Nothing
tests/testthat/_snaps/mlogit.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
M1 (Intercept)
1 1 1
2 2 1
3 2 1
4 1 1
5 3 1
6 3 1
7 3 1
8 3 1
9 1 1
10 2 1
11 1 1
12 2 1
13 1 1
14 4 1
15 2 1
16 3 1
17 3 1
18 1 1
19 2 1
20 2 1
21 1 1
22 4 1
23 1 1
24 2 1
25 1 1
26 1 1
27 2 1
28 1 1
29 3 1
30 1 1
31 4 1
32 1 1
33 4 1
34 2 1
35 1 1
36 1 1
37 1 1
38 2 1
39 2 1
40 2 1
41 4 1
42 4 1
43 4 1
44 4 1
45 2 1
46 1 1
47 3 1
48 3 1
49 2 1
50 2 1
51 3 1
52 1 1
53 2 1
54 3 1
55 2 1
56 1 1
57 4 1
58 1 1
59 4 1
60 1 1
61 1 1
62 1 1
63 4 1
64 1 1
65 2 1
66 4 1
67 4 1
68 3 1
69 3 1
70 2 1
71 3 1
72 2 1
73 4 1
74 2 1
75 1 1
76 3 1
77 2 1
78 4 1
79 4 1
80 1 1
81 1 1
82 4 1
83 3 1
84 4 1
85 2 1
86 4 1
87 3 1
88 3 1
89 3 1
90 1 1
91 1 1
92 1 1
93 1 1
94 3 1
95 2 1
96 1 1
97 3 1
98 4 1
99 2 1
100 1 1
$m0a$mu_reg_multinomial
[1] 0
$m0a$tau_reg_multinomial
[1] 1e-04
$m0b
$m0b$M_lvlone
M2 (Intercept)
1 4 1
2 1 1
3 3 1
4 3 1
5 4 1
6 4 1
7 1 1
8 1 1
9 2 1
10 2 1
11 3 1
12 3 1
13 2 1
14 3 1
15 2 1
16 1 1
17 4 1
18 2 1
19 3 1
20 3 1
21 2 1
22 2 1
23 3 1
24 3 1
25 2 1
26 2 1
27 1 1
28 3 1
29 4 1
30 2 1
31 NA 1
32 4 1
33 4 1
34 4 1
35 2 1
36 1 1
37 3 1
38 4 1
39 3 1
40 3 1
41 1 1
42 4 1
43 1 1
44 2 1
45 2 1
46 1 1
47 4 1
48 2 1
49 4 1
50 1 1
51 4 1
52 4 1
53 4 1
54 3 1
55 3 1
56 3 1
57 2 1
58 3 1
59 3 1
60 4 1
61 2 1
62 2 1
63 1 1
64 2 1
65 2 1
66 3 1
67 2 1
68 4 1
69 NA 1
70 1 1
71 2 1
72 3 1
73 4 1
74 4 1
75 1 1
76 4 1
77 NA 1
78 2 1
79 2 1
80 2 1
81 4 1
82 3 1
83 3 1
84 1 1
85 2 1
86 1 1
87 3 1
88 1 1
89 2 1
90 1 1
91 3 1
92 1 1
93 4 1
94 4 1
95 1 1
96 4 1
97 4 1
98 3 1
99 3 1
100 4 1
$m0b$mu_reg_multinomial
[1] 0
$m0b$tau_reg_multinomial
[1] 1e-04
$m1a
$m1a$M_lvlone
M1 (Intercept) C1
1 1 1 1.410531
2 2 1 1.434183
3 2 1 1.430994
4 1 1 1.453096
5 3 1 1.438344
6 3 1 1.453207
7 3 1 1.425176
8 3 1 1.437908
9 1 1 1.416911
10 2 1 1.448638
11 1 1 1.428375
12 2 1 1.450130
13 1 1 1.420545
14 4 1 1.423005
15 2 1 1.435902
16 3 1 1.423901
17 3 1 1.457208
18 1 1 1.414280
19 2 1 1.443383
20 2 1 1.434954
21 1 1 1.429499
22 4 1 1.441897
23 1 1 1.423713
24 2 1 1.435395
25 1 1 1.425944
26 1 1 1.437115
27 2 1 1.441326
28 1 1 1.422953
29 3 1 1.437797
30 1 1 1.472121
31 4 1 1.421782
32 1 1 1.457672
33 4 1 1.430842
34 2 1 1.431523
35 1 1 1.421395
36 1 1 1.434496
37 1 1 1.425383
38 2 1 1.421802
39 2 1 1.430094
40 2 1 1.447621
41 4 1 1.434797
42 4 1 1.446091
43 4 1 1.445306
44 4 1 1.448783
45 2 1 1.450617
46 1 1 1.415055
47 3 1 1.436590
48 3 1 1.433938
49 2 1 1.414941
50 2 1 1.421807
51 3 1 1.453203
52 1 1 1.452129
53 2 1 1.431510
54 3 1 1.430082
55 2 1 1.443492
56 1 1 1.436460
57 4 1 1.418119
58 1 1 1.434971
59 4 1 1.445599
60 1 1 1.437097
61 1 1 1.428360
62 1 1 1.440550
63 4 1 1.443014
64 1 1 1.424298
65 2 1 1.448823
66 4 1 1.425834
67 4 1 1.427102
68 3 1 1.414240
69 3 1 1.456218
70 2 1 1.470594
71 3 1 1.425058
72 2 1 1.432371
73 4 1 1.441656
74 2 1 1.434952
75 1 1 1.402860
76 3 1 1.453363
77 2 1 1.432909
78 4 1 1.435103
79 4 1 1.434462
80 1 1 1.434661
81 1 1 1.445881
82 4 1 1.442548
83 3 1 1.430097
84 4 1 1.430119
85 2 1 1.430315
86 4 1 1.437584
87 3 1 1.409738
88 3 1 1.422388
89 3 1 1.422509
90 1 1 1.439432
91 1 1 1.430175
92 1 1 1.418002
93 1 1 1.423812
94 3 1 1.423473
95 2 1 1.434412
96 1 1 1.450844
97 3 1 1.433371
98 4 1 1.444378
99 2 1 1.422523
100 1 1 1.410394
$m1a$spM_lvlone
center scale
M1 NA NA
(Intercept) NA NA
C1 1.434101 0.01299651
$m1a$mu_reg_multinomial
[1] 0
$m1a$tau_reg_multinomial
[1] 1e-04
$m1b
$m1b$M_lvlone
M2 (Intercept) C1
1 4 1 1.410531
2 1 1 1.434183
3 3 1 1.430994
4 3 1 1.453096
5 4 1 1.438344
6 4 1 1.453207
7 1 1 1.425176
8 1 1 1.437908
9 2 1 1.416911
10 2 1 1.448638
11 3 1 1.428375
12 3 1 1.450130
13 2 1 1.420545
14 3 1 1.423005
15 2 1 1.435902
16 1 1 1.423901
17 4 1 1.457208
18 2 1 1.414280
19 3 1 1.443383
20 3 1 1.434954
21 2 1 1.429499
22 2 1 1.441897
23 3 1 1.423713
24 3 1 1.435395
25 2 1 1.425944
26 2 1 1.437115
27 1 1 1.441326
28 3 1 1.422953
29 4 1 1.437797
30 2 1 1.472121
31 NA 1 1.421782
32 4 1 1.457672
33 4 1 1.430842
34 4 1 1.431523
35 2 1 1.421395
36 1 1 1.434496
37 3 1 1.425383
38 4 1 1.421802
39 3 1 1.430094
40 3 1 1.447621
41 1 1 1.434797
42 4 1 1.446091
43 1 1 1.445306
44 2 1 1.448783
45 2 1 1.450617
46 1 1 1.415055
47 4 1 1.436590
48 2 1 1.433938
49 4 1 1.414941
50 1 1 1.421807
51 4 1 1.453203
52 4 1 1.452129
53 4 1 1.431510
54 3 1 1.430082
55 3 1 1.443492
56 3 1 1.436460
57 2 1 1.418119
58 3 1 1.434971
59 3 1 1.445599
60 4 1 1.437097
61 2 1 1.428360
62 2 1 1.440550
63 1 1 1.443014
64 2 1 1.424298
65 2 1 1.448823
66 3 1 1.425834
67 2 1 1.427102
68 4 1 1.414240
69 NA 1 1.456218
70 1 1 1.470594
71 2 1 1.425058
72 3 1 1.432371
73 4 1 1.441656
74 4 1 1.434952
75 1 1 1.402860
76 4 1 1.453363
77 NA 1 1.432909
78 2 1 1.435103
79 2 1 1.434462
80 2 1 1.434661
81 4 1 1.445881
82 3 1 1.442548
83 3 1 1.430097
84 1 1 1.430119
85 2 1 1.430315
86 1 1 1.437584
87 3 1 1.409738
88 1 1 1.422388
89 2 1 1.422509
90 1 1 1.439432
91 3 1 1.430175
92 1 1 1.418002
93 4 1 1.423812
94 4 1 1.423473
95 1 1 1.434412
96 4 1 1.450844
97 4 1 1.433371
98 3 1 1.444378
99 3 1 1.422523
100 4 1 1.410394
$m1b$spM_lvlone
center scale
M2 NA NA
(Intercept) NA NA
C1 1.434101 0.01299651
$m1b$mu_reg_multinomial
[1] 0
$m1b$tau_reg_multinomial
[1] 1e-04
$m2a
$m2a$M_lvlone
M1 C2 (Intercept)
1 1 0.144065882 1
2 2 0.032778478 1
3 2 0.343008492 1
4 1 -0.361887858 1
5 3 -0.389600647 1
6 3 -0.205306841 1
7 3 0.079434830 1
8 3 -0.331246757 1
9 1 -0.329638800 1
10 2 0.167597533 1
11 1 0.860207989 1
12 2 0.022730640 1
13 1 0.217171172 1
14 4 -0.403002412 1
15 2 0.087369742 1
16 3 -0.183870429 1
17 3 -0.194577002 1
18 1 -0.349718516 1
19 2 -0.508781244 1
20 2 0.494883111 1
21 1 0.258041067 1
22 4 -0.922621989 1
23 1 0.431254949 1
24 2 -0.294218881 1
25 1 -0.425548895 1
26 1 0.057176054 1
27 2 0.289090158 1
28 1 -0.473079489 1
29 3 -0.385664863 1
30 1 -0.154780107 1
31 4 0.100536296 1
32 1 0.634791958 1
33 4 -0.387252617 1
34 2 -0.181741088 1
35 1 -0.311562695 1
36 1 -0.044115907 1
37 1 -0.657409991 1
38 2 0.159577214 1
39 2 -0.460416933 1
40 2 NA 1
41 4 -0.248909867 1
42 4 -0.609021545 1
43 4 0.025471883 1
44 4 0.066648592 1
45 2 -0.276108719 1
46 1 -0.179737577 1
47 3 0.181190937 1
48 3 -0.453871693 1
49 2 0.448629602 1
50 2 -0.529811821 1
51 3 -0.028304571 1
52 1 -0.520318482 1
53 2 0.171317619 1
54 3 0.432732046 1
55 2 -0.346286005 1
56 1 -0.469375653 1
57 4 0.031021711 1
58 1 -0.118837515 1
59 4 0.507769984 1
60 1 0.271797031 1
61 1 -0.124442204 1
62 1 0.277677389 1
63 4 -0.102893730 1
64 1 NA 1
65 2 -0.678303052 1
66 4 0.478880037 1
67 4 -0.428028760 1
68 3 0.048119185 1
69 3 0.216932805 1
70 2 -0.234575269 1
71 3 0.006827078 1
72 2 -0.456055171 1
73 4 0.346486708 1
74 2 0.205092215 1
75 1 -0.136596858 1
76 3 -0.500179043 1
77 2 0.527352086 1
78 4 0.022742250 1
79 4 NA 1
80 1 -0.002032440 1
81 1 -0.154246160 1
82 4 0.140201825 1
83 3 -0.141417121 1
84 4 NA 1
85 2 -0.021285339 1
86 4 -0.010196306 1
87 3 -0.089747520 1
88 3 -0.083699898 1
89 3 -0.044061996 1
90 1 -0.209291697 1
91 1 0.639036426 1
92 1 0.094698299 1
93 1 -0.055510622 1
94 3 -0.421318463 1
95 2 0.125295503 1
96 1 0.213084904 1
97 3 -0.161914659 1
98 4 -0.034767685 1
99 2 -0.320681689 1
100 1 0.058192962 1
$m2a$spM_lvlone
center scale
M1 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_multinomial
[1] 0
$m2a$tau_reg_multinomial
[1] 1e-04
$m2b
$m2b$M_lvlone
M2 C2 (Intercept)
1 4 0.144065882 1
2 1 0.032778478 1
3 3 0.343008492 1
4 3 -0.361887858 1
5 4 -0.389600647 1
6 4 -0.205306841 1
7 1 0.079434830 1
8 1 -0.331246757 1
9 2 -0.329638800 1
10 2 0.167597533 1
11 3 0.860207989 1
12 3 0.022730640 1
13 2 0.217171172 1
14 3 -0.403002412 1
15 2 0.087369742 1
16 1 -0.183870429 1
17 4 -0.194577002 1
18 2 -0.349718516 1
19 3 -0.508781244 1
20 3 0.494883111 1
21 2 0.258041067 1
22 2 -0.922621989 1
23 3 0.431254949 1
24 3 -0.294218881 1
25 2 -0.425548895 1
26 2 0.057176054 1
27 1 0.289090158 1
28 3 -0.473079489 1
29 4 -0.385664863 1
30 2 -0.154780107 1
31 NA 0.100536296 1
32 4 0.634791958 1
33 4 -0.387252617 1
34 4 -0.181741088 1
35 2 -0.311562695 1
36 1 -0.044115907 1
37 3 -0.657409991 1
38 4 0.159577214 1
39 3 -0.460416933 1
40 3 NA 1
41 1 -0.248909867 1
42 4 -0.609021545 1
43 1 0.025471883 1
44 2 0.066648592 1
45 2 -0.276108719 1
46 1 -0.179737577 1
47 4 0.181190937 1
48 2 -0.453871693 1
49 4 0.448629602 1
50 1 -0.529811821 1
51 4 -0.028304571 1
52 4 -0.520318482 1
53 4 0.171317619 1
54 3 0.432732046 1
55 3 -0.346286005 1
56 3 -0.469375653 1
57 2 0.031021711 1
58 3 -0.118837515 1
59 3 0.507769984 1
60 4 0.271797031 1
61 2 -0.124442204 1
62 2 0.277677389 1
63 1 -0.102893730 1
64 2 NA 1
65 2 -0.678303052 1
66 3 0.478880037 1
67 2 -0.428028760 1
68 4 0.048119185 1
69 NA 0.216932805 1
70 1 -0.234575269 1
71 2 0.006827078 1
72 3 -0.456055171 1
73 4 0.346486708 1
74 4 0.205092215 1
75 1 -0.136596858 1
76 4 -0.500179043 1
77 NA 0.527352086 1
78 2 0.022742250 1
79 2 NA 1
80 2 -0.002032440 1
81 4 -0.154246160 1
82 3 0.140201825 1
83 3 -0.141417121 1
84 1 NA 1
85 2 -0.021285339 1
86 1 -0.010196306 1
87 3 -0.089747520 1
88 1 -0.083699898 1
89 2 -0.044061996 1
90 1 -0.209291697 1
91 3 0.639036426 1
92 1 0.094698299 1
93 4 -0.055510622 1
94 4 -0.421318463 1
95 1 0.125295503 1
96 4 0.213084904 1
97 4 -0.161914659 1
98 3 -0.034767685 1
99 3 -0.320681689 1
100 4 0.058192962 1
$m2b$spM_lvlone
center scale
M2 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_multinomial
[1] 0
$m2b$tau_reg_multinomial
[1] 1e-04
$m3a
$m3a$M_lvlone
C1 (Intercept) M12 M13 M14
1 1.410531 1 0 0 0
2 1.434183 1 1 0 0
3 1.430994 1 1 0 0
4 1.453096 1 0 0 0
5 1.438344 1 0 1 0
6 1.453207 1 0 1 0
7 1.425176 1 0 1 0
8 1.437908 1 0 1 0
9 1.416911 1 0 0 0
10 1.448638 1 1 0 0
11 1.428375 1 0 0 0
12 1.450130 1 1 0 0
13 1.420545 1 0 0 0
14 1.423005 1 0 0 1
15 1.435902 1 1 0 0
16 1.423901 1 0 1 0
17 1.457208 1 0 1 0
18 1.414280 1 0 0 0
19 1.443383 1 1 0 0
20 1.434954 1 1 0 0
21 1.429499 1 0 0 0
22 1.441897 1 0 0 1
23 1.423713 1 0 0 0
24 1.435395 1 1 0 0
25 1.425944 1 0 0 0
26 1.437115 1 0 0 0
27 1.441326 1 1 0 0
28 1.422953 1 0 0 0
29 1.437797 1 0 1 0
30 1.472121 1 0 0 0
31 1.421782 1 0 0 1
32 1.457672 1 0 0 0
33 1.430842 1 0 0 1
34 1.431523 1 1 0 0
35 1.421395 1 0 0 0
36 1.434496 1 0 0 0
37 1.425383 1 0 0 0
38 1.421802 1 1 0 0
39 1.430094 1 1 0 0
40 1.447621 1 1 0 0
41 1.434797 1 0 0 1
42 1.446091 1 0 0 1
43 1.445306 1 0 0 1
44 1.448783 1 0 0 1
45 1.450617 1 1 0 0
46 1.415055 1 0 0 0
47 1.436590 1 0 1 0
48 1.433938 1 0 1 0
49 1.414941 1 1 0 0
50 1.421807 1 1 0 0
51 1.453203 1 0 1 0
52 1.452129 1 0 0 0
53 1.431510 1 1 0 0
54 1.430082 1 0 1 0
55 1.443492 1 1 0 0
56 1.436460 1 0 0 0
57 1.418119 1 0 0 1
58 1.434971 1 0 0 0
59 1.445599 1 0 0 1
60 1.437097 1 0 0 0
61 1.428360 1 0 0 0
62 1.440550 1 0 0 0
63 1.443014 1 0 0 1
64 1.424298 1 0 0 0
65 1.448823 1 1 0 0
66 1.425834 1 0 0 1
67 1.427102 1 0 0 1
68 1.414240 1 0 1 0
69 1.456218 1 0 1 0
70 1.470594 1 1 0 0
71 1.425058 1 0 1 0
72 1.432371 1 1 0 0
73 1.441656 1 0 0 1
74 1.434952 1 1 0 0
75 1.402860 1 0 0 0
76 1.453363 1 0 1 0
77 1.432909 1 1 0 0
78 1.435103 1 0 0 1
79 1.434462 1 0 0 1
80 1.434661 1 0 0 0
81 1.445881 1 0 0 0
82 1.442548 1 0 0 1
83 1.430097 1 0 1 0
84 1.430119 1 0 0 1
85 1.430315 1 1 0 0
86 1.437584 1 0 0 1
87 1.409738 1 0 1 0
88 1.422388 1 0 1 0
89 1.422509 1 0 1 0
90 1.439432 1 0 0 0
91 1.430175 1 0 0 0
92 1.418002 1 0 0 0
93 1.423812 1 0 0 0
94 1.423473 1 0 1 0
95 1.434412 1 1 0 0
96 1.450844 1 0 0 0
97 1.433371 1 0 1 0
98 1.444378 1 0 0 1
99 1.422523 1 1 0 0
100 1.410394 1 0 0 0
$m3a$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 M2 (Intercept) M22 M23 M24
1 1.410531 4 1 NA NA NA
2 1.434183 1 1 NA NA NA
3 1.430994 3 1 NA NA NA
4 1.453096 3 1 NA NA NA
5 1.438344 4 1 NA NA NA
6 1.453207 4 1 NA NA NA
7 1.425176 1 1 NA NA NA
8 1.437908 1 1 NA NA NA
9 1.416911 2 1 NA NA NA
10 1.448638 2 1 NA NA NA
11 1.428375 3 1 NA NA NA
12 1.450130 3 1 NA NA NA
13 1.420545 2 1 NA NA NA
14 1.423005 3 1 NA NA NA
15 1.435902 2 1 NA NA NA
16 1.423901 1 1 NA NA NA
17 1.457208 4 1 NA NA NA
18 1.414280 2 1 NA NA NA
19 1.443383 3 1 NA NA NA
20 1.434954 3 1 NA NA NA
21 1.429499 2 1 NA NA NA
22 1.441897 2 1 NA NA NA
23 1.423713 3 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 3 1 NA NA NA
29 1.437797 4 1 NA NA NA
30 1.472121 2 1 NA NA NA
31 1.421782 NA 1 NA NA NA
32 1.457672 4 1 NA NA NA
33 1.430842 4 1 NA NA NA
34 1.431523 4 1 NA NA NA
35 1.421395 2 1 NA NA NA
36 1.434496 1 1 NA NA NA
37 1.425383 3 1 NA NA NA
38 1.421802 4 1 NA NA NA
39 1.430094 3 1 NA NA NA
40 1.447621 3 1 NA NA NA
41 1.434797 1 1 NA NA NA
42 1.446091 4 1 NA NA NA
43 1.445306 1 1 NA NA NA
44 1.448783 2 1 NA NA NA
45 1.450617 2 1 NA NA NA
46 1.415055 1 1 NA NA NA
47 1.436590 4 1 NA NA NA
48 1.433938 2 1 NA NA NA
49 1.414941 4 1 NA NA NA
50 1.421807 1 1 NA NA NA
51 1.453203 4 1 NA NA NA
52 1.452129 4 1 NA NA NA
53 1.431510 4 1 NA NA NA
54 1.430082 3 1 NA NA NA
55 1.443492 3 1 NA NA NA
56 1.436460 3 1 NA NA NA
57 1.418119 2 1 NA NA NA
58 1.434971 3 1 NA NA NA
59 1.445599 3 1 NA NA NA
60 1.437097 4 1 NA NA NA
61 1.428360 2 1 NA NA NA
62 1.440550 2 1 NA NA NA
63 1.443014 1 1 NA NA NA
64 1.424298 2 1 NA NA NA
65 1.448823 2 1 NA NA NA
66 1.425834 3 1 NA NA NA
67 1.427102 2 1 NA NA NA
68 1.414240 4 1 NA NA NA
69 1.456218 NA 1 NA NA NA
70 1.470594 1 1 NA NA NA
71 1.425058 2 1 NA NA NA
72 1.432371 3 1 NA NA NA
73 1.441656 4 1 NA NA NA
74 1.434952 4 1 NA NA NA
75 1.402860 1 1 NA NA NA
76 1.453363 4 1 NA NA NA
77 1.432909 NA 1 NA NA NA
78 1.435103 2 1 NA NA NA
79 1.434462 2 1 NA NA NA
80 1.434661 2 1 NA NA NA
81 1.445881 4 1 NA NA NA
82 1.442548 3 1 NA NA NA
83 1.430097 3 1 NA NA NA
84 1.430119 1 1 NA NA NA
85 1.430315 2 1 NA NA NA
86 1.437584 1 1 NA NA NA
87 1.409738 3 1 NA NA NA
88 1.422388 1 1 NA NA NA
89 1.422509 2 1 NA NA NA
90 1.439432 1 1 NA NA NA
91 1.430175 3 1 NA NA NA
92 1.418002 1 1 NA NA NA
93 1.423812 4 1 NA NA NA
94 1.423473 4 1 NA NA NA
95 1.434412 1 1 NA NA NA
96 1.450844 4 1 NA NA NA
97 1.433371 4 1 NA NA NA
98 1.444378 3 1 NA NA NA
99 1.422523 3 1 NA NA NA
100 1.410394 4 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_reg_multinomial
[1] 0
$m3b$tau_reg_multinomial
[1] 1e-04
$m4a
$m4a$M_lvlone
M1 C2 M2 O2 (Intercept) M22 M23 M24 O22 O23 O24 abs(C1 - C2)
1 1 0.144065882 4 4 1 NA NA NA NA NA NA NA
2 2 0.032778478 1 4 1 NA NA NA NA NA NA NA
3 2 0.343008492 3 4 1 NA NA NA NA NA NA NA
4 1 -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 3 -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 3 -0.331246757 1 2 1 NA NA NA NA NA NA NA
9 1 -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 2 0.022730640 3 1 1 NA NA NA NA NA NA NA
13 1 0.217171172 2 1 1 NA NA NA NA NA NA NA
14 4 -0.403002412 3 1 1 NA NA NA NA NA NA NA
15 2 0.087369742 2 4 1 NA NA NA NA NA NA NA
16 3 -0.183870429 1 3 1 NA NA NA NA NA NA NA
17 3 -0.194577002 4 3 1 NA NA NA NA NA NA NA
18 1 -0.349718516 2 1 1 NA NA NA NA NA NA NA
19 2 -0.508781244 3 3 1 NA NA NA NA NA NA NA
20 2 0.494883111 3 1 1 NA NA NA NA NA NA NA
21 1 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 1 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 1 0.057176054 2 2 1 NA NA NA NA NA NA NA
27 2 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 3 -0.385664863 4 3 1 NA NA NA NA NA NA NA
30 1 -0.154780107 2 3 1 NA NA NA NA NA NA NA
31 4 0.100536296 NA 2 1 NA NA NA NA NA NA NA
32 1 0.634791958 4 2 1 NA NA NA NA NA NA NA
33 4 -0.387252617 4 1 1 NA NA NA NA NA NA NA
34 2 -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 1 -0.044115907 1 3 1 NA NA NA NA NA NA NA
37 1 -0.657409991 3 3 1 NA NA NA NA NA NA NA
38 2 0.159577214 4 1 1 NA NA NA NA NA NA NA
39 2 -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 4 -0.248909867 1 3 1 NA NA NA NA NA NA NA
42 4 -0.609021545 4 3 1 NA NA NA NA NA NA NA
43 4 0.025471883 1 3 1 NA NA NA NA NA NA NA
44 4 0.066648592 2 4 1 NA NA NA NA NA NA NA
45 2 -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 3 0.181190937 4 4 1 NA NA NA NA NA NA NA
48 3 -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 3 -0.028304571 4 1 1 NA NA NA NA NA NA NA
52 1 -0.520318482 4 3 1 NA NA NA NA NA NA NA
53 2 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 1 -0.469375653 3 3 1 NA NA NA NA NA NA NA
57 4 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 4 0.507769984 3 4 1 NA NA NA NA NA NA NA
60 1 0.271797031 4 3 1 NA NA NA NA NA NA NA
61 1 -0.124442204 2 4 1 NA NA NA NA NA NA NA
62 1 0.277677389 2 1 1 NA NA NA NA NA NA NA
63 4 -0.102893730 1 4 1 NA NA NA NA NA NA NA
64 1 NA 2 4 1 NA NA NA NA NA NA NA
65 2 -0.678303052 2 4 1 NA NA NA NA NA NA NA
66 4 0.478880037 3 1 1 NA NA NA NA NA NA NA
67 4 -0.428028760 2 3 1 NA NA NA NA NA NA NA
68 3 0.048119185 4 3 1 NA NA NA NA NA NA NA
69 3 0.216932805 NA 4 1 NA NA NA NA NA NA NA
70 2 -0.234575269 1 1 1 NA NA NA NA NA NA NA
71 3 0.006827078 2 4 1 NA NA NA NA NA NA NA
72 2 -0.456055171 3 4 1 NA NA NA NA NA NA NA
73 4 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 1 -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 2 0.527352086 NA 2 1 NA NA NA NA NA NA NA
78 4 0.022742250 2 3 1 NA NA NA NA NA NA NA
79 4 NA 2 2 1 NA NA NA NA NA NA NA
80 1 -0.002032440 2 1 1 NA NA NA NA NA NA NA
81 1 -0.154246160 4 4 1 NA NA NA NA NA NA NA
82 4 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 4 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 3 -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 1 -0.209291697 1 4 1 NA NA NA NA NA NA NA
91 1 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 1 -0.055510622 4 NA 1 NA NA NA NA NA NA NA
94 3 -0.421318463 4 3 1 NA NA NA NA NA NA NA
95 2 0.125295503 1 1 1 NA NA NA NA NA NA NA
96 1 0.213084904 4 3 1 NA NA NA NA NA NA NA
97 3 -0.161914659 4 2 1 NA NA NA NA NA NA NA
98 4 -0.034767685 3 2 1 NA NA NA NA NA NA NA
99 2 -0.320681689 3 4 1 NA NA NA NA NA NA NA
100 1 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
M1 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
M1 C2 M2 (Intercept)
1 1 0.144065882 4 1
2 2 0.032778478 1 1
3 2 0.343008492 3 1
4 1 -0.361887858 3 1
5 3 -0.389600647 4 1
6 3 -0.205306841 4 1
7 3 0.079434830 1 1
8 3 -0.331246757 1 1
9 1 -0.329638800 2 1
10 2 0.167597533 2 1
11 1 0.860207989 3 1
12 2 0.022730640 3 1
13 1 0.217171172 2 1
14 4 -0.403002412 3 1
15 2 0.087369742 2 1
16 3 -0.183870429 1 1
17 3 -0.194577002 4 1
18 1 -0.349718516 2 1
19 2 -0.508781244 3 1
20 2 0.494883111 3 1
21 1 0.258041067 2 1
22 4 -0.922621989 2 1
23 1 0.431254949 3 1
24 2 -0.294218881 3 1
25 1 -0.425548895 2 1
26 1 0.057176054 2 1
27 2 0.289090158 1 1
28 1 -0.473079489 3 1
29 3 -0.385664863 4 1
30 1 -0.154780107 2 1
31 4 0.100536296 NA 1
32 1 0.634791958 4 1
33 4 -0.387252617 4 1
34 2 -0.181741088 4 1
35 1 -0.311562695 2 1
36 1 -0.044115907 1 1
37 1 -0.657409991 3 1
38 2 0.159577214 4 1
39 2 -0.460416933 3 1
40 2 NA 3 1
41 4 -0.248909867 1 1
42 4 -0.609021545 4 1
43 4 0.025471883 1 1
44 4 0.066648592 2 1
45 2 -0.276108719 2 1
46 1 -0.179737577 1 1
47 3 0.181190937 4 1
48 3 -0.453871693 2 1
49 2 0.448629602 4 1
50 2 -0.529811821 1 1
51 3 -0.028304571 4 1
52 1 -0.520318482 4 1
53 2 0.171317619 4 1
54 3 0.432732046 3 1
55 2 -0.346286005 3 1
56 1 -0.469375653 3 1
57 4 0.031021711 2 1
58 1 -0.118837515 3 1
59 4 0.507769984 3 1
60 1 0.271797031 4 1
61 1 -0.124442204 2 1
62 1 0.277677389 2 1
63 4 -0.102893730 1 1
64 1 NA 2 1
65 2 -0.678303052 2 1
66 4 0.478880037 3 1
67 4 -0.428028760 2 1
68 3 0.048119185 4 1
69 3 0.216932805 NA 1
70 2 -0.234575269 1 1
71 3 0.006827078 2 1
72 2 -0.456055171 3 1
73 4 0.346486708 4 1
74 2 0.205092215 4 1
75 1 -0.136596858 1 1
76 3 -0.500179043 4 1
77 2 0.527352086 NA 1
78 4 0.022742250 2 1
79 4 NA 2 1
80 1 -0.002032440 2 1
81 1 -0.154246160 4 1
82 4 0.140201825 3 1
83 3 -0.141417121 3 1
84 4 NA 1 1
85 2 -0.021285339 2 1
86 4 -0.010196306 1 1
87 3 -0.089747520 3 1
88 3 -0.083699898 1 1
89 3 -0.044061996 2 1
90 1 -0.209291697 1 1
91 1 0.639036426 3 1
92 1 0.094698299 1 1
93 1 -0.055510622 4 1
94 3 -0.421318463 4 1
95 2 0.125295503 1 1
96 1 0.213084904 4 1
97 3 -0.161914659 4 1
98 4 -0.034767685 3 1
99 2 -0.320681689 3 1
100 1 0.058192962 4 1
ifelse(as.numeric(M2) > as.numeric(O1), 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(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) M22 M23 M24
1 NA NA NA NA
2 NA NA NA NA
3 NA NA NA NA
4 NA NA NA NA
5 NA NA NA NA
6 NA NA NA NA
7 NA NA NA NA
8 NA NA NA NA
9 NA NA NA NA
10 NA NA NA NA
11 NA NA NA NA
12 NA NA NA NA
13 NA NA NA NA
14 NA NA NA NA
15 NA NA NA NA
16 NA NA NA NA
17 NA NA NA NA
18 NA NA NA NA
19 NA NA NA NA
20 NA NA NA NA
21 NA NA NA NA
22 NA NA NA NA
23 NA NA NA NA
24 NA NA NA NA
25 NA NA NA NA
26 NA NA NA NA
27 NA NA NA NA
28 NA NA NA NA
29 NA NA NA NA
30 NA NA NA NA
31 NA NA NA NA
32 NA NA NA NA
33 NA NA NA NA
34 NA NA NA NA
35 NA NA NA NA
36 NA NA NA NA
37 NA NA NA NA
38 NA NA NA NA
39 NA NA NA NA
40 NA NA NA NA
41 NA NA NA NA
42 NA NA NA NA
43 NA NA NA NA
44 NA NA NA NA
45 NA NA NA NA
46 NA NA NA NA
47 NA NA NA NA
48 NA NA NA NA
49 NA NA NA NA
50 NA NA NA NA
51 NA NA NA NA
52 NA NA NA NA
53 NA NA NA NA
54 NA NA NA NA
55 NA NA NA NA
56 NA NA NA NA
57 NA NA NA NA
58 NA NA NA NA
59 NA NA NA NA
60 NA NA NA NA
61 NA NA NA NA
62 NA NA NA NA
63 NA NA NA NA
64 NA NA NA NA
65 NA NA NA NA
66 NA NA NA NA
67 NA NA NA NA
68 NA NA NA NA
69 NA NA NA NA
70 NA NA NA NA
71 NA NA NA NA
72 NA NA NA NA
73 NA NA NA NA
74 NA NA NA NA
75 NA NA NA NA
76 NA NA NA NA
77 NA NA NA NA
78 NA NA NA NA
79 NA NA NA NA
80 NA NA NA NA
81 NA NA NA NA
82 NA NA NA NA
83 NA NA NA NA
84 NA NA NA NA
85 NA NA NA NA
86 NA NA NA NA
87 NA NA NA NA
88 NA NA NA NA
89 NA NA NA NA
90 NA NA NA NA
91 NA NA NA NA
92 NA NA NA NA
93 NA NA NA NA
94 NA NA NA NA
95 NA NA NA NA
96 NA NA NA NA
97 NA NA NA NA
98 NA NA NA NA
99 NA NA NA NA
100 NA NA NA NA
O1.L O1.Q O1.C C1 O1
1 -0.2236068 -0.5 0.6708204 1.410531 2
2 0.6708204 0.5 0.2236068 1.434183 4
3 0.2236068 -0.5 -0.6708204 1.430994 3
4 -0.2236068 -0.5 0.6708204 1.453096 2
5 0.2236068 -0.5 -0.6708204 1.438344 3
6 -0.6708204 0.5 -0.2236068 1.453207 1
7 0.2236068 -0.5 -0.6708204 1.425176 3
8 0.6708204 0.5 0.2236068 1.437908 4
9 0.6708204 0.5 0.2236068 1.416911 4
10 -0.2236068 -0.5 0.6708204 1.448638 2
11 -0.6708204 0.5 -0.2236068 1.428375 1
12 0.2236068 -0.5 -0.6708204 1.450130 3
13 0.2236068 -0.5 -0.6708204 1.420545 3
14 -0.6708204 0.5 -0.2236068 1.423005 1
15 -0.6708204 0.5 -0.2236068 1.435902 1
16 0.6708204 0.5 0.2236068 1.423901 4
17 -0.2236068 -0.5 0.6708204 1.457208 2
18 0.2236068 -0.5 -0.6708204 1.414280 3
19 0.6708204 0.5 0.2236068 1.443383 4
20 -0.6708204 0.5 -0.2236068 1.434954 1
21 0.2236068 -0.5 -0.6708204 1.429499 3
22 0.6708204 0.5 0.2236068 1.441897 4
23 0.6708204 0.5 0.2236068 1.423713 4
24 -0.2236068 -0.5 0.6708204 1.435395 2
25 -0.6708204 0.5 -0.2236068 1.425944 1
26 0.2236068 -0.5 -0.6708204 1.437115 3
27 0.6708204 0.5 0.2236068 1.441326 4
28 -0.6708204 0.5 -0.2236068 1.422953 1
29 0.6708204 0.5 0.2236068 1.437797 4
30 0.6708204 0.5 0.2236068 1.472121 4
31 -0.2236068 -0.5 0.6708204 1.421782 2
32 0.2236068 -0.5 -0.6708204 1.457672 3
33 0.2236068 -0.5 -0.6708204 1.430842 3
34 -0.6708204 0.5 -0.2236068 1.431523 1
35 -0.6708204 0.5 -0.2236068 1.421395 1
36 0.6708204 0.5 0.2236068 1.434496 4
37 0.6708204 0.5 0.2236068 1.425383 4
38 0.6708204 0.5 0.2236068 1.421802 4
39 -0.6708204 0.5 -0.2236068 1.430094 1
40 -0.2236068 -0.5 0.6708204 1.447621 2
41 -0.6708204 0.5 -0.2236068 1.434797 1
42 -0.6708204 0.5 -0.2236068 1.446091 1
43 -0.2236068 -0.5 0.6708204 1.445306 2
44 -0.2236068 -0.5 0.6708204 1.448783 2
45 -0.6708204 0.5 -0.2236068 1.450617 1
46 -0.6708204 0.5 -0.2236068 1.415055 1
47 0.6708204 0.5 0.2236068 1.436590 4
48 0.6708204 0.5 0.2236068 1.433938 4
49 -0.2236068 -0.5 0.6708204 1.414941 2
50 -0.2236068 -0.5 0.6708204 1.421807 2
51 -0.6708204 0.5 -0.2236068 1.453203 1
52 0.2236068 -0.5 -0.6708204 1.452129 3
53 -0.6708204 0.5 -0.2236068 1.431510 1
54 0.2236068 -0.5 -0.6708204 1.430082 3
55 -0.2236068 -0.5 0.6708204 1.443492 2
56 0.6708204 0.5 0.2236068 1.436460 4
57 -0.2236068 -0.5 0.6708204 1.418119 2
58 -0.6708204 0.5 -0.2236068 1.434971 1
59 -0.6708204 0.5 -0.2236068 1.445599 1
60 0.6708204 0.5 0.2236068 1.437097 4
61 -0.2236068 -0.5 0.6708204 1.428360 2
62 0.6708204 0.5 0.2236068 1.440550 4
63 0.2236068 -0.5 -0.6708204 1.443014 3
64 -0.2236068 -0.5 0.6708204 1.424298 2
65 0.2236068 -0.5 -0.6708204 1.448823 3
66 0.2236068 -0.5 -0.6708204 1.425834 3
67 -0.2236068 -0.5 0.6708204 1.427102 2
68 -0.6708204 0.5 -0.2236068 1.414240 1
69 -0.6708204 0.5 -0.2236068 1.456218 1
70 -0.6708204 0.5 -0.2236068 1.470594 1
71 -0.6708204 0.5 -0.2236068 1.425058 1
72 0.2236068 -0.5 -0.6708204 1.432371 3
73 -0.2236068 -0.5 0.6708204 1.441656 2
74 -0.2236068 -0.5 0.6708204 1.434952 2
75 0.2236068 -0.5 -0.6708204 1.402860 3
76 0.2236068 -0.5 -0.6708204 1.453363 3
77 0.6708204 0.5 0.2236068 1.432909 4
78 0.2236068 -0.5 -0.6708204 1.435103 3
79 -0.2236068 -0.5 0.6708204 1.434462 2
80 -0.2236068 -0.5 0.6708204 1.434661 2
81 0.2236068 -0.5 -0.6708204 1.445881 3
82 -0.6708204 0.5 -0.2236068 1.442548 1
83 0.2236068 -0.5 -0.6708204 1.430097 3
84 -0.2236068 -0.5 0.6708204 1.430119 2
85 -0.2236068 -0.5 0.6708204 1.430315 2
86 0.6708204 0.5 0.2236068 1.437584 4
87 0.2236068 -0.5 -0.6708204 1.409738 3
88 -0.2236068 -0.5 0.6708204 1.422388 2
89 0.2236068 -0.5 -0.6708204 1.422509 3
90 0.2236068 -0.5 -0.6708204 1.439432 3
91 0.6708204 0.5 0.2236068 1.430175 4
92 -0.6708204 0.5 -0.2236068 1.418002 1
93 0.6708204 0.5 0.2236068 1.423812 4
94 -0.6708204 0.5 -0.2236068 1.423473 1
95 -0.6708204 0.5 -0.2236068 1.434412 1
96 0.2236068 -0.5 -0.6708204 1.450844 3
97 -0.6708204 0.5 -0.2236068 1.433371 1
98 0.2236068 -0.5 -0.6708204 1.444378 3
99 0.2236068 -0.5 -0.6708204 1.422523 3
100 0.2236068 -0.5 -0.6708204 1.410394 3
$m4b$spM_lvlone
center
M1 NA
C2 -0.06490582
M2 NA
(Intercept) NA
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0.39175258
abs(C1 - C2) 1.49900534
log(C1) 0.36049727
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0.60211251
M22 NA
M23 NA
M24 NA
O1.L NA
O1.Q NA
O1.C NA
C1 1.43410054
O1 NA
scale
M1 NA
C2 0.333173465
M2 NA
(Intercept) NA
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0.490677700
abs(C1 - C2) 0.334214181
log(C1) 0.009050336
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0.778929778
M22 NA
M23 NA
M24 NA
O1.L NA
O1.Q NA
O1.C NA
C1 0.012996511
O1 NA
$m4b$mu_reg_norm
[1] 0
$m4b$tau_reg_norm
[1] 1e-04
$m4b$shape_tau_norm
[1] 0.01
$m4b$rate_tau_norm
[1] 0.01
$m4b$mu_reg_multinomial
[1] 0
$m4b$tau_reg_multinomial
[1] 1e-04
jagsmodel remains the same
Code
lapply(models, "[[", "jagsmodel")
Output
$m0a
model {
# Multinomial logit model for M1 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4])
p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ])))
p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ])))
p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ])))
p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ])))
log(phi_M1[i, 1]) <- 0
log(phi_M1[i, 2]) <- M_lvlone[i, 2] * beta[1]
log(phi_M1[i, 3]) <- M_lvlone[i, 2] * beta[2]
log(phi_M1[i, 4]) <- M_lvlone[i, 2] * beta[3]
}
# Priors for the model for M1
for (k in 1:3) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
}
$m0b
model {
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ 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, 2] * beta[1]
log(phi_M2[i, 3]) <- M_lvlone[i, 2] * beta[2]
log(phi_M2[i, 4]) <- M_lvlone[i, 2] * beta[3]
}
# Priors for the model for M2
for (k in 1:3) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
}
$m1a
model {
# Multinomial logit model for M1 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4])
p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ])))
p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ])))
p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ])))
p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ])))
log(phi_M1[i, 1]) <- 0
log(phi_M1[i, 2]) <- M_lvlone[i, 2] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2]
log(phi_M1[i, 3]) <- M_lvlone[i, 2] * beta[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4]
log(phi_M1[i, 4]) <- M_lvlone[i, 2] * beta[5] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[6]
}
# Priors for the model for M1
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
}
$m1b
model {
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ 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, 2] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2]
log(phi_M2[i, 3]) <- M_lvlone[i, 2] * beta[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4]
log(phi_M2[i, 4]) <- M_lvlone[i, 2] * beta[5] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[6]
}
# Priors for the model for M2
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
}
$m2a
model {
# Multinomial logit model for M1 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4])
p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ])))
p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ])))
p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ])))
p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ])))
log(phi_M1[i, 1]) <- 0
log(phi_M1[i, 2]) <- M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2]
log(phi_M1[i, 3]) <- M_lvlone[i, 3] * beta[3] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4]
log(phi_M1[i, 4]) <- M_lvlone[i, 3] * beta[5] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6]
}
# Priors for the model for M1
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# 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 {
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ 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, 3] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2]
log(phi_M2[i, 3]) <- M_lvlone[i, 3] * beta[3] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4]
log(phi_M2[i, 4]) <- M_lvlone[i, 3] * beta[5] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6]
}
# Priors for the model for M2
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# 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)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ 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, 3] * alpha[1]
log(phi_M2[i, 3]) <- M_lvlone[i, 3] * alpha[2]
log(phi_M2[i, 4]) <- M_lvlone[i, 3] * alpha[3]
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 M2
for (k in 1:3) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
}
$m4a
model {
# Multinomial logit model for M1 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4])
p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ])))
p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ])))
p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ])))
p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ])))
log(phi_M1[i, 1]) <- 0
log(phi_M1[i, 2]) <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] +
M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] +
M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] +
M_lvlone[i, 11] * beta[7] +
(M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * 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] +
(M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[12]
log(phi_M1[i, 3]) <- M_lvlone[i, 5] * beta[13] + M_lvlone[i, 6] * beta[14] +
M_lvlone[i, 7] * beta[15] + M_lvlone[i, 8] * beta[16] +
M_lvlone[i, 9] * beta[17] + M_lvlone[i, 10] * beta[18] +
M_lvlone[i, 11] * beta[19] +
(M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[20] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[21] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[22] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[23] +
(M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[24]
log(phi_M1[i, 4]) <- M_lvlone[i, 5] * beta[25] + M_lvlone[i, 6] * beta[26] +
M_lvlone[i, 7] * beta[27] + M_lvlone[i, 8] * beta[28] +
M_lvlone[i, 9] * beta[29] + M_lvlone[i, 10] * beta[30] +
M_lvlone[i, 11] * beta[31] +
(M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[32] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[33] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[34] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[35] +
(M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[36]
}
# Priors for the model for M1
for (k in 1:36) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# 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 {
# Multinomial logit model for M1 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4])
p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ])))
p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ])))
p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ])))
p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ])))
log(phi_M1[i, 1]) <- 0
log(phi_M1[i, 2]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[3] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5]
log(phi_M1[i, 3]) <- M_lvlone[i, 4] * beta[6] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[7] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[9] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[10]
log(phi_M1[i, 4]) <- M_lvlone[i, 4] * beta[11] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[12] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[14] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[15]
}
# Priors for the model for M1
for (k in 1:15) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# 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)
# 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, 4] * alpha[9] + M_lvlone[i, 12] * alpha[10] +
M_lvlone[i, 13] * alpha[11] + M_lvlone[i, 14] * alpha[12] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 4] * alpha[14] + M_lvlone[i, 12] * alpha[15] +
M_lvlone[i, 13] * alpha[16] + M_lvlone[i, 14] * alpha[17] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 4] * alpha[19] + M_lvlone[i, 12] * alpha[20] +
M_lvlone[i, 13] * alpha[21] + M_lvlone[i, 14] * alpha[22] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[23]
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 M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6]
}
}
GRcrit and MCerror give same result
Code
lapply(models0, GR_crit, multivariate = FALSE)
Output
$m0a
Potential scale reduction factors:
Point est. Upper C.I.
M12: (Intercept) NaN NaN
M13: (Intercept) NaN NaN
M14: (Intercept) NaN NaN
$m0b
Potential scale reduction factors:
Point est. Upper C.I.
M22: (Intercept) NaN NaN
M23: (Intercept) NaN NaN
M24: (Intercept) NaN NaN
$m1a
Potential scale reduction factors:
Point est. Upper C.I.
M12: (Intercept) NaN NaN
M12: C1 NaN NaN
M13: (Intercept) NaN NaN
M13: C1 NaN NaN
M14: (Intercept) NaN NaN
M14: C1 NaN NaN
$m1b
Potential scale reduction factors:
Point est. Upper C.I.
M22: (Intercept) NaN NaN
M22: C1 NaN NaN
M23: (Intercept) NaN NaN
M23: C1 NaN NaN
M24: (Intercept) NaN NaN
M24: C1 NaN NaN
$m2a
Potential scale reduction factors:
Point est. Upper C.I.
M12: (Intercept) NaN NaN
M12: C2 NaN NaN
M13: (Intercept) NaN NaN
M13: C2 NaN NaN
M14: (Intercept) NaN NaN
M14: C2 NaN NaN
$m2b
Potential scale reduction factors:
Point est. Upper C.I.
M22: (Intercept) NaN NaN
M22: C2 NaN NaN
M23: (Intercept) NaN NaN
M23: C2 NaN NaN
M24: (Intercept) NaN NaN
M24: C2 NaN NaN
$m3a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
M12 NaN NaN
M13 NaN NaN
M14 NaN NaN
sigma_C1 NaN NaN
$m3b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
sigma_C1 NaN NaN
$m4a
Potential scale reduction factors:
Point est. Upper C.I.
M12: (Intercept) NaN NaN
M12: M22 NaN NaN
M12: M23 NaN NaN
M12: M24 NaN NaN
M12: O22 NaN NaN
M12: O23 NaN NaN
M12: O24 NaN NaN
M12: abs(C1 - C2) NaN NaN
M12: log(C1) NaN NaN
M12: O22:abs(C1 - C2) NaN NaN
M12: O23:abs(C1 - C2) NaN NaN
M12: O24:abs(C1 - C2) NaN NaN
M13: (Intercept) NaN NaN
M13: M22 NaN NaN
M13: M23 NaN NaN
M13: M24 NaN NaN
M13: O22 NaN NaN
M13: O23 NaN NaN
M13: O24 NaN NaN
M13: abs(C1 - C2) NaN NaN
M13: log(C1) NaN NaN
M13: O22:abs(C1 - C2) NaN NaN
M13: O23:abs(C1 - C2) NaN NaN
M13: O24:abs(C1 - C2) NaN NaN
M14: (Intercept) NaN NaN
M14: M22 NaN NaN
M14: M23 NaN NaN
M14: M24 NaN NaN
M14: O22 NaN NaN
M14: O23 NaN NaN
M14: O24 NaN NaN
M14: abs(C1 - C2) NaN NaN
M14: log(C1) NaN NaN
M14: O22:abs(C1 - C2) NaN NaN
M14: O23:abs(C1 - C2) NaN NaN
M14: O24:abs(C1 - C2) NaN NaN
$m4b
Potential scale reduction factors:
Point est.
M12: (Intercept) NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M12: abs(C1 - C2) NaN
M12: log(C1) NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN
M13: (Intercept) NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M13: abs(C1 - C2) NaN
M13: log(C1) NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN
M14: (Intercept) NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M14: abs(C1 - C2) NaN
M14: log(C1) NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN
Upper C.I.
M12: (Intercept) NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M12: abs(C1 - C2) NaN
M12: log(C1) NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN
M13: (Intercept) NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M13: abs(C1 - C2) NaN
M13: log(C1) NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN
M14: (Intercept) NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M14: abs(C1 - C2) NaN
M14: log(C1) NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 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"
$m0a
est MCSE SD MCSE/SD
M12: (Intercept) 0 0 0 NaN
M13: (Intercept) 0 0 0 NaN
M14: (Intercept) 0 0 0 NaN
$m0b
est MCSE SD MCSE/SD
M22: (Intercept) 0 0 0 NaN
M23: (Intercept) 0 0 0 NaN
M24: (Intercept) 0 0 0 NaN
$m1a
est MCSE SD MCSE/SD
M12: (Intercept) 0 0 0 NaN
M12: C1 0 0 0 NaN
M13: (Intercept) 0 0 0 NaN
M13: C1 0 0 0 NaN
M14: (Intercept) 0 0 0 NaN
M14: C1 0 0 0 NaN
$m1b
est MCSE SD MCSE/SD
M22: (Intercept) 0 0 0 NaN
M22: C1 0 0 0 NaN
M23: (Intercept) 0 0 0 NaN
M23: C1 0 0 0 NaN
M24: (Intercept) 0 0 0 NaN
M24: C1 0 0 0 NaN
$m2a
est MCSE SD MCSE/SD
M12: (Intercept) 0 0 0 NaN
M12: C2 0 0 0 NaN
M13: (Intercept) 0 0 0 NaN
M13: C2 0 0 0 NaN
M14: (Intercept) 0 0 0 NaN
M14: C2 0 0 0 NaN
$m2b
est MCSE SD MCSE/SD
M22: (Intercept) 0 0 0 NaN
M22: C2 0 0 0 NaN
M23: (Intercept) 0 0 0 NaN
M23: C2 0 0 0 NaN
M24: (Intercept) 0 0 0 NaN
M24: C2 0 0 0 NaN
$m3a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
M12 0 0 0 NaN
M13 0 0 0 NaN
M14 0 0 0 NaN
sigma_C1 0 0 0 NaN
$m3b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
sigma_C1 0 0 0 NaN
$m4a
est MCSE SD MCSE/SD
M12: (Intercept) 0 0 0 NaN
M12: M22 0 0 0 NaN
M12: M23 0 0 0 NaN
M12: M24 0 0 0 NaN
M12: O22 0 0 0 NaN
M12: O23 0 0 0 NaN
M12: O24 0 0 0 NaN
M12: abs(C1 - C2) 0 0 0 NaN
M12: log(C1) 0 0 0 NaN
M12: O22:abs(C1 - C2) 0 0 0 NaN
M12: O23:abs(C1 - C2) 0 0 0 NaN
M12: O24:abs(C1 - C2) 0 0 0 NaN
M13: (Intercept) 0 0 0 NaN
M13: M22 0 0 0 NaN
M13: M23 0 0 0 NaN
M13: M24 0 0 0 NaN
M13: O22 0 0 0 NaN
M13: O23 0 0 0 NaN
M13: O24 0 0 0 NaN
M13: abs(C1 - C2) 0 0 0 NaN
M13: log(C1) 0 0 0 NaN
M13: O22:abs(C1 - C2) 0 0 0 NaN
M13: O23:abs(C1 - C2) 0 0 0 NaN
M13: O24:abs(C1 - C2) 0 0 0 NaN
M14: (Intercept) 0 0 0 NaN
M14: M22 0 0 0 NaN
M14: M23 0 0 0 NaN
M14: M24 0 0 0 NaN
M14: O22 0 0 0 NaN
M14: O23 0 0 0 NaN
M14: O24 0 0 0 NaN
M14: abs(C1 - C2) 0 0 0 NaN
M14: log(C1) 0 0 0 NaN
M14: O22:abs(C1 - C2) 0 0 0 NaN
M14: O23:abs(C1 - C2) 0 0 0 NaN
M14: O24:abs(C1 - C2) 0 0 0 NaN
$m4b
est MCSE SD
M12: (Intercept) 0 0 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M12: abs(C1 - C2) 0 0 0
M12: log(C1) 0 0 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
M13: (Intercept) 0 0 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M13: abs(C1 - C2) 0 0 0
M13: log(C1) 0 0 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
M14: (Intercept) 0 0 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M14: abs(C1 - C2) 0 0 0
M14: log(C1) 0 0 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
MCSE/SD
M12: (Intercept) NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M12: abs(C1 - C2) NaN
M12: log(C1) NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN
M13: (Intercept) NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M13: abs(C1 - C2) NaN
M13: log(C1) NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN
M14: (Intercept) NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M14: abs(C1 - C2) NaN
M14: log(C1) NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN
summary output remained the same
Code
lapply(models0, print)
Output
Call:
mlogit_imp(formula = M1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept) (Intercept) (Intercept)
0 0 0
Call:
mlogit_imp(formula = M2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M2"
Coefficients:
(Intercept) (Intercept) (Intercept)
0 0 0
Call:
mlogit_imp(formula = M1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept) C1 (Intercept) C1 (Intercept) C1
0 0 0 0 0 0
Call:
mlogit_imp(formula = M2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M2"
Coefficients:
(Intercept) C1 (Intercept) C1 (Intercept) C1
0 0 0 0 0 0
Call:
mlogit_imp(formula = M1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept) C2 (Intercept) C2 (Intercept) C2
0 0 0 0 0 0
Call:
mlogit_imp(formula = M2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M2"
Coefficients:
(Intercept) C2 (Intercept) C2 (Intercept) C2
0 0 0 0 0 0
Call:
lm_imp(formula = C1 ~ M1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) M12 M13 M14
0 0 0 0
Residual standard deviation:
sigma_C1
0
Call:
lm_imp(formula = C1 ~ M2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) M22 M23 M24
0 0 0 0
Residual standard deviation:
sigma_C1
0
Call:
mlogit_imp(formula = M1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_M1"),
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
Call:
mlogit_imp(formula = M1 ~ ifelse(as.numeric(M2) > as.numeric(O1),
1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5,
n.iter = 10, monitor_params = list(other = "p_M1"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
$m0a
Call:
mlogit_imp(formula = M1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept) (Intercept) (Intercept)
0 0 0
$m0b
Call:
mlogit_imp(formula = M2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M2"
Coefficients:
(Intercept) (Intercept) (Intercept)
0 0 0
$m1a
Call:
mlogit_imp(formula = M1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept) C1 (Intercept) C1 (Intercept) C1
0 0 0 0 0 0
$m1b
Call:
mlogit_imp(formula = M2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M2"
Coefficients:
(Intercept) C1 (Intercept) C1 (Intercept) C1
0 0 0 0 0 0
$m2a
Call:
mlogit_imp(formula = M1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept) C2 (Intercept) C2 (Intercept) C2
0 0 0 0 0 0
$m2b
Call:
mlogit_imp(formula = M2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M2"
Coefficients:
(Intercept) C2 (Intercept) C2 (Intercept) C2
0 0 0 0 0 0
$m3a
Call:
lm_imp(formula = C1 ~ M1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) M12 M13 M14
0 0 0 0
Residual standard deviation:
sigma_C1
0
$m3b
Call:
lm_imp(formula = C1 ~ M2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) M22 M23 M24
0 0 0 0
Residual standard deviation:
sigma_C1
0
$m4a
Call:
mlogit_imp(formula = M1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_M1"),
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
$m4b
Call:
mlogit_imp(formula = M1 ~ ifelse(as.numeric(M2) > as.numeric(O1),
1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5,
n.iter = 10, monitor_params = list(other = "p_M1"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
Code
lapply(models0, coef)
Output
$m0a
$m0a$M1
(Intercept) (Intercept) (Intercept)
0 0 0
$m0b
$m0b$M2
(Intercept) (Intercept) (Intercept)
0 0 0
$m1a
$m1a$M1
(Intercept) C1 (Intercept) C1 (Intercept) C1
0 0 0 0 0 0
$m1b
$m1b$M2
(Intercept) C1 (Intercept) C1 (Intercept) C1
0 0 0 0 0 0
$m2a
$m2a$M1
(Intercept) C2 (Intercept) C2 (Intercept) C2
0 0 0 0 0 0
$m2b
$m2b$M2
(Intercept) C2 (Intercept) C2 (Intercept) C2
0 0 0 0 0 0
$m3a
$m3a$C1
(Intercept) M12 M13 M14 sigma_C1
0 0 0 0 0
$m3b
$m3b$C1
(Intercept) M22 M23 M24 sigma_C1
0 0 0 0 0
$m4a
$m4a$M1
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
$m4b
$m4b$M1
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
Code
lapply(models0, confint)
Output
$m0a
$m0a$M1
2.5% 97.5%
(Intercept) 0 0
(Intercept) 0 0
(Intercept) 0 0
$m0b
$m0b$M2
2.5% 97.5%
(Intercept) 0 0
(Intercept) 0 0
(Intercept) 0 0
$m1a
$m1a$M1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
(Intercept) 0 0
C1 0 0
(Intercept) 0 0
C1 0 0
$m1b
$m1b$M2
2.5% 97.5%
(Intercept) 0 0
C1 0 0
(Intercept) 0 0
C1 0 0
(Intercept) 0 0
C1 0 0
$m2a
$m2a$M1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
(Intercept) 0 0
C2 0 0
(Intercept) 0 0
C2 0 0
$m2b
$m2b$M2
2.5% 97.5%
(Intercept) 0 0
C2 0 0
(Intercept) 0 0
C2 0 0
(Intercept) 0 0
C2 0 0
$m3a
$m3a$C1
2.5% 97.5%
(Intercept) 0 0
M12 0 0
M13 0 0
M14 0 0
sigma_C1 0 0
$m3b
$m3b$C1
2.5% 97.5%
(Intercept) 0 0
M22 0 0
M23 0 0
M24 0 0
sigma_C1 0 0
$m4a
$m4a$M1
2.5% 97.5%
(Intercept) 0 0
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
(Intercept) 0 0
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
(Intercept) 0 0
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
$m4b
$m4b$M1
2.5% 97.5%
(Intercept) 0 0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0
(Intercept) 0 0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0
(Intercept) 0 0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 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"
$m0a
Bayesian multinomial logit model fitted with JointAI
Call:
mlogit_imp(formula = M1 ~ 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
M12: (Intercept) 0 0 0 0 0 NaN NaN
M13: (Intercept) 0 0 0 0 0 NaN NaN
M14: (Intercept) 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 multinomial logit model fitted with JointAI
Call:
mlogit_imp(formula = M2 ~ 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
M22: (Intercept) 0 0 0 0 0 NaN NaN
M23: (Intercept) 0 0 0 0 0 NaN NaN
M24: (Intercept) 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 multinomial logit model fitted with JointAI
Call:
mlogit_imp(formula = M1 ~ 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
M12: (Intercept) 0 0 0 0 0 NaN NaN
M12: C1 0 0 0 0 0 NaN NaN
M13: (Intercept) 0 0 0 0 0 NaN NaN
M13: C1 0 0 0 0 0 NaN NaN
M14: (Intercept) 0 0 0 0 0 NaN NaN
M14: C1 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 multinomial logit model fitted with JointAI
Call:
mlogit_imp(formula = M2 ~ 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: (Intercept) 0 0 0 0 0 NaN NaN
M22: C1 0 0 0 0 0 NaN NaN
M23: (Intercept) 0 0 0 0 0 NaN NaN
M23: C1 0 0 0 0 0 NaN NaN
M24: (Intercept) 0 0 0 0 0 NaN NaN
M24: C1 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 multinomial logit model fitted with JointAI
Call:
mlogit_imp(formula = M1 ~ 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
M12: (Intercept) 0 0 0 0 0 NaN NaN
M12: C2 0 0 0 0 0 NaN NaN
M13: (Intercept) 0 0 0 0 0 NaN NaN
M13: C2 0 0 0 0 0 NaN NaN
M14: (Intercept) 0 0 0 0 0 NaN NaN
M14: C2 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 multinomial logit model fitted with JointAI
Call:
mlogit_imp(formula = M2 ~ 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
M22: (Intercept) 0 0 0 0 0 NaN NaN
M22: C2 0 0 0 0 0 NaN NaN
M23: (Intercept) 0 0 0 0 0 NaN NaN
M23: C2 0 0 0 0 0 NaN NaN
M24: (Intercept) 0 0 0 0 0 NaN NaN
M24: C2 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 ~ M1, 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
M12 0 0 0 0 0 NaN NaN
M13 0 0 0 0 0 NaN NaN
M14 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 ~ M2, 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
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 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 multinomial logit model fitted with JointAI
Call:
mlogit_imp(formula = M1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_M1"),
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M12: (Intercept) 0 0 0 0 0 NaN NaN
M12: M22 0 0 0 0 0 NaN NaN
M12: M23 0 0 0 0 0 NaN NaN
M12: M24 0 0 0 0 0 NaN NaN
M12: O22 0 0 0 0 0 NaN NaN
M12: O23 0 0 0 0 0 NaN NaN
M12: O24 0 0 0 0 0 NaN NaN
M12: abs(C1 - C2) 0 0 0 0 0 NaN NaN
M12: log(C1) 0 0 0 0 0 NaN NaN
M12: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M12: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M12: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M13: (Intercept) 0 0 0 0 0 NaN NaN
M13: M22 0 0 0 0 0 NaN NaN
M13: M23 0 0 0 0 0 NaN NaN
M13: M24 0 0 0 0 0 NaN NaN
M13: O22 0 0 0 0 0 NaN NaN
M13: O23 0 0 0 0 0 NaN NaN
M13: O24 0 0 0 0 0 NaN NaN
M13: abs(C1 - C2) 0 0 0 0 0 NaN NaN
M13: log(C1) 0 0 0 0 0 NaN NaN
M13: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M13: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M13: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M14: (Intercept) 0 0 0 0 0 NaN NaN
M14: M22 0 0 0 0 0 NaN NaN
M14: M23 0 0 0 0 0 NaN NaN
M14: M24 0 0 0 0 0 NaN NaN
M14: O22 0 0 0 0 0 NaN NaN
M14: O23 0 0 0 0 0 NaN NaN
M14: O24 0 0 0 0 0 NaN NaN
M14: abs(C1 - C2) 0 0 0 0 0 NaN NaN
M14: log(C1) 0 0 0 0 0 NaN NaN
M14: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M14: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M14: O24:abs(C1 - C2) 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 multinomial logit model fitted with JointAI
Call:
mlogit_imp(formula = M1 ~ ifelse(as.numeric(M2) > as.numeric(O1),
1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5,
n.iter = 10, monitor_params = list(other = "p_M1"), seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5%
M12: (Intercept) 0 0 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M12: abs(C1 - C2) 0 0 0
M12: log(C1) 0 0 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
M13: (Intercept) 0 0 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M13: abs(C1 - C2) 0 0 0
M13: log(C1) 0 0 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
M14: (Intercept) 0 0 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M14: abs(C1 - C2) 0 0 0
M14: log(C1) 0 0 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
97.5%
M12: (Intercept) 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M12: abs(C1 - C2) 0
M12: log(C1) 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
M13: (Intercept) 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M13: abs(C1 - C2) 0
M13: log(C1) 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
M14: (Intercept) 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M14: abs(C1 - C2) 0
M14: log(C1) 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
tail-prob.
M12: (Intercept) 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M12: abs(C1 - C2) 0
M12: log(C1) 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
M13: (Intercept) 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M13: abs(C1 - C2) 0
M13: log(C1) 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
M14: (Intercept) 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M14: abs(C1 - C2) 0
M14: log(C1) 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
GR-crit MCE/SD
M12: (Intercept) NaN NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN
M12: abs(C1 - C2) NaN NaN
M12: log(C1) NaN NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN
M13: (Intercept) NaN NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN
M13: abs(C1 - C2) NaN NaN
M13: log(C1) NaN NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN
M14: (Intercept) NaN NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN
M14: abs(C1 - C2) NaN NaN
M14: log(C1) NaN NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 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"
$m0a
$m0a$M1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M12: (Intercept) 0 0 0 0 0 NaN NaN
M13: (Intercept) 0 0 0 0 0 NaN NaN
M14: (Intercept) 0 0 0 0 0 NaN NaN
$m0b
$m0b$M2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22: (Intercept) 0 0 0 0 0 NaN NaN
M23: (Intercept) 0 0 0 0 0 NaN NaN
M24: (Intercept) 0 0 0 0 0 NaN NaN
$m1a
$m1a$M1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M12: (Intercept) 0 0 0 0 0 NaN NaN
M12: C1 0 0 0 0 0 NaN NaN
M13: (Intercept) 0 0 0 0 0 NaN NaN
M13: C1 0 0 0 0 0 NaN NaN
M14: (Intercept) 0 0 0 0 0 NaN NaN
M14: C1 0 0 0 0 0 NaN NaN
$m1b
$m1b$M2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22: (Intercept) 0 0 0 0 0 NaN NaN
M22: C1 0 0 0 0 0 NaN NaN
M23: (Intercept) 0 0 0 0 0 NaN NaN
M23: C1 0 0 0 0 0 NaN NaN
M24: (Intercept) 0 0 0 0 0 NaN NaN
M24: C1 0 0 0 0 0 NaN NaN
$m2a
$m2a$M1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M12: (Intercept) 0 0 0 0 0 NaN NaN
M12: C2 0 0 0 0 0 NaN NaN
M13: (Intercept) 0 0 0 0 0 NaN NaN
M13: C2 0 0 0 0 0 NaN NaN
M14: (Intercept) 0 0 0 0 0 NaN NaN
M14: C2 0 0 0 0 0 NaN NaN
$m2b
$m2b$M2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22: (Intercept) 0 0 0 0 0 NaN NaN
M22: C2 0 0 0 0 0 NaN NaN
M23: (Intercept) 0 0 0 0 0 NaN NaN
M23: C2 0 0 0 0 0 NaN NaN
M24: (Intercept) 0 0 0 0 0 NaN NaN
M24: 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
M12 0 0 0 0 0 NaN NaN
M13 0 0 0 0 0 NaN NaN
M14 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
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
$m4a
$m4a$M1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M12: (Intercept) 0 0 0 0 0 NaN NaN
M12: M22 0 0 0 0 0 NaN NaN
M12: M23 0 0 0 0 0 NaN NaN
M12: M24 0 0 0 0 0 NaN NaN
M12: O22 0 0 0 0 0 NaN NaN
M12: O23 0 0 0 0 0 NaN NaN
M12: O24 0 0 0 0 0 NaN NaN
M12: abs(C1 - C2) 0 0 0 0 0 NaN NaN
M12: log(C1) 0 0 0 0 0 NaN NaN
M12: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M12: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M12: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M13: (Intercept) 0 0 0 0 0 NaN NaN
M13: M22 0 0 0 0 0 NaN NaN
M13: M23 0 0 0 0 0 NaN NaN
M13: M24 0 0 0 0 0 NaN NaN
M13: O22 0 0 0 0 0 NaN NaN
M13: O23 0 0 0 0 0 NaN NaN
M13: O24 0 0 0 0 0 NaN NaN
M13: abs(C1 - C2) 0 0 0 0 0 NaN NaN
M13: log(C1) 0 0 0 0 0 NaN NaN
M13: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M13: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M13: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M14: (Intercept) 0 0 0 0 0 NaN NaN
M14: M22 0 0 0 0 0 NaN NaN
M14: M23 0 0 0 0 0 NaN NaN
M14: M24 0 0 0 0 0 NaN NaN
M14: O22 0 0 0 0 0 NaN NaN
M14: O23 0 0 0 0 0 NaN NaN
M14: O24 0 0 0 0 0 NaN NaN
M14: abs(C1 - C2) 0 0 0 0 0 NaN NaN
M14: log(C1) 0 0 0 0 0 NaN NaN
M14: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M14: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M14: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
$m4b
$m4b$M1
Mean SD 2.5%
M12: (Intercept) 0 0 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M12: abs(C1 - C2) 0 0 0
M12: log(C1) 0 0 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
M13: (Intercept) 0 0 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M13: abs(C1 - C2) 0 0 0
M13: log(C1) 0 0 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
M14: (Intercept) 0 0 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M14: abs(C1 - C2) 0 0 0
M14: log(C1) 0 0 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
97.5%
M12: (Intercept) 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M12: abs(C1 - C2) 0
M12: log(C1) 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
M13: (Intercept) 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M13: abs(C1 - C2) 0
M13: log(C1) 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
M14: (Intercept) 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M14: abs(C1 - C2) 0
M14: log(C1) 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
tail-prob.
M12: (Intercept) 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M12: abs(C1 - C2) 0
M12: log(C1) 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
M13: (Intercept) 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M13: abs(C1 - C2) 0
M13: log(C1) 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
M14: (Intercept) 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M14: abs(C1 - C2) 0
M14: log(C1) 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
GR-crit MCE/SD
M12: (Intercept) NaN NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN
M12: abs(C1 - C2) NaN NaN
M12: log(C1) NaN NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN
M13: (Intercept) NaN NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN
M13: abs(C1 - C2) NaN NaN
M13: log(C1) NaN NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN
M14: (Intercept) NaN NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN
M14: abs(C1 - C2) NaN NaN
M14: log(C1) NaN NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN
Try the JointAI package in your browser
Any scripts or data that you put into this service are public.
JointAI documentation built on April 27, 2023, 5:15 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
data_list remains the same
Code
lapply(models, "[[", "data_list")
Output
$m0a
$m0a$M_lvlone
M1 (Intercept)
1 1 1
2 2 1
3 2 1
4 1 1
5 3 1
6 3 1
7 3 1
8 3 1
9 1 1
10 2 1
11 1 1
12 2 1
13 1 1
14 4 1
15 2 1
16 3 1
17 3 1
18 1 1
19 2 1
20 2 1
21 1 1
22 4 1
23 1 1
24 2 1
25 1 1
26 1 1
27 2 1
28 1 1
29 3 1
30 1 1
31 4 1
32 1 1
33 4 1
34 2 1
35 1 1
36 1 1
37 1 1
38 2 1
39 2 1
40 2 1
41 4 1
42 4 1
43 4 1
44 4 1
45 2 1
46 1 1
47 3 1
48 3 1
49 2 1
50 2 1
51 3 1
52 1 1
53 2 1
54 3 1
55 2 1
56 1 1
57 4 1
58 1 1
59 4 1
60 1 1
61 1 1
62 1 1
63 4 1
64 1 1
65 2 1
66 4 1
67 4 1
68 3 1
69 3 1
70 2 1
71 3 1
72 2 1
73 4 1
74 2 1
75 1 1
76 3 1
77 2 1
78 4 1
79 4 1
80 1 1
81 1 1
82 4 1
83 3 1
84 4 1
85 2 1
86 4 1
87 3 1
88 3 1
89 3 1
90 1 1
91 1 1
92 1 1
93 1 1
94 3 1
95 2 1
96 1 1
97 3 1
98 4 1
99 2 1
100 1 1
$m0a$mu_reg_multinomial
[1] 0
$m0a$tau_reg_multinomial
[1] 1e-04
$m0b
$m0b$M_lvlone
M2 (Intercept)
1 4 1
2 1 1
3 3 1
4 3 1
5 4 1
6 4 1
7 1 1
8 1 1
9 2 1
10 2 1
11 3 1
12 3 1
13 2 1
14 3 1
15 2 1
16 1 1
17 4 1
18 2 1
19 3 1
20 3 1
21 2 1
22 2 1
23 3 1
24 3 1
25 2 1
26 2 1
27 1 1
28 3 1
29 4 1
30 2 1
31 NA 1
32 4 1
33 4 1
34 4 1
35 2 1
36 1 1
37 3 1
38 4 1
39 3 1
40 3 1
41 1 1
42 4 1
43 1 1
44 2 1
45 2 1
46 1 1
47 4 1
48 2 1
49 4 1
50 1 1
51 4 1
52 4 1
53 4 1
54 3 1
55 3 1
56 3 1
57 2 1
58 3 1
59 3 1
60 4 1
61 2 1
62 2 1
63 1 1
64 2 1
65 2 1
66 3 1
67 2 1
68 4 1
69 NA 1
70 1 1
71 2 1
72 3 1
73 4 1
74 4 1
75 1 1
76 4 1
77 NA 1
78 2 1
79 2 1
80 2 1
81 4 1
82 3 1
83 3 1
84 1 1
85 2 1
86 1 1
87 3 1
88 1 1
89 2 1
90 1 1
91 3 1
92 1 1
93 4 1
94 4 1
95 1 1
96 4 1
97 4 1
98 3 1
99 3 1
100 4 1
$m0b$mu_reg_multinomial
[1] 0
$m0b$tau_reg_multinomial
[1] 1e-04
$m1a
$m1a$M_lvlone
M1 (Intercept) C1
1 1 1 1.410531
2 2 1 1.434183
3 2 1 1.430994
4 1 1 1.453096
5 3 1 1.438344
6 3 1 1.453207
7 3 1 1.425176
8 3 1 1.437908
9 1 1 1.416911
10 2 1 1.448638
11 1 1 1.428375
12 2 1 1.450130
13 1 1 1.420545
14 4 1 1.423005
15 2 1 1.435902
16 3 1 1.423901
17 3 1 1.457208
18 1 1 1.414280
19 2 1 1.443383
20 2 1 1.434954
21 1 1 1.429499
22 4 1 1.441897
23 1 1 1.423713
24 2 1 1.435395
25 1 1 1.425944
26 1 1 1.437115
27 2 1 1.441326
28 1 1 1.422953
29 3 1 1.437797
30 1 1 1.472121
31 4 1 1.421782
32 1 1 1.457672
33 4 1 1.430842
34 2 1 1.431523
35 1 1 1.421395
36 1 1 1.434496
37 1 1 1.425383
38 2 1 1.421802
39 2 1 1.430094
40 2 1 1.447621
41 4 1 1.434797
42 4 1 1.446091
43 4 1 1.445306
44 4 1 1.448783
45 2 1 1.450617
46 1 1 1.415055
47 3 1 1.436590
48 3 1 1.433938
49 2 1 1.414941
50 2 1 1.421807
51 3 1 1.453203
52 1 1 1.452129
53 2 1 1.431510
54 3 1 1.430082
55 2 1 1.443492
56 1 1 1.436460
57 4 1 1.418119
58 1 1 1.434971
59 4 1 1.445599
60 1 1 1.437097
61 1 1 1.428360
62 1 1 1.440550
63 4 1 1.443014
64 1 1 1.424298
65 2 1 1.448823
66 4 1 1.425834
67 4 1 1.427102
68 3 1 1.414240
69 3 1 1.456218
70 2 1 1.470594
71 3 1 1.425058
72 2 1 1.432371
73 4 1 1.441656
74 2 1 1.434952
75 1 1 1.402860
76 3 1 1.453363
77 2 1 1.432909
78 4 1 1.435103
79 4 1 1.434462
80 1 1 1.434661
81 1 1 1.445881
82 4 1 1.442548
83 3 1 1.430097
84 4 1 1.430119
85 2 1 1.430315
86 4 1 1.437584
87 3 1 1.409738
88 3 1 1.422388
89 3 1 1.422509
90 1 1 1.439432
91 1 1 1.430175
92 1 1 1.418002
93 1 1 1.423812
94 3 1 1.423473
95 2 1 1.434412
96 1 1 1.450844
97 3 1 1.433371
98 4 1 1.444378
99 2 1 1.422523
100 1 1 1.410394
$m1a$spM_lvlone
center scale
M1 NA NA
(Intercept) NA NA
C1 1.434101 0.01299651
$m1a$mu_reg_multinomial
[1] 0
$m1a$tau_reg_multinomial
[1] 1e-04
$m1b
$m1b$M_lvlone
M2 (Intercept) C1
1 4 1 1.410531
2 1 1 1.434183
3 3 1 1.430994
4 3 1 1.453096
5 4 1 1.438344
6 4 1 1.453207
7 1 1 1.425176
8 1 1 1.437908
9 2 1 1.416911
10 2 1 1.448638
11 3 1 1.428375
12 3 1 1.450130
13 2 1 1.420545
14 3 1 1.423005
15 2 1 1.435902
16 1 1 1.423901
17 4 1 1.457208
18 2 1 1.414280
19 3 1 1.443383
20 3 1 1.434954
21 2 1 1.429499
22 2 1 1.441897
23 3 1 1.423713
24 3 1 1.435395
25 2 1 1.425944
26 2 1 1.437115
27 1 1 1.441326
28 3 1 1.422953
29 4 1 1.437797
30 2 1 1.472121
31 NA 1 1.421782
32 4 1 1.457672
33 4 1 1.430842
34 4 1 1.431523
35 2 1 1.421395
36 1 1 1.434496
37 3 1 1.425383
38 4 1 1.421802
39 3 1 1.430094
40 3 1 1.447621
41 1 1 1.434797
42 4 1 1.446091
43 1 1 1.445306
44 2 1 1.448783
45 2 1 1.450617
46 1 1 1.415055
47 4 1 1.436590
48 2 1 1.433938
49 4 1 1.414941
50 1 1 1.421807
51 4 1 1.453203
52 4 1 1.452129
53 4 1 1.431510
54 3 1 1.430082
55 3 1 1.443492
56 3 1 1.436460
57 2 1 1.418119
58 3 1 1.434971
59 3 1 1.445599
60 4 1 1.437097
61 2 1 1.428360
62 2 1 1.440550
63 1 1 1.443014
64 2 1 1.424298
65 2 1 1.448823
66 3 1 1.425834
67 2 1 1.427102
68 4 1 1.414240
69 NA 1 1.456218
70 1 1 1.470594
71 2 1 1.425058
72 3 1 1.432371
73 4 1 1.441656
74 4 1 1.434952
75 1 1 1.402860
76 4 1 1.453363
77 NA 1 1.432909
78 2 1 1.435103
79 2 1 1.434462
80 2 1 1.434661
81 4 1 1.445881
82 3 1 1.442548
83 3 1 1.430097
84 1 1 1.430119
85 2 1 1.430315
86 1 1 1.437584
87 3 1 1.409738
88 1 1 1.422388
89 2 1 1.422509
90 1 1 1.439432
91 3 1 1.430175
92 1 1 1.418002
93 4 1 1.423812
94 4 1 1.423473
95 1 1 1.434412
96 4 1 1.450844
97 4 1 1.433371
98 3 1 1.444378
99 3 1 1.422523
100 4 1 1.410394
$m1b$spM_lvlone
center scale
M2 NA NA
(Intercept) NA NA
C1 1.434101 0.01299651
$m1b$mu_reg_multinomial
[1] 0
$m1b$tau_reg_multinomial
[1] 1e-04
$m2a
$m2a$M_lvlone
M1 C2 (Intercept)
1 1 0.144065882 1
2 2 0.032778478 1
3 2 0.343008492 1
4 1 -0.361887858 1
5 3 -0.389600647 1
6 3 -0.205306841 1
7 3 0.079434830 1
8 3 -0.331246757 1
9 1 -0.329638800 1
10 2 0.167597533 1
11 1 0.860207989 1
12 2 0.022730640 1
13 1 0.217171172 1
14 4 -0.403002412 1
15 2 0.087369742 1
16 3 -0.183870429 1
17 3 -0.194577002 1
18 1 -0.349718516 1
19 2 -0.508781244 1
20 2 0.494883111 1
21 1 0.258041067 1
22 4 -0.922621989 1
23 1 0.431254949 1
24 2 -0.294218881 1
25 1 -0.425548895 1
26 1 0.057176054 1
27 2 0.289090158 1
28 1 -0.473079489 1
29 3 -0.385664863 1
30 1 -0.154780107 1
31 4 0.100536296 1
32 1 0.634791958 1
33 4 -0.387252617 1
34 2 -0.181741088 1
35 1 -0.311562695 1
36 1 -0.044115907 1
37 1 -0.657409991 1
38 2 0.159577214 1
39 2 -0.460416933 1
40 2 NA 1
41 4 -0.248909867 1
42 4 -0.609021545 1
43 4 0.025471883 1
44 4 0.066648592 1
45 2 -0.276108719 1
46 1 -0.179737577 1
47 3 0.181190937 1
48 3 -0.453871693 1
49 2 0.448629602 1
50 2 -0.529811821 1
51 3 -0.028304571 1
52 1 -0.520318482 1
53 2 0.171317619 1
54 3 0.432732046 1
55 2 -0.346286005 1
56 1 -0.469375653 1
57 4 0.031021711 1
58 1 -0.118837515 1
59 4 0.507769984 1
60 1 0.271797031 1
61 1 -0.124442204 1
62 1 0.277677389 1
63 4 -0.102893730 1
64 1 NA 1
65 2 -0.678303052 1
66 4 0.478880037 1
67 4 -0.428028760 1
68 3 0.048119185 1
69 3 0.216932805 1
70 2 -0.234575269 1
71 3 0.006827078 1
72 2 -0.456055171 1
73 4 0.346486708 1
74 2 0.205092215 1
75 1 -0.136596858 1
76 3 -0.500179043 1
77 2 0.527352086 1
78 4 0.022742250 1
79 4 NA 1
80 1 -0.002032440 1
81 1 -0.154246160 1
82 4 0.140201825 1
83 3 -0.141417121 1
84 4 NA 1
85 2 -0.021285339 1
86 4 -0.010196306 1
87 3 -0.089747520 1
88 3 -0.083699898 1
89 3 -0.044061996 1
90 1 -0.209291697 1
91 1 0.639036426 1
92 1 0.094698299 1
93 1 -0.055510622 1
94 3 -0.421318463 1
95 2 0.125295503 1
96 1 0.213084904 1
97 3 -0.161914659 1
98 4 -0.034767685 1
99 2 -0.320681689 1
100 1 0.058192962 1
$m2a$spM_lvlone
center scale
M1 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_multinomial
[1] 0
$m2a$tau_reg_multinomial
[1] 1e-04
$m2b
$m2b$M_lvlone
M2 C2 (Intercept)
1 4 0.144065882 1
2 1 0.032778478 1
3 3 0.343008492 1
4 3 -0.361887858 1
5 4 -0.389600647 1
6 4 -0.205306841 1
7 1 0.079434830 1
8 1 -0.331246757 1
9 2 -0.329638800 1
10 2 0.167597533 1
11 3 0.860207989 1
12 3 0.022730640 1
13 2 0.217171172 1
14 3 -0.403002412 1
15 2 0.087369742 1
16 1 -0.183870429 1
17 4 -0.194577002 1
18 2 -0.349718516 1
19 3 -0.508781244 1
20 3 0.494883111 1
21 2 0.258041067 1
22 2 -0.922621989 1
23 3 0.431254949 1
24 3 -0.294218881 1
25 2 -0.425548895 1
26 2 0.057176054 1
27 1 0.289090158 1
28 3 -0.473079489 1
29 4 -0.385664863 1
30 2 -0.154780107 1
31 NA 0.100536296 1
32 4 0.634791958 1
33 4 -0.387252617 1
34 4 -0.181741088 1
35 2 -0.311562695 1
36 1 -0.044115907 1
37 3 -0.657409991 1
38 4 0.159577214 1
39 3 -0.460416933 1
40 3 NA 1
41 1 -0.248909867 1
42 4 -0.609021545 1
43 1 0.025471883 1
44 2 0.066648592 1
45 2 -0.276108719 1
46 1 -0.179737577 1
47 4 0.181190937 1
48 2 -0.453871693 1
49 4 0.448629602 1
50 1 -0.529811821 1
51 4 -0.028304571 1
52 4 -0.520318482 1
53 4 0.171317619 1
54 3 0.432732046 1
55 3 -0.346286005 1
56 3 -0.469375653 1
57 2 0.031021711 1
58 3 -0.118837515 1
59 3 0.507769984 1
60 4 0.271797031 1
61 2 -0.124442204 1
62 2 0.277677389 1
63 1 -0.102893730 1
64 2 NA 1
65 2 -0.678303052 1
66 3 0.478880037 1
67 2 -0.428028760 1
68 4 0.048119185 1
69 NA 0.216932805 1
70 1 -0.234575269 1
71 2 0.006827078 1
72 3 -0.456055171 1
73 4 0.346486708 1
74 4 0.205092215 1
75 1 -0.136596858 1
76 4 -0.500179043 1
77 NA 0.527352086 1
78 2 0.022742250 1
79 2 NA 1
80 2 -0.002032440 1
81 4 -0.154246160 1
82 3 0.140201825 1
83 3 -0.141417121 1
84 1 NA 1
85 2 -0.021285339 1
86 1 -0.010196306 1
87 3 -0.089747520 1
88 1 -0.083699898 1
89 2 -0.044061996 1
90 1 -0.209291697 1
91 3 0.639036426 1
92 1 0.094698299 1
93 4 -0.055510622 1
94 4 -0.421318463 1
95 1 0.125295503 1
96 4 0.213084904 1
97 4 -0.161914659 1
98 3 -0.034767685 1
99 3 -0.320681689 1
100 4 0.058192962 1
$m2b$spM_lvlone
center scale
M2 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_multinomial
[1] 0
$m2b$tau_reg_multinomial
[1] 1e-04
$m3a
$m3a$M_lvlone
C1 (Intercept) M12 M13 M14
1 1.410531 1 0 0 0
2 1.434183 1 1 0 0
3 1.430994 1 1 0 0
4 1.453096 1 0 0 0
5 1.438344 1 0 1 0
6 1.453207 1 0 1 0
7 1.425176 1 0 1 0
8 1.437908 1 0 1 0
9 1.416911 1 0 0 0
10 1.448638 1 1 0 0
11 1.428375 1 0 0 0
12 1.450130 1 1 0 0
13 1.420545 1 0 0 0
14 1.423005 1 0 0 1
15 1.435902 1 1 0 0
16 1.423901 1 0 1 0
17 1.457208 1 0 1 0
18 1.414280 1 0 0 0
19 1.443383 1 1 0 0
20 1.434954 1 1 0 0
21 1.429499 1 0 0 0
22 1.441897 1 0 0 1
23 1.423713 1 0 0 0
24 1.435395 1 1 0 0
25 1.425944 1 0 0 0
26 1.437115 1 0 0 0
27 1.441326 1 1 0 0
28 1.422953 1 0 0 0
29 1.437797 1 0 1 0
30 1.472121 1 0 0 0
31 1.421782 1 0 0 1
32 1.457672 1 0 0 0
33 1.430842 1 0 0 1
34 1.431523 1 1 0 0
35 1.421395 1 0 0 0
36 1.434496 1 0 0 0
37 1.425383 1 0 0 0
38 1.421802 1 1 0 0
39 1.430094 1 1 0 0
40 1.447621 1 1 0 0
41 1.434797 1 0 0 1
42 1.446091 1 0 0 1
43 1.445306 1 0 0 1
44 1.448783 1 0 0 1
45 1.450617 1 1 0 0
46 1.415055 1 0 0 0
47 1.436590 1 0 1 0
48 1.433938 1 0 1 0
49 1.414941 1 1 0 0
50 1.421807 1 1 0 0
51 1.453203 1 0 1 0
52 1.452129 1 0 0 0
53 1.431510 1 1 0 0
54 1.430082 1 0 1 0
55 1.443492 1 1 0 0
56 1.436460 1 0 0 0
57 1.418119 1 0 0 1
58 1.434971 1 0 0 0
59 1.445599 1 0 0 1
60 1.437097 1 0 0 0
61 1.428360 1 0 0 0
62 1.440550 1 0 0 0
63 1.443014 1 0 0 1
64 1.424298 1 0 0 0
65 1.448823 1 1 0 0
66 1.425834 1 0 0 1
67 1.427102 1 0 0 1
68 1.414240 1 0 1 0
69 1.456218 1 0 1 0
70 1.470594 1 1 0 0
71 1.425058 1 0 1 0
72 1.432371 1 1 0 0
73 1.441656 1 0 0 1
74 1.434952 1 1 0 0
75 1.402860 1 0 0 0
76 1.453363 1 0 1 0
77 1.432909 1 1 0 0
78 1.435103 1 0 0 1
79 1.434462 1 0 0 1
80 1.434661 1 0 0 0
81 1.445881 1 0 0 0
82 1.442548 1 0 0 1
83 1.430097 1 0 1 0
84 1.430119 1 0 0 1
85 1.430315 1 1 0 0
86 1.437584 1 0 0 1
87 1.409738 1 0 1 0
88 1.422388 1 0 1 0
89 1.422509 1 0 1 0
90 1.439432 1 0 0 0
91 1.430175 1 0 0 0
92 1.418002 1 0 0 0
93 1.423812 1 0 0 0
94 1.423473 1 0 1 0
95 1.434412 1 1 0 0
96 1.450844 1 0 0 0
97 1.433371 1 0 1 0
98 1.444378 1 0 0 1
99 1.422523 1 1 0 0
100 1.410394 1 0 0 0
$m3a$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 M2 (Intercept) M22 M23 M24
1 1.410531 4 1 NA NA NA
2 1.434183 1 1 NA NA NA
3 1.430994 3 1 NA NA NA
4 1.453096 3 1 NA NA NA
5 1.438344 4 1 NA NA NA
6 1.453207 4 1 NA NA NA
7 1.425176 1 1 NA NA NA
8 1.437908 1 1 NA NA NA
9 1.416911 2 1 NA NA NA
10 1.448638 2 1 NA NA NA
11 1.428375 3 1 NA NA NA
12 1.450130 3 1 NA NA NA
13 1.420545 2 1 NA NA NA
14 1.423005 3 1 NA NA NA
15 1.435902 2 1 NA NA NA
16 1.423901 1 1 NA NA NA
17 1.457208 4 1 NA NA NA
18 1.414280 2 1 NA NA NA
19 1.443383 3 1 NA NA NA
20 1.434954 3 1 NA NA NA
21 1.429499 2 1 NA NA NA
22 1.441897 2 1 NA NA NA
23 1.423713 3 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 3 1 NA NA NA
29 1.437797 4 1 NA NA NA
30 1.472121 2 1 NA NA NA
31 1.421782 NA 1 NA NA NA
32 1.457672 4 1 NA NA NA
33 1.430842 4 1 NA NA NA
34 1.431523 4 1 NA NA NA
35 1.421395 2 1 NA NA NA
36 1.434496 1 1 NA NA NA
37 1.425383 3 1 NA NA NA
38 1.421802 4 1 NA NA NA
39 1.430094 3 1 NA NA NA
40 1.447621 3 1 NA NA NA
41 1.434797 1 1 NA NA NA
42 1.446091 4 1 NA NA NA
43 1.445306 1 1 NA NA NA
44 1.448783 2 1 NA NA NA
45 1.450617 2 1 NA NA NA
46 1.415055 1 1 NA NA NA
47 1.436590 4 1 NA NA NA
48 1.433938 2 1 NA NA NA
49 1.414941 4 1 NA NA NA
50 1.421807 1 1 NA NA NA
51 1.453203 4 1 NA NA NA
52 1.452129 4 1 NA NA NA
53 1.431510 4 1 NA NA NA
54 1.430082 3 1 NA NA NA
55 1.443492 3 1 NA NA NA
56 1.436460 3 1 NA NA NA
57 1.418119 2 1 NA NA NA
58 1.434971 3 1 NA NA NA
59 1.445599 3 1 NA NA NA
60 1.437097 4 1 NA NA NA
61 1.428360 2 1 NA NA NA
62 1.440550 2 1 NA NA NA
63 1.443014 1 1 NA NA NA
64 1.424298 2 1 NA NA NA
65 1.448823 2 1 NA NA NA
66 1.425834 3 1 NA NA NA
67 1.427102 2 1 NA NA NA
68 1.414240 4 1 NA NA NA
69 1.456218 NA 1 NA NA NA
70 1.470594 1 1 NA NA NA
71 1.425058 2 1 NA NA NA
72 1.432371 3 1 NA NA NA
73 1.441656 4 1 NA NA NA
74 1.434952 4 1 NA NA NA
75 1.402860 1 1 NA NA NA
76 1.453363 4 1 NA NA NA
77 1.432909 NA 1 NA NA NA
78 1.435103 2 1 NA NA NA
79 1.434462 2 1 NA NA NA
80 1.434661 2 1 NA NA NA
81 1.445881 4 1 NA NA NA
82 1.442548 3 1 NA NA NA
83 1.430097 3 1 NA NA NA
84 1.430119 1 1 NA NA NA
85 1.430315 2 1 NA NA NA
86 1.437584 1 1 NA NA NA
87 1.409738 3 1 NA NA NA
88 1.422388 1 1 NA NA NA
89 1.422509 2 1 NA NA NA
90 1.439432 1 1 NA NA NA
91 1.430175 3 1 NA NA NA
92 1.418002 1 1 NA NA NA
93 1.423812 4 1 NA NA NA
94 1.423473 4 1 NA NA NA
95 1.434412 1 1 NA NA NA
96 1.450844 4 1 NA NA NA
97 1.433371 4 1 NA NA NA
98 1.444378 3 1 NA NA NA
99 1.422523 3 1 NA NA NA
100 1.410394 4 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_reg_multinomial
[1] 0
$m3b$tau_reg_multinomial
[1] 1e-04
$m4a
$m4a$M_lvlone
M1 C2 M2 O2 (Intercept) M22 M23 M24 O22 O23 O24 abs(C1 - C2)
1 1 0.144065882 4 4 1 NA NA NA NA NA NA NA
2 2 0.032778478 1 4 1 NA NA NA NA NA NA NA
3 2 0.343008492 3 4 1 NA NA NA NA NA NA NA
4 1 -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 3 -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 3 -0.331246757 1 2 1 NA NA NA NA NA NA NA
9 1 -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 2 0.022730640 3 1 1 NA NA NA NA NA NA NA
13 1 0.217171172 2 1 1 NA NA NA NA NA NA NA
14 4 -0.403002412 3 1 1 NA NA NA NA NA NA NA
15 2 0.087369742 2 4 1 NA NA NA NA NA NA NA
16 3 -0.183870429 1 3 1 NA NA NA NA NA NA NA
17 3 -0.194577002 4 3 1 NA NA NA NA NA NA NA
18 1 -0.349718516 2 1 1 NA NA NA NA NA NA NA
19 2 -0.508781244 3 3 1 NA NA NA NA NA NA NA
20 2 0.494883111 3 1 1 NA NA NA NA NA NA NA
21 1 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 1 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 1 0.057176054 2 2 1 NA NA NA NA NA NA NA
27 2 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 3 -0.385664863 4 3 1 NA NA NA NA NA NA NA
30 1 -0.154780107 2 3 1 NA NA NA NA NA NA NA
31 4 0.100536296 NA 2 1 NA NA NA NA NA NA NA
32 1 0.634791958 4 2 1 NA NA NA NA NA NA NA
33 4 -0.387252617 4 1 1 NA NA NA NA NA NA NA
34 2 -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 1 -0.044115907 1 3 1 NA NA NA NA NA NA NA
37 1 -0.657409991 3 3 1 NA NA NA NA NA NA NA
38 2 0.159577214 4 1 1 NA NA NA NA NA NA NA
39 2 -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 4 -0.248909867 1 3 1 NA NA NA NA NA NA NA
42 4 -0.609021545 4 3 1 NA NA NA NA NA NA NA
43 4 0.025471883 1 3 1 NA NA NA NA NA NA NA
44 4 0.066648592 2 4 1 NA NA NA NA NA NA NA
45 2 -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 3 0.181190937 4 4 1 NA NA NA NA NA NA NA
48 3 -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 3 -0.028304571 4 1 1 NA NA NA NA NA NA NA
52 1 -0.520318482 4 3 1 NA NA NA NA NA NA NA
53 2 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 1 -0.469375653 3 3 1 NA NA NA NA NA NA NA
57 4 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 4 0.507769984 3 4 1 NA NA NA NA NA NA NA
60 1 0.271797031 4 3 1 NA NA NA NA NA NA NA
61 1 -0.124442204 2 4 1 NA NA NA NA NA NA NA
62 1 0.277677389 2 1 1 NA NA NA NA NA NA NA
63 4 -0.102893730 1 4 1 NA NA NA NA NA NA NA
64 1 NA 2 4 1 NA NA NA NA NA NA NA
65 2 -0.678303052 2 4 1 NA NA NA NA NA NA NA
66 4 0.478880037 3 1 1 NA NA NA NA NA NA NA
67 4 -0.428028760 2 3 1 NA NA NA NA NA NA NA
68 3 0.048119185 4 3 1 NA NA NA NA NA NA NA
69 3 0.216932805 NA 4 1 NA NA NA NA NA NA NA
70 2 -0.234575269 1 1 1 NA NA NA NA NA NA NA
71 3 0.006827078 2 4 1 NA NA NA NA NA NA NA
72 2 -0.456055171 3 4 1 NA NA NA NA NA NA NA
73 4 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 1 -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 2 0.527352086 NA 2 1 NA NA NA NA NA NA NA
78 4 0.022742250 2 3 1 NA NA NA NA NA NA NA
79 4 NA 2 2 1 NA NA NA NA NA NA NA
80 1 -0.002032440 2 1 1 NA NA NA NA NA NA NA
81 1 -0.154246160 4 4 1 NA NA NA NA NA NA NA
82 4 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 4 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 3 -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 1 -0.209291697 1 4 1 NA NA NA NA NA NA NA
91 1 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 1 -0.055510622 4 NA 1 NA NA NA NA NA NA NA
94 3 -0.421318463 4 3 1 NA NA NA NA NA NA NA
95 2 0.125295503 1 1 1 NA NA NA NA NA NA NA
96 1 0.213084904 4 3 1 NA NA NA NA NA NA NA
97 3 -0.161914659 4 2 1 NA NA NA NA NA NA NA
98 4 -0.034767685 3 2 1 NA NA NA NA NA NA NA
99 2 -0.320681689 3 4 1 NA NA NA NA NA NA NA
100 1 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
M1 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
M1 C2 M2 (Intercept)
1 1 0.144065882 4 1
2 2 0.032778478 1 1
3 2 0.343008492 3 1
4 1 -0.361887858 3 1
5 3 -0.389600647 4 1
6 3 -0.205306841 4 1
7 3 0.079434830 1 1
8 3 -0.331246757 1 1
9 1 -0.329638800 2 1
10 2 0.167597533 2 1
11 1 0.860207989 3 1
12 2 0.022730640 3 1
13 1 0.217171172 2 1
14 4 -0.403002412 3 1
15 2 0.087369742 2 1
16 3 -0.183870429 1 1
17 3 -0.194577002 4 1
18 1 -0.349718516 2 1
19 2 -0.508781244 3 1
20 2 0.494883111 3 1
21 1 0.258041067 2 1
22 4 -0.922621989 2 1
23 1 0.431254949 3 1
24 2 -0.294218881 3 1
25 1 -0.425548895 2 1
26 1 0.057176054 2 1
27 2 0.289090158 1 1
28 1 -0.473079489 3 1
29 3 -0.385664863 4 1
30 1 -0.154780107 2 1
31 4 0.100536296 NA 1
32 1 0.634791958 4 1
33 4 -0.387252617 4 1
34 2 -0.181741088 4 1
35 1 -0.311562695 2 1
36 1 -0.044115907 1 1
37 1 -0.657409991 3 1
38 2 0.159577214 4 1
39 2 -0.460416933 3 1
40 2 NA 3 1
41 4 -0.248909867 1 1
42 4 -0.609021545 4 1
43 4 0.025471883 1 1
44 4 0.066648592 2 1
45 2 -0.276108719 2 1
46 1 -0.179737577 1 1
47 3 0.181190937 4 1
48 3 -0.453871693 2 1
49 2 0.448629602 4 1
50 2 -0.529811821 1 1
51 3 -0.028304571 4 1
52 1 -0.520318482 4 1
53 2 0.171317619 4 1
54 3 0.432732046 3 1
55 2 -0.346286005 3 1
56 1 -0.469375653 3 1
57 4 0.031021711 2 1
58 1 -0.118837515 3 1
59 4 0.507769984 3 1
60 1 0.271797031 4 1
61 1 -0.124442204 2 1
62 1 0.277677389 2 1
63 4 -0.102893730 1 1
64 1 NA 2 1
65 2 -0.678303052 2 1
66 4 0.478880037 3 1
67 4 -0.428028760 2 1
68 3 0.048119185 4 1
69 3 0.216932805 NA 1
70 2 -0.234575269 1 1
71 3 0.006827078 2 1
72 2 -0.456055171 3 1
73 4 0.346486708 4 1
74 2 0.205092215 4 1
75 1 -0.136596858 1 1
76 3 -0.500179043 4 1
77 2 0.527352086 NA 1
78 4 0.022742250 2 1
79 4 NA 2 1
80 1 -0.002032440 2 1
81 1 -0.154246160 4 1
82 4 0.140201825 3 1
83 3 -0.141417121 3 1
84 4 NA 1 1
85 2 -0.021285339 2 1
86 4 -0.010196306 1 1
87 3 -0.089747520 3 1
88 3 -0.083699898 1 1
89 3 -0.044061996 2 1
90 1 -0.209291697 1 1
91 1 0.639036426 3 1
92 1 0.094698299 1 1
93 1 -0.055510622 4 1
94 3 -0.421318463 4 1
95 2 0.125295503 1 1
96 1 0.213084904 4 1
97 3 -0.161914659 4 1
98 4 -0.034767685 3 1
99 2 -0.320681689 3 1
100 1 0.058192962 4 1
ifelse(as.numeric(M2) > as.numeric(O1), 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(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) M22 M23 M24
1 NA NA NA NA
2 NA NA NA NA
3 NA NA NA NA
4 NA NA NA NA
5 NA NA NA NA
6 NA NA NA NA
7 NA NA NA NA
8 NA NA NA NA
9 NA NA NA NA
10 NA NA NA NA
11 NA NA NA NA
12 NA NA NA NA
13 NA NA NA NA
14 NA NA NA NA
15 NA NA NA NA
16 NA NA NA NA
17 NA NA NA NA
18 NA NA NA NA
19 NA NA NA NA
20 NA NA NA NA
21 NA NA NA NA
22 NA NA NA NA
23 NA NA NA NA
24 NA NA NA NA
25 NA NA NA NA
26 NA NA NA NA
27 NA NA NA NA
28 NA NA NA NA
29 NA NA NA NA
30 NA NA NA NA
31 NA NA NA NA
32 NA NA NA NA
33 NA NA NA NA
34 NA NA NA NA
35 NA NA NA NA
36 NA NA NA NA
37 NA NA NA NA
38 NA NA NA NA
39 NA NA NA NA
40 NA NA NA NA
41 NA NA NA NA
42 NA NA NA NA
43 NA NA NA NA
44 NA NA NA NA
45 NA NA NA NA
46 NA NA NA NA
47 NA NA NA NA
48 NA NA NA NA
49 NA NA NA NA
50 NA NA NA NA
51 NA NA NA NA
52 NA NA NA NA
53 NA NA NA NA
54 NA NA NA NA
55 NA NA NA NA
56 NA NA NA NA
57 NA NA NA NA
58 NA NA NA NA
59 NA NA NA NA
60 NA NA NA NA
61 NA NA NA NA
62 NA NA NA NA
63 NA NA NA NA
64 NA NA NA NA
65 NA NA NA NA
66 NA NA NA NA
67 NA NA NA NA
68 NA NA NA NA
69 NA NA NA NA
70 NA NA NA NA
71 NA NA NA NA
72 NA NA NA NA
73 NA NA NA NA
74 NA NA NA NA
75 NA NA NA NA
76 NA NA NA NA
77 NA NA NA NA
78 NA NA NA NA
79 NA NA NA NA
80 NA NA NA NA
81 NA NA NA NA
82 NA NA NA NA
83 NA NA NA NA
84 NA NA NA NA
85 NA NA NA NA
86 NA NA NA NA
87 NA NA NA NA
88 NA NA NA NA
89 NA NA NA NA
90 NA NA NA NA
91 NA NA NA NA
92 NA NA NA NA
93 NA NA NA NA
94 NA NA NA NA
95 NA NA NA NA
96 NA NA NA NA
97 NA NA NA NA
98 NA NA NA NA
99 NA NA NA NA
100 NA NA NA NA
O1.L O1.Q O1.C C1 O1
1 -0.2236068 -0.5 0.6708204 1.410531 2
2 0.6708204 0.5 0.2236068 1.434183 4
3 0.2236068 -0.5 -0.6708204 1.430994 3
4 -0.2236068 -0.5 0.6708204 1.453096 2
5 0.2236068 -0.5 -0.6708204 1.438344 3
6 -0.6708204 0.5 -0.2236068 1.453207 1
7 0.2236068 -0.5 -0.6708204 1.425176 3
8 0.6708204 0.5 0.2236068 1.437908 4
9 0.6708204 0.5 0.2236068 1.416911 4
10 -0.2236068 -0.5 0.6708204 1.448638 2
11 -0.6708204 0.5 -0.2236068 1.428375 1
12 0.2236068 -0.5 -0.6708204 1.450130 3
13 0.2236068 -0.5 -0.6708204 1.420545 3
14 -0.6708204 0.5 -0.2236068 1.423005 1
15 -0.6708204 0.5 -0.2236068 1.435902 1
16 0.6708204 0.5 0.2236068 1.423901 4
17 -0.2236068 -0.5 0.6708204 1.457208 2
18 0.2236068 -0.5 -0.6708204 1.414280 3
19 0.6708204 0.5 0.2236068 1.443383 4
20 -0.6708204 0.5 -0.2236068 1.434954 1
21 0.2236068 -0.5 -0.6708204 1.429499 3
22 0.6708204 0.5 0.2236068 1.441897 4
23 0.6708204 0.5 0.2236068 1.423713 4
24 -0.2236068 -0.5 0.6708204 1.435395 2
25 -0.6708204 0.5 -0.2236068 1.425944 1
26 0.2236068 -0.5 -0.6708204 1.437115 3
27 0.6708204 0.5 0.2236068 1.441326 4
28 -0.6708204 0.5 -0.2236068 1.422953 1
29 0.6708204 0.5 0.2236068 1.437797 4
30 0.6708204 0.5 0.2236068 1.472121 4
31 -0.2236068 -0.5 0.6708204 1.421782 2
32 0.2236068 -0.5 -0.6708204 1.457672 3
33 0.2236068 -0.5 -0.6708204 1.430842 3
34 -0.6708204 0.5 -0.2236068 1.431523 1
35 -0.6708204 0.5 -0.2236068 1.421395 1
36 0.6708204 0.5 0.2236068 1.434496 4
37 0.6708204 0.5 0.2236068 1.425383 4
38 0.6708204 0.5 0.2236068 1.421802 4
39 -0.6708204 0.5 -0.2236068 1.430094 1
40 -0.2236068 -0.5 0.6708204 1.447621 2
41 -0.6708204 0.5 -0.2236068 1.434797 1
42 -0.6708204 0.5 -0.2236068 1.446091 1
43 -0.2236068 -0.5 0.6708204 1.445306 2
44 -0.2236068 -0.5 0.6708204 1.448783 2
45 -0.6708204 0.5 -0.2236068 1.450617 1
46 -0.6708204 0.5 -0.2236068 1.415055 1
47 0.6708204 0.5 0.2236068 1.436590 4
48 0.6708204 0.5 0.2236068 1.433938 4
49 -0.2236068 -0.5 0.6708204 1.414941 2
50 -0.2236068 -0.5 0.6708204 1.421807 2
51 -0.6708204 0.5 -0.2236068 1.453203 1
52 0.2236068 -0.5 -0.6708204 1.452129 3
53 -0.6708204 0.5 -0.2236068 1.431510 1
54 0.2236068 -0.5 -0.6708204 1.430082 3
55 -0.2236068 -0.5 0.6708204 1.443492 2
56 0.6708204 0.5 0.2236068 1.436460 4
57 -0.2236068 -0.5 0.6708204 1.418119 2
58 -0.6708204 0.5 -0.2236068 1.434971 1
59 -0.6708204 0.5 -0.2236068 1.445599 1
60 0.6708204 0.5 0.2236068 1.437097 4
61 -0.2236068 -0.5 0.6708204 1.428360 2
62 0.6708204 0.5 0.2236068 1.440550 4
63 0.2236068 -0.5 -0.6708204 1.443014 3
64 -0.2236068 -0.5 0.6708204 1.424298 2
65 0.2236068 -0.5 -0.6708204 1.448823 3
66 0.2236068 -0.5 -0.6708204 1.425834 3
67 -0.2236068 -0.5 0.6708204 1.427102 2
68 -0.6708204 0.5 -0.2236068 1.414240 1
69 -0.6708204 0.5 -0.2236068 1.456218 1
70 -0.6708204 0.5 -0.2236068 1.470594 1
71 -0.6708204 0.5 -0.2236068 1.425058 1
72 0.2236068 -0.5 -0.6708204 1.432371 3
73 -0.2236068 -0.5 0.6708204 1.441656 2
74 -0.2236068 -0.5 0.6708204 1.434952 2
75 0.2236068 -0.5 -0.6708204 1.402860 3
76 0.2236068 -0.5 -0.6708204 1.453363 3
77 0.6708204 0.5 0.2236068 1.432909 4
78 0.2236068 -0.5 -0.6708204 1.435103 3
79 -0.2236068 -0.5 0.6708204 1.434462 2
80 -0.2236068 -0.5 0.6708204 1.434661 2
81 0.2236068 -0.5 -0.6708204 1.445881 3
82 -0.6708204 0.5 -0.2236068 1.442548 1
83 0.2236068 -0.5 -0.6708204 1.430097 3
84 -0.2236068 -0.5 0.6708204 1.430119 2
85 -0.2236068 -0.5 0.6708204 1.430315 2
86 0.6708204 0.5 0.2236068 1.437584 4
87 0.2236068 -0.5 -0.6708204 1.409738 3
88 -0.2236068 -0.5 0.6708204 1.422388 2
89 0.2236068 -0.5 -0.6708204 1.422509 3
90 0.2236068 -0.5 -0.6708204 1.439432 3
91 0.6708204 0.5 0.2236068 1.430175 4
92 -0.6708204 0.5 -0.2236068 1.418002 1
93 0.6708204 0.5 0.2236068 1.423812 4
94 -0.6708204 0.5 -0.2236068 1.423473 1
95 -0.6708204 0.5 -0.2236068 1.434412 1
96 0.2236068 -0.5 -0.6708204 1.450844 3
97 -0.6708204 0.5 -0.2236068 1.433371 1
98 0.2236068 -0.5 -0.6708204 1.444378 3
99 0.2236068 -0.5 -0.6708204 1.422523 3
100 0.2236068 -0.5 -0.6708204 1.410394 3
$m4b$spM_lvlone
center
M1 NA
C2 -0.06490582
M2 NA
(Intercept) NA
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0.39175258
abs(C1 - C2) 1.49900534
log(C1) 0.36049727
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0.60211251
M22 NA
M23 NA
M24 NA
O1.L NA
O1.Q NA
O1.C NA
C1 1.43410054
O1 NA
scale
M1 NA
C2 0.333173465
M2 NA
(Intercept) NA
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0.490677700
abs(C1 - C2) 0.334214181
log(C1) 0.009050336
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0.778929778
M22 NA
M23 NA
M24 NA
O1.L NA
O1.Q NA
O1.C NA
C1 0.012996511
O1 NA
$m4b$mu_reg_norm
[1] 0
$m4b$tau_reg_norm
[1] 1e-04
$m4b$shape_tau_norm
[1] 0.01
$m4b$rate_tau_norm
[1] 0.01
$m4b$mu_reg_multinomial
[1] 0
$m4b$tau_reg_multinomial
[1] 1e-04
jagsmodel remains the same
Code
lapply(models, "[[", "jagsmodel")
Output
$m0a
model {
# Multinomial logit model for M1 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4])
p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ])))
p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ])))
p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ])))
p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ])))
log(phi_M1[i, 1]) <- 0
log(phi_M1[i, 2]) <- M_lvlone[i, 2] * beta[1]
log(phi_M1[i, 3]) <- M_lvlone[i, 2] * beta[2]
log(phi_M1[i, 4]) <- M_lvlone[i, 2] * beta[3]
}
# Priors for the model for M1
for (k in 1:3) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
}
$m0b
model {
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ 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, 2] * beta[1]
log(phi_M2[i, 3]) <- M_lvlone[i, 2] * beta[2]
log(phi_M2[i, 4]) <- M_lvlone[i, 2] * beta[3]
}
# Priors for the model for M2
for (k in 1:3) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
}
$m1a
model {
# Multinomial logit model for M1 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4])
p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ])))
p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ])))
p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ])))
p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ])))
log(phi_M1[i, 1]) <- 0
log(phi_M1[i, 2]) <- M_lvlone[i, 2] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2]
log(phi_M1[i, 3]) <- M_lvlone[i, 2] * beta[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4]
log(phi_M1[i, 4]) <- M_lvlone[i, 2] * beta[5] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[6]
}
# Priors for the model for M1
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
}
$m1b
model {
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ 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, 2] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2]
log(phi_M2[i, 3]) <- M_lvlone[i, 2] * beta[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4]
log(phi_M2[i, 4]) <- M_lvlone[i, 2] * beta[5] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[6]
}
# Priors for the model for M2
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
}
$m2a
model {
# Multinomial logit model for M1 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4])
p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ])))
p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ])))
p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ])))
p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ])))
log(phi_M1[i, 1]) <- 0
log(phi_M1[i, 2]) <- M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2]
log(phi_M1[i, 3]) <- M_lvlone[i, 3] * beta[3] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4]
log(phi_M1[i, 4]) <- M_lvlone[i, 3] * beta[5] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6]
}
# Priors for the model for M1
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# 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 {
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ 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, 3] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2]
log(phi_M2[i, 3]) <- M_lvlone[i, 3] * beta[3] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4]
log(phi_M2[i, 4]) <- M_lvlone[i, 3] * beta[5] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6]
}
# Priors for the model for M2
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# 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)
# Multinomial logit model for M2 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ 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, 3] * alpha[1]
log(phi_M2[i, 3]) <- M_lvlone[i, 3] * alpha[2]
log(phi_M2[i, 4]) <- M_lvlone[i, 3] * alpha[3]
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 M2
for (k in 1:3) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
}
$m4a
model {
# Multinomial logit model for M1 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4])
p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ])))
p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ])))
p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ])))
p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ])))
log(phi_M1[i, 1]) <- 0
log(phi_M1[i, 2]) <- M_lvlone[i, 5] * beta[1] + M_lvlone[i, 6] * beta[2] +
M_lvlone[i, 7] * beta[3] + M_lvlone[i, 8] * beta[4] +
M_lvlone[i, 9] * beta[5] + M_lvlone[i, 10] * beta[6] +
M_lvlone[i, 11] * beta[7] +
(M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * 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] +
(M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[12]
log(phi_M1[i, 3]) <- M_lvlone[i, 5] * beta[13] + M_lvlone[i, 6] * beta[14] +
M_lvlone[i, 7] * beta[15] + M_lvlone[i, 8] * beta[16] +
M_lvlone[i, 9] * beta[17] + M_lvlone[i, 10] * beta[18] +
M_lvlone[i, 11] * beta[19] +
(M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[20] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[21] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[22] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[23] +
(M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[24]
log(phi_M1[i, 4]) <- M_lvlone[i, 5] * beta[25] + M_lvlone[i, 6] * beta[26] +
M_lvlone[i, 7] * beta[27] + M_lvlone[i, 8] * beta[28] +
M_lvlone[i, 9] * beta[29] + M_lvlone[i, 10] * beta[30] +
M_lvlone[i, 11] * beta[31] +
(M_lvlone[i, 12] - spM_lvlone[12, 1])/spM_lvlone[12, 2] * beta[32] +
(M_lvlone[i, 13] - spM_lvlone[13, 1])/spM_lvlone[13, 2] * beta[33] +
(M_lvlone[i, 14] - spM_lvlone[14, 1])/spM_lvlone[14, 2] * beta[34] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * beta[35] +
(M_lvlone[i, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * beta[36]
}
# Priors for the model for M1
for (k in 1:36) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# 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 {
# Multinomial logit model for M1 ------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dcat(p_M1[i, 1:4])
p_M1[i, 1] <- min(1-1e-7, max(1e-7, phi_M1[i, 1] / sum(phi_M1[i, ])))
p_M1[i, 2] <- min(1-1e-7, max(1e-7, phi_M1[i, 2] / sum(phi_M1[i, ])))
p_M1[i, 3] <- min(1-1e-7, max(1e-7, phi_M1[i, 3] / sum(phi_M1[i, ])))
p_M1[i, 4] <- min(1-1e-7, max(1e-7, phi_M1[i, 4] / sum(phi_M1[i, ])))
log(phi_M1[i, 1]) <- 0
log(phi_M1[i, 2]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[3] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[4] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[5]
log(phi_M1[i, 3]) <- M_lvlone[i, 4] * beta[6] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[7] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[8] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[9] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[10]
log(phi_M1[i, 4]) <- M_lvlone[i, 4] * beta[11] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[12] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[13] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[14] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[15]
}
# Priors for the model for M1
for (k in 1:15) {
beta[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# 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)
# 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, 4] * alpha[9] + M_lvlone[i, 12] * alpha[10] +
M_lvlone[i, 13] * alpha[11] + M_lvlone[i, 14] * alpha[12] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[13]
log(phi_M2[i, 3]) <- M_lvlone[i, 4] * alpha[14] + M_lvlone[i, 12] * alpha[15] +
M_lvlone[i, 13] * alpha[16] + M_lvlone[i, 14] * alpha[17] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[18]
log(phi_M2[i, 4]) <- M_lvlone[i, 4] * alpha[19] + M_lvlone[i, 12] * alpha[20] +
M_lvlone[i, 13] * alpha[21] + M_lvlone[i, 14] * alpha[22] +
(M_lvlone[i, 15] - spM_lvlone[15, 1])/spM_lvlone[15, 2] * alpha[23]
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 M2
for (k in 9:23) {
alpha[k] ~ dnorm(mu_reg_multinomial, tau_reg_multinomial)
}
# Re-calculate interaction terms
for (i in 1:100) {
M_lvlone[i, 8] <- M_lvlone[i, 5] * M_lvlone[i, 6]
}
}
GRcrit and MCerror give same result
Code
lapply(models0, GR_crit, multivariate = FALSE)
Output
$m0a
Potential scale reduction factors:
Point est. Upper C.I.
M12: (Intercept) NaN NaN
M13: (Intercept) NaN NaN
M14: (Intercept) NaN NaN
$m0b
Potential scale reduction factors:
Point est. Upper C.I.
M22: (Intercept) NaN NaN
M23: (Intercept) NaN NaN
M24: (Intercept) NaN NaN
$m1a
Potential scale reduction factors:
Point est. Upper C.I.
M12: (Intercept) NaN NaN
M12: C1 NaN NaN
M13: (Intercept) NaN NaN
M13: C1 NaN NaN
M14: (Intercept) NaN NaN
M14: C1 NaN NaN
$m1b
Potential scale reduction factors:
Point est. Upper C.I.
M22: (Intercept) NaN NaN
M22: C1 NaN NaN
M23: (Intercept) NaN NaN
M23: C1 NaN NaN
M24: (Intercept) NaN NaN
M24: C1 NaN NaN
$m2a
Potential scale reduction factors:
Point est. Upper C.I.
M12: (Intercept) NaN NaN
M12: C2 NaN NaN
M13: (Intercept) NaN NaN
M13: C2 NaN NaN
M14: (Intercept) NaN NaN
M14: C2 NaN NaN
$m2b
Potential scale reduction factors:
Point est. Upper C.I.
M22: (Intercept) NaN NaN
M22: C2 NaN NaN
M23: (Intercept) NaN NaN
M23: C2 NaN NaN
M24: (Intercept) NaN NaN
M24: C2 NaN NaN
$m3a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
M12 NaN NaN
M13 NaN NaN
M14 NaN NaN
sigma_C1 NaN NaN
$m3b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
sigma_C1 NaN NaN
$m4a
Potential scale reduction factors:
Point est. Upper C.I.
M12: (Intercept) NaN NaN
M12: M22 NaN NaN
M12: M23 NaN NaN
M12: M24 NaN NaN
M12: O22 NaN NaN
M12: O23 NaN NaN
M12: O24 NaN NaN
M12: abs(C1 - C2) NaN NaN
M12: log(C1) NaN NaN
M12: O22:abs(C1 - C2) NaN NaN
M12: O23:abs(C1 - C2) NaN NaN
M12: O24:abs(C1 - C2) NaN NaN
M13: (Intercept) NaN NaN
M13: M22 NaN NaN
M13: M23 NaN NaN
M13: M24 NaN NaN
M13: O22 NaN NaN
M13: O23 NaN NaN
M13: O24 NaN NaN
M13: abs(C1 - C2) NaN NaN
M13: log(C1) NaN NaN
M13: O22:abs(C1 - C2) NaN NaN
M13: O23:abs(C1 - C2) NaN NaN
M13: O24:abs(C1 - C2) NaN NaN
M14: (Intercept) NaN NaN
M14: M22 NaN NaN
M14: M23 NaN NaN
M14: M24 NaN NaN
M14: O22 NaN NaN
M14: O23 NaN NaN
M14: O24 NaN NaN
M14: abs(C1 - C2) NaN NaN
M14: log(C1) NaN NaN
M14: O22:abs(C1 - C2) NaN NaN
M14: O23:abs(C1 - C2) NaN NaN
M14: O24:abs(C1 - C2) NaN NaN
$m4b
Potential scale reduction factors:
Point est.
M12: (Intercept) NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M12: abs(C1 - C2) NaN
M12: log(C1) NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN
M13: (Intercept) NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M13: abs(C1 - C2) NaN
M13: log(C1) NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN
M14: (Intercept) NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M14: abs(C1 - C2) NaN
M14: log(C1) NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN
Upper C.I.
M12: (Intercept) NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M12: abs(C1 - C2) NaN
M12: log(C1) NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN
M13: (Intercept) NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M13: abs(C1 - C2) NaN
M13: log(C1) NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN
M14: (Intercept) NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M14: abs(C1 - C2) NaN
M14: log(C1) NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 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"
$m0a
est MCSE SD MCSE/SD
M12: (Intercept) 0 0 0 NaN
M13: (Intercept) 0 0 0 NaN
M14: (Intercept) 0 0 0 NaN
$m0b
est MCSE SD MCSE/SD
M22: (Intercept) 0 0 0 NaN
M23: (Intercept) 0 0 0 NaN
M24: (Intercept) 0 0 0 NaN
$m1a
est MCSE SD MCSE/SD
M12: (Intercept) 0 0 0 NaN
M12: C1 0 0 0 NaN
M13: (Intercept) 0 0 0 NaN
M13: C1 0 0 0 NaN
M14: (Intercept) 0 0 0 NaN
M14: C1 0 0 0 NaN
$m1b
est MCSE SD MCSE/SD
M22: (Intercept) 0 0 0 NaN
M22: C1 0 0 0 NaN
M23: (Intercept) 0 0 0 NaN
M23: C1 0 0 0 NaN
M24: (Intercept) 0 0 0 NaN
M24: C1 0 0 0 NaN
$m2a
est MCSE SD MCSE/SD
M12: (Intercept) 0 0 0 NaN
M12: C2 0 0 0 NaN
M13: (Intercept) 0 0 0 NaN
M13: C2 0 0 0 NaN
M14: (Intercept) 0 0 0 NaN
M14: C2 0 0 0 NaN
$m2b
est MCSE SD MCSE/SD
M22: (Intercept) 0 0 0 NaN
M22: C2 0 0 0 NaN
M23: (Intercept) 0 0 0 NaN
M23: C2 0 0 0 NaN
M24: (Intercept) 0 0 0 NaN
M24: C2 0 0 0 NaN
$m3a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
M12 0 0 0 NaN
M13 0 0 0 NaN
M14 0 0 0 NaN
sigma_C1 0 0 0 NaN
$m3b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
M22 0 0 0 NaN
M23 0 0 0 NaN
M24 0 0 0 NaN
sigma_C1 0 0 0 NaN
$m4a
est MCSE SD MCSE/SD
M12: (Intercept) 0 0 0 NaN
M12: M22 0 0 0 NaN
M12: M23 0 0 0 NaN
M12: M24 0 0 0 NaN
M12: O22 0 0 0 NaN
M12: O23 0 0 0 NaN
M12: O24 0 0 0 NaN
M12: abs(C1 - C2) 0 0 0 NaN
M12: log(C1) 0 0 0 NaN
M12: O22:abs(C1 - C2) 0 0 0 NaN
M12: O23:abs(C1 - C2) 0 0 0 NaN
M12: O24:abs(C1 - C2) 0 0 0 NaN
M13: (Intercept) 0 0 0 NaN
M13: M22 0 0 0 NaN
M13: M23 0 0 0 NaN
M13: M24 0 0 0 NaN
M13: O22 0 0 0 NaN
M13: O23 0 0 0 NaN
M13: O24 0 0 0 NaN
M13: abs(C1 - C2) 0 0 0 NaN
M13: log(C1) 0 0 0 NaN
M13: O22:abs(C1 - C2) 0 0 0 NaN
M13: O23:abs(C1 - C2) 0 0 0 NaN
M13: O24:abs(C1 - C2) 0 0 0 NaN
M14: (Intercept) 0 0 0 NaN
M14: M22 0 0 0 NaN
M14: M23 0 0 0 NaN
M14: M24 0 0 0 NaN
M14: O22 0 0 0 NaN
M14: O23 0 0 0 NaN
M14: O24 0 0 0 NaN
M14: abs(C1 - C2) 0 0 0 NaN
M14: log(C1) 0 0 0 NaN
M14: O22:abs(C1 - C2) 0 0 0 NaN
M14: O23:abs(C1 - C2) 0 0 0 NaN
M14: O24:abs(C1 - C2) 0 0 0 NaN
$m4b
est MCSE SD
M12: (Intercept) 0 0 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M12: abs(C1 - C2) 0 0 0
M12: log(C1) 0 0 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
M13: (Intercept) 0 0 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M13: abs(C1 - C2) 0 0 0
M13: log(C1) 0 0 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
M14: (Intercept) 0 0 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M14: abs(C1 - C2) 0 0 0
M14: log(C1) 0 0 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
MCSE/SD
M12: (Intercept) NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M12: abs(C1 - C2) NaN
M12: log(C1) NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN
M13: (Intercept) NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M13: abs(C1 - C2) NaN
M13: log(C1) NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN
M14: (Intercept) NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN
M14: abs(C1 - C2) NaN
M14: log(C1) NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN
summary output remained the same
Code
lapply(models0, print)
Output
Call:
mlogit_imp(formula = M1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept) (Intercept) (Intercept)
0 0 0
Call:
mlogit_imp(formula = M2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M2"
Coefficients:
(Intercept) (Intercept) (Intercept)
0 0 0
Call:
mlogit_imp(formula = M1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept) C1 (Intercept) C1 (Intercept) C1
0 0 0 0 0 0
Call:
mlogit_imp(formula = M2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M2"
Coefficients:
(Intercept) C1 (Intercept) C1 (Intercept) C1
0 0 0 0 0 0
Call:
mlogit_imp(formula = M1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept) C2 (Intercept) C2 (Intercept) C2
0 0 0 0 0 0
Call:
mlogit_imp(formula = M2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M2"
Coefficients:
(Intercept) C2 (Intercept) C2 (Intercept) C2
0 0 0 0 0 0
Call:
lm_imp(formula = C1 ~ M1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) M12 M13 M14
0 0 0 0
Residual standard deviation:
sigma_C1
0
Call:
lm_imp(formula = C1 ~ M2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) M22 M23 M24
0 0 0 0
Residual standard deviation:
sigma_C1
0
Call:
mlogit_imp(formula = M1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_M1"),
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
Call:
mlogit_imp(formula = M1 ~ ifelse(as.numeric(M2) > as.numeric(O1),
1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5,
n.iter = 10, monitor_params = list(other = "p_M1"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
$m0a
Call:
mlogit_imp(formula = M1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept) (Intercept) (Intercept)
0 0 0
$m0b
Call:
mlogit_imp(formula = M2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M2"
Coefficients:
(Intercept) (Intercept) (Intercept)
0 0 0
$m1a
Call:
mlogit_imp(formula = M1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept) C1 (Intercept) C1 (Intercept) C1
0 0 0 0 0 0
$m1b
Call:
mlogit_imp(formula = M2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M2"
Coefficients:
(Intercept) C1 (Intercept) C1 (Intercept) C1
0 0 0 0 0 0
$m2a
Call:
mlogit_imp(formula = M1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept) C2 (Intercept) C2 (Intercept) C2
0 0 0 0 0 0
$m2b
Call:
mlogit_imp(formula = M2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M2"
Coefficients:
(Intercept) C2 (Intercept) C2 (Intercept) C2
0 0 0 0 0 0
$m3a
Call:
lm_imp(formula = C1 ~ M1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) M12 M13 M14
0 0 0 0
Residual standard deviation:
sigma_C1
0
$m3b
Call:
lm_imp(formula = C1 ~ M2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) M22 M23 M24
0 0 0 0
Residual standard deviation:
sigma_C1
0
$m4a
Call:
mlogit_imp(formula = M1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_M1"),
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
$m4b
Call:
mlogit_imp(formula = M1 ~ ifelse(as.numeric(M2) > as.numeric(O1),
1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5,
n.iter = 10, monitor_params = list(other = "p_M1"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian multinomial logit model for "M1"
Coefficients:
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
Code
lapply(models0, coef)
Output
$m0a
$m0a$M1
(Intercept) (Intercept) (Intercept)
0 0 0
$m0b
$m0b$M2
(Intercept) (Intercept) (Intercept)
0 0 0
$m1a
$m1a$M1
(Intercept) C1 (Intercept) C1 (Intercept) C1
0 0 0 0 0 0
$m1b
$m1b$M2
(Intercept) C1 (Intercept) C1 (Intercept) C1
0 0 0 0 0 0
$m2a
$m2a$M1
(Intercept) C2 (Intercept) C2 (Intercept) C2
0 0 0 0 0 0
$m2b
$m2b$M2
(Intercept) C2 (Intercept) C2 (Intercept) C2
0 0 0 0 0 0
$m3a
$m3a$C1
(Intercept) M12 M13 M14 sigma_C1
0 0 0 0 0
$m3b
$m3b$C1
(Intercept) M22 M23 M24 sigma_C1
0 0 0 0 0
$m4a
$m4a$M1
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
(Intercept) M22 M23 M24
0 0 0 0
O22 O23 O24 abs(C1 - C2)
0 0 0 0
log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
0 0 0 0
$m4b
$m4b$M1
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
(Intercept)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0)
0
abs(C1 - C2)
0
log(C1)
0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2)
0
Code
lapply(models0, confint)
Output
$m0a
$m0a$M1
2.5% 97.5%
(Intercept) 0 0
(Intercept) 0 0
(Intercept) 0 0
$m0b
$m0b$M2
2.5% 97.5%
(Intercept) 0 0
(Intercept) 0 0
(Intercept) 0 0
$m1a
$m1a$M1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
(Intercept) 0 0
C1 0 0
(Intercept) 0 0
C1 0 0
$m1b
$m1b$M2
2.5% 97.5%
(Intercept) 0 0
C1 0 0
(Intercept) 0 0
C1 0 0
(Intercept) 0 0
C1 0 0
$m2a
$m2a$M1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
(Intercept) 0 0
C2 0 0
(Intercept) 0 0
C2 0 0
$m2b
$m2b$M2
2.5% 97.5%
(Intercept) 0 0
C2 0 0
(Intercept) 0 0
C2 0 0
(Intercept) 0 0
C2 0 0
$m3a
$m3a$C1
2.5% 97.5%
(Intercept) 0 0
M12 0 0
M13 0 0
M14 0 0
sigma_C1 0 0
$m3b
$m3b$C1
2.5% 97.5%
(Intercept) 0 0
M22 0 0
M23 0 0
M24 0 0
sigma_C1 0 0
$m4a
$m4a$M1
2.5% 97.5%
(Intercept) 0 0
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
(Intercept) 0 0
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
(Intercept) 0 0
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
$m4b
$m4b$M1
2.5% 97.5%
(Intercept) 0 0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0
(Intercept) 0 0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0
(Intercept) 0 0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0
abs(C1 - C2) 0 0
log(C1) 0 0
ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 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"
$m0a
Bayesian multinomial logit model fitted with JointAI
Call:
mlogit_imp(formula = M1 ~ 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
M12: (Intercept) 0 0 0 0 0 NaN NaN
M13: (Intercept) 0 0 0 0 0 NaN NaN
M14: (Intercept) 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 multinomial logit model fitted with JointAI
Call:
mlogit_imp(formula = M2 ~ 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
M22: (Intercept) 0 0 0 0 0 NaN NaN
M23: (Intercept) 0 0 0 0 0 NaN NaN
M24: (Intercept) 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 multinomial logit model fitted with JointAI
Call:
mlogit_imp(formula = M1 ~ 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
M12: (Intercept) 0 0 0 0 0 NaN NaN
M12: C1 0 0 0 0 0 NaN NaN
M13: (Intercept) 0 0 0 0 0 NaN NaN
M13: C1 0 0 0 0 0 NaN NaN
M14: (Intercept) 0 0 0 0 0 NaN NaN
M14: C1 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 multinomial logit model fitted with JointAI
Call:
mlogit_imp(formula = M2 ~ 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: (Intercept) 0 0 0 0 0 NaN NaN
M22: C1 0 0 0 0 0 NaN NaN
M23: (Intercept) 0 0 0 0 0 NaN NaN
M23: C1 0 0 0 0 0 NaN NaN
M24: (Intercept) 0 0 0 0 0 NaN NaN
M24: C1 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 multinomial logit model fitted with JointAI
Call:
mlogit_imp(formula = M1 ~ 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
M12: (Intercept) 0 0 0 0 0 NaN NaN
M12: C2 0 0 0 0 0 NaN NaN
M13: (Intercept) 0 0 0 0 0 NaN NaN
M13: C2 0 0 0 0 0 NaN NaN
M14: (Intercept) 0 0 0 0 0 NaN NaN
M14: C2 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 multinomial logit model fitted with JointAI
Call:
mlogit_imp(formula = M2 ~ 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
M22: (Intercept) 0 0 0 0 0 NaN NaN
M22: C2 0 0 0 0 0 NaN NaN
M23: (Intercept) 0 0 0 0 0 NaN NaN
M23: C2 0 0 0 0 0 NaN NaN
M24: (Intercept) 0 0 0 0 0 NaN NaN
M24: C2 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 ~ M1, 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
M12 0 0 0 0 0 NaN NaN
M13 0 0 0 0 0 NaN NaN
M14 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 ~ M2, 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
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 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 multinomial logit model fitted with JointAI
Call:
mlogit_imp(formula = M1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_M1"),
seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M12: (Intercept) 0 0 0 0 0 NaN NaN
M12: M22 0 0 0 0 0 NaN NaN
M12: M23 0 0 0 0 0 NaN NaN
M12: M24 0 0 0 0 0 NaN NaN
M12: O22 0 0 0 0 0 NaN NaN
M12: O23 0 0 0 0 0 NaN NaN
M12: O24 0 0 0 0 0 NaN NaN
M12: abs(C1 - C2) 0 0 0 0 0 NaN NaN
M12: log(C1) 0 0 0 0 0 NaN NaN
M12: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M12: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M12: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M13: (Intercept) 0 0 0 0 0 NaN NaN
M13: M22 0 0 0 0 0 NaN NaN
M13: M23 0 0 0 0 0 NaN NaN
M13: M24 0 0 0 0 0 NaN NaN
M13: O22 0 0 0 0 0 NaN NaN
M13: O23 0 0 0 0 0 NaN NaN
M13: O24 0 0 0 0 0 NaN NaN
M13: abs(C1 - C2) 0 0 0 0 0 NaN NaN
M13: log(C1) 0 0 0 0 0 NaN NaN
M13: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M13: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M13: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M14: (Intercept) 0 0 0 0 0 NaN NaN
M14: M22 0 0 0 0 0 NaN NaN
M14: M23 0 0 0 0 0 NaN NaN
M14: M24 0 0 0 0 0 NaN NaN
M14: O22 0 0 0 0 0 NaN NaN
M14: O23 0 0 0 0 0 NaN NaN
M14: O24 0 0 0 0 0 NaN NaN
M14: abs(C1 - C2) 0 0 0 0 0 NaN NaN
M14: log(C1) 0 0 0 0 0 NaN NaN
M14: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M14: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M14: O24:abs(C1 - C2) 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 multinomial logit model fitted with JointAI
Call:
mlogit_imp(formula = M1 ~ ifelse(as.numeric(M2) > as.numeric(O1),
1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5,
n.iter = 10, monitor_params = list(other = "p_M1"), seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5%
M12: (Intercept) 0 0 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M12: abs(C1 - C2) 0 0 0
M12: log(C1) 0 0 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
M13: (Intercept) 0 0 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M13: abs(C1 - C2) 0 0 0
M13: log(C1) 0 0 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
M14: (Intercept) 0 0 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M14: abs(C1 - C2) 0 0 0
M14: log(C1) 0 0 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
97.5%
M12: (Intercept) 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M12: abs(C1 - C2) 0
M12: log(C1) 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
M13: (Intercept) 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M13: abs(C1 - C2) 0
M13: log(C1) 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
M14: (Intercept) 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M14: abs(C1 - C2) 0
M14: log(C1) 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
tail-prob.
M12: (Intercept) 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M12: abs(C1 - C2) 0
M12: log(C1) 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
M13: (Intercept) 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M13: abs(C1 - C2) 0
M13: log(C1) 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
M14: (Intercept) 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M14: abs(C1 - C2) 0
M14: log(C1) 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
GR-crit MCE/SD
M12: (Intercept) NaN NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN
M12: abs(C1 - C2) NaN NaN
M12: log(C1) NaN NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN
M13: (Intercept) NaN NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN
M13: abs(C1 - C2) NaN NaN
M13: log(C1) NaN NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN
M14: (Intercept) NaN NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN
M14: abs(C1 - C2) NaN NaN
M14: log(C1) NaN NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 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"
$m0a
$m0a$M1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M12: (Intercept) 0 0 0 0 0 NaN NaN
M13: (Intercept) 0 0 0 0 0 NaN NaN
M14: (Intercept) 0 0 0 0 0 NaN NaN
$m0b
$m0b$M2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22: (Intercept) 0 0 0 0 0 NaN NaN
M23: (Intercept) 0 0 0 0 0 NaN NaN
M24: (Intercept) 0 0 0 0 0 NaN NaN
$m1a
$m1a$M1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M12: (Intercept) 0 0 0 0 0 NaN NaN
M12: C1 0 0 0 0 0 NaN NaN
M13: (Intercept) 0 0 0 0 0 NaN NaN
M13: C1 0 0 0 0 0 NaN NaN
M14: (Intercept) 0 0 0 0 0 NaN NaN
M14: C1 0 0 0 0 0 NaN NaN
$m1b
$m1b$M2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22: (Intercept) 0 0 0 0 0 NaN NaN
M22: C1 0 0 0 0 0 NaN NaN
M23: (Intercept) 0 0 0 0 0 NaN NaN
M23: C1 0 0 0 0 0 NaN NaN
M24: (Intercept) 0 0 0 0 0 NaN NaN
M24: C1 0 0 0 0 0 NaN NaN
$m2a
$m2a$M1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M12: (Intercept) 0 0 0 0 0 NaN NaN
M12: C2 0 0 0 0 0 NaN NaN
M13: (Intercept) 0 0 0 0 0 NaN NaN
M13: C2 0 0 0 0 0 NaN NaN
M14: (Intercept) 0 0 0 0 0 NaN NaN
M14: C2 0 0 0 0 0 NaN NaN
$m2b
$m2b$M2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22: (Intercept) 0 0 0 0 0 NaN NaN
M22: C2 0 0 0 0 0 NaN NaN
M23: (Intercept) 0 0 0 0 0 NaN NaN
M23: C2 0 0 0 0 0 NaN NaN
M24: (Intercept) 0 0 0 0 0 NaN NaN
M24: 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
M12 0 0 0 0 0 NaN NaN
M13 0 0 0 0 0 NaN NaN
M14 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
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
$m4a
$m4a$M1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M12: (Intercept) 0 0 0 0 0 NaN NaN
M12: M22 0 0 0 0 0 NaN NaN
M12: M23 0 0 0 0 0 NaN NaN
M12: M24 0 0 0 0 0 NaN NaN
M12: O22 0 0 0 0 0 NaN NaN
M12: O23 0 0 0 0 0 NaN NaN
M12: O24 0 0 0 0 0 NaN NaN
M12: abs(C1 - C2) 0 0 0 0 0 NaN NaN
M12: log(C1) 0 0 0 0 0 NaN NaN
M12: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M12: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M12: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M13: (Intercept) 0 0 0 0 0 NaN NaN
M13: M22 0 0 0 0 0 NaN NaN
M13: M23 0 0 0 0 0 NaN NaN
M13: M24 0 0 0 0 0 NaN NaN
M13: O22 0 0 0 0 0 NaN NaN
M13: O23 0 0 0 0 0 NaN NaN
M13: O24 0 0 0 0 0 NaN NaN
M13: abs(C1 - C2) 0 0 0 0 0 NaN NaN
M13: log(C1) 0 0 0 0 0 NaN NaN
M13: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M13: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M13: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M14: (Intercept) 0 0 0 0 0 NaN NaN
M14: M22 0 0 0 0 0 NaN NaN
M14: M23 0 0 0 0 0 NaN NaN
M14: M24 0 0 0 0 0 NaN NaN
M14: O22 0 0 0 0 0 NaN NaN
M14: O23 0 0 0 0 0 NaN NaN
M14: O24 0 0 0 0 0 NaN NaN
M14: abs(C1 - C2) 0 0 0 0 0 NaN NaN
M14: log(C1) 0 0 0 0 0 NaN NaN
M14: O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M14: O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
M14: O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
$m4b
$m4b$M1
Mean SD 2.5%
M12: (Intercept) 0 0 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M12: abs(C1 - C2) 0 0 0
M12: log(C1) 0 0 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
M13: (Intercept) 0 0 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M13: abs(C1 - C2) 0 0 0
M13: log(C1) 0 0 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
M14: (Intercept) 0 0 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0 0 0
M14: abs(C1 - C2) 0 0 0
M14: log(C1) 0 0 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0 0 0
97.5%
M12: (Intercept) 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M12: abs(C1 - C2) 0
M12: log(C1) 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
M13: (Intercept) 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M13: abs(C1 - C2) 0
M13: log(C1) 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
M14: (Intercept) 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M14: abs(C1 - C2) 0
M14: log(C1) 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
tail-prob.
M12: (Intercept) 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M12: abs(C1 - C2) 0
M12: log(C1) 0
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
M13: (Intercept) 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M13: abs(C1 - C2) 0
M13: log(C1) 0
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
M14: (Intercept) 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) 0
M14: abs(C1 - C2) 0
M14: log(C1) 0
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) 0
GR-crit MCE/SD
M12: (Intercept) NaN NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN
M12: abs(C1 - C2) NaN NaN
M12: log(C1) NaN NaN
M12: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN
M13: (Intercept) NaN NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN
M13: abs(C1 - C2) NaN NaN
M13: log(C1) NaN NaN
M13: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN
M14: (Intercept) NaN NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0) NaN NaN
M14: abs(C1 - C2) NaN NaN
M14: log(C1) NaN NaN
M14: ifelse(as.numeric(M2) > as.numeric(O1), 1, 0):abs(C1 - C2) NaN NaN
Try the JointAI package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.