interaction terms
for (ii in 1:100) {
M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2]
M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2]
M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2]
}
}
$m6a
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
eta_o1_1[i] <- beta[3] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] +
beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] +
beta[7] * M_lvlone[i, 3]
eta_o1_2[i] <- beta[5] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] +
beta[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] +
beta[8] * M_lvlone[i, 3]
p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1]))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1]))
p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2])))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2]
}
# Priors for the model for o1
for (k in 1:8) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
logit(mu_b2[i]) <- b_b2_id[group_id[i], 1]
M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
for (ii in 1:100) {
b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ])
mu_b_b2_id[ii, 1] <- M_id[ii, 3] * alpha[1] +
(M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] +
M_id[ii, 5] * alpha[4] + M_id[ii, 6] * alpha[5]
}
# Priors for the model for b2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 3] * alpha[6] +
(M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[7] +
M_id[ii, 5] * alpha[8] + M_id[ii, 6] * alpha[9]
}
# Priors for the model for C2
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 2] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[10]
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 5] <- ifelse(M_id[ii, 2] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 2] == 3, 1, 0)
}
# Priors for the model for O2
for (k in 10:10) {
alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
}
$m6b
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] +
beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1]))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1]))
p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2])))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] +
M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] +
M_id[ii, 9] * beta[5]
}
# Priors for the model for o1
for (k in 1:10) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1]
}
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, 4] * alpha[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] +
M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] +
M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] +
M_id[ii, 9] * alpha[7]
}
# Priors for the model for c1
for (k in 1:7) {
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])
# 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, 4] * alpha[8] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] +
M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11]
log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] +
M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15]
log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] +
M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19]
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 8:19) {
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, 4] * alpha[20] + M_id[ii, 8] * alpha[21] +
M_id[ii, 9] * alpha[22]
}
# Priors for the model for C2
for (k in 20:22) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 3] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- 0
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0)
M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0)
}
# Priors for the model for O2
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2]
}
}
$m6c
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
eta_o1_1[i] <- beta[6] * (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]
eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1]))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1]))
p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2])))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] +
M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] +
M_id[ii, 9] * beta[5]
}
# Priors for the model for o1
for (k in 1:11) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1]
}
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, 4] * alpha[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] +
M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] +
M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] +
M_id[ii, 9] * alpha[7]
}
# Priors for the model for c1
for (k in 1:7) {
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])
# 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, 4] * alpha[8] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] +
M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11]
log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] +
M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15]
log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] +
M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19]
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 8:19) {
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, 4] * alpha[20] + M_id[ii, 8] * alpha[21] +
M_id[ii, 9] * alpha[22]
}
# Priors for the model for C2
for (k in 20:22) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 3] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- 0
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0)
M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0)
}
# Priors for the model for O2
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2]
}
}
$m6d
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
eta_o1_1[i] <- beta[9] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[11] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
eta_o1_2[i] <- beta[10] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[12] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1]))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1]))
p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2])))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] +
M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] +
M_id[ii, 9] * beta[5] +
(M_id[ii, 10] - spM_id[10, 1])/spM_id[10, 2] * beta[6] +
(M_id[ii, 11] - spM_id[11, 1])/spM_id[11, 2] * beta[7] +
(M_id[ii, 12] - spM_id[12, 1])/spM_id[12, 2] * beta[8]
}
# Priors for the model for o1
for (k in 1:12) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1]
}
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, 4] * alpha[1] + M_id[ii, 5] * alpha[2] +
M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] +
M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7]
}
# Priors for the model for c1
for (k in 1:7) {
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])
# 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, 4] * alpha[8] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] +
M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11]
log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] +
M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15]
log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] +
M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19]
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 8:19) {
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, 4] * alpha[20] + M_id[ii, 8] * alpha[21] +
M_id[ii, 9] * alpha[22]
}
# Priors for the model for C2
for (k in 20:22) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 3] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- 0
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0)
M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0)
}
# Priors for the model for O2
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
# Re-calculate interaction terms
for (ii in 1:100) {
M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2]
M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2]
M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2]
}
}
$m6e
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
eta_o1_1[i] <- beta[1] * M_id[group_id[i], 5] + beta[2] * M_id[group_id[i], 6] +
beta[3] * M_id[group_id[i], 7] +
beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[5] * M_id[group_id[i], 8] + beta[6] * M_id[group_id[i], 9] +
beta[7] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] +
beta[8] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] +
beta[9] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] +
beta[19] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
eta_o1_2[i] <- beta[10] * M_id[group_id[i], 5] + beta[11] * M_id[group_id[i], 6] +
beta[12] * M_id[group_id[i], 7] +
beta[13] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[14] * M_id[group_id[i], 8] + beta[15] * M_id[group_id[i], 9] +
beta[16] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] +
beta[17] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] +
beta[18] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] +
beta[20] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1]))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1]))
p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2])))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- 0
}
# Priors for the model for o1
for (k in 1:20) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1]
}
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, 4] * alpha[1] + M_id[ii, 5] * alpha[2] +
M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] +
M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7]
}
# Priors for the model for c1
for (k in 1:7) {
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])
# 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, 4] * alpha[8] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] +
M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11]
log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] +
M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15]
log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] +
M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19]
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 8:19) {
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, 4] * alpha[20] + M_id[ii, 8] * alpha[21] +
M_id[ii, 9] * alpha[22]
}
# Priors for the model for C2
for (k in 20:22) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 3] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- 0
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0)
M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0)
}
# Priors for the model for O2
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
# Re-calculate interaction terms
for (ii in 1:100) {
M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2]
M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2]
M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2]
}
}
$m7a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] + beta[3] * M_lvlone[i, 4] +
beta[4] * M_lvlone[i, 5] + beta[5] * M_lvlone[i, 6] +
beta[6] * M_lvlone[i, 7] + beta[7] * M_lvlone[i, 8] +
beta[8] * M_lvlone[i, 9] + beta[9] * M_lvlone[i, 10] +
beta[10] * M_lvlone[i, 11] +
beta[11] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2]
}
# Priors for the model for y
for (k in 1:11) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1] + alpha[2] * M_lvlone[i, 9] +
alpha[3] * M_lvlone[i, 10] + alpha[4] * M_lvlone[i, 11] +
alpha[5] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 2, 1, 0)
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 3, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 2] == 4, 1, 0)
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[1]
}
# Priors for the model for o2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
# Cumulative logit mixed effects model for x ------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dcat(p_x[i, 1:4])
eta_x[i] <- b_x_id[group_id[i], 1] +
alpha[7] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2]
p_x[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_x[i, 2:4])))
p_x[i, 2] <- max(1e-10, min(1-1e-10, psum_x[i, 1] - psum_x[i, 2]))
p_x[i, 3] <- max(1e-10, min(1-1e-10, psum_x[i, 2] - psum_x[i, 3]))
p_x[i, 4] <- max(1e-10, min(1-1e-10, psum_x[i, 3]))
logit(psum_x[i, 1]) <- gamma_x[1] + eta_x[i]
logit(psum_x[i, 2]) <- gamma_x[2] + eta_x[i]
logit(psum_x[i, 3]) <- gamma_x[3] + eta_x[i]
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 10] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
for (ii in 1:100) {
b_x_id[ii, 1:1] ~ dnorm(mu_b_x_id[ii, ], invD_x_id[ , ])
mu_b_x_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[6]
}
# Priors for the model for x
for (k in 6:7) {
alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_x[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_x[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_x[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_x[2] <- gamma_x[1] - exp(delta_x[1])
gamma_x[3] <- gamma_x[2] - exp(delta_x[2])
invD_x_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_x_id[1, 1] <- 1 / (invD_x_id[1, 1])
}
$m7b
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] + beta[2] * M_lvlone[i, 5] +
beta[3] * M_lvlone[i, 6] + beta[4] * M_lvlone[i, 7] +
beta[5] * M_lvlone[i, 8] + beta[6] * M_lvlone[i, 9] +
beta[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[8] * M_lvlone[i, 10]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for y
for (k in 1:8) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + alpha[2] * M_lvlone[i, 5] +
alpha[3] * M_lvlone[i, 6] + alpha[4] * M_lvlone[i, 7] +
alpha[5] * M_lvlone[i, 8] + alpha[6] * M_lvlone[i, 9] +
alpha[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
M_lvlone[i, 10] <- 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]
}
# Priors for the model for b2
for (k in 1:7) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] + alpha[9] * M_lvlone[i, 5] +
alpha[10] * M_lvlone[i, 6] + alpha[11] * M_lvlone[i, 7] +
alpha[12] * M_lvlone[i, 8] + alpha[13] * M_lvlone[i, 9]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8]
}
# Priors for the model for c2
for (k in 8:13) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- 0
}
# Priors for the model for o2
delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
}
GRcrit and MCerror give same result
Code
lapply(models0, GR_crit, multivariate = FALSE)
Output
$m0a
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m0b
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o2[1] NaN NaN
gamma_o2[2] NaN NaN
gamma_o2[3] NaN NaN
D_o2_id[1,1] NaN NaN
$m1a
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
C1 NaN NaN
D_o1_id[1,1] NaN NaN
$m1b
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o2[1] NaN NaN
gamma_o2[2] NaN NaN
gamma_o2[3] NaN NaN
C1 NaN NaN
D_o2_id[1,1] NaN NaN
$m1c
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
c1 NaN NaN
D_o1_id[1,1] NaN NaN
$m1d
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o2[1] NaN NaN
gamma_o2[2] NaN NaN
gamma_o2[3] NaN NaN
c1 NaN NaN
D_o2_id[1,1] NaN NaN
$m2a
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
C2 NaN NaN
D_o1_id[1,1] NaN NaN
$m2b
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o2[1] NaN NaN
gamma_o2[2] NaN NaN
gamma_o2[3] NaN NaN
C2 NaN NaN
D_o2_id[1,1] NaN NaN
$m2c
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
c2 NaN NaN
D_o1_id[1,1] NaN NaN
$m2d
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o2[1] NaN NaN
gamma_o2[2] NaN NaN
gamma_o2[3] NaN NaN
c2 NaN NaN
D_o2_id[1,1] NaN NaN
$m3a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
o1.L NaN NaN
o1.Q 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
o22 NaN NaN
o23 NaN NaN
o24 NaN NaN
sigma_c1 NaN NaN
D_c1_id[1,1] NaN NaN
$m4a
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
abs(C1 - C2) NaN NaN
log(C1) NaN NaN
o22 NaN NaN
o23 NaN NaN
o24 NaN NaN
o22:abs(C1 - C2) NaN NaN
o23:abs(C1 - C2) NaN NaN
o24:abs(C1 - C2) NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m4b
Potential scale reduction factors:
Point est.
abs(C1 - C2) NaN
log(C1) NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
gamma_o1[1] NaN
gamma_o1[2] NaN
D_o1_id[1,1] NaN
Upper C.I.
abs(C1 - C2) NaN
log(C1) NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
gamma_o1[1] NaN
gamma_o1[2] NaN
D_o1_id[1,1] NaN
$m4c
Potential scale reduction factors:
Point est. Upper C.I.
C1 NaN NaN
B21 NaN NaN
time NaN NaN
c1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
D_o1_id[1,2] NaN NaN
D_o1_id[2,2] NaN NaN
D_o1_id[1,3] NaN NaN
D_o1_id[2,3] NaN NaN
D_o1_id[3,3] NaN NaN
D_o1_id[1,4] NaN NaN
D_o1_id[2,4] NaN NaN
D_o1_id[3,4] NaN NaN
D_o1_id[4,4] NaN NaN
$m4d
Potential scale reduction factors:
Point est. Upper C.I.
C1 NaN NaN
time NaN NaN
I(time^2) NaN NaN
b21 NaN NaN
c1 NaN NaN
C1:time NaN NaN
b21:c1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
D_o1_id[1,2] NaN NaN
D_o1_id[2,2] NaN NaN
$m4e
Potential scale reduction factors:
Point est. Upper C.I.
C1 NaN NaN
log(time) NaN NaN
I(time^2) NaN NaN
p1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m5a
Potential scale reduction factors:
Point est. Upper C.I.
O22 NaN NaN
O23 NaN NaN
o12: C1 NaN NaN
o12: C2 NaN NaN
o13: C1 NaN NaN
o13: C2 NaN NaN
o12: b21 NaN NaN
o13: b21 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m5b
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
o13: C2 NaN NaN
c1:C2 NaN NaN
o12: C2 NaN NaN
o12: c1 NaN NaN
o13: c1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m5c
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
o12: C2 NaN NaN
o13: C2 NaN NaN
o12: c1 NaN NaN
o12: c1:C2 NaN NaN
o13: c1 NaN NaN
o13: c1:C2 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m5d
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
M22:C2 NaN NaN
M23:C2 NaN NaN
M24:C2 NaN NaN
o12: C2 NaN NaN
o13: C2 NaN NaN
o12: c1 NaN NaN
o13: c1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m5e
Potential scale reduction factors:
Point est. Upper C.I.
o12: M22 NaN NaN
o12: M23 NaN NaN
o12: M24 NaN NaN
o12: C2 NaN NaN
o12: O22 NaN NaN
o12: O23 NaN NaN
o12: M22:C2 NaN NaN
o12: M23:C2 NaN NaN
o12: M24:C2 NaN NaN
o13: M22 NaN NaN
o13: M23 NaN NaN
o13: M24 NaN NaN
o13: C2 NaN NaN
o13: O22 NaN NaN
o13: O23 NaN NaN
o13: M22:C2 NaN NaN
o13: M23:C2 NaN NaN
o13: M24:C2 NaN NaN
o12: c1 NaN NaN
o13: c1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m6a
Potential scale reduction factors:
Point est. Upper C.I.
O22 NaN NaN
O23 NaN NaN
o12: C1 NaN NaN
o12: C2 NaN NaN
o13: C1 NaN NaN
o13: C2 NaN NaN
o12: b21 NaN NaN
o13: b21 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m6b
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
o13: C2 NaN NaN
c1:C2 NaN NaN
o12: C2 NaN NaN
o12: c1 NaN NaN
o13: c1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m6c
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
o12: C2 NaN NaN
o13: C2 NaN NaN
o12: c1 NaN NaN
o12: c1:C2 NaN NaN
o13: c1 NaN NaN
o13: c1:C2 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m6d
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
M22:C2 NaN NaN
M23:C2 NaN NaN
M24:C2 NaN NaN
o12: C2 NaN NaN
o13: C2 NaN NaN
o12: c1 NaN NaN
o13: c1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m6e
Potential scale reduction factors:
Point est. Upper C.I.
o12: M22 NaN NaN
o12: M23 NaN NaN
o12: M24 NaN NaN
o12: C2 NaN NaN
o12: O22 NaN NaN
o12: O23 NaN NaN
o12: M22:C2 NaN NaN
o12: M23:C2 NaN NaN
o12: M24:C2 NaN NaN
o13: M22 NaN NaN
o13: M23 NaN NaN
o13: M24 NaN NaN
o13: C2 NaN NaN
o13: O22 NaN NaN
o13: O23 NaN NaN
o13: M22:C2 NaN NaN
o13: M23:C2 NaN NaN
o13: M24:C2 NaN NaN
o12: c1 NaN NaN
o13: c1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m7a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
o1.L NaN NaN
o1.Q NaN NaN
o22 NaN NaN
o23 NaN NaN
o24 NaN NaN
x2 NaN NaN
x3 NaN NaN
x4 NaN NaN
time NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m7b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
o22 NaN NaN
o23 NaN NaN
o24 NaN NaN
o1.L NaN NaN
o1.Q NaN NaN
c2 NaN NaN
b21 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
Code
lapply(models0, MC_error)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a
est MCSE SD MCSE/SD
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m0b
est MCSE SD MCSE/SD
gamma_o2[1] 0 0 0 NaN
gamma_o2[2] 0 0 0 NaN
gamma_o2[3] 0 0 0 NaN
D_o2_id[1,1] 0 0 0 NaN
$m1a
est MCSE SD MCSE/SD
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
C1 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m1b
est MCSE SD MCSE/SD
gamma_o2[1] 0 0 0 NaN
gamma_o2[2] 0 0 0 NaN
gamma_o2[3] 0 0 0 NaN
C1 0 0 0 NaN
D_o2_id[1,1] 0 0 0 NaN
$m1c
est MCSE SD MCSE/SD
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
c1 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m1d
est MCSE SD MCSE/SD
gamma_o2[1] 0 0 0 NaN
gamma_o2[2] 0 0 0 NaN
gamma_o2[3] 0 0 0 NaN
c1 0 0 0 NaN
D_o2_id[1,1] 0 0 0 NaN
$m2a
est MCSE SD MCSE/SD
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
C2 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m2b
est MCSE SD MCSE/SD
gamma_o2[1] 0 0 0 NaN
gamma_o2[2] 0 0 0 NaN
gamma_o2[3] 0 0 0 NaN
C2 0 0 0 NaN
D_o2_id[1,1] 0 0 0 NaN
$m2c
est MCSE SD MCSE/SD
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
c2 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m2d
est MCSE SD MCSE/SD
gamma_o2[1] 0 0 0 NaN
gamma_o2[2] 0 0 0 NaN
gamma_o2[3] 0 0 0 NaN
c2 0 0 0 NaN
D_o2_id[1,1] 0 0 0 NaN
$m3a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
o1.L 0 0 0 NaN
o1.Q 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
o22 0 0 0 NaN
o23 0 0 0 NaN
o24 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
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
abs(C1 - C2) 0 0 0 NaN
log(C1) 0 0 0 NaN
o22 0 0 0 NaN
o23 0 0 0 NaN
o24 0 0 0 NaN
o22:abs(C1 - C2) 0 0 0 NaN
o23:abs(C1 - C2) 0 0 0 NaN
o24:abs(C1 - C2) 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m4b
est MCSE SD MCSE/SD
abs(C1 - C2) 0 0 0 NaN
log(C1) 0 0 0 NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 0 0 NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m4c
est MCSE SD MCSE/SD
C1 0 0 0 NaN
B21 0 0 0 NaN
time 0 0 0 NaN
c1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
D_o1_id[1,2] 0 0 0 NaN
D_o1_id[2,2] 0 0 0 NaN
D_o1_id[1,3] 0 0 0 NaN
D_o1_id[2,3] 0 0 0 NaN
D_o1_id[3,3] 0 0 0 NaN
D_o1_id[1,4] 0 0 0 NaN
D_o1_id[2,4] 0 0 0 NaN
D_o1_id[3,4] 0 0 0 NaN
D_o1_id[4,4] 0 0 0 NaN
$m4d
est MCSE SD MCSE/SD
C1 0 0 0 NaN
time 0 0 0 NaN
I(time^2) 0 0 0 NaN
b21 0 0 0 NaN
c1 0 0 0 NaN
C1:time 0 0 0 NaN
b21:c1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
D_o1_id[1,2] 0 0 0 NaN
D_o1_id[2,2] 0 0 0 NaN
$m4e
est MCSE SD MCSE/SD
C1 0 0 0 NaN
log(time) 0 0 0 NaN
I(time^2) 0 0 0 NaN
p1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m5a
est MCSE SD MCSE/SD
O22 0 0 0 NaN
O23 0 0 0 NaN
o12: C1 0 0 0 NaN
o12: C2 0 0 0 NaN
o13: C1 0 0 0 NaN
o13: C2 0 0 0 NaN
o12: b21 0 0 0 NaN
o13: b21 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m5b
est MCSE SD MCSE/SD
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
O22 0 0 0 NaN
O23 0 0 0 NaN
o13: C2 0 0 0 NaN
c1:C2 0 0 0 NaN
o12: C2 0 0 0 NaN
o12: c1 0 0 0 NaN
o13: c1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m5c
est MCSE SD MCSE/SD
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
O22 0 0 0 NaN
O23 0 0 0 NaN
o12: C2 0 0 0 NaN
o13: C2 0 0 0 NaN
o12: c1 0 0 0 NaN
o12: c1:C2 0 0 0 NaN
o13: c1 0 0 0 NaN
o13: c1:C2 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m5d
est MCSE SD MCSE/SD
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
O22 0 0 0 NaN
O23 0 0 0 NaN
M22:C2 0 0 0 NaN
M23:C2 0 0 0 NaN
M24:C2 0 0 0 NaN
o12: C2 0 0 0 NaN
o13: C2 0 0 0 NaN
o12: c1 0 0 0 NaN
o13: c1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m5e
est MCSE SD MCSE/SD
o12: M22 0 0 0 NaN
o12: M23 0 0 0 NaN
o12: M24 0 0 0 NaN
o12: C2 0 0 0 NaN
o12: O22 0 0 0 NaN
o12: O23 0 0 0 NaN
o12: M22:C2 0 0 0 NaN
o12: M23:C2 0 0 0 NaN
o12: M24:C2 0 0 0 NaN
o13: M22 0 0 0 NaN
o13: M23 0 0 0 NaN
o13: M24 0 0 0 NaN
o13: C2 0 0 0 NaN
o13: O22 0 0 0 NaN
o13: O23 0 0 0 NaN
o13: M22:C2 0 0 0 NaN
o13: M23:C2 0 0 0 NaN
o13: M24:C2 0 0 0 NaN
o12: c1 0 0 0 NaN
o13: c1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m6a
est MCSE SD MCSE/SD
O22 0 0 0 NaN
O23 0 0 0 NaN
o12: C1 0 0 0 NaN
o12: C2 0 0 0 NaN
o13: C1 0 0 0 NaN
o13: C2 0 0 0 NaN
o12: b21 0 0 0 NaN
o13: b21 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m6b
est MCSE SD MCSE/SD
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
O22 0 0 0 NaN
O23 0 0 0 NaN
o13: C2 0 0 0 NaN
c1:C2 0 0 0 NaN
o12: C2 0 0 0 NaN
o12: c1 0 0 0 NaN
o13: c1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m6c
est MCSE SD MCSE/SD
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
O22 0 0 0 NaN
O23 0 0 0 NaN
o12: C2 0 0 0 NaN
o13: C2 0 0 0 NaN
o12: c1 0 0 0 NaN
o12: c1:C2 0 0 0 NaN
o13: c1 0 0 0 NaN
o13: c1:C2 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m6d
est MCSE SD MCSE/SD
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
O22 0 0 0 NaN
O23 0 0 0 NaN
M22:C2 0 0 0 NaN
M23:C2 0 0 0 NaN
M24:C2 0 0 0 NaN
o12: C2 0 0 0 NaN
o13: C2 0 0 0 NaN
o12: c1 0 0 0 NaN
o13: c1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m6e
est MCSE SD MCSE/SD
o12: M22 0 0 0 NaN
o12: M23 0 0 0 NaN
o12: M24 0 0 0 NaN
o12: C2 0 0 0 NaN
o12: O22 0 0 0 NaN
o12: O23 0 0 0 NaN
o12: M22:C2 0 0 0 NaN
o12: M23:C2 0 0 0 NaN
o12: M24:C2 0 0 0 NaN
o13: M22 0 0 0 NaN
o13: M23 0 0 0 NaN
o13: M24 0 0 0 NaN
o13: C2 0 0 0 NaN
o13: O22 0 0 0 NaN
o13: O23 0 0 0 NaN
o13: M22:C2 0 0 0 NaN
o13: M23:C2 0 0 0 NaN
o13: M24:C2 0 0 0 NaN
o12: c1 0 0 0 NaN
o13: c1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m7a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
o1.L 0 0 0 NaN
o1.Q 0 0 0 NaN
o22 0 0 0 NaN
o23 0 0 0 NaN
o24 0 0 0 NaN
x2 0 0 0 NaN
x3 0 0 0 NaN
x4 0 0 0 NaN
time 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m7b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
o22 0 0 0 NaN
o23 0 0 0 NaN
o24 0 0 0 NaN
o1.L 0 0 0 NaN
o1.Q 0 0 0 NaN
c2 0 0 0 NaN
b21 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
summary output remained the same on Windows
Code
lapply(models0, print)
Output
Call:
clmm_imp(fixed = o1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2
0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o2 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3
0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C1
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o2 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3 C1
0 0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 c1
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o2 ~ c1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3 c1
0 0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C2
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o2 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3 C2
0 0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 c2
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o2 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3 c2
0 0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
Call:
lme_imp(fixed = c1 ~ o1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) o1.L o1.Q
0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
Call:
lme_imp(fixed = c1 ~ o2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) o22 o23 o24
0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
Call:
clmm_imp(fixed = o1 ~ M2 + o2 * abs(C1 - C2) + log(C1) + (1 |
id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23
0 0 0 0
M24 abs(C1 - C2) log(C1) o22
0 0 0 0
o23 o24 o22:abs(C1 - C2) o23:abs(C1 - C2)
0 0 0 0
o24:abs(C1 - C2)
0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ ifelse(as.numeric(o2) > 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 cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1
0
o1 > 2
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ time + c1 + C1 + B2 + (c1 * time | id),
data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C1 B21 time c1
0 0 0 0 0 0
Random effects covariance matrix:
$id
o1 o1 o1 o1
(Intercept) c1 time c1:time
o1 (Intercept) 0 0 0 0
o1 c1 0 0 0 0
o1 time 0 0 0 0
o1 c1:time 0 0 0 0
Call:
clmm_imp(fixed = o1 ~ 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 cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C1 time I(time^2) b21 c1 C1:time
0 0 0 0 0 0 0 0
b21:c1
0
Random effects covariance matrix:
$id
o1 o1
(Intercept) time
o1 (Intercept) 0 0
o1 time 0 0
Call:
clmm_imp(fixed = o1 ~ 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 cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C1 log(time) I(time^2) p1
0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~C1 + C2 + b2), seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 O22 O23 C1 C2 C1 C2 b21 b21
0 0 0 0 0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1
0 0 0 0 0 0 0 0 0 0 0
c1
0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 * C2), seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2
0 0 0 0 0 0 0 0 0 0 0
c1 c1:C2
0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2
0 0 0 0 0 0 0 0 0 0 0
C2 c1 c1
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = ~c1 + M2 * C2 + O2, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1
0 0 0 0 0 0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~C1 + C2 + b2), rev = "o1", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 O22 O23 C1 C2 C1 C2 b21 b21
0 0 0 0 0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1
0 0 0 0 0 0 0 0 0 0 0
c1
0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 * C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2
0 0 0 0 0 0 0 0 0 0 0
c1 c1:C2
0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2
0 0 0 0 0 0 0 0 0 0 0
C2 c1 c1
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = ~c1 + M2 * C2 + O2, rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1
0 0 0 0 0 0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
lme_imp(fixed = y ~ C1 + o1 + o2 + x + time, data = longDF, random = ~1 |
id, n.adapt = 5, n.iter = 10, seed = 2020)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 o1.L o1.Q o22 o23
0 0 0 0 0 0
o24 x2 x3 x4 time
0 0 0 0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ o2 + o1 + c2 + b2, data = longDF, random = ~1 |
id, n.adapt = 5, n.iter = 10, seed = 2020)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) o22 o23 o24 o1.L o1.Q
0 0 0 0 0 0
c2 b21
0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m0a
Call:
clmm_imp(fixed = o1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2
0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m0b
Call:
clmm_imp(fixed = o2 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3
0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
$m1a
Call:
clmm_imp(fixed = o1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C1
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m1b
Call:
clmm_imp(fixed = o2 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3 C1
0 0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
$m1c
Call:
clmm_imp(fixed = o1 ~ c1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 c1
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m1d
Call:
clmm_imp(fixed = o2 ~ c1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3 c1
0 0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
$m2a
Call:
clmm_imp(fixed = o1 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C2
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m2b
Call:
clmm_imp(fixed = o2 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3 C2
0 0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
$m2c
Call:
clmm_imp(fixed = o1 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 c2
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m2d
Call:
clmm_imp(fixed = o2 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3 c2
0 0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
$m3a
Call:
lme_imp(fixed = c1 ~ o1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) o1.L o1.Q
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 ~ o2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) o22 o23 o24
0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
$m4a
Call:
clmm_imp(fixed = o1 ~ M2 + o2 * abs(C1 - C2) + log(C1) + (1 |
id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23
0 0 0 0
M24 abs(C1 - C2) log(C1) o22
0 0 0 0
o23 o24 o22:abs(C1 - C2) o23:abs(C1 - C2)
0 0 0 0
o24:abs(C1 - C2)
0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m4b
Call:
clmm_imp(fixed = o1 ~ ifelse(as.numeric(o2) > 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 cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1
0
o1 > 2
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m4c
Call:
clmm_imp(fixed = o1 ~ time + c1 + C1 + B2 + (c1 * time | id),
data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C1 B21 time c1
0 0 0 0 0 0
Random effects covariance matrix:
$id
o1 o1 o1 o1
(Intercept) c1 time c1:time
o1 (Intercept) 0 0 0 0
o1 c1 0 0 0 0
o1 time 0 0 0 0
o1 c1:time 0 0 0 0
$m4d
Call:
clmm_imp(fixed = o1 ~ 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 cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C1 time I(time^2) b21 c1 C1:time
0 0 0 0 0 0 0 0
b21:c1
0
Random effects covariance matrix:
$id
o1 o1
(Intercept) time
o1 (Intercept) 0 0
o1 time 0 0
$m4e
Call:
clmm_imp(fixed = o1 ~ 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 cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C1 log(time) I(time^2) p1
0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m5a
Call:
clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~C1 + C2 + b2), seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 O22 O23 C1 C2 C1 C2 b21 b21
0 0 0 0 0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m5b
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1
0 0 0 0 0 0 0 0 0 0 0
c1
0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m5c
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 * C2), seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2
0 0 0 0 0 0 0 0 0 0 0
c1 c1:C2
0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m5d
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2
0 0 0 0 0 0 0 0 0 0 0
C2 c1 c1
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m5e
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = ~c1 + M2 * C2 + O2, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1
0 0 0 0 0 0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m6a
Call:
clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~C1 + C2 + b2), rev = "o1", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 O22 O23 C1 C2 C1 C2 b21 b21
0 0 0 0 0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m6b
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1
0 0 0 0 0 0 0 0 0 0 0
c1
0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m6c
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 * C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2
0 0 0 0 0 0 0 0 0 0 0
c1 c1:C2
0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m6d
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2
0 0 0 0 0 0 0 0 0 0 0
C2 c1 c1
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m6e
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = ~c1 + M2 * C2 + O2, rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1
0 0 0 0 0 0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m7a
Call:
lme_imp(fixed = y ~ C1 + o1 + o2 + x + time, data = longDF, random = ~1 |
id, n.adapt = 5, n.iter = 10, seed = 2020)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 o1.L o1.Q o22 o23
0 0 0 0 0 0
o24 x2 x3 x4 time
0 0 0 0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m7b
Call:
lme_imp(fixed = y ~ o2 + o1 + c2 + b2, data = longDF, random = ~1 |
id, n.adapt = 5, n.iter = 10, seed = 2020)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) o22 o23 o24 o1.L o1.Q
0 0 0 0 0 0
c2 b21
0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Code
lapply(models0, coef)
Output
$m0a
$m0a$o1
D_o1_id[1,1] o1 > 1 o1 > 2
0 0 0
$m0b
$m0b$o2
D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3
0 0 0 0
$m1a
$m1a$o1
C1 D_o1_id[1,1] o1 > 1 o1 > 2
0 0 0 0
$m1b
$m1b$o2
C1 D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3
0 0 0 0 0
$m1c
$m1c$o1
c1 D_o1_id[1,1] o1 > 1 o1 > 2
0 0 0 0
$m1d
$m1d$o2
c1 D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3
0 0 0 0 0
$m2a
$m2a$o1
C2 D_o1_id[1,1] o1 > 1 o1 > 2
0 0 0 0
$m2b
$m2b$o2
C2 D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3
0 0 0 0 0
$m2c
$m2c$o1
c2 D_o1_id[1,1] o1 > 1 o1 > 2
0 0 0 0
$m2d
$m2d$o2
c2 D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3
0 0 0 0 0
$m3a
$m3a$c1
(Intercept) o1.L o1.Q sigma_c1 D_c1_id[1,1]
0 0 0 0 0
$m3b
$m3b$c1
(Intercept) o22 o23 o24 sigma_c1 D_c1_id[1,1]
0 0 0 0 0 0
$m4a
$m4a$o1
M22 M23 M24 abs(C1 - C2)
0 0 0 0
log(C1) o22 o23 o24
0 0 0 0
o22:abs(C1 - C2) o23:abs(C1 - C2) o24:abs(C1 - C2) D_o1_id[1,1]
0 0 0 0
o1 > 1 o1 > 2
0 0
$m4b
$m4b$o1
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
D_o1_id[1,1]
0
o1 > 1
0
o1 > 2
0
$m4c
$m4c$o1
C1 B21 time c1 D_o1_id[1,1] D_o1_id[1,2]
0 0 0 0 0 0
D_o1_id[2,2] D_o1_id[1,3] D_o1_id[2,3] D_o1_id[3,3] D_o1_id[1,4] D_o1_id[2,4]
0 0 0 0 0 0
D_o1_id[3,4] D_o1_id[4,4] o1 > 1 o1 > 2
0 0 0 0
$m4d
$m4d$o1
C1 time I(time^2) b21 c1 C1:time
0 0 0 0 0 0
b21:c1 D_o1_id[1,1] D_o1_id[1,2] D_o1_id[2,2] o1 > 1 o1 > 2
0 0 0 0 0 0
$m4e
$m4e$o1
C1 log(time) I(time^2) p1 D_o1_id[1,1] o1 > 1
0 0 0 0 0 0
o1 > 2
0
$m5a
$m5a$o1
O22 O23 C1 C2 C1 C2
0 0 0 0 0 0
b21 b21 D_o1_id[1,1] o1 > 1 o1 > 2
0 0 0 0 0
$m5b
$m5b$o1
M22 M23 M24 O22 O23 c1:C2
0 0 0 0 0 0
C2 C2 c1 c1 D_o1_id[1,1] o1 > 1
0 0 0 0 0 0
o1 > 2
0
$m5c
$m5c$o1
M22 M23 M24 O22 O23 C2
0 0 0 0 0 0
C2 c1 c1:C2 c1 c1:C2 D_o1_id[1,1]
0 0 0 0 0 0
o1 > 1 o1 > 2
0 0
$m5d
$m5d$o1
M22 M23 M24 O22 O23 M22:C2
0 0 0 0 0 0
M23:C2 M24:C2 C2 C2 c1 c1
0 0 0 0 0 0
D_o1_id[1,1] o1 > 1 o1 > 2
0 0 0
$m5e
$m5e$o1
M22 M23 M24 C2 O22 O23
0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 M22 M23 M24
0 0 0 0 0 0
C2 O22 O23 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0
c1 c1 D_o1_id[1,1] o1 > 1 o1 > 2
0 0 0 0 0
$m6a
$m6a$o1
O22 O23 C1 C2 C1 C2
0 0 0 0 0 0
b21 b21 D_o1_id[1,1] o1 ≤ 1 o1 ≤ 2
0 0 0 0 0
$m6b
$m6b$o1
M22 M23 M24 O22 O23 c1:C2
0 0 0 0 0 0
C2 C2 c1 c1 D_o1_id[1,1] o1 ≤ 1
0 0 0 0 0 0
o1 ≤ 2
0
$m6c
$m6c$o1
M22 M23 M24 O22 O23 C2
0 0 0 0 0 0
C2 c1 c1:C2 c1 c1:C2 D_o1_id[1,1]
0 0 0 0 0 0
o1 ≤ 1 o1 ≤ 2
0 0
$m6d
$m6d$o1
M22 M23 M24 O22 O23 M22:C2
0 0 0 0 0 0
M23:C2 M24:C2 C2 C2 c1 c1
0 0 0 0 0 0
D_o1_id[1,1] o1 ≤ 1 o1 ≤ 2
0 0 0
$m6e
$m6e$o1
M22 M23 M24 C2 O22 O23
0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 M22 M23 M24
0 0 0 0 0 0
C2 O22 O23 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0
c1 c1 D_o1_id[1,1] o1 ≤ 1 o1 ≤ 2
0 0 0 0 0
$m7a
$m7a$y
(Intercept) C1 o1.L o1.Q o22 o23
0 0 0 0 0 0
o24 x2 x3 x4 time sigma_y
0 0 0 0 0 0
D_y_id[1,1]
0
$m7b
$m7b$y
(Intercept) o22 o23 o24 o1.L o1.Q
0 0 0 0 0 0
c2 b21 sigma_y D_y_id[1,1]
0 0 0 0
Code
lapply(models0, confint)
Output
$m0a
$m0a$o1
2.5% 97.5%
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m0b
$m0b$o2
2.5% 97.5%
D_o2_id[1,1] 0 0
o2 > 1 0 0
o2 > 2 0 0
o2 > 3 0 0
$m1a
$m1a$o1
2.5% 97.5%
C1 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m1b
$m1b$o2
2.5% 97.5%
C1 0 0
D_o2_id[1,1] 0 0
o2 > 1 0 0
o2 > 2 0 0
o2 > 3 0 0
$m1c
$m1c$o1
2.5% 97.5%
c1 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m1d
$m1d$o2
2.5% 97.5%
c1 0 0
D_o2_id[1,1] 0 0
o2 > 1 0 0
o2 > 2 0 0
o2 > 3 0 0
$m2a
$m2a$o1
2.5% 97.5%
C2 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m2b
$m2b$o2
2.5% 97.5%
C2 0 0
D_o2_id[1,1] 0 0
o2 > 1 0 0
o2 > 2 0 0
o2 > 3 0 0
$m2c
$m2c$o1
2.5% 97.5%
c2 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m2d
$m2d$o2
2.5% 97.5%
c2 0 0
D_o2_id[1,1] 0 0
o2 > 1 0 0
o2 > 2 0 0
o2 > 3 0 0
$m3a
$m3a$c1
2.5% 97.5%
(Intercept) 0 0
o1.L 0 0
o1.Q 0 0
sigma_c1 0 0
D_c1_id[1,1] 0 0
$m3b
$m3b$c1
2.5% 97.5%
(Intercept) 0 0
o22 0 0
o23 0 0
o24 0 0
sigma_c1 0 0
D_c1_id[1,1] 0 0
$m4a
$m4a$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
o22 0 0
o23 0 0
o24 0 0
o22:abs(C1 - C2) 0 0
o23:abs(C1 - C2) 0 0
o24:abs(C1 - C2) 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m4b
$m4b$o1
2.5% 97.5%
abs(C1 - C2) 0 0
log(C1) 0 0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m4c
$m4c$o1
2.5% 97.5%
C1 0 0
B21 0 0
time 0 0
c1 0 0
D_o1_id[1,1] 0 0
D_o1_id[1,2] 0 0
D_o1_id[2,2] 0 0
D_o1_id[1,3] 0 0
D_o1_id[2,3] 0 0
D_o1_id[3,3] 0 0
D_o1_id[1,4] 0 0
D_o1_id[2,4] 0 0
D_o1_id[3,4] 0 0
D_o1_id[4,4] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m4d
$m4d$o1
2.5% 97.5%
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_o1_id[1,1] 0 0
D_o1_id[1,2] 0 0
D_o1_id[2,2] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m4e
$m4e$o1
2.5% 97.5%
C1 0 0
log(time) 0 0
I(time^2) 0 0
p1 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m5a
$m5a$o1
2.5% 97.5%
O22 0 0
O23 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
b21 0 0
b21 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m5b
$m5b$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
c1:C2 0 0
C2 0 0
C2 0 0
c1 0 0
c1 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m5c
$m5c$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
C2 0 0
C2 0 0
c1 0 0
c1:C2 0 0
c1 0 0
c1:C2 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m5d
$m5d$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
C2 0 0
C2 0 0
c1 0 0
c1 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m5e
$m5e$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
c1 0 0
c1 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m6a
$m6a$o1
2.5% 97.5%
O22 0 0
O23 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
b21 0 0
b21 0 0
D_o1_id[1,1] 0 0
o1 ≤ 1 0 0
o1 ≤ 2 0 0
$m6b
$m6b$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
c1:C2 0 0
C2 0 0
C2 0 0
c1 0 0
c1 0 0
D_o1_id[1,1] 0 0
o1 ≤ 1 0 0
o1 ≤ 2 0 0
$m6c
$m6c$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
C2 0 0
C2 0 0
c1 0 0
c1:C2 0 0
c1 0 0
c1:C2 0 0
D_o1_id[1,1] 0 0
o1 ≤ 1 0 0
o1 ≤ 2 0 0
$m6d
$m6d$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
C2 0 0
C2 0 0
c1 0 0
c1 0 0
D_o1_id[1,1] 0 0
o1 ≤ 1 0 0
o1 ≤ 2 0 0
$m6e
$m6e$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
c1 0 0
c1 0 0
D_o1_id[1,1] 0 0
o1 ≤ 1 0 0
o1 ≤ 2 0 0
$m7a
$m7a$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
o1.L 0 0
o1.Q 0 0
o22 0 0
o23 0 0
o24 0 0
x2 0 0
x3 0 0
x4 0 0
time 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m7b
$m7b$y
2.5% 97.5%
(Intercept) 0 0
o22 0 0
o23 0 0
o24 0 0
o1.L 0 0
o1.Q 0 0
c2 0 0
b21 0 0
sigma_y 0 0
D_y_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"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ 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
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o2 ~ 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
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o2 > 1 0 0 0 0 0 NaN NaN
o2 > 2 0 0 0 0 0 NaN NaN
o2 > 3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o2_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ 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
C1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o2 ~ 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
C1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o2 > 1 0 0 0 0 0 NaN NaN
o2 > 2 0 0 0 0 0 NaN NaN
o2 > 3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o2_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ 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
c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o2 ~ 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
c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o2 > 1 0 0 0 0 0 NaN NaN
o2 > 2 0 0 0 0 0 NaN NaN
o2 > 3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o2_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o2 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o2 > 1 0 0 0 0 0 NaN NaN
o2 > 2 0 0 0 0 0 NaN NaN
o2 > 3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o2_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
c2 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o2 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
c2 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o2 > 1 0 0 0 0 0 NaN NaN
o2 > 2 0 0 0 0 0 NaN NaN
o2 > 3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o2_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 ~ o1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
o1.L 0 0 0 0 0 NaN NaN
o1.Q 0 0 0 0 0 NaN NaN
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 ~ o2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ M2 + o2 * 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
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
o22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
o23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
o24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ ifelse(as.numeric(o2) > 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% 97.5%
abs(C1 - C2) 0 0 0 0
log(C1) 0 0 0 0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 0 0 0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 0
tail-prob. GR-crit
abs(C1 - C2) 0 NaN
log(C1) 0 NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 NaN
MCE/SD
abs(C1 - C2) NaN
log(C1) NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ 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
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1] 0 0 0 0 NaN NaN
D_o1_id[1,2] 0 0 0 0 0 NaN NaN
D_o1_id[2,2] 0 0 0 0 NaN NaN
D_o1_id[1,3] 0 0 0 0 0 NaN NaN
D_o1_id[2,3] 0 0 0 0 0 NaN NaN
D_o1_id[3,3] 0 0 0 0 NaN NaN
D_o1_id[1,4] 0 0 0 0 0 NaN NaN
D_o1_id[2,4] 0 0 0 0 0 NaN NaN
D_o1_id[3,4] 0 0 0 0 0 NaN NaN
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ 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
C1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
I(time^2) 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
C1:time 0 0 0 0 0 NaN NaN
b21:c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1] 0 0 0 0 NaN NaN
D_o1_id[1,2] 0 0 0 0 0 NaN NaN
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ 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
C1 0 0 0 0 0 NaN NaN
log(time) 0 0 0 0 0 NaN NaN
I(time^2) 0 0 0 0 0 NaN NaN
p1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m5a
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~C1 + C2 + b2), seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C1 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C1 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: b21 0 0 0 0 0 NaN NaN
o13: b21 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m5b
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
c1:C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m5c
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 * C2), seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o12: c1:C2 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
o13: c1:C2 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m5d
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
M22:C2 0 0 0 0 0 NaN NaN
M23:C2 0 0 0 0 0 NaN NaN
M24:C2 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m5e
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = ~c1 + M2 * C2 + O2, seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o12: M22 0 0 0 0 0 NaN NaN
o12: M23 0 0 0 0 0 NaN NaN
o12: M24 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o12: O22 0 0 0 0 0 NaN NaN
o12: O23 0 0 0 0 0 NaN NaN
o12: M22:C2 0 0 0 0 0 NaN NaN
o12: M23:C2 0 0 0 0 0 NaN NaN
o12: M24:C2 0 0 0 0 0 NaN NaN
o13: M22 0 0 0 0 0 NaN NaN
o13: M23 0 0 0 0 0 NaN NaN
o13: M24 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o13: O22 0 0 0 0 0 NaN NaN
o13: O23 0 0 0 0 0 NaN NaN
o13: M22:C2 0 0 0 0 0 NaN NaN
o13: M23:C2 0 0 0 0 0 NaN NaN
o13: M24:C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m6a
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~C1 + C2 + b2), rev = "o1", seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C1 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C1 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: b21 0 0 0 0 0 NaN NaN
o13: b21 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 ≤ 1 0 0 0 0 0 NaN NaN
o1 ≤ 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m6b
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
c1:C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 ≤ 1 0 0 0 0 0 NaN NaN
o1 ≤ 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m6c
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 * C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o12: c1:C2 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
o13: c1:C2 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 ≤ 1 0 0 0 0 0 NaN NaN
o1 ≤ 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m6d
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
M22:C2 0 0 0 0 0 NaN NaN
M23:C2 0 0 0 0 0 NaN NaN
M24:C2 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 ≤ 1 0 0 0 0 0 NaN NaN
o1 ≤ 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m6e
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = ~c1 + M2 * C2 + O2, rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o12: M22 0 0 0 0 0 NaN NaN
o12: M23 0 0 0 0 0 NaN NaN
o12: M24 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o12: O22 0 0 0 0 0 NaN NaN
o12: O23 0 0 0 0 0 NaN NaN
o12: M22:C2 0 0 0 0 0 NaN NaN
o12: M23:C2 0 0 0 0 0 NaN NaN
o12: M24:C2 0 0 0 0 0 NaN NaN
o13: M22 0 0 0 0 0 NaN NaN
o13: M23 0 0 0 0 0 NaN NaN
o13: M24 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o13: O22 0 0 0 0 0 NaN NaN
o13: O23 0 0 0 0 0 NaN NaN
o13: M22:C2 0 0 0 0 0 NaN NaN
o13: M23:C2 0 0 0 0 0 NaN NaN
o13: M24:C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 ≤ 1 0 0 0 0 0 NaN NaN
o1 ≤ 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m7a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + o1 + o2 + x + time, data = longDF, random = ~1 |
id, n.adapt = 5, n.iter = 10, seed = 2020)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
o1.L 0 0 0 0 0 NaN NaN
o1.Q 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
x2 0 0 0 0 0 NaN NaN
x3 0 0 0 0 0 NaN NaN
x4 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
$m7b
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ o2 + o1 + c2 + b2, data = longDF, random = ~1 |
id, n.adapt = 5, n.iter = 10, seed = 2020)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
o1.L 0 0 0 0 0 NaN NaN
o1.Q 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Code
lapply(models0, function(x) coef(summary(x)))
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a
$m0a$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
$m0b
$m0b$o2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
$m1a
$m1a$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C1 0 0 0 0 0 NaN NaN
$m1b
$m1b$o2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C1 0 0 0 0 0 NaN NaN
$m1c
$m1c$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
c1 0 0 0 0 0 NaN NaN
$m1d
$m1d$o2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
c1 0 0 0 0 0 NaN NaN
$m2a
$m2a$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
$m2b
$m2b$o2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
$m2c
$m2c$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
c2 0 0 0 0 0 NaN NaN
$m2d
$m2d$o2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
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
o1.L 0 0 0 0 0 NaN NaN
o1.Q 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
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
$m4a
$m4a$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
o22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
o23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
o24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
$m4b
$m4b$o1
Mean SD 2.5% 97.5%
abs(C1 - C2) 0 0 0 0
log(C1) 0 0 0 0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 0 0 0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 0
tail-prob. GR-crit
abs(C1 - C2) 0 NaN
log(C1) 0 NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 NaN
MCE/SD
abs(C1 - C2) NaN
log(C1) NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
$m4c
$m4c$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
$m4d
$m4d$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
I(time^2) 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
C1:time 0 0 0 0 0 NaN NaN
b21:c1 0 0 0 0 0 NaN NaN
$m4e
$m4e$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C1 0 0 0 0 0 NaN NaN
log(time) 0 0 0 0 0 NaN NaN
I(time^2) 0 0 0 0 0 NaN NaN
p1 0 0 0 0 0 NaN NaN
$m5a
$m5a$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C1 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C1 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: b21 0 0 0 0 0 NaN NaN
o13: b21 0 0 0 0 0 NaN NaN
$m5b
$m5b$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
c1:C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
$m5c
$m5c$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o12: c1:C2 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
o13: c1:C2 0 0 0 0 0 NaN NaN
$m5d
$m5d$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
M22:C2 0 0 0 0 0 NaN NaN
M23:C2 0 0 0 0 0 NaN NaN
M24:C2 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
$m5e
$m5e$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o12: M22 0 0 0 0 0 NaN NaN
o12: M23 0 0 0 0 0 NaN NaN
o12: M24 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o12: O22 0 0 0 0 0 NaN NaN
o12: O23 0 0 0 0 0 NaN NaN
o12: M22:C2 0 0 0 0 0 NaN NaN
o12: M23:C2 0 0 0 0 0 NaN NaN
o12: M24:C2 0 0 0 0 0 NaN NaN
o13: M22 0 0 0 0 0 NaN NaN
o13: M23 0 0 0 0 0 NaN NaN
o13: M24 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o13: O22 0 0 0 0 0 NaN NaN
o13: O23 0 0 0 0 0 NaN NaN
o13: M22:C2 0 0 0 0 0 NaN NaN
o13: M23:C2 0 0 0 0 0 NaN NaN
o13: M24:C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
$m6a
$m6a$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C1 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C1 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: b21 0 0 0 0 0 NaN NaN
o13: b21 0 0 0 0 0 NaN NaN
$m6b
$m6b$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
c1:C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
$m6c
$m6c$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o12: c1:C2 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
o13: c1:C2 0 0 0 0 0 NaN NaN
$m6d
$m6d$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
M22:C2 0 0 0 0 0 NaN NaN
M23:C2 0 0 0 0 0 NaN NaN
M24:C2 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
$m6e
$m6e$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o12: M22 0 0 0 0 0 NaN NaN
o12: M23 0 0 0 0 0 NaN NaN
o12: M24 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o12: O22 0 0 0 0 0 NaN NaN
o12: O23 0 0 0 0 0 NaN NaN
o12: M22:C2 0 0 0 0 0 NaN NaN
o12: M23:C2 0 0 0 0 0 NaN NaN
o12: M24:C2 0 0 0 0 0 NaN NaN
o13: M22 0 0 0 0 0 NaN NaN
o13: M23 0 0 0 0 0 NaN NaN
o13: M24 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o13: O22 0 0 0 0 0 NaN NaN
o13: O23 0 0 0 0 0 NaN NaN
o13: M22:C2 0 0 0 0 0 NaN NaN
o13: M23:C2 0 0 0 0 0 NaN NaN
o13: M24:C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
$m7a
$m7a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
o1.L 0 0 0 0 0 NaN NaN
o1.Q 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
x2 0 0 0 0 0 NaN NaN
x3 0 0 0 0 0 NaN NaN
x4 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
$m7b
$m7b$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
o1.L 0 0 0 0 0 NaN NaN
o1.Q 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
b21 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
o1
1 2
1.1 1
1.2 1
1.3 2
2 3
2.1 1
2.2 2
3 1
3.1 3
3.2 2
4 3
4.1 2
4.2 1
4.3 2
5 2
5.1 2
5.2 3
5.3 2
6 3
7 3
7.1 1
7.2 1
8 2
8.1 2
8.2 3
8.3 3
8.4 2
8.5 2
9 2
9.1 2
9.2 3
10 2
10.1 1
11 2
11.1 3
11.2 3
11.3 1
11.4 3
12 1
13 2
13.1 1
14 3
14.1 3
14.2 2
14.3 1
15 3
15.1 3
15.2 2
15.3 2
16 3
16.1 1
16.2 3
16.3 1
16.4 1
16.5 2
17 2
17.1 2
17.2 3
17.3 2
17.4 3
18 2
19 3
19.1 2
19.2 2
19.3 2
20 1
20.1 2
20.2 2
20.3 1
20.4 1
20.5 2
21 2
21.1 2
21.2 2
22 1
22.1 1
23 1
23.1 2
24 1
25 2
25.1 1
25.2 1
25.3 1
25.4 2
25.5 2
26 2
26.1 3
26.2 2
26.3 1
27 2
27.1 2
28 1
28.1 1
28.2 1
28.3 1
29 2
29.1 2
29.2 1
29.3 2
30 2
30.1 2
30.2 2
31 2
32 3
32.1 1
32.2 3
32.3 3
33 2
33.1 2
34 3
34.1 2
34.2 2
34.3 1
35 2
35.1 3
35.2 2
36 3
36.1 2
36.2 3
36.3 3
36.4 3
37 3
37.1 3
37.2 3
38 2
39 1
39.1 2
39.2 2
39.3 1
39.4 1
39.5 1
40 3
40.1 1
40.2 1
40.3 2
41 3
41.1 2
41.2 1
41.3 1
41.4 3
42 2
42.1 1
43 2
43.1 2
43.2 1
44 3
44.1 2
44.2 1
44.3 1
45 1
45.1 1
46 1
46.1 2
46.2 3
47 1
47.1 1
47.2 1
47.3 3
47.4 2
48 3
48.1 2
49 1
50 2
51 2
52 2
52.1 3
52.2 2
52.3 1
52.4 3
52.5 2
53 2
53.1 1
53.2 1
54 1
54.1 2
54.2 3
54.3 1
54.4 2
55 1
55.1 1
55.2 2
55.3 1
55.4 1
56 3
56.1 2
56.2 3
56.3 3
56.4 1
56.5 3
57 2
57.1 2
57.2 2
57.3 2
58 3
58.1 2
58.2 3
58.3 3
58.4 2
58.5 3
59 3
59.1 1
60 3
61 1
61.1 2
61.2 2
61.3 1
61.4 2
62 2
62.1 3
62.2 1
62.3 2
63 1
63.1 3
64 2
65 1
65.1 2
65.2 1
65.3 1
66 1
66.1 1
66.2 3
67 3
68 1
68.1 2
68.2 2
68.3 2
68.4 2
69 2
70 1
70.1 3
71 3
71.1 3
71.2 3
71.3 2
71.4 2
72 1
72.1 1
72.2 2
72.3 2
72.4 1
72.5 1
73 1
74 1
75 3
76 3
76.1 3
76.2 1
77 2
78 2
79 1
79.1 2
79.2 1
80 2
80.1 2
80.2 3
81 3
81.1 2
81.2 3
81.3 3
82 3
82.1 2
82.2 2
83 3
83.1 3
83.2 3
83.3 2
84 3
84.1 3
85 3
85.1 2
85.2 2
85.3 2
85.4 1
85.5 3
86 2
86.1 3
86.2 1
86.3 1
86.4 1
86.5 3
87 1
87.1 1
87.2 1
88 3
88.1 1
88.2 1
88.3 1
89 1
90 2
90.1 1
90.2 3
90.3 1
91 1
91.1 2
91.2 1
92 2
93 1
93.1 3
93.2 1
93.3 3
93.4 1
94 2
94.1 3
94.2 1
94.3 3
94.4 2
94.5 3
95 3
95.1 1
95.2 2
96 1
96.1 3
96.2 1
96.3 1
96.4 2
96.5 3
97 3
97.1 3
98 3
98.1 1
98.2 1
99 2
99.1 2
99.2 1
100 1
100.1 2
100.2 2
100.3 1
100.4 1
$m0a$mu_delta_ordinal
[1] 0
$m0a$tau_delta_ordinal
[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
o2
1 1
1.1 1
1.2 3
1.3 1
2 4
2.1 4
2.2 2
3 2
3.1 4
3.2 2
4 4
4.1 3
4.2 NA
4.3 2
5 2
5.1 4
5.2 2
5.3 4
6 3
7 1
7.1 NA
7.2 4
8 1
8.1 3
8.2 1
8.3 4
8.4 3
8.5 3
9 2
9.1 2
9.2 4
10 1
10.1 4
11 3
11.1 1
11.2 4
11.3 3
11.4 3
12 3
13 NA
13.1 1
14 1
14.1 4
14.2 3
14.3 4
15 1
15.1 4
15.2 NA
15.3 2
16 NA
16.1 NA
16.2 1
16.3 3
16.4 3
16.5 1
17 3
17.1 2
17.2 2
17.3 3
17.4 1
18 4
19 1
19.1 NA
19.2 NA
19.3 2
20 1
20.1 4
20.2 3
20.3 3
20.4 1
20.5 3
21 3
21.1 1
21.2 2
22 4
22.1 NA
23 4
23.1 NA
24 3
25 1
25.1 3
25.2 2
25.3 1
25.4 1
25.5 NA
26 3
26.1 4
26.2 3
26.3 1
27 4
27.1 4
28 1
28.1 2
28.2 3
28.3 3
29 4
29.1 4
29.2 3
29.3 2
30 3
30.1 4
30.2 4
31 2
32 NA
32.1 2
32.2 4
32.3 3
33 4
33.1 4
34 NA
34.1 NA
34.2 NA
34.3 NA
35 4
35.1 1
35.2 NA
36 1
36.1 1
36.2 2
36.3 2
36.4 1
37 4
37.1 2
37.2 2
38 NA
39 NA
39.1 2
39.2 2
39.3 3
39.4 3
39.5 1
40 1
40.1 2
40.2 NA
40.3 2
41 4
41.1 3
41.2 4
41.3 NA
41.4 4
42 4
42.1 4
43 NA
43.1 NA
43.2 3
44 2
44.1 4
44.2 NA
44.3 1
45 3
45.1 4
46 NA
46.1 4
46.2 4
47 3
47.1 2
47.2 1
47.3 2
47.4 1
48 4
48.1 NA
49 4
50 4
51 4
52 NA
52.1 NA
52.2 3
52.3 NA
52.4 4
52.5 1
53 2
53.1 1
53.2 3
54 3
54.1 4
54.2 4
54.3 3
54.4 NA
55 4
55.1 1
55.2 4
55.3 NA
55.4 1
56 1
56.1 2
56.2 2
56.3 3
56.4 4
56.5 4
57 2
57.1 2
57.2 4
57.3 NA
58 1
58.1 2
58.2 2
58.3 4
58.4 1
58.5 NA
59 4
59.1 1
60 1
61 1
61.1 1
61.2 NA
61.3 1
61.4 3
62 NA
62.1 NA
62.2 NA
62.3 3
63 4
63.1 3
64 4
65 2
65.1 1
65.2 3
65.3 NA
66 1
66.1 3
66.2 2
67 3
68 3
68.1 4
68.2 3
68.3 1
68.4 4
69 4
70 4
70.1 4
71 2
71.1 NA
71.2 4
71.3 3
71.4 1
72 1
72.1 NA
72.2 4
72.3 1
72.4 3
72.5 1
73 2
74 4
75 1
76 2
76.1 1
76.2 1
77 3
78 3
79 NA
79.1 3
79.2 NA
80 2
80.1 3
80.2 1
81 3
81.1 NA
81.2 3
81.3 2
82 NA
82.1 3
82.2 1
83 4
83.1 NA
83.2 2
83.3 NA
84 2
84.1 1
85 1
85.1 4
85.2 3
85.3 3
85.4 NA
85.5 2
86 1
86.1 3
86.2 1
86.3 2
86.4 3
86.5 4
87 NA
87.1 3
87.2 3
88 NA
88.1 1
88.2 2
88.3 NA
89 3
90 2
90.1 2
90.2 2
90.3 4
91 2
91.1 NA
91.2 3
92 2
93 3
93.1 2
93.2 3
93.3 2
93.4 4
94 NA
94.1 2
94.2 NA
94.3 3
94.4 4
94.5 3
95 NA
95.1 2
95.2 3
96 3
96.1 NA
96.2 4
96.3 3
96.4 NA
96.5 1
97 2
97.1 1
98 2
98.1 1
98.2 3
99 NA
99.1 NA
99.2 4
100 1
100.1 NA
100.2 1
100.3 4
100.4 1
$m0b$mu_delta_ordinal
[1] 0
$m0b$tau_delta_ordinal
[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
o1
1 2
1.1 1
1.2 1
1.3 2
2 3
2.1 1
2.2 2
3 1
3.1 3
3.2 2
4 3
4.1 2
4.2 1
4.3 2
5 2
5.1 2
5.2 3
5.3 2
6 3
7 3
7.1 1
7.2 1
8 2
8.1 2
8.2 3
8.3 3
8.4 2
8.5 2
9 2
9.1 2
9.2 3
10 2
10.1 1
11 2
11.1 3
11.2 3
11.3 1
11.4 3
12 1
13 2
13.1 1
14 3
14.1 3
14.2 2
14.3 1
15 3
15.1 3
15.2 2
15.3 2
16 3
16.1 1
16.2 3
16.3 1
16.4 1
16.5 2
17 2
17.1 2
17.2 3
17.3 2
17.4 3
18 2
19 3
19.1 2
19.2 2
19.3 2
20 1
20.1 2
20.2 2
20.3 1
20.4 1
20.5 2
21 2
21.1 2
21.2 2
22 1
22.1 1
23 1
23.1 2
24 1
25 2
25.1 1
25.2 1
25.3 1
25.4 2
25.5 2
26 2
26.1 3
26.2 2
26.3 1
27 2
27.1 2
28 1
28.1 1
28.2 1
28.3 1
29 2
29.1 2
29.2 1
29.3 2
30 2
30.1 2
30.2 2
31 2
32 3
32.1 1
32.2 3
32.3 3
33 2
33.1 2
34 3
34.1 2
34.2 2
34.3 1
35 2
35.1 3
35.2 2
36 3
36.1 2
36.2 3
36.3 3
36.4 3
37 3
37.1 3
37.2 3
38 2
39 1
39.1 2
39.2 2
39.3 1
39.4 1
39.5 1
40 3
40.1 1
40.2 1
40.3 2
41 3
41.1 2
41.2 1
41.3 1
41.4 3
42 2
42.1 1
43 2
43.1 2
43.2 1
44 3
44.1 2
44.2 1
44.3 1
45 1
45.1 1
46 1
46.1 2
46.2 3
47 1
47.1 1
47.2 1
47.3 3
47.4 2
48 3
48.1 2
49 1
50 2
51 2
52 2
52.1 3
52.2 2
52.3 1
52.4 3
52.5 2
53 2
53.1 1
53.2 1
54 1
54.1 2
54.2 3
54.3 1
54.4 2
55 1
55.1 1
55.2 2
55.3 1
55.4 1
56 3
56.1 2
56.2 3
56.3 3
56.4 1
56.5 3
57 2
57.1 2
57.2 2
57.3 2
58 3
58.1 2
58.2 3
58.3 3
58.4 2
58.5 3
59 3
59.1 1
60 3
61 1
61.1 2
61.2 2
61.3 1
61.4 2
62 2
62.1 3
62.2 1
62.3 2
63 1
63.1 3
64 2
65 1
65.1 2
65.2 1
65.3 1
66 1
66.1 1
66.2 3
67 3
68 1
68.1 2
68.2 2
68.3 2
68.4 2
69 2
70 1
70.1 3
71 3
71.1 3
71.2 3
71.3 2
71.4 2
72 1
72.1 1
72.2 2
72.3 2
72.4 1
72.5 1
73 1
74 1
75 3
76 3
76.1 3
76.2 1
77 2
78 2
79 1
79.1 2
79.2 1
80 2
80.1 2
80.2 3
81 3
81.1 2
81.2 3
81.3 3
82 3
82.1 2
82.2 2
83 3
83.1 3
83.2 3
83.3 2
84 3
84.1 3
85 3
85.1 2
85.2 2
85.3 2
85.4 1
85.5 3
86 2
86.1 3
86.2 1
86.3 1
86.4 1
86.5 3
87 1
87.1 1
87.2 1
88 3
88.1 1
88.2 1
88.3 1
89 1
90 2
90.1 1
90.2 3
90.3 1
91 1
91.1 2
91.2 1
92 2
93 1
93.1 3
93.2 1
93.3 3
93.4 1
94 2
94.1 3
94.2 1
94.3 3
94.4 2
94.5 3
95 3
95.1 1
95.2 2
96 1
96.1 3
96.2 1
96.3 1
96.4 2
96.5 3
97 3
97.1 3
98 3
98.1 1
98.2 1
99 2
99.1 2
99.2 1
100 1
100.1 2
100.2 2
100.3 1
100.4 1
$m1a$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1a$mu_reg_ordinal
[1] 0
$m1a$tau_reg_ordinal
[1] 1e-04
$m1a$mu_delta_ordinal
[1] 0
$m1a$tau_delta_ordinal
[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
o2
1 1
1.1 1
1.2 3
1.3 1
2 4
2.1 4
2.2 2
3 2
3.1 4
3.2 2
4 4
4.1 3
4.2 NA
4.3 2
5 2
5.1 4
5.2 2
5.3 4
6 3
7 1
7.1 NA
7.2 4
8 1
8.1 3
8.2 1
8.3 4
8.4 3
8.5 3
9 2
9.1 2
9.2 4
10 1
10.1 4
11 3
11.1 1
11.2 4
11.3 3
11.4 3
12 3
13 NA
13.1 1
14 1
14.1 4
14.2 3
14.3 4
15 1
15.1 4
15.2 NA
15.3 2
16 NA
16.1 NA
16.2 1
16.3 3
16.4 3
16.5 1
17 3
17.1 2
17.2 2
17.3 3
17.4 1
18 4
19 1
19.1 NA
19.2 NA
19.3 2
20 1
20.1 4
20.2 3
20.3 3
20.4 1
20.5 3
21 3
21.1 1
21.2 2
22 4
22.1 NA
23 4
23.1 NA
24 3
25 1
25.1 3
25.2 2
25.3 1
25.4 1
25.5 NA
26 3
26.1 4
26.2 3
26.3 1
27 4
27.1 4
28 1
28.1 2
28.2 3
28.3 3
29 4
29.1 4
29.2 3
29.3 2
30 3
30.1 4
30.2 4
31 2
32 NA
32.1 2
32.2 4
32.3 3
33 4
33.1 4
34 NA
34.1 NA
34.2 NA
34.3 NA
35 4
35.1 1
35.2 NA
36 1
36.1 1
36.2 2
36.3 2
36.4 1
37 4
37.1 2
37.2 2
38 NA
39 NA
39.1 2
39.2 2
39.3 3
39.4 3
39.5 1
40 1
40.1 2
40.2 NA
40.3 2
41 4
41.1 3
41.2 4
41.3 NA
41.4 4
42 4
42.1 4
43 NA
43.1 NA
43.2 3
44 2
44.1 4
44.2 NA
44.3 1
45 3
45.1 4
46 NA
46.1 4
46.2 4
47 3
47.1 2
47.2 1
47.3 2
47.4 1
48 4
48.1 NA
49 4
50 4
51 4
52 NA
52.1 NA
52.2 3
52.3 NA
52.4 4
52.5 1
53 2
53.1 1
53.2 3
54 3
54.1 4
54.2 4
54.3 3
54.4 NA
55 4
55.1 1
55.2 4
55.3 NA
55.4 1
56 1
56.1 2
56.2 2
56.3 3
56.4 4
56.5 4
57 2
57.1 2
57.2 4
57.3 NA
58 1
58.1 2
58.2 2
58.3 4
58.4 1
58.5 NA
59 4
59.1 1
60 1
61 1
61.1 1
61.2 NA
61.3 1
61.4 3
62 NA
62.1 NA
62.2 NA
62.3 3
63 4
63.1 3
64 4
65 2
65.1 1
65.2 3
65.3 NA
66 1
66.1 3
66.2 2
67 3
68 3
68.1 4
68.2 3
68.3 1
68.4 4
69 4
70 4
70.1 4
71 2
71.1 NA
71.2 4
71.3 3
71.4 1
72 1
72.1 NA
72.2 4
72.3 1
72.4 3
72.5 1
73 2
74 4
75 1
76 2
76.1 1
76.2 1
77 3
78 3
79 NA
79.1 3
79.2 NA
80 2
80.1 3
80.2 1
81 3
81.1 NA
81.2 3
81.3 2
82 NA
82.1 3
82.2 1
83 4
83.1 NA
83.2 2
83.3 NA
84 2
84.1 1
85 1
85.1 4
85.2 3
85.3 3
85.4 NA
85.5 2
86 1
86.1 3
86.2 1
86.3 2
86.4 3
86.5 4
87 NA
87.1 3
87.2 3
88 NA
88.1 1
88.2 2
88.3 NA
89 3
90 2
90.1 2
90.2 2
90.3 4
91 2
91.1 NA
91.2 3
92 2
93 3
93.1 2
93.2 3
93.3 2
93.4 4
94 NA
94.1 2
94.2 NA
94.3 3
94.4 4
94.5 3
95 NA
95.1 2
95.2 3
96 3
96.1 NA
96.2 4
96.3 3
96.4 NA
96.5 1
97 2
97.1 1
98 2
98.1 1
98.2 3
99 NA
99.1 NA
99.2 4
100 1
100.1 NA
100.2 1
100.3 4
100.4 1
$m1b$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m1b$mu_reg_ordinal
[1] 0
$m1b$tau_reg_ordinal
[1] 1e-04
$m1b$mu_delta_ordinal
[1] 0
$m1b$tau_delta_ordinal
[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
o1 c1
1 2 0.7592026489
1.1 1 0.9548337990
1.2 1 0.5612235156
1.3 2 1.1873391025
2 3 0.9192204198
2.1 1 -0.1870730476
2.2 2 1.2517512331
3 1 -0.0605087604
3.1 3 0.3788637747
3.2 2 0.9872578281
4 3 1.4930175328
4.1 2 -0.7692526880
4.2 1 0.9180841450
4.3 2 -0.0541170782
5 2 -0.1376784521
5.1 2 -0.2740585866
5.2 3 0.4670496929
5.3 2 0.1740288049
6 3 0.9868044683
7 3 -0.1280320918
7.1 1 0.4242971219
7.2 1 0.0777182491
8 2 -0.5791408712
8.1 2 0.3128604232
8.2 3 0.6258446356
8.3 3 -0.1040137707
8.4 2 0.0481450285
8.5 2 0.3831763675
9 2 -0.1757592269
9.1 2 -0.1791541200
9.2 3 -0.0957042935
10 2 -0.5598409704
10.1 1 -0.2318340451
11 2 0.5086859475
11.1 3 0.4951758188
11.2 3 -1.1022162541
11.3 1 -0.0611636705
11.4 3 -0.4971774316
12 1 -0.2433996286
13 2 0.8799673116
13.1 1 0.1079022586
14 3 0.9991752617
14.1 3 -0.1094019046
14.2 2 0.1518967560
14.3 1 0.3521012473
15 3 0.3464447888
15.1 3 -0.4767313971
15.2 2 0.5759767791
15.3 2 -0.1713452662
16 3 0.4564754473
16.1 1 1.0652558311
16.2 3 0.6971872493
16.3 1 0.5259331838
16.4 1 0.2046601798
16.5 2 1.0718540464
17 2 0.6048676222
17.1 2 0.2323298304
17.2 3 1.2617499032
17.3 2 -0.3913230895
17.4 3 0.9577299112
18 2 -0.0050324072
19 3 -0.4187468937
19.1 2 -0.4478828944
19.2 2 -1.1966721302
19.3 2 -0.5877091668
20 1 0.6838223064
20.1 2 0.3278571109
20.2 2 -0.8489831990
20.3 1 1.3169975191
20.4 1 0.0444804531
20.5 2 -0.4535207652
21 2 -0.4030302960
21.1 2 -0.4069674045
21.2 2 1.0650265940
22 1 -0.0673274516
22.1 1 0.9601388170
23 1 0.5556634840
23.1 2 1.4407865964
24 1 0.3856376411
25 2 0.3564400705
25.1 1 0.0982553434
25.2 1 0.1928682598
25.3 1 -0.0192488594
25.4 2 0.4466012931
25.5 2 1.1425193342
26 2 0.5341531449
26.1 3 1.2268695927
26.2 2 0.3678294939
26.3 1 0.5948516018
27 2 -0.3342844147
27.1 2 -0.4835141229
28 1 -0.7145915499
28.1 1 0.5063671955
28.2 1 -0.2067413142
28.3 1 0.1196789973
29 2 0.1392699487
29.1 2 0.7960234776
29.2 1 1.0398214352
29.3 2 0.0813246429
30 2 -0.3296323050
30.1 2 1.3635850954
30.2 2 0.7354171050
31 2 0.3708398217
32 3 -0.0474059668
32.1 1 1.2507771489
32.2 3 0.1142915519
32.3 3 0.6773270619
33 2 0.1774293842
33.1 2 0.6159606291
34 3 0.8590979166
34.1 2 0.0546216775
34.2 2 -0.0897224473
34.3 1 0.4163395571
35 2 -1.4693520528
35.1 3 -0.3031734330
35.2 2 -0.6045512101
36 3 0.9823048960
36.1 2 1.4466051416
36.2 3 1.1606752905
36.3 3 0.8373091576
36.4 3 0.2640591685
37 3 0.1177313455
37.1 3 -0.1415483779
37.2 3 0.0054610124
38 2 0.8078948077
39 1 0.9876451040
39.1 2 -0.3431222274
39.2 2 -1.7909380751
39.3 1 -0.1798746191
39.4 1 -0.1850961689
39.5 1 0.4544226146
40 3 0.5350190436
40.1 1 0.4189342752
40.2 1 0.4211994981
40.3 2 0.0916687506
41 3 -0.1035047421
41.1 2 -0.4684202411
41.2 1 0.5972615368
41.3 1 0.9885613862
41.4 3 -0.3908036794
42 2 -0.0338893961
42.1 1 -0.4498363172
43 2 0.8965546110
43.1 2 0.6199122090
43.2 1 0.1804894429
44 3 1.3221409285
44.1 2 0.3416426284
44.2 1 0.5706610068
44.3 1 1.2679497430
45 1 0.1414983160
45.1 1 0.7220892521
46 1 1.5391054233
46.1 2 0.3889107049
46.2 3 0.1248719493
47 1 0.2014101100
47.1 1 0.2982973539
47.2 1 1.1518107179
47.3 3 0.5196802157
47.4 2 0.3702301552
48 3 -0.2128602862
48.1 2 -0.5337239976
49 1 -0.5236770035
50 2 0.3897705981
51 2 -0.7213343736
52 2 0.3758235358
52.1 3 0.7138067080
52.2 2 0.8872895233
52.3 1 -0.9664587437
52.4 3 0.0254566848
52.5 2 0.4155259424
53 2 0.5675736897
53.1 1 -0.3154088781
53.2 1 0.2162315769
54 1 -0.0880802382
54.1 2 0.4129127672
54.2 3 1.0119546775
54.3 1 -0.1112901990
54.4 2 0.8587727145
55 1 -0.0116453589
55.1 1 0.5835528661
55.2 2 -1.0010857254
55.3 1 -0.4796526070
55.4 1 -0.1202746964
56 3 0.5176377612
56.1 2 -1.1136932588
56.2 3 -0.0168103281
56.3 3 0.3933023606
56.4 1 0.3714625139
56.5 3 0.7811448179
57 2 -1.0868304872
57.1 2 0.8018626997
57.2 2 -0.1159517011
57.3 2 0.6785562445
58 3 1.6476207996
58.1 2 0.3402652711
58.2 3 -0.1111300753
58.3 3 -0.5409234285
58.4 2 -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 1 1.1717723589
61.4 2 -0.0629201596
62 2 -0.3979137604
62.1 3 0.6830738372
62.2 1 0.4301745954
62.3 2 -0.0333139957
63 1 0.3345678035
63.1 3 0.3643769511
64 2 0.3949911859
65 1 1.2000091513
65.1 2 0.0110122646
65.2 1 -0.5776452043
65.3 1 -0.1372183563
66 1 -0.5081302805
66.1 1 -0.1447837412
66.2 3 0.1906241379
67 3 1.6716027681
68 1 0.5691848839
68.1 2 0.1004860389
68.2 2 -0.0061241827
68.3 2 0.7443745962
68.4 2 0.8726923437
69 2 0.0381382683
70 1 0.8126204217
70.1 3 0.4691503050
71 3 -0.5529062591
71.1 3 -0.1103252087
71.2 3 1.7178492547
71.3 2 -1.0118346755
71.4 2 1.8623785017
72 1 -0.4521659275
72.1 1 0.1375317317
72.2 2 -0.4170988856
72.3 2 0.7107266765
72.4 1 0.1451969143
72.5 1 1.6298050306
73 1 -0.0307469467
74 1 0.3730017941
75 3 -0.4908003566
76 3 -0.9888876620
76.1 3 0.0003798292
76.2 1 -0.8421863763
77 2 -0.4986802480
78 2 0.0417330969
79 1 -0.3767450660
79.1 2 0.1516000028
79.2 1 -0.1888160741
80 2 -0.0041558414
80.1 2 -0.0329337062
80.2 3 0.5046816157
81 3 -0.9493950353
81.1 2 0.2443038954
81.2 3 0.6476958410
81.3 3 0.4182528210
82 3 1.1088801952
82.1 2 0.9334157763
82.2 2 0.4958140634
83 3 0.5104724530
83.1 3 -0.0513309106
83.2 3 -0.2067792494
83.3 2 -0.0534169155
84 3 -0.0255753653
84.1 3 -1.8234189877
85 3 -0.0114038622
85.1 2 -0.0577615939
85.2 2 -0.2241856342
85.3 2 -0.0520175929
85.4 1 0.2892733846
85.5 3 -0.3740417009
86 2 0.4293735089
86.1 3 -0.1363456521
86.2 1 0.1230989293
86.3 1 0.3305413955
86.4 1 2.6003411822
86.5 3 -0.1420690052
87 1 1.0457427869
87.1 1 -0.2973007190
87.2 1 0.4396872616
88 3 -0.0601928334
88.1 1 -1.0124347595
88.2 1 0.5730917016
88.3 1 -0.0029455332
89 1 1.5465903721
90 2 0.0626760573
90.1 1 1.1896872985
90.2 3 0.2597888783
90.3 1 0.6599799887
91 1 1.1213651365
91.1 2 1.2046371625
91.2 1 0.3395603754
92 2 0.4674939332
93 1 0.2677965647
93.1 3 1.6424445368
93.2 1 0.7101700066
93.3 3 1.1222322893
93.4 1 1.4628960401
94 2 -0.2904211940
94.1 3 0.0147813580
94.2 1 -0.4536774482
94.3 3 0.6793464917
94.4 2 -0.9411356550
94.5 3 0.5683867264
95 3 0.2375652188
95.1 1 0.0767152977
95.2 2 -0.6886731251
96 1 0.7813892121
96.1 3 0.3391519695
96.2 1 -0.4857246503
96.3 1 0.8771471244
96.4 2 1.9030768981
96.5 3 -0.1684332749
97 3 1.3775130083
97.1 3 -1.7323228619
98 3 -1.2648518889
98.1 1 -0.9042716241
98.2 1 -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 2 0.8861545820
100.3 1 0.1277046316
100.4 1 -0.0834577654
$m1c$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
$m1c$mu_reg_ordinal
[1] 0
$m1c$tau_reg_ordinal
[1] 1e-04
$m1c$mu_delta_ordinal
[1] 0
$m1c$tau_delta_ordinal
[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
o2 c1
1 1 0.7592026489
1.1 1 0.9548337990
1.2 3 0.5612235156
1.3 1 1.1873391025
2 4 0.9192204198
2.1 4 -0.1870730476
2.2 2 1.2517512331
3 2 -0.0605087604
3.1 4 0.3788637747
3.2 2 0.9872578281
4 4 1.4930175328
4.1 3 -0.7692526880
4.2 NA 0.9180841450
4.3 2 -0.0541170782
5 2 -0.1376784521
5.1 4 -0.2740585866
5.2 2 0.4670496929
5.3 4 0.1740288049
6 3 0.9868044683
7 1 -0.1280320918
7.1 NA 0.4242971219
7.2 4 0.0777182491
8 1 -0.5791408712
8.1 3 0.3128604232
8.2 1 0.6258446356
8.3 4 -0.1040137707
8.4 3 0.0481450285
8.5 3 0.3831763675
9 2 -0.1757592269
9.1 2 -0.1791541200
9.2 4 -0.0957042935
10 1 -0.5598409704
10.1 4 -0.2318340451
11 3 0.5086859475
11.1 1 0.4951758188
11.2 4 -1.1022162541
11.3 3 -0.0611636705
11.4 3 -0.4971774316
12 3 -0.2433996286
13 NA 0.8799673116
13.1 1 0.1079022586
14 1 0.9991752617
14.1 4 -0.1094019046
14.2 3 0.1518967560
14.3 4 0.3521012473
15 1 0.3464447888
15.1 4 -0.4767313971
15.2 NA 0.5759767791
15.3 2 -0.1713452662
16 NA 0.4564754473
16.1 NA 1.0652558311
16.2 1 0.6971872493
16.3 3 0.5259331838
16.4 3 0.2046601798
16.5 1 1.0718540464
17 3 0.6048676222
17.1 2 0.2323298304
17.2 2 1.2617499032
17.3 3 -0.3913230895
17.4 1 0.9577299112
18 4 -0.0050324072
19 1 -0.4187468937
19.1 NA -0.4478828944
19.2 NA -1.1966721302
19.3 2 -0.5877091668
20 1 0.6838223064
20.1 4 0.3278571109
20.2 3 -0.8489831990
20.3 3 1.3169975191
20.4 1 0.0444804531
20.5 3 -0.4535207652
21 3 -0.4030302960
21.1 1 -0.4069674045
21.2 2 1.0650265940
22 4 -0.0673274516
22.1 NA 0.9601388170
23 4 0.5556634840
23.1 NA 1.4407865964
24 3 0.3856376411
25 1 0.3564400705
25.1 3 0.0982553434
25.2 2 0.1928682598
25.3 1 -0.0192488594
25.4 1 0.4466012931
25.5 NA 1.1425193342
26 3 0.5341531449
26.1 4 1.2268695927
26.2 3 0.3678294939
26.3 1 0.5948516018
27 4 -0.3342844147
27.1 4 -0.4835141229
28 1 -0.7145915499
28.1 2 0.5063671955
28.2 3 -0.2067413142
28.3 3 0.1196789973
29 4 0.1392699487
29.1 4 0.7960234776
29.2 3 1.0398214352
29.3 2 0.0813246429
30 3 -0.3296323050
30.1 4 1.3635850954
30.2 4 0.7354171050
31 2 0.3708398217
32 NA -0.0474059668
32.1 2 1.2507771489
32.2 4 0.1142915519
32.3 3 0.6773270619
33 4 0.1774293842
33.1 4 0.6159606291
34 NA 0.8590979166
34.1 NA 0.0546216775
34.2 NA -0.0897224473
34.3 NA 0.4163395571
35 4 -1.4693520528
35.1 1 -0.3031734330
35.2 NA -0.6045512101
36 1 0.9823048960
36.1 1 1.4466051416
36.2 2 1.1606752905
36.3 2 0.8373091576
36.4 1 0.2640591685
37 4 0.1177313455
37.1 2 -0.1415483779
37.2 2 0.0054610124
38 NA 0.8078948077
39 NA 0.9876451040
39.1 2 -0.3431222274
39.2 2 -1.7909380751
39.3 3 -0.1798746191
39.4 3 -0.1850961689
39.5 1 0.4544226146
40 1 0.5350190436
40.1 2 0.4189342752
40.2 NA 0.4211994981
40.3 2 0.0916687506
41 4 -0.1035047421
41.1 3 -0.4684202411
41.2 4 0.5972615368
41.3 NA 0.9885613862
41.4 4 -0.3908036794
42 4 -0.0338893961
42.1 4 -0.4498363172
43 NA 0.8965546110
43.1 NA 0.6199122090
43.2 3 0.1804894429
44 2 1.3221409285
44.1 4 0.3416426284
44.2 NA 0.5706610068
44.3 1 1.2679497430
45 3 0.1414983160
45.1 4 0.7220892521
46 NA 1.5391054233
46.1 4 0.3889107049
46.2 4 0.1248719493
47 3 0.2014101100
47.1 2 0.2982973539
47.2 1 1.1518107179
47.3 2 0.5196802157
47.4 1 0.3702301552
48 4 -0.2128602862
48.1 NA -0.5337239976
49 4 -0.5236770035
50 4 0.3897705981
51 4 -0.7213343736
52 NA 0.3758235358
52.1 NA 0.7138067080
52.2 3 0.8872895233
52.3 NA -0.9664587437
52.4 4 0.0254566848
52.5 1 0.4155259424
53 2 0.5675736897
53.1 1 -0.3154088781
53.2 3 0.2162315769
54 3 -0.0880802382
54.1 4 0.4129127672
54.2 4 1.0119546775
54.3 3 -0.1112901990
54.4 NA 0.8587727145
55 4 -0.0116453589
55.1 1 0.5835528661
55.2 4 -1.0010857254
55.3 NA -0.4796526070
55.4 1 -0.1202746964
56 1 0.5176377612
56.1 2 -1.1136932588
56.2 2 -0.0168103281
56.3 3 0.3933023606
56.4 4 0.3714625139
56.5 4 0.7811448179
57 2 -1.0868304872
57.1 2 0.8018626997
57.2 4 -0.1159517011
57.3 NA 0.6785562445
58 1 1.6476207996
58.1 2 0.3402652711
58.2 2 -0.1111300753
58.3 4 -0.5409234285
58.4 1 -0.1271327672
58.5 NA 0.8713264822
59 4 0.4766421367
59.1 1 1.0028089765
60 1 0.5231452932
61 1 -0.7190130614
61.1 1 0.8353702312
61.2 NA 1.0229058138
61.3 1 1.1717723589
61.4 3 -0.0629201596
62 NA -0.3979137604
62.1 NA 0.6830738372
62.2 NA 0.4301745954
62.3 3 -0.0333139957
63 4 0.3345678035
63.1 3 0.3643769511
64 4 0.3949911859
65 2 1.2000091513
65.1 1 0.0110122646
65.2 3 -0.5776452043
65.3 NA -0.1372183563
66 1 -0.5081302805
66.1 3 -0.1447837412
66.2 2 0.1906241379
67 3 1.6716027681
68 3 0.5691848839
68.1 4 0.1004860389
68.2 3 -0.0061241827
68.3 1 0.7443745962
68.4 4 0.8726923437
69 4 0.0381382683
70 4 0.8126204217
70.1 4 0.4691503050
71 2 -0.5529062591
71.1 NA -0.1103252087
71.2 4 1.7178492547
71.3 3 -1.0118346755
71.4 1 1.8623785017
72 1 -0.4521659275
72.1 NA 0.1375317317
72.2 4 -0.4170988856
72.3 1 0.7107266765
72.4 3 0.1451969143
72.5 1 1.6298050306
73 2 -0.0307469467
74 4 0.3730017941
75 1 -0.4908003566
76 2 -0.9888876620
76.1 1 0.0003798292
76.2 1 -0.8421863763
77 3 -0.4986802480
78 3 0.0417330969
79 NA -0.3767450660
79.1 3 0.1516000028
79.2 NA -0.1888160741
80 2 -0.0041558414
80.1 3 -0.0329337062
80.2 1 0.5046816157
81 3 -0.9493950353
81.1 NA 0.2443038954
81.2 3 0.6476958410
81.3 2 0.4182528210
82 NA 1.1088801952
82.1 3 0.9334157763
82.2 1 0.4958140634
83 4 0.5104724530
83.1 NA -0.0513309106
83.2 2 -0.2067792494
83.3 NA -0.0534169155
84 2 -0.0255753653
84.1 1 -1.8234189877
85 1 -0.0114038622
85.1 4 -0.0577615939
85.2 3 -0.2241856342
85.3 3 -0.0520175929
85.4 NA 0.2892733846
85.5 2 -0.3740417009
86 1 0.4293735089
86.1 3 -0.1363456521
86.2 1 0.1230989293
86.3 2 0.3305413955
86.4 3 2.6003411822
86.5 4 -0.1420690052
87 NA 1.0457427869
87.1 3 -0.2973007190
87.2 3 0.4396872616
88 NA -0.0601928334
88.1 1 -1.0124347595
88.2 2 0.5730917016
88.3 NA -0.0029455332
89 3 1.5465903721
90 2 0.0626760573
90.1 2 1.1896872985
90.2 2 0.2597888783
90.3 4 0.6599799887
91 2 1.1213651365
91.1 NA 1.2046371625
91.2 3 0.3395603754
92 2 0.4674939332
93 3 0.2677965647
93.1 2 1.6424445368
93.2 3 0.7101700066
93.3 2 1.1222322893
93.4 4 1.4628960401
94 NA -0.2904211940
94.1 2 0.0147813580
94.2 NA -0.4536774482
94.3 3 0.6793464917
94.4 4 -0.9411356550
94.5 3 0.5683867264
95 NA 0.2375652188
95.1 2 0.0767152977
95.2 3 -0.6886731251
96 3 0.7813892121
96.1 NA 0.3391519695
96.2 4 -0.4857246503
96.3 3 0.8771471244
96.4 NA 1.9030768981
96.5 1 -0.1684332749
97 2 1.3775130083
97.1 1 -1.7323228619
98 2 -1.2648518889
98.1 1 -0.9042716241
98.2 3 -0.1560385207
99 NA 0.7993356425
99.1 NA 1.0355522332
99.2 4 -0.1150895843
100 1 0.0369067906
100.1 NA 1.6023713093
100.2 1 0.8861545820
100.3 4 0.1277046316
100.4 1 -0.0834577654
$m1d$spM_lvlone
center scale
o2 NA NA
c1 0.2559996 0.6718095
$m1d$mu_reg_ordinal
[1] 0
$m1d$tau_reg_ordinal
[1] 1e-04
$m1d$mu_delta_ordinal
[1] 0
$m1d$tau_delta_ordinal
[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
o1
1 2
1.1 1
1.2 1
1.3 2
2 3
2.1 1
2.2 2
3 1
3.1 3
3.2 2
4 3
4.1 2
4.2 1
4.3 2
5 2
5.1 2
5.2 3
5.3 2
6 3
7 3
7.1 1
7.2 1
8 2
8.1 2
8.2 3
8.3 3
8.4 2
8.5 2
9 2
9.1 2
9.2 3
10 2
10.1 1
11 2
11.1 3
11.2 3
11.3 1
11.4 3
12 1
13 2
13.1 1
14 3
14.1 3
14.2 2
14.3 1
15 3
15.1 3
15.2 2
15.3 2
16 3
16.1 1
16.2 3
16.3 1
16.4 1
16.5 2
17 2
17.1 2
17.2 3
17.3 2
17.4 3
18 2
19 3
19.1 2
19.2 2
19.3 2
20 1
20.1 2
20.2 2
20.3 1
20.4 1
20.5 2
21 2
21.1 2
21.2 2
22 1
22.1 1
23 1
23.1 2
24 1
25 2
25.1 1
25.2 1
25.3 1
25.4 2
25.5 2
26 2
26.1 3
26.2 2
26.3 1
27 2
27.1 2
28 1
28.1 1
28.2 1
28.3 1
29 2
29.1 2
29.2 1
29.3 2
30 2
30.1 2
30.2 2
31 2
32 3
32.1 1
32.2 3
32.3 3
33 2
33.1 2
34 3
34.1 2
34.2 2
34.3 1
35 2
35.1 3
35.2 2
36 3
36.1 2
36.2 3
36.3 3
36.4 3
37 3
37.1 3
37.2 3
38 2
39 1
39.1 2
39.2 2
39.3 1
39.4 1
39.5 1
40 3
40.1 1
40.2 1
40.3 2
41 3
41.1 2
41.2 1
41.3 1
41.4 3
42 2
42.1 1
43 2
43.1 2
43.2 1
44 3
44.1 2
44.2 1
44.3 1
45 1
45.1 1
46 1
46.1 2
46.2 3
47 1
47.1 1
47.2 1
47.3 3
47.4 2
48 3
48.1 2
49 1
50 2
51 2
52 2
52.1 3
52.2 2
52.3 1
52.4 3
52.5 2
53 2
53.1 1
53.2 1
54 1
54.1 2
54.2 3
54.3 1
54.4 2
55 1
55.1 1
55.2 2
55.3 1
55.4 1
56 3
56.1 2
56.2 3
56.3 3
56.4 1
56.5 3
57 2
57.1 2
57.2 2
57.3 2
58 3
58.1 2
58.2 3
58.3 3
58.4 2
58.5 3
59 3
59.1 1
60 3
61 1
61.1 2
61.2 2
61.3 1
61.4 2
62 2
62.1 3
62.2 1
62.3 2
63 1
63.1 3
64 2
65 1
65.1 2
65.2 1
65.3 1
66 1
66.1 1
66.2 3
67 3
68 1
68.1 2
68.2 2
68.3 2
68.4 2
69 2
70 1
70.1 3
71 3
71.1 3
71.2 3
71.3 2
71.4 2
72 1
72.1 1
72.2 2
72.3 2
72.4 1
72.5 1
73 1
74 1
75 3
76 3
76.1 3
76.2 1
77 2
78 2
79 1
79.1 2
79.2 1
80 2
80.1 2
80.2 3
81 3
81.1 2
81.2 3
81.3 3
82 3
82.1 2
82.2 2
83 3
83.1 3
83.2 3
83.3 2
84 3
84.1 3
85 3
85.1 2
85.2 2
85.3 2
85.4 1
85.5 3
86 2
86.1 3
86.2 1
86.3 1
86.4 1
86.5 3
87 1
87.1 1
87.2 1
88 3
88.1 1
88.2 1
88.3 1
89 1
90 2
90.1 1
90.2 3
90.3 1
91 1
91.1 2
91.2 1
92 2
93 1
93.1 3
93.2 1
93.3 3
93.4 1
94 2
94.1 3
94.2 1
94.3 3
94.4 2
94.5 3
95 3
95.1 1
95.2 2
96 1
96.1 3
96.2 1
96.3 1
96.4 2
96.5 3
97 3
97.1 3
98 3
98.1 1
98.2 1
99 2
99.1 2
99.2 1
100 1
100.1 2
100.2 2
100.3 1
100.4 1
$m2a$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_ordinal
[1] 0
$m2a$tau_reg_ordinal
[1] 1e-04
$m2a$mu_delta_ordinal
[1] 0
$m2a$tau_delta_ordinal
[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
o2
1 1
1.1 1
1.2 3
1.3 1
2 4
2.1 4
2.2 2
3 2
3.1 4
3.2 2
4 4
4.1 3
4.2 NA
4.3 2
5 2
5.1 4
5.2 2
5.3 4
6 3
7 1
7.1 NA
7.2 4
8 1
8.1 3
8.2 1
8.3 4
8.4 3
8.5 3
9 2
9.1 2
9.2 4
10 1
10.1 4
11 3
11.1 1
11.2 4
11.3 3
11.4 3
12 3
13 NA
13.1 1
14 1
14.1 4
14.2 3
14.3 4
15 1
15.1 4
15.2 NA
15.3 2
16 NA
16.1 NA
16.2 1
16.3 3
16.4 3
16.5 1
17 3
17.1 2
17.2 2
17.3 3
17.4 1
18 4
19 1
19.1 NA
19.2 NA
19.3 2
20 1
20.1 4
20.2 3
20.3 3
20.4 1
20.5 3
21 3
21.1 1
21.2 2
22 4
22.1 NA
23 4
23.1 NA
24 3
25 1
25.1 3
25.2 2
25.3 1
25.4 1
25.5 NA
26 3
26.1 4
26.2 3
26.3 1
27 4
27.1 4
28 1
28.1 2
28.2 3
28.3 3
29 4
29.1 4
29.2 3
29.3 2
30 3
30.1 4
30.2 4
31 2
32 NA
32.1 2
32.2 4
32.3 3
33 4
33.1 4
34 NA
34.1 NA
34.2 NA
34.3 NA
35 4
35.1 1
35.2 NA
36 1
36.1 1
36.2 2
36.3 2
36.4 1
37 4
37.1 2
37.2 2
38 NA
39 NA
39.1 2
39.2 2
39.3 3
39.4 3
39.5 1
40 1
40.1 2
40.2 NA
40.3 2
41 4
41.1 3
41.2 4
41.3 NA
41.4 4
42 4
42.1 4
43 NA
43.1 NA
43.2 3
44 2
44.1 4
44.2 NA
44.3 1
45 3
45.1 4
46 NA
46.1 4
46.2 4
47 3
47.1 2
47.2 1
47.3 2
47.4 1
48 4
48.1 NA
49 4
50 4
51 4
52 NA
52.1 NA
52.2 3
52.3 NA
52.4 4
52.5 1
53 2
53.1 1
53.2 3
54 3
54.1 4
54.2 4
54.3 3
54.4 NA
55 4
55.1 1
55.2 4
55.3 NA
55.4 1
56 1
56.1 2
56.2 2
56.3 3
56.4 4
56.5 4
57 2
57.1 2
57.2 4
57.3 NA
58 1
58.1 2
58.2 2
58.3 4
58.4 1
58.5 NA
59 4
59.1 1
60 1
61 1
61.1 1
61.2 NA
61.3 1
61.4 3
62 NA
62.1 NA
62.2 NA
62.3 3
63 4
63.1 3
64 4
65 2
65.1 1
65.2 3
65.3 NA
66 1
66.1 3
66.2 2
67 3
68 3
68.1 4
68.2 3
68.3 1
68.4 4
69 4
70 4
70.1 4
71 2
71.1 NA
71.2 4
71.3 3
71.4 1
72 1
72.1 NA
72.2 4
72.3 1
72.4 3
72.5 1
73 2
74 4
75 1
76 2
76.1 1
76.2 1
77 3
78 3
79 NA
79.1 3
79.2 NA
80 2
80.1 3
80.2 1
81 3
81.1 NA
81.2 3
81.3 2
82 NA
82.1 3
82.2 1
83 4
83.1 NA
83.2 2
83.3 NA
84 2
84.1 1
85 1
85.1 4
85.2 3
85.3 3
85.4 NA
85.5 2
86 1
86.1 3
86.2 1
86.3 2
86.4 3
86.5 4
87 NA
87.1 3
87.2 3
88 NA
88.1 1
88.2 2
88.3 NA
89 3
90 2
90.1 2
90.2 2
90.3 4
91 2
91.1 NA
91.2 3
92 2
93 3
93.1 2
93.2 3
93.3 2
93.4 4
94 NA
94.1 2
94.2 NA
94.3 3
94.4 4
94.5 3
95 NA
95.1 2
95.2 3
96 3
96.1 NA
96.2 4
96.3 3
96.4 NA
96.5 1
97 2
97.1 1
98 2
98.1 1
98.2 3
99 NA
99.1 NA
99.2 4
100 1
100.1 NA
100.2 1
100.3 4
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_ordinal
[1] 0
$m2b$tau_reg_ordinal
[1] 1e-04
$m2b$mu_delta_ordinal
[1] 0
$m2b$tau_delta_ordinal
[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
o1 c2
1 2 NA
1.1 1 -0.08061445
1.2 1 -0.26523782
1.3 2 -0.30260393
2 3 -0.33443795
2.1 1 -0.11819800
2.2 2 -0.31532280
3 1 -0.12920657
3.1 3 NA
3.2 2 NA
4 3 -0.31177403
4.1 2 -0.23894886
4.2 1 -0.15533613
4.3 2 -0.14644545
5 2 -0.28360457
5.1 2 -0.20135143
5.2 3 -0.28293375
5.3 2 NA
6 3 -0.08617066
7 3 -0.22243495
7.1 1 NA
7.2 1 NA
8 2 NA
8.1 2 NA
8.2 3 NA
8.3 3 -0.35148972
8.4 2 0.03661023
8.5 2 -0.08424534
9 2 NA
9.1 2 -0.43509340
9.2 3 -0.22527490
10 2 NA
10.1 1 NA
11 2 -0.08587475
11.1 3 -0.06157340
11.2 3 -0.12436018
11.3 1 -0.21377934
11.4 3 -0.32208329
12 1 NA
13 2 NA
13.1 1 -0.40300449
14 3 -0.28992072
14.1 3 NA
14.2 2 NA
14.3 1 -0.21979936
15 3 NA
15.1 3 -0.29092263
15.2 2 -0.19392239
15.3 2 -0.25718384
16 3 -0.45041108
16.1 1 -0.07599066
16.2 3 -0.32385667
16.3 1 -0.38326110
16.4 1 -0.22845856
16.5 2 -0.25497157
17 2 NA
17.1 2 -0.22105143
17.2 3 NA
17.3 2 NA
17.4 3 -0.15098046
18 2 -0.09870041
19 3 -0.26680239
19.1 2 -0.15815241
19.2 2 -0.14717437
19.3 2 -0.21271374
20 1 -0.22087628
20.1 2 NA
20.2 2 -0.30127439
20.3 1 -0.11782590
20.4 1 -0.19857957
20.5 2 -0.24338208
21 2 -0.31407992
21.1 2 -0.12424941
21.2 2 -0.27672716
22 1 -0.23790593
22.1 1 -0.15996535
23 1 -0.18236682
23.1 2 -0.20823302
24 1 -0.29026416
25 2 -0.36139273
25.1 1 -0.19571118
25.2 1 -0.21379355
25.3 1 -0.33876012
25.4 2 NA
25.5 2 -0.04068446
26 2 -0.16846716
26.1 3 -0.10440642
26.2 2 -0.26884827
26.3 1 NA
27 2 -0.19520794
27.1 2 -0.17622638
28 1 -0.32164962
28.1 1 -0.27003852
28.2 1 -0.07235801
28.3 1 -0.13462982
29 2 -0.32432030
29.1 2 -0.27034171
29.2 1 -0.10197448
29.3 2 -0.27606945
30 2 -0.06949300
30.1 2 -0.11511035
30.2 2 -0.16215882
31 2 0.05707733
32 3 -0.18446298
32.1 1 -0.14270013
32.2 3 -0.20530798
32.3 3 -0.14705649
33 2 -0.15252819
33.1 2 NA
34 3 -0.30378735
34.1 2 -0.11982431
34.2 2 -0.24278671
34.3 1 -0.19971833
35 2 NA
35.1 3 -0.24165780
35.2 2 NA
36 3 -0.49062180
36.1 2 -0.25651700
36.2 3 NA
36.3 3 -0.30401274
36.4 3 NA
37 3 -0.15276529
37.1 3 -0.30016169
37.2 3 0.06809545
38 2 -0.11218486
39 1 -0.38072211
39.1 2 -0.32094428
39.2 2 NA
39.3 1 -0.40173480
39.4 1 -0.20041848
39.5 1 -0.26027990
40 3 -0.19751956
40.1 1 -0.08399467
40.2 1 -0.20864416
40.3 2 NA
41 3 -0.26096953
41.1 2 -0.23953874
41.2 1 -0.03079344
41.3 1 NA
41.4 3 NA
42 2 -0.16084527
42.1 1 -0.13812521
43 2 -0.08864017
43.1 2 -0.12583158
43.2 1 -0.29253959
44 3 -0.22697597
44.1 2 NA
44.2 1 NA
44.3 1 -0.40544012
45 1 -0.19274788
45.1 1 -0.34860483
46 1 -0.28547861
46.1 2 -0.21977836
46.2 3 NA
47 1 -0.08597098
47.1 1 -0.35424828
47.2 1 -0.24262576
47.3 3 -0.30426315
47.4 2 NA
48 3 NA
48.1 2 NA
49 1 -0.42198781
50 2 -0.19959516
51 2 -0.16556964
52 2 -0.07438732
52.1 3 -0.37537080
52.2 2 -0.24222066
52.3 1 -0.31520603
52.4 3 -0.44619160
52.5 2 -0.11011682
53 2 -0.23278716
53.1 1 -0.28317264
53.2 1 -0.19517481
54 1 -0.10122856
54.1 2 -0.28325504
54.2 3 -0.16753120
54.3 1 -0.22217672
54.4 2 -0.34609328
55 1 -0.32428190
55.1 1 -0.24235382
55.2 2 -0.24065814
55.3 1 -0.23665476
55.4 1 NA
56 3 NA
56.1 2 -0.30357450
56.2 3 -0.51301630
56.3 3 -0.23743117
56.4 1 -0.17264917
56.5 3 -0.39188329
57 2 -0.18501692
57.1 2 -0.27274841
57.2 2 NA
57.3 2 -0.09898509
58 3 -0.29901358
58.1 2 -0.35390896
58.2 3 -0.16687336
58.3 3 -0.11784506
58.4 2 -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 1 -0.34184391
61.4 2 -0.30891967
62 2 NA
62.1 3 -0.10504143
62.2 1 -0.20104997
62.3 2 -0.08138677
63 1 -0.12036319
63.1 3 -0.13624992
64 2 NA
65 1 -0.34450396
65.1 2 -0.32514650
65.2 1 -0.10984996
65.3 1 -0.19275692
66 1 NA
66.1 1 NA
66.2 3 -0.11687008
67 3 NA
68 1 -0.13605235
68.1 2 -0.19790827
68.2 2 -0.17750123
68.3 2 NA
68.4 2 -0.12570562
69 2 -0.32152751
70 1 -0.28190462
70.1 3 -0.11503263
71 3 -0.13029093
71.1 3 NA
71.2 3 -0.39075433
71.3 2 -0.21401028
71.4 2 -0.40219281
72 1 -0.40337108
72.1 1 -0.25978914
72.2 2 NA
72.3 2 -0.09809866
72.4 1 -0.14240019
72.5 1 -0.14794204
73 1 -0.23509343
74 1 -0.27963171
75 3 -0.12905034
76 3 0.04775562
76.1 3 -0.19399157
76.2 1 -0.02754574
77 2 -0.19053195
78 2 -0.17172929
79 1 -0.03958515
79.1 2 -0.20328809
79.2 1 -0.23901634
80 2 -0.34031873
80.1 2 -0.19526756
80.2 3 NA
81 3 -0.18401980
81.1 2 -0.16889476
81.2 3 -0.37343047
81.3 3 NA
82 3 -0.08328168
82.1 2 -0.22167084
82.2 2 -0.20971187
83 3 -0.34228255
83.1 3 -0.34075730
83.2 3 -0.32503954
83.3 2 NA
84 3 -0.20676741
84.1 3 -0.20310458
85 3 -0.12107593
85.1 2 NA
85.2 2 -0.32509207
85.3 2 NA
85.4 1 -0.30730810
85.5 3 NA
86 2 -0.10854862
86.1 3 -0.25751662
86.2 1 -0.38943076
86.3 1 -0.24454702
86.4 1 -0.12338992
86.5 3 -0.23976984
87 1 NA
87.1 1 -0.34366972
87.2 1 NA
88 3 -0.31563888
88.1 1 -0.20304028
88.2 1 -0.40311895
88.3 1 -0.12308715
89 1 -0.18527715
90 2 -0.25029126
90.1 1 -0.26974303
90.2 3 -0.28804531
90.3 1 -0.19180615
91 1 -0.26591197
91.1 2 -0.09153470
91.2 1 -0.48414390
92 2 NA
93 1 -0.11939966
93.1 3 NA
93.2 1 -0.21089379
93.3 3 NA
93.4 1 -0.23618836
94 2 NA
94.1 3 -0.10217284
94.2 1 -0.36713471
94.3 3 -0.13806763
94.4 2 -0.42353804
94.5 3 -0.15513707
95 3 -0.24149687
95.1 1 -0.21315958
95.2 2 -0.15777208
96 1 -0.16780948
96.1 3 -0.32504815
96.2 1 -0.20395970
96.3 1 -0.06221501
96.4 2 -0.14801097
96.5 3 -0.28658893
97 3 -0.34484656
97.1 3 -0.35658805
98 3 -0.36913003
98.1 1 NA
98.2 1 -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 2 -0.12072016
100.3 1 -0.27657520
100.4 1 -0.14631556
$m2c$spM_lvlone
center scale
o1 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_ordinal
[1] 0
$m2c$tau_reg_ordinal
[1] 1e-04
$m2c$mu_delta_ordinal
[1] 0
$m2c$tau_delta_ordinal
[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
o2 c2
1 1 NA
1.1 1 -0.08061445
1.2 3 -0.26523782
1.3 1 -0.30260393
2 4 -0.33443795
2.1 4 -0.11819800
2.2 2 -0.31532280
3 2 -0.12920657
3.1 4 NA
3.2 2 NA
4 4 -0.31177403
4.1 3 -0.23894886
4.2 NA -0.15533613
4.3 2 -0.14644545
5 2 -0.28360457
5.1 4 -0.20135143
5.2 2 -0.28293375
5.3 4 NA
6 3 -0.08617066
7 1 -0.22243495
7.1 NA NA
7.2 4 NA
8 1 NA
8.1 3 NA
8.2 1 NA
8.3 4 -0.35148972
8.4 3 0.03661023
8.5 3 -0.08424534
9 2 NA
9.1 2 -0.43509340
9.2 4 -0.22527490
10 1 NA
10.1 4 NA
11 3 -0.08587475
11.1 1 -0.06157340
11.2 4 -0.12436018
11.3 3 -0.21377934
11.4 3 -0.32208329
12 3 NA
13 NA NA
13.1 1 -0.40300449
14 1 -0.28992072
14.1 4 NA
14.2 3 NA
14.3 4 -0.21979936
15 1 NA
15.1 4 -0.29092263
15.2 NA -0.19392239
15.3 2 -0.25718384
16 NA -0.45041108
16.1 NA -0.07599066
16.2 1 -0.32385667
16.3 3 -0.38326110
16.4 3 -0.22845856
16.5 1 -0.25497157
17 3 NA
17.1 2 -0.22105143
17.2 2 NA
17.3 3 NA
17.4 1 -0.15098046
18 4 -0.09870041
19 1 -0.26680239
19.1 NA -0.15815241
19.2 NA -0.14717437
19.3 2 -0.21271374
20 1 -0.22087628
20.1 4 NA
20.2 3 -0.30127439
20.3 3 -0.11782590
20.4 1 -0.19857957
20.5 3 -0.24338208
21 3 -0.31407992
21.1 1 -0.12424941
21.2 2 -0.27672716
22 4 -0.23790593
22.1 NA -0.15996535
23 4 -0.18236682
23.1 NA -0.20823302
24 3 -0.29026416
25 1 -0.36139273
25.1 3 -0.19571118
25.2 2 -0.21379355
25.3 1 -0.33876012
25.4 1 NA
25.5 NA -0.04068446
26 3 -0.16846716
26.1 4 -0.10440642
26.2 3 -0.26884827
26.3 1 NA
27 4 -0.19520794
27.1 4 -0.17622638
28 1 -0.32164962
28.1 2 -0.27003852
28.2 3 -0.07235801
28.3 3 -0.13462982
29 4 -0.32432030
29.1 4 -0.27034171
29.2 3 -0.10197448
29.3 2 -0.27606945
30 3 -0.06949300
30.1 4 -0.11511035
30.2 4 -0.16215882
31 2 0.05707733
32 NA -0.18446298
32.1 2 -0.14270013
32.2 4 -0.20530798
32.3 3 -0.14705649
33 4 -0.15252819
33.1 4 NA
34 NA -0.30378735
34.1 NA -0.11982431
34.2 NA -0.24278671
34.3 NA -0.19971833
35 4 NA
35.1 1 -0.24165780
35.2 NA NA
36 1 -0.49062180
36.1 1 -0.25651700
36.2 2 NA
36.3 2 -0.30401274
36.4 1 NA
37 4 -0.15276529
37.1 2 -0.30016169
37.2 2 0.06809545
38 NA -0.11218486
39 NA -0.38072211
39.1 2 -0.32094428
39.2 2 NA
39.3 3 -0.40173480
39.4 3 -0.20041848
39.5 1 -0.26027990
40 1 -0.19751956
40.1 2 -0.08399467
40.2 NA -0.20864416
40.3 2 NA
41 4 -0.26096953
41.1 3 -0.23953874
41.2 4 -0.03079344
41.3 NA NA
41.4 4 NA
42 4 -0.16084527
42.1 4 -0.13812521
43 NA -0.08864017
43.1 NA -0.12583158
43.2 3 -0.29253959
44 2 -0.22697597
44.1 4 NA
44.2 NA NA
44.3 1 -0.40544012
45 3 -0.19274788
45.1 4 -0.34860483
46 NA -0.28547861
46.1 4 -0.21977836
46.2 4 NA
47 3 -0.08597098
47.1 2 -0.35424828
47.2 1 -0.24262576
47.3 2 -0.30426315
47.4 1 NA
48 4 NA
48.1 NA NA
49 4 -0.42198781
50 4 -0.19959516
51 4 -0.16556964
52 NA -0.07438732
52.1 NA -0.37537080
52.2 3 -0.24222066
52.3 NA -0.31520603
52.4 4 -0.44619160
52.5 1 -0.11011682
53 2 -0.23278716
53.1 1 -0.28317264
53.2 3 -0.19517481
54 3 -0.10122856
54.1 4 -0.28325504
54.2 4 -0.16753120
54.3 3 -0.22217672
54.4 NA -0.34609328
55 4 -0.32428190
55.1 1 -0.24235382
55.2 4 -0.24065814
55.3 NA -0.23665476
55.4 1 NA
56 1 NA
56.1 2 -0.30357450
56.2 2 -0.51301630
56.3 3 -0.23743117
56.4 4 -0.17264917
56.5 4 -0.39188329
57 2 -0.18501692
57.1 2 -0.27274841
57.2 4 NA
57.3 NA -0.09898509
58 1 -0.29901358
58.1 2 -0.35390896
58.2 2 -0.16687336
58.3 4 -0.11784506
58.4 1 -0.05321983
58.5 NA -0.54457568
59 4 -0.27255364
59.1 1 NA
60 1 NA
61 1 -0.30550120
61.1 1 -0.35579892
61.2 NA NA
61.3 1 -0.34184391
61.4 3 -0.30891967
62 NA NA
62.1 NA -0.10504143
62.2 NA -0.20104997
62.3 3 -0.08138677
63 4 -0.12036319
63.1 3 -0.13624992
64 4 NA
65 2 -0.34450396
65.1 1 -0.32514650
65.2 3 -0.10984996
65.3 NA -0.19275692
66 1 NA
66.1 3 NA
66.2 2 -0.11687008
67 3 NA
68 3 -0.13605235
68.1 4 -0.19790827
68.2 3 -0.17750123
68.3 1 NA
68.4 4 -0.12570562
69 4 -0.32152751
70 4 -0.28190462
70.1 4 -0.11503263
71 2 -0.13029093
71.1 NA NA
71.2 4 -0.39075433
71.3 3 -0.21401028
71.4 1 -0.40219281
72 1 -0.40337108
72.1 NA -0.25978914
72.2 4 NA
72.3 1 -0.09809866
72.4 3 -0.14240019
72.5 1 -0.14794204
73 2 -0.23509343
74 4 -0.27963171
75 1 -0.12905034
76 2 0.04775562
76.1 1 -0.19399157
76.2 1 -0.02754574
77 3 -0.19053195
78 3 -0.17172929
79 NA -0.03958515
79.1 3 -0.20328809
79.2 NA -0.23901634
80 2 -0.34031873
80.1 3 -0.19526756
80.2 1 NA
81 3 -0.18401980
81.1 NA -0.16889476
81.2 3 -0.37343047
81.3 2 NA
82 NA -0.08328168
82.1 3 -0.22167084
82.2 1 -0.20971187
83 4 -0.34228255
83.1 NA -0.34075730
83.2 2 -0.32503954
83.3 NA NA
84 2 -0.20676741
84.1 1 -0.20310458
85 1 -0.12107593
85.1 4 NA
85.2 3 -0.32509207
85.3 3 NA
85.4 NA -0.30730810
85.5 2 NA
86 1 -0.10854862
86.1 3 -0.25751662
86.2 1 -0.38943076
86.3 2 -0.24454702
86.4 3 -0.12338992
86.5 4 -0.23976984
87 NA NA
87.1 3 -0.34366972
87.2 3 NA
88 NA -0.31563888
88.1 1 -0.20304028
88.2 2 -0.40311895
88.3 NA -0.12308715
89 3 -0.18527715
90 2 -0.25029126
90.1 2 -0.26974303
90.2 2 -0.28804531
90.3 4 -0.19180615
91 2 -0.26591197
91.1 NA -0.09153470
91.2 3 -0.48414390
92 2 NA
93 3 -0.11939966
93.1 2 NA
93.2 3 -0.21089379
93.3 2 NA
93.4 4 -0.23618836
94 NA NA
94.1 2 -0.10217284
94.2 NA -0.36713471
94.3 3 -0.13806763
94.4 4 -0.42353804
94.5 3 -0.15513707
95 NA -0.24149687
95.1 2 -0.21315958
95.2 3 -0.15777208
96 3 -0.16780948
96.1 NA -0.32504815
96.2 4 -0.20395970
96.3 3 -0.06221501
96.4 NA -0.14801097
96.5 1 -0.28658893
97 2 -0.34484656
97.1 1 -0.35658805
98 2 -0.36913003
98.1 1 NA
98.2 3 -0.17154225
99 NA -0.24753132
99.1 NA -0.27947829
99.2 4 -0.09033035
100 1 -0.17326698
100.1 NA NA
100.2 1 -0.12072016
100.3 4 -0.27657520
100.4 1 -0.14631556
$m2d$spM_lvlone
center scale
o2 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_ordinal
[1] 0
$m2d$tau_reg_ordinal
[1] 1e-04
$m2d$mu_delta_ordinal
[1] 0
$m2d$tau_delta_ordinal
[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 o1.L o1.Q
1 0.7592026489 -7.850462e-17 -0.8164966
1.1 0.9548337990 -7.071068e-01 0.4082483
1.2 0.5612235156 -7.071068e-01 0.4082483
1.3 1.1873391025 -7.850462e-17 -0.8164966
2 0.9192204198 7.071068e-01 0.4082483
2.1 -0.1870730476 -7.071068e-01 0.4082483
2.2 1.2517512331 -7.850462e-17 -0.8164966
3 -0.0605087604 -7.071068e-01 0.4082483
3.1 0.3788637747 7.071068e-01 0.4082483
3.2 0.9872578281 -7.850462e-17 -0.8164966
4 1.4930175328 7.071068e-01 0.4082483
4.1 -0.7692526880 -7.850462e-17 -0.8164966
4.2 0.9180841450 -7.071068e-01 0.4082483
4.3 -0.0541170782 -7.850462e-17 -0.8164966
5 -0.1376784521 -7.850462e-17 -0.8164966
5.1 -0.2740585866 -7.850462e-17 -0.8164966
5.2 0.4670496929 7.071068e-01 0.4082483
5.3 0.1740288049 -7.850462e-17 -0.8164966
6 0.9868044683 7.071068e-01 0.4082483
7 -0.1280320918 7.071068e-01 0.4082483
7.1 0.4242971219 -7.071068e-01 0.4082483
7.2 0.0777182491 -7.071068e-01 0.4082483
8 -0.5791408712 -7.850462e-17 -0.8164966
8.1 0.3128604232 -7.850462e-17 -0.8164966
8.2 0.6258446356 7.071068e-01 0.4082483
8.3 -0.1040137707 7.071068e-01 0.4082483
8.4 0.0481450285 -7.850462e-17 -0.8164966
8.5 0.3831763675 -7.850462e-17 -0.8164966
9 -0.1757592269 -7.850462e-17 -0.8164966
9.1 -0.1791541200 -7.850462e-17 -0.8164966
9.2 -0.0957042935 7.071068e-01 0.4082483
10 -0.5598409704 -7.850462e-17 -0.8164966
10.1 -0.2318340451 -7.071068e-01 0.4082483
11 0.5086859475 -7.850462e-17 -0.8164966
11.1 0.4951758188 7.071068e-01 0.4082483
11.2 -1.1022162541 7.071068e-01 0.4082483
11.3 -0.0611636705 -7.071068e-01 0.4082483
11.4 -0.4971774316 7.071068e-01 0.4082483
12 -0.2433996286 -7.071068e-01 0.4082483
13 0.8799673116 -7.850462e-17 -0.8164966
13.1 0.1079022586 -7.071068e-01 0.4082483
14 0.9991752617 7.071068e-01 0.4082483
14.1 -0.1094019046 7.071068e-01 0.4082483
14.2 0.1518967560 -7.850462e-17 -0.8164966
14.3 0.3521012473 -7.071068e-01 0.4082483
15 0.3464447888 7.071068e-01 0.4082483
15.1 -0.4767313971 7.071068e-01 0.4082483
15.2 0.5759767791 -7.850462e-17 -0.8164966
15.3 -0.1713452662 -7.850462e-17 -0.8164966
16 0.4564754473 7.071068e-01 0.4082483
16.1 1.0652558311 -7.071068e-01 0.4082483
16.2 0.6971872493 7.071068e-01 0.4082483
16.3 0.5259331838 -7.071068e-01 0.4082483
16.4 0.2046601798 -7.071068e-01 0.4082483
16.5 1.0718540464 -7.850462e-17 -0.8164966
17 0.6048676222 -7.850462e-17 -0.8164966
17.1 0.2323298304 -7.850462e-17 -0.8164966
17.2 1.2617499032 7.071068e-01 0.4082483
17.3 -0.3913230895 -7.850462e-17 -0.8164966
17.4 0.9577299112 7.071068e-01 0.4082483
18 -0.0050324072 -7.850462e-17 -0.8164966
19 -0.4187468937 7.071068e-01 0.4082483
19.1 -0.4478828944 -7.850462e-17 -0.8164966
19.2 -1.1966721302 -7.850462e-17 -0.8164966
19.3 -0.5877091668 -7.850462e-17 -0.8164966
20 0.6838223064 -7.071068e-01 0.4082483
20.1 0.3278571109 -7.850462e-17 -0.8164966
20.2 -0.8489831990 -7.850462e-17 -0.8164966
20.3 1.3169975191 -7.071068e-01 0.4082483
20.4 0.0444804531 -7.071068e-01 0.4082483
20.5 -0.4535207652 -7.850462e-17 -0.8164966
21 -0.4030302960 -7.850462e-17 -0.8164966
21.1 -0.4069674045 -7.850462e-17 -0.8164966
21.2 1.0650265940 -7.850462e-17 -0.8164966
22 -0.0673274516 -7.071068e-01 0.4082483
22.1 0.9601388170 -7.071068e-01 0.4082483
23 0.5556634840 -7.071068e-01 0.4082483
23.1 1.4407865964 -7.850462e-17 -0.8164966
24 0.3856376411 -7.071068e-01 0.4082483
25 0.3564400705 -7.850462e-17 -0.8164966
25.1 0.0982553434 -7.071068e-01 0.4082483
25.2 0.1928682598 -7.071068e-01 0.4082483
25.3 -0.0192488594 -7.071068e-01 0.4082483
25.4 0.4466012931 -7.850462e-17 -0.8164966
25.5 1.1425193342 -7.850462e-17 -0.8164966
26 0.5341531449 -7.850462e-17 -0.8164966
26.1 1.2268695927 7.071068e-01 0.4082483
26.2 0.3678294939 -7.850462e-17 -0.8164966
26.3 0.5948516018 -7.071068e-01 0.4082483
27 -0.3342844147 -7.850462e-17 -0.8164966
27.1 -0.4835141229 -7.850462e-17 -0.8164966
28 -0.7145915499 -7.071068e-01 0.4082483
28.1 0.5063671955 -7.071068e-01 0.4082483
28.2 -0.2067413142 -7.071068e-01 0.4082483
28.3 0.1196789973 -7.071068e-01 0.4082483
29 0.1392699487 -7.850462e-17 -0.8164966
29.1 0.7960234776 -7.850462e-17 -0.8164966
29.2 1.0398214352 -7.071068e-01 0.4082483
29.3 0.0813246429 -7.850462e-17 -0.8164966
30 -0.3296323050 -7.850462e-17 -0.8164966
30.1 1.3635850954 -7.850462e-17 -0.8164966
30.2 0.7354171050 -7.850462e-17 -0.8164966
31 0.3708398217 -7.850462e-17 -0.8164966
32 -0.0474059668 7.071068e-01 0.4082483
32.1 1.2507771489 -7.071068e-01 0.4082483
32.2 0.1142915519 7.071068e-01 0.4082483
32.3 0.6773270619 7.071068e-01 0.4082483
33 0.1774293842 -7.850462e-17 -0.8164966
33.1 0.6159606291 -7.850462e-17 -0.8164966
34 0.8590979166 7.071068e-01 0.4082483
34.1 0.0546216775 -7.850462e-17 -0.8164966
34.2 -0.0897224473 -7.850462e-17 -0.8164966
34.3 0.4163395571 -7.071068e-01 0.4082483
35 -1.4693520528 -7.850462e-17 -0.8164966
35.1 -0.3031734330 7.071068e-01 0.4082483
35.2 -0.6045512101 -7.850462e-17 -0.8164966
36 0.9823048960 7.071068e-01 0.4082483
36.1 1.4466051416 -7.850462e-17 -0.8164966
36.2 1.1606752905 7.071068e-01 0.4082483
36.3 0.8373091576 7.071068e-01 0.4082483
36.4 0.2640591685 7.071068e-01 0.4082483
37 0.1177313455 7.071068e-01 0.4082483
37.1 -0.1415483779 7.071068e-01 0.4082483
37.2 0.0054610124 7.071068e-01 0.4082483
38 0.8078948077 -7.850462e-17 -0.8164966
39 0.9876451040 -7.071068e-01 0.4082483
39.1 -0.3431222274 -7.850462e-17 -0.8164966
39.2 -1.7909380751 -7.850462e-17 -0.8164966
39.3 -0.1798746191 -7.071068e-01 0.4082483
39.4 -0.1850961689 -7.071068e-01 0.4082483
39.5 0.4544226146 -7.071068e-01 0.4082483
40 0.5350190436 7.071068e-01 0.4082483
40.1 0.4189342752 -7.071068e-01 0.4082483
40.2 0.4211994981 -7.071068e-01 0.4082483
40.3 0.0916687506 -7.850462e-17 -0.8164966
41 -0.1035047421 7.071068e-01 0.4082483
41.1 -0.4684202411 -7.850462e-17 -0.8164966
41.2 0.5972615368 -7.071068e-01 0.4082483
41.3 0.9885613862 -7.071068e-01 0.4082483
41.4 -0.3908036794 7.071068e-01 0.4082483
42 -0.0338893961 -7.850462e-17 -0.8164966
42.1 -0.4498363172 -7.071068e-01 0.4082483
43 0.8965546110 -7.850462e-17 -0.8164966
43.1 0.6199122090 -7.850462e-17 -0.8164966
43.2 0.1804894429 -7.071068e-01 0.4082483
44 1.3221409285 7.071068e-01 0.4082483
44.1 0.3416426284 -7.850462e-17 -0.8164966
44.2 0.5706610068 -7.071068e-01 0.4082483
44.3 1.2679497430 -7.071068e-01 0.4082483
45 0.1414983160 -7.071068e-01 0.4082483
45.1 0.7220892521 -7.071068e-01 0.4082483
46 1.5391054233 -7.071068e-01 0.4082483
46.1 0.3889107049 -7.850462e-17 -0.8164966
46.2 0.1248719493 7.071068e-01 0.4082483
47 0.2014101100 -7.071068e-01 0.4082483
47.1 0.2982973539 -7.071068e-01 0.4082483
47.2 1.1518107179 -7.071068e-01 0.4082483
47.3 0.5196802157 7.071068e-01 0.4082483
47.4 0.3702301552 -7.850462e-17 -0.8164966
48 -0.2128602862 7.071068e-01 0.4082483
48.1 -0.5337239976 -7.850462e-17 -0.8164966
49 -0.5236770035 -7.071068e-01 0.4082483
50 0.3897705981 -7.850462e-17 -0.8164966
51 -0.7213343736 -7.850462e-17 -0.8164966
52 0.3758235358 -7.850462e-17 -0.8164966
52.1 0.7138067080 7.071068e-01 0.4082483
52.2 0.8872895233 -7.850462e-17 -0.8164966
52.3 -0.9664587437 -7.071068e-01 0.4082483
52.4 0.0254566848 7.071068e-01 0.4082483
52.5 0.4155259424 -7.850462e-17 -0.8164966
53 0.5675736897 -7.850462e-17 -0.8164966
53.1 -0.3154088781 -7.071068e-01 0.4082483
53.2 0.2162315769 -7.071068e-01 0.4082483
54 -0.0880802382 -7.071068e-01 0.4082483
54.1 0.4129127672 -7.850462e-17 -0.8164966
54.2 1.0119546775 7.071068e-01 0.4082483
54.3 -0.1112901990 -7.071068e-01 0.4082483
54.4 0.8587727145 -7.850462e-17 -0.8164966
55 -0.0116453589 -7.071068e-01 0.4082483
55.1 0.5835528661 -7.071068e-01 0.4082483
55.2 -1.0010857254 -7.850462e-17 -0.8164966
55.3 -0.4796526070 -7.071068e-01 0.4082483
55.4 -0.1202746964 -7.071068e-01 0.4082483
56 0.5176377612 7.071068e-01 0.4082483
56.1 -1.1136932588 -7.850462e-17 -0.8164966
56.2 -0.0168103281 7.071068e-01 0.4082483
56.3 0.3933023606 7.071068e-01 0.4082483
56.4 0.3714625139 -7.071068e-01 0.4082483
56.5 0.7811448179 7.071068e-01 0.4082483
57 -1.0868304872 -7.850462e-17 -0.8164966
57.1 0.8018626997 -7.850462e-17 -0.8164966
57.2 -0.1159517011 -7.850462e-17 -0.8164966
57.3 0.6785562445 -7.850462e-17 -0.8164966
58 1.6476207996 7.071068e-01 0.4082483
58.1 0.3402652711 -7.850462e-17 -0.8164966
58.2 -0.1111300753 7.071068e-01 0.4082483
58.3 -0.5409234285 7.071068e-01 0.4082483
58.4 -0.1271327672 -7.850462e-17 -0.8164966
58.5 0.8713264822 7.071068e-01 0.4082483
59 0.4766421367 7.071068e-01 0.4082483
59.1 1.0028089765 -7.071068e-01 0.4082483
60 0.5231452932 7.071068e-01 0.4082483
61 -0.7190130614 -7.071068e-01 0.4082483
61.1 0.8353702312 -7.850462e-17 -0.8164966
61.2 1.0229058138 -7.850462e-17 -0.8164966
61.3 1.1717723589 -7.071068e-01 0.4082483
61.4 -0.0629201596 -7.850462e-17 -0.8164966
62 -0.3979137604 -7.850462e-17 -0.8164966
62.1 0.6830738372 7.071068e-01 0.4082483
62.2 0.4301745954 -7.071068e-01 0.4082483
62.3 -0.0333139957 -7.850462e-17 -0.8164966
63 0.3345678035 -7.071068e-01 0.4082483
63.1 0.3643769511 7.071068e-01 0.4082483
64 0.3949911859 -7.850462e-17 -0.8164966
65 1.2000091513 -7.071068e-01 0.4082483
65.1 0.0110122646 -7.850462e-17 -0.8164966
65.2 -0.5776452043 -7.071068e-01 0.4082483
65.3 -0.1372183563 -7.071068e-01 0.4082483
66 -0.5081302805 -7.071068e-01 0.4082483
66.1 -0.1447837412 -7.071068e-01 0.4082483
66.2 0.1906241379 7.071068e-01 0.4082483
67 1.6716027681 7.071068e-01 0.4082483
68 0.5691848839 -7.071068e-01 0.4082483
68.1 0.1004860389 -7.850462e-17 -0.8164966
68.2 -0.0061241827 -7.850462e-17 -0.8164966
68.3 0.7443745962 -7.850462e-17 -0.8164966
68.4 0.8726923437 -7.850462e-17 -0.8164966
69 0.0381382683 -7.850462e-17 -0.8164966
70 0.8126204217 -7.071068e-01 0.4082483
70.1 0.4691503050 7.071068e-01 0.4082483
71 -0.5529062591 7.071068e-01 0.4082483
71.1 -0.1103252087 7.071068e-01 0.4082483
71.2 1.7178492547 7.071068e-01 0.4082483
71.3 -1.0118346755 -7.850462e-17 -0.8164966
71.4 1.8623785017 -7.850462e-17 -0.8164966
72 -0.4521659275 -7.071068e-01 0.4082483
72.1 0.1375317317 -7.071068e-01 0.4082483
72.2 -0.4170988856 -7.850462e-17 -0.8164966
72.3 0.7107266765 -7.850462e-17 -0.8164966
72.4 0.1451969143 -7.071068e-01 0.4082483
72.5 1.6298050306 -7.071068e-01 0.4082483
73 -0.0307469467 -7.071068e-01 0.4082483
74 0.3730017941 -7.071068e-01 0.4082483
75 -0.4908003566 7.071068e-01 0.4082483
76 -0.9888876620 7.071068e-01 0.4082483
76.1 0.0003798292 7.071068e-01 0.4082483
76.2 -0.8421863763 -7.071068e-01 0.4082483
77 -0.4986802480 -7.850462e-17 -0.8164966
78 0.0417330969 -7.850462e-17 -0.8164966
79 -0.3767450660 -7.071068e-01 0.4082483
79.1 0.1516000028 -7.850462e-17 -0.8164966
79.2 -0.1888160741 -7.071068e-01 0.4082483
80 -0.0041558414 -7.850462e-17 -0.8164966
80.1 -0.0329337062 -7.850462e-17 -0.8164966
80.2 0.5046816157 7.071068e-01 0.4082483
81 -0.9493950353 7.071068e-01 0.4082483
81.1 0.2443038954 -7.850462e-17 -0.8164966
81.2 0.6476958410 7.071068e-01 0.4082483
81.3 0.4182528210 7.071068e-01 0.4082483
82 1.1088801952 7.071068e-01 0.4082483
82.1 0.9334157763 -7.850462e-17 -0.8164966
82.2 0.4958140634 -7.850462e-17 -0.8164966
83 0.5104724530 7.071068e-01 0.4082483
83.1 -0.0513309106 7.071068e-01 0.4082483
83.2 -0.2067792494 7.071068e-01 0.4082483
83.3 -0.0534169155 -7.850462e-17 -0.8164966
84 -0.0255753653 7.071068e-01 0.4082483
84.1 -1.8234189877 7.071068e-01 0.4082483
85 -0.0114038622 7.071068e-01 0.4082483
85.1 -0.0577615939 -7.850462e-17 -0.8164966
85.2 -0.2241856342 -7.850462e-17 -0.8164966
85.3 -0.0520175929 -7.850462e-17 -0.8164966
85.4 0.2892733846 -7.071068e-01 0.4082483
85.5 -0.3740417009 7.071068e-01 0.4082483
86 0.4293735089 -7.850462e-17 -0.8164966
86.1 -0.1363456521 7.071068e-01 0.4082483
86.2 0.1230989293 -7.071068e-01 0.4082483
86.3 0.3305413955 -7.071068e-01 0.4082483
86.4 2.6003411822 -7.071068e-01 0.4082483
86.5 -0.1420690052 7.071068e-01 0.4082483
87 1.0457427869 -7.071068e-01 0.4082483
87.1 -0.2973007190 -7.071068e-01 0.4082483
87.2 0.4396872616 -7.071068e-01 0.4082483
88 -0.0601928334 7.071068e-01 0.4082483
88.1 -1.0124347595 -7.071068e-01 0.4082483
88.2 0.5730917016 -7.071068e-01 0.4082483
88.3 -0.0029455332 -7.071068e-01 0.4082483
89 1.5465903721 -7.071068e-01 0.4082483
90 0.0626760573 -7.850462e-17 -0.8164966
90.1 1.1896872985 -7.071068e-01 0.4082483
90.2 0.2597888783 7.071068e-01 0.4082483
90.3 0.6599799887 -7.071068e-01 0.4082483
91 1.1213651365 -7.071068e-01 0.4082483
91.1 1.2046371625 -7.850462e-17 -0.8164966
91.2 0.3395603754 -7.071068e-01 0.4082483
92 0.4674939332 -7.850462e-17 -0.8164966
93 0.2677965647 -7.071068e-01 0.4082483
93.1 1.6424445368 7.071068e-01 0.4082483
93.2 0.7101700066 -7.071068e-01 0.4082483
93.3 1.1222322893 7.071068e-01 0.4082483
93.4 1.4628960401 -7.071068e-01 0.4082483
94 -0.2904211940 -7.850462e-17 -0.8164966
94.1 0.0147813580 7.071068e-01 0.4082483
94.2 -0.4536774482 -7.071068e-01 0.4082483
94.3 0.6793464917 7.071068e-01 0.4082483
94.4 -0.9411356550 -7.850462e-17 -0.8164966
94.5 0.5683867264 7.071068e-01 0.4082483
95 0.2375652188 7.071068e-01 0.4082483
95.1 0.0767152977 -7.071068e-01 0.4082483
95.2 -0.6886731251 -7.850462e-17 -0.8164966
96 0.7813892121 -7.071068e-01 0.4082483
96.1 0.3391519695 7.071068e-01 0.4082483
96.2 -0.4857246503 -7.071068e-01 0.4082483
96.3 0.8771471244 -7.071068e-01 0.4082483
96.4 1.9030768981 -7.850462e-17 -0.8164966
96.5 -0.1684332749 7.071068e-01 0.4082483
97 1.3775130083 7.071068e-01 0.4082483
97.1 -1.7323228619 7.071068e-01 0.4082483
98 -1.2648518889 7.071068e-01 0.4082483
98.1 -0.9042716241 -7.071068e-01 0.4082483
98.2 -0.1560385207 -7.071068e-01 0.4082483
99 0.7993356425 -7.850462e-17 -0.8164966
99.1 1.0355522332 -7.850462e-17 -0.8164966
99.2 -0.1150895843 -7.071068e-01 0.4082483
100 0.0369067906 -7.071068e-01 0.4082483
100.1 1.6023713093 -7.850462e-17 -0.8164966
100.2 0.8861545820 -7.850462e-17 -0.8164966
100.3 0.1277046316 -7.071068e-01 0.4082483
100.4 -0.0834577654 -7.071068e-01 0.4082483
$m3a$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 o2 o22 o23 o24
1 0.7592026489 1 NA NA NA
1.1 0.9548337990 1 NA NA NA
1.2 0.5612235156 3 NA NA NA
1.3 1.1873391025 1 NA NA NA
2 0.9192204198 4 NA NA NA
2.1 -0.1870730476 4 NA NA NA
2.2 1.2517512331 2 NA NA NA
3 -0.0605087604 2 NA NA NA
3.1 0.3788637747 4 NA NA NA
3.2 0.9872578281 2 NA NA NA
4 1.4930175328 4 NA NA NA
4.1 -0.7692526880 3 NA NA NA
4.2 0.9180841450 NA NA NA NA
4.3 -0.0541170782 2 NA NA NA
5 -0.1376784521 2 NA NA NA
5.1 -0.2740585866 4 NA NA NA
5.2 0.4670496929 2 NA NA NA
5.3 0.1740288049 4 NA NA NA
6 0.9868044683 3 NA NA NA
7 -0.1280320918 1 NA NA NA
7.1 0.4242971219 NA NA NA NA
7.2 0.0777182491 4 NA NA NA
8 -0.5791408712 1 NA NA NA
8.1 0.3128604232 3 NA NA NA
8.2 0.6258446356 1 NA NA NA
8.3 -0.1040137707 4 NA NA NA
8.4 0.0481450285 3 NA NA NA
8.5 0.3831763675 3 NA NA NA
9 -0.1757592269 2 NA NA NA
9.1 -0.1791541200 2 NA NA NA
9.2 -0.0957042935 4 NA NA NA
10 -0.5598409704 1 NA NA NA
10.1 -0.2318340451 4 NA NA NA
11 0.5086859475 3 NA NA NA
11.1 0.4951758188 1 NA NA NA
11.2 -1.1022162541 4 NA NA NA
11.3 -0.0611636705 3 NA NA NA
11.4 -0.4971774316 3 NA NA NA
12 -0.2433996286 3 NA NA NA
13 0.8799673116 NA NA NA NA
13.1 0.1079022586 1 NA NA NA
14 0.9991752617 1 NA NA NA
14.1 -0.1094019046 4 NA NA NA
14.2 0.1518967560 3 NA NA NA
14.3 0.3521012473 4 NA NA NA
15 0.3464447888 1 NA NA NA
15.1 -0.4767313971 4 NA NA NA
15.2 0.5759767791 NA NA NA NA
15.3 -0.1713452662 2 NA NA NA
16 0.4564754473 NA NA NA NA
16.1 1.0652558311 NA NA NA NA
16.2 0.6971872493 1 NA NA NA
16.3 0.5259331838 3 NA NA NA
16.4 0.2046601798 3 NA NA NA
16.5 1.0718540464 1 NA NA NA
17 0.6048676222 3 NA NA NA
17.1 0.2323298304 2 NA NA NA
17.2 1.2617499032 2 NA NA NA
17.3 -0.3913230895 3 NA NA NA
17.4 0.9577299112 1 NA NA NA
18 -0.0050324072 4 NA NA NA
19 -0.4187468937 1 NA NA NA
19.1 -0.4478828944 NA NA NA NA
19.2 -1.1966721302 NA NA NA NA
19.3 -0.5877091668 2 NA NA NA
20 0.6838223064 1 NA NA NA
20.1 0.3278571109 4 NA NA NA
20.2 -0.8489831990 3 NA NA NA
20.3 1.3169975191 3 NA NA NA
20.4 0.0444804531 1 NA NA NA
20.5 -0.4535207652 3 NA NA NA
21 -0.4030302960 3 NA NA NA
21.1 -0.4069674045 1 NA NA NA
21.2 1.0650265940 2 NA NA NA
22 -0.0673274516 4 NA NA NA
22.1 0.9601388170 NA NA NA NA
23 0.5556634840 4 NA NA NA
23.1 1.4407865964 NA NA NA NA
24 0.3856376411 3 NA NA NA
25 0.3564400705 1 NA NA NA
25.1 0.0982553434 3 NA NA NA
25.2 0.1928682598 2 NA NA NA
25.3 -0.0192488594 1 NA NA NA
25.4 0.4466012931 1 NA NA NA
25.5 1.1425193342 NA NA NA NA
26 0.5341531449 3 NA NA NA
26.1 1.2268695927 4 NA NA NA
26.2 0.3678294939 3 NA NA NA
26.3 0.5948516018 1 NA NA NA
27 -0.3342844147 4 NA NA NA
27.1 -0.4835141229 4 NA NA NA
28 -0.7145915499 1 NA NA NA
28.1 0.5063671955 2 NA NA NA
28.2 -0.2067413142 3 NA NA NA
28.3 0.1196789973 3 NA NA NA
29 0.1392699487 4 NA NA NA
29.1 0.7960234776 4 NA NA NA
29.2 1.0398214352 3 NA NA NA
29.3 0.0813246429 2 NA NA NA
30 -0.3296323050 3 NA NA NA
30.1 1.3635850954 4 NA NA NA
30.2 0.7354171050 4 NA NA NA
31 0.3708398217 2 NA NA NA
32 -0.0474059668 NA NA NA NA
32.1 1.2507771489 2 NA NA NA
32.2 0.1142915519 4 NA NA NA
32.3 0.6773270619 3 NA NA NA
33 0.1774293842 4 NA NA NA
33.1 0.6159606291 4 NA NA NA
34 0.8590979166 NA NA NA NA
34.1 0.0546216775 NA NA NA NA
34.2 -0.0897224473 NA NA NA NA
34.3 0.4163395571 NA NA NA NA
35 -1.4693520528 4 NA NA NA
35.1 -0.3031734330 1 NA NA NA
35.2 -0.6045512101 NA NA NA NA
36 0.9823048960 1 NA NA NA
36.1 1.4466051416 1 NA NA NA
36.2 1.1606752905 2 NA NA NA
36.3 0.8373091576 2 NA NA NA
36.4 0.2640591685 1 NA NA NA
37 0.1177313455 4 NA NA NA
37.1 -0.1415483779 2 NA NA NA
37.2 0.0054610124 2 NA NA NA
38 0.8078948077 NA NA NA NA
39 0.9876451040 NA NA NA NA
39.1 -0.3431222274 2 NA NA NA
39.2 -1.7909380751 2 NA NA NA
39.3 -0.1798746191 3 NA NA NA
39.4 -0.1850961689 3 NA NA NA
39.5 0.4544226146 1 NA NA NA
40 0.5350190436 1 NA NA NA
40.1 0.4189342752 2 NA NA NA
40.2 0.4211994981 NA NA NA NA
40.3 0.0916687506 2 NA NA NA
41 -0.1035047421 4 NA NA NA
41.1 -0.4684202411 3 NA NA NA
41.2 0.5972615368 4 NA NA NA
41.3 0.9885613862 NA NA NA NA
41.4 -0.3908036794 4 NA NA NA
42 -0.0338893961 4 NA NA NA
42.1 -0.4498363172 4 NA NA NA
43 0.8965546110 NA NA NA NA
43.1 0.6199122090 NA NA NA NA
43.2 0.1804894429 3 NA NA NA
44 1.3221409285 2 NA NA NA
44.1 0.3416426284 4 NA NA NA
44.2 0.5706610068 NA NA NA NA
44.3 1.2679497430 1 NA NA NA
45 0.1414983160 3 NA NA NA
45.1 0.7220892521 4 NA NA NA
46 1.5391054233 NA NA NA NA
46.1 0.3889107049 4 NA NA NA
46.2 0.1248719493 4 NA NA NA
47 0.2014101100 3 NA NA NA
47.1 0.2982973539 2 NA NA NA
47.2 1.1518107179 1 NA NA NA
47.3 0.5196802157 2 NA NA NA
47.4 0.3702301552 1 NA NA NA
48 -0.2128602862 4 NA NA NA
48.1 -0.5337239976 NA NA NA NA
49 -0.5236770035 4 NA NA NA
50 0.3897705981 4 NA NA NA
51 -0.7213343736 4 NA NA NA
52 0.3758235358 NA NA NA NA
52.1 0.7138067080 NA NA NA NA
52.2 0.8872895233 3 NA NA NA
52.3 -0.9664587437 NA NA NA NA
52.4 0.0254566848 4 NA NA NA
52.5 0.4155259424 1 NA NA NA
53 0.5675736897 2 NA NA NA
53.1 -0.3154088781 1 NA NA NA
53.2 0.2162315769 3 NA NA NA
54 -0.0880802382 3 NA NA NA
54.1 0.4129127672 4 NA NA NA
54.2 1.0119546775 4 NA NA NA
54.3 -0.1112901990 3 NA NA NA
54.4 0.8587727145 NA NA NA NA
55 -0.0116453589 4 NA NA NA
55.1 0.5835528661 1 NA NA NA
55.2 -1.0010857254 4 NA NA NA
55.3 -0.4796526070 NA NA NA NA
55.4 -0.1202746964 1 NA NA NA
56 0.5176377612 1 NA NA NA
56.1 -1.1136932588 2 NA NA NA
56.2 -0.0168103281 2 NA NA NA
56.3 0.3933023606 3 NA NA NA
56.4 0.3714625139 4 NA NA NA
56.5 0.7811448179 4 NA NA NA
57 -1.0868304872 2 NA NA NA
57.1 0.8018626997 2 NA NA NA
57.2 -0.1159517011 4 NA NA NA
57.3 0.6785562445 NA NA NA NA
58 1.6476207996 1 NA NA NA
58.1 0.3402652711 2 NA NA NA
58.2 -0.1111300753 2 NA NA NA
58.3 -0.5409234285 4 NA NA NA
58.4 -0.1271327672 1 NA NA NA
58.5 0.8713264822 NA NA NA NA
59 0.4766421367 4 NA NA NA
59.1 1.0028089765 1 NA NA NA
60 0.5231452932 1 NA NA NA
61 -0.7190130614 1 NA NA NA
61.1 0.8353702312 1 NA NA NA
61.2 1.0229058138 NA NA NA NA
61.3 1.1717723589 1 NA NA NA
61.4 -0.0629201596 3 NA NA NA
62 -0.3979137604 NA NA NA NA
62.1 0.6830738372 NA NA NA NA
62.2 0.4301745954 NA NA NA NA
62.3 -0.0333139957 3 NA NA NA
63 0.3345678035 4 NA NA NA
63.1 0.3643769511 3 NA NA NA
64 0.3949911859 4 NA NA NA
65 1.2000091513 2 NA NA NA
65.1 0.0110122646 1 NA NA NA
65.2 -0.5776452043 3 NA NA NA
65.3 -0.1372183563 NA NA NA NA
66 -0.5081302805 1 NA NA NA
66.1 -0.1447837412 3 NA NA NA
66.2 0.1906241379 2 NA NA NA
67 1.6716027681 3 NA NA NA
68 0.5691848839 3 NA NA NA
68.1 0.1004860389 4 NA NA NA
68.2 -0.0061241827 3 NA NA NA
68.3 0.7443745962 1 NA NA NA
68.4 0.8726923437 4 NA NA NA
69 0.0381382683 4 NA NA NA
70 0.8126204217 4 NA NA NA
70.1 0.4691503050 4 NA NA NA
71 -0.5529062591 2 NA NA NA
71.1 -0.1103252087 NA NA NA NA
71.2 1.7178492547 4 NA NA NA
71.3 -1.0118346755 3 NA NA NA
71.4 1.8623785017 1 NA NA NA
72 -0.4521659275 1 NA NA NA
72.1 0.1375317317 NA NA NA NA
72.2 -0.4170988856 4 NA NA NA
72.3 0.7107266765 1 NA NA NA
72.4 0.1451969143 3 NA NA NA
72.5 1.6298050306 1 NA NA NA
73 -0.0307469467 2 NA NA NA
74 0.3730017941 4 NA NA NA
75 -0.4908003566 1 NA NA NA
76 -0.9888876620 2 NA NA NA
76.1 0.0003798292 1 NA NA NA
76.2 -0.8421863763 1 NA NA NA
77 -0.4986802480 3 NA NA NA
78 0.0417330969 3 NA NA NA
79 -0.3767450660 NA NA NA NA
79.1 0.1516000028 3 NA NA NA
79.2 -0.1888160741 NA NA NA NA
80 -0.0041558414 2 NA NA NA
80.1 -0.0329337062 3 NA NA NA
80.2 0.5046816157 1 NA NA NA
81 -0.9493950353 3 NA NA NA
81.1 0.2443038954 NA NA NA NA
81.2 0.6476958410 3 NA NA NA
81.3 0.4182528210 2 NA NA NA
82 1.1088801952 NA NA NA NA
82.1 0.9334157763 3 NA NA NA
82.2 0.4958140634 1 NA NA NA
83 0.5104724530 4 NA NA NA
83.1 -0.0513309106 NA NA NA NA
83.2 -0.2067792494 2 NA NA NA
83.3 -0.0534169155 NA NA NA NA
84 -0.0255753653 2 NA NA NA
84.1 -1.8234189877 1 NA NA NA
85 -0.0114038622 1 NA NA NA
85.1 -0.0577615939 4 NA NA NA
85.2 -0.2241856342 3 NA NA NA
85.3 -0.0520175929 3 NA NA NA
85.4 0.2892733846 NA NA NA NA
85.5 -0.3740417009 2 NA NA NA
86 0.4293735089 1 NA NA NA
86.1 -0.1363456521 3 NA NA NA
86.2 0.1230989293 1 NA NA NA
86.3 0.3305413955 2 NA NA NA
86.4 2.6003411822 3 NA NA NA
86.5 -0.1420690052 4 NA NA NA
87 1.0457427869 NA NA NA NA
87.1 -0.2973007190 3 NA NA NA
87.2 0.4396872616 3 NA NA NA
88 -0.0601928334 NA NA NA NA
88.1 -1.0124347595 1 NA NA NA
88.2 0.5730917016 2 NA NA NA
88.3 -0.0029455332 NA NA NA NA
89 1.5465903721 3 NA NA NA
90 0.0626760573 2 NA NA NA
90.1 1.1896872985 2 NA NA NA
90.2 0.2597888783 2 NA NA NA
90.3 0.6599799887 4 NA NA NA
91 1.1213651365 2 NA NA NA
91.1 1.2046371625 NA NA NA NA
91.2 0.3395603754 3 NA NA NA
92 0.4674939332 2 NA NA NA
93 0.2677965647 3 NA NA NA
93.1 1.6424445368 2 NA NA NA
93.2 0.7101700066 3 NA NA NA
93.3 1.1222322893 2 NA NA NA
93.4 1.4628960401 4 NA NA NA
94 -0.2904211940 NA NA NA NA
94.1 0.0147813580 2 NA NA NA
94.2 -0.4536774482 NA NA NA NA
94.3 0.6793464917 3 NA NA NA
94.4 -0.9411356550 4 NA NA NA
94.5 0.5683867264 3 NA NA NA
95 0.2375652188 NA NA NA NA
95.1 0.0767152977 2 NA NA NA
95.2 -0.6886731251 3 NA NA NA
96 0.7813892121 3 NA NA NA
96.1 0.3391519695 NA NA NA NA
96.2 -0.4857246503 4 NA NA NA
96.3 0.8771471244 3 NA NA NA
96.4 1.9030768981 NA NA NA NA
96.5 -0.1684332749 1 NA NA NA
97 1.3775130083 2 NA NA NA
97.1 -1.7323228619 1 NA NA NA
98 -1.2648518889 2 NA NA NA
98.1 -0.9042716241 1 NA NA NA
98.2 -0.1560385207 3 NA NA NA
99 0.7993356425 NA NA NA NA
99.1 1.0355522332 NA NA NA NA
99.2 -0.1150895843 4 NA NA NA
100 0.0369067906 1 NA NA NA
100.1 1.6023713093 NA NA NA NA
100.2 0.8861545820 1 NA NA NA
100.3 0.1277046316 4 NA NA NA
100.4 -0.0834577654 1 NA 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_delta_ordinal
[1] 0
$m3b$tau_delta_ordinal
[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
o1 o2 o22 o23 o24 o22:abs(C1 - C2) o23:abs(C1 - C2) o24:abs(C1 - C2)
1 2 1 NA NA NA NA NA NA
1.1 1 1 NA NA NA NA NA NA
1.2 1 3 NA NA NA NA NA NA
1.3 2 1 NA NA NA NA NA NA
2 3 4 NA NA NA NA NA NA
2.1 1 4 NA NA NA NA NA NA
2.2 2 2 NA NA NA NA NA NA
3 1 2 NA NA NA NA NA NA
3.1 3 4 NA NA NA NA NA NA
3.2 2 2 NA NA NA NA NA NA
4 3 4 NA NA NA NA NA NA
4.1 2 3 NA NA NA NA NA NA
4.2 1 NA NA NA NA NA NA NA
4.3 2 2 NA NA NA NA NA NA
5 2 2 NA NA NA NA NA NA
5.1 2 4 NA NA NA NA NA NA
5.2 3 2 NA NA NA NA NA NA
5.3 2 4 NA NA NA NA NA NA
6 3 3 NA NA NA NA NA NA
7 3 1 NA NA NA NA NA NA
7.1 1 NA NA NA NA NA NA NA
7.2 1 4 NA NA NA NA NA NA
8 2 1 NA NA NA NA NA NA
8.1 2 3 NA NA NA NA NA NA
8.2 3 1 NA NA NA NA NA NA
8.3 3 4 NA NA NA NA NA NA
8.4 2 3 NA NA NA NA NA NA
8.5 2 3 NA NA NA NA NA NA
9 2 2 NA NA NA NA NA NA
9.1 2 2 NA NA NA NA NA NA
9.2 3 4 NA NA NA NA NA NA
10 2 1 NA NA NA NA NA NA
10.1 1 4 NA NA NA NA NA NA
11 2 3 NA NA NA NA NA NA
11.1 3 1 NA NA NA NA NA NA
11.2 3 4 NA NA NA NA NA NA
11.3 1 3 NA NA NA NA NA NA
11.4 3 3 NA NA NA NA NA NA
12 1 3 NA NA NA NA NA NA
13 2 NA NA NA NA NA NA NA
13.1 1 1 NA NA NA NA NA NA
14 3 1 NA NA NA NA NA NA
14.1 3 4 NA NA NA NA NA NA
14.2 2 3 NA NA NA NA NA NA
14.3 1 4 NA NA NA NA NA NA
15 3 1 NA NA NA NA NA NA
15.1 3 4 NA NA NA NA NA NA
15.2 2 NA NA NA NA NA NA NA
15.3 2 2 NA NA NA NA NA NA
16 3 NA NA NA NA NA NA NA
16.1 1 NA NA NA NA NA NA NA
16.2 3 1 NA NA NA NA NA NA
16.3 1 3 NA NA NA NA NA NA
16.4 1 3 NA NA NA NA NA NA
16.5 2 1 NA NA NA NA NA NA
17 2 3 NA NA NA NA NA NA
17.1 2 2 NA NA NA NA NA NA
17.2 3 2 NA NA NA NA NA NA
17.3 2 3 NA NA NA NA NA NA
17.4 3 1 NA NA NA NA NA NA
18 2 4 NA NA NA NA NA NA
19 3 1 NA NA NA NA NA NA
19.1 2 NA NA NA NA NA NA NA
19.2 2 NA NA NA NA NA NA NA
19.3 2 2 NA NA NA NA NA NA
20 1 1 NA NA NA NA NA NA
20.1 2 4 NA NA NA NA NA NA
20.2 2 3 NA NA NA NA NA NA
20.3 1 3 NA NA NA NA NA NA
20.4 1 1 NA NA NA NA NA NA
20.5 2 3 NA NA NA NA NA NA
21 2 3 NA NA NA NA NA NA
21.1 2 1 NA NA NA NA NA NA
21.2 2 2 NA NA NA NA NA NA
22 1 4 NA NA NA NA NA NA
22.1 1 NA NA NA NA NA NA NA
23 1 4 NA NA NA NA NA NA
23.1 2 NA NA NA NA NA NA NA
24 1 3 NA NA NA NA NA NA
25 2 1 NA NA NA NA NA NA
25.1 1 3 NA NA NA NA NA NA
25.2 1 2 NA NA NA NA NA NA
25.3 1 1 NA NA NA NA NA NA
25.4 2 1 NA NA NA NA NA NA
25.5 2 NA NA NA NA NA NA NA
26 2 3 NA NA NA NA NA NA
26.1 3 4 NA NA NA NA NA NA
26.2 2 3 NA NA NA NA NA NA
26.3 1 1 NA NA NA NA NA NA
27 2 4 NA NA NA NA NA NA
27.1 2 4 NA NA NA NA NA NA
28 1 1 NA NA NA NA NA NA
28.1 1 2 NA NA NA NA NA NA
28.2 1 3 NA NA NA NA NA NA
28.3 1 3 NA NA NA NA NA NA
29 2 4 NA NA NA NA NA NA
29.1 2 4 NA NA NA NA NA NA
29.2 1 3 NA NA NA NA NA NA
29.3 2 2 NA NA NA NA NA NA
30 2 3 NA NA NA NA NA NA
30.1 2 4 NA NA NA NA NA NA
30.2 2 4 NA NA NA NA NA NA
31 2 2 NA NA NA NA NA NA
32 3 NA NA NA NA NA NA NA
32.1 1 2 NA NA NA NA NA NA
32.2 3 4 NA NA NA NA NA NA
32.3 3 3 NA NA NA NA NA NA
33 2 4 NA NA NA NA NA NA
33.1 2 4 NA NA NA NA NA NA
34 3 NA NA NA NA NA NA NA
34.1 2 NA NA NA NA NA NA NA
34.2 2 NA NA NA NA NA NA NA
34.3 1 NA NA NA NA NA NA NA
35 2 4 NA NA NA NA NA NA
35.1 3 1 NA NA NA NA NA NA
35.2 2 NA NA NA NA NA NA NA
36 3 1 NA NA NA NA NA NA
36.1 2 1 NA NA NA NA NA NA
36.2 3 2 NA NA NA NA NA NA
36.3 3 2 NA NA NA NA NA NA
36.4 3 1 NA NA NA NA NA NA
37 3 4 NA NA NA NA NA NA
37.1 3 2 NA NA NA NA NA NA
37.2 3 2 NA NA NA NA NA NA
38 2 NA NA NA NA NA NA NA
39 1 NA NA NA NA NA NA NA
39.1 2 2 NA NA NA NA NA NA
39.2 2 2 NA NA NA NA NA NA
39.3 1 3 NA NA NA NA NA NA
39.4 1 3 NA NA NA NA NA NA
39.5 1 1 NA NA NA NA NA NA
40 3 1 NA NA NA NA NA NA
40.1 1 2 NA NA NA NA NA NA
40.2 1 NA NA NA NA NA NA NA
40.3 2 2 NA NA NA NA NA NA
41 3 4 NA NA NA NA NA NA
41.1 2 3 NA NA NA NA NA NA
41.2 1 4 NA NA NA NA NA NA
41.3 1 NA NA NA NA NA NA NA
41.4 3 4 NA NA NA NA NA NA
42 2 4 NA NA NA NA NA NA
42.1 1 4 NA NA NA NA NA NA
43 2 NA NA NA NA NA NA NA
43.1 2 NA NA NA NA NA NA NA
43.2 1 3 NA NA NA NA NA NA
44 3 2 NA NA NA NA NA NA
44.1 2 4 NA NA NA NA NA NA
44.2 1 NA NA NA NA NA NA NA
44.3 1 1 NA NA NA NA NA NA
45 1 3 NA NA NA NA NA NA
45.1 1 4 NA NA NA NA NA NA
46 1 NA NA NA NA NA NA NA
46.1 2 4 NA NA NA NA NA NA
46.2 3 4 NA NA NA NA NA NA
47 1 3 NA NA NA NA NA NA
47.1 1 2 NA NA NA NA NA NA
47.2 1 1 NA NA NA NA NA NA
47.3 3 2 NA NA NA NA NA NA
47.4 2 1 NA NA NA NA NA NA
48 3 4 NA NA NA NA NA NA
48.1 2 NA NA NA NA NA NA NA
49 1 4 NA NA NA NA NA NA
50 2 4 NA NA NA NA NA NA
51 2 4 NA NA NA NA NA NA
52 2 NA NA NA NA NA NA NA
52.1 3 NA NA NA NA NA NA NA
52.2 2 3 NA NA NA NA NA NA
52.3 1 NA NA NA NA NA NA NA
52.4 3 4 NA NA NA NA NA NA
52.5 2 1 NA NA NA NA NA NA
53 2 2 NA NA NA NA NA NA
53.1 1 1 NA NA NA NA NA NA
53.2 1 3 NA NA NA NA NA NA
54 1 3 NA NA NA NA NA NA
54.1 2 4 NA NA NA NA NA NA
54.2 3 4 NA NA NA NA NA NA
54.3 1 3 NA NA NA NA NA NA
54.4 2 NA NA NA NA NA NA NA
55 1 4 NA NA NA NA NA NA
55.1 1 1 NA NA NA NA NA NA
55.2 2 4 NA NA NA NA NA NA
55.3 1 NA NA NA NA NA NA NA
55.4 1 1 NA NA NA NA NA NA
56 3 1 NA NA NA NA NA NA
56.1 2 2 NA NA NA NA NA NA
56.2 3 2 NA NA NA NA NA NA
56.3 3 3 NA NA NA NA NA NA
56.4 1 4 NA NA NA NA NA NA
56.5 3 4 NA NA NA NA NA NA
57 2 2 NA NA NA NA NA NA
57.1 2 2 NA NA NA NA NA NA
57.2 2 4 NA NA NA NA NA NA
57.3 2 NA NA NA NA NA NA NA
58 3 1 NA NA NA NA NA NA
58.1 2 2 NA NA NA NA NA NA
58.2 3 2 NA NA NA NA NA NA
58.3 3 4 NA NA NA NA NA NA
58.4 2 1 NA NA NA NA NA NA
58.5 3 NA NA NA NA NA NA NA
59 3 4 NA NA NA NA NA NA
59.1 1 1 NA NA NA NA NA NA
60 3 1 NA NA NA NA NA NA
61 1 1 NA NA NA NA NA NA
61.1 2 1 NA NA NA NA NA NA
61.2 2 NA NA NA NA NA NA NA
61.3 1 1 NA NA NA NA NA NA
61.4 2 3 NA NA NA NA NA NA
62 2 NA NA NA NA NA NA NA
62.1 3 NA NA NA NA NA NA NA
62.2 1 NA NA NA NA NA NA NA
62.3 2 3 NA NA NA NA NA NA
63 1 4 NA NA NA NA NA NA
63.1 3 3 NA NA NA NA NA NA
64 2 4 NA NA NA NA NA NA
65 1 2 NA NA NA NA NA NA
65.1 2 1 NA NA NA NA NA NA
65.2 1 3 NA NA NA NA NA NA
65.3 1 NA NA NA NA NA NA NA
66 1 1 NA NA NA NA NA NA
66.1 1 3 NA NA NA NA NA NA
66.2 3 2 NA NA NA NA NA NA
67 3 3 NA NA NA NA NA NA
68 1 3 NA NA NA NA NA NA
68.1 2 4 NA NA NA NA NA NA
68.2 2 3 NA NA NA NA NA NA
68.3 2 1 NA NA NA NA NA NA
68.4 2 4 NA NA NA NA NA NA
69 2 4 NA NA NA NA NA NA
70 1 4 NA NA NA NA NA NA
70.1 3 4 NA NA NA NA NA NA
71 3 2 NA NA NA NA NA NA
71.1 3 NA NA NA NA NA NA NA
71.2 3 4 NA NA NA NA NA NA
71.3 2 3 NA NA NA NA NA NA
71.4 2 1 NA NA NA NA NA NA
72 1 1 NA NA NA NA NA NA
72.1 1 NA NA NA NA NA NA NA
72.2 2 4 NA NA NA NA NA NA
72.3 2 1 NA NA NA NA NA NA
72.4 1 3 NA NA NA NA NA NA
72.5 1 1 NA NA NA NA NA NA
73 1 2 NA NA NA NA NA NA
74 1 4 NA NA NA NA NA NA
75 3 1 NA NA NA NA NA NA
76 3 2 NA NA NA NA NA NA
76.1 3 1 NA NA NA NA NA NA
76.2 1 1 NA NA NA NA NA NA
77 2 3 NA NA NA NA NA NA
78 2 3 NA NA NA NA NA NA
79 1 NA NA NA NA NA NA NA
79.1 2 3 NA NA NA NA NA NA
79.2 1 NA NA NA NA NA NA NA
80 2 2 NA NA NA NA NA NA
80.1 2 3 NA NA NA NA NA NA
80.2 3 1 NA NA NA NA NA NA
81 3 3 NA NA NA NA NA NA
81.1 2 NA NA NA NA NA NA NA
81.2 3 3 NA NA NA NA NA NA
81.3 3 2 NA NA NA NA NA NA
82 3 NA NA NA NA NA NA NA
82.1 2 3 NA NA NA NA NA NA
82.2 2 1 NA NA NA NA NA NA
83 3 4 NA NA NA NA NA NA
83.1 3 NA NA NA NA NA NA NA
83.2 3 2 NA NA NA NA NA NA
83.3 2 NA NA NA NA NA NA NA
84 3 2 NA NA NA NA NA NA
84.1 3 1 NA NA NA NA NA NA
85 3 1 NA NA NA NA NA NA
85.1 2 4 NA NA NA NA NA NA
85.2 2 3 NA NA NA NA NA NA
85.3 2 3 NA NA NA NA NA NA
85.4 1 NA NA NA NA NA NA NA
85.5 3 2 NA NA NA NA NA NA
86 2 1 NA NA NA NA NA NA
86.1 3 3 NA NA NA NA NA NA
86.2 1 1 NA NA NA NA NA NA
86.3 1 2 NA NA NA NA NA NA
86.4 1 3 NA NA NA NA NA NA
86.5 3 4 NA NA NA NA NA NA
87 1 NA NA NA NA NA NA NA
87.1 1 3 NA NA NA NA NA NA
87.2 1 3 NA NA NA NA NA NA
88 3 NA NA NA NA NA NA NA
88.1 1 1 NA NA NA NA NA NA
88.2 1 2 NA NA NA NA NA NA
88.3 1 NA NA NA NA NA NA NA
89 1 3 NA NA NA NA NA NA
90 2 2 NA NA NA NA NA NA
90.1 1 2 NA NA NA NA NA NA
90.2 3 2 NA NA NA NA NA NA
90.3 1 4 NA NA NA NA NA NA
91 1 2 NA NA NA NA NA NA
91.1 2 NA NA NA NA NA NA NA
91.2 1 3 NA NA NA NA NA NA
92 2 2 NA NA NA NA NA NA
93 1 3 NA NA NA NA NA NA
93.1 3 2 NA NA NA NA NA NA
93.2 1 3 NA NA NA NA NA NA
93.3 3 2 NA NA NA NA NA NA
93.4 1 4 NA NA NA NA NA NA
94 2 NA NA NA NA NA NA NA
94.1 3 2 NA NA NA NA NA NA
94.2 1 NA NA NA NA NA NA NA
94.3 3 3 NA NA NA NA NA NA
94.4 2 4 NA NA NA NA NA NA
94.5 3 3 NA NA NA NA NA NA
95 3 NA NA NA NA NA NA NA
95.1 1 2 NA NA NA NA NA NA
95.2 2 3 NA NA NA NA NA NA
96 1 3 NA NA NA NA NA NA
96.1 3 NA NA NA NA NA NA NA
96.2 1 4 NA NA NA NA NA NA
96.3 1 3 NA NA NA NA NA NA
96.4 2 NA NA NA NA NA NA NA
96.5 3 1 NA NA NA NA NA NA
97 3 2 NA NA NA NA NA NA
97.1 3 1 NA NA NA NA NA NA
98 3 2 NA NA NA NA NA NA
98.1 1 1 NA NA NA NA NA NA
98.2 1 3 NA NA NA NA NA NA
99 2 NA NA NA NA NA NA NA
99.1 2 NA NA NA NA NA NA NA
99.2 1 4 NA NA NA NA NA NA
100 1 1 NA NA NA NA NA NA
100.1 2 NA NA NA NA NA NA NA
100.2 2 1 NA NA NA NA NA NA
100.3 1 4 NA NA NA NA NA NA
100.4 1 1 NA NA 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
o1 NA NA
o2 NA NA
o22 NA NA
o23 NA NA
o24 NA NA
o22:abs(C1 - C2) 0.2846191 0.6429094
o23:abs(C1 - C2) 0.3769770 0.7128409
o24:abs(C1 - C2) 0.3704272 0.7139761
$m4a$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
$m4a$mu_reg_ordinal
[1] 0
$m4a$tau_reg_ordinal
[1] 1e-04
$m4a$mu_delta_ordinal
[1] 0
$m4a$tau_delta_ordinal
[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
o1 o2 ifelse(as.numeric(o2) > as.numeric(M1), 1, 0)
1 2 1 NA
1.1 1 1 NA
1.2 1 3 NA
1.3 2 1 NA
2 3 4 NA
2.1 1 4 NA
2.2 2 2 NA
3 1 2 NA
3.1 3 4 NA
3.2 2 2 NA
4 3 4 NA
4.1 2 3 NA
4.2 1 NA NA
4.3 2 2 NA
5 2 2 NA
5.1 2 4 NA
5.2 3 2 NA
5.3 2 4 NA
6 3 3 NA
7 3 1 NA
7.1 1 NA NA
7.2 1 4 NA
8 2 1 NA
8.1 2 3 NA
8.2 3 1 NA
8.3 3 4 NA
8.4 2 3 NA
8.5 2 3 NA
9 2 2 NA
9.1 2 2 NA
9.2 3 4 NA
10 2 1 NA
10.1 1 4 NA
11 2 3 NA
11.1 3 1 NA
11.2 3 4 NA
11.3 1 3 NA
11.4 3 3 NA
12 1 3 NA
13 2 NA NA
13.1 1 1 NA
14 3 1 NA
14.1 3 4 NA
14.2 2 3 NA
14.3 1 4 NA
15 3 1 NA
15.1 3 4 NA
15.2 2 NA NA
15.3 2 2 NA
16 3 NA NA
16.1 1 NA NA
16.2 3 1 NA
16.3 1 3 NA
16.4 1 3 NA
16.5 2 1 NA
17 2 3 NA
17.1 2 2 NA
17.2 3 2 NA
17.3 2 3 NA
17.4 3 1 NA
18 2 4 NA
19 3 1 NA
19.1 2 NA NA
19.2 2 NA NA
19.3 2 2 NA
20 1 1 NA
20.1 2 4 NA
20.2 2 3 NA
20.3 1 3 NA
20.4 1 1 NA
20.5 2 3 NA
21 2 3 NA
21.1 2 1 NA
21.2 2 2 NA
22 1 4 NA
22.1 1 NA NA
23 1 4 NA
23.1 2 NA NA
24 1 3 NA
25 2 1 NA
25.1 1 3 NA
25.2 1 2 NA
25.3 1 1 NA
25.4 2 1 NA
25.5 2 NA NA
26 2 3 NA
26.1 3 4 NA
26.2 2 3 NA
26.3 1 1 NA
27 2 4 NA
27.1 2 4 NA
28 1 1 NA
28.1 1 2 NA
28.2 1 3 NA
28.3 1 3 NA
29 2 4 NA
29.1 2 4 NA
29.2 1 3 NA
29.3 2 2 NA
30 2 3 NA
30.1 2 4 NA
30.2 2 4 NA
31 2 2 NA
32 3 NA NA
32.1 1 2 NA
32.2 3 4 NA
32.3 3 3 NA
33 2 4 NA
33.1 2 4 NA
34 3 NA NA
34.1 2 NA NA
34.2 2 NA NA
34.3 1 NA NA
35 2 4 NA
35.1 3 1 NA
35.2 2 NA NA
36 3 1 NA
36.1 2 1 NA
36.2 3 2 NA
36.3 3 2 NA
36.4 3 1 NA
37 3 4 NA
37.1 3 2 NA
37.2 3 2 NA
38 2 NA NA
39 1 NA NA
39.1 2 2 NA
39.2 2 2 NA
39.3 1 3 NA
39.4 1 3 NA
39.5 1 1 NA
40 3 1 NA
40.1 1 2 NA
40.2 1 NA NA
40.3 2 2 NA
41 3 4 NA
41.1 2 3 NA
41.2 1 4 NA
41.3 1 NA NA
41.4 3 4 NA
42 2 4 NA
42.1 1 4 NA
43 2 NA NA
43.1 2 NA NA
43.2 1 3 NA
44 3 2 NA
44.1 2 4 NA
44.2 1 NA NA
44.3 1 1 NA
45 1 3 NA
45.1 1 4 NA
46 1 NA NA
46.1 2 4 NA
46.2 3 4 NA
47 1 3 NA
47.1 1 2 NA
47.2 1 1 NA
47.3 3 2 NA
47.4 2 1 NA
48 3 4 NA
48.1 2 NA NA
49 1 4 NA
50 2 4 NA
51 2 4 NA
52 2 NA NA
52.1 3 NA NA
52.2 2 3 NA
52.3 1 NA NA
52.4 3 4 NA
52.5 2 1 NA
53 2 2 NA
53.1 1 1 NA
53.2 1 3 NA
54 1 3 NA
54.1 2 4 NA
54.2 3 4 NA
54.3 1 3 NA
54.4 2 NA NA
55 1 4 NA
55.1 1 1 NA
55.2 2 4 NA
55.3 1 NA NA
55.4 1 1 NA
56 3 1 NA
56.1 2 2 NA
56.2 3 2 NA
56.3 3 3 NA
56.4 1 4 NA
56.5 3 4 NA
57 2 2 NA
57.1 2 2 NA
57.2 2 4 NA
57.3 2 NA NA
58 3 1 NA
58.1 2 2 NA
58.2 3 2 NA
58.3 3 4 NA
58.4 2 1 NA
58.5 3 NA NA
59 3 4 NA
59.1 1 1 NA
60 3 1 NA
61 1 1 NA
61.1 2 1 NA
61.2 2 NA NA
61.3 1 1 NA
61.4 2 3 NA
62 2 NA NA
62.1 3 NA NA
62.2 1 NA NA
62.3 2 3 NA
63 1 4 NA
63.1 3 3 NA
64 2 4 NA
65 1 2 NA
65.1 2 1 NA
65.2 1 3 NA
65.3 1 NA NA
66 1 1 NA
66.1 1 3 NA
66.2 3 2 NA
67 3 3 NA
68 1 3 NA
68.1 2 4 NA
68.2 2 3 NA
68.3 2 1 NA
68.4 2 4 NA
69 2 4 NA
70 1 4 NA
70.1 3 4 NA
71 3 2 NA
71.1 3 NA NA
71.2 3 4 NA
71.3 2 3 NA
71.4 2 1 NA
72 1 1 NA
72.1 1 NA NA
72.2 2 4 NA
72.3 2 1 NA
72.4 1 3 NA
72.5 1 1 NA
73 1 2 NA
74 1 4 NA
75 3 1 NA
76 3 2 NA
76.1 3 1 NA
76.2 1 1 NA
77 2 3 NA
78 2 3 NA
79 1 NA NA
79.1 2 3 NA
79.2 1 NA NA
80 2 2 NA
80.1 2 3 NA
80.2 3 1 NA
81 3 3 NA
81.1 2 NA NA
81.2 3 3 NA
81.3 3 2 NA
82 3 NA NA
82.1 2 3 NA
82.2 2 1 NA
83 3 4 NA
83.1 3 NA NA
83.2 3 2 NA
83.3 2 NA NA
84 3 2 NA
84.1 3 1 NA
85 3 1 NA
85.1 2 4 NA
85.2 2 3 NA
85.3 2 3 NA
85.4 1 NA NA
85.5 3 2 NA
86 2 1 NA
86.1 3 3 NA
86.2 1 1 NA
86.3 1 2 NA
86.4 1 3 NA
86.5 3 4 NA
87 1 NA NA
87.1 1 3 NA
87.2 1 3 NA
88 3 NA NA
88.1 1 1 NA
88.2 1 2 NA
88.3 1 NA NA
89 1 3 NA
90 2 2 NA
90.1 1 2 NA
90.2 3 2 NA
90.3 1 4 NA
91 1 2 NA
91.1 2 NA NA
91.2 1 3 NA
92 2 2 NA
93 1 3 NA
93.1 3 2 NA
93.2 1 3 NA
93.3 3 2 NA
93.4 1 4 NA
94 2 NA NA
94.1 3 2 NA
94.2 1 NA NA
94.3 3 3 NA
94.4 2 4 NA
94.5 3 3 NA
95 3 NA NA
95.1 1 2 NA
95.2 2 3 NA
96 1 3 NA
96.1 3 NA NA
96.2 1 4 NA
96.3 1 3 NA
96.4 2 NA NA
96.5 3 1 NA
97 3 2 NA
97.1 3 1 NA
98 3 2 NA
98.1 1 1 NA
98.2 1 3 NA
99 2 NA NA
99.1 2 NA NA
99.2 1 4 NA
100 1 1 NA
100.1 2 NA NA
100.2 2 1 NA
100.3 1 4 NA
100.4 1 1 NA
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) o22 o23 o24
1 NA NA NA NA
1.1 NA NA NA NA
1.2 NA NA NA NA
1.3 NA NA NA NA
2 NA NA NA NA
2.1 NA NA NA NA
2.2 NA NA NA NA
3 NA NA NA NA
3.1 NA NA NA NA
3.2 NA NA NA NA
4 NA NA NA NA
4.1 NA NA NA NA
4.2 NA NA NA NA
4.3 NA NA NA NA
5 NA NA NA NA
5.1 NA NA NA NA
5.2 NA NA NA NA
5.3 NA NA NA NA
6 NA NA NA NA
7 NA NA NA NA
7.1 NA NA NA NA
7.2 NA NA NA NA
8 NA NA NA NA
8.1 NA NA NA NA
8.2 NA NA NA NA
8.3 NA NA NA NA
8.4 NA NA NA NA
8.5 NA NA NA NA
9 NA NA NA NA
9.1 NA NA NA NA
9.2 NA NA NA NA
10 NA NA NA NA
10.1 NA NA NA NA
11 NA NA NA NA
11.1 NA NA NA NA
11.2 NA NA NA NA
11.3 NA NA NA NA
11.4 NA NA NA NA
12 NA NA NA NA
13 NA NA NA NA
13.1 NA NA NA NA
14 NA NA NA NA
14.1 NA NA NA NA
14.2 NA NA NA NA
14.3 NA NA NA NA
15 NA NA NA NA
15.1 NA NA NA NA
15.2 NA NA NA NA
15.3 NA NA NA NA
16 NA NA NA NA
16.1 NA NA NA NA
16.2 NA NA NA NA
16.3 NA NA NA NA
16.4 NA NA NA NA
16.5 NA NA NA NA
17 NA NA NA NA
17.1 NA NA NA NA
17.2 NA NA NA NA
17.3 NA NA NA NA
17.4 NA NA NA NA
18 NA NA NA NA
19 NA NA NA NA
19.1 NA NA NA NA
19.2 NA NA NA NA
19.3 NA NA NA NA
20 NA NA NA NA
20.1 NA NA NA NA
20.2 NA NA NA NA
20.3 NA NA NA NA
20.4 NA NA NA NA
20.5 NA NA NA NA
21 NA NA NA NA
21.1 NA NA NA NA
21.2 NA NA NA NA
22 NA NA NA NA
22.1 NA NA NA NA
23 NA NA NA NA
23.1 NA NA NA NA
24 NA NA NA NA
25 NA NA NA NA
25.1 NA NA NA NA
25.2 NA NA NA NA
25.3 NA NA NA NA
25.4 NA NA NA NA
25.5 NA NA NA NA
26 NA NA NA NA
26.1 NA NA NA NA
26.2 NA NA NA NA
26.3 NA NA NA NA
27 NA NA NA NA
27.1 NA NA NA NA
28 NA NA NA NA
28.1 NA NA NA NA
28.2 NA NA NA NA
28.3 NA NA NA NA
29 NA NA NA NA
29.1 NA NA NA NA
29.2 NA NA NA NA
29.3 NA NA NA NA
30 NA NA NA NA
30.1 NA NA NA NA
30.2 NA NA NA NA
31 NA NA NA NA
32 NA NA NA NA
32.1 NA NA NA NA
32.2 NA NA NA NA
32.3 NA NA NA NA
33 NA NA NA NA
33.1 NA NA NA NA
34 NA NA NA NA
34.1 NA NA NA NA
34.2 NA NA NA NA
34.3 NA NA NA NA
35 NA NA NA NA
35.1 NA NA NA NA
35.2 NA NA NA NA
36 NA NA NA NA
36.1 NA NA NA NA
36.2 NA NA NA NA
36.3 NA NA NA NA
36.4 NA NA NA NA
37 NA NA NA NA
37.1 NA NA NA NA
37.2 NA NA NA NA
38 NA NA NA NA
39 NA NA NA NA
39.1 NA NA NA NA
39.2 NA NA NA NA
39.3 NA NA NA NA
39.4 NA NA NA NA
39.5 NA NA NA NA
40 NA NA NA NA
40.1 NA NA NA NA
40.2 NA NA NA NA
40.3 NA NA NA NA
41 NA NA NA NA
41.1 NA NA NA NA
41.2 NA NA NA NA
41.3 NA NA NA NA
41.4 NA NA NA NA
42 NA NA NA NA
42.1 NA NA NA NA
43 NA NA NA NA
43.1 NA NA NA NA
43.2 NA NA NA NA
44 NA NA NA NA
44.1 NA NA NA NA
44.2 NA NA NA NA
44.3 NA NA NA NA
45 NA NA NA NA
45.1 NA NA NA NA
46 NA NA NA NA
46.1 NA NA NA NA
46.2 NA NA NA NA
47 NA NA NA NA
47.1 NA NA NA NA
47.2 NA NA NA NA
47.3 NA NA NA NA
47.4 NA NA NA NA
48 NA NA NA NA
48.1 NA NA NA NA
49 NA NA NA NA
50 NA NA NA NA
51 NA NA NA NA
52 NA NA NA NA
52.1 NA NA NA NA
52.2 NA NA NA NA
52.3 NA NA NA NA
52.4 NA NA NA NA
52.5 NA NA NA NA
53 NA NA NA NA
53.1 NA NA NA NA
53.2 NA NA NA NA
54 NA NA NA NA
54.1 NA NA NA NA
54.2 NA NA NA NA
54.3 NA NA NA NA
54.4 NA NA NA NA
55 NA NA NA NA
55.1 NA NA NA NA
55.2 NA NA NA NA
55.3 NA NA NA NA
55.4 NA NA NA NA
56 NA NA NA NA
56.1 NA NA NA NA
56.2 NA NA NA NA
56.3 NA NA NA NA
56.4 NA NA NA NA
56.5 NA NA NA NA
57 NA NA NA NA
57.1 NA NA NA NA
57.2 NA NA NA NA
57.3 NA NA NA NA
58 NA NA NA NA
58.1 NA NA NA NA
58.2 NA NA NA NA
58.3 NA NA NA NA
58.4 NA NA NA NA
58.5 NA NA NA NA
59 NA NA NA NA
59.1 NA NA NA NA
60 NA NA NA NA
61 NA NA NA NA
61.1 NA NA NA NA
61.2 NA NA NA NA
61.3 NA NA NA NA
61.4 NA NA NA NA
62 NA NA NA NA
62.1 NA NA NA NA
62.2 NA NA NA NA
62.3 NA NA NA NA
63 NA NA NA NA
63.1 NA NA NA NA
64 NA NA NA NA
65 NA NA NA NA
65.1 NA NA NA NA
65.2 NA NA NA NA
65.3 NA NA NA NA
66 NA NA NA NA
66.1 NA NA NA NA
66.2 NA NA NA NA
67 NA NA NA NA
68 NA NA NA NA
68.1 NA NA NA NA
68.2 NA NA NA NA
68.3 NA NA NA NA
68.4 NA NA NA NA
69 NA NA NA NA
70 NA NA NA NA
70.1 NA NA NA NA
71 NA NA NA NA
71.1 NA NA NA NA
71.2 NA NA NA NA
71.3 NA NA NA NA
71.4 NA NA NA NA
72 NA NA NA NA
72.1 NA NA NA NA
72.2 NA NA NA NA
72.3 NA NA NA NA
72.4 NA NA NA NA
72.5 NA NA NA NA
73 NA NA NA NA
74 NA NA NA NA
75 NA NA NA NA
76 NA NA NA NA
76.1 NA NA NA NA
76.2 NA NA NA NA
77 NA NA NA NA
78 NA NA NA NA
79 NA NA NA NA
79.1 NA NA NA NA
79.2 NA NA NA NA
80 NA NA NA NA
80.1 NA NA NA NA
80.2 NA NA NA NA
81 NA NA NA NA
81.1 NA NA NA NA
81.2 NA NA NA NA
81.3 NA NA NA NA
82 NA NA NA NA
82.1 NA NA NA NA
82.2 NA NA NA NA
83 NA NA NA NA
83.1 NA NA NA NA
83.2 NA NA NA NA
83.3 NA NA NA NA
84 NA NA NA NA
84.1 NA NA NA NA
85 NA NA NA NA
85.1 NA NA NA NA
85.2 NA NA NA NA
85.3 NA NA NA NA
85.4 NA NA NA NA
85.5 NA NA NA NA
86 NA NA NA NA
86.1 NA NA NA NA
86.2 NA NA NA NA
86.3 NA NA NA NA
86.4 NA NA NA NA
86.5 NA NA NA NA
87 NA NA NA NA
87.1 NA NA NA NA
87.2 NA NA NA NA
88 NA NA NA NA
88.1 NA NA NA NA
88.2 NA NA NA NA
88.3 NA NA NA NA
89 NA NA NA NA
90 NA NA NA NA
90.1 NA NA NA NA
90.2 NA NA NA NA
90.3 NA NA NA NA
91 NA NA NA NA
91.1 NA NA NA NA
91.2 NA NA NA NA
92 NA NA NA NA
93 NA NA NA NA
93.1 NA NA NA NA
93.2 NA NA NA NA
93.3 NA NA NA NA
93.4 NA NA NA NA
94 NA NA NA NA
94.1 NA NA NA NA
94.2 NA NA NA NA
94.3 NA NA NA NA
94.4 NA NA NA NA
94.5 NA NA NA NA
95 NA NA NA NA
95.1 NA NA NA NA
95.2 NA NA NA NA
96 NA NA NA NA
96.1 NA NA NA NA
96.2 NA NA NA NA
96.3 NA NA NA NA
96.4 NA NA NA NA
96.5 NA NA NA NA
97 NA NA NA NA
97.1 NA NA NA NA
98 NA NA NA NA
98.1 NA NA NA NA
98.2 NA NA NA NA
99 NA NA NA NA
99.1 NA NA NA NA
99.2 NA NA NA NA
100 NA NA NA NA
100.1 NA NA NA NA
100.2 NA NA NA NA
100.3 NA NA NA NA
100.4 NA 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
o1 NA NA
o2 NA NA
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0.3740741 0.4847815
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0.5451138 0.8377542
o22 NA NA
o23 NA NA
o24 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_ordinal
[1] 0
$m4b$tau_reg_ordinal
[1] 1e-04
$m4b$mu_delta_ordinal
[1] 0
$m4b$tau_delta_ordinal
[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
o1 time c1 c1:time
1 2 0.5090421822 0.7592026489 3.864662e-01
1.1 1 0.6666076288 0.9548337990 6.364995e-01
1.2 1 2.1304941282 0.5612235156 1.195683e+00
1.3 2 2.4954441458 1.1873391025 2.962938e+00
2 3 3.0164990982 0.9192204198 2.772828e+00
2.1 1 3.2996806887 -0.1870730476 -6.172813e-01
2.2 2 4.1747569619 1.2517512331 5.225757e+00
3 1 0.8478727890 -0.0605087604 -5.130373e-02
3.1 3 3.0654308549 0.3788637747 1.161381e+00
3.2 2 4.7381553578 0.9872578281 4.677781e+00
4 3 0.3371432109 1.4930175328 5.033607e-01
4.1 2 1.0693019140 -0.7692526880 -8.225634e-01
4.2 1 2.6148973033 0.9180841450 2.400696e+00
4.3 2 3.1336532847 -0.0541170782 -1.695842e-01
5 2 1.0762525082 -0.1376784521 -1.481768e-01
5.1 2 1.7912546196 -0.2740585866 -4.909087e-01
5.2 3 2.7960080339 0.4670496929 1.305875e+00
5.3 2 2.8119940578 0.1740288049 4.893680e-01
6 3 1.7815462884 0.9868044683 1.758038e+00
7 3 3.3074087673 -0.1280320918 -4.234545e-01
7.1 1 3.7008403614 0.4242971219 1.570256e+00
7.2 1 4.7716691741 0.0777182491 3.708458e-01
8 2 1.1246398522 -0.5791408712 -6.513249e-01
8.1 2 1.8027009873 0.3128604232 5.639938e-01
8.2 3 1.8175825174 0.6258446356 1.137524e+00
8.3 3 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 2 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 2 2.2975509102 -0.5598409704 -1.286263e+00
10.1 1 4.1734118364 -0.2318340451 -9.675389e-01
11 2 1.1832662905 0.5086859475 6.019109e-01
11.1 3 1.2346051680 0.4951758188 6.113466e-01
11.2 3 1.6435316263 -1.1022162541 -1.811527e+00
11.3 1 3.3859017969 -0.0611636705 -2.070942e-01
11.4 3 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 1 3.5621061502 0.1079022586 3.843593e-01
14 3 4.0364430007 0.9991752617 4.033114e+00
14.1 3 4.4710561272 -0.1094019046 -4.891421e-01
14.2 2 4.6359198843 0.1518967560 7.041812e-01
14.3 1 4.6886152599 0.3521012473 1.650867e+00
15 3 0.5402063532 0.3464447888 1.871517e-01
15.1 3 1.1893180816 -0.4767313971 -5.669853e-01
15.2 2 1.5094739688 0.5759767791 8.694220e-01
15.3 2 4.9193474615 -0.1713452662 -8.429069e-01
16 3 1.2417913869 0.4564754473 5.668473e-01
16.1 1 2.5675726333 1.0652558311 2.735122e+00
16.2 3 2.6524101500 0.6971872493 1.849227e+00
16.3 1 3.5585018690 0.5259331838 1.871534e+00
16.4 1 3.7612454291 0.2046601798 7.697772e-01
16.5 2 3.9851612889 1.0718540464 4.271511e+00
17 2 1.5925356350 0.6048676222 9.632732e-01
17.1 2 2.4374032998 0.2323298304 5.662815e-01
17.2 3 3.0256489082 1.2617499032 3.817612e+00
17.3 2 3.3329089405 -0.3913230895 -1.304244e+00
17.4 3 3.8693758985 0.9577299112 3.705817e+00
18 2 2.4374292302 -0.0050324072 -1.226614e-02
19 3 0.9772165376 -0.4187468937 -4.092064e-01
19.1 2 1.1466335913 -0.4478828944 -5.135576e-01
19.2 2 2.2599126538 -1.1966721302 -2.704374e+00
19.3 2 4.2114245973 -0.5877091668 -2.475093e+00
20 1 1.7170160066 0.6838223064 1.174134e+00
20.1 2 1.7562902288 0.3278571109 5.758122e-01
20.2 2 2.2515566566 -0.8489831990 -1.911534e+00
20.3 1 2.2609123867 1.3169975191 2.977616e+00
20.4 1 3.4913365287 0.0444804531 1.552962e-01
20.5 2 4.1730977828 -0.4535207652 -1.892586e+00
21 2 1.6936582839 -0.4030302960 -6.825956e-01
21.1 2 2.9571191233 -0.4069674045 -1.203451e+00
21.2 2 3.7887385779 1.0650265940 4.035107e+00
22 1 2.4696226232 -0.0673274516 -1.662734e-01
22.1 1 3.1626627257 0.9601388170 3.036595e+00
23 1 1.5414533857 0.5556634840 8.565294e-01
23.1 2 2.3369736120 1.4407865964 3.367080e+00
24 1 2.8283136466 0.3856376411 1.090704e+00
25 2 0.5381704110 0.3564400705 1.918255e-01
25.1 1 1.6069735331 0.0982553434 1.578937e-01
25.2 1 1.6358226922 0.1928682598 3.154983e-01
25.3 1 3.2646870392 -0.0192488594 -6.284150e-02
25.4 2 4.0782226040 0.4466012931 1.821339e+00
25.5 2 4.1560292873 1.1425193342 4.748344e+00
26 2 0.2412706357 0.5341531449 1.288755e-01
26.1 3 2.4451737676 1.2268695927 2.999909e+00
26.2 2 3.5988757887 0.3678294939 1.323773e+00
26.3 1 4.1822362854 0.5948516018 2.487810e+00
27 2 3.6955824879 -0.3342844147 -1.235376e+00
27.1 2 4.2451434687 -0.4835141229 -2.052587e+00
28 1 0.5746519344 -0.7145915499 -4.106414e-01
28.1 1 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 2 1.1898884235 0.1392699487 1.657157e-01
29.1 2 1.7624059319 0.7960234776 1.402916e+00
29.2 1 2.0210406382 1.0398214352 2.101521e+00
29.3 2 3.4078777023 0.0813246429 2.771444e-01
30 2 2.2635366488 -0.3296323050 -7.461348e-01
30.1 2 3.5938334477 1.3635850954 4.900498e+00
30.2 2 3.6138710892 0.7354171050 2.657703e+00
31 2 4.3988140998 0.3708398217 1.631255e+00
32 3 1.6745209007 -0.0474059668 -7.938228e-02
32.1 1 2.9128167813 1.2507771489 3.643285e+00
32.2 3 2.9676558380 0.1142915519 3.391780e-01
32.3 3 4.2099863547 0.6773270619 2.851538e+00
33 2 0.0093385763 0.1774293842 1.656938e-03
33.1 2 3.4591242753 0.6159606291 2.130684e+00
34 3 1.4998774312 0.8590979166 1.288542e+00
34.1 2 3.8242761395 0.0546216775 2.088884e-01
34.2 2 3.9072251692 -0.0897224473 -3.505658e-01
34.3 1 3.9582124643 0.4163395571 1.647960e+00
35 2 1.3294299203 -1.4693520528 -1.953401e+00
35.1 3 1.5276966314 -0.3031734330 -4.631570e-01
35.2 2 4.5025920868 -0.6045512101 -2.722047e+00
36 3 0.7123168337 0.9823048960 6.997123e-01
36.1 2 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 3 2.0018732361 0.1177313455 2.356832e-01
37.1 3 3.6656836793 -0.1415483779 -5.188716e-01
37.2 3 3.9663937816 0.0054610124 2.166053e-02
38 2 0.9826511063 0.8078948077 7.938787e-01
39 1 0.6921808305 0.9876451040 6.836290e-01
39.1 2 0.9027792048 -0.3431222274 -3.097636e-01
39.2 2 1.3055654289 -1.7909380751 -2.338187e+00
39.3 1 1.5412842878 -0.1798746191 -2.772379e-01
39.4 1 3.1834997435 -0.1850961689 -5.892536e-01
39.5 1 4.1394166439 0.4544226146 1.881045e+00
40 3 1.1330395646 0.5350190436 6.061977e-01
40.1 1 2.6940994046 0.4189342752 1.128651e+00
40.2 1 3.0396614212 0.4211994981 1.280304e+00
40.3 2 4.6762977762 0.0916687506 4.286704e-01
41 3 1.9337158254 -0.1035047421 -2.001488e-01
41.1 2 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 3 3.5482964432 -0.3908036794 -1.386687e+00
42 2 0.4859252973 -0.0338893961 -1.646771e-02
42.1 1 4.3293134298 -0.4498363172 -1.947482e+00
43 2 0.5616614548 0.8965546110 5.035602e-01
43.1 2 1.0743579536 0.6199122090 6.660076e-01
43.2 1 2.6131797966 0.1804894429 4.716514e-01
44 3 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 1 0.1957449992 0.1414983160 2.769759e-02
45.1 1 1.9963831536 0.7220892521 1.441567e+00
46 1 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 1 2.4097121472 0.2982973539 7.188108e-01
47.2 1 2.9975794035 1.1518107179 3.452644e+00
47.3 3 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 2 2.9098354396 -0.5337239976 -1.553049e+00
49 1 2.7179756400 -0.5236770035 -1.423341e+00
50 2 1.1762060679 0.3897705981 4.584505e-01
51 2 1.4304436720 -0.7213343736 -1.031828e+00
52 2 2.1266646020 0.3758235358 7.992506e-01
52.1 3 3.1000545993 0.7138067080 2.212840e+00
52.2 2 3.1268477370 0.8872895233 2.774419e+00
52.3 1 3.5711459327 -0.9664587437 -3.451365e+00
52.4 3 4.7983659909 0.0254566848 1.221505e-01
52.5 2 4.9818264414 0.4155259424 2.070078e+00
53 2 0.4965799209 0.5675736897 2.818457e-01
53.1 1 3.5505357443 -0.3154088781 -1.119870e+00
53.2 1 4.5790420019 0.2162315769 9.901335e-01
54 1 1.4034724841 -0.0880802382 -1.236182e-01
54.1 2 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 2 4.0143877396 0.8587727145 3.447447e+00
55 1 0.6118522980 -0.0116453589 -7.125240e-03
55.1 1 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 3 1.2231332215 0.5176377612 6.331399e-01
56.1 2 2.3573202139 -1.1136932588 -2.625332e+00
56.2 3 2.5674936292 -0.0168103281 -4.316041e-02
56.3 3 2.9507164378 0.3933023606 1.160524e+00
56.4 1 3.2272730360 0.3714625139 1.198811e+00
56.5 3 3.4175522043 0.7811448179 2.669603e+00
57 2 0.2370331448 -1.0868304872 -2.576148e-01
57.1 2 0.2481445030 0.8018626997 1.989778e-01
57.2 2 1.1405586067 -0.1159517011 -1.322497e-01
57.3 2 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 3 1.6791862890 -0.1111300753 -1.866081e-01
58.3 3 2.6320121693 -0.5409234285 -1.423717e+00
58.4 2 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 1 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 3 0.6987666548 0.6830738372 4.773092e-01
62.2 1 3.4584309917 0.4301745954 1.487729e+00
62.3 2 4.8028772371 -0.0333139957 -1.600030e-01
63 1 2.8097350930 0.3345678035 9.400469e-01
63.1 3 3.9653754211 0.3643769511 1.444891e+00
64 2 4.1191305732 0.3949911859 1.627020e+00
65 1 0.7076152589 1.2000091513 8.491448e-01
65.1 2 2.0252246363 0.0110122646 2.230231e-02
65.2 1 3.1127382827 -0.5776452043 -1.798058e+00
65.3 1 3.1969087943 -0.1372183563 -4.386746e-01
66 1 3.4943454154 -0.5081302805 -1.775583e+00
66.1 1 3.7677437009 -0.1447837412 -5.455080e-01
66.2 3 3.9486138616 0.1906241379 7.527011e-01
67 3 4.1728388879 1.6716027681 6.975329e+00
68 1 0.1291919907 0.5691848839 7.353413e-02
68.1 2 1.7809643946 0.1004860389 1.789621e-01
68.2 2 2.0493205660 -0.0061241827 -1.255041e-02
68.3 2 2.9406870750 0.7443745962 2.188973e+00
68.4 2 4.0406670363 0.8726923437 3.526259e+00
69 2 4.1451198701 0.0381382683 1.580877e-01
70 1 0.1992557163 0.8126204217 1.619193e-01
70.1 3 0.4829774413 0.4691503050 2.265890e-01
71 3 0.7741605386 -0.5529062591 -4.280382e-01
71.1 3 1.4883817220 -0.1103252087 -1.642060e-01
71.2 3 4.0758526395 1.7178492547 7.001700e+00
71.3 2 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 1 1.1799991806 0.1375317317 1.622873e-01
72.2 2 1.8917567329 -0.4170988856 -7.890496e-01
72.3 2 3.4853593935 0.7107266765 2.477138e+00
72.4 1 3.6884259700 0.1451969143 5.355481e-01
72.5 1 4.0854155901 1.6298050306 6.658431e+00
73 1 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 1 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 1 0.0583819521 -0.3767450660 -2.199511e-02
79.1 2 2.4406372628 0.1516000028 3.700006e-01
79.2 1 3.2962526032 -0.1888160741 -6.223855e-01
80 2 0.8985060186 -0.0041558414 -3.734049e-03
80.1 2 1.3434670598 -0.0329337062 -4.424535e-02
80.2 3 2.8025900386 0.5046816157 1.414416e+00
81 3 0.0101324962 -0.9493950353 -9.619742e-03
81.1 2 0.9421709494 0.2443038954 2.301760e-01
81.2 3 3.0542453879 0.6476958410 1.978222e+00
81.3 3 3.3456630446 0.4182528210 1.399333e+00
82 3 1.3791010005 1.1088801952 1.529258e+00
82.1 2 1.7601010622 0.9334157763 1.642906e+00
82.2 2 2.6233131927 0.4958140634 1.300676e+00
83 3 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 2 4.7663642222 -0.0534169155 -2.546045e-01
84 3 2.7254060237 -0.0255753653 -6.970325e-02
84.1 3 3.3364784659 -1.8234189877 -6.083798e+00
85 3 0.2977756259 -0.0114038622 -3.395792e-03
85.1 2 1.7394116637 -0.0577615939 -1.004712e-01
85.2 2 2.6846330194 -0.2241856342 -6.018562e-01
85.3 2 3.1608762743 -0.0520175929 -1.644212e-01
85.4 1 3.9452053758 0.2892733846 1.141243e+00
85.5 3 4.5092553482 -0.3740417009 -1.686650e+00
86 2 0.8476278360 0.4293735089 3.639489e-01
86.1 3 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 3 3.8206058508 -0.1420690052 -5.427897e-01
87 1 2.7069531474 1.0457427869 2.830777e+00
87.1 1 3.4462517721 -0.2973007190 -1.024573e+00
87.2 1 4.5241666853 0.4396872616 1.989218e+00
88 3 0.0005892443 -0.0601928334 -3.546828e-05
88.1 1 0.7116099866 -1.0124347595 -7.204587e-01
88.2 1 2.4952722900 0.5730917016 1.430020e+00
88.3 1 3.2995816297 -0.0029455332 -9.719027e-03
89 1 0.6462086167 1.5465903721 9.994200e-01
90 2 0.1696030737 0.0626760573 1.063005e-02
90.1 1 2.5980385230 1.1896872985 3.090853e+00
90.2 3 2.6651392167 0.2597888783 6.923735e-01
90.3 1 3.1242690247 0.6599799887 2.061955e+00
91 1 0.6382618390 1.1213651365 7.157246e-01
91.1 2 2.6224059286 1.2046371625 3.159048e+00
91.2 1 4.7772527603 0.3395603754 1.622166e+00
92 2 0.0737052364 0.4674939332 3.445675e-02
93 1 0.2788909199 0.2677965647 7.468603e-02
93.1 3 1.0357759963 1.6424445368 1.701205e+00
93.2 1 2.4916551099 0.7101700066 1.769499e+00
93.3 3 2.8876129608 1.1222322893 3.240573e+00
93.4 1 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 1 1.9445500884 -0.4536774482 -8.821985e-01
94.3 3 3.0710722448 0.6793464917 2.086322e+00
94.4 2 3.0872731935 -0.9411356550 -2.905543e+00
94.5 3 4.3805759016 0.5683867264 2.489861e+00
95 3 2.0199063048 0.2375652188 4.798595e-01
95.1 1 4.0184444457 0.0767152977 3.082762e-01
95.2 2 4.5596531732 -0.6886731251 -3.140111e+00
96 1 0.0311333477 0.7813892121 2.432726e-02
96.1 3 0.1324267720 0.3391519695 4.491280e-02
96.2 1 0.6701303425 -0.4857246503 -3.254988e-01
96.3 1 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 3 0.2041732438 -1.2648518889 -2.582489e-01
98.1 1 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 2 4.6789444226 1.0355522332 4.845291e+00
99.2 1 4.9927084171 -0.1150895843 -5.746087e-01
100 1 1.0691387602 0.0369067906 3.945848e-02
100.1 2 1.5109344281 1.6023713093 2.421078e+00
100.2 2 2.1502332564 0.8861545820 1.905439e+00
100.3 1 3.8745574222 0.1277046316 4.947989e-01
100.4 1 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
o1 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_ordinal
[1] 0
$m4c$tau_reg_ordinal
[1] 1e-04
$m4c$mu_delta_ordinal
[1] 0
$m4c$tau_delta_ordinal
[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_o1_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_o1_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
o1 b2 time I(time^2) b21 c1 C1:time b21:c1
1 2 NA 0.5090421822 2.591239e-01 NA 0.7592026489 0.3652818145 NA
1.1 1 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 2 0 2.4954441458 6.227241e+00 NA 1.1873391025 1.7906971118 NA
2 3 0 3.0164990982 9.099267e+00 NA 0.9192204198 2.2645370243 NA
2.1 1 NA 3.2996806887 1.088789e+01 NA -0.1870730476 2.4771262462 NA
2.2 2 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 3 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 3 1 0.3371432109 1.136655e-01 NA 1.4930175328 0.2518241168 NA
4.1 2 0 1.0693019140 1.143407e+00 NA -0.7692526880 0.7986991917 NA
4.2 1 0 2.6148973033 6.837688e+00 NA 0.9180841450 1.9531587247 NA
4.3 2 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 2 0 1.7912546196 3.208593e+00 NA -0.2740585866 1.2787981866 NA
5.2 3 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 3 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 1 NA 3.7008403614 1.369622e+01 NA 0.4242971219 2.7186109493 NA
7.2 1 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 2 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 3 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 2 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 2 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 2 0 1.1832662905 1.400119e+00 NA 0.5086859475 0.8800481723 NA
11.1 3 0 1.2346051680 1.524250e+00 NA 0.4951758188 0.9182311964 NA
11.2 3 0 1.6435316263 2.701196e+00 NA -1.1022162541 1.2223681309 NA
11.3 1 0 3.3859017969 1.146433e+01 NA -0.0611636705 2.5182469169 NA
11.4 3 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 1 0 3.5621061502 1.268860e+01 NA 0.1079022586 2.6823320911 NA
14 3 NA 4.0364430007 1.629287e+01 NA 0.9991752617 2.8631042655 NA
14.1 3 NA 4.4710561272 1.999034e+01 NA -0.1094019046 3.1713813046 NA
14.2 2 NA 4.6359198843 2.149175e+01 NA 0.1518967560 3.2883214239 NA
14.3 1 NA 4.6886152599 2.198311e+01 NA 0.3521012473 3.3256989750 NA
15 3 0 0.5402063532 2.918229e-01 NA 0.3464447888 0.3960356618 NA
15.1 3 0 1.1893180816 1.414477e+00 NA -0.4767313971 0.8719119477 NA
15.2 2 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 3 1 1.2417913869 1.542046e+00 NA 0.4564754473 0.8706683147 NA
16.1 1 NA 2.5675726333 6.592429e+00 NA 1.0652558311 1.8002251917 NA
16.2 3 NA 2.6524101500 7.035280e+00 NA 0.6971872493 1.8597080795 NA
16.3 1 0 3.5585018690 1.266294e+01 NA 0.5259331838 2.4950042800 NA
16.4 1 0 3.7612454291 1.414697e+01 NA 0.2046601798 2.6371556877 NA
16.5 2 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 2 0 2.4374032998 5.940935e+00 NA 0.2323298304 1.8115744548 NA
17.2 3 0 3.0256489082 9.154551e+00 NA 1.2617499032 2.2487818375 NA
17.3 2 NA 3.3329089405 1.110828e+01 NA -0.3913230895 2.4771496359 NA
17.4 3 0 3.8693758985 1.497207e+01 NA 0.9577299112 2.8758730794 NA
18 2 0 2.4374292302 5.941061e+00 NA -0.0050324072 1.8390868081 NA
19 3 NA 0.9772165376 9.549522e-01 NA -0.4187468937 0.7356962360 NA
19.1 2 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 2 1 4.2114245973 1.773610e+01 NA -0.5877091668 3.1705656887 NA
20 1 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 2 1 2.2515566566 5.069507e+00 NA -0.8489831990 1.7140797861 NA
20.3 1 0 2.2609123867 5.111725e+00 NA 1.3169975191 1.7212021776 NA
20.4 1 0 3.4913365287 1.218943e+01 NA 0.0444804531 2.6579075206 NA
20.5 2 0 4.1730977828 1.741475e+01 NA -0.4535207652 3.1769231897 NA
21 2 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 2 NA 3.7887385779 1.435454e+01 NA 1.0650265940 2.7474868216 NA
22 1 0 2.4696226232 6.099036e+00 NA -0.0673274516 1.8029800300 NA
22.1 1 0 3.1626627257 1.000244e+01 NA 0.9601388170 2.3089429463 NA
23 1 0 1.5414533857 2.376079e+00 NA 0.5556634840 1.0924663221 NA
23.1 2 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 2 0 0.5381704110 2.896274e-01 NA 0.3564400705 0.4208837547 NA
25.1 1 NA 1.6069735331 2.582364e+00 NA 0.0982553434 1.2567562995 NA
25.2 1 1 1.6358226922 2.675916e+00 NA 0.1928682598 1.2793181910 NA
25.3 1 0 3.2646870392 1.065818e+01 NA -0.0192488594 2.5531945101 NA
25.4 2 0 4.0782226040 1.663190e+01 NA 0.4466012931 3.1894314642 NA
25.5 2 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
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26.3 1 0 4.1822362854 1.749110e+01 NA 0.5948516018 2.9770402626 NA
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28.3 1 0 4.4705521734 1.998584e+01 NA 0.1196789973 3.3481543470 NA
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29.1 2 0 1.7624059319 3.106075e+00 NA 0.7960234776 1.3237233767 NA
29.2 1 0 2.0210406382 4.084605e+00 NA 1.0398214352 1.5179810109 NA
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31 2 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
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32.2 3 NA 2.9676558380 8.806981e+00 NA 0.1142915519 2.1728874266 NA
32.3 3 NA 4.2099863547 1.772399e+01 NA 0.6773270619 3.0825091978 NA
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33.1 2 1 3.4591242753 1.196554e+01 NA 0.6159606291 2.5273792639 NA
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36 3 NA 0.7123168337 5.073953e-01 NA 0.9823048960 0.5209093415 NA
36.1 2 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 3 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 3 0 3.9663937816 1.573228e+01 NA 0.0054610124 2.8163124234 NA
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39 1 1 0.6921808305 4.791143e-01 NA 0.9876451040 0.5122448722 NA
39.1 2 0 0.9027792048 8.150103e-01 NA -0.3431222274 0.6680971185 NA
39.2 2 NA 1.3055654289 1.704501e+00 NA -1.7909380751 0.9661769970 NA
39.3 1 NA 1.5412842878 2.375557e+00 NA -0.1798746191 1.1406195291 NA
39.4 1 0 3.1834997435 1.013467e+01 NA -0.1850961689 2.3559326511 NA
39.5 1 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 1 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 2 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
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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 3 0 3.5482964432 1.259041e+01 NA -0.3908036794 2.6580166349 NA
42 2 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 2 0 0.5616614548 3.154636e-01 NA 0.8965546110 0.4228080758 NA
43.1 2 1 1.0743579536 1.154245e+00 NA 0.6199122090 0.8087562626 NA
43.2 1 0 2.6131797966 6.828709e+00 NA 0.1804894429 1.9671521198 NA
44 3 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 1 NA 0.1957449992 3.831610e-02 NA 0.1414983160 0.1438246201 NA
45.1 1 1 1.9963831536 3.985546e+00 NA 0.7220892521 1.4668525369 NA
46 1 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 1 0 2.4097121472 5.806713e+00 NA 0.2982973539 1.7775195440 NA
47.2 1 0 2.9975794035 8.985482e+00 NA 1.1518107179 2.2111586981 NA
47.3 3 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 2 1 2.9098354396 8.467142e+00 NA -0.5337239976 2.1376170787 NA
49 1 NA 2.7179756400 7.387392e+00 NA -0.5236770035 1.9921136553 NA
50 2 0 1.1762060679 1.383461e+00 NA 0.3897705981 0.8539769445 NA
51 2 0 1.4304436720 2.046169e+00 NA -0.7213343736 1.0360574125 NA
52 2 0 2.1266646020 4.522702e+00 NA 0.3758235358 1.5520541611 NA
52.1 3 0 3.1000545993 9.610339e+00 NA 0.7138067080 2.2624407422 NA
52.2 2 0 3.1268477370 9.777177e+00 NA 0.8872895233 2.2819945547 NA
52.3 1 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 2 0 4.9818264414 2.481859e+01 NA 0.4155259424 3.6357705164 NA
53 2 0 0.4965799209 2.465916e-01 NA 0.5675736897 0.3602558557 NA
53.1 1 0 3.5505357443 1.260630e+01 NA -0.3154088781 2.5758216127 NA
53.2 1 NA 4.5790420019 2.096763e+01 NA 0.2162315769 3.3219762321 NA
54 1 NA 1.4034724841 1.969735e+00 NA -0.0880802382 1.0585083082 NA
54.1 2 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 2 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 1 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 3 0 1.2231332215 1.496055e+00 NA 0.5176377612 0.9198742485 NA
56.1 2 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 3 1 2.9507164378 8.706727e+00 NA 0.3933023606 2.2191270894 NA
56.4 1 0 3.2272730360 1.041529e+01 NA 0.3714625139 2.4271153024 NA
56.5 3 0 3.4175522043 1.167966e+01 NA 0.7811448179 2.5702173815 NA
57 2 0 0.2370331448 5.618471e-02 NA -1.0868304872 0.1711369455 NA
57.1 2 0 0.2481445030 6.157569e-02 NA 0.8018626997 0.1791592999 NA
57.2 2 0 1.1405586067 1.300874e+00 NA -0.1159517011 0.8234785742 NA
57.3 2 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 3 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 2 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 1 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 3 1 0.6987666548 4.882748e-01 NA 0.6830738372 0.5024056819 NA
62.2 1 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 1 NA 2.8097350930 7.894611e+00 NA 0.3345678035 2.0604786922 NA
63.1 3 0 3.9653754211 1.572420e+01 NA 0.3643769511 2.9079508535 NA
64 2 0 4.1191305732 1.696724e+01 NA 0.3949911859 3.0153034804 NA
65 1 0 0.7076152589 5.007194e-01 NA 1.2000091513 0.5291342322 NA
65.1 2 0 2.0252246363 4.101535e+00 NA 0.0110122646 1.5144044302 NA
65.2 1 0 3.1127382827 9.689140e+00 NA -0.5776452043 2.3276156931 NA
65.3 1 0 3.1969087943 1.022023e+01 NA -0.1372183563 2.3905559682 NA
66 1 NA 3.4943454154 1.221045e+01 NA -0.5081302805 2.5662383121 NA
66.1 1 0 3.7677437009 1.419589e+01 NA -0.1447837412 2.7670213119 NA
66.2 3 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 1 0 0.1291919907 1.669057e-02 NA 0.5691848839 0.0966709548 NA
68.1 2 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 2 0 2.9406870750 8.647640e+00 NA 0.7443745962 2.2004384781 NA
68.4 2 NA 4.0406670363 1.632699e+01 NA 0.8726923437 3.0235244339 NA
69 2 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 3 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 3 1 1.4883817220 2.215280e+00 NA -0.1103252087 1.1576038195 NA
71.2 3 0 4.0758526395 1.661257e+01 NA 1.7178492547 3.1700352897 NA
71.3 2 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 1 0 1.1799991806 1.392398e+00 NA 0.1375317317 0.8741602904 NA
72.2 2 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 1 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 1 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 1 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
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79 1 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 1 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 2 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 3 0 0.0101324962 1.026675e-04 NA -0.9493950353 0.0073905742 NA
81.1 2 0 0.9421709494 8.876861e-01 NA 0.2443038954 0.6872131160 NA
81.2 3 NA 3.0542453879 9.328415e+00 NA 0.6476958410 2.2277459217 NA
81.3 3 0 3.3456630446 1.119346e+01 NA 0.4182528210 2.4403039888 NA
82 3 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 2 1 2.6233131927 6.881772e+00 NA 0.4958140634 1.9095908060 NA
83 3 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 2 NA 4.7663642222 2.271823e+01 NA -0.0534169155 3.5007244248 NA
84 3 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 3 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 2 0 2.6846330194 7.207254e+00 NA -0.2241856342 1.9661489512 NA
85.3 2 0 3.1608762743 9.991139e+00 NA -0.0520175929 2.3149359808 NA
85.4 1 0 3.9452053758 1.556465e+01 NA 0.2892733846 2.8893563314 NA
85.5 3 0 4.5092553482 2.033338e+01 NA -0.3740417009 3.3024505062 NA
86 2 0 0.8476278360 7.184729e-01 NA 0.4293735089 0.6422134099 NA
86.1 3 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 3 0 3.8206058508 1.459703e+01 NA -0.1420690052 2.8947188929 NA
87 1 NA 2.7069531474 7.327595e+00 NA 1.0457427869 2.0291697589 NA
87.1 1 NA 3.4462517721 1.187665e+01 NA -0.2973007190 2.5833582987 NA
87.2 1 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 1 NA 0.7116099866 5.063888e-01 NA -1.0124347595 0.5177132747 NA
88.2 1 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 1 0 0.6462086167 4.175856e-01 NA 1.5465903721 0.4685431339 NA
90 2 0 0.1696030737 2.876520e-02 NA 0.0626760573 0.1244083036 NA
90.1 1 0 2.5980385230 6.749804e+00 NA 1.1896872985 1.9057294087 NA
90.2 3 0 2.6651392167 7.102967e+00 NA 0.2597888783 1.9549495277 NA
90.3 1 NA 3.1242690247 9.761057e+00 NA 0.6599799887 2.2917332858 NA
91 1 0 0.6382618390 4.073782e-01 NA 1.1213651365 0.4687382105 NA
91.1 2 0 2.6224059286 6.877013e+00 NA 1.2046371625 1.9258896380 NA
91.2 1 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 1 NA 0.2788909199 7.778015e-02 NA 0.2677965647 0.2060853219 NA
93.1 3 0 1.0357759963 1.072832e+00 NA 1.6424445368 0.7653825006 NA
93.2 1 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 1 0 4.4639474002 1.992683e+01 NA 1.4628960401 3.2986159518 NA
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94.1 3 0 1.0552454425 1.113543e+00 NA 0.0147813580 0.7661029975 NA
94.2 1 0 1.9445500884 3.781275e+00 NA -0.4536774482 1.4117337932 NA
94.3 3 NA 3.0710722448 9.431485e+00 NA 0.6793464917 2.2295833342 NA
94.4 2 0 3.0872731935 9.531256e+00 NA -0.9411356550 2.2413451432 NA
94.5 3 1 4.3805759016 1.918945e+01 NA 0.5683867264 3.1802765437 NA
95 3 0 2.0199063048 4.080021e+00 NA 0.2375652188 1.4710654666 NA
95.1 1 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 1 0 0.0311333477 9.692853e-04 NA 0.7813892121 0.0226702966 NA
96.1 3 0 0.1324267720 1.753685e-02 NA 0.3391519695 0.0964288912 NA
96.2 1 0 0.6701303425 4.490747e-01 NA -0.4857246503 0.4879672359 NA
96.3 1 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 3 0 0.2041732438 4.168671e-02 NA -1.2648518889 0.1536799385 NA
98.1 1 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 2 0 4.6789444226 2.189252e+01 NA 1.0355522332 3.4026730772 NA
99.2 1 0 4.9927084171 2.492714e+01 NA -0.1150895843 3.6308519569 NA
100 1 NA 1.0691387602 1.143058e+00 NA 0.0369067906 0.7893943379 NA
100.1 2 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 1 NA 3.8745574222 1.501220e+01 NA 0.1277046316 2.8607640137 NA
100.4 1 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
o1 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_ordinal
[1] 0
$m4d$tau_reg_ordinal
[1] 1e-04
$m4d$mu_delta_ordinal
[1] 0
$m4d$tau_delta_ordinal
[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_o1_id
[,1] [,2]
[1,] NA 0
[2,] 0 NA
$m4d$KinvD_o1_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
o1 log(time) I(time^2) p1 time
1 2 -0.67522439 2.591239e-01 5 0.5090421822
1.1 1 -0.40555367 4.443657e-01 3 0.6666076288
1.2 1 0.75635394 4.539005e+00 8 2.1304941282
1.3 2 0.91446673 6.227241e+00 6 2.4954441458
2 3 1.10409692 9.099267e+00 5 3.0164990982
2.1 1 1.19382570 1.088789e+01 3 3.2996806887
2.2 2 1.42905614 1.742860e+01 2 4.1747569619
3 1 -0.16502467 7.188883e-01 7 0.8478727890
3.1 3 1.12018813 9.396866e+00 2 3.0654308549
3.2 2 1.55564789 2.245012e+01 8 4.7381553578
4 3 -1.08724748 1.136655e-01 2 0.3371432109
4.1 2 0.06700602 1.143407e+00 4 1.0693019140
4.2 1 0.96122482 6.837688e+00 2 2.6148973033
4.3 2 1.14219951 9.819783e+00 6 3.1336532847
5 2 0.07348511 1.158319e+00 6 1.0762525082
5.1 2 0.58291628 3.208593e+00 2 1.7912546196
5.2 3 1.02819270 7.817661e+00 3 2.7960080339
5.3 2 1.03389386 7.907311e+00 2 2.8119940578
6 3 0.57748169 3.173907e+00 4 1.7815462884
7 3 1.19616503 1.093895e+01 2 3.3074087673
7.1 1 1.30855992 1.369622e+01 6 3.7008403614
7.2 1 1.56269618 2.276883e+01 4 4.7716691741
8 2 0.11746285 1.264815e+00 2 1.1246398522
8.1 2 0.58928609 3.249731e+00 2 1.8027009873
8.2 3 0.59750733 3.303606e+00 1 1.8175825174
8.3 3 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 2 -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 2 0.83184373 5.278740e+00 4 2.2975509102
10.1 1 1.42873389 1.741737e+01 5 4.1734118364
11 2 0.16827866 1.400119e+00 2 1.1832662905
11.1 3 0.21075122 1.524250e+00 4 1.2346051680
11.2 3 0.49684736 2.701196e+00 6 1.6435316263
11.3 1 1.21962028 1.146433e+01 2 3.3859017969
11.4 3 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 1 1.27035199 1.268860e+01 6 3.5621061502
14 3 1.39536386 1.629287e+01 3 4.0364430007
14.1 3 1.49762465 1.999034e+01 2 4.4710561272
14.2 2 1.53383464 2.149175e+01 4 4.6359198843
14.3 1 1.54513729 2.198311e+01 2 4.6886152599
15 3 -0.61580408 2.918229e-01 4 0.5402063532
15.1 3 0.17338010 1.414477e+00 7 1.1893180816
15.2 2 0.41176123 2.278512e+00 4 1.5094739688
15.3 2 1.59317589 2.419998e+01 3 4.9193474615
16 3 0.21655500 1.542046e+00 3 1.2417913869
16.1 1 0.94296095 6.592429e+00 2 2.5675726333
16.2 3 0.97546872 7.035280e+00 5 2.6524101500
16.3 1 1.26933963 1.266294e+01 3 3.5585018690
16.4 1 1.32475013 1.414697e+01 2 3.7612454291
16.5 2 1.38257779 1.588151e+01 6 3.9851612889
17 2 0.46532748 2.536170e+00 3 1.5925356350
17.1 2 0.89093325 5.940935e+00 1 2.4374032998
17.2 3 1.10712558 9.154551e+00 4 3.0256489082
17.3 2 1.20384548 1.110828e+01 5 3.3329089405
17.4 3 1.35309323 1.497207e+01 5 3.8693758985
18 2 0.89094389 5.941061e+00 8 2.4374292302
19 3 -0.02304702 9.549522e-01 5 0.9772165376
19.1 2 0.13683034 1.314769e+00 6 1.1466335913
19.2 2 0.81532616 5.107205e+00 4 2.2599126538
19.3 2 1.43780097 1.773610e+01 3 4.2114245973
20 1 0.54058790 2.948144e+00 5 1.7170160066
20.1 2 0.56320376 3.084555e+00 8 1.7562902288
20.2 2 0.81162182 5.069507e+00 3 2.2515566566
20.3 1 0.81576844 5.111725e+00 3 2.2609123867
20.4 1 1.25028462 1.218943e+01 3 3.4913365287
20.5 2 1.42865863 1.741475e+01 3 4.1730977828
21 2 0.52689085 2.868478e+00 3 1.6936582839
21.1 2 1.08421553 8.744554e+00 3 2.9571191233
21.2 2 1.33203313 1.435454e+01 4 3.7887385779
22 1 0.90406535 6.099036e+00 6 2.4696226232
22.1 1 1.15141431 1.000244e+01 3 3.1626627257
23 1 0.43272573 2.376079e+00 3 1.5414533857
23.1 2 0.84885676 5.461446e+00 2 2.3369736120
24 1 1.03968065 7.999358e+00 1 2.8283136466
25 2 -0.61958002 2.896274e-01 2 0.5381704110
25.1 1 0.47435262 2.582364e+00 0 1.6069735331
25.2 1 0.49214585 2.675916e+00 6 1.6358226922
25.3 1 1.18316390 1.065818e+01 6 3.2646870392
25.4 2 1.40566126 1.663190e+01 2 4.0782226040
25.5 2 1.42456012 1.727258e+01 2 4.1560292873
26 2 -1.42183601 5.821152e-02 6 0.2412706357
26.1 3 0.89411619 5.978875e+00 0 2.4451737676
26.2 2 1.28062152 1.295191e+01 1 3.5988757887
26.3 1 1.43084610 1.749110e+01 4 4.1822362854
27 2 1.30713818 1.365733e+01 2 3.6955824879
27.1 2 1.44577562 1.802124e+01 4 4.2451434687
28 1 -0.55399075 3.302248e-01 5 0.5746519344
28.1 1 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 2 0.17385954 1.415834e+00 4 1.1898884235
29.1 2 0.56667988 3.106075e+00 1 1.7624059319
29.2 1 0.70361255 4.084605e+00 4 2.0210406382
29.3 2 1.22608972 1.161363e+01 3 3.4078777023
30 2 0.81692848 5.123598e+00 5 2.2635366488
30.1 2 1.27921945 1.291564e+01 5 3.5938334477
30.2 2 1.28477952 1.306006e+01 6 3.6138710892
31 2 1.48133498 1.934957e+01 1 4.3988140998
32 3 0.51552709 2.804020e+00 2 1.6745209007
32.1 1 1.06912058 8.484502e+00 5 2.9128167813
32.2 3 1.08777236 8.806981e+00 5 2.9676558380
32.3 3 1.43745941 1.772399e+01 6 4.2099863547
33 2 -4.67360146 8.720901e-05 4 0.0093385763
33.1 2 1.24101546 1.196554e+01 7 3.4591242753
34 3 0.40538339 2.249632e+00 2 1.4998774312
34.1 2 1.34136920 1.462509e+01 5 3.8242761395
34.2 2 1.36282745 1.526641e+01 6 3.9072251692
34.3 1 1.37579253 1.566745e+01 2 3.9582124643
35 2 0.28475022 1.767384e+00 3 1.3294299203
35.1 3 0.42376113 2.333857e+00 2 1.5276966314
35.2 2 1.50465325 2.027334e+01 3 4.5025920868
36 3 -0.33923248 5.073953e-01 3 0.7123168337
36.1 2 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 3 0.69408336 4.007496e+00 4 2.0018732361
37.1 3 1.29901486 1.343724e+01 6 3.6656836793
37.2 3 1.37785731 1.573228e+01 8 3.9663937816
38 2 -0.01750115 9.656032e-01 3 0.9826511063
39 1 -0.36790804 4.791143e-01 2 0.6921808305
39.1 2 -0.10227727 8.150103e-01 3 0.9027792048
39.2 2 0.26663623 1.704501e+00 6 1.3055654289
39.3 1 0.43261602 2.375557e+00 4 1.5412842878
39.4 1 1.15798114 1.013467e+01 3 3.1834997435
39.5 1 1.42055487 1.713477e+01 6 4.1394166439
40 3 0.12490390 1.283779e+00 1 1.1330395646
40.1 1 0.99106398 7.258172e+00 3 2.6940994046
40.2 1 1.11174613 9.239542e+00 0 3.0396614212
40.3 2 1.54250672 2.186776e+01 4 4.6762977762
41 3 0.65944345 3.739257e+00 1 1.9337158254
41.1 2 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 3 1.26646761 1.259041e+01 2 3.5482964432
42 2 -0.72170038 2.361234e-01 1 0.4859252973
42.1 1 1.46540897 1.874295e+01 3 4.3293134298
43 2 -0.57685600 3.154636e-01 5 0.5616614548
43.1 2 0.07172323 1.154245e+00 2 1.0743579536
43.2 1 0.96056779 6.828709e+00 3 2.6131797966
44 3 -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 1 -1.63094249 3.831610e-02 4 0.1957449992
45.1 1 0.69133712 3.985546e+00 2 1.9963831536
46 1 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 1 0.87950730 5.806713e+00 5 2.4097121472
47.2 1 1.09780510 8.985482e+00 5 2.9975794035
47.3 3 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 2 1.06809653 8.467142e+00 5 2.9098354396
49 1 0.99988735 7.387392e+00 4 2.7179756400
50 2 0.16229406 1.383461e+00 1 1.1762060679
51 2 0.35798466 2.046169e+00 9 1.4304436720
52 2 0.75455484 4.522702e+00 3 2.1266646020
52.1 3 1.13141972 9.610339e+00 3 3.1000545993
52.2 2 1.14002538 9.777177e+00 4 3.1268477370
52.3 1 1.27288653 1.275308e+01 11 3.5711459327
52.4 3 1.56827544 2.302432e+01 3 4.7983659909
52.5 2 1.60579658 2.481859e+01 3 4.9818264414
53 2 -0.70001084 2.465916e-01 5 0.4965799209
53.1 1 1.26709851 1.260630e+01 3 3.5505357443
53.2 1 1.52148981 2.096763e+01 2 4.5790420019
54 1 0.33894951 1.969735e+00 1 1.4034724841
54.1 2 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 2 1.38988484 1.611531e+01 6 4.0143877396
55 1 -0.49126437 3.743632e-01 1 0.6118522980
55.1 1 -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 3 0.20141578 1.496055e+00 5 1.2231332215
56.1 2 0.85752547 5.556959e+00 5 2.3573202139
56.2 3 0.94293018 6.592024e+00 2 2.5674936292
56.3 3 1.08204800 8.706727e+00 3 2.9507164378
56.4 1 1.17163752 1.041529e+01 6 3.2272730360
56.5 3 1.22892457 1.167966e+01 1 3.4175522043
57 2 -1.43955530 5.618471e-02 3 0.2370331448
57.1 2 -1.39374403 6.157569e-02 6 0.2481445030
57.2 2 0.13151815 1.300874e+00 3 1.1405586067
57.3 2 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 3 0.51830932 2.819667e+00 2 1.6791862890
58.3 3 0.96774864 6.927488e+00 4 2.6320121693
58.4 2 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 1 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 3 -0.35843842 4.882748e-01 1 0.6987666548
62.2 1 1.24081502 1.196074e+01 2 3.4584309917
62.3 2 1.56921516 2.306763e+01 4 4.8028772371
63 1 1.03309021 7.894611e+00 6 2.8097350930
63.1 3 1.37760053 1.572420e+01 2 3.9653754211
64 2 1.41564212 1.696724e+01 2 4.1191305732
65 1 -0.34585475 5.007194e-01 3 0.7076152589
65.1 2 0.70568063 4.101535e+00 4 2.0252246363
65.2 1 1.13550282 9.689140e+00 2 3.1127382827
65.3 1 1.16218434 1.022023e+01 2 3.1969087943
66 1 1.25114607 1.221045e+01 6 3.4943454154
66.1 1 1.32647633 1.419589e+01 0 3.7677437009
66.2 3 1.37336460 1.559155e+01 5 3.9486138616
67 3 1.42859659 1.741258e+01 8 4.1728388879
68 1 -2.04645568 1.669057e-02 5 0.1291919907
68.1 2 0.57715501 3.171834e+00 5 1.7809643946
68.2 2 0.71750831 4.199715e+00 4 2.0493205660
68.3 2 1.07864325 8.647640e+00 3 2.9406870750
68.4 2 1.39640979 1.632699e+01 1 4.0406670363
69 2 1.42193171 1.718202e+01 5 4.1451198701
70 1 -1.61316627 3.970284e-02 6 0.1992557163
70.1 3 -0.72778533 2.332672e-01 2 0.4829774413
71 3 -0.25597601 5.993245e-01 4 0.7741605386
71.1 3 0.39768944 2.215280e+00 2 1.4883817220
71.2 3 1.40507996 1.661257e+01 5 4.0758526395
71.3 2 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 1 0.16551374 1.392398e+00 4 1.1799991806
72.2 2 0.63750589 3.578744e+00 8 1.8917567329
72.3 2 1.24857116 1.214773e+01 6 3.4853593935
72.4 1 1.30519980 1.360449e+01 4 3.6884259700
72.5 1 1.40742346 1.669062e+01 1 4.0854155901
73 1 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 1 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 1 -2.84074848 3.408452e-03 2 0.0583819521
79.1 2 0.89225918 5.956710e+00 6 2.4406372628
79.2 1 1.19278625 1.086528e+01 5 3.2962526032
80 2 -0.10702187 8.073131e-01 5 0.8985060186
80.1 2 0.29525363 1.804904e+00 1 1.3434670598
80.2 3 1.03054400 7.854511e+00 4 2.8025900386
81 3 -4.59200757 1.026675e-04 4 0.0101324962
81.1 2 -0.05956855 8.876861e-01 5 0.9421709494
81.2 3 1.11653255 9.328415e+00 2 3.0542453879
81.3 3 1.20766489 1.119346e+01 5 3.3456630446
82 3 0.32143184 1.901920e+00 1 1.3791010005
82.1 2 0.56537123 3.097956e+00 2 1.7601010622
82.2 2 0.96443810 6.881772e+00 5 2.6233131927
83 3 -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 2 1.56158380 2.271823e+01 4 4.7663642222
84 3 1.00261742 7.427838e+00 1 2.7254060237
84.1 3 1.20491590 1.113209e+01 5 3.3364784659
85 3 -1.21141501 8.867032e-02 6 0.2977756259
85.1 2 0.55354693 3.025553e+00 5 1.7394116637
85.2 2 0.98754404 7.207254e+00 3 2.6846330194
85.3 2 1.15084929 9.991139e+00 2 3.1608762743
85.4 1 1.37250101 1.556465e+01 2 3.9452053758
85.5 3 1.50613203 2.033338e+01 6 4.5092553482
86 2 -0.16531361 7.184729e-01 3 0.8476278360
86.1 3 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 3 1.34040901 1.459703e+01 4 3.8206058508
87 1 0.99582370 7.327595e+00 3 2.7069531474
87.1 1 1.23728720 1.187665e+01 6 3.4462517721
87.2 1 1.50943340 2.046808e+01 2 4.5241666853
88 3 -7.43666969 3.472088e-07 1 0.0005892443
88.1 1 -0.34022529 5.063888e-01 6 0.7116099866
88.2 1 0.91439786 6.226384e+00 1 2.4952722900
88.3 1 1.19379568 1.088724e+01 6 3.2995816297
89 1 -0.43663289 4.175856e-01 7 0.6462086167
90 2 -1.77429443 2.876520e-02 3 0.1696030737
90.1 1 0.95475675 6.749804e+00 8 2.5980385230
90.2 3 0.98025630 7.102967e+00 4 2.6651392167
90.3 1 1.13920034 9.761057e+00 2 3.1242690247
91 1 -0.44900667 4.073782e-01 4 0.6382618390
91.1 2 0.96409219 6.877013e+00 2 2.6224059286
91.2 1 1.56386564 2.282214e+01 5 4.7772527603
92 2 -2.60768143 5.432462e-03 3 0.0737052364
93 1 -1.27693454 7.778015e-02 3 0.2788909199
93.1 3 0.03515090 1.072832e+00 3 1.0357759963
93.2 1 0.91294719 6.208345e+00 4 2.4916551099
93.3 3 1.06043020 8.338309e+00 2 2.8876129608
93.4 1 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 1 0.66503063 3.781275e+00 2 1.9445500884
94.3 3 1.12202677 9.431485e+00 6 3.0710722448
94.4 2 1.12728824 9.531256e+00 5 3.0872731935
94.5 3 1.47718020 1.918945e+01 5 4.3805759016
95 3 0.70305113 4.080021e+00 8 2.0199063048
95.1 1 1.39089487 1.614790e+01 4 4.0184444457
95.2 2 1.51724656 2.079044e+01 1 4.5596531732
96 1 -3.46947576 9.692853e-04 2 0.0311333477
96.1 3 -2.02172545 1.753685e-02 3 0.1324267720
96.2 1 -0.40028304 4.490747e-01 2 0.6701303425
96.3 1 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 3 -1.58878641 4.168671e-02 7 0.2041732438
98.1 1 -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 2 1.54307253 2.189252e+01 4 4.6789444226
99.2 1 1.60797853 2.492714e+01 5 4.9927084171
100 1 0.06685343 1.143058e+00 2 1.0691387602
100.1 2 0.41272829 2.282923e+00 3 1.5109344281
100.2 2 0.76557633 4.623503e+00 3 2.1502332564
100.3 1 1.35443144 1.501220e+01 7 3.8745574222
100.4 1 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
o1 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_ordinal
[1] 0
$m4e$tau_reg_ordinal
[1] 1e-04
$m4e$mu_delta_ordinal
[1] 0
$m4e$tau_delta_ordinal
[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"
$m5a
$m5a$M_id
C2 O2 (Intercept) C1 O22 O23
1 -1.381594459 3 1 0.7175865 NA NA
2 0.344426024 2 1 0.7507170 NA NA
3 NA 2 1 0.7255954 NA NA
4 -0.228910007 3 1 0.7469352 NA NA
5 NA 1 1 0.7139120 NA NA
6 -2.143955482 3 1 0.7332505 NA NA
7 -1.156567023 NA 1 0.7345929 NA NA
8 -0.598827660 1 1 0.7652589 NA NA
9 NA 1 1 0.7200622 NA NA
10 -1.006719032 3 1 0.7423879 NA NA
11 0.239801450 3 1 0.7437448 NA NA
12 -1.064969789 NA 1 0.7446470 NA NA
13 -0.538082688 3 1 0.7530186 NA NA
14 NA 2 1 0.7093137 NA NA
15 -1.781049276 2 1 0.7331192 NA NA
16 NA 3 1 0.7011390 NA NA
17 NA 3 1 0.7432395 NA NA
18 -0.014579883 NA 1 0.7545191 NA NA
19 -2.121550136 2 1 0.7528487 NA NA
20 NA 1 1 0.7612865 NA NA
21 -0.363239698 1 1 0.7251719 NA NA
22 -0.121568514 3 1 0.7300630 NA NA
23 -0.951271111 3 1 0.7087249 NA NA
24 NA 3 1 0.7391938 NA NA
25 -0.974288621 2 1 0.7820641 NA NA
26 -1.130632418 1 1 0.7118298 NA NA
27 0.114339868 3 1 0.7230857 NA NA
28 0.238334648 3 1 0.7489353 NA NA
29 0.840744958 2 1 0.7510888 NA NA
30 NA 1 1 0.7300717 NA NA
31 NA 2 1 0.7550721 NA NA
32 -1.466312154 3 1 0.7321898 NA NA
33 -0.637352277 2 1 0.7306414 NA NA
34 NA 2 1 0.7427216 NA NA
35 NA 3 1 0.7193042 NA NA
36 NA 1 1 0.7312888 NA NA
37 NA 3 1 0.7100436 NA NA
38 NA 2 1 0.7670184 NA NA
39 0.006728205 3 1 0.7400449 NA NA
40 NA 2 1 0.7397304 NA NA
41 -1.663281353 2 1 0.7490966 NA NA
42 0.161184794 2 1 0.7419274 NA NA
43 0.457939180 3 1 0.7527810 NA NA
44 -0.307070331 2 1 0.7408315 NA NA
45 NA 1 1 0.7347550 NA NA
46 -1.071668276 NA 1 0.7332398 NA NA
47 -0.814751321 1 1 0.7376481 NA NA
48 -0.547630662 2 1 0.7346179 NA NA
49 NA 1 1 0.7329402 NA NA
50 -1.350213782 2 1 0.7260436 NA NA
51 0.719054706 2 1 0.7242910 NA NA
52 NA 3 1 0.7298067 NA NA
53 -1.207130750 3 1 0.7254741 NA NA
54 NA 1 1 0.7542067 NA NA
55 -0.408600991 2 1 0.7389952 NA NA
56 -0.271380529 NA 1 0.7520638 NA NA
57 -1.361925974 3 1 0.7219958 NA NA
58 NA 1 1 0.7259632 NA NA
59 NA 2 1 0.7458606 NA NA
60 -0.323712205 2 1 0.7672421 NA NA
61 NA 3 1 0.7257179 NA NA
62 NA 2 1 0.7189892 NA NA
63 -1.386906880 1 1 0.7333356 NA NA
64 NA 3 1 0.7320243 NA NA
65 NA 1 1 0.7477711 NA NA
66 -0.565191691 3 1 0.7343974 NA NA
67 -0.382899912 3 1 0.7491624 NA NA
68 NA 2 1 0.7482736 NA NA
69 -0.405642769 NA 1 0.7338267 NA NA
70 NA 2 1 0.7607742 NA NA
71 -0.843748427 NA 1 0.7777600 NA NA
72 0.116003683 NA 1 0.7408143 NA NA
73 -0.778634325 1 1 0.7248271 NA NA
74 NA 2 1 0.7364916 NA NA
75 NA 2 1 0.7464926 NA NA
76 NA 1 1 0.7355430 NA NA
77 -0.632974758 3 1 0.7208449 NA NA
78 NA 2 1 0.7373573 NA NA
79 -0.778064615 NA 1 0.7598079 NA NA
80 NA 1 1 0.7360415 NA NA
81 NA 1 1 0.7293932 NA NA
82 -0.246123253 3 1 0.7279309 NA NA
83 -1.239659782 NA 1 0.7344643 NA NA
84 -0.467772280 3 1 0.7384350 NA NA
85 NA 3 1 0.7323716 NA NA
86 -2.160485036 2 1 0.7576597 NA NA
87 -0.657675572 1 1 0.7496139 NA NA
88 NA 1 1 0.7275239 NA NA
89 -0.696710744 3 1 0.7250648 NA NA
90 NA 1 1 0.7335262 NA NA
91 -0.179395847 2 1 0.7343980 NA NA
92 -0.441545568 NA 1 0.7380425 NA NA
93 -0.685799334 1 1 0.7389460 NA NA
94 NA 3 1 0.7259951 NA NA
95 0.191929445 2 1 0.7282840 NA NA
96 NA 2 1 0.7281676 NA NA
97 -0.069760671 3 1 0.7245642 NA NA
98 NA 2 1 0.7526938 NA NA
99 NA 2 1 0.7272309 NA NA
100 NA 3 1 0.7383460 NA NA
$m5a$M_lvlone
o1 b2 b21
1 2 NA NA
1.1 1 0 NA
1.2 1 NA NA
1.3 2 0 NA
2 3 0 NA
2.1 1 NA NA
2.2 2 NA NA
3 1 0 NA
3.1 3 NA NA
3.2 2 1 NA
4 3 1 NA
4.1 2 0 NA
4.2 1 0 NA
4.3 2 0 NA
5 2 NA NA
5.1 2 0 NA
5.2 3 NA NA
5.3 2 NA NA
6 3 NA NA
7 3 NA NA
7.1 1 NA NA
7.2 1 0 NA
8 2 0 NA
8.1 2 0 NA
8.2 3 NA NA
8.3 3 1 NA
8.4 2 0 NA
8.5 2 1 NA
9 2 0 NA
9.1 2 NA NA
9.2 3 NA NA
10 2 NA NA
10.1 1 0 NA
11 2 0 NA
11.1 3 0 NA
11.2 3 0 NA
11.3 1 0 NA
11.4 3 0 NA
12 1 0 NA
13 2 NA NA
13.1 1 0 NA
14 3 NA NA
14.1 3 NA NA
14.2 2 NA NA
14.3 1 NA NA
15 3 0 NA
15.1 3 0 NA
15.2 2 0 NA
15.3 2 0 NA
16 3 1 NA
16.1 1 NA NA
16.2 3 NA NA
16.3 1 0 NA
16.4 1 0 NA
16.5 2 NA NA
17 2 0 NA
17.1 2 0 NA
17.2 3 0 NA
17.3 2 NA NA
17.4 3 0 NA
18 2 0 NA
19 3 NA NA
19.1 2 NA NA
19.2 2 0 NA
19.3 2 1 NA
20 1 NA NA
20.1 2 0 NA
20.2 2 1 NA
20.3 1 0 NA
20.4 1 0 NA
20.5 2 0 NA
21 2 0 NA
21.1 2 0 NA
21.2 2 NA NA
22 1 0 NA
22.1 1 0 NA
23 1 0 NA
23.1 2 NA NA
24 1 0 NA
25 2 0 NA
25.1 1 NA NA
25.2 1 1 NA
25.3 1 0 NA
25.4 2 0 NA
25.5 2 NA NA
26 2 NA NA
26.1 3 0 NA
26.2 2 0 NA
26.3 1 0 NA
27 2 0 NA
27.1 2 0 NA
28 1 NA NA
28.1 1 0 NA
28.2 1 0 NA
28.3 1 0 NA
29 2 0 NA
29.1 2 0 NA
29.2 1 0 NA
29.3 2 0 NA
30 2 NA NA
30.1 2 0 NA
30.2 2 0 NA
31 2 0 NA
32 3 0 NA
32.1 1 0 NA
32.2 3 NA NA
32.3 3 NA NA
33 2 0 NA
33.1 2 1 NA
34 3 NA NA
34.1 2 0 NA
34.2 2 NA NA
34.3 1 NA NA
35 2 0 NA
35.1 3 0 NA
35.2 2 NA NA
36 3 NA NA
36.1 2 NA NA
36.2 3 0 NA
36.3 3 0 NA
36.4 3 0 NA
37 3 0 NA
37.1 3 0 NA
37.2 3 0 NA
38 2 0 NA
39 1 1 NA
39.1 2 0 NA
39.2 2 NA NA
39.3 1 NA NA
39.4 1 0 NA
39.5 1 1 NA
40 3 0 NA
40.1 1 1 NA
40.2 1 0 NA
40.3 2 NA NA
41 3 0 NA
41.1 2 NA NA
41.2 1 0 NA
41.3 1 NA NA
41.4 3 0 NA
42 2 0 NA
42.1 1 1 NA
43 2 0 NA
43.1 2 1 NA
43.2 1 0 NA
44 3 0 NA
44.1 2 0 NA
44.2 1 0 NA
44.3 1 0 NA
45 1 NA NA
45.1 1 1 NA
46 1 0 NA
46.1 2 0 NA
46.2 3 0 NA
47 1 0 NA
47.1 1 0 NA
47.2 1 0 NA
47.3 3 NA NA
47.4 2 0 NA
48 3 1 NA
48.1 2 1 NA
49 1 NA NA
50 2 0 NA
51 2 0 NA
52 2 0 NA
52.1 3 0 NA
52.2 2 0 NA
52.3 1 0 NA
52.4 3 0 NA
52.5 2 0 NA
53 2 0 NA
53.1 1 0 NA
53.2 1 NA NA
54 1 NA NA
54.1 2 NA NA
54.2 3 NA NA
54.3 1 NA NA
54.4 2 0 NA
55 1 0 NA
55.1 1 0 NA
55.2 2 NA NA
55.3 1 NA NA
55.4 1 0 NA
56 3 0 NA
56.1 2 NA NA
56.2 3 NA NA
56.3 3 1 NA
56.4 1 0 NA
56.5 3 0 NA
57 2 0 NA
57.1 2 0 NA
57.2 2 0 NA
57.3 2 NA NA
58 3 0 NA
58.1 2 NA NA
58.2 3 1 NA
58.3 3 1 NA
58.4 2 0 NA
58.5 3 0 NA
59 3 NA NA
59.1 1 1 NA
60 3 0 NA
61 1 NA NA
61.1 2 1 NA
61.2 2 1 NA
61.3 1 0 NA
61.4 2 0 NA
62 2 NA NA
62.1 3 1 NA
62.2 1 0 NA
62.3 2 0 NA
63 1 NA NA
63.1 3 0 NA
64 2 0 NA
65 1 0 NA
65.1 2 0 NA
65.2 1 0 NA
65.3 1 0 NA
66 1 NA NA
66.1 1 0 NA
66.2 3 0 NA
67 3 NA NA
68 1 0 NA
68.1 2 0 NA
68.2 2 NA NA
68.3 2 0 NA
68.4 2 NA NA
69 2 0 NA
70 1 0 NA
70.1 3 0 NA
71 3 0 NA
71.1 3 1 NA
71.2 3 0 NA
71.3 2 1 NA
71.4 2 0 NA
72 1 0 NA
72.1 1 0 NA
72.2 2 NA NA
72.3 2 0 NA
72.4 1 0 NA
72.5 1 0 NA
73 1 0 NA
74 1 0 NA
75 3 NA NA
76 3 0 NA
76.1 3 0 NA
76.2 1 0 NA
77 2 NA NA
78 2 0 NA
79 1 NA NA
79.1 2 0 NA
79.2 1 NA NA
80 2 NA NA
80.1 2 0 NA
80.2 3 NA NA
81 3 0 NA
81.1 2 0 NA
81.2 3 NA NA
81.3 3 0 NA
82 3 NA NA
82.1 2 0 NA
82.2 2 1 NA
83 3 NA NA
83.1 3 0 NA
83.2 3 0 NA
83.3 2 NA NA
84 3 0 NA
84.1 3 NA NA
85 3 1 NA
85.1 2 NA NA
85.2 2 0 NA
85.3 2 0 NA
85.4 1 0 NA
85.5 3 0 NA
86 2 0 NA
86.1 3 NA NA
86.2 1 NA NA
86.3 1 0 NA
86.4 1 NA NA
86.5 3 0 NA
87 1 NA NA
87.1 1 NA NA
87.2 1 NA NA
88 3 0 NA
88.1 1 NA NA
88.2 1 0 NA
88.3 1 0 NA
89 1 0 NA
90 2 0 NA
90.1 1 0 NA
90.2 3 0 NA
90.3 1 NA NA
91 1 0 NA
91.1 2 0 NA
91.2 1 0 NA
92 2 0 NA
93 1 NA NA
93.1 3 0 NA
93.2 1 NA NA
93.3 3 0 NA
93.4 1 0 NA
94 2 NA NA
94.1 3 0 NA
94.2 1 0 NA
94.3 3 NA NA
94.4 2 0 NA
94.5 3 1 NA
95 3 0 NA
95.1 1 NA NA
95.2 2 0 NA
96 1 0 NA
96.1 3 0 NA
96.2 1 0 NA
96.3 1 NA NA
96.4 2 1 NA
96.5 3 1 NA
97 3 0 NA
97.1 3 0 NA
98 3 0 NA
98.1 1 0 NA
98.2 1 1 NA
99 2 0 NA
99.1 2 0 NA
99.2 1 0 NA
100 1 NA NA
100.1 2 NA NA
100.2 2 0 NA
100.3 1 NA NA
100.4 1 0 NA
$m5a$spM_id
center scale
C2 -0.6240921 0.68571078
O2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
O22 NA NA
O23 NA NA
$m5a$mu_reg_norm
[1] 0
$m5a$tau_reg_norm
[1] 1e-04
$m5a$shape_tau_norm
[1] 0.01
$m5a$rate_tau_norm
[1] 0.01
$m5a$mu_reg_binom
[1] 0
$m5a$tau_reg_binom
[1] 1e-04
$m5a$mu_reg_ordinal
[1] 0
$m5a$tau_reg_ordinal
[1] 1e-04
$m5a$mu_delta_ordinal
[1] 0
$m5a$tau_delta_ordinal
[1] 1e-04
$m5a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m5a$shape_diag_RinvD
[1] "0.01"
$m5a$rate_diag_RinvD
[1] "0.001"
$m5b
$m5b$M_id
M2 C2 O2 (Intercept) M22 M23 M24 O22 O23
1 NA -1.381594459 3 1 NA NA NA NA NA
2 1 0.344426024 2 1 NA NA NA NA NA
3 2 NA 2 1 NA NA NA NA NA
4 2 -0.228910007 3 1 NA NA NA NA NA
5 1 NA 1 1 NA NA NA NA NA
6 NA -2.143955482 3 1 NA NA NA NA NA
7 NA -1.156567023 NA 1 NA NA NA NA NA
8 2 -0.598827660 1 1 NA NA NA NA NA
9 NA NA 1 1 NA NA NA NA NA
10 NA -1.006719032 3 1 NA NA NA NA NA
11 3 0.239801450 3 1 NA NA NA NA NA
12 NA -1.064969789 NA 1 NA NA NA NA NA
13 NA -0.538082688 3 1 NA NA NA NA NA
14 2 NA 2 1 NA NA NA NA NA
15 2 -1.781049276 2 1 NA NA NA NA NA
16 NA NA 3 1 NA NA NA NA NA
17 3 NA 3 1 NA NA NA NA NA
18 3 -0.014579883 NA 1 NA NA NA NA NA
19 2 -2.121550136 2 1 NA NA NA NA NA
20 NA NA 1 1 NA NA NA NA NA
21 NA -0.363239698 1 1 NA NA NA NA NA
22 1 -0.121568514 3 1 NA NA NA NA NA
23 2 -0.951271111 3 1 NA NA NA NA NA
24 NA NA 3 1 NA NA NA NA NA
25 4 -0.974288621 2 1 NA NA NA NA NA
26 NA -1.130632418 1 1 NA NA NA NA NA
27 NA 0.114339868 3 1 NA NA NA NA NA
28 2 0.238334648 3 1 NA NA NA NA NA
29 4 0.840744958 2 1 NA NA NA NA NA
30 2 NA 1 1 NA NA NA NA NA
31 2 NA 2 1 NA NA NA NA NA
32 3 -1.466312154 3 1 NA NA NA NA NA
33 1 -0.637352277 2 1 NA NA NA NA NA
34 4 NA 2 1 NA NA NA NA NA
35 4 NA 3 1 NA NA NA NA NA
36 NA NA 1 1 NA NA NA NA NA
37 NA NA 3 1 NA NA NA NA NA
38 NA NA 2 1 NA NA NA NA NA
39 4 0.006728205 3 1 NA NA NA NA NA
40 NA NA 2 1 NA NA NA NA NA
41 2 -1.663281353 2 1 NA NA NA NA NA
42 NA 0.161184794 2 1 NA NA NA NA NA
43 NA 0.457939180 3 1 NA NA NA NA NA
44 3 -0.307070331 2 1 NA NA NA NA NA
45 NA NA 1 1 NA NA NA NA NA
46 NA -1.071668276 NA 1 NA NA NA NA NA
47 4 -0.814751321 1 1 NA NA NA NA NA
48 3 -0.547630662 2 1 NA NA NA NA NA
49 4 NA 1 1 NA NA NA NA NA
50 3 -1.350213782 2 1 NA NA NA NA NA
51 NA 0.719054706 2 1 NA NA NA NA NA
52 1 NA 3 1 NA NA NA NA NA
53 NA -1.207130750 3 1 NA NA NA NA NA
54 NA NA 1 1 NA NA NA NA NA
55 NA -0.408600991 2 1 NA NA NA NA NA
56 NA -0.271380529 NA 1 NA NA NA NA NA
57 4 -1.361925974 3 1 NA NA NA NA NA
58 1 NA 1 1 NA NA NA NA NA
59 2 NA 2 1 NA NA NA NA NA
60 3 -0.323712205 2 1 NA NA NA NA NA
61 2 NA 3 1 NA NA NA NA NA
62 3 NA 2 1 NA NA NA NA NA
63 2 -1.386906880 1 1 NA NA NA NA NA
64 NA NA 3 1 NA NA NA NA NA
65 NA NA 1 1 NA NA NA NA NA
66 2 -0.565191691 3 1 NA NA NA NA NA
67 NA -0.382899912 3 1 NA NA NA NA NA
68 3 NA 2 1 NA NA NA NA NA
69 3 -0.405642769 NA 1 NA NA NA NA NA
70 4 NA 2 1 NA NA NA NA NA
71 2 -0.843748427 NA 1 NA NA NA NA NA
72 NA 0.116003683 NA 1 NA NA NA NA NA
73 4 -0.778634325 1 1 NA NA NA NA NA
74 NA NA 2 1 NA NA NA NA NA
75 NA NA 2 1 NA NA NA NA NA
76 4 NA 1 1 NA NA NA NA NA
77 2 -0.632974758 3 1 NA NA NA NA NA
78 2 NA 2 1 NA NA NA NA NA
79 NA -0.778064615 NA 1 NA NA NA NA NA
80 1 NA 1 1 NA NA NA NA NA
81 NA NA 1 1 NA NA NA NA NA
82 3 -0.246123253 3 1 NA NA NA NA NA
83 4 -1.239659782 NA 1 NA NA NA NA NA
84 3 -0.467772280 3 1 NA NA NA NA NA
85 NA NA 3 1 NA NA NA NA NA
86 3 -2.160485036 2 1 NA NA NA NA NA
87 NA -0.657675572 1 1 NA NA NA NA NA
88 NA NA 1 1 NA NA NA NA NA
89 NA -0.696710744 3 1 NA NA NA NA NA
90 4 NA 1 1 NA NA NA NA NA
91 NA -0.179395847 2 1 NA NA NA NA NA
92 2 -0.441545568 NA 1 NA NA NA NA NA
93 4 -0.685799334 1 1 NA NA NA NA NA
94 NA NA 3 1 NA NA NA NA NA
95 NA 0.191929445 2 1 NA NA NA NA NA
96 NA NA 2 1 NA NA NA NA NA
97 1 -0.069760671 3 1 NA NA NA NA NA
98 NA NA 2 1 NA NA NA NA NA
99 2 NA 2 1 NA NA NA NA NA
100 NA NA 3 1 NA NA NA NA NA
$m5b$M_lvlone
o1 c1 c1:C2
1 2 0.7592026489 NA
1.1 1 0.9548337990 NA
1.2 1 0.5612235156 NA
1.3 2 1.1873391025 NA
2 3 0.9192204198 NA
2.1 1 -0.1870730476 NA
2.2 2 1.2517512331 NA
3 1 -0.0605087604 NA
3.1 3 0.3788637747 NA
3.2 2 0.9872578281 NA
4 3 1.4930175328 NA
4.1 2 -0.7692526880 NA
4.2 1 0.9180841450 NA
4.3 2 -0.0541170782 NA
5 2 -0.1376784521 NA
5.1 2 -0.2740585866 NA
5.2 3 0.4670496929 NA
5.3 2 0.1740288049 NA
6 3 0.9868044683 NA
7 3 -0.1280320918 NA
7.1 1 0.4242971219 NA
7.2 1 0.0777182491 NA
8 2 -0.5791408712 NA
8.1 2 0.3128604232 NA
8.2 3 0.6258446356 NA
8.3 3 -0.1040137707 NA
8.4 2 0.0481450285 NA
8.5 2 0.3831763675 NA
9 2 -0.1757592269 NA
9.1 2 -0.1791541200 NA
9.2 3 -0.0957042935 NA
10 2 -0.5598409704 NA
10.1 1 -0.2318340451 NA
11 2 0.5086859475 NA
11.1 3 0.4951758188 NA
11.2 3 -1.1022162541 NA
11.3 1 -0.0611636705 NA
11.4 3 -0.4971774316 NA
12 1 -0.2433996286 NA
13 2 0.8799673116 NA
13.1 1 0.1079022586 NA
14 3 0.9991752617 NA
14.1 3 -0.1094019046 NA
14.2 2 0.1518967560 NA
14.3 1 0.3521012473 NA
15 3 0.3464447888 NA
15.1 3 -0.4767313971 NA
15.2 2 0.5759767791 NA
15.3 2 -0.1713452662 NA
16 3 0.4564754473 NA
16.1 1 1.0652558311 NA
16.2 3 0.6971872493 NA
16.3 1 0.5259331838 NA
16.4 1 0.2046601798 NA
16.5 2 1.0718540464 NA
17 2 0.6048676222 NA
17.1 2 0.2323298304 NA
17.2 3 1.2617499032 NA
17.3 2 -0.3913230895 NA
17.4 3 0.9577299112 NA
18 2 -0.0050324072 NA
19 3 -0.4187468937 NA
19.1 2 -0.4478828944 NA
19.2 2 -1.1966721302 NA
19.3 2 -0.5877091668 NA
20 1 0.6838223064 NA
20.1 2 0.3278571109 NA
20.2 2 -0.8489831990 NA
20.3 1 1.3169975191 NA
20.4 1 0.0444804531 NA
20.5 2 -0.4535207652 NA
21 2 -0.4030302960 NA
21.1 2 -0.4069674045 NA
21.2 2 1.0650265940 NA
22 1 -0.0673274516 NA
22.1 1 0.9601388170 NA
23 1 0.5556634840 NA
23.1 2 1.4407865964 NA
24 1 0.3856376411 NA
25 2 0.3564400705 NA
25.1 1 0.0982553434 NA
25.2 1 0.1928682598 NA
25.3 1 -0.0192488594 NA
25.4 2 0.4466012931 NA
25.5 2 1.1425193342 NA
26 2 0.5341531449 NA
26.1 3 1.2268695927 NA
26.2 2 0.3678294939 NA
26.3 1 0.5948516018 NA
27 2 -0.3342844147 NA
27.1 2 -0.4835141229 NA
28 1 -0.7145915499 NA
28.1 1 0.5063671955 NA
28.2 1 -0.2067413142 NA
28.3 1 0.1196789973 NA
29 2 0.1392699487 NA
29.1 2 0.7960234776 NA
29.2 1 1.0398214352 NA
29.3 2 0.0813246429 NA
30 2 -0.3296323050 NA
30.1 2 1.3635850954 NA
30.2 2 0.7354171050 NA
31 2 0.3708398217 NA
32 3 -0.0474059668 NA
32.1 1 1.2507771489 NA
32.2 3 0.1142915519 NA
32.3 3 0.6773270619 NA
33 2 0.1774293842 NA
33.1 2 0.6159606291 NA
34 3 0.8590979166 NA
34.1 2 0.0546216775 NA
34.2 2 -0.0897224473 NA
34.3 1 0.4163395571 NA
35 2 -1.4693520528 NA
35.1 3 -0.3031734330 NA
35.2 2 -0.6045512101 NA
36 3 0.9823048960 NA
36.1 2 1.4466051416 NA
36.2 3 1.1606752905 NA
36.3 3 0.8373091576 NA
36.4 3 0.2640591685 NA
37 3 0.1177313455 NA
37.1 3 -0.1415483779 NA
37.2 3 0.0054610124 NA
38 2 0.8078948077 NA
39 1 0.9876451040 NA
39.1 2 -0.3431222274 NA
39.2 2 -1.7909380751 NA
39.3 1 -0.1798746191 NA
39.4 1 -0.1850961689 NA
39.5 1 0.4544226146 NA
40 3 0.5350190436 NA
40.1 1 0.4189342752 NA
40.2 1 0.4211994981 NA
40.3 2 0.0916687506 NA
41 3 -0.1035047421 NA
41.1 2 -0.4684202411 NA
41.2 1 0.5972615368 NA
41.3 1 0.9885613862 NA
41.4 3 -0.3908036794 NA
42 2 -0.0338893961 NA
42.1 1 -0.4498363172 NA
43 2 0.8965546110 NA
43.1 2 0.6199122090 NA
43.2 1 0.1804894429 NA
44 3 1.3221409285 NA
44.1 2 0.3416426284 NA
44.2 1 0.5706610068 NA
44.3 1 1.2679497430 NA
45 1 0.1414983160 NA
45.1 1 0.7220892521 NA
46 1 1.5391054233 NA
46.1 2 0.3889107049 NA
46.2 3 0.1248719493 NA
47 1 0.2014101100 NA
47.1 1 0.2982973539 NA
47.2 1 1.1518107179 NA
47.3 3 0.5196802157 NA
47.4 2 0.3702301552 NA
48 3 -0.2128602862 NA
48.1 2 -0.5337239976 NA
49 1 -0.5236770035 NA
50 2 0.3897705981 NA
51 2 -0.7213343736 NA
52 2 0.3758235358 NA
52.1 3 0.7138067080 NA
52.2 2 0.8872895233 NA
52.3 1 -0.9664587437 NA
52.4 3 0.0254566848 NA
52.5 2 0.4155259424 NA
53 2 0.5675736897 NA
53.1 1 -0.3154088781 NA
53.2 1 0.2162315769 NA
54 1 -0.0880802382 NA
54.1 2 0.4129127672 NA
54.2 3 1.0119546775 NA
54.3 1 -0.1112901990 NA
54.4 2 0.8587727145 NA
55 1 -0.0116453589 NA
55.1 1 0.5835528661 NA
55.2 2 -1.0010857254 NA
55.3 1 -0.4796526070 NA
55.4 1 -0.1202746964 NA
56 3 0.5176377612 NA
56.1 2 -1.1136932588 NA
56.2 3 -0.0168103281 NA
56.3 3 0.3933023606 NA
56.4 1 0.3714625139 NA
56.5 3 0.7811448179 NA
57 2 -1.0868304872 NA
57.1 2 0.8018626997 NA
57.2 2 -0.1159517011 NA
57.3 2 0.6785562445 NA
58 3 1.6476207996 NA
58.1 2 0.3402652711 NA
58.2 3 -0.1111300753 NA
58.3 3 -0.5409234285 NA
58.4 2 -0.1271327672 NA
58.5 3 0.8713264822 NA
59 3 0.4766421367 NA
59.1 1 1.0028089765 NA
60 3 0.5231452932 NA
61 1 -0.7190130614 NA
61.1 2 0.8353702312 NA
61.2 2 1.0229058138 NA
61.3 1 1.1717723589 NA
61.4 2 -0.0629201596 NA
62 2 -0.3979137604 NA
62.1 3 0.6830738372 NA
62.2 1 0.4301745954 NA
62.3 2 -0.0333139957 NA
63 1 0.3345678035 NA
63.1 3 0.3643769511 NA
64 2 0.3949911859 NA
65 1 1.2000091513 NA
65.1 2 0.0110122646 NA
65.2 1 -0.5776452043 NA
65.3 1 -0.1372183563 NA
66 1 -0.5081302805 NA
66.1 1 -0.1447837412 NA
66.2 3 0.1906241379 NA
67 3 1.6716027681 NA
68 1 0.5691848839 NA
68.1 2 0.1004860389 NA
68.2 2 -0.0061241827 NA
68.3 2 0.7443745962 NA
68.4 2 0.8726923437 NA
69 2 0.0381382683 NA
70 1 0.8126204217 NA
70.1 3 0.4691503050 NA
71 3 -0.5529062591 NA
71.1 3 -0.1103252087 NA
71.2 3 1.7178492547 NA
71.3 2 -1.0118346755 NA
71.4 2 1.8623785017 NA
72 1 -0.4521659275 NA
72.1 1 0.1375317317 NA
72.2 2 -0.4170988856 NA
72.3 2 0.7107266765 NA
72.4 1 0.1451969143 NA
72.5 1 1.6298050306 NA
73 1 -0.0307469467 NA
74 1 0.3730017941 NA
75 3 -0.4908003566 NA
76 3 -0.9888876620 NA
76.1 3 0.0003798292 NA
76.2 1 -0.8421863763 NA
77 2 -0.4986802480 NA
78 2 0.0417330969 NA
79 1 -0.3767450660 NA
79.1 2 0.1516000028 NA
79.2 1 -0.1888160741 NA
80 2 -0.0041558414 NA
80.1 2 -0.0329337062 NA
80.2 3 0.5046816157 NA
81 3 -0.9493950353 NA
81.1 2 0.2443038954 NA
81.2 3 0.6476958410 NA
81.3 3 0.4182528210 NA
82 3 1.1088801952 NA
82.1 2 0.9334157763 NA
82.2 2 0.4958140634 NA
83 3 0.5104724530 NA
83.1 3 -0.0513309106 NA
83.2 3 -0.2067792494 NA
83.3 2 -0.0534169155 NA
84 3 -0.0255753653 NA
84.1 3 -1.8234189877 NA
85 3 -0.0114038622 NA
85.1 2 -0.0577615939 NA
85.2 2 -0.2241856342 NA
85.3 2 -0.0520175929 NA
85.4 1 0.2892733846 NA
85.5 3 -0.3740417009 NA
86 2 0.4293735089 NA
86.1 3 -0.1363456521 NA
86.2 1 0.1230989293 NA
86.3 1 0.3305413955 NA
86.4 1 2.6003411822 NA
86.5 3 -0.1420690052 NA
87 1 1.0457427869 NA
87.1 1 -0.2973007190 NA
87.2 1 0.4396872616 NA
88 3 -0.0601928334 NA
88.1 1 -1.0124347595 NA
88.2 1 0.5730917016 NA
88.3 1 -0.0029455332 NA
89 1 1.5465903721 NA
90 2 0.0626760573 NA
90.1 1 1.1896872985 NA
90.2 3 0.2597888783 NA
90.3 1 0.6599799887 NA
91 1 1.1213651365 NA
91.1 2 1.2046371625 NA
91.2 1 0.3395603754 NA
92 2 0.4674939332 NA
93 1 0.2677965647 NA
93.1 3 1.6424445368 NA
93.2 1 0.7101700066 NA
93.3 3 1.1222322893 NA
93.4 1 1.4628960401 NA
94 2 -0.2904211940 NA
94.1 3 0.0147813580 NA
94.2 1 -0.4536774482 NA
94.3 3 0.6793464917 NA
94.4 2 -0.9411356550 NA
94.5 3 0.5683867264 NA
95 3 0.2375652188 NA
95.1 1 0.0767152977 NA
95.2 2 -0.6886731251 NA
96 1 0.7813892121 NA
96.1 3 0.3391519695 NA
96.2 1 -0.4857246503 NA
96.3 1 0.8771471244 NA
96.4 2 1.9030768981 NA
96.5 3 -0.1684332749 NA
97 3 1.3775130083 NA
97.1 3 -1.7323228619 NA
98 3 -1.2648518889 NA
98.1 1 -0.9042716241 NA
98.2 1 -0.1560385207 NA
99 2 0.7993356425 NA
99.1 2 1.0355522332 NA
99.2 1 -0.1150895843 NA
100 1 0.0369067906 NA
100.1 2 1.6023713093 NA
100.2 2 0.8861545820 NA
100.3 1 0.1277046316 NA
100.4 1 -0.0834577654 NA
$m5b$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.6857108
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
$m5b$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
c1:C2 -0.1866456 0.7201206
$m5b$mu_reg_norm
[1] 0
$m5b$tau_reg_norm
[1] 1e-04
$m5b$shape_tau_norm
[1] 0.01
$m5b$rate_tau_norm
[1] 0.01
$m5b$mu_reg_multinomial
[1] 0
$m5b$tau_reg_multinomial
[1] 1e-04
$m5b$mu_reg_ordinal
[1] 0
$m5b$tau_reg_ordinal
[1] 1e-04
$m5b$mu_delta_ordinal
[1] 0
$m5b$tau_delta_ordinal
[1] 1e-04
$m5b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m5b$shape_diag_RinvD
[1] "0.01"
$m5b$rate_diag_RinvD
[1] "0.001"
$m5c
$m5c$M_id
M2 C2 O2 (Intercept) M22 M23 M24 O22 O23
1 NA -1.381594459 3 1 NA NA NA NA NA
2 1 0.344426024 2 1 NA NA NA NA NA
3 2 NA 2 1 NA NA NA NA NA
4 2 -0.228910007 3 1 NA NA NA NA NA
5 1 NA 1 1 NA NA NA NA NA
6 NA -2.143955482 3 1 NA NA NA NA NA
7 NA -1.156567023 NA 1 NA NA NA NA NA
8 2 -0.598827660 1 1 NA NA NA NA NA
9 NA NA 1 1 NA NA NA NA NA
10 NA -1.006719032 3 1 NA NA NA NA NA
11 3 0.239801450 3 1 NA NA NA NA NA
12 NA -1.064969789 NA 1 NA NA NA NA NA
13 NA -0.538082688 3 1 NA NA NA NA NA
14 2 NA 2 1 NA NA NA NA NA
15 2 -1.781049276 2 1 NA NA NA NA NA
16 NA NA 3 1 NA NA NA NA NA
17 3 NA 3 1 NA NA NA NA NA
18 3 -0.014579883 NA 1 NA NA NA NA NA
19 2 -2.121550136 2 1 NA NA NA NA NA
20 NA NA 1 1 NA NA NA NA NA
21 NA -0.363239698 1 1 NA NA NA NA NA
22 1 -0.121568514 3 1 NA NA NA NA NA
23 2 -0.951271111 3 1 NA NA NA NA NA
24 NA NA 3 1 NA NA NA NA NA
25 4 -0.974288621 2 1 NA NA NA NA NA
26 NA -1.130632418 1 1 NA NA NA NA NA
27 NA 0.114339868 3 1 NA NA NA NA NA
28 2 0.238334648 3 1 NA NA NA NA NA
29 4 0.840744958 2 1 NA NA NA NA NA
30 2 NA 1 1 NA NA NA NA NA
31 2 NA 2 1 NA NA NA NA NA
32 3 -1.466312154 3 1 NA NA NA NA NA
33 1 -0.637352277 2 1 NA NA NA NA NA
34 4 NA 2 1 NA NA NA NA NA
35 4 NA 3 1 NA NA NA NA NA
36 NA NA 1 1 NA NA NA NA NA
37 NA NA 3 1 NA NA NA NA NA
38 NA NA 2 1 NA NA NA NA NA
39 4 0.006728205 3 1 NA NA NA NA NA
40 NA NA 2 1 NA NA NA NA NA
41 2 -1.663281353 2 1 NA NA NA NA NA
42 NA 0.161184794 2 1 NA NA NA NA NA
43 NA 0.457939180 3 1 NA NA NA NA NA
44 3 -0.307070331 2 1 NA NA NA NA NA
45 NA NA 1 1 NA NA NA NA NA
46 NA -1.071668276 NA 1 NA NA NA NA NA
47 4 -0.814751321 1 1 NA NA NA NA NA
48 3 -0.547630662 2 1 NA NA NA NA NA
49 4 NA 1 1 NA NA NA NA NA
50 3 -1.350213782 2 1 NA NA NA NA NA
51 NA 0.719054706 2 1 NA NA NA NA NA
52 1 NA 3 1 NA NA NA NA NA
53 NA -1.207130750 3 1 NA NA NA NA NA
54 NA NA 1 1 NA NA NA NA NA
55 NA -0.408600991 2 1 NA NA NA NA NA
56 NA -0.271380529 NA 1 NA NA NA NA NA
57 4 -1.361925974 3 1 NA NA NA NA NA
58 1 NA 1 1 NA NA NA NA NA
59 2 NA 2 1 NA NA NA NA NA
60 3 -0.323712205 2 1 NA NA NA NA NA
61 2 NA 3 1 NA NA NA NA NA
62 3 NA 2 1 NA NA NA NA NA
63 2 -1.386906880 1 1 NA NA NA NA NA
64 NA NA 3 1 NA NA NA NA NA
65 NA NA 1 1 NA NA NA NA NA
66 2 -0.565191691 3 1 NA NA NA NA NA
67 NA -0.382899912 3 1 NA NA NA NA NA
68 3 NA 2 1 NA NA NA NA NA
69 3 -0.405642769 NA 1 NA NA NA NA NA
70 4 NA 2 1 NA NA NA NA NA
71 2 -0.843748427 NA 1 NA NA NA NA NA
72 NA 0.116003683 NA 1 NA NA NA NA NA
73 4 -0.778634325 1 1 NA NA NA NA NA
74 NA NA 2 1 NA NA NA NA NA
75 NA NA 2 1 NA NA NA NA NA
76 4 NA 1 1 NA NA NA NA NA
77 2 -0.632974758 3 1 NA NA NA NA NA
78 2 NA 2 1 NA NA NA NA NA
79 NA -0.778064615 NA 1 NA NA NA NA NA
80 1 NA 1 1 NA NA NA NA NA
81 NA NA 1 1 NA NA NA NA NA
82 3 -0.246123253 3 1 NA NA NA NA NA
83 4 -1.239659782 NA 1 NA NA NA NA NA
84 3 -0.467772280 3 1 NA NA NA NA NA
85 NA NA 3 1 NA NA NA NA NA
86 3 -2.160485036 2 1 NA NA NA NA NA
87 NA -0.657675572 1 1 NA NA NA NA NA
88 NA NA 1 1 NA NA NA NA NA
89 NA -0.696710744 3 1 NA NA NA NA NA
90 4 NA 1 1 NA NA NA NA NA
91 NA -0.179395847 2 1 NA NA NA NA NA
92 2 -0.441545568 NA 1 NA NA NA NA NA
93 4 -0.685799334 1 1 NA NA NA NA NA
94 NA NA 3 1 NA NA NA NA NA
95 NA 0.191929445 2 1 NA NA NA NA NA
96 NA NA 2 1 NA NA NA NA NA
97 1 -0.069760671 3 1 NA NA NA NA NA
98 NA NA 2 1 NA NA NA NA NA
99 2 NA 2 1 NA NA NA NA NA
100 NA NA 3 1 NA NA NA NA NA
$m5c$M_lvlone
o1 c1 c1:C2
1 2 0.7592026489 NA
1.1 1 0.9548337990 NA
1.2 1 0.5612235156 NA
1.3 2 1.1873391025 NA
2 3 0.9192204198 NA
2.1 1 -0.1870730476 NA
2.2 2 1.2517512331 NA
3 1 -0.0605087604 NA
3.1 3 0.3788637747 NA
3.2 2 0.9872578281 NA
4 3 1.4930175328 NA
4.1 2 -0.7692526880 NA
4.2 1 0.9180841450 NA
4.3 2 -0.0541170782 NA
5 2 -0.1376784521 NA
5.1 2 -0.2740585866 NA
5.2 3 0.4670496929 NA
5.3 2 0.1740288049 NA
6 3 0.9868044683 NA
7 3 -0.1280320918 NA
7.1 1 0.4242971219 NA
7.2 1 0.0777182491 NA
8 2 -0.5791408712 NA
8.1 2 0.3128604232 NA
8.2 3 0.6258446356 NA
8.3 3 -0.1040137707 NA
8.4 2 0.0481450285 NA
8.5 2 0.3831763675 NA
9 2 -0.1757592269 NA
9.1 2 -0.1791541200 NA
9.2 3 -0.0957042935 NA
10 2 -0.5598409704 NA
10.1 1 -0.2318340451 NA
11 2 0.5086859475 NA
11.1 3 0.4951758188 NA
11.2 3 -1.1022162541 NA
11.3 1 -0.0611636705 NA
11.4 3 -0.4971774316 NA
12 1 -0.2433996286 NA
13 2 0.8799673116 NA
13.1 1 0.1079022586 NA
14 3 0.9991752617 NA
14.1 3 -0.1094019046 NA
14.2 2 0.1518967560 NA
14.3 1 0.3521012473 NA
15 3 0.3464447888 NA
15.1 3 -0.4767313971 NA
15.2 2 0.5759767791 NA
15.3 2 -0.1713452662 NA
16 3 0.4564754473 NA
16.1 1 1.0652558311 NA
16.2 3 0.6971872493 NA
16.3 1 0.5259331838 NA
16.4 1 0.2046601798 NA
16.5 2 1.0718540464 NA
17 2 0.6048676222 NA
17.1 2 0.2323298304 NA
17.2 3 1.2617499032 NA
17.3 2 -0.3913230895 NA
17.4 3 0.9577299112 NA
18 2 -0.0050324072 NA
19 3 -0.4187468937 NA
19.1 2 -0.4478828944 NA
19.2 2 -1.1966721302 NA
19.3 2 -0.5877091668 NA
20 1 0.6838223064 NA
20.1 2 0.3278571109 NA
20.2 2 -0.8489831990 NA
20.3 1 1.3169975191 NA
20.4 1 0.0444804531 NA
20.5 2 -0.4535207652 NA
21 2 -0.4030302960 NA
21.1 2 -0.4069674045 NA
21.2 2 1.0650265940 NA
22 1 -0.0673274516 NA
22.1 1 0.9601388170 NA
23 1 0.5556634840 NA
23.1 2 1.4407865964 NA
24 1 0.3856376411 NA
25 2 0.3564400705 NA
25.1 1 0.0982553434 NA
25.2 1 0.1928682598 NA
25.3 1 -0.0192488594 NA
25.4 2 0.4466012931 NA
25.5 2 1.1425193342 NA
26 2 0.5341531449 NA
26.1 3 1.2268695927 NA
26.2 2 0.3678294939 NA
26.3 1 0.5948516018 NA
27 2 -0.3342844147 NA
27.1 2 -0.4835141229 NA
28 1 -0.7145915499 NA
28.1 1 0.5063671955 NA
28.2 1 -0.2067413142 NA
28.3 1 0.1196789973 NA
29 2 0.1392699487 NA
29.1 2 0.7960234776 NA
29.2 1 1.0398214352 NA
29.3 2 0.0813246429 NA
30 2 -0.3296323050 NA
30.1 2 1.3635850954 NA
30.2 2 0.7354171050 NA
31 2 0.3708398217 NA
32 3 -0.0474059668 NA
32.1 1 1.2507771489 NA
32.2 3 0.1142915519 NA
32.3 3 0.6773270619 NA
33 2 0.1774293842 NA
33.1 2 0.6159606291 NA
34 3 0.8590979166 NA
34.1 2 0.0546216775 NA
34.2 2 -0.0897224473 NA
34.3 1 0.4163395571 NA
35 2 -1.4693520528 NA
35.1 3 -0.3031734330 NA
35.2 2 -0.6045512101 NA
36 3 0.9823048960 NA
36.1 2 1.4466051416 NA
36.2 3 1.1606752905 NA
36.3 3 0.8373091576 NA
36.4 3 0.2640591685 NA
37 3 0.1177313455 NA
37.1 3 -0.1415483779 NA
37.2 3 0.0054610124 NA
38 2 0.8078948077 NA
39 1 0.9876451040 NA
39.1 2 -0.3431222274 NA
39.2 2 -1.7909380751 NA
39.3 1 -0.1798746191 NA
39.4 1 -0.1850961689 NA
39.5 1 0.4544226146 NA
40 3 0.5350190436 NA
40.1 1 0.4189342752 NA
40.2 1 0.4211994981 NA
40.3 2 0.0916687506 NA
41 3 -0.1035047421 NA
41.1 2 -0.4684202411 NA
41.2 1 0.5972615368 NA
41.3 1 0.9885613862 NA
41.4 3 -0.3908036794 NA
42 2 -0.0338893961 NA
42.1 1 -0.4498363172 NA
43 2 0.8965546110 NA
43.1 2 0.6199122090 NA
43.2 1 0.1804894429 NA
44 3 1.3221409285 NA
44.1 2 0.3416426284 NA
44.2 1 0.5706610068 NA
44.3 1 1.2679497430 NA
45 1 0.1414983160 NA
45.1 1 0.7220892521 NA
46 1 1.5391054233 NA
46.1 2 0.3889107049 NA
46.2 3 0.1248719493 NA
47 1 0.2014101100 NA
47.1 1 0.2982973539 NA
47.2 1 1.1518107179 NA
47.3 3 0.5196802157 NA
47.4 2 0.3702301552 NA
48 3 -0.2128602862 NA
48.1 2 -0.5337239976 NA
49 1 -0.5236770035 NA
50 2 0.3897705981 NA
51 2 -0.7213343736 NA
52 2 0.3758235358 NA
52.1 3 0.7138067080 NA
52.2 2 0.8872895233 NA
52.3 1 -0.9664587437 NA
52.4 3 0.0254566848 NA
52.5 2 0.4155259424 NA
53 2 0.5675736897 NA
53.1 1 -0.3154088781 NA
53.2 1 0.2162315769 NA
54 1 -0.0880802382 NA
54.1 2 0.4129127672 NA
54.2 3 1.0119546775 NA
54.3 1 -0.1112901990 NA
54.4 2 0.8587727145 NA
55 1 -0.0116453589 NA
55.1 1 0.5835528661 NA
55.2 2 -1.0010857254 NA
55.3 1 -0.4796526070 NA
55.4 1 -0.1202746964 NA
56 3 0.5176377612 NA
56.1 2 -1.1136932588 NA
56.2 3 -0.0168103281 NA
56.3 3 0.3933023606 NA
56.4 1 0.3714625139 NA
56.5 3 0.7811448179 NA
57 2 -1.0868304872 NA
57.1 2 0.8018626997 NA
57.2 2 -0.1159517011 NA
57.3 2 0.6785562445 NA
58 3 1.6476207996 NA
58.1 2 0.3402652711 NA
58.2 3 -0.1111300753 NA
58.3 3 -0.5409234285 NA
58.4 2 -0.1271327672 NA
58.5 3 0.8713264822 NA
59 3 0.4766421367 NA
59.1 1 1.0028089765 NA
60 3 0.5231452932 NA
61 1 -0.7190130614 NA
61.1 2 0.8353702312 NA
61.2 2 1.0229058138 NA
61.3 1 1.1717723589 NA
61.4 2 -0.0629201596 NA
62 2 -0.3979137604 NA
62.1 3 0.6830738372 NA
62.2 1 0.4301745954 NA
62.3 2 -0.0333139957 NA
63 1 0.3345678035 NA
63.1 3 0.3643769511 NA
64 2 0.3949911859 NA
65 1 1.2000091513 NA
65.1 2 0.0110122646 NA
65.2 1 -0.5776452043 NA
65.3 1 -0.1372183563 NA
66 1 -0.5081302805 NA
66.1 1 -0.1447837412 NA
66.2 3 0.1906241379 NA
67 3 1.6716027681 NA
68 1 0.5691848839 NA
68.1 2 0.1004860389 NA
68.2 2 -0.0061241827 NA
68.3 2 0.7443745962 NA
68.4 2 0.8726923437 NA
69 2 0.0381382683 NA
70 1 0.8126204217 NA
70.1 3 0.4691503050 NA
71 3 -0.5529062591 NA
71.1 3 -0.1103252087 NA
71.2 3 1.7178492547 NA
71.3 2 -1.0118346755 NA
71.4 2 1.8623785017 NA
72 1 -0.4521659275 NA
72.1 1 0.1375317317 NA
72.2 2 -0.4170988856 NA
72.3 2 0.7107266765 NA
72.4 1 0.1451969143 NA
72.5 1 1.6298050306 NA
73 1 -0.0307469467 NA
74 1 0.3730017941 NA
75 3 -0.4908003566 NA
76 3 -0.9888876620 NA
76.1 3 0.0003798292 NA
76.2 1 -0.8421863763 NA
77 2 -0.4986802480 NA
78 2 0.0417330969 NA
79 1 -0.3767450660 NA
79.1 2 0.1516000028 NA
79.2 1 -0.1888160741 NA
80 2 -0.0041558414 NA
80.1 2 -0.0329337062 NA
80.2 3 0.5046816157 NA
81 3 -0.9493950353 NA
81.1 2 0.2443038954 NA
81.2 3 0.6476958410 NA
81.3 3 0.4182528210 NA
82 3 1.1088801952 NA
82.1 2 0.9334157763 NA
82.2 2 0.4958140634 NA
83 3 0.5104724530 NA
83.1 3 -0.0513309106 NA
83.2 3 -0.2067792494 NA
83.3 2 -0.0534169155 NA
84 3 -0.0255753653 NA
84.1 3 -1.8234189877 NA
85 3 -0.0114038622 NA
85.1 2 -0.0577615939 NA
85.2 2 -0.2241856342 NA
85.3 2 -0.0520175929 NA
85.4 1 0.2892733846 NA
85.5 3 -0.3740417009 NA
86 2 0.4293735089 NA
86.1 3 -0.1363456521 NA
86.2 1 0.1230989293 NA
86.3 1 0.3305413955 NA
86.4 1 2.6003411822 NA
86.5 3 -0.1420690052 NA
87 1 1.0457427869 NA
87.1 1 -0.2973007190 NA
87.2 1 0.4396872616 NA
88 3 -0.0601928334 NA
88.1 1 -1.0124347595 NA
88.2 1 0.5730917016 NA
88.3 1 -0.0029455332 NA
89 1 1.5465903721 NA
90 2 0.0626760573 NA
90.1 1 1.1896872985 NA
90.2 3 0.2597888783 NA
90.3 1 0.6599799887 NA
91 1 1.1213651365 NA
91.1 2 1.2046371625 NA
91.2 1 0.3395603754 NA
92 2 0.4674939332 NA
93 1 0.2677965647 NA
93.1 3 1.6424445368 NA
93.2 1 0.7101700066 NA
93.3 3 1.1222322893 NA
93.4 1 1.4628960401 NA
94 2 -0.2904211940 NA
94.1 3 0.0147813580 NA
94.2 1 -0.4536774482 NA
94.3 3 0.6793464917 NA
94.4 2 -0.9411356550 NA
94.5 3 0.5683867264 NA
95 3 0.2375652188 NA
95.1 1 0.0767152977 NA
95.2 2 -0.6886731251 NA
96 1 0.7813892121 NA
96.1 3 0.3391519695 NA
96.2 1 -0.4857246503 NA
96.3 1 0.8771471244 NA
96.4 2 1.9030768981 NA
96.5 3 -0.1684332749 NA
97 3 1.3775130083 NA
97.1 3 -1.7323228619 NA
98 3 -1.2648518889 NA
98.1 1 -0.9042716241 NA
98.2 1 -0.1560385207 NA
99 2 0.7993356425 NA
99.1 2 1.0355522332 NA
99.2 1 -0.1150895843 NA
100 1 0.0369067906 NA
100.1 2 1.6023713093 NA
100.2 2 0.8861545820 NA
100.3 1 0.1277046316 NA
100.4 1 -0.0834577654 NA
$m5c$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.6857108
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
$m5c$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
c1:C2 -0.1866456 0.7201206
$m5c$mu_reg_norm
[1] 0
$m5c$tau_reg_norm
[1] 1e-04
$m5c$shape_tau_norm
[1] 0.01
$m5c$rate_tau_norm
[1] 0.01
$m5c$mu_reg_multinomial
[1] 0
$m5c$tau_reg_multinomial
[1] 1e-04
$m5c$mu_reg_ordinal
[1] 0
$m5c$tau_reg_ordinal
[1] 1e-04
$m5c$mu_delta_ordinal
[1] 0
$m5c$tau_delta_ordinal
[1] 1e-04
$m5c$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
$m5c$shape_diag_RinvD
[1] "0.01"
$m5c$rate_diag_RinvD
[1] "0.001"
$m5d
$m5d$M_id
M2 C2 O2 (Intercept) M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2
1 NA -1.381594459 3 1 NA NA NA NA NA NA NA NA
2 1 0.344426024 2 1 NA NA NA NA NA NA NA NA
3 2 NA 2 1 NA NA NA NA NA NA NA NA
4 2 -0.228910007 3 1 NA NA NA NA NA NA NA NA
5 1 NA 1 1 NA NA NA NA NA NA NA NA
6 NA -2.143955482 3 1 NA NA NA NA NA NA NA NA
7 NA -1.156567023 NA 1 NA NA NA NA NA NA NA NA
8 2 -0.598827660 1 1 NA NA NA NA NA NA NA NA
9 NA NA 1 1 NA NA NA NA NA NA NA NA
10 NA -1.006719032 3 1 NA NA NA NA NA NA NA NA
11 3 0.239801450 3 1 NA NA NA NA NA NA NA NA
12 NA -1.064969789 NA 1 NA NA NA NA NA NA NA NA
13 NA -0.538082688 3 1 NA NA NA NA NA NA NA NA
14 2 NA 2 1 NA NA NA NA NA NA NA NA
15 2 -1.781049276 2 1 NA NA NA NA NA NA NA NA
16 NA NA 3 1 NA NA NA NA NA NA NA NA
17 3 NA 3 1 NA NA NA NA NA NA NA NA
18 3 -0.014579883 NA 1 NA NA NA NA NA NA NA NA
19 2 -2.121550136 2 1 NA NA NA NA NA NA NA NA
20 NA NA 1 1 NA NA NA NA NA NA NA NA
21 NA -0.363239698 1 1 NA NA NA NA NA NA NA NA
22 1 -0.121568514 3 1 NA NA NA NA NA NA NA NA
23 2 -0.951271111 3 1 NA NA NA NA NA NA NA NA
24 NA NA 3 1 NA NA NA NA NA NA NA NA
25 4 -0.974288621 2 1 NA NA NA NA NA NA NA NA
26 NA -1.130632418 1 1 NA NA NA NA NA NA NA NA
27 NA 0.114339868 3 1 NA NA NA NA NA NA NA NA
28 2 0.238334648 3 1 NA NA NA NA NA NA NA NA
29 4 0.840744958 2 1 NA NA NA NA NA NA NA NA
30 2 NA 1 1 NA NA NA NA NA NA NA NA
31 2 NA 2 1 NA NA NA NA NA NA NA NA
32 3 -1.466312154 3 1 NA NA NA NA NA NA NA NA
33 1 -0.637352277 2 1 NA NA NA NA NA NA NA NA
34 4 NA 2 1 NA NA NA NA NA NA NA NA
35 4 NA 3 1 NA NA NA NA NA NA NA NA
36 NA NA 1 1 NA NA NA NA NA NA NA NA
37 NA NA 3 1 NA NA NA NA NA NA NA NA
38 NA NA 2 1 NA NA NA NA NA NA NA NA
39 4 0.006728205 3 1 NA NA NA NA NA NA NA NA
40 NA NA 2 1 NA NA NA NA NA NA NA NA
41 2 -1.663281353 2 1 NA NA NA NA NA NA NA NA
42 NA 0.161184794 2 1 NA NA NA NA NA NA NA NA
43 NA 0.457939180 3 1 NA NA NA NA NA NA NA NA
44 3 -0.307070331 2 1 NA NA NA NA NA NA NA NA
45 NA NA 1 1 NA NA NA NA NA NA NA NA
46 NA -1.071668276 NA 1 NA NA NA NA NA NA NA NA
47 4 -0.814751321 1 1 NA NA NA NA NA NA NA NA
48 3 -0.547630662 2 1 NA NA NA NA NA NA NA NA
49 4 NA 1 1 NA NA NA NA NA NA NA NA
50 3 -1.350213782 2 1 NA NA NA NA NA NA NA NA
51 NA 0.719054706 2 1 NA NA NA NA NA NA NA NA
52 1 NA 3 1 NA NA NA NA NA NA NA NA
53 NA -1.207130750 3 1 NA NA NA NA NA NA NA NA
54 NA NA 1 1 NA NA NA NA NA NA NA NA
55 NA -0.408600991 2 1 NA NA NA NA NA NA NA NA
56 NA -0.271380529 NA 1 NA NA NA NA NA NA NA NA
57 4 -1.361925974 3 1 NA NA NA NA NA NA NA NA
58 1 NA 1 1 NA NA NA NA NA NA NA NA
59 2 NA 2 1 NA NA NA NA NA NA NA NA
60 3 -0.323712205 2 1 NA NA NA NA NA NA NA NA
61 2 NA 3 1 NA NA NA NA NA NA NA NA
62 3 NA 2 1 NA NA NA NA NA NA NA NA
63 2 -1.386906880 1 1 NA NA NA NA NA NA NA NA
64 NA NA 3 1 NA NA NA NA NA NA NA NA
65 NA NA 1 1 NA NA NA NA NA NA NA NA
66 2 -0.565191691 3 1 NA NA NA NA NA NA NA NA
67 NA -0.382899912 3 1 NA NA NA NA NA NA NA NA
68 3 NA 2 1 NA NA NA NA NA NA NA NA
69 3 -0.405642769 NA 1 NA NA NA NA NA NA NA NA
70 4 NA 2 1 NA NA NA NA NA NA NA NA
71 2 -0.843748427 NA 1 NA NA NA NA NA NA NA NA
72 NA 0.116003683 NA 1 NA NA NA NA NA NA NA NA
73 4 -0.778634325 1 1 NA NA NA NA NA NA NA NA
74 NA NA 2 1 NA NA NA NA NA NA NA NA
75 NA NA 2 1 NA NA NA NA NA NA NA NA
76 4 NA 1 1 NA NA NA NA NA NA NA NA
77 2 -0.632974758 3 1 NA NA NA NA NA NA NA NA
78 2 NA 2 1 NA NA NA NA NA NA NA NA
79 NA -0.778064615 NA 1 NA NA NA NA NA NA NA NA
80 1 NA 1 1 NA NA NA NA NA NA NA NA
81 NA NA 1 1 NA NA NA NA NA NA NA NA
82 3 -0.246123253 3 1 NA NA NA NA NA NA NA NA
83 4 -1.239659782 NA 1 NA NA NA NA NA NA NA NA
84 3 -0.467772280 3 1 NA NA NA NA NA NA NA NA
85 NA NA 3 1 NA NA NA NA NA NA NA NA
86 3 -2.160485036 2 1 NA NA NA NA NA NA NA NA
87 NA -0.657675572 1 1 NA NA NA NA NA NA NA NA
88 NA NA 1 1 NA NA NA NA NA NA NA NA
89 NA -0.696710744 3 1 NA NA NA NA NA NA NA NA
90 4 NA 1 1 NA NA NA NA NA NA NA NA
91 NA -0.179395847 2 1 NA NA NA NA NA NA NA NA
92 2 -0.441545568 NA 1 NA NA NA NA NA NA NA NA
93 4 -0.685799334 1 1 NA NA NA NA NA NA NA NA
94 NA NA 3 1 NA NA NA NA NA NA NA NA
95 NA 0.191929445 2 1 NA NA NA NA NA NA NA NA
96 NA NA 2 1 NA NA NA NA NA NA NA NA
97 1 -0.069760671 3 1 NA NA NA NA NA NA NA NA
98 NA NA 2 1 NA NA NA NA NA NA NA NA
99 2 NA 2 1 NA NA NA NA NA NA NA NA
100 NA NA 3 1 NA NA NA NA NA NA NA NA
$m5d$M_lvlone
o1 c1
1 2 0.7592026489
1.1 1 0.9548337990
1.2 1 0.5612235156
1.3 2 1.1873391025
2 3 0.9192204198
2.1 1 -0.1870730476
2.2 2 1.2517512331
3 1 -0.0605087604
3.1 3 0.3788637747
3.2 2 0.9872578281
4 3 1.4930175328
4.1 2 -0.7692526880
4.2 1 0.9180841450
4.3 2 -0.0541170782
5 2 -0.1376784521
5.1 2 -0.2740585866
5.2 3 0.4670496929
5.3 2 0.1740288049
6 3 0.9868044683
7 3 -0.1280320918
7.1 1 0.4242971219
7.2 1 0.0777182491
8 2 -0.5791408712
8.1 2 0.3128604232
8.2 3 0.6258446356
8.3 3 -0.1040137707
8.4 2 0.0481450285
8.5 2 0.3831763675
9 2 -0.1757592269
9.1 2 -0.1791541200
9.2 3 -0.0957042935
10 2 -0.5598409704
10.1 1 -0.2318340451
11 2 0.5086859475
11.1 3 0.4951758188
11.2 3 -1.1022162541
11.3 1 -0.0611636705
11.4 3 -0.4971774316
12 1 -0.2433996286
13 2 0.8799673116
13.1 1 0.1079022586
14 3 0.9991752617
14.1 3 -0.1094019046
14.2 2 0.1518967560
14.3 1 0.3521012473
15 3 0.3464447888
15.1 3 -0.4767313971
15.2 2 0.5759767791
15.3 2 -0.1713452662
16 3 0.4564754473
16.1 1 1.0652558311
16.2 3 0.6971872493
16.3 1 0.5259331838
16.4 1 0.2046601798
16.5 2 1.0718540464
17 2 0.6048676222
17.1 2 0.2323298304
17.2 3 1.2617499032
17.3 2 -0.3913230895
17.4 3 0.9577299112
18 2 -0.0050324072
19 3 -0.4187468937
19.1 2 -0.4478828944
19.2 2 -1.1966721302
19.3 2 -0.5877091668
20 1 0.6838223064
20.1 2 0.3278571109
20.2 2 -0.8489831990
20.3 1 1.3169975191
20.4 1 0.0444804531
20.5 2 -0.4535207652
21 2 -0.4030302960
21.1 2 -0.4069674045
21.2 2 1.0650265940
22 1 -0.0673274516
22.1 1 0.9601388170
23 1 0.5556634840
23.1 2 1.4407865964
24 1 0.3856376411
25 2 0.3564400705
25.1 1 0.0982553434
25.2 1 0.1928682598
25.3 1 -0.0192488594
25.4 2 0.4466012931
25.5 2 1.1425193342
26 2 0.5341531449
26.1 3 1.2268695927
26.2 2 0.3678294939
26.3 1 0.5948516018
27 2 -0.3342844147
27.1 2 -0.4835141229
28 1 -0.7145915499
28.1 1 0.5063671955
28.2 1 -0.2067413142
28.3 1 0.1196789973
29 2 0.1392699487
29.1 2 0.7960234776
29.2 1 1.0398214352
29.3 2 0.0813246429
30 2 -0.3296323050
30.1 2 1.3635850954
30.2 2 0.7354171050
31 2 0.3708398217
32 3 -0.0474059668
32.1 1 1.2507771489
32.2 3 0.1142915519
32.3 3 0.6773270619
33 2 0.1774293842
33.1 2 0.6159606291
34 3 0.8590979166
34.1 2 0.0546216775
34.2 2 -0.0897224473
34.3 1 0.4163395571
35 2 -1.4693520528
35.1 3 -0.3031734330
35.2 2 -0.6045512101
36 3 0.9823048960
36.1 2 1.4466051416
36.2 3 1.1606752905
36.3 3 0.8373091576
36.4 3 0.2640591685
37 3 0.1177313455
37.1 3 -0.1415483779
37.2 3 0.0054610124
38 2 0.8078948077
39 1 0.9876451040
39.1 2 -0.3431222274
39.2 2 -1.7909380751
39.3 1 -0.1798746191
39.4 1 -0.1850961689
39.5 1 0.4544226146
40 3 0.5350190436
40.1 1 0.4189342752
40.2 1 0.4211994981
40.3 2 0.0916687506
41 3 -0.1035047421
41.1 2 -0.4684202411
41.2 1 0.5972615368
41.3 1 0.9885613862
41.4 3 -0.3908036794
42 2 -0.0338893961
42.1 1 -0.4498363172
43 2 0.8965546110
43.1 2 0.6199122090
43.2 1 0.1804894429
44 3 1.3221409285
44.1 2 0.3416426284
44.2 1 0.5706610068
44.3 1 1.2679497430
45 1 0.1414983160
45.1 1 0.7220892521
46 1 1.5391054233
46.1 2 0.3889107049
46.2 3 0.1248719493
47 1 0.2014101100
47.1 1 0.2982973539
47.2 1 1.1518107179
47.3 3 0.5196802157
47.4 2 0.3702301552
48 3 -0.2128602862
48.1 2 -0.5337239976
49 1 -0.5236770035
50 2 0.3897705981
51 2 -0.7213343736
52 2 0.3758235358
52.1 3 0.7138067080
52.2 2 0.8872895233
52.3 1 -0.9664587437
52.4 3 0.0254566848
52.5 2 0.4155259424
53 2 0.5675736897
53.1 1 -0.3154088781
53.2 1 0.2162315769
54 1 -0.0880802382
54.1 2 0.4129127672
54.2 3 1.0119546775
54.3 1 -0.1112901990
54.4 2 0.8587727145
55 1 -0.0116453589
55.1 1 0.5835528661
55.2 2 -1.0010857254
55.3 1 -0.4796526070
55.4 1 -0.1202746964
56 3 0.5176377612
56.1 2 -1.1136932588
56.2 3 -0.0168103281
56.3 3 0.3933023606
56.4 1 0.3714625139
56.5 3 0.7811448179
57 2 -1.0868304872
57.1 2 0.8018626997
57.2 2 -0.1159517011
57.3 2 0.6785562445
58 3 1.6476207996
58.1 2 0.3402652711
58.2 3 -0.1111300753
58.3 3 -0.5409234285
58.4 2 -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 1 1.1717723589
61.4 2 -0.0629201596
62 2 -0.3979137604
62.1 3 0.6830738372
62.2 1 0.4301745954
62.3 2 -0.0333139957
63 1 0.3345678035
63.1 3 0.3643769511
64 2 0.3949911859
65 1 1.2000091513
65.1 2 0.0110122646
65.2 1 -0.5776452043
65.3 1 -0.1372183563
66 1 -0.5081302805
66.1 1 -0.1447837412
66.2 3 0.1906241379
67 3 1.6716027681
68 1 0.5691848839
68.1 2 0.1004860389
68.2 2 -0.0061241827
68.3 2 0.7443745962
68.4 2 0.8726923437
69 2 0.0381382683
70 1 0.8126204217
70.1 3 0.4691503050
71 3 -0.5529062591
71.1 3 -0.1103252087
71.2 3 1.7178492547
71.3 2 -1.0118346755
71.4 2 1.8623785017
72 1 -0.4521659275
72.1 1 0.1375317317
72.2 2 -0.4170988856
72.3 2 0.7107266765
72.4 1 0.1451969143
72.5 1 1.6298050306
73 1 -0.0307469467
74 1 0.3730017941
75 3 -0.4908003566
76 3 -0.9888876620
76.1 3 0.0003798292
76.2 1 -0.8421863763
77 2 -0.4986802480
78 2 0.0417330969
79 1 -0.3767450660
79.1 2 0.1516000028
79.2 1 -0.1888160741
80 2 -0.0041558414
80.1 2 -0.0329337062
80.2 3 0.5046816157
81 3 -0.9493950353
81.1 2 0.2443038954
81.2 3 0.6476958410
81.3 3 0.4182528210
82 3 1.1088801952
82.1 2 0.9334157763
82.2 2 0.4958140634
83 3 0.5104724530
83.1 3 -0.0513309106
83.2 3 -0.2067792494
83.3 2 -0.0534169155
84 3 -0.0255753653
84.1 3 -1.8234189877
85 3 -0.0114038622
85.1 2 -0.0577615939
85.2 2 -0.2241856342
85.3 2 -0.0520175929
85.4 1 0.2892733846
85.5 3 -0.3740417009
86 2 0.4293735089
86.1 3 -0.1363456521
86.2 1 0.1230989293
86.3 1 0.3305413955
86.4 1 2.6003411822
86.5 3 -0.1420690052
87 1 1.0457427869
87.1 1 -0.2973007190
87.2 1 0.4396872616
88 3 -0.0601928334
88.1 1 -1.0124347595
88.2 1 0.5730917016
88.3 1 -0.0029455332
89 1 1.5465903721
90 2 0.0626760573
90.1 1 1.1896872985
90.2 3 0.2597888783
90.3 1 0.6599799887
91 1 1.1213651365
91.1 2 1.2046371625
91.2 1 0.3395603754
92 2 0.4674939332
93 1 0.2677965647
93.1 3 1.6424445368
93.2 1 0.7101700066
93.3 3 1.1222322893
93.4 1 1.4628960401
94 2 -0.2904211940
94.1 3 0.0147813580
94.2 1 -0.4536774482
94.3 3 0.6793464917
94.4 2 -0.9411356550
94.5 3 0.5683867264
95 3 0.2375652188
95.1 1 0.0767152977
95.2 2 -0.6886731251
96 1 0.7813892121
96.1 3 0.3391519695
96.2 1 -0.4857246503
96.3 1 0.8771471244
96.4 2 1.9030768981
96.5 3 -0.1684332749
97 3 1.3775130083
97.1 3 -1.7323228619
98 3 -1.2648518889
98.1 1 -0.9042716241
98.2 1 -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 2 0.8861545820
100.3 1 0.1277046316
100.4 1 -0.0834577654
$m5d$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.6857108
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
M22:C2 -0.3136263 0.5921089
M23:C2 -0.2014212 0.4917409
M24:C2 -0.1430739 0.4225863
$m5d$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
$m5d$mu_reg_norm
[1] 0
$m5d$tau_reg_norm
[1] 1e-04
$m5d$shape_tau_norm
[1] 0.01
$m5d$rate_tau_norm
[1] 0.01
$m5d$mu_reg_multinomial
[1] 0
$m5d$tau_reg_multinomial
[1] 1e-04
$m5d$mu_reg_ordinal
[1] 0
$m5d$tau_reg_ordinal
[1] 1e-04
$m5d$mu_delta_ordinal
[1] 0
$m5d$tau_delta_ordinal
[1] 1e-04
$m5d$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
$m5d$shape_diag_RinvD
[1] "0.01"
$m5d$rate_diag_RinvD
[1] "0.001"
$m5e
$m5e$M_id
M2 C2 O2 (Intercept) M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2
1 NA -1.381594459 3 1 NA NA NA NA NA NA NA NA
2 1 0.344426024 2 1 NA NA NA NA NA NA NA NA
3 2 NA 2 1 NA NA NA NA NA NA NA NA
4 2 -0.228910007 3 1 NA NA NA NA NA NA NA NA
5 1 NA 1 1 NA NA NA NA NA NA NA NA
6 NA -2.143955482 3 1 NA NA NA NA NA NA NA NA
7 NA -1.156567023 NA 1 NA NA NA NA NA NA NA NA
8 2 -0.598827660 1 1 NA NA NA NA NA NA NA NA
9 NA NA 1 1 NA NA NA NA NA NA NA NA
10 NA -1.006719032 3 1 NA NA NA NA NA NA NA NA
11 3 0.239801450 3 1 NA NA NA NA NA NA NA NA
12 NA -1.064969789 NA 1 NA NA NA NA NA NA NA NA
13 NA -0.538082688 3 1 NA NA NA NA NA NA NA NA
14 2 NA 2 1 NA NA NA NA NA NA NA NA
15 2 -1.781049276 2 1 NA NA NA NA NA NA NA NA
16 NA NA 3 1 NA NA NA NA NA NA NA NA
17 3 NA 3 1 NA NA NA NA NA NA NA NA
18 3 -0.014579883 NA 1 NA NA NA NA NA NA NA NA
19 2 -2.121550136 2 1 NA NA NA NA NA NA NA NA
20 NA NA 1 1 NA NA NA NA NA NA NA NA
21 NA -0.363239698 1 1 NA NA NA NA NA NA NA NA
22 1 -0.121568514 3 1 NA NA NA NA NA NA NA NA
23 2 -0.951271111 3 1 NA NA NA NA NA NA NA NA
24 NA NA 3 1 NA NA NA NA NA NA NA NA
25 4 -0.974288621 2 1 NA NA NA NA NA NA NA NA
26 NA -1.130632418 1 1 NA NA NA NA NA NA NA NA
27 NA 0.114339868 3 1 NA NA NA NA NA NA NA NA
28 2 0.238334648 3 1 NA NA NA NA NA NA NA NA
29 4 0.840744958 2 1 NA NA NA NA NA NA NA NA
30 2 NA 1 1 NA NA NA NA NA NA NA NA
31 2 NA 2 1 NA NA NA NA NA NA NA NA
32 3 -1.466312154 3 1 NA NA NA NA NA NA NA NA
33 1 -0.637352277 2 1 NA NA NA NA NA NA NA NA
34 4 NA 2 1 NA NA NA NA NA NA NA NA
35 4 NA 3 1 NA NA NA NA NA NA NA NA
36 NA NA 1 1 NA NA NA NA NA NA NA NA
37 NA NA 3 1 NA NA NA NA NA NA NA NA
38 NA NA 2 1 NA NA NA NA NA NA NA NA
39 4 0.006728205 3 1 NA NA NA NA NA NA NA NA
40 NA NA 2 1 NA NA NA NA NA NA NA NA
41 2 -1.663281353 2 1 NA NA NA NA NA NA NA NA
42 NA 0.161184794 2 1 NA NA NA NA NA NA NA NA
43 NA 0.457939180 3 1 NA NA NA NA NA NA NA NA
44 3 -0.307070331 2 1 NA NA NA NA NA NA NA NA
45 NA NA 1 1 NA NA NA NA NA NA NA NA
46 NA -1.071668276 NA 1 NA NA NA NA NA NA NA NA
47 4 -0.814751321 1 1 NA NA NA NA NA NA NA NA
48 3 -0.547630662 2 1 NA NA NA NA NA NA NA NA
49 4 NA 1 1 NA NA NA NA NA NA NA NA
50 3 -1.350213782 2 1 NA NA NA NA NA NA NA NA
51 NA 0.719054706 2 1 NA NA NA NA NA NA NA NA
52 1 NA 3 1 NA NA NA NA NA NA NA NA
53 NA -1.207130750 3 1 NA NA NA NA NA NA NA NA
54 NA NA 1 1 NA NA NA NA NA NA NA NA
55 NA -0.408600991 2 1 NA NA NA NA NA NA NA NA
56 NA -0.271380529 NA 1 NA NA NA NA NA NA NA NA
57 4 -1.361925974 3 1 NA NA NA NA NA NA NA NA
58 1 NA 1 1 NA NA NA NA NA NA NA NA
59 2 NA 2 1 NA NA NA NA NA NA NA NA
60 3 -0.323712205 2 1 NA NA NA NA NA NA NA NA
61 2 NA 3 1 NA NA NA NA NA NA NA NA
62 3 NA 2 1 NA NA NA NA NA NA NA NA
63 2 -1.386906880 1 1 NA NA NA NA NA NA NA NA
64 NA NA 3 1 NA NA NA NA NA NA NA NA
65 NA NA 1 1 NA NA NA NA NA NA NA NA
66 2 -0.565191691 3 1 NA NA NA NA NA NA NA NA
67 NA -0.382899912 3 1 NA NA NA NA NA NA NA NA
68 3 NA 2 1 NA NA NA NA NA NA NA NA
69 3 -0.405642769 NA 1 NA NA NA NA NA NA NA NA
70 4 NA 2 1 NA NA NA NA NA NA NA NA
71 2 -0.843748427 NA 1 NA NA NA NA NA NA NA NA
72 NA 0.116003683 NA 1 NA NA NA NA NA NA NA NA
73 4 -0.778634325 1 1 NA NA NA NA NA NA NA NA
74 NA NA 2 1 NA NA NA NA NA NA NA NA
75 NA NA 2 1 NA NA NA NA NA NA NA NA
76 4 NA 1 1 NA NA NA NA NA NA NA NA
77 2 -0.632974758 3 1 NA NA NA NA NA NA NA NA
78 2 NA 2 1 NA NA NA NA NA NA NA NA
79 NA -0.778064615 NA 1 NA NA NA NA NA NA NA NA
80 1 NA 1 1 NA NA NA NA NA NA NA NA
81 NA NA 1 1 NA NA NA NA NA NA NA NA
82 3 -0.246123253 3 1 NA NA NA NA NA NA NA NA
83 4 -1.239659782 NA 1 NA NA NA NA NA NA NA NA
84 3 -0.467772280 3 1 NA NA NA NA NA NA NA NA
85 NA NA 3 1 NA NA NA NA NA NA NA NA
86 3 -2.160485036 2 1 NA NA NA NA NA NA NA NA
87 NA -0.657675572 1 1 NA NA NA NA NA NA NA NA
88 NA NA 1 1 NA NA NA NA NA NA NA NA
89 NA -0.696710744 3 1 NA NA NA NA NA NA NA NA
90 4 NA 1 1 NA NA NA NA NA NA NA NA
91 NA -0.179395847 2 1 NA NA NA NA NA NA NA NA
92 2 -0.441545568 NA 1 NA NA NA NA NA NA NA NA
93 4 -0.685799334 1 1 NA NA NA NA NA NA NA NA
94 NA NA 3 1 NA NA NA NA NA NA NA NA
95 NA 0.191929445 2 1 NA NA NA NA NA NA NA NA
96 NA NA 2 1 NA NA NA NA NA NA NA NA
97 1 -0.069760671 3 1 NA NA NA NA NA NA NA NA
98 NA NA 2 1 NA NA NA NA NA NA NA NA
99 2 NA 2 1 NA NA NA NA NA NA NA NA
100 NA NA 3 1 NA NA NA NA NA NA NA NA
$m5e$M_lvlone
o1 c1
1 2 0.7592026489
1.1 1 0.9548337990
1.2 1 0.5612235156
1.3 2 1.1873391025
2 3 0.9192204198
2.1 1 -0.1870730476
2.2 2 1.2517512331
3 1 -0.0605087604
3.1 3 0.3788637747
3.2 2 0.9872578281
4 3 1.4930175328
4.1 2 -0.7692526880
4.2 1 0.9180841450
4.3 2 -0.0541170782
5 2 -0.1376784521
5.1 2 -0.2740585866
5.2 3 0.4670496929
5.3 2 0.1740288049
6 3 0.9868044683
7 3 -0.1280320918
7.1 1 0.4242971219
7.2 1 0.0777182491
8 2 -0.5791408712
8.1 2 0.3128604232
8.2 3 0.6258446356
8.3 3 -0.1040137707
8.4 2 0.0481450285
8.5 2 0.3831763675
9 2 -0.1757592269
9.1 2 -0.1791541200
9.2 3 -0.0957042935
10 2 -0.5598409704
10.1 1 -0.2318340451
11 2 0.5086859475
11.1 3 0.4951758188
11.2 3 -1.1022162541
11.3 1 -0.0611636705
11.4 3 -0.4971774316
12 1 -0.2433996286
13 2 0.8799673116
13.1 1 0.1079022586
14 3 0.9991752617
14.1 3 -0.1094019046
14.2 2 0.1518967560
14.3 1 0.3521012473
15 3 0.3464447888
15.1 3 -0.4767313971
15.2 2 0.5759767791
15.3 2 -0.1713452662
16 3 0.4564754473
16.1 1 1.0652558311
16.2 3 0.6971872493
16.3 1 0.5259331838
16.4 1 0.2046601798
16.5 2 1.0718540464
17 2 0.6048676222
17.1 2 0.2323298304
17.2 3 1.2617499032
17.3 2 -0.3913230895
17.4 3 0.9577299112
18 2 -0.0050324072
19 3 -0.4187468937
19.1 2 -0.4478828944
19.2 2 -1.1966721302
19.3 2 -0.5877091668
20 1 0.6838223064
20.1 2 0.3278571109
20.2 2 -0.8489831990
20.3 1 1.3169975191
20.4 1 0.0444804531
20.5 2 -0.4535207652
21 2 -0.4030302960
21.1 2 -0.4069674045
21.2 2 1.0650265940
22 1 -0.0673274516
22.1 1 0.9601388170
23 1 0.5556634840
23.1 2 1.4407865964
24 1 0.3856376411
25 2 0.3564400705
25.1 1 0.0982553434
25.2 1 0.1928682598
25.3 1 -0.0192488594
25.4 2 0.4466012931
25.5 2 1.1425193342
26 2 0.5341531449
26.1 3 1.2268695927
26.2 2 0.3678294939
26.3 1 0.5948516018
27 2 -0.3342844147
27.1 2 -0.4835141229
28 1 -0.7145915499
28.1 1 0.5063671955
28.2 1 -0.2067413142
28.3 1 0.1196789973
29 2 0.1392699487
29.1 2 0.7960234776
29.2 1 1.0398214352
29.3 2 0.0813246429
30 2 -0.3296323050
30.1 2 1.3635850954
30.2 2 0.7354171050
31 2 0.3708398217
32 3 -0.0474059668
32.1 1 1.2507771489
32.2 3 0.1142915519
32.3 3 0.6773270619
33 2 0.1774293842
33.1 2 0.6159606291
34 3 0.8590979166
34.1 2 0.0546216775
34.2 2 -0.0897224473
34.3 1 0.4163395571
35 2 -1.4693520528
35.1 3 -0.3031734330
35.2 2 -0.6045512101
36 3 0.9823048960
36.1 2 1.4466051416
36.2 3 1.1606752905
36.3 3 0.8373091576
36.4 3 0.2640591685
37 3 0.1177313455
37.1 3 -0.1415483779
37.2 3 0.0054610124
38 2 0.8078948077
39 1 0.9876451040
39.1 2 -0.3431222274
39.2 2 -1.7909380751
39.3 1 -0.1798746191
39.4 1 -0.1850961689
39.5 1 0.4544226146
40 3 0.5350190436
40.1 1 0.4189342752
40.2 1 0.4211994981
40.3 2 0.0916687506
41 3 -0.1035047421
41.1 2 -0.4684202411
41.2 1 0.5972615368
41.3 1 0.9885613862
41.4 3 -0.3908036794
42 2 -0.0338893961
42.1 1 -0.4498363172
43 2 0.8965546110
43.1 2 0.6199122090
43.2 1 0.1804894429
44 3 1.3221409285
44.1 2 0.3416426284
44.2 1 0.5706610068
44.3 1 1.2679497430
45 1 0.1414983160
45.1 1 0.7220892521
46 1 1.5391054233
46.1 2 0.3889107049
46.2 3 0.1248719493
47 1 0.2014101100
47.1 1 0.2982973539
47.2 1 1.1518107179
47.3 3 0.5196802157
47.4 2 0.3702301552
48 3 -0.2128602862
48.1 2 -0.5337239976
49 1 -0.5236770035
50 2 0.3897705981
51 2 -0.7213343736
52 2 0.3758235358
52.1 3 0.7138067080
52.2 2 0.8872895233
52.3 1 -0.9664587437
52.4 3 0.0254566848
52.5 2 0.4155259424
53 2 0.5675736897
53.1 1 -0.3154088781
53.2 1 0.2162315769
54 1 -0.0880802382
54.1 2 0.4129127672
54.2 3 1.0119546775
54.3 1 -0.1112901990
54.4 2 0.8587727145
55 1 -0.0116453589
55.1 1 0.5835528661
55.2 2 -1.0010857254
55.3 1 -0.4796526070
55.4 1 -0.1202746964
56 3 0.5176377612
56.1 2 -1.1136932588
56.2 3 -0.0168103281
56.3 3 0.3933023606
56.4 1 0.3714625139
56.5 3 0.7811448179
57 2 -1.0868304872
57.1 2 0.8018626997
57.2 2 -0.1159517011
57.3 2 0.6785562445
58 3 1.6476207996
58.1 2 0.3402652711
58.2 3 -0.1111300753
58.3 3 -0.5409234285
58.4 2 -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 1 1.1717723589
61.4 2 -0.0629201596
62 2 -0.3979137604
62.1 3 0.6830738372
62.2 1 0.4301745954
62.3 2 -0.0333139957
63 1 0.3345678035
63.1 3 0.3643769511
64 2 0.3949911859
65 1 1.2000091513
65.1 2 0.0110122646
65.2 1 -0.5776452043
65.3 1 -0.1372183563
66 1 -0.5081302805
66.1 1 -0.1447837412
66.2 3 0.1906241379
67 3 1.6716027681
68 1 0.5691848839
68.1 2 0.1004860389
68.2 2 -0.0061241827
68.3 2 0.7443745962
68.4 2 0.8726923437
69 2 0.0381382683
70 1 0.8126204217
70.1 3 0.4691503050
71 3 -0.5529062591
71.1 3 -0.1103252087
71.2 3 1.7178492547
71.3 2 -1.0118346755
71.4 2 1.8623785017
72 1 -0.4521659275
72.1 1 0.1375317317
72.2 2 -0.4170988856
72.3 2 0.7107266765
72.4 1 0.1451969143
72.5 1 1.6298050306
73 1 -0.0307469467
74 1 0.3730017941
75 3 -0.4908003566
76 3 -0.9888876620
76.1 3 0.0003798292
76.2 1 -0.8421863763
77 2 -0.4986802480
78 2 0.0417330969
79 1 -0.3767450660
79.1 2 0.1516000028
79.2 1 -0.1888160741
80 2 -0.0041558414
80.1 2 -0.0329337062
80.2 3 0.5046816157
81 3 -0.9493950353
81.1 2 0.2443038954
81.2 3 0.6476958410
81.3 3 0.4182528210
82 3 1.1088801952
82.1 2 0.9334157763
82.2 2 0.4958140634
83 3 0.5104724530
83.1 3 -0.0513309106
83.2 3 -0.2067792494
83.3 2 -0.0534169155
84 3 -0.0255753653
84.1 3 -1.8234189877
85 3 -0.0114038622
85.1 2 -0.0577615939
85.2 2 -0.2241856342
85.3 2 -0.0520175929
85.4 1 0.2892733846
85.5 3 -0.3740417009
86 2 0.4293735089
86.1 3 -0.1363456521
86.2 1 0.1230989293
86.3 1 0.3305413955
86.4 1 2.6003411822
86.5 3 -0.1420690052
87 1 1.0457427869
87.1 1 -0.2973007190
87.2 1 0.4396872616
88 3 -0.0601928334
88.1 1 -1.0124347595
88.2 1 0.5730917016
88.3 1 -0.0029455332
89 1 1.5465903721
90 2 0.0626760573
90.1 1 1.1896872985
90.2 3 0.2597888783
90.3 1 0.6599799887
91 1 1.1213651365
91.1 2 1.2046371625
91.2 1 0.3395603754
92 2 0.4674939332
93 1 0.2677965647
93.1 3 1.6424445368
93.2 1 0.7101700066
93.3 3 1.1222322893
93.4 1 1.4628960401
94 2 -0.2904211940
94.1 3 0.0147813580
94.2 1 -0.4536774482
94.3 3 0.6793464917
94.4 2 -0.9411356550
94.5 3 0.5683867264
95 3 0.2375652188
95.1 1 0.0767152977
95.2 2 -0.6886731251
96 1 0.7813892121
96.1 3 0.3391519695
96.2 1 -0.4857246503
96.3 1 0.8771471244
96.4 2 1.9030768981
96.5 3 -0.1684332749
97 3 1.3775130083
97.1 3 -1.7323228619
98 3 -1.2648518889
98.1 1 -0.9042716241
98.2 1 -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 2 0.8861545820
100.3 1 0.1277046316
100.4 1 -0.0834577654
$m5e$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.6857108
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
M22:C2 -0.3136263 0.5921089
M23:C2 -0.2014212 0.4917409
M24:C2 -0.1430739 0.4225863
$m5e$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
$m5e$mu_reg_norm
[1] 0
$m5e$tau_reg_norm
[1] 1e-04
$m5e$shape_tau_norm
[1] 0.01
$m5e$rate_tau_norm
[1] 0.01
$m5e$mu_reg_multinomial
[1] 0
$m5e$tau_reg_multinomial
[1] 1e-04
$m5e$mu_reg_ordinal
[1] 0
$m5e$tau_reg_ordinal
[1] 1e-04
$m5e$mu_delta_ordinal
[1] 0
$m5e$tau_delta_ordinal
[1] 1e-04
$m5e$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
$m5e$shape_diag_RinvD
[1] "0.01"
$m5e$rate_diag_RinvD
[1] "0.001"
$m6a
$m6a$M_id
C2 O2 (Intercept) C1 O22 O23
1 -1.381594459 3 1 0.7175865 NA NA
2 0.344426024 2 1 0.7507170 NA NA
3 NA 2 1 0.7255954 NA NA
4 -0.228910007 3 1 0.7469352 NA NA
5 NA 1 1 0.7139120 NA NA
6 -2.143955482 3 1 0.7332505 NA NA
7 -1.156567023 NA 1 0.7345929 NA NA
8 -0.598827660 1 1 0.7652589 NA NA
9 NA 1 1 0.7200622 NA NA
10 -1.006719032 3 1 0.7423879 NA NA
11 0.239801450 3 1 0.7437448 NA NA
12 -1.064969789 NA 1 0.7446470 NA NA
13 -0.538082688 3 1 0.7530186 NA NA
14 NA 2 1 0.7093137 NA NA
15 -1.781049276 2 1 0.7331192 NA NA
16 NA 3 1 0.7011390 NA NA
17 NA 3 1 0.7432395 NA NA
18 -0.014579883 NA 1 0.7545191 NA NA
19 -2.121550136 2 1 0.7528487 NA NA
20 NA 1 1 0.7612865 NA NA
21 -0.363239698 1 1 0.7251719 NA NA
22 -0.121568514 3 1 0.7300630 NA NA
23 -0.951271111 3 1 0.7087249 NA NA
24 NA 3 1 0.7391938 NA NA
25 -0.974288621 2 1 0.7820641 NA NA
26 -1.130632418 1 1 0.7118298 NA NA
27 0.114339868 3 1 0.7230857 NA NA
28 0.238334648 3 1 0.7489353 NA NA
29 0.840744958 2 1 0.7510888 NA NA
30 NA 1 1 0.7300717 NA NA
31 NA 2 1 0.7550721 NA NA
32 -1.466312154 3 1 0.7321898 NA NA
33 -0.637352277 2 1 0.7306414 NA NA
34 NA 2 1 0.7427216 NA NA
35 NA 3 1 0.7193042 NA NA
36 NA 1 1 0.7312888 NA NA
37 NA 3 1 0.7100436 NA NA
38 NA 2 1 0.7670184 NA NA
39 0.006728205 3 1 0.7400449 NA NA
40 NA 2 1 0.7397304 NA NA
41 -1.663281353 2 1 0.7490966 NA NA
42 0.161184794 2 1 0.7419274 NA NA
43 0.457939180 3 1 0.7527810 NA NA
44 -0.307070331 2 1 0.7408315 NA NA
45 NA 1 1 0.7347550 NA NA
46 -1.071668276 NA 1 0.7332398 NA NA
47 -0.814751321 1 1 0.7376481 NA NA
48 -0.547630662 2 1 0.7346179 NA NA
49 NA 1 1 0.7329402 NA NA
50 -1.350213782 2 1 0.7260436 NA NA
51 0.719054706 2 1 0.7242910 NA NA
52 NA 3 1 0.7298067 NA NA
53 -1.207130750 3 1 0.7254741 NA NA
54 NA 1 1 0.7542067 NA NA
55 -0.408600991 2 1 0.7389952 NA NA
56 -0.271380529 NA 1 0.7520638 NA NA
57 -1.361925974 3 1 0.7219958 NA NA
58 NA 1 1 0.7259632 NA NA
59 NA 2 1 0.7458606 NA NA
60 -0.323712205 2 1 0.7672421 NA NA
61 NA 3 1 0.7257179 NA NA
62 NA 2 1 0.7189892 NA NA
63 -1.386906880 1 1 0.7333356 NA NA
64 NA 3 1 0.7320243 NA NA
65 NA 1 1 0.7477711 NA NA
66 -0.565191691 3 1 0.7343974 NA NA
67 -0.382899912 3 1 0.7491624 NA NA
68 NA 2 1 0.7482736 NA NA
69 -0.405642769 NA 1 0.7338267 NA NA
70 NA 2 1 0.7607742 NA NA
71 -0.843748427 NA 1 0.7777600 NA NA
72 0.116003683 NA 1 0.7408143 NA NA
73 -0.778634325 1 1 0.7248271 NA NA
74 NA 2 1 0.7364916 NA NA
75 NA 2 1 0.7464926 NA NA
76 NA 1 1 0.7355430 NA NA
77 -0.632974758 3 1 0.7208449 NA NA
78 NA 2 1 0.7373573 NA NA
79 -0.778064615 NA 1 0.7598079 NA NA
80 NA 1 1 0.7360415 NA NA
81 NA 1 1 0.7293932 NA NA
82 -0.246123253 3 1 0.7279309 NA NA
83 -1.239659782 NA 1 0.7344643 NA NA
84 -0.467772280 3 1 0.7384350 NA NA
85 NA 3 1 0.7323716 NA NA
86 -2.160485036 2 1 0.7576597 NA NA
87 -0.657675572 1 1 0.7496139 NA NA
88 NA 1 1 0.7275239 NA NA
89 -0.696710744 3 1 0.7250648 NA NA
90 NA 1 1 0.7335262 NA NA
91 -0.179395847 2 1 0.7343980 NA NA
92 -0.441545568 NA 1 0.7380425 NA NA
93 -0.685799334 1 1 0.7389460 NA NA
94 NA 3 1 0.7259951 NA NA
95 0.191929445 2 1 0.7282840 NA NA
96 NA 2 1 0.7281676 NA NA
97 -0.069760671 3 1 0.7245642 NA NA
98 NA 2 1 0.7526938 NA NA
99 NA 2 1 0.7272309 NA NA
100 NA 3 1 0.7383460 NA NA
$m6a$M_lvlone
o1 b2 b21
1 2 NA NA
1.1 1 0 NA
1.2 1 NA NA
1.3 2 0 NA
2 3 0 NA
2.1 1 NA NA
2.2 2 NA NA
3 1 0 NA
3.1 3 NA NA
3.2 2 1 NA
4 3 1 NA
4.1 2 0 NA
4.2 1 0 NA
4.3 2 0 NA
5 2 NA NA
5.1 2 0 NA
5.2 3 NA NA
5.3 2 NA NA
6 3 NA NA
7 3 NA NA
7.1 1 NA NA
7.2 1 0 NA
8 2 0 NA
8.1 2 0 NA
8.2 3 NA NA
8.3 3 1 NA
8.4 2 0 NA
8.5 2 1 NA
9 2 0 NA
9.1 2 NA NA
9.2 3 NA NA
10 2 NA NA
10.1 1 0 NA
11 2 0 NA
11.1 3 0 NA
11.2 3 0 NA
11.3 1 0 NA
11.4 3 0 NA
12 1 0 NA
13 2 NA NA
13.1 1 0 NA
14 3 NA NA
14.1 3 NA NA
14.2 2 NA NA
14.3 1 NA NA
15 3 0 NA
15.1 3 0 NA
15.2 2 0 NA
15.3 2 0 NA
16 3 1 NA
16.1 1 NA NA
16.2 3 NA NA
16.3 1 0 NA
16.4 1 0 NA
16.5 2 NA NA
17 2 0 NA
17.1 2 0 NA
17.2 3 0 NA
17.3 2 NA NA
17.4 3 0 NA
18 2 0 NA
19 3 NA NA
19.1 2 NA NA
19.2 2 0 NA
19.3 2 1 NA
20 1 NA NA
20.1 2 0 NA
20.2 2 1 NA
20.3 1 0 NA
20.4 1 0 NA
20.5 2 0 NA
21 2 0 NA
21.1 2 0 NA
21.2 2 NA NA
22 1 0 NA
22.1 1 0 NA
23 1 0 NA
23.1 2 NA NA
24 1 0 NA
25 2 0 NA
25.1 1 NA NA
25.2 1 1 NA
25.3 1 0 NA
25.4 2 0 NA
25.5 2 NA NA
26 2 NA NA
26.1 3 0 NA
26.2 2 0 NA
26.3 1 0 NA
27 2 0 NA
27.1 2 0 NA
28 1 NA NA
28.1 1 0 NA
28.2 1 0 NA
28.3 1 0 NA
29 2 0 NA
29.1 2 0 NA
29.2 1 0 NA
29.3 2 0 NA
30 2 NA NA
30.1 2 0 NA
30.2 2 0 NA
31 2 0 NA
32 3 0 NA
32.1 1 0 NA
32.2 3 NA NA
32.3 3 NA NA
33 2 0 NA
33.1 2 1 NA
34 3 NA NA
34.1 2 0 NA
34.2 2 NA NA
34.3 1 NA NA
35 2 0 NA
35.1 3 0 NA
35.2 2 NA NA
36 3 NA NA
36.1 2 NA NA
36.2 3 0 NA
36.3 3 0 NA
36.4 3 0 NA
37 3 0 NA
37.1 3 0 NA
37.2 3 0 NA
38 2 0 NA
39 1 1 NA
39.1 2 0 NA
39.2 2 NA NA
39.3 1 NA NA
39.4 1 0 NA
39.5 1 1 NA
40 3 0 NA
40.1 1 1 NA
40.2 1 0 NA
40.3 2 NA NA
41 3 0 NA
41.1 2 NA NA
41.2 1 0 NA
41.3 1 NA NA
41.4 3 0 NA
42 2 0 NA
42.1 1 1 NA
43 2 0 NA
43.1 2 1 NA
43.2 1 0 NA
44 3 0 NA
44.1 2 0 NA
44.2 1 0 NA
44.3 1 0 NA
45 1 NA NA
45.1 1 1 NA
46 1 0 NA
46.1 2 0 NA
46.2 3 0 NA
47 1 0 NA
47.1 1 0 NA
47.2 1 0 NA
47.3 3 NA NA
47.4 2 0 NA
48 3 1 NA
48.1 2 1 NA
49 1 NA NA
50 2 0 NA
51 2 0 NA
52 2 0 NA
52.1 3 0 NA
52.2 2 0 NA
52.3 1 0 NA
52.4 3 0 NA
52.5 2 0 NA
53 2 0 NA
53.1 1 0 NA
53.2 1 NA NA
54 1 NA NA
54.1 2 NA NA
54.2 3 NA NA
54.3 1 NA NA
54.4 2 0 NA
55 1 0 NA
55.1 1 0 NA
55.2 2 NA NA
55.3 1 NA NA
55.4 1 0 NA
56 3 0 NA
56.1 2 NA NA
56.2 3 NA NA
56.3 3 1 NA
56.4 1 0 NA
56.5 3 0 NA
57 2 0 NA
57.1 2 0 NA
57.2 2 0 NA
57.3 2 NA NA
58 3 0 NA
58.1 2 NA NA
58.2 3 1 NA
58.3 3 1 NA
58.4 2 0 NA
58.5 3 0 NA
59 3 NA NA
59.1 1 1 NA
60 3 0 NA
61 1 NA NA
61.1 2 1 NA
61.2 2 1 NA
61.3 1 0 NA
61.4 2 0 NA
62 2 NA NA
62.1 3 1 NA
62.2 1 0 NA
62.3 2 0 NA
63 1 NA NA
63.1 3 0 NA
64 2 0 NA
65 1 0 NA
65.1 2 0 NA
65.2 1 0 NA
65.3 1 0 NA
66 1 NA NA
66.1 1 0 NA
66.2 3 0 NA
67 3 NA NA
68 1 0 NA
68.1 2 0 NA
68.2 2 NA NA
68.3 2 0 NA
68.4 2 NA NA
69 2 0 NA
70 1 0 NA
70.1 3 0 NA
71 3 0 NA
71.1 3 1 NA
71.2 3 0 NA
71.3 2 1 NA
71.4 2 0 NA
72 1 0 NA
72.1 1 0 NA
72.2 2 NA NA
72.3 2 0 NA
72.4 1 0 NA
72.5 1 0 NA
73 1 0 NA
74 1 0 NA
75 3 NA NA
76 3 0 NA
76.1 3 0 NA
76.2 1 0 NA
77 2 NA NA
78 2 0 NA
79 1 NA NA
79.1 2 0 NA
79.2 1 NA NA
80 2 NA NA
80.1 2 0 NA
80.2 3 NA NA
81 3 0 NA
81.1 2 0 NA
81.2 3 NA NA
81.3 3 0 NA
82 3 NA NA
82.1 2 0 NA
82.2 2 1 NA
83 3 NA NA
83.1 3 0 NA
83.2 3 0 NA
83.3 2 NA NA
84 3 0 NA
84.1 3 NA NA
85 3 1 NA
85.1 2 NA NA
85.2 2 0 NA
85.3 2 0 NA
85.4 1 0 NA
85.5 3 0 NA
86 2 0 NA
86.1 3 NA NA
86.2 1 NA NA
86.3 1 0 NA
86.4 1 NA NA
86.5 3 0 NA
87 1 NA NA
87.1 1 NA NA
87.2 1 NA NA
88 3 0 NA
88.1 1 NA NA
88.2 1 0 NA
88.3 1 0 NA
89 1 0 NA
90 2 0 NA
90.1 1 0 NA
90.2 3 0 NA
90.3 1 NA NA
91 1 0 NA
91.1 2 0 NA
91.2 1 0 NA
92 2 0 NA
93 1 NA NA
93.1 3 0 NA
93.2 1 NA NA
93.3 3 0 NA
93.4 1 0 NA
94 2 NA NA
94.1 3 0 NA
94.2 1 0 NA
94.3 3 NA NA
94.4 2 0 NA
94.5 3 1 NA
95 3 0 NA
95.1 1 NA NA
95.2 2 0 NA
96 1 0 NA
96.1 3 0 NA
96.2 1 0 NA
96.3 1 NA NA
96.4 2 1 NA
96.5 3 1 NA
97 3 0 NA
97.1 3 0 NA
98 3 0 NA
98.1 1 0 NA
98.2 1 1 NA
99 2 0 NA
99.1 2 0 NA
99.2 1 0 NA
100 1 NA NA
100.1 2 NA NA
100.2 2 0 NA
100.3 1 NA NA
100.4 1 0 NA
$m6a$spM_id
center scale
C2 -0.6240921 0.68571078
O2 NA NA
(Intercept) NA NA
C1 0.7372814 0.01472882
O22 NA NA
O23 NA NA
$m6a$mu_reg_norm
[1] 0
$m6a$tau_reg_norm
[1] 1e-04
$m6a$shape_tau_norm
[1] 0.01
$m6a$rate_tau_norm
[1] 0.01
$m6a$mu_reg_binom
[1] 0
$m6a$tau_reg_binom
[1] 1e-04
$m6a$mu_reg_ordinal
[1] 0
$m6a$tau_reg_ordinal
[1] 1e-04
$m6a$mu_delta_ordinal
[1] 0
$m6a$tau_delta_ordinal
[1] 1e-04
$m6a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m6a$shape_diag_RinvD
[1] "0.01"
$m6a$rate_diag_RinvD
[1] "0.001"
$m6b
$m6b$M_id
M2 C2 O2 (Intercept) M22 M23 M24 O22 O23
1 NA -1.381594459 3 1 NA NA NA NA NA
2 1 0.344426024 2 1 NA NA NA NA NA
3 2 NA 2 1 NA NA NA NA NA
4 2 -0.228910007 3 1 NA NA NA NA NA
5 1 NA 1 1 NA NA NA NA NA
6 NA -2.143955482 3 1 NA NA NA NA NA
7 NA -1.156567023 NA 1 NA NA NA NA NA
8 2 -0.598827660 1 1 NA NA NA NA NA
9 NA NA 1 1 NA NA NA NA NA
10 NA -1.006719032 3 1 NA NA NA NA NA
11 3 0.239801450 3 1 NA NA NA NA NA
12 NA -1.064969789 NA 1 NA NA NA NA NA
13 NA -0.538082688 3 1 NA NA NA NA NA
14 2 NA 2 1 NA NA NA NA NA
15 2 -1.781049276 2 1 NA NA NA NA NA
16 NA NA 3 1 NA NA NA NA NA
17 3 NA 3 1 NA NA NA NA NA
18 3 -0.014579883 NA 1 NA NA NA NA NA
19 2 -2.121550136 2 1 NA NA NA NA NA
20 NA NA 1 1 NA NA NA NA NA
21 NA -0.363239698 1 1 NA NA NA NA NA
22 1 -0.121568514 3 1 NA NA NA NA NA
23 2 -0.951271111 3 1 NA NA NA NA NA
24 NA NA 3 1 NA NA NA NA NA
25 4 -0.974288621 2 1 NA NA NA NA NA
26 NA -1.130632418 1 1 NA NA NA NA NA
27 NA 0.114339868 3 1 NA NA NA NA NA
28 2 0.238334648 3 1 NA NA NA NA NA
29 4 0.840744958 2 1 NA NA NA NA NA
30 2 NA 1 1 NA NA NA NA NA
31 2 NA 2 1 NA NA NA NA NA
32 3 -1.466312154 3 1 NA NA NA NA NA
33 1 -0.637352277 2 1 NA NA NA NA NA
34 4 NA 2 1 NA NA NA NA NA
35 4 NA 3 1 NA NA NA NA NA
36 NA NA 1 1 NA NA NA NA NA
37 NA NA 3 1 NA NA NA NA NA
38 NA NA 2 1 NA NA NA NA NA
39 4 0.006728205 3 1 NA NA NA NA NA
40 NA NA 2 1 NA NA NA NA NA
41 2 -1.663281353 2 1 NA NA NA NA NA
42 NA 0.161184794 2 1 NA NA NA NA NA
43 NA 0.457939180 3 1 NA NA NA NA NA
44 3 -0.307070331 2 1 NA NA NA NA NA
45 NA NA 1 1 NA NA NA NA NA
46 NA -1.071668276 NA 1 NA NA NA NA NA
47 4 -0.814751321 1 1 NA NA NA NA NA
48 3 -0.547630662 2 1 NA NA NA NA NA
49 4 NA 1 1 NA NA NA NA NA
50 3 -1.350213782 2 1 NA NA NA NA NA
51 NA 0.719054706 2 1 NA NA NA NA NA
52 1 NA 3 1 NA NA NA NA NA
53 NA -1.207130750 3 1 NA NA NA NA NA
54 NA NA 1 1 NA NA NA NA NA
55 NA -0.408600991 2 1 NA NA NA NA NA
56 NA -0.271380529 NA 1 NA NA NA NA NA
57 4 -1.361925974 3 1 NA NA NA NA NA
58 1 NA 1 1 NA NA NA NA NA
59 2 NA 2 1 NA NA NA NA NA
60 3 -0.323712205 2 1 NA NA NA NA NA
61 2 NA 3 1 NA NA NA NA NA
62 3 NA 2 1 NA NA NA NA NA
63 2 -1.386906880 1 1 NA NA NA NA NA
64 NA NA 3 1 NA NA NA NA NA
65 NA NA 1 1 NA NA NA NA NA
66 2 -0.565191691 3 1 NA NA NA NA NA
67 NA -0.382899912 3 1 NA NA NA NA NA
68 3 NA 2 1 NA NA NA NA NA
69 3 -0.405642769 NA 1 NA NA NA NA NA
70 4 NA 2 1 NA NA NA NA NA
71 2 -0.843748427 NA 1 NA NA NA NA NA
72 NA 0.116003683 NA 1 NA NA NA NA NA
73 4 -0.778634325 1 1 NA NA NA NA NA
74 NA NA 2 1 NA NA NA NA NA
75 NA NA 2 1 NA NA NA NA NA
76 4 NA 1 1 NA NA NA NA NA
77 2 -0.632974758 3 1 NA NA NA NA NA
78 2 NA 2 1 NA NA NA NA NA
79 NA -0.778064615 NA 1 NA NA NA NA NA
80 1 NA 1 1 NA NA NA NA NA
81 NA NA 1 1 NA NA NA NA NA
82 3 -0.246123253 3 1 NA NA NA NA NA
83 4 -1.239659782 NA 1 NA NA NA NA NA
84 3 -0.467772280 3 1 NA NA NA NA NA
85 NA NA 3 1 NA NA NA NA NA
86 3 -2.160485036 2 1 NA NA NA NA NA
87 NA -0.657675572 1 1 NA NA NA NA NA
88 NA NA 1 1 NA NA NA NA NA
89 NA -0.696710744 3 1 NA NA NA NA NA
90 4 NA 1 1 NA NA NA NA NA
91 NA -0.179395847 2 1 NA NA NA NA NA
92 2 -0.441545568 NA 1 NA NA NA NA NA
93 4 -0.685799334 1 1 NA NA NA NA NA
94 NA NA 3 1 NA NA NA NA NA
95 NA 0.191929445 2 1 NA NA NA NA NA
96 NA NA 2 1 NA NA NA NA NA
97 1 -0.069760671 3 1 NA NA NA NA NA
98 NA NA 2 1 NA NA NA NA NA
99 2 NA 2 1 NA NA NA NA NA
100 NA NA 3 1 NA NA NA NA NA
$m6b$M_lvlone
o1 c1 c1:C2
1 2 0.7592026489 NA
1.1 1 0.9548337990 NA
1.2 1 0.5612235156 NA
1.3 2 1.1873391025 NA
2 3 0.9192204198 NA
2.1 1 -0.1870730476 NA
2.2 2 1.2517512331 NA
3 1 -0.0605087604 NA
3.1 3 0.3788637747 NA
3.2 2 0.9872578281 NA
4 3 1.4930175328 NA
4.1 2 -0.7692526880 NA
4.2 1 0.9180841450 NA
4.3 2 -0.0541170782 NA
5 2 -0.1376784521 NA
5.1 2 -0.2740585866 NA
5.2 3 0.4670496929 NA
5.3 2 0.1740288049 NA
6 3 0.9868044683 NA
7 3 -0.1280320918 NA
7.1 1 0.4242971219 NA
7.2 1 0.0777182491 NA
8 2 -0.5791408712 NA
8.1 2 0.3128604232 NA
8.2 3 0.6258446356 NA
8.3 3 -0.1040137707 NA
8.4 2 0.0481450285 NA
8.5 2 0.3831763675 NA
9 2 -0.1757592269 NA
9.1 2 -0.1791541200 NA
9.2 3 -0.0957042935 NA
10 2 -0.5598409704 NA
10.1 1 -0.2318340451 NA
11 2 0.5086859475 NA
11.1 3 0.4951758188 NA
11.2 3 -1.1022162541 NA
11.3 1 -0.0611636705 NA
11.4 3 -0.4971774316 NA
12 1 -0.2433996286 NA
13 2 0.8799673116 NA
13.1 1 0.1079022586 NA
14 3 0.9991752617 NA
14.1 3 -0.1094019046 NA
14.2 2 0.1518967560 NA
14.3 1 0.3521012473 NA
15 3 0.3464447888 NA
15.1 3 -0.4767313971 NA
15.2 2 0.5759767791 NA
15.3 2 -0.1713452662 NA
16 3 0.4564754473 NA
16.1 1 1.0652558311 NA
16.2 3 0.6971872493 NA
16.3 1 0.5259331838 NA
16.4 1 0.2046601798 NA
16.5 2 1.0718540464 NA
17 2 0.6048676222 NA
17.1 2 0.2323298304 NA
17.2 3 1.2617499032 NA
17.3 2 -0.3913230895 NA
17.4 3 0.9577299112 NA
18 2 -0.0050324072 NA
19 3 -0.4187468937 NA
19.1 2 -0.4478828944 NA
19.2 2 -1.1966721302 NA
19.3 2 -0.5877091668 NA
20 1 0.6838223064 NA
20.1 2 0.3278571109 NA
20.2 2 -0.8489831990 NA
20.3 1 1.3169975191 NA
20.4 1 0.0444804531 NA
20.5 2 -0.4535207652 NA
21 2 -0.4030302960 NA
21.1 2 -0.4069674045 NA
21.2 2 1.0650265940 NA
22 1 -0.0673274516 NA
22.1 1 0.9601388170 NA
23 1 0.5556634840 NA
23.1 2 1.4407865964 NA
24 1 0.3856376411 NA
25 2 0.3564400705 NA
25.1 1 0.0982553434 NA
25.2 1 0.1928682598 NA
25.3 1 -0.0192488594 NA
25.4 2 0.4466012931 NA
25.5 2 1.1425193342 NA
26 2 0.5341531449 NA
26.1 3 1.2268695927 NA
26.2 2 0.3678294939 NA
26.3 1 0.5948516018 NA
27 2 -0.3342844147 NA
27.1 2 -0.4835141229 NA
28 1 -0.7145915499 NA
28.1 1 0.5063671955 NA
28.2 1 -0.2067413142 NA
28.3 1 0.1196789973 NA
29 2 0.1392699487 NA
29.1 2 0.7960234776 NA
29.2 1 1.0398214352 NA
29.3 2 0.0813246429 NA
30 2 -0.3296323050 NA
30.1 2 1.3635850954 NA
30.2 2 0.7354171050 NA
31 2 0.3708398217 NA
32 3 -0.0474059668 NA
32.1 1 1.2507771489 NA
32.2 3 0.1142915519 NA
32.3 3 0.6773270619 NA
33 2 0.1774293842 NA
33.1 2 0.6159606291 NA
34 3 0.8590979166 NA
34.1 2 0.0546216775 NA
34.2 2 -0.0897224473 NA
34.3 1 0.4163395571 NA
35 2 -1.4693520528 NA
35.1 3 -0.3031734330 NA
35.2 2 -0.6045512101 NA
36 3 0.9823048960 NA
36.1 2 1.4466051416 NA
36.2 3 1.1606752905 NA
36.3 3 0.8373091576 NA
36.4 3 0.2640591685 NA
37 3 0.1177313455 NA
37.1 3 -0.1415483779 NA
37.2 3 0.0054610124 NA
38 2 0.8078948077 NA
39 1 0.9876451040 NA
39.1 2 -0.3431222274 NA
39.2 2 -1.7909380751 NA
39.3 1 -0.1798746191 NA
39.4 1 -0.1850961689 NA
39.5 1 0.4544226146 NA
40 3 0.5350190436 NA
40.1 1 0.4189342752 NA
40.2 1 0.4211994981 NA
40.3 2 0.0916687506 NA
41 3 -0.1035047421 NA
41.1 2 -0.4684202411 NA
41.2 1 0.5972615368 NA
41.3 1 0.9885613862 NA
41.4 3 -0.3908036794 NA
42 2 -0.0338893961 NA
42.1 1 -0.4498363172 NA
43 2 0.8965546110 NA
43.1 2 0.6199122090 NA
43.2 1 0.1804894429 NA
44 3 1.3221409285 NA
44.1 2 0.3416426284 NA
44.2 1 0.5706610068 NA
44.3 1 1.2679497430 NA
45 1 0.1414983160 NA
45.1 1 0.7220892521 NA
46 1 1.5391054233 NA
46.1 2 0.3889107049 NA
46.2 3 0.1248719493 NA
47 1 0.2014101100 NA
47.1 1 0.2982973539 NA
47.2 1 1.1518107179 NA
47.3 3 0.5196802157 NA
47.4 2 0.3702301552 NA
48 3 -0.2128602862 NA
48.1 2 -0.5337239976 NA
49 1 -0.5236770035 NA
50 2 0.3897705981 NA
51 2 -0.7213343736 NA
52 2 0.3758235358 NA
52.1 3 0.7138067080 NA
52.2 2 0.8872895233 NA
52.3 1 -0.9664587437 NA
52.4 3 0.0254566848 NA
52.5 2 0.4155259424 NA
53 2 0.5675736897 NA
53.1 1 -0.3154088781 NA
53.2 1 0.2162315769 NA
54 1 -0.0880802382 NA
54.1 2 0.4129127672 NA
54.2 3 1.0119546775 NA
54.3 1 -0.1112901990 NA
54.4 2 0.8587727145 NA
55 1 -0.0116453589 NA
55.1 1 0.5835528661 NA
55.2 2 -1.0010857254 NA
55.3 1 -0.4796526070 NA
55.4 1 -0.1202746964 NA
56 3 0.5176377612 NA
56.1 2 -1.1136932588 NA
56.2 3 -0.0168103281 NA
56.3 3 0.3933023606 NA
56.4 1 0.3714625139 NA
56.5 3 0.7811448179 NA
57 2 -1.0868304872 NA
57.1 2 0.8018626997 NA
57.2 2 -0.1159517011 NA
57.3 2 0.6785562445 NA
58 3 1.6476207996 NA
58.1 2 0.3402652711 NA
58.2 3 -0.1111300753 NA
58.3 3 -0.5409234285 NA
58.4 2 -0.1271327672 NA
58.5 3 0.8713264822 NA
59 3 0.4766421367 NA
59.1 1 1.0028089765 NA
60 3 0.5231452932 NA
61 1 -0.7190130614 NA
61.1 2 0.8353702312 NA
61.2 2 1.0229058138 NA
61.3 1 1.1717723589 NA
61.4 2 -0.0629201596 NA
62 2 -0.3979137604 NA
62.1 3 0.6830738372 NA
62.2 1 0.4301745954 NA
62.3 2 -0.0333139957 NA
63 1 0.3345678035 NA
63.1 3 0.3643769511 NA
64 2 0.3949911859 NA
65 1 1.2000091513 NA
65.1 2 0.0110122646 NA
65.2 1 -0.5776452043 NA
65.3 1 -0.1372183563 NA
66 1 -0.5081302805 NA
66.1 1 -0.1447837412 NA
66.2 3 0.1906241379 NA
67 3 1.6716027681 NA
68 1 0.5691848839 NA
68.1 2 0.1004860389 NA
68.2 2 -0.0061241827 NA
68.3 2 0.7443745962 NA
68.4 2 0.8726923437 NA
69 2 0.0381382683 NA
70 1 0.8126204217 NA
70.1 3 0.4691503050 NA
71 3 -0.5529062591 NA
71.1 3 -0.1103252087 NA
71.2 3 1.7178492547 NA
71.3 2 -1.0118346755 NA
71.4 2 1.8623785017 NA
72 1 -0.4521659275 NA
72.1 1 0.1375317317 NA
72.2 2 -0.4170988856 NA
72.3 2 0.7107266765 NA
72.4 1 0.1451969143 NA
72.5 1 1.6298050306 NA
73 1 -0.0307469467 NA
74 1 0.3730017941 NA
75 3 -0.4908003566 NA
76 3 -0.9888876620 NA
76.1 3 0.0003798292 NA
76.2 1 -0.8421863763 NA
77 2 -0.4986802480 NA
78 2 0.0417330969 NA
79 1 -0.3767450660 NA
79.1 2 0.1516000028 NA
79.2 1 -0.1888160741 NA
80 2 -0.0041558414 NA
80.1 2 -0.0329337062 NA
80.2 3 0.5046816157 NA
81 3 -0.9493950353 NA
81.1 2 0.2443038954 NA
81.2 3 0.6476958410 NA
81.3 3 0.4182528210 NA
82 3 1.1088801952 NA
82.1 2 0.9334157763 NA
82.2 2 0.4958140634 NA
83 3 0.5104724530 NA
83.1 3 -0.0513309106 NA
83.2 3 -0.2067792494 NA
83.3 2 -0.0534169155 NA
84 3 -0.0255753653 NA
84.1 3 -1.8234189877 NA
85 3 -0.0114038622 NA
85.1 2 -0.0577615939 NA
85.2 2 -0.2241856342 NA
85.3 2 -0.0520175929 NA
85.4 1 0.2892733846 NA
85.5 3 -0.3740417009 NA
86 2 0.4293735089 NA
86.1 3 -0.1363456521 NA
86.2 1 0.1230989293 NA
86.3 1 0.3305413955 NA
86.4 1 2.6003411822 NA
86.5 3 -0.1420690052 NA
87 1 1.0457427869 NA
87.1 1 -0.2973007190 NA
87.2 1 0.4396872616 NA
88 3 -0.0601928334 NA
88.1 1 -1.0124347595 NA
88.2 1 0.5730917016 NA
88.3 1 -0.0029455332 NA
89 1 1.5465903721 NA
90 2 0.0626760573 NA
90.1 1 1.1896872985 NA
90.2 3 0.2597888783 NA
90.3 1 0.6599799887 NA
91 1 1.1213651365 NA
91.1 2 1.2046371625 NA
91.2 1 0.3395603754 NA
92 2 0.4674939332 NA
93 1 0.2677965647 NA
93.1 3 1.6424445368 NA
93.2 1 0.7101700066 NA
93.3 3 1.1222322893 NA
93.4 1 1.4628960401 NA
94 2 -0.2904211940 NA
94.1 3 0.0147813580 NA
94.2 1 -0.4536774482 NA
94.3 3 0.6793464917 NA
94.4 2 -0.9411356550 NA
94.5 3 0.5683867264 NA
95 3 0.2375652188 NA
95.1 1 0.0767152977 NA
95.2 2 -0.6886731251 NA
96 1 0.7813892121 NA
96.1 3 0.3391519695 NA
96.2 1 -0.4857246503 NA
96.3 1 0.8771471244 NA
96.4 2 1.9030768981 NA
96.5 3 -0.1684332749 NA
97 3 1.3775130083 NA
97.1 3 -1.7323228619 NA
98 3 -1.2648518889 NA
98.1 1 -0.9042716241 NA
98.2 1 -0.1560385207 NA
99 2 0.7993356425 NA
99.1 2 1.0355522332 NA
99.2 1 -0.1150895843 NA
100 1 0.0369067906 NA
100.1 2 1.6023713093 NA
100.2 2 0.8861545820 NA
100.3 1 0.1277046316 NA
100.4 1 -0.0834577654 NA
$m6b$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.6857108
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
$m6b$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
c1:C2 -0.1866456 0.7201206
$m6b$mu_reg_norm
[1] 0
$m6b$tau_reg_norm
[1] 1e-04
$m6b$shape_tau_norm
[1] 0.01
$m6b$rate_tau_norm
[1] 0.01
$m6b$mu_reg_multinomial
[1] 0
$m6b$tau_reg_multinomial
[1] 1e-04
$m6b$mu_reg_ordinal
[1] 0
$m6b$tau_reg_ordinal
[1] 1e-04
$m6b$mu_delta_ordinal
[1] 0
$m6b$tau_delta_ordinal
[1] 1e-04
$m6b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m6b$shape_diag_RinvD
[1] "0.01"
$m6b$rate_diag_RinvD
[1] "0.001"
$m6c
$m6c$M_id
M2 C2 O2 (Intercept) M22 M23 M24 O22 O23
1 NA -1.381594459 3 1 NA NA NA NA NA
2 1 0.344426024 2 1 NA NA NA NA NA
3 2 NA 2 1 NA NA NA NA NA
4 2 -0.228910007 3 1 NA NA NA NA NA
5 1 NA 1 1 NA NA NA NA NA
6 NA -2.143955482 3 1 NA NA NA NA NA
7 NA -1.156567023 NA 1 NA NA NA NA NA
8 2 -0.598827660 1 1 NA NA NA NA NA
9 NA NA 1 1 NA NA NA NA NA
10 NA -1.006719032 3 1 NA NA NA NA NA
11 3 0.239801450 3 1 NA NA NA NA NA
12 NA -1.064969789 NA 1 NA NA NA NA NA
13 NA -0.538082688 3 1 NA NA NA NA NA
14 2 NA 2 1 NA NA NA NA NA
15 2 -1.781049276 2 1 NA NA NA NA NA
16 NA NA 3 1 NA NA NA NA NA
17 3 NA 3 1 NA NA NA NA NA
18 3 -0.014579883 NA 1 NA NA NA NA NA
19 2 -2.121550136 2 1 NA NA NA NA NA
20 NA NA 1 1 NA NA NA NA NA
21 NA -0.363239698 1 1 NA NA NA NA NA
22 1 -0.121568514 3 1 NA NA NA NA NA
23 2 -0.951271111 3 1 NA NA NA NA NA
24 NA NA 3 1 NA NA NA NA NA
25 4 -0.974288621 2 1 NA NA NA NA NA
26 NA -1.130632418 1 1 NA NA NA NA NA
27 NA 0.114339868 3 1 NA NA NA NA NA
28 2 0.238334648 3 1 NA NA NA NA NA
29 4 0.840744958 2 1 NA NA NA NA NA
30 2 NA 1 1 NA NA NA NA NA
31 2 NA 2 1 NA NA NA NA NA
32 3 -1.466312154 3 1 NA NA NA NA NA
33 1 -0.637352277 2 1 NA NA NA NA NA
34 4 NA 2 1 NA NA NA NA NA
35 4 NA 3 1 NA NA NA NA NA
36 NA NA 1 1 NA NA NA NA NA
37 NA NA 3 1 NA NA NA NA NA
38 NA NA 2 1 NA NA NA NA NA
39 4 0.006728205 3 1 NA NA NA NA NA
40 NA NA 2 1 NA NA NA NA NA
41 2 -1.663281353 2 1 NA NA NA NA NA
42 NA 0.161184794 2 1 NA NA NA NA NA
43 NA 0.457939180 3 1 NA NA NA NA NA
44 3 -0.307070331 2 1 NA NA NA NA NA
45 NA NA 1 1 NA NA NA NA NA
46 NA -1.071668276 NA 1 NA NA NA NA NA
47 4 -0.814751321 1 1 NA NA NA NA NA
48 3 -0.547630662 2 1 NA NA NA NA NA
49 4 NA 1 1 NA NA NA NA NA
50 3 -1.350213782 2 1 NA NA NA NA NA
51 NA 0.719054706 2 1 NA NA NA NA NA
52 1 NA 3 1 NA NA NA NA NA
53 NA -1.207130750 3 1 NA NA NA NA NA
54 NA NA 1 1 NA NA NA NA NA
55 NA -0.408600991 2 1 NA NA NA NA NA
56 NA -0.271380529 NA 1 NA NA NA NA NA
57 4 -1.361925974 3 1 NA NA NA NA NA
58 1 NA 1 1 NA NA NA NA NA
59 2 NA 2 1 NA NA NA NA NA
60 3 -0.323712205 2 1 NA NA NA NA NA
61 2 NA 3 1 NA NA NA NA NA
62 3 NA 2 1 NA NA NA NA NA
63 2 -1.386906880 1 1 NA NA NA NA NA
64 NA NA 3 1 NA NA NA NA NA
65 NA NA 1 1 NA NA NA NA NA
66 2 -0.565191691 3 1 NA NA NA NA NA
67 NA -0.382899912 3 1 NA NA NA NA NA
68 3 NA 2 1 NA NA NA NA NA
69 3 -0.405642769 NA 1 NA NA NA NA NA
70 4 NA 2 1 NA NA NA NA NA
71 2 -0.843748427 NA 1 NA NA NA NA NA
72 NA 0.116003683 NA 1 NA NA NA NA NA
73 4 -0.778634325 1 1 NA NA NA NA NA
74 NA NA 2 1 NA NA NA NA NA
75 NA NA 2 1 NA NA NA NA NA
76 4 NA 1 1 NA NA NA NA NA
77 2 -0.632974758 3 1 NA NA NA NA NA
78 2 NA 2 1 NA NA NA NA NA
79 NA -0.778064615 NA 1 NA NA NA NA NA
80 1 NA 1 1 NA NA NA NA NA
81 NA NA 1 1 NA NA NA NA NA
82 3 -0.246123253 3 1 NA NA NA NA NA
83 4 -1.239659782 NA 1 NA NA NA NA NA
84 3 -0.467772280 3 1 NA NA NA NA NA
85 NA NA 3 1 NA NA NA NA NA
86 3 -2.160485036 2 1 NA NA NA NA NA
87 NA -0.657675572 1 1 NA NA NA NA NA
88 NA NA 1 1 NA NA NA NA NA
89 NA -0.696710744 3 1 NA NA NA NA NA
90 4 NA 1 1 NA NA NA NA NA
91 NA -0.179395847 2 1 NA NA NA NA NA
92 2 -0.441545568 NA 1 NA NA NA NA NA
93 4 -0.685799334 1 1 NA NA NA NA NA
94 NA NA 3 1 NA NA NA NA NA
95 NA 0.191929445 2 1 NA NA NA NA NA
96 NA NA 2 1 NA NA NA NA NA
97 1 -0.069760671 3 1 NA NA NA NA NA
98 NA NA 2 1 NA NA NA NA NA
99 2 NA 2 1 NA NA NA NA NA
100 NA NA 3 1 NA NA NA NA NA
$m6c$M_lvlone
o1 c1 c1:C2
1 2 0.7592026489 NA
1.1 1 0.9548337990 NA
1.2 1 0.5612235156 NA
1.3 2 1.1873391025 NA
2 3 0.9192204198 NA
2.1 1 -0.1870730476 NA
2.2 2 1.2517512331 NA
3 1 -0.0605087604 NA
3.1 3 0.3788637747 NA
3.2 2 0.9872578281 NA
4 3 1.4930175328 NA
4.1 2 -0.7692526880 NA
4.2 1 0.9180841450 NA
4.3 2 -0.0541170782 NA
5 2 -0.1376784521 NA
5.1 2 -0.2740585866 NA
5.2 3 0.4670496929 NA
5.3 2 0.1740288049 NA
6 3 0.9868044683 NA
7 3 -0.1280320918 NA
7.1 1 0.4242971219 NA
7.2 1 0.0777182491 NA
8 2 -0.5791408712 NA
8.1 2 0.3128604232 NA
8.2 3 0.6258446356 NA
8.3 3 -0.1040137707 NA
8.4 2 0.0481450285 NA
8.5 2 0.3831763675 NA
9 2 -0.1757592269 NA
9.1 2 -0.1791541200 NA
9.2 3 -0.0957042935 NA
10 2 -0.5598409704 NA
10.1 1 -0.2318340451 NA
11 2 0.5086859475 NA
11.1 3 0.4951758188 NA
11.2 3 -1.1022162541 NA
11.3 1 -0.0611636705 NA
11.4 3 -0.4971774316 NA
12 1 -0.2433996286 NA
13 2 0.8799673116 NA
13.1 1 0.1079022586 NA
14 3 0.9991752617 NA
14.1 3 -0.1094019046 NA
14.2 2 0.1518967560 NA
14.3 1 0.3521012473 NA
15 3 0.3464447888 NA
15.1 3 -0.4767313971 NA
15.2 2 0.5759767791 NA
15.3 2 -0.1713452662 NA
16 3 0.4564754473 NA
16.1 1 1.0652558311 NA
16.2 3 0.6971872493 NA
16.3 1 0.5259331838 NA
16.4 1 0.2046601798 NA
16.5 2 1.0718540464 NA
17 2 0.6048676222 NA
17.1 2 0.2323298304 NA
17.2 3 1.2617499032 NA
17.3 2 -0.3913230895 NA
17.4 3 0.9577299112 NA
18 2 -0.0050324072 NA
19 3 -0.4187468937 NA
19.1 2 -0.4478828944 NA
19.2 2 -1.1966721302 NA
19.3 2 -0.5877091668 NA
20 1 0.6838223064 NA
20.1 2 0.3278571109 NA
20.2 2 -0.8489831990 NA
20.3 1 1.3169975191 NA
20.4 1 0.0444804531 NA
20.5 2 -0.4535207652 NA
21 2 -0.4030302960 NA
21.1 2 -0.4069674045 NA
21.2 2 1.0650265940 NA
22 1 -0.0673274516 NA
22.1 1 0.9601388170 NA
23 1 0.5556634840 NA
23.1 2 1.4407865964 NA
24 1 0.3856376411 NA
25 2 0.3564400705 NA
25.1 1 0.0982553434 NA
25.2 1 0.1928682598 NA
25.3 1 -0.0192488594 NA
25.4 2 0.4466012931 NA
25.5 2 1.1425193342 NA
26 2 0.5341531449 NA
26.1 3 1.2268695927 NA
26.2 2 0.3678294939 NA
26.3 1 0.5948516018 NA
27 2 -0.3342844147 NA
27.1 2 -0.4835141229 NA
28 1 -0.7145915499 NA
28.1 1 0.5063671955 NA
28.2 1 -0.2067413142 NA
28.3 1 0.1196789973 NA
29 2 0.1392699487 NA
29.1 2 0.7960234776 NA
29.2 1 1.0398214352 NA
29.3 2 0.0813246429 NA
30 2 -0.3296323050 NA
30.1 2 1.3635850954 NA
30.2 2 0.7354171050 NA
31 2 0.3708398217 NA
32 3 -0.0474059668 NA
32.1 1 1.2507771489 NA
32.2 3 0.1142915519 NA
32.3 3 0.6773270619 NA
33 2 0.1774293842 NA
33.1 2 0.6159606291 NA
34 3 0.8590979166 NA
34.1 2 0.0546216775 NA
34.2 2 -0.0897224473 NA
34.3 1 0.4163395571 NA
35 2 -1.4693520528 NA
35.1 3 -0.3031734330 NA
35.2 2 -0.6045512101 NA
36 3 0.9823048960 NA
36.1 2 1.4466051416 NA
36.2 3 1.1606752905 NA
36.3 3 0.8373091576 NA
36.4 3 0.2640591685 NA
37 3 0.1177313455 NA
37.1 3 -0.1415483779 NA
37.2 3 0.0054610124 NA
38 2 0.8078948077 NA
39 1 0.9876451040 NA
39.1 2 -0.3431222274 NA
39.2 2 -1.7909380751 NA
39.3 1 -0.1798746191 NA
39.4 1 -0.1850961689 NA
39.5 1 0.4544226146 NA
40 3 0.5350190436 NA
40.1 1 0.4189342752 NA
40.2 1 0.4211994981 NA
40.3 2 0.0916687506 NA
41 3 -0.1035047421 NA
41.1 2 -0.4684202411 NA
41.2 1 0.5972615368 NA
41.3 1 0.9885613862 NA
41.4 3 -0.3908036794 NA
42 2 -0.0338893961 NA
42.1 1 -0.4498363172 NA
43 2 0.8965546110 NA
43.1 2 0.6199122090 NA
43.2 1 0.1804894429 NA
44 3 1.3221409285 NA
44.1 2 0.3416426284 NA
44.2 1 0.5706610068 NA
44.3 1 1.2679497430 NA
45 1 0.1414983160 NA
45.1 1 0.7220892521 NA
46 1 1.5391054233 NA
46.1 2 0.3889107049 NA
46.2 3 0.1248719493 NA
47 1 0.2014101100 NA
47.1 1 0.2982973539 NA
47.2 1 1.1518107179 NA
47.3 3 0.5196802157 NA
47.4 2 0.3702301552 NA
48 3 -0.2128602862 NA
48.1 2 -0.5337239976 NA
49 1 -0.5236770035 NA
50 2 0.3897705981 NA
51 2 -0.7213343736 NA
52 2 0.3758235358 NA
52.1 3 0.7138067080 NA
52.2 2 0.8872895233 NA
52.3 1 -0.9664587437 NA
52.4 3 0.0254566848 NA
52.5 2 0.4155259424 NA
53 2 0.5675736897 NA
53.1 1 -0.3154088781 NA
53.2 1 0.2162315769 NA
54 1 -0.0880802382 NA
54.1 2 0.4129127672 NA
54.2 3 1.0119546775 NA
54.3 1 -0.1112901990 NA
54.4 2 0.8587727145 NA
55 1 -0.0116453589 NA
55.1 1 0.5835528661 NA
55.2 2 -1.0010857254 NA
55.3 1 -0.4796526070 NA
55.4 1 -0.1202746964 NA
56 3 0.5176377612 NA
56.1 2 -1.1136932588 NA
56.2 3 -0.0168103281 NA
56.3 3 0.3933023606 NA
56.4 1 0.3714625139 NA
56.5 3 0.7811448179 NA
57 2 -1.0868304872 NA
57.1 2 0.8018626997 NA
57.2 2 -0.1159517011 NA
57.3 2 0.6785562445 NA
58 3 1.6476207996 NA
58.1 2 0.3402652711 NA
58.2 3 -0.1111300753 NA
58.3 3 -0.5409234285 NA
58.4 2 -0.1271327672 NA
58.5 3 0.8713264822 NA
59 3 0.4766421367 NA
59.1 1 1.0028089765 NA
60 3 0.5231452932 NA
61 1 -0.7190130614 NA
61.1 2 0.8353702312 NA
61.2 2 1.0229058138 NA
61.3 1 1.1717723589 NA
61.4 2 -0.0629201596 NA
62 2 -0.3979137604 NA
62.1 3 0.6830738372 NA
62.2 1 0.4301745954 NA
62.3 2 -0.0333139957 NA
63 1 0.3345678035 NA
63.1 3 0.3643769511 NA
64 2 0.3949911859 NA
65 1 1.2000091513 NA
65.1 2 0.0110122646 NA
65.2 1 -0.5776452043 NA
65.3 1 -0.1372183563 NA
66 1 -0.5081302805 NA
66.1 1 -0.1447837412 NA
66.2 3 0.1906241379 NA
67 3 1.6716027681 NA
68 1 0.5691848839 NA
68.1 2 0.1004860389 NA
68.2 2 -0.0061241827 NA
68.3 2 0.7443745962 NA
68.4 2 0.8726923437 NA
69 2 0.0381382683 NA
70 1 0.8126204217 NA
70.1 3 0.4691503050 NA
71 3 -0.5529062591 NA
71.1 3 -0.1103252087 NA
71.2 3 1.7178492547 NA
71.3 2 -1.0118346755 NA
71.4 2 1.8623785017 NA
72 1 -0.4521659275 NA
72.1 1 0.1375317317 NA
72.2 2 -0.4170988856 NA
72.3 2 0.7107266765 NA
72.4 1 0.1451969143 NA
72.5 1 1.6298050306 NA
73 1 -0.0307469467 NA
74 1 0.3730017941 NA
75 3 -0.4908003566 NA
76 3 -0.9888876620 NA
76.1 3 0.0003798292 NA
76.2 1 -0.8421863763 NA
77 2 -0.4986802480 NA
78 2 0.0417330969 NA
79 1 -0.3767450660 NA
79.1 2 0.1516000028 NA
79.2 1 -0.1888160741 NA
80 2 -0.0041558414 NA
80.1 2 -0.0329337062 NA
80.2 3 0.5046816157 NA
81 3 -0.9493950353 NA
81.1 2 0.2443038954 NA
81.2 3 0.6476958410 NA
81.3 3 0.4182528210 NA
82 3 1.1088801952 NA
82.1 2 0.9334157763 NA
82.2 2 0.4958140634 NA
83 3 0.5104724530 NA
83.1 3 -0.0513309106 NA
83.2 3 -0.2067792494 NA
83.3 2 -0.0534169155 NA
84 3 -0.0255753653 NA
84.1 3 -1.8234189877 NA
85 3 -0.0114038622 NA
85.1 2 -0.0577615939 NA
85.2 2 -0.2241856342 NA
85.3 2 -0.0520175929 NA
85.4 1 0.2892733846 NA
85.5 3 -0.3740417009 NA
86 2 0.4293735089 NA
86.1 3 -0.1363456521 NA
86.2 1 0.1230989293 NA
86.3 1 0.3305413955 NA
86.4 1 2.6003411822 NA
86.5 3 -0.1420690052 NA
87 1 1.0457427869 NA
87.1 1 -0.2973007190 NA
87.2 1 0.4396872616 NA
88 3 -0.0601928334 NA
88.1 1 -1.0124347595 NA
88.2 1 0.5730917016 NA
88.3 1 -0.0029455332 NA
89 1 1.5465903721 NA
90 2 0.0626760573 NA
90.1 1 1.1896872985 NA
90.2 3 0.2597888783 NA
90.3 1 0.6599799887 NA
91 1 1.1213651365 NA
91.1 2 1.2046371625 NA
91.2 1 0.3395603754 NA
92 2 0.4674939332 NA
93 1 0.2677965647 NA
93.1 3 1.6424445368 NA
93.2 1 0.7101700066 NA
93.3 3 1.1222322893 NA
93.4 1 1.4628960401 NA
94 2 -0.2904211940 NA
94.1 3 0.0147813580 NA
94.2 1 -0.4536774482 NA
94.3 3 0.6793464917 NA
94.4 2 -0.9411356550 NA
94.5 3 0.5683867264 NA
95 3 0.2375652188 NA
95.1 1 0.0767152977 NA
95.2 2 -0.6886731251 NA
96 1 0.7813892121 NA
96.1 3 0.3391519695 NA
96.2 1 -0.4857246503 NA
96.3 1 0.8771471244 NA
96.4 2 1.9030768981 NA
96.5 3 -0.1684332749 NA
97 3 1.3775130083 NA
97.1 3 -1.7323228619 NA
98 3 -1.2648518889 NA
98.1 1 -0.9042716241 NA
98.2 1 -0.1560385207 NA
99 2 0.7993356425 NA
99.1 2 1.0355522332 NA
99.2 1 -0.1150895843 NA
100 1 0.0369067906 NA
100.1 2 1.6023713093 NA
100.2 2 0.8861545820 NA
100.3 1 0.1277046316 NA
100.4 1 -0.0834577654 NA
$m6c$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.6857108
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
$m6c$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
c1:C2 -0.1866456 0.7201206
$m6c$mu_reg_norm
[1] 0
$m6c$tau_reg_norm
[1] 1e-04
$m6c$shape_tau_norm
[1] 0.01
$m6c$rate_tau_norm
[1] 0.01
$m6c$mu_reg_multinomial
[1] 0
$m6c$tau_reg_multinomial
[1] 1e-04
$m6c$mu_reg_ordinal
[1] 0
$m6c$tau_reg_ordinal
[1] 1e-04
$m6c$mu_delta_ordinal
[1] 0
$m6c$tau_delta_ordinal
[1] 1e-04
$m6c$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
$m6c$shape_diag_RinvD
[1] "0.01"
$m6c$rate_diag_RinvD
[1] "0.001"
$m6d
$m6d$M_id
M2 C2 O2 (Intercept) M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2
1 NA -1.381594459 3 1 NA NA NA NA NA NA NA NA
2 1 0.344426024 2 1 NA NA NA NA NA NA NA NA
3 2 NA 2 1 NA NA NA NA NA NA NA NA
4 2 -0.228910007 3 1 NA NA NA NA NA NA NA NA
5 1 NA 1 1 NA NA NA NA NA NA NA NA
6 NA -2.143955482 3 1 NA NA NA NA NA NA NA NA
7 NA -1.156567023 NA 1 NA NA NA NA NA NA NA NA
8 2 -0.598827660 1 1 NA NA NA NA NA NA NA NA
9 NA NA 1 1 NA NA NA NA NA NA NA NA
10 NA -1.006719032 3 1 NA NA NA NA NA NA NA NA
11 3 0.239801450 3 1 NA NA NA NA NA NA NA NA
12 NA -1.064969789 NA 1 NA NA NA NA NA NA NA NA
13 NA -0.538082688 3 1 NA NA NA NA NA NA NA NA
14 2 NA 2 1 NA NA NA NA NA NA NA NA
15 2 -1.781049276 2 1 NA NA NA NA NA NA NA NA
16 NA NA 3 1 NA NA NA NA NA NA NA NA
17 3 NA 3 1 NA NA NA NA NA NA NA NA
18 3 -0.014579883 NA 1 NA NA NA NA NA NA NA NA
19 2 -2.121550136 2 1 NA NA NA NA NA NA NA NA
20 NA NA 1 1 NA NA NA NA NA NA NA NA
21 NA -0.363239698 1 1 NA NA NA NA NA NA NA NA
22 1 -0.121568514 3 1 NA NA NA NA NA NA NA NA
23 2 -0.951271111 3 1 NA NA NA NA NA NA NA NA
24 NA NA 3 1 NA NA NA NA NA NA NA NA
25 4 -0.974288621 2 1 NA NA NA NA NA NA NA NA
26 NA -1.130632418 1 1 NA NA NA NA NA NA NA NA
27 NA 0.114339868 3 1 NA NA NA NA NA NA NA NA
28 2 0.238334648 3 1 NA NA NA NA NA NA NA NA
29 4 0.840744958 2 1 NA NA NA NA NA NA NA NA
30 2 NA 1 1 NA NA NA NA NA NA NA NA
31 2 NA 2 1 NA NA NA NA NA NA NA NA
32 3 -1.466312154 3 1 NA NA NA NA NA NA NA NA
33 1 -0.637352277 2 1 NA NA NA NA NA NA NA NA
34 4 NA 2 1 NA NA NA NA NA NA NA NA
35 4 NA 3 1 NA NA NA NA NA NA NA NA
36 NA NA 1 1 NA NA NA NA NA NA NA NA
37 NA NA 3 1 NA NA NA NA NA NA NA NA
38 NA NA 2 1 NA NA NA NA NA NA NA NA
39 4 0.006728205 3 1 NA NA NA NA NA NA NA NA
40 NA NA 2 1 NA NA NA NA NA NA NA NA
41 2 -1.663281353 2 1 NA NA NA NA NA NA NA NA
42 NA 0.161184794 2 1 NA NA NA NA NA NA NA NA
43 NA 0.457939180 3 1 NA NA NA NA NA NA NA NA
44 3 -0.307070331 2 1 NA NA NA NA NA NA NA NA
45 NA NA 1 1 NA NA NA NA NA NA NA NA
46 NA -1.071668276 NA 1 NA NA NA NA NA NA NA NA
47 4 -0.814751321 1 1 NA NA NA NA NA NA NA NA
48 3 -0.547630662 2 1 NA NA NA NA NA NA NA NA
49 4 NA 1 1 NA NA NA NA NA NA NA NA
50 3 -1.350213782 2 1 NA NA NA NA NA NA NA NA
51 NA 0.719054706 2 1 NA NA NA NA NA NA NA NA
52 1 NA 3 1 NA NA NA NA NA NA NA NA
53 NA -1.207130750 3 1 NA NA NA NA NA NA NA NA
54 NA NA 1 1 NA NA NA NA NA NA NA NA
55 NA -0.408600991 2 1 NA NA NA NA NA NA NA NA
56 NA -0.271380529 NA 1 NA NA NA NA NA NA NA NA
57 4 -1.361925974 3 1 NA NA NA NA NA NA NA NA
58 1 NA 1 1 NA NA NA NA NA NA NA NA
59 2 NA 2 1 NA NA NA NA NA NA NA NA
60 3 -0.323712205 2 1 NA NA NA NA NA NA NA NA
61 2 NA 3 1 NA NA NA NA NA NA NA NA
62 3 NA 2 1 NA NA NA NA NA NA NA NA
63 2 -1.386906880 1 1 NA NA NA NA NA NA NA NA
64 NA NA 3 1 NA NA NA NA NA NA NA NA
65 NA NA 1 1 NA NA NA NA NA NA NA NA
66 2 -0.565191691 3 1 NA NA NA NA NA NA NA NA
67 NA -0.382899912 3 1 NA NA NA NA NA NA NA NA
68 3 NA 2 1 NA NA NA NA NA NA NA NA
69 3 -0.405642769 NA 1 NA NA NA NA NA NA NA NA
70 4 NA 2 1 NA NA NA NA NA NA NA NA
71 2 -0.843748427 NA 1 NA NA NA NA NA NA NA NA
72 NA 0.116003683 NA 1 NA NA NA NA NA NA NA NA
73 4 -0.778634325 1 1 NA NA NA NA NA NA NA NA
74 NA NA 2 1 NA NA NA NA NA NA NA NA
75 NA NA 2 1 NA NA NA NA NA NA NA NA
76 4 NA 1 1 NA NA NA NA NA NA NA NA
77 2 -0.632974758 3 1 NA NA NA NA NA NA NA NA
78 2 NA 2 1 NA NA NA NA NA NA NA NA
79 NA -0.778064615 NA 1 NA NA NA NA NA NA NA NA
80 1 NA 1 1 NA NA NA NA NA NA NA NA
81 NA NA 1 1 NA NA NA NA NA NA NA NA
82 3 -0.246123253 3 1 NA NA NA NA NA NA NA NA
83 4 -1.239659782 NA 1 NA NA NA NA NA NA NA NA
84 3 -0.467772280 3 1 NA NA NA NA NA NA NA NA
85 NA NA 3 1 NA NA NA NA NA NA NA NA
86 3 -2.160485036 2 1 NA NA NA NA NA NA NA NA
87 NA -0.657675572 1 1 NA NA NA NA NA NA NA NA
88 NA NA 1 1 NA NA NA NA NA NA NA NA
89 NA -0.696710744 3 1 NA NA NA NA NA NA NA NA
90 4 NA 1 1 NA NA NA NA NA NA NA NA
91 NA -0.179395847 2 1 NA NA NA NA NA NA NA NA
92 2 -0.441545568 NA 1 NA NA NA NA NA NA NA NA
93 4 -0.685799334 1 1 NA NA NA NA NA NA NA NA
94 NA NA 3 1 NA NA NA NA NA NA NA NA
95 NA 0.191929445 2 1 NA NA NA NA NA NA NA NA
96 NA NA 2 1 NA NA NA NA NA NA NA NA
97 1 -0.069760671 3 1 NA NA NA NA NA NA NA NA
98 NA NA 2 1 NA NA NA NA NA NA NA NA
99 2 NA 2 1 NA NA NA NA NA NA NA NA
100 NA NA 3 1 NA NA NA NA NA NA NA NA
$m6d$M_lvlone
o1 c1
1 2 0.7592026489
1.1 1 0.9548337990
1.2 1 0.5612235156
1.3 2 1.1873391025
2 3 0.9192204198
2.1 1 -0.1870730476
2.2 2 1.2517512331
3 1 -0.0605087604
3.1 3 0.3788637747
3.2 2 0.9872578281
4 3 1.4930175328
4.1 2 -0.7692526880
4.2 1 0.9180841450
4.3 2 -0.0541170782
5 2 -0.1376784521
5.1 2 -0.2740585866
5.2 3 0.4670496929
5.3 2 0.1740288049
6 3 0.9868044683
7 3 -0.1280320918
7.1 1 0.4242971219
7.2 1 0.0777182491
8 2 -0.5791408712
8.1 2 0.3128604232
8.2 3 0.6258446356
8.3 3 -0.1040137707
8.4 2 0.0481450285
8.5 2 0.3831763675
9 2 -0.1757592269
9.1 2 -0.1791541200
9.2 3 -0.0957042935
10 2 -0.5598409704
10.1 1 -0.2318340451
11 2 0.5086859475
11.1 3 0.4951758188
11.2 3 -1.1022162541
11.3 1 -0.0611636705
11.4 3 -0.4971774316
12 1 -0.2433996286
13 2 0.8799673116
13.1 1 0.1079022586
14 3 0.9991752617
14.1 3 -0.1094019046
14.2 2 0.1518967560
14.3 1 0.3521012473
15 3 0.3464447888
15.1 3 -0.4767313971
15.2 2 0.5759767791
15.3 2 -0.1713452662
16 3 0.4564754473
16.1 1 1.0652558311
16.2 3 0.6971872493
16.3 1 0.5259331838
16.4 1 0.2046601798
16.5 2 1.0718540464
17 2 0.6048676222
17.1 2 0.2323298304
17.2 3 1.2617499032
17.3 2 -0.3913230895
17.4 3 0.9577299112
18 2 -0.0050324072
19 3 -0.4187468937
19.1 2 -0.4478828944
19.2 2 -1.1966721302
19.3 2 -0.5877091668
20 1 0.6838223064
20.1 2 0.3278571109
20.2 2 -0.8489831990
20.3 1 1.3169975191
20.4 1 0.0444804531
20.5 2 -0.4535207652
21 2 -0.4030302960
21.1 2 -0.4069674045
21.2 2 1.0650265940
22 1 -0.0673274516
22.1 1 0.9601388170
23 1 0.5556634840
23.1 2 1.4407865964
24 1 0.3856376411
25 2 0.3564400705
25.1 1 0.0982553434
25.2 1 0.1928682598
25.3 1 -0.0192488594
25.4 2 0.4466012931
25.5 2 1.1425193342
26 2 0.5341531449
26.1 3 1.2268695927
26.2 2 0.3678294939
26.3 1 0.5948516018
27 2 -0.3342844147
27.1 2 -0.4835141229
28 1 -0.7145915499
28.1 1 0.5063671955
28.2 1 -0.2067413142
28.3 1 0.1196789973
29 2 0.1392699487
29.1 2 0.7960234776
29.2 1 1.0398214352
29.3 2 0.0813246429
30 2 -0.3296323050
30.1 2 1.3635850954
30.2 2 0.7354171050
31 2 0.3708398217
32 3 -0.0474059668
32.1 1 1.2507771489
32.2 3 0.1142915519
32.3 3 0.6773270619
33 2 0.1774293842
33.1 2 0.6159606291
34 3 0.8590979166
34.1 2 0.0546216775
34.2 2 -0.0897224473
34.3 1 0.4163395571
35 2 -1.4693520528
35.1 3 -0.3031734330
35.2 2 -0.6045512101
36 3 0.9823048960
36.1 2 1.4466051416
36.2 3 1.1606752905
36.3 3 0.8373091576
36.4 3 0.2640591685
37 3 0.1177313455
37.1 3 -0.1415483779
37.2 3 0.0054610124
38 2 0.8078948077
39 1 0.9876451040
39.1 2 -0.3431222274
39.2 2 -1.7909380751
39.3 1 -0.1798746191
39.4 1 -0.1850961689
39.5 1 0.4544226146
40 3 0.5350190436
40.1 1 0.4189342752
40.2 1 0.4211994981
40.3 2 0.0916687506
41 3 -0.1035047421
41.1 2 -0.4684202411
41.2 1 0.5972615368
41.3 1 0.9885613862
41.4 3 -0.3908036794
42 2 -0.0338893961
42.1 1 -0.4498363172
43 2 0.8965546110
43.1 2 0.6199122090
43.2 1 0.1804894429
44 3 1.3221409285
44.1 2 0.3416426284
44.2 1 0.5706610068
44.3 1 1.2679497430
45 1 0.1414983160
45.1 1 0.7220892521
46 1 1.5391054233
46.1 2 0.3889107049
46.2 3 0.1248719493
47 1 0.2014101100
47.1 1 0.2982973539
47.2 1 1.1518107179
47.3 3 0.5196802157
47.4 2 0.3702301552
48 3 -0.2128602862
48.1 2 -0.5337239976
49 1 -0.5236770035
50 2 0.3897705981
51 2 -0.7213343736
52 2 0.3758235358
52.1 3 0.7138067080
52.2 2 0.8872895233
52.3 1 -0.9664587437
52.4 3 0.0254566848
52.5 2 0.4155259424
53 2 0.5675736897
53.1 1 -0.3154088781
53.2 1 0.2162315769
54 1 -0.0880802382
54.1 2 0.4129127672
54.2 3 1.0119546775
54.3 1 -0.1112901990
54.4 2 0.8587727145
55 1 -0.0116453589
55.1 1 0.5835528661
55.2 2 -1.0010857254
55.3 1 -0.4796526070
55.4 1 -0.1202746964
56 3 0.5176377612
56.1 2 -1.1136932588
56.2 3 -0.0168103281
56.3 3 0.3933023606
56.4 1 0.3714625139
56.5 3 0.7811448179
57 2 -1.0868304872
57.1 2 0.8018626997
57.2 2 -0.1159517011
57.3 2 0.6785562445
58 3 1.6476207996
58.1 2 0.3402652711
58.2 3 -0.1111300753
58.3 3 -0.5409234285
58.4 2 -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 1 1.1717723589
61.4 2 -0.0629201596
62 2 -0.3979137604
62.1 3 0.6830738372
62.2 1 0.4301745954
62.3 2 -0.0333139957
63 1 0.3345678035
63.1 3 0.3643769511
64 2 0.3949911859
65 1 1.2000091513
65.1 2 0.0110122646
65.2 1 -0.5776452043
65.3 1 -0.1372183563
66 1 -0.5081302805
66.1 1 -0.1447837412
66.2 3 0.1906241379
67 3 1.6716027681
68 1 0.5691848839
68.1 2 0.1004860389
68.2 2 -0.0061241827
68.3 2 0.7443745962
68.4 2 0.8726923437
69 2 0.0381382683
70 1 0.8126204217
70.1 3 0.4691503050
71 3 -0.5529062591
71.1 3 -0.1103252087
71.2 3 1.7178492547
71.3 2 -1.0118346755
71.4 2 1.8623785017
72 1 -0.4521659275
72.1 1 0.1375317317
72.2 2 -0.4170988856
72.3 2 0.7107266765
72.4 1 0.1451969143
72.5 1 1.6298050306
73 1 -0.0307469467
74 1 0.3730017941
75 3 -0.4908003566
76 3 -0.9888876620
76.1 3 0.0003798292
76.2 1 -0.8421863763
77 2 -0.4986802480
78 2 0.0417330969
79 1 -0.3767450660
79.1 2 0.1516000028
79.2 1 -0.1888160741
80 2 -0.0041558414
80.1 2 -0.0329337062
80.2 3 0.5046816157
81 3 -0.9493950353
81.1 2 0.2443038954
81.2 3 0.6476958410
81.3 3 0.4182528210
82 3 1.1088801952
82.1 2 0.9334157763
82.2 2 0.4958140634
83 3 0.5104724530
83.1 3 -0.0513309106
83.2 3 -0.2067792494
83.3 2 -0.0534169155
84 3 -0.0255753653
84.1 3 -1.8234189877
85 3 -0.0114038622
85.1 2 -0.0577615939
85.2 2 -0.2241856342
85.3 2 -0.0520175929
85.4 1 0.2892733846
85.5 3 -0.3740417009
86 2 0.4293735089
86.1 3 -0.1363456521
86.2 1 0.1230989293
86.3 1 0.3305413955
86.4 1 2.6003411822
86.5 3 -0.1420690052
87 1 1.0457427869
87.1 1 -0.2973007190
87.2 1 0.4396872616
88 3 -0.0601928334
88.1 1 -1.0124347595
88.2 1 0.5730917016
88.3 1 -0.0029455332
89 1 1.5465903721
90 2 0.0626760573
90.1 1 1.1896872985
90.2 3 0.2597888783
90.3 1 0.6599799887
91 1 1.1213651365
91.1 2 1.2046371625
91.2 1 0.3395603754
92 2 0.4674939332
93 1 0.2677965647
93.1 3 1.6424445368
93.2 1 0.7101700066
93.3 3 1.1222322893
93.4 1 1.4628960401
94 2 -0.2904211940
94.1 3 0.0147813580
94.2 1 -0.4536774482
94.3 3 0.6793464917
94.4 2 -0.9411356550
94.5 3 0.5683867264
95 3 0.2375652188
95.1 1 0.0767152977
95.2 2 -0.6886731251
96 1 0.7813892121
96.1 3 0.3391519695
96.2 1 -0.4857246503
96.3 1 0.8771471244
96.4 2 1.9030768981
96.5 3 -0.1684332749
97 3 1.3775130083
97.1 3 -1.7323228619
98 3 -1.2648518889
98.1 1 -0.9042716241
98.2 1 -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 2 0.8861545820
100.3 1 0.1277046316
100.4 1 -0.0834577654
$m6d$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.6857108
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
M22:C2 -0.3136263 0.5921089
M23:C2 -0.2014212 0.4917409
M24:C2 -0.1430739 0.4225863
$m6d$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
$m6d$mu_reg_norm
[1] 0
$m6d$tau_reg_norm
[1] 1e-04
$m6d$shape_tau_norm
[1] 0.01
$m6d$rate_tau_norm
[1] 0.01
$m6d$mu_reg_multinomial
[1] 0
$m6d$tau_reg_multinomial
[1] 1e-04
$m6d$mu_reg_ordinal
[1] 0
$m6d$tau_reg_ordinal
[1] 1e-04
$m6d$mu_delta_ordinal
[1] 0
$m6d$tau_delta_ordinal
[1] 1e-04
$m6d$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
$m6d$shape_diag_RinvD
[1] "0.01"
$m6d$rate_diag_RinvD
[1] "0.001"
$m6e
$m6e$M_id
M2 C2 O2 (Intercept) M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2
1 NA -1.381594459 3 1 NA NA NA NA NA NA NA NA
2 1 0.344426024 2 1 NA NA NA NA NA NA NA NA
3 2 NA 2 1 NA NA NA NA NA NA NA NA
4 2 -0.228910007 3 1 NA NA NA NA NA NA NA NA
5 1 NA 1 1 NA NA NA NA NA NA NA NA
6 NA -2.143955482 3 1 NA NA NA NA NA NA NA NA
7 NA -1.156567023 NA 1 NA NA NA NA NA NA NA NA
8 2 -0.598827660 1 1 NA NA NA NA NA NA NA NA
9 NA NA 1 1 NA NA NA NA NA NA NA NA
10 NA -1.006719032 3 1 NA NA NA NA NA NA NA NA
11 3 0.239801450 3 1 NA NA NA NA NA NA NA NA
12 NA -1.064969789 NA 1 NA NA NA NA NA NA NA NA
13 NA -0.538082688 3 1 NA NA NA NA NA NA NA NA
14 2 NA 2 1 NA NA NA NA NA NA NA NA
15 2 -1.781049276 2 1 NA NA NA NA NA NA NA NA
16 NA NA 3 1 NA NA NA NA NA NA NA NA
17 3 NA 3 1 NA NA NA NA NA NA NA NA
18 3 -0.014579883 NA 1 NA NA NA NA NA NA NA NA
19 2 -2.121550136 2 1 NA NA NA NA NA NA NA NA
20 NA NA 1 1 NA NA NA NA NA NA NA NA
21 NA -0.363239698 1 1 NA NA NA NA NA NA NA NA
22 1 -0.121568514 3 1 NA NA NA NA NA NA NA NA
23 2 -0.951271111 3 1 NA NA NA NA NA NA NA NA
24 NA NA 3 1 NA NA NA NA NA NA NA NA
25 4 -0.974288621 2 1 NA NA NA NA NA NA NA NA
26 NA -1.130632418 1 1 NA NA NA NA NA NA NA NA
27 NA 0.114339868 3 1 NA NA NA NA NA NA NA NA
28 2 0.238334648 3 1 NA NA NA NA NA NA NA NA
29 4 0.840744958 2 1 NA NA NA NA NA NA NA NA
30 2 NA 1 1 NA NA NA NA NA NA NA NA
31 2 NA 2 1 NA NA NA NA NA NA NA NA
32 3 -1.466312154 3 1 NA NA NA NA NA NA NA NA
33 1 -0.637352277 2 1 NA NA NA NA NA NA NA NA
34 4 NA 2 1 NA NA NA NA NA NA NA NA
35 4 NA 3 1 NA NA NA NA NA NA NA NA
36 NA NA 1 1 NA NA NA NA NA NA NA NA
37 NA NA 3 1 NA NA NA NA NA NA NA NA
38 NA NA 2 1 NA NA NA NA NA NA NA NA
39 4 0.006728205 3 1 NA NA NA NA NA NA NA NA
40 NA NA 2 1 NA NA NA NA NA NA NA NA
41 2 -1.663281353 2 1 NA NA NA NA NA NA NA NA
42 NA 0.161184794 2 1 NA NA NA NA NA NA NA NA
43 NA 0.457939180 3 1 NA NA NA NA NA NA NA NA
44 3 -0.307070331 2 1 NA NA NA NA NA NA NA NA
45 NA NA 1 1 NA NA NA NA NA NA NA NA
46 NA -1.071668276 NA 1 NA NA NA NA NA NA NA NA
47 4 -0.814751321 1 1 NA NA NA NA NA NA NA NA
48 3 -0.547630662 2 1 NA NA NA NA NA NA NA NA
49 4 NA 1 1 NA NA NA NA NA NA NA NA
50 3 -1.350213782 2 1 NA NA NA NA NA NA NA NA
51 NA 0.719054706 2 1 NA NA NA NA NA NA NA NA
52 1 NA 3 1 NA NA NA NA NA NA NA NA
53 NA -1.207130750 3 1 NA NA NA NA NA NA NA NA
54 NA NA 1 1 NA NA NA NA NA NA NA NA
55 NA -0.408600991 2 1 NA NA NA NA NA NA NA NA
56 NA -0.271380529 NA 1 NA NA NA NA NA NA NA NA
57 4 -1.361925974 3 1 NA NA NA NA NA NA NA NA
58 1 NA 1 1 NA NA NA NA NA NA NA NA
59 2 NA 2 1 NA NA NA NA NA NA NA NA
60 3 -0.323712205 2 1 NA NA NA NA NA NA NA NA
61 2 NA 3 1 NA NA NA NA NA NA NA NA
62 3 NA 2 1 NA NA NA NA NA NA NA NA
63 2 -1.386906880 1 1 NA NA NA NA NA NA NA NA
64 NA NA 3 1 NA NA NA NA NA NA NA NA
65 NA NA 1 1 NA NA NA NA NA NA NA NA
66 2 -0.565191691 3 1 NA NA NA NA NA NA NA NA
67 NA -0.382899912 3 1 NA NA NA NA NA NA NA NA
68 3 NA 2 1 NA NA NA NA NA NA NA NA
69 3 -0.405642769 NA 1 NA NA NA NA NA NA NA NA
70 4 NA 2 1 NA NA NA NA NA NA NA NA
71 2 -0.843748427 NA 1 NA NA NA NA NA NA NA NA
72 NA 0.116003683 NA 1 NA NA NA NA NA NA NA NA
73 4 -0.778634325 1 1 NA NA NA NA NA NA NA NA
74 NA NA 2 1 NA NA NA NA NA NA NA NA
75 NA NA 2 1 NA NA NA NA NA NA NA NA
76 4 NA 1 1 NA NA NA NA NA NA NA NA
77 2 -0.632974758 3 1 NA NA NA NA NA NA NA NA
78 2 NA 2 1 NA NA NA NA NA NA NA NA
79 NA -0.778064615 NA 1 NA NA NA NA NA NA NA NA
80 1 NA 1 1 NA NA NA NA NA NA NA NA
81 NA NA 1 1 NA NA NA NA NA NA NA NA
82 3 -0.246123253 3 1 NA NA NA NA NA NA NA NA
83 4 -1.239659782 NA 1 NA NA NA NA NA NA NA NA
84 3 -0.467772280 3 1 NA NA NA NA NA NA NA NA
85 NA NA 3 1 NA NA NA NA NA NA NA NA
86 3 -2.160485036 2 1 NA NA NA NA NA NA NA NA
87 NA -0.657675572 1 1 NA NA NA NA NA NA NA NA
88 NA NA 1 1 NA NA NA NA NA NA NA NA
89 NA -0.696710744 3 1 NA NA NA NA NA NA NA NA
90 4 NA 1 1 NA NA NA NA NA NA NA NA
91 NA -0.179395847 2 1 NA NA NA NA NA NA NA NA
92 2 -0.441545568 NA 1 NA NA NA NA NA NA NA NA
93 4 -0.685799334 1 1 NA NA NA NA NA NA NA NA
94 NA NA 3 1 NA NA NA NA NA NA NA NA
95 NA 0.191929445 2 1 NA NA NA NA NA NA NA NA
96 NA NA 2 1 NA NA NA NA NA NA NA NA
97 1 -0.069760671 3 1 NA NA NA NA NA NA NA NA
98 NA NA 2 1 NA NA NA NA NA NA NA NA
99 2 NA 2 1 NA NA NA NA NA NA NA NA
100 NA NA 3 1 NA NA NA NA NA NA NA NA
$m6e$M_lvlone
o1 c1
1 2 0.7592026489
1.1 1 0.9548337990
1.2 1 0.5612235156
1.3 2 1.1873391025
2 3 0.9192204198
2.1 1 -0.1870730476
2.2 2 1.2517512331
3 1 -0.0605087604
3.1 3 0.3788637747
3.2 2 0.9872578281
4 3 1.4930175328
4.1 2 -0.7692526880
4.2 1 0.9180841450
4.3 2 -0.0541170782
5 2 -0.1376784521
5.1 2 -0.2740585866
5.2 3 0.4670496929
5.3 2 0.1740288049
6 3 0.9868044683
7 3 -0.1280320918
7.1 1 0.4242971219
7.2 1 0.0777182491
8 2 -0.5791408712
8.1 2 0.3128604232
8.2 3 0.6258446356
8.3 3 -0.1040137707
8.4 2 0.0481450285
8.5 2 0.3831763675
9 2 -0.1757592269
9.1 2 -0.1791541200
9.2 3 -0.0957042935
10 2 -0.5598409704
10.1 1 -0.2318340451
11 2 0.5086859475
11.1 3 0.4951758188
11.2 3 -1.1022162541
11.3 1 -0.0611636705
11.4 3 -0.4971774316
12 1 -0.2433996286
13 2 0.8799673116
13.1 1 0.1079022586
14 3 0.9991752617
14.1 3 -0.1094019046
14.2 2 0.1518967560
14.3 1 0.3521012473
15 3 0.3464447888
15.1 3 -0.4767313971
15.2 2 0.5759767791
15.3 2 -0.1713452662
16 3 0.4564754473
16.1 1 1.0652558311
16.2 3 0.6971872493
16.3 1 0.5259331838
16.4 1 0.2046601798
16.5 2 1.0718540464
17 2 0.6048676222
17.1 2 0.2323298304
17.2 3 1.2617499032
17.3 2 -0.3913230895
17.4 3 0.9577299112
18 2 -0.0050324072
19 3 -0.4187468937
19.1 2 -0.4478828944
19.2 2 -1.1966721302
19.3 2 -0.5877091668
20 1 0.6838223064
20.1 2 0.3278571109
20.2 2 -0.8489831990
20.3 1 1.3169975191
20.4 1 0.0444804531
20.5 2 -0.4535207652
21 2 -0.4030302960
21.1 2 -0.4069674045
21.2 2 1.0650265940
22 1 -0.0673274516
22.1 1 0.9601388170
23 1 0.5556634840
23.1 2 1.4407865964
24 1 0.3856376411
25 2 0.3564400705
25.1 1 0.0982553434
25.2 1 0.1928682598
25.3 1 -0.0192488594
25.4 2 0.4466012931
25.5 2 1.1425193342
26 2 0.5341531449
26.1 3 1.2268695927
26.2 2 0.3678294939
26.3 1 0.5948516018
27 2 -0.3342844147
27.1 2 -0.4835141229
28 1 -0.7145915499
28.1 1 0.5063671955
28.2 1 -0.2067413142
28.3 1 0.1196789973
29 2 0.1392699487
29.1 2 0.7960234776
29.2 1 1.0398214352
29.3 2 0.0813246429
30 2 -0.3296323050
30.1 2 1.3635850954
30.2 2 0.7354171050
31 2 0.3708398217
32 3 -0.0474059668
32.1 1 1.2507771489
32.2 3 0.1142915519
32.3 3 0.6773270619
33 2 0.1774293842
33.1 2 0.6159606291
34 3 0.8590979166
34.1 2 0.0546216775
34.2 2 -0.0897224473
34.3 1 0.4163395571
35 2 -1.4693520528
35.1 3 -0.3031734330
35.2 2 -0.6045512101
36 3 0.9823048960
36.1 2 1.4466051416
36.2 3 1.1606752905
36.3 3 0.8373091576
36.4 3 0.2640591685
37 3 0.1177313455
37.1 3 -0.1415483779
37.2 3 0.0054610124
38 2 0.8078948077
39 1 0.9876451040
39.1 2 -0.3431222274
39.2 2 -1.7909380751
39.3 1 -0.1798746191
39.4 1 -0.1850961689
39.5 1 0.4544226146
40 3 0.5350190436
40.1 1 0.4189342752
40.2 1 0.4211994981
40.3 2 0.0916687506
41 3 -0.1035047421
41.1 2 -0.4684202411
41.2 1 0.5972615368
41.3 1 0.9885613862
41.4 3 -0.3908036794
42 2 -0.0338893961
42.1 1 -0.4498363172
43 2 0.8965546110
43.1 2 0.6199122090
43.2 1 0.1804894429
44 3 1.3221409285
44.1 2 0.3416426284
44.2 1 0.5706610068
44.3 1 1.2679497430
45 1 0.1414983160
45.1 1 0.7220892521
46 1 1.5391054233
46.1 2 0.3889107049
46.2 3 0.1248719493
47 1 0.2014101100
47.1 1 0.2982973539
47.2 1 1.1518107179
47.3 3 0.5196802157
47.4 2 0.3702301552
48 3 -0.2128602862
48.1 2 -0.5337239976
49 1 -0.5236770035
50 2 0.3897705981
51 2 -0.7213343736
52 2 0.3758235358
52.1 3 0.7138067080
52.2 2 0.8872895233
52.3 1 -0.9664587437
52.4 3 0.0254566848
52.5 2 0.4155259424
53 2 0.5675736897
53.1 1 -0.3154088781
53.2 1 0.2162315769
54 1 -0.0880802382
54.1 2 0.4129127672
54.2 3 1.0119546775
54.3 1 -0.1112901990
54.4 2 0.8587727145
55 1 -0.0116453589
55.1 1 0.5835528661
55.2 2 -1.0010857254
55.3 1 -0.4796526070
55.4 1 -0.1202746964
56 3 0.5176377612
56.1 2 -1.1136932588
56.2 3 -0.0168103281
56.3 3 0.3933023606
56.4 1 0.3714625139
56.5 3 0.7811448179
57 2 -1.0868304872
57.1 2 0.8018626997
57.2 2 -0.1159517011
57.3 2 0.6785562445
58 3 1.6476207996
58.1 2 0.3402652711
58.2 3 -0.1111300753
58.3 3 -0.5409234285
58.4 2 -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 1 1.1717723589
61.4 2 -0.0629201596
62 2 -0.3979137604
62.1 3 0.6830738372
62.2 1 0.4301745954
62.3 2 -0.0333139957
63 1 0.3345678035
63.1 3 0.3643769511
64 2 0.3949911859
65 1 1.2000091513
65.1 2 0.0110122646
65.2 1 -0.5776452043
65.3 1 -0.1372183563
66 1 -0.5081302805
66.1 1 -0.1447837412
66.2 3 0.1906241379
67 3 1.6716027681
68 1 0.5691848839
68.1 2 0.1004860389
68.2 2 -0.0061241827
68.3 2 0.7443745962
68.4 2 0.8726923437
69 2 0.0381382683
70 1 0.8126204217
70.1 3 0.4691503050
71 3 -0.5529062591
71.1 3 -0.1103252087
71.2 3 1.7178492547
71.3 2 -1.0118346755
71.4 2 1.8623785017
72 1 -0.4521659275
72.1 1 0.1375317317
72.2 2 -0.4170988856
72.3 2 0.7107266765
72.4 1 0.1451969143
72.5 1 1.6298050306
73 1 -0.0307469467
74 1 0.3730017941
75 3 -0.4908003566
76 3 -0.9888876620
76.1 3 0.0003798292
76.2 1 -0.8421863763
77 2 -0.4986802480
78 2 0.0417330969
79 1 -0.3767450660
79.1 2 0.1516000028
79.2 1 -0.1888160741
80 2 -0.0041558414
80.1 2 -0.0329337062
80.2 3 0.5046816157
81 3 -0.9493950353
81.1 2 0.2443038954
81.2 3 0.6476958410
81.3 3 0.4182528210
82 3 1.1088801952
82.1 2 0.9334157763
82.2 2 0.4958140634
83 3 0.5104724530
83.1 3 -0.0513309106
83.2 3 -0.2067792494
83.3 2 -0.0534169155
84 3 -0.0255753653
84.1 3 -1.8234189877
85 3 -0.0114038622
85.1 2 -0.0577615939
85.2 2 -0.2241856342
85.3 2 -0.0520175929
85.4 1 0.2892733846
85.5 3 -0.3740417009
86 2 0.4293735089
86.1 3 -0.1363456521
86.2 1 0.1230989293
86.3 1 0.3305413955
86.4 1 2.6003411822
86.5 3 -0.1420690052
87 1 1.0457427869
87.1 1 -0.2973007190
87.2 1 0.4396872616
88 3 -0.0601928334
88.1 1 -1.0124347595
88.2 1 0.5730917016
88.3 1 -0.0029455332
89 1 1.5465903721
90 2 0.0626760573
90.1 1 1.1896872985
90.2 3 0.2597888783
90.3 1 0.6599799887
91 1 1.1213651365
91.1 2 1.2046371625
91.2 1 0.3395603754
92 2 0.4674939332
93 1 0.2677965647
93.1 3 1.6424445368
93.2 1 0.7101700066
93.3 3 1.1222322893
93.4 1 1.4628960401
94 2 -0.2904211940
94.1 3 0.0147813580
94.2 1 -0.4536774482
94.3 3 0.6793464917
94.4 2 -0.9411356550
94.5 3 0.5683867264
95 3 0.2375652188
95.1 1 0.0767152977
95.2 2 -0.6886731251
96 1 0.7813892121
96.1 3 0.3391519695
96.2 1 -0.4857246503
96.3 1 0.8771471244
96.4 2 1.9030768981
96.5 3 -0.1684332749
97 3 1.3775130083
97.1 3 -1.7323228619
98 3 -1.2648518889
98.1 1 -0.9042716241
98.2 1 -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 2 0.8861545820
100.3 1 0.1277046316
100.4 1 -0.0834577654
$m6e$spM_id
center scale
M2 NA NA
C2 -0.6240921 0.6857108
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
M22:C2 -0.3136263 0.5921089
M23:C2 -0.2014212 0.4917409
M24:C2 -0.1430739 0.4225863
$m6e$spM_lvlone
center scale
o1 NA NA
c1 0.2559996 0.6718095
$m6e$mu_reg_norm
[1] 0
$m6e$tau_reg_norm
[1] 1e-04
$m6e$shape_tau_norm
[1] 0.01
$m6e$rate_tau_norm
[1] 0.01
$m6e$mu_reg_multinomial
[1] 0
$m6e$tau_reg_multinomial
[1] 1e-04
$m6e$mu_reg_ordinal
[1] 0
$m6e$tau_reg_ordinal
[1] 1e-04
$m6e$mu_delta_ordinal
[1] 0
$m6e$tau_delta_ordinal
[1] 1e-04
$m6e$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
$m6e$shape_diag_RinvD
[1] "0.01"
$m6e$rate_diag_RinvD
[1] "0.001"
$m7a
$m7a$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
$m7a$M_lvlone
y o2 x o1.L o1.Q o22 o23 o24 x2 x3 x4
1 -13.0493856 1 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
1.1 -9.3335901 1 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
1.2 -22.3469852 3 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
1.3 -15.0417337 1 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
2 -12.0655434 4 1 7.071068e-01 0.4082483 NA NA NA NA NA NA
2.1 -15.8674476 4 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
2.2 -7.8800006 2 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
3 -11.4820604 2 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
3.1 -10.5983220 4 1 7.071068e-01 0.4082483 NA NA NA NA NA NA
3.2 -22.4519157 2 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
4 -1.2697775 4 4 7.071068e-01 0.4082483 NA NA NA NA NA NA
4.1 -11.1215184 3 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
4.2 -3.6134138 NA 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
4.3 -14.5982385 2 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
5 -6.8457515 2 NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA
5.1 -7.0551214 4 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
5.2 -12.3418980 2 2 7.071068e-01 0.4082483 NA NA NA NA NA NA
5.3 -9.2366906 4 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
6 -5.1648211 3 2 7.071068e-01 0.4082483 NA NA NA NA NA NA
7 -10.0599502 1 4 7.071068e-01 0.4082483 NA NA NA NA NA NA
7.1 -18.3267285 NA 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
7.2 -12.5138426 4 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
8 -1.6305331 1 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
8.1 -9.6520453 3 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
8.2 -1.5278462 1 4 7.071068e-01 0.4082483 NA NA NA NA NA NA
8.3 -7.4172211 4 4 7.071068e-01 0.4082483 NA NA NA NA NA NA
8.4 -7.1238609 3 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
8.5 -8.8706950 3 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
9 -0.1634429 2 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
9.1 -2.6034300 2 NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA
9.2 -6.7272369 4 3 7.071068e-01 0.4082483 NA NA NA NA NA NA
10 -6.4172202 1 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
10.1 -11.4834569 4 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA
11 -8.7911356 3 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
11.1 -19.6645080 1 1 7.071068e-01 0.4082483 NA NA NA NA NA NA
11.2 -20.2030932 4 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA
11.3 -21.3082176 3 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
11.4 -14.5802901 3 2 7.071068e-01 0.4082483 NA NA NA NA NA NA
12 -15.2006287 3 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
13 0.8058816 NA 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
13.1 -13.6379208 1 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
14 -15.3422873 1 3 7.071068e-01 0.4082483 NA NA NA NA NA NA
14.1 -10.0965208 4 4 7.071068e-01 0.4082483 NA NA NA NA NA NA
14.2 -16.6452027 3 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
14.3 -15.8389733 4 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
15 -8.9424594 1 3 7.071068e-01 0.4082483 NA NA NA NA NA NA
15.1 -22.0101983 4 3 7.071068e-01 0.4082483 NA NA NA NA NA NA
15.2 -7.3975599 NA 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
15.3 -10.3567334 2 NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA
16 -1.9691302 NA 2 7.071068e-01 0.4082483 NA NA NA NA NA NA
16.1 -9.9308357 NA 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
16.2 -6.9626923 1 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA
16.3 -3.2862557 3 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
16.4 -3.3972355 3 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
16.5 -11.5767835 1 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
17 -10.5474144 3 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
17.1 -7.6215009 2 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
17.2 -16.5386939 2 3 7.071068e-01 0.4082483 NA NA NA NA NA NA
17.3 -20.0004774 3 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
17.4 -18.8505475 1 1 7.071068e-01 0.4082483 NA NA NA NA NA NA
18 -19.7302351 4 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
19 -14.6177568 1 4 7.071068e-01 0.4082483 NA NA NA NA NA NA
19.1 -17.8043866 NA 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
19.2 -15.1641705 NA 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
19.3 -16.6898418 2 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
20 -12.9059229 1 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
20.1 -16.8191201 4 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
20.2 -6.1010131 3 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
20.3 -7.9415371 3 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
20.4 -9.3904458 1 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
20.5 -13.3504189 3 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
21 -7.6974718 3 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
21.1 -11.9335526 1 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
21.2 -12.7064929 2 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
22 -21.5022909 4 NA -7.071068e-01 0.4082483 NA NA NA NA NA NA
22.1 -12.7745451 NA 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
23 -3.5146508 4 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
23.1 -4.6724048 NA 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
24 -2.5619821 3 NA -7.071068e-01 0.4082483 NA NA NA NA NA NA
25 -6.2944970 1 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
25.1 -3.8630505 3 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
25.2 -14.4205140 2 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
25.3 -19.6735037 1 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA
25.4 -9.0288933 1 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
25.5 -9.0509738 NA 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
26 -19.7340685 3 NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA
26.1 -14.1692728 4 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA
26.2 -17.2819976 3 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
26.3 -24.6265576 1 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
27 -7.3354999 4 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
27.1 -11.1488468 4 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
28 -11.7996597 1 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA
28.1 -8.2030122 2 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA
28.2 -26.4317815 3 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
28.3 -18.5016071 3 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
29 -5.8551395 4 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
29.1 -2.0209442 4 NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA
29.2 -5.6368080 3 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA
29.3 -3.8110961 2 NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA
30 -12.7217702 3 NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA
30.1 -17.0170140 4 NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA
30.2 -25.4236089 4 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
31 -17.0783921 2 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
32 -18.4338764 NA 3 7.071068e-01 0.4082483 NA NA NA NA NA NA
32.1 -19.4317212 2 NA -7.071068e-01 0.4082483 NA NA NA NA NA NA
32.2 -19.4738978 4 4 7.071068e-01 0.4082483 NA NA NA NA NA NA
32.3 -21.4922645 3 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA
33 2.0838099 4 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
33.1 -13.3172274 4 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
34 -10.0296691 NA 1 7.071068e-01 0.4082483 NA NA NA NA NA NA
34.1 -25.9426553 NA 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
34.2 -18.5688138 NA 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
34.3 -15.4173859 NA 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
35 -14.3958113 4 NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA
35.1 -12.9457541 1 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA
35.2 -16.1380691 NA 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
36 -12.8166968 1 4 7.071068e-01 0.4082483 NA NA NA NA NA NA
36.1 -14.3989481 1 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
36.2 -12.2436943 2 4 7.071068e-01 0.4082483 NA NA NA NA NA NA
36.3 -15.0104638 2 3 7.071068e-01 0.4082483 NA NA NA NA NA NA
36.4 -10.1775457 1 1 7.071068e-01 0.4082483 NA NA NA NA NA NA
37 -15.2223495 4 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA
37.1 -14.7526195 2 2 7.071068e-01 0.4082483 NA NA NA NA NA NA
37.2 -19.8168430 2 3 7.071068e-01 0.4082483 NA NA NA NA NA NA
38 -2.7065118 NA 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
39 -8.7288138 NA 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
39.1 -9.2746473 2 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
39.2 -18.2695344 2 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
39.3 -13.8219083 3 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
39.4 -16.2254704 3 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
39.5 -21.7283648 1 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
40 1.8291916 1 1 7.071068e-01 0.4082483 NA NA NA NA NA NA
40.1 -6.6916432 2 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
40.2 -1.6278171 NA 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
40.3 -10.5749790 2 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
41 -3.1556121 4 2 7.071068e-01 0.4082483 NA NA NA NA NA NA
41.1 -11.5895327 3 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
41.2 -18.9352091 4 NA -7.071068e-01 0.4082483 NA NA NA NA NA NA
41.3 -15.9788960 NA 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
41.4 -9.6070508 4 2 7.071068e-01 0.4082483 NA NA NA NA NA NA
42 -5.2159485 4 NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA
42.1 -15.9878743 4 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
43 -16.6104361 NA 3 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
43.1 -9.5549441 NA 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
43.2 -14.2003491 3 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
44 -8.1969033 2 3 7.071068e-01 0.4082483 NA NA NA NA NA NA
44.1 -19.9270197 4 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
44.2 -22.6521171 NA 4 -7.071068e-01 0.4082483 NA NA NA NA NA NA
44.3 -21.1903736 1 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
45 -0.5686627 3 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
45.1 -7.5645740 4 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
46 -19.1624789 NA 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
46.1 -18.4487574 4 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
46.2 -15.8222682 4 3 7.071068e-01 0.4082483 NA NA NA NA NA NA
47 -5.4165074 3 NA -7.071068e-01 0.4082483 NA NA NA NA NA NA
47.1 -15.0975029 2 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
47.2 -12.9971413 1 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
47.3 -10.6844521 2 2 7.071068e-01 0.4082483 NA NA NA NA NA NA
47.4 -18.2214784 1 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
48 -8.3101471 4 1 7.071068e-01 0.4082483 NA NA NA NA NA NA
48.1 -18.3854275 NA 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
49 -13.0130319 4 1 -7.071068e-01 0.4082483 NA NA NA NA NA NA
50 -10.4579977 4 1 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
51 -19.3157621 4 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
52 -4.4747188 NA NA -7.850462e-17 -0.8164966 NA NA NA NA NA NA
52.1 -4.3163827 NA NA 7.071068e-01 0.4082483 NA NA NA NA NA NA
52.2 -6.9761408 3 4 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
52.3 -20.1764756 NA 2 -7.071068e-01 0.4082483 NA NA NA NA NA NA
52.4 -8.9036692 4 NA 7.071068e-01 0.4082483 NA NA NA NA NA NA
52.5 -5.6949642 1 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
53 -10.3141887 2 2 -7.850462e-17 -0.8164966 NA NA NA NA NA NA
53.1 -8.2642654 1 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA
53.2 -9.1691554 3 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA
54 -6.2198754 3 3 -7.071068e-01 0.4082483 NA NA NA NA NA NA
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time
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91 0.6382618390
91.1 2.6224059286
91.2 4.7772527603
92 0.0737052364
93 0.2788909199
93.1 1.0357759963
93.2 2.4916551099
93.3 2.8876129608
93.4 4.4639474002
94 0.8488043118
94.1 1.0552454425
94.2 1.9445500884
94.3 3.0710722448
94.4 3.0872731935
94.5 4.3805759016
95 2.0199063048
95.1 4.0184444457
95.2 4.5596531732
96 0.0311333477
96.1 0.1324267720
96.2 0.6701303425
96.3 2.1775037691
96.4 2.2246142488
96.5 4.2377650598
97 1.1955102731
97.1 4.9603108643
98 0.2041732438
98.1 0.4309578973
98.2 3.5172611906
99 0.3531786101
99.1 4.6789444226
99.2 4.9927084171
100 1.0691387602
100.1 1.5109344281
100.2 2.1502332564
100.3 3.8745574222
100.4 4.6567608765
$m7a$spM_id
center scale
(Intercept) NA NA
C1 0.7372814 0.01472882
$m7a$spM_lvlone
center scale
y -11.17337 6.249662
o2 NA NA
x NA NA
o1.L NA NA
o1.Q NA NA
o22 NA NA
o23 NA NA
o24 NA NA
x2 NA NA
x3 NA NA
x4 NA NA
time 2.53394 1.381809
$m7a$mu_reg_norm
[1] 0
$m7a$tau_reg_norm
[1] 1e-04
$m7a$shape_tau_norm
[1] 0.01
$m7a$rate_tau_norm
[1] 0.01
$m7a$mu_reg_ordinal
[1] 0
$m7a$tau_reg_ordinal
[1] 1e-04
$m7a$mu_delta_ordinal
[1] 0
$m7a$tau_delta_ordinal
[1] 1e-04
$m7a$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m7a$shape_diag_RinvD
[1] "0.01"
$m7a$rate_diag_RinvD
[1] "0.001"
$m7b
$m7b$M_id
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
$m7b$M_lvlone
y b2 c2 o2 o22 o23 o24 o1.L o1.Q b21
1 -13.0493856 NA NA 1 NA NA NA -7.850462e-17 -0.8164966 NA
1.1 -9.3335901 0 -0.08061445 1 NA NA NA -7.071068e-01 0.4082483 NA
1.2 -22.3469852 NA -0.26523782 3 NA NA NA -7.071068e-01 0.4082483 NA
1.3 -15.0417337 0 -0.30260393 1 NA NA NA -7.850462e-17 -0.8164966 NA
2 -12.0655434 0 -0.33443795 4 NA NA NA 7.071068e-01 0.4082483 NA
2.1 -15.8674476 NA -0.11819800 4 NA NA NA -7.071068e-01 0.4082483 NA
2.2 -7.8800006 NA -0.31532280 2 NA NA NA -7.850462e-17 -0.8164966 NA
3 -11.4820604 0 -0.12920657 2 NA NA NA -7.071068e-01 0.4082483 NA
3.1 -10.5983220 NA NA 4 NA NA NA 7.071068e-01 0.4082483 NA
3.2 -22.4519157 1 NA 2 NA NA NA -7.850462e-17 -0.8164966 NA
4 -1.2697775 1 -0.31177403 4 NA NA NA 7.071068e-01 0.4082483 NA
4.1 -11.1215184 0 -0.23894886 3 NA NA NA -7.850462e-17 -0.8164966 NA
4.2 -3.6134138 0 -0.15533613 NA NA NA NA -7.071068e-01 0.4082483 NA
4.3 -14.5982385 0 -0.14644545 2 NA NA NA -7.850462e-17 -0.8164966 NA
5 -6.8457515 NA -0.28360457 2 NA NA NA -7.850462e-17 -0.8164966 NA
5.1 -7.0551214 0 -0.20135143 4 NA NA NA -7.850462e-17 -0.8164966 NA
5.2 -12.3418980 NA -0.28293375 2 NA NA NA 7.071068e-01 0.4082483 NA
5.3 -9.2366906 NA NA 4 NA NA NA -7.850462e-17 -0.8164966 NA
6 -5.1648211 NA -0.08617066 3 NA NA NA 7.071068e-01 0.4082483 NA
7 -10.0599502 NA -0.22243495 1 NA NA NA 7.071068e-01 0.4082483 NA
7.1 -18.3267285 NA NA NA NA NA NA -7.071068e-01 0.4082483 NA
7.2 -12.5138426 0 NA 4 NA NA NA -7.071068e-01 0.4082483 NA
8 -1.6305331 0 NA 1 NA NA NA -7.850462e-17 -0.8164966 NA
8.1 -9.6520453 0 NA 3 NA NA NA -7.850462e-17 -0.8164966 NA
8.2 -1.5278462 NA NA 1 NA NA NA 7.071068e-01 0.4082483 NA
8.3 -7.4172211 1 -0.35148972 4 NA NA NA 7.071068e-01 0.4082483 NA
8.4 -7.1238609 0 0.03661023 3 NA NA NA -7.850462e-17 -0.8164966 NA
8.5 -8.8706950 1 -0.08424534 3 NA NA NA -7.850462e-17 -0.8164966 NA
9 -0.1634429 0 NA 2 NA NA NA -7.850462e-17 -0.8164966 NA
9.1 -2.6034300 NA -0.43509340 2 NA NA NA -7.850462e-17 -0.8164966 NA
9.2 -6.7272369 NA -0.22527490 4 NA NA NA 7.071068e-01 0.4082483 NA
10 -6.4172202 NA NA 1 NA NA NA -7.850462e-17 -0.8164966 NA
10.1 -11.4834569 0 NA 4 NA NA NA -7.071068e-01 0.4082483 NA
11 -8.7911356 0 -0.08587475 3 NA NA NA -7.850462e-17 -0.8164966 NA
11.1 -19.6645080 0 -0.06157340 1 NA NA NA 7.071068e-01 0.4082483 NA
11.2 -20.2030932 0 -0.12436018 4 NA NA NA 7.071068e-01 0.4082483 NA
11.3 -21.3082176 0 -0.21377934 3 NA NA NA -7.071068e-01 0.4082483 NA
11.4 -14.5802901 0 -0.32208329 3 NA NA NA 7.071068e-01 0.4082483 NA
12 -15.2006287 0 NA 3 NA NA NA -7.071068e-01 0.4082483 NA
13 0.8058816 NA NA NA NA NA NA -7.850462e-17 -0.8164966 NA
13.1 -13.6379208 0 -0.40300449 1 NA NA NA -7.071068e-01 0.4082483 NA
14 -15.3422873 NA -0.28992072 1 NA NA NA 7.071068e-01 0.4082483 NA
14.1 -10.0965208 NA NA 4 NA NA NA 7.071068e-01 0.4082483 NA
14.2 -16.6452027 NA NA 3 NA NA NA -7.850462e-17 -0.8164966 NA
14.3 -15.8389733 NA -0.21979936 4 NA NA NA -7.071068e-01 0.4082483 NA
15 -8.9424594 0 NA 1 NA NA NA 7.071068e-01 0.4082483 NA
15.1 -22.0101983 0 -0.29092263 4 NA NA NA 7.071068e-01 0.4082483 NA
15.2 -7.3975599 0 -0.19392239 NA NA NA NA -7.850462e-17 -0.8164966 NA
15.3 -10.3567334 0 -0.25718384 2 NA NA NA -7.850462e-17 -0.8164966 NA
16 -1.9691302 1 -0.45041108 NA NA NA NA 7.071068e-01 0.4082483 NA
16.1 -9.9308357 NA -0.07599066 NA NA NA NA -7.071068e-01 0.4082483 NA
16.2 -6.9626923 NA -0.32385667 1 NA NA NA 7.071068e-01 0.4082483 NA
16.3 -3.2862557 0 -0.38326110 3 NA NA NA -7.071068e-01 0.4082483 NA
16.4 -3.3972355 0 -0.22845856 3 NA NA NA -7.071068e-01 0.4082483 NA
16.5 -11.5767835 NA -0.25497157 1 NA NA NA -7.850462e-17 -0.8164966 NA
17 -10.5474144 0 NA 3 NA NA NA -7.850462e-17 -0.8164966 NA
17.1 -7.6215009 0 -0.22105143 2 NA NA NA -7.850462e-17 -0.8164966 NA
17.2 -16.5386939 0 NA 2 NA NA NA 7.071068e-01 0.4082483 NA
17.3 -20.0004774 NA NA 3 NA NA NA -7.850462e-17 -0.8164966 NA
17.4 -18.8505475 0 -0.15098046 1 NA NA NA 7.071068e-01 0.4082483 NA
18 -19.7302351 0 -0.09870041 4 NA NA NA -7.850462e-17 -0.8164966 NA
19 -14.6177568 NA -0.26680239 1 NA NA NA 7.071068e-01 0.4082483 NA
19.1 -17.8043866 NA -0.15815241 NA NA NA NA -7.850462e-17 -0.8164966 NA
19.2 -15.1641705 0 -0.14717437 NA NA NA NA -7.850462e-17 -0.8164966 NA
19.3 -16.6898418 1 -0.21271374 2 NA NA NA -7.850462e-17 -0.8164966 NA
20 -12.9059229 NA -0.22087628 1 NA NA NA -7.071068e-01 0.4082483 NA
20.1 -16.8191201 0 NA 4 NA NA NA -7.850462e-17 -0.8164966 NA
20.2 -6.1010131 1 -0.30127439 3 NA NA NA -7.850462e-17 -0.8164966 NA
20.3 -7.9415371 0 -0.11782590 3 NA NA NA -7.071068e-01 0.4082483 NA
20.4 -9.3904458 0 -0.19857957 1 NA NA NA -7.071068e-01 0.4082483 NA
20.5 -13.3504189 0 -0.24338208 3 NA NA NA -7.850462e-17 -0.8164966 NA
21 -7.6974718 0 -0.31407992 3 NA NA NA -7.850462e-17 -0.8164966 NA
21.1 -11.9335526 0 -0.12424941 1 NA NA NA -7.850462e-17 -0.8164966 NA
21.2 -12.7064929 NA -0.27672716 2 NA NA NA -7.850462e-17 -0.8164966 NA
22 -21.5022909 0 -0.23790593 4 NA NA NA -7.071068e-01 0.4082483 NA
22.1 -12.7745451 0 -0.15996535 NA NA NA NA -7.071068e-01 0.4082483 NA
23 -3.5146508 0 -0.18236682 4 NA NA NA -7.071068e-01 0.4082483 NA
23.1 -4.6724048 NA -0.20823302 NA NA NA NA -7.850462e-17 -0.8164966 NA
24 -2.5619821 0 -0.29026416 3 NA NA NA -7.071068e-01 0.4082483 NA
25 -6.2944970 0 -0.36139273 1 NA NA NA -7.850462e-17 -0.8164966 NA
25.1 -3.8630505 NA -0.19571118 3 NA NA NA -7.071068e-01 0.4082483 NA
25.2 -14.4205140 1 -0.21379355 2 NA NA NA -7.071068e-01 0.4082483 NA
25.3 -19.6735037 0 -0.33876012 1 NA NA NA -7.071068e-01 0.4082483 NA
25.4 -9.0288933 0 NA 1 NA NA NA -7.850462e-17 -0.8164966 NA
25.5 -9.0509738 NA -0.04068446 NA NA NA NA -7.850462e-17 -0.8164966 NA
26 -19.7340685 NA -0.16846716 3 NA NA NA -7.850462e-17 -0.8164966 NA
26.1 -14.1692728 0 -0.10440642 4 NA NA NA 7.071068e-01 0.4082483 NA
26.2 -17.2819976 0 -0.26884827 3 NA NA NA -7.850462e-17 -0.8164966 NA
26.3 -24.6265576 0 NA 1 NA NA NA -7.071068e-01 0.4082483 NA
27 -7.3354999 0 -0.19520794 4 NA NA NA -7.850462e-17 -0.8164966 NA
27.1 -11.1488468 0 -0.17622638 4 NA NA NA -7.850462e-17 -0.8164966 NA
28 -11.7996597 NA -0.32164962 1 NA NA NA -7.071068e-01 0.4082483 NA
28.1 -8.2030122 0 -0.27003852 2 NA NA NA -7.071068e-01 0.4082483 NA
28.2 -26.4317815 0 -0.07235801 3 NA NA NA -7.071068e-01 0.4082483 NA
28.3 -18.5016071 0 -0.13462982 3 NA NA NA -7.071068e-01 0.4082483 NA
29 -5.8551395 0 -0.32432030 4 NA NA NA -7.850462e-17 -0.8164966 NA
29.1 -2.0209442 0 -0.27034171 4 NA NA NA -7.850462e-17 -0.8164966 NA
29.2 -5.6368080 0 -0.10197448 3 NA NA NA -7.071068e-01 0.4082483 NA
29.3 -3.8110961 0 -0.27606945 2 NA NA NA -7.850462e-17 -0.8164966 NA
30 -12.7217702 NA -0.06949300 3 NA NA NA -7.850462e-17 -0.8164966 NA
30.1 -17.0170140 0 -0.11511035 4 NA NA NA -7.850462e-17 -0.8164966 NA
30.2 -25.4236089 0 -0.16215882 4 NA NA NA -7.850462e-17 -0.8164966 NA
31 -17.0783921 0 0.05707733 2 NA NA NA -7.850462e-17 -0.8164966 NA
32 -18.4338764 0 -0.18446298 NA NA NA NA 7.071068e-01 0.4082483 NA
32.1 -19.4317212 0 -0.14270013 2 NA NA NA -7.071068e-01 0.4082483 NA
32.2 -19.4738978 NA -0.20530798 4 NA NA NA 7.071068e-01 0.4082483 NA
32.3 -21.4922645 NA -0.14705649 3 NA NA NA 7.071068e-01 0.4082483 NA
33 2.0838099 0 -0.15252819 4 NA NA NA -7.850462e-17 -0.8164966 NA
33.1 -13.3172274 1 NA 4 NA NA NA -7.850462e-17 -0.8164966 NA
34 -10.0296691 NA -0.30378735 NA NA NA NA 7.071068e-01 0.4082483 NA
34.1 -25.9426553 0 -0.11982431 NA NA NA NA -7.850462e-17 -0.8164966 NA
34.2 -18.5688138 NA -0.24278671 NA NA NA NA -7.850462e-17 -0.8164966 NA
34.3 -15.4173859 NA -0.19971833 NA NA NA NA -7.071068e-01 0.4082483 NA
35 -14.3958113 0 NA 4 NA NA NA -7.850462e-17 -0.8164966 NA
35.1 -12.9457541 0 -0.24165780 1 NA NA NA 7.071068e-01 0.4082483 NA
35.2 -16.1380691 NA NA NA NA NA NA -7.850462e-17 -0.8164966 NA
36 -12.8166968 NA -0.49062180 1 NA NA NA 7.071068e-01 0.4082483 NA
36.1 -14.3989481 NA -0.25651700 1 NA NA NA -7.850462e-17 -0.8164966 NA
36.2 -12.2436943 0 NA 2 NA NA NA 7.071068e-01 0.4082483 NA
36.3 -15.0104638 0 -0.30401274 2 NA NA NA 7.071068e-01 0.4082483 NA
36.4 -10.1775457 0 NA 1 NA NA NA 7.071068e-01 0.4082483 NA
37 -15.2223495 0 -0.15276529 4 NA NA NA 7.071068e-01 0.4082483 NA
37.1 -14.7526195 0 -0.30016169 2 NA NA NA 7.071068e-01 0.4082483 NA
37.2 -19.8168430 0 0.06809545 2 NA NA NA 7.071068e-01 0.4082483 NA
38 -2.7065118 0 -0.11218486 NA NA NA NA -7.850462e-17 -0.8164966 NA
39 -8.7288138 1 -0.38072211 NA NA NA NA -7.071068e-01 0.4082483 NA
39.1 -9.2746473 0 -0.32094428 2 NA NA NA -7.850462e-17 -0.8164966 NA
39.2 -18.2695344 NA NA 2 NA NA NA -7.850462e-17 -0.8164966 NA
39.3 -13.8219083 NA -0.40173480 3 NA NA NA -7.071068e-01 0.4082483 NA
39.4 -16.2254704 0 -0.20041848 3 NA NA NA -7.071068e-01 0.4082483 NA
39.5 -21.7283648 1 -0.26027990 1 NA NA NA -7.071068e-01 0.4082483 NA
40 1.8291916 0 -0.19751956 1 NA NA NA 7.071068e-01 0.4082483 NA
40.1 -6.6916432 1 -0.08399467 2 NA NA NA -7.071068e-01 0.4082483 NA
40.2 -1.6278171 0 -0.20864416 NA NA NA NA -7.071068e-01 0.4082483 NA
40.3 -10.5749790 NA NA 2 NA NA NA -7.850462e-17 -0.8164966 NA
41 -3.1556121 0 -0.26096953 4 NA NA NA 7.071068e-01 0.4082483 NA
41.1 -11.5895327 NA -0.23953874 3 NA NA NA -7.850462e-17 -0.8164966 NA
41.2 -18.9352091 0 -0.03079344 4 NA NA NA -7.071068e-01 0.4082483 NA
41.3 -15.9788960 NA NA NA NA NA NA -7.071068e-01 0.4082483 NA
41.4 -9.6070508 0 NA 4 NA NA NA 7.071068e-01 0.4082483 NA
42 -5.2159485 0 -0.16084527 4 NA NA NA -7.850462e-17 -0.8164966 NA
42.1 -15.9878743 1 -0.13812521 4 NA NA NA -7.071068e-01 0.4082483 NA
43 -16.6104361 0 -0.08864017 NA NA NA NA -7.850462e-17 -0.8164966 NA
43.1 -9.5549441 1 -0.12583158 NA NA NA NA -7.850462e-17 -0.8164966 NA
43.2 -14.2003491 0 -0.29253959 3 NA NA NA -7.071068e-01 0.4082483 NA
44 -8.1969033 0 -0.22697597 2 NA NA NA 7.071068e-01 0.4082483 NA
44.1 -19.9270197 0 NA 4 NA NA NA -7.850462e-17 -0.8164966 NA
44.2 -22.6521171 0 NA NA NA NA NA -7.071068e-01 0.4082483 NA
44.3 -21.1903736 0 -0.40544012 1 NA NA NA -7.071068e-01 0.4082483 NA
45 -0.5686627 NA -0.19274788 3 NA NA NA -7.071068e-01 0.4082483 NA
45.1 -7.5645740 1 -0.34860483 4 NA NA NA -7.071068e-01 0.4082483 NA
46 -19.1624789 0 -0.28547861 NA NA NA NA -7.071068e-01 0.4082483 NA
46.1 -18.4487574 0 -0.21977836 4 NA NA NA -7.850462e-17 -0.8164966 NA
46.2 -15.8222682 0 NA 4 NA NA NA 7.071068e-01 0.4082483 NA
47 -5.4165074 0 -0.08597098 3 NA NA NA -7.071068e-01 0.4082483 NA
47.1 -15.0975029 0 -0.35424828 2 NA NA NA -7.071068e-01 0.4082483 NA
47.2 -12.9971413 0 -0.24262576 1 NA NA NA -7.071068e-01 0.4082483 NA
47.3 -10.6844521 NA -0.30426315 2 NA NA NA 7.071068e-01 0.4082483 NA
47.4 -18.2214784 0 NA 1 NA NA NA -7.850462e-17 -0.8164966 NA
48 -8.3101471 1 NA 4 NA NA NA 7.071068e-01 0.4082483 NA
48.1 -18.3854275 1 NA NA NA NA NA -7.850462e-17 -0.8164966 NA
49 -13.0130319 NA -0.42198781 4 NA NA NA -7.071068e-01 0.4082483 NA
50 -10.4579977 0 -0.19959516 4 NA NA NA -7.850462e-17 -0.8164966 NA
51 -19.3157621 0 -0.16556964 4 NA NA NA -7.850462e-17 -0.8164966 NA
52 -4.4747188 0 -0.07438732 NA NA NA NA -7.850462e-17 -0.8164966 NA
52.1 -4.3163827 0 -0.37537080 NA NA NA NA 7.071068e-01 0.4082483 NA
52.2 -6.9761408 0 -0.24222066 3 NA NA NA -7.850462e-17 -0.8164966 NA
52.3 -20.1764756 0 -0.31520603 NA NA NA NA -7.071068e-01 0.4082483 NA
52.4 -8.9036692 0 -0.44619160 4 NA NA NA 7.071068e-01 0.4082483 NA
52.5 -5.6949642 0 -0.11011682 1 NA NA NA -7.850462e-17 -0.8164966 NA
53 -10.3141887 0 -0.23278716 2 NA NA NA -7.850462e-17 -0.8164966 NA
53.1 -8.2642654 0 -0.28317264 1 NA NA NA -7.071068e-01 0.4082483 NA
53.2 -9.1691554 NA -0.19517481 3 NA NA NA -7.071068e-01 0.4082483 NA
54 -6.2198754 NA -0.10122856 3 NA NA NA -7.071068e-01 0.4082483 NA
54.1 -15.7192609 NA -0.28325504 4 NA NA NA -7.850462e-17 -0.8164966 NA
54.2 -13.0978998 NA -0.16753120 4 NA NA NA 7.071068e-01 0.4082483 NA
54.3 -5.1195299 NA -0.22217672 3 NA NA NA -7.071068e-01 0.4082483 NA
54.4 -16.5771751 0 -0.34609328 NA NA NA NA -7.850462e-17 -0.8164966 NA
55 -5.7348534 0 -0.32428190 4 NA NA NA -7.071068e-01 0.4082483 NA
55.1 -7.3217494 0 -0.24235382 1 NA NA NA -7.071068e-01 0.4082483 NA
55.2 -12.2171938 NA -0.24065814 4 NA NA NA -7.850462e-17 -0.8164966 NA
55.3 -12.9821266 NA -0.23665476 NA NA NA NA -7.071068e-01 0.4082483 NA
55.4 -14.8599983 0 NA 1 NA NA NA -7.071068e-01 0.4082483 NA
56 -14.1764282 0 NA 1 NA NA NA 7.071068e-01 0.4082483 NA
56.1 -12.5343602 NA -0.30357450 2 NA NA NA -7.850462e-17 -0.8164966 NA
56.2 -8.4573382 NA -0.51301630 2 NA NA NA 7.071068e-01 0.4082483 NA
56.3 -12.4633969 1 -0.23743117 3 NA NA NA 7.071068e-01 0.4082483 NA
56.4 -17.3841863 0 -0.17264917 4 NA NA NA -7.071068e-01 0.4082483 NA
56.5 -14.8147645 0 -0.39188329 4 NA NA NA 7.071068e-01 0.4082483 NA
57 -3.1403293 0 -0.18501692 2 NA NA NA -7.850462e-17 -0.8164966 NA
57.1 -11.1509248 0 -0.27274841 2 NA NA NA -7.850462e-17 -0.8164966 NA
57.2 -6.3940143 0 NA 4 NA NA NA -7.850462e-17 -0.8164966 NA
57.3 -9.3473241 NA -0.09898509 NA NA NA NA -7.850462e-17 -0.8164966 NA
58 -12.0245677 0 -0.29901358 1 NA NA NA 7.071068e-01 0.4082483 NA
58.1 -9.2112246 NA -0.35390896 2 NA NA NA -7.850462e-17 -0.8164966 NA
58.2 -1.2071742 1 -0.16687336 2 NA NA NA 7.071068e-01 0.4082483 NA
58.3 -11.0141711 1 -0.11784506 4 NA NA NA 7.071068e-01 0.4082483 NA
58.4 -5.3721214 0 -0.05321983 1 NA NA NA -7.850462e-17 -0.8164966 NA
58.5 -7.8523047 0 -0.54457568 NA NA NA NA 7.071068e-01 0.4082483 NA
59 -13.2946560 NA -0.27255364 4 NA NA NA 7.071068e-01 0.4082483 NA
59.1 -10.0530648 1 NA 1 NA NA NA -7.071068e-01 0.4082483 NA
60 -19.2209402 0 NA 1 NA NA NA 7.071068e-01 0.4082483 NA
61 -4.6699914 NA -0.30550120 1 NA NA NA -7.071068e-01 0.4082483 NA
61.1 -3.5981894 1 -0.35579892 1 NA NA NA -7.850462e-17 -0.8164966 NA
61.2 -1.4713611 1 NA NA NA NA NA -7.850462e-17 -0.8164966 NA
61.3 -3.8819786 0 -0.34184391 1 NA NA NA -7.071068e-01 0.4082483 NA
61.4 0.1041413 0 -0.30891967 3 NA NA NA -7.850462e-17 -0.8164966 NA
62 -2.8591600 NA NA NA NA NA NA -7.850462e-17 -0.8164966 NA
62.1 -6.9461986 1 -0.10504143 NA NA NA NA 7.071068e-01 0.4082483 NA
62.2 -16.7910593 0 -0.20104997 NA NA NA NA -7.071068e-01 0.4082483 NA
62.3 -17.9844596 0 -0.08138677 3 NA NA NA -7.850462e-17 -0.8164966 NA
63 -24.0335535 NA -0.12036319 4 NA NA NA -7.071068e-01 0.4082483 NA
63.1 -11.7765300 0 -0.13624992 3 NA NA NA 7.071068e-01 0.4082483 NA
64 -20.5963897 0 NA 4 NA NA NA -7.850462e-17 -0.8164966 NA
65 -2.7969169 0 -0.34450396 2 NA NA NA -7.071068e-01 0.4082483 NA
65.1 -11.1778694 0 -0.32514650 1 NA NA NA -7.850462e-17 -0.8164966 NA
65.2 -5.2830399 0 -0.10984996 3 NA NA NA -7.071068e-01 0.4082483 NA
65.3 -7.9353390 0 -0.19275692 NA NA NA NA -7.071068e-01 0.4082483 NA
66 -13.2318328 NA NA 1 NA NA NA -7.071068e-01 0.4082483 NA
66.1 -1.9090560 0 NA 3 NA NA NA -7.071068e-01 0.4082483 NA
66.2 -16.6643889 0 -0.11687008 2 NA NA NA 7.071068e-01 0.4082483 NA
67 -25.6073277 NA NA 3 NA NA NA 7.071068e-01 0.4082483 NA
68 -13.4806759 0 -0.13605235 3 NA NA NA -7.071068e-01 0.4082483 NA
68.1 -18.4557183 0 -0.19790827 4 NA NA NA -7.850462e-17 -0.8164966 NA
68.2 -13.3982327 NA -0.17750123 3 NA NA NA -7.850462e-17 -0.8164966 NA
68.3 -12.4977127 0 NA 1 NA NA NA -7.850462e-17 -0.8164966 NA
68.4 -11.7073990 NA -0.12570562 4 NA NA NA -7.850462e-17 -0.8164966 NA
69 -14.5290675 0 -0.32152751 4 NA NA NA -7.850462e-17 -0.8164966 NA
70 -15.2122709 0 -0.28190462 4 NA NA NA -7.071068e-01 0.4082483 NA
70.1 -7.8681167 0 -0.11503263 4 NA NA NA 7.071068e-01 0.4082483 NA
71 -10.3352703 0 -0.13029093 2 NA NA NA 7.071068e-01 0.4082483 NA
71.1 -7.5699888 1 NA NA NA NA NA 7.071068e-01 0.4082483 NA
71.2 -18.4680702 0 -0.39075433 4 NA NA NA 7.071068e-01 0.4082483 NA
71.3 -21.4316644 1 -0.21401028 3 NA NA NA -7.850462e-17 -0.8164966 NA
71.4 -8.1137650 0 -0.40219281 1 NA NA NA -7.850462e-17 -0.8164966 NA
72 -9.1848162 0 -0.40337108 1 NA NA NA -7.071068e-01 0.4082483 NA
72.1 -23.7538846 0 -0.25978914 NA NA NA NA -7.071068e-01 0.4082483 NA
72.2 -26.3421306 NA NA 4 NA NA NA -7.850462e-17 -0.8164966 NA
72.3 -27.2843801 0 -0.09809866 1 NA NA NA -7.850462e-17 -0.8164966 NA
72.4 -20.8541617 0 -0.14240019 3 NA NA NA -7.071068e-01 0.4082483 NA
72.5 -12.8948965 0 -0.14794204 1 NA NA NA -7.071068e-01 0.4082483 NA
73 -2.6091307 0 -0.23509343 2 NA NA NA -7.071068e-01 0.4082483 NA
74 -8.2790175 0 -0.27963171 4 NA NA NA -7.071068e-01 0.4082483 NA
75 -12.5029612 NA -0.12905034 1 NA NA NA 7.071068e-01 0.4082483 NA
76 -6.0061671 0 0.04775562 2 NA NA NA 7.071068e-01 0.4082483 NA
76.1 -8.8149114 0 -0.19399157 1 NA NA NA 7.071068e-01 0.4082483 NA
76.2 -11.8359043 0 -0.02754574 1 NA NA NA -7.071068e-01 0.4082483 NA
77 0.4772521 NA -0.19053195 3 NA NA NA -7.850462e-17 -0.8164966 NA
78 -9.4105229 0 -0.17172929 3 NA NA NA -7.850462e-17 -0.8164966 NA
79 -1.0217265 NA -0.03958515 NA NA NA NA -7.071068e-01 0.4082483 NA
79.1 -11.8125257 0 -0.20328809 3 NA NA NA -7.850462e-17 -0.8164966 NA
79.2 -10.5465186 NA -0.23901634 NA NA NA NA -7.071068e-01 0.4082483 NA
80 -12.7366807 NA -0.34031873 2 NA NA NA -7.850462e-17 -0.8164966 NA
80.1 -9.0584783 0 -0.19526756 3 NA NA NA -7.850462e-17 -0.8164966 NA
80.2 -16.6381566 NA NA 1 NA NA NA 7.071068e-01 0.4082483 NA
81 0.5547913 0 -0.18401980 3 NA NA NA 7.071068e-01 0.4082483 NA
81.1 -4.0892715 0 -0.16889476 NA NA NA NA -7.850462e-17 -0.8164966 NA
81.2 1.8283303 NA -0.37343047 3 NA NA NA 7.071068e-01 0.4082483 NA
81.3 -5.2166381 0 NA 2 NA NA NA 7.071068e-01 0.4082483 NA
82 -3.0749381 NA -0.08328168 NA NA NA NA 7.071068e-01 0.4082483 NA
82.1 -10.5506696 0 -0.22167084 3 NA NA NA -7.850462e-17 -0.8164966 NA
82.2 -18.2226347 1 -0.20971187 1 NA NA NA -7.850462e-17 -0.8164966 NA
83 -12.5872635 NA -0.34228255 4 NA NA NA 7.071068e-01 0.4082483 NA
83.1 -11.9756502 0 -0.34075730 NA NA NA NA 7.071068e-01 0.4082483 NA
83.2 -10.6744217 0 -0.32503954 2 NA NA NA 7.071068e-01 0.4082483 NA
83.3 -19.2714012 NA NA NA NA NA NA -7.850462e-17 -0.8164966 NA
84 -2.6320312 0 -0.20676741 2 NA NA NA 7.071068e-01 0.4082483 NA
84.1 -9.8140094 NA -0.20310458 1 NA NA NA 7.071068e-01 0.4082483 NA
85 -12.3886736 1 -0.12107593 1 NA NA NA 7.071068e-01 0.4082483 NA
85.1 -12.9196365 NA NA 4 NA NA NA -7.850462e-17 -0.8164966 NA
85.2 -9.6433248 0 -0.32509207 3 NA NA NA -7.850462e-17 -0.8164966 NA
85.3 -6.3296340 0 NA 3 NA NA NA -7.850462e-17 -0.8164966 NA
85.4 -7.0405525 0 -0.30730810 NA NA NA NA -7.071068e-01 0.4082483 NA
85.5 -13.6714939 0 NA 2 NA NA NA 7.071068e-01 0.4082483 NA
86 -10.8756412 0 -0.10854862 1 NA NA NA -7.850462e-17 -0.8164966 NA
86.1 -12.0055331 NA -0.25751662 3 NA NA NA 7.071068e-01 0.4082483 NA
86.2 -13.3724699 NA -0.38943076 1 NA NA NA -7.071068e-01 0.4082483 NA
86.3 -13.3252145 0 -0.24454702 2 NA NA NA -7.071068e-01 0.4082483 NA
86.4 -14.9191290 NA -0.12338992 3 NA NA NA -7.071068e-01 0.4082483 NA
86.5 -17.7515546 0 -0.23976984 4 NA NA NA 7.071068e-01 0.4082483 NA
87 -10.7027963 NA NA NA NA NA NA -7.071068e-01 0.4082483 NA
87.1 -22.4941954 NA -0.34366972 3 NA NA NA -7.071068e-01 0.4082483 NA
87.2 -14.9616716 NA NA 3 NA NA NA -7.071068e-01 0.4082483 NA
88 -2.2264493 0 -0.31563888 NA NA NA NA 7.071068e-01 0.4082483 NA
88.1 -8.9626474 NA -0.20304028 1 NA NA NA -7.071068e-01 0.4082483 NA
88.2 -2.5095281 0 -0.40311895 2 NA NA NA -7.071068e-01 0.4082483 NA
88.3 -16.3345673 0 -0.12308715 NA NA NA NA -7.071068e-01 0.4082483 NA
89 -11.0459647 0 -0.18527715 3 NA NA NA -7.071068e-01 0.4082483 NA
90 -4.5610239 0 -0.25029126 2 NA NA NA -7.850462e-17 -0.8164966 NA
90.1 -11.7036651 0 -0.26974303 2 NA NA NA -7.071068e-01 0.4082483 NA
90.2 -5.3838521 0 -0.28804531 2 NA NA NA 7.071068e-01 0.4082483 NA
90.3 -4.1636999 NA -0.19180615 4 NA NA NA -7.071068e-01 0.4082483 NA
91 -7.1462503 0 -0.26591197 2 NA NA NA -7.071068e-01 0.4082483 NA
91.1 -12.8374475 0 -0.09153470 NA NA NA NA -7.850462e-17 -0.8164966 NA
91.2 -18.2576707 0 -0.48414390 3 NA NA NA -7.071068e-01 0.4082483 NA
92 -6.4119222 0 NA 2 NA NA NA -7.850462e-17 -0.8164966 NA
93 5.2122168 NA -0.11939966 3 NA NA NA -7.071068e-01 0.4082483 NA
93.1 3.1211725 0 NA 2 NA NA NA 7.071068e-01 0.4082483 NA
93.2 -3.6841177 NA -0.21089379 3 NA NA NA -7.071068e-01 0.4082483 NA
93.3 2.6223542 0 NA 2 NA NA NA 7.071068e-01 0.4082483 NA
93.4 -11.1877696 0 -0.23618836 4 NA NA NA -7.071068e-01 0.4082483 NA
94 -6.9602492 NA NA NA NA NA NA -7.850462e-17 -0.8164966 NA
94.1 -7.4318416 0 -0.10217284 2 NA NA NA 7.071068e-01 0.4082483 NA
94.2 -4.3498045 0 -0.36713471 NA NA NA NA -7.071068e-01 0.4082483 NA
94.3 -11.6340088 NA -0.13806763 3 NA NA NA 7.071068e-01 0.4082483 NA
94.4 -12.9357964 0 -0.42353804 4 NA NA NA -7.850462e-17 -0.8164966 NA
94.5 -14.7648530 1 -0.15513707 3 NA NA NA 7.071068e-01 0.4082483 NA
95 -12.8849309 0 -0.24149687 NA NA NA NA 7.071068e-01 0.4082483 NA
95.1 -9.7451502 NA -0.21315958 2 NA NA NA -7.071068e-01 0.4082483 NA
95.2 -0.8535063 0 -0.15777208 3 NA NA NA -7.850462e-17 -0.8164966 NA
96 -4.9139832 0 -0.16780948 3 NA NA NA -7.071068e-01 0.4082483 NA
96.1 -3.9582653 0 -0.32504815 NA NA NA NA 7.071068e-01 0.4082483 NA
96.2 -9.6555492 0 -0.20395970 4 NA NA NA -7.071068e-01 0.4082483 NA
96.3 -11.8690793 NA -0.06221501 3 NA NA NA -7.071068e-01 0.4082483 NA
96.4 -11.0224373 1 -0.14801097 NA NA NA NA -7.850462e-17 -0.8164966 NA
96.5 -10.9530403 1 -0.28658893 1 NA NA NA 7.071068e-01 0.4082483 NA
97 -9.8540471 0 -0.34484656 2 NA NA NA 7.071068e-01 0.4082483 NA
97.1 -19.2262840 0 -0.35658805 1 NA NA NA 7.071068e-01 0.4082483 NA
98 -11.9651231 0 -0.36913003 2 NA NA NA 7.071068e-01 0.4082483 NA
98.1 -2.6515128 0 NA 1 NA NA NA -7.071068e-01 0.4082483 NA
98.2 -12.2606382 1 -0.17154225 3 NA NA NA -7.071068e-01 0.4082483 NA
99 -11.4720500 0 -0.24753132 NA NA NA NA -7.850462e-17 -0.8164966 NA
99.1 -14.0596866 0 -0.27947829 NA NA NA NA -7.850462e-17 -0.8164966 NA
99.2 -17.3939469 0 -0.09033035 4 NA NA NA -7.071068e-01 0.4082483 NA
100 1.1005874 NA -0.17326698 1 NA NA NA -7.071068e-01 0.4082483 NA
100.1 -3.8226248 NA NA NA NA NA NA -7.850462e-17 -0.8164966 NA
100.2 -0.9123182 0 -0.12072016 1 NA NA NA -7.850462e-17 -0.8164966 NA
100.3 -15.8389474 NA -0.27657520 4 NA NA NA -7.071068e-01 0.4082483 NA
100.4 -12.8093826 0 -0.14631556 1 NA NA NA -7.071068e-01 0.4082483 NA
$m7b$spM_lvlone
center scale
y -11.1733710 6.2496619
b2 NA NA
c2 -0.2237158 0.1059527
o2 NA NA
o22 NA NA
o23 NA NA
o24 NA NA
o1.L NA NA
o1.Q NA NA
b21 NA NA
$m7b$mu_reg_norm
[1] 0
$m7b$tau_reg_norm
[1] 1e-04
$m7b$shape_tau_norm
[1] 0.01
$m7b$rate_tau_norm
[1] 0.01
$m7b$mu_reg_binom
[1] 0
$m7b$tau_reg_binom
[1] 1e-04
$m7b$mu_delta_ordinal
[1] 0
$m7b$tau_delta_ordinal
[1] 1e-04
$m7b$group_id
[1] 1 1 1 1 2 2 2 3 3 3 4 4 4 4 5 5 5 5
[19] 6 7 7 7 8 8 8 8 8 8 9 9 9 10 10 11 11 11
[37] 11 11 12 13 13 14 14 14 14 15 15 15 15 16 16 16 16 16
[55] 16 17 17 17 17 17 18 19 19 19 19 20 20 20 20 20 20 21
[73] 21 21 22 22 23 23 24 25 25 25 25 25 25 26 26 26 26 27
[91] 27 28 28 28 28 29 29 29 29 30 30 30 31 32 32 32 32 33
[109] 33 34 34 34 34 35 35 35 36 36 36 36 36 37 37 37 38 39
[127] 39 39 39 39 39 40 40 40 40 41 41 41 41 41 42 42 43 43
[145] 43 44 44 44 44 45 45 46 46 46 47 47 47 47 47 48 48 49
[163] 50 51 52 52 52 52 52 52 53 53 53 54 54 54 54 54 55 55
[181] 55 55 55 56 56 56 56 56 56 57 57 57 57 58 58 58 58 58
[199] 58 59 59 60 61 61 61 61 61 62 62 62 62 63 63 64 65 65
[217] 65 65 66 66 66 67 68 68 68 68 68 69 70 70 71 71 71 71
[235] 71 72 72 72 72 72 72 73 74 75 76 76 76 77 78 79 79 79
[253] 80 80 80 81 81 81 81 82 82 82 83 83 83 83 84 84 85 85
[271] 85 85 85 85 86 86 86 86 86 86 87 87 87 88 88 88 88 89
[289] 90 90 90 90 91 91 91 92 93 93 93 93 93 94 94 94 94 94
[307] 94 95 95 95 96 96 96 96 96 96 97 97 98 98 98 99 99 99
[325] 100 100 100 100 100
$m7b$shape_diag_RinvD
[1] "0.01"
$m7b$rate_diag_RinvD
[1] "0.001"
jagsmodel remains the same
Code
lapply(models, "[[", "jagsmodel")
Output
$m0a
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- 0
}
# Priors for the model for o1
delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
}
$m0b
model {
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- 0
}
# Priors for the model for o2
delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
}
$m1a
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1]
}
# Priors for the model for o1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
}
$m1b
model {
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1]
}
# Priors for the model for o2
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
}
$m1c
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] +
beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- 0
}
# Priors for the model for o1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
}
$m1d
model {
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1] +
beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- 0
}
# Priors for the model for o2
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
}
$m2a
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1]
}
# Priors for the model for o1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_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 {
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- (M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * beta[1]
}
# Priors for the model for o2
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
# Normal 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 {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] +
beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- 0
}
# Priors for the model for o1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_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 {
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1] +
beta[1] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- 0
}
# Priors for the model for o2
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
# Normal mixed effects model for 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] + beta[4] * M_lvlone[i, 5]
}
for (ii in 1:100) {
b_c1_id[ii, 1:1] ~ dnorm(mu_b_c1_id[ii, ], invD_c1_id[ , ])
mu_b_c1_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for c1
for (k in 1:4) {
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])
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0)
M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0)
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 4, 1, 0)
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- 0
}
# Priors for the model for o2
delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
}
$m4a
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] + beta[6] * M_lvlone[i, 3] +
beta[7] * M_lvlone[i, 4] + beta[8] * M_lvlone[i, 5] +
beta[9] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] +
beta[10] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] +
beta[11] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 4] * beta[1] + M_id[ii, 5] * beta[2] +
M_id[ii, 6] * beta[3] +
(M_id[ii, 7] - spM_id[7, 1])/spM_id[7, 2] * beta[4] +
(M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * beta[5]
}
# Priors for the model for o1
for (k in 1:11) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 2, 1, 0)
M_lvlone[i, 4] <- ifelse(M_lvlone[i, 2] == 3, 1, 0)
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 4, 1, 0)
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- M_id[ii, 4] * alpha[1] + M_id[ii, 5] * alpha[2] +
M_id[ii, 6] * alpha[3] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[4] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5]
}
# Priors for the model for o2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
# 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[6] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[7] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[8]
log(phi_M2[ii, 3]) <- M_id[ii, 3] * alpha[9] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[10] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[11]
log(phi_M2[ii, 4]) <- M_id[ii, 3] * alpha[12] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[13] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[14]
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 6:14) {
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[15] +
(M_id[ii, 9] - spM_id[9, 1])/spM_id[9, 2] * alpha[16]
M_id[ii, 7] <- abs(M_id[ii, 9] - M_id[ii, 2])
}
# Priors for the model for C2
for (k in 15:16) {
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, 6] <- M_lvlone[i, 3] * M_id[group_id[i], 7]
M_lvlone[i, 7] <- M_lvlone[i, 4] * M_id[group_id[i], 7]
M_lvlone[i, 8] <- M_lvlone[i, 5] * M_id[group_id[i], 7]
}
}
$m4b
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] +
beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[1] +
(M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * beta[2]
}
# Priors for the model for o1
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 2, 1, 0)
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 3, 1, 0)
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 4, 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_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- M_id[ii, 5] * alpha[1] + M_id[ii, 6] * alpha[2] +
M_id[ii, 7] * alpha[3] +
(M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[4] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[5]
}
# Priors for the model for o2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
# Normal 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[6] + M_id[ii, 5] * alpha[7] + M_id[ii, 6] * alpha[8] +
M_id[ii, 7] * alpha[9] +
(M_id[ii, 8] - spM_id[8, 1])/spM_id[8, 2] * alpha[10]
M_id[ii, 3] <- abs(M_id[ii, 8] - M_id[ii, 1])
}
# Priors for the model for C2
for (k in 6:10) {
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 {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] +
b_o1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
b_o1_id[group_id[i], 3] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
b_o1_id[group_id[i], 4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:4] ~ dmnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] * beta[1] +
M_id[ii, 4] * beta[2]
mu_b_o1_id[ii, 2] <- beta[4]
mu_b_o1_id[ii, 3] <- beta[3]
mu_b_o1_id[ii, 4] <- 0
}
# Priors for the model for o1
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
for (k in 1:4) {
RinvD_o1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_o1_id[1:4, 1:4] ~ dwish(RinvD_o1_id[ , ], KinvD_o1_id)
D_o1_id[1:4, 1:4] <- inverse(invD_o1_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 {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] +
b_o1_id[group_id[i], 2] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[3] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[4] * M_lvlone[i, 5] +
beta[5] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] +
beta[7] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:2] ~ dmnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1]
mu_b_o1_id[ii, 2] <- beta[2] + (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[6]
}
# Priors for the model for o1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
for (k in 1:2) {
RinvD_o1_id[k, k] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)
}
invD_o1_id[1:2, 1:2] ~ dwish(RinvD_o1_id[ , ], KinvD_o1_id)
D_o1_id[1:2, 1:2] <- inverse(invD_o1_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 {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] +
beta[2] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[3] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[4] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[1]
}
# Priors for the model for o1
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal_ridge_beta[k])
tau_reg_ordinal_ridge_beta[k] ~ dgamma(0.01, 0.01)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
}
$m5a
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
eta_o1_1[i] <- beta[3] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] +
beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] +
beta[7] * M_lvlone[i, 3]
eta_o1_2[i] <- beta[5] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] +
beta[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] +
beta[8] * M_lvlone[i, 3]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2]
}
# Priors for the model for o1
for (k in 1:8) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
logit(mu_b2[i]) <- b_b2_id[group_id[i], 1]
M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
for (ii in 1:100) {
b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ])
mu_b_b2_id[ii, 1] <- M_id[ii, 3] * alpha[1] +
(M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] +
M_id[ii, 5] * alpha[4] + M_id[ii, 6] * alpha[5]
}
# Priors for the model for b2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 3] * alpha[6] +
(M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[7] +
M_id[ii, 5] * alpha[8] + M_id[ii, 6] * alpha[9]
}
# Priors for the model for C2
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 2] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[10]
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 5] <- ifelse(M_id[ii, 2] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 2] == 3, 1, 0)
}
# Priors for the model for O2
for (k in 10:10) {
alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
}
$m5b
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] +
beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] +
M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] +
M_id[ii, 9] * beta[5]
}
# Priors for the model for o1
for (k in 1:10) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1]
}
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, 4] * alpha[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] +
M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] +
M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] +
M_id[ii, 9] * alpha[7]
}
# Priors for the model for c1
for (k in 1:7) {
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])
# 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, 4] * alpha[8] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] +
M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11]
log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] +
M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15]
log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] +
M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19]
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 8:19) {
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, 4] * alpha[20] + M_id[ii, 8] * alpha[21] +
M_id[ii, 9] * alpha[22]
}
# Priors for the model for C2
for (k in 20:22) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 3] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- 0
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0)
M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0)
}
# Priors for the model for O2
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2]
}
}
$m5c
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
eta_o1_1[i] <- beta[6] * (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]
eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] +
M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] +
M_id[ii, 9] * beta[5]
}
# Priors for the model for o1
for (k in 1:11) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1]
}
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, 4] * alpha[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] +
M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] +
M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] +
M_id[ii, 9] * alpha[7]
}
# Priors for the model for c1
for (k in 1:7) {
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])
# 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, 4] * alpha[8] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] +
M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11]
log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] +
M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15]
log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] +
M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19]
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 8:19) {
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, 4] * alpha[20] + M_id[ii, 8] * alpha[21] +
M_id[ii, 9] * alpha[22]
}
# Priors for the model for C2
for (k in 20:22) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 3] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- 0
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0)
M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0)
}
# Priors for the model for O2
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2]
}
}
$m5d
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
eta_o1_1[i] <- beta[9] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[11] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
eta_o1_2[i] <- beta[10] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[12] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] +
M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] +
M_id[ii, 9] * beta[5] +
(M_id[ii, 10] - spM_id[10, 1])/spM_id[10, 2] * beta[6] +
(M_id[ii, 11] - spM_id[11, 1])/spM_id[11, 2] * beta[7] +
(M_id[ii, 12] - spM_id[12, 1])/spM_id[12, 2] * beta[8]
}
# Priors for the model for o1
for (k in 1:12) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1]
}
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, 4] * alpha[1] + M_id[ii, 5] * alpha[2] +
M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] +
M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7]
}
# Priors for the model for c1
for (k in 1:7) {
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])
# 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, 4] * alpha[8] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] +
M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11]
log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] +
M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15]
log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] +
M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19]
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 8:19) {
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, 4] * alpha[20] + M_id[ii, 8] * alpha[21] +
M_id[ii, 9] * alpha[22]
}
# Priors for the model for C2
for (k in 20:22) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 3] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- 0
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0)
M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0)
}
# Priors for the model for O2
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
# Re-calculate interaction terms
for (ii in 1:100) {
M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2]
M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2]
M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2]
}
}
$m5e
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
eta_o1_1[i] <- beta[1] * M_id[group_id[i], 5] + beta[2] * M_id[group_id[i], 6] +
beta[3] * M_id[group_id[i], 7] +
beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[5] * M_id[group_id[i], 8] + beta[6] * M_id[group_id[i], 9] +
beta[7] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] +
beta[8] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] +
beta[9] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] +
beta[19] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
eta_o1_2[i] <- beta[10] * M_id[group_id[i], 5] + beta[11] * M_id[group_id[i], 6] +
beta[12] * M_id[group_id[i], 7] +
beta[13] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[14] * M_id[group_id[i], 8] + beta[15] * M_id[group_id[i], 9] +
beta[16] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] +
beta[17] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] +
beta[18] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] +
beta[20] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 2:3])))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 1] - psum_o1[i, 2]))
p_o1[i, 3] <- max(1e-10, min(1-1e-10, psum_o1[i, 2]))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- 0
}
# Priors for the model for o1
for (k in 1:20) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] - exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1]
}
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, 4] * alpha[1] + M_id[ii, 5] * alpha[2] +
M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] +
M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7]
}
# Priors for the model for c1
for (k in 1:7) {
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])
# 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, 4] * alpha[8] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] +
M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11]
log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] +
M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15]
log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] +
M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19]
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 8:19) {
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, 4] * alpha[20] + M_id[ii, 8] * alpha[21] +
M_id[ii, 9] * alpha[22]
}
# Priors for the model for C2
for (k in 20:22) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 3] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- 0
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0)
M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0)
}
# Priors for the model for O2
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
# Re-calculate interaction terms
for (ii in 1:100) {
M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2]
M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2]
M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2]
}
}
$m6a
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
eta_o1_1[i] <- beta[3] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] +
beta[4] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] +
beta[7] * M_lvlone[i, 3]
eta_o1_2[i] <- beta[5] * (M_id[group_id[i], 4] - spM_id[4, 1])/spM_id[4, 2] +
beta[6] * (M_id[group_id[i], 1] - spM_id[1, 1])/spM_id[1, 2] +
beta[8] * M_lvlone[i, 3]
p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1]))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1]))
p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2])))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2]
}
# Priors for the model for o1
for (k in 1:8) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
logit(mu_b2[i]) <- b_b2_id[group_id[i], 1]
M_lvlone[i, 3] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
for (ii in 1:100) {
b_b2_id[ii, 1:1] ~ dnorm(mu_b_b2_id[ii, ], invD_b2_id[ , ])
mu_b_b2_id[ii, 1] <- M_id[ii, 3] * alpha[1] +
(M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[2] +
(M_id[ii, 1] - spM_id[1, 1])/spM_id[1, 2] * alpha[3] +
M_id[ii, 5] * alpha[4] + M_id[ii, 6] * alpha[5]
}
# Priors for the model for b2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal model for C2 -----------------------------------------------------------
for (ii in 1:100) {
M_id[ii, 1] ~ dnorm(mu_C2[ii], tau_C2)
mu_C2[ii] <- M_id[ii, 3] * alpha[6] +
(M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[7] +
M_id[ii, 5] * alpha[8] + M_id[ii, 6] * alpha[9]
}
# Priors for the model for C2
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 2] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * alpha[10]
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 5] <- ifelse(M_id[ii, 2] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 2] == 3, 1, 0)
}
# Priors for the model for O2
for (k in 10:10) {
alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
}
$m6b
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1] +
beta[8] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
eta_o1_1[i] <- beta[6] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[9] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1]))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1]))
p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2])))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] +
M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] +
M_id[ii, 9] * beta[5]
}
# Priors for the model for o1
for (k in 1:10) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1]
}
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, 4] * alpha[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] +
M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] +
M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] +
M_id[ii, 9] * alpha[7]
}
# Priors for the model for c1
for (k in 1:7) {
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])
# 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, 4] * alpha[8] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] +
M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11]
log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] +
M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15]
log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] +
M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19]
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 8:19) {
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, 4] * alpha[20] + M_id[ii, 8] * alpha[21] +
M_id[ii, 9] * alpha[22]
}
# Priors for the model for C2
for (k in 20:22) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 3] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- 0
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0)
M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0)
}
# Priors for the model for O2
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2]
}
}
$m6c
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
eta_o1_1[i] <- beta[6] * (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]
eta_o1_2[i] <- beta[7] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[10] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] +
beta[11] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1]))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1]))
p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2])))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] +
M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] +
M_id[ii, 9] * beta[5]
}
# Priors for the model for o1
for (k in 1:11) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1]
}
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, 4] * alpha[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[2] +
M_id[ii, 5] * alpha[3] + M_id[ii, 6] * alpha[4] +
M_id[ii, 7] * alpha[5] + M_id[ii, 8] * alpha[6] +
M_id[ii, 9] * alpha[7]
}
# Priors for the model for c1
for (k in 1:7) {
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])
# 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, 4] * alpha[8] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] +
M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11]
log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] +
M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15]
log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] +
M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19]
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 8:19) {
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, 4] * alpha[20] + M_id[ii, 8] * alpha[21] +
M_id[ii, 9] * alpha[22]
}
# Priors for the model for C2
for (k in 20:22) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 3] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- 0
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0)
M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0)
}
# Priors for the model for O2
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
# Re-calculate interaction terms
for (i in 1:329) {
M_lvlone[i, 3] <- M_lvlone[i, 2] * M_id[group_id[i], 2]
}
}
$m6d
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
eta_o1_1[i] <- beta[9] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[11] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
eta_o1_2[i] <- beta[10] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[12] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1]))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1]))
p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2])))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- M_id[ii, 5] * beta[1] + M_id[ii, 6] * beta[2] +
M_id[ii, 7] * beta[3] + M_id[ii, 8] * beta[4] +
M_id[ii, 9] * beta[5] +
(M_id[ii, 10] - spM_id[10, 1])/spM_id[10, 2] * beta[6] +
(M_id[ii, 11] - spM_id[11, 1])/spM_id[11, 2] * beta[7] +
(M_id[ii, 12] - spM_id[12, 1])/spM_id[12, 2] * beta[8]
}
# Priors for the model for o1
for (k in 1:12) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1]
}
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, 4] * alpha[1] + M_id[ii, 5] * alpha[2] +
M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] +
M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7]
}
# Priors for the model for c1
for (k in 1:7) {
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])
# 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, 4] * alpha[8] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] +
M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11]
log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] +
M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15]
log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] +
M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19]
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 8:19) {
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, 4] * alpha[20] + M_id[ii, 8] * alpha[21] +
M_id[ii, 9] * alpha[22]
}
# Priors for the model for C2
for (k in 20:22) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 3] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- 0
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0)
M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0)
}
# Priors for the model for O2
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
# Re-calculate interaction terms
for (ii in 1:100) {
M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2]
M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2]
M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2]
}
}
$m6e
model {
# Cumulative logit mixed effects model for o1 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dcat(p_o1[i, 1:3])
eta_o1[i] <- b_o1_id[group_id[i], 1]
eta_o1_1[i] <- beta[1] * M_id[group_id[i], 5] + beta[2] * M_id[group_id[i], 6] +
beta[3] * M_id[group_id[i], 7] +
beta[4] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[5] * M_id[group_id[i], 8] + beta[6] * M_id[group_id[i], 9] +
beta[7] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] +
beta[8] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] +
beta[9] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] +
beta[19] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
eta_o1_2[i] <- beta[10] * M_id[group_id[i], 5] + beta[11] * M_id[group_id[i], 6] +
beta[12] * M_id[group_id[i], 7] +
beta[13] * (M_id[group_id[i], 2] - spM_id[2, 1])/spM_id[2, 2] +
beta[14] * M_id[group_id[i], 8] + beta[15] * M_id[group_id[i], 9] +
beta[16] * (M_id[group_id[i], 10] - spM_id[10, 1])/spM_id[10, 2] +
beta[17] * (M_id[group_id[i], 11] - spM_id[11, 1])/spM_id[11, 2] +
beta[18] * (M_id[group_id[i], 12] - spM_id[12, 1])/spM_id[12, 2] +
beta[20] * (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2]
p_o1[i, 1] <- max(1e-10, min(1-1e-10, psum_o1[i, 1]))
p_o1[i, 2] <- max(1e-10, min(1-1e-10, psum_o1[i, 2] - psum_o1[i, 1]))
p_o1[i, 3] <- 1 - max(1e-10, min(1-1e-10, sum(p_o1[i, 1:2])))
logit(psum_o1[i, 1]) <- gamma_o1[1] + eta_o1[i] + eta_o1_1[i]
logit(psum_o1[i, 2]) <- gamma_o1[2] + eta_o1[i] + eta_o1_2[i]
}
for (ii in 1:100) {
b_o1_id[ii, 1:1] ~ dnorm(mu_b_o1_id[ii, ], invD_o1_id[ , ])
mu_b_o1_id[ii, 1] <- 0
}
# Priors for the model for o1
for (k in 1:20) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o1[2] <- gamma_o1[1] + exp(delta_o1[1])
invD_o1_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o1_id[1, 1] <- 1 / (invD_o1_id[1, 1])
# Normal mixed effects model for c1 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dnorm(mu_c1[i], tau_c1)
mu_c1[i] <- b_c1_id[group_id[i], 1]
}
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, 4] * alpha[1] + M_id[ii, 5] * alpha[2] +
M_id[ii, 6] * alpha[3] + M_id[ii, 7] * alpha[4] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[5] +
M_id[ii, 8] * alpha[6] + M_id[ii, 9] * alpha[7]
}
# Priors for the model for c1
for (k in 1:7) {
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])
# 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, 4] * alpha[8] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[9] +
M_id[ii, 8] * alpha[10] + M_id[ii, 9] * alpha[11]
log(phi_M2[ii, 3]) <- M_id[ii, 4] * alpha[12] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[13] +
M_id[ii, 8] * alpha[14] + M_id[ii, 9] * alpha[15]
log(phi_M2[ii, 4]) <- M_id[ii, 4] * alpha[16] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[17] +
M_id[ii, 8] * alpha[18] + M_id[ii, 9] * alpha[19]
M_id[ii, 5] <- ifelse(M_id[ii, 1] == 2, 1, 0)
M_id[ii, 6] <- ifelse(M_id[ii, 1] == 3, 1, 0)
M_id[ii, 7] <- ifelse(M_id[ii, 1] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 8:19) {
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, 4] * alpha[20] + M_id[ii, 8] * alpha[21] +
M_id[ii, 9] * alpha[22]
}
# Priors for the model for C2
for (k in 20:22) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Cumulative logit model for O2 -------------------------------------------------
for (ii in 1:100) {
M_id[ii, 3] ~ dcat(p_O2[ii, 1:3])
eta_O2[ii] <- 0
p_O2[ii, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O2[ii, 2:3])))
p_O2[ii, 2] <- max(1e-10, min(1-1e-10, psum_O2[ii, 1] - psum_O2[ii, 2]))
p_O2[ii, 3] <- max(1e-10, min(1-1e-10, psum_O2[ii, 2]))
logit(psum_O2[ii, 1]) <- gamma_O2[1] + eta_O2[ii]
logit(psum_O2[ii, 2]) <- gamma_O2[2] + eta_O2[ii]
M_id[ii, 8] <- ifelse(M_id[ii, 3] == 2, 1, 0)
M_id[ii, 9] <- ifelse(M_id[ii, 3] == 3, 1, 0)
}
# Priors for the model for O2
delta_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_O2[2] <- gamma_O2[1] - exp(delta_O2[1])
# Re-calculate interaction terms
for (ii in 1:100) {
M_id[ii, 10] <- M_id[ii, 5] * M_id[ii, 2]
M_id[ii, 11] <- M_id[ii, 6] * M_id[ii, 2]
M_id[ii, 12] <- M_id[ii, 7] * M_id[ii, 2]
}
}
$m7a
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] + beta[3] * M_lvlone[i, 4] +
beta[4] * M_lvlone[i, 5] + beta[5] * M_lvlone[i, 6] +
beta[6] * M_lvlone[i, 7] + beta[7] * M_lvlone[i, 8] +
beta[8] * M_lvlone[i, 9] + beta[9] * M_lvlone[i, 10] +
beta[10] * M_lvlone[i, 11] +
beta[11] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1] +
(M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * beta[2]
}
# Priors for the model for y
for (k in 1:11) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1] + alpha[2] * M_lvlone[i, 9] +
alpha[3] * M_lvlone[i, 10] + alpha[4] * M_lvlone[i, 11] +
alpha[5] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 2, 1, 0)
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 3, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 2] == 4, 1, 0)
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[1]
}
# Priors for the model for o2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
# Cumulative logit mixed effects model for x ------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dcat(p_x[i, 1:4])
eta_x[i] <- b_x_id[group_id[i], 1] +
alpha[7] * (M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2]
p_x[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_x[i, 2:4])))
p_x[i, 2] <- max(1e-10, min(1-1e-10, psum_x[i, 1] - psum_x[i, 2]))
p_x[i, 3] <- max(1e-10, min(1-1e-10, psum_x[i, 2] - psum_x[i, 3]))
p_x[i, 4] <- max(1e-10, min(1-1e-10, psum_x[i, 3]))
logit(psum_x[i, 1]) <- gamma_x[1] + eta_x[i]
logit(psum_x[i, 2]) <- gamma_x[2] + eta_x[i]
logit(psum_x[i, 3]) <- gamma_x[3] + eta_x[i]
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 10] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
for (ii in 1:100) {
b_x_id[ii, 1:1] ~ dnorm(mu_b_x_id[ii, ], invD_x_id[ , ])
mu_b_x_id[ii, 1] <- (M_id[ii, 2] - spM_id[2, 1])/spM_id[2, 2] * alpha[6]
}
# Priors for the model for x
for (k in 6:7) {
alpha[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
} delta_x[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_x[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_x[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_x[2] <- gamma_x[1] - exp(delta_x[1])
gamma_x[3] <- gamma_x[2] - exp(delta_x[2])
invD_x_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_x_id[1, 1] <- 1 / (invD_x_id[1, 1])
}
$m7b
model {
# Normal mixed effects model for y ----------------------------------------------
for (i in 1:329) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- b_y_id[group_id[i], 1] + beta[2] * M_lvlone[i, 5] +
beta[3] * M_lvlone[i, 6] + beta[4] * M_lvlone[i, 7] +
beta[5] * M_lvlone[i, 8] + beta[6] * M_lvlone[i, 9] +
beta[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] +
beta[8] * M_lvlone[i, 10]
}
for (ii in 1:100) {
b_y_id[ii, 1:1] ~ dnorm(mu_b_y_id[ii, ], invD_y_id[ , ])
mu_b_y_id[ii, 1] <- M_id[ii, 1] * beta[1]
}
# Priors for the model for y
for (k in 1:8) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
invD_y_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_y_id[1, 1] <- 1 / (invD_y_id[1, 1])
# Binomial mixed effects model for b2 -------------------------------------------
for (i in 1:329) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_b2[i])))
logit(mu_b2[i]) <- b_b2_id[group_id[i], 1] + alpha[2] * M_lvlone[i, 5] +
alpha[3] * M_lvlone[i, 6] + alpha[4] * M_lvlone[i, 7] +
alpha[5] * M_lvlone[i, 8] + alpha[6] * M_lvlone[i, 9] +
alpha[7] * (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2]
M_lvlone[i, 10] <- 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]
}
# Priors for the model for b2
for (k in 1:7) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
invD_b2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_b2_id[1, 1] <- 1 / (invD_b2_id[1, 1])
# Normal mixed effects model for c2 ---------------------------------------------
for (i in 1:329) {
M_lvlone[i, 3] ~ dnorm(mu_c2[i], tau_c2)
mu_c2[i] <- b_c2_id[group_id[i], 1] + alpha[9] * M_lvlone[i, 5] +
alpha[10] * M_lvlone[i, 6] + alpha[11] * M_lvlone[i, 7] +
alpha[12] * M_lvlone[i, 8] + alpha[13] * M_lvlone[i, 9]
}
for (ii in 1:100) {
b_c2_id[ii, 1:1] ~ dnorm(mu_b_c2_id[ii, ], invD_c2_id[ , ])
mu_b_c2_id[ii, 1] <- M_id[ii, 1] * alpha[8]
}
# Priors for the model for c2
for (k in 8:13) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_c2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_c2 <- sqrt(1/tau_c2)
invD_c2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_c2_id[1, 1] <- 1 / (invD_c2_id[1, 1])
# Cumulative logit mixed effects model for o2 -----------------------------------
for (i in 1:329) {
M_lvlone[i, 4] ~ dcat(p_o2[i, 1:4])
eta_o2[i] <- b_o2_id[group_id[i], 1]
p_o2[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_o2[i, 2:4])))
p_o2[i, 2] <- max(1e-10, min(1-1e-10, psum_o2[i, 1] - psum_o2[i, 2]))
p_o2[i, 3] <- max(1e-10, min(1-1e-10, psum_o2[i, 2] - psum_o2[i, 3]))
p_o2[i, 4] <- max(1e-10, min(1-1e-10, psum_o2[i, 3]))
logit(psum_o2[i, 1]) <- gamma_o2[1] + eta_o2[i]
logit(psum_o2[i, 2]) <- gamma_o2[2] + eta_o2[i]
logit(psum_o2[i, 3]) <- gamma_o2[3] + eta_o2[i]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
for (ii in 1:100) {
b_o2_id[ii, 1:1] ~ dnorm(mu_b_o2_id[ii, ], invD_o2_id[ , ])
mu_b_o2_id[ii, 1] <- 0
}
# Priors for the model for o2
delta_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_o2[2] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
gamma_o2[2] <- gamma_o2[1] - exp(delta_o2[1])
gamma_o2[3] <- gamma_o2[2] - exp(delta_o2[2])
invD_o2_id[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_o2_id[1, 1] <- 1 / (invD_o2_id[1, 1])
}
GRcrit and MCerror give same result
Code
lapply(models0, GR_crit, multivariate = FALSE)
Output
$m0a
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m0b
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o2[1] NaN NaN
gamma_o2[2] NaN NaN
gamma_o2[3] NaN NaN
D_o2_id[1,1] NaN NaN
$m1a
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
C1 NaN NaN
D_o1_id[1,1] NaN NaN
$m1b
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o2[1] NaN NaN
gamma_o2[2] NaN NaN
gamma_o2[3] NaN NaN
C1 NaN NaN
D_o2_id[1,1] NaN NaN
$m1c
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
c1 NaN NaN
D_o1_id[1,1] NaN NaN
$m1d
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o2[1] NaN NaN
gamma_o2[2] NaN NaN
gamma_o2[3] NaN NaN
c1 NaN NaN
D_o2_id[1,1] NaN NaN
$m2a
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
C2 NaN NaN
D_o1_id[1,1] NaN NaN
$m2b
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o2[1] NaN NaN
gamma_o2[2] NaN NaN
gamma_o2[3] NaN NaN
C2 NaN NaN
D_o2_id[1,1] NaN NaN
$m2c
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
c2 NaN NaN
D_o1_id[1,1] NaN NaN
$m2d
Potential scale reduction factors:
Point est. Upper C.I.
gamma_o2[1] NaN NaN
gamma_o2[2] NaN NaN
gamma_o2[3] NaN NaN
c2 NaN NaN
D_o2_id[1,1] NaN NaN
$m3a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
o1.L NaN NaN
o1.Q 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
o22 NaN NaN
o23 NaN NaN
o24 NaN NaN
sigma_c1 NaN NaN
D_c1_id[1,1] NaN NaN
$m4a
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
abs(C1 - C2) NaN NaN
log(C1) NaN NaN
o22 NaN NaN
o23 NaN NaN
o24 NaN NaN
o22:abs(C1 - C2) NaN NaN
o23:abs(C1 - C2) NaN NaN
o24:abs(C1 - C2) NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m4b
Potential scale reduction factors:
Point est.
abs(C1 - C2) NaN
log(C1) NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
gamma_o1[1] NaN
gamma_o1[2] NaN
D_o1_id[1,1] NaN
Upper C.I.
abs(C1 - C2) NaN
log(C1) NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
gamma_o1[1] NaN
gamma_o1[2] NaN
D_o1_id[1,1] NaN
$m4c
Potential scale reduction factors:
Point est. Upper C.I.
C1 NaN NaN
B21 NaN NaN
time NaN NaN
c1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
D_o1_id[1,2] NaN NaN
D_o1_id[2,2] NaN NaN
D_o1_id[1,3] NaN NaN
D_o1_id[2,3] NaN NaN
D_o1_id[3,3] NaN NaN
D_o1_id[1,4] NaN NaN
D_o1_id[2,4] NaN NaN
D_o1_id[3,4] NaN NaN
D_o1_id[4,4] NaN NaN
$m4d
Potential scale reduction factors:
Point est. Upper C.I.
C1 NaN NaN
time NaN NaN
I(time^2) NaN NaN
b21 NaN NaN
c1 NaN NaN
C1:time NaN NaN
b21:c1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
D_o1_id[1,2] NaN NaN
D_o1_id[2,2] NaN NaN
$m4e
Potential scale reduction factors:
Point est. Upper C.I.
C1 NaN NaN
log(time) NaN NaN
I(time^2) NaN NaN
p1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m5a
Potential scale reduction factors:
Point est. Upper C.I.
O22 NaN NaN
O23 NaN NaN
o12: C1 NaN NaN
o12: C2 NaN NaN
o13: C1 NaN NaN
o13: C2 NaN NaN
o12: b21 NaN NaN
o13: b21 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m5b
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
o13: C2 NaN NaN
c1:C2 NaN NaN
o12: C2 NaN NaN
o12: c1 NaN NaN
o13: c1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m5c
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
o12: C2 NaN NaN
o13: C2 NaN NaN
o12: c1 NaN NaN
o12: c1:C2 NaN NaN
o13: c1 NaN NaN
o13: c1:C2 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m5d
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
M22:C2 NaN NaN
M23:C2 NaN NaN
M24:C2 NaN NaN
o12: C2 NaN NaN
o13: C2 NaN NaN
o12: c1 NaN NaN
o13: c1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m5e
Potential scale reduction factors:
Point est. Upper C.I.
o12: M22 NaN NaN
o12: M23 NaN NaN
o12: M24 NaN NaN
o12: C2 NaN NaN
o12: O22 NaN NaN
o12: O23 NaN NaN
o12: M22:C2 NaN NaN
o12: M23:C2 NaN NaN
o12: M24:C2 NaN NaN
o13: M22 NaN NaN
o13: M23 NaN NaN
o13: M24 NaN NaN
o13: C2 NaN NaN
o13: O22 NaN NaN
o13: O23 NaN NaN
o13: M22:C2 NaN NaN
o13: M23:C2 NaN NaN
o13: M24:C2 NaN NaN
o12: c1 NaN NaN
o13: c1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m6a
Potential scale reduction factors:
Point est. Upper C.I.
O22 NaN NaN
O23 NaN NaN
o12: C1 NaN NaN
o12: C2 NaN NaN
o13: C1 NaN NaN
o13: C2 NaN NaN
o12: b21 NaN NaN
o13: b21 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m6b
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
o13: C2 NaN NaN
c1:C2 NaN NaN
o12: C2 NaN NaN
o12: c1 NaN NaN
o13: c1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m6c
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
o12: C2 NaN NaN
o13: C2 NaN NaN
o12: c1 NaN NaN
o12: c1:C2 NaN NaN
o13: c1 NaN NaN
o13: c1:C2 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m6d
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
M22:C2 NaN NaN
M23:C2 NaN NaN
M24:C2 NaN NaN
o12: C2 NaN NaN
o13: C2 NaN NaN
o12: c1 NaN NaN
o13: c1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m6e
Potential scale reduction factors:
Point est. Upper C.I.
o12: M22 NaN NaN
o12: M23 NaN NaN
o12: M24 NaN NaN
o12: C2 NaN NaN
o12: O22 NaN NaN
o12: O23 NaN NaN
o12: M22:C2 NaN NaN
o12: M23:C2 NaN NaN
o12: M24:C2 NaN NaN
o13: M22 NaN NaN
o13: M23 NaN NaN
o13: M24 NaN NaN
o13: C2 NaN NaN
o13: O22 NaN NaN
o13: O23 NaN NaN
o13: M22:C2 NaN NaN
o13: M23:C2 NaN NaN
o13: M24:C2 NaN NaN
o12: c1 NaN NaN
o13: c1 NaN NaN
gamma_o1[1] NaN NaN
gamma_o1[2] NaN NaN
D_o1_id[1,1] NaN NaN
$m7a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
o1.L NaN NaN
o1.Q NaN NaN
o22 NaN NaN
o23 NaN NaN
o24 NaN NaN
x2 NaN NaN
x3 NaN NaN
x4 NaN NaN
time NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
$m7b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
o22 NaN NaN
o23 NaN NaN
o24 NaN NaN
o1.L NaN NaN
o1.Q NaN NaN
c2 NaN NaN
b21 NaN NaN
sigma_y NaN NaN
D_y_id[1,1] NaN NaN
Code
lapply(models0, MC_error)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a
est MCSE SD MCSE/SD
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m0b
est MCSE SD MCSE/SD
gamma_o2[1] 0 0 0 NaN
gamma_o2[2] 0 0 0 NaN
gamma_o2[3] 0 0 0 NaN
D_o2_id[1,1] 0 0 0 NaN
$m1a
est MCSE SD MCSE/SD
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
C1 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m1b
est MCSE SD MCSE/SD
gamma_o2[1] 0 0 0 NaN
gamma_o2[2] 0 0 0 NaN
gamma_o2[3] 0 0 0 NaN
C1 0 0 0 NaN
D_o2_id[1,1] 0 0 0 NaN
$m1c
est MCSE SD MCSE/SD
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
c1 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m1d
est MCSE SD MCSE/SD
gamma_o2[1] 0 0 0 NaN
gamma_o2[2] 0 0 0 NaN
gamma_o2[3] 0 0 0 NaN
c1 0 0 0 NaN
D_o2_id[1,1] 0 0 0 NaN
$m2a
est MCSE SD MCSE/SD
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
C2 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m2b
est MCSE SD MCSE/SD
gamma_o2[1] 0 0 0 NaN
gamma_o2[2] 0 0 0 NaN
gamma_o2[3] 0 0 0 NaN
C2 0 0 0 NaN
D_o2_id[1,1] 0 0 0 NaN
$m2c
est MCSE SD MCSE/SD
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
c2 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m2d
est MCSE SD MCSE/SD
gamma_o2[1] 0 0 0 NaN
gamma_o2[2] 0 0 0 NaN
gamma_o2[3] 0 0 0 NaN
c2 0 0 0 NaN
D_o2_id[1,1] 0 0 0 NaN
$m3a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
o1.L 0 0 0 NaN
o1.Q 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
o22 0 0 0 NaN
o23 0 0 0 NaN
o24 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
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
abs(C1 - C2) 0 0 0 NaN
log(C1) 0 0 0 NaN
o22 0 0 0 NaN
o23 0 0 0 NaN
o24 0 0 0 NaN
o22:abs(C1 - C2) 0 0 0 NaN
o23:abs(C1 - C2) 0 0 0 NaN
o24:abs(C1 - C2) 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m4b
est MCSE SD MCSE/SD
abs(C1 - C2) 0 0 0 NaN
log(C1) 0 0 0 NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 0 0 NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m4c
est MCSE SD MCSE/SD
C1 0 0 0 NaN
B21 0 0 0 NaN
time 0 0 0 NaN
c1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
D_o1_id[1,2] 0 0 0 NaN
D_o1_id[2,2] 0 0 0 NaN
D_o1_id[1,3] 0 0 0 NaN
D_o1_id[2,3] 0 0 0 NaN
D_o1_id[3,3] 0 0 0 NaN
D_o1_id[1,4] 0 0 0 NaN
D_o1_id[2,4] 0 0 0 NaN
D_o1_id[3,4] 0 0 0 NaN
D_o1_id[4,4] 0 0 0 NaN
$m4d
est MCSE SD MCSE/SD
C1 0 0 0 NaN
time 0 0 0 NaN
I(time^2) 0 0 0 NaN
b21 0 0 0 NaN
c1 0 0 0 NaN
C1:time 0 0 0 NaN
b21:c1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
D_o1_id[1,2] 0 0 0 NaN
D_o1_id[2,2] 0 0 0 NaN
$m4e
est MCSE SD MCSE/SD
C1 0 0 0 NaN
log(time) 0 0 0 NaN
I(time^2) 0 0 0 NaN
p1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m5a
est MCSE SD MCSE/SD
O22 0 0 0 NaN
O23 0 0 0 NaN
o12: C1 0 0 0 NaN
o12: C2 0 0 0 NaN
o13: C1 0 0 0 NaN
o13: C2 0 0 0 NaN
o12: b21 0 0 0 NaN
o13: b21 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m5b
est MCSE SD MCSE/SD
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
O22 0 0 0 NaN
O23 0 0 0 NaN
o13: C2 0 0 0 NaN
c1:C2 0 0 0 NaN
o12: C2 0 0 0 NaN
o12: c1 0 0 0 NaN
o13: c1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m5c
est MCSE SD MCSE/SD
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
O22 0 0 0 NaN
O23 0 0 0 NaN
o12: C2 0 0 0 NaN
o13: C2 0 0 0 NaN
o12: c1 0 0 0 NaN
o12: c1:C2 0 0 0 NaN
o13: c1 0 0 0 NaN
o13: c1:C2 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m5d
est MCSE SD MCSE/SD
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
O22 0 0 0 NaN
O23 0 0 0 NaN
M22:C2 0 0 0 NaN
M23:C2 0 0 0 NaN
M24:C2 0 0 0 NaN
o12: C2 0 0 0 NaN
o13: C2 0 0 0 NaN
o12: c1 0 0 0 NaN
o13: c1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m5e
est MCSE SD MCSE/SD
o12: M22 0 0 0 NaN
o12: M23 0 0 0 NaN
o12: M24 0 0 0 NaN
o12: C2 0 0 0 NaN
o12: O22 0 0 0 NaN
o12: O23 0 0 0 NaN
o12: M22:C2 0 0 0 NaN
o12: M23:C2 0 0 0 NaN
o12: M24:C2 0 0 0 NaN
o13: M22 0 0 0 NaN
o13: M23 0 0 0 NaN
o13: M24 0 0 0 NaN
o13: C2 0 0 0 NaN
o13: O22 0 0 0 NaN
o13: O23 0 0 0 NaN
o13: M22:C2 0 0 0 NaN
o13: M23:C2 0 0 0 NaN
o13: M24:C2 0 0 0 NaN
o12: c1 0 0 0 NaN
o13: c1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m6a
est MCSE SD MCSE/SD
O22 0 0 0 NaN
O23 0 0 0 NaN
o12: C1 0 0 0 NaN
o12: C2 0 0 0 NaN
o13: C1 0 0 0 NaN
o13: C2 0 0 0 NaN
o12: b21 0 0 0 NaN
o13: b21 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m6b
est MCSE SD MCSE/SD
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
O22 0 0 0 NaN
O23 0 0 0 NaN
o13: C2 0 0 0 NaN
c1:C2 0 0 0 NaN
o12: C2 0 0 0 NaN
o12: c1 0 0 0 NaN
o13: c1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m6c
est MCSE SD MCSE/SD
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
O22 0 0 0 NaN
O23 0 0 0 NaN
o12: C2 0 0 0 NaN
o13: C2 0 0 0 NaN
o12: c1 0 0 0 NaN
o12: c1:C2 0 0 0 NaN
o13: c1 0 0 0 NaN
o13: c1:C2 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m6d
est MCSE SD MCSE/SD
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
O22 0 0 0 NaN
O23 0 0 0 NaN
M22:C2 0 0 0 NaN
M23:C2 0 0 0 NaN
M24:C2 0 0 0 NaN
o12: C2 0 0 0 NaN
o13: C2 0 0 0 NaN
o12: c1 0 0 0 NaN
o13: c1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m6e
est MCSE SD MCSE/SD
o12: M22 0 0 0 NaN
o12: M23 0 0 0 NaN
o12: M24 0 0 0 NaN
o12: C2 0 0 0 NaN
o12: O22 0 0 0 NaN
o12: O23 0 0 0 NaN
o12: M22:C2 0 0 0 NaN
o12: M23:C2 0 0 0 NaN
o12: M24:C2 0 0 0 NaN
o13: M22 0 0 0 NaN
o13: M23 0 0 0 NaN
o13: M24 0 0 0 NaN
o13: C2 0 0 0 NaN
o13: O22 0 0 0 NaN
o13: O23 0 0 0 NaN
o13: M22:C2 0 0 0 NaN
o13: M23:C2 0 0 0 NaN
o13: M24:C2 0 0 0 NaN
o12: c1 0 0 0 NaN
o13: c1 0 0 0 NaN
gamma_o1[1] 0 0 0 NaN
gamma_o1[2] 0 0 0 NaN
D_o1_id[1,1] 0 0 0 NaN
$m7a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
o1.L 0 0 0 NaN
o1.Q 0 0 0 NaN
o22 0 0 0 NaN
o23 0 0 0 NaN
o24 0 0 0 NaN
x2 0 0 0 NaN
x3 0 0 0 NaN
x4 0 0 0 NaN
time 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
$m7b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
o22 0 0 0 NaN
o23 0 0 0 NaN
o24 0 0 0 NaN
o1.L 0 0 0 NaN
o1.Q 0 0 0 NaN
c2 0 0 0 NaN
b21 0 0 0 NaN
sigma_y 0 0 0 NaN
D_y_id[1,1] 0 0 0 NaN
summary output remained the same on Windows
Code
lapply(models0, print)
Output
Call:
clmm_imp(fixed = o1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2
0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o2 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3
0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C1
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o2 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3 C1
0 0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 c1
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o2 ~ c1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3 c1
0 0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C2
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o2 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3 C2
0 0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 c2
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o2 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3 c2
0 0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
Call:
lme_imp(fixed = c1 ~ o1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) o1.L o1.Q
0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
Call:
lme_imp(fixed = c1 ~ o2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) o22 o23 o24
0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
Call:
clmm_imp(fixed = o1 ~ M2 + o2 * abs(C1 - C2) + log(C1) + (1 |
id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23
0 0 0 0
M24 abs(C1 - C2) log(C1) o22
0 0 0 0
o23 o24 o22:abs(C1 - C2) o23:abs(C1 - C2)
0 0 0 0
o24:abs(C1 - C2)
0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ ifelse(as.numeric(o2) > 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 cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1
0
o1 > 2
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ time + c1 + C1 + B2 + (c1 * time | id),
data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C1 B21 time c1
0 0 0 0 0 0
Random effects covariance matrix:
$id
o1 o1 o1 o1
(Intercept) c1 time c1:time
o1 (Intercept) 0 0 0 0
o1 c1 0 0 0 0
o1 time 0 0 0 0
o1 c1:time 0 0 0 0
Call:
clmm_imp(fixed = o1 ~ 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 cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C1 time I(time^2) b21 c1 C1:time
0 0 0 0 0 0 0 0
b21:c1
0
Random effects covariance matrix:
$id
o1 o1
(Intercept) time
o1 (Intercept) 0 0
o1 time 0 0
Call:
clmm_imp(fixed = o1 ~ 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 cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C1 log(time) I(time^2) p1
0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~C1 + C2 + b2), seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 O22 O23 C1 C2 C1 C2 b21 b21
0 0 0 0 0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1
0 0 0 0 0 0 0 0 0 0 0
c1
0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 * C2), seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2
0 0 0 0 0 0 0 0 0 0 0
c1 c1:C2
0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2
0 0 0 0 0 0 0 0 0 0 0
C2 c1 c1
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = ~c1 + M2 * C2 + O2, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1
0 0 0 0 0 0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~C1 + C2 + b2), rev = "o1", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 O22 O23 C1 C2 C1 C2 b21 b21
0 0 0 0 0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1
0 0 0 0 0 0 0 0 0 0 0
c1
0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 * C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2
0 0 0 0 0 0 0 0 0 0 0
c1 c1:C2
0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2
0 0 0 0 0 0 0 0 0 0 0
C2 c1 c1
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = ~c1 + M2 * C2 + O2, rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1
0 0 0 0 0 0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
Call:
lme_imp(fixed = y ~ C1 + o1 + o2 + x + time, data = longDF, random = ~1 |
id, n.adapt = 5, n.iter = 10, seed = 2020)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 o1.L o1.Q o22 o23
0 0 0 0 0 0
o24 x2 x3 x4 time
0 0 0 0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Call:
lme_imp(fixed = y ~ o2 + o1 + c2 + b2, data = longDF, random = ~1 |
id, n.adapt = 5, n.iter = 10, seed = 2020)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) o22 o23 o24 o1.L o1.Q
0 0 0 0 0 0
c2 b21
0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m0a
Call:
clmm_imp(fixed = o1 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2
0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m0b
Call:
clmm_imp(fixed = o2 ~ 1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3
0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
$m1a
Call:
clmm_imp(fixed = o1 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C1
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m1b
Call:
clmm_imp(fixed = o2 ~ C1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3 C1
0 0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
$m1c
Call:
clmm_imp(fixed = o1 ~ c1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 c1
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m1d
Call:
clmm_imp(fixed = o2 ~ c1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3 c1
0 0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
$m2a
Call:
clmm_imp(fixed = o1 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C2
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m2b
Call:
clmm_imp(fixed = o2 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3 C2
0 0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
$m2c
Call:
clmm_imp(fixed = o1 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 c2
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m2d
Call:
clmm_imp(fixed = o2 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o2"
Fixed effects:
o2 > 1 o2 > 2 o2 > 3 c2
0 0 0 0
Random effects covariance matrix:
$id
o2
(Intercept)
o2 (Intercept) 0
$m3a
Call:
lme_imp(fixed = c1 ~ o1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) o1.L o1.Q
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 ~ o2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020)
Bayesian linear mixed model for "c1"
Fixed effects:
(Intercept) o22 o23 o24
0 0 0 0
Random effects covariance matrix:
$id
c1
(Intercept)
c1 (Intercept) 0
Residual standard deviation:
sigma_c1
0
$m4a
Call:
clmm_imp(fixed = o1 ~ M2 + o2 * abs(C1 - C2) + log(C1) + (1 |
id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23
0 0 0 0
M24 abs(C1 - C2) log(C1) o22
0 0 0 0
o23 o24 o22:abs(C1 - C2) o23:abs(C1 - C2)
0 0 0 0
o24:abs(C1 - C2)
0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m4b
Call:
clmm_imp(fixed = o1 ~ ifelse(as.numeric(o2) > 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 cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1
0
o1 > 2
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m4c
Call:
clmm_imp(fixed = o1 ~ time + c1 + C1 + B2 + (c1 * time | id),
data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C1 B21 time c1
0 0 0 0 0 0
Random effects covariance matrix:
$id
o1 o1 o1 o1
(Intercept) c1 time c1:time
o1 (Intercept) 0 0 0 0
o1 c1 0 0 0 0
o1 time 0 0 0 0
o1 c1:time 0 0 0 0
$m4d
Call:
clmm_imp(fixed = o1 ~ 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 cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C1 time I(time^2) b21 c1 C1:time
0 0 0 0 0 0 0 0
b21:c1
0
Random effects covariance matrix:
$id
o1 o1
(Intercept) time
o1 (Intercept) 0 0
o1 time 0 0
$m4e
Call:
clmm_imp(fixed = o1 ~ 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 cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 C1 log(time) I(time^2) p1
0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m5a
Call:
clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~C1 + C2 + b2), seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 O22 O23 C1 C2 C1 C2 b21 b21
0 0 0 0 0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m5b
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1
0 0 0 0 0 0 0 0 0 0 0
c1
0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m5c
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 * C2), seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2
0 0 0 0 0 0 0 0 0 0 0
c1 c1:C2
0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m5d
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2
0 0 0 0 0 0 0 0 0 0 0
C2 c1 c1
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m5e
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = ~c1 + M2 * C2 + O2, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 > 1 o1 > 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1
0 0 0 0 0 0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m6a
Call:
clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~C1 + C2 + b2), rev = "o1", seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 O22 O23 C1 C2 C1 C2 b21 b21
0 0 0 0 0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m6b
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 c1:C2 C2 C2 c1
0 0 0 0 0 0 0 0 0 0 0
c1
0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m6c
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 * C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 C2 C2 c1 c1:C2
0 0 0 0 0 0 0 0 0 0 0
c1 c1:C2
0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m6d
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 M22 M23 M24 O22 O23 M22:C2 M23:C2 M24:C2 C2
0 0 0 0 0 0 0 0 0 0 0
C2 c1 c1
0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m6e
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = ~c1 + M2 * C2 + O2, rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian cumulative logit mixed model for "o1"
Fixed effects:
o1 ≤ 1 o1 ≤ 2 M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
M22 M23 M24 C2 O22 O23 M22:C2 M23:C2 M24:C2 c1 c1
0 0 0 0 0 0 0 0 0 0 0
Random effects covariance matrix:
$id
o1
(Intercept)
o1 (Intercept) 0
$m7a
Call:
lme_imp(fixed = y ~ C1 + o1 + o2 + x + time, data = longDF, random = ~1 |
id, n.adapt = 5, n.iter = 10, seed = 2020)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) C1 o1.L o1.Q o22 o23
0 0 0 0 0 0
o24 x2 x3 x4 time
0 0 0 0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
$m7b
Call:
lme_imp(fixed = y ~ o2 + o1 + c2 + b2, data = longDF, random = ~1 |
id, n.adapt = 5, n.iter = 10, seed = 2020)
Bayesian linear mixed model for "y"
Fixed effects:
(Intercept) o22 o23 o24 o1.L o1.Q
0 0 0 0 0 0
c2 b21
0 0
Random effects covariance matrix:
$id
y
(Intercept)
y (Intercept) 0
Residual standard deviation:
sigma_y
0
Code
lapply(models0, coef)
Output
$m0a
$m0a$o1
D_o1_id[1,1] o1 > 1 o1 > 2
0 0 0
$m0b
$m0b$o2
D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3
0 0 0 0
$m1a
$m1a$o1
C1 D_o1_id[1,1] o1 > 1 o1 > 2
0 0 0 0
$m1b
$m1b$o2
C1 D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3
0 0 0 0 0
$m1c
$m1c$o1
c1 D_o1_id[1,1] o1 > 1 o1 > 2
0 0 0 0
$m1d
$m1d$o2
c1 D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3
0 0 0 0 0
$m2a
$m2a$o1
C2 D_o1_id[1,1] o1 > 1 o1 > 2
0 0 0 0
$m2b
$m2b$o2
C2 D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3
0 0 0 0 0
$m2c
$m2c$o1
c2 D_o1_id[1,1] o1 > 1 o1 > 2
0 0 0 0
$m2d
$m2d$o2
c2 D_o2_id[1,1] o2 > 1 o2 > 2 o2 > 3
0 0 0 0 0
$m3a
$m3a$c1
(Intercept) o1.L o1.Q sigma_c1 D_c1_id[1,1]
0 0 0 0 0
$m3b
$m3b$c1
(Intercept) o22 o23 o24 sigma_c1 D_c1_id[1,1]
0 0 0 0 0 0
$m4a
$m4a$o1
M22 M23 M24 abs(C1 - C2)
0 0 0 0
log(C1) o22 o23 o24
0 0 0 0
o22:abs(C1 - C2) o23:abs(C1 - C2) o24:abs(C1 - C2) D_o1_id[1,1]
0 0 0 0
o1 > 1 o1 > 2
0 0
$m4b
$m4b$o1
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0)
0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
D_o1_id[1,1]
0
o1 > 1
0
o1 > 2
0
$m4c
$m4c$o1
C1 B21 time c1 D_o1_id[1,1] D_o1_id[1,2]
0 0 0 0 0 0
D_o1_id[2,2] D_o1_id[1,3] D_o1_id[2,3] D_o1_id[3,3] D_o1_id[1,4] D_o1_id[2,4]
0 0 0 0 0 0
D_o1_id[3,4] D_o1_id[4,4] o1 > 1 o1 > 2
0 0 0 0
$m4d
$m4d$o1
C1 time I(time^2) b21 c1 C1:time
0 0 0 0 0 0
b21:c1 D_o1_id[1,1] D_o1_id[1,2] D_o1_id[2,2] o1 > 1 o1 > 2
0 0 0 0 0 0
$m4e
$m4e$o1
C1 log(time) I(time^2) p1 D_o1_id[1,1] o1 > 1
0 0 0 0 0 0
o1 > 2
0
$m5a
$m5a$o1
O22 O23 C1 C2 C1 C2
0 0 0 0 0 0
b21 b21 D_o1_id[1,1] o1 > 1 o1 > 2
0 0 0 0 0
$m5b
$m5b$o1
M22 M23 M24 O22 O23 c1:C2
0 0 0 0 0 0
C2 C2 c1 c1 D_o1_id[1,1] o1 > 1
0 0 0 0 0 0
o1 > 2
0
$m5c
$m5c$o1
M22 M23 M24 O22 O23 C2
0 0 0 0 0 0
C2 c1 c1:C2 c1 c1:C2 D_o1_id[1,1]
0 0 0 0 0 0
o1 > 1 o1 > 2
0 0
$m5d
$m5d$o1
M22 M23 M24 O22 O23 M22:C2
0 0 0 0 0 0
M23:C2 M24:C2 C2 C2 c1 c1
0 0 0 0 0 0
D_o1_id[1,1] o1 > 1 o1 > 2
0 0 0
$m5e
$m5e$o1
M22 M23 M24 C2 O22 O23
0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 M22 M23 M24
0 0 0 0 0 0
C2 O22 O23 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0
c1 c1 D_o1_id[1,1] o1 > 1 o1 > 2
0 0 0 0 0
$m6a
$m6a$o1
O22 O23 C1 C2 C1 C2
0 0 0 0 0 0
b21 b21 D_o1_id[1,1] o1 ≤ 1 o1 ≤ 2
0 0 0 0 0
$m6b
$m6b$o1
M22 M23 M24 O22 O23 c1:C2
0 0 0 0 0 0
C2 C2 c1 c1 D_o1_id[1,1] o1 ≤ 1
0 0 0 0 0 0
o1 ≤ 2
0
$m6c
$m6c$o1
M22 M23 M24 O22 O23 C2
0 0 0 0 0 0
C2 c1 c1:C2 c1 c1:C2 D_o1_id[1,1]
0 0 0 0 0 0
o1 ≤ 1 o1 ≤ 2
0 0
$m6d
$m6d$o1
M22 M23 M24 O22 O23 M22:C2
0 0 0 0 0 0
M23:C2 M24:C2 C2 C2 c1 c1
0 0 0 0 0 0
D_o1_id[1,1] o1 ≤ 1 o1 ≤ 2
0 0 0
$m6e
$m6e$o1
M22 M23 M24 C2 O22 O23
0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 M22 M23 M24
0 0 0 0 0 0
C2 O22 O23 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0
c1 c1 D_o1_id[1,1] o1 ≤ 1 o1 ≤ 2
0 0 0 0 0
$m7a
$m7a$y
(Intercept) C1 o1.L o1.Q o22 o23
0 0 0 0 0 0
o24 x2 x3 x4 time sigma_y
0 0 0 0 0 0
D_y_id[1,1]
0
$m7b
$m7b$y
(Intercept) o22 o23 o24 o1.L o1.Q
0 0 0 0 0 0
c2 b21 sigma_y D_y_id[1,1]
0 0 0 0
Code
lapply(models0, confint)
Output
$m0a
$m0a$o1
2.5% 97.5%
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m0b
$m0b$o2
2.5% 97.5%
D_o2_id[1,1] 0 0
o2 > 1 0 0
o2 > 2 0 0
o2 > 3 0 0
$m1a
$m1a$o1
2.5% 97.5%
C1 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m1b
$m1b$o2
2.5% 97.5%
C1 0 0
D_o2_id[1,1] 0 0
o2 > 1 0 0
o2 > 2 0 0
o2 > 3 0 0
$m1c
$m1c$o1
2.5% 97.5%
c1 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m1d
$m1d$o2
2.5% 97.5%
c1 0 0
D_o2_id[1,1] 0 0
o2 > 1 0 0
o2 > 2 0 0
o2 > 3 0 0
$m2a
$m2a$o1
2.5% 97.5%
C2 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m2b
$m2b$o2
2.5% 97.5%
C2 0 0
D_o2_id[1,1] 0 0
o2 > 1 0 0
o2 > 2 0 0
o2 > 3 0 0
$m2c
$m2c$o1
2.5% 97.5%
c2 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m2d
$m2d$o2
2.5% 97.5%
c2 0 0
D_o2_id[1,1] 0 0
o2 > 1 0 0
o2 > 2 0 0
o2 > 3 0 0
$m3a
$m3a$c1
2.5% 97.5%
(Intercept) 0 0
o1.L 0 0
o1.Q 0 0
sigma_c1 0 0
D_c1_id[1,1] 0 0
$m3b
$m3b$c1
2.5% 97.5%
(Intercept) 0 0
o22 0 0
o23 0 0
o24 0 0
sigma_c1 0 0
D_c1_id[1,1] 0 0
$m4a
$m4a$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
o22 0 0
o23 0 0
o24 0 0
o22:abs(C1 - C2) 0 0
o23:abs(C1 - C2) 0 0
o24:abs(C1 - C2) 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m4b
$m4b$o1
2.5% 97.5%
abs(C1 - C2) 0 0
log(C1) 0 0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m4c
$m4c$o1
2.5% 97.5%
C1 0 0
B21 0 0
time 0 0
c1 0 0
D_o1_id[1,1] 0 0
D_o1_id[1,2] 0 0
D_o1_id[2,2] 0 0
D_o1_id[1,3] 0 0
D_o1_id[2,3] 0 0
D_o1_id[3,3] 0 0
D_o1_id[1,4] 0 0
D_o1_id[2,4] 0 0
D_o1_id[3,4] 0 0
D_o1_id[4,4] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m4d
$m4d$o1
2.5% 97.5%
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_o1_id[1,1] 0 0
D_o1_id[1,2] 0 0
D_o1_id[2,2] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m4e
$m4e$o1
2.5% 97.5%
C1 0 0
log(time) 0 0
I(time^2) 0 0
p1 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m5a
$m5a$o1
2.5% 97.5%
O22 0 0
O23 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
b21 0 0
b21 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m5b
$m5b$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
c1:C2 0 0
C2 0 0
C2 0 0
c1 0 0
c1 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m5c
$m5c$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
C2 0 0
C2 0 0
c1 0 0
c1:C2 0 0
c1 0 0
c1:C2 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m5d
$m5d$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
C2 0 0
C2 0 0
c1 0 0
c1 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m5e
$m5e$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
c1 0 0
c1 0 0
D_o1_id[1,1] 0 0
o1 > 1 0 0
o1 > 2 0 0
$m6a
$m6a$o1
2.5% 97.5%
O22 0 0
O23 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
b21 0 0
b21 0 0
D_o1_id[1,1] 0 0
o1 ≤ 1 0 0
o1 ≤ 2 0 0
$m6b
$m6b$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
c1:C2 0 0
C2 0 0
C2 0 0
c1 0 0
c1 0 0
D_o1_id[1,1] 0 0
o1 ≤ 1 0 0
o1 ≤ 2 0 0
$m6c
$m6c$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
C2 0 0
C2 0 0
c1 0 0
c1:C2 0 0
c1 0 0
c1:C2 0 0
D_o1_id[1,1] 0 0
o1 ≤ 1 0 0
o1 ≤ 2 0 0
$m6d
$m6d$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
C2 0 0
C2 0 0
c1 0 0
c1 0 0
D_o1_id[1,1] 0 0
o1 ≤ 1 0 0
o1 ≤ 2 0 0
$m6e
$m6e$o1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
c1 0 0
c1 0 0
D_o1_id[1,1] 0 0
o1 ≤ 1 0 0
o1 ≤ 2 0 0
$m7a
$m7a$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
o1.L 0 0
o1.Q 0 0
o22 0 0
o23 0 0
o24 0 0
x2 0 0
x3 0 0
x4 0 0
time 0 0
sigma_y 0 0
D_y_id[1,1] 0 0
$m7b
$m7b$y
2.5% 97.5%
(Intercept) 0 0
o22 0 0
o23 0 0
o24 0 0
o1.L 0 0
o1.Q 0 0
c2 0 0
b21 0 0
sigma_y 0 0
D_y_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"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ 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
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o2 ~ 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
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o2 > 1 0 0 0 0 0 NaN NaN
o2 > 2 0 0 0 0 0 NaN NaN
o2 > 3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o2_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ 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
C1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o2 ~ 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
C1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o2 > 1 0 0 0 0 0 NaN NaN
o2 > 2 0 0 0 0 0 NaN NaN
o2 > 3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o2_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ 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
c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o2 ~ 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
c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o2 > 1 0 0 0 0 0 NaN NaN
o2 > 2 0 0 0 0 0 NaN NaN
o2 > 3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o2_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o2 ~ C2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o2 > 1 0 0 0 0 0 NaN NaN
o2 > 2 0 0 0 0 0 NaN NaN
o2 > 3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o2_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
c2 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o2 ~ c2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
c2 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o2 > 1 0 0 0 0 0 NaN NaN
o2 > 2 0 0 0 0 0 NaN NaN
o2 > 3 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o2_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 ~ o1 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
o1.L 0 0 0 0 0 NaN NaN
o1.Q 0 0 0 0 0 NaN NaN
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 ~ o2 + (1 | id), data = longDF, n.adapt = 5,
n.iter = 10, seed = 2020)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ M2 + o2 * 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
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
o22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
o23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
o24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ ifelse(as.numeric(o2) > 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% 97.5%
abs(C1 - C2) 0 0 0 0
log(C1) 0 0 0 0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 0 0 0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 0
tail-prob. GR-crit
abs(C1 - C2) 0 NaN
log(C1) 0 NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 NaN
MCE/SD
abs(C1 - C2) NaN
log(C1) NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ 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
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1] 0 0 0 0 NaN NaN
D_o1_id[1,2] 0 0 0 0 0 NaN NaN
D_o1_id[2,2] 0 0 0 0 NaN NaN
D_o1_id[1,3] 0 0 0 0 0 NaN NaN
D_o1_id[2,3] 0 0 0 0 0 NaN NaN
D_o1_id[3,3] 0 0 0 0 NaN NaN
D_o1_id[1,4] 0 0 0 0 0 NaN NaN
D_o1_id[2,4] 0 0 0 0 0 NaN NaN
D_o1_id[3,4] 0 0 0 0 0 NaN NaN
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ 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
C1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
I(time^2) 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
C1:time 0 0 0 0 0 NaN NaN
b21:c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1] 0 0 0 0 NaN NaN
D_o1_id[1,2] 0 0 0 0 0 NaN NaN
D_o1_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 cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ 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
C1 0 0 0 0 0 NaN NaN
log(time) 0 0 0 0 0 NaN NaN
I(time^2) 0 0 0 0 0 NaN NaN
p1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m5a
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~C1 + C2 + b2), seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C1 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C1 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: b21 0 0 0 0 0 NaN NaN
o13: b21 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m5b
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
c1:C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m5c
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 * C2), seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o12: c1:C2 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
o13: c1:C2 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m5d
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
M22:C2 0 0 0 0 0 NaN NaN
M23:C2 0 0 0 0 0 NaN NaN
M24:C2 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m5e
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = ~c1 + M2 * C2 + O2, seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o12: M22 0 0 0 0 0 NaN NaN
o12: M23 0 0 0 0 0 NaN NaN
o12: M24 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o12: O22 0 0 0 0 0 NaN NaN
o12: O23 0 0 0 0 0 NaN NaN
o12: M22:C2 0 0 0 0 0 NaN NaN
o12: M23:C2 0 0 0 0 0 NaN NaN
o12: M24:C2 0 0 0 0 0 NaN NaN
o13: M22 0 0 0 0 0 NaN NaN
o13: M23 0 0 0 0 0 NaN NaN
o13: M24 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o13: O22 0 0 0 0 0 NaN NaN
o13: O23 0 0 0 0 0 NaN NaN
o13: M22:C2 0 0 0 0 0 NaN NaN
o13: M23:C2 0 0 0 0 0 NaN NaN
o13: M24:C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1 0 0 0 0 0 NaN NaN
o1 > 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m6a
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~C1 + C2 + b2), rev = "o1", seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C1 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C1 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: b21 0 0 0 0 0 NaN NaN
o13: b21 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 ≤ 1 0 0 0 0 0 NaN NaN
o1 ≤ 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m6b
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
c1:C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 ≤ 1 0 0 0 0 0 NaN NaN
o1 ≤ 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m6c
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 * C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o12: c1:C2 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
o13: c1:C2 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 ≤ 1 0 0 0 0 0 NaN NaN
o1 ≤ 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m6d
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
M22:C2 0 0 0 0 0 NaN NaN
M23:C2 0 0 0 0 0 NaN NaN
M24:C2 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 ≤ 1 0 0 0 0 0 NaN NaN
o1 ≤ 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m6e
Bayesian cumulative logit mixed model fitted with JointAI
Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"),
nonprop = ~c1 + M2 * C2 + O2, rev = "o1", seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o12: M22 0 0 0 0 0 NaN NaN
o12: M23 0 0 0 0 0 NaN NaN
o12: M24 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o12: O22 0 0 0 0 0 NaN NaN
o12: O23 0 0 0 0 0 NaN NaN
o12: M22:C2 0 0 0 0 0 NaN NaN
o12: M23:C2 0 0 0 0 0 NaN NaN
o12: M24:C2 0 0 0 0 0 NaN NaN
o13: M22 0 0 0 0 0 NaN NaN
o13: M23 0 0 0 0 0 NaN NaN
o13: M24 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o13: O22 0 0 0 0 0 NaN NaN
o13: O23 0 0 0 0 0 NaN NaN
o13: M22:C2 0 0 0 0 0 NaN NaN
o13: M23:C2 0 0 0 0 0 NaN NaN
o13: M24:C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
Posterior summary of the intercepts:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 ≤ 1 0 0 0 0 0 NaN NaN
o1 ≤ 2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_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
$m7a
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ C1 + o1 + o2 + x + time, data = longDF, random = ~1 |
id, n.adapt = 5, n.iter = 10, seed = 2020)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
o1.L 0 0 0 0 0 NaN NaN
o1.Q 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
x2 0 0 0 0 0 NaN NaN
x3 0 0 0 0 0 NaN NaN
x4 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
$m7b
Bayesian linear mixed model fitted with JointAI
Call:
lme_imp(fixed = y ~ o2 + o1 + c2 + b2, data = longDF, random = ~1 |
id, n.adapt = 5, n.iter = 10, seed = 2020)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
o1.L 0 0 0 0 0 NaN NaN
o1.Q 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1] 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 329
Number of groups:
- id: 100
Code
lapply(models0, function(x) coef(summary(x)))
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a
$m0a$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
$m0b
$m0b$o2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
$m1a
$m1a$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C1 0 0 0 0 0 NaN NaN
$m1b
$m1b$o2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C1 0 0 0 0 0 NaN NaN
$m1c
$m1c$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
c1 0 0 0 0 0 NaN NaN
$m1d
$m1d$o2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
c1 0 0 0 0 0 NaN NaN
$m2a
$m2a$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
$m2b
$m2b$o2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2 0 0 0 0 0 NaN NaN
$m2c
$m2c$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
c2 0 0 0 0 0 NaN NaN
$m2d
$m2d$o2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
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
o1.L 0 0 0 0 0 NaN NaN
o1.Q 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
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
$m4a
$m4a$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
o22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
o23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
o24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
$m4b
$m4b$o1
Mean SD 2.5% 97.5%
abs(C1 - C2) 0 0 0 0
log(C1) 0 0 0 0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 0 0 0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 0
tail-prob. GR-crit
abs(C1 - C2) 0 NaN
log(C1) 0 NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 0 NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 NaN
MCE/SD
abs(C1 - C2) NaN
log(C1) NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
$m4c
$m4c$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C1 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
$m4d
$m4d$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C1 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
I(time^2) 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
c1 0 0 0 0 0 NaN NaN
C1:time 0 0 0 0 0 NaN NaN
b21:c1 0 0 0 0 0 NaN NaN
$m4e
$m4e$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C1 0 0 0 0 0 NaN NaN
log(time) 0 0 0 0 0 NaN NaN
I(time^2) 0 0 0 0 0 NaN NaN
p1 0 0 0 0 0 NaN NaN
$m5a
$m5a$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C1 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C1 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: b21 0 0 0 0 0 NaN NaN
o13: b21 0 0 0 0 0 NaN NaN
$m5b
$m5b$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
c1:C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
$m5c
$m5c$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o12: c1:C2 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
o13: c1:C2 0 0 0 0 0 NaN NaN
$m5d
$m5d$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
M22:C2 0 0 0 0 0 NaN NaN
M23:C2 0 0 0 0 0 NaN NaN
M24:C2 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
$m5e
$m5e$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o12: M22 0 0 0 0 0 NaN NaN
o12: M23 0 0 0 0 0 NaN NaN
o12: M24 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o12: O22 0 0 0 0 0 NaN NaN
o12: O23 0 0 0 0 0 NaN NaN
o12: M22:C2 0 0 0 0 0 NaN NaN
o12: M23:C2 0 0 0 0 0 NaN NaN
o12: M24:C2 0 0 0 0 0 NaN NaN
o13: M22 0 0 0 0 0 NaN NaN
o13: M23 0 0 0 0 0 NaN NaN
o13: M24 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o13: O22 0 0 0 0 0 NaN NaN
o13: O23 0 0 0 0 0 NaN NaN
o13: M22:C2 0 0 0 0 0 NaN NaN
o13: M23:C2 0 0 0 0 0 NaN NaN
o13: M24:C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
$m6a
$m6a$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C1 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C1 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: b21 0 0 0 0 0 NaN NaN
o13: b21 0 0 0 0 0 NaN NaN
$m6b
$m6b$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
c1:C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
$m6c
$m6c$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o12: c1:C2 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
o13: c1:C2 0 0 0 0 0 NaN NaN
$m6d
$m6d$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
M22:C2 0 0 0 0 0 NaN NaN
M23:C2 0 0 0 0 0 NaN NaN
M24:C2 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
$m6e
$m6e$o1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o12: M22 0 0 0 0 0 NaN NaN
o12: M23 0 0 0 0 0 NaN NaN
o12: M24 0 0 0 0 0 NaN NaN
o12: C2 0 0 0 0 0 NaN NaN
o12: O22 0 0 0 0 0 NaN NaN
o12: O23 0 0 0 0 0 NaN NaN
o12: M22:C2 0 0 0 0 0 NaN NaN
o12: M23:C2 0 0 0 0 0 NaN NaN
o12: M24:C2 0 0 0 0 0 NaN NaN
o13: M22 0 0 0 0 0 NaN NaN
o13: M23 0 0 0 0 0 NaN NaN
o13: M24 0 0 0 0 0 NaN NaN
o13: C2 0 0 0 0 0 NaN NaN
o13: O22 0 0 0 0 0 NaN NaN
o13: O23 0 0 0 0 0 NaN NaN
o13: M22:C2 0 0 0 0 0 NaN NaN
o13: M23:C2 0 0 0 0 0 NaN NaN
o13: M24:C2 0 0 0 0 0 NaN NaN
o12: c1 0 0 0 0 0 NaN NaN
o13: c1 0 0 0 0 0 NaN NaN
$m7a
$m7a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
o1.L 0 0 0 0 0 NaN NaN
o1.Q 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
x2 0 0 0 0 0 NaN NaN
x3 0 0 0 0 0 NaN NaN
x4 0 0 0 0 0 NaN NaN
time 0 0 0 0 0 NaN NaN
$m7b
$m7b$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
o22 0 0 0 0 0 NaN NaN
o23 0 0 0 0 0 NaN NaN
o24 0 0 0 0 0 NaN NaN
o1.L 0 0 0 0 0 NaN NaN
o1.Q 0 0 0 0 0 NaN NaN
c2 0 0 0 0 0 NaN NaN
b21 0 0 0 0 0 NaN NaN
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