Nothing
tests/testthat/_snaps/clm.md
In JointAI: Joint Analysis and Imputation of Incomplete Data
data_list remains the same
Code
lapply(models, "[[", "data_list")
Output
$m0a
$m0a$M_lvlone
O1 (Intercept)
1 2 1
2 4 1
3 3 1
4 2 1
5 3 1
6 1 1
7 3 1
8 4 1
9 4 1
10 2 1
11 1 1
12 3 1
13 3 1
14 1 1
15 1 1
16 4 1
17 2 1
18 3 1
19 4 1
20 1 1
21 3 1
22 4 1
23 4 1
24 2 1
25 1 1
26 3 1
27 4 1
28 1 1
29 4 1
30 4 1
31 2 1
32 3 1
33 3 1
34 1 1
35 1 1
36 4 1
37 4 1
38 4 1
39 1 1
40 2 1
41 1 1
42 1 1
43 2 1
44 2 1
45 1 1
46 1 1
47 4 1
48 4 1
49 2 1
50 2 1
51 1 1
52 3 1
53 1 1
54 3 1
55 2 1
56 4 1
57 2 1
58 1 1
59 1 1
60 4 1
61 2 1
62 4 1
63 3 1
64 2 1
65 3 1
66 3 1
67 2 1
68 1 1
69 1 1
70 1 1
71 1 1
72 3 1
73 2 1
74 2 1
75 3 1
76 3 1
77 4 1
78 3 1
79 2 1
80 2 1
81 3 1
82 1 1
83 3 1
84 2 1
85 2 1
86 4 1
87 3 1
88 2 1
89 3 1
90 3 1
91 4 1
92 1 1
93 4 1
94 1 1
95 1 1
96 3 1
97 1 1
98 3 1
99 3 1
100 3 1
$m0a$mu_delta_ordinal
[1] 0
$m0a$tau_delta_ordinal
[1] 1e-04
$m0b
$m0b$M_lvlone
O2 (Intercept)
1 4 1
2 4 1
3 4 1
4 1 1
5 2 1
6 3 1
7 4 1
8 2 1
9 4 1
10 3 1
11 2 1
12 1 1
13 1 1
14 1 1
15 4 1
16 3 1
17 3 1
18 1 1
19 3 1
20 1 1
21 3 1
22 3 1
23 2 1
24 3 1
25 2 1
26 2 1
27 1 1
28 4 1
29 3 1
30 3 1
31 2 1
32 2 1
33 1 1
34 1 1
35 4 1
36 3 1
37 3 1
38 1 1
39 2 1
40 3 1
41 3 1
42 3 1
43 3 1
44 4 1
45 4 1
46 1 1
47 4 1
48 4 1
49 1 1
50 2 1
51 1 1
52 3 1
53 2 1
54 1 1
55 2 1
56 3 1
57 NA 1
58 4 1
59 4 1
60 3 1
61 4 1
62 1 1
63 4 1
64 4 1
65 4 1
66 1 1
67 3 1
68 3 1
69 4 1
70 1 1
71 4 1
72 4 1
73 2 1
74 4 1
75 3 1
76 2 1
77 2 1
78 3 1
79 2 1
80 1 1
81 4 1
82 2 1
83 4 1
84 1 1
85 1 1
86 2 1
87 3 1
88 3 1
89 2 1
90 4 1
91 2 1
92 1 1
93 NA 1
94 3 1
95 1 1
96 3 1
97 2 1
98 2 1
99 4 1
100 3 1
$m0b$mu_delta_ordinal
[1] 0
$m0b$tau_delta_ordinal
[1] 1e-04
$m1a
$m1a$M_lvlone
O1 (Intercept) C1
1 2 1 1.410531
2 4 1 1.434183
3 3 1 1.430994
4 2 1 1.453096
5 3 1 1.438344
6 1 1 1.453207
7 3 1 1.425176
8 4 1 1.437908
9 4 1 1.416911
10 2 1 1.448638
11 1 1 1.428375
12 3 1 1.450130
13 3 1 1.420545
14 1 1 1.423005
15 1 1 1.435902
16 4 1 1.423901
17 2 1 1.457208
18 3 1 1.414280
19 4 1 1.443383
20 1 1 1.434954
21 3 1 1.429499
22 4 1 1.441897
23 4 1 1.423713
24 2 1 1.435395
25 1 1 1.425944
26 3 1 1.437115
27 4 1 1.441326
28 1 1 1.422953
29 4 1 1.437797
30 4 1 1.472121
31 2 1 1.421782
32 3 1 1.457672
33 3 1 1.430842
34 1 1 1.431523
35 1 1 1.421395
36 4 1 1.434496
37 4 1 1.425383
38 4 1 1.421802
39 1 1 1.430094
40 2 1 1.447621
41 1 1 1.434797
42 1 1 1.446091
43 2 1 1.445306
44 2 1 1.448783
45 1 1 1.450617
46 1 1 1.415055
47 4 1 1.436590
48 4 1 1.433938
49 2 1 1.414941
50 2 1 1.421807
51 1 1 1.453203
52 3 1 1.452129
53 1 1 1.431510
54 3 1 1.430082
55 2 1 1.443492
56 4 1 1.436460
57 2 1 1.418119
58 1 1 1.434971
59 1 1 1.445599
60 4 1 1.437097
61 2 1 1.428360
62 4 1 1.440550
63 3 1 1.443014
64 2 1 1.424298
65 3 1 1.448823
66 3 1 1.425834
67 2 1 1.427102
68 1 1 1.414240
69 1 1 1.456218
70 1 1 1.470594
71 1 1 1.425058
72 3 1 1.432371
73 2 1 1.441656
74 2 1 1.434952
75 3 1 1.402860
76 3 1 1.453363
77 4 1 1.432909
78 3 1 1.435103
79 2 1 1.434462
80 2 1 1.434661
81 3 1 1.445881
82 1 1 1.442548
83 3 1 1.430097
84 2 1 1.430119
85 2 1 1.430315
86 4 1 1.437584
87 3 1 1.409738
88 2 1 1.422388
89 3 1 1.422509
90 3 1 1.439432
91 4 1 1.430175
92 1 1 1.418002
93 4 1 1.423812
94 1 1 1.423473
95 1 1 1.434412
96 3 1 1.450844
97 1 1 1.433371
98 3 1 1.444378
99 3 1 1.422523
100 3 1 1.410394
$m1a$spM_lvlone
center scale
O1 NA NA
(Intercept) NA NA
C1 1.434101 0.01299651
$m1a$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
$m1b
$m1b$M_lvlone
O2 (Intercept) C1
1 4 1 1.410531
2 4 1 1.434183
3 4 1 1.430994
4 1 1 1.453096
5 2 1 1.438344
6 3 1 1.453207
7 4 1 1.425176
8 2 1 1.437908
9 4 1 1.416911
10 3 1 1.448638
11 2 1 1.428375
12 1 1 1.450130
13 1 1 1.420545
14 1 1 1.423005
15 4 1 1.435902
16 3 1 1.423901
17 3 1 1.457208
18 1 1 1.414280
19 3 1 1.443383
20 1 1 1.434954
21 3 1 1.429499
22 3 1 1.441897
23 2 1 1.423713
24 3 1 1.435395
25 2 1 1.425944
26 2 1 1.437115
27 1 1 1.441326
28 4 1 1.422953
29 3 1 1.437797
30 3 1 1.472121
31 2 1 1.421782
32 2 1 1.457672
33 1 1 1.430842
34 1 1 1.431523
35 4 1 1.421395
36 3 1 1.434496
37 3 1 1.425383
38 1 1 1.421802
39 2 1 1.430094
40 3 1 1.447621
41 3 1 1.434797
42 3 1 1.446091
43 3 1 1.445306
44 4 1 1.448783
45 4 1 1.450617
46 1 1 1.415055
47 4 1 1.436590
48 4 1 1.433938
49 1 1 1.414941
50 2 1 1.421807
51 1 1 1.453203
52 3 1 1.452129
53 2 1 1.431510
54 1 1 1.430082
55 2 1 1.443492
56 3 1 1.436460
57 NA 1 1.418119
58 4 1 1.434971
59 4 1 1.445599
60 3 1 1.437097
61 4 1 1.428360
62 1 1 1.440550
63 4 1 1.443014
64 4 1 1.424298
65 4 1 1.448823
66 1 1 1.425834
67 3 1 1.427102
68 3 1 1.414240
69 4 1 1.456218
70 1 1 1.470594
71 4 1 1.425058
72 4 1 1.432371
73 2 1 1.441656
74 4 1 1.434952
75 3 1 1.402860
76 2 1 1.453363
77 2 1 1.432909
78 3 1 1.435103
79 2 1 1.434462
80 1 1 1.434661
81 4 1 1.445881
82 2 1 1.442548
83 4 1 1.430097
84 1 1 1.430119
85 1 1 1.430315
86 2 1 1.437584
87 3 1 1.409738
88 3 1 1.422388
89 2 1 1.422509
90 4 1 1.439432
91 2 1 1.430175
92 1 1 1.418002
93 NA 1 1.423812
94 3 1 1.423473
95 1 1 1.434412
96 3 1 1.450844
97 2 1 1.433371
98 2 1 1.444378
99 4 1 1.422523
100 3 1 1.410394
$m1b$spM_lvlone
center scale
O2 NA NA
(Intercept) NA NA
C1 1.434101 0.01299651
$m1b$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
$m2a
$m2a$M_lvlone
O1 C2 (Intercept)
1 2 0.144065882 1
2 4 0.032778478 1
3 3 0.343008492 1
4 2 -0.361887858 1
5 3 -0.389600647 1
6 1 -0.205306841 1
7 3 0.079434830 1
8 4 -0.331246757 1
9 4 -0.329638800 1
10 2 0.167597533 1
11 1 0.860207989 1
12 3 0.022730640 1
13 3 0.217171172 1
14 1 -0.403002412 1
15 1 0.087369742 1
16 4 -0.183870429 1
17 2 -0.194577002 1
18 3 -0.349718516 1
19 4 -0.508781244 1
20 1 0.494883111 1
21 3 0.258041067 1
22 4 -0.922621989 1
23 4 0.431254949 1
24 2 -0.294218881 1
25 1 -0.425548895 1
26 3 0.057176054 1
27 4 0.289090158 1
28 1 -0.473079489 1
29 4 -0.385664863 1
30 4 -0.154780107 1
31 2 0.100536296 1
32 3 0.634791958 1
33 3 -0.387252617 1
34 1 -0.181741088 1
35 1 -0.311562695 1
36 4 -0.044115907 1
37 4 -0.657409991 1
38 4 0.159577214 1
39 1 -0.460416933 1
40 2 NA 1
41 1 -0.248909867 1
42 1 -0.609021545 1
43 2 0.025471883 1
44 2 0.066648592 1
45 1 -0.276108719 1
46 1 -0.179737577 1
47 4 0.181190937 1
48 4 -0.453871693 1
49 2 0.448629602 1
50 2 -0.529811821 1
51 1 -0.028304571 1
52 3 -0.520318482 1
53 1 0.171317619 1
54 3 0.432732046 1
55 2 -0.346286005 1
56 4 -0.469375653 1
57 2 0.031021711 1
58 1 -0.118837515 1
59 1 0.507769984 1
60 4 0.271797031 1
61 2 -0.124442204 1
62 4 0.277677389 1
63 3 -0.102893730 1
64 2 NA 1
65 3 -0.678303052 1
66 3 0.478880037 1
67 2 -0.428028760 1
68 1 0.048119185 1
69 1 0.216932805 1
70 1 -0.234575269 1
71 1 0.006827078 1
72 3 -0.456055171 1
73 2 0.346486708 1
74 2 0.205092215 1
75 3 -0.136596858 1
76 3 -0.500179043 1
77 4 0.527352086 1
78 3 0.022742250 1
79 2 NA 1
80 2 -0.002032440 1
81 3 -0.154246160 1
82 1 0.140201825 1
83 3 -0.141417121 1
84 2 NA 1
85 2 -0.021285339 1
86 4 -0.010196306 1
87 3 -0.089747520 1
88 2 -0.083699898 1
89 3 -0.044061996 1
90 3 -0.209291697 1
91 4 0.639036426 1
92 1 0.094698299 1
93 4 -0.055510622 1
94 1 -0.421318463 1
95 1 0.125295503 1
96 3 0.213084904 1
97 1 -0.161914659 1
98 3 -0.034767685 1
99 3 -0.320681689 1
100 3 0.058192962 1
$m2a$spM_lvlone
center scale
O1 NA NA
C2 -0.06490582 0.3331735
(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
$m2b
$m2b$M_lvlone
O2 C2 (Intercept)
1 4 0.144065882 1
2 4 0.032778478 1
3 4 0.343008492 1
4 1 -0.361887858 1
5 2 -0.389600647 1
6 3 -0.205306841 1
7 4 0.079434830 1
8 2 -0.331246757 1
9 4 -0.329638800 1
10 3 0.167597533 1
11 2 0.860207989 1
12 1 0.022730640 1
13 1 0.217171172 1
14 1 -0.403002412 1
15 4 0.087369742 1
16 3 -0.183870429 1
17 3 -0.194577002 1
18 1 -0.349718516 1
19 3 -0.508781244 1
20 1 0.494883111 1
21 3 0.258041067 1
22 3 -0.922621989 1
23 2 0.431254949 1
24 3 -0.294218881 1
25 2 -0.425548895 1
26 2 0.057176054 1
27 1 0.289090158 1
28 4 -0.473079489 1
29 3 -0.385664863 1
30 3 -0.154780107 1
31 2 0.100536296 1
32 2 0.634791958 1
33 1 -0.387252617 1
34 1 -0.181741088 1
35 4 -0.311562695 1
36 3 -0.044115907 1
37 3 -0.657409991 1
38 1 0.159577214 1
39 2 -0.460416933 1
40 3 NA 1
41 3 -0.248909867 1
42 3 -0.609021545 1
43 3 0.025471883 1
44 4 0.066648592 1
45 4 -0.276108719 1
46 1 -0.179737577 1
47 4 0.181190937 1
48 4 -0.453871693 1
49 1 0.448629602 1
50 2 -0.529811821 1
51 1 -0.028304571 1
52 3 -0.520318482 1
53 2 0.171317619 1
54 1 0.432732046 1
55 2 -0.346286005 1
56 3 -0.469375653 1
57 NA 0.031021711 1
58 4 -0.118837515 1
59 4 0.507769984 1
60 3 0.271797031 1
61 4 -0.124442204 1
62 1 0.277677389 1
63 4 -0.102893730 1
64 4 NA 1
65 4 -0.678303052 1
66 1 0.478880037 1
67 3 -0.428028760 1
68 3 0.048119185 1
69 4 0.216932805 1
70 1 -0.234575269 1
71 4 0.006827078 1
72 4 -0.456055171 1
73 2 0.346486708 1
74 4 0.205092215 1
75 3 -0.136596858 1
76 2 -0.500179043 1
77 2 0.527352086 1
78 3 0.022742250 1
79 2 NA 1
80 1 -0.002032440 1
81 4 -0.154246160 1
82 2 0.140201825 1
83 4 -0.141417121 1
84 1 NA 1
85 1 -0.021285339 1
86 2 -0.010196306 1
87 3 -0.089747520 1
88 3 -0.083699898 1
89 2 -0.044061996 1
90 4 -0.209291697 1
91 2 0.639036426 1
92 1 0.094698299 1
93 NA -0.055510622 1
94 3 -0.421318463 1
95 1 0.125295503 1
96 3 0.213084904 1
97 2 -0.161914659 1
98 2 -0.034767685 1
99 4 -0.320681689 1
100 3 0.058192962 1
$m2b$spM_lvlone
center scale
O2 NA NA
C2 -0.06490582 0.3331735
(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
$m3a
$m3a$M_lvlone
C1 (Intercept) O1.L O1.Q O1.C
1 1.410531 1 -0.2236068 -0.5 0.6708204
2 1.434183 1 0.6708204 0.5 0.2236068
3 1.430994 1 0.2236068 -0.5 -0.6708204
4 1.453096 1 -0.2236068 -0.5 0.6708204
5 1.438344 1 0.2236068 -0.5 -0.6708204
6 1.453207 1 -0.6708204 0.5 -0.2236068
7 1.425176 1 0.2236068 -0.5 -0.6708204
8 1.437908 1 0.6708204 0.5 0.2236068
9 1.416911 1 0.6708204 0.5 0.2236068
10 1.448638 1 -0.2236068 -0.5 0.6708204
11 1.428375 1 -0.6708204 0.5 -0.2236068
12 1.450130 1 0.2236068 -0.5 -0.6708204
13 1.420545 1 0.2236068 -0.5 -0.6708204
14 1.423005 1 -0.6708204 0.5 -0.2236068
15 1.435902 1 -0.6708204 0.5 -0.2236068
16 1.423901 1 0.6708204 0.5 0.2236068
17 1.457208 1 -0.2236068 -0.5 0.6708204
18 1.414280 1 0.2236068 -0.5 -0.6708204
19 1.443383 1 0.6708204 0.5 0.2236068
20 1.434954 1 -0.6708204 0.5 -0.2236068
21 1.429499 1 0.2236068 -0.5 -0.6708204
22 1.441897 1 0.6708204 0.5 0.2236068
23 1.423713 1 0.6708204 0.5 0.2236068
24 1.435395 1 -0.2236068 -0.5 0.6708204
25 1.425944 1 -0.6708204 0.5 -0.2236068
26 1.437115 1 0.2236068 -0.5 -0.6708204
27 1.441326 1 0.6708204 0.5 0.2236068
28 1.422953 1 -0.6708204 0.5 -0.2236068
29 1.437797 1 0.6708204 0.5 0.2236068
30 1.472121 1 0.6708204 0.5 0.2236068
31 1.421782 1 -0.2236068 -0.5 0.6708204
32 1.457672 1 0.2236068 -0.5 -0.6708204
33 1.430842 1 0.2236068 -0.5 -0.6708204
34 1.431523 1 -0.6708204 0.5 -0.2236068
35 1.421395 1 -0.6708204 0.5 -0.2236068
36 1.434496 1 0.6708204 0.5 0.2236068
37 1.425383 1 0.6708204 0.5 0.2236068
38 1.421802 1 0.6708204 0.5 0.2236068
39 1.430094 1 -0.6708204 0.5 -0.2236068
40 1.447621 1 -0.2236068 -0.5 0.6708204
41 1.434797 1 -0.6708204 0.5 -0.2236068
42 1.446091 1 -0.6708204 0.5 -0.2236068
43 1.445306 1 -0.2236068 -0.5 0.6708204
44 1.448783 1 -0.2236068 -0.5 0.6708204
45 1.450617 1 -0.6708204 0.5 -0.2236068
46 1.415055 1 -0.6708204 0.5 -0.2236068
47 1.436590 1 0.6708204 0.5 0.2236068
48 1.433938 1 0.6708204 0.5 0.2236068
49 1.414941 1 -0.2236068 -0.5 0.6708204
50 1.421807 1 -0.2236068 -0.5 0.6708204
51 1.453203 1 -0.6708204 0.5 -0.2236068
52 1.452129 1 0.2236068 -0.5 -0.6708204
53 1.431510 1 -0.6708204 0.5 -0.2236068
54 1.430082 1 0.2236068 -0.5 -0.6708204
55 1.443492 1 -0.2236068 -0.5 0.6708204
56 1.436460 1 0.6708204 0.5 0.2236068
57 1.418119 1 -0.2236068 -0.5 0.6708204
58 1.434971 1 -0.6708204 0.5 -0.2236068
59 1.445599 1 -0.6708204 0.5 -0.2236068
60 1.437097 1 0.6708204 0.5 0.2236068
61 1.428360 1 -0.2236068 -0.5 0.6708204
62 1.440550 1 0.6708204 0.5 0.2236068
63 1.443014 1 0.2236068 -0.5 -0.6708204
64 1.424298 1 -0.2236068 -0.5 0.6708204
65 1.448823 1 0.2236068 -0.5 -0.6708204
66 1.425834 1 0.2236068 -0.5 -0.6708204
67 1.427102 1 -0.2236068 -0.5 0.6708204
68 1.414240 1 -0.6708204 0.5 -0.2236068
69 1.456218 1 -0.6708204 0.5 -0.2236068
70 1.470594 1 -0.6708204 0.5 -0.2236068
71 1.425058 1 -0.6708204 0.5 -0.2236068
72 1.432371 1 0.2236068 -0.5 -0.6708204
73 1.441656 1 -0.2236068 -0.5 0.6708204
74 1.434952 1 -0.2236068 -0.5 0.6708204
75 1.402860 1 0.2236068 -0.5 -0.6708204
76 1.453363 1 0.2236068 -0.5 -0.6708204
77 1.432909 1 0.6708204 0.5 0.2236068
78 1.435103 1 0.2236068 -0.5 -0.6708204
79 1.434462 1 -0.2236068 -0.5 0.6708204
80 1.434661 1 -0.2236068 -0.5 0.6708204
81 1.445881 1 0.2236068 -0.5 -0.6708204
82 1.442548 1 -0.6708204 0.5 -0.2236068
83 1.430097 1 0.2236068 -0.5 -0.6708204
84 1.430119 1 -0.2236068 -0.5 0.6708204
85 1.430315 1 -0.2236068 -0.5 0.6708204
86 1.437584 1 0.6708204 0.5 0.2236068
87 1.409738 1 0.2236068 -0.5 -0.6708204
88 1.422388 1 -0.2236068 -0.5 0.6708204
89 1.422509 1 0.2236068 -0.5 -0.6708204
90 1.439432 1 0.2236068 -0.5 -0.6708204
91 1.430175 1 0.6708204 0.5 0.2236068
92 1.418002 1 -0.6708204 0.5 -0.2236068
93 1.423812 1 0.6708204 0.5 0.2236068
94 1.423473 1 -0.6708204 0.5 -0.2236068
95 1.434412 1 -0.6708204 0.5 -0.2236068
96 1.450844 1 0.2236068 -0.5 -0.6708204
97 1.433371 1 -0.6708204 0.5 -0.2236068
98 1.444378 1 0.2236068 -0.5 -0.6708204
99 1.422523 1 0.2236068 -0.5 -0.6708204
100 1.410394 1 0.2236068 -0.5 -0.6708204
$m3a$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
$m3b
$m3b$M_lvlone
C1 O2 (Intercept) O22 O23 O24
1 1.410531 4 1 NA NA NA
2 1.434183 4 1 NA NA NA
3 1.430994 4 1 NA NA NA
4 1.453096 1 1 NA NA NA
5 1.438344 2 1 NA NA NA
6 1.453207 3 1 NA NA NA
7 1.425176 4 1 NA NA NA
8 1.437908 2 1 NA NA NA
9 1.416911 4 1 NA NA NA
10 1.448638 3 1 NA NA NA
11 1.428375 2 1 NA NA NA
12 1.450130 1 1 NA NA NA
13 1.420545 1 1 NA NA NA
14 1.423005 1 1 NA NA NA
15 1.435902 4 1 NA NA NA
16 1.423901 3 1 NA NA NA
17 1.457208 3 1 NA NA NA
18 1.414280 1 1 NA NA NA
19 1.443383 3 1 NA NA NA
20 1.434954 1 1 NA NA NA
21 1.429499 3 1 NA NA NA
22 1.441897 3 1 NA NA NA
23 1.423713 2 1 NA NA NA
24 1.435395 3 1 NA NA NA
25 1.425944 2 1 NA NA NA
26 1.437115 2 1 NA NA NA
27 1.441326 1 1 NA NA NA
28 1.422953 4 1 NA NA NA
29 1.437797 3 1 NA NA NA
30 1.472121 3 1 NA NA NA
31 1.421782 2 1 NA NA NA
32 1.457672 2 1 NA NA NA
33 1.430842 1 1 NA NA NA
34 1.431523 1 1 NA NA NA
35 1.421395 4 1 NA NA NA
36 1.434496 3 1 NA NA NA
37 1.425383 3 1 NA NA NA
38 1.421802 1 1 NA NA NA
39 1.430094 2 1 NA NA NA
40 1.447621 3 1 NA NA NA
41 1.434797 3 1 NA NA NA
42 1.446091 3 1 NA NA NA
43 1.445306 3 1 NA NA NA
44 1.448783 4 1 NA NA NA
45 1.450617 4 1 NA NA NA
46 1.415055 1 1 NA NA NA
47 1.436590 4 1 NA NA NA
48 1.433938 4 1 NA NA NA
49 1.414941 1 1 NA NA NA
50 1.421807 2 1 NA NA NA
51 1.453203 1 1 NA NA NA
52 1.452129 3 1 NA NA NA
53 1.431510 2 1 NA NA NA
54 1.430082 1 1 NA NA NA
55 1.443492 2 1 NA NA NA
56 1.436460 3 1 NA NA NA
57 1.418119 NA 1 NA NA NA
58 1.434971 4 1 NA NA NA
59 1.445599 4 1 NA NA NA
60 1.437097 3 1 NA NA NA
61 1.428360 4 1 NA NA NA
62 1.440550 1 1 NA NA NA
63 1.443014 4 1 NA NA NA
64 1.424298 4 1 NA NA NA
65 1.448823 4 1 NA NA NA
66 1.425834 1 1 NA NA NA
67 1.427102 3 1 NA NA NA
68 1.414240 3 1 NA NA NA
69 1.456218 4 1 NA NA NA
70 1.470594 1 1 NA NA NA
71 1.425058 4 1 NA NA NA
72 1.432371 4 1 NA NA NA
73 1.441656 2 1 NA NA NA
74 1.434952 4 1 NA NA NA
75 1.402860 3 1 NA NA NA
76 1.453363 2 1 NA NA NA
77 1.432909 2 1 NA NA NA
78 1.435103 3 1 NA NA NA
79 1.434462 2 1 NA NA NA
80 1.434661 1 1 NA NA NA
81 1.445881 4 1 NA NA NA
82 1.442548 2 1 NA NA NA
83 1.430097 4 1 NA NA NA
84 1.430119 1 1 NA NA NA
85 1.430315 1 1 NA NA NA
86 1.437584 2 1 NA NA NA
87 1.409738 3 1 NA NA NA
88 1.422388 3 1 NA NA NA
89 1.422509 2 1 NA NA NA
90 1.439432 4 1 NA NA NA
91 1.430175 2 1 NA NA NA
92 1.418002 1 1 NA NA NA
93 1.423812 NA 1 NA NA NA
94 1.423473 3 1 NA NA NA
95 1.434412 1 1 NA NA NA
96 1.450844 3 1 NA NA NA
97 1.433371 2 1 NA NA NA
98 1.444378 2 1 NA NA NA
99 1.422523 4 1 NA NA NA
100 1.410394 3 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
$m4a
$m4a$M_lvlone
O1 C2 M2 O2 (Intercept) M22 M23 M24 O22 O23 O24 abs(C1 - C2)
1 2 0.144065882 4 4 1 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 NA NA NA NA NA NA NA
40 2 NA 3 3 1 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 NA NA NA NA NA NA NA
64 2 NA 2 4 1 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 NA NA NA NA NA NA NA
79 2 NA 2 2 1 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 NA NA NA NA NA NA NA
84 2 NA 1 1 1 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 NA NA NA NA NA NA NA
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) C1
1 0.3439662 NA NA NA 1.410531
2 0.3605954 NA NA NA 1.434183
3 0.3583696 NA NA NA 1.430994
4 0.3736964 NA NA NA 1.453096
5 0.3634928 NA NA NA 1.438344
6 0.3737730 NA NA NA 1.453207
7 0.3542952 NA NA NA 1.425176
8 0.3631892 NA NA NA 1.437908
9 0.3484794 NA NA NA 1.416911
10 0.3706241 NA NA NA 1.448638
11 0.3565373 NA NA NA 1.428375
12 0.3716534 NA NA NA 1.450130
13 0.3510408 NA NA NA 1.420545
14 0.3527707 NA NA NA 1.423005
15 0.3617934 NA NA NA 1.435902
16 0.3534000 NA NA NA 1.423901
17 0.3765220 NA NA NA 1.457208
18 0.3466206 NA NA NA 1.414280
19 0.3669896 NA NA NA 1.443383
20 0.3611331 NA NA NA 1.434954
21 0.3573242 NA NA NA 1.429499
22 0.3659595 NA NA NA 1.441897
23 0.3532680 NA NA NA 1.423713
24 0.3614400 NA NA NA 1.435395
25 0.3548341 NA NA NA 1.425944
26 0.3626380 NA NA NA 1.437115
27 0.3655634 NA NA NA 1.441326
28 0.3527344 NA NA NA 1.422953
29 0.3631120 NA NA NA 1.437797
30 0.3867045 NA NA NA 1.472121
31 0.3519109 NA NA NA 1.421782
32 0.3768405 NA NA NA 1.457672
33 0.3582630 NA NA NA 1.430842
34 0.3587390 NA NA NA 1.431523
35 0.3516387 NA NA NA 1.421395
36 0.3608133 NA NA NA 1.434496
37 0.3544406 NA NA NA 1.425383
38 0.3519254 NA NA NA 1.421802
39 0.3577404 NA NA NA 1.430094
40 0.3699214 NA NA NA 1.447621
41 0.3610235 NA NA NA 1.434797
42 0.3688639 NA NA NA 1.446091
43 0.3683210 NA NA NA 1.445306
44 0.3707242 NA NA NA 1.448783
45 0.3719890 NA NA NA 1.450617
46 0.3471687 NA NA NA 1.415055
47 0.3622725 NA NA NA 1.436590
48 0.3604242 NA NA NA 1.433938
49 0.3470878 NA NA NA 1.414941
50 0.3519288 NA NA NA 1.421807
51 0.3737703 NA NA NA 1.453203
52 0.3730309 NA NA NA 1.452129
53 0.3587298 NA NA NA 1.431510
54 0.3577317 NA NA NA 1.430082
55 0.3670651 NA NA NA 1.443492
56 0.3621821 NA NA NA 1.436460
57 0.3493310 NA NA NA 1.418119
58 0.3611449 NA NA NA 1.434971
59 0.3685236 NA NA NA 1.445599
60 0.3626252 NA NA NA 1.437097
61 0.3565271 NA NA NA 1.428360
62 0.3650248 NA NA NA 1.440550
63 0.3667342 NA NA NA 1.443014
64 0.3536790 NA NA NA 1.424298
65 0.3707512 NA NA NA 1.448823
66 0.3547570 NA NA NA 1.425834
67 0.3556460 NA NA NA 1.427102
68 0.3465922 NA NA NA 1.414240
69 0.3758430 NA NA NA 1.456218
70 0.3856661 NA NA NA 1.470594
71 0.3542125 NA NA NA 1.425058
72 0.3593309 NA NA NA 1.432371
73 0.3657925 NA NA NA 1.441656
74 0.3611311 NA NA NA 1.434952
75 0.3385130 NA NA NA 1.402860
76 0.3738804 NA NA NA 1.453363
77 0.3597065 NA NA NA 1.432909
78 0.3612366 NA NA NA 1.435103
79 0.3607899 NA NA NA 1.434462
80 0.3609283 NA NA NA 1.434661
81 0.3687189 NA NA NA 1.445881
82 0.3664112 NA NA NA 1.442548
83 0.3577425 NA NA NA 1.430097
84 0.3577579 NA NA NA 1.430119
85 0.3578947 NA NA NA 1.430315
86 0.3629637 NA NA NA 1.437584
87 0.3434041 NA NA NA 1.409738
88 0.3523374 NA NA NA 1.422388
89 0.3524220 NA NA NA 1.422509
90 0.3642486 NA NA NA 1.439432
91 0.3577968 NA NA NA 1.430175
92 0.3492491 NA NA NA 1.418002
93 0.3533376 NA NA NA 1.423812
94 0.3530999 NA NA NA 1.423473
95 0.3607553 NA NA NA 1.434412
96 0.3721453 NA NA NA 1.450844
97 0.3600291 NA NA NA 1.433371
98 0.3676785 NA NA NA 1.444378
99 0.3524318 NA NA NA 1.422523
100 0.3438689 NA NA NA 1.410394
$m4a$spM_lvlone
center scale
O1 NA NA
C2 -0.06490582 0.333173465
M2 NA NA
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
abs(C1 - C2) 1.49900534 0.334214181
log(C1) 0.36049727 0.009050336
O22:abs(C1 - C2) 0.31342466 0.618807150
O23:abs(C1 - C2) 0.47068368 0.762352624
O24:abs(C1 - C2) 0.40568706 0.692690317
C1 1.43410054 0.012996511
$m4a$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
$m4b
$m4b$M_lvlone
O1 C2 O2 (Intercept)
1 2 0.144065882 4 1
2 4 0.032778478 4 1
3 3 0.343008492 4 1
4 2 -0.361887858 1 1
5 3 -0.389600647 2 1
6 1 -0.205306841 3 1
7 3 0.079434830 4 1
8 4 -0.331246757 2 1
9 4 -0.329638800 4 1
10 2 0.167597533 3 1
11 1 0.860207989 2 1
12 3 0.022730640 1 1
13 3 0.217171172 1 1
14 1 -0.403002412 1 1
15 1 0.087369742 4 1
16 4 -0.183870429 3 1
17 2 -0.194577002 3 1
18 3 -0.349718516 1 1
19 4 -0.508781244 3 1
20 1 0.494883111 1 1
21 3 0.258041067 3 1
22 4 -0.922621989 3 1
23 4 0.431254949 2 1
24 2 -0.294218881 3 1
25 1 -0.425548895 2 1
26 3 0.057176054 2 1
27 4 0.289090158 1 1
28 1 -0.473079489 4 1
29 4 -0.385664863 3 1
30 4 -0.154780107 3 1
31 2 0.100536296 2 1
32 3 0.634791958 2 1
33 3 -0.387252617 1 1
34 1 -0.181741088 1 1
35 1 -0.311562695 4 1
36 4 -0.044115907 3 1
37 4 -0.657409991 3 1
38 4 0.159577214 1 1
39 1 -0.460416933 2 1
40 2 NA 3 1
41 1 -0.248909867 3 1
42 1 -0.609021545 3 1
43 2 0.025471883 3 1
44 2 0.066648592 4 1
45 1 -0.276108719 4 1
46 1 -0.179737577 1 1
47 4 0.181190937 4 1
48 4 -0.453871693 4 1
49 2 0.448629602 1 1
50 2 -0.529811821 2 1
51 1 -0.028304571 1 1
52 3 -0.520318482 3 1
53 1 0.171317619 2 1
54 3 0.432732046 1 1
55 2 -0.346286005 2 1
56 4 -0.469375653 3 1
57 2 0.031021711 NA 1
58 1 -0.118837515 4 1
59 1 0.507769984 4 1
60 4 0.271797031 3 1
61 2 -0.124442204 4 1
62 4 0.277677389 1 1
63 3 -0.102893730 4 1
64 2 NA 4 1
65 3 -0.678303052 4 1
66 3 0.478880037 1 1
67 2 -0.428028760 3 1
68 1 0.048119185 3 1
69 1 0.216932805 4 1
70 1 -0.234575269 1 1
71 1 0.006827078 4 1
72 3 -0.456055171 4 1
73 2 0.346486708 2 1
74 2 0.205092215 4 1
75 3 -0.136596858 3 1
76 3 -0.500179043 2 1
77 4 0.527352086 2 1
78 3 0.022742250 3 1
79 2 NA 2 1
80 2 -0.002032440 1 1
81 3 -0.154246160 4 1
82 1 0.140201825 2 1
83 3 -0.141417121 4 1
84 2 NA 1 1
85 2 -0.021285339 1 1
86 4 -0.010196306 2 1
87 3 -0.089747520 3 1
88 2 -0.083699898 3 1
89 3 -0.044061996 2 1
90 3 -0.209291697 4 1
91 4 0.639036426 2 1
92 1 0.094698299 1 1
93 4 -0.055510622 NA 1
94 1 -0.421318463 3 1
95 1 0.125295503 1 1
96 3 0.213084904 3 1
97 1 -0.161914659 2 1
98 3 -0.034767685 2 1
99 3 -0.320681689 4 1
100 3 0.058192962 3 1
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) abs(C1 - C2) log(C1)
1 NA NA 0.3439662
2 NA NA 0.3605954
3 NA NA 0.3583696
4 NA NA 0.3736964
5 NA NA 0.3634928
6 NA NA 0.3737730
7 NA NA 0.3542952
8 NA NA 0.3631892
9 NA NA 0.3484794
10 NA NA 0.3706241
11 NA NA 0.3565373
12 NA NA 0.3716534
13 NA NA 0.3510408
14 NA NA 0.3527707
15 NA NA 0.3617934
16 NA NA 0.3534000
17 NA NA 0.3765220
18 NA NA 0.3466206
19 NA NA 0.3669896
20 NA NA 0.3611331
21 NA NA 0.3573242
22 NA NA 0.3659595
23 NA NA 0.3532680
24 NA NA 0.3614400
25 NA NA 0.3548341
26 NA NA 0.3626380
27 NA NA 0.3655634
28 NA NA 0.3527344
29 NA NA 0.3631120
30 NA NA 0.3867045
31 NA NA 0.3519109
32 NA NA 0.3768405
33 NA NA 0.3582630
34 NA NA 0.3587390
35 NA NA 0.3516387
36 NA NA 0.3608133
37 NA NA 0.3544406
38 NA NA 0.3519254
39 NA NA 0.3577404
40 NA NA 0.3699214
41 NA NA 0.3610235
42 NA NA 0.3688639
43 NA NA 0.3683210
44 NA NA 0.3707242
45 NA NA 0.3719890
46 NA NA 0.3471687
47 NA NA 0.3622725
48 NA NA 0.3604242
49 NA NA 0.3470878
50 NA NA 0.3519288
51 NA NA 0.3737703
52 NA NA 0.3730309
53 NA NA 0.3587298
54 NA NA 0.3577317
55 NA NA 0.3670651
56 NA NA 0.3621821
57 NA NA 0.3493310
58 NA NA 0.3611449
59 NA NA 0.3685236
60 NA NA 0.3626252
61 NA NA 0.3565271
62 NA NA 0.3650248
63 NA NA 0.3667342
64 NA NA 0.3536790
65 NA NA 0.3707512
66 NA NA 0.3547570
67 NA NA 0.3556460
68 NA NA 0.3465922
69 NA NA 0.3758430
70 NA NA 0.3856661
71 NA NA 0.3542125
72 NA NA 0.3593309
73 NA NA 0.3657925
74 NA NA 0.3611311
75 NA NA 0.3385130
76 NA NA 0.3738804
77 NA NA 0.3597065
78 NA NA 0.3612366
79 NA NA 0.3607899
80 NA NA 0.3609283
81 NA NA 0.3687189
82 NA NA 0.3664112
83 NA NA 0.3577425
84 NA NA 0.3577579
85 NA NA 0.3578947
86 NA NA 0.3629637
87 NA NA 0.3434041
88 NA NA 0.3523374
89 NA NA 0.3524220
90 NA NA 0.3642486
91 NA NA 0.3577968
92 NA NA 0.3492491
93 NA NA 0.3533376
94 NA NA 0.3530999
95 NA NA 0.3607553
96 NA NA 0.3721453
97 NA NA 0.3600291
98 NA NA 0.3676785
99 NA NA 0.3524318
100 NA NA 0.3438689
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) O22 O23 O24 M12
1 NA NA NA NA 0
2 NA NA NA NA 1
3 NA NA NA NA 1
4 NA NA NA NA 0
5 NA NA NA NA 0
6 NA NA NA NA 0
7 NA NA NA NA 0
8 NA NA NA NA 0
9 NA NA NA NA 0
10 NA NA NA NA 1
11 NA NA NA NA 0
12 NA NA NA NA 1
13 NA NA NA NA 0
14 NA NA NA NA 0
15 NA NA NA NA 1
16 NA NA NA NA 0
17 NA NA NA NA 0
18 NA NA NA NA 0
19 NA NA NA NA 1
20 NA NA NA NA 1
21 NA NA NA NA 0
22 NA NA NA NA 0
23 NA NA NA NA 0
24 NA NA NA NA 1
25 NA NA NA NA 0
26 NA NA NA NA 0
27 NA NA NA NA 1
28 NA NA NA NA 0
29 NA NA NA NA 0
30 NA NA NA NA 0
31 NA NA NA NA 0
32 NA NA NA NA 0
33 NA NA NA NA 0
34 NA NA NA NA 1
35 NA NA NA NA 0
36 NA NA NA NA 0
37 NA NA NA NA 0
38 NA NA NA NA 1
39 NA NA NA NA 1
40 NA NA NA NA 1
41 NA NA NA NA 0
42 NA NA NA NA 0
43 NA NA NA NA 0
44 NA NA NA NA 0
45 NA NA NA NA 1
46 NA NA NA NA 0
47 NA NA NA NA 0
48 NA NA NA NA 0
49 NA NA NA NA 1
50 NA NA NA NA 1
51 NA NA NA NA 0
52 NA NA NA NA 0
53 NA NA NA NA 1
54 NA NA NA NA 0
55 NA NA NA NA 1
56 NA NA NA NA 0
57 NA NA NA NA 0
58 NA NA NA NA 0
59 NA NA NA NA 0
60 NA NA NA NA 0
61 NA NA NA NA 0
62 NA NA NA NA 0
63 NA NA NA NA 0
64 NA NA NA NA 0
65 NA NA NA NA 1
66 NA NA NA NA 0
67 NA NA NA NA 0
68 NA NA NA NA 0
69 NA NA NA NA 0
70 NA NA NA NA 1
71 NA NA NA NA 0
72 NA NA NA NA 1
73 NA NA NA NA 0
74 NA NA NA NA 1
75 NA NA NA NA 0
76 NA NA NA NA 0
77 NA NA NA NA 1
78 NA NA NA NA 0
79 NA NA NA NA 0
80 NA NA NA NA 0
81 NA NA NA NA 0
82 NA NA NA NA 0
83 NA NA NA NA 0
84 NA NA NA NA 0
85 NA NA NA NA 1
86 NA NA NA NA 0
87 NA NA NA NA 0
88 NA NA NA NA 0
89 NA NA NA NA 0
90 NA NA NA NA 0
91 NA NA NA NA 0
92 NA NA NA NA 0
93 NA NA NA NA 0
94 NA NA NA NA 0
95 NA NA NA NA 1
96 NA NA NA NA 0
97 NA NA NA NA 0
98 NA NA NA NA 0
99 NA NA NA NA 1
100 NA NA NA NA 0
M13 M14 C1 M1
1 0 0 1.410531 1
2 0 0 1.434183 2
3 0 0 1.430994 2
4 0 0 1.453096 1
5 1 0 1.438344 3
6 1 0 1.453207 3
7 1 0 1.425176 3
8 1 0 1.437908 3
9 0 0 1.416911 1
10 0 0 1.448638 2
11 0 0 1.428375 1
12 0 0 1.450130 2
13 0 0 1.420545 1
14 0 1 1.423005 4
15 0 0 1.435902 2
16 1 0 1.423901 3
17 1 0 1.457208 3
18 0 0 1.414280 1
19 0 0 1.443383 2
20 0 0 1.434954 2
21 0 0 1.429499 1
22 0 1 1.441897 4
23 0 0 1.423713 1
24 0 0 1.435395 2
25 0 0 1.425944 1
26 0 0 1.437115 1
27 0 0 1.441326 2
28 0 0 1.422953 1
29 1 0 1.437797 3
30 0 0 1.472121 1
31 0 1 1.421782 4
32 0 0 1.457672 1
33 0 1 1.430842 4
34 0 0 1.431523 2
35 0 0 1.421395 1
36 0 0 1.434496 1
37 0 0 1.425383 1
38 0 0 1.421802 2
39 0 0 1.430094 2
40 0 0 1.447621 2
41 0 1 1.434797 4
42 0 1 1.446091 4
43 0 1 1.445306 4
44 0 1 1.448783 4
45 0 0 1.450617 2
46 0 0 1.415055 1
47 1 0 1.436590 3
48 1 0 1.433938 3
49 0 0 1.414941 2
50 0 0 1.421807 2
51 1 0 1.453203 3
52 0 0 1.452129 1
53 0 0 1.431510 2
54 1 0 1.430082 3
55 0 0 1.443492 2
56 0 0 1.436460 1
57 0 1 1.418119 4
58 0 0 1.434971 1
59 0 1 1.445599 4
60 0 0 1.437097 1
61 0 0 1.428360 1
62 0 0 1.440550 1
63 0 1 1.443014 4
64 0 0 1.424298 1
65 0 0 1.448823 2
66 0 1 1.425834 4
67 0 1 1.427102 4
68 1 0 1.414240 3
69 1 0 1.456218 3
70 0 0 1.470594 2
71 1 0 1.425058 3
72 0 0 1.432371 2
73 0 1 1.441656 4
74 0 0 1.434952 2
75 0 0 1.402860 1
76 1 0 1.453363 3
77 0 0 1.432909 2
78 0 1 1.435103 4
79 0 1 1.434462 4
80 0 0 1.434661 1
81 0 0 1.445881 1
82 0 1 1.442548 4
83 1 0 1.430097 3
84 0 1 1.430119 4
85 0 0 1.430315 2
86 0 1 1.437584 4
87 1 0 1.409738 3
88 1 0 1.422388 3
89 1 0 1.422509 3
90 0 0 1.439432 1
91 0 0 1.430175 1
92 0 0 1.418002 1
93 0 0 1.423812 1
94 1 0 1.423473 3
95 0 0 1.434412 2
96 0 0 1.450844 1
97 1 0 1.433371 3
98 0 1 1.444378 4
99 0 0 1.422523 2
100 0 0 1.410394 1
$m4b$spM_lvlone
center
O1 NA
C2 -0.06490582
O2 NA
(Intercept) NA
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0.43877551
abs(C1 - C2) 1.49900534
log(C1) 0.36049727
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0.65074863
O22 NA
O23 NA
O24 NA
M12 NA
M13 NA
M14 NA
C1 1.43410054
M1 NA
scale
O1 NA
C2 0.333173465
O2 NA
(Intercept) NA
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0.498788771
abs(C1 - C2) 0.334214181
log(C1) 0.009050336
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0.780466974
O22 NA
O23 NA
O24 NA
M12 NA
M13 NA
M14 NA
C1 0.012996511
M1 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
$m5a
$m5a$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA
$m5a$spM_lvlone
center scale
O1 NA NA
C2 -0.06490582 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.43410054 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 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_multinomial
[1] 0
$m5a$tau_reg_multinomial
[1] 1e-04
$m5a$mu_reg_ordinal
[1] 0
$m5a$tau_reg_ordinal
[1] 1e-04
$m5a$mu_delta_ordinal
[1] 0
$m5a$tau_delta_ordinal
[1] 1e-04
$m5b
$m5b$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24 C1:C2
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA NA
$m5b$spM_lvlone
center scale
O1 NA NA
C2 -0.06490582 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.43410054 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
C1:C2 -0.09333951 0.47826999
$m5b$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
$m5c
$m5c$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24 C1:C2
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA NA
$m5c$spM_lvlone
center scale
O1 NA NA
C2 -0.06490582 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.43410054 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
C1:C2 -0.09333951 0.47826999
$m5c$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
$m5d
$m5d$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24 M22:C2
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA NA
M23:C2 M24:C2
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
91 NA NA
92 NA NA
93 NA NA
94 NA NA
95 NA NA
96 NA NA
97 NA NA
98 NA NA
99 NA NA
100 NA NA
$m5d$spM_lvlone
center scale
O1 NA NA
C2 -0.064905817 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.434100545 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
M22:C2 -0.035803577 0.16299962
M23:C2 -0.008443652 0.22326710
M24:C2 -0.014114090 0.17029222
$m5d$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
$m5e
$m5e$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24 M22:C2
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA NA
M23:C2 M24:C2
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
91 NA NA
92 NA NA
93 NA NA
94 NA NA
95 NA NA
96 NA NA
97 NA NA
98 NA NA
99 NA NA
100 NA NA
$m5e$spM_lvlone
center scale
O1 NA NA
C2 -0.064905817 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.434100545 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
M22:C2 -0.035803577 0.16299962
M23:C2 -0.008443652 0.22326710
M24:C2 -0.014114090 0.17029222
$m5e$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
$m6a
$m6a$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA
$m6a$spM_lvlone
center scale
O1 NA NA
C2 -0.06490582 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.43410054 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 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_multinomial
[1] 0
$m6a$tau_reg_multinomial
[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
$m6b
$m6b$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24 C1:C2
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA NA
$m6b$spM_lvlone
center scale
O1 NA NA
C2 -0.06490582 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.43410054 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
C1:C2 -0.09333951 0.47826999
$m6b$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
$m6c
$m6c$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24 C1:C2
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA NA
$m6c$spM_lvlone
center scale
O1 NA NA
C2 -0.06490582 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.43410054 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
C1:C2 -0.09333951 0.47826999
$m6c$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
$m6d
$m6d$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24 M22:C2
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA NA
M23:C2 M24:C2
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
91 NA NA
92 NA NA
93 NA NA
94 NA NA
95 NA NA
96 NA NA
97 NA NA
98 NA NA
99 NA NA
100 NA NA
$m6d$spM_lvlone
center scale
O1 NA NA
C2 -0.064905817 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.434100545 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
M22:C2 -0.035803577 0.16299962
M23:C2 -0.008443652 0.22326710
M24:C2 -0.014114090 0.17029222
$m6d$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
$m6e
$m6e$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24 M22:C2
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA NA
M23:C2 M24:C2
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
91 NA NA
92 NA NA
93 NA NA
94 NA NA
95 NA NA
96 NA NA
97 NA NA
98 NA NA
99 NA NA
100 NA NA
$m6e$spM_lvlone
center scale
O1 NA NA
C2 -0.064905817 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.434100545 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
M22:C2 -0.035803577 0.16299962
M23:C2 -0.008443652 0.22326710
M24:C2 -0.014114090 0.17029222
$m6e$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
jagsmodel remains the same
Code
lapply(models, "[[", "jagsmodel")
Output
$m0a
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- 0
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i]
logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i]
}
# Priors for the model for O1
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
}
$m0b
model {
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- 0
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]
}
# 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])
}
$m1a
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[1]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i]
logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i]
}
# 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)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
}
$m1b
model {
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[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]
}
# 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])
}
$m2a
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[1]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i]
logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i]
}
# 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)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 3] * 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 model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[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]
}
# 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])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 3] * 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)
}
$m3a
model {
# Normal model for C1 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1)
mu_C1[i] <- M_lvlone[i, 2] * beta[1] + M_lvlone[i, 3] * beta[2] +
M_lvlone[i, 4] * beta[3] + M_lvlone[i, 5] * beta[4]
}
# 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)
}
$m3b
model {
# Normal model for C1 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1)
mu_C1[i] <- M_lvlone[i, 3] * beta[1] + M_lvlone[i, 4] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4]
}
# 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)
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- 0
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, 4] <- ifelse(M_lvlone[i, 2] == 2, 1, 0)
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 3, 1, 0)
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 4, 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])
}
$m4a
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 6] * beta[1] + M_lvlone[i, 7] * beta[2] +
M_lvlone[i, 8] * beta[3] + M_lvlone[i, 9] * beta[4] +
M_lvlone[i, 10] * beta[5] + M_lvlone[i, 11] * beta[6] +
(M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[7] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[8] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[9] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[10] +
(M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[11]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i]
logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i]
}
# 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)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] +
(M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8]
M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2])
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
(M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
(M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
(M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23]
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24]
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, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12]
M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12]
M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12]
}
}
$m4b
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[2] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[3] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[4]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i]
logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i]
}
# 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)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[1] + M_lvlone[i, 9] * alpha[2] +
M_lvlone[i, 10] * alpha[3] + M_lvlone[i, 11] * alpha[4] +
M_lvlone[i, 12] * alpha[5] + M_lvlone[i, 13] * alpha[6] +
M_lvlone[i, 14] * alpha[7] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[8]
M_lvlone[i, 6] <- abs(M_lvlone[i, 15] - M_lvlone[i, 2])
}
# Priors for the model for C2
for (k in 1:8) {
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 (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- M_lvlone[i, 12] * alpha[9] + M_lvlone[i, 13] * alpha[10] +
M_lvlone[i, 14] * alpha[11] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[12]
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, 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)
M_lvlone[i, 5] <- ifelse((M_lvlone[i, 3]) > (M_lvlone[i, 16]), 1, 0)
}
# Priors for the model for O2
for (k in 9:12) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6]
}
}
$m5a
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] +
M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] +
M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6]
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[9] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[10]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[11] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[12]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# 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)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
}
$m5b
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] +
M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] +
M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7]
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[9]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# Priors for the model for O1
for (k in 1:13) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2]
}
}
$m5c
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] +
M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] +
M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6]
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[12]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[14] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[15]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# Priors for the model for O1
for (k in 1:15) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2]
}
}
$m5d
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] +
M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] +
M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[8] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[9]
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[14] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[15]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# Priors for the model for O1
for (k in 1:15) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2]
M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2]
M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2]
}
}
$m5e
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- 0
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[1] +
M_lvlone[i, 7] * beta[2] + M_lvlone[i, 8] * beta[3] +
M_lvlone[i, 9] * beta[4] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] +
M_lvlone[i, 10] * beta[6] + M_lvlone[i, 11] * beta[7] +
M_lvlone[i, 12] * beta[8] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] +
M_lvlone[i, 7] * beta[13] + M_lvlone[i, 8] * beta[14] +
M_lvlone[i, 9] * beta[15] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[16] +
M_lvlone[i, 10] * beta[17] + M_lvlone[i, 11] * beta[18] +
M_lvlone[i, 12] * beta[19] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[20] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[21] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[22]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[23] +
M_lvlone[i, 7] * beta[24] + M_lvlone[i, 8] * beta[25] +
M_lvlone[i, 9] * beta[26] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[27] +
M_lvlone[i, 10] * beta[28] + M_lvlone[i, 11] * beta[29] +
M_lvlone[i, 12] * beta[30] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[31] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[32] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[33]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# Priors for the model for O1
for (k in 1:33) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2]
M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2]
M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2]
}
}
$m6a
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] +
M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] +
M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6]
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[9] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[10]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[11] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[12]
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] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2]))
p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3])))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# 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)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
}
$m6b
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] +
M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] +
M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7]
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[9]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13]
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] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2]))
p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3])))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# Priors for the model for O1
for (k in 1:13) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2]
}
}
$m6c
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] +
M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] +
M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6]
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[12]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[14] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[15]
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] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2]))
p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3])))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# Priors for the model for O1
for (k in 1:15) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2]
}
}
$m6d
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] +
M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] +
M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[8] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[9]
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[14] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[15]
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] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2]))
p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3])))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# Priors for the model for O1
for (k in 1:15) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2]
M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2]
M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2]
}
}
$m6e
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- 0
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[1] +
M_lvlone[i, 7] * beta[2] + M_lvlone[i, 8] * beta[3] +
M_lvlone[i, 9] * beta[4] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] +
M_lvlone[i, 10] * beta[6] + M_lvlone[i, 11] * beta[7] +
M_lvlone[i, 12] * beta[8] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] +
M_lvlone[i, 7] * beta[13] + M_lvlone[i, 8] * beta[14] +
M_lvlone[i, 9] * beta[15] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[16] +
M_lvlone[i, 10] * beta[17] + M_lvlone[i, 11] * beta[18] +
M_lvlone[i, 12] * beta[19] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[20] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[21] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[22]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[23] +
M_lvlone[i, 7] * beta[24] + M_lvlone[i, 8] * beta[25] +
M_lvlone[i, 9] * beta[26] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[27] +
M_lvlone[i, 10] * beta[28] + M_lvlone[i, 11] * beta[29] +
M_lvlone[i, 12] * beta[30] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[31] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[32] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[33]
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] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2]))
p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3])))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# Priors for the model for O1
for (k in 1:33) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2]
M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2]
M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2]
}
}
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
gamma_O1[3] 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
$m1a
Potential scale reduction factors:
Point est. Upper C.I.
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] NaN NaN
C1 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
$m2a
Potential scale reduction factors:
Point est. Upper C.I.
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] NaN NaN
C2 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
$m3a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
sigma_C1 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
$m4a
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
O24 NaN NaN
abs(C1 - C2) NaN NaN
log(C1) 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
gamma_O1[3] NaN NaN
$m4b
Potential scale reduction factors:
Point est.
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) NaN
abs(C1 - C2) NaN
log(C1) NaN
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
gamma_O1[1] NaN
gamma_O1[2] NaN
gamma_O1[3] NaN
Upper C.I.
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) NaN
abs(C1 - C2) NaN
log(C1) NaN
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
gamma_O1[1] NaN
gamma_O1[2] NaN
gamma_O1[3] NaN
$m5a
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
O24 NaN NaN
O12: C1 NaN NaN
O12: C2 NaN NaN
O13: C1 NaN NaN
O13: C2 NaN NaN
O14: C1 NaN NaN
O14: C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] 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
O24 NaN NaN
C1:C2 NaN NaN
O12: C1 NaN NaN
O12: C2 NaN NaN
O13: C1 NaN NaN
O13: C2 NaN NaN
O14: C1 NaN NaN
O14: C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] 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
O24 NaN NaN
O12: C1 NaN NaN
O12: C2 NaN NaN
O12: C1:C2 NaN NaN
O13: C1 NaN NaN
O13: C2 NaN NaN
O13: C1:C2 NaN NaN
O14: C1 NaN NaN
O14: C2 NaN NaN
O14: C1:C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] 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
O24 NaN NaN
M22:C2 NaN NaN
M23:C2 NaN NaN
M24:C2 NaN NaN
O12: C1 NaN NaN
O12: C2 NaN NaN
O13: C1 NaN NaN
O13: C2 NaN NaN
O14: C1 NaN NaN
O14: C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] NaN NaN
$m5e
Potential scale reduction factors:
Point est. Upper C.I.
O12: C1 NaN NaN
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: O24 NaN NaN
O12: M22:C2 NaN NaN
O12: M23:C2 NaN NaN
O12: M24:C2 NaN NaN
O13: C1 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: O24 NaN NaN
O13: M22:C2 NaN NaN
O13: M23:C2 NaN NaN
O13: M24:C2 NaN NaN
O14: C1 NaN NaN
O14: M22 NaN NaN
O14: M23 NaN NaN
O14: M24 NaN NaN
O14: C2 NaN NaN
O14: O22 NaN NaN
O14: O23 NaN NaN
O14: O24 NaN NaN
O14: M22:C2 NaN NaN
O14: M23:C2 NaN NaN
O14: M24:C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] NaN NaN
$m6a
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
O24 NaN NaN
O12: C1 NaN NaN
O12: C2 NaN NaN
O13: C1 NaN NaN
O13: C2 NaN NaN
O14: C1 NaN NaN
O14: C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] 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
O24 NaN NaN
C1:C2 NaN NaN
O12: C1 NaN NaN
O12: C2 NaN NaN
O13: C1 NaN NaN
O13: C2 NaN NaN
O14: C1 NaN NaN
O14: C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] 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
O24 NaN NaN
O12: C1 NaN NaN
O12: C2 NaN NaN
O12: C1:C2 NaN NaN
O13: C1 NaN NaN
O13: C2 NaN NaN
O13: C1:C2 NaN NaN
O14: C1 NaN NaN
O14: C2 NaN NaN
O14: C1:C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] 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
O24 NaN NaN
M22:C2 NaN NaN
M23:C2 NaN NaN
M24:C2 NaN NaN
O12: C1 NaN NaN
O12: C2 NaN NaN
O13: C1 NaN NaN
O13: C2 NaN NaN
O14: C1 NaN NaN
O14: C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] NaN NaN
$m6e
Potential scale reduction factors:
Point est. Upper C.I.
O12: C1 NaN NaN
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: O24 NaN NaN
O12: M22:C2 NaN NaN
O12: M23:C2 NaN NaN
O12: M24:C2 NaN NaN
O13: C1 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: O24 NaN NaN
O13: M22:C2 NaN NaN
O13: M23:C2 NaN NaN
O13: M24:C2 NaN NaN
O14: C1 NaN NaN
O14: M22 NaN NaN
O14: M23 NaN NaN
O14: M24 NaN NaN
O14: C2 NaN NaN
O14: O22 NaN NaN
O14: O23 NaN NaN
O14: O24 NaN NaN
O14: M22:C2 NaN NaN
O14: M23:C2 NaN NaN
O14: M24:C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] 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"
$m0a
est MCSE SD MCSE/SD
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 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
$m1a
est MCSE SD MCSE/SD
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 0 0 0 NaN
C1 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
$m2a
est MCSE SD MCSE/SD
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 0 0 0 NaN
C2 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
$m3a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
sigma_C1 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
$m4a
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
O24 0 0 0 NaN
abs(C1 - C2) 0 0 0 NaN
log(C1) 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
gamma_O1[3] 0 0 0 NaN
$m4b
est MCSE SD MCSE/SD
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 0 0 NaN
abs(C1 - C2) 0 0 0 NaN
log(C1) 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
gamma_O1[3] 0 0 0 NaN
$m5a
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
O24 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
O14: C1 0 0 0 NaN
O14: C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 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
O24 0 0 0 NaN
C1:C2 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
O14: C1 0 0 0 NaN
O14: C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 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
O24 0 0 0 NaN
O12: C1 0 0 0 NaN
O12: C2 0 0 0 NaN
O12: C1:C2 0 0 0 NaN
O13: C1 0 0 0 NaN
O13: C2 0 0 0 NaN
O13: C1:C2 0 0 0 NaN
O14: C1 0 0 0 NaN
O14: C2 0 0 0 NaN
O14: C1:C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 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
O24 0 0 0 NaN
M22:C2 0 0 0 NaN
M23:C2 0 0 0 NaN
M24:C2 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
O14: C1 0 0 0 NaN
O14: C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 0 0 0 NaN
$m5e
est MCSE SD MCSE/SD
O12: C1 0 0 0 NaN
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: O24 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: C1 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: O24 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
O14: C1 0 0 0 NaN
O14: M22 0 0 0 NaN
O14: M23 0 0 0 NaN
O14: M24 0 0 0 NaN
O14: C2 0 0 0 NaN
O14: O22 0 0 0 NaN
O14: O23 0 0 0 NaN
O14: O24 0 0 0 NaN
O14: M22:C2 0 0 0 NaN
O14: M23:C2 0 0 0 NaN
O14: M24:C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 0 0 0 NaN
$m6a
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
O24 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
O14: C1 0 0 0 NaN
O14: C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 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
O24 0 0 0 NaN
C1:C2 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
O14: C1 0 0 0 NaN
O14: C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 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
O24 0 0 0 NaN
O12: C1 0 0 0 NaN
O12: C2 0 0 0 NaN
O12: C1:C2 0 0 0 NaN
O13: C1 0 0 0 NaN
O13: C2 0 0 0 NaN
O13: C1:C2 0 0 0 NaN
O14: C1 0 0 0 NaN
O14: C2 0 0 0 NaN
O14: C1:C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 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
O24 0 0 0 NaN
M22:C2 0 0 0 NaN
M23:C2 0 0 0 NaN
M24:C2 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
O14: C1 0 0 0 NaN
O14: C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 0 0 0 NaN
$m6e
est MCSE SD MCSE/SD
O12: C1 0 0 0 NaN
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: O24 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: C1 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: O24 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
O14: C1 0 0 0 NaN
O14: M22 0 0 0 NaN
O14: M23 0 0 0 NaN
O14: M24 0 0 0 NaN
O14: C2 0 0 0 NaN
O14: O22 0 0 0 NaN
O14: O23 0 0 0 NaN
O14: O24 0 0 0 NaN
O14: M22:C2 0 0 0 NaN
O14: M23:C2 0 0 0 NaN
O14: M24:C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 0 0 0 NaN
summary output remained the same on Windows
Code
lapply(models0, print)
Output
Call:
clm_imp(formula = O1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3
0 0 0
Call:
clm_imp(formula = O2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O2"
Coefficients:
O2 > 1 O2 > 2 O2 > 3
0 0 0
Call:
clm_imp(formula = O1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 C1
0 0 0 0
Call:
clm_imp(formula = O2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O2"
Coefficients:
O2 > 1 O2 > 2 O2 > 3 C1
0 0 0 0
Call:
clm_imp(formula = O1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 C2
0 0 0 0
Call:
clm_imp(formula = O2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O2"
Coefficients:
O2 > 1 O2 > 2 O2 > 3 C2
0 0 0 0
Call:
lm_imp(formula = C1 ~ O1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) O1.L O1.Q O1.C
0 0 0 0
Residual standard deviation:
sigma_C1
0
Call:
lm_imp(formula = C1 ~ O2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) O22 O23 O24
0 0 0 0
Residual standard deviation:
sigma_C1
0
Call:
clm_imp(formula = O1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22
0 0 0 0
M23 M24 O22 O23
0 0 0 0
O24 abs(C1 - C2) log(C1) O22:abs(C1 - C2)
0 0 0 0
O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0
Call:
clm_imp(formula = O1 ~ ifelse(as.numeric(O2) > as.numeric(M1),
1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1
0
O1 > 2
0
O1 > 3
0
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
Call:
clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1 C2 C1 C2
0 0 0 0
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1:C2 C1
0 0 0 0 0 0 0 0 0 0 0
C2 C1 C2 C1 C2
0 0 0 0 0
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1:C2 C1 C2 C1:C2 C1 C2 C1:C2
0 0 0 0 0 0 0
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2
0 0 0 0 0 0 0 0 0 0 0
M24:C2 C1 C2 C1 C2 C1 C2
0 0 0 0 0 0 0
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2
0 0 0
Call:
clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1 C2 C1 C2
0 0 0 0
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1:C2 C1
0 0 0 0 0 0 0 0 0 0 0
C2 C1 C2 C1 C2
0 0 0 0 0
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1:C2 C1 C2 C1:C2 C1 C2 C1:C2
0 0 0 0 0 0 0
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2
0 0 0 0 0 0 0 0 0 0 0
M24:C2 C1 C2 C1 C2 C1 C2
0 0 0 0 0 0 0
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2
0 0 0
$m0a
Call:
clm_imp(formula = O1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3
0 0 0
$m0b
Call:
clm_imp(formula = O2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O2"
Coefficients:
O2 > 1 O2 > 2 O2 > 3
0 0 0
$m1a
Call:
clm_imp(formula = O1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 C1
0 0 0 0
$m1b
Call:
clm_imp(formula = O2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O2"
Coefficients:
O2 > 1 O2 > 2 O2 > 3 C1
0 0 0 0
$m2a
Call:
clm_imp(formula = O1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 C2
0 0 0 0
$m2b
Call:
clm_imp(formula = O2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O2"
Coefficients:
O2 > 1 O2 > 2 O2 > 3 C2
0 0 0 0
$m3a
Call:
lm_imp(formula = C1 ~ O1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) O1.L O1.Q O1.C
0 0 0 0
Residual standard deviation:
sigma_C1
0
$m3b
Call:
lm_imp(formula = C1 ~ O2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) O22 O23 O24
0 0 0 0
Residual standard deviation:
sigma_C1
0
$m4a
Call:
clm_imp(formula = O1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22
0 0 0 0
M23 M24 O22 O23
0 0 0 0
O24 abs(C1 - C2) log(C1) O22:abs(C1 - C2)
0 0 0 0
O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0
$m4b
Call:
clm_imp(formula = O1 ~ ifelse(as.numeric(O2) > as.numeric(M1),
1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1
0
O1 > 2
0
O1 > 3
0
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
$m5a
Call:
clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1 C2 C1 C2
0 0 0 0
$m5b
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1:C2 C1
0 0 0 0 0 0 0 0 0 0 0
C2 C1 C2 C1 C2
0 0 0 0 0
$m5c
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1:C2 C1 C2 C1:C2 C1 C2 C1:C2
0 0 0 0 0 0 0
$m5d
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2
0 0 0 0 0 0 0 0 0 0 0
M24:C2 C1 C2 C1 C2 C1 C2
0 0 0 0 0 0 0
$m5e
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2
0 0 0
$m6a
Call:
clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1 C2 C1 C2
0 0 0 0
$m6b
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1:C2 C1
0 0 0 0 0 0 0 0 0 0 0
C2 C1 C2 C1 C2
0 0 0 0 0
$m6c
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1:C2 C1 C2 C1:C2 C1 C2 C1:C2
0 0 0 0 0 0 0
$m6d
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2
0 0 0 0 0 0 0 0 0 0 0
M24:C2 C1 C2 C1 C2 C1 C2
0 0 0 0 0 0 0
$m6e
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2
0 0 0
Code
lapply(models0, coef)
Output
$m0a
$m0a$O1
O1 > 1 O1 > 2 O1 > 3
0 0 0
$m0b
$m0b$O2
O2 > 1 O2 > 2 O2 > 3
0 0 0
$m1a
$m1a$O1
C1 O1 > 1 O1 > 2 O1 > 3
0 0 0 0
$m1b
$m1b$O2
C1 O2 > 1 O2 > 2 O2 > 3
0 0 0 0
$m2a
$m2a$O1
C2 O1 > 1 O1 > 2 O1 > 3
0 0 0 0
$m2b
$m2b$O2
C2 O2 > 1 O2 > 2 O2 > 3
0 0 0 0
$m3a
$m3a$C1
(Intercept) O1.L O1.Q O1.C sigma_C1
0 0 0 0 0
$m3b
$m3b$C1
(Intercept) O22 O23 O24 sigma_C1
0 0 0 0 0
$m4a
$m4a$O1
M22 M23 M24 O22
0 0 0 0
O23 O24 abs(C1 - C2) log(C1)
0 0 0 0
O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) O1 > 1
0 0 0 0
O1 > 2 O1 > 3
0 0
$m4b
$m4b$O1
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
O1 > 1
0
O1 > 2
0
O1 > 3
0
$m5a
$m5a$O1
M22 M23 M24 O22 O23 O24 C1 C2 C1 C2 C1
0 0 0 0 0 0 0 0 0 0 0
C2 O1 > 1 O1 > 2 O1 > 3
0 0 0 0
$m5b
$m5b$O1
M22 M23 M24 O22 O23 O24 C1:C2 C1 C2 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1 C2 O1 > 1 O1 > 2 O1 > 3
0 0 0 0 0
$m5c
$m5c$O1
M22 M23 M24 O22 O23 O24 C1 C2 C1:C2 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1:C2 C1 C2 C1:C2 O1 > 1 O1 > 2 O1 > 3
0 0 0 0 0 0 0
$m5d
$m5d$O1
M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 M24:C2 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1 C2 C1 C2 O1 > 1 O1 > 2 O1 > 3
0 0 0 0 0 0 0
$m5e
$m5e$O1
C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
O1 > 1 O1 > 2 O1 > 3
0 0 0
$m6a
$m6a$O1
M22 M23 M24 O22 O23 O24 C1 C2 C1 C2 C1
0 0 0 0 0 0 0 0 0 0 0
C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3
0 0 0 0
$m6b
$m6b$O1
M22 M23 M24 O22 O23 O24 C1:C2 C1 C2 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1 C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3
0 0 0 0 0
$m6c
$m6c$O1
M22 M23 M24 O22 O23 O24 C1 C2 C1:C2 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1:C2 C1 C2 C1:C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3
0 0 0 0 0 0 0
$m6d
$m6d$O1
M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 M24:C2 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1 C2 C1 C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3
0 0 0 0 0 0 0
$m6e
$m6e$O1
C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3
0 0 0
Code
lapply(models0, confint)
Output
$m0a
$m0a$O1
2.5% 97.5%
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m0b
$m0b$O2
2.5% 97.5%
O2 > 1 0 0
O2 > 2 0 0
O2 > 3 0 0
$m1a
$m1a$O1
2.5% 97.5%
C1 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m1b
$m1b$O2
2.5% 97.5%
C1 0 0
O2 > 1 0 0
O2 > 2 0 0
O2 > 3 0 0
$m2a
$m2a$O1
2.5% 97.5%
C2 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m2b
$m2b$O2
2.5% 97.5%
C2 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
O1.C 0 0
sigma_C1 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
$m4a
$m4a$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
O22:abs(C1 - C2) 0 0
O23:abs(C1 - C2) 0 0
O24:abs(C1 - C2) 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m4b
$m4b$O1
2.5% 97.5%
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m5a
$m5a$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m5b
$m5b$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
C1:C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m5c
$m5c$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
C1 0 0
C2 0 0
C1:C2 0 0
C1 0 0
C2 0 0
C1:C2 0 0
C1 0 0
C2 0 0
C1:C2 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m5d
$m5d$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m5e
$m5e$O1
2.5% 97.5%
C1 0 0
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
O24 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
C1 0 0
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
O24 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
C1 0 0
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
O24 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m6a
$m6a$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
O1 ≤ 1 0 0
O1 ≤ 2 0 0
O1 ≤ 3 0 0
$m6b
$m6b$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
C1:C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
O1 ≤ 1 0 0
O1 ≤ 2 0 0
O1 ≤ 3 0 0
$m6c
$m6c$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
C1 0 0
C2 0 0
C1:C2 0 0
C1 0 0
C2 0 0
C1:C2 0 0
C1 0 0
C2 0 0
C1:C2 0 0
O1 ≤ 1 0 0
O1 ≤ 2 0 0
O1 ≤ 3 0 0
$m6d
$m6d$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
O1 ≤ 1 0 0
O1 ≤ 2 0 0
O1 ≤ 3 0 0
$m6e
$m6e$O1
2.5% 97.5%
C1 0 0
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
O24 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
C1 0 0
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
O24 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
C1 0 0
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
O24 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
O1 ≤ 1 0 0
O1 ≤ 2 0 0
O1 ≤ 3 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"
$m0a
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ 1, data = wideDF, 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m0b
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O2 ~ 1, data = wideDF, 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
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m1a
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1, data = wideDF, 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m1b
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O2 ~ C1, data = wideDF, 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
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m2a
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C2, data = wideDF, 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m2b
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O2 ~ C2, data = wideDF, 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
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m3a
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = C1 ~ O1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 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: 100
$m3b
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = C1 ~ O2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
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 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: 100
$m4a
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
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
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m4b
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ ifelse(as.numeric(O2) > as.numeric(M1),
1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5%
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 0 0 0
abs(C1 - C2) 0 0 0 0
log(C1) 0 0 0 0
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 0
tail-prob. GR-crit
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 NaN
abs(C1 - C2) 0 NaN
log(C1) 0 NaN
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 NaN
MCE/SD
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) NaN
abs(C1 - C2) NaN
log(C1) 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m5a
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, 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
O24 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
O14: C1 0 0 0 0 0 NaN NaN
O14: 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m5b
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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
O24 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
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
O14: C1 0 0 0 0 0 NaN NaN
O14: 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m5c
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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
O24 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
O12: C1: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
O13: C1:C2 0 0 0 0 0 NaN NaN
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
O14: 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m5d
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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
O24 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: 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
O14: C1 0 0 0 0 0 NaN NaN
O14: 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m5e
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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: C1 0 0 0 0 0 NaN NaN
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: O24 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: C1 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: O24 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
O14: C1 0 0 0 0 0 NaN NaN
O14: M22 0 0 0 0 0 NaN NaN
O14: M23 0 0 0 0 0 NaN NaN
O14: M24 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
O14: O22 0 0 0 0 0 NaN NaN
O14: O23 0 0 0 0 0 NaN NaN
O14: O24 0 0 0 0 0 NaN NaN
O14: M22:C2 0 0 0 0 0 NaN NaN
O14: M23:C2 0 0 0 0 0 NaN NaN
O14: M24: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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m6a
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, 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
O24 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
O14: C1 0 0 0 0 0 NaN NaN
O14: 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
O1 ≤ 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m6b
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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
O24 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
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
O14: C1 0 0 0 0 0 NaN NaN
O14: 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
O1 ≤ 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m6c
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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
O24 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
O12: C1: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
O13: C1:C2 0 0 0 0 0 NaN NaN
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
O14: 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
O1 ≤ 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m6d
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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
O24 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: 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
O14: C1 0 0 0 0 0 NaN NaN
O14: 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
O1 ≤ 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m6e
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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: C1 0 0 0 0 0 NaN NaN
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: O24 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: C1 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: O24 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
O14: C1 0 0 0 0 0 NaN NaN
O14: M22 0 0 0 0 0 NaN NaN
O14: M23 0 0 0 0 0 NaN NaN
O14: M24 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
O14: O22 0 0 0 0 0 NaN NaN
O14: O23 0 0 0 0 0 NaN NaN
O14: O24 0 0 0 0 0 NaN NaN
O14: M22:C2 0 0 0 0 0 NaN NaN
O14: M23:C2 0 0 0 0 0 NaN NaN
O14: M24: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
O1 ≤ 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 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"
$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
$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
$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
O1.C 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
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 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%
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 0 0 0
abs(C1 - C2) 0 0 0 0
log(C1) 0 0 0 0
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 0
tail-prob. GR-crit
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 NaN
abs(C1 - C2) 0 NaN
log(C1) 0 NaN
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 NaN
MCE/SD
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) NaN
abs(C1 - C2) NaN
log(C1) NaN
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
$m5a
$m5a$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
O24 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
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 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
O24 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
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
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 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
O24 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
O12: C1: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
O13: C1:C2 0 0 0 0 0 NaN NaN
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
O14: 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
O24 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: 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
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
$m5e
$m5e$O1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O12: C1 0 0 0 0 0 NaN NaN
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: O24 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: C1 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: O24 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
O14: C1 0 0 0 0 0 NaN NaN
O14: M22 0 0 0 0 0 NaN NaN
O14: M23 0 0 0 0 0 NaN NaN
O14: M24 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
O14: O22 0 0 0 0 0 NaN NaN
O14: O23 0 0 0 0 0 NaN NaN
O14: O24 0 0 0 0 0 NaN NaN
O14: M22:C2 0 0 0 0 0 NaN NaN
O14: M23:C2 0 0 0 0 0 NaN NaN
O14: M24:C2 0 0 0 0 0 NaN NaN
$m6a
$m6a$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
O24 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
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 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
O24 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
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
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 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
O24 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
O12: C1: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
O13: C1:C2 0 0 0 0 0 NaN NaN
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
O14: 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
O24 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: 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
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
$m6e
$m6e$O1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O12: C1 0 0 0 0 0 NaN NaN
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: O24 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: C1 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: O24 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
O14: C1 0 0 0 0 0 NaN NaN
O14: M22 0 0 0 0 0 NaN NaN
O14: M23 0 0 0 0 0 NaN NaN
O14: M24 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
O14: O22 0 0 0 0 0 NaN NaN
O14: O23 0 0 0 0 0 NaN NaN
O14: O24 0 0 0 0 0 NaN NaN
O14: M22:C2 0 0 0 0 0 NaN NaN
O14: M23:C2 0 0 0 0 0 NaN NaN
O14: M24:C2 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_lvlone
O1 (Intercept)
1 2 1
2 4 1
3 3 1
4 2 1
5 3 1
6 1 1
7 3 1
8 4 1
9 4 1
10 2 1
11 1 1
12 3 1
13 3 1
14 1 1
15 1 1
16 4 1
17 2 1
18 3 1
19 4 1
20 1 1
21 3 1
22 4 1
23 4 1
24 2 1
25 1 1
26 3 1
27 4 1
28 1 1
29 4 1
30 4 1
31 2 1
32 3 1
33 3 1
34 1 1
35 1 1
36 4 1
37 4 1
38 4 1
39 1 1
40 2 1
41 1 1
42 1 1
43 2 1
44 2 1
45 1 1
46 1 1
47 4 1
48 4 1
49 2 1
50 2 1
51 1 1
52 3 1
53 1 1
54 3 1
55 2 1
56 4 1
57 2 1
58 1 1
59 1 1
60 4 1
61 2 1
62 4 1
63 3 1
64 2 1
65 3 1
66 3 1
67 2 1
68 1 1
69 1 1
70 1 1
71 1 1
72 3 1
73 2 1
74 2 1
75 3 1
76 3 1
77 4 1
78 3 1
79 2 1
80 2 1
81 3 1
82 1 1
83 3 1
84 2 1
85 2 1
86 4 1
87 3 1
88 2 1
89 3 1
90 3 1
91 4 1
92 1 1
93 4 1
94 1 1
95 1 1
96 3 1
97 1 1
98 3 1
99 3 1
100 3 1
$m0a$mu_delta_ordinal
[1] 0
$m0a$tau_delta_ordinal
[1] 1e-04
$m0b
$m0b$M_lvlone
O2 (Intercept)
1 4 1
2 4 1
3 4 1
4 1 1
5 2 1
6 3 1
7 4 1
8 2 1
9 4 1
10 3 1
11 2 1
12 1 1
13 1 1
14 1 1
15 4 1
16 3 1
17 3 1
18 1 1
19 3 1
20 1 1
21 3 1
22 3 1
23 2 1
24 3 1
25 2 1
26 2 1
27 1 1
28 4 1
29 3 1
30 3 1
31 2 1
32 2 1
33 1 1
34 1 1
35 4 1
36 3 1
37 3 1
38 1 1
39 2 1
40 3 1
41 3 1
42 3 1
43 3 1
44 4 1
45 4 1
46 1 1
47 4 1
48 4 1
49 1 1
50 2 1
51 1 1
52 3 1
53 2 1
54 1 1
55 2 1
56 3 1
57 NA 1
58 4 1
59 4 1
60 3 1
61 4 1
62 1 1
63 4 1
64 4 1
65 4 1
66 1 1
67 3 1
68 3 1
69 4 1
70 1 1
71 4 1
72 4 1
73 2 1
74 4 1
75 3 1
76 2 1
77 2 1
78 3 1
79 2 1
80 1 1
81 4 1
82 2 1
83 4 1
84 1 1
85 1 1
86 2 1
87 3 1
88 3 1
89 2 1
90 4 1
91 2 1
92 1 1
93 NA 1
94 3 1
95 1 1
96 3 1
97 2 1
98 2 1
99 4 1
100 3 1
$m0b$mu_delta_ordinal
[1] 0
$m0b$tau_delta_ordinal
[1] 1e-04
$m1a
$m1a$M_lvlone
O1 (Intercept) C1
1 2 1 1.410531
2 4 1 1.434183
3 3 1 1.430994
4 2 1 1.453096
5 3 1 1.438344
6 1 1 1.453207
7 3 1 1.425176
8 4 1 1.437908
9 4 1 1.416911
10 2 1 1.448638
11 1 1 1.428375
12 3 1 1.450130
13 3 1 1.420545
14 1 1 1.423005
15 1 1 1.435902
16 4 1 1.423901
17 2 1 1.457208
18 3 1 1.414280
19 4 1 1.443383
20 1 1 1.434954
21 3 1 1.429499
22 4 1 1.441897
23 4 1 1.423713
24 2 1 1.435395
25 1 1 1.425944
26 3 1 1.437115
27 4 1 1.441326
28 1 1 1.422953
29 4 1 1.437797
30 4 1 1.472121
31 2 1 1.421782
32 3 1 1.457672
33 3 1 1.430842
34 1 1 1.431523
35 1 1 1.421395
36 4 1 1.434496
37 4 1 1.425383
38 4 1 1.421802
39 1 1 1.430094
40 2 1 1.447621
41 1 1 1.434797
42 1 1 1.446091
43 2 1 1.445306
44 2 1 1.448783
45 1 1 1.450617
46 1 1 1.415055
47 4 1 1.436590
48 4 1 1.433938
49 2 1 1.414941
50 2 1 1.421807
51 1 1 1.453203
52 3 1 1.452129
53 1 1 1.431510
54 3 1 1.430082
55 2 1 1.443492
56 4 1 1.436460
57 2 1 1.418119
58 1 1 1.434971
59 1 1 1.445599
60 4 1 1.437097
61 2 1 1.428360
62 4 1 1.440550
63 3 1 1.443014
64 2 1 1.424298
65 3 1 1.448823
66 3 1 1.425834
67 2 1 1.427102
68 1 1 1.414240
69 1 1 1.456218
70 1 1 1.470594
71 1 1 1.425058
72 3 1 1.432371
73 2 1 1.441656
74 2 1 1.434952
75 3 1 1.402860
76 3 1 1.453363
77 4 1 1.432909
78 3 1 1.435103
79 2 1 1.434462
80 2 1 1.434661
81 3 1 1.445881
82 1 1 1.442548
83 3 1 1.430097
84 2 1 1.430119
85 2 1 1.430315
86 4 1 1.437584
87 3 1 1.409738
88 2 1 1.422388
89 3 1 1.422509
90 3 1 1.439432
91 4 1 1.430175
92 1 1 1.418002
93 4 1 1.423812
94 1 1 1.423473
95 1 1 1.434412
96 3 1 1.450844
97 1 1 1.433371
98 3 1 1.444378
99 3 1 1.422523
100 3 1 1.410394
$m1a$spM_lvlone
center scale
O1 NA NA
(Intercept) NA NA
C1 1.434101 0.01299651
$m1a$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
$m1b
$m1b$M_lvlone
O2 (Intercept) C1
1 4 1 1.410531
2 4 1 1.434183
3 4 1 1.430994
4 1 1 1.453096
5 2 1 1.438344
6 3 1 1.453207
7 4 1 1.425176
8 2 1 1.437908
9 4 1 1.416911
10 3 1 1.448638
11 2 1 1.428375
12 1 1 1.450130
13 1 1 1.420545
14 1 1 1.423005
15 4 1 1.435902
16 3 1 1.423901
17 3 1 1.457208
18 1 1 1.414280
19 3 1 1.443383
20 1 1 1.434954
21 3 1 1.429499
22 3 1 1.441897
23 2 1 1.423713
24 3 1 1.435395
25 2 1 1.425944
26 2 1 1.437115
27 1 1 1.441326
28 4 1 1.422953
29 3 1 1.437797
30 3 1 1.472121
31 2 1 1.421782
32 2 1 1.457672
33 1 1 1.430842
34 1 1 1.431523
35 4 1 1.421395
36 3 1 1.434496
37 3 1 1.425383
38 1 1 1.421802
39 2 1 1.430094
40 3 1 1.447621
41 3 1 1.434797
42 3 1 1.446091
43 3 1 1.445306
44 4 1 1.448783
45 4 1 1.450617
46 1 1 1.415055
47 4 1 1.436590
48 4 1 1.433938
49 1 1 1.414941
50 2 1 1.421807
51 1 1 1.453203
52 3 1 1.452129
53 2 1 1.431510
54 1 1 1.430082
55 2 1 1.443492
56 3 1 1.436460
57 NA 1 1.418119
58 4 1 1.434971
59 4 1 1.445599
60 3 1 1.437097
61 4 1 1.428360
62 1 1 1.440550
63 4 1 1.443014
64 4 1 1.424298
65 4 1 1.448823
66 1 1 1.425834
67 3 1 1.427102
68 3 1 1.414240
69 4 1 1.456218
70 1 1 1.470594
71 4 1 1.425058
72 4 1 1.432371
73 2 1 1.441656
74 4 1 1.434952
75 3 1 1.402860
76 2 1 1.453363
77 2 1 1.432909
78 3 1 1.435103
79 2 1 1.434462
80 1 1 1.434661
81 4 1 1.445881
82 2 1 1.442548
83 4 1 1.430097
84 1 1 1.430119
85 1 1 1.430315
86 2 1 1.437584
87 3 1 1.409738
88 3 1 1.422388
89 2 1 1.422509
90 4 1 1.439432
91 2 1 1.430175
92 1 1 1.418002
93 NA 1 1.423812
94 3 1 1.423473
95 1 1 1.434412
96 3 1 1.450844
97 2 1 1.433371
98 2 1 1.444378
99 4 1 1.422523
100 3 1 1.410394
$m1b$spM_lvlone
center scale
O2 NA NA
(Intercept) NA NA
C1 1.434101 0.01299651
$m1b$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
$m2a
$m2a$M_lvlone
O1 C2 (Intercept)
1 2 0.144065882 1
2 4 0.032778478 1
3 3 0.343008492 1
4 2 -0.361887858 1
5 3 -0.389600647 1
6 1 -0.205306841 1
7 3 0.079434830 1
8 4 -0.331246757 1
9 4 -0.329638800 1
10 2 0.167597533 1
11 1 0.860207989 1
12 3 0.022730640 1
13 3 0.217171172 1
14 1 -0.403002412 1
15 1 0.087369742 1
16 4 -0.183870429 1
17 2 -0.194577002 1
18 3 -0.349718516 1
19 4 -0.508781244 1
20 1 0.494883111 1
21 3 0.258041067 1
22 4 -0.922621989 1
23 4 0.431254949 1
24 2 -0.294218881 1
25 1 -0.425548895 1
26 3 0.057176054 1
27 4 0.289090158 1
28 1 -0.473079489 1
29 4 -0.385664863 1
30 4 -0.154780107 1
31 2 0.100536296 1
32 3 0.634791958 1
33 3 -0.387252617 1
34 1 -0.181741088 1
35 1 -0.311562695 1
36 4 -0.044115907 1
37 4 -0.657409991 1
38 4 0.159577214 1
39 1 -0.460416933 1
40 2 NA 1
41 1 -0.248909867 1
42 1 -0.609021545 1
43 2 0.025471883 1
44 2 0.066648592 1
45 1 -0.276108719 1
46 1 -0.179737577 1
47 4 0.181190937 1
48 4 -0.453871693 1
49 2 0.448629602 1
50 2 -0.529811821 1
51 1 -0.028304571 1
52 3 -0.520318482 1
53 1 0.171317619 1
54 3 0.432732046 1
55 2 -0.346286005 1
56 4 -0.469375653 1
57 2 0.031021711 1
58 1 -0.118837515 1
59 1 0.507769984 1
60 4 0.271797031 1
61 2 -0.124442204 1
62 4 0.277677389 1
63 3 -0.102893730 1
64 2 NA 1
65 3 -0.678303052 1
66 3 0.478880037 1
67 2 -0.428028760 1
68 1 0.048119185 1
69 1 0.216932805 1
70 1 -0.234575269 1
71 1 0.006827078 1
72 3 -0.456055171 1
73 2 0.346486708 1
74 2 0.205092215 1
75 3 -0.136596858 1
76 3 -0.500179043 1
77 4 0.527352086 1
78 3 0.022742250 1
79 2 NA 1
80 2 -0.002032440 1
81 3 -0.154246160 1
82 1 0.140201825 1
83 3 -0.141417121 1
84 2 NA 1
85 2 -0.021285339 1
86 4 -0.010196306 1
87 3 -0.089747520 1
88 2 -0.083699898 1
89 3 -0.044061996 1
90 3 -0.209291697 1
91 4 0.639036426 1
92 1 0.094698299 1
93 4 -0.055510622 1
94 1 -0.421318463 1
95 1 0.125295503 1
96 3 0.213084904 1
97 1 -0.161914659 1
98 3 -0.034767685 1
99 3 -0.320681689 1
100 3 0.058192962 1
$m2a$spM_lvlone
center scale
O1 NA NA
C2 -0.06490582 0.3331735
(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
$m2b
$m2b$M_lvlone
O2 C2 (Intercept)
1 4 0.144065882 1
2 4 0.032778478 1
3 4 0.343008492 1
4 1 -0.361887858 1
5 2 -0.389600647 1
6 3 -0.205306841 1
7 4 0.079434830 1
8 2 -0.331246757 1
9 4 -0.329638800 1
10 3 0.167597533 1
11 2 0.860207989 1
12 1 0.022730640 1
13 1 0.217171172 1
14 1 -0.403002412 1
15 4 0.087369742 1
16 3 -0.183870429 1
17 3 -0.194577002 1
18 1 -0.349718516 1
19 3 -0.508781244 1
20 1 0.494883111 1
21 3 0.258041067 1
22 3 -0.922621989 1
23 2 0.431254949 1
24 3 -0.294218881 1
25 2 -0.425548895 1
26 2 0.057176054 1
27 1 0.289090158 1
28 4 -0.473079489 1
29 3 -0.385664863 1
30 3 -0.154780107 1
31 2 0.100536296 1
32 2 0.634791958 1
33 1 -0.387252617 1
34 1 -0.181741088 1
35 4 -0.311562695 1
36 3 -0.044115907 1
37 3 -0.657409991 1
38 1 0.159577214 1
39 2 -0.460416933 1
40 3 NA 1
41 3 -0.248909867 1
42 3 -0.609021545 1
43 3 0.025471883 1
44 4 0.066648592 1
45 4 -0.276108719 1
46 1 -0.179737577 1
47 4 0.181190937 1
48 4 -0.453871693 1
49 1 0.448629602 1
50 2 -0.529811821 1
51 1 -0.028304571 1
52 3 -0.520318482 1
53 2 0.171317619 1
54 1 0.432732046 1
55 2 -0.346286005 1
56 3 -0.469375653 1
57 NA 0.031021711 1
58 4 -0.118837515 1
59 4 0.507769984 1
60 3 0.271797031 1
61 4 -0.124442204 1
62 1 0.277677389 1
63 4 -0.102893730 1
64 4 NA 1
65 4 -0.678303052 1
66 1 0.478880037 1
67 3 -0.428028760 1
68 3 0.048119185 1
69 4 0.216932805 1
70 1 -0.234575269 1
71 4 0.006827078 1
72 4 -0.456055171 1
73 2 0.346486708 1
74 4 0.205092215 1
75 3 -0.136596858 1
76 2 -0.500179043 1
77 2 0.527352086 1
78 3 0.022742250 1
79 2 NA 1
80 1 -0.002032440 1
81 4 -0.154246160 1
82 2 0.140201825 1
83 4 -0.141417121 1
84 1 NA 1
85 1 -0.021285339 1
86 2 -0.010196306 1
87 3 -0.089747520 1
88 3 -0.083699898 1
89 2 -0.044061996 1
90 4 -0.209291697 1
91 2 0.639036426 1
92 1 0.094698299 1
93 NA -0.055510622 1
94 3 -0.421318463 1
95 1 0.125295503 1
96 3 0.213084904 1
97 2 -0.161914659 1
98 2 -0.034767685 1
99 4 -0.320681689 1
100 3 0.058192962 1
$m2b$spM_lvlone
center scale
O2 NA NA
C2 -0.06490582 0.3331735
(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
$m3a
$m3a$M_lvlone
C1 (Intercept) O1.L O1.Q O1.C
1 1.410531 1 -0.2236068 -0.5 0.6708204
2 1.434183 1 0.6708204 0.5 0.2236068
3 1.430994 1 0.2236068 -0.5 -0.6708204
4 1.453096 1 -0.2236068 -0.5 0.6708204
5 1.438344 1 0.2236068 -0.5 -0.6708204
6 1.453207 1 -0.6708204 0.5 -0.2236068
7 1.425176 1 0.2236068 -0.5 -0.6708204
8 1.437908 1 0.6708204 0.5 0.2236068
9 1.416911 1 0.6708204 0.5 0.2236068
10 1.448638 1 -0.2236068 -0.5 0.6708204
11 1.428375 1 -0.6708204 0.5 -0.2236068
12 1.450130 1 0.2236068 -0.5 -0.6708204
13 1.420545 1 0.2236068 -0.5 -0.6708204
14 1.423005 1 -0.6708204 0.5 -0.2236068
15 1.435902 1 -0.6708204 0.5 -0.2236068
16 1.423901 1 0.6708204 0.5 0.2236068
17 1.457208 1 -0.2236068 -0.5 0.6708204
18 1.414280 1 0.2236068 -0.5 -0.6708204
19 1.443383 1 0.6708204 0.5 0.2236068
20 1.434954 1 -0.6708204 0.5 -0.2236068
21 1.429499 1 0.2236068 -0.5 -0.6708204
22 1.441897 1 0.6708204 0.5 0.2236068
23 1.423713 1 0.6708204 0.5 0.2236068
24 1.435395 1 -0.2236068 -0.5 0.6708204
25 1.425944 1 -0.6708204 0.5 -0.2236068
26 1.437115 1 0.2236068 -0.5 -0.6708204
27 1.441326 1 0.6708204 0.5 0.2236068
28 1.422953 1 -0.6708204 0.5 -0.2236068
29 1.437797 1 0.6708204 0.5 0.2236068
30 1.472121 1 0.6708204 0.5 0.2236068
31 1.421782 1 -0.2236068 -0.5 0.6708204
32 1.457672 1 0.2236068 -0.5 -0.6708204
33 1.430842 1 0.2236068 -0.5 -0.6708204
34 1.431523 1 -0.6708204 0.5 -0.2236068
35 1.421395 1 -0.6708204 0.5 -0.2236068
36 1.434496 1 0.6708204 0.5 0.2236068
37 1.425383 1 0.6708204 0.5 0.2236068
38 1.421802 1 0.6708204 0.5 0.2236068
39 1.430094 1 -0.6708204 0.5 -0.2236068
40 1.447621 1 -0.2236068 -0.5 0.6708204
41 1.434797 1 -0.6708204 0.5 -0.2236068
42 1.446091 1 -0.6708204 0.5 -0.2236068
43 1.445306 1 -0.2236068 -0.5 0.6708204
44 1.448783 1 -0.2236068 -0.5 0.6708204
45 1.450617 1 -0.6708204 0.5 -0.2236068
46 1.415055 1 -0.6708204 0.5 -0.2236068
47 1.436590 1 0.6708204 0.5 0.2236068
48 1.433938 1 0.6708204 0.5 0.2236068
49 1.414941 1 -0.2236068 -0.5 0.6708204
50 1.421807 1 -0.2236068 -0.5 0.6708204
51 1.453203 1 -0.6708204 0.5 -0.2236068
52 1.452129 1 0.2236068 -0.5 -0.6708204
53 1.431510 1 -0.6708204 0.5 -0.2236068
54 1.430082 1 0.2236068 -0.5 -0.6708204
55 1.443492 1 -0.2236068 -0.5 0.6708204
56 1.436460 1 0.6708204 0.5 0.2236068
57 1.418119 1 -0.2236068 -0.5 0.6708204
58 1.434971 1 -0.6708204 0.5 -0.2236068
59 1.445599 1 -0.6708204 0.5 -0.2236068
60 1.437097 1 0.6708204 0.5 0.2236068
61 1.428360 1 -0.2236068 -0.5 0.6708204
62 1.440550 1 0.6708204 0.5 0.2236068
63 1.443014 1 0.2236068 -0.5 -0.6708204
64 1.424298 1 -0.2236068 -0.5 0.6708204
65 1.448823 1 0.2236068 -0.5 -0.6708204
66 1.425834 1 0.2236068 -0.5 -0.6708204
67 1.427102 1 -0.2236068 -0.5 0.6708204
68 1.414240 1 -0.6708204 0.5 -0.2236068
69 1.456218 1 -0.6708204 0.5 -0.2236068
70 1.470594 1 -0.6708204 0.5 -0.2236068
71 1.425058 1 -0.6708204 0.5 -0.2236068
72 1.432371 1 0.2236068 -0.5 -0.6708204
73 1.441656 1 -0.2236068 -0.5 0.6708204
74 1.434952 1 -0.2236068 -0.5 0.6708204
75 1.402860 1 0.2236068 -0.5 -0.6708204
76 1.453363 1 0.2236068 -0.5 -0.6708204
77 1.432909 1 0.6708204 0.5 0.2236068
78 1.435103 1 0.2236068 -0.5 -0.6708204
79 1.434462 1 -0.2236068 -0.5 0.6708204
80 1.434661 1 -0.2236068 -0.5 0.6708204
81 1.445881 1 0.2236068 -0.5 -0.6708204
82 1.442548 1 -0.6708204 0.5 -0.2236068
83 1.430097 1 0.2236068 -0.5 -0.6708204
84 1.430119 1 -0.2236068 -0.5 0.6708204
85 1.430315 1 -0.2236068 -0.5 0.6708204
86 1.437584 1 0.6708204 0.5 0.2236068
87 1.409738 1 0.2236068 -0.5 -0.6708204
88 1.422388 1 -0.2236068 -0.5 0.6708204
89 1.422509 1 0.2236068 -0.5 -0.6708204
90 1.439432 1 0.2236068 -0.5 -0.6708204
91 1.430175 1 0.6708204 0.5 0.2236068
92 1.418002 1 -0.6708204 0.5 -0.2236068
93 1.423812 1 0.6708204 0.5 0.2236068
94 1.423473 1 -0.6708204 0.5 -0.2236068
95 1.434412 1 -0.6708204 0.5 -0.2236068
96 1.450844 1 0.2236068 -0.5 -0.6708204
97 1.433371 1 -0.6708204 0.5 -0.2236068
98 1.444378 1 0.2236068 -0.5 -0.6708204
99 1.422523 1 0.2236068 -0.5 -0.6708204
100 1.410394 1 0.2236068 -0.5 -0.6708204
$m3a$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
$m3b
$m3b$M_lvlone
C1 O2 (Intercept) O22 O23 O24
1 1.410531 4 1 NA NA NA
2 1.434183 4 1 NA NA NA
3 1.430994 4 1 NA NA NA
4 1.453096 1 1 NA NA NA
5 1.438344 2 1 NA NA NA
6 1.453207 3 1 NA NA NA
7 1.425176 4 1 NA NA NA
8 1.437908 2 1 NA NA NA
9 1.416911 4 1 NA NA NA
10 1.448638 3 1 NA NA NA
11 1.428375 2 1 NA NA NA
12 1.450130 1 1 NA NA NA
13 1.420545 1 1 NA NA NA
14 1.423005 1 1 NA NA NA
15 1.435902 4 1 NA NA NA
16 1.423901 3 1 NA NA NA
17 1.457208 3 1 NA NA NA
18 1.414280 1 1 NA NA NA
19 1.443383 3 1 NA NA NA
20 1.434954 1 1 NA NA NA
21 1.429499 3 1 NA NA NA
22 1.441897 3 1 NA NA NA
23 1.423713 2 1 NA NA NA
24 1.435395 3 1 NA NA NA
25 1.425944 2 1 NA NA NA
26 1.437115 2 1 NA NA NA
27 1.441326 1 1 NA NA NA
28 1.422953 4 1 NA NA NA
29 1.437797 3 1 NA NA NA
30 1.472121 3 1 NA NA NA
31 1.421782 2 1 NA NA NA
32 1.457672 2 1 NA NA NA
33 1.430842 1 1 NA NA NA
34 1.431523 1 1 NA NA NA
35 1.421395 4 1 NA NA NA
36 1.434496 3 1 NA NA NA
37 1.425383 3 1 NA NA NA
38 1.421802 1 1 NA NA NA
39 1.430094 2 1 NA NA NA
40 1.447621 3 1 NA NA NA
41 1.434797 3 1 NA NA NA
42 1.446091 3 1 NA NA NA
43 1.445306 3 1 NA NA NA
44 1.448783 4 1 NA NA NA
45 1.450617 4 1 NA NA NA
46 1.415055 1 1 NA NA NA
47 1.436590 4 1 NA NA NA
48 1.433938 4 1 NA NA NA
49 1.414941 1 1 NA NA NA
50 1.421807 2 1 NA NA NA
51 1.453203 1 1 NA NA NA
52 1.452129 3 1 NA NA NA
53 1.431510 2 1 NA NA NA
54 1.430082 1 1 NA NA NA
55 1.443492 2 1 NA NA NA
56 1.436460 3 1 NA NA NA
57 1.418119 NA 1 NA NA NA
58 1.434971 4 1 NA NA NA
59 1.445599 4 1 NA NA NA
60 1.437097 3 1 NA NA NA
61 1.428360 4 1 NA NA NA
62 1.440550 1 1 NA NA NA
63 1.443014 4 1 NA NA NA
64 1.424298 4 1 NA NA NA
65 1.448823 4 1 NA NA NA
66 1.425834 1 1 NA NA NA
67 1.427102 3 1 NA NA NA
68 1.414240 3 1 NA NA NA
69 1.456218 4 1 NA NA NA
70 1.470594 1 1 NA NA NA
71 1.425058 4 1 NA NA NA
72 1.432371 4 1 NA NA NA
73 1.441656 2 1 NA NA NA
74 1.434952 4 1 NA NA NA
75 1.402860 3 1 NA NA NA
76 1.453363 2 1 NA NA NA
77 1.432909 2 1 NA NA NA
78 1.435103 3 1 NA NA NA
79 1.434462 2 1 NA NA NA
80 1.434661 1 1 NA NA NA
81 1.445881 4 1 NA NA NA
82 1.442548 2 1 NA NA NA
83 1.430097 4 1 NA NA NA
84 1.430119 1 1 NA NA NA
85 1.430315 1 1 NA NA NA
86 1.437584 2 1 NA NA NA
87 1.409738 3 1 NA NA NA
88 1.422388 3 1 NA NA NA
89 1.422509 2 1 NA NA NA
90 1.439432 4 1 NA NA NA
91 1.430175 2 1 NA NA NA
92 1.418002 1 1 NA NA NA
93 1.423812 NA 1 NA NA NA
94 1.423473 3 1 NA NA NA
95 1.434412 1 1 NA NA NA
96 1.450844 3 1 NA NA NA
97 1.433371 2 1 NA NA NA
98 1.444378 2 1 NA NA NA
99 1.422523 4 1 NA NA NA
100 1.410394 3 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
$m4a
$m4a$M_lvlone
O1 C2 M2 O2 (Intercept) M22 M23 M24 O22 O23 O24 abs(C1 - C2)
1 2 0.144065882 4 4 1 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 NA NA NA NA NA NA NA
40 2 NA 3 3 1 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 NA NA NA NA NA NA NA
64 2 NA 2 4 1 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 NA NA NA NA NA NA NA
79 2 NA 2 2 1 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 NA NA NA NA NA NA NA
84 2 NA 1 1 1 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 NA NA NA NA NA NA NA
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) C1
1 0.3439662 NA NA NA 1.410531
2 0.3605954 NA NA NA 1.434183
3 0.3583696 NA NA NA 1.430994
4 0.3736964 NA NA NA 1.453096
5 0.3634928 NA NA NA 1.438344
6 0.3737730 NA NA NA 1.453207
7 0.3542952 NA NA NA 1.425176
8 0.3631892 NA NA NA 1.437908
9 0.3484794 NA NA NA 1.416911
10 0.3706241 NA NA NA 1.448638
11 0.3565373 NA NA NA 1.428375
12 0.3716534 NA NA NA 1.450130
13 0.3510408 NA NA NA 1.420545
14 0.3527707 NA NA NA 1.423005
15 0.3617934 NA NA NA 1.435902
16 0.3534000 NA NA NA 1.423901
17 0.3765220 NA NA NA 1.457208
18 0.3466206 NA NA NA 1.414280
19 0.3669896 NA NA NA 1.443383
20 0.3611331 NA NA NA 1.434954
21 0.3573242 NA NA NA 1.429499
22 0.3659595 NA NA NA 1.441897
23 0.3532680 NA NA NA 1.423713
24 0.3614400 NA NA NA 1.435395
25 0.3548341 NA NA NA 1.425944
26 0.3626380 NA NA NA 1.437115
27 0.3655634 NA NA NA 1.441326
28 0.3527344 NA NA NA 1.422953
29 0.3631120 NA NA NA 1.437797
30 0.3867045 NA NA NA 1.472121
31 0.3519109 NA NA NA 1.421782
32 0.3768405 NA NA NA 1.457672
33 0.3582630 NA NA NA 1.430842
34 0.3587390 NA NA NA 1.431523
35 0.3516387 NA NA NA 1.421395
36 0.3608133 NA NA NA 1.434496
37 0.3544406 NA NA NA 1.425383
38 0.3519254 NA NA NA 1.421802
39 0.3577404 NA NA NA 1.430094
40 0.3699214 NA NA NA 1.447621
41 0.3610235 NA NA NA 1.434797
42 0.3688639 NA NA NA 1.446091
43 0.3683210 NA NA NA 1.445306
44 0.3707242 NA NA NA 1.448783
45 0.3719890 NA NA NA 1.450617
46 0.3471687 NA NA NA 1.415055
47 0.3622725 NA NA NA 1.436590
48 0.3604242 NA NA NA 1.433938
49 0.3470878 NA NA NA 1.414941
50 0.3519288 NA NA NA 1.421807
51 0.3737703 NA NA NA 1.453203
52 0.3730309 NA NA NA 1.452129
53 0.3587298 NA NA NA 1.431510
54 0.3577317 NA NA NA 1.430082
55 0.3670651 NA NA NA 1.443492
56 0.3621821 NA NA NA 1.436460
57 0.3493310 NA NA NA 1.418119
58 0.3611449 NA NA NA 1.434971
59 0.3685236 NA NA NA 1.445599
60 0.3626252 NA NA NA 1.437097
61 0.3565271 NA NA NA 1.428360
62 0.3650248 NA NA NA 1.440550
63 0.3667342 NA NA NA 1.443014
64 0.3536790 NA NA NA 1.424298
65 0.3707512 NA NA NA 1.448823
66 0.3547570 NA NA NA 1.425834
67 0.3556460 NA NA NA 1.427102
68 0.3465922 NA NA NA 1.414240
69 0.3758430 NA NA NA 1.456218
70 0.3856661 NA NA NA 1.470594
71 0.3542125 NA NA NA 1.425058
72 0.3593309 NA NA NA 1.432371
73 0.3657925 NA NA NA 1.441656
74 0.3611311 NA NA NA 1.434952
75 0.3385130 NA NA NA 1.402860
76 0.3738804 NA NA NA 1.453363
77 0.3597065 NA NA NA 1.432909
78 0.3612366 NA NA NA 1.435103
79 0.3607899 NA NA NA 1.434462
80 0.3609283 NA NA NA 1.434661
81 0.3687189 NA NA NA 1.445881
82 0.3664112 NA NA NA 1.442548
83 0.3577425 NA NA NA 1.430097
84 0.3577579 NA NA NA 1.430119
85 0.3578947 NA NA NA 1.430315
86 0.3629637 NA NA NA 1.437584
87 0.3434041 NA NA NA 1.409738
88 0.3523374 NA NA NA 1.422388
89 0.3524220 NA NA NA 1.422509
90 0.3642486 NA NA NA 1.439432
91 0.3577968 NA NA NA 1.430175
92 0.3492491 NA NA NA 1.418002
93 0.3533376 NA NA NA 1.423812
94 0.3530999 NA NA NA 1.423473
95 0.3607553 NA NA NA 1.434412
96 0.3721453 NA NA NA 1.450844
97 0.3600291 NA NA NA 1.433371
98 0.3676785 NA NA NA 1.444378
99 0.3524318 NA NA NA 1.422523
100 0.3438689 NA NA NA 1.410394
$m4a$spM_lvlone
center scale
O1 NA NA
C2 -0.06490582 0.333173465
M2 NA NA
O2 NA NA
(Intercept) NA NA
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
abs(C1 - C2) 1.49900534 0.334214181
log(C1) 0.36049727 0.009050336
O22:abs(C1 - C2) 0.31342466 0.618807150
O23:abs(C1 - C2) 0.47068368 0.762352624
O24:abs(C1 - C2) 0.40568706 0.692690317
C1 1.43410054 0.012996511
$m4a$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
$m4b
$m4b$M_lvlone
O1 C2 O2 (Intercept)
1 2 0.144065882 4 1
2 4 0.032778478 4 1
3 3 0.343008492 4 1
4 2 -0.361887858 1 1
5 3 -0.389600647 2 1
6 1 -0.205306841 3 1
7 3 0.079434830 4 1
8 4 -0.331246757 2 1
9 4 -0.329638800 4 1
10 2 0.167597533 3 1
11 1 0.860207989 2 1
12 3 0.022730640 1 1
13 3 0.217171172 1 1
14 1 -0.403002412 1 1
15 1 0.087369742 4 1
16 4 -0.183870429 3 1
17 2 -0.194577002 3 1
18 3 -0.349718516 1 1
19 4 -0.508781244 3 1
20 1 0.494883111 1 1
21 3 0.258041067 3 1
22 4 -0.922621989 3 1
23 4 0.431254949 2 1
24 2 -0.294218881 3 1
25 1 -0.425548895 2 1
26 3 0.057176054 2 1
27 4 0.289090158 1 1
28 1 -0.473079489 4 1
29 4 -0.385664863 3 1
30 4 -0.154780107 3 1
31 2 0.100536296 2 1
32 3 0.634791958 2 1
33 3 -0.387252617 1 1
34 1 -0.181741088 1 1
35 1 -0.311562695 4 1
36 4 -0.044115907 3 1
37 4 -0.657409991 3 1
38 4 0.159577214 1 1
39 1 -0.460416933 2 1
40 2 NA 3 1
41 1 -0.248909867 3 1
42 1 -0.609021545 3 1
43 2 0.025471883 3 1
44 2 0.066648592 4 1
45 1 -0.276108719 4 1
46 1 -0.179737577 1 1
47 4 0.181190937 4 1
48 4 -0.453871693 4 1
49 2 0.448629602 1 1
50 2 -0.529811821 2 1
51 1 -0.028304571 1 1
52 3 -0.520318482 3 1
53 1 0.171317619 2 1
54 3 0.432732046 1 1
55 2 -0.346286005 2 1
56 4 -0.469375653 3 1
57 2 0.031021711 NA 1
58 1 -0.118837515 4 1
59 1 0.507769984 4 1
60 4 0.271797031 3 1
61 2 -0.124442204 4 1
62 4 0.277677389 1 1
63 3 -0.102893730 4 1
64 2 NA 4 1
65 3 -0.678303052 4 1
66 3 0.478880037 1 1
67 2 -0.428028760 3 1
68 1 0.048119185 3 1
69 1 0.216932805 4 1
70 1 -0.234575269 1 1
71 1 0.006827078 4 1
72 3 -0.456055171 4 1
73 2 0.346486708 2 1
74 2 0.205092215 4 1
75 3 -0.136596858 3 1
76 3 -0.500179043 2 1
77 4 0.527352086 2 1
78 3 0.022742250 3 1
79 2 NA 2 1
80 2 -0.002032440 1 1
81 3 -0.154246160 4 1
82 1 0.140201825 2 1
83 3 -0.141417121 4 1
84 2 NA 1 1
85 2 -0.021285339 1 1
86 4 -0.010196306 2 1
87 3 -0.089747520 3 1
88 2 -0.083699898 3 1
89 3 -0.044061996 2 1
90 3 -0.209291697 4 1
91 4 0.639036426 2 1
92 1 0.094698299 1 1
93 4 -0.055510622 NA 1
94 1 -0.421318463 3 1
95 1 0.125295503 1 1
96 3 0.213084904 3 1
97 1 -0.161914659 2 1
98 3 -0.034767685 2 1
99 3 -0.320681689 4 1
100 3 0.058192962 3 1
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) abs(C1 - C2) log(C1)
1 NA NA 0.3439662
2 NA NA 0.3605954
3 NA NA 0.3583696
4 NA NA 0.3736964
5 NA NA 0.3634928
6 NA NA 0.3737730
7 NA NA 0.3542952
8 NA NA 0.3631892
9 NA NA 0.3484794
10 NA NA 0.3706241
11 NA NA 0.3565373
12 NA NA 0.3716534
13 NA NA 0.3510408
14 NA NA 0.3527707
15 NA NA 0.3617934
16 NA NA 0.3534000
17 NA NA 0.3765220
18 NA NA 0.3466206
19 NA NA 0.3669896
20 NA NA 0.3611331
21 NA NA 0.3573242
22 NA NA 0.3659595
23 NA NA 0.3532680
24 NA NA 0.3614400
25 NA NA 0.3548341
26 NA NA 0.3626380
27 NA NA 0.3655634
28 NA NA 0.3527344
29 NA NA 0.3631120
30 NA NA 0.3867045
31 NA NA 0.3519109
32 NA NA 0.3768405
33 NA NA 0.3582630
34 NA NA 0.3587390
35 NA NA 0.3516387
36 NA NA 0.3608133
37 NA NA 0.3544406
38 NA NA 0.3519254
39 NA NA 0.3577404
40 NA NA 0.3699214
41 NA NA 0.3610235
42 NA NA 0.3688639
43 NA NA 0.3683210
44 NA NA 0.3707242
45 NA NA 0.3719890
46 NA NA 0.3471687
47 NA NA 0.3622725
48 NA NA 0.3604242
49 NA NA 0.3470878
50 NA NA 0.3519288
51 NA NA 0.3737703
52 NA NA 0.3730309
53 NA NA 0.3587298
54 NA NA 0.3577317
55 NA NA 0.3670651
56 NA NA 0.3621821
57 NA NA 0.3493310
58 NA NA 0.3611449
59 NA NA 0.3685236
60 NA NA 0.3626252
61 NA NA 0.3565271
62 NA NA 0.3650248
63 NA NA 0.3667342
64 NA NA 0.3536790
65 NA NA 0.3707512
66 NA NA 0.3547570
67 NA NA 0.3556460
68 NA NA 0.3465922
69 NA NA 0.3758430
70 NA NA 0.3856661
71 NA NA 0.3542125
72 NA NA 0.3593309
73 NA NA 0.3657925
74 NA NA 0.3611311
75 NA NA 0.3385130
76 NA NA 0.3738804
77 NA NA 0.3597065
78 NA NA 0.3612366
79 NA NA 0.3607899
80 NA NA 0.3609283
81 NA NA 0.3687189
82 NA NA 0.3664112
83 NA NA 0.3577425
84 NA NA 0.3577579
85 NA NA 0.3578947
86 NA NA 0.3629637
87 NA NA 0.3434041
88 NA NA 0.3523374
89 NA NA 0.3524220
90 NA NA 0.3642486
91 NA NA 0.3577968
92 NA NA 0.3492491
93 NA NA 0.3533376
94 NA NA 0.3530999
95 NA NA 0.3607553
96 NA NA 0.3721453
97 NA NA 0.3600291
98 NA NA 0.3676785
99 NA NA 0.3524318
100 NA NA 0.3438689
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) O22 O23 O24 M12
1 NA NA NA NA 0
2 NA NA NA NA 1
3 NA NA NA NA 1
4 NA NA NA NA 0
5 NA NA NA NA 0
6 NA NA NA NA 0
7 NA NA NA NA 0
8 NA NA NA NA 0
9 NA NA NA NA 0
10 NA NA NA NA 1
11 NA NA NA NA 0
12 NA NA NA NA 1
13 NA NA NA NA 0
14 NA NA NA NA 0
15 NA NA NA NA 1
16 NA NA NA NA 0
17 NA NA NA NA 0
18 NA NA NA NA 0
19 NA NA NA NA 1
20 NA NA NA NA 1
21 NA NA NA NA 0
22 NA NA NA NA 0
23 NA NA NA NA 0
24 NA NA NA NA 1
25 NA NA NA NA 0
26 NA NA NA NA 0
27 NA NA NA NA 1
28 NA NA NA NA 0
29 NA NA NA NA 0
30 NA NA NA NA 0
31 NA NA NA NA 0
32 NA NA NA NA 0
33 NA NA NA NA 0
34 NA NA NA NA 1
35 NA NA NA NA 0
36 NA NA NA NA 0
37 NA NA NA NA 0
38 NA NA NA NA 1
39 NA NA NA NA 1
40 NA NA NA NA 1
41 NA NA NA NA 0
42 NA NA NA NA 0
43 NA NA NA NA 0
44 NA NA NA NA 0
45 NA NA NA NA 1
46 NA NA NA NA 0
47 NA NA NA NA 0
48 NA NA NA NA 0
49 NA NA NA NA 1
50 NA NA NA NA 1
51 NA NA NA NA 0
52 NA NA NA NA 0
53 NA NA NA NA 1
54 NA NA NA NA 0
55 NA NA NA NA 1
56 NA NA NA NA 0
57 NA NA NA NA 0
58 NA NA NA NA 0
59 NA NA NA NA 0
60 NA NA NA NA 0
61 NA NA NA NA 0
62 NA NA NA NA 0
63 NA NA NA NA 0
64 NA NA NA NA 0
65 NA NA NA NA 1
66 NA NA NA NA 0
67 NA NA NA NA 0
68 NA NA NA NA 0
69 NA NA NA NA 0
70 NA NA NA NA 1
71 NA NA NA NA 0
72 NA NA NA NA 1
73 NA NA NA NA 0
74 NA NA NA NA 1
75 NA NA NA NA 0
76 NA NA NA NA 0
77 NA NA NA NA 1
78 NA NA NA NA 0
79 NA NA NA NA 0
80 NA NA NA NA 0
81 NA NA NA NA 0
82 NA NA NA NA 0
83 NA NA NA NA 0
84 NA NA NA NA 0
85 NA NA NA NA 1
86 NA NA NA NA 0
87 NA NA NA NA 0
88 NA NA NA NA 0
89 NA NA NA NA 0
90 NA NA NA NA 0
91 NA NA NA NA 0
92 NA NA NA NA 0
93 NA NA NA NA 0
94 NA NA NA NA 0
95 NA NA NA NA 1
96 NA NA NA NA 0
97 NA NA NA NA 0
98 NA NA NA NA 0
99 NA NA NA NA 1
100 NA NA NA NA 0
M13 M14 C1 M1
1 0 0 1.410531 1
2 0 0 1.434183 2
3 0 0 1.430994 2
4 0 0 1.453096 1
5 1 0 1.438344 3
6 1 0 1.453207 3
7 1 0 1.425176 3
8 1 0 1.437908 3
9 0 0 1.416911 1
10 0 0 1.448638 2
11 0 0 1.428375 1
12 0 0 1.450130 2
13 0 0 1.420545 1
14 0 1 1.423005 4
15 0 0 1.435902 2
16 1 0 1.423901 3
17 1 0 1.457208 3
18 0 0 1.414280 1
19 0 0 1.443383 2
20 0 0 1.434954 2
21 0 0 1.429499 1
22 0 1 1.441897 4
23 0 0 1.423713 1
24 0 0 1.435395 2
25 0 0 1.425944 1
26 0 0 1.437115 1
27 0 0 1.441326 2
28 0 0 1.422953 1
29 1 0 1.437797 3
30 0 0 1.472121 1
31 0 1 1.421782 4
32 0 0 1.457672 1
33 0 1 1.430842 4
34 0 0 1.431523 2
35 0 0 1.421395 1
36 0 0 1.434496 1
37 0 0 1.425383 1
38 0 0 1.421802 2
39 0 0 1.430094 2
40 0 0 1.447621 2
41 0 1 1.434797 4
42 0 1 1.446091 4
43 0 1 1.445306 4
44 0 1 1.448783 4
45 0 0 1.450617 2
46 0 0 1.415055 1
47 1 0 1.436590 3
48 1 0 1.433938 3
49 0 0 1.414941 2
50 0 0 1.421807 2
51 1 0 1.453203 3
52 0 0 1.452129 1
53 0 0 1.431510 2
54 1 0 1.430082 3
55 0 0 1.443492 2
56 0 0 1.436460 1
57 0 1 1.418119 4
58 0 0 1.434971 1
59 0 1 1.445599 4
60 0 0 1.437097 1
61 0 0 1.428360 1
62 0 0 1.440550 1
63 0 1 1.443014 4
64 0 0 1.424298 1
65 0 0 1.448823 2
66 0 1 1.425834 4
67 0 1 1.427102 4
68 1 0 1.414240 3
69 1 0 1.456218 3
70 0 0 1.470594 2
71 1 0 1.425058 3
72 0 0 1.432371 2
73 0 1 1.441656 4
74 0 0 1.434952 2
75 0 0 1.402860 1
76 1 0 1.453363 3
77 0 0 1.432909 2
78 0 1 1.435103 4
79 0 1 1.434462 4
80 0 0 1.434661 1
81 0 0 1.445881 1
82 0 1 1.442548 4
83 1 0 1.430097 3
84 0 1 1.430119 4
85 0 0 1.430315 2
86 0 1 1.437584 4
87 1 0 1.409738 3
88 1 0 1.422388 3
89 1 0 1.422509 3
90 0 0 1.439432 1
91 0 0 1.430175 1
92 0 0 1.418002 1
93 0 0 1.423812 1
94 1 0 1.423473 3
95 0 0 1.434412 2
96 0 0 1.450844 1
97 1 0 1.433371 3
98 0 1 1.444378 4
99 0 0 1.422523 2
100 0 0 1.410394 1
$m4b$spM_lvlone
center
O1 NA
C2 -0.06490582
O2 NA
(Intercept) NA
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0.43877551
abs(C1 - C2) 1.49900534
log(C1) 0.36049727
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0.65074863
O22 NA
O23 NA
O24 NA
M12 NA
M13 NA
M14 NA
C1 1.43410054
M1 NA
scale
O1 NA
C2 0.333173465
O2 NA
(Intercept) NA
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0.498788771
abs(C1 - C2) 0.334214181
log(C1) 0.009050336
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0.780466974
O22 NA
O23 NA
O24 NA
M12 NA
M13 NA
M14 NA
C1 0.012996511
M1 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
$m5a
$m5a$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA
$m5a$spM_lvlone
center scale
O1 NA NA
C2 -0.06490582 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.43410054 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 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_multinomial
[1] 0
$m5a$tau_reg_multinomial
[1] 1e-04
$m5a$mu_reg_ordinal
[1] 0
$m5a$tau_reg_ordinal
[1] 1e-04
$m5a$mu_delta_ordinal
[1] 0
$m5a$tau_delta_ordinal
[1] 1e-04
$m5b
$m5b$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24 C1:C2
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA NA
$m5b$spM_lvlone
center scale
O1 NA NA
C2 -0.06490582 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.43410054 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
C1:C2 -0.09333951 0.47826999
$m5b$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
$m5c
$m5c$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24 C1:C2
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA NA
$m5c$spM_lvlone
center scale
O1 NA NA
C2 -0.06490582 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.43410054 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
C1:C2 -0.09333951 0.47826999
$m5c$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
$m5d
$m5d$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24 M22:C2
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA NA
M23:C2 M24:C2
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
91 NA NA
92 NA NA
93 NA NA
94 NA NA
95 NA NA
96 NA NA
97 NA NA
98 NA NA
99 NA NA
100 NA NA
$m5d$spM_lvlone
center scale
O1 NA NA
C2 -0.064905817 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.434100545 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
M22:C2 -0.035803577 0.16299962
M23:C2 -0.008443652 0.22326710
M24:C2 -0.014114090 0.17029222
$m5d$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
$m5e
$m5e$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24 M22:C2
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA NA
M23:C2 M24:C2
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
91 NA NA
92 NA NA
93 NA NA
94 NA NA
95 NA NA
96 NA NA
97 NA NA
98 NA NA
99 NA NA
100 NA NA
$m5e$spM_lvlone
center scale
O1 NA NA
C2 -0.064905817 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.434100545 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
M22:C2 -0.035803577 0.16299962
M23:C2 -0.008443652 0.22326710
M24:C2 -0.014114090 0.17029222
$m5e$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
$m6a
$m6a$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA
$m6a$spM_lvlone
center scale
O1 NA NA
C2 -0.06490582 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.43410054 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 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_multinomial
[1] 0
$m6a$tau_reg_multinomial
[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
$m6b
$m6b$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24 C1:C2
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA NA
$m6b$spM_lvlone
center scale
O1 NA NA
C2 -0.06490582 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.43410054 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
C1:C2 -0.09333951 0.47826999
$m6b$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
$m6c
$m6c$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24 C1:C2
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA NA
$m6c$spM_lvlone
center scale
O1 NA NA
C2 -0.06490582 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.43410054 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
C1:C2 -0.09333951 0.47826999
$m6c$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
$m6d
$m6d$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24 M22:C2
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA NA
M23:C2 M24:C2
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
91 NA NA
92 NA NA
93 NA NA
94 NA NA
95 NA NA
96 NA NA
97 NA NA
98 NA NA
99 NA NA
100 NA NA
$m6d$spM_lvlone
center scale
O1 NA NA
C2 -0.064905817 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.434100545 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
M22:C2 -0.035803577 0.16299962
M23:C2 -0.008443652 0.22326710
M24:C2 -0.014114090 0.17029222
$m6d$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
$m6e
$m6e$M_lvlone
O1 C2 M2 O2 (Intercept) C1 M22 M23 M24 O22 O23 O24 M22:C2
1 2 0.144065882 4 4 1 1.410531 NA NA NA NA NA NA NA
2 4 0.032778478 1 4 1 1.434183 NA NA NA NA NA NA NA
3 3 0.343008492 3 4 1 1.430994 NA NA NA NA NA NA NA
4 2 -0.361887858 3 1 1 1.453096 NA NA NA NA NA NA NA
5 3 -0.389600647 4 2 1 1.438344 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 1.453207 NA NA NA NA NA NA NA
7 3 0.079434830 1 4 1 1.425176 NA NA NA NA NA NA NA
8 4 -0.331246757 1 2 1 1.437908 NA NA NA NA NA NA NA
9 4 -0.329638800 2 4 1 1.416911 NA NA NA NA NA NA NA
10 2 0.167597533 2 3 1 1.448638 NA NA NA NA NA NA NA
11 1 0.860207989 3 2 1 1.428375 NA NA NA NA NA NA NA
12 3 0.022730640 3 1 1 1.450130 NA NA NA NA NA NA NA
13 3 0.217171172 2 1 1 1.420545 NA NA NA NA NA NA NA
14 1 -0.403002412 3 1 1 1.423005 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 1.435902 NA NA NA NA NA NA NA
16 4 -0.183870429 1 3 1 1.423901 NA NA NA NA NA NA NA
17 2 -0.194577002 4 3 1 1.457208 NA NA NA NA NA NA NA
18 3 -0.349718516 2 1 1 1.414280 NA NA NA NA NA NA NA
19 4 -0.508781244 3 3 1 1.443383 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 1.434954 NA NA NA NA NA NA NA
21 3 0.258041067 2 3 1 1.429499 NA NA NA NA NA NA NA
22 4 -0.922621989 2 3 1 1.441897 NA NA NA NA NA NA NA
23 4 0.431254949 3 2 1 1.423713 NA NA NA NA NA NA NA
24 2 -0.294218881 3 3 1 1.435395 NA NA NA NA NA NA NA
25 1 -0.425548895 2 2 1 1.425944 NA NA NA NA NA NA NA
26 3 0.057176054 2 2 1 1.437115 NA NA NA NA NA NA NA
27 4 0.289090158 1 1 1 1.441326 NA NA NA NA NA NA NA
28 1 -0.473079489 3 4 1 1.422953 NA NA NA NA NA NA NA
29 4 -0.385664863 4 3 1 1.437797 NA NA NA NA NA NA NA
30 4 -0.154780107 2 3 1 1.472121 NA NA NA NA NA NA NA
31 2 0.100536296 NA 2 1 1.421782 NA NA NA NA NA NA NA
32 3 0.634791958 4 2 1 1.457672 NA NA NA NA NA NA NA
33 3 -0.387252617 4 1 1 1.430842 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 1.431523 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 1.421395 NA NA NA NA NA NA NA
36 4 -0.044115907 1 3 1 1.434496 NA NA NA NA NA NA NA
37 4 -0.657409991 3 3 1 1.425383 NA NA NA NA NA NA NA
38 4 0.159577214 4 1 1 1.421802 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 1.430094 NA NA NA NA NA NA NA
40 2 NA 3 3 1 1.447621 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 1.434797 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 1.446091 NA NA NA NA NA NA NA
43 2 0.025471883 1 3 1 1.445306 NA NA NA NA NA NA NA
44 2 0.066648592 2 4 1 1.448783 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 1.450617 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 1.415055 NA NA NA NA NA NA NA
47 4 0.181190937 4 4 1 1.436590 NA NA NA NA NA NA NA
48 4 -0.453871693 2 4 1 1.433938 NA NA NA NA NA NA NA
49 2 0.448629602 4 1 1 1.414941 NA NA NA NA NA NA NA
50 2 -0.529811821 1 2 1 1.421807 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 1.453203 NA NA NA NA NA NA NA
52 3 -0.520318482 4 3 1 1.452129 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 1.431510 NA NA NA NA NA NA NA
54 3 0.432732046 3 1 1 1.430082 NA NA NA NA NA NA NA
55 2 -0.346286005 3 2 1 1.443492 NA NA NA NA NA NA NA
56 4 -0.469375653 3 3 1 1.436460 NA NA NA NA NA NA NA
57 2 0.031021711 2 NA 1 1.418119 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 1.434971 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 1.445599 NA NA NA NA NA NA NA
60 4 0.271797031 4 3 1 1.437097 NA NA NA NA NA NA NA
61 2 -0.124442204 2 4 1 1.428360 NA NA NA NA NA NA NA
62 4 0.277677389 2 1 1 1.440550 NA NA NA NA NA NA NA
63 3 -0.102893730 1 4 1 1.443014 NA NA NA NA NA NA NA
64 2 NA 2 4 1 1.424298 NA NA NA NA NA NA NA
65 3 -0.678303052 2 4 1 1.448823 NA NA NA NA NA NA NA
66 3 0.478880037 3 1 1 1.425834 NA NA NA NA NA NA NA
67 2 -0.428028760 2 3 1 1.427102 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 1.414240 NA NA NA NA NA NA NA
69 1 0.216932805 NA 4 1 1.456218 NA NA NA NA NA NA NA
70 1 -0.234575269 1 1 1 1.470594 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 1.425058 NA NA NA NA NA NA NA
72 3 -0.456055171 3 4 1 1.432371 NA NA NA NA NA NA NA
73 2 0.346486708 4 2 1 1.441656 NA NA NA NA NA NA NA
74 2 0.205092215 4 4 1 1.434952 NA NA NA NA NA NA NA
75 3 -0.136596858 1 3 1 1.402860 NA NA NA NA NA NA NA
76 3 -0.500179043 4 2 1 1.453363 NA NA NA NA NA NA NA
77 4 0.527352086 NA 2 1 1.432909 NA NA NA NA NA NA NA
78 3 0.022742250 2 3 1 1.435103 NA NA NA NA NA NA NA
79 2 NA 2 2 1 1.434462 NA NA NA NA NA NA NA
80 2 -0.002032440 2 1 1 1.434661 NA NA NA NA NA NA NA
81 3 -0.154246160 4 4 1 1.445881 NA NA NA NA NA NA NA
82 1 0.140201825 3 2 1 1.442548 NA NA NA NA NA NA NA
83 3 -0.141417121 3 4 1 1.430097 NA NA NA NA NA NA NA
84 2 NA 1 1 1 1.430119 NA NA NA NA NA NA NA
85 2 -0.021285339 2 1 1 1.430315 NA NA NA NA NA NA NA
86 4 -0.010196306 1 2 1 1.437584 NA NA NA NA NA NA NA
87 3 -0.089747520 3 3 1 1.409738 NA NA NA NA NA NA NA
88 2 -0.083699898 1 3 1 1.422388 NA NA NA NA NA NA NA
89 3 -0.044061996 2 2 1 1.422509 NA NA NA NA NA NA NA
90 3 -0.209291697 1 4 1 1.439432 NA NA NA NA NA NA NA
91 4 0.639036426 3 2 1 1.430175 NA NA NA NA NA NA NA
92 1 0.094698299 1 1 1 1.418002 NA NA NA NA NA NA NA
93 4 -0.055510622 4 NA 1 1.423812 NA NA NA NA NA NA NA
94 1 -0.421318463 4 3 1 1.423473 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 1.434412 NA NA NA NA NA NA NA
96 3 0.213084904 4 3 1 1.450844 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 1.433371 NA NA NA NA NA NA NA
98 3 -0.034767685 3 2 1 1.444378 NA NA NA NA NA NA NA
99 3 -0.320681689 3 4 1 1.422523 NA NA NA NA NA NA NA
100 3 0.058192962 4 3 1 1.410394 NA NA NA NA NA NA NA
M23:C2 M24:C2
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
91 NA NA
92 NA NA
93 NA NA
94 NA NA
95 NA NA
96 NA NA
97 NA NA
98 NA NA
99 NA NA
100 NA NA
$m6e$spM_lvlone
center scale
O1 NA NA
C2 -0.064905817 0.33317347
M2 NA NA
O2 NA NA
(Intercept) NA NA
C1 1.434100545 0.01299651
M22 NA NA
M23 NA NA
M24 NA NA
O22 NA NA
O23 NA NA
O24 NA NA
M22:C2 -0.035803577 0.16299962
M23:C2 -0.008443652 0.22326710
M24:C2 -0.014114090 0.17029222
$m6e$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
jagsmodel remains the same
Code
lapply(models, "[[", "jagsmodel")
Output
$m0a
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- 0
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i]
logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i]
}
# Priors for the model for O1
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
}
$m0b
model {
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- 0
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]
}
# 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])
}
$m1a
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[1]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i]
logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i]
}
# 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)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
}
$m1b
model {
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[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]
}
# 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])
}
$m2a
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[1]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i]
logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i]
}
# 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)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 3] * 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 model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[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]
}
# 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])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 3] * 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)
}
$m3a
model {
# Normal model for C1 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1)
mu_C1[i] <- M_lvlone[i, 2] * beta[1] + M_lvlone[i, 3] * beta[2] +
M_lvlone[i, 4] * beta[3] + M_lvlone[i, 5] * beta[4]
}
# 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)
}
$m3b
model {
# Normal model for C1 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_C1[i], tau_C1)
mu_C1[i] <- M_lvlone[i, 3] * beta[1] + M_lvlone[i, 4] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4]
}
# 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)
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- 0
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, 4] <- ifelse(M_lvlone[i, 2] == 2, 1, 0)
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 3, 1, 0)
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 2] == 4, 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])
}
$m4a
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 6] * beta[1] + M_lvlone[i, 7] * beta[2] +
M_lvlone[i, 8] * beta[3] + M_lvlone[i, 9] * beta[4] +
M_lvlone[i, 10] * beta[5] + M_lvlone[i, 11] * beta[6] +
(M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[7] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[8] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[9] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[10] +
(M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[11]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i]
logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i]
}
# 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)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] + M_lvlone[i, 6] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] +
(M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[8]
M_lvlone[i, 12] <- abs(M_lvlone[i, 17] - M_lvlone[i, 2])
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] + M_lvlone[i, 9] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
(M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] + M_lvlone[i, 9] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
(M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] + M_lvlone[i, 9] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
(M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[23]
M_lvlone[i, 6] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 17] - spM_lvlone[17, 1])/spM_lvlone[17, 2] * alpha[24]
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, 9] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 10] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 14] <- M_lvlone[i, 9] * M_lvlone[i, 12]
M_lvlone[i, 15] <- M_lvlone[i, 10] * M_lvlone[i, 12]
M_lvlone[i, 16] <- M_lvlone[i, 11] * M_lvlone[i, 12]
}
}
$m4b
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- (M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[2] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[3] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[4]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
logit(psum_O1[i, 1]) <- gamma_O1[1] + eta_O1[i]
logit(psum_O1[i, 2]) <- gamma_O1[2] + eta_O1[i]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i]
}
# 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)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[1] + M_lvlone[i, 9] * alpha[2] +
M_lvlone[i, 10] * alpha[3] + M_lvlone[i, 11] * alpha[4] +
M_lvlone[i, 12] * alpha[5] + M_lvlone[i, 13] * alpha[6] +
M_lvlone[i, 14] * alpha[7] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[8]
M_lvlone[i, 6] <- abs(M_lvlone[i, 15] - M_lvlone[i, 2])
}
# Priors for the model for C2
for (k in 1:8) {
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 (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- M_lvlone[i, 12] * alpha[9] + M_lvlone[i, 13] * alpha[10] +
M_lvlone[i, 14] * alpha[11] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[12]
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, 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)
M_lvlone[i, 5] <- ifelse((M_lvlone[i, 3]) > (M_lvlone[i, 16]), 1, 0)
}
# Priors for the model for O2
for (k in 9:12) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6]
}
}
$m5a
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] +
M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] +
M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6]
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[9] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[10]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[11] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[12]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# 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)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
}
$m5b
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] +
M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] +
M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7]
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[9]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# Priors for the model for O1
for (k in 1:13) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2]
}
}
$m5c
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] +
M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] +
M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6]
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[12]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[14] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[15]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# Priors for the model for O1
for (k in 1:15) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2]
}
}
$m5d
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] +
M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] +
M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[8] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[9]
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[14] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[15]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# Priors for the model for O1
for (k in 1:15) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2]
M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2]
M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2]
}
}
$m5e
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- 0
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[1] +
M_lvlone[i, 7] * beta[2] + M_lvlone[i, 8] * beta[3] +
M_lvlone[i, 9] * beta[4] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] +
M_lvlone[i, 10] * beta[6] + M_lvlone[i, 11] * beta[7] +
M_lvlone[i, 12] * beta[8] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] +
M_lvlone[i, 7] * beta[13] + M_lvlone[i, 8] * beta[14] +
M_lvlone[i, 9] * beta[15] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[16] +
M_lvlone[i, 10] * beta[17] + M_lvlone[i, 11] * beta[18] +
M_lvlone[i, 12] * beta[19] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[20] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[21] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[22]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[23] +
M_lvlone[i, 7] * beta[24] + M_lvlone[i, 8] * beta[25] +
M_lvlone[i, 9] * beta[26] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[27] +
M_lvlone[i, 10] * beta[28] + M_lvlone[i, 11] * beta[29] +
M_lvlone[i, 12] * beta[30] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[31] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[32] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[33]
p_O1[i, 1] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 2:4])))
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] - psum_O1[i, 3]))
p_O1[i, 4] <- max(1e-10, min(1-1e-10, psum_O1[i, 3]))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# Priors for the model for O1
for (k in 1:33) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] - exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2]
M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2]
M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2]
}
}
$m6a
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] +
M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] +
M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6]
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[9] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[10]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[11] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[12]
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] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2]))
p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3])))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# 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)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
}
$m6b
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] +
M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] +
M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7]
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[9]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13]
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] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2]))
p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3])))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# Priors for the model for O1
for (k in 1:13) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2]
}
}
$m6c
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] +
M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] +
M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6]
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[7] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[8] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[12]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[14] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[15]
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] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2]))
p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3])))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# Priors for the model for O1
for (k in 1:15) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 13] <- M_lvlone[i, 6] * M_lvlone[i, 2]
}
}
$m6d
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- M_lvlone[i, 7] * beta[1] + M_lvlone[i, 8] * beta[2] +
M_lvlone[i, 9] * beta[3] + M_lvlone[i, 10] * beta[4] +
M_lvlone[i, 11] * beta[5] + M_lvlone[i, 12] * beta[6] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[7] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[8] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[9]
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[10] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[11]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[13]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[14] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[15]
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] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2]))
p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3])))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# Priors for the model for O1
for (k in 1:15) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2]
M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2]
M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2]
}
}
$m6e
model {
# Cumulative logit model for O1 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_O1[i, 1:4])
eta_O1[i] <- 0
eta_O1_1[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[1] +
M_lvlone[i, 7] * beta[2] + M_lvlone[i, 8] * beta[3] +
M_lvlone[i, 9] * beta[4] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] +
M_lvlone[i, 10] * beta[6] + M_lvlone[i, 11] * beta[7] +
M_lvlone[i, 12] * beta[8] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[9] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[10] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[11]
eta_O1_2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[12] +
M_lvlone[i, 7] * beta[13] + M_lvlone[i, 8] * beta[14] +
M_lvlone[i, 9] * beta[15] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[16] +
M_lvlone[i, 10] * beta[17] + M_lvlone[i, 11] * beta[18] +
M_lvlone[i, 12] * beta[19] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[20] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[21] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[22]
eta_O1_3[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[23] +
M_lvlone[i, 7] * beta[24] + M_lvlone[i, 8] * beta[25] +
M_lvlone[i, 9] * beta[26] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[27] +
M_lvlone[i, 10] * beta[28] + M_lvlone[i, 11] * beta[29] +
M_lvlone[i, 12] * beta[30] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[31] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[32] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[33]
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] <- max(1e-10, min(1-1e-10, psum_O1[i, 3] - psum_O1[i, 2]))
p_O1[i, 4] <- 1 - max(1e-10, min(1-1e-10, sum(p_O1[i, 1:3])))
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]
logit(psum_O1[i, 3]) <- gamma_O1[3] + eta_O1[i] + eta_O1_3[i]
}
# Priors for the model for O1
for (k in 1:33) {
beta[k] ~ dnorm(mu_reg_ordinal, tau_reg_ordinal)
}
delta_O1[1] ~ dnorm(mu_delta_ordinal, tau_delta_ordinal)
delta_O1[2] ~ 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])
gamma_O1[3] <- gamma_O1[2] + exp(delta_O1[2])
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
M_lvlone[i, 7] * alpha[3] + M_lvlone[i, 8] * alpha[4] +
M_lvlone[i, 9] * alpha[5] + M_lvlone[i, 10] * alpha[6] +
M_lvlone[i, 11] * alpha[7] + M_lvlone[i, 12] * alpha[8]
}
# Priors for the model for C2
for (k in 1:8) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dcat(p_M2[i, 1:4])
p_M2[i, 1] <- min(1-1e-7, max(1e-7, phi_M2[i, 1] / sum(phi_M2[i, ])))
p_M2[i, 2] <- min(1-1e-7, max(1e-7, phi_M2[i, 2] / sum(phi_M2[i, ])))
p_M2[i, 3] <- min(1-1e-7, max(1e-7, phi_M2[i, 3] / sum(phi_M2[i, ])))
p_M2[i, 4] <- min(1-1e-7, max(1e-7, phi_M2[i, 4] / sum(phi_M2[i, ])))
log(phi_M2[i, 1]) <- 0
log(phi_M2[i, 2]) <- M_lvlone[i, 5] * alpha[9] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[10] +
M_lvlone[i, 10] * alpha[11] + M_lvlone[i, 11] * alpha[12] +
M_lvlone[i, 12] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 5] * alpha[14] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[15] +
M_lvlone[i, 10] * alpha[16] + M_lvlone[i, 11] * alpha[17] +
M_lvlone[i, 12] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 5] * alpha[19] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[20] +
M_lvlone[i, 10] * alpha[21] + M_lvlone[i, 11] * alpha[22] +
M_lvlone[i, 12] * alpha[23]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 3] == 2, 1, 0)
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 3] == 3, 1, 0)
M_lvlone[i, 9] <- ifelse(M_lvlone[i, 3] == 4, 1, 0)
}
# Priors for the model for M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Cumulative logit model for O2 -------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dcat(p_O2[i, 1:4])
eta_O2[i] <- (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[24]
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, 10] <- ifelse(M_lvlone[i, 4] == 2, 1, 0)
M_lvlone[i, 11] <- ifelse(M_lvlone[i, 4] == 3, 1, 0)
M_lvlone[i, 12] <- ifelse(M_lvlone[i, 4] == 4, 1, 0)
}
# Priors for the model for O2
for (k in 24:24) {
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])
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 13] <- M_lvlone[i, 7] * M_lvlone[i, 2]
M_lvlone[i, 14] <- M_lvlone[i, 8] * M_lvlone[i, 2]
M_lvlone[i, 15] <- M_lvlone[i, 9] * M_lvlone[i, 2]
}
}
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
gamma_O1[3] 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
$m1a
Potential scale reduction factors:
Point est. Upper C.I.
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] NaN NaN
C1 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
$m2a
Potential scale reduction factors:
Point est. Upper C.I.
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] NaN NaN
C2 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
$m3a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
sigma_C1 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
$m4a
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
O24 NaN NaN
abs(C1 - C2) NaN NaN
log(C1) 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
gamma_O1[3] NaN NaN
$m4b
Potential scale reduction factors:
Point est.
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) NaN
abs(C1 - C2) NaN
log(C1) NaN
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
gamma_O1[1] NaN
gamma_O1[2] NaN
gamma_O1[3] NaN
Upper C.I.
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) NaN
abs(C1 - C2) NaN
log(C1) NaN
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
gamma_O1[1] NaN
gamma_O1[2] NaN
gamma_O1[3] NaN
$m5a
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
O24 NaN NaN
O12: C1 NaN NaN
O12: C2 NaN NaN
O13: C1 NaN NaN
O13: C2 NaN NaN
O14: C1 NaN NaN
O14: C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] 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
O24 NaN NaN
C1:C2 NaN NaN
O12: C1 NaN NaN
O12: C2 NaN NaN
O13: C1 NaN NaN
O13: C2 NaN NaN
O14: C1 NaN NaN
O14: C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] 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
O24 NaN NaN
O12: C1 NaN NaN
O12: C2 NaN NaN
O12: C1:C2 NaN NaN
O13: C1 NaN NaN
O13: C2 NaN NaN
O13: C1:C2 NaN NaN
O14: C1 NaN NaN
O14: C2 NaN NaN
O14: C1:C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] 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
O24 NaN NaN
M22:C2 NaN NaN
M23:C2 NaN NaN
M24:C2 NaN NaN
O12: C1 NaN NaN
O12: C2 NaN NaN
O13: C1 NaN NaN
O13: C2 NaN NaN
O14: C1 NaN NaN
O14: C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] NaN NaN
$m5e
Potential scale reduction factors:
Point est. Upper C.I.
O12: C1 NaN NaN
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: O24 NaN NaN
O12: M22:C2 NaN NaN
O12: M23:C2 NaN NaN
O12: M24:C2 NaN NaN
O13: C1 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: O24 NaN NaN
O13: M22:C2 NaN NaN
O13: M23:C2 NaN NaN
O13: M24:C2 NaN NaN
O14: C1 NaN NaN
O14: M22 NaN NaN
O14: M23 NaN NaN
O14: M24 NaN NaN
O14: C2 NaN NaN
O14: O22 NaN NaN
O14: O23 NaN NaN
O14: O24 NaN NaN
O14: M22:C2 NaN NaN
O14: M23:C2 NaN NaN
O14: M24:C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] NaN NaN
$m6a
Potential scale reduction factors:
Point est. Upper C.I.
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
O24 NaN NaN
O12: C1 NaN NaN
O12: C2 NaN NaN
O13: C1 NaN NaN
O13: C2 NaN NaN
O14: C1 NaN NaN
O14: C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] 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
O24 NaN NaN
C1:C2 NaN NaN
O12: C1 NaN NaN
O12: C2 NaN NaN
O13: C1 NaN NaN
O13: C2 NaN NaN
O14: C1 NaN NaN
O14: C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] 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
O24 NaN NaN
O12: C1 NaN NaN
O12: C2 NaN NaN
O12: C1:C2 NaN NaN
O13: C1 NaN NaN
O13: C2 NaN NaN
O13: C1:C2 NaN NaN
O14: C1 NaN NaN
O14: C2 NaN NaN
O14: C1:C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] 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
O24 NaN NaN
M22:C2 NaN NaN
M23:C2 NaN NaN
M24:C2 NaN NaN
O12: C1 NaN NaN
O12: C2 NaN NaN
O13: C1 NaN NaN
O13: C2 NaN NaN
O14: C1 NaN NaN
O14: C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] NaN NaN
$m6e
Potential scale reduction factors:
Point est. Upper C.I.
O12: C1 NaN NaN
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: O24 NaN NaN
O12: M22:C2 NaN NaN
O12: M23:C2 NaN NaN
O12: M24:C2 NaN NaN
O13: C1 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: O24 NaN NaN
O13: M22:C2 NaN NaN
O13: M23:C2 NaN NaN
O13: M24:C2 NaN NaN
O14: C1 NaN NaN
O14: M22 NaN NaN
O14: M23 NaN NaN
O14: M24 NaN NaN
O14: C2 NaN NaN
O14: O22 NaN NaN
O14: O23 NaN NaN
O14: O24 NaN NaN
O14: M22:C2 NaN NaN
O14: M23:C2 NaN NaN
O14: M24:C2 NaN NaN
gamma_O1[1] NaN NaN
gamma_O1[2] NaN NaN
gamma_O1[3] 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"
$m0a
est MCSE SD MCSE/SD
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 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
$m1a
est MCSE SD MCSE/SD
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 0 0 0 NaN
C1 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
$m2a
est MCSE SD MCSE/SD
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 0 0 0 NaN
C2 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
$m3a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
sigma_C1 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
$m4a
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
O24 0 0 0 NaN
abs(C1 - C2) 0 0 0 NaN
log(C1) 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
gamma_O1[3] 0 0 0 NaN
$m4b
est MCSE SD MCSE/SD
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 0 0 NaN
abs(C1 - C2) 0 0 0 NaN
log(C1) 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
gamma_O1[3] 0 0 0 NaN
$m5a
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
O24 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
O14: C1 0 0 0 NaN
O14: C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 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
O24 0 0 0 NaN
C1:C2 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
O14: C1 0 0 0 NaN
O14: C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 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
O24 0 0 0 NaN
O12: C1 0 0 0 NaN
O12: C2 0 0 0 NaN
O12: C1:C2 0 0 0 NaN
O13: C1 0 0 0 NaN
O13: C2 0 0 0 NaN
O13: C1:C2 0 0 0 NaN
O14: C1 0 0 0 NaN
O14: C2 0 0 0 NaN
O14: C1:C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 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
O24 0 0 0 NaN
M22:C2 0 0 0 NaN
M23:C2 0 0 0 NaN
M24:C2 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
O14: C1 0 0 0 NaN
O14: C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 0 0 0 NaN
$m5e
est MCSE SD MCSE/SD
O12: C1 0 0 0 NaN
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: O24 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: C1 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: O24 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
O14: C1 0 0 0 NaN
O14: M22 0 0 0 NaN
O14: M23 0 0 0 NaN
O14: M24 0 0 0 NaN
O14: C2 0 0 0 NaN
O14: O22 0 0 0 NaN
O14: O23 0 0 0 NaN
O14: O24 0 0 0 NaN
O14: M22:C2 0 0 0 NaN
O14: M23:C2 0 0 0 NaN
O14: M24:C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 0 0 0 NaN
$m6a
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
O24 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
O14: C1 0 0 0 NaN
O14: C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 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
O24 0 0 0 NaN
C1:C2 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
O14: C1 0 0 0 NaN
O14: C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 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
O24 0 0 0 NaN
O12: C1 0 0 0 NaN
O12: C2 0 0 0 NaN
O12: C1:C2 0 0 0 NaN
O13: C1 0 0 0 NaN
O13: C2 0 0 0 NaN
O13: C1:C2 0 0 0 NaN
O14: C1 0 0 0 NaN
O14: C2 0 0 0 NaN
O14: C1:C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 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
O24 0 0 0 NaN
M22:C2 0 0 0 NaN
M23:C2 0 0 0 NaN
M24:C2 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
O14: C1 0 0 0 NaN
O14: C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 0 0 0 NaN
$m6e
est MCSE SD MCSE/SD
O12: C1 0 0 0 NaN
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: O24 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: C1 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: O24 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
O14: C1 0 0 0 NaN
O14: M22 0 0 0 NaN
O14: M23 0 0 0 NaN
O14: M24 0 0 0 NaN
O14: C2 0 0 0 NaN
O14: O22 0 0 0 NaN
O14: O23 0 0 0 NaN
O14: O24 0 0 0 NaN
O14: M22:C2 0 0 0 NaN
O14: M23:C2 0 0 0 NaN
O14: M24:C2 0 0 0 NaN
gamma_O1[1] 0 0 0 NaN
gamma_O1[2] 0 0 0 NaN
gamma_O1[3] 0 0 0 NaN
summary output remained the same on Windows
Code
lapply(models0, print)
Output
Call:
clm_imp(formula = O1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3
0 0 0
Call:
clm_imp(formula = O2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O2"
Coefficients:
O2 > 1 O2 > 2 O2 > 3
0 0 0
Call:
clm_imp(formula = O1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 C1
0 0 0 0
Call:
clm_imp(formula = O2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O2"
Coefficients:
O2 > 1 O2 > 2 O2 > 3 C1
0 0 0 0
Call:
clm_imp(formula = O1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 C2
0 0 0 0
Call:
clm_imp(formula = O2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O2"
Coefficients:
O2 > 1 O2 > 2 O2 > 3 C2
0 0 0 0
Call:
lm_imp(formula = C1 ~ O1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) O1.L O1.Q O1.C
0 0 0 0
Residual standard deviation:
sigma_C1
0
Call:
lm_imp(formula = C1 ~ O2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) O22 O23 O24
0 0 0 0
Residual standard deviation:
sigma_C1
0
Call:
clm_imp(formula = O1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22
0 0 0 0
M23 M24 O22 O23
0 0 0 0
O24 abs(C1 - C2) log(C1) O22:abs(C1 - C2)
0 0 0 0
O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0
Call:
clm_imp(formula = O1 ~ ifelse(as.numeric(O2) > as.numeric(M1),
1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1
0
O1 > 2
0
O1 > 3
0
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
Call:
clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1 C2 C1 C2
0 0 0 0
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1:C2 C1
0 0 0 0 0 0 0 0 0 0 0
C2 C1 C2 C1 C2
0 0 0 0 0
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1:C2 C1 C2 C1:C2 C1 C2 C1:C2
0 0 0 0 0 0 0
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2
0 0 0 0 0 0 0 0 0 0 0
M24:C2 C1 C2 C1 C2 C1 C2
0 0 0 0 0 0 0
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2
0 0 0
Call:
clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1 C2 C1 C2
0 0 0 0
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1:C2 C1
0 0 0 0 0 0 0 0 0 0 0
C2 C1 C2 C1 C2
0 0 0 0 0
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1:C2 C1 C2 C1:C2 C1 C2 C1:C2
0 0 0 0 0 0 0
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2
0 0 0 0 0 0 0 0 0 0 0
M24:C2 C1 C2 C1 C2 C1 C2
0 0 0 0 0 0 0
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2
0 0 0
$m0a
Call:
clm_imp(formula = O1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3
0 0 0
$m0b
Call:
clm_imp(formula = O2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O2"
Coefficients:
O2 > 1 O2 > 2 O2 > 3
0 0 0
$m1a
Call:
clm_imp(formula = O1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 C1
0 0 0 0
$m1b
Call:
clm_imp(formula = O2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O2"
Coefficients:
O2 > 1 O2 > 2 O2 > 3 C1
0 0 0 0
$m2a
Call:
clm_imp(formula = O1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 C2
0 0 0 0
$m2b
Call:
clm_imp(formula = O2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O2"
Coefficients:
O2 > 1 O2 > 2 O2 > 3 C2
0 0 0 0
$m3a
Call:
lm_imp(formula = C1 ~ O1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) O1.L O1.Q O1.C
0 0 0 0
Residual standard deviation:
sigma_C1
0
$m3b
Call:
lm_imp(formula = C1 ~ O2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) O22 O23 O24
0 0 0 0
Residual standard deviation:
sigma_C1
0
$m4a
Call:
clm_imp(formula = O1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22
0 0 0 0
M23 M24 O22 O23
0 0 0 0
O24 abs(C1 - C2) log(C1) O22:abs(C1 - C2)
0 0 0 0
O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0
$m4b
Call:
clm_imp(formula = O1 ~ ifelse(as.numeric(O2) > as.numeric(M1),
1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian cumulative logit model for "O1"
Coefficients:
O1 > 1
0
O1 > 2
0
O1 > 3
0
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
$m5a
Call:
clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1 C2 C1 C2
0 0 0 0
$m5b
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1:C2 C1
0 0 0 0 0 0 0 0 0 0 0
C2 C1 C2 C1 C2
0 0 0 0 0
$m5c
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1:C2 C1 C2 C1:C2 C1 C2 C1:C2
0 0 0 0 0 0 0
$m5d
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2
0 0 0 0 0 0 0 0 0 0 0
M24:C2 C1 C2 C1 C2 C1 C2
0 0 0 0 0 0 0
$m5e
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 > 1 O1 > 2 O1 > 3 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2
0 0 0
$m6a
Call:
clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1 C2 C1 C2
0 0 0 0
$m6b
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1:C2 C1
0 0 0 0 0 0 0 0 0 0 0
C2 C1 C2 C1 C2
0 0 0 0 0
$m6c
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1:C2 C1 C2 C1:C2 C1 C2 C1:C2
0 0 0 0 0 0 0
$m6d
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 M22 M23 M24 O22 O23 O24 M22:C2 M23:C2
0 0 0 0 0 0 0 0 0 0 0
M24:C2 C1 C2 C1 C2 C1 C2
0 0 0 0 0 0 0
$m6e
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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 model for "O1"
Coefficients:
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2 C1 M22 M23 M24 C2 O22 O23 O24
0 0 0 0 0 0 0 0 0 0 0
M22:C2 M23:C2 M24:C2
0 0 0
Code
lapply(models0, coef)
Output
$m0a
$m0a$O1
O1 > 1 O1 > 2 O1 > 3
0 0 0
$m0b
$m0b$O2
O2 > 1 O2 > 2 O2 > 3
0 0 0
$m1a
$m1a$O1
C1 O1 > 1 O1 > 2 O1 > 3
0 0 0 0
$m1b
$m1b$O2
C1 O2 > 1 O2 > 2 O2 > 3
0 0 0 0
$m2a
$m2a$O1
C2 O1 > 1 O1 > 2 O1 > 3
0 0 0 0
$m2b
$m2b$O2
C2 O2 > 1 O2 > 2 O2 > 3
0 0 0 0
$m3a
$m3a$C1
(Intercept) O1.L O1.Q O1.C sigma_C1
0 0 0 0 0
$m3b
$m3b$C1
(Intercept) O22 O23 O24 sigma_C1
0 0 0 0 0
$m4a
$m4a$O1
M22 M23 M24 O22
0 0 0 0
O23 O24 abs(C1 - C2) log(C1)
0 0 0 0
O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) O1 > 1
0 0 0 0
O1 > 2 O1 > 3
0 0
$m4b
$m4b$O1
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2)
0
O1 > 1
0
O1 > 2
0
O1 > 3
0
$m5a
$m5a$O1
M22 M23 M24 O22 O23 O24 C1 C2 C1 C2 C1
0 0 0 0 0 0 0 0 0 0 0
C2 O1 > 1 O1 > 2 O1 > 3
0 0 0 0
$m5b
$m5b$O1
M22 M23 M24 O22 O23 O24 C1:C2 C1 C2 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1 C2 O1 > 1 O1 > 2 O1 > 3
0 0 0 0 0
$m5c
$m5c$O1
M22 M23 M24 O22 O23 O24 C1 C2 C1:C2 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1:C2 C1 C2 C1:C2 O1 > 1 O1 > 2 O1 > 3
0 0 0 0 0 0 0
$m5d
$m5d$O1
M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 M24:C2 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1 C2 C1 C2 O1 > 1 O1 > 2 O1 > 3
0 0 0 0 0 0 0
$m5e
$m5e$O1
C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
O1 > 1 O1 > 2 O1 > 3
0 0 0
$m6a
$m6a$O1
M22 M23 M24 O22 O23 O24 C1 C2 C1 C2 C1
0 0 0 0 0 0 0 0 0 0 0
C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3
0 0 0 0
$m6b
$m6b$O1
M22 M23 M24 O22 O23 O24 C1:C2 C1 C2 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1 C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3
0 0 0 0 0
$m6c
$m6c$O1
M22 M23 M24 O22 O23 O24 C1 C2 C1:C2 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1:C2 C1 C2 C1:C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3
0 0 0 0 0 0 0
$m6d
$m6d$O1
M22 M23 M24 O22 O23 O24 M22:C2 M23:C2 M24:C2 C1 C2
0 0 0 0 0 0 0 0 0 0 0
C1 C2 C1 C2 O1 ≤ 1 O1 ≤ 2 O1 ≤ 3
0 0 0 0 0 0 0
$m6e
$m6e$O1
C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
C1 M22 M23 M24 C2 O22 O23 O24 M22:C2 M23:C2 M24:C2
0 0 0 0 0 0 0 0 0 0 0
O1 ≤ 1 O1 ≤ 2 O1 ≤ 3
0 0 0
Code
lapply(models0, confint)
Output
$m0a
$m0a$O1
2.5% 97.5%
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m0b
$m0b$O2
2.5% 97.5%
O2 > 1 0 0
O2 > 2 0 0
O2 > 3 0 0
$m1a
$m1a$O1
2.5% 97.5%
C1 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m1b
$m1b$O2
2.5% 97.5%
C1 0 0
O2 > 1 0 0
O2 > 2 0 0
O2 > 3 0 0
$m2a
$m2a$O1
2.5% 97.5%
C2 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m2b
$m2b$O2
2.5% 97.5%
C2 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
O1.C 0 0
sigma_C1 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
$m4a
$m4a$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
O22:abs(C1 - C2) 0 0
O23:abs(C1 - C2) 0 0
O24:abs(C1 - C2) 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m4b
$m4b$O1
2.5% 97.5%
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m5a
$m5a$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m5b
$m5b$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
C1:C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m5c
$m5c$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
C1 0 0
C2 0 0
C1:C2 0 0
C1 0 0
C2 0 0
C1:C2 0 0
C1 0 0
C2 0 0
C1:C2 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m5d
$m5d$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m5e
$m5e$O1
2.5% 97.5%
C1 0 0
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
O24 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
C1 0 0
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
O24 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
C1 0 0
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
O24 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
O1 > 1 0 0
O1 > 2 0 0
O1 > 3 0 0
$m6a
$m6a$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
O1 ≤ 1 0 0
O1 ≤ 2 0 0
O1 ≤ 3 0 0
$m6b
$m6b$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
C1:C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
O1 ≤ 1 0 0
O1 ≤ 2 0 0
O1 ≤ 3 0 0
$m6c
$m6c$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
C1 0 0
C2 0 0
C1:C2 0 0
C1 0 0
C2 0 0
C1:C2 0 0
C1 0 0
C2 0 0
C1:C2 0 0
O1 ≤ 1 0 0
O1 ≤ 2 0 0
O1 ≤ 3 0 0
$m6d
$m6d$O1
2.5% 97.5%
M22 0 0
M23 0 0
M24 0 0
O22 0 0
O23 0 0
O24 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
C1 0 0
C2 0 0
O1 ≤ 1 0 0
O1 ≤ 2 0 0
O1 ≤ 3 0 0
$m6e
$m6e$O1
2.5% 97.5%
C1 0 0
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
O24 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
C1 0 0
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
O24 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
C1 0 0
M22 0 0
M23 0 0
M24 0 0
C2 0 0
O22 0 0
O23 0 0
O24 0 0
M22:C2 0 0
M23:C2 0 0
M24:C2 0 0
O1 ≤ 1 0 0
O1 ≤ 2 0 0
O1 ≤ 3 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"
$m0a
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ 1, data = wideDF, 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m0b
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O2 ~ 1, data = wideDF, 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
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m1a
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1, data = wideDF, 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m1b
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O2 ~ C1, data = wideDF, 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
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m2a
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C2, data = wideDF, 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m2b
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O2 ~ C2, data = wideDF, 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
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m3a
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = C1 ~ O1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 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: 100
$m3b
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = C1 ~ O2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
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 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: 100
$m4a
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
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
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m4b
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ ifelse(as.numeric(O2) > as.numeric(M1),
1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5%
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 0 0 0
abs(C1 - C2) 0 0 0 0
log(C1) 0 0 0 0
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 0
tail-prob. GR-crit
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 NaN
abs(C1 - C2) 0 NaN
log(C1) 0 NaN
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 NaN
MCE/SD
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) NaN
abs(C1 - C2) NaN
log(C1) 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m5a
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, 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
O24 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
O14: C1 0 0 0 0 0 NaN NaN
O14: 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m5b
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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
O24 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
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
O14: C1 0 0 0 0 0 NaN NaN
O14: 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m5c
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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
O24 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
O12: C1: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
O13: C1:C2 0 0 0 0 0 NaN NaN
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
O14: 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m5d
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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
O24 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: 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
O14: C1 0 0 0 0 0 NaN NaN
O14: 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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m5e
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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: C1 0 0 0 0 0 NaN NaN
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: O24 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: C1 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: O24 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
O14: C1 0 0 0 0 0 NaN NaN
O14: M22 0 0 0 0 0 NaN NaN
O14: M23 0 0 0 0 0 NaN NaN
O14: M24 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
O14: O22 0 0 0 0 0 NaN NaN
O14: O23 0 0 0 0 0 NaN NaN
O14: O24 0 0 0 0 0 NaN NaN
O14: M22:C2 0 0 0 0 0 NaN NaN
O14: M23:C2 0 0 0 0 0 NaN NaN
O14: M24: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
O1 > 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m6a
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, 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
O24 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
O14: C1 0 0 0 0 0 NaN NaN
O14: 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
O1 ≤ 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m6b
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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
O24 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
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
O14: C1 0 0 0 0 0 NaN NaN
O14: 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
O1 ≤ 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m6c
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, 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
O24 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
O12: C1: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
O13: C1:C2 0 0 0 0 0 NaN NaN
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
O14: 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
O1 ≤ 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m6d
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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
O24 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: 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
O14: C1 0 0 0 0 0 NaN NaN
O14: 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
O1 ≤ 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
$m6e
Bayesian cumulative logit model fitted with JointAI
Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, 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: C1 0 0 0 0 0 NaN NaN
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: O24 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: C1 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: O24 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
O14: C1 0 0 0 0 0 NaN NaN
O14: M22 0 0 0 0 0 NaN NaN
O14: M23 0 0 0 0 0 NaN NaN
O14: M24 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
O14: O22 0 0 0 0 0 NaN NaN
O14: O23 0 0 0 0 0 NaN NaN
O14: O24 0 0 0 0 0 NaN NaN
O14: M22:C2 0 0 0 0 0 NaN NaN
O14: M23:C2 0 0 0 0 0 NaN NaN
O14: M24: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
O1 ≤ 3 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 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"
$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
$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
$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
O1.C 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
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 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%
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 0 0 0
abs(C1 - C2) 0 0 0 0
log(C1) 0 0 0 0
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 0 0 0
tail-prob. GR-crit
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) 0 NaN
abs(C1 - C2) 0 NaN
log(C1) 0 NaN
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) 0 NaN
MCE/SD
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0) NaN
abs(C1 - C2) NaN
log(C1) NaN
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2) NaN
$m5a
$m5a$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
O24 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
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 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
O24 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
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
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 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
O24 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
O12: C1: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
O13: C1:C2 0 0 0 0 0 NaN NaN
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
O14: 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
O24 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: 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
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
$m5e
$m5e$O1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O12: C1 0 0 0 0 0 NaN NaN
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: O24 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: C1 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: O24 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
O14: C1 0 0 0 0 0 NaN NaN
O14: M22 0 0 0 0 0 NaN NaN
O14: M23 0 0 0 0 0 NaN NaN
O14: M24 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
O14: O22 0 0 0 0 0 NaN NaN
O14: O23 0 0 0 0 0 NaN NaN
O14: O24 0 0 0 0 0 NaN NaN
O14: M22:C2 0 0 0 0 0 NaN NaN
O14: M23:C2 0 0 0 0 0 NaN NaN
O14: M24:C2 0 0 0 0 0 NaN NaN
$m6a
$m6a$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
O24 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
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 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
O24 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
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
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 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
O24 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
O12: C1: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
O13: C1:C2 0 0 0 0 0 NaN NaN
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
O14: 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
O24 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: 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
O14: C1 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
$m6e
$m6e$O1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O12: C1 0 0 0 0 0 NaN NaN
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: O24 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: C1 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: O24 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
O14: C1 0 0 0 0 0 NaN NaN
O14: M22 0 0 0 0 0 NaN NaN
O14: M23 0 0 0 0 0 NaN NaN
O14: M24 0 0 0 0 0 NaN NaN
O14: C2 0 0 0 0 0 NaN NaN
O14: O22 0 0 0 0 0 NaN NaN
O14: O23 0 0 0 0 0 NaN NaN
O14: O24 0 0 0 0 0 NaN NaN
O14: M22:C2 0 0 0 0 0 NaN NaN
O14: M23:C2 0 0 0 0 0 NaN NaN
O14: M24:C2 0 0 0 0 0 NaN NaN
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