Nothing
tests/testthat/_snaps/glm.md
In JointAI: Joint Analysis and Imputation of Incomplete Data
data_list remains the same
Code
lapply(models, "[[", "data_list")
Output
$m0a1
$m0a1$M_lvlone
y (Intercept)
1 -4.76915977 1
2 -2.69277172 1
3 -1.17551547 1
4 -4.57464473 1
5 -2.20260004 1
6 -3.48995315 1
7 -0.44987258 1
8 -2.29588848 1
9 -4.49135812 1
10 -5.52545368 1
11 -4.16286741 1
12 -2.93455761 1
13 -0.04202496 1
14 -1.63149775 1
15 -0.97786151 1
16 -1.79100431 1
17 -6.26520032 1
18 -1.36028709 1
19 -1.15396597 1
20 -3.21707239 1
21 -1.59389898 1
22 -5.50335066 1
23 0.57290123 1
24 -8.22270323 1
25 -1.41364158 1
26 -6.28031574 1
27 -3.15624425 1
28 -3.55693639 1
29 -1.11821124 1
30 -2.82834175 1
31 -3.72259860 1
32 -1.75256656 1
33 -5.55044409 1
34 -7.45068147 1
35 -0.97491919 1
36 -2.98356481 1
37 -1.86039471 1
38 -7.28754607 1
39 -8.66234796 1
40 -4.16291375 1
41 -3.48250771 1
42 -7.27930410 1
43 -6.12866190 1
44 -4.96880803 1
45 -4.76746713 1
46 -1.91249177 1
47 -0.61884029 1
48 -0.20496175 1
49 -7.12636055 1
50 -6.23103837 1
51 -3.32561065 1
52 -2.95942339 1
53 -4.44915114 1
54 -0.81566463 1
55 -6.50029573 1
56 -2.74718050 1
57 -6.35015663 1
58 -2.69505883 1
59 -1.55660833 1
60 -3.76240209 1
61 -3.92885797 1
62 -1.72044748 1
63 -0.56602625 1
64 -4.42235015 1
65 -2.39122287 1
66 -0.81807247 1
67 -6.48196782 1
68 -1.37306273 1
69 -4.99886487 1
70 -5.82288217 1
71 -2.68234219 1
72 -3.96170442 1
73 -7.19573667 1
74 -5.08799713 1
75 -1.32967262 1
76 -2.56532332 1
77 -3.21002900 1
78 -3.40559790 1
79 -4.56223913 1
80 -2.04250454 1
81 -2.20378059 1
82 -3.37471317 1
83 -0.95345385 1
84 -4.89337660 1
85 -9.82258463 1
86 -4.51800734 1
87 -0.18662049 1
88 -2.87120881 1
89 1.29290150 1
90 -1.39497744 1
91 1.14575040 1
92 0.92801246 1
93 -2.59938157 1
94 -3.26905923 1
95 -3.26861434 1
96 -5.71017484 1
97 -3.76781806 1
98 -2.02677390 1
99 -2.96199765 1
100 -4.81129496 1
$m0a1$mu_reg_norm
[1] 0
$m0a1$tau_reg_norm
[1] 1e-04
$m0a1$shape_tau_norm
[1] 0.01
$m0a1$rate_tau_norm
[1] 0.01
$m0a2
$m0a2$M_lvlone
y (Intercept)
1 -4.76915977 1
2 -2.69277172 1
3 -1.17551547 1
4 -4.57464473 1
5 -2.20260004 1
6 -3.48995315 1
7 -0.44987258 1
8 -2.29588848 1
9 -4.49135812 1
10 -5.52545368 1
11 -4.16286741 1
12 -2.93455761 1
13 -0.04202496 1
14 -1.63149775 1
15 -0.97786151 1
16 -1.79100431 1
17 -6.26520032 1
18 -1.36028709 1
19 -1.15396597 1
20 -3.21707239 1
21 -1.59389898 1
22 -5.50335066 1
23 0.57290123 1
24 -8.22270323 1
25 -1.41364158 1
26 -6.28031574 1
27 -3.15624425 1
28 -3.55693639 1
29 -1.11821124 1
30 -2.82834175 1
31 -3.72259860 1
32 -1.75256656 1
33 -5.55044409 1
34 -7.45068147 1
35 -0.97491919 1
36 -2.98356481 1
37 -1.86039471 1
38 -7.28754607 1
39 -8.66234796 1
40 -4.16291375 1
41 -3.48250771 1
42 -7.27930410 1
43 -6.12866190 1
44 -4.96880803 1
45 -4.76746713 1
46 -1.91249177 1
47 -0.61884029 1
48 -0.20496175 1
49 -7.12636055 1
50 -6.23103837 1
51 -3.32561065 1
52 -2.95942339 1
53 -4.44915114 1
54 -0.81566463 1
55 -6.50029573 1
56 -2.74718050 1
57 -6.35015663 1
58 -2.69505883 1
59 -1.55660833 1
60 -3.76240209 1
61 -3.92885797 1
62 -1.72044748 1
63 -0.56602625 1
64 -4.42235015 1
65 -2.39122287 1
66 -0.81807247 1
67 -6.48196782 1
68 -1.37306273 1
69 -4.99886487 1
70 -5.82288217 1
71 -2.68234219 1
72 -3.96170442 1
73 -7.19573667 1
74 -5.08799713 1
75 -1.32967262 1
76 -2.56532332 1
77 -3.21002900 1
78 -3.40559790 1
79 -4.56223913 1
80 -2.04250454 1
81 -2.20378059 1
82 -3.37471317 1
83 -0.95345385 1
84 -4.89337660 1
85 -9.82258463 1
86 -4.51800734 1
87 -0.18662049 1
88 -2.87120881 1
89 1.29290150 1
90 -1.39497744 1
91 1.14575040 1
92 0.92801246 1
93 -2.59938157 1
94 -3.26905923 1
95 -3.26861434 1
96 -5.71017484 1
97 -3.76781806 1
98 -2.02677390 1
99 -2.96199765 1
100 -4.81129496 1
$m0a2$mu_reg_norm
[1] 0
$m0a2$tau_reg_norm
[1] 1e-04
$m0a2$shape_tau_norm
[1] 0.01
$m0a2$rate_tau_norm
[1] 0.01
$m0a3
$m0a3$M_lvlone
y (Intercept)
1 -4.76915977 1
2 -2.69277172 1
3 -1.17551547 1
4 -4.57464473 1
5 -2.20260004 1
6 -3.48995315 1
7 -0.44987258 1
8 -2.29588848 1
9 -4.49135812 1
10 -5.52545368 1
11 -4.16286741 1
12 -2.93455761 1
13 -0.04202496 1
14 -1.63149775 1
15 -0.97786151 1
16 -1.79100431 1
17 -6.26520032 1
18 -1.36028709 1
19 -1.15396597 1
20 -3.21707239 1
21 -1.59389898 1
22 -5.50335066 1
23 0.57290123 1
24 -8.22270323 1
25 -1.41364158 1
26 -6.28031574 1
27 -3.15624425 1
28 -3.55693639 1
29 -1.11821124 1
30 -2.82834175 1
31 -3.72259860 1
32 -1.75256656 1
33 -5.55044409 1
34 -7.45068147 1
35 -0.97491919 1
36 -2.98356481 1
37 -1.86039471 1
38 -7.28754607 1
39 -8.66234796 1
40 -4.16291375 1
41 -3.48250771 1
42 -7.27930410 1
43 -6.12866190 1
44 -4.96880803 1
45 -4.76746713 1
46 -1.91249177 1
47 -0.61884029 1
48 -0.20496175 1
49 -7.12636055 1
50 -6.23103837 1
51 -3.32561065 1
52 -2.95942339 1
53 -4.44915114 1
54 -0.81566463 1
55 -6.50029573 1
56 -2.74718050 1
57 -6.35015663 1
58 -2.69505883 1
59 -1.55660833 1
60 -3.76240209 1
61 -3.92885797 1
62 -1.72044748 1
63 -0.56602625 1
64 -4.42235015 1
65 -2.39122287 1
66 -0.81807247 1
67 -6.48196782 1
68 -1.37306273 1
69 -4.99886487 1
70 -5.82288217 1
71 -2.68234219 1
72 -3.96170442 1
73 -7.19573667 1
74 -5.08799713 1
75 -1.32967262 1
76 -2.56532332 1
77 -3.21002900 1
78 -3.40559790 1
79 -4.56223913 1
80 -2.04250454 1
81 -2.20378059 1
82 -3.37471317 1
83 -0.95345385 1
84 -4.89337660 1
85 -9.82258463 1
86 -4.51800734 1
87 -0.18662049 1
88 -2.87120881 1
89 1.29290150 1
90 -1.39497744 1
91 1.14575040 1
92 0.92801246 1
93 -2.59938157 1
94 -3.26905923 1
95 -3.26861434 1
96 -5.71017484 1
97 -3.76781806 1
98 -2.02677390 1
99 -2.96199765 1
100 -4.81129496 1
$m0a3$mu_reg_norm
[1] 0
$m0a3$tau_reg_norm
[1] 1e-04
$m0a3$shape_tau_norm
[1] 0.01
$m0a3$rate_tau_norm
[1] 0.01
$m0a4
$m0a4$M_lvlone
y (Intercept)
1 -4.76915977 1
2 -2.69277172 1
3 -1.17551547 1
4 -4.57464473 1
5 -2.20260004 1
6 -3.48995315 1
7 -0.44987258 1
8 -2.29588848 1
9 -4.49135812 1
10 -5.52545368 1
11 -4.16286741 1
12 -2.93455761 1
13 -0.04202496 1
14 -1.63149775 1
15 -0.97786151 1
16 -1.79100431 1
17 -6.26520032 1
18 -1.36028709 1
19 -1.15396597 1
20 -3.21707239 1
21 -1.59389898 1
22 -5.50335066 1
23 0.57290123 1
24 -8.22270323 1
25 -1.41364158 1
26 -6.28031574 1
27 -3.15624425 1
28 -3.55693639 1
29 -1.11821124 1
30 -2.82834175 1
31 -3.72259860 1
32 -1.75256656 1
33 -5.55044409 1
34 -7.45068147 1
35 -0.97491919 1
36 -2.98356481 1
37 -1.86039471 1
38 -7.28754607 1
39 -8.66234796 1
40 -4.16291375 1
41 -3.48250771 1
42 -7.27930410 1
43 -6.12866190 1
44 -4.96880803 1
45 -4.76746713 1
46 -1.91249177 1
47 -0.61884029 1
48 -0.20496175 1
49 -7.12636055 1
50 -6.23103837 1
51 -3.32561065 1
52 -2.95942339 1
53 -4.44915114 1
54 -0.81566463 1
55 -6.50029573 1
56 -2.74718050 1
57 -6.35015663 1
58 -2.69505883 1
59 -1.55660833 1
60 -3.76240209 1
61 -3.92885797 1
62 -1.72044748 1
63 -0.56602625 1
64 -4.42235015 1
65 -2.39122287 1
66 -0.81807247 1
67 -6.48196782 1
68 -1.37306273 1
69 -4.99886487 1
70 -5.82288217 1
71 -2.68234219 1
72 -3.96170442 1
73 -7.19573667 1
74 -5.08799713 1
75 -1.32967262 1
76 -2.56532332 1
77 -3.21002900 1
78 -3.40559790 1
79 -4.56223913 1
80 -2.04250454 1
81 -2.20378059 1
82 -3.37471317 1
83 -0.95345385 1
84 -4.89337660 1
85 -9.82258463 1
86 -4.51800734 1
87 -0.18662049 1
88 -2.87120881 1
89 1.29290150 1
90 -1.39497744 1
91 1.14575040 1
92 0.92801246 1
93 -2.59938157 1
94 -3.26905923 1
95 -3.26861434 1
96 -5.71017484 1
97 -3.76781806 1
98 -2.02677390 1
99 -2.96199765 1
100 -4.81129496 1
$m0a4$mu_reg_norm
[1] 0
$m0a4$tau_reg_norm
[1] 1e-04
$m0a4$shape_tau_norm
[1] 0.01
$m0a4$rate_tau_norm
[1] 0.01
$m0b1
$m0b1$M_lvlone
B1 (Intercept)
1 1 1
2 1 1
3 1 1
4 1 1
5 1 1
6 1 1
7 0 1
8 0 1
9 1 1
10 1 1
11 1 1
12 0 1
13 1 1
14 0 1
15 1 1
16 1 1
17 1 1
18 1 1
19 1 1
20 1 1
21 1 1
22 1 1
23 1 1
24 1 1
25 0 1
26 1 1
27 1 1
28 1 1
29 1 1
30 0 1
31 0 1
32 1 1
33 1 1
34 1 1
35 1 1
36 0 1
37 1 1
38 1 1
39 1 1
40 1 1
41 1 1
42 1 1
43 1 1
44 1 1
45 1 1
46 1 1
47 0 1
48 1 1
49 1 1
50 0 1
51 1 1
52 1 1
53 1 1
54 1 1
55 0 1
56 1 1
57 1 1
58 1 1
59 1 1
60 0 1
61 1 1
62 1 1
63 0 1
64 1 1
65 1 1
66 0 1
67 0 1
68 1 1
69 0 1
70 0 1
71 1 1
72 1 1
73 0 1
74 1 1
75 1 1
76 0 1
77 0 1
78 0 1
79 1 1
80 1 1
81 1 1
82 1 1
83 1 1
84 1 1
85 1 1
86 1 1
87 1 1
88 0 1
89 1 1
90 1 1
91 1 1
92 1 1
93 1 1
94 1 1
95 1 1
96 1 1
97 1 1
98 1 1
99 1 1
100 1 1
$m0b1$mu_reg_binom
[1] 0
$m0b1$tau_reg_binom
[1] 1e-04
$m0b2
$m0b2$M_lvlone
B1 (Intercept)
1 1 1
2 1 1
3 1 1
4 1 1
5 1 1
6 1 1
7 0 1
8 0 1
9 1 1
10 1 1
11 1 1
12 0 1
13 1 1
14 0 1
15 1 1
16 1 1
17 1 1
18 1 1
19 1 1
20 1 1
21 1 1
22 1 1
23 1 1
24 1 1
25 0 1
26 1 1
27 1 1
28 1 1
29 1 1
30 0 1
31 0 1
32 1 1
33 1 1
34 1 1
35 1 1
36 0 1
37 1 1
38 1 1
39 1 1
40 1 1
41 1 1
42 1 1
43 1 1
44 1 1
45 1 1
46 1 1
47 0 1
48 1 1
49 1 1
50 0 1
51 1 1
52 1 1
53 1 1
54 1 1
55 0 1
56 1 1
57 1 1
58 1 1
59 1 1
60 0 1
61 1 1
62 1 1
63 0 1
64 1 1
65 1 1
66 0 1
67 0 1
68 1 1
69 0 1
70 0 1
71 1 1
72 1 1
73 0 1
74 1 1
75 1 1
76 0 1
77 0 1
78 0 1
79 1 1
80 1 1
81 1 1
82 1 1
83 1 1
84 1 1
85 1 1
86 1 1
87 1 1
88 0 1
89 1 1
90 1 1
91 1 1
92 1 1
93 1 1
94 1 1
95 1 1
96 1 1
97 1 1
98 1 1
99 1 1
100 1 1
$m0b2$mu_reg_binom
[1] 0
$m0b2$tau_reg_binom
[1] 1e-04
$m0b3
$m0b3$M_lvlone
B1 (Intercept)
1 1 1
2 1 1
3 1 1
4 1 1
5 1 1
6 1 1
7 0 1
8 0 1
9 1 1
10 1 1
11 1 1
12 0 1
13 1 1
14 0 1
15 1 1
16 1 1
17 1 1
18 1 1
19 1 1
20 1 1
21 1 1
22 1 1
23 1 1
24 1 1
25 0 1
26 1 1
27 1 1
28 1 1
29 1 1
30 0 1
31 0 1
32 1 1
33 1 1
34 1 1
35 1 1
36 0 1
37 1 1
38 1 1
39 1 1
40 1 1
41 1 1
42 1 1
43 1 1
44 1 1
45 1 1
46 1 1
47 0 1
48 1 1
49 1 1
50 0 1
51 1 1
52 1 1
53 1 1
54 1 1
55 0 1
56 1 1
57 1 1
58 1 1
59 1 1
60 0 1
61 1 1
62 1 1
63 0 1
64 1 1
65 1 1
66 0 1
67 0 1
68 1 1
69 0 1
70 0 1
71 1 1
72 1 1
73 0 1
74 1 1
75 1 1
76 0 1
77 0 1
78 0 1
79 1 1
80 1 1
81 1 1
82 1 1
83 1 1
84 1 1
85 1 1
86 1 1
87 1 1
88 0 1
89 1 1
90 1 1
91 1 1
92 1 1
93 1 1
94 1 1
95 1 1
96 1 1
97 1 1
98 1 1
99 1 1
100 1 1
$m0b3$mu_reg_binom
[1] 0
$m0b3$tau_reg_binom
[1] 1e-04
$m0b4
$m0b4$M_lvlone
B1 (Intercept)
1 1 1
2 1 1
3 1 1
4 1 1
5 1 1
6 1 1
7 0 1
8 0 1
9 1 1
10 1 1
11 1 1
12 0 1
13 1 1
14 0 1
15 1 1
16 1 1
17 1 1
18 1 1
19 1 1
20 1 1
21 1 1
22 1 1
23 1 1
24 1 1
25 0 1
26 1 1
27 1 1
28 1 1
29 1 1
30 0 1
31 0 1
32 1 1
33 1 1
34 1 1
35 1 1
36 0 1
37 1 1
38 1 1
39 1 1
40 1 1
41 1 1
42 1 1
43 1 1
44 1 1
45 1 1
46 1 1
47 0 1
48 1 1
49 1 1
50 0 1
51 1 1
52 1 1
53 1 1
54 1 1
55 0 1
56 1 1
57 1 1
58 1 1
59 1 1
60 0 1
61 1 1
62 1 1
63 0 1
64 1 1
65 1 1
66 0 1
67 0 1
68 1 1
69 0 1
70 0 1
71 1 1
72 1 1
73 0 1
74 1 1
75 1 1
76 0 1
77 0 1
78 0 1
79 1 1
80 1 1
81 1 1
82 1 1
83 1 1
84 1 1
85 1 1
86 1 1
87 1 1
88 0 1
89 1 1
90 1 1
91 1 1
92 1 1
93 1 1
94 1 1
95 1 1
96 1 1
97 1 1
98 1 1
99 1 1
100 1 1
$m0b4$mu_reg_binom
[1] 0
$m0b4$tau_reg_binom
[1] 1e-04
$m0c1
$m0c1$M_lvlone
L1 (Intercept)
1 0.9364352 1
2 0.8943541 1
3 0.2868460 1
4 0.9068418 1
5 0.7621346 1
6 0.5858621 1
7 0.7194403 1
8 0.7593154 1
9 0.5863705 1
10 0.7342586 1
11 0.7218028 1
12 0.7241254 1
13 0.7200126 1
14 0.5289014 1
15 0.7322482 1
16 0.7462471 1
17 0.9119922 1
18 0.6262513 1
19 0.4587835 1
20 0.7173364 1
21 0.7288999 1
22 0.7160420 1
23 0.5795514 1
24 0.7210413 1
25 0.7816086 1
26 0.6747483 1
27 0.4746725 1
28 0.9270652 1
29 0.5306249 1
30 0.8913764 1
31 0.8090308 1
32 0.4610800 1
33 0.7183814 1
34 0.6375974 1
35 0.9202563 1
36 0.7263222 1
37 1.0638781 1
38 0.6053893 1
39 0.7945509 1
40 0.6355032 1
41 0.9939049 1
42 1.0690739 1
43 0.7009106 1
44 0.7595403 1
45 0.8356414 1
46 0.4929132 1
47 0.5298192 1
48 0.5363034 1
49 0.8494053 1
50 0.6292812 1
51 0.9561312 1
52 0.9735411 1
53 0.7156259 1
54 0.5184434 1
55 0.7948965 1
56 0.5191792 1
57 0.9233108 1
58 0.8025356 1
59 0.8546624 1
60 0.8639819 1
61 0.7521237 1
62 0.5590215 1
63 0.5972103 1
64 0.6071272 1
65 0.8837829 1
66 0.7775301 1
67 0.6756191 1
68 0.7857549 1
69 0.9119262 1
70 0.5816103 1
71 0.4886093 1
72 0.8292467 1
73 0.6767456 1
74 0.7328840 1
75 0.7946099 1
76 0.7734810 1
77 0.5296147 1
78 0.7723288 1
79 0.8079308 1
80 0.5214822 1
81 0.6264777 1
82 0.8332107 1
83 0.4544158 1
84 0.6482660 1
85 0.7272109 1
86 0.7302426 1
87 0.6768061 1
88 0.8115758 1
89 0.9775567 1
90 0.6408465 1
91 0.5917453 1
92 0.7224845 1
93 0.4501596 1
94 0.5190455 1
95 0.7305821 1
96 0.9696445 1
97 0.7087457 1
98 0.9964080 1
99 0.9084899 1
100 0.9296776 1
$m0c1$mu_reg_gamma
[1] 0
$m0c1$tau_reg_gamma
[1] 1e-04
$m0c1$shape_tau_gamma
[1] 0.01
$m0c1$rate_tau_gamma
[1] 0.01
$m0c2
$m0c2$M_lvlone
L1 (Intercept)
1 0.9364352 1
2 0.8943541 1
3 0.2868460 1
4 0.9068418 1
5 0.7621346 1
6 0.5858621 1
7 0.7194403 1
8 0.7593154 1
9 0.5863705 1
10 0.7342586 1
11 0.7218028 1
12 0.7241254 1
13 0.7200126 1
14 0.5289014 1
15 0.7322482 1
16 0.7462471 1
17 0.9119922 1
18 0.6262513 1
19 0.4587835 1
20 0.7173364 1
21 0.7288999 1
22 0.7160420 1
23 0.5795514 1
24 0.7210413 1
25 0.7816086 1
26 0.6747483 1
27 0.4746725 1
28 0.9270652 1
29 0.5306249 1
30 0.8913764 1
31 0.8090308 1
32 0.4610800 1
33 0.7183814 1
34 0.6375974 1
35 0.9202563 1
36 0.7263222 1
37 1.0638781 1
38 0.6053893 1
39 0.7945509 1
40 0.6355032 1
41 0.9939049 1
42 1.0690739 1
43 0.7009106 1
44 0.7595403 1
45 0.8356414 1
46 0.4929132 1
47 0.5298192 1
48 0.5363034 1
49 0.8494053 1
50 0.6292812 1
51 0.9561312 1
52 0.9735411 1
53 0.7156259 1
54 0.5184434 1
55 0.7948965 1
56 0.5191792 1
57 0.9233108 1
58 0.8025356 1
59 0.8546624 1
60 0.8639819 1
61 0.7521237 1
62 0.5590215 1
63 0.5972103 1
64 0.6071272 1
65 0.8837829 1
66 0.7775301 1
67 0.6756191 1
68 0.7857549 1
69 0.9119262 1
70 0.5816103 1
71 0.4886093 1
72 0.8292467 1
73 0.6767456 1
74 0.7328840 1
75 0.7946099 1
76 0.7734810 1
77 0.5296147 1
78 0.7723288 1
79 0.8079308 1
80 0.5214822 1
81 0.6264777 1
82 0.8332107 1
83 0.4544158 1
84 0.6482660 1
85 0.7272109 1
86 0.7302426 1
87 0.6768061 1
88 0.8115758 1
89 0.9775567 1
90 0.6408465 1
91 0.5917453 1
92 0.7224845 1
93 0.4501596 1
94 0.5190455 1
95 0.7305821 1
96 0.9696445 1
97 0.7087457 1
98 0.9964080 1
99 0.9084899 1
100 0.9296776 1
$m0c2$mu_reg_gamma
[1] 0
$m0c2$tau_reg_gamma
[1] 1e-04
$m0c2$shape_tau_gamma
[1] 0.01
$m0c2$rate_tau_gamma
[1] 0.01
$m0d1
$m0d1$M_lvlone
P1 (Intercept)
1 1 1
2 3 1
3 3 1
4 3 1
5 5 1
6 3 1
7 0 1
8 2 1
9 4 1
10 3 1
11 4 1
12 3 1
13 2 1
14 6 1
15 2 1
16 5 1
17 2 1
18 2 1
19 1 1
20 2 1
21 2 1
22 2 1
23 1 1
24 0 1
25 2 1
26 4 1
27 3 1
28 5 1
29 5 1
30 0 1
31 3 1
32 2 1
33 2 1
34 3 1
35 1 1
36 4 1
37 2 1
38 2 1
39 8 1
40 4 1
41 3 1
42 3 1
43 2 1
44 3 1
45 2 1
46 3 1
47 4 1
48 3 1
49 2 1
50 4 1
51 1 1
52 2 1
53 4 1
54 3 1
55 1 1
56 3 1
57 3 1
58 4 1
59 1 1
60 5 1
61 5 1
62 0 1
63 2 1
64 0 1
65 2 1
66 4 1
67 2 1
68 3 1
69 1 1
70 3 1
71 1 1
72 5 1
73 0 1
74 4 1
75 1 1
76 3 1
77 2 1
78 1 1
79 2 1
80 4 1
81 6 1
82 3 1
83 1 1
84 3 1
85 1 1
86 5 1
87 2 1
88 2 1
89 1 1
90 5 1
91 1 1
92 5 1
93 1 1
94 1 1
95 1 1
96 3 1
97 2 1
98 0 1
99 2 1
100 4 1
$m0d1$mu_reg_poisson
[1] 0
$m0d1$tau_reg_poisson
[1] 1e-04
$m0d2
$m0d2$M_lvlone
P1 (Intercept)
1 1 1
2 3 1
3 3 1
4 3 1
5 5 1
6 3 1
7 0 1
8 2 1
9 4 1
10 3 1
11 4 1
12 3 1
13 2 1
14 6 1
15 2 1
16 5 1
17 2 1
18 2 1
19 1 1
20 2 1
21 2 1
22 2 1
23 1 1
24 0 1
25 2 1
26 4 1
27 3 1
28 5 1
29 5 1
30 0 1
31 3 1
32 2 1
33 2 1
34 3 1
35 1 1
36 4 1
37 2 1
38 2 1
39 8 1
40 4 1
41 3 1
42 3 1
43 2 1
44 3 1
45 2 1
46 3 1
47 4 1
48 3 1
49 2 1
50 4 1
51 1 1
52 2 1
53 4 1
54 3 1
55 1 1
56 3 1
57 3 1
58 4 1
59 1 1
60 5 1
61 5 1
62 0 1
63 2 1
64 0 1
65 2 1
66 4 1
67 2 1
68 3 1
69 1 1
70 3 1
71 1 1
72 5 1
73 0 1
74 4 1
75 1 1
76 3 1
77 2 1
78 1 1
79 2 1
80 4 1
81 6 1
82 3 1
83 1 1
84 3 1
85 1 1
86 5 1
87 2 1
88 2 1
89 1 1
90 5 1
91 1 1
92 5 1
93 1 1
94 1 1
95 1 1
96 3 1
97 2 1
98 0 1
99 2 1
100 4 1
$m0d2$mu_reg_poisson
[1] 0
$m0d2$tau_reg_poisson
[1] 1e-04
$m0e1
$m0e1$M_lvlone
L1 (Intercept)
1 0.9364352 1
2 0.8943541 1
3 0.2868460 1
4 0.9068418 1
5 0.7621346 1
6 0.5858621 1
7 0.7194403 1
8 0.7593154 1
9 0.5863705 1
10 0.7342586 1
11 0.7218028 1
12 0.7241254 1
13 0.7200126 1
14 0.5289014 1
15 0.7322482 1
16 0.7462471 1
17 0.9119922 1
18 0.6262513 1
19 0.4587835 1
20 0.7173364 1
21 0.7288999 1
22 0.7160420 1
23 0.5795514 1
24 0.7210413 1
25 0.7816086 1
26 0.6747483 1
27 0.4746725 1
28 0.9270652 1
29 0.5306249 1
30 0.8913764 1
31 0.8090308 1
32 0.4610800 1
33 0.7183814 1
34 0.6375974 1
35 0.9202563 1
36 0.7263222 1
37 1.0638781 1
38 0.6053893 1
39 0.7945509 1
40 0.6355032 1
41 0.9939049 1
42 1.0690739 1
43 0.7009106 1
44 0.7595403 1
45 0.8356414 1
46 0.4929132 1
47 0.5298192 1
48 0.5363034 1
49 0.8494053 1
50 0.6292812 1
51 0.9561312 1
52 0.9735411 1
53 0.7156259 1
54 0.5184434 1
55 0.7948965 1
56 0.5191792 1
57 0.9233108 1
58 0.8025356 1
59 0.8546624 1
60 0.8639819 1
61 0.7521237 1
62 0.5590215 1
63 0.5972103 1
64 0.6071272 1
65 0.8837829 1
66 0.7775301 1
67 0.6756191 1
68 0.7857549 1
69 0.9119262 1
70 0.5816103 1
71 0.4886093 1
72 0.8292467 1
73 0.6767456 1
74 0.7328840 1
75 0.7946099 1
76 0.7734810 1
77 0.5296147 1
78 0.7723288 1
79 0.8079308 1
80 0.5214822 1
81 0.6264777 1
82 0.8332107 1
83 0.4544158 1
84 0.6482660 1
85 0.7272109 1
86 0.7302426 1
87 0.6768061 1
88 0.8115758 1
89 0.9775567 1
90 0.6408465 1
91 0.5917453 1
92 0.7224845 1
93 0.4501596 1
94 0.5190455 1
95 0.7305821 1
96 0.9696445 1
97 0.7087457 1
98 0.9964080 1
99 0.9084899 1
100 0.9296776 1
$m0e1$mu_reg_norm
[1] 0
$m0e1$tau_reg_norm
[1] 1e-04
$m0e1$shape_tau_norm
[1] 0.01
$m0e1$rate_tau_norm
[1] 0.01
$m0f1
$m0f1$M_lvlone
Be1 (Intercept)
1 0.69649948 1
2 0.56085128 1
3 0.35796663 1
4 0.53961336 1
5 0.06191042 1
6 0.51256785 1
7 0.13154723 1
8 0.35032766 1
9 0.21796890 1
10 0.10476230 1
11 0.66083800 1
12 0.66884267 1
13 0.69840279 1
14 0.50398472 1
15 0.52807655 1
16 0.40135087 1
17 0.45554802 1
18 0.68717635 1
19 0.35880655 1
20 0.36341035 1
21 0.71468563 1
22 0.44558172 1
23 0.33262526 1
24 0.66812751 1
25 0.23180310 1
26 0.37786624 1
27 0.88834598 1
28 0.46487057 1
29 0.47018802 1
30 0.91617346 1
31 0.67589111 1
32 0.61623852 1
33 0.44182889 1
34 0.29868153 1
35 0.44235110 1
36 0.72557250 1
37 0.74809277 1
38 0.26452559 1
39 0.41597215 1
40 0.29080530 1
41 0.80342568 1
42 0.76614332 1
43 0.29734466 1
44 0.42809509 1
45 0.12861202 1
46 0.44369392 1
47 0.35290028 1
48 0.88288407 1
49 0.37880332 1
50 0.60663793 1
51 0.15505292 1
52 0.65796074 1
53 0.63416487 1
54 0.83040459 1
55 0.64947589 1
56 0.67541381 1
57 0.53637356 1
58 0.39157422 1
59 0.88168026 1
60 0.32582606 1
61 0.64492753 1
62 0.34804110 1
63 0.49241010 1
64 0.43387493 1
65 0.21806182 1
66 0.60021691 1
67 0.30567313 1
68 0.22476988 1
69 0.23155216 1
70 0.29610794 1
71 0.83435168 1
72 0.65543408 1
73 0.59684715 1
74 0.80640183 1
75 0.52288624 1
76 0.41546840 1
77 0.44756212 1
78 0.68093413 1
79 0.29261828 1
80 0.21008516 1
81 0.44710869 1
82 0.70470991 1
83 0.31300581 1
84 0.44774544 1
85 0.68031201 1
86 0.44456865 1
87 0.79031803 1
88 0.22231438 1
89 0.30114327 1
90 0.45339193 1
91 0.35526875 1
92 0.68684691 1
93 0.81430167 1
94 0.60104343 1
95 0.82012448 1
96 0.55669948 1
97 0.76622465 1
98 0.50112270 1
99 0.53468983 1
100 0.58249327 1
$m0f1$mu_reg_beta
[1] 0
$m0f1$tau_reg_beta
[1] 1e-04
$m0f1$shape_tau_beta
[1] 0.01
$m0f1$rate_tau_beta
[1] 0.01
$m1a
$m1a$M_lvlone
y (Intercept) C1
1 -4.76915977 1 1.410531
2 -2.69277172 1 1.434183
3 -1.17551547 1 1.430994
4 -4.57464473 1 1.453096
5 -2.20260004 1 1.438344
6 -3.48995315 1 1.453207
7 -0.44987258 1 1.425176
8 -2.29588848 1 1.437908
9 -4.49135812 1 1.416911
10 -5.52545368 1 1.448638
11 -4.16286741 1 1.428375
12 -2.93455761 1 1.450130
13 -0.04202496 1 1.420545
14 -1.63149775 1 1.423005
15 -0.97786151 1 1.435902
16 -1.79100431 1 1.423901
17 -6.26520032 1 1.457208
18 -1.36028709 1 1.414280
19 -1.15396597 1 1.443383
20 -3.21707239 1 1.434954
21 -1.59389898 1 1.429499
22 -5.50335066 1 1.441897
23 0.57290123 1 1.423713
24 -8.22270323 1 1.435395
25 -1.41364158 1 1.425944
26 -6.28031574 1 1.437115
27 -3.15624425 1 1.441326
28 -3.55693639 1 1.422953
29 -1.11821124 1 1.437797
30 -2.82834175 1 1.472121
31 -3.72259860 1 1.421782
32 -1.75256656 1 1.457672
33 -5.55044409 1 1.430842
34 -7.45068147 1 1.431523
35 -0.97491919 1 1.421395
36 -2.98356481 1 1.434496
37 -1.86039471 1 1.425383
38 -7.28754607 1 1.421802
39 -8.66234796 1 1.430094
40 -4.16291375 1 1.447621
41 -3.48250771 1 1.434797
42 -7.27930410 1 1.446091
43 -6.12866190 1 1.445306
44 -4.96880803 1 1.448783
45 -4.76746713 1 1.450617
46 -1.91249177 1 1.415055
47 -0.61884029 1 1.436590
48 -0.20496175 1 1.433938
49 -7.12636055 1 1.414941
50 -6.23103837 1 1.421807
51 -3.32561065 1 1.453203
52 -2.95942339 1 1.452129
53 -4.44915114 1 1.431510
54 -0.81566463 1 1.430082
55 -6.50029573 1 1.443492
56 -2.74718050 1 1.436460
57 -6.35015663 1 1.418119
58 -2.69505883 1 1.434971
59 -1.55660833 1 1.445599
60 -3.76240209 1 1.437097
61 -3.92885797 1 1.428360
62 -1.72044748 1 1.440550
63 -0.56602625 1 1.443014
64 -4.42235015 1 1.424298
65 -2.39122287 1 1.448823
66 -0.81807247 1 1.425834
67 -6.48196782 1 1.427102
68 -1.37306273 1 1.414240
69 -4.99886487 1 1.456218
70 -5.82288217 1 1.470594
71 -2.68234219 1 1.425058
72 -3.96170442 1 1.432371
73 -7.19573667 1 1.441656
74 -5.08799713 1 1.434952
75 -1.32967262 1 1.402860
76 -2.56532332 1 1.453363
77 -3.21002900 1 1.432909
78 -3.40559790 1 1.435103
79 -4.56223913 1 1.434462
80 -2.04250454 1 1.434661
81 -2.20378059 1 1.445881
82 -3.37471317 1 1.442548
83 -0.95345385 1 1.430097
84 -4.89337660 1 1.430119
85 -9.82258463 1 1.430315
86 -4.51800734 1 1.437584
87 -0.18662049 1 1.409738
88 -2.87120881 1 1.422388
89 1.29290150 1 1.422509
90 -1.39497744 1 1.439432
91 1.14575040 1 1.430175
92 0.92801246 1 1.418002
93 -2.59938157 1 1.423812
94 -3.26905923 1 1.423473
95 -3.26861434 1 1.434412
96 -5.71017484 1 1.450844
97 -3.76781806 1 1.433371
98 -2.02677390 1 1.444378
99 -2.96199765 1 1.422523
100 -4.81129496 1 1.410394
$m1a$spM_lvlone
center scale
y -3.344283 2.27649507
(Intercept) NA NA
C1 1.434101 0.01299651
$m1a$mu_reg_norm
[1] 0
$m1a$tau_reg_norm
[1] 1e-04
$m1a$shape_tau_norm
[1] 0.01
$m1a$rate_tau_norm
[1] 0.01
$m1b
$m1b$M_lvlone
B1 (Intercept) C1
1 1 1 1.410531
2 1 1 1.434183
3 1 1 1.430994
4 1 1 1.453096
5 1 1 1.438344
6 1 1 1.453207
7 0 1 1.425176
8 0 1 1.437908
9 1 1 1.416911
10 1 1 1.448638
11 1 1 1.428375
12 0 1 1.450130
13 1 1 1.420545
14 0 1 1.423005
15 1 1 1.435902
16 1 1 1.423901
17 1 1 1.457208
18 1 1 1.414280
19 1 1 1.443383
20 1 1 1.434954
21 1 1 1.429499
22 1 1 1.441897
23 1 1 1.423713
24 1 1 1.435395
25 0 1 1.425944
26 1 1 1.437115
27 1 1 1.441326
28 1 1 1.422953
29 1 1 1.437797
30 0 1 1.472121
31 0 1 1.421782
32 1 1 1.457672
33 1 1 1.430842
34 1 1 1.431523
35 1 1 1.421395
36 0 1 1.434496
37 1 1 1.425383
38 1 1 1.421802
39 1 1 1.430094
40 1 1 1.447621
41 1 1 1.434797
42 1 1 1.446091
43 1 1 1.445306
44 1 1 1.448783
45 1 1 1.450617
46 1 1 1.415055
47 0 1 1.436590
48 1 1 1.433938
49 1 1 1.414941
50 0 1 1.421807
51 1 1 1.453203
52 1 1 1.452129
53 1 1 1.431510
54 1 1 1.430082
55 0 1 1.443492
56 1 1 1.436460
57 1 1 1.418119
58 1 1 1.434971
59 1 1 1.445599
60 0 1 1.437097
61 1 1 1.428360
62 1 1 1.440550
63 0 1 1.443014
64 1 1 1.424298
65 1 1 1.448823
66 0 1 1.425834
67 0 1 1.427102
68 1 1 1.414240
69 0 1 1.456218
70 0 1 1.470594
71 1 1 1.425058
72 1 1 1.432371
73 0 1 1.441656
74 1 1 1.434952
75 1 1 1.402860
76 0 1 1.453363
77 0 1 1.432909
78 0 1 1.435103
79 1 1 1.434462
80 1 1 1.434661
81 1 1 1.445881
82 1 1 1.442548
83 1 1 1.430097
84 1 1 1.430119
85 1 1 1.430315
86 1 1 1.437584
87 1 1 1.409738
88 0 1 1.422388
89 1 1 1.422509
90 1 1 1.439432
91 1 1 1.430175
92 1 1 1.418002
93 1 1 1.423812
94 1 1 1.423473
95 1 1 1.434412
96 1 1 1.450844
97 1 1 1.433371
98 1 1 1.444378
99 1 1 1.422523
100 1 1 1.410394
$m1b$spM_lvlone
center scale
B1 NA NA
(Intercept) NA NA
C1 1.434101 0.01299651
$m1b$mu_reg_binom
[1] 0
$m1b$tau_reg_binom
[1] 1e-04
$m1c
$m1c$M_lvlone
L1 (Intercept) C1
1 0.9364352 1 1.410531
2 0.8943541 1 1.434183
3 0.2868460 1 1.430994
4 0.9068418 1 1.453096
5 0.7621346 1 1.438344
6 0.5858621 1 1.453207
7 0.7194403 1 1.425176
8 0.7593154 1 1.437908
9 0.5863705 1 1.416911
10 0.7342586 1 1.448638
11 0.7218028 1 1.428375
12 0.7241254 1 1.450130
13 0.7200126 1 1.420545
14 0.5289014 1 1.423005
15 0.7322482 1 1.435902
16 0.7462471 1 1.423901
17 0.9119922 1 1.457208
18 0.6262513 1 1.414280
19 0.4587835 1 1.443383
20 0.7173364 1 1.434954
21 0.7288999 1 1.429499
22 0.7160420 1 1.441897
23 0.5795514 1 1.423713
24 0.7210413 1 1.435395
25 0.7816086 1 1.425944
26 0.6747483 1 1.437115
27 0.4746725 1 1.441326
28 0.9270652 1 1.422953
29 0.5306249 1 1.437797
30 0.8913764 1 1.472121
31 0.8090308 1 1.421782
32 0.4610800 1 1.457672
33 0.7183814 1 1.430842
34 0.6375974 1 1.431523
35 0.9202563 1 1.421395
36 0.7263222 1 1.434496
37 1.0638781 1 1.425383
38 0.6053893 1 1.421802
39 0.7945509 1 1.430094
40 0.6355032 1 1.447621
41 0.9939049 1 1.434797
42 1.0690739 1 1.446091
43 0.7009106 1 1.445306
44 0.7595403 1 1.448783
45 0.8356414 1 1.450617
46 0.4929132 1 1.415055
47 0.5298192 1 1.436590
48 0.5363034 1 1.433938
49 0.8494053 1 1.414941
50 0.6292812 1 1.421807
51 0.9561312 1 1.453203
52 0.9735411 1 1.452129
53 0.7156259 1 1.431510
54 0.5184434 1 1.430082
55 0.7948965 1 1.443492
56 0.5191792 1 1.436460
57 0.9233108 1 1.418119
58 0.8025356 1 1.434971
59 0.8546624 1 1.445599
60 0.8639819 1 1.437097
61 0.7521237 1 1.428360
62 0.5590215 1 1.440550
63 0.5972103 1 1.443014
64 0.6071272 1 1.424298
65 0.8837829 1 1.448823
66 0.7775301 1 1.425834
67 0.6756191 1 1.427102
68 0.7857549 1 1.414240
69 0.9119262 1 1.456218
70 0.5816103 1 1.470594
71 0.4886093 1 1.425058
72 0.8292467 1 1.432371
73 0.6767456 1 1.441656
74 0.7328840 1 1.434952
75 0.7946099 1 1.402860
76 0.7734810 1 1.453363
77 0.5296147 1 1.432909
78 0.7723288 1 1.435103
79 0.8079308 1 1.434462
80 0.5214822 1 1.434661
81 0.6264777 1 1.445881
82 0.8332107 1 1.442548
83 0.4544158 1 1.430097
84 0.6482660 1 1.430119
85 0.7272109 1 1.430315
86 0.7302426 1 1.437584
87 0.6768061 1 1.409738
88 0.8115758 1 1.422388
89 0.9775567 1 1.422509
90 0.6408465 1 1.439432
91 0.5917453 1 1.430175
92 0.7224845 1 1.418002
93 0.4501596 1 1.423812
94 0.5190455 1 1.423473
95 0.7305821 1 1.434412
96 0.9696445 1 1.450844
97 0.7087457 1 1.433371
98 0.9964080 1 1.444378
99 0.9084899 1 1.422523
100 0.9296776 1 1.410394
$m1c$spM_lvlone
center scale
L1 0.7248851 0.15692291
(Intercept) NA NA
C1 1.4341005 0.01299651
$m1c$mu_reg_gamma
[1] 0
$m1c$tau_reg_gamma
[1] 1e-04
$m1c$shape_tau_gamma
[1] 0.01
$m1c$rate_tau_gamma
[1] 0.01
$m1d
$m1d$M_lvlone
P1 (Intercept) C1
1 1 1 1.410531
2 3 1 1.434183
3 3 1 1.430994
4 3 1 1.453096
5 5 1 1.438344
6 3 1 1.453207
7 0 1 1.425176
8 2 1 1.437908
9 4 1 1.416911
10 3 1 1.448638
11 4 1 1.428375
12 3 1 1.450130
13 2 1 1.420545
14 6 1 1.423005
15 2 1 1.435902
16 5 1 1.423901
17 2 1 1.457208
18 2 1 1.414280
19 1 1 1.443383
20 2 1 1.434954
21 2 1 1.429499
22 2 1 1.441897
23 1 1 1.423713
24 0 1 1.435395
25 2 1 1.425944
26 4 1 1.437115
27 3 1 1.441326
28 5 1 1.422953
29 5 1 1.437797
30 0 1 1.472121
31 3 1 1.421782
32 2 1 1.457672
33 2 1 1.430842
34 3 1 1.431523
35 1 1 1.421395
36 4 1 1.434496
37 2 1 1.425383
38 2 1 1.421802
39 8 1 1.430094
40 4 1 1.447621
41 3 1 1.434797
42 3 1 1.446091
43 2 1 1.445306
44 3 1 1.448783
45 2 1 1.450617
46 3 1 1.415055
47 4 1 1.436590
48 3 1 1.433938
49 2 1 1.414941
50 4 1 1.421807
51 1 1 1.453203
52 2 1 1.452129
53 4 1 1.431510
54 3 1 1.430082
55 1 1 1.443492
56 3 1 1.436460
57 3 1 1.418119
58 4 1 1.434971
59 1 1 1.445599
60 5 1 1.437097
61 5 1 1.428360
62 0 1 1.440550
63 2 1 1.443014
64 0 1 1.424298
65 2 1 1.448823
66 4 1 1.425834
67 2 1 1.427102
68 3 1 1.414240
69 1 1 1.456218
70 3 1 1.470594
71 1 1 1.425058
72 5 1 1.432371
73 0 1 1.441656
74 4 1 1.434952
75 1 1 1.402860
76 3 1 1.453363
77 2 1 1.432909
78 1 1 1.435103
79 2 1 1.434462
80 4 1 1.434661
81 6 1 1.445881
82 3 1 1.442548
83 1 1 1.430097
84 3 1 1.430119
85 1 1 1.430315
86 5 1 1.437584
87 2 1 1.409738
88 2 1 1.422388
89 1 1 1.422509
90 5 1 1.439432
91 1 1 1.430175
92 5 1 1.418002
93 1 1 1.423812
94 1 1 1.423473
95 1 1 1.434412
96 3 1 1.450844
97 2 1 1.433371
98 0 1 1.444378
99 2 1 1.422523
100 4 1 1.410394
$m1d$spM_lvlone
center scale
P1 2.610000 1.56279341
(Intercept) NA NA
C1 1.434101 0.01299651
$m1d$mu_reg_poisson
[1] 0
$m1d$tau_reg_poisson
[1] 1e-04
$m1e
$m1e$M_lvlone
L1 (Intercept) C1
1 0.9364352 1 1.410531
2 0.8943541 1 1.434183
3 0.2868460 1 1.430994
4 0.9068418 1 1.453096
5 0.7621346 1 1.438344
6 0.5858621 1 1.453207
7 0.7194403 1 1.425176
8 0.7593154 1 1.437908
9 0.5863705 1 1.416911
10 0.7342586 1 1.448638
11 0.7218028 1 1.428375
12 0.7241254 1 1.450130
13 0.7200126 1 1.420545
14 0.5289014 1 1.423005
15 0.7322482 1 1.435902
16 0.7462471 1 1.423901
17 0.9119922 1 1.457208
18 0.6262513 1 1.414280
19 0.4587835 1 1.443383
20 0.7173364 1 1.434954
21 0.7288999 1 1.429499
22 0.7160420 1 1.441897
23 0.5795514 1 1.423713
24 0.7210413 1 1.435395
25 0.7816086 1 1.425944
26 0.6747483 1 1.437115
27 0.4746725 1 1.441326
28 0.9270652 1 1.422953
29 0.5306249 1 1.437797
30 0.8913764 1 1.472121
31 0.8090308 1 1.421782
32 0.4610800 1 1.457672
33 0.7183814 1 1.430842
34 0.6375974 1 1.431523
35 0.9202563 1 1.421395
36 0.7263222 1 1.434496
37 1.0638781 1 1.425383
38 0.6053893 1 1.421802
39 0.7945509 1 1.430094
40 0.6355032 1 1.447621
41 0.9939049 1 1.434797
42 1.0690739 1 1.446091
43 0.7009106 1 1.445306
44 0.7595403 1 1.448783
45 0.8356414 1 1.450617
46 0.4929132 1 1.415055
47 0.5298192 1 1.436590
48 0.5363034 1 1.433938
49 0.8494053 1 1.414941
50 0.6292812 1 1.421807
51 0.9561312 1 1.453203
52 0.9735411 1 1.452129
53 0.7156259 1 1.431510
54 0.5184434 1 1.430082
55 0.7948965 1 1.443492
56 0.5191792 1 1.436460
57 0.9233108 1 1.418119
58 0.8025356 1 1.434971
59 0.8546624 1 1.445599
60 0.8639819 1 1.437097
61 0.7521237 1 1.428360
62 0.5590215 1 1.440550
63 0.5972103 1 1.443014
64 0.6071272 1 1.424298
65 0.8837829 1 1.448823
66 0.7775301 1 1.425834
67 0.6756191 1 1.427102
68 0.7857549 1 1.414240
69 0.9119262 1 1.456218
70 0.5816103 1 1.470594
71 0.4886093 1 1.425058
72 0.8292467 1 1.432371
73 0.6767456 1 1.441656
74 0.7328840 1 1.434952
75 0.7946099 1 1.402860
76 0.7734810 1 1.453363
77 0.5296147 1 1.432909
78 0.7723288 1 1.435103
79 0.8079308 1 1.434462
80 0.5214822 1 1.434661
81 0.6264777 1 1.445881
82 0.8332107 1 1.442548
83 0.4544158 1 1.430097
84 0.6482660 1 1.430119
85 0.7272109 1 1.430315
86 0.7302426 1 1.437584
87 0.6768061 1 1.409738
88 0.8115758 1 1.422388
89 0.9775567 1 1.422509
90 0.6408465 1 1.439432
91 0.5917453 1 1.430175
92 0.7224845 1 1.418002
93 0.4501596 1 1.423812
94 0.5190455 1 1.423473
95 0.7305821 1 1.434412
96 0.9696445 1 1.450844
97 0.7087457 1 1.433371
98 0.9964080 1 1.444378
99 0.9084899 1 1.422523
100 0.9296776 1 1.410394
$m1e$spM_lvlone
center scale
L1 0.7248851 0.15692291
(Intercept) NA NA
C1 1.4341005 0.01299651
$m1e$mu_reg_norm
[1] 0
$m1e$tau_reg_norm
[1] 1e-04
$m1e$shape_tau_norm
[1] 0.01
$m1e$rate_tau_norm
[1] 0.01
$m1f
$m1f$M_lvlone
Be1 (Intercept) C1
1 0.69649948 1 1.410531
2 0.56085128 1 1.434183
3 0.35796663 1 1.430994
4 0.53961336 1 1.453096
5 0.06191042 1 1.438344
6 0.51256785 1 1.453207
7 0.13154723 1 1.425176
8 0.35032766 1 1.437908
9 0.21796890 1 1.416911
10 0.10476230 1 1.448638
11 0.66083800 1 1.428375
12 0.66884267 1 1.450130
13 0.69840279 1 1.420545
14 0.50398472 1 1.423005
15 0.52807655 1 1.435902
16 0.40135087 1 1.423901
17 0.45554802 1 1.457208
18 0.68717635 1 1.414280
19 0.35880655 1 1.443383
20 0.36341035 1 1.434954
21 0.71468563 1 1.429499
22 0.44558172 1 1.441897
23 0.33262526 1 1.423713
24 0.66812751 1 1.435395
25 0.23180310 1 1.425944
26 0.37786624 1 1.437115
27 0.88834598 1 1.441326
28 0.46487057 1 1.422953
29 0.47018802 1 1.437797
30 0.91617346 1 1.472121
31 0.67589111 1 1.421782
32 0.61623852 1 1.457672
33 0.44182889 1 1.430842
34 0.29868153 1 1.431523
35 0.44235110 1 1.421395
36 0.72557250 1 1.434496
37 0.74809277 1 1.425383
38 0.26452559 1 1.421802
39 0.41597215 1 1.430094
40 0.29080530 1 1.447621
41 0.80342568 1 1.434797
42 0.76614332 1 1.446091
43 0.29734466 1 1.445306
44 0.42809509 1 1.448783
45 0.12861202 1 1.450617
46 0.44369392 1 1.415055
47 0.35290028 1 1.436590
48 0.88288407 1 1.433938
49 0.37880332 1 1.414941
50 0.60663793 1 1.421807
51 0.15505292 1 1.453203
52 0.65796074 1 1.452129
53 0.63416487 1 1.431510
54 0.83040459 1 1.430082
55 0.64947589 1 1.443492
56 0.67541381 1 1.436460
57 0.53637356 1 1.418119
58 0.39157422 1 1.434971
59 0.88168026 1 1.445599
60 0.32582606 1 1.437097
61 0.64492753 1 1.428360
62 0.34804110 1 1.440550
63 0.49241010 1 1.443014
64 0.43387493 1 1.424298
65 0.21806182 1 1.448823
66 0.60021691 1 1.425834
67 0.30567313 1 1.427102
68 0.22476988 1 1.414240
69 0.23155216 1 1.456218
70 0.29610794 1 1.470594
71 0.83435168 1 1.425058
72 0.65543408 1 1.432371
73 0.59684715 1 1.441656
74 0.80640183 1 1.434952
75 0.52288624 1 1.402860
76 0.41546840 1 1.453363
77 0.44756212 1 1.432909
78 0.68093413 1 1.435103
79 0.29261828 1 1.434462
80 0.21008516 1 1.434661
81 0.44710869 1 1.445881
82 0.70470991 1 1.442548
83 0.31300581 1 1.430097
84 0.44774544 1 1.430119
85 0.68031201 1 1.430315
86 0.44456865 1 1.437584
87 0.79031803 1 1.409738
88 0.22231438 1 1.422388
89 0.30114327 1 1.422509
90 0.45339193 1 1.439432
91 0.35526875 1 1.430175
92 0.68684691 1 1.418002
93 0.81430167 1 1.423812
94 0.60104343 1 1.423473
95 0.82012448 1 1.434412
96 0.55669948 1 1.450844
97 0.76622465 1 1.433371
98 0.50112270 1 1.444378
99 0.53468983 1 1.422523
100 0.58249327 1 1.410394
$m1f$spM_lvlone
center scale
Be1 0.503988 0.20498987
(Intercept) NA NA
C1 1.434101 0.01299651
$m1f$mu_reg_beta
[1] 0
$m1f$tau_reg_beta
[1] 1e-04
$m1f$shape_tau_beta
[1] 0.01
$m1f$rate_tau_beta
[1] 0.01
$m2a
$m2a$M_lvlone
y C2 (Intercept)
1 -4.76915977 0.144065882 1
2 -2.69277172 0.032778478 1
3 -1.17551547 0.343008492 1
4 -4.57464473 -0.361887858 1
5 -2.20260004 -0.389600647 1
6 -3.48995315 -0.205306841 1
7 -0.44987258 0.079434830 1
8 -2.29588848 -0.331246757 1
9 -4.49135812 -0.329638800 1
10 -5.52545368 0.167597533 1
11 -4.16286741 0.860207989 1
12 -2.93455761 0.022730640 1
13 -0.04202496 0.217171172 1
14 -1.63149775 -0.403002412 1
15 -0.97786151 0.087369742 1
16 -1.79100431 -0.183870429 1
17 -6.26520032 -0.194577002 1
18 -1.36028709 -0.349718516 1
19 -1.15396597 -0.508781244 1
20 -3.21707239 0.494883111 1
21 -1.59389898 0.258041067 1
22 -5.50335066 -0.922621989 1
23 0.57290123 0.431254949 1
24 -8.22270323 -0.294218881 1
25 -1.41364158 -0.425548895 1
26 -6.28031574 0.057176054 1
27 -3.15624425 0.289090158 1
28 -3.55693639 -0.473079489 1
29 -1.11821124 -0.385664863 1
30 -2.82834175 -0.154780107 1
31 -3.72259860 0.100536296 1
32 -1.75256656 0.634791958 1
33 -5.55044409 -0.387252617 1
34 -7.45068147 -0.181741088 1
35 -0.97491919 -0.311562695 1
36 -2.98356481 -0.044115907 1
37 -1.86039471 -0.657409991 1
38 -7.28754607 0.159577214 1
39 -8.66234796 -0.460416933 1
40 -4.16291375 NA 1
41 -3.48250771 -0.248909867 1
42 -7.27930410 -0.609021545 1
43 -6.12866190 0.025471883 1
44 -4.96880803 0.066648592 1
45 -4.76746713 -0.276108719 1
46 -1.91249177 -0.179737577 1
47 -0.61884029 0.181190937 1
48 -0.20496175 -0.453871693 1
49 -7.12636055 0.448629602 1
50 -6.23103837 -0.529811821 1
51 -3.32561065 -0.028304571 1
52 -2.95942339 -0.520318482 1
53 -4.44915114 0.171317619 1
54 -0.81566463 0.432732046 1
55 -6.50029573 -0.346286005 1
56 -2.74718050 -0.469375653 1
57 -6.35015663 0.031021711 1
58 -2.69505883 -0.118837515 1
59 -1.55660833 0.507769984 1
60 -3.76240209 0.271797031 1
61 -3.92885797 -0.124442204 1
62 -1.72044748 0.277677389 1
63 -0.56602625 -0.102893730 1
64 -4.42235015 NA 1
65 -2.39122287 -0.678303052 1
66 -0.81807247 0.478880037 1
67 -6.48196782 -0.428028760 1
68 -1.37306273 0.048119185 1
69 -4.99886487 0.216932805 1
70 -5.82288217 -0.234575269 1
71 -2.68234219 0.006827078 1
72 -3.96170442 -0.456055171 1
73 -7.19573667 0.346486708 1
74 -5.08799713 0.205092215 1
75 -1.32967262 -0.136596858 1
76 -2.56532332 -0.500179043 1
77 -3.21002900 0.527352086 1
78 -3.40559790 0.022742250 1
79 -4.56223913 NA 1
80 -2.04250454 -0.002032440 1
81 -2.20378059 -0.154246160 1
82 -3.37471317 0.140201825 1
83 -0.95345385 -0.141417121 1
84 -4.89337660 NA 1
85 -9.82258463 -0.021285339 1
86 -4.51800734 -0.010196306 1
87 -0.18662049 -0.089747520 1
88 -2.87120881 -0.083699898 1
89 1.29290150 -0.044061996 1
90 -1.39497744 -0.209291697 1
91 1.14575040 0.639036426 1
92 0.92801246 0.094698299 1
93 -2.59938157 -0.055510622 1
94 -3.26905923 -0.421318463 1
95 -3.26861434 0.125295503 1
96 -5.71017484 0.213084904 1
97 -3.76781806 -0.161914659 1
98 -2.02677390 -0.034767685 1
99 -2.96199765 -0.320681689 1
100 -4.81129496 0.058192962 1
$m2a$spM_lvlone
center scale
y -3.34428345 2.2764951
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
$m2b
$m2b$M_lvlone
B2 C2 (Intercept)
1 1 0.144065882 1
2 1 0.032778478 1
3 1 0.343008492 1
4 1 -0.361887858 1
5 1 -0.389600647 1
6 NA -0.205306841 1
7 1 0.079434830 1
8 1 -0.331246757 1
9 1 -0.329638800 1
10 NA 0.167597533 1
11 1 0.860207989 1
12 1 0.022730640 1
13 1 0.217171172 1
14 1 -0.403002412 1
15 1 0.087369742 1
16 1 -0.183870429 1
17 1 -0.194577002 1
18 1 -0.349718516 1
19 NA -0.508781244 1
20 NA 0.494883111 1
21 1 0.258041067 1
22 NA -0.922621989 1
23 NA 0.431254949 1
24 1 -0.294218881 1
25 NA -0.425548895 1
26 NA 0.057176054 1
27 1 0.289090158 1
28 1 -0.473079489 1
29 1 -0.385664863 1
30 1 -0.154780107 1
31 NA 0.100536296 1
32 1 0.634791958 1
33 1 -0.387252617 1
34 0 -0.181741088 1
35 1 -0.311562695 1
36 1 -0.044115907 1
37 1 -0.657409991 1
38 NA 0.159577214 1
39 1 -0.460416933 1
40 NA NA 1
41 1 -0.248909867 1
42 1 -0.609021545 1
43 1 0.025471883 1
44 1 0.066648592 1
45 1 -0.276108719 1
46 1 -0.179737577 1
47 0 0.181190937 1
48 1 -0.453871693 1
49 0 0.448629602 1
50 1 -0.529811821 1
51 1 -0.028304571 1
52 1 -0.520318482 1
53 1 0.171317619 1
54 1 0.432732046 1
55 1 -0.346286005 1
56 1 -0.469375653 1
57 1 0.031021711 1
58 NA -0.118837515 1
59 1 0.507769984 1
60 NA 0.271797031 1
61 1 -0.124442204 1
62 1 0.277677389 1
63 1 -0.102893730 1
64 1 NA 1
65 1 -0.678303052 1
66 0 0.478880037 1
67 1 -0.428028760 1
68 1 0.048119185 1
69 NA 0.216932805 1
70 1 -0.234575269 1
71 1 0.006827078 1
72 1 -0.456055171 1
73 1 0.346486708 1
74 1 0.205092215 1
75 1 -0.136596858 1
76 1 -0.500179043 1
77 1 0.527352086 1
78 1 0.022742250 1
79 1 NA 1
80 1 -0.002032440 1
81 0 -0.154246160 1
82 NA 0.140201825 1
83 1 -0.141417121 1
84 1 NA 1
85 1 -0.021285339 1
86 NA -0.010196306 1
87 NA -0.089747520 1
88 1 -0.083699898 1
89 1 -0.044061996 1
90 1 -0.209291697 1
91 1 0.639036426 1
92 NA 0.094698299 1
93 1 -0.055510622 1
94 1 -0.421318463 1
95 1 0.125295503 1
96 1 0.213084904 1
97 NA -0.161914659 1
98 1 -0.034767685 1
99 0 -0.320681689 1
100 NA 0.058192962 1
$m2b$spM_lvlone
center scale
B2 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_binom
[1] 0
$m2b$tau_reg_binom
[1] 1e-04
$m2c
$m2c$M_lvlone
L1mis C2 (Intercept)
1 0.9364352 0.144065882 1
2 0.8943541 0.032778478 1
3 0.2868460 0.343008492 1
4 0.9068418 -0.361887858 1
5 0.7621346 -0.389600647 1
6 NA -0.205306841 1
7 NA 0.079434830 1
8 0.7593154 -0.331246757 1
9 0.5863705 -0.329638800 1
10 0.7342586 0.167597533 1
11 0.7218028 0.860207989 1
12 NA 0.022730640 1
13 0.7200126 0.217171172 1
14 0.5289014 -0.403002412 1
15 0.7322482 0.087369742 1
16 0.7462471 -0.183870429 1
17 0.9119922 -0.194577002 1
18 NA -0.349718516 1
19 NA -0.508781244 1
20 NA 0.494883111 1
21 0.7288999 0.258041067 1
22 0.7160420 -0.922621989 1
23 NA 0.431254949 1
24 0.7210413 -0.294218881 1
25 0.7816086 -0.425548895 1
26 0.6747483 0.057176054 1
27 0.4746725 0.289090158 1
28 0.9270652 -0.473079489 1
29 0.5306249 -0.385664863 1
30 0.8913764 -0.154780107 1
31 NA 0.100536296 1
32 0.4610800 0.634791958 1
33 0.7183814 -0.387252617 1
34 0.6375974 -0.181741088 1
35 0.9202563 -0.311562695 1
36 0.7263222 -0.044115907 1
37 NA -0.657409991 1
38 NA 0.159577214 1
39 0.7945509 -0.460416933 1
40 0.6355032 NA 1
41 0.9939049 -0.248909867 1
42 1.0690739 -0.609021545 1
43 0.7009106 0.025471883 1
44 0.7595403 0.066648592 1
45 0.8356414 -0.276108719 1
46 0.4929132 -0.179737577 1
47 NA 0.181190937 1
48 0.5363034 -0.453871693 1
49 0.8494053 0.448629602 1
50 0.6292812 -0.529811821 1
51 0.9561312 -0.028304571 1
52 0.9735411 -0.520318482 1
53 0.7156259 0.171317619 1
54 0.5184434 0.432732046 1
55 0.7948965 -0.346286005 1
56 0.5191792 -0.469375653 1
57 0.9233108 0.031021711 1
58 0.8025356 -0.118837515 1
59 0.8546624 0.507769984 1
60 0.8639819 0.271797031 1
61 0.7521237 -0.124442204 1
62 0.5590215 0.277677389 1
63 0.5972103 -0.102893730 1
64 0.6071272 NA 1
65 0.8837829 -0.678303052 1
66 0.7775301 0.478880037 1
67 NA -0.428028760 1
68 0.7857549 0.048119185 1
69 0.9119262 0.216932805 1
70 0.5816103 -0.234575269 1
71 0.4886093 0.006827078 1
72 NA -0.456055171 1
73 NA 0.346486708 1
74 0.7328840 0.205092215 1
75 0.7946099 -0.136596858 1
76 0.7734810 -0.500179043 1
77 0.5296147 0.527352086 1
78 0.7723288 0.022742250 1
79 0.8079308 NA 1
80 NA -0.002032440 1
81 NA -0.154246160 1
82 NA 0.140201825 1
83 0.4544158 -0.141417121 1
84 0.6482660 NA 1
85 0.7272109 -0.021285339 1
86 NA -0.010196306 1
87 0.6768061 -0.089747520 1
88 0.8115758 -0.083699898 1
89 NA -0.044061996 1
90 0.6408465 -0.209291697 1
91 0.5917453 0.639036426 1
92 0.7224845 0.094698299 1
93 0.4501596 -0.055510622 1
94 0.5190455 -0.421318463 1
95 0.7305821 0.125295503 1
96 0.9696445 0.213084904 1
97 0.7087457 -0.161914659 1
98 0.9964080 -0.034767685 1
99 NA -0.320681689 1
100 0.9296776 0.058192962 1
$m2c$spM_lvlone
center scale
L1mis 0.72862466 0.1577261
C2 -0.06490582 0.3331735
(Intercept) NA NA
$m2c$mu_reg_norm
[1] 0
$m2c$tau_reg_norm
[1] 1e-04
$m2c$shape_tau_norm
[1] 0.01
$m2c$rate_tau_norm
[1] 0.01
$m2c$mu_reg_gamma
[1] 0
$m2c$tau_reg_gamma
[1] 1e-04
$m2c$shape_tau_gamma
[1] 0.01
$m2c$rate_tau_gamma
[1] 0.01
$m2d
$m2d$M_lvlone
P2 C2 (Intercept)
1 0 0.144065882 1
2 NA 0.032778478 1
3 1 0.343008492 1
4 1 -0.361887858 1
5 0 -0.389600647 1
6 1 -0.205306841 1
7 1 0.079434830 1
8 0 -0.331246757 1
9 2 -0.329638800 1
10 NA 0.167597533 1
11 3 0.860207989 1
12 0 0.022730640 1
13 5 0.217171172 1
14 0 -0.403002412 1
15 1 0.087369742 1
16 4 -0.183870429 1
17 NA -0.194577002 1
18 1 -0.349718516 1
19 NA -0.508781244 1
20 3 0.494883111 1
21 3 0.258041067 1
22 NA -0.922621989 1
23 6 0.431254949 1
24 4 -0.294218881 1
25 NA -0.425548895 1
26 1 0.057176054 1
27 1 0.289090158 1
28 2 -0.473079489 1
29 2 -0.385664863 1
30 NA -0.154780107 1
31 5 0.100536296 1
32 2 0.634791958 1
33 0 -0.387252617 1
34 2 -0.181741088 1
35 NA -0.311562695 1
36 2 -0.044115907 1
37 4 -0.657409991 1
38 2 0.159577214 1
39 2 -0.460416933 1
40 NA NA 1
41 2 -0.248909867 1
42 6 -0.609021545 1
43 1 0.025471883 1
44 2 0.066648592 1
45 1 -0.276108719 1
46 2 -0.179737577 1
47 NA 0.181190937 1
48 2 -0.453871693 1
49 NA 0.448629602 1
50 2 -0.529811821 1
51 2 -0.028304571 1
52 1 -0.520318482 1
53 0 0.171317619 1
54 3 0.432732046 1
55 1 -0.346286005 1
56 6 -0.469375653 1
57 NA 0.031021711 1
58 7 -0.118837515 1
59 1 0.507769984 1
60 2 0.271797031 1
61 NA -0.124442204 1
62 2 0.277677389 1
63 2 -0.102893730 1
64 1 NA 1
65 0 -0.678303052 1
66 2 0.478880037 1
67 NA -0.428028760 1
68 NA 0.048119185 1
69 3 0.216932805 1
70 1 -0.234575269 1
71 NA 0.006827078 1
72 NA -0.456055171 1
73 3 0.346486708 1
74 2 0.205092215 1
75 1 -0.136596858 1
76 3 -0.500179043 1
77 2 0.527352086 1
78 2 0.022742250 1
79 0 NA 1
80 1 -0.002032440 1
81 2 -0.154246160 1
82 1 0.140201825 1
83 NA -0.141417121 1
84 NA NA 1
85 5 -0.021285339 1
86 0 -0.010196306 1
87 NA -0.089747520 1
88 2 -0.083699898 1
89 1 -0.044061996 1
90 3 -0.209291697 1
91 2 0.639036426 1
92 6 0.094698299 1
93 0 -0.055510622 1
94 4 -0.421318463 1
95 3 0.125295503 1
96 3 0.213084904 1
97 3 -0.161914659 1
98 3 -0.034767685 1
99 5 -0.320681689 1
100 2 0.058192962 1
$m2d$spM_lvlone
center scale
P2 2.15000000 1.6466306
C2 -0.06490582 0.3331735
(Intercept) NA NA
$m2d$mu_reg_norm
[1] 0
$m2d$tau_reg_norm
[1] 1e-04
$m2d$shape_tau_norm
[1] 0.01
$m2d$rate_tau_norm
[1] 0.01
$m2d$mu_reg_poisson
[1] 0
$m2d$tau_reg_poisson
[1] 1e-04
$m2e
$m2e$M_lvlone
L1mis C2 (Intercept)
1 0.9364352 0.144065882 1
2 0.8943541 0.032778478 1
3 0.2868460 0.343008492 1
4 0.9068418 -0.361887858 1
5 0.7621346 -0.389600647 1
6 NA -0.205306841 1
7 NA 0.079434830 1
8 0.7593154 -0.331246757 1
9 0.5863705 -0.329638800 1
10 0.7342586 0.167597533 1
11 0.7218028 0.860207989 1
12 NA 0.022730640 1
13 0.7200126 0.217171172 1
14 0.5289014 -0.403002412 1
15 0.7322482 0.087369742 1
16 0.7462471 -0.183870429 1
17 0.9119922 -0.194577002 1
18 NA -0.349718516 1
19 NA -0.508781244 1
20 NA 0.494883111 1
21 0.7288999 0.258041067 1
22 0.7160420 -0.922621989 1
23 NA 0.431254949 1
24 0.7210413 -0.294218881 1
25 0.7816086 -0.425548895 1
26 0.6747483 0.057176054 1
27 0.4746725 0.289090158 1
28 0.9270652 -0.473079489 1
29 0.5306249 -0.385664863 1
30 0.8913764 -0.154780107 1
31 NA 0.100536296 1
32 0.4610800 0.634791958 1
33 0.7183814 -0.387252617 1
34 0.6375974 -0.181741088 1
35 0.9202563 -0.311562695 1
36 0.7263222 -0.044115907 1
37 NA -0.657409991 1
38 NA 0.159577214 1
39 0.7945509 -0.460416933 1
40 0.6355032 NA 1
41 0.9939049 -0.248909867 1
42 1.0690739 -0.609021545 1
43 0.7009106 0.025471883 1
44 0.7595403 0.066648592 1
45 0.8356414 -0.276108719 1
46 0.4929132 -0.179737577 1
47 NA 0.181190937 1
48 0.5363034 -0.453871693 1
49 0.8494053 0.448629602 1
50 0.6292812 -0.529811821 1
51 0.9561312 -0.028304571 1
52 0.9735411 -0.520318482 1
53 0.7156259 0.171317619 1
54 0.5184434 0.432732046 1
55 0.7948965 -0.346286005 1
56 0.5191792 -0.469375653 1
57 0.9233108 0.031021711 1
58 0.8025356 -0.118837515 1
59 0.8546624 0.507769984 1
60 0.8639819 0.271797031 1
61 0.7521237 -0.124442204 1
62 0.5590215 0.277677389 1
63 0.5972103 -0.102893730 1
64 0.6071272 NA 1
65 0.8837829 -0.678303052 1
66 0.7775301 0.478880037 1
67 NA -0.428028760 1
68 0.7857549 0.048119185 1
69 0.9119262 0.216932805 1
70 0.5816103 -0.234575269 1
71 0.4886093 0.006827078 1
72 NA -0.456055171 1
73 NA 0.346486708 1
74 0.7328840 0.205092215 1
75 0.7946099 -0.136596858 1
76 0.7734810 -0.500179043 1
77 0.5296147 0.527352086 1
78 0.7723288 0.022742250 1
79 0.8079308 NA 1
80 NA -0.002032440 1
81 NA -0.154246160 1
82 NA 0.140201825 1
83 0.4544158 -0.141417121 1
84 0.6482660 NA 1
85 0.7272109 -0.021285339 1
86 NA -0.010196306 1
87 0.6768061 -0.089747520 1
88 0.8115758 -0.083699898 1
89 NA -0.044061996 1
90 0.6408465 -0.209291697 1
91 0.5917453 0.639036426 1
92 0.7224845 0.094698299 1
93 0.4501596 -0.055510622 1
94 0.5190455 -0.421318463 1
95 0.7305821 0.125295503 1
96 0.9696445 0.213084904 1
97 0.7087457 -0.161914659 1
98 0.9964080 -0.034767685 1
99 NA -0.320681689 1
100 0.9296776 0.058192962 1
$m2e$spM_lvlone
center scale
L1mis 0.72862466 0.1577261
C2 -0.06490582 0.3331735
(Intercept) NA NA
$m2e$mu_reg_norm
[1] 0
$m2e$tau_reg_norm
[1] 1e-04
$m2e$shape_tau_norm
[1] 0.01
$m2e$rate_tau_norm
[1] 0.01
$m2f
$m2f$M_lvlone
Be2 C2 (Intercept)
1 0.13821330 0.144065882 1
2 NA 0.032778478 1
3 0.85221266 0.343008492 1
4 0.61517266 -0.361887858 1
5 0.56718424 -0.389600647 1
6 0.16127199 -0.205306841 1
7 NA 0.079434830 1
8 0.51062047 -0.331246757 1
9 0.29560086 -0.329638800 1
10 0.43261394 0.167597533 1
11 0.54537238 0.860207989 1
12 0.36458613 0.022730640 1
13 0.84543642 0.217171172 1
14 0.88041616 -0.403002412 1
15 0.47940969 0.087369742 1
16 0.25520352 -0.183870429 1
17 0.53793620 -0.194577002 1
18 0.41924865 -0.349718516 1
19 0.19038933 -0.508781244 1
20 NA 0.494883111 1
21 0.26763985 0.258041067 1
22 NA -0.922621989 1
23 NA 0.431254949 1
24 0.39688480 -0.294218881 1
25 0.20117762 -0.425548895 1
26 0.56039795 0.057176054 1
27 0.69959156 0.289090158 1
28 0.16198957 -0.473079489 1
29 0.73477348 -0.385664863 1
30 NA -0.154780107 1
31 0.69439759 0.100536296 1
32 NA 0.634791958 1
33 NA -0.387252617 1
34 0.68680241 -0.181741088 1
35 0.20563215 -0.311562695 1
36 0.39312999 -0.044115907 1
37 0.33592359 -0.657409991 1
38 0.80799798 0.159577214 1
39 0.70399665 -0.460416933 1
40 0.14770504 NA 1
41 0.32976608 -0.248909867 1
42 0.57875125 -0.609021545 1
43 0.69765999 0.025471883 1
44 0.92706981 0.066648592 1
45 0.59881110 -0.276108719 1
46 NA -0.179737577 1
47 0.57021551 0.181190937 1
48 0.31297307 -0.453871693 1
49 0.45752036 0.448629602 1
50 0.76707228 -0.529811821 1
51 0.79670238 -0.028304571 1
52 0.31851588 -0.520318482 1
53 0.27413726 0.171317619 1
54 0.87099655 0.432732046 1
55 0.14767954 -0.346286005 1
56 0.72225832 -0.469375653 1
57 0.91165899 0.031021711 1
58 NA -0.118837515 1
59 0.74875442 0.507769984 1
60 0.57086552 0.271797031 1
61 0.17368573 -0.124442204 1
62 NA 0.277677389 1
63 0.60538003 -0.102893730 1
64 NA NA 1
65 0.44987490 -0.678303052 1
66 0.71105443 0.478880037 1
67 0.09500493 -0.428028760 1
68 0.37292542 0.048119185 1
69 0.41025328 0.216932805 1
70 0.87473911 -0.234575269 1
71 0.57325664 0.006827078 1
72 0.76227946 -0.456055171 1
73 0.56061854 0.346486708 1
74 0.61145842 0.205092215 1
75 NA -0.136596858 1
76 0.23795025 -0.500179043 1
77 0.28135640 0.527352086 1
78 NA 0.022742250 1
79 0.43010097 NA 1
80 0.30775746 -0.002032440 1
81 0.43379094 -0.154246160 1
82 0.70103825 0.140201825 1
83 0.19501290 -0.141417121 1
84 0.42336380 NA 1
85 NA -0.021285339 1
86 0.49004839 -0.010196306 1
87 NA -0.089747520 1
88 0.71840773 -0.083699898 1
89 0.81565945 -0.044061996 1
90 0.83308857 -0.209291697 1
91 0.56239647 0.639036426 1
92 NA 0.094698299 1
93 NA -0.055510622 1
94 NA -0.421318463 1
95 0.73286310 0.125295503 1
96 0.39788846 0.213084904 1
97 NA -0.161914659 1
98 0.81066470 -0.034767685 1
99 0.40892733 -0.320681689 1
100 0.76834275 0.058192962 1
$m2f$spM_lvlone
center scale
Be2 0.51799407 0.2297468
C2 -0.06490582 0.3331735
(Intercept) NA NA
$m2f$mu_reg_norm
[1] 0
$m2f$tau_reg_norm
[1] 1e-04
$m2f$shape_tau_norm
[1] 0.01
$m2f$rate_tau_norm
[1] 0.01
$m2f$mu_reg_beta
[1] 0
$m2f$tau_reg_beta
[1] 1e-04
$m2f$shape_tau_beta
[1] 0.01
$m2f$rate_tau_beta
[1] 0.01
$m3a
$m3a$M_lvlone
C1 B2 P2 L1mis Be2 C2 (Intercept) B21
1 1.410531 1 0 0.9364352 0.13821330 0.144065882 1 NA
2 1.434183 1 NA 0.8943541 NA 0.032778478 1 NA
3 1.430994 1 1 0.2868460 0.85221266 0.343008492 1 NA
4 1.453096 1 1 0.9068418 0.61517266 -0.361887858 1 NA
5 1.438344 1 0 0.7621346 0.56718424 -0.389600647 1 NA
6 1.453207 NA 1 NA 0.16127199 -0.205306841 1 NA
7 1.425176 1 1 NA NA 0.079434830 1 NA
8 1.437908 1 0 0.7593154 0.51062047 -0.331246757 1 NA
9 1.416911 1 2 0.5863705 0.29560086 -0.329638800 1 NA
10 1.448638 NA NA 0.7342586 0.43261394 0.167597533 1 NA
11 1.428375 1 3 0.7218028 0.54537238 0.860207989 1 NA
12 1.450130 1 0 NA 0.36458613 0.022730640 1 NA
13 1.420545 1 5 0.7200126 0.84543642 0.217171172 1 NA
14 1.423005 1 0 0.5289014 0.88041616 -0.403002412 1 NA
15 1.435902 1 1 0.7322482 0.47940969 0.087369742 1 NA
16 1.423901 1 4 0.7462471 0.25520352 -0.183870429 1 NA
17 1.457208 1 NA 0.9119922 0.53793620 -0.194577002 1 NA
18 1.414280 1 1 NA 0.41924865 -0.349718516 1 NA
19 1.443383 NA NA NA 0.19038933 -0.508781244 1 NA
20 1.434954 NA 3 NA NA 0.494883111 1 NA
21 1.429499 1 3 0.7288999 0.26763985 0.258041067 1 NA
22 1.441897 NA NA 0.7160420 NA -0.922621989 1 NA
23 1.423713 NA 6 NA NA 0.431254949 1 NA
24 1.435395 1 4 0.7210413 0.39688480 -0.294218881 1 NA
25 1.425944 NA NA 0.7816086 0.20117762 -0.425548895 1 NA
26 1.437115 NA 1 0.6747483 0.56039795 0.057176054 1 NA
27 1.441326 1 1 0.4746725 0.69959156 0.289090158 1 NA
28 1.422953 1 2 0.9270652 0.16198957 -0.473079489 1 NA
29 1.437797 1 2 0.5306249 0.73477348 -0.385664863 1 NA
30 1.472121 1 NA 0.8913764 NA -0.154780107 1 NA
31 1.421782 NA 5 NA 0.69439759 0.100536296 1 NA
32 1.457672 1 2 0.4610800 NA 0.634791958 1 NA
33 1.430842 1 0 0.7183814 NA -0.387252617 1 NA
34 1.431523 0 2 0.6375974 0.68680241 -0.181741088 1 NA
35 1.421395 1 NA 0.9202563 0.20563215 -0.311562695 1 NA
36 1.434496 1 2 0.7263222 0.39312999 -0.044115907 1 NA
37 1.425383 1 4 NA 0.33592359 -0.657409991 1 NA
38 1.421802 NA 2 NA 0.80799798 0.159577214 1 NA
39 1.430094 1 2 0.7945509 0.70399665 -0.460416933 1 NA
40 1.447621 NA NA 0.6355032 0.14770504 NA 1 NA
41 1.434797 1 2 0.9939049 0.32976608 -0.248909867 1 NA
42 1.446091 1 6 1.0690739 0.57875125 -0.609021545 1 NA
43 1.445306 1 1 0.7009106 0.69765999 0.025471883 1 NA
44 1.448783 1 2 0.7595403 0.92706981 0.066648592 1 NA
45 1.450617 1 1 0.8356414 0.59881110 -0.276108719 1 NA
46 1.415055 1 2 0.4929132 NA -0.179737577 1 NA
47 1.436590 0 NA NA 0.57021551 0.181190937 1 NA
48 1.433938 1 2 0.5363034 0.31297307 -0.453871693 1 NA
49 1.414941 0 NA 0.8494053 0.45752036 0.448629602 1 NA
50 1.421807 1 2 0.6292812 0.76707228 -0.529811821 1 NA
51 1.453203 1 2 0.9561312 0.79670238 -0.028304571 1 NA
52 1.452129 1 1 0.9735411 0.31851588 -0.520318482 1 NA
53 1.431510 1 0 0.7156259 0.27413726 0.171317619 1 NA
54 1.430082 1 3 0.5184434 0.87099655 0.432732046 1 NA
55 1.443492 1 1 0.7948965 0.14767954 -0.346286005 1 NA
56 1.436460 1 6 0.5191792 0.72225832 -0.469375653 1 NA
57 1.418119 1 NA 0.9233108 0.91165899 0.031021711 1 NA
58 1.434971 NA 7 0.8025356 NA -0.118837515 1 NA
59 1.445599 1 1 0.8546624 0.74875442 0.507769984 1 NA
60 1.437097 NA 2 0.8639819 0.57086552 0.271797031 1 NA
61 1.428360 1 NA 0.7521237 0.17368573 -0.124442204 1 NA
62 1.440550 1 2 0.5590215 NA 0.277677389 1 NA
63 1.443014 1 2 0.5972103 0.60538003 -0.102893730 1 NA
64 1.424298 1 1 0.6071272 NA NA 1 NA
65 1.448823 1 0 0.8837829 0.44987490 -0.678303052 1 NA
66 1.425834 0 2 0.7775301 0.71105443 0.478880037 1 NA
67 1.427102 1 NA NA 0.09500493 -0.428028760 1 NA
68 1.414240 1 NA 0.7857549 0.37292542 0.048119185 1 NA
69 1.456218 NA 3 0.9119262 0.41025328 0.216932805 1 NA
70 1.470594 1 1 0.5816103 0.87473911 -0.234575269 1 NA
71 1.425058 1 NA 0.4886093 0.57325664 0.006827078 1 NA
72 1.432371 1 NA NA 0.76227946 -0.456055171 1 NA
73 1.441656 1 3 NA 0.56061854 0.346486708 1 NA
74 1.434952 1 2 0.7328840 0.61145842 0.205092215 1 NA
75 1.402860 1 1 0.7946099 NA -0.136596858 1 NA
76 1.453363 1 3 0.7734810 0.23795025 -0.500179043 1 NA
77 1.432909 1 2 0.5296147 0.28135640 0.527352086 1 NA
78 1.435103 1 2 0.7723288 NA 0.022742250 1 NA
79 1.434462 1 0 0.8079308 0.43010097 NA 1 NA
80 1.434661 1 1 NA 0.30775746 -0.002032440 1 NA
81 1.445881 0 2 NA 0.43379094 -0.154246160 1 NA
82 1.442548 NA 1 NA 0.70103825 0.140201825 1 NA
83 1.430097 1 NA 0.4544158 0.19501290 -0.141417121 1 NA
84 1.430119 1 NA 0.6482660 0.42336380 NA 1 NA
85 1.430315 1 5 0.7272109 NA -0.021285339 1 NA
86 1.437584 NA 0 NA 0.49004839 -0.010196306 1 NA
87 1.409738 NA NA 0.6768061 NA -0.089747520 1 NA
88 1.422388 1 2 0.8115758 0.71840773 -0.083699898 1 NA
89 1.422509 1 1 NA 0.81565945 -0.044061996 1 NA
90 1.439432 1 3 0.6408465 0.83308857 -0.209291697 1 NA
91 1.430175 1 2 0.5917453 0.56239647 0.639036426 1 NA
92 1.418002 NA 6 0.7224845 NA 0.094698299 1 NA
93 1.423812 1 0 0.4501596 NA -0.055510622 1 NA
94 1.423473 1 4 0.5190455 NA -0.421318463 1 NA
95 1.434412 1 3 0.7305821 0.73286310 0.125295503 1 NA
96 1.450844 1 3 0.9696445 0.39788846 0.213084904 1 NA
97 1.433371 NA 3 0.7087457 NA -0.161914659 1 NA
98 1.444378 1 3 0.9964080 0.81066470 -0.034767685 1 NA
99 1.422523 0 5 NA 0.40892733 -0.320681689 1 NA
100 1.410394 NA 2 0.9296776 0.76834275 0.058192962 1 NA
$m3a$spM_lvlone
center scale
C1 1.43410054 0.01299651
B2 NA NA
P2 2.15000000 1.64663062
L1mis 0.72862466 0.15772614
Be2 0.51799407 0.22974678
C2 -0.06490582 0.33317347
(Intercept) NA NA
B21 NA NA
$m3a$mu_reg_norm
[1] 0
$m3a$tau_reg_norm
[1] 1e-04
$m3a$shape_tau_norm
[1] 0.01
$m3a$rate_tau_norm
[1] 0.01
$m3a$mu_reg_gamma
[1] 0
$m3a$tau_reg_gamma
[1] 1e-04
$m3a$shape_tau_gamma
[1] 0.01
$m3a$rate_tau_gamma
[1] 0.01
$m3a$mu_reg_beta
[1] 0
$m3a$tau_reg_beta
[1] 1e-04
$m3a$shape_tau_beta
[1] 0.01
$m3a$rate_tau_beta
[1] 0.01
$m3a$mu_reg_binom
[1] 0
$m3a$tau_reg_binom
[1] 1e-04
$m3a$mu_reg_poisson
[1] 0
$m3a$tau_reg_poisson
[1] 1e-04
$m3b
$m3b$M_lvlone
C1 B2 P2 L1mis C2 (Intercept) B21
1 1.410531 1 0 0.9364352 0.144065882 1 NA
2 1.434183 1 NA 0.8943541 0.032778478 1 NA
3 1.430994 1 1 0.2868460 0.343008492 1 NA
4 1.453096 1 1 0.9068418 -0.361887858 1 NA
5 1.438344 1 0 0.7621346 -0.389600647 1 NA
6 1.453207 NA 1 NA -0.205306841 1 NA
7 1.425176 1 1 NA 0.079434830 1 NA
8 1.437908 1 0 0.7593154 -0.331246757 1 NA
9 1.416911 1 2 0.5863705 -0.329638800 1 NA
10 1.448638 NA NA 0.7342586 0.167597533 1 NA
11 1.428375 1 3 0.7218028 0.860207989 1 NA
12 1.450130 1 0 NA 0.022730640 1 NA
13 1.420545 1 5 0.7200126 0.217171172 1 NA
14 1.423005 1 0 0.5289014 -0.403002412 1 NA
15 1.435902 1 1 0.7322482 0.087369742 1 NA
16 1.423901 1 4 0.7462471 -0.183870429 1 NA
17 1.457208 1 NA 0.9119922 -0.194577002 1 NA
18 1.414280 1 1 NA -0.349718516 1 NA
19 1.443383 NA NA NA -0.508781244 1 NA
20 1.434954 NA 3 NA 0.494883111 1 NA
21 1.429499 1 3 0.7288999 0.258041067 1 NA
22 1.441897 NA NA 0.7160420 -0.922621989 1 NA
23 1.423713 NA 6 NA 0.431254949 1 NA
24 1.435395 1 4 0.7210413 -0.294218881 1 NA
25 1.425944 NA NA 0.7816086 -0.425548895 1 NA
26 1.437115 NA 1 0.6747483 0.057176054 1 NA
27 1.441326 1 1 0.4746725 0.289090158 1 NA
28 1.422953 1 2 0.9270652 -0.473079489 1 NA
29 1.437797 1 2 0.5306249 -0.385664863 1 NA
30 1.472121 1 NA 0.8913764 -0.154780107 1 NA
31 1.421782 NA 5 NA 0.100536296 1 NA
32 1.457672 1 2 0.4610800 0.634791958 1 NA
33 1.430842 1 0 0.7183814 -0.387252617 1 NA
34 1.431523 0 2 0.6375974 -0.181741088 1 NA
35 1.421395 1 NA 0.9202563 -0.311562695 1 NA
36 1.434496 1 2 0.7263222 -0.044115907 1 NA
37 1.425383 1 4 NA -0.657409991 1 NA
38 1.421802 NA 2 NA 0.159577214 1 NA
39 1.430094 1 2 0.7945509 -0.460416933 1 NA
40 1.447621 NA NA 0.6355032 NA 1 NA
41 1.434797 1 2 0.9939049 -0.248909867 1 NA
42 1.446091 1 6 1.0690739 -0.609021545 1 NA
43 1.445306 1 1 0.7009106 0.025471883 1 NA
44 1.448783 1 2 0.7595403 0.066648592 1 NA
45 1.450617 1 1 0.8356414 -0.276108719 1 NA
46 1.415055 1 2 0.4929132 -0.179737577 1 NA
47 1.436590 0 NA NA 0.181190937 1 NA
48 1.433938 1 2 0.5363034 -0.453871693 1 NA
49 1.414941 0 NA 0.8494053 0.448629602 1 NA
50 1.421807 1 2 0.6292812 -0.529811821 1 NA
51 1.453203 1 2 0.9561312 -0.028304571 1 NA
52 1.452129 1 1 0.9735411 -0.520318482 1 NA
53 1.431510 1 0 0.7156259 0.171317619 1 NA
54 1.430082 1 3 0.5184434 0.432732046 1 NA
55 1.443492 1 1 0.7948965 -0.346286005 1 NA
56 1.436460 1 6 0.5191792 -0.469375653 1 NA
57 1.418119 1 NA 0.9233108 0.031021711 1 NA
58 1.434971 NA 7 0.8025356 -0.118837515 1 NA
59 1.445599 1 1 0.8546624 0.507769984 1 NA
60 1.437097 NA 2 0.8639819 0.271797031 1 NA
61 1.428360 1 NA 0.7521237 -0.124442204 1 NA
62 1.440550 1 2 0.5590215 0.277677389 1 NA
63 1.443014 1 2 0.5972103 -0.102893730 1 NA
64 1.424298 1 1 0.6071272 NA 1 NA
65 1.448823 1 0 0.8837829 -0.678303052 1 NA
66 1.425834 0 2 0.7775301 0.478880037 1 NA
67 1.427102 1 NA NA -0.428028760 1 NA
68 1.414240 1 NA 0.7857549 0.048119185 1 NA
69 1.456218 NA 3 0.9119262 0.216932805 1 NA
70 1.470594 1 1 0.5816103 -0.234575269 1 NA
71 1.425058 1 NA 0.4886093 0.006827078 1 NA
72 1.432371 1 NA NA -0.456055171 1 NA
73 1.441656 1 3 NA 0.346486708 1 NA
74 1.434952 1 2 0.7328840 0.205092215 1 NA
75 1.402860 1 1 0.7946099 -0.136596858 1 NA
76 1.453363 1 3 0.7734810 -0.500179043 1 NA
77 1.432909 1 2 0.5296147 0.527352086 1 NA
78 1.435103 1 2 0.7723288 0.022742250 1 NA
79 1.434462 1 0 0.8079308 NA 1 NA
80 1.434661 1 1 NA -0.002032440 1 NA
81 1.445881 0 2 NA -0.154246160 1 NA
82 1.442548 NA 1 NA 0.140201825 1 NA
83 1.430097 1 NA 0.4544158 -0.141417121 1 NA
84 1.430119 1 NA 0.6482660 NA 1 NA
85 1.430315 1 5 0.7272109 -0.021285339 1 NA
86 1.437584 NA 0 NA -0.010196306 1 NA
87 1.409738 NA NA 0.6768061 -0.089747520 1 NA
88 1.422388 1 2 0.8115758 -0.083699898 1 NA
89 1.422509 1 1 NA -0.044061996 1 NA
90 1.439432 1 3 0.6408465 -0.209291697 1 NA
91 1.430175 1 2 0.5917453 0.639036426 1 NA
92 1.418002 NA 6 0.7224845 0.094698299 1 NA
93 1.423812 1 0 0.4501596 -0.055510622 1 NA
94 1.423473 1 4 0.5190455 -0.421318463 1 NA
95 1.434412 1 3 0.7305821 0.125295503 1 NA
96 1.450844 1 3 0.9696445 0.213084904 1 NA
97 1.433371 NA 3 0.7087457 -0.161914659 1 NA
98 1.444378 1 3 0.9964080 -0.034767685 1 NA
99 1.422523 0 5 NA -0.320681689 1 NA
100 1.410394 NA 2 0.9296776 0.058192962 1 NA
$m3b$spM_lvlone
center scale
C1 1.43410054 0.01299651
B2 NA NA
P2 2.15000000 1.64663062
L1mis 0.72862466 0.15772614
C2 -0.06490582 0.33317347
(Intercept) NA NA
B21 NA NA
$m3b$mu_reg_norm
[1] 0
$m3b$tau_reg_norm
[1] 1e-04
$m3b$shape_tau_norm
[1] 0.01
$m3b$rate_tau_norm
[1] 0.01
$m3b$mu_reg_binom
[1] 0
$m3b$tau_reg_binom
[1] 1e-04
$m3b$mu_reg_poisson
[1] 0
$m3b$tau_reg_poisson
[1] 1e-04
$m3c
$m3c$M_lvlone
C1 B2 P2 L1mis C2 (Intercept) B21
1 1.410531 1 0 0.9364352 0.144065882 1 NA
2 1.434183 1 NA 0.8943541 0.032778478 1 NA
3 1.430994 1 1 0.2868460 0.343008492 1 NA
4 1.453096 1 1 0.9068418 -0.361887858 1 NA
5 1.438344 1 0 0.7621346 -0.389600647 1 NA
6 1.453207 NA 1 NA -0.205306841 1 NA
7 1.425176 1 1 NA 0.079434830 1 NA
8 1.437908 1 0 0.7593154 -0.331246757 1 NA
9 1.416911 1 2 0.5863705 -0.329638800 1 NA
10 1.448638 NA NA 0.7342586 0.167597533 1 NA
11 1.428375 1 3 0.7218028 0.860207989 1 NA
12 1.450130 1 0 NA 0.022730640 1 NA
13 1.420545 1 5 0.7200126 0.217171172 1 NA
14 1.423005 1 0 0.5289014 -0.403002412 1 NA
15 1.435902 1 1 0.7322482 0.087369742 1 NA
16 1.423901 1 4 0.7462471 -0.183870429 1 NA
17 1.457208 1 NA 0.9119922 -0.194577002 1 NA
18 1.414280 1 1 NA -0.349718516 1 NA
19 1.443383 NA NA NA -0.508781244 1 NA
20 1.434954 NA 3 NA 0.494883111 1 NA
21 1.429499 1 3 0.7288999 0.258041067 1 NA
22 1.441897 NA NA 0.7160420 -0.922621989 1 NA
23 1.423713 NA 6 NA 0.431254949 1 NA
24 1.435395 1 4 0.7210413 -0.294218881 1 NA
25 1.425944 NA NA 0.7816086 -0.425548895 1 NA
26 1.437115 NA 1 0.6747483 0.057176054 1 NA
27 1.441326 1 1 0.4746725 0.289090158 1 NA
28 1.422953 1 2 0.9270652 -0.473079489 1 NA
29 1.437797 1 2 0.5306249 -0.385664863 1 NA
30 1.472121 1 NA 0.8913764 -0.154780107 1 NA
31 1.421782 NA 5 NA 0.100536296 1 NA
32 1.457672 1 2 0.4610800 0.634791958 1 NA
33 1.430842 1 0 0.7183814 -0.387252617 1 NA
34 1.431523 0 2 0.6375974 -0.181741088 1 NA
35 1.421395 1 NA 0.9202563 -0.311562695 1 NA
36 1.434496 1 2 0.7263222 -0.044115907 1 NA
37 1.425383 1 4 NA -0.657409991 1 NA
38 1.421802 NA 2 NA 0.159577214 1 NA
39 1.430094 1 2 0.7945509 -0.460416933 1 NA
40 1.447621 NA NA 0.6355032 NA 1 NA
41 1.434797 1 2 0.9939049 -0.248909867 1 NA
42 1.446091 1 6 1.0690739 -0.609021545 1 NA
43 1.445306 1 1 0.7009106 0.025471883 1 NA
44 1.448783 1 2 0.7595403 0.066648592 1 NA
45 1.450617 1 1 0.8356414 -0.276108719 1 NA
46 1.415055 1 2 0.4929132 -0.179737577 1 NA
47 1.436590 0 NA NA 0.181190937 1 NA
48 1.433938 1 2 0.5363034 -0.453871693 1 NA
49 1.414941 0 NA 0.8494053 0.448629602 1 NA
50 1.421807 1 2 0.6292812 -0.529811821 1 NA
51 1.453203 1 2 0.9561312 -0.028304571 1 NA
52 1.452129 1 1 0.9735411 -0.520318482 1 NA
53 1.431510 1 0 0.7156259 0.171317619 1 NA
54 1.430082 1 3 0.5184434 0.432732046 1 NA
55 1.443492 1 1 0.7948965 -0.346286005 1 NA
56 1.436460 1 6 0.5191792 -0.469375653 1 NA
57 1.418119 1 NA 0.9233108 0.031021711 1 NA
58 1.434971 NA 7 0.8025356 -0.118837515 1 NA
59 1.445599 1 1 0.8546624 0.507769984 1 NA
60 1.437097 NA 2 0.8639819 0.271797031 1 NA
61 1.428360 1 NA 0.7521237 -0.124442204 1 NA
62 1.440550 1 2 0.5590215 0.277677389 1 NA
63 1.443014 1 2 0.5972103 -0.102893730 1 NA
64 1.424298 1 1 0.6071272 NA 1 NA
65 1.448823 1 0 0.8837829 -0.678303052 1 NA
66 1.425834 0 2 0.7775301 0.478880037 1 NA
67 1.427102 1 NA NA -0.428028760 1 NA
68 1.414240 1 NA 0.7857549 0.048119185 1 NA
69 1.456218 NA 3 0.9119262 0.216932805 1 NA
70 1.470594 1 1 0.5816103 -0.234575269 1 NA
71 1.425058 1 NA 0.4886093 0.006827078 1 NA
72 1.432371 1 NA NA -0.456055171 1 NA
73 1.441656 1 3 NA 0.346486708 1 NA
74 1.434952 1 2 0.7328840 0.205092215 1 NA
75 1.402860 1 1 0.7946099 -0.136596858 1 NA
76 1.453363 1 3 0.7734810 -0.500179043 1 NA
77 1.432909 1 2 0.5296147 0.527352086 1 NA
78 1.435103 1 2 0.7723288 0.022742250 1 NA
79 1.434462 1 0 0.8079308 NA 1 NA
80 1.434661 1 1 NA -0.002032440 1 NA
81 1.445881 0 2 NA -0.154246160 1 NA
82 1.442548 NA 1 NA 0.140201825 1 NA
83 1.430097 1 NA 0.4544158 -0.141417121 1 NA
84 1.430119 1 NA 0.6482660 NA 1 NA
85 1.430315 1 5 0.7272109 -0.021285339 1 NA
86 1.437584 NA 0 NA -0.010196306 1 NA
87 1.409738 NA NA 0.6768061 -0.089747520 1 NA
88 1.422388 1 2 0.8115758 -0.083699898 1 NA
89 1.422509 1 1 NA -0.044061996 1 NA
90 1.439432 1 3 0.6408465 -0.209291697 1 NA
91 1.430175 1 2 0.5917453 0.639036426 1 NA
92 1.418002 NA 6 0.7224845 0.094698299 1 NA
93 1.423812 1 0 0.4501596 -0.055510622 1 NA
94 1.423473 1 4 0.5190455 -0.421318463 1 NA
95 1.434412 1 3 0.7305821 0.125295503 1 NA
96 1.450844 1 3 0.9696445 0.213084904 1 NA
97 1.433371 NA 3 0.7087457 -0.161914659 1 NA
98 1.444378 1 3 0.9964080 -0.034767685 1 NA
99 1.422523 0 5 NA -0.320681689 1 NA
100 1.410394 NA 2 0.9296776 0.058192962 1 NA
$m3c$spM_lvlone
center scale
C1 1.43410054 0.01299651
B2 NA NA
P2 2.15000000 1.64663062
L1mis 0.72862466 0.15772614
C2 -0.06490582 0.33317347
(Intercept) NA NA
B21 NA NA
$m3c$mu_reg_norm
[1] 0
$m3c$tau_reg_norm
[1] 1e-04
$m3c$shape_tau_norm
[1] 0.01
$m3c$rate_tau_norm
[1] 0.01
$m3c$mu_reg_gamma
[1] 0
$m3c$tau_reg_gamma
[1] 1e-04
$m3c$shape_tau_gamma
[1] 0.01
$m3c$rate_tau_gamma
[1] 0.01
$m3c$mu_reg_binom
[1] 0
$m3c$tau_reg_binom
[1] 1e-04
$m3c$mu_reg_poisson
[1] 0
$m3c$tau_reg_poisson
[1] 1e-04
$m3d
$m3d$M_lvlone
C1 B2 P2 L1mis Be2 C2 (Intercept) B21
1 1.410531 1 0 0.9364352 0.13821330 0.144065882 1 NA
2 1.434183 1 NA 0.8943541 NA 0.032778478 1 NA
3 1.430994 1 1 0.2868460 0.85221266 0.343008492 1 NA
4 1.453096 1 1 0.9068418 0.61517266 -0.361887858 1 NA
5 1.438344 1 0 0.7621346 0.56718424 -0.389600647 1 NA
6 1.453207 NA 1 NA 0.16127199 -0.205306841 1 NA
7 1.425176 1 1 NA NA 0.079434830 1 NA
8 1.437908 1 0 0.7593154 0.51062047 -0.331246757 1 NA
9 1.416911 1 2 0.5863705 0.29560086 -0.329638800 1 NA
10 1.448638 NA NA 0.7342586 0.43261394 0.167597533 1 NA
11 1.428375 1 3 0.7218028 0.54537238 0.860207989 1 NA
12 1.450130 1 0 NA 0.36458613 0.022730640 1 NA
13 1.420545 1 5 0.7200126 0.84543642 0.217171172 1 NA
14 1.423005 1 0 0.5289014 0.88041616 -0.403002412 1 NA
15 1.435902 1 1 0.7322482 0.47940969 0.087369742 1 NA
16 1.423901 1 4 0.7462471 0.25520352 -0.183870429 1 NA
17 1.457208 1 NA 0.9119922 0.53793620 -0.194577002 1 NA
18 1.414280 1 1 NA 0.41924865 -0.349718516 1 NA
19 1.443383 NA NA NA 0.19038933 -0.508781244 1 NA
20 1.434954 NA 3 NA NA 0.494883111 1 NA
21 1.429499 1 3 0.7288999 0.26763985 0.258041067 1 NA
22 1.441897 NA NA 0.7160420 NA -0.922621989 1 NA
23 1.423713 NA 6 NA NA 0.431254949 1 NA
24 1.435395 1 4 0.7210413 0.39688480 -0.294218881 1 NA
25 1.425944 NA NA 0.7816086 0.20117762 -0.425548895 1 NA
26 1.437115 NA 1 0.6747483 0.56039795 0.057176054 1 NA
27 1.441326 1 1 0.4746725 0.69959156 0.289090158 1 NA
28 1.422953 1 2 0.9270652 0.16198957 -0.473079489 1 NA
29 1.437797 1 2 0.5306249 0.73477348 -0.385664863 1 NA
30 1.472121 1 NA 0.8913764 NA -0.154780107 1 NA
31 1.421782 NA 5 NA 0.69439759 0.100536296 1 NA
32 1.457672 1 2 0.4610800 NA 0.634791958 1 NA
33 1.430842 1 0 0.7183814 NA -0.387252617 1 NA
34 1.431523 0 2 0.6375974 0.68680241 -0.181741088 1 NA
35 1.421395 1 NA 0.9202563 0.20563215 -0.311562695 1 NA
36 1.434496 1 2 0.7263222 0.39312999 -0.044115907 1 NA
37 1.425383 1 4 NA 0.33592359 -0.657409991 1 NA
38 1.421802 NA 2 NA 0.80799798 0.159577214 1 NA
39 1.430094 1 2 0.7945509 0.70399665 -0.460416933 1 NA
40 1.447621 NA NA 0.6355032 0.14770504 NA 1 NA
41 1.434797 1 2 0.9939049 0.32976608 -0.248909867 1 NA
42 1.446091 1 6 1.0690739 0.57875125 -0.609021545 1 NA
43 1.445306 1 1 0.7009106 0.69765999 0.025471883 1 NA
44 1.448783 1 2 0.7595403 0.92706981 0.066648592 1 NA
45 1.450617 1 1 0.8356414 0.59881110 -0.276108719 1 NA
46 1.415055 1 2 0.4929132 NA -0.179737577 1 NA
47 1.436590 0 NA NA 0.57021551 0.181190937 1 NA
48 1.433938 1 2 0.5363034 0.31297307 -0.453871693 1 NA
49 1.414941 0 NA 0.8494053 0.45752036 0.448629602 1 NA
50 1.421807 1 2 0.6292812 0.76707228 -0.529811821 1 NA
51 1.453203 1 2 0.9561312 0.79670238 -0.028304571 1 NA
52 1.452129 1 1 0.9735411 0.31851588 -0.520318482 1 NA
53 1.431510 1 0 0.7156259 0.27413726 0.171317619 1 NA
54 1.430082 1 3 0.5184434 0.87099655 0.432732046 1 NA
55 1.443492 1 1 0.7948965 0.14767954 -0.346286005 1 NA
56 1.436460 1 6 0.5191792 0.72225832 -0.469375653 1 NA
57 1.418119 1 NA 0.9233108 0.91165899 0.031021711 1 NA
58 1.434971 NA 7 0.8025356 NA -0.118837515 1 NA
59 1.445599 1 1 0.8546624 0.74875442 0.507769984 1 NA
60 1.437097 NA 2 0.8639819 0.57086552 0.271797031 1 NA
61 1.428360 1 NA 0.7521237 0.17368573 -0.124442204 1 NA
62 1.440550 1 2 0.5590215 NA 0.277677389 1 NA
63 1.443014 1 2 0.5972103 0.60538003 -0.102893730 1 NA
64 1.424298 1 1 0.6071272 NA NA 1 NA
65 1.448823 1 0 0.8837829 0.44987490 -0.678303052 1 NA
66 1.425834 0 2 0.7775301 0.71105443 0.478880037 1 NA
67 1.427102 1 NA NA 0.09500493 -0.428028760 1 NA
68 1.414240 1 NA 0.7857549 0.37292542 0.048119185 1 NA
69 1.456218 NA 3 0.9119262 0.41025328 0.216932805 1 NA
70 1.470594 1 1 0.5816103 0.87473911 -0.234575269 1 NA
71 1.425058 1 NA 0.4886093 0.57325664 0.006827078 1 NA
72 1.432371 1 NA NA 0.76227946 -0.456055171 1 NA
73 1.441656 1 3 NA 0.56061854 0.346486708 1 NA
74 1.434952 1 2 0.7328840 0.61145842 0.205092215 1 NA
75 1.402860 1 1 0.7946099 NA -0.136596858 1 NA
76 1.453363 1 3 0.7734810 0.23795025 -0.500179043 1 NA
77 1.432909 1 2 0.5296147 0.28135640 0.527352086 1 NA
78 1.435103 1 2 0.7723288 NA 0.022742250 1 NA
79 1.434462 1 0 0.8079308 0.43010097 NA 1 NA
80 1.434661 1 1 NA 0.30775746 -0.002032440 1 NA
81 1.445881 0 2 NA 0.43379094 -0.154246160 1 NA
82 1.442548 NA 1 NA 0.70103825 0.140201825 1 NA
83 1.430097 1 NA 0.4544158 0.19501290 -0.141417121 1 NA
84 1.430119 1 NA 0.6482660 0.42336380 NA 1 NA
85 1.430315 1 5 0.7272109 NA -0.021285339 1 NA
86 1.437584 NA 0 NA 0.49004839 -0.010196306 1 NA
87 1.409738 NA NA 0.6768061 NA -0.089747520 1 NA
88 1.422388 1 2 0.8115758 0.71840773 -0.083699898 1 NA
89 1.422509 1 1 NA 0.81565945 -0.044061996 1 NA
90 1.439432 1 3 0.6408465 0.83308857 -0.209291697 1 NA
91 1.430175 1 2 0.5917453 0.56239647 0.639036426 1 NA
92 1.418002 NA 6 0.7224845 NA 0.094698299 1 NA
93 1.423812 1 0 0.4501596 NA -0.055510622 1 NA
94 1.423473 1 4 0.5190455 NA -0.421318463 1 NA
95 1.434412 1 3 0.7305821 0.73286310 0.125295503 1 NA
96 1.450844 1 3 0.9696445 0.39788846 0.213084904 1 NA
97 1.433371 NA 3 0.7087457 NA -0.161914659 1 NA
98 1.444378 1 3 0.9964080 0.81066470 -0.034767685 1 NA
99 1.422523 0 5 NA 0.40892733 -0.320681689 1 NA
100 1.410394 NA 2 0.9296776 0.76834275 0.058192962 1 NA
$m3d$spM_lvlone
center scale
C1 1.43410054 0.01299651
B2 NA NA
P2 2.15000000 1.64663062
L1mis 0.72862466 0.15772614
Be2 0.51799407 0.22974678
C2 -0.06490582 0.33317347
(Intercept) NA NA
B21 NA NA
$m3d$mu_reg_norm
[1] 0
$m3d$tau_reg_norm
[1] 1e-04
$m3d$shape_tau_norm
[1] 0.01
$m3d$rate_tau_norm
[1] 0.01
$m3d$mu_reg_gamma
[1] 0
$m3d$tau_reg_gamma
[1] 1e-04
$m3d$shape_tau_gamma
[1] 0.01
$m3d$rate_tau_gamma
[1] 0.01
$m3d$mu_reg_binom
[1] 0
$m3d$tau_reg_binom
[1] 1e-04
$m3d$mu_reg_poisson
[1] 0
$m3d$tau_reg_poisson
[1] 1e-04
$m4a
$m4a$M_lvlone
y C2 M2 O2 (Intercept) M22 M23 M24 O22 O23 O24
1 -4.76915977 0.144065882 4 4 1 NA NA NA NA NA NA
2 -2.69277172 0.032778478 1 4 1 NA NA NA NA NA NA
3 -1.17551547 0.343008492 3 4 1 NA NA NA NA NA NA
4 -4.57464473 -0.361887858 3 1 1 NA NA NA NA NA NA
5 -2.20260004 -0.389600647 4 2 1 NA NA NA NA NA NA
6 -3.48995315 -0.205306841 4 3 1 NA NA NA NA NA NA
7 -0.44987258 0.079434830 1 4 1 NA NA NA NA NA NA
8 -2.29588848 -0.331246757 1 2 1 NA NA NA NA NA NA
9 -4.49135812 -0.329638800 2 4 1 NA NA NA NA NA NA
10 -5.52545368 0.167597533 2 3 1 NA NA NA NA NA NA
11 -4.16286741 0.860207989 3 2 1 NA NA NA NA NA NA
12 -2.93455761 0.022730640 3 1 1 NA NA NA NA NA NA
13 -0.04202496 0.217171172 2 1 1 NA NA NA NA NA NA
14 -1.63149775 -0.403002412 3 1 1 NA NA NA NA NA NA
15 -0.97786151 0.087369742 2 4 1 NA NA NA NA NA NA
16 -1.79100431 -0.183870429 1 3 1 NA NA NA NA NA NA
17 -6.26520032 -0.194577002 4 3 1 NA NA NA NA NA NA
18 -1.36028709 -0.349718516 2 1 1 NA NA NA NA NA NA
19 -1.15396597 -0.508781244 3 3 1 NA NA NA NA NA NA
20 -3.21707239 0.494883111 3 1 1 NA NA NA NA NA NA
21 -1.59389898 0.258041067 2 3 1 NA NA NA NA NA NA
22 -5.50335066 -0.922621989 2 3 1 NA NA NA NA NA NA
23 0.57290123 0.431254949 3 2 1 NA NA NA NA NA NA
24 -8.22270323 -0.294218881 3 3 1 NA NA NA NA NA NA
25 -1.41364158 -0.425548895 2 2 1 NA NA NA NA NA NA
26 -6.28031574 0.057176054 2 2 1 NA NA NA NA NA NA
27 -3.15624425 0.289090158 1 1 1 NA NA NA NA NA NA
28 -3.55693639 -0.473079489 3 4 1 NA NA NA NA NA NA
29 -1.11821124 -0.385664863 4 3 1 NA NA NA NA NA NA
30 -2.82834175 -0.154780107 2 3 1 NA NA NA NA NA NA
31 -3.72259860 0.100536296 NA 2 1 NA NA NA NA NA NA
32 -1.75256656 0.634791958 4 2 1 NA NA NA NA NA NA
33 -5.55044409 -0.387252617 4 1 1 NA NA NA NA NA NA
34 -7.45068147 -0.181741088 4 1 1 NA NA NA NA NA NA
35 -0.97491919 -0.311562695 2 4 1 NA NA NA NA NA NA
36 -2.98356481 -0.044115907 1 3 1 NA NA NA NA NA NA
37 -1.86039471 -0.657409991 3 3 1 NA NA NA NA NA NA
38 -7.28754607 0.159577214 4 1 1 NA NA NA NA NA NA
39 -8.66234796 -0.460416933 3 2 1 NA NA NA NA NA NA
40 -4.16291375 NA 3 3 1 NA NA NA NA NA NA
41 -3.48250771 -0.248909867 1 3 1 NA NA NA NA NA NA
42 -7.27930410 -0.609021545 4 3 1 NA NA NA NA NA NA
43 -6.12866190 0.025471883 1 3 1 NA NA NA NA NA NA
44 -4.96880803 0.066648592 2 4 1 NA NA NA NA NA NA
45 -4.76746713 -0.276108719 2 4 1 NA NA NA NA NA NA
46 -1.91249177 -0.179737577 1 1 1 NA NA NA NA NA NA
47 -0.61884029 0.181190937 4 4 1 NA NA NA NA NA NA
48 -0.20496175 -0.453871693 2 4 1 NA NA NA NA NA NA
49 -7.12636055 0.448629602 4 1 1 NA NA NA NA NA NA
50 -6.23103837 -0.529811821 1 2 1 NA NA NA NA NA NA
51 -3.32561065 -0.028304571 4 1 1 NA NA NA NA NA NA
52 -2.95942339 -0.520318482 4 3 1 NA NA NA NA NA NA
53 -4.44915114 0.171317619 4 2 1 NA NA NA NA NA NA
54 -0.81566463 0.432732046 3 1 1 NA NA NA NA NA NA
55 -6.50029573 -0.346286005 3 2 1 NA NA NA NA NA NA
56 -2.74718050 -0.469375653 3 3 1 NA NA NA NA NA NA
57 -6.35015663 0.031021711 2 NA 1 NA NA NA NA NA NA
58 -2.69505883 -0.118837515 3 4 1 NA NA NA NA NA NA
59 -1.55660833 0.507769984 3 4 1 NA NA NA NA NA NA
60 -3.76240209 0.271797031 4 3 1 NA NA NA NA NA NA
61 -3.92885797 -0.124442204 2 4 1 NA NA NA NA NA NA
62 -1.72044748 0.277677389 2 1 1 NA NA NA NA NA NA
63 -0.56602625 -0.102893730 1 4 1 NA NA NA NA NA NA
64 -4.42235015 NA 2 4 1 NA NA NA NA NA NA
65 -2.39122287 -0.678303052 2 4 1 NA NA NA NA NA NA
66 -0.81807247 0.478880037 3 1 1 NA NA NA NA NA NA
67 -6.48196782 -0.428028760 2 3 1 NA NA NA NA NA NA
68 -1.37306273 0.048119185 4 3 1 NA NA NA NA NA NA
69 -4.99886487 0.216932805 NA 4 1 NA NA NA NA NA NA
70 -5.82288217 -0.234575269 1 1 1 NA NA NA NA NA NA
71 -2.68234219 0.006827078 2 4 1 NA NA NA NA NA NA
72 -3.96170442 -0.456055171 3 4 1 NA NA NA NA NA NA
73 -7.19573667 0.346486708 4 2 1 NA NA NA NA NA NA
74 -5.08799713 0.205092215 4 4 1 NA NA NA NA NA NA
75 -1.32967262 -0.136596858 1 3 1 NA NA NA NA NA NA
76 -2.56532332 -0.500179043 4 2 1 NA NA NA NA NA NA
77 -3.21002900 0.527352086 NA 2 1 NA NA NA NA NA NA
78 -3.40559790 0.022742250 2 3 1 NA NA NA NA NA NA
79 -4.56223913 NA 2 2 1 NA NA NA NA NA NA
80 -2.04250454 -0.002032440 2 1 1 NA NA NA NA NA NA
81 -2.20378059 -0.154246160 4 4 1 NA NA NA NA NA NA
82 -3.37471317 0.140201825 3 2 1 NA NA NA NA NA NA
83 -0.95345385 -0.141417121 3 4 1 NA NA NA NA NA NA
84 -4.89337660 NA 1 1 1 NA NA NA NA NA NA
85 -9.82258463 -0.021285339 2 1 1 NA NA NA NA NA NA
86 -4.51800734 -0.010196306 1 2 1 NA NA NA NA NA NA
87 -0.18662049 -0.089747520 3 3 1 NA NA NA NA NA NA
88 -2.87120881 -0.083699898 1 3 1 NA NA NA NA NA NA
89 1.29290150 -0.044061996 2 2 1 NA NA NA NA NA NA
90 -1.39497744 -0.209291697 1 4 1 NA NA NA NA NA NA
91 1.14575040 0.639036426 3 2 1 NA NA NA NA NA NA
92 0.92801246 0.094698299 1 1 1 NA NA NA NA NA NA
93 -2.59938157 -0.055510622 4 NA 1 NA NA NA NA NA NA
94 -3.26905923 -0.421318463 4 3 1 NA NA NA NA NA NA
95 -3.26861434 0.125295503 1 1 1 NA NA NA NA NA NA
96 -5.71017484 0.213084904 4 3 1 NA NA NA NA NA NA
97 -3.76781806 -0.161914659 4 2 1 NA NA NA NA NA NA
98 -2.02677390 -0.034767685 3 2 1 NA NA NA NA NA NA
99 -2.96199765 -0.320681689 3 4 1 NA NA NA NA NA NA
100 -4.81129496 0.058192962 4 3 1 NA NA NA NA NA NA
abs(C1 - C2) log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
1 NA 0.3439662 NA NA NA
2 NA 0.3605954 NA NA NA
3 NA 0.3583696 NA NA NA
4 NA 0.3736964 NA NA NA
5 NA 0.3634928 NA NA NA
6 NA 0.3737730 NA NA NA
7 NA 0.3542952 NA NA NA
8 NA 0.3631892 NA NA NA
9 NA 0.3484794 NA NA NA
10 NA 0.3706241 NA NA NA
11 NA 0.3565373 NA NA NA
12 NA 0.3716534 NA NA NA
13 NA 0.3510408 NA NA NA
14 NA 0.3527707 NA NA NA
15 NA 0.3617934 NA NA NA
16 NA 0.3534000 NA NA NA
17 NA 0.3765220 NA NA NA
18 NA 0.3466206 NA NA NA
19 NA 0.3669896 NA NA NA
20 NA 0.3611331 NA NA NA
21 NA 0.3573242 NA NA NA
22 NA 0.3659595 NA NA NA
23 NA 0.3532680 NA NA NA
24 NA 0.3614400 NA NA NA
25 NA 0.3548341 NA NA NA
26 NA 0.3626380 NA NA NA
27 NA 0.3655634 NA NA NA
28 NA 0.3527344 NA NA NA
29 NA 0.3631120 NA NA NA
30 NA 0.3867045 NA NA NA
31 NA 0.3519109 NA NA NA
32 NA 0.3768405 NA NA NA
33 NA 0.3582630 NA NA NA
34 NA 0.3587390 NA NA NA
35 NA 0.3516387 NA NA NA
36 NA 0.3608133 NA NA NA
37 NA 0.3544406 NA NA NA
38 NA 0.3519254 NA NA NA
39 NA 0.3577404 NA NA NA
40 NA 0.3699214 NA NA NA
41 NA 0.3610235 NA NA NA
42 NA 0.3688639 NA NA NA
43 NA 0.3683210 NA NA NA
44 NA 0.3707242 NA NA NA
45 NA 0.3719890 NA NA NA
46 NA 0.3471687 NA NA NA
47 NA 0.3622725 NA NA NA
48 NA 0.3604242 NA NA NA
49 NA 0.3470878 NA NA NA
50 NA 0.3519288 NA NA NA
51 NA 0.3737703 NA NA NA
52 NA 0.3730309 NA NA NA
53 NA 0.3587298 NA NA NA
54 NA 0.3577317 NA NA NA
55 NA 0.3670651 NA NA NA
56 NA 0.3621821 NA NA NA
57 NA 0.3493310 NA NA NA
58 NA 0.3611449 NA NA NA
59 NA 0.3685236 NA NA NA
60 NA 0.3626252 NA NA NA
61 NA 0.3565271 NA NA NA
62 NA 0.3650248 NA NA NA
63 NA 0.3667342 NA NA NA
64 NA 0.3536790 NA NA NA
65 NA 0.3707512 NA NA NA
66 NA 0.3547570 NA NA NA
67 NA 0.3556460 NA NA NA
68 NA 0.3465922 NA NA NA
69 NA 0.3758430 NA NA NA
70 NA 0.3856661 NA NA NA
71 NA 0.3542125 NA NA NA
72 NA 0.3593309 NA NA NA
73 NA 0.3657925 NA NA NA
74 NA 0.3611311 NA NA NA
75 NA 0.3385130 NA NA NA
76 NA 0.3738804 NA NA NA
77 NA 0.3597065 NA NA NA
78 NA 0.3612366 NA NA NA
79 NA 0.3607899 NA NA NA
80 NA 0.3609283 NA NA NA
81 NA 0.3687189 NA NA NA
82 NA 0.3664112 NA NA NA
83 NA 0.3577425 NA NA NA
84 NA 0.3577579 NA NA NA
85 NA 0.3578947 NA NA NA
86 NA 0.3629637 NA NA NA
87 NA 0.3434041 NA NA NA
88 NA 0.3523374 NA NA NA
89 NA 0.3524220 NA NA NA
90 NA 0.3642486 NA NA NA
91 NA 0.3577968 NA NA NA
92 NA 0.3492491 NA NA NA
93 NA 0.3533376 NA NA NA
94 NA 0.3530999 NA NA NA
95 NA 0.3607553 NA NA NA
96 NA 0.3721453 NA NA NA
97 NA 0.3600291 NA NA NA
98 NA 0.3676785 NA NA NA
99 NA 0.3524318 NA NA NA
100 NA 0.3438689 NA NA NA
C1
1 1.410531
2 1.434183
3 1.430994
4 1.453096
5 1.438344
6 1.453207
7 1.425176
8 1.437908
9 1.416911
10 1.448638
11 1.428375
12 1.450130
13 1.420545
14 1.423005
15 1.435902
16 1.423901
17 1.457208
18 1.414280
19 1.443383
20 1.434954
21 1.429499
22 1.441897
23 1.423713
24 1.435395
25 1.425944
26 1.437115
27 1.441326
28 1.422953
29 1.437797
30 1.472121
31 1.421782
32 1.457672
33 1.430842
34 1.431523
35 1.421395
36 1.434496
37 1.425383
38 1.421802
39 1.430094
40 1.447621
41 1.434797
42 1.446091
43 1.445306
44 1.448783
45 1.450617
46 1.415055
47 1.436590
48 1.433938
49 1.414941
50 1.421807
51 1.453203
52 1.452129
53 1.431510
54 1.430082
55 1.443492
56 1.436460
57 1.418119
58 1.434971
59 1.445599
60 1.437097
61 1.428360
62 1.440550
63 1.443014
64 1.424298
65 1.448823
66 1.425834
67 1.427102
68 1.414240
69 1.456218
70 1.470594
71 1.425058
72 1.432371
73 1.441656
74 1.434952
75 1.402860
76 1.453363
77 1.432909
78 1.435103
79 1.434462
80 1.434661
81 1.445881
82 1.442548
83 1.430097
84 1.430119
85 1.430315
86 1.437584
87 1.409738
88 1.422388
89 1.422509
90 1.439432
91 1.430175
92 1.418002
93 1.423812
94 1.423473
95 1.434412
96 1.450844
97 1.433371
98 1.444378
99 1.422523
100 1.410394
$m4a$spM_lvlone
center scale
y -3.34428345 2.276495066
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
B1 L1mis Be2 C2 (Intercept) abs(C1 - C2) log(Be2)
1 1 0.9364352 0.13821330 0.144065882 1 NA NA
2 1 0.8943541 NA 0.032778478 1 NA NA
3 1 0.2868460 0.85221266 0.343008492 1 NA NA
4 1 0.9068418 0.61517266 -0.361887858 1 NA NA
5 1 0.7621346 0.56718424 -0.389600647 1 NA NA
6 1 NA 0.16127199 -0.205306841 1 NA NA
7 0 NA NA 0.079434830 1 NA NA
8 0 0.7593154 0.51062047 -0.331246757 1 NA NA
9 1 0.5863705 0.29560086 -0.329638800 1 NA NA
10 1 0.7342586 0.43261394 0.167597533 1 NA NA
11 1 0.7218028 0.54537238 0.860207989 1 NA NA
12 0 NA 0.36458613 0.022730640 1 NA NA
13 1 0.7200126 0.84543642 0.217171172 1 NA NA
14 0 0.5289014 0.88041616 -0.403002412 1 NA NA
15 1 0.7322482 0.47940969 0.087369742 1 NA NA
16 1 0.7462471 0.25520352 -0.183870429 1 NA NA
17 1 0.9119922 0.53793620 -0.194577002 1 NA NA
18 1 NA 0.41924865 -0.349718516 1 NA NA
19 1 NA 0.19038933 -0.508781244 1 NA NA
20 1 NA NA 0.494883111 1 NA NA
21 1 0.7288999 0.26763985 0.258041067 1 NA NA
22 1 0.7160420 NA -0.922621989 1 NA NA
23 1 NA NA 0.431254949 1 NA NA
24 1 0.7210413 0.39688480 -0.294218881 1 NA NA
25 0 0.7816086 0.20117762 -0.425548895 1 NA NA
26 1 0.6747483 0.56039795 0.057176054 1 NA NA
27 1 0.4746725 0.69959156 0.289090158 1 NA NA
28 1 0.9270652 0.16198957 -0.473079489 1 NA NA
29 1 0.5306249 0.73477348 -0.385664863 1 NA NA
30 0 0.8913764 NA -0.154780107 1 NA NA
31 0 NA 0.69439759 0.100536296 1 NA NA
32 1 0.4610800 NA 0.634791958 1 NA NA
33 1 0.7183814 NA -0.387252617 1 NA NA
34 1 0.6375974 0.68680241 -0.181741088 1 NA NA
35 1 0.9202563 0.20563215 -0.311562695 1 NA NA
36 0 0.7263222 0.39312999 -0.044115907 1 NA NA
37 1 NA 0.33592359 -0.657409991 1 NA NA
38 1 NA 0.80799798 0.159577214 1 NA NA
39 1 0.7945509 0.70399665 -0.460416933 1 NA NA
40 1 0.6355032 0.14770504 NA 1 NA NA
41 1 0.9939049 0.32976608 -0.248909867 1 NA NA
42 1 1.0690739 0.57875125 -0.609021545 1 NA NA
43 1 0.7009106 0.69765999 0.025471883 1 NA NA
44 1 0.7595403 0.92706981 0.066648592 1 NA NA
45 1 0.8356414 0.59881110 -0.276108719 1 NA NA
46 1 0.4929132 NA -0.179737577 1 NA NA
47 0 NA 0.57021551 0.181190937 1 NA NA
48 1 0.5363034 0.31297307 -0.453871693 1 NA NA
49 1 0.8494053 0.45752036 0.448629602 1 NA NA
50 0 0.6292812 0.76707228 -0.529811821 1 NA NA
51 1 0.9561312 0.79670238 -0.028304571 1 NA NA
52 1 0.9735411 0.31851588 -0.520318482 1 NA NA
53 1 0.7156259 0.27413726 0.171317619 1 NA NA
54 1 0.5184434 0.87099655 0.432732046 1 NA NA
55 0 0.7948965 0.14767954 -0.346286005 1 NA NA
56 1 0.5191792 0.72225832 -0.469375653 1 NA NA
57 1 0.9233108 0.91165899 0.031021711 1 NA NA
58 1 0.8025356 NA -0.118837515 1 NA NA
59 1 0.8546624 0.74875442 0.507769984 1 NA NA
60 0 0.8639819 0.57086552 0.271797031 1 NA NA
61 1 0.7521237 0.17368573 -0.124442204 1 NA NA
62 1 0.5590215 NA 0.277677389 1 NA NA
63 0 0.5972103 0.60538003 -0.102893730 1 NA NA
64 1 0.6071272 NA NA 1 NA NA
65 1 0.8837829 0.44987490 -0.678303052 1 NA NA
66 0 0.7775301 0.71105443 0.478880037 1 NA NA
67 0 NA 0.09500493 -0.428028760 1 NA NA
68 1 0.7857549 0.37292542 0.048119185 1 NA NA
69 0 0.9119262 0.41025328 0.216932805 1 NA NA
70 0 0.5816103 0.87473911 -0.234575269 1 NA NA
71 1 0.4886093 0.57325664 0.006827078 1 NA NA
72 1 NA 0.76227946 -0.456055171 1 NA NA
73 0 NA 0.56061854 0.346486708 1 NA NA
74 1 0.7328840 0.61145842 0.205092215 1 NA NA
75 1 0.7946099 NA -0.136596858 1 NA NA
76 0 0.7734810 0.23795025 -0.500179043 1 NA NA
77 0 0.5296147 0.28135640 0.527352086 1 NA NA
78 0 0.7723288 NA 0.022742250 1 NA NA
79 1 0.8079308 0.43010097 NA 1 NA NA
80 1 NA 0.30775746 -0.002032440 1 NA NA
81 1 NA 0.43379094 -0.154246160 1 NA NA
82 1 NA 0.70103825 0.140201825 1 NA NA
83 1 0.4544158 0.19501290 -0.141417121 1 NA NA
84 1 0.6482660 0.42336380 NA 1 NA NA
85 1 0.7272109 NA -0.021285339 1 NA NA
86 1 NA 0.49004839 -0.010196306 1 NA NA
87 1 0.6768061 NA -0.089747520 1 NA NA
88 0 0.8115758 0.71840773 -0.083699898 1 NA NA
89 1 NA 0.81565945 -0.044061996 1 NA NA
90 1 0.6408465 0.83308857 -0.209291697 1 NA NA
91 1 0.5917453 0.56239647 0.639036426 1 NA NA
92 1 0.7224845 NA 0.094698299 1 NA NA
93 1 0.4501596 NA -0.055510622 1 NA NA
94 1 0.5190455 NA -0.421318463 1 NA NA
95 1 0.7305821 0.73286310 0.125295503 1 NA NA
96 1 0.9696445 0.39788846 0.213084904 1 NA NA
97 1 0.7087457 NA -0.161914659 1 NA NA
98 1 0.9964080 0.81066470 -0.034767685 1 NA NA
99 1 NA 0.40892733 -0.320681689 1 NA NA
100 1 0.9296776 0.76834275 0.058192962 1 NA NA
C1
1 1.410531
2 1.434183
3 1.430994
4 1.453096
5 1.438344
6 1.453207
7 1.425176
8 1.437908
9 1.416911
10 1.448638
11 1.428375
12 1.450130
13 1.420545
14 1.423005
15 1.435902
16 1.423901
17 1.457208
18 1.414280
19 1.443383
20 1.434954
21 1.429499
22 1.441897
23 1.423713
24 1.435395
25 1.425944
26 1.437115
27 1.441326
28 1.422953
29 1.437797
30 1.472121
31 1.421782
32 1.457672
33 1.430842
34 1.431523
35 1.421395
36 1.434496
37 1.425383
38 1.421802
39 1.430094
40 1.447621
41 1.434797
42 1.446091
43 1.445306
44 1.448783
45 1.450617
46 1.415055
47 1.436590
48 1.433938
49 1.414941
50 1.421807
51 1.453203
52 1.452129
53 1.431510
54 1.430082
55 1.443492
56 1.436460
57 1.418119
58 1.434971
59 1.445599
60 1.437097
61 1.428360
62 1.440550
63 1.443014
64 1.424298
65 1.448823
66 1.425834
67 1.427102
68 1.414240
69 1.456218
70 1.470594
71 1.425058
72 1.432371
73 1.441656
74 1.434952
75 1.402860
76 1.453363
77 1.432909
78 1.435103
79 1.434462
80 1.434661
81 1.445881
82 1.442548
83 1.430097
84 1.430119
85 1.430315
86 1.437584
87 1.409738
88 1.422388
89 1.422509
90 1.439432
91 1.430175
92 1.418002
93 1.423812
94 1.423473
95 1.434412
96 1.450844
97 1.433371
98 1.444378
99 1.422523
100 1.410394
$m4b$spM_lvlone
center scale
B1 NA NA
L1mis 0.72862466 0.15772614
Be2 0.51799407 0.22974678
C2 -0.06490582 0.33317347
(Intercept) NA NA
abs(C1 - C2) 1.49900534 0.33421418
log(Be2) -0.78337703 0.54590062
C1 1.43410054 0.01299651
$m4b$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_gamma
[1] 0
$m4b$tau_reg_gamma
[1] 1e-04
$m4b$shape_tau_gamma
[1] 0.01
$m4b$rate_tau_gamma
[1] 0.01
$m4b$mu_reg_beta
[1] 0
$m4b$tau_reg_beta
[1] 1e-04
$m4b$shape_tau_beta
[1] 0.01
$m4b$rate_tau_beta
[1] 0.01
$m4b$mu_reg_binom
[1] 0
$m4b$tau_reg_binom
[1] 1e-04
$m5a1
$m5a1$M_lvlone
y B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 -4.76915977 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 -2.69277172 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 -1.17551547 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 -4.57464473 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 -2.20260004 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 -3.48995315 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 -0.44987258 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 -2.29588848 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 -4.49135812 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 -5.52545368 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 -4.16286741 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 -2.93455761 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 -0.04202496 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 -1.63149775 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 -0.97786151 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 -1.79100431 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 -6.26520032 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 -1.36028709 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 -1.15396597 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 -3.21707239 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 -1.59389898 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 -5.50335066 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 0.57290123 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 -8.22270323 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 -1.41364158 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 -6.28031574 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 -3.15624425 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 -3.55693639 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 -1.11821124 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 -2.82834175 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 -3.72259860 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 -1.75256656 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 -5.55044409 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 -7.45068147 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 -0.97491919 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 -2.98356481 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 -1.86039471 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 -7.28754607 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 -8.66234796 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 -4.16291375 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 -3.48250771 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 -7.27930410 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 -6.12866190 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 -4.96880803 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 -4.76746713 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 -1.91249177 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 -0.61884029 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 -0.20496175 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 -7.12636055 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 -6.23103837 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 -3.32561065 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 -2.95942339 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 -4.44915114 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 -0.81566463 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 -6.50029573 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 -2.74718050 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 -6.35015663 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 -2.69505883 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 -1.55660833 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 -3.76240209 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 -3.92885797 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 -1.72044748 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 -0.56602625 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 -4.42235015 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 -2.39122287 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 -0.81807247 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 -6.48196782 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 -1.37306273 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 -4.99886487 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 -5.82288217 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 -2.68234219 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 -3.96170442 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 -7.19573667 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 -5.08799713 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 -1.32967262 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 -2.56532332 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 -3.21002900 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 -3.40559790 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 -4.56223913 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 -2.04250454 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 -2.20378059 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 -3.37471317 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 -0.95345385 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 -4.89337660 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 -9.82258463 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 -4.51800734 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 -0.18662049 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 -2.87120881 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 1.29290150 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 -1.39497744 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 1.14575040 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 0.92801246 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 -2.59938157 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 -3.26905923 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 -3.26861434 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 -5.71017484 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 -3.76781806 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 -2.02677390 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 -2.96199765 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 -4.81129496 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5a1$spM_lvlone
center scale
y -3.34428345 2.2764951
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5a1$mu_reg_norm
[1] 0
$m5a1$tau_reg_norm
[1] 1e-04
$m5a1$shape_tau_norm
[1] 0.01
$m5a1$rate_tau_norm
[1] 0.01
$m5a1$mu_reg_binom
[1] 0
$m5a1$tau_reg_binom
[1] 1e-04
$m5a2
$m5a2$M_lvlone
y B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 -4.76915977 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 -2.69277172 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 -1.17551547 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 -4.57464473 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 -2.20260004 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 -3.48995315 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 -0.44987258 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 -2.29588848 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 -4.49135812 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 -5.52545368 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 -4.16286741 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 -2.93455761 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 -0.04202496 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 -1.63149775 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 -0.97786151 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 -1.79100431 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 -6.26520032 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 -1.36028709 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 -1.15396597 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 -3.21707239 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 -1.59389898 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 -5.50335066 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 0.57290123 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 -8.22270323 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 -1.41364158 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 -6.28031574 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 -3.15624425 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 -3.55693639 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 -1.11821124 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 -2.82834175 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 -3.72259860 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 -1.75256656 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 -5.55044409 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 -7.45068147 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 -0.97491919 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 -2.98356481 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 -1.86039471 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 -7.28754607 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 -8.66234796 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 -4.16291375 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 -3.48250771 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 -7.27930410 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 -6.12866190 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 -4.96880803 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 -4.76746713 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 -1.91249177 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 -0.61884029 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 -0.20496175 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 -7.12636055 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 -6.23103837 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 -3.32561065 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 -2.95942339 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 -4.44915114 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 -0.81566463 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 -6.50029573 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 -2.74718050 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 -6.35015663 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 -2.69505883 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 -1.55660833 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 -3.76240209 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 -3.92885797 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 -1.72044748 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 -0.56602625 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 -4.42235015 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 -2.39122287 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 -0.81807247 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 -6.48196782 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 -1.37306273 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 -4.99886487 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 -5.82288217 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 -2.68234219 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 -3.96170442 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 -7.19573667 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 -5.08799713 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 -1.32967262 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 -2.56532332 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 -3.21002900 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 -3.40559790 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 -4.56223913 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 -2.04250454 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 -2.20378059 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 -3.37471317 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 -0.95345385 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 -4.89337660 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 -9.82258463 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 -4.51800734 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 -0.18662049 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 -2.87120881 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 1.29290150 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 -1.39497744 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 1.14575040 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 0.92801246 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 -2.59938157 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 -3.26905923 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 -3.26861434 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 -5.71017484 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 -3.76781806 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 -2.02677390 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 -2.96199765 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 -4.81129496 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5a2$spM_lvlone
center scale
y -3.34428345 2.2764951
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5a2$mu_reg_norm
[1] 0
$m5a2$tau_reg_norm
[1] 1e-04
$m5a2$shape_tau_norm
[1] 0.01
$m5a2$rate_tau_norm
[1] 0.01
$m5a2$mu_reg_binom
[1] 0
$m5a2$tau_reg_binom
[1] 1e-04
$m5a3
$m5a3$M_lvlone
y B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 -4.76915977 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 -2.69277172 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 -1.17551547 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 -4.57464473 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 -2.20260004 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 -3.48995315 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 -0.44987258 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 -2.29588848 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 -4.49135812 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 -5.52545368 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 -4.16286741 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 -2.93455761 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 -0.04202496 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 -1.63149775 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 -0.97786151 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 -1.79100431 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 -6.26520032 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 -1.36028709 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 -1.15396597 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 -3.21707239 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 -1.59389898 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 -5.50335066 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 0.57290123 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 -8.22270323 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 -1.41364158 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 -6.28031574 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 -3.15624425 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 -3.55693639 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 -1.11821124 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 -2.82834175 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 -3.72259860 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 -1.75256656 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 -5.55044409 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 -7.45068147 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 -0.97491919 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 -2.98356481 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 -1.86039471 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 -7.28754607 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 -8.66234796 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 -4.16291375 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 -3.48250771 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 -7.27930410 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 -6.12866190 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 -4.96880803 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 -4.76746713 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 -1.91249177 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 -0.61884029 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 -0.20496175 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 -7.12636055 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 -6.23103837 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 -3.32561065 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 -2.95942339 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 -4.44915114 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 -0.81566463 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 -6.50029573 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 -2.74718050 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 -6.35015663 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 -2.69505883 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 -1.55660833 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 -3.76240209 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 -3.92885797 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 -1.72044748 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 -0.56602625 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 -4.42235015 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 -2.39122287 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 -0.81807247 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 -6.48196782 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 -1.37306273 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 -4.99886487 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 -5.82288217 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 -2.68234219 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 -3.96170442 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 -7.19573667 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 -5.08799713 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 -1.32967262 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 -2.56532332 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 -3.21002900 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 -3.40559790 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 -4.56223913 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 -2.04250454 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 -2.20378059 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 -3.37471317 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 -0.95345385 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 -4.89337660 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 -9.82258463 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 -4.51800734 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 -0.18662049 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 -2.87120881 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 1.29290150 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 -1.39497744 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 1.14575040 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 0.92801246 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 -2.59938157 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 -3.26905923 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 -3.26861434 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 -5.71017484 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 -3.76781806 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 -2.02677390 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 -2.96199765 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 -4.81129496 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5a3$spM_lvlone
center scale
y -3.34428345 2.2764951
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5a3$mu_reg_norm
[1] 0
$m5a3$tau_reg_norm
[1] 1e-04
$m5a3$shape_tau_norm
[1] 0.01
$m5a3$rate_tau_norm
[1] 0.01
$m5a3$mu_reg_binom
[1] 0
$m5a3$tau_reg_binom
[1] 1e-04
$m5b1
$m5b1$M_lvlone
B1 B2 C2 (Intercept) B21 C1 O1.L O1.Q O1.C
1 1 1 0.144065882 1 NA 1.410531 -0.2236068 -0.5 0.6708204
2 1 1 0.032778478 1 NA 1.434183 0.6708204 0.5 0.2236068
3 1 1 0.343008492 1 NA 1.430994 0.2236068 -0.5 -0.6708204
4 1 1 -0.361887858 1 NA 1.453096 -0.2236068 -0.5 0.6708204
5 1 1 -0.389600647 1 NA 1.438344 0.2236068 -0.5 -0.6708204
6 1 NA -0.205306841 1 NA 1.453207 -0.6708204 0.5 -0.2236068
7 0 1 0.079434830 1 NA 1.425176 0.2236068 -0.5 -0.6708204
8 0 1 -0.331246757 1 NA 1.437908 0.6708204 0.5 0.2236068
9 1 1 -0.329638800 1 NA 1.416911 0.6708204 0.5 0.2236068
10 1 NA 0.167597533 1 NA 1.448638 -0.2236068 -0.5 0.6708204
11 1 1 0.860207989 1 NA 1.428375 -0.6708204 0.5 -0.2236068
12 0 1 0.022730640 1 NA 1.450130 0.2236068 -0.5 -0.6708204
13 1 1 0.217171172 1 NA 1.420545 0.2236068 -0.5 -0.6708204
14 0 1 -0.403002412 1 NA 1.423005 -0.6708204 0.5 -0.2236068
15 1 1 0.087369742 1 NA 1.435902 -0.6708204 0.5 -0.2236068
16 1 1 -0.183870429 1 NA 1.423901 0.6708204 0.5 0.2236068
17 1 1 -0.194577002 1 NA 1.457208 -0.2236068 -0.5 0.6708204
18 1 1 -0.349718516 1 NA 1.414280 0.2236068 -0.5 -0.6708204
19 1 NA -0.508781244 1 NA 1.443383 0.6708204 0.5 0.2236068
20 1 NA 0.494883111 1 NA 1.434954 -0.6708204 0.5 -0.2236068
21 1 1 0.258041067 1 NA 1.429499 0.2236068 -0.5 -0.6708204
22 1 NA -0.922621989 1 NA 1.441897 0.6708204 0.5 0.2236068
23 1 NA 0.431254949 1 NA 1.423713 0.6708204 0.5 0.2236068
24 1 1 -0.294218881 1 NA 1.435395 -0.2236068 -0.5 0.6708204
25 0 NA -0.425548895 1 NA 1.425944 -0.6708204 0.5 -0.2236068
26 1 NA 0.057176054 1 NA 1.437115 0.2236068 -0.5 -0.6708204
27 1 1 0.289090158 1 NA 1.441326 0.6708204 0.5 0.2236068
28 1 1 -0.473079489 1 NA 1.422953 -0.6708204 0.5 -0.2236068
29 1 1 -0.385664863 1 NA 1.437797 0.6708204 0.5 0.2236068
30 0 1 -0.154780107 1 NA 1.472121 0.6708204 0.5 0.2236068
31 0 NA 0.100536296 1 NA 1.421782 -0.2236068 -0.5 0.6708204
32 1 1 0.634791958 1 NA 1.457672 0.2236068 -0.5 -0.6708204
33 1 1 -0.387252617 1 NA 1.430842 0.2236068 -0.5 -0.6708204
34 1 0 -0.181741088 1 NA 1.431523 -0.6708204 0.5 -0.2236068
35 1 1 -0.311562695 1 NA 1.421395 -0.6708204 0.5 -0.2236068
36 0 1 -0.044115907 1 NA 1.434496 0.6708204 0.5 0.2236068
37 1 1 -0.657409991 1 NA 1.425383 0.6708204 0.5 0.2236068
38 1 NA 0.159577214 1 NA 1.421802 0.6708204 0.5 0.2236068
39 1 1 -0.460416933 1 NA 1.430094 -0.6708204 0.5 -0.2236068
40 1 NA NA 1 NA 1.447621 -0.2236068 -0.5 0.6708204
41 1 1 -0.248909867 1 NA 1.434797 -0.6708204 0.5 -0.2236068
42 1 1 -0.609021545 1 NA 1.446091 -0.6708204 0.5 -0.2236068
43 1 1 0.025471883 1 NA 1.445306 -0.2236068 -0.5 0.6708204
44 1 1 0.066648592 1 NA 1.448783 -0.2236068 -0.5 0.6708204
45 1 1 -0.276108719 1 NA 1.450617 -0.6708204 0.5 -0.2236068
46 1 1 -0.179737577 1 NA 1.415055 -0.6708204 0.5 -0.2236068
47 0 0 0.181190937 1 NA 1.436590 0.6708204 0.5 0.2236068
48 1 1 -0.453871693 1 NA 1.433938 0.6708204 0.5 0.2236068
49 1 0 0.448629602 1 NA 1.414941 -0.2236068 -0.5 0.6708204
50 0 1 -0.529811821 1 NA 1.421807 -0.2236068 -0.5 0.6708204
51 1 1 -0.028304571 1 NA 1.453203 -0.6708204 0.5 -0.2236068
52 1 1 -0.520318482 1 NA 1.452129 0.2236068 -0.5 -0.6708204
53 1 1 0.171317619 1 NA 1.431510 -0.6708204 0.5 -0.2236068
54 1 1 0.432732046 1 NA 1.430082 0.2236068 -0.5 -0.6708204
55 0 1 -0.346286005 1 NA 1.443492 -0.2236068 -0.5 0.6708204
56 1 1 -0.469375653 1 NA 1.436460 0.6708204 0.5 0.2236068
57 1 1 0.031021711 1 NA 1.418119 -0.2236068 -0.5 0.6708204
58 1 NA -0.118837515 1 NA 1.434971 -0.6708204 0.5 -0.2236068
59 1 1 0.507769984 1 NA 1.445599 -0.6708204 0.5 -0.2236068
60 0 NA 0.271797031 1 NA 1.437097 0.6708204 0.5 0.2236068
61 1 1 -0.124442204 1 NA 1.428360 -0.2236068 -0.5 0.6708204
62 1 1 0.277677389 1 NA 1.440550 0.6708204 0.5 0.2236068
63 0 1 -0.102893730 1 NA 1.443014 0.2236068 -0.5 -0.6708204
64 1 1 NA 1 NA 1.424298 -0.2236068 -0.5 0.6708204
65 1 1 -0.678303052 1 NA 1.448823 0.2236068 -0.5 -0.6708204
66 0 0 0.478880037 1 NA 1.425834 0.2236068 -0.5 -0.6708204
67 0 1 -0.428028760 1 NA 1.427102 -0.2236068 -0.5 0.6708204
68 1 1 0.048119185 1 NA 1.414240 -0.6708204 0.5 -0.2236068
69 0 NA 0.216932805 1 NA 1.456218 -0.6708204 0.5 -0.2236068
70 0 1 -0.234575269 1 NA 1.470594 -0.6708204 0.5 -0.2236068
71 1 1 0.006827078 1 NA 1.425058 -0.6708204 0.5 -0.2236068
72 1 1 -0.456055171 1 NA 1.432371 0.2236068 -0.5 -0.6708204
73 0 1 0.346486708 1 NA 1.441656 -0.2236068 -0.5 0.6708204
74 1 1 0.205092215 1 NA 1.434952 -0.2236068 -0.5 0.6708204
75 1 1 -0.136596858 1 NA 1.402860 0.2236068 -0.5 -0.6708204
76 0 1 -0.500179043 1 NA 1.453363 0.2236068 -0.5 -0.6708204
77 0 1 0.527352086 1 NA 1.432909 0.6708204 0.5 0.2236068
78 0 1 0.022742250 1 NA 1.435103 0.2236068 -0.5 -0.6708204
79 1 1 NA 1 NA 1.434462 -0.2236068 -0.5 0.6708204
80 1 1 -0.002032440 1 NA 1.434661 -0.2236068 -0.5 0.6708204
81 1 0 -0.154246160 1 NA 1.445881 0.2236068 -0.5 -0.6708204
82 1 NA 0.140201825 1 NA 1.442548 -0.6708204 0.5 -0.2236068
83 1 1 -0.141417121 1 NA 1.430097 0.2236068 -0.5 -0.6708204
84 1 1 NA 1 NA 1.430119 -0.2236068 -0.5 0.6708204
85 1 1 -0.021285339 1 NA 1.430315 -0.2236068 -0.5 0.6708204
86 1 NA -0.010196306 1 NA 1.437584 0.6708204 0.5 0.2236068
87 1 NA -0.089747520 1 NA 1.409738 0.2236068 -0.5 -0.6708204
88 0 1 -0.083699898 1 NA 1.422388 -0.2236068 -0.5 0.6708204
89 1 1 -0.044061996 1 NA 1.422509 0.2236068 -0.5 -0.6708204
90 1 1 -0.209291697 1 NA 1.439432 0.2236068 -0.5 -0.6708204
91 1 1 0.639036426 1 NA 1.430175 0.6708204 0.5 0.2236068
92 1 NA 0.094698299 1 NA 1.418002 -0.6708204 0.5 -0.2236068
93 1 1 -0.055510622 1 NA 1.423812 0.6708204 0.5 0.2236068
94 1 1 -0.421318463 1 NA 1.423473 -0.6708204 0.5 -0.2236068
95 1 1 0.125295503 1 NA 1.434412 -0.6708204 0.5 -0.2236068
96 1 1 0.213084904 1 NA 1.450844 0.2236068 -0.5 -0.6708204
97 1 NA -0.161914659 1 NA 1.433371 -0.6708204 0.5 -0.2236068
98 1 1 -0.034767685 1 NA 1.444378 0.2236068 -0.5 -0.6708204
99 1 0 -0.320681689 1 NA 1.422523 0.2236068 -0.5 -0.6708204
100 1 NA 0.058192962 1 NA 1.410394 0.2236068 -0.5 -0.6708204
$m5b1$spM_lvlone
center scale
B1 NA NA
B2 NA NA
C2 -0.06490582 0.33317347
(Intercept) NA NA
B21 NA NA
C1 1.43410054 0.01299651
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5b1$mu_reg_norm
[1] 0
$m5b1$tau_reg_norm
[1] 1e-04
$m5b1$shape_tau_norm
[1] 0.01
$m5b1$rate_tau_norm
[1] 0.01
$m5b1$mu_reg_binom
[1] 0
$m5b1$tau_reg_binom
[1] 1e-04
$m5b2
$m5b2$M_lvlone
B1 B2 C2 (Intercept) B21 C1 O1.L O1.Q O1.C
1 1 1 0.144065882 1 NA 1.410531 -0.2236068 -0.5 0.6708204
2 1 1 0.032778478 1 NA 1.434183 0.6708204 0.5 0.2236068
3 1 1 0.343008492 1 NA 1.430994 0.2236068 -0.5 -0.6708204
4 1 1 -0.361887858 1 NA 1.453096 -0.2236068 -0.5 0.6708204
5 1 1 -0.389600647 1 NA 1.438344 0.2236068 -0.5 -0.6708204
6 1 NA -0.205306841 1 NA 1.453207 -0.6708204 0.5 -0.2236068
7 0 1 0.079434830 1 NA 1.425176 0.2236068 -0.5 -0.6708204
8 0 1 -0.331246757 1 NA 1.437908 0.6708204 0.5 0.2236068
9 1 1 -0.329638800 1 NA 1.416911 0.6708204 0.5 0.2236068
10 1 NA 0.167597533 1 NA 1.448638 -0.2236068 -0.5 0.6708204
11 1 1 0.860207989 1 NA 1.428375 -0.6708204 0.5 -0.2236068
12 0 1 0.022730640 1 NA 1.450130 0.2236068 -0.5 -0.6708204
13 1 1 0.217171172 1 NA 1.420545 0.2236068 -0.5 -0.6708204
14 0 1 -0.403002412 1 NA 1.423005 -0.6708204 0.5 -0.2236068
15 1 1 0.087369742 1 NA 1.435902 -0.6708204 0.5 -0.2236068
16 1 1 -0.183870429 1 NA 1.423901 0.6708204 0.5 0.2236068
17 1 1 -0.194577002 1 NA 1.457208 -0.2236068 -0.5 0.6708204
18 1 1 -0.349718516 1 NA 1.414280 0.2236068 -0.5 -0.6708204
19 1 NA -0.508781244 1 NA 1.443383 0.6708204 0.5 0.2236068
20 1 NA 0.494883111 1 NA 1.434954 -0.6708204 0.5 -0.2236068
21 1 1 0.258041067 1 NA 1.429499 0.2236068 -0.5 -0.6708204
22 1 NA -0.922621989 1 NA 1.441897 0.6708204 0.5 0.2236068
23 1 NA 0.431254949 1 NA 1.423713 0.6708204 0.5 0.2236068
24 1 1 -0.294218881 1 NA 1.435395 -0.2236068 -0.5 0.6708204
25 0 NA -0.425548895 1 NA 1.425944 -0.6708204 0.5 -0.2236068
26 1 NA 0.057176054 1 NA 1.437115 0.2236068 -0.5 -0.6708204
27 1 1 0.289090158 1 NA 1.441326 0.6708204 0.5 0.2236068
28 1 1 -0.473079489 1 NA 1.422953 -0.6708204 0.5 -0.2236068
29 1 1 -0.385664863 1 NA 1.437797 0.6708204 0.5 0.2236068
30 0 1 -0.154780107 1 NA 1.472121 0.6708204 0.5 0.2236068
31 0 NA 0.100536296 1 NA 1.421782 -0.2236068 -0.5 0.6708204
32 1 1 0.634791958 1 NA 1.457672 0.2236068 -0.5 -0.6708204
33 1 1 -0.387252617 1 NA 1.430842 0.2236068 -0.5 -0.6708204
34 1 0 -0.181741088 1 NA 1.431523 -0.6708204 0.5 -0.2236068
35 1 1 -0.311562695 1 NA 1.421395 -0.6708204 0.5 -0.2236068
36 0 1 -0.044115907 1 NA 1.434496 0.6708204 0.5 0.2236068
37 1 1 -0.657409991 1 NA 1.425383 0.6708204 0.5 0.2236068
38 1 NA 0.159577214 1 NA 1.421802 0.6708204 0.5 0.2236068
39 1 1 -0.460416933 1 NA 1.430094 -0.6708204 0.5 -0.2236068
40 1 NA NA 1 NA 1.447621 -0.2236068 -0.5 0.6708204
41 1 1 -0.248909867 1 NA 1.434797 -0.6708204 0.5 -0.2236068
42 1 1 -0.609021545 1 NA 1.446091 -0.6708204 0.5 -0.2236068
43 1 1 0.025471883 1 NA 1.445306 -0.2236068 -0.5 0.6708204
44 1 1 0.066648592 1 NA 1.448783 -0.2236068 -0.5 0.6708204
45 1 1 -0.276108719 1 NA 1.450617 -0.6708204 0.5 -0.2236068
46 1 1 -0.179737577 1 NA 1.415055 -0.6708204 0.5 -0.2236068
47 0 0 0.181190937 1 NA 1.436590 0.6708204 0.5 0.2236068
48 1 1 -0.453871693 1 NA 1.433938 0.6708204 0.5 0.2236068
49 1 0 0.448629602 1 NA 1.414941 -0.2236068 -0.5 0.6708204
50 0 1 -0.529811821 1 NA 1.421807 -0.2236068 -0.5 0.6708204
51 1 1 -0.028304571 1 NA 1.453203 -0.6708204 0.5 -0.2236068
52 1 1 -0.520318482 1 NA 1.452129 0.2236068 -0.5 -0.6708204
53 1 1 0.171317619 1 NA 1.431510 -0.6708204 0.5 -0.2236068
54 1 1 0.432732046 1 NA 1.430082 0.2236068 -0.5 -0.6708204
55 0 1 -0.346286005 1 NA 1.443492 -0.2236068 -0.5 0.6708204
56 1 1 -0.469375653 1 NA 1.436460 0.6708204 0.5 0.2236068
57 1 1 0.031021711 1 NA 1.418119 -0.2236068 -0.5 0.6708204
58 1 NA -0.118837515 1 NA 1.434971 -0.6708204 0.5 -0.2236068
59 1 1 0.507769984 1 NA 1.445599 -0.6708204 0.5 -0.2236068
60 0 NA 0.271797031 1 NA 1.437097 0.6708204 0.5 0.2236068
61 1 1 -0.124442204 1 NA 1.428360 -0.2236068 -0.5 0.6708204
62 1 1 0.277677389 1 NA 1.440550 0.6708204 0.5 0.2236068
63 0 1 -0.102893730 1 NA 1.443014 0.2236068 -0.5 -0.6708204
64 1 1 NA 1 NA 1.424298 -0.2236068 -0.5 0.6708204
65 1 1 -0.678303052 1 NA 1.448823 0.2236068 -0.5 -0.6708204
66 0 0 0.478880037 1 NA 1.425834 0.2236068 -0.5 -0.6708204
67 0 1 -0.428028760 1 NA 1.427102 -0.2236068 -0.5 0.6708204
68 1 1 0.048119185 1 NA 1.414240 -0.6708204 0.5 -0.2236068
69 0 NA 0.216932805 1 NA 1.456218 -0.6708204 0.5 -0.2236068
70 0 1 -0.234575269 1 NA 1.470594 -0.6708204 0.5 -0.2236068
71 1 1 0.006827078 1 NA 1.425058 -0.6708204 0.5 -0.2236068
72 1 1 -0.456055171 1 NA 1.432371 0.2236068 -0.5 -0.6708204
73 0 1 0.346486708 1 NA 1.441656 -0.2236068 -0.5 0.6708204
74 1 1 0.205092215 1 NA 1.434952 -0.2236068 -0.5 0.6708204
75 1 1 -0.136596858 1 NA 1.402860 0.2236068 -0.5 -0.6708204
76 0 1 -0.500179043 1 NA 1.453363 0.2236068 -0.5 -0.6708204
77 0 1 0.527352086 1 NA 1.432909 0.6708204 0.5 0.2236068
78 0 1 0.022742250 1 NA 1.435103 0.2236068 -0.5 -0.6708204
79 1 1 NA 1 NA 1.434462 -0.2236068 -0.5 0.6708204
80 1 1 -0.002032440 1 NA 1.434661 -0.2236068 -0.5 0.6708204
81 1 0 -0.154246160 1 NA 1.445881 0.2236068 -0.5 -0.6708204
82 1 NA 0.140201825 1 NA 1.442548 -0.6708204 0.5 -0.2236068
83 1 1 -0.141417121 1 NA 1.430097 0.2236068 -0.5 -0.6708204
84 1 1 NA 1 NA 1.430119 -0.2236068 -0.5 0.6708204
85 1 1 -0.021285339 1 NA 1.430315 -0.2236068 -0.5 0.6708204
86 1 NA -0.010196306 1 NA 1.437584 0.6708204 0.5 0.2236068
87 1 NA -0.089747520 1 NA 1.409738 0.2236068 -0.5 -0.6708204
88 0 1 -0.083699898 1 NA 1.422388 -0.2236068 -0.5 0.6708204
89 1 1 -0.044061996 1 NA 1.422509 0.2236068 -0.5 -0.6708204
90 1 1 -0.209291697 1 NA 1.439432 0.2236068 -0.5 -0.6708204
91 1 1 0.639036426 1 NA 1.430175 0.6708204 0.5 0.2236068
92 1 NA 0.094698299 1 NA 1.418002 -0.6708204 0.5 -0.2236068
93 1 1 -0.055510622 1 NA 1.423812 0.6708204 0.5 0.2236068
94 1 1 -0.421318463 1 NA 1.423473 -0.6708204 0.5 -0.2236068
95 1 1 0.125295503 1 NA 1.434412 -0.6708204 0.5 -0.2236068
96 1 1 0.213084904 1 NA 1.450844 0.2236068 -0.5 -0.6708204
97 1 NA -0.161914659 1 NA 1.433371 -0.6708204 0.5 -0.2236068
98 1 1 -0.034767685 1 NA 1.444378 0.2236068 -0.5 -0.6708204
99 1 0 -0.320681689 1 NA 1.422523 0.2236068 -0.5 -0.6708204
100 1 NA 0.058192962 1 NA 1.410394 0.2236068 -0.5 -0.6708204
$m5b2$spM_lvlone
center scale
B1 NA NA
B2 NA NA
C2 -0.06490582 0.33317347
(Intercept) NA NA
B21 NA NA
C1 1.43410054 0.01299651
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5b2$mu_reg_norm
[1] 0
$m5b2$tau_reg_norm
[1] 1e-04
$m5b2$shape_tau_norm
[1] 0.01
$m5b2$rate_tau_norm
[1] 0.01
$m5b2$mu_reg_binom
[1] 0
$m5b2$tau_reg_binom
[1] 1e-04
$m5b3
$m5b3$M_lvlone
B1 B2 C2 (Intercept) B21 C1 O1.L O1.Q O1.C
1 1 1 0.144065882 1 NA 1.410531 -0.2236068 -0.5 0.6708204
2 1 1 0.032778478 1 NA 1.434183 0.6708204 0.5 0.2236068
3 1 1 0.343008492 1 NA 1.430994 0.2236068 -0.5 -0.6708204
4 1 1 -0.361887858 1 NA 1.453096 -0.2236068 -0.5 0.6708204
5 1 1 -0.389600647 1 NA 1.438344 0.2236068 -0.5 -0.6708204
6 1 NA -0.205306841 1 NA 1.453207 -0.6708204 0.5 -0.2236068
7 0 1 0.079434830 1 NA 1.425176 0.2236068 -0.5 -0.6708204
8 0 1 -0.331246757 1 NA 1.437908 0.6708204 0.5 0.2236068
9 1 1 -0.329638800 1 NA 1.416911 0.6708204 0.5 0.2236068
10 1 NA 0.167597533 1 NA 1.448638 -0.2236068 -0.5 0.6708204
11 1 1 0.860207989 1 NA 1.428375 -0.6708204 0.5 -0.2236068
12 0 1 0.022730640 1 NA 1.450130 0.2236068 -0.5 -0.6708204
13 1 1 0.217171172 1 NA 1.420545 0.2236068 -0.5 -0.6708204
14 0 1 -0.403002412 1 NA 1.423005 -0.6708204 0.5 -0.2236068
15 1 1 0.087369742 1 NA 1.435902 -0.6708204 0.5 -0.2236068
16 1 1 -0.183870429 1 NA 1.423901 0.6708204 0.5 0.2236068
17 1 1 -0.194577002 1 NA 1.457208 -0.2236068 -0.5 0.6708204
18 1 1 -0.349718516 1 NA 1.414280 0.2236068 -0.5 -0.6708204
19 1 NA -0.508781244 1 NA 1.443383 0.6708204 0.5 0.2236068
20 1 NA 0.494883111 1 NA 1.434954 -0.6708204 0.5 -0.2236068
21 1 1 0.258041067 1 NA 1.429499 0.2236068 -0.5 -0.6708204
22 1 NA -0.922621989 1 NA 1.441897 0.6708204 0.5 0.2236068
23 1 NA 0.431254949 1 NA 1.423713 0.6708204 0.5 0.2236068
24 1 1 -0.294218881 1 NA 1.435395 -0.2236068 -0.5 0.6708204
25 0 NA -0.425548895 1 NA 1.425944 -0.6708204 0.5 -0.2236068
26 1 NA 0.057176054 1 NA 1.437115 0.2236068 -0.5 -0.6708204
27 1 1 0.289090158 1 NA 1.441326 0.6708204 0.5 0.2236068
28 1 1 -0.473079489 1 NA 1.422953 -0.6708204 0.5 -0.2236068
29 1 1 -0.385664863 1 NA 1.437797 0.6708204 0.5 0.2236068
30 0 1 -0.154780107 1 NA 1.472121 0.6708204 0.5 0.2236068
31 0 NA 0.100536296 1 NA 1.421782 -0.2236068 -0.5 0.6708204
32 1 1 0.634791958 1 NA 1.457672 0.2236068 -0.5 -0.6708204
33 1 1 -0.387252617 1 NA 1.430842 0.2236068 -0.5 -0.6708204
34 1 0 -0.181741088 1 NA 1.431523 -0.6708204 0.5 -0.2236068
35 1 1 -0.311562695 1 NA 1.421395 -0.6708204 0.5 -0.2236068
36 0 1 -0.044115907 1 NA 1.434496 0.6708204 0.5 0.2236068
37 1 1 -0.657409991 1 NA 1.425383 0.6708204 0.5 0.2236068
38 1 NA 0.159577214 1 NA 1.421802 0.6708204 0.5 0.2236068
39 1 1 -0.460416933 1 NA 1.430094 -0.6708204 0.5 -0.2236068
40 1 NA NA 1 NA 1.447621 -0.2236068 -0.5 0.6708204
41 1 1 -0.248909867 1 NA 1.434797 -0.6708204 0.5 -0.2236068
42 1 1 -0.609021545 1 NA 1.446091 -0.6708204 0.5 -0.2236068
43 1 1 0.025471883 1 NA 1.445306 -0.2236068 -0.5 0.6708204
44 1 1 0.066648592 1 NA 1.448783 -0.2236068 -0.5 0.6708204
45 1 1 -0.276108719 1 NA 1.450617 -0.6708204 0.5 -0.2236068
46 1 1 -0.179737577 1 NA 1.415055 -0.6708204 0.5 -0.2236068
47 0 0 0.181190937 1 NA 1.436590 0.6708204 0.5 0.2236068
48 1 1 -0.453871693 1 NA 1.433938 0.6708204 0.5 0.2236068
49 1 0 0.448629602 1 NA 1.414941 -0.2236068 -0.5 0.6708204
50 0 1 -0.529811821 1 NA 1.421807 -0.2236068 -0.5 0.6708204
51 1 1 -0.028304571 1 NA 1.453203 -0.6708204 0.5 -0.2236068
52 1 1 -0.520318482 1 NA 1.452129 0.2236068 -0.5 -0.6708204
53 1 1 0.171317619 1 NA 1.431510 -0.6708204 0.5 -0.2236068
54 1 1 0.432732046 1 NA 1.430082 0.2236068 -0.5 -0.6708204
55 0 1 -0.346286005 1 NA 1.443492 -0.2236068 -0.5 0.6708204
56 1 1 -0.469375653 1 NA 1.436460 0.6708204 0.5 0.2236068
57 1 1 0.031021711 1 NA 1.418119 -0.2236068 -0.5 0.6708204
58 1 NA -0.118837515 1 NA 1.434971 -0.6708204 0.5 -0.2236068
59 1 1 0.507769984 1 NA 1.445599 -0.6708204 0.5 -0.2236068
60 0 NA 0.271797031 1 NA 1.437097 0.6708204 0.5 0.2236068
61 1 1 -0.124442204 1 NA 1.428360 -0.2236068 -0.5 0.6708204
62 1 1 0.277677389 1 NA 1.440550 0.6708204 0.5 0.2236068
63 0 1 -0.102893730 1 NA 1.443014 0.2236068 -0.5 -0.6708204
64 1 1 NA 1 NA 1.424298 -0.2236068 -0.5 0.6708204
65 1 1 -0.678303052 1 NA 1.448823 0.2236068 -0.5 -0.6708204
66 0 0 0.478880037 1 NA 1.425834 0.2236068 -0.5 -0.6708204
67 0 1 -0.428028760 1 NA 1.427102 -0.2236068 -0.5 0.6708204
68 1 1 0.048119185 1 NA 1.414240 -0.6708204 0.5 -0.2236068
69 0 NA 0.216932805 1 NA 1.456218 -0.6708204 0.5 -0.2236068
70 0 1 -0.234575269 1 NA 1.470594 -0.6708204 0.5 -0.2236068
71 1 1 0.006827078 1 NA 1.425058 -0.6708204 0.5 -0.2236068
72 1 1 -0.456055171 1 NA 1.432371 0.2236068 -0.5 -0.6708204
73 0 1 0.346486708 1 NA 1.441656 -0.2236068 -0.5 0.6708204
74 1 1 0.205092215 1 NA 1.434952 -0.2236068 -0.5 0.6708204
75 1 1 -0.136596858 1 NA 1.402860 0.2236068 -0.5 -0.6708204
76 0 1 -0.500179043 1 NA 1.453363 0.2236068 -0.5 -0.6708204
77 0 1 0.527352086 1 NA 1.432909 0.6708204 0.5 0.2236068
78 0 1 0.022742250 1 NA 1.435103 0.2236068 -0.5 -0.6708204
79 1 1 NA 1 NA 1.434462 -0.2236068 -0.5 0.6708204
80 1 1 -0.002032440 1 NA 1.434661 -0.2236068 -0.5 0.6708204
81 1 0 -0.154246160 1 NA 1.445881 0.2236068 -0.5 -0.6708204
82 1 NA 0.140201825 1 NA 1.442548 -0.6708204 0.5 -0.2236068
83 1 1 -0.141417121 1 NA 1.430097 0.2236068 -0.5 -0.6708204
84 1 1 NA 1 NA 1.430119 -0.2236068 -0.5 0.6708204
85 1 1 -0.021285339 1 NA 1.430315 -0.2236068 -0.5 0.6708204
86 1 NA -0.010196306 1 NA 1.437584 0.6708204 0.5 0.2236068
87 1 NA -0.089747520 1 NA 1.409738 0.2236068 -0.5 -0.6708204
88 0 1 -0.083699898 1 NA 1.422388 -0.2236068 -0.5 0.6708204
89 1 1 -0.044061996 1 NA 1.422509 0.2236068 -0.5 -0.6708204
90 1 1 -0.209291697 1 NA 1.439432 0.2236068 -0.5 -0.6708204
91 1 1 0.639036426 1 NA 1.430175 0.6708204 0.5 0.2236068
92 1 NA 0.094698299 1 NA 1.418002 -0.6708204 0.5 -0.2236068
93 1 1 -0.055510622 1 NA 1.423812 0.6708204 0.5 0.2236068
94 1 1 -0.421318463 1 NA 1.423473 -0.6708204 0.5 -0.2236068
95 1 1 0.125295503 1 NA 1.434412 -0.6708204 0.5 -0.2236068
96 1 1 0.213084904 1 NA 1.450844 0.2236068 -0.5 -0.6708204
97 1 NA -0.161914659 1 NA 1.433371 -0.6708204 0.5 -0.2236068
98 1 1 -0.034767685 1 NA 1.444378 0.2236068 -0.5 -0.6708204
99 1 0 -0.320681689 1 NA 1.422523 0.2236068 -0.5 -0.6708204
100 1 NA 0.058192962 1 NA 1.410394 0.2236068 -0.5 -0.6708204
$m5b3$spM_lvlone
center scale
B1 NA NA
B2 NA NA
C2 -0.06490582 0.33317347
(Intercept) NA NA
B21 NA NA
C1 1.43410054 0.01299651
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5b3$mu_reg_norm
[1] 0
$m5b3$tau_reg_norm
[1] 1e-04
$m5b3$shape_tau_norm
[1] 0.01
$m5b3$rate_tau_norm
[1] 0.01
$m5b3$mu_reg_binom
[1] 0
$m5b3$tau_reg_binom
[1] 1e-04
$m5b4
$m5b4$M_lvlone
B1 B2 C2 (Intercept) B21 C1 O1.L O1.Q O1.C
1 1 1 0.144065882 1 NA 1.410531 -0.2236068 -0.5 0.6708204
2 1 1 0.032778478 1 NA 1.434183 0.6708204 0.5 0.2236068
3 1 1 0.343008492 1 NA 1.430994 0.2236068 -0.5 -0.6708204
4 1 1 -0.361887858 1 NA 1.453096 -0.2236068 -0.5 0.6708204
5 1 1 -0.389600647 1 NA 1.438344 0.2236068 -0.5 -0.6708204
6 1 NA -0.205306841 1 NA 1.453207 -0.6708204 0.5 -0.2236068
7 0 1 0.079434830 1 NA 1.425176 0.2236068 -0.5 -0.6708204
8 0 1 -0.331246757 1 NA 1.437908 0.6708204 0.5 0.2236068
9 1 1 -0.329638800 1 NA 1.416911 0.6708204 0.5 0.2236068
10 1 NA 0.167597533 1 NA 1.448638 -0.2236068 -0.5 0.6708204
11 1 1 0.860207989 1 NA 1.428375 -0.6708204 0.5 -0.2236068
12 0 1 0.022730640 1 NA 1.450130 0.2236068 -0.5 -0.6708204
13 1 1 0.217171172 1 NA 1.420545 0.2236068 -0.5 -0.6708204
14 0 1 -0.403002412 1 NA 1.423005 -0.6708204 0.5 -0.2236068
15 1 1 0.087369742 1 NA 1.435902 -0.6708204 0.5 -0.2236068
16 1 1 -0.183870429 1 NA 1.423901 0.6708204 0.5 0.2236068
17 1 1 -0.194577002 1 NA 1.457208 -0.2236068 -0.5 0.6708204
18 1 1 -0.349718516 1 NA 1.414280 0.2236068 -0.5 -0.6708204
19 1 NA -0.508781244 1 NA 1.443383 0.6708204 0.5 0.2236068
20 1 NA 0.494883111 1 NA 1.434954 -0.6708204 0.5 -0.2236068
21 1 1 0.258041067 1 NA 1.429499 0.2236068 -0.5 -0.6708204
22 1 NA -0.922621989 1 NA 1.441897 0.6708204 0.5 0.2236068
23 1 NA 0.431254949 1 NA 1.423713 0.6708204 0.5 0.2236068
24 1 1 -0.294218881 1 NA 1.435395 -0.2236068 -0.5 0.6708204
25 0 NA -0.425548895 1 NA 1.425944 -0.6708204 0.5 -0.2236068
26 1 NA 0.057176054 1 NA 1.437115 0.2236068 -0.5 -0.6708204
27 1 1 0.289090158 1 NA 1.441326 0.6708204 0.5 0.2236068
28 1 1 -0.473079489 1 NA 1.422953 -0.6708204 0.5 -0.2236068
29 1 1 -0.385664863 1 NA 1.437797 0.6708204 0.5 0.2236068
30 0 1 -0.154780107 1 NA 1.472121 0.6708204 0.5 0.2236068
31 0 NA 0.100536296 1 NA 1.421782 -0.2236068 -0.5 0.6708204
32 1 1 0.634791958 1 NA 1.457672 0.2236068 -0.5 -0.6708204
33 1 1 -0.387252617 1 NA 1.430842 0.2236068 -0.5 -0.6708204
34 1 0 -0.181741088 1 NA 1.431523 -0.6708204 0.5 -0.2236068
35 1 1 -0.311562695 1 NA 1.421395 -0.6708204 0.5 -0.2236068
36 0 1 -0.044115907 1 NA 1.434496 0.6708204 0.5 0.2236068
37 1 1 -0.657409991 1 NA 1.425383 0.6708204 0.5 0.2236068
38 1 NA 0.159577214 1 NA 1.421802 0.6708204 0.5 0.2236068
39 1 1 -0.460416933 1 NA 1.430094 -0.6708204 0.5 -0.2236068
40 1 NA NA 1 NA 1.447621 -0.2236068 -0.5 0.6708204
41 1 1 -0.248909867 1 NA 1.434797 -0.6708204 0.5 -0.2236068
42 1 1 -0.609021545 1 NA 1.446091 -0.6708204 0.5 -0.2236068
43 1 1 0.025471883 1 NA 1.445306 -0.2236068 -0.5 0.6708204
44 1 1 0.066648592 1 NA 1.448783 -0.2236068 -0.5 0.6708204
45 1 1 -0.276108719 1 NA 1.450617 -0.6708204 0.5 -0.2236068
46 1 1 -0.179737577 1 NA 1.415055 -0.6708204 0.5 -0.2236068
47 0 0 0.181190937 1 NA 1.436590 0.6708204 0.5 0.2236068
48 1 1 -0.453871693 1 NA 1.433938 0.6708204 0.5 0.2236068
49 1 0 0.448629602 1 NA 1.414941 -0.2236068 -0.5 0.6708204
50 0 1 -0.529811821 1 NA 1.421807 -0.2236068 -0.5 0.6708204
51 1 1 -0.028304571 1 NA 1.453203 -0.6708204 0.5 -0.2236068
52 1 1 -0.520318482 1 NA 1.452129 0.2236068 -0.5 -0.6708204
53 1 1 0.171317619 1 NA 1.431510 -0.6708204 0.5 -0.2236068
54 1 1 0.432732046 1 NA 1.430082 0.2236068 -0.5 -0.6708204
55 0 1 -0.346286005 1 NA 1.443492 -0.2236068 -0.5 0.6708204
56 1 1 -0.469375653 1 NA 1.436460 0.6708204 0.5 0.2236068
57 1 1 0.031021711 1 NA 1.418119 -0.2236068 -0.5 0.6708204
58 1 NA -0.118837515 1 NA 1.434971 -0.6708204 0.5 -0.2236068
59 1 1 0.507769984 1 NA 1.445599 -0.6708204 0.5 -0.2236068
60 0 NA 0.271797031 1 NA 1.437097 0.6708204 0.5 0.2236068
61 1 1 -0.124442204 1 NA 1.428360 -0.2236068 -0.5 0.6708204
62 1 1 0.277677389 1 NA 1.440550 0.6708204 0.5 0.2236068
63 0 1 -0.102893730 1 NA 1.443014 0.2236068 -0.5 -0.6708204
64 1 1 NA 1 NA 1.424298 -0.2236068 -0.5 0.6708204
65 1 1 -0.678303052 1 NA 1.448823 0.2236068 -0.5 -0.6708204
66 0 0 0.478880037 1 NA 1.425834 0.2236068 -0.5 -0.6708204
67 0 1 -0.428028760 1 NA 1.427102 -0.2236068 -0.5 0.6708204
68 1 1 0.048119185 1 NA 1.414240 -0.6708204 0.5 -0.2236068
69 0 NA 0.216932805 1 NA 1.456218 -0.6708204 0.5 -0.2236068
70 0 1 -0.234575269 1 NA 1.470594 -0.6708204 0.5 -0.2236068
71 1 1 0.006827078 1 NA 1.425058 -0.6708204 0.5 -0.2236068
72 1 1 -0.456055171 1 NA 1.432371 0.2236068 -0.5 -0.6708204
73 0 1 0.346486708 1 NA 1.441656 -0.2236068 -0.5 0.6708204
74 1 1 0.205092215 1 NA 1.434952 -0.2236068 -0.5 0.6708204
75 1 1 -0.136596858 1 NA 1.402860 0.2236068 -0.5 -0.6708204
76 0 1 -0.500179043 1 NA 1.453363 0.2236068 -0.5 -0.6708204
77 0 1 0.527352086 1 NA 1.432909 0.6708204 0.5 0.2236068
78 0 1 0.022742250 1 NA 1.435103 0.2236068 -0.5 -0.6708204
79 1 1 NA 1 NA 1.434462 -0.2236068 -0.5 0.6708204
80 1 1 -0.002032440 1 NA 1.434661 -0.2236068 -0.5 0.6708204
81 1 0 -0.154246160 1 NA 1.445881 0.2236068 -0.5 -0.6708204
82 1 NA 0.140201825 1 NA 1.442548 -0.6708204 0.5 -0.2236068
83 1 1 -0.141417121 1 NA 1.430097 0.2236068 -0.5 -0.6708204
84 1 1 NA 1 NA 1.430119 -0.2236068 -0.5 0.6708204
85 1 1 -0.021285339 1 NA 1.430315 -0.2236068 -0.5 0.6708204
86 1 NA -0.010196306 1 NA 1.437584 0.6708204 0.5 0.2236068
87 1 NA -0.089747520 1 NA 1.409738 0.2236068 -0.5 -0.6708204
88 0 1 -0.083699898 1 NA 1.422388 -0.2236068 -0.5 0.6708204
89 1 1 -0.044061996 1 NA 1.422509 0.2236068 -0.5 -0.6708204
90 1 1 -0.209291697 1 NA 1.439432 0.2236068 -0.5 -0.6708204
91 1 1 0.639036426 1 NA 1.430175 0.6708204 0.5 0.2236068
92 1 NA 0.094698299 1 NA 1.418002 -0.6708204 0.5 -0.2236068
93 1 1 -0.055510622 1 NA 1.423812 0.6708204 0.5 0.2236068
94 1 1 -0.421318463 1 NA 1.423473 -0.6708204 0.5 -0.2236068
95 1 1 0.125295503 1 NA 1.434412 -0.6708204 0.5 -0.2236068
96 1 1 0.213084904 1 NA 1.450844 0.2236068 -0.5 -0.6708204
97 1 NA -0.161914659 1 NA 1.433371 -0.6708204 0.5 -0.2236068
98 1 1 -0.034767685 1 NA 1.444378 0.2236068 -0.5 -0.6708204
99 1 0 -0.320681689 1 NA 1.422523 0.2236068 -0.5 -0.6708204
100 1 NA 0.058192962 1 NA 1.410394 0.2236068 -0.5 -0.6708204
$m5b4$spM_lvlone
center scale
B1 NA NA
B2 NA NA
C2 -0.06490582 0.33317347
(Intercept) NA NA
B21 NA NA
C1 1.43410054 0.01299651
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5b4$mu_reg_norm
[1] 0
$m5b4$tau_reg_norm
[1] 1e-04
$m5b4$shape_tau_norm
[1] 0.01
$m5b4$rate_tau_norm
[1] 0.01
$m5b4$mu_reg_binom
[1] 0
$m5b4$tau_reg_binom
[1] 1e-04
$m5c1
$m5c1$M_lvlone
L1 B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 0.9364352 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 0.8943541 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 0.2868460 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 0.9068418 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 0.7621346 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 0.5858621 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 0.7194403 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 0.7593154 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 0.5863705 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 0.7342586 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 0.7218028 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 0.7241254 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 0.7200126 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 0.5289014 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 0.7322482 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 0.7462471 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 0.9119922 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 0.6262513 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 0.4587835 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 0.7173364 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 0.7288999 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 0.7160420 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 0.5795514 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 0.7210413 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 0.7816086 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 0.6747483 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 0.4746725 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 0.9270652 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 0.5306249 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 0.8913764 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 0.8090308 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 0.4610800 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 0.7183814 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 0.6375974 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 0.9202563 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 0.7263222 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 1.0638781 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 0.6053893 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 0.7945509 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 0.6355032 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 0.9939049 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 1.0690739 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 0.7009106 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 0.7595403 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 0.8356414 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 0.4929132 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 0.5298192 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 0.5363034 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 0.8494053 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 0.6292812 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 0.9561312 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 0.9735411 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 0.7156259 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 0.5184434 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 0.7948965 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 0.5191792 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 0.9233108 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 0.8025356 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 0.8546624 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 0.8639819 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 0.7521237 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 0.5590215 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 0.5972103 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 0.6071272 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 0.8837829 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 0.7775301 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 0.6756191 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 0.7857549 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 0.9119262 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 0.5816103 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 0.4886093 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 0.8292467 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 0.6767456 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 0.7328840 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 0.7946099 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 0.7734810 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 0.5296147 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 0.7723288 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 0.8079308 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 0.5214822 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 0.6264777 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 0.8332107 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 0.4544158 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 0.6482660 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 0.7272109 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 0.7302426 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 0.6768061 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 0.8115758 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 0.9775567 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 0.6408465 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 0.5917453 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 0.7224845 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 0.4501596 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 0.5190455 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 0.7305821 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 0.9696445 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 0.7087457 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 0.9964080 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 0.9084899 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 0.9296776 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5c1$spM_lvlone
center scale
L1 0.72488512 0.1569229
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5c1$mu_reg_norm
[1] 0
$m5c1$tau_reg_norm
[1] 1e-04
$m5c1$shape_tau_norm
[1] 0.01
$m5c1$rate_tau_norm
[1] 0.01
$m5c1$mu_reg_gamma
[1] 0
$m5c1$tau_reg_gamma
[1] 1e-04
$m5c1$shape_tau_gamma
[1] 0.01
$m5c1$rate_tau_gamma
[1] 0.01
$m5c1$mu_reg_binom
[1] 0
$m5c1$tau_reg_binom
[1] 1e-04
$m5c2
$m5c2$M_lvlone
L1 B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 0.9364352 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 0.8943541 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 0.2868460 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 0.9068418 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 0.7621346 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 0.5858621 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 0.7194403 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 0.7593154 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 0.5863705 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 0.7342586 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 0.7218028 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 0.7241254 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 0.7200126 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 0.5289014 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 0.7322482 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 0.7462471 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 0.9119922 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 0.6262513 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 0.4587835 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 0.7173364 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 0.7288999 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 0.7160420 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 0.5795514 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 0.7210413 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 0.7816086 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 0.6747483 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 0.4746725 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 0.9270652 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 0.5306249 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 0.8913764 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 0.8090308 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 0.4610800 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 0.7183814 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 0.6375974 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 0.9202563 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 0.7263222 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 1.0638781 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 0.6053893 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 0.7945509 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 0.6355032 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 0.9939049 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 1.0690739 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 0.7009106 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 0.7595403 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 0.8356414 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 0.4929132 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 0.5298192 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 0.5363034 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 0.8494053 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 0.6292812 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 0.9561312 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 0.9735411 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 0.7156259 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 0.5184434 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 0.7948965 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 0.5191792 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 0.9233108 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 0.8025356 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 0.8546624 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 0.8639819 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 0.7521237 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 0.5590215 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 0.5972103 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 0.6071272 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 0.8837829 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 0.7775301 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 0.6756191 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 0.7857549 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 0.9119262 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 0.5816103 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 0.4886093 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 0.8292467 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 0.6767456 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 0.7328840 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 0.7946099 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 0.7734810 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 0.5296147 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 0.7723288 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 0.8079308 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 0.5214822 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 0.6264777 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 0.8332107 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 0.4544158 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 0.6482660 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 0.7272109 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 0.7302426 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 0.6768061 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 0.8115758 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 0.9775567 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 0.6408465 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 0.5917453 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 0.7224845 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 0.4501596 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 0.5190455 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 0.7305821 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 0.9696445 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 0.7087457 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 0.9964080 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 0.9084899 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 0.9296776 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5c2$spM_lvlone
center scale
L1 0.72488512 0.1569229
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5c2$mu_reg_norm
[1] 0
$m5c2$tau_reg_norm
[1] 1e-04
$m5c2$shape_tau_norm
[1] 0.01
$m5c2$rate_tau_norm
[1] 0.01
$m5c2$mu_reg_gamma
[1] 0
$m5c2$tau_reg_gamma
[1] 1e-04
$m5c2$shape_tau_gamma
[1] 0.01
$m5c2$rate_tau_gamma
[1] 0.01
$m5c2$mu_reg_binom
[1] 0
$m5c2$tau_reg_binom
[1] 1e-04
$m5d1
$m5d1$M_lvlone
P1 B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 1 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 3 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 3 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 3 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 5 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 3 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 0 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 2 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 4 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 3 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 4 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 3 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 2 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 6 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 2 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 5 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 2 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 2 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 1 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 2 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 2 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 2 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 1 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 0 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 2 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 4 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 3 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 5 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 5 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 0 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 3 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 2 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 2 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 3 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 1 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 4 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 2 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 2 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 8 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 4 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 3 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 3 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 2 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 3 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 2 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 3 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 4 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 3 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 2 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 4 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 1 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 2 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 4 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 3 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 1 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 3 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 3 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 4 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 1 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 5 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 5 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 0 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 2 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 0 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 2 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 4 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 2 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 3 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 1 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 3 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 1 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 5 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 0 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 4 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 1 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 3 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 2 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 1 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 2 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 4 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 6 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 3 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 1 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 3 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 1 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 5 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 2 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 2 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 1 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 5 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 1 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 5 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 1 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 1 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 1 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 3 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 2 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 0 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 2 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 4 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5d1$spM_lvlone
center scale
P1 2.61000000 1.5627934
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5d1$mu_reg_norm
[1] 0
$m5d1$tau_reg_norm
[1] 1e-04
$m5d1$shape_tau_norm
[1] 0.01
$m5d1$rate_tau_norm
[1] 0.01
$m5d1$mu_reg_binom
[1] 0
$m5d1$tau_reg_binom
[1] 1e-04
$m5d1$mu_reg_poisson
[1] 0
$m5d1$tau_reg_poisson
[1] 1e-04
$m5d2
$m5d2$M_lvlone
P1 B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 1 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 3 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 3 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 3 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 5 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 3 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 0 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 2 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 4 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 3 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 4 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 3 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 2 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 6 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 2 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 5 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 2 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 2 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 1 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 2 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 2 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 2 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 1 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 0 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 2 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 4 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 3 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 5 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 5 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 0 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 3 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 2 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 2 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 3 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 1 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 4 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 2 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 2 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 8 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 4 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 3 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 3 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 2 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 3 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 2 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 3 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 4 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 3 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 2 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 4 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 1 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 2 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 4 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 3 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 1 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 3 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 3 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 4 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 1 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 5 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 5 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 0 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 2 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 0 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 2 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 4 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 2 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 3 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 1 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 3 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 1 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 5 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 0 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 4 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 1 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 3 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 2 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 1 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 2 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 4 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 6 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 3 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 1 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 3 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 1 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 5 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 2 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 2 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 1 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 5 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 1 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 5 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 1 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 1 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 1 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 3 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 2 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 0 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 2 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 4 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5d2$spM_lvlone
center scale
P1 2.61000000 1.5627934
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5d2$mu_reg_norm
[1] 0
$m5d2$tau_reg_norm
[1] 1e-04
$m5d2$shape_tau_norm
[1] 0.01
$m5d2$rate_tau_norm
[1] 0.01
$m5d2$mu_reg_binom
[1] 0
$m5d2$tau_reg_binom
[1] 1e-04
$m5d2$mu_reg_poisson
[1] 0
$m5d2$tau_reg_poisson
[1] 1e-04
$m5e1
$m5e1$M_lvlone
L1 B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 0.9364352 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 0.8943541 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 0.2868460 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 0.9068418 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 0.7621346 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 0.5858621 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 0.7194403 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 0.7593154 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 0.5863705 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 0.7342586 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 0.7218028 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 0.7241254 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 0.7200126 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 0.5289014 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 0.7322482 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 0.7462471 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 0.9119922 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 0.6262513 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 0.4587835 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 0.7173364 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 0.7288999 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 0.7160420 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 0.5795514 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 0.7210413 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 0.7816086 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 0.6747483 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 0.4746725 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 0.9270652 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 0.5306249 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 0.8913764 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 0.8090308 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 0.4610800 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 0.7183814 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 0.6375974 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 0.9202563 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 0.7263222 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 1.0638781 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 0.6053893 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 0.7945509 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 0.6355032 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 0.9939049 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 1.0690739 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 0.7009106 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 0.7595403 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 0.8356414 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 0.4929132 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 0.5298192 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 0.5363034 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 0.8494053 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 0.6292812 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 0.9561312 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 0.9735411 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 0.7156259 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 0.5184434 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 0.7948965 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 0.5191792 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 0.9233108 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 0.8025356 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 0.8546624 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 0.8639819 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 0.7521237 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 0.5590215 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 0.5972103 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 0.6071272 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 0.8837829 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 0.7775301 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 0.6756191 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 0.7857549 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 0.9119262 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 0.5816103 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 0.4886093 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 0.8292467 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 0.6767456 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 0.7328840 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 0.7946099 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 0.7734810 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 0.5296147 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 0.7723288 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 0.8079308 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 0.5214822 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 0.6264777 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 0.8332107 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 0.4544158 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 0.6482660 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 0.7272109 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 0.7302426 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 0.6768061 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 0.8115758 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 0.9775567 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 0.6408465 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 0.5917453 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 0.7224845 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 0.4501596 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 0.5190455 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 0.7305821 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 0.9696445 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 0.7087457 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 0.9964080 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 0.9084899 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 0.9296776 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5e1$spM_lvlone
center scale
L1 0.72488512 0.1569229
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5e1$mu_reg_norm
[1] 0
$m5e1$tau_reg_norm
[1] 1e-04
$m5e1$shape_tau_norm
[1] 0.01
$m5e1$rate_tau_norm
[1] 0.01
$m5e1$mu_reg_binom
[1] 0
$m5e1$tau_reg_binom
[1] 1e-04
$m5f1
$m5f1$M_lvlone
Be1 B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 0.69649948 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 0.56085128 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 0.35796663 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 0.53961336 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 0.06191042 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 0.51256785 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 0.13154723 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 0.35032766 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 0.21796890 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 0.10476230 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 0.66083800 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 0.66884267 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 0.69840279 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 0.50398472 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 0.52807655 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 0.40135087 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 0.45554802 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 0.68717635 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 0.35880655 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 0.36341035 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 0.71468563 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 0.44558172 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 0.33262526 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 0.66812751 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 0.23180310 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 0.37786624 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 0.88834598 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 0.46487057 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 0.47018802 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 0.91617346 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 0.67589111 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 0.61623852 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 0.44182889 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 0.29868153 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 0.44235110 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 0.72557250 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 0.74809277 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 0.26452559 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 0.41597215 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 0.29080530 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 0.80342568 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 0.76614332 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 0.29734466 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 0.42809509 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 0.12861202 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 0.44369392 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 0.35290028 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 0.88288407 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 0.37880332 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 0.60663793 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 0.15505292 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 0.65796074 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 0.63416487 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 0.83040459 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 0.64947589 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 0.67541381 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 0.53637356 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 0.39157422 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 0.88168026 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 0.32582606 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 0.64492753 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 0.34804110 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 0.49241010 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 0.43387493 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 0.21806182 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 0.60021691 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 0.30567313 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 0.22476988 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 0.23155216 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 0.29610794 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 0.83435168 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 0.65543408 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 0.59684715 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 0.80640183 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 0.52288624 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 0.41546840 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 0.44756212 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 0.68093413 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 0.29261828 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 0.21008516 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 0.44710869 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 0.70470991 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 0.31300581 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 0.44774544 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 0.68031201 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 0.44456865 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 0.79031803 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 0.22231438 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 0.30114327 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 0.45339193 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 0.35526875 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 0.68684691 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 0.81430167 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 0.60104343 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 0.82012448 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 0.55669948 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 0.76622465 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 0.50112270 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 0.53468983 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 0.58249327 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5f1$spM_lvlone
center scale
Be1 0.50398804 0.2049899
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5f1$mu_reg_norm
[1] 0
$m5f1$tau_reg_norm
[1] 1e-04
$m5f1$shape_tau_norm
[1] 0.01
$m5f1$rate_tau_norm
[1] 0.01
$m5f1$mu_reg_beta
[1] 0
$m5f1$tau_reg_beta
[1] 1e-04
$m5f1$shape_tau_beta
[1] 0.01
$m5f1$rate_tau_beta
[1] 0.01
$m5f1$mu_reg_binom
[1] 0
$m5f1$tau_reg_binom
[1] 1e-04
$m6a
$m6a$M_lvlone
y C2 M2 O2 (Intercept) M22 M23 M24 O22 O23 O24
1 -4.76915977 0.144065882 4 4 1 NA NA NA NA NA NA
2 -2.69277172 0.032778478 1 4 1 NA NA NA NA NA NA
3 -1.17551547 0.343008492 3 4 1 NA NA NA NA NA NA
4 -4.57464473 -0.361887858 3 1 1 NA NA NA NA NA NA
5 -2.20260004 -0.389600647 4 2 1 NA NA NA NA NA NA
6 -3.48995315 -0.205306841 4 3 1 NA NA NA NA NA NA
7 -0.44987258 0.079434830 1 4 1 NA NA NA NA NA NA
8 -2.29588848 -0.331246757 1 2 1 NA NA NA NA NA NA
9 -4.49135812 -0.329638800 2 4 1 NA NA NA NA NA NA
10 -5.52545368 0.167597533 2 3 1 NA NA NA NA NA NA
11 -4.16286741 0.860207989 3 2 1 NA NA NA NA NA NA
12 -2.93455761 0.022730640 3 1 1 NA NA NA NA NA NA
13 -0.04202496 0.217171172 2 1 1 NA NA NA NA NA NA
14 -1.63149775 -0.403002412 3 1 1 NA NA NA NA NA NA
15 -0.97786151 0.087369742 2 4 1 NA NA NA NA NA NA
16 -1.79100431 -0.183870429 1 3 1 NA NA NA NA NA NA
17 -6.26520032 -0.194577002 4 3 1 NA NA NA NA NA NA
18 -1.36028709 -0.349718516 2 1 1 NA NA NA NA NA NA
19 -1.15396597 -0.508781244 3 3 1 NA NA NA NA NA NA
20 -3.21707239 0.494883111 3 1 1 NA NA NA NA NA NA
21 -1.59389898 0.258041067 2 3 1 NA NA NA NA NA NA
22 -5.50335066 -0.922621989 2 3 1 NA NA NA NA NA NA
23 0.57290123 0.431254949 3 2 1 NA NA NA NA NA NA
24 -8.22270323 -0.294218881 3 3 1 NA NA NA NA NA NA
25 -1.41364158 -0.425548895 2 2 1 NA NA NA NA NA NA
26 -6.28031574 0.057176054 2 2 1 NA NA NA NA NA NA
27 -3.15624425 0.289090158 1 1 1 NA NA NA NA NA NA
28 -3.55693639 -0.473079489 3 4 1 NA NA NA NA NA NA
29 -1.11821124 -0.385664863 4 3 1 NA NA NA NA NA NA
30 -2.82834175 -0.154780107 2 3 1 NA NA NA NA NA NA
31 -3.72259860 0.100536296 NA 2 1 NA NA NA NA NA NA
32 -1.75256656 0.634791958 4 2 1 NA NA NA NA NA NA
33 -5.55044409 -0.387252617 4 1 1 NA NA NA NA NA NA
34 -7.45068147 -0.181741088 4 1 1 NA NA NA NA NA NA
35 -0.97491919 -0.311562695 2 4 1 NA NA NA NA NA NA
36 -2.98356481 -0.044115907 1 3 1 NA NA NA NA NA NA
37 -1.86039471 -0.657409991 3 3 1 NA NA NA NA NA NA
38 -7.28754607 0.159577214 4 1 1 NA NA NA NA NA NA
39 -8.66234796 -0.460416933 3 2 1 NA NA NA NA NA NA
40 -4.16291375 NA 3 3 1 NA NA NA NA NA NA
41 -3.48250771 -0.248909867 1 3 1 NA NA NA NA NA NA
42 -7.27930410 -0.609021545 4 3 1 NA NA NA NA NA NA
43 -6.12866190 0.025471883 1 3 1 NA NA NA NA NA NA
44 -4.96880803 0.066648592 2 4 1 NA NA NA NA NA NA
45 -4.76746713 -0.276108719 2 4 1 NA NA NA NA NA NA
46 -1.91249177 -0.179737577 1 1 1 NA NA NA NA NA NA
47 -0.61884029 0.181190937 4 4 1 NA NA NA NA NA NA
48 -0.20496175 -0.453871693 2 4 1 NA NA NA NA NA NA
49 -7.12636055 0.448629602 4 1 1 NA NA NA NA NA NA
50 -6.23103837 -0.529811821 1 2 1 NA NA NA NA NA NA
51 -3.32561065 -0.028304571 4 1 1 NA NA NA NA NA NA
52 -2.95942339 -0.520318482 4 3 1 NA NA NA NA NA NA
53 -4.44915114 0.171317619 4 2 1 NA NA NA NA NA NA
54 -0.81566463 0.432732046 3 1 1 NA NA NA NA NA NA
55 -6.50029573 -0.346286005 3 2 1 NA NA NA NA NA NA
56 -2.74718050 -0.469375653 3 3 1 NA NA NA NA NA NA
57 -6.35015663 0.031021711 2 NA 1 NA NA NA NA NA NA
58 -2.69505883 -0.118837515 3 4 1 NA NA NA NA NA NA
59 -1.55660833 0.507769984 3 4 1 NA NA NA NA NA NA
60 -3.76240209 0.271797031 4 3 1 NA NA NA NA NA NA
61 -3.92885797 -0.124442204 2 4 1 NA NA NA NA NA NA
62 -1.72044748 0.277677389 2 1 1 NA NA NA NA NA NA
63 -0.56602625 -0.102893730 1 4 1 NA NA NA NA NA NA
64 -4.42235015 NA 2 4 1 NA NA NA NA NA NA
65 -2.39122287 -0.678303052 2 4 1 NA NA NA NA NA NA
66 -0.81807247 0.478880037 3 1 1 NA NA NA NA NA NA
67 -6.48196782 -0.428028760 2 3 1 NA NA NA NA NA NA
68 -1.37306273 0.048119185 4 3 1 NA NA NA NA NA NA
69 -4.99886487 0.216932805 NA 4 1 NA NA NA NA NA NA
70 -5.82288217 -0.234575269 1 1 1 NA NA NA NA NA NA
71 -2.68234219 0.006827078 2 4 1 NA NA NA NA NA NA
72 -3.96170442 -0.456055171 3 4 1 NA NA NA NA NA NA
73 -7.19573667 0.346486708 4 2 1 NA NA NA NA NA NA
74 -5.08799713 0.205092215 4 4 1 NA NA NA NA NA NA
75 -1.32967262 -0.136596858 1 3 1 NA NA NA NA NA NA
76 -2.56532332 -0.500179043 4 2 1 NA NA NA NA NA NA
77 -3.21002900 0.527352086 NA 2 1 NA NA NA NA NA NA
78 -3.40559790 0.022742250 2 3 1 NA NA NA NA NA NA
79 -4.56223913 NA 2 2 1 NA NA NA NA NA NA
80 -2.04250454 -0.002032440 2 1 1 NA NA NA NA NA NA
81 -2.20378059 -0.154246160 4 4 1 NA NA NA NA NA NA
82 -3.37471317 0.140201825 3 2 1 NA NA NA NA NA NA
83 -0.95345385 -0.141417121 3 4 1 NA NA NA NA NA NA
84 -4.89337660 NA 1 1 1 NA NA NA NA NA NA
85 -9.82258463 -0.021285339 2 1 1 NA NA NA NA NA NA
86 -4.51800734 -0.010196306 1 2 1 NA NA NA NA NA NA
87 -0.18662049 -0.089747520 3 3 1 NA NA NA NA NA NA
88 -2.87120881 -0.083699898 1 3 1 NA NA NA NA NA NA
89 1.29290150 -0.044061996 2 2 1 NA NA NA NA NA NA
90 -1.39497744 -0.209291697 1 4 1 NA NA NA NA NA NA
91 1.14575040 0.639036426 3 2 1 NA NA NA NA NA NA
92 0.92801246 0.094698299 1 1 1 NA NA NA NA NA NA
93 -2.59938157 -0.055510622 4 NA 1 NA NA NA NA NA NA
94 -3.26905923 -0.421318463 4 3 1 NA NA NA NA NA NA
95 -3.26861434 0.125295503 1 1 1 NA NA NA NA NA NA
96 -5.71017484 0.213084904 4 3 1 NA NA NA NA NA NA
97 -3.76781806 -0.161914659 4 2 1 NA NA NA NA NA NA
98 -2.02677390 -0.034767685 3 2 1 NA NA NA NA NA NA
99 -2.96199765 -0.320681689 3 4 1 NA NA NA NA NA NA
100 -4.81129496 0.058192962 4 3 1 NA NA NA NA NA NA
abs(C1 - C2) log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
1 NA 0.3439662 NA NA NA
2 NA 0.3605954 NA NA NA
3 NA 0.3583696 NA NA NA
4 NA 0.3736964 NA NA NA
5 NA 0.3634928 NA NA NA
6 NA 0.3737730 NA NA NA
7 NA 0.3542952 NA NA NA
8 NA 0.3631892 NA NA NA
9 NA 0.3484794 NA NA NA
10 NA 0.3706241 NA NA NA
11 NA 0.3565373 NA NA NA
12 NA 0.3716534 NA NA NA
13 NA 0.3510408 NA NA NA
14 NA 0.3527707 NA NA NA
15 NA 0.3617934 NA NA NA
16 NA 0.3534000 NA NA NA
17 NA 0.3765220 NA NA NA
18 NA 0.3466206 NA NA NA
19 NA 0.3669896 NA NA NA
20 NA 0.3611331 NA NA NA
21 NA 0.3573242 NA NA NA
22 NA 0.3659595 NA NA NA
23 NA 0.3532680 NA NA NA
24 NA 0.3614400 NA NA NA
25 NA 0.3548341 NA NA NA
26 NA 0.3626380 NA NA NA
27 NA 0.3655634 NA NA NA
28 NA 0.3527344 NA NA NA
29 NA 0.3631120 NA NA NA
30 NA 0.3867045 NA NA NA
31 NA 0.3519109 NA NA NA
32 NA 0.3768405 NA NA NA
33 NA 0.3582630 NA NA NA
34 NA 0.3587390 NA NA NA
35 NA 0.3516387 NA NA NA
36 NA 0.3608133 NA NA NA
37 NA 0.3544406 NA NA NA
38 NA 0.3519254 NA NA NA
39 NA 0.3577404 NA NA NA
40 NA 0.3699214 NA NA NA
41 NA 0.3610235 NA NA NA
42 NA 0.3688639 NA NA NA
43 NA 0.3683210 NA NA NA
44 NA 0.3707242 NA NA NA
45 NA 0.3719890 NA NA NA
46 NA 0.3471687 NA NA NA
47 NA 0.3622725 NA NA NA
48 NA 0.3604242 NA NA NA
49 NA 0.3470878 NA NA NA
50 NA 0.3519288 NA NA NA
51 NA 0.3737703 NA NA NA
52 NA 0.3730309 NA NA NA
53 NA 0.3587298 NA NA NA
54 NA 0.3577317 NA NA NA
55 NA 0.3670651 NA NA NA
56 NA 0.3621821 NA NA NA
57 NA 0.3493310 NA NA NA
58 NA 0.3611449 NA NA NA
59 NA 0.3685236 NA NA NA
60 NA 0.3626252 NA NA NA
61 NA 0.3565271 NA NA NA
62 NA 0.3650248 NA NA NA
63 NA 0.3667342 NA NA NA
64 NA 0.3536790 NA NA NA
65 NA 0.3707512 NA NA NA
66 NA 0.3547570 NA NA NA
67 NA 0.3556460 NA NA NA
68 NA 0.3465922 NA NA NA
69 NA 0.3758430 NA NA NA
70 NA 0.3856661 NA NA NA
71 NA 0.3542125 NA NA NA
72 NA 0.3593309 NA NA NA
73 NA 0.3657925 NA NA NA
74 NA 0.3611311 NA NA NA
75 NA 0.3385130 NA NA NA
76 NA 0.3738804 NA NA NA
77 NA 0.3597065 NA NA NA
78 NA 0.3612366 NA NA NA
79 NA 0.3607899 NA NA NA
80 NA 0.3609283 NA NA NA
81 NA 0.3687189 NA NA NA
82 NA 0.3664112 NA NA NA
83 NA 0.3577425 NA NA NA
84 NA 0.3577579 NA NA NA
85 NA 0.3578947 NA NA NA
86 NA 0.3629637 NA NA NA
87 NA 0.3434041 NA NA NA
88 NA 0.3523374 NA NA NA
89 NA 0.3524220 NA NA NA
90 NA 0.3642486 NA NA NA
91 NA 0.3577968 NA NA NA
92 NA 0.3492491 NA NA NA
93 NA 0.3533376 NA NA NA
94 NA 0.3530999 NA NA NA
95 NA 0.3607553 NA NA NA
96 NA 0.3721453 NA NA NA
97 NA 0.3600291 NA NA NA
98 NA 0.3676785 NA NA NA
99 NA 0.3524318 NA NA NA
100 NA 0.3438689 NA NA NA
C1
1 1.410531
2 1.434183
3 1.430994
4 1.453096
5 1.438344
6 1.453207
7 1.425176
8 1.437908
9 1.416911
10 1.448638
11 1.428375
12 1.450130
13 1.420545
14 1.423005
15 1.435902
16 1.423901
17 1.457208
18 1.414280
19 1.443383
20 1.434954
21 1.429499
22 1.441897
23 1.423713
24 1.435395
25 1.425944
26 1.437115
27 1.441326
28 1.422953
29 1.437797
30 1.472121
31 1.421782
32 1.457672
33 1.430842
34 1.431523
35 1.421395
36 1.434496
37 1.425383
38 1.421802
39 1.430094
40 1.447621
41 1.434797
42 1.446091
43 1.445306
44 1.448783
45 1.450617
46 1.415055
47 1.436590
48 1.433938
49 1.414941
50 1.421807
51 1.453203
52 1.452129
53 1.431510
54 1.430082
55 1.443492
56 1.436460
57 1.418119
58 1.434971
59 1.445599
60 1.437097
61 1.428360
62 1.440550
63 1.443014
64 1.424298
65 1.448823
66 1.425834
67 1.427102
68 1.414240
69 1.456218
70 1.470594
71 1.425058
72 1.432371
73 1.441656
74 1.434952
75 1.402860
76 1.453363
77 1.432909
78 1.435103
79 1.434462
80 1.434661
81 1.445881
82 1.442548
83 1.430097
84 1.430119
85 1.430315
86 1.437584
87 1.409738
88 1.422388
89 1.422509
90 1.439432
91 1.430175
92 1.418002
93 1.423812
94 1.423473
95 1.434412
96 1.450844
97 1.433371
98 1.444378
99 1.422523
100 1.410394
$m6a$spM_lvlone
center scale
y -3.34428345 2.276495066
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
$m6a$mu_reg_norm
[1] 0
$m6a$tau_reg_norm
[1] 1e-04
$m6a$shape_tau_norm
[1] 0.01
$m6a$rate_tau_norm
[1] 0.01
$m6a$mu_reg_multinomial
[1] 0
$m6a$tau_reg_multinomial
[1] 1e-04
$m6a$mu_reg_ordinal
[1] 0
$m6a$tau_reg_ordinal
[1] 1e-04
$m6a$mu_delta_ordinal
[1] 0
$m6a$tau_delta_ordinal
[1] 1e-04
$m6b
$m6b$M_lvlone
B1 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 1 0.032778478 1 4 1 NA NA NA NA NA NA NA
3 1 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 1 -0.389600647 4 2 1 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 NA NA NA NA NA NA NA
7 0 0.079434830 1 4 1 NA NA NA NA NA NA NA
8 0 -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 1 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 0 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 0 -0.403002412 3 1 1 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 NA NA NA NA NA NA NA
16 1 -0.183870429 1 3 1 NA NA NA NA NA NA NA
17 1 -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 1 -0.508781244 3 3 1 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 NA NA NA NA NA NA NA
21 1 0.258041067 2 3 1 NA NA NA NA NA NA NA
22 1 -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 1 -0.294218881 3 3 1 NA NA NA NA NA NA NA
25 0 -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 1 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 1 -0.385664863 4 3 1 NA NA NA NA NA NA NA
30 0 -0.154780107 2 3 1 NA NA NA NA NA NA NA
31 0 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 1 -0.387252617 4 1 1 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 NA NA NA NA NA NA NA
36 0 -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 1 0.159577214 4 1 1 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 NA NA NA NA NA NA NA
40 1 NA 3 3 1 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 NA NA NA NA NA NA NA
43 1 0.025471883 1 3 1 NA NA NA NA NA NA NA
44 1 0.066648592 2 4 1 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 NA NA NA NA NA NA NA
47 0 0.181190937 4 4 1 NA NA NA NA NA NA NA
48 1 -0.453871693 2 4 1 NA NA NA NA NA NA NA
49 1 0.448629602 4 1 1 NA NA NA NA NA NA NA
50 0 -0.529811821 1 2 1 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 NA NA NA NA NA NA NA
52 1 -0.520318482 4 3 1 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 NA NA NA NA NA NA NA
54 1 0.432732046 3 1 1 NA NA NA NA NA NA NA
55 0 -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 1 0.031021711 2 NA 1 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 NA NA NA NA NA NA NA
60 0 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 0 -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 1 -0.678303052 2 4 1 NA NA NA NA NA NA NA
66 0 0.478880037 3 1 1 NA NA NA NA NA NA NA
67 0 -0.428028760 2 3 1 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 NA NA NA NA NA NA NA
69 0 0.216932805 NA 4 1 NA NA NA NA NA NA NA
70 0 -0.234575269 1 1 1 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 NA NA NA NA NA NA NA
72 1 -0.456055171 3 4 1 NA NA NA NA NA NA NA
73 0 0.346486708 4 2 1 NA NA NA NA NA NA NA
74 1 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 0 -0.500179043 4 2 1 NA NA NA NA NA NA NA
77 0 0.527352086 NA 2 1 NA NA NA NA NA NA NA
78 0 0.022742250 2 3 1 NA NA NA NA NA NA NA
79 1 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 1 0.140201825 3 2 1 NA NA NA NA NA NA NA
83 1 -0.141417121 3 4 1 NA NA NA NA NA NA NA
84 1 NA 1 1 1 NA NA NA NA NA NA NA
85 1 -0.021285339 2 1 1 NA NA NA NA NA NA NA
86 1 -0.010196306 1 2 1 NA NA NA NA NA NA NA
87 1 -0.089747520 3 3 1 NA NA NA NA NA NA NA
88 0 -0.083699898 1 3 1 NA NA NA NA NA NA NA
89 1 -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 1 -0.421318463 4 3 1 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 NA NA NA NA NA NA NA
96 1 0.213084904 4 3 1 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 NA NA NA NA NA NA NA
98 1 -0.034767685 3 2 1 NA NA NA NA NA NA NA
99 1 -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
$m6b$spM_lvlone
center scale
B1 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
$m6b$mu_reg_norm
[1] 0
$m6b$tau_reg_norm
[1] 1e-04
$m6b$shape_tau_norm
[1] 0.01
$m6b$rate_tau_norm
[1] 0.01
$m6b$mu_reg_binom
[1] 0
$m6b$tau_reg_binom
[1] 1e-04
$m6b$mu_reg_multinomial
[1] 0
$m6b$tau_reg_multinomial
[1] 1e-04
$m6b$mu_reg_ordinal
[1] 0
$m6b$tau_reg_ordinal
[1] 1e-04
$m6b$mu_delta_ordinal
[1] 0
$m6b$tau_delta_ordinal
[1] 1e-04
$m6c
$m6c$M_lvlone
C1 C2 M2 O2 (Intercept) M22 M23 M24 O22 O23 O24 abs(y - C2)
1 1.410531 0.144065882 4 4 1 NA NA NA NA NA NA NA
2 1.434183 0.032778478 1 4 1 NA NA NA NA NA NA NA
3 1.430994 0.343008492 3 4 1 NA NA NA NA NA NA NA
4 1.453096 -0.361887858 3 1 1 NA NA NA NA NA NA NA
5 1.438344 -0.389600647 4 2 1 NA NA NA NA NA NA NA
6 1.453207 -0.205306841 4 3 1 NA NA NA NA NA NA NA
7 1.425176 0.079434830 1 4 1 NA NA NA NA NA NA NA
8 1.437908 -0.331246757 1 2 1 NA NA NA NA NA NA NA
9 1.416911 -0.329638800 2 4 1 NA NA NA NA NA NA NA
10 1.448638 0.167597533 2 3 1 NA NA NA NA NA NA NA
11 1.428375 0.860207989 3 2 1 NA NA NA NA NA NA NA
12 1.450130 0.022730640 3 1 1 NA NA NA NA NA NA NA
13 1.420545 0.217171172 2 1 1 NA NA NA NA NA NA NA
14 1.423005 -0.403002412 3 1 1 NA NA NA NA NA NA NA
15 1.435902 0.087369742 2 4 1 NA NA NA NA NA NA NA
16 1.423901 -0.183870429 1 3 1 NA NA NA NA NA NA NA
17 1.457208 -0.194577002 4 3 1 NA NA NA NA NA NA NA
18 1.414280 -0.349718516 2 1 1 NA NA NA NA NA NA NA
19 1.443383 -0.508781244 3 3 1 NA NA NA NA NA NA NA
20 1.434954 0.494883111 3 1 1 NA NA NA NA NA NA NA
21 1.429499 0.258041067 2 3 1 NA NA NA NA NA NA NA
22 1.441897 -0.922621989 2 3 1 NA NA NA NA NA NA NA
23 1.423713 0.431254949 3 2 1 NA NA NA NA NA NA NA
24 1.435395 -0.294218881 3 3 1 NA NA NA NA NA NA NA
25 1.425944 -0.425548895 2 2 1 NA NA NA NA NA NA NA
26 1.437115 0.057176054 2 2 1 NA NA NA NA NA NA NA
27 1.441326 0.289090158 1 1 1 NA NA NA NA NA NA NA
28 1.422953 -0.473079489 3 4 1 NA NA NA NA NA NA NA
29 1.437797 -0.385664863 4 3 1 NA NA NA NA NA NA NA
30 1.472121 -0.154780107 2 3 1 NA NA NA NA NA NA NA
31 1.421782 0.100536296 NA 2 1 NA NA NA NA NA NA NA
32 1.457672 0.634791958 4 2 1 NA NA NA NA NA NA NA
33 1.430842 -0.387252617 4 1 1 NA NA NA NA NA NA NA
34 1.431523 -0.181741088 4 1 1 NA NA NA NA NA NA NA
35 1.421395 -0.311562695 2 4 1 NA NA NA NA NA NA NA
36 1.434496 -0.044115907 1 3 1 NA NA NA NA NA NA NA
37 1.425383 -0.657409991 3 3 1 NA NA NA NA NA NA NA
38 1.421802 0.159577214 4 1 1 NA NA NA NA NA NA NA
39 1.430094 -0.460416933 3 2 1 NA NA NA NA NA NA NA
40 1.447621 NA 3 3 1 NA NA NA NA NA NA NA
41 1.434797 -0.248909867 1 3 1 NA NA NA NA NA NA NA
42 1.446091 -0.609021545 4 3 1 NA NA NA NA NA NA NA
43 1.445306 0.025471883 1 3 1 NA NA NA NA NA NA NA
44 1.448783 0.066648592 2 4 1 NA NA NA NA NA NA NA
45 1.450617 -0.276108719 2 4 1 NA NA NA NA NA NA NA
46 1.415055 -0.179737577 1 1 1 NA NA NA NA NA NA NA
47 1.436590 0.181190937 4 4 1 NA NA NA NA NA NA NA
48 1.433938 -0.453871693 2 4 1 NA NA NA NA NA NA NA
49 1.414941 0.448629602 4 1 1 NA NA NA NA NA NA NA
50 1.421807 -0.529811821 1 2 1 NA NA NA NA NA NA NA
51 1.453203 -0.028304571 4 1 1 NA NA NA NA NA NA NA
52 1.452129 -0.520318482 4 3 1 NA NA NA NA NA NA NA
53 1.431510 0.171317619 4 2 1 NA NA NA NA NA NA NA
54 1.430082 0.432732046 3 1 1 NA NA NA NA NA NA NA
55 1.443492 -0.346286005 3 2 1 NA NA NA NA NA NA NA
56 1.436460 -0.469375653 3 3 1 NA NA NA NA NA NA NA
57 1.418119 0.031021711 2 NA 1 NA NA NA NA NA NA NA
58 1.434971 -0.118837515 3 4 1 NA NA NA NA NA NA NA
59 1.445599 0.507769984 3 4 1 NA NA NA NA NA NA NA
60 1.437097 0.271797031 4 3 1 NA NA NA NA NA NA NA
61 1.428360 -0.124442204 2 4 1 NA NA NA NA NA NA NA
62 1.440550 0.277677389 2 1 1 NA NA NA NA NA NA NA
63 1.443014 -0.102893730 1 4 1 NA NA NA NA NA NA NA
64 1.424298 NA 2 4 1 NA NA NA NA NA NA NA
65 1.448823 -0.678303052 2 4 1 NA NA NA NA NA NA NA
66 1.425834 0.478880037 3 1 1 NA NA NA NA NA NA NA
67 1.427102 -0.428028760 2 3 1 NA NA NA NA NA NA NA
68 1.414240 0.048119185 4 3 1 NA NA NA NA NA NA NA
69 1.456218 0.216932805 NA 4 1 NA NA NA NA NA NA NA
70 1.470594 -0.234575269 1 1 1 NA NA NA NA NA NA NA
71 1.425058 0.006827078 2 4 1 NA NA NA NA NA NA NA
72 1.432371 -0.456055171 3 4 1 NA NA NA NA NA NA NA
73 1.441656 0.346486708 4 2 1 NA NA NA NA NA NA NA
74 1.434952 0.205092215 4 4 1 NA NA NA NA NA NA NA
75 1.402860 -0.136596858 1 3 1 NA NA NA NA NA NA NA
76 1.453363 -0.500179043 4 2 1 NA NA NA NA NA NA NA
77 1.432909 0.527352086 NA 2 1 NA NA NA NA NA NA NA
78 1.435103 0.022742250 2 3 1 NA NA NA NA NA NA NA
79 1.434462 NA 2 2 1 NA NA NA NA NA NA NA
80 1.434661 -0.002032440 2 1 1 NA NA NA NA NA NA NA
81 1.445881 -0.154246160 4 4 1 NA NA NA NA NA NA NA
82 1.442548 0.140201825 3 2 1 NA NA NA NA NA NA NA
83 1.430097 -0.141417121 3 4 1 NA NA NA NA NA NA NA
84 1.430119 NA 1 1 1 NA NA NA NA NA NA NA
85 1.430315 -0.021285339 2 1 1 NA NA NA NA NA NA NA
86 1.437584 -0.010196306 1 2 1 NA NA NA NA NA NA NA
87 1.409738 -0.089747520 3 3 1 NA NA NA NA NA NA NA
88 1.422388 -0.083699898 1 3 1 NA NA NA NA NA NA NA
89 1.422509 -0.044061996 2 2 1 NA NA NA NA NA NA NA
90 1.439432 -0.209291697 1 4 1 NA NA NA NA NA NA NA
91 1.430175 0.639036426 3 2 1 NA NA NA NA NA NA NA
92 1.418002 0.094698299 1 1 1 NA NA NA NA NA NA NA
93 1.423812 -0.055510622 4 NA 1 NA NA NA NA NA NA NA
94 1.423473 -0.421318463 4 3 1 NA NA NA NA NA NA NA
95 1.434412 0.125295503 1 1 1 NA NA NA NA NA NA NA
96 1.450844 0.213084904 4 3 1 NA NA NA NA NA NA NA
97 1.433371 -0.161914659 4 2 1 NA NA NA NA NA NA NA
98 1.444378 -0.034767685 3 2 1 NA NA NA NA NA NA NA
99 1.422523 -0.320681689 3 4 1 NA NA NA NA NA NA NA
100 1.410394 0.058192962 4 3 1 NA NA NA NA NA NA NA
O22:abs(y - C2) O23:abs(y - C2) O24:abs(y - C2) y
1 NA NA NA -4.76915977
2 NA NA NA -2.69277172
3 NA NA NA -1.17551547
4 NA NA NA -4.57464473
5 NA NA NA -2.20260004
6 NA NA NA -3.48995315
7 NA NA NA -0.44987258
8 NA NA NA -2.29588848
9 NA NA NA -4.49135812
10 NA NA NA -5.52545368
11 NA NA NA -4.16286741
12 NA NA NA -2.93455761
13 NA NA NA -0.04202496
14 NA NA NA -1.63149775
15 NA NA NA -0.97786151
16 NA NA NA -1.79100431
17 NA NA NA -6.26520032
18 NA NA NA -1.36028709
19 NA NA NA -1.15396597
20 NA NA NA -3.21707239
21 NA NA NA -1.59389898
22 NA NA NA -5.50335066
23 NA NA NA 0.57290123
24 NA NA NA -8.22270323
25 NA NA NA -1.41364158
26 NA NA NA -6.28031574
27 NA NA NA -3.15624425
28 NA NA NA -3.55693639
29 NA NA NA -1.11821124
30 NA NA NA -2.82834175
31 NA NA NA -3.72259860
32 NA NA NA -1.75256656
33 NA NA NA -5.55044409
34 NA NA NA -7.45068147
35 NA NA NA -0.97491919
36 NA NA NA -2.98356481
37 NA NA NA -1.86039471
38 NA NA NA -7.28754607
39 NA NA NA -8.66234796
40 NA NA NA -4.16291375
41 NA NA NA -3.48250771
42 NA NA NA -7.27930410
43 NA NA NA -6.12866190
44 NA NA NA -4.96880803
45 NA NA NA -4.76746713
46 NA NA NA -1.91249177
47 NA NA NA -0.61884029
48 NA NA NA -0.20496175
49 NA NA NA -7.12636055
50 NA NA NA -6.23103837
51 NA NA NA -3.32561065
52 NA NA NA -2.95942339
53 NA NA NA -4.44915114
54 NA NA NA -0.81566463
55 NA NA NA -6.50029573
56 NA NA NA -2.74718050
57 NA NA NA -6.35015663
58 NA NA NA -2.69505883
59 NA NA NA -1.55660833
60 NA NA NA -3.76240209
61 NA NA NA -3.92885797
62 NA NA NA -1.72044748
63 NA NA NA -0.56602625
64 NA NA NA -4.42235015
65 NA NA NA -2.39122287
66 NA NA NA -0.81807247
67 NA NA NA -6.48196782
68 NA NA NA -1.37306273
69 NA NA NA -4.99886487
70 NA NA NA -5.82288217
71 NA NA NA -2.68234219
72 NA NA NA -3.96170442
73 NA NA NA -7.19573667
74 NA NA NA -5.08799713
75 NA NA NA -1.32967262
76 NA NA NA -2.56532332
77 NA NA NA -3.21002900
78 NA NA NA -3.40559790
79 NA NA NA -4.56223913
80 NA NA NA -2.04250454
81 NA NA NA -2.20378059
82 NA NA NA -3.37471317
83 NA NA NA -0.95345385
84 NA NA NA -4.89337660
85 NA NA NA -9.82258463
86 NA NA NA -4.51800734
87 NA NA NA -0.18662049
88 NA NA NA -2.87120881
89 NA NA NA 1.29290150
90 NA NA NA -1.39497744
91 NA NA NA 1.14575040
92 NA NA NA 0.92801246
93 NA NA NA -2.59938157
94 NA NA NA -3.26905923
95 NA NA NA -3.26861434
96 NA NA NA -5.71017484
97 NA NA NA -3.76781806
98 NA NA NA -2.02677390
99 NA NA NA -2.96199765
100 NA NA NA -4.81129496
$m6c$spM_lvlone
center scale
C1 1.43410054 0.01299651
C2 -0.06490582 0.33317347
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(y - C2) 3.29470420 2.19275349
O22:abs(y - C2) 0.80813977 1.84992792
O23:abs(y - C2) 0.98554111 1.92203764
O24:abs(y - C2) 0.67287100 1.40175060
y -3.34428345 2.27649507
$m6c$mu_reg_norm
[1] 0
$m6c$tau_reg_norm
[1] 1e-04
$m6c$shape_tau_norm
[1] 0.01
$m6c$rate_tau_norm
[1] 0.01
$m6c$mu_reg_gamma
[1] 0
$m6c$tau_reg_gamma
[1] 1e-04
$m6c$shape_tau_gamma
[1] 0.01
$m6c$rate_tau_gamma
[1] 0.01
$m6c$mu_reg_multinomial
[1] 0
$m6c$tau_reg_multinomial
[1] 1e-04
$m6c$mu_reg_ordinal
[1] 0
$m6c$tau_reg_ordinal
[1] 1e-04
$m6c$mu_delta_ordinal
[1] 0
$m6c$tau_delta_ordinal
[1] 1e-04
$m6d
$m6d$M_lvlone
SBP bili creat (Intercept) age genderfemale log(bili) exp(creat)
10 108.00000 0.9 1.10 1 35 0 NA NA
14 105.33333 1.0 0.77 1 38 0 NA NA
41 110.00000 0.9 1.14 1 78 1 NA NA
77 106.00000 0.7 0.99 1 23 0 NA NA
91 114.66667 0.6 0.90 1 40 0 NA NA
105 139.33333 1.2 0.88 1 54 0 NA NA
114 124.00000 0.3 0.68 1 31 1 NA NA
135 100.00000 0.5 0.66 1 27 1 NA NA
149 114.66667 0.4 1.05 1 37 0 NA NA
154 156.66667 NA NA 1 50 1 NA NA
155 127.33333 0.8 0.98 1 63 0 NA NA
176 106.66667 0.6 0.67 1 26 1 NA NA
215 114.00000 0.9 0.74 1 35 1 NA NA
220 126.00000 NA NA 1 44 0 NA NA
224 86.00000 1.0 0.76 1 34 1 NA NA
226 117.33333 0.6 0.93 1 60 0 NA NA
264 128.00000 0.6 0.79 1 24 0 NA NA
282 113.33333 NA NA 1 48 0 NA NA
286 117.33333 0.4 0.57 1 68 1 NA NA
300 115.33333 0.7 0.83 1 37 0 NA NA
301 126.66667 0.6 0.77 1 35 0 NA NA
311 110.00000 0.6 0.72 1 59 0 NA NA
317 124.66667 0.6 0.76 1 20 1 NA NA
337 111.33333 0.9 0.91 1 71 1 NA NA
383 153.33333 NA NA 1 53 0 NA NA
391 115.33333 0.8 0.91 1 23 0 NA NA
392 126.66667 1.0 0.83 1 32 0 NA NA
420 98.00000 0.8 0.66 1 36 1 NA NA
422 166.66667 0.6 1.22 1 48 0 NA NA
461 124.66667 1.0 0.99 1 56 0 NA NA
475 112.66667 0.5 0.64 1 40 1 NA NA
483 106.66667 0.6 0.88 1 27 0 NA NA
501 112.66667 0.8 0.95 1 23 0 NA NA
533 110.66667 0.8 0.72 1 44 1 NA NA
538 127.33333 0.4 0.73 1 62 1 NA NA
550 134.00000 1.2 0.73 1 59 1 NA NA
557 135.33333 0.7 1.11 1 54 1 NA NA
589 128.66667 0.7 0.84 1 44 0 NA NA
598 118.66667 0.6 0.68 1 62 1 NA NA
621 120.66667 0.5 0.75 1 31 0 NA NA
631 116.66667 0.7 0.68 1 61 1 NA NA
637 118.66667 0.6 0.79 1 45 1 NA NA
650 111.33333 0.5 0.80 1 41 1 NA NA
673 135.33333 0.4 0.73 1 49 1 NA NA
696 140.66667 1.1 0.81 1 38 0 NA NA
703 106.00000 0.7 0.95 1 36 0 NA NA
704 124.66667 0.4 0.66 1 58 0 NA NA
726 112.66667 0.6 1.05 1 21 1 NA NA
739 107.33333 0.4 0.78 1 50 0 NA NA
747 105.33333 0.8 0.62 1 35 1 NA NA
755 115.33333 0.5 0.47 1 37 1 NA NA
756 123.33333 1.4 0.95 1 37 0 NA NA
766 117.33333 0.8 0.84 1 47 0 NA NA
777 124.00000 0.7 1.00 1 31 0 NA NA
793 109.33333 0.6 0.92 1 42 1 NA NA
818 127.33333 1.0 0.79 1 38 0 NA NA
850 98.66667 1.0 0.75 1 31 0 NA NA
862 108.66667 1.1 0.93 1 43 0 NA NA
866 108.00000 0.5 0.69 1 35 1 NA NA
867 109.33333 0.7 0.80 1 23 1 NA NA
887 160.66667 0.7 0.64 1 54 1 NA NA
894 138.66667 0.7 0.61 1 45 1 NA NA
913 99.33333 0.9 0.72 1 27 1 NA NA
974 114.00000 0.6 0.58 1 23 0 NA NA
976 137.33333 0.8 1.07 1 57 0 NA NA
980 117.33333 0.5 0.69 1 41 1 NA NA
1028 118.66667 0.8 0.74 1 30 0 NA NA
1039 124.66667 1.0 1.07 1 58 0 NA NA
1040 112.00000 0.7 0.97 1 29 1 NA NA
1046 110.66667 1.0 0.62 1 43 1 NA NA
1055 112.00000 1.0 0.69 1 35 0 NA NA
1092 114.66667 0.5 0.68 1 37 1 NA NA
1108 108.66667 0.7 1.03 1 21 0 NA NA
1150 141.33333 1.1 1.15 1 71 0 NA NA
1153 122.00000 0.4 0.94 1 26 0 NA NA
1165 98.00000 1.1 0.92 1 45 0 NA NA
1174 116.66667 0.7 0.84 1 63 1 NA NA
1212 124.66667 0.5 1.35 1 61 0 NA NA
1231 134.66667 0.9 1.10 1 56 0 NA NA
1245 130.00000 NA NA 1 66 1 NA NA
1247 108.66667 0.6 0.82 1 52 1 NA NA
1273 126.66667 0.4 1.00 1 42 0 NA NA
1278 103.33333 0.6 0.69 1 29 0 NA NA
1299 112.00000 0.7 1.10 1 39 0 NA NA
1346 99.33333 0.5 0.77 1 23 1 NA NA
1352 102.00000 0.4 1.04 1 46 1 NA NA
1360 103.00000 0.5 1.02 1 42 0 NA NA
1397 106.66667 0.7 0.66 1 31 1 NA NA
1399 106.66667 0.5 1.15 1 33 1 NA NA
1410 167.33333 0.6 0.72 1 70 1 NA NA
1439 130.00000 1.1 0.69 1 44 0 NA NA
1481 93.33333 0.7 0.77 1 58 1 NA NA
1494 120.66667 0.7 1.05 1 70 0 NA NA
1499 130.00000 0.8 1.29 1 38 0 NA NA
1509 111.33333 1.1 0.88 1 73 0 NA NA
1512 127.33333 0.4 0.77 1 47 1 NA NA
1520 120.00000 0.8 0.71 1 56 1 NA NA
1560 144.00000 0.8 1.08 1 32 0 NA NA
1602 118.00000 0.5 1.15 1 28 0 NA NA
1608 140.66667 0.7 0.89 1 58 0 NA NA
1619 122.00000 0.8 0.90 1 34 0 NA NA
1642 128.66667 0.8 1.18 1 30 0 NA NA
1648 100.00000 1.0 0.73 1 33 1 NA NA
1663 124.00000 0.4 0.96 1 51 0 NA NA
1671 140.66667 0.5 0.86 1 74 1 NA NA
1691 122.00000 1.0 1.12 1 56 0 NA NA
1701 119.33333 0.7 0.77 1 56 1 NA NA
1726 154.66667 0.6 1.12 1 31 0 NA NA
1733 106.66667 0.5 0.93 1 38 1 NA NA
1743 114.66667 0.5 1.13 1 74 0 NA NA
1753 118.66667 1.4 0.85 1 42 1 NA NA
1761 112.66667 0.6 0.68 1 47 1 NA NA
1765 125.33333 0.6 0.99 1 49 0 NA NA
1766 114.00000 1.2 0.98 1 61 0 NA NA
1795 177.33333 0.8 0.63 1 65 1 NA NA
1804 122.66667 0.8 1.01 1 43 0 NA NA
1809 116.00000 0.7 0.79 1 26 0 NA NA
1813 96.66667 NA NA 1 36 0 NA NA
1858 97.33333 1.1 0.83 1 43 1 NA NA
1878 122.00000 0.6 0.96 1 51 0 NA NA
1889 128.00000 0.7 0.98 1 34 0 NA NA
1933 104.66667 1.2 0.52 1 77 1 NA NA
1940 110.66667 0.7 0.83 1 48 1 NA NA
1988 136.00000 0.7 0.64 1 62 1 NA NA
1993 116.66667 0.7 0.72 1 45 1 NA NA
1997 123.33333 0.5 1.01 1 56 0 NA NA
2005 122.00000 0.6 0.93 1 78 1 NA NA
2032 126.66667 0.7 0.77 1 20 0 NA NA
2034 116.00000 0.6 0.98 1 25 0 NA NA
2036 122.00000 0.4 0.67 1 52 1 NA NA
2054 111.33333 0.7 0.64 1 43 1 NA NA
2086 124.66667 0.3 0.56 1 47 1 NA NA
2122 141.33333 0.7 0.68 1 71 1 NA NA
2124 115.33333 0.5 0.96 1 27 0 NA NA
2133 134.66667 0.5 1.38 1 60 0 NA NA
2163 128.66667 0.5 0.64 1 53 1 NA NA
2174 148.66667 0.6 0.85 1 55 1 NA NA
2175 125.33333 1.0 0.72 1 64 0 NA NA
2195 109.33333 1.3 0.85 1 42 1 NA NA
2197 94.00000 0.7 0.87 1 22 0 NA NA
2202 118.66667 0.7 0.88 1 20 0 NA NA
2222 140.66667 0.6 0.66 1 75 1 NA NA
2231 104.00000 0.8 0.83 1 32 0 NA NA
2248 107.33333 0.5 0.82 1 29 1 NA NA
2260 142.00000 0.5 0.76 1 45 1 NA NA
2265 93.33333 0.6 0.56 1 40 1 NA NA
2268 110.00000 0.8 0.82 1 61 1 NA NA
2306 106.66667 0.9 0.95 1 32 0 NA NA
2313 138.00000 0.6 0.86 1 48 1 NA NA
2333 126.00000 0.7 1.06 1 70 0 NA NA
2337 124.00000 0.4 0.50 1 43 1 NA NA
2351 136.00000 0.6 1.03 1 33 0 NA NA
2375 98.66667 1.0 0.82 1 34 0 NA NA
2378 134.66667 0.6 0.77 1 25 0 NA NA
2385 101.33333 0.5 0.74 1 48 1 NA NA
2401 114.66667 0.7 0.84 1 69 1 NA NA
2417 122.66667 0.7 0.68 1 68 1 NA NA
2428 140.66667 0.6 0.74 1 65 0 NA NA
2431 115.33333 0.6 0.69 1 22 1 NA NA
2440 116.66667 0.4 0.65 1 44 1 NA NA
2446 132.00000 0.5 0.73 1 30 0 NA NA
2453 127.33333 0.7 0.80 1 60 0 NA NA
2460 94.66667 0.5 0.65 1 22 1 NA NA
2475 116.00000 0.8 0.92 1 39 0 NA NA
2491 102.66667 0.7 0.64 1 43 1 NA NA
2493 114.00000 0.5 0.83 1 46 1 NA NA
2519 116.00000 0.8 0.73 1 38 0 NA NA
2549 115.33333 0.8 0.85 1 36 0 NA NA
2551 111.33333 0.8 0.58 1 68 1 NA NA
2552 86.00000 0.6 0.69 1 36 1 NA NA
2554 112.66667 0.9 0.89 1 21 0 NA NA
2562 93.33333 NA NA 1 62 0 NA NA
2590 98.66667 1.1 0.84 1 23 1 NA NA
2615 125.33333 1.2 0.91 1 22 0 NA NA
2618 145.33333 1.1 0.82 1 37 0 NA NA
2631 106.00000 1.1 0.65 1 37 1 NA NA
2648 116.66667 0.8 1.12 1 43 0 NA NA
2661 141.33333 0.5 0.94 1 35 0 NA NA
2672 126.66667 0.9 0.84 1 29 0 NA NA
2676 111.33333 NA NA 1 41 0 NA NA
2681 102.66667 0.9 0.79 1 21 1 NA NA
2718 111.33333 0.7 0.95 1 20 0 NA NA
2733 142.66667 0.6 0.80 1 53 1 NA NA
2752 98.66667 1.0 1.01 1 24 0 NA NA
2763 124.00000 0.8 0.94 1 28 0 NA NA
2764 129.33333 1.0 1.08 1 27 0 NA NA
$m6d$spM_lvlone
center scale
SBP 119.2956989 15.3559299
bili 0.7207865 0.2266570
creat 0.8437640 0.1711968
(Intercept) NA NA
age 43.5107527 15.0631963
genderfemale NA NA
log(bili) -0.3758477 0.3135642
exp(creat) 2.3601663 0.4232889
$m6d$mu_reg_norm
[1] 0
$m6d$tau_reg_norm
[1] 1e-04
$m6d$shape_tau_norm
[1] 0.01
$m6d$rate_tau_norm
[1] 0.01
$m6e
$m6e$M_lvlone
SBP bili creat (Intercept) age genderfemale log(bili) exp(creat)
10 108.00000 0.9 1.10 1 35 0 NA NA
14 105.33333 1.0 0.77 1 38 0 NA NA
41 110.00000 0.9 1.14 1 78 1 NA NA
77 106.00000 0.7 0.99 1 23 0 NA NA
91 114.66667 0.6 0.90 1 40 0 NA NA
105 139.33333 1.2 0.88 1 54 0 NA NA
114 124.00000 0.3 0.68 1 31 1 NA NA
135 100.00000 0.5 0.66 1 27 1 NA NA
149 114.66667 0.4 1.05 1 37 0 NA NA
154 156.66667 NA NA 1 50 1 NA NA
155 127.33333 0.8 0.98 1 63 0 NA NA
176 106.66667 0.6 0.67 1 26 1 NA NA
215 114.00000 0.9 0.74 1 35 1 NA NA
220 126.00000 NA NA 1 44 0 NA NA
224 86.00000 1.0 0.76 1 34 1 NA NA
226 117.33333 0.6 0.93 1 60 0 NA NA
264 128.00000 0.6 0.79 1 24 0 NA NA
282 113.33333 NA NA 1 48 0 NA NA
286 117.33333 0.4 0.57 1 68 1 NA NA
300 115.33333 0.7 0.83 1 37 0 NA NA
301 126.66667 0.6 0.77 1 35 0 NA NA
311 110.00000 0.6 0.72 1 59 0 NA NA
317 124.66667 0.6 0.76 1 20 1 NA NA
337 111.33333 0.9 0.91 1 71 1 NA NA
383 153.33333 NA NA 1 53 0 NA NA
391 115.33333 0.8 0.91 1 23 0 NA NA
392 126.66667 1.0 0.83 1 32 0 NA NA
420 98.00000 0.8 0.66 1 36 1 NA NA
422 166.66667 0.6 1.22 1 48 0 NA NA
461 124.66667 1.0 0.99 1 56 0 NA NA
475 112.66667 0.5 0.64 1 40 1 NA NA
483 106.66667 0.6 0.88 1 27 0 NA NA
501 112.66667 0.8 0.95 1 23 0 NA NA
533 110.66667 0.8 0.72 1 44 1 NA NA
538 127.33333 0.4 0.73 1 62 1 NA NA
550 134.00000 1.2 0.73 1 59 1 NA NA
557 135.33333 0.7 1.11 1 54 1 NA NA
589 128.66667 0.7 0.84 1 44 0 NA NA
598 118.66667 0.6 0.68 1 62 1 NA NA
621 120.66667 0.5 0.75 1 31 0 NA NA
631 116.66667 0.7 0.68 1 61 1 NA NA
637 118.66667 0.6 0.79 1 45 1 NA NA
650 111.33333 0.5 0.80 1 41 1 NA NA
673 135.33333 0.4 0.73 1 49 1 NA NA
696 140.66667 1.1 0.81 1 38 0 NA NA
703 106.00000 0.7 0.95 1 36 0 NA NA
704 124.66667 0.4 0.66 1 58 0 NA NA
726 112.66667 0.6 1.05 1 21 1 NA NA
739 107.33333 0.4 0.78 1 50 0 NA NA
747 105.33333 0.8 0.62 1 35 1 NA NA
755 115.33333 0.5 0.47 1 37 1 NA NA
756 123.33333 1.4 0.95 1 37 0 NA NA
766 117.33333 0.8 0.84 1 47 0 NA NA
777 124.00000 0.7 1.00 1 31 0 NA NA
793 109.33333 0.6 0.92 1 42 1 NA NA
818 127.33333 1.0 0.79 1 38 0 NA NA
850 98.66667 1.0 0.75 1 31 0 NA NA
862 108.66667 1.1 0.93 1 43 0 NA NA
866 108.00000 0.5 0.69 1 35 1 NA NA
867 109.33333 0.7 0.80 1 23 1 NA NA
887 160.66667 0.7 0.64 1 54 1 NA NA
894 138.66667 0.7 0.61 1 45 1 NA NA
913 99.33333 0.9 0.72 1 27 1 NA NA
974 114.00000 0.6 0.58 1 23 0 NA NA
976 137.33333 0.8 1.07 1 57 0 NA NA
980 117.33333 0.5 0.69 1 41 1 NA NA
1028 118.66667 0.8 0.74 1 30 0 NA NA
1039 124.66667 1.0 1.07 1 58 0 NA NA
1040 112.00000 0.7 0.97 1 29 1 NA NA
1046 110.66667 1.0 0.62 1 43 1 NA NA
1055 112.00000 1.0 0.69 1 35 0 NA NA
1092 114.66667 0.5 0.68 1 37 1 NA NA
1108 108.66667 0.7 1.03 1 21 0 NA NA
1150 141.33333 1.1 1.15 1 71 0 NA NA
1153 122.00000 0.4 0.94 1 26 0 NA NA
1165 98.00000 1.1 0.92 1 45 0 NA NA
1174 116.66667 0.7 0.84 1 63 1 NA NA
1212 124.66667 0.5 1.35 1 61 0 NA NA
1231 134.66667 0.9 1.10 1 56 0 NA NA
1245 130.00000 NA NA 1 66 1 NA NA
1247 108.66667 0.6 0.82 1 52 1 NA NA
1273 126.66667 0.4 1.00 1 42 0 NA NA
1278 103.33333 0.6 0.69 1 29 0 NA NA
1299 112.00000 0.7 1.10 1 39 0 NA NA
1346 99.33333 0.5 0.77 1 23 1 NA NA
1352 102.00000 0.4 1.04 1 46 1 NA NA
1360 103.00000 0.5 1.02 1 42 0 NA NA
1397 106.66667 0.7 0.66 1 31 1 NA NA
1399 106.66667 0.5 1.15 1 33 1 NA NA
1410 167.33333 0.6 0.72 1 70 1 NA NA
1439 130.00000 1.1 0.69 1 44 0 NA NA
1481 93.33333 0.7 0.77 1 58 1 NA NA
1494 120.66667 0.7 1.05 1 70 0 NA NA
1499 130.00000 0.8 1.29 1 38 0 NA NA
1509 111.33333 1.1 0.88 1 73 0 NA NA
1512 127.33333 0.4 0.77 1 47 1 NA NA
1520 120.00000 0.8 0.71 1 56 1 NA NA
1560 144.00000 0.8 1.08 1 32 0 NA NA
1602 118.00000 0.5 1.15 1 28 0 NA NA
1608 140.66667 0.7 0.89 1 58 0 NA NA
1619 122.00000 0.8 0.90 1 34 0 NA NA
1642 128.66667 0.8 1.18 1 30 0 NA NA
1648 100.00000 1.0 0.73 1 33 1 NA NA
1663 124.00000 0.4 0.96 1 51 0 NA NA
1671 140.66667 0.5 0.86 1 74 1 NA NA
1691 122.00000 1.0 1.12 1 56 0 NA NA
1701 119.33333 0.7 0.77 1 56 1 NA NA
1726 154.66667 0.6 1.12 1 31 0 NA NA
1733 106.66667 0.5 0.93 1 38 1 NA NA
1743 114.66667 0.5 1.13 1 74 0 NA NA
1753 118.66667 1.4 0.85 1 42 1 NA NA
1761 112.66667 0.6 0.68 1 47 1 NA NA
1765 125.33333 0.6 0.99 1 49 0 NA NA
1766 114.00000 1.2 0.98 1 61 0 NA NA
1795 177.33333 0.8 0.63 1 65 1 NA NA
1804 122.66667 0.8 1.01 1 43 0 NA NA
1809 116.00000 0.7 0.79 1 26 0 NA NA
1813 96.66667 NA NA 1 36 0 NA NA
1858 97.33333 1.1 0.83 1 43 1 NA NA
1878 122.00000 0.6 0.96 1 51 0 NA NA
1889 128.00000 0.7 0.98 1 34 0 NA NA
1933 104.66667 1.2 0.52 1 77 1 NA NA
1940 110.66667 0.7 0.83 1 48 1 NA NA
1988 136.00000 0.7 0.64 1 62 1 NA NA
1993 116.66667 0.7 0.72 1 45 1 NA NA
1997 123.33333 0.5 1.01 1 56 0 NA NA
2005 122.00000 0.6 0.93 1 78 1 NA NA
2032 126.66667 0.7 0.77 1 20 0 NA NA
2034 116.00000 0.6 0.98 1 25 0 NA NA
2036 122.00000 0.4 0.67 1 52 1 NA NA
2054 111.33333 0.7 0.64 1 43 1 NA NA
2086 124.66667 0.3 0.56 1 47 1 NA NA
2122 141.33333 0.7 0.68 1 71 1 NA NA
2124 115.33333 0.5 0.96 1 27 0 NA NA
2133 134.66667 0.5 1.38 1 60 0 NA NA
2163 128.66667 0.5 0.64 1 53 1 NA NA
2174 148.66667 0.6 0.85 1 55 1 NA NA
2175 125.33333 1.0 0.72 1 64 0 NA NA
2195 109.33333 1.3 0.85 1 42 1 NA NA
2197 94.00000 0.7 0.87 1 22 0 NA NA
2202 118.66667 0.7 0.88 1 20 0 NA NA
2222 140.66667 0.6 0.66 1 75 1 NA NA
2231 104.00000 0.8 0.83 1 32 0 NA NA
2248 107.33333 0.5 0.82 1 29 1 NA NA
2260 142.00000 0.5 0.76 1 45 1 NA NA
2265 93.33333 0.6 0.56 1 40 1 NA NA
2268 110.00000 0.8 0.82 1 61 1 NA NA
2306 106.66667 0.9 0.95 1 32 0 NA NA
2313 138.00000 0.6 0.86 1 48 1 NA NA
2333 126.00000 0.7 1.06 1 70 0 NA NA
2337 124.00000 0.4 0.50 1 43 1 NA NA
2351 136.00000 0.6 1.03 1 33 0 NA NA
2375 98.66667 1.0 0.82 1 34 0 NA NA
2378 134.66667 0.6 0.77 1 25 0 NA NA
2385 101.33333 0.5 0.74 1 48 1 NA NA
2401 114.66667 0.7 0.84 1 69 1 NA NA
2417 122.66667 0.7 0.68 1 68 1 NA NA
2428 140.66667 0.6 0.74 1 65 0 NA NA
2431 115.33333 0.6 0.69 1 22 1 NA NA
2440 116.66667 0.4 0.65 1 44 1 NA NA
2446 132.00000 0.5 0.73 1 30 0 NA NA
2453 127.33333 0.7 0.80 1 60 0 NA NA
2460 94.66667 0.5 0.65 1 22 1 NA NA
2475 116.00000 0.8 0.92 1 39 0 NA NA
2491 102.66667 0.7 0.64 1 43 1 NA NA
2493 114.00000 0.5 0.83 1 46 1 NA NA
2519 116.00000 0.8 0.73 1 38 0 NA NA
2549 115.33333 0.8 0.85 1 36 0 NA NA
2551 111.33333 0.8 0.58 1 68 1 NA NA
2552 86.00000 0.6 0.69 1 36 1 NA NA
2554 112.66667 0.9 0.89 1 21 0 NA NA
2562 93.33333 NA NA 1 62 0 NA NA
2590 98.66667 1.1 0.84 1 23 1 NA NA
2615 125.33333 1.2 0.91 1 22 0 NA NA
2618 145.33333 1.1 0.82 1 37 0 NA NA
2631 106.00000 1.1 0.65 1 37 1 NA NA
2648 116.66667 0.8 1.12 1 43 0 NA NA
2661 141.33333 0.5 0.94 1 35 0 NA NA
2672 126.66667 0.9 0.84 1 29 0 NA NA
2676 111.33333 NA NA 1 41 0 NA NA
2681 102.66667 0.9 0.79 1 21 1 NA NA
2718 111.33333 0.7 0.95 1 20 0 NA NA
2733 142.66667 0.6 0.80 1 53 1 NA NA
2752 98.66667 1.0 1.01 1 24 0 NA NA
2763 124.00000 0.8 0.94 1 28 0 NA NA
2764 129.33333 1.0 1.08 1 27 0 NA NA
$m6e$spM_lvlone
center scale
SBP 119.2956989 15.3559299
bili 0.7207865 0.2266570
creat 0.8437640 0.1711968
(Intercept) NA NA
age 43.5107527 15.0631963
genderfemale NA NA
log(bili) -0.3758477 0.3135642
exp(creat) 2.3601663 0.4232889
$m6e$mu_reg_norm
[1] 0
$m6e$tau_reg_norm
[1] 1e-04
$m6e$shape_tau_norm
[1] 0.01
$m6e$rate_tau_norm
[1] 0.01
$m6f
$m6f$M_lvlone
SBP bili creat (Intercept) age genderfemale log(bili) exp(creat)
10 108.00000 0.9 1.10 1 35 0 NA NA
14 105.33333 1.0 0.77 1 38 0 NA NA
41 110.00000 0.9 1.14 1 78 1 NA NA
77 106.00000 0.7 0.99 1 23 0 NA NA
91 114.66667 0.6 0.90 1 40 0 NA NA
105 139.33333 1.2 0.88 1 54 0 NA NA
114 124.00000 0.3 0.68 1 31 1 NA NA
135 100.00000 0.5 0.66 1 27 1 NA NA
149 114.66667 0.4 1.05 1 37 0 NA NA
154 156.66667 NA NA 1 50 1 NA NA
155 127.33333 0.8 0.98 1 63 0 NA NA
176 106.66667 0.6 0.67 1 26 1 NA NA
215 114.00000 0.9 0.74 1 35 1 NA NA
220 126.00000 NA NA 1 44 0 NA NA
224 86.00000 1.0 0.76 1 34 1 NA NA
226 117.33333 0.6 0.93 1 60 0 NA NA
264 128.00000 0.6 0.79 1 24 0 NA NA
282 113.33333 NA NA 1 48 0 NA NA
286 117.33333 0.4 0.57 1 68 1 NA NA
300 115.33333 0.7 0.83 1 37 0 NA NA
301 126.66667 0.6 0.77 1 35 0 NA NA
311 110.00000 0.6 0.72 1 59 0 NA NA
317 124.66667 0.6 0.76 1 20 1 NA NA
337 111.33333 0.9 0.91 1 71 1 NA NA
383 153.33333 NA NA 1 53 0 NA NA
391 115.33333 0.8 0.91 1 23 0 NA NA
392 126.66667 1.0 0.83 1 32 0 NA NA
420 98.00000 0.8 0.66 1 36 1 NA NA
422 166.66667 0.6 1.22 1 48 0 NA NA
461 124.66667 1.0 0.99 1 56 0 NA NA
475 112.66667 0.5 0.64 1 40 1 NA NA
483 106.66667 0.6 0.88 1 27 0 NA NA
501 112.66667 0.8 0.95 1 23 0 NA NA
533 110.66667 0.8 0.72 1 44 1 NA NA
538 127.33333 0.4 0.73 1 62 1 NA NA
550 134.00000 1.2 0.73 1 59 1 NA NA
557 135.33333 0.7 1.11 1 54 1 NA NA
589 128.66667 0.7 0.84 1 44 0 NA NA
598 118.66667 0.6 0.68 1 62 1 NA NA
621 120.66667 0.5 0.75 1 31 0 NA NA
631 116.66667 0.7 0.68 1 61 1 NA NA
637 118.66667 0.6 0.79 1 45 1 NA NA
650 111.33333 0.5 0.80 1 41 1 NA NA
673 135.33333 0.4 0.73 1 49 1 NA NA
696 140.66667 1.1 0.81 1 38 0 NA NA
703 106.00000 0.7 0.95 1 36 0 NA NA
704 124.66667 0.4 0.66 1 58 0 NA NA
726 112.66667 0.6 1.05 1 21 1 NA NA
739 107.33333 0.4 0.78 1 50 0 NA NA
747 105.33333 0.8 0.62 1 35 1 NA NA
755 115.33333 0.5 0.47 1 37 1 NA NA
756 123.33333 1.4 0.95 1 37 0 NA NA
766 117.33333 0.8 0.84 1 47 0 NA NA
777 124.00000 0.7 1.00 1 31 0 NA NA
793 109.33333 0.6 0.92 1 42 1 NA NA
818 127.33333 1.0 0.79 1 38 0 NA NA
850 98.66667 1.0 0.75 1 31 0 NA NA
862 108.66667 1.1 0.93 1 43 0 NA NA
866 108.00000 0.5 0.69 1 35 1 NA NA
867 109.33333 0.7 0.80 1 23 1 NA NA
887 160.66667 0.7 0.64 1 54 1 NA NA
894 138.66667 0.7 0.61 1 45 1 NA NA
913 99.33333 0.9 0.72 1 27 1 NA NA
974 114.00000 0.6 0.58 1 23 0 NA NA
976 137.33333 0.8 1.07 1 57 0 NA NA
980 117.33333 0.5 0.69 1 41 1 NA NA
1028 118.66667 0.8 0.74 1 30 0 NA NA
1039 124.66667 1.0 1.07 1 58 0 NA NA
1040 112.00000 0.7 0.97 1 29 1 NA NA
1046 110.66667 1.0 0.62 1 43 1 NA NA
1055 112.00000 1.0 0.69 1 35 0 NA NA
1092 114.66667 0.5 0.68 1 37 1 NA NA
1108 108.66667 0.7 1.03 1 21 0 NA NA
1150 141.33333 1.1 1.15 1 71 0 NA NA
1153 122.00000 0.4 0.94 1 26 0 NA NA
1165 98.00000 1.1 0.92 1 45 0 NA NA
1174 116.66667 0.7 0.84 1 63 1 NA NA
1212 124.66667 0.5 1.35 1 61 0 NA NA
1231 134.66667 0.9 1.10 1 56 0 NA NA
1245 130.00000 NA NA 1 66 1 NA NA
1247 108.66667 0.6 0.82 1 52 1 NA NA
1273 126.66667 0.4 1.00 1 42 0 NA NA
1278 103.33333 0.6 0.69 1 29 0 NA NA
1299 112.00000 0.7 1.10 1 39 0 NA NA
1346 99.33333 0.5 0.77 1 23 1 NA NA
1352 102.00000 0.4 1.04 1 46 1 NA NA
1360 103.00000 0.5 1.02 1 42 0 NA NA
1397 106.66667 0.7 0.66 1 31 1 NA NA
1399 106.66667 0.5 1.15 1 33 1 NA NA
1410 167.33333 0.6 0.72 1 70 1 NA NA
1439 130.00000 1.1 0.69 1 44 0 NA NA
1481 93.33333 0.7 0.77 1 58 1 NA NA
1494 120.66667 0.7 1.05 1 70 0 NA NA
1499 130.00000 0.8 1.29 1 38 0 NA NA
1509 111.33333 1.1 0.88 1 73 0 NA NA
1512 127.33333 0.4 0.77 1 47 1 NA NA
1520 120.00000 0.8 0.71 1 56 1 NA NA
1560 144.00000 0.8 1.08 1 32 0 NA NA
1602 118.00000 0.5 1.15 1 28 0 NA NA
1608 140.66667 0.7 0.89 1 58 0 NA NA
1619 122.00000 0.8 0.90 1 34 0 NA NA
1642 128.66667 0.8 1.18 1 30 0 NA NA
1648 100.00000 1.0 0.73 1 33 1 NA NA
1663 124.00000 0.4 0.96 1 51 0 NA NA
1671 140.66667 0.5 0.86 1 74 1 NA NA
1691 122.00000 1.0 1.12 1 56 0 NA NA
1701 119.33333 0.7 0.77 1 56 1 NA NA
1726 154.66667 0.6 1.12 1 31 0 NA NA
1733 106.66667 0.5 0.93 1 38 1 NA NA
1743 114.66667 0.5 1.13 1 74 0 NA NA
1753 118.66667 1.4 0.85 1 42 1 NA NA
1761 112.66667 0.6 0.68 1 47 1 NA NA
1765 125.33333 0.6 0.99 1 49 0 NA NA
1766 114.00000 1.2 0.98 1 61 0 NA NA
1795 177.33333 0.8 0.63 1 65 1 NA NA
1804 122.66667 0.8 1.01 1 43 0 NA NA
1809 116.00000 0.7 0.79 1 26 0 NA NA
1813 96.66667 NA NA 1 36 0 NA NA
1858 97.33333 1.1 0.83 1 43 1 NA NA
1878 122.00000 0.6 0.96 1 51 0 NA NA
1889 128.00000 0.7 0.98 1 34 0 NA NA
1933 104.66667 1.2 0.52 1 77 1 NA NA
1940 110.66667 0.7 0.83 1 48 1 NA NA
1988 136.00000 0.7 0.64 1 62 1 NA NA
1993 116.66667 0.7 0.72 1 45 1 NA NA
1997 123.33333 0.5 1.01 1 56 0 NA NA
2005 122.00000 0.6 0.93 1 78 1 NA NA
2032 126.66667 0.7 0.77 1 20 0 NA NA
2034 116.00000 0.6 0.98 1 25 0 NA NA
2036 122.00000 0.4 0.67 1 52 1 NA NA
2054 111.33333 0.7 0.64 1 43 1 NA NA
2086 124.66667 0.3 0.56 1 47 1 NA NA
2122 141.33333 0.7 0.68 1 71 1 NA NA
2124 115.33333 0.5 0.96 1 27 0 NA NA
2133 134.66667 0.5 1.38 1 60 0 NA NA
2163 128.66667 0.5 0.64 1 53 1 NA NA
2174 148.66667 0.6 0.85 1 55 1 NA NA
2175 125.33333 1.0 0.72 1 64 0 NA NA
2195 109.33333 1.3 0.85 1 42 1 NA NA
2197 94.00000 0.7 0.87 1 22 0 NA NA
2202 118.66667 0.7 0.88 1 20 0 NA NA
2222 140.66667 0.6 0.66 1 75 1 NA NA
2231 104.00000 0.8 0.83 1 32 0 NA NA
2248 107.33333 0.5 0.82 1 29 1 NA NA
2260 142.00000 0.5 0.76 1 45 1 NA NA
2265 93.33333 0.6 0.56 1 40 1 NA NA
2268 110.00000 0.8 0.82 1 61 1 NA NA
2306 106.66667 0.9 0.95 1 32 0 NA NA
2313 138.00000 0.6 0.86 1 48 1 NA NA
2333 126.00000 0.7 1.06 1 70 0 NA NA
2337 124.00000 0.4 0.50 1 43 1 NA NA
2351 136.00000 0.6 1.03 1 33 0 NA NA
2375 98.66667 1.0 0.82 1 34 0 NA NA
2378 134.66667 0.6 0.77 1 25 0 NA NA
2385 101.33333 0.5 0.74 1 48 1 NA NA
2401 114.66667 0.7 0.84 1 69 1 NA NA
2417 122.66667 0.7 0.68 1 68 1 NA NA
2428 140.66667 0.6 0.74 1 65 0 NA NA
2431 115.33333 0.6 0.69 1 22 1 NA NA
2440 116.66667 0.4 0.65 1 44 1 NA NA
2446 132.00000 0.5 0.73 1 30 0 NA NA
2453 127.33333 0.7 0.80 1 60 0 NA NA
2460 94.66667 0.5 0.65 1 22 1 NA NA
2475 116.00000 0.8 0.92 1 39 0 NA NA
2491 102.66667 0.7 0.64 1 43 1 NA NA
2493 114.00000 0.5 0.83 1 46 1 NA NA
2519 116.00000 0.8 0.73 1 38 0 NA NA
2549 115.33333 0.8 0.85 1 36 0 NA NA
2551 111.33333 0.8 0.58 1 68 1 NA NA
2552 86.00000 0.6 0.69 1 36 1 NA NA
2554 112.66667 0.9 0.89 1 21 0 NA NA
2562 93.33333 NA NA 1 62 0 NA NA
2590 98.66667 1.1 0.84 1 23 1 NA NA
2615 125.33333 1.2 0.91 1 22 0 NA NA
2618 145.33333 1.1 0.82 1 37 0 NA NA
2631 106.00000 1.1 0.65 1 37 1 NA NA
2648 116.66667 0.8 1.12 1 43 0 NA NA
2661 141.33333 0.5 0.94 1 35 0 NA NA
2672 126.66667 0.9 0.84 1 29 0 NA NA
2676 111.33333 NA NA 1 41 0 NA NA
2681 102.66667 0.9 0.79 1 21 1 NA NA
2718 111.33333 0.7 0.95 1 20 0 NA NA
2733 142.66667 0.6 0.80 1 53 1 NA NA
2752 98.66667 1.0 1.01 1 24 0 NA NA
2763 124.00000 0.8 0.94 1 28 0 NA NA
2764 129.33333 1.0 1.08 1 27 0 NA NA
$m6f$spM_lvlone
center scale
SBP 119.2956989 15.3559299
bili 0.7207865 0.2266570
creat 0.8437640 0.1711968
(Intercept) NA NA
age 43.5107527 15.0631963
genderfemale NA NA
log(bili) -0.3758477 0.3135642
exp(creat) 2.3601663 0.4232889
$m6f$mu_reg_norm
[1] 0
$m6f$tau_reg_norm
[1] 1e-04
$m6f$shape_tau_norm
[1] 0.01
$m6f$rate_tau_norm
[1] 0.01
$m6f$mu_reg_gamma
[1] 0
$m6f$tau_reg_gamma
[1] 1e-04
$m6f$shape_tau_gamma
[1] 0.01
$m6f$rate_tau_gamma
[1] 0.01
$mod7a
$mod7a$M_lvlone
SBP bili (Intercept) ns(age, df = 2)1 ns(age, df = 2)2 genderfemale
10 108.00000 0.9 1 0.40123555 -0.219432742 0
14 105.33333 1.0 1 0.46083535 -0.235722523 0
41 110.00000 0.9 1 0.31290629 0.806930162 1
77 106.00000 0.7 1 0.08775523 -0.053921737 0
91 114.66667 0.6 1 0.49442764 -0.238374584 0
105 139.33333 1.2 1 0.57496216 -0.050288463 0
114 124.00000 0.3 1 0.30749447 -0.178633788 1
135 100.00000 0.5 1 0.20151795 -0.121481193 1
149 114.66667 0.4 1 0.44212741 -0.231841236 0
154 156.66667 NA 1 0.57740015 -0.138040409 1
155 127.33333 0.8 1 0.51397109 0.221341024 0
176 106.66667 0.6 1 0.17363342 -0.105334677 1
215 114.00000 0.9 1 0.40123555 -0.219432742 1
220 126.00000 NA 1 0.54452072 -0.220834542 0
224 86.00000 1.0 1 0.37919068 -0.211091372 1
226 117.33333 0.6 1 0.54156624 0.121087888 0
264 128.00000 0.6 1 0.11668254 -0.071462030 0
282 113.33333 NA 1 0.57165173 -0.172603893 0
286 117.33333 0.4 1 0.45580036 0.404707513 1
300 115.33333 0.7 1 0.44212741 -0.231841236 0
301 126.66667 0.6 1 0.40123555 -0.219432742 0
311 110.00000 0.6 1 0.54929184 0.089638669 0
317 124.66667 0.6 1 0.00000000 0.000000000 1
337 111.33333 0.9 1 0.41545995 0.521995691 1
383 153.33333 NA 1 0.57720730 -0.074412613 0
391 115.33333 0.8 1 0.08775523 -0.053921737 0
392 126.66667 1.0 1 0.33225062 -0.190598976 0
420 98.00000 0.8 1 0.42223764 -0.226380337 1
422 166.66667 0.6 1 0.57165173 -0.172603893 0
461 124.66667 1.0 1 0.56745550 0.001991464 0
475 112.66667 0.5 1 0.49442764 -0.238374584 1
483 106.66667 0.6 1 0.20151795 -0.121481193 0
501 112.66667 0.8 1 0.08775523 -0.053921737 0
533 110.66667 0.8 1 0.54452072 -0.220834542 1
538 127.33333 0.4 1 0.52386338 0.186995848 1
550 134.00000 1.2 1 0.54929184 0.089638669 1
557 135.33333 0.7 1 0.57496216 -0.050288463 1
589 128.66667 0.7 1 0.54452072 -0.220834542 0
598 118.66667 0.6 1 0.52386338 0.186995848 1
621 120.66667 0.5 1 0.30749447 -0.178633788 0
631 116.66667 0.7 1 0.53307593 0.153559208 1
637 118.66667 0.6 1 0.55336396 -0.211530878 1
650 111.33333 0.5 1 0.50917296 -0.236959521 1
673 135.33333 0.4 1 0.57514196 -0.156145511 1
696 140.66667 1.1 1 0.46083535 -0.235722523 0
703 106.00000 0.7 1 0.42223764 -0.226380337 0
704 124.66667 0.4 1 0.55621024 0.059268337 0
726 112.66667 0.6 1 0.02934443 -0.018097803 1
739 107.33333 0.4 1 0.57740015 -0.138040409 0
747 105.33333 0.8 1 0.40123555 -0.219432742 1
755 115.33333 0.5 1 0.44212741 -0.231841236 1
756 123.33333 1.4 1 0.44212741 -0.231841236 0
766 117.33333 0.8 1 0.56688698 -0.187358770 0
777 124.00000 0.7 1 0.30749447 -0.178633788 0
793 109.33333 0.6 1 0.52245837 -0.233593171 1
818 127.33333 1.0 1 0.46083535 -0.235722523 0
850 98.66667 1.0 1 0.30749447 -0.178633788 0
862 108.66667 1.1 1 0.53423302 -0.228207567 0
866 108.00000 0.5 1 0.40123555 -0.219432742 1
867 109.33333 0.7 1 0.08775523 -0.053921737 1
887 160.66667 0.7 1 0.57496216 -0.050288463 1
894 138.66667 0.7 1 0.55336396 -0.211530878 1
913 99.33333 0.9 1 0.20151795 -0.121481193 1
974 114.00000 0.6 1 0.08775523 -0.053921737 0
976 137.33333 0.8 1 0.56227896 0.030033674 0
980 117.33333 0.5 1 0.50917296 -0.236959521 1
1028 118.66667 0.8 1 0.28197362 -0.165646497 0
1039 124.66667 1.0 1 0.55621024 0.059268337 0
1040 112.00000 0.7 1 0.25575756 -0.151730022 1
1046 110.66667 1.0 1 0.53423302 -0.228207567 1
1055 112.00000 1.0 1 0.40123555 -0.219432742 0
1092 114.66667 0.5 1 0.44212741 -0.231841236 1
1108 108.66667 0.7 1 0.02934443 -0.018097803 0
1150 141.33333 1.1 1 0.41545995 0.521995691 0
1153 122.00000 0.4 1 0.17363342 -0.105334677 0
1165 98.00000 1.1 1 0.55336396 -0.211530878 0
1174 116.66667 0.7 1 0.51397109 0.221341024 1
1212 124.66667 0.5 1 0.53307593 0.153559208 0
1231 134.66667 0.9 1 0.56745550 0.001991464 0
1245 130.00000 NA 1 0.48064057 0.329259929 1
1247 108.66667 0.6 1 0.57839033 -0.097117177 1
1273 126.66667 0.4 1 0.52245837 -0.233593171 0
1278 103.33333 0.6 1 0.25575756 -0.151730022 0
1299 112.00000 0.7 1 0.47829193 -0.237931278 0
1346 99.33333 0.5 1 0.08775523 -0.053921737 1
1352 102.00000 0.4 1 0.56080521 -0.200353360 1
1360 103.00000 0.5 1 0.52245837 -0.233593171 0
1397 106.66667 0.7 1 0.30749447 -0.178633788 1
1399 106.66667 0.5 1 0.35617252 -0.201449144 1
1410 167.33333 0.6 1 0.42926079 0.482426436 1
1439 130.00000 1.1 1 0.54452072 -0.220834542 0
1481 93.33333 0.7 1 0.55621024 0.059268337 1
1494 120.66667 0.7 1 0.42926079 0.482426436 0
1499 130.00000 0.8 1 0.46083535 -0.235722523 0
1509 111.33333 1.1 1 0.38700859 0.602269871 0
1512 127.33333 0.4 1 0.56688698 -0.187358770 1
1520 120.00000 0.8 1 0.56745550 0.001991464 1
1560 144.00000 0.8 1 0.33225062 -0.190598976 0
1602 118.00000 0.5 1 0.22891583 -0.136977281 0
1608 140.66667 0.7 1 0.55621024 0.059268337 0
1619 122.00000 0.8 1 0.37919068 -0.211091372 0
1642 128.66667 0.8 1 0.28197362 -0.165646497 0
1648 100.00000 1.0 1 0.35617252 -0.201449144 1
1663 124.00000 0.4 1 0.57846877 -0.118345370 0
1671 140.66667 0.5 1 0.37244303 0.642861228 1
1691 122.00000 1.0 1 0.56745550 0.001991464 0
1701 119.33333 0.7 1 0.56745550 0.001991464 1
1726 154.66667 0.6 1 0.30749447 -0.178633788 0
1733 106.66667 0.5 1 0.46083535 -0.235722523 1
1743 114.66667 0.5 1 0.37244303 0.642861228 0
1753 118.66667 1.4 1 0.52245837 -0.233593171 1
1761 112.66667 0.6 1 0.56688698 -0.187358770 1
1765 125.33333 0.6 1 0.57514196 -0.156145511 0
1766 114.00000 1.2 1 0.53307593 0.153559208 0
1795 177.33333 0.8 1 0.49231720 0.292529847 1
1804 122.66667 0.8 1 0.53423302 -0.228207567 0
1809 116.00000 0.7 1 0.17363342 -0.105334677 0
1813 96.66667 NA 1 0.42223764 -0.226380337 0
1858 97.33333 1.1 1 0.53423302 -0.228207567 1
1878 122.00000 0.6 1 0.57846877 -0.118345370 0
1889 128.00000 0.7 1 0.37919068 -0.211091372 0
1933 104.66667 1.2 1 0.32789669 0.765770970 1
1940 110.66667 0.7 1 0.57165173 -0.172603893 1
1988 136.00000 0.7 1 0.52386338 0.186995848 1
1993 116.66667 0.7 1 0.55336396 -0.211530878 1
1997 123.33333 0.5 1 0.56745550 0.001991464 0
2005 122.00000 0.6 1 0.31290629 0.806930162 1
2032 126.66667 0.7 1 0.00000000 0.000000000 0
2034 116.00000 0.6 1 0.14533178 -0.088630649 0
2036 122.00000 0.4 1 0.57839033 -0.097117177 1
2054 111.33333 0.7 1 0.53423302 -0.228207567 1
2086 124.66667 0.3 1 0.56688698 -0.187358770 1
2122 141.33333 0.7 1 0.41545995 0.521995691 1
2124 115.33333 0.5 1 0.20151795 -0.121481193 0
2133 134.66667 0.5 1 0.54156624 0.121087888 0
2163 128.66667 0.5 1 0.57720730 -0.074412613 1
2174 148.66667 0.6 1 0.57169740 -0.024801509 1
2175 125.33333 1.0 1 0.50344153 0.256537951 0
2195 109.33333 1.3 1 0.52245837 -0.233593171 1
2197 94.00000 0.7 1 0.05861935 -0.036102689 0
2202 118.66667 0.7 1 0.00000000 0.000000000 0
2222 140.66667 0.6 1 0.35770754 0.683679720 1
2231 104.00000 0.8 1 0.33225062 -0.190598976 0
2248 107.33333 0.5 1 0.25575756 -0.151730022 1
2260 142.00000 0.5 1 0.55336396 -0.211530878 1
2265 93.33333 0.6 1 0.49442764 -0.238374584 1
2268 110.00000 0.8 1 0.53307593 0.153559208 1
2306 106.66667 0.9 1 0.33225062 -0.190598976 0
2313 138.00000 0.6 1 0.57165173 -0.172603893 1
2333 126.00000 0.7 1 0.42926079 0.482426436 0
2337 124.00000 0.4 1 0.53423302 -0.228207567 1
2351 136.00000 0.6 1 0.35617252 -0.201449144 0
2375 98.66667 1.0 1 0.37919068 -0.211091372 0
2378 134.66667 0.6 1 0.14533178 -0.088630649 0
2385 101.33333 0.5 1 0.57165173 -0.172603893 1
2401 114.66667 0.7 1 0.44272175 0.443311449 1
2417 122.66667 0.7 1 0.45580036 0.404707513 1
2428 140.66667 0.6 1 0.49231720 0.292529847 0
2431 115.33333 0.6 1 0.05861935 -0.036102689 1
2440 116.66667 0.4 1 0.54452072 -0.220834542 1
2446 132.00000 0.5 1 0.28197362 -0.165646497 0
2453 127.33333 0.7 1 0.54156624 0.121087888 0
2460 94.66667 0.5 1 0.05861935 -0.036102689 1
2475 116.00000 0.8 1 0.47829193 -0.237931278 0
2491 102.66667 0.7 1 0.53423302 -0.228207567 1
2493 114.00000 0.5 1 0.56080521 -0.200353360 1
2519 116.00000 0.8 1 0.46083535 -0.235722523 0
2549 115.33333 0.8 1 0.42223764 -0.226380337 0
2551 111.33333 0.8 1 0.45580036 0.404707513 1
2552 86.00000 0.6 1 0.42223764 -0.226380337 1
2554 112.66667 0.9 1 0.02934443 -0.018097803 0
2562 93.33333 NA 1 0.52386338 0.186995848 0
2590 98.66667 1.1 1 0.08775523 -0.053921737 1
2615 125.33333 1.2 1 0.05861935 -0.036102689 0
2618 145.33333 1.1 1 0.44212741 -0.231841236 0
2631 106.00000 1.1 1 0.44212741 -0.231841236 1
2648 116.66667 0.8 1 0.53423302 -0.228207567 0
2661 141.33333 0.5 1 0.40123555 -0.219432742 0
2672 126.66667 0.9 1 0.25575756 -0.151730022 0
2676 111.33333 NA 1 0.50917296 -0.236959521 0
2681 102.66667 0.9 1 0.02934443 -0.018097803 1
2718 111.33333 0.7 1 0.00000000 0.000000000 0
2733 142.66667 0.6 1 0.57720730 -0.074412613 1
2752 98.66667 1.0 1 0.11668254 -0.071462030 0
2763 124.00000 0.8 1 0.22891583 -0.136977281 0
2764 129.33333 1.0 1 0.20151795 -0.121481193 0
I(bili^2) I(bili^3) age
10 NA NA 35
14 NA NA 38
41 NA NA 78
77 NA NA 23
91 NA NA 40
105 NA NA 54
114 NA NA 31
135 NA NA 27
149 NA NA 37
154 NA NA 50
155 NA NA 63
176 NA NA 26
215 NA NA 35
220 NA NA 44
224 NA NA 34
226 NA NA 60
264 NA NA 24
282 NA NA 48
286 NA NA 68
300 NA NA 37
301 NA NA 35
311 NA NA 59
317 NA NA 20
337 NA NA 71
383 NA NA 53
391 NA NA 23
392 NA NA 32
420 NA NA 36
422 NA NA 48
461 NA NA 56
475 NA NA 40
483 NA NA 27
501 NA NA 23
533 NA NA 44
538 NA NA 62
550 NA NA 59
557 NA NA 54
589 NA NA 44
598 NA NA 62
621 NA NA 31
631 NA NA 61
637 NA NA 45
650 NA NA 41
673 NA NA 49
696 NA NA 38
703 NA NA 36
704 NA NA 58
726 NA NA 21
739 NA NA 50
747 NA NA 35
755 NA NA 37
756 NA NA 37
766 NA NA 47
777 NA NA 31
793 NA NA 42
818 NA NA 38
850 NA NA 31
862 NA NA 43
866 NA NA 35
867 NA NA 23
887 NA NA 54
894 NA NA 45
913 NA NA 27
974 NA NA 23
976 NA NA 57
980 NA NA 41
1028 NA NA 30
1039 NA NA 58
1040 NA NA 29
1046 NA NA 43
1055 NA NA 35
1092 NA NA 37
1108 NA NA 21
1150 NA NA 71
1153 NA NA 26
1165 NA NA 45
1174 NA NA 63
1212 NA NA 61
1231 NA NA 56
1245 NA NA 66
1247 NA NA 52
1273 NA NA 42
1278 NA NA 29
1299 NA NA 39
1346 NA NA 23
1352 NA NA 46
1360 NA NA 42
1397 NA NA 31
1399 NA NA 33
1410 NA NA 70
1439 NA NA 44
1481 NA NA 58
1494 NA NA 70
1499 NA NA 38
1509 NA NA 73
1512 NA NA 47
1520 NA NA 56
1560 NA NA 32
1602 NA NA 28
1608 NA NA 58
1619 NA NA 34
1642 NA NA 30
1648 NA NA 33
1663 NA NA 51
1671 NA NA 74
1691 NA NA 56
1701 NA NA 56
1726 NA NA 31
1733 NA NA 38
1743 NA NA 74
1753 NA NA 42
1761 NA NA 47
1765 NA NA 49
1766 NA NA 61
1795 NA NA 65
1804 NA NA 43
1809 NA NA 26
1813 NA NA 36
1858 NA NA 43
1878 NA NA 51
1889 NA NA 34
1933 NA NA 77
1940 NA NA 48
1988 NA NA 62
1993 NA NA 45
1997 NA NA 56
2005 NA NA 78
2032 NA NA 20
2034 NA NA 25
2036 NA NA 52
2054 NA NA 43
2086 NA NA 47
2122 NA NA 71
2124 NA NA 27
2133 NA NA 60
2163 NA NA 53
2174 NA NA 55
2175 NA NA 64
2195 NA NA 42
2197 NA NA 22
2202 NA NA 20
2222 NA NA 75
2231 NA NA 32
2248 NA NA 29
2260 NA NA 45
2265 NA NA 40
2268 NA NA 61
2306 NA NA 32
2313 NA NA 48
2333 NA NA 70
2337 NA NA 43
2351 NA NA 33
2375 NA NA 34
2378 NA NA 25
2385 NA NA 48
2401 NA NA 69
2417 NA NA 68
2428 NA NA 65
2431 NA NA 22
2440 NA NA 44
2446 NA NA 30
2453 NA NA 60
2460 NA NA 22
2475 NA NA 39
2491 NA NA 43
2493 NA NA 46
2519 NA NA 38
2549 NA NA 36
2551 NA NA 68
2552 NA NA 36
2554 NA NA 21
2562 NA NA 62
2590 NA NA 23
2615 NA NA 22
2618 NA NA 37
2631 NA NA 37
2648 NA NA 43
2661 NA NA 35
2672 NA NA 29
2676 NA NA 41
2681 NA NA 21
2718 NA NA 20
2733 NA NA 53
2752 NA NA 24
2763 NA NA 28
2764 NA NA 27
$mod7a$spM_lvlone
center scale
SBP 119.29569892 15.3559299
bili 0.72078652 0.2266570
(Intercept) NA NA
ns(age, df = 2)1 0.40886544 0.1673890
ns(age, df = 2)2 -0.04985511 0.2381012
genderfemale NA NA
I(bili^2) 0.57061798 0.3661097
I(bili^3) 0.49253371 0.4876694
age 43.51075269 15.0631963
$mod7a$mu_reg_norm
[1] 0
$mod7a$tau_reg_norm
[1] 1e-04
$mod7a$shape_tau_norm
[1] 0.01
$mod7a$rate_tau_norm
[1] 0.01
jagsmodel remains the same
Code
lapply(models, "[[", "jagsmodel")
Output
$m0a1
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
}
$m0a2
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
}
$m0a3
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
log(mu_y[i]) <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
}
$m0a4
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- 1/max(1e-10, inv_mu_y[i])
inv_mu_y[i] <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
}
$m0b1
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
logit(mu_B1[i]) <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for B1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
}
$m0b2
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
probit(mu_B1[i]) <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for B1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
}
$m0b3
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
log(mu_B1[i]) <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for B1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
}
$m0b4
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
cloglog(mu_B1[i]) <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for B1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
}
$m0c1
model {
# Gamma model for L1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i])
shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2)
rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2)
mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i])
inv_mu_L1[i] <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for L1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1 <- sqrt(1/tau_L1)
}
$m0c2
model {
# Gamma model for L1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i])
shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2)
rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2)
log(mu_L1[i]) <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for L1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1 <- sqrt(1/tau_L1)
}
$m0d1
model {
# Poisson model for P1 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i]))
log(mu_P1[i]) <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for P1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
}
$m0d2
model {
# Poisson model for P1 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i]))
mu_P1[i] <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for P1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
}
$m0e1
model {
# Log-normal model for L1 -------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1)
mu_L1[i] <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for L1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1 <- sqrt(1/tau_L1)
}
$m0f1
model {
# Beta model for Be1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15)
shape1_Be1[i] <- mu_Be1[i] * tau_Be1
shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1
logit(mu_Be1[i]) <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for Be1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta)
}
$m1a
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- M_lvlone[i, 2] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2]
}
# Priors for the model for y
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
}
$m1b
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
logit(mu_B1[i]) <- M_lvlone[i, 2] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2]
}
# Priors for the model for B1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
}
$m1c
model {
# Gamma model for L1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i])
shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2)
rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2)
mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i])
inv_mu_L1[i] <- M_lvlone[i, 2] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2]
}
# Priors for the model for L1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1 <- sqrt(1/tau_L1)
}
$m1d
model {
# Poisson model for P1 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i]))
log(mu_P1[i]) <- M_lvlone[i, 2] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2]
}
# Priors for the model for P1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
}
$m1e
model {
# Log-normal model for L1 -------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1)
mu_L1[i] <- M_lvlone[i, 2] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2]
}
# Priors for the model for L1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1 <- sqrt(1/tau_L1)
}
$m1f
model {
# Beta model for Be1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15)
shape1_Be1[i] <- mu_Be1[i] * tau_Be1
shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1
logit(mu_Be1[i]) <- M_lvlone[i, 2] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2]
}
# Priors for the model for Be1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta)
}
$m2a
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2]
}
# Priors for the model for y
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
# 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 {
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2]
}
# Priors for the model for B2
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# 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)
}
$m2c
model {
# Gamma model for L1mis ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i])
inv_mu_L1mis[i] <- M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2]
}
# Priors for the model for L1mis
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
# 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)
}
$m2d
model {
# Poisson model for P2 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P2[i]))
log(mu_P2[i]) <- M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2]
}
# Priors for the model for P2
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
# 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)
}
$m2e
model {
# Log-normal model for L1mis ----------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dlnorm(mu_L1mis[i], tau_L1mis)
mu_L1mis[i] <- M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2]
}
# Priors for the model for L1mis
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1mis <- sqrt(1/tau_L1mis)
# 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)
}
$m2f
model {
# Beta model for Be2 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15)
shape1_Be2[i] <- mu_Be2[i] * tau_Be2
shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2
logit(mu_Be2[i]) <- M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2]
}
# Priors for the model for Be2
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta)
# 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, 7] * beta[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[2] +
M_lvlone[i, 8] * beta[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[6]
}
# Priors for the model for C1
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C1 <- sqrt(1/tau_C1)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 7] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[5]
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Poisson model for P2 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i]))
log(mu_P2[i]) <- M_lvlone[i, 7] * alpha[6] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[7] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[8] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9]
}
# Priors for the model for P2
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
# Gamma model for L1mis ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i])
inv_mu_L1mis[i] <- M_lvlone[i, 7] * alpha[10] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[11] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[12]
}
# Priors for the model for L1mis
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
# Beta model for Be2 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 5] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15)
shape1_Be2[i] <- mu_Be2[i] * tau_Be2
shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2
logit(mu_Be2[i]) <- M_lvlone[i, 7] * alpha[13] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[14]
}
# Priors for the model for Be2
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta)
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 6] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 7] * alpha[15]
}
# Priors for the model for C2
for (k in 15:15) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m3b
model {
# 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, 6] * beta[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] +
M_lvlone[i, 7] * beta[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5]
}
# Priors for the model for C1
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C1 <- sqrt(1/tau_C1)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
probit(mu_B2[i]) <- M_lvlone[i, 6] * alpha[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Poisson model for P2 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i]))
mu_P2[i] <- M_lvlone[i, 6] * alpha[5] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[7]
}
# Priors for the model for P2
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
# Log-normal model for L1mis ----------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dlnorm(mu_L1mis[i], tau_L1mis)
mu_L1mis[i] <- M_lvlone[i, 6] * alpha[8] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9]
}
# Priors for the model for L1mis
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1mis <- sqrt(1/tau_L1mis)
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 5] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- 1/max(1e-10, inv_mu_C2[i])
inv_mu_C2[i] <- M_lvlone[i, 6] * alpha[10]
}
# Priors for the model for C2
for (k in 10:10) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m3c
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, 6] * beta[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] +
M_lvlone[i, 7] * beta[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5]
}
# Priors for the model for C1
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C1 <- sqrt(1/tau_C1)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
log(mu_B2[i]) <- M_lvlone[i, 6] * alpha[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Poisson model for P2 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i]))
mu_P2[i] <- M_lvlone[i, 6] * alpha[5] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[7]
}
# Priors for the model for P2
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
# Gamma model for L1mis ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
log(mu_L1mis[i]) <- M_lvlone[i, 6] * alpha[8] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9]
}
# Priors for the model for L1mis
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 5] ~ dnorm(mu_C2[i], tau_C2)
log(mu_C2[i]) <- M_lvlone[i, 6] * alpha[10]
}
# Priors for the model for C2
for (k in 10:10) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m3d
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, 7] * beta[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[2] +
M_lvlone[i, 8] * beta[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[6]
}
# Priors for the model for C1
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C1 <- sqrt(1/tau_C1)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
log(mu_B2[i]) <- M_lvlone[i, 7] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[5]
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Poisson model for P2 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i]))
mu_P2[i] <- M_lvlone[i, 7] * alpha[6] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[7] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[8] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9]
}
# Priors for the model for P2
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
# Gamma model for L1mis ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
log(mu_L1mis[i]) <- M_lvlone[i, 7] * alpha[10] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[11] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[12]
}
# Priors for the model for L1mis
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
# Normal model for Be2 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 5] ~ dnorm(mu_Be2[i], tau_Be2)T(0, 1)
mu_Be2[i] <- M_lvlone[i, 7] * alpha[13] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[14]
}
# Priors for the model for Be2
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_Be2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_Be2 <- sqrt(1/tau_Be2)
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 6] ~ dnorm(mu_C2[i], tau_C2)
log(mu_C2[i]) <- M_lvlone[i, 7] * alpha[15]
}
# Priors for the model for C2
for (k in 15:15) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m4a
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- 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]
}
# Priors for the model for y
for (k in 1:12) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
# 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 {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
logit(mu_B1[i]) <- M_lvlone[i, 5] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 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]
}
# Priors for the model for B1
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Gamma model for L1mis ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i])
inv_mu_L1mis[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * alpha[2] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4]
}
# Priors for the model for L1mis
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
# Beta model for Be2 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15)
shape1_Be2[i] <- mu_Be2[i] * tau_Be2
shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2
logit(mu_Be2[i]) <- M_lvlone[i, 5] * alpha[5] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * alpha[6] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[7]
M_lvlone[i, 7] <- log(M_lvlone[i, 3])
}
# Priors for the model for Be2
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta)
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dnorm(mu_C2[i], tau_C2)
log(mu_C2[i]) <- M_lvlone[i, 5] * alpha[8] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * alpha[9]
M_lvlone[i, 6] <- abs(M_lvlone[i, 8] - M_lvlone[i, 4])
}
# Priors for the model for C2
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5a1
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5a2
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
log(mu_y[i]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5a3
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- 1/max(1e-10, inv_mu_y[i])
inv_mu_y[i] <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5b1
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
logit(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for B1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] +
M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] +
M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5b2
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
probit(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for B1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] +
M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] +
M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5b3
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
log(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for B1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] +
M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] +
M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5b4
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
cloglog(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for B1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] +
M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] +
M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5c1
model {
# Gamma model for L1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i])
shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2)
rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2)
mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i])
inv_mu_L1[i] <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for L1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1 <- sqrt(1/tau_L1)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5c2
model {
# Gamma model for L1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i])
shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2)
rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2)
log(mu_L1[i]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for L1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1 <- sqrt(1/tau_L1)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5d1
model {
# Poisson model for P1 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i]))
log(mu_P1[i]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for P1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5d2
model {
# Poisson model for P1 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i]))
mu_P1[i] <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for P1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5e1
model {
# Log-normal model for L1 -------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1)
mu_L1[i] <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for L1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1 <- sqrt(1/tau_L1)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5f1
model {
# Beta model for Be1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15)
shape1_Be1[i] <- mu_Be1[i] * tau_Be1
shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1
logit(mu_Be1[i]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for Be1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m6a
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- 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]
}
# Priors for the model for y
for (k in 1:12) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
# 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]
}
}
$m6b
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
logit(mu_B1[i]) <- 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]
}
# Priors for the model for B1
for (k in 1:12) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# 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]
}
}
$m6c
model {
# Gamma model for C1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dgamma(shape_C1[i], rate_C1[i])
shape_C1[i] <- pow(mu_C1[i], 2) / pow(sigma_C1, 2)
rate_C1[i] <- mu_C1[i] / pow(sigma_C1, 2)
log(mu_C1[i]) <- 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]
}
# Priors for the model for C1
for (k in 1:11) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_C1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_C1 <- sqrt(1/tau_C1)
# 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, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * alpha[8]
M_lvlone[i, 12] <- abs(M_lvlone[i, 16] - 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, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 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, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 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, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 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, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 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, 13] <- M_lvlone[i, 9] * M_lvlone[i, 12]
M_lvlone[i, 14] <- M_lvlone[i, 10] * M_lvlone[i, 12]
M_lvlone[i, 15] <- M_lvlone[i, 11] * M_lvlone[i, 12]
}
}
$m6d
model {
# Normal model for SBP ----------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP)
mu_SBP[i] <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] +
M_lvlone[i, 6] * 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]
}
# Priors for the model for SBP
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_SBP <- sqrt(1/tau_SBP)
# Normal model for bili ---------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 2] ~ dnorm(mu_bili[i], tau_bili)T(1e-05, 1e+10)
mu_bili[i] <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4]
M_lvlone[i, 7] <- log(M_lvlone[i, 2])
}
# Priors for the model for bili
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_bili ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_bili <- sqrt(1/tau_bili)
# Normal model for creat --------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 3] ~ dnorm(mu_creat[i], tau_creat)
mu_creat[i] <- M_lvlone[i, 4] * alpha[5] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] +
M_lvlone[i, 6] * alpha[7]
M_lvlone[i, 8] <- exp(M_lvlone[i, 3])
}
# Priors for the model for creat
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_creat ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_creat <- sqrt(1/tau_creat)
}
$m6e
model {
# Normal model for SBP ----------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP)
mu_SBP[i] <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] +
M_lvlone[i, 6] * 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]
}
# Priors for the model for SBP
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_SBP <- sqrt(1/tau_SBP)
# Log-normal model for bili -----------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 2] ~ dlnorm(mu_bili[i], tau_bili)
mu_bili[i] <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4]
M_lvlone[i, 7] <- log(M_lvlone[i, 2])
}
# Priors for the model for bili
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_bili ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_bili <- sqrt(1/tau_bili)
# Normal model for creat --------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 3] ~ dnorm(mu_creat[i], tau_creat)
mu_creat[i] <- M_lvlone[i, 4] * alpha[5] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] +
M_lvlone[i, 6] * alpha[7]
M_lvlone[i, 8] <- exp(M_lvlone[i, 3])
}
# Priors for the model for creat
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_creat ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_creat <- sqrt(1/tau_creat)
}
$m6f
model {
# Normal model for SBP ----------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP)
mu_SBP[i] <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] +
M_lvlone[i, 6] * 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]
}
# Priors for the model for SBP
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_SBP <- sqrt(1/tau_SBP)
# Gamma model for bili ----------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 2] ~ dgamma(shape_bili[i], rate_bili[i])
shape_bili[i] <- pow(mu_bili[i], 2) / pow(sigma_bili, 2)
rate_bili[i] <- mu_bili[i] / pow(sigma_bili, 2)
mu_bili[i] <- 1/max(1e-10, inv_mu_bili[i])
inv_mu_bili[i] <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4]
M_lvlone[i, 7] <- log(M_lvlone[i, 2])
}
# Priors for the model for bili
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_bili ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_bili <- sqrt(1/tau_bili)
# Normal model for creat --------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 3] ~ dnorm(mu_creat[i], tau_creat)
mu_creat[i] <- M_lvlone[i, 4] * alpha[5] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] +
M_lvlone[i, 6] * alpha[7]
M_lvlone[i, 8] <- exp(M_lvlone[i, 3])
}
# Priors for the model for creat
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_creat ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_creat <- sqrt(1/tau_creat)
}
$mod7a
model {
# Normal model for SBP ----------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP)
mu_SBP[i] <- M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[2] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[3] +
M_lvlone[i, 6] * beta[4] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[5] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[6]
}
# Priors for the model for SBP
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_SBP <- sqrt(1/tau_SBP)
# Normal model for bili ---------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 2] ~ dnorm(mu_bili[i], tau_bili)
mu_bili[i] <- M_lvlone[i, 3] * alpha[1] +
(M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3]
M_lvlone[i, 7] <- M_lvlone[i, 2]^2
M_lvlone[i, 8] <- M_lvlone[i, 2]^3
}
# Priors for the model for bili
for (k in 1:3) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_bili ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_bili <- sqrt(1/tau_bili)
}
GRcrit and MCerror give same result
Code
lapply(models0, GR_crit, multivariate = FALSE)
Output
$m0a1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_y NaN NaN
$m0a2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_y NaN NaN
$m0a3
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_y NaN NaN
$m0a4
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_y NaN NaN
$m0b1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
$m0b2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
$m0b3
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
$m0b4
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
$m0c1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_L1 NaN NaN
$m0c2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_L1 NaN NaN
$m0d1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
$m0d2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
$m0e1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_L1 NaN NaN
$m0f1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
tau_Be1 NaN NaN
$m1a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
sigma_y NaN NaN
$m1b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
$m1c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
sigma_L1 NaN NaN
$m1d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
$m1e
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
sigma_L1 NaN NaN
$m1f
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
tau_Be1 NaN NaN
$m2a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
sigma_y NaN NaN
$m2b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
$m2c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
sigma_L1mis NaN NaN
$m2d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
$m2e
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
sigma_L1mis NaN NaN
$m2f
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
tau_Be2 NaN NaN
$m3a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
P2 NaN NaN
L1mis NaN NaN
Be2 NaN NaN
sigma_C1 NaN NaN
$m3b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
P2 NaN NaN
L1mis NaN NaN
sigma_C1 NaN NaN
$m3c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
P2 NaN NaN
L1mis NaN NaN
sigma_C1 NaN NaN
$m3d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
P2 NaN NaN
L1mis NaN NaN
Be2 NaN NaN
sigma_C1 NaN NaN
$m4a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
O24 NaN NaN
abs(C1 - C2) NaN NaN
log(C1) NaN NaN
O22:abs(C1 - C2) NaN NaN
O23:abs(C1 - C2) NaN NaN
O24:abs(C1 - C2) NaN NaN
sigma_y NaN NaN
$m4b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
L1mis NaN NaN
abs(C1 - C2) NaN NaN
log(Be2) NaN NaN
$m5a1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
sigma_y NaN NaN
$m5a2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
sigma_y NaN NaN
$m5a3
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
sigma_y NaN NaN
$m5b1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
C1 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
$m5b2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
C1 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
$m5b3
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
C1 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
$m5b4
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
C1 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
$m5c1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
sigma_L1 NaN NaN
$m5c2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
sigma_L1 NaN NaN
$m5d1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
$m5d2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
$m5e1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
sigma_L1 NaN NaN
$m5f1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
tau_Be1 NaN NaN
$m6a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
O24 NaN NaN
abs(C1 - C2) NaN NaN
log(C1) NaN NaN
O22:abs(C1 - C2) NaN NaN
O23:abs(C1 - C2) NaN NaN
O24:abs(C1 - C2) NaN NaN
sigma_y NaN NaN
$m6b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
O24 NaN NaN
abs(C1 - C2) NaN NaN
log(C1) NaN NaN
O22:abs(C1 - C2) NaN NaN
O23:abs(C1 - C2) NaN NaN
O24:abs(C1 - C2) NaN NaN
$m6c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
O24 NaN NaN
abs(y - C2) NaN NaN
O22:abs(y - C2) NaN NaN
O23:abs(y - C2) NaN NaN
O24:abs(y - C2) NaN NaN
sigma_C1 NaN NaN
$m6d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
age NaN NaN
genderfemale NaN NaN
log(bili) NaN NaN
exp(creat) NaN NaN
sigma_SBP NaN NaN
$m6e
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
age NaN NaN
genderfemale NaN NaN
log(bili) NaN NaN
exp(creat) NaN NaN
sigma_SBP NaN NaN
$m6f
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
age NaN NaN
genderfemale NaN NaN
log(bili) NaN NaN
exp(creat) NaN NaN
sigma_SBP NaN NaN
$mod7a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
ns(age, df = 2)1 NaN NaN
ns(age, df = 2)2 NaN NaN
genderfemale NaN NaN
I(bili^2) NaN NaN
I(bili^3) NaN NaN
sigma_SBP NaN NaN
Code
lapply(models0, MC_error)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_y 0 0 0 NaN
$m0a2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_y 0 0 0 NaN
$m0a3
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_y 0 0 0 NaN
$m0a4
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_y 0 0 0 NaN
$m0b1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
$m0b2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
$m0b3
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
$m0b4
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
$m0c1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_L1 0 0 0 NaN
$m0c2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_L1 0 0 0 NaN
$m0d1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
$m0d2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
$m0e1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_L1 0 0 0 NaN
$m0f1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
tau_Be1 0 0 0 NaN
$m1a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
sigma_y 0 0 0 NaN
$m1b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
$m1c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
sigma_L1 0 0 0 NaN
$m1d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
$m1e
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
sigma_L1 0 0 0 NaN
$m1f
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
tau_Be1 0 0 0 NaN
$m2a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
sigma_y 0 0 0 NaN
$m2b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
$m2c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
sigma_L1mis 0 0 0 NaN
$m2d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
$m2e
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
sigma_L1mis 0 0 0 NaN
$m2f
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
tau_Be2 0 0 0 NaN
$m3a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
P2 0 0 0 NaN
L1mis 0 0 0 NaN
Be2 0 0 0 NaN
sigma_C1 0 0 0 NaN
$m3b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
P2 0 0 0 NaN
L1mis 0 0 0 NaN
sigma_C1 0 0 0 NaN
$m3c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
P2 0 0 0 NaN
L1mis 0 0 0 NaN
sigma_C1 0 0 0 NaN
$m3d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
P2 0 0 0 NaN
L1mis 0 0 0 NaN
Be2 0 0 0 NaN
sigma_C1 0 0 0 NaN
$m4a
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
O22 0 0 0 NaN
O23 0 0 0 NaN
O24 0 0 0 NaN
abs(C1 - C2) 0 0 0 NaN
log(C1) 0 0 0 NaN
O22:abs(C1 - C2) 0 0 0 NaN
O23:abs(C1 - C2) 0 0 0 NaN
O24:abs(C1 - C2) 0 0 0 NaN
sigma_y 0 0 0 NaN
$m4b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
L1mis 0 0 0 NaN
abs(C1 - C2) 0 0 0 NaN
log(Be2) 0 0 0 NaN
$m5a1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
sigma_y 0 0 0 NaN
$m5a2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
sigma_y 0 0 0 NaN
$m5a3
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
sigma_y 0 0 0 NaN
$m5b1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
C1 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
$m5b2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
C1 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
$m5b3
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
C1 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
$m5b4
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
C1 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
$m5c1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
sigma_L1 0 0 0 NaN
$m5c2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
sigma_L1 0 0 0 NaN
$m5d1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
$m5d2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
$m5e1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
sigma_L1 0 0 0 NaN
$m5f1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
tau_Be1 0 0 0 NaN
$m6a
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
O22 0 0 0 NaN
O23 0 0 0 NaN
O24 0 0 0 NaN
abs(C1 - C2) 0 0 0 NaN
log(C1) 0 0 0 NaN
O22:abs(C1 - C2) 0 0 0 NaN
O23:abs(C1 - C2) 0 0 0 NaN
O24:abs(C1 - C2) 0 0 0 NaN
sigma_y 0 0 0 NaN
$m6b
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
O22 0 0 0 NaN
O23 0 0 0 NaN
O24 0 0 0 NaN
abs(C1 - C2) 0 0 0 NaN
log(C1) 0 0 0 NaN
O22:abs(C1 - C2) 0 0 0 NaN
O23:abs(C1 - C2) 0 0 0 NaN
O24:abs(C1 - C2) 0 0 0 NaN
$m6c
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
O22 0 0 0 NaN
O23 0 0 0 NaN
O24 0 0 0 NaN
abs(y - C2) 0 0 0 NaN
O22:abs(y - C2) 0 0 0 NaN
O23:abs(y - C2) 0 0 0 NaN
O24:abs(y - C2) 0 0 0 NaN
sigma_C1 0 0 0 NaN
$m6d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
age 0 0 0 NaN
genderfemale 0 0 0 NaN
log(bili) 0 0 0 NaN
exp(creat) 0 0 0 NaN
sigma_SBP 0 0 0 NaN
$m6e
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
age 0 0 0 NaN
genderfemale 0 0 0 NaN
log(bili) 0 0 0 NaN
exp(creat) 0 0 0 NaN
sigma_SBP 0 0 0 NaN
$m6f
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
age 0 0 0 NaN
genderfemale 0 0 0 NaN
log(bili) 0 0 0 NaN
exp(creat) 0 0 0 NaN
sigma_SBP 0 0 0 NaN
$mod7a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
ns(age, df = 2)1 0 0 0 NaN
ns(age, df = 2)2 0 0 0 NaN
genderfemale 0 0 0 NaN
I(bili^2) 0 0 0 NaN
I(bili^3) 0 0 0 NaN
sigma_SBP 0 0 0 NaN
summary output remained the same
Code
lapply(models0, print)
Output
Call:
lm_imp(formula = y ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "identity"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "log"), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "inverse"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "logit"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept)
0
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "probit"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept)
0
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "log"), data = wideDF,
n.adapt = 150, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept)
0
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "cloglog"),
data = wideDF, n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept)
0
Call:
glm_imp(formula = L1 ~ 1, family = Gamma(link = "inverse"), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_L1
0
Call:
glm_imp(formula = L1 ~ 1, family = Gamma(link = "log"), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_L1
0
Call:
glm_imp(formula = P1 ~ 1, family = poisson(link = "log"), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept)
0
Call:
glm_imp(formula = P1 ~ 1, family = poisson(link = "identity"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept)
0
Call:
lognorm_imp(formula = L1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal model for "L1"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_L1
0
Call:
betareg_imp(formula = Be1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta model for "Be1"
Coefficients:
(Intercept)
0
Call:
lm_imp(formula = y ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C1
0 0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = B1 ~ C1, family = binomial(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C1
0 0
Call:
glm_imp(formula = L1 ~ C1, family = Gamma(), data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept) C1
0 0
Residual standard deviation:
sigma_L1
0
Call:
glm_imp(formula = P1 ~ C1, family = poisson(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept) C1
0 0
Call:
lognorm_imp(formula = L1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal model for "L1"
Coefficients:
(Intercept) C1
0 0
Residual standard deviation:
sigma_L1
0
Call:
betareg_imp(formula = Be1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta model for "Be1"
Coefficients:
(Intercept) C1
0 0
Call:
lm_imp(formula = y ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C2
0 0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = B2 ~ C2, family = binomial(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial model for "B2"
Coefficients:
(Intercept) C2
0 0
Call:
glm_imp(formula = L1mis ~ C2, family = Gamma(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma model for "L1mis"
Coefficients:
(Intercept) C2
0 0
Residual standard deviation:
sigma_L1mis
0
Call:
glm_imp(formula = P2 ~ C2, family = poisson(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson model for "P2"
Coefficients:
(Intercept) C2
0 0
Call:
lognorm_imp(formula = L1mis ~ C2, data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal model for "L1mis"
Coefficients:
(Intercept) C2
0 0
Residual standard deviation:
sigma_L1mis
0
Call:
betareg_imp(formula = Be2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta model for "Be2"
Coefficients:
(Intercept) C2
0 0
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF,
n.adapt = 5, n.iter = 10, models = c(P2 = "glm_poisson_log",
L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) C2 B21 P2 L1mis Be2
0 0 0 0 0 0
Residual standard deviation:
sigma_C1
0
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5,
n.iter = 10, models = c(C2 = "glm_gaussian_inverse", P2 = "glm_poisson_identity",
B2 = "glm_binomial_probit", L1mis = "lognorm"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) C2 B21 P2 L1mis
0 0 0 0 0
Residual standard deviation:
sigma_C1
0
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5,
n.iter = 10, models = c(C2 = "glm_gaussian_log", P2 = "glm_poisson_identity",
L1mis = "glm_gamma_log", B2 = "glm_binomial_log"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) C2 B21 P2 L1mis
0 0 0 0 0
Residual standard deviation:
sigma_C1
0
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF,
n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
P2 = "glm_poisson_identity", L1mis = "glm_gamma_log",
B2 = "glm_binomial_log"), seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(Be2 = c(0, 1)))
Bayesian linear model for "C1"
Coefficients:
(Intercept) C2 B21 P2 L1mis Be2
0 0 0 0 0 0
Residual standard deviation:
sigma_C1
0
Call:
lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
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
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = B1 ~ L1mis + abs(C1 - C2) + log(Be2), family = binomial(),
data = wideDF, n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) L1mis abs(C1 - C2) log(Be2)
0 0 0 0
Call:
lm_imp(formula = y ~ C2 + B2 + B1 + O1, data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "inverse"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "logit"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "probit"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "cloglog"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Call:
glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "inverse"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_L1
0
Call:
glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_L1
0
Call:
glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Call:
glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "identity"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Call:
lognorm_imp(formula = L1 ~ C2 + B2 + B1 + O1, data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal model for "L1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_L1
0
Call:
betareg_imp(formula = Be1 ~ C2 + B2 + B1 + O1, data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta model for "Be1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Call:
lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
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
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = B1 ~ M2 + O2 * abs(C1 - C2) + log(C1), family = "binomial",
data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
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
Call:
glm_imp(formula = C1 ~ M2 + O2 * abs(y - C2), family = Gamma(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian Gamma model for "C1"
Coefficients:
(Intercept) M22 M23 M24 O22
0 0 0 0 0
O23 O24 abs(y - C2) O22:abs(y - C2) O23:abs(y - C2)
0 0 0 0 0
O24:abs(y - C2)
0
Residual standard deviation:
sigma_C1
0
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(bili = c(1e-05, 1e+10)))
Bayesian linear model for "SBP"
Coefficients:
(Intercept) age genderfemale log(bili) exp(creat)
0 0 0 0 0
Residual standard deviation:
sigma_SBP
0
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "lognorm",
creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "SBP"
Coefficients:
(Intercept) age genderfemale log(bili) exp(creat)
0 0 0 0 0
Residual standard deviation:
sigma_SBP
0
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "glm_gamma_inverse",
creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "SBP"
Coefficients:
(Intercept) age genderfemale log(bili) exp(creat)
0 0 0 0 0
Residual standard deviation:
sigma_SBP
0
Call:
lm_imp(formula = SBP ~ ns(age, df = 2) + gender + I(bili^2) +
I(bili^3), data = NHANES, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear model for "SBP"
Coefficients:
(Intercept) ns(age, df = 2)1 ns(age, df = 2)2 genderfemale
0 0 0 0
I(bili^2) I(bili^3)
0 0
Residual standard deviation:
sigma_SBP
0
$m0a1
Call:
lm_imp(formula = y ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_y
0
$m0a2
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "identity"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_y
0
$m0a3
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "log"), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_y
0
$m0a4
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "inverse"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_y
0
$m0b1
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "logit"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept)
0
$m0b2
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "probit"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept)
0
$m0b3
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "log"), data = wideDF,
n.adapt = 150, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept)
0
$m0b4
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "cloglog"),
data = wideDF, n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept)
0
$m0c1
Call:
glm_imp(formula = L1 ~ 1, family = Gamma(link = "inverse"), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_L1
0
$m0c2
Call:
glm_imp(formula = L1 ~ 1, family = Gamma(link = "log"), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_L1
0
$m0d1
Call:
glm_imp(formula = P1 ~ 1, family = poisson(link = "log"), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept)
0
$m0d2
Call:
glm_imp(formula = P1 ~ 1, family = poisson(link = "identity"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept)
0
$m0e1
Call:
lognorm_imp(formula = L1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal model for "L1"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_L1
0
$m0f1
Call:
betareg_imp(formula = Be1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta model for "Be1"
Coefficients:
(Intercept)
0
$m1a
Call:
lm_imp(formula = y ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C1
0 0
Residual standard deviation:
sigma_y
0
$m1b
Call:
glm_imp(formula = B1 ~ C1, family = binomial(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C1
0 0
$m1c
Call:
glm_imp(formula = L1 ~ C1, family = Gamma(), data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept) C1
0 0
Residual standard deviation:
sigma_L1
0
$m1d
Call:
glm_imp(formula = P1 ~ C1, family = poisson(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept) C1
0 0
$m1e
Call:
lognorm_imp(formula = L1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal model for "L1"
Coefficients:
(Intercept) C1
0 0
Residual standard deviation:
sigma_L1
0
$m1f
Call:
betareg_imp(formula = Be1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta model for "Be1"
Coefficients:
(Intercept) C1
0 0
$m2a
Call:
lm_imp(formula = y ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C2
0 0
Residual standard deviation:
sigma_y
0
$m2b
Call:
glm_imp(formula = B2 ~ C2, family = binomial(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial model for "B2"
Coefficients:
(Intercept) C2
0 0
$m2c
Call:
glm_imp(formula = L1mis ~ C2, family = Gamma(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma model for "L1mis"
Coefficients:
(Intercept) C2
0 0
Residual standard deviation:
sigma_L1mis
0
$m2d
Call:
glm_imp(formula = P2 ~ C2, family = poisson(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson model for "P2"
Coefficients:
(Intercept) C2
0 0
$m2e
Call:
lognorm_imp(formula = L1mis ~ C2, data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal model for "L1mis"
Coefficients:
(Intercept) C2
0 0
Residual standard deviation:
sigma_L1mis
0
$m2f
Call:
betareg_imp(formula = Be2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta model for "Be2"
Coefficients:
(Intercept) C2
0 0
$m3a
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF,
n.adapt = 5, n.iter = 10, models = c(P2 = "glm_poisson_log",
L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) C2 B21 P2 L1mis Be2
0 0 0 0 0 0
Residual standard deviation:
sigma_C1
0
$m3b
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5,
n.iter = 10, models = c(C2 = "glm_gaussian_inverse", P2 = "glm_poisson_identity",
B2 = "glm_binomial_probit", L1mis = "lognorm"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) C2 B21 P2 L1mis
0 0 0 0 0
Residual standard deviation:
sigma_C1
0
$m3c
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5,
n.iter = 10, models = c(C2 = "glm_gaussian_log", P2 = "glm_poisson_identity",
L1mis = "glm_gamma_log", B2 = "glm_binomial_log"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) C2 B21 P2 L1mis
0 0 0 0 0
Residual standard deviation:
sigma_C1
0
$m3d
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF,
n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
P2 = "glm_poisson_identity", L1mis = "glm_gamma_log",
B2 = "glm_binomial_log"), seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(Be2 = c(0, 1)))
Bayesian linear model for "C1"
Coefficients:
(Intercept) C2 B21 P2 L1mis Be2
0 0 0 0 0 0
Residual standard deviation:
sigma_C1
0
$m4a
Call:
lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
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
Residual standard deviation:
sigma_y
0
$m4b
Call:
glm_imp(formula = B1 ~ L1mis + abs(C1 - C2) + log(Be2), family = binomial(),
data = wideDF, n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) L1mis abs(C1 - C2) log(Be2)
0 0 0 0
$m5a1
Call:
lm_imp(formula = y ~ C2 + B2 + B1 + O1, data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_y
0
$m5a2
Call:
glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_y
0
$m5a3
Call:
glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "inverse"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_y
0
$m5b1
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "logit"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5b2
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "probit"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5b3
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5b4
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "cloglog"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5c1
Call:
glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "inverse"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_L1
0
$m5c2
Call:
glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_L1
0
$m5d1
Call:
glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5d2
Call:
glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "identity"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5e1
Call:
lognorm_imp(formula = L1 ~ C2 + B2 + B1 + O1, data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal model for "L1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_L1
0
$m5f1
Call:
betareg_imp(formula = Be1 ~ C2 + B2 + B1 + O1, data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta model for "Be1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m6a
Call:
lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
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
Residual standard deviation:
sigma_y
0
$m6b
Call:
glm_imp(formula = B1 ~ M2 + O2 * abs(C1 - C2) + log(C1), family = "binomial",
data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
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
$m6c
Call:
glm_imp(formula = C1 ~ M2 + O2 * abs(y - C2), family = Gamma(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian Gamma model for "C1"
Coefficients:
(Intercept) M22 M23 M24 O22
0 0 0 0 0
O23 O24 abs(y - C2) O22:abs(y - C2) O23:abs(y - C2)
0 0 0 0 0
O24:abs(y - C2)
0
Residual standard deviation:
sigma_C1
0
$m6d
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(bili = c(1e-05, 1e+10)))
Bayesian linear model for "SBP"
Coefficients:
(Intercept) age genderfemale log(bili) exp(creat)
0 0 0 0 0
Residual standard deviation:
sigma_SBP
0
$m6e
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "lognorm",
creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "SBP"
Coefficients:
(Intercept) age genderfemale log(bili) exp(creat)
0 0 0 0 0
Residual standard deviation:
sigma_SBP
0
$m6f
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "glm_gamma_inverse",
creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "SBP"
Coefficients:
(Intercept) age genderfemale log(bili) exp(creat)
0 0 0 0 0
Residual standard deviation:
sigma_SBP
0
$mod7a
Call:
lm_imp(formula = SBP ~ ns(age, df = 2) + gender + I(bili^2) +
I(bili^3), data = NHANES, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear model for "SBP"
Coefficients:
(Intercept) ns(age, df = 2)1 ns(age, df = 2)2 genderfemale
0 0 0 0
I(bili^2) I(bili^3)
0 0
Residual standard deviation:
sigma_SBP
0
Code
lapply(models0, coef)
Output
$m0a1
$m0a1$y
(Intercept) sigma_y
0 0
$m0a2
$m0a2$y
(Intercept) sigma_y
0 0
$m0a3
$m0a3$y
(Intercept) sigma_y
0 0
$m0a4
$m0a4$y
(Intercept) sigma_y
0 0
$m0b1
$m0b1$B1
(Intercept)
0
$m0b2
$m0b2$B1
(Intercept)
0
$m0b3
$m0b3$B1
(Intercept)
0
$m0b4
$m0b4$B1
(Intercept)
0
$m0c1
$m0c1$L1
(Intercept) sigma_L1
0 0
$m0c2
$m0c2$L1
(Intercept) sigma_L1
0 0
$m0d1
$m0d1$P1
(Intercept)
0
$m0d2
$m0d2$P1
(Intercept)
0
$m0e1
$m0e1$L1
(Intercept) sigma_L1
0 0
$m0f1
$m0f1$Be1
(Intercept) tau_Be1
0 0
$m1a
$m1a$y
(Intercept) C1 sigma_y
0 0 0
$m1b
$m1b$B1
(Intercept) C1
0 0
$m1c
$m1c$L1
(Intercept) C1 sigma_L1
0 0 0
$m1d
$m1d$P1
(Intercept) C1
0 0
$m1e
$m1e$L1
(Intercept) C1 sigma_L1
0 0 0
$m1f
$m1f$Be1
(Intercept) C1 tau_Be1
0 0 0
$m2a
$m2a$y
(Intercept) C2 sigma_y
0 0 0
$m2b
$m2b$B2
(Intercept) C2
0 0
$m2c
$m2c$L1mis
(Intercept) C2 sigma_L1mis
0 0 0
$m2d
$m2d$P2
(Intercept) C2
0 0
$m2e
$m2e$L1mis
(Intercept) C2 sigma_L1mis
0 0 0
$m2f
$m2f$Be2
(Intercept) C2 tau_Be2
0 0 0
$m3a
$m3a$C1
(Intercept) C2 B21 P2 L1mis Be2
0 0 0 0 0 0
sigma_C1
0
$m3b
$m3b$C1
(Intercept) C2 B21 P2 L1mis sigma_C1
0 0 0 0 0 0
$m3c
$m3c$C1
(Intercept) C2 B21 P2 L1mis sigma_C1
0 0 0 0 0 0
$m3d
$m3d$C1
(Intercept) C2 B21 P2 L1mis Be2
0 0 0 0 0 0
sigma_C1
0
$m4a
$m4a$y
(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
sigma_y
0
$m4b
$m4b$B1
(Intercept) L1mis abs(C1 - C2) log(Be2)
0 0 0 0
$m5a1
$m5a1$y
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C sigma_y
0 0
$m5a2
$m5a2$y
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C sigma_y
0 0
$m5a3
$m5a3$y
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C sigma_y
0 0
$m5b1
$m5b1$B1
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5b2
$m5b2$B1
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5b3
$m5b3$B1
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5b4
$m5b4$B1
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5c1
$m5c1$L1
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C sigma_L1
0 0
$m5c2
$m5c2$L1
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C sigma_L1
0 0
$m5d1
$m5d1$P1
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5d2
$m5d2$P1
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5e1
$m5e1$L1
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C sigma_L1
0 0
$m5f1
$m5f1$Be1
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C tau_Be1
0 0
$m6a
$m6a$y
(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
sigma_y
0
$m6b
$m6b$B1
(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
$m6c
$m6c$C1
(Intercept) M22 M23 M24 O22
0 0 0 0 0
O23 O24 abs(y - C2) O22:abs(y - C2) O23:abs(y - C2)
0 0 0 0 0
O24:abs(y - C2) sigma_C1
0 0
$m6d
$m6d$SBP
(Intercept) age genderfemale log(bili) exp(creat) sigma_SBP
0 0 0 0 0 0
$m6e
$m6e$SBP
(Intercept) age genderfemale log(bili) exp(creat) sigma_SBP
0 0 0 0 0 0
$m6f
$m6f$SBP
(Intercept) age genderfemale log(bili) exp(creat) sigma_SBP
0 0 0 0 0 0
$mod7a
$mod7a$SBP
(Intercept) ns(age, df = 2)1 ns(age, df = 2)2 genderfemale
0 0 0 0
I(bili^2) I(bili^3) sigma_SBP
0 0 0
Code
lapply(models0, confint)
Output
$m0a1
$m0a1$y
2.5% 97.5%
(Intercept) 0 0
sigma_y 0 0
$m0a2
$m0a2$y
2.5% 97.5%
(Intercept) 0 0
sigma_y 0 0
$m0a3
$m0a3$y
2.5% 97.5%
(Intercept) 0 0
sigma_y 0 0
$m0a4
$m0a4$y
2.5% 97.5%
(Intercept) 0 0
sigma_y 0 0
$m0b1
$m0b1$B1
2.5% 97.5%
(Intercept) 0 0
$m0b2
$m0b2$B1
2.5% 97.5%
(Intercept) 0 0
$m0b3
$m0b3$B1
2.5% 97.5%
(Intercept) 0 0
$m0b4
$m0b4$B1
2.5% 97.5%
(Intercept) 0 0
$m0c1
$m0c1$L1
2.5% 97.5%
(Intercept) 0 0
sigma_L1 0 0
$m0c2
$m0c2$L1
2.5% 97.5%
(Intercept) 0 0
sigma_L1 0 0
$m0d1
$m0d1$P1
2.5% 97.5%
(Intercept) 0 0
$m0d2
$m0d2$P1
2.5% 97.5%
(Intercept) 0 0
$m0e1
$m0e1$L1
2.5% 97.5%
(Intercept) 0 0
sigma_L1 0 0
$m0f1
$m0f1$Be1
2.5% 97.5%
(Intercept) 0 0
tau_Be1 0 0
$m1a
$m1a$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
sigma_y 0 0
$m1b
$m1b$B1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
$m1c
$m1c$L1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
sigma_L1 0 0
$m1d
$m1d$P1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
$m1e
$m1e$L1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
sigma_L1 0 0
$m1f
$m1f$Be1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
tau_Be1 0 0
$m2a
$m2a$y
2.5% 97.5%
(Intercept) 0 0
C2 0 0
sigma_y 0 0
$m2b
$m2b$B2
2.5% 97.5%
(Intercept) 0 0
C2 0 0
$m2c
$m2c$L1mis
2.5% 97.5%
(Intercept) 0 0
C2 0 0
sigma_L1mis 0 0
$m2d
$m2d$P2
2.5% 97.5%
(Intercept) 0 0
C2 0 0
$m2e
$m2e$L1mis
2.5% 97.5%
(Intercept) 0 0
C2 0 0
sigma_L1mis 0 0
$m2f
$m2f$Be2
2.5% 97.5%
(Intercept) 0 0
C2 0 0
tau_Be2 0 0
$m3a
$m3a$C1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
P2 0 0
L1mis 0 0
Be2 0 0
sigma_C1 0 0
$m3b
$m3b$C1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
P2 0 0
L1mis 0 0
sigma_C1 0 0
$m3c
$m3c$C1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
P2 0 0
L1mis 0 0
sigma_C1 0 0
$m3d
$m3d$C1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
P2 0 0
L1mis 0 0
Be2 0 0
sigma_C1 0 0
$m4a
$m4a$y
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
sigma_y 0 0
$m4b
$m4b$B1
2.5% 97.5%
(Intercept) 0 0
L1mis 0 0
abs(C1 - C2) 0 0
log(Be2) 0 0
$m5a1
$m5a1$y
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
sigma_y 0 0
$m5a2
$m5a2$y
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
sigma_y 0 0
$m5a3
$m5a3$y
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
sigma_y 0 0
$m5b1
$m5b1$B1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
C1 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
$m5b2
$m5b2$B1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
C1 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
$m5b3
$m5b3$B1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
C1 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
$m5b4
$m5b4$B1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
C1 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
$m5c1
$m5c1$L1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
sigma_L1 0 0
$m5c2
$m5c2$L1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
sigma_L1 0 0
$m5d1
$m5d1$P1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
$m5d2
$m5d2$P1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
$m5e1
$m5e1$L1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
sigma_L1 0 0
$m5f1
$m5f1$Be1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
tau_Be1 0 0
$m6a
$m6a$y
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
sigma_y 0 0
$m6b
$m6b$B1
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
$m6c
$m6c$C1
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(y - C2) 0 0
O22:abs(y - C2) 0 0
O23:abs(y - C2) 0 0
O24:abs(y - C2) 0 0
sigma_C1 0 0
$m6d
$m6d$SBP
2.5% 97.5%
(Intercept) 0 0
age 0 0
genderfemale 0 0
log(bili) 0 0
exp(creat) 0 0
sigma_SBP 0 0
$m6e
$m6e$SBP
2.5% 97.5%
(Intercept) 0 0
age 0 0
genderfemale 0 0
log(bili) 0 0
exp(creat) 0 0
sigma_SBP 0 0
$m6f
$m6f$SBP
2.5% 97.5%
(Intercept) 0 0
age 0 0
genderfemale 0 0
log(bili) 0 0
exp(creat) 0 0
sigma_SBP 0 0
$mod7a
$mod7a$SBP
2.5% 97.5%
(Intercept) 0 0
ns(age, df = 2)1 0 0
ns(age, df = 2)2 0 0
genderfemale 0 0
I(bili^2) 0 0
I(bili^3) 0 0
sigma_SBP 0 0
Code
lapply(models0, summary, missinfo = TRUE)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a1
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = y ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
y 0 0
$m0a2
Bayesian linear model fitted with JointAI
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "identity"),
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
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
y 0 0
$m0a3
Bayesian linear model fitted with JointAI
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "log"), 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
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
y 0 0
$m0a4
Bayesian linear model fitted with JointAI
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "inverse"),
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
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
y 0 0
$m0b1
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "logit"),
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
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
B1 0 0
$m0b2
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "probit"),
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
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
B1 0 0
$m0b3
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "log"), data = wideDF,
n.adapt = 150, 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
MCMC settings:
Iterations = 151:160
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
B1 0 0
$m0b4
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "cloglog"),
data = wideDF, n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 51:60
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
B1 0 0
$m0c1
Bayesian Gamma model fitted with JointAI
Call:
glm_imp(formula = L1 ~ 1, family = Gamma(link = "inverse"), 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
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
L1 0 0
$m0c2
Bayesian Gamma model fitted with JointAI
Call:
glm_imp(formula = L1 ~ 1, family = Gamma(link = "log"), 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
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
L1 0 0
$m0d1
Bayesian poisson model fitted with JointAI
Call:
glm_imp(formula = P1 ~ 1, family = poisson(link = "log"), 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
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
P1 0 0
$m0d2
Bayesian poisson model fitted with JointAI
Call:
glm_imp(formula = P1 ~ 1, family = poisson(link = "identity"),
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
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
P1 0 0
$m0e1
Bayesian log-normal model fitted with JointAI
Call:
lognorm_imp(formula = L1 ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
L1 0 0
$m0f1
Bayesian beta model fitted with JointAI
Call:
betareg_imp(formula = Be1 ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be1 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
Be1 0 0
$m1a
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = y ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
y 0 0
C1 0 0
$m1b
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ C1, family = binomial(), 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
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
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
B1 0 0
C1 0 0
$m1c
Bayesian Gamma model fitted with JointAI
Call:
glm_imp(formula = L1 ~ C1, family = Gamma(), 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
C1 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
L1 0 0
C1 0 0
$m1d
Bayesian poisson model fitted with JointAI
Call:
glm_imp(formula = P1 ~ C1, family = poisson(), 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
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
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
P1 0 0
C1 0 0
$m1e
Bayesian log-normal model fitted with JointAI
Call:
lognorm_imp(formula = L1 ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
L1 0 0
C1 0 0
$m1f
Bayesian beta model fitted with JointAI
Call:
betareg_imp(formula = Be1 ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be1 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
Be1 0 0
C1 0 0
$m2a
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = y ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 96 96
Number and proportion of missing values:
# NA % NA
y 0 0
C2 4 4
$m2b
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B2 ~ C2, family = binomial(), 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
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
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
C2 4 4
B2 20 20
$m2c
Bayesian Gamma model fitted with JointAI
Call:
glm_imp(formula = L1mis ~ C2, family = Gamma(), 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
C2 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1mis 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 76 76
Number and proportion of missing values:
# NA % NA
C2 4 4
L1mis 20 20
$m2d
Bayesian poisson model fitted with JointAI
Call:
glm_imp(formula = P2 ~ C2, family = poisson(), 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
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
Number and proportion of complete cases:
# %
lvlone 78 78
Number and proportion of missing values:
# NA % NA
C2 4 4
P2 20 20
$m2e
Bayesian log-normal model fitted with JointAI
Call:
lognorm_imp(formula = L1mis ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1mis 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 76 76
Number and proportion of missing values:
# NA % NA
C2 4 4
L1mis 20 20
$m2f
Bayesian beta model fitted with JointAI
Call:
betareg_imp(formula = Be2 ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be2 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
C2 4 4
Be2 20 20
$m3a
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF,
n.adapt = 5, n.iter = 10, models = c(P2 = "glm_poisson_log",
L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
P2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Be2 0 0 0 0 0 NaN NaN
Posterior summary of 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
Number and proportion of complete cases:
# %
lvlone 46 46
Number and proportion of missing values:
# NA % NA
C1 0 0
C2 4 4
B2 20 20
P2 20 20
L1mis 20 20
Be2 20 20
$m3b
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5,
n.iter = 10, models = c(C2 = "glm_gaussian_inverse", P2 = "glm_poisson_identity",
B2 = "glm_binomial_probit", L1mis = "lognorm"), seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
P2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Posterior summary of 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
Number and proportion of complete cases:
# %
lvlone 55 55
Number and proportion of missing values:
# NA % NA
C1 0 0
C2 4 4
B2 20 20
P2 20 20
L1mis 20 20
$m3c
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5,
n.iter = 10, models = c(C2 = "glm_gaussian_log", P2 = "glm_poisson_identity",
L1mis = "glm_gamma_log", B2 = "glm_binomial_log"), seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
P2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Posterior summary of 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
Number and proportion of complete cases:
# %
lvlone 55 55
Number and proportion of missing values:
# NA % NA
C1 0 0
C2 4 4
B2 20 20
P2 20 20
L1mis 20 20
$m3d
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF,
n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
P2 = "glm_poisson_identity", L1mis = "glm_gamma_log",
B2 = "glm_binomial_log"), seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(Be2 = c(0, 1)))
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
P2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Be2 0 0 0 0 0 NaN NaN
Posterior summary of 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
Number and proportion of complete cases:
# %
lvlone 46 46
Number and proportion of missing values:
# NA % NA
C1 0 0
C2 4 4
B2 20 20
P2 20 20
L1mis 20 20
Be2 20 20
$m4a
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(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
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 91 91
Number and proportion of missing values:
# NA % NA
y 0 0
C1 0 0
O2 2 2
M2 3 3
C2 4 4
$m4b
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ L1mis + abs(C1 - C2) + log(Be2), family = binomial(),
data = wideDF, n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(Be2) 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 60 60
Number and proportion of missing values:
# NA % NA
B1 0 0
C1 0 0
C2 4 4
L1mis 20 20
Be2 20 20
$m5a1
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = y ~ C2 + B2 + B1 + O1, data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
y 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5a2
Bayesian linear model fitted with JointAI
Call:
glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "log"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
y 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5a3
Bayesian linear model fitted with JointAI
Call:
glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "inverse"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
y 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5b1
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "logit"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 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
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
B1 0 0
C1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5b2
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "probit"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 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
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
B1 0 0
C1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5b3
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "log"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 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
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
B1 0 0
C1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5b4
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "cloglog"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 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
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
B1 0 0
C1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5c1
Bayesian Gamma model fitted with JointAI
Call:
glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "inverse"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
L1 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5c2
Bayesian Gamma model fitted with JointAI
Call:
glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "log"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
L1 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5d1
Bayesian poisson model fitted with JointAI
Call:
glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "log"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 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
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
P1 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5d2
Bayesian poisson model fitted with JointAI
Call:
glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "identity"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 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
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
P1 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5e1
Bayesian log-normal model fitted with JointAI
Call:
lognorm_imp(formula = L1 ~ C2 + B2 + B1 + O1, data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
L1 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5f1
Bayesian beta model fitted with JointAI
Call:
betareg_imp(formula = Be1 ~ C2 + B2 + B1 + O1, data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be1 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
Be1 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m6a
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 5, 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
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:10
Sample size per chain = 5
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 91 91
Number and proportion of missing values:
# NA % NA
y 0 0
C1 0 0
O2 2 2
M2 3 3
C2 4 4
$m6b
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ M2 + O2 * abs(C1 - C2) + log(C1), family = "binomial",
data = wideDF, n.adapt = 5, n.iter = 5, 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
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:10
Sample size per chain = 5
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 91 91
Number and proportion of missing values:
# NA % NA
B1 0 0
C1 0 0
O2 2 2
M2 3 3
C2 4 4
$m6c
Bayesian Gamma model fitted with JointAI
Call:
glm_imp(formula = C1 ~ M2 + O2 * abs(y - C2), family = Gamma(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 5, 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
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(y - C2) 0 0 0 0 0 NaN NaN
O22:abs(y - C2) 0 0 0 0 0 NaN NaN
O23:abs(y - C2) 0 0 0 0 0 NaN NaN
O24:abs(y - C2) 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 = 6:10
Sample size per chain = 5
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 91 91
Number and proportion of missing values:
# NA % NA
C1 0 0
y 0 0
O2 2 2
M2 3 3
C2 4 4
$m6d
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(bili = c(1e-05, 1e+10)))
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
genderfemale 0 0 0 0 0 NaN NaN
log(bili) 0 0 0 0 0 NaN NaN
exp(creat) 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_SBP 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:10
Sample size per chain = 5
Thinning interval = 1
Number of chains = 3
Number of observations: 186
Number and proportion of complete cases:
# %
lvlone 178 95.7
Number and proportion of missing values:
# NA % NA
SBP 0 0.0
age 0 0.0
gender 0 0.0
bili 8 4.3
creat 8 4.3
$m6e
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "lognorm",
creat = "lm"), 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
age 0 0 0 0 0 NaN NaN
genderfemale 0 0 0 0 0 NaN NaN
log(bili) 0 0 0 0 0 NaN NaN
exp(creat) 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_SBP 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:10
Sample size per chain = 5
Thinning interval = 1
Number of chains = 3
Number of observations: 186
Number and proportion of complete cases:
# %
lvlone 178 95.7
Number and proportion of missing values:
# NA % NA
SBP 0 0.0
age 0 0.0
gender 0 0.0
bili 8 4.3
creat 8 4.3
$m6f
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "glm_gamma_inverse",
creat = "lm"), 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
age 0 0 0 0 0 NaN NaN
genderfemale 0 0 0 0 0 NaN NaN
log(bili) 0 0 0 0 0 NaN NaN
exp(creat) 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_SBP 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:10
Sample size per chain = 5
Thinning interval = 1
Number of chains = 3
Number of observations: 186
Number and proportion of complete cases:
# %
lvlone 178 95.7
Number and proportion of missing values:
# NA % NA
SBP 0 0.0
age 0 0.0
gender 0 0.0
bili 8 4.3
creat 8 4.3
$mod7a
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = SBP ~ ns(age, df = 2) + gender + I(bili^2) +
I(bili^3), data = NHANES, 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
ns(age, df = 2)1 0 0 0 0 0 NaN NaN
ns(age, df = 2)2 0 0 0 0 0 NaN NaN
genderfemale 0 0 0 0 0 NaN NaN
I(bili^2) 0 0 0 0 0 NaN NaN
I(bili^3) 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_SBP 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: 186
Number and proportion of complete cases:
# %
lvlone 178 95.7
Number and proportion of missing values:
# NA % NA
SBP 0 0.0
age 0 0.0
gender 0 0.0
bili 8 4.3
Code
lapply(models0, function(x) coef(summary(x)))
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a1
$m0a1$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0a2
$m0a2$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0a3
$m0a3$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0a4
$m0a4$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0b1
$m0b1$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0b2
$m0b2$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0b3
$m0b3$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0b4
$m0b4$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0c1
$m0c1$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0c2
$m0c2$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0d1
$m0d1$P1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0d2
$m0d2$P1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0e1
$m0e1$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0f1
$m0f1$Be1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m1a
$m1a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1b
$m1b$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1c
$m1c$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1d
$m1d$P1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1e
$m1e$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1f
$m1f$Be1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m2a
$m2a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
$m2b
$m2b$B2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
$m2c
$m2c$L1mis
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
$m2d
$m2d$P2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
$m2e
$m2e$L1mis
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
$m2f
$m2f$Be2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
$m3a
$m3a$C1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
P2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Be2 0 0 0 0 0 NaN NaN
$m3b
$m3b$C1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
P2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
$m3c
$m3c$C1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
P2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
$m3d
$m3d$C1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
P2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Be2 0 0 0 0 0 NaN NaN
$m4a
$m4a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
$m4b
$m4b$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(Be2) 0 0 0 0 0 NaN NaN
$m5a1
$m5a1$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5a2
$m5a2$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5a3
$m5a3$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5b1
$m5b1$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5b2
$m5b2$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5b3
$m5b3$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5b4
$m5b4$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5c1
$m5c1$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5c2
$m5c2$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5d1
$m5d1$P1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5d2
$m5d2$P1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5e1
$m5e1$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5f1
$m5f1$Be1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m6a
$m6a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
$m6b
$m6b$B1
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
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
$m6c
$m6c$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
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(y - C2) 0 0 0 0 0 NaN NaN
O22:abs(y - C2) 0 0 0 0 0 NaN NaN
O23:abs(y - C2) 0 0 0 0 0 NaN NaN
O24:abs(y - C2) 0 0 0 0 0 NaN NaN
$m6d
$m6d$SBP
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
genderfemale 0 0 0 0 0 NaN NaN
log(bili) 0 0 0 0 0 NaN NaN
exp(creat) 0 0 0 0 0 NaN NaN
$m6e
$m6e$SBP
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
genderfemale 0 0 0 0 0 NaN NaN
log(bili) 0 0 0 0 0 NaN NaN
exp(creat) 0 0 0 0 0 NaN NaN
$m6f
$m6f$SBP
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
genderfemale 0 0 0 0 0 NaN NaN
log(bili) 0 0 0 0 0 NaN NaN
exp(creat) 0 0 0 0 0 NaN NaN
$mod7a
$mod7a$SBP
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
ns(age, df = 2)1 0 0 0 0 0 NaN NaN
ns(age, df = 2)2 0 0 0 0 0 NaN NaN
genderfemale 0 0 0 0 0 NaN NaN
I(bili^2) 0 0 0 0 0 NaN NaN
I(bili^3) 0 0 0 0 0 NaN NaN
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data_list remains the same
Code
lapply(models, "[[", "data_list")
Output
$m0a1
$m0a1$M_lvlone
y (Intercept)
1 -4.76915977 1
2 -2.69277172 1
3 -1.17551547 1
4 -4.57464473 1
5 -2.20260004 1
6 -3.48995315 1
7 -0.44987258 1
8 -2.29588848 1
9 -4.49135812 1
10 -5.52545368 1
11 -4.16286741 1
12 -2.93455761 1
13 -0.04202496 1
14 -1.63149775 1
15 -0.97786151 1
16 -1.79100431 1
17 -6.26520032 1
18 -1.36028709 1
19 -1.15396597 1
20 -3.21707239 1
21 -1.59389898 1
22 -5.50335066 1
23 0.57290123 1
24 -8.22270323 1
25 -1.41364158 1
26 -6.28031574 1
27 -3.15624425 1
28 -3.55693639 1
29 -1.11821124 1
30 -2.82834175 1
31 -3.72259860 1
32 -1.75256656 1
33 -5.55044409 1
34 -7.45068147 1
35 -0.97491919 1
36 -2.98356481 1
37 -1.86039471 1
38 -7.28754607 1
39 -8.66234796 1
40 -4.16291375 1
41 -3.48250771 1
42 -7.27930410 1
43 -6.12866190 1
44 -4.96880803 1
45 -4.76746713 1
46 -1.91249177 1
47 -0.61884029 1
48 -0.20496175 1
49 -7.12636055 1
50 -6.23103837 1
51 -3.32561065 1
52 -2.95942339 1
53 -4.44915114 1
54 -0.81566463 1
55 -6.50029573 1
56 -2.74718050 1
57 -6.35015663 1
58 -2.69505883 1
59 -1.55660833 1
60 -3.76240209 1
61 -3.92885797 1
62 -1.72044748 1
63 -0.56602625 1
64 -4.42235015 1
65 -2.39122287 1
66 -0.81807247 1
67 -6.48196782 1
68 -1.37306273 1
69 -4.99886487 1
70 -5.82288217 1
71 -2.68234219 1
72 -3.96170442 1
73 -7.19573667 1
74 -5.08799713 1
75 -1.32967262 1
76 -2.56532332 1
77 -3.21002900 1
78 -3.40559790 1
79 -4.56223913 1
80 -2.04250454 1
81 -2.20378059 1
82 -3.37471317 1
83 -0.95345385 1
84 -4.89337660 1
85 -9.82258463 1
86 -4.51800734 1
87 -0.18662049 1
88 -2.87120881 1
89 1.29290150 1
90 -1.39497744 1
91 1.14575040 1
92 0.92801246 1
93 -2.59938157 1
94 -3.26905923 1
95 -3.26861434 1
96 -5.71017484 1
97 -3.76781806 1
98 -2.02677390 1
99 -2.96199765 1
100 -4.81129496 1
$m0a1$mu_reg_norm
[1] 0
$m0a1$tau_reg_norm
[1] 1e-04
$m0a1$shape_tau_norm
[1] 0.01
$m0a1$rate_tau_norm
[1] 0.01
$m0a2
$m0a2$M_lvlone
y (Intercept)
1 -4.76915977 1
2 -2.69277172 1
3 -1.17551547 1
4 -4.57464473 1
5 -2.20260004 1
6 -3.48995315 1
7 -0.44987258 1
8 -2.29588848 1
9 -4.49135812 1
10 -5.52545368 1
11 -4.16286741 1
12 -2.93455761 1
13 -0.04202496 1
14 -1.63149775 1
15 -0.97786151 1
16 -1.79100431 1
17 -6.26520032 1
18 -1.36028709 1
19 -1.15396597 1
20 -3.21707239 1
21 -1.59389898 1
22 -5.50335066 1
23 0.57290123 1
24 -8.22270323 1
25 -1.41364158 1
26 -6.28031574 1
27 -3.15624425 1
28 -3.55693639 1
29 -1.11821124 1
30 -2.82834175 1
31 -3.72259860 1
32 -1.75256656 1
33 -5.55044409 1
34 -7.45068147 1
35 -0.97491919 1
36 -2.98356481 1
37 -1.86039471 1
38 -7.28754607 1
39 -8.66234796 1
40 -4.16291375 1
41 -3.48250771 1
42 -7.27930410 1
43 -6.12866190 1
44 -4.96880803 1
45 -4.76746713 1
46 -1.91249177 1
47 -0.61884029 1
48 -0.20496175 1
49 -7.12636055 1
50 -6.23103837 1
51 -3.32561065 1
52 -2.95942339 1
53 -4.44915114 1
54 -0.81566463 1
55 -6.50029573 1
56 -2.74718050 1
57 -6.35015663 1
58 -2.69505883 1
59 -1.55660833 1
60 -3.76240209 1
61 -3.92885797 1
62 -1.72044748 1
63 -0.56602625 1
64 -4.42235015 1
65 -2.39122287 1
66 -0.81807247 1
67 -6.48196782 1
68 -1.37306273 1
69 -4.99886487 1
70 -5.82288217 1
71 -2.68234219 1
72 -3.96170442 1
73 -7.19573667 1
74 -5.08799713 1
75 -1.32967262 1
76 -2.56532332 1
77 -3.21002900 1
78 -3.40559790 1
79 -4.56223913 1
80 -2.04250454 1
81 -2.20378059 1
82 -3.37471317 1
83 -0.95345385 1
84 -4.89337660 1
85 -9.82258463 1
86 -4.51800734 1
87 -0.18662049 1
88 -2.87120881 1
89 1.29290150 1
90 -1.39497744 1
91 1.14575040 1
92 0.92801246 1
93 -2.59938157 1
94 -3.26905923 1
95 -3.26861434 1
96 -5.71017484 1
97 -3.76781806 1
98 -2.02677390 1
99 -2.96199765 1
100 -4.81129496 1
$m0a2$mu_reg_norm
[1] 0
$m0a2$tau_reg_norm
[1] 1e-04
$m0a2$shape_tau_norm
[1] 0.01
$m0a2$rate_tau_norm
[1] 0.01
$m0a3
$m0a3$M_lvlone
y (Intercept)
1 -4.76915977 1
2 -2.69277172 1
3 -1.17551547 1
4 -4.57464473 1
5 -2.20260004 1
6 -3.48995315 1
7 -0.44987258 1
8 -2.29588848 1
9 -4.49135812 1
10 -5.52545368 1
11 -4.16286741 1
12 -2.93455761 1
13 -0.04202496 1
14 -1.63149775 1
15 -0.97786151 1
16 -1.79100431 1
17 -6.26520032 1
18 -1.36028709 1
19 -1.15396597 1
20 -3.21707239 1
21 -1.59389898 1
22 -5.50335066 1
23 0.57290123 1
24 -8.22270323 1
25 -1.41364158 1
26 -6.28031574 1
27 -3.15624425 1
28 -3.55693639 1
29 -1.11821124 1
30 -2.82834175 1
31 -3.72259860 1
32 -1.75256656 1
33 -5.55044409 1
34 -7.45068147 1
35 -0.97491919 1
36 -2.98356481 1
37 -1.86039471 1
38 -7.28754607 1
39 -8.66234796 1
40 -4.16291375 1
41 -3.48250771 1
42 -7.27930410 1
43 -6.12866190 1
44 -4.96880803 1
45 -4.76746713 1
46 -1.91249177 1
47 -0.61884029 1
48 -0.20496175 1
49 -7.12636055 1
50 -6.23103837 1
51 -3.32561065 1
52 -2.95942339 1
53 -4.44915114 1
54 -0.81566463 1
55 -6.50029573 1
56 -2.74718050 1
57 -6.35015663 1
58 -2.69505883 1
59 -1.55660833 1
60 -3.76240209 1
61 -3.92885797 1
62 -1.72044748 1
63 -0.56602625 1
64 -4.42235015 1
65 -2.39122287 1
66 -0.81807247 1
67 -6.48196782 1
68 -1.37306273 1
69 -4.99886487 1
70 -5.82288217 1
71 -2.68234219 1
72 -3.96170442 1
73 -7.19573667 1
74 -5.08799713 1
75 -1.32967262 1
76 -2.56532332 1
77 -3.21002900 1
78 -3.40559790 1
79 -4.56223913 1
80 -2.04250454 1
81 -2.20378059 1
82 -3.37471317 1
83 -0.95345385 1
84 -4.89337660 1
85 -9.82258463 1
86 -4.51800734 1
87 -0.18662049 1
88 -2.87120881 1
89 1.29290150 1
90 -1.39497744 1
91 1.14575040 1
92 0.92801246 1
93 -2.59938157 1
94 -3.26905923 1
95 -3.26861434 1
96 -5.71017484 1
97 -3.76781806 1
98 -2.02677390 1
99 -2.96199765 1
100 -4.81129496 1
$m0a3$mu_reg_norm
[1] 0
$m0a3$tau_reg_norm
[1] 1e-04
$m0a3$shape_tau_norm
[1] 0.01
$m0a3$rate_tau_norm
[1] 0.01
$m0a4
$m0a4$M_lvlone
y (Intercept)
1 -4.76915977 1
2 -2.69277172 1
3 -1.17551547 1
4 -4.57464473 1
5 -2.20260004 1
6 -3.48995315 1
7 -0.44987258 1
8 -2.29588848 1
9 -4.49135812 1
10 -5.52545368 1
11 -4.16286741 1
12 -2.93455761 1
13 -0.04202496 1
14 -1.63149775 1
15 -0.97786151 1
16 -1.79100431 1
17 -6.26520032 1
18 -1.36028709 1
19 -1.15396597 1
20 -3.21707239 1
21 -1.59389898 1
22 -5.50335066 1
23 0.57290123 1
24 -8.22270323 1
25 -1.41364158 1
26 -6.28031574 1
27 -3.15624425 1
28 -3.55693639 1
29 -1.11821124 1
30 -2.82834175 1
31 -3.72259860 1
32 -1.75256656 1
33 -5.55044409 1
34 -7.45068147 1
35 -0.97491919 1
36 -2.98356481 1
37 -1.86039471 1
38 -7.28754607 1
39 -8.66234796 1
40 -4.16291375 1
41 -3.48250771 1
42 -7.27930410 1
43 -6.12866190 1
44 -4.96880803 1
45 -4.76746713 1
46 -1.91249177 1
47 -0.61884029 1
48 -0.20496175 1
49 -7.12636055 1
50 -6.23103837 1
51 -3.32561065 1
52 -2.95942339 1
53 -4.44915114 1
54 -0.81566463 1
55 -6.50029573 1
56 -2.74718050 1
57 -6.35015663 1
58 -2.69505883 1
59 -1.55660833 1
60 -3.76240209 1
61 -3.92885797 1
62 -1.72044748 1
63 -0.56602625 1
64 -4.42235015 1
65 -2.39122287 1
66 -0.81807247 1
67 -6.48196782 1
68 -1.37306273 1
69 -4.99886487 1
70 -5.82288217 1
71 -2.68234219 1
72 -3.96170442 1
73 -7.19573667 1
74 -5.08799713 1
75 -1.32967262 1
76 -2.56532332 1
77 -3.21002900 1
78 -3.40559790 1
79 -4.56223913 1
80 -2.04250454 1
81 -2.20378059 1
82 -3.37471317 1
83 -0.95345385 1
84 -4.89337660 1
85 -9.82258463 1
86 -4.51800734 1
87 -0.18662049 1
88 -2.87120881 1
89 1.29290150 1
90 -1.39497744 1
91 1.14575040 1
92 0.92801246 1
93 -2.59938157 1
94 -3.26905923 1
95 -3.26861434 1
96 -5.71017484 1
97 -3.76781806 1
98 -2.02677390 1
99 -2.96199765 1
100 -4.81129496 1
$m0a4$mu_reg_norm
[1] 0
$m0a4$tau_reg_norm
[1] 1e-04
$m0a4$shape_tau_norm
[1] 0.01
$m0a4$rate_tau_norm
[1] 0.01
$m0b1
$m0b1$M_lvlone
B1 (Intercept)
1 1 1
2 1 1
3 1 1
4 1 1
5 1 1
6 1 1
7 0 1
8 0 1
9 1 1
10 1 1
11 1 1
12 0 1
13 1 1
14 0 1
15 1 1
16 1 1
17 1 1
18 1 1
19 1 1
20 1 1
21 1 1
22 1 1
23 1 1
24 1 1
25 0 1
26 1 1
27 1 1
28 1 1
29 1 1
30 0 1
31 0 1
32 1 1
33 1 1
34 1 1
35 1 1
36 0 1
37 1 1
38 1 1
39 1 1
40 1 1
41 1 1
42 1 1
43 1 1
44 1 1
45 1 1
46 1 1
47 0 1
48 1 1
49 1 1
50 0 1
51 1 1
52 1 1
53 1 1
54 1 1
55 0 1
56 1 1
57 1 1
58 1 1
59 1 1
60 0 1
61 1 1
62 1 1
63 0 1
64 1 1
65 1 1
66 0 1
67 0 1
68 1 1
69 0 1
70 0 1
71 1 1
72 1 1
73 0 1
74 1 1
75 1 1
76 0 1
77 0 1
78 0 1
79 1 1
80 1 1
81 1 1
82 1 1
83 1 1
84 1 1
85 1 1
86 1 1
87 1 1
88 0 1
89 1 1
90 1 1
91 1 1
92 1 1
93 1 1
94 1 1
95 1 1
96 1 1
97 1 1
98 1 1
99 1 1
100 1 1
$m0b1$mu_reg_binom
[1] 0
$m0b1$tau_reg_binom
[1] 1e-04
$m0b2
$m0b2$M_lvlone
B1 (Intercept)
1 1 1
2 1 1
3 1 1
4 1 1
5 1 1
6 1 1
7 0 1
8 0 1
9 1 1
10 1 1
11 1 1
12 0 1
13 1 1
14 0 1
15 1 1
16 1 1
17 1 1
18 1 1
19 1 1
20 1 1
21 1 1
22 1 1
23 1 1
24 1 1
25 0 1
26 1 1
27 1 1
28 1 1
29 1 1
30 0 1
31 0 1
32 1 1
33 1 1
34 1 1
35 1 1
36 0 1
37 1 1
38 1 1
39 1 1
40 1 1
41 1 1
42 1 1
43 1 1
44 1 1
45 1 1
46 1 1
47 0 1
48 1 1
49 1 1
50 0 1
51 1 1
52 1 1
53 1 1
54 1 1
55 0 1
56 1 1
57 1 1
58 1 1
59 1 1
60 0 1
61 1 1
62 1 1
63 0 1
64 1 1
65 1 1
66 0 1
67 0 1
68 1 1
69 0 1
70 0 1
71 1 1
72 1 1
73 0 1
74 1 1
75 1 1
76 0 1
77 0 1
78 0 1
79 1 1
80 1 1
81 1 1
82 1 1
83 1 1
84 1 1
85 1 1
86 1 1
87 1 1
88 0 1
89 1 1
90 1 1
91 1 1
92 1 1
93 1 1
94 1 1
95 1 1
96 1 1
97 1 1
98 1 1
99 1 1
100 1 1
$m0b2$mu_reg_binom
[1] 0
$m0b2$tau_reg_binom
[1] 1e-04
$m0b3
$m0b3$M_lvlone
B1 (Intercept)
1 1 1
2 1 1
3 1 1
4 1 1
5 1 1
6 1 1
7 0 1
8 0 1
9 1 1
10 1 1
11 1 1
12 0 1
13 1 1
14 0 1
15 1 1
16 1 1
17 1 1
18 1 1
19 1 1
20 1 1
21 1 1
22 1 1
23 1 1
24 1 1
25 0 1
26 1 1
27 1 1
28 1 1
29 1 1
30 0 1
31 0 1
32 1 1
33 1 1
34 1 1
35 1 1
36 0 1
37 1 1
38 1 1
39 1 1
40 1 1
41 1 1
42 1 1
43 1 1
44 1 1
45 1 1
46 1 1
47 0 1
48 1 1
49 1 1
50 0 1
51 1 1
52 1 1
53 1 1
54 1 1
55 0 1
56 1 1
57 1 1
58 1 1
59 1 1
60 0 1
61 1 1
62 1 1
63 0 1
64 1 1
65 1 1
66 0 1
67 0 1
68 1 1
69 0 1
70 0 1
71 1 1
72 1 1
73 0 1
74 1 1
75 1 1
76 0 1
77 0 1
78 0 1
79 1 1
80 1 1
81 1 1
82 1 1
83 1 1
84 1 1
85 1 1
86 1 1
87 1 1
88 0 1
89 1 1
90 1 1
91 1 1
92 1 1
93 1 1
94 1 1
95 1 1
96 1 1
97 1 1
98 1 1
99 1 1
100 1 1
$m0b3$mu_reg_binom
[1] 0
$m0b3$tau_reg_binom
[1] 1e-04
$m0b4
$m0b4$M_lvlone
B1 (Intercept)
1 1 1
2 1 1
3 1 1
4 1 1
5 1 1
6 1 1
7 0 1
8 0 1
9 1 1
10 1 1
11 1 1
12 0 1
13 1 1
14 0 1
15 1 1
16 1 1
17 1 1
18 1 1
19 1 1
20 1 1
21 1 1
22 1 1
23 1 1
24 1 1
25 0 1
26 1 1
27 1 1
28 1 1
29 1 1
30 0 1
31 0 1
32 1 1
33 1 1
34 1 1
35 1 1
36 0 1
37 1 1
38 1 1
39 1 1
40 1 1
41 1 1
42 1 1
43 1 1
44 1 1
45 1 1
46 1 1
47 0 1
48 1 1
49 1 1
50 0 1
51 1 1
52 1 1
53 1 1
54 1 1
55 0 1
56 1 1
57 1 1
58 1 1
59 1 1
60 0 1
61 1 1
62 1 1
63 0 1
64 1 1
65 1 1
66 0 1
67 0 1
68 1 1
69 0 1
70 0 1
71 1 1
72 1 1
73 0 1
74 1 1
75 1 1
76 0 1
77 0 1
78 0 1
79 1 1
80 1 1
81 1 1
82 1 1
83 1 1
84 1 1
85 1 1
86 1 1
87 1 1
88 0 1
89 1 1
90 1 1
91 1 1
92 1 1
93 1 1
94 1 1
95 1 1
96 1 1
97 1 1
98 1 1
99 1 1
100 1 1
$m0b4$mu_reg_binom
[1] 0
$m0b4$tau_reg_binom
[1] 1e-04
$m0c1
$m0c1$M_lvlone
L1 (Intercept)
1 0.9364352 1
2 0.8943541 1
3 0.2868460 1
4 0.9068418 1
5 0.7621346 1
6 0.5858621 1
7 0.7194403 1
8 0.7593154 1
9 0.5863705 1
10 0.7342586 1
11 0.7218028 1
12 0.7241254 1
13 0.7200126 1
14 0.5289014 1
15 0.7322482 1
16 0.7462471 1
17 0.9119922 1
18 0.6262513 1
19 0.4587835 1
20 0.7173364 1
21 0.7288999 1
22 0.7160420 1
23 0.5795514 1
24 0.7210413 1
25 0.7816086 1
26 0.6747483 1
27 0.4746725 1
28 0.9270652 1
29 0.5306249 1
30 0.8913764 1
31 0.8090308 1
32 0.4610800 1
33 0.7183814 1
34 0.6375974 1
35 0.9202563 1
36 0.7263222 1
37 1.0638781 1
38 0.6053893 1
39 0.7945509 1
40 0.6355032 1
41 0.9939049 1
42 1.0690739 1
43 0.7009106 1
44 0.7595403 1
45 0.8356414 1
46 0.4929132 1
47 0.5298192 1
48 0.5363034 1
49 0.8494053 1
50 0.6292812 1
51 0.9561312 1
52 0.9735411 1
53 0.7156259 1
54 0.5184434 1
55 0.7948965 1
56 0.5191792 1
57 0.9233108 1
58 0.8025356 1
59 0.8546624 1
60 0.8639819 1
61 0.7521237 1
62 0.5590215 1
63 0.5972103 1
64 0.6071272 1
65 0.8837829 1
66 0.7775301 1
67 0.6756191 1
68 0.7857549 1
69 0.9119262 1
70 0.5816103 1
71 0.4886093 1
72 0.8292467 1
73 0.6767456 1
74 0.7328840 1
75 0.7946099 1
76 0.7734810 1
77 0.5296147 1
78 0.7723288 1
79 0.8079308 1
80 0.5214822 1
81 0.6264777 1
82 0.8332107 1
83 0.4544158 1
84 0.6482660 1
85 0.7272109 1
86 0.7302426 1
87 0.6768061 1
88 0.8115758 1
89 0.9775567 1
90 0.6408465 1
91 0.5917453 1
92 0.7224845 1
93 0.4501596 1
94 0.5190455 1
95 0.7305821 1
96 0.9696445 1
97 0.7087457 1
98 0.9964080 1
99 0.9084899 1
100 0.9296776 1
$m0c1$mu_reg_gamma
[1] 0
$m0c1$tau_reg_gamma
[1] 1e-04
$m0c1$shape_tau_gamma
[1] 0.01
$m0c1$rate_tau_gamma
[1] 0.01
$m0c2
$m0c2$M_lvlone
L1 (Intercept)
1 0.9364352 1
2 0.8943541 1
3 0.2868460 1
4 0.9068418 1
5 0.7621346 1
6 0.5858621 1
7 0.7194403 1
8 0.7593154 1
9 0.5863705 1
10 0.7342586 1
11 0.7218028 1
12 0.7241254 1
13 0.7200126 1
14 0.5289014 1
15 0.7322482 1
16 0.7462471 1
17 0.9119922 1
18 0.6262513 1
19 0.4587835 1
20 0.7173364 1
21 0.7288999 1
22 0.7160420 1
23 0.5795514 1
24 0.7210413 1
25 0.7816086 1
26 0.6747483 1
27 0.4746725 1
28 0.9270652 1
29 0.5306249 1
30 0.8913764 1
31 0.8090308 1
32 0.4610800 1
33 0.7183814 1
34 0.6375974 1
35 0.9202563 1
36 0.7263222 1
37 1.0638781 1
38 0.6053893 1
39 0.7945509 1
40 0.6355032 1
41 0.9939049 1
42 1.0690739 1
43 0.7009106 1
44 0.7595403 1
45 0.8356414 1
46 0.4929132 1
47 0.5298192 1
48 0.5363034 1
49 0.8494053 1
50 0.6292812 1
51 0.9561312 1
52 0.9735411 1
53 0.7156259 1
54 0.5184434 1
55 0.7948965 1
56 0.5191792 1
57 0.9233108 1
58 0.8025356 1
59 0.8546624 1
60 0.8639819 1
61 0.7521237 1
62 0.5590215 1
63 0.5972103 1
64 0.6071272 1
65 0.8837829 1
66 0.7775301 1
67 0.6756191 1
68 0.7857549 1
69 0.9119262 1
70 0.5816103 1
71 0.4886093 1
72 0.8292467 1
73 0.6767456 1
74 0.7328840 1
75 0.7946099 1
76 0.7734810 1
77 0.5296147 1
78 0.7723288 1
79 0.8079308 1
80 0.5214822 1
81 0.6264777 1
82 0.8332107 1
83 0.4544158 1
84 0.6482660 1
85 0.7272109 1
86 0.7302426 1
87 0.6768061 1
88 0.8115758 1
89 0.9775567 1
90 0.6408465 1
91 0.5917453 1
92 0.7224845 1
93 0.4501596 1
94 0.5190455 1
95 0.7305821 1
96 0.9696445 1
97 0.7087457 1
98 0.9964080 1
99 0.9084899 1
100 0.9296776 1
$m0c2$mu_reg_gamma
[1] 0
$m0c2$tau_reg_gamma
[1] 1e-04
$m0c2$shape_tau_gamma
[1] 0.01
$m0c2$rate_tau_gamma
[1] 0.01
$m0d1
$m0d1$M_lvlone
P1 (Intercept)
1 1 1
2 3 1
3 3 1
4 3 1
5 5 1
6 3 1
7 0 1
8 2 1
9 4 1
10 3 1
11 4 1
12 3 1
13 2 1
14 6 1
15 2 1
16 5 1
17 2 1
18 2 1
19 1 1
20 2 1
21 2 1
22 2 1
23 1 1
24 0 1
25 2 1
26 4 1
27 3 1
28 5 1
29 5 1
30 0 1
31 3 1
32 2 1
33 2 1
34 3 1
35 1 1
36 4 1
37 2 1
38 2 1
39 8 1
40 4 1
41 3 1
42 3 1
43 2 1
44 3 1
45 2 1
46 3 1
47 4 1
48 3 1
49 2 1
50 4 1
51 1 1
52 2 1
53 4 1
54 3 1
55 1 1
56 3 1
57 3 1
58 4 1
59 1 1
60 5 1
61 5 1
62 0 1
63 2 1
64 0 1
65 2 1
66 4 1
67 2 1
68 3 1
69 1 1
70 3 1
71 1 1
72 5 1
73 0 1
74 4 1
75 1 1
76 3 1
77 2 1
78 1 1
79 2 1
80 4 1
81 6 1
82 3 1
83 1 1
84 3 1
85 1 1
86 5 1
87 2 1
88 2 1
89 1 1
90 5 1
91 1 1
92 5 1
93 1 1
94 1 1
95 1 1
96 3 1
97 2 1
98 0 1
99 2 1
100 4 1
$m0d1$mu_reg_poisson
[1] 0
$m0d1$tau_reg_poisson
[1] 1e-04
$m0d2
$m0d2$M_lvlone
P1 (Intercept)
1 1 1
2 3 1
3 3 1
4 3 1
5 5 1
6 3 1
7 0 1
8 2 1
9 4 1
10 3 1
11 4 1
12 3 1
13 2 1
14 6 1
15 2 1
16 5 1
17 2 1
18 2 1
19 1 1
20 2 1
21 2 1
22 2 1
23 1 1
24 0 1
25 2 1
26 4 1
27 3 1
28 5 1
29 5 1
30 0 1
31 3 1
32 2 1
33 2 1
34 3 1
35 1 1
36 4 1
37 2 1
38 2 1
39 8 1
40 4 1
41 3 1
42 3 1
43 2 1
44 3 1
45 2 1
46 3 1
47 4 1
48 3 1
49 2 1
50 4 1
51 1 1
52 2 1
53 4 1
54 3 1
55 1 1
56 3 1
57 3 1
58 4 1
59 1 1
60 5 1
61 5 1
62 0 1
63 2 1
64 0 1
65 2 1
66 4 1
67 2 1
68 3 1
69 1 1
70 3 1
71 1 1
72 5 1
73 0 1
74 4 1
75 1 1
76 3 1
77 2 1
78 1 1
79 2 1
80 4 1
81 6 1
82 3 1
83 1 1
84 3 1
85 1 1
86 5 1
87 2 1
88 2 1
89 1 1
90 5 1
91 1 1
92 5 1
93 1 1
94 1 1
95 1 1
96 3 1
97 2 1
98 0 1
99 2 1
100 4 1
$m0d2$mu_reg_poisson
[1] 0
$m0d2$tau_reg_poisson
[1] 1e-04
$m0e1
$m0e1$M_lvlone
L1 (Intercept)
1 0.9364352 1
2 0.8943541 1
3 0.2868460 1
4 0.9068418 1
5 0.7621346 1
6 0.5858621 1
7 0.7194403 1
8 0.7593154 1
9 0.5863705 1
10 0.7342586 1
11 0.7218028 1
12 0.7241254 1
13 0.7200126 1
14 0.5289014 1
15 0.7322482 1
16 0.7462471 1
17 0.9119922 1
18 0.6262513 1
19 0.4587835 1
20 0.7173364 1
21 0.7288999 1
22 0.7160420 1
23 0.5795514 1
24 0.7210413 1
25 0.7816086 1
26 0.6747483 1
27 0.4746725 1
28 0.9270652 1
29 0.5306249 1
30 0.8913764 1
31 0.8090308 1
32 0.4610800 1
33 0.7183814 1
34 0.6375974 1
35 0.9202563 1
36 0.7263222 1
37 1.0638781 1
38 0.6053893 1
39 0.7945509 1
40 0.6355032 1
41 0.9939049 1
42 1.0690739 1
43 0.7009106 1
44 0.7595403 1
45 0.8356414 1
46 0.4929132 1
47 0.5298192 1
48 0.5363034 1
49 0.8494053 1
50 0.6292812 1
51 0.9561312 1
52 0.9735411 1
53 0.7156259 1
54 0.5184434 1
55 0.7948965 1
56 0.5191792 1
57 0.9233108 1
58 0.8025356 1
59 0.8546624 1
60 0.8639819 1
61 0.7521237 1
62 0.5590215 1
63 0.5972103 1
64 0.6071272 1
65 0.8837829 1
66 0.7775301 1
67 0.6756191 1
68 0.7857549 1
69 0.9119262 1
70 0.5816103 1
71 0.4886093 1
72 0.8292467 1
73 0.6767456 1
74 0.7328840 1
75 0.7946099 1
76 0.7734810 1
77 0.5296147 1
78 0.7723288 1
79 0.8079308 1
80 0.5214822 1
81 0.6264777 1
82 0.8332107 1
83 0.4544158 1
84 0.6482660 1
85 0.7272109 1
86 0.7302426 1
87 0.6768061 1
88 0.8115758 1
89 0.9775567 1
90 0.6408465 1
91 0.5917453 1
92 0.7224845 1
93 0.4501596 1
94 0.5190455 1
95 0.7305821 1
96 0.9696445 1
97 0.7087457 1
98 0.9964080 1
99 0.9084899 1
100 0.9296776 1
$m0e1$mu_reg_norm
[1] 0
$m0e1$tau_reg_norm
[1] 1e-04
$m0e1$shape_tau_norm
[1] 0.01
$m0e1$rate_tau_norm
[1] 0.01
$m0f1
$m0f1$M_lvlone
Be1 (Intercept)
1 0.69649948 1
2 0.56085128 1
3 0.35796663 1
4 0.53961336 1
5 0.06191042 1
6 0.51256785 1
7 0.13154723 1
8 0.35032766 1
9 0.21796890 1
10 0.10476230 1
11 0.66083800 1
12 0.66884267 1
13 0.69840279 1
14 0.50398472 1
15 0.52807655 1
16 0.40135087 1
17 0.45554802 1
18 0.68717635 1
19 0.35880655 1
20 0.36341035 1
21 0.71468563 1
22 0.44558172 1
23 0.33262526 1
24 0.66812751 1
25 0.23180310 1
26 0.37786624 1
27 0.88834598 1
28 0.46487057 1
29 0.47018802 1
30 0.91617346 1
31 0.67589111 1
32 0.61623852 1
33 0.44182889 1
34 0.29868153 1
35 0.44235110 1
36 0.72557250 1
37 0.74809277 1
38 0.26452559 1
39 0.41597215 1
40 0.29080530 1
41 0.80342568 1
42 0.76614332 1
43 0.29734466 1
44 0.42809509 1
45 0.12861202 1
46 0.44369392 1
47 0.35290028 1
48 0.88288407 1
49 0.37880332 1
50 0.60663793 1
51 0.15505292 1
52 0.65796074 1
53 0.63416487 1
54 0.83040459 1
55 0.64947589 1
56 0.67541381 1
57 0.53637356 1
58 0.39157422 1
59 0.88168026 1
60 0.32582606 1
61 0.64492753 1
62 0.34804110 1
63 0.49241010 1
64 0.43387493 1
65 0.21806182 1
66 0.60021691 1
67 0.30567313 1
68 0.22476988 1
69 0.23155216 1
70 0.29610794 1
71 0.83435168 1
72 0.65543408 1
73 0.59684715 1
74 0.80640183 1
75 0.52288624 1
76 0.41546840 1
77 0.44756212 1
78 0.68093413 1
79 0.29261828 1
80 0.21008516 1
81 0.44710869 1
82 0.70470991 1
83 0.31300581 1
84 0.44774544 1
85 0.68031201 1
86 0.44456865 1
87 0.79031803 1
88 0.22231438 1
89 0.30114327 1
90 0.45339193 1
91 0.35526875 1
92 0.68684691 1
93 0.81430167 1
94 0.60104343 1
95 0.82012448 1
96 0.55669948 1
97 0.76622465 1
98 0.50112270 1
99 0.53468983 1
100 0.58249327 1
$m0f1$mu_reg_beta
[1] 0
$m0f1$tau_reg_beta
[1] 1e-04
$m0f1$shape_tau_beta
[1] 0.01
$m0f1$rate_tau_beta
[1] 0.01
$m1a
$m1a$M_lvlone
y (Intercept) C1
1 -4.76915977 1 1.410531
2 -2.69277172 1 1.434183
3 -1.17551547 1 1.430994
4 -4.57464473 1 1.453096
5 -2.20260004 1 1.438344
6 -3.48995315 1 1.453207
7 -0.44987258 1 1.425176
8 -2.29588848 1 1.437908
9 -4.49135812 1 1.416911
10 -5.52545368 1 1.448638
11 -4.16286741 1 1.428375
12 -2.93455761 1 1.450130
13 -0.04202496 1 1.420545
14 -1.63149775 1 1.423005
15 -0.97786151 1 1.435902
16 -1.79100431 1 1.423901
17 -6.26520032 1 1.457208
18 -1.36028709 1 1.414280
19 -1.15396597 1 1.443383
20 -3.21707239 1 1.434954
21 -1.59389898 1 1.429499
22 -5.50335066 1 1.441897
23 0.57290123 1 1.423713
24 -8.22270323 1 1.435395
25 -1.41364158 1 1.425944
26 -6.28031574 1 1.437115
27 -3.15624425 1 1.441326
28 -3.55693639 1 1.422953
29 -1.11821124 1 1.437797
30 -2.82834175 1 1.472121
31 -3.72259860 1 1.421782
32 -1.75256656 1 1.457672
33 -5.55044409 1 1.430842
34 -7.45068147 1 1.431523
35 -0.97491919 1 1.421395
36 -2.98356481 1 1.434496
37 -1.86039471 1 1.425383
38 -7.28754607 1 1.421802
39 -8.66234796 1 1.430094
40 -4.16291375 1 1.447621
41 -3.48250771 1 1.434797
42 -7.27930410 1 1.446091
43 -6.12866190 1 1.445306
44 -4.96880803 1 1.448783
45 -4.76746713 1 1.450617
46 -1.91249177 1 1.415055
47 -0.61884029 1 1.436590
48 -0.20496175 1 1.433938
49 -7.12636055 1 1.414941
50 -6.23103837 1 1.421807
51 -3.32561065 1 1.453203
52 -2.95942339 1 1.452129
53 -4.44915114 1 1.431510
54 -0.81566463 1 1.430082
55 -6.50029573 1 1.443492
56 -2.74718050 1 1.436460
57 -6.35015663 1 1.418119
58 -2.69505883 1 1.434971
59 -1.55660833 1 1.445599
60 -3.76240209 1 1.437097
61 -3.92885797 1 1.428360
62 -1.72044748 1 1.440550
63 -0.56602625 1 1.443014
64 -4.42235015 1 1.424298
65 -2.39122287 1 1.448823
66 -0.81807247 1 1.425834
67 -6.48196782 1 1.427102
68 -1.37306273 1 1.414240
69 -4.99886487 1 1.456218
70 -5.82288217 1 1.470594
71 -2.68234219 1 1.425058
72 -3.96170442 1 1.432371
73 -7.19573667 1 1.441656
74 -5.08799713 1 1.434952
75 -1.32967262 1 1.402860
76 -2.56532332 1 1.453363
77 -3.21002900 1 1.432909
78 -3.40559790 1 1.435103
79 -4.56223913 1 1.434462
80 -2.04250454 1 1.434661
81 -2.20378059 1 1.445881
82 -3.37471317 1 1.442548
83 -0.95345385 1 1.430097
84 -4.89337660 1 1.430119
85 -9.82258463 1 1.430315
86 -4.51800734 1 1.437584
87 -0.18662049 1 1.409738
88 -2.87120881 1 1.422388
89 1.29290150 1 1.422509
90 -1.39497744 1 1.439432
91 1.14575040 1 1.430175
92 0.92801246 1 1.418002
93 -2.59938157 1 1.423812
94 -3.26905923 1 1.423473
95 -3.26861434 1 1.434412
96 -5.71017484 1 1.450844
97 -3.76781806 1 1.433371
98 -2.02677390 1 1.444378
99 -2.96199765 1 1.422523
100 -4.81129496 1 1.410394
$m1a$spM_lvlone
center scale
y -3.344283 2.27649507
(Intercept) NA NA
C1 1.434101 0.01299651
$m1a$mu_reg_norm
[1] 0
$m1a$tau_reg_norm
[1] 1e-04
$m1a$shape_tau_norm
[1] 0.01
$m1a$rate_tau_norm
[1] 0.01
$m1b
$m1b$M_lvlone
B1 (Intercept) C1
1 1 1 1.410531
2 1 1 1.434183
3 1 1 1.430994
4 1 1 1.453096
5 1 1 1.438344
6 1 1 1.453207
7 0 1 1.425176
8 0 1 1.437908
9 1 1 1.416911
10 1 1 1.448638
11 1 1 1.428375
12 0 1 1.450130
13 1 1 1.420545
14 0 1 1.423005
15 1 1 1.435902
16 1 1 1.423901
17 1 1 1.457208
18 1 1 1.414280
19 1 1 1.443383
20 1 1 1.434954
21 1 1 1.429499
22 1 1 1.441897
23 1 1 1.423713
24 1 1 1.435395
25 0 1 1.425944
26 1 1 1.437115
27 1 1 1.441326
28 1 1 1.422953
29 1 1 1.437797
30 0 1 1.472121
31 0 1 1.421782
32 1 1 1.457672
33 1 1 1.430842
34 1 1 1.431523
35 1 1 1.421395
36 0 1 1.434496
37 1 1 1.425383
38 1 1 1.421802
39 1 1 1.430094
40 1 1 1.447621
41 1 1 1.434797
42 1 1 1.446091
43 1 1 1.445306
44 1 1 1.448783
45 1 1 1.450617
46 1 1 1.415055
47 0 1 1.436590
48 1 1 1.433938
49 1 1 1.414941
50 0 1 1.421807
51 1 1 1.453203
52 1 1 1.452129
53 1 1 1.431510
54 1 1 1.430082
55 0 1 1.443492
56 1 1 1.436460
57 1 1 1.418119
58 1 1 1.434971
59 1 1 1.445599
60 0 1 1.437097
61 1 1 1.428360
62 1 1 1.440550
63 0 1 1.443014
64 1 1 1.424298
65 1 1 1.448823
66 0 1 1.425834
67 0 1 1.427102
68 1 1 1.414240
69 0 1 1.456218
70 0 1 1.470594
71 1 1 1.425058
72 1 1 1.432371
73 0 1 1.441656
74 1 1 1.434952
75 1 1 1.402860
76 0 1 1.453363
77 0 1 1.432909
78 0 1 1.435103
79 1 1 1.434462
80 1 1 1.434661
81 1 1 1.445881
82 1 1 1.442548
83 1 1 1.430097
84 1 1 1.430119
85 1 1 1.430315
86 1 1 1.437584
87 1 1 1.409738
88 0 1 1.422388
89 1 1 1.422509
90 1 1 1.439432
91 1 1 1.430175
92 1 1 1.418002
93 1 1 1.423812
94 1 1 1.423473
95 1 1 1.434412
96 1 1 1.450844
97 1 1 1.433371
98 1 1 1.444378
99 1 1 1.422523
100 1 1 1.410394
$m1b$spM_lvlone
center scale
B1 NA NA
(Intercept) NA NA
C1 1.434101 0.01299651
$m1b$mu_reg_binom
[1] 0
$m1b$tau_reg_binom
[1] 1e-04
$m1c
$m1c$M_lvlone
L1 (Intercept) C1
1 0.9364352 1 1.410531
2 0.8943541 1 1.434183
3 0.2868460 1 1.430994
4 0.9068418 1 1.453096
5 0.7621346 1 1.438344
6 0.5858621 1 1.453207
7 0.7194403 1 1.425176
8 0.7593154 1 1.437908
9 0.5863705 1 1.416911
10 0.7342586 1 1.448638
11 0.7218028 1 1.428375
12 0.7241254 1 1.450130
13 0.7200126 1 1.420545
14 0.5289014 1 1.423005
15 0.7322482 1 1.435902
16 0.7462471 1 1.423901
17 0.9119922 1 1.457208
18 0.6262513 1 1.414280
19 0.4587835 1 1.443383
20 0.7173364 1 1.434954
21 0.7288999 1 1.429499
22 0.7160420 1 1.441897
23 0.5795514 1 1.423713
24 0.7210413 1 1.435395
25 0.7816086 1 1.425944
26 0.6747483 1 1.437115
27 0.4746725 1 1.441326
28 0.9270652 1 1.422953
29 0.5306249 1 1.437797
30 0.8913764 1 1.472121
31 0.8090308 1 1.421782
32 0.4610800 1 1.457672
33 0.7183814 1 1.430842
34 0.6375974 1 1.431523
35 0.9202563 1 1.421395
36 0.7263222 1 1.434496
37 1.0638781 1 1.425383
38 0.6053893 1 1.421802
39 0.7945509 1 1.430094
40 0.6355032 1 1.447621
41 0.9939049 1 1.434797
42 1.0690739 1 1.446091
43 0.7009106 1 1.445306
44 0.7595403 1 1.448783
45 0.8356414 1 1.450617
46 0.4929132 1 1.415055
47 0.5298192 1 1.436590
48 0.5363034 1 1.433938
49 0.8494053 1 1.414941
50 0.6292812 1 1.421807
51 0.9561312 1 1.453203
52 0.9735411 1 1.452129
53 0.7156259 1 1.431510
54 0.5184434 1 1.430082
55 0.7948965 1 1.443492
56 0.5191792 1 1.436460
57 0.9233108 1 1.418119
58 0.8025356 1 1.434971
59 0.8546624 1 1.445599
60 0.8639819 1 1.437097
61 0.7521237 1 1.428360
62 0.5590215 1 1.440550
63 0.5972103 1 1.443014
64 0.6071272 1 1.424298
65 0.8837829 1 1.448823
66 0.7775301 1 1.425834
67 0.6756191 1 1.427102
68 0.7857549 1 1.414240
69 0.9119262 1 1.456218
70 0.5816103 1 1.470594
71 0.4886093 1 1.425058
72 0.8292467 1 1.432371
73 0.6767456 1 1.441656
74 0.7328840 1 1.434952
75 0.7946099 1 1.402860
76 0.7734810 1 1.453363
77 0.5296147 1 1.432909
78 0.7723288 1 1.435103
79 0.8079308 1 1.434462
80 0.5214822 1 1.434661
81 0.6264777 1 1.445881
82 0.8332107 1 1.442548
83 0.4544158 1 1.430097
84 0.6482660 1 1.430119
85 0.7272109 1 1.430315
86 0.7302426 1 1.437584
87 0.6768061 1 1.409738
88 0.8115758 1 1.422388
89 0.9775567 1 1.422509
90 0.6408465 1 1.439432
91 0.5917453 1 1.430175
92 0.7224845 1 1.418002
93 0.4501596 1 1.423812
94 0.5190455 1 1.423473
95 0.7305821 1 1.434412
96 0.9696445 1 1.450844
97 0.7087457 1 1.433371
98 0.9964080 1 1.444378
99 0.9084899 1 1.422523
100 0.9296776 1 1.410394
$m1c$spM_lvlone
center scale
L1 0.7248851 0.15692291
(Intercept) NA NA
C1 1.4341005 0.01299651
$m1c$mu_reg_gamma
[1] 0
$m1c$tau_reg_gamma
[1] 1e-04
$m1c$shape_tau_gamma
[1] 0.01
$m1c$rate_tau_gamma
[1] 0.01
$m1d
$m1d$M_lvlone
P1 (Intercept) C1
1 1 1 1.410531
2 3 1 1.434183
3 3 1 1.430994
4 3 1 1.453096
5 5 1 1.438344
6 3 1 1.453207
7 0 1 1.425176
8 2 1 1.437908
9 4 1 1.416911
10 3 1 1.448638
11 4 1 1.428375
12 3 1 1.450130
13 2 1 1.420545
14 6 1 1.423005
15 2 1 1.435902
16 5 1 1.423901
17 2 1 1.457208
18 2 1 1.414280
19 1 1 1.443383
20 2 1 1.434954
21 2 1 1.429499
22 2 1 1.441897
23 1 1 1.423713
24 0 1 1.435395
25 2 1 1.425944
26 4 1 1.437115
27 3 1 1.441326
28 5 1 1.422953
29 5 1 1.437797
30 0 1 1.472121
31 3 1 1.421782
32 2 1 1.457672
33 2 1 1.430842
34 3 1 1.431523
35 1 1 1.421395
36 4 1 1.434496
37 2 1 1.425383
38 2 1 1.421802
39 8 1 1.430094
40 4 1 1.447621
41 3 1 1.434797
42 3 1 1.446091
43 2 1 1.445306
44 3 1 1.448783
45 2 1 1.450617
46 3 1 1.415055
47 4 1 1.436590
48 3 1 1.433938
49 2 1 1.414941
50 4 1 1.421807
51 1 1 1.453203
52 2 1 1.452129
53 4 1 1.431510
54 3 1 1.430082
55 1 1 1.443492
56 3 1 1.436460
57 3 1 1.418119
58 4 1 1.434971
59 1 1 1.445599
60 5 1 1.437097
61 5 1 1.428360
62 0 1 1.440550
63 2 1 1.443014
64 0 1 1.424298
65 2 1 1.448823
66 4 1 1.425834
67 2 1 1.427102
68 3 1 1.414240
69 1 1 1.456218
70 3 1 1.470594
71 1 1 1.425058
72 5 1 1.432371
73 0 1 1.441656
74 4 1 1.434952
75 1 1 1.402860
76 3 1 1.453363
77 2 1 1.432909
78 1 1 1.435103
79 2 1 1.434462
80 4 1 1.434661
81 6 1 1.445881
82 3 1 1.442548
83 1 1 1.430097
84 3 1 1.430119
85 1 1 1.430315
86 5 1 1.437584
87 2 1 1.409738
88 2 1 1.422388
89 1 1 1.422509
90 5 1 1.439432
91 1 1 1.430175
92 5 1 1.418002
93 1 1 1.423812
94 1 1 1.423473
95 1 1 1.434412
96 3 1 1.450844
97 2 1 1.433371
98 0 1 1.444378
99 2 1 1.422523
100 4 1 1.410394
$m1d$spM_lvlone
center scale
P1 2.610000 1.56279341
(Intercept) NA NA
C1 1.434101 0.01299651
$m1d$mu_reg_poisson
[1] 0
$m1d$tau_reg_poisson
[1] 1e-04
$m1e
$m1e$M_lvlone
L1 (Intercept) C1
1 0.9364352 1 1.410531
2 0.8943541 1 1.434183
3 0.2868460 1 1.430994
4 0.9068418 1 1.453096
5 0.7621346 1 1.438344
6 0.5858621 1 1.453207
7 0.7194403 1 1.425176
8 0.7593154 1 1.437908
9 0.5863705 1 1.416911
10 0.7342586 1 1.448638
11 0.7218028 1 1.428375
12 0.7241254 1 1.450130
13 0.7200126 1 1.420545
14 0.5289014 1 1.423005
15 0.7322482 1 1.435902
16 0.7462471 1 1.423901
17 0.9119922 1 1.457208
18 0.6262513 1 1.414280
19 0.4587835 1 1.443383
20 0.7173364 1 1.434954
21 0.7288999 1 1.429499
22 0.7160420 1 1.441897
23 0.5795514 1 1.423713
24 0.7210413 1 1.435395
25 0.7816086 1 1.425944
26 0.6747483 1 1.437115
27 0.4746725 1 1.441326
28 0.9270652 1 1.422953
29 0.5306249 1 1.437797
30 0.8913764 1 1.472121
31 0.8090308 1 1.421782
32 0.4610800 1 1.457672
33 0.7183814 1 1.430842
34 0.6375974 1 1.431523
35 0.9202563 1 1.421395
36 0.7263222 1 1.434496
37 1.0638781 1 1.425383
38 0.6053893 1 1.421802
39 0.7945509 1 1.430094
40 0.6355032 1 1.447621
41 0.9939049 1 1.434797
42 1.0690739 1 1.446091
43 0.7009106 1 1.445306
44 0.7595403 1 1.448783
45 0.8356414 1 1.450617
46 0.4929132 1 1.415055
47 0.5298192 1 1.436590
48 0.5363034 1 1.433938
49 0.8494053 1 1.414941
50 0.6292812 1 1.421807
51 0.9561312 1 1.453203
52 0.9735411 1 1.452129
53 0.7156259 1 1.431510
54 0.5184434 1 1.430082
55 0.7948965 1 1.443492
56 0.5191792 1 1.436460
57 0.9233108 1 1.418119
58 0.8025356 1 1.434971
59 0.8546624 1 1.445599
60 0.8639819 1 1.437097
61 0.7521237 1 1.428360
62 0.5590215 1 1.440550
63 0.5972103 1 1.443014
64 0.6071272 1 1.424298
65 0.8837829 1 1.448823
66 0.7775301 1 1.425834
67 0.6756191 1 1.427102
68 0.7857549 1 1.414240
69 0.9119262 1 1.456218
70 0.5816103 1 1.470594
71 0.4886093 1 1.425058
72 0.8292467 1 1.432371
73 0.6767456 1 1.441656
74 0.7328840 1 1.434952
75 0.7946099 1 1.402860
76 0.7734810 1 1.453363
77 0.5296147 1 1.432909
78 0.7723288 1 1.435103
79 0.8079308 1 1.434462
80 0.5214822 1 1.434661
81 0.6264777 1 1.445881
82 0.8332107 1 1.442548
83 0.4544158 1 1.430097
84 0.6482660 1 1.430119
85 0.7272109 1 1.430315
86 0.7302426 1 1.437584
87 0.6768061 1 1.409738
88 0.8115758 1 1.422388
89 0.9775567 1 1.422509
90 0.6408465 1 1.439432
91 0.5917453 1 1.430175
92 0.7224845 1 1.418002
93 0.4501596 1 1.423812
94 0.5190455 1 1.423473
95 0.7305821 1 1.434412
96 0.9696445 1 1.450844
97 0.7087457 1 1.433371
98 0.9964080 1 1.444378
99 0.9084899 1 1.422523
100 0.9296776 1 1.410394
$m1e$spM_lvlone
center scale
L1 0.7248851 0.15692291
(Intercept) NA NA
C1 1.4341005 0.01299651
$m1e$mu_reg_norm
[1] 0
$m1e$tau_reg_norm
[1] 1e-04
$m1e$shape_tau_norm
[1] 0.01
$m1e$rate_tau_norm
[1] 0.01
$m1f
$m1f$M_lvlone
Be1 (Intercept) C1
1 0.69649948 1 1.410531
2 0.56085128 1 1.434183
3 0.35796663 1 1.430994
4 0.53961336 1 1.453096
5 0.06191042 1 1.438344
6 0.51256785 1 1.453207
7 0.13154723 1 1.425176
8 0.35032766 1 1.437908
9 0.21796890 1 1.416911
10 0.10476230 1 1.448638
11 0.66083800 1 1.428375
12 0.66884267 1 1.450130
13 0.69840279 1 1.420545
14 0.50398472 1 1.423005
15 0.52807655 1 1.435902
16 0.40135087 1 1.423901
17 0.45554802 1 1.457208
18 0.68717635 1 1.414280
19 0.35880655 1 1.443383
20 0.36341035 1 1.434954
21 0.71468563 1 1.429499
22 0.44558172 1 1.441897
23 0.33262526 1 1.423713
24 0.66812751 1 1.435395
25 0.23180310 1 1.425944
26 0.37786624 1 1.437115
27 0.88834598 1 1.441326
28 0.46487057 1 1.422953
29 0.47018802 1 1.437797
30 0.91617346 1 1.472121
31 0.67589111 1 1.421782
32 0.61623852 1 1.457672
33 0.44182889 1 1.430842
34 0.29868153 1 1.431523
35 0.44235110 1 1.421395
36 0.72557250 1 1.434496
37 0.74809277 1 1.425383
38 0.26452559 1 1.421802
39 0.41597215 1 1.430094
40 0.29080530 1 1.447621
41 0.80342568 1 1.434797
42 0.76614332 1 1.446091
43 0.29734466 1 1.445306
44 0.42809509 1 1.448783
45 0.12861202 1 1.450617
46 0.44369392 1 1.415055
47 0.35290028 1 1.436590
48 0.88288407 1 1.433938
49 0.37880332 1 1.414941
50 0.60663793 1 1.421807
51 0.15505292 1 1.453203
52 0.65796074 1 1.452129
53 0.63416487 1 1.431510
54 0.83040459 1 1.430082
55 0.64947589 1 1.443492
56 0.67541381 1 1.436460
57 0.53637356 1 1.418119
58 0.39157422 1 1.434971
59 0.88168026 1 1.445599
60 0.32582606 1 1.437097
61 0.64492753 1 1.428360
62 0.34804110 1 1.440550
63 0.49241010 1 1.443014
64 0.43387493 1 1.424298
65 0.21806182 1 1.448823
66 0.60021691 1 1.425834
67 0.30567313 1 1.427102
68 0.22476988 1 1.414240
69 0.23155216 1 1.456218
70 0.29610794 1 1.470594
71 0.83435168 1 1.425058
72 0.65543408 1 1.432371
73 0.59684715 1 1.441656
74 0.80640183 1 1.434952
75 0.52288624 1 1.402860
76 0.41546840 1 1.453363
77 0.44756212 1 1.432909
78 0.68093413 1 1.435103
79 0.29261828 1 1.434462
80 0.21008516 1 1.434661
81 0.44710869 1 1.445881
82 0.70470991 1 1.442548
83 0.31300581 1 1.430097
84 0.44774544 1 1.430119
85 0.68031201 1 1.430315
86 0.44456865 1 1.437584
87 0.79031803 1 1.409738
88 0.22231438 1 1.422388
89 0.30114327 1 1.422509
90 0.45339193 1 1.439432
91 0.35526875 1 1.430175
92 0.68684691 1 1.418002
93 0.81430167 1 1.423812
94 0.60104343 1 1.423473
95 0.82012448 1 1.434412
96 0.55669948 1 1.450844
97 0.76622465 1 1.433371
98 0.50112270 1 1.444378
99 0.53468983 1 1.422523
100 0.58249327 1 1.410394
$m1f$spM_lvlone
center scale
Be1 0.503988 0.20498987
(Intercept) NA NA
C1 1.434101 0.01299651
$m1f$mu_reg_beta
[1] 0
$m1f$tau_reg_beta
[1] 1e-04
$m1f$shape_tau_beta
[1] 0.01
$m1f$rate_tau_beta
[1] 0.01
$m2a
$m2a$M_lvlone
y C2 (Intercept)
1 -4.76915977 0.144065882 1
2 -2.69277172 0.032778478 1
3 -1.17551547 0.343008492 1
4 -4.57464473 -0.361887858 1
5 -2.20260004 -0.389600647 1
6 -3.48995315 -0.205306841 1
7 -0.44987258 0.079434830 1
8 -2.29588848 -0.331246757 1
9 -4.49135812 -0.329638800 1
10 -5.52545368 0.167597533 1
11 -4.16286741 0.860207989 1
12 -2.93455761 0.022730640 1
13 -0.04202496 0.217171172 1
14 -1.63149775 -0.403002412 1
15 -0.97786151 0.087369742 1
16 -1.79100431 -0.183870429 1
17 -6.26520032 -0.194577002 1
18 -1.36028709 -0.349718516 1
19 -1.15396597 -0.508781244 1
20 -3.21707239 0.494883111 1
21 -1.59389898 0.258041067 1
22 -5.50335066 -0.922621989 1
23 0.57290123 0.431254949 1
24 -8.22270323 -0.294218881 1
25 -1.41364158 -0.425548895 1
26 -6.28031574 0.057176054 1
27 -3.15624425 0.289090158 1
28 -3.55693639 -0.473079489 1
29 -1.11821124 -0.385664863 1
30 -2.82834175 -0.154780107 1
31 -3.72259860 0.100536296 1
32 -1.75256656 0.634791958 1
33 -5.55044409 -0.387252617 1
34 -7.45068147 -0.181741088 1
35 -0.97491919 -0.311562695 1
36 -2.98356481 -0.044115907 1
37 -1.86039471 -0.657409991 1
38 -7.28754607 0.159577214 1
39 -8.66234796 -0.460416933 1
40 -4.16291375 NA 1
41 -3.48250771 -0.248909867 1
42 -7.27930410 -0.609021545 1
43 -6.12866190 0.025471883 1
44 -4.96880803 0.066648592 1
45 -4.76746713 -0.276108719 1
46 -1.91249177 -0.179737577 1
47 -0.61884029 0.181190937 1
48 -0.20496175 -0.453871693 1
49 -7.12636055 0.448629602 1
50 -6.23103837 -0.529811821 1
51 -3.32561065 -0.028304571 1
52 -2.95942339 -0.520318482 1
53 -4.44915114 0.171317619 1
54 -0.81566463 0.432732046 1
55 -6.50029573 -0.346286005 1
56 -2.74718050 -0.469375653 1
57 -6.35015663 0.031021711 1
58 -2.69505883 -0.118837515 1
59 -1.55660833 0.507769984 1
60 -3.76240209 0.271797031 1
61 -3.92885797 -0.124442204 1
62 -1.72044748 0.277677389 1
63 -0.56602625 -0.102893730 1
64 -4.42235015 NA 1
65 -2.39122287 -0.678303052 1
66 -0.81807247 0.478880037 1
67 -6.48196782 -0.428028760 1
68 -1.37306273 0.048119185 1
69 -4.99886487 0.216932805 1
70 -5.82288217 -0.234575269 1
71 -2.68234219 0.006827078 1
72 -3.96170442 -0.456055171 1
73 -7.19573667 0.346486708 1
74 -5.08799713 0.205092215 1
75 -1.32967262 -0.136596858 1
76 -2.56532332 -0.500179043 1
77 -3.21002900 0.527352086 1
78 -3.40559790 0.022742250 1
79 -4.56223913 NA 1
80 -2.04250454 -0.002032440 1
81 -2.20378059 -0.154246160 1
82 -3.37471317 0.140201825 1
83 -0.95345385 -0.141417121 1
84 -4.89337660 NA 1
85 -9.82258463 -0.021285339 1
86 -4.51800734 -0.010196306 1
87 -0.18662049 -0.089747520 1
88 -2.87120881 -0.083699898 1
89 1.29290150 -0.044061996 1
90 -1.39497744 -0.209291697 1
91 1.14575040 0.639036426 1
92 0.92801246 0.094698299 1
93 -2.59938157 -0.055510622 1
94 -3.26905923 -0.421318463 1
95 -3.26861434 0.125295503 1
96 -5.71017484 0.213084904 1
97 -3.76781806 -0.161914659 1
98 -2.02677390 -0.034767685 1
99 -2.96199765 -0.320681689 1
100 -4.81129496 0.058192962 1
$m2a$spM_lvlone
center scale
y -3.34428345 2.2764951
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
$m2b
$m2b$M_lvlone
B2 C2 (Intercept)
1 1 0.144065882 1
2 1 0.032778478 1
3 1 0.343008492 1
4 1 -0.361887858 1
5 1 -0.389600647 1
6 NA -0.205306841 1
7 1 0.079434830 1
8 1 -0.331246757 1
9 1 -0.329638800 1
10 NA 0.167597533 1
11 1 0.860207989 1
12 1 0.022730640 1
13 1 0.217171172 1
14 1 -0.403002412 1
15 1 0.087369742 1
16 1 -0.183870429 1
17 1 -0.194577002 1
18 1 -0.349718516 1
19 NA -0.508781244 1
20 NA 0.494883111 1
21 1 0.258041067 1
22 NA -0.922621989 1
23 NA 0.431254949 1
24 1 -0.294218881 1
25 NA -0.425548895 1
26 NA 0.057176054 1
27 1 0.289090158 1
28 1 -0.473079489 1
29 1 -0.385664863 1
30 1 -0.154780107 1
31 NA 0.100536296 1
32 1 0.634791958 1
33 1 -0.387252617 1
34 0 -0.181741088 1
35 1 -0.311562695 1
36 1 -0.044115907 1
37 1 -0.657409991 1
38 NA 0.159577214 1
39 1 -0.460416933 1
40 NA NA 1
41 1 -0.248909867 1
42 1 -0.609021545 1
43 1 0.025471883 1
44 1 0.066648592 1
45 1 -0.276108719 1
46 1 -0.179737577 1
47 0 0.181190937 1
48 1 -0.453871693 1
49 0 0.448629602 1
50 1 -0.529811821 1
51 1 -0.028304571 1
52 1 -0.520318482 1
53 1 0.171317619 1
54 1 0.432732046 1
55 1 -0.346286005 1
56 1 -0.469375653 1
57 1 0.031021711 1
58 NA -0.118837515 1
59 1 0.507769984 1
60 NA 0.271797031 1
61 1 -0.124442204 1
62 1 0.277677389 1
63 1 -0.102893730 1
64 1 NA 1
65 1 -0.678303052 1
66 0 0.478880037 1
67 1 -0.428028760 1
68 1 0.048119185 1
69 NA 0.216932805 1
70 1 -0.234575269 1
71 1 0.006827078 1
72 1 -0.456055171 1
73 1 0.346486708 1
74 1 0.205092215 1
75 1 -0.136596858 1
76 1 -0.500179043 1
77 1 0.527352086 1
78 1 0.022742250 1
79 1 NA 1
80 1 -0.002032440 1
81 0 -0.154246160 1
82 NA 0.140201825 1
83 1 -0.141417121 1
84 1 NA 1
85 1 -0.021285339 1
86 NA -0.010196306 1
87 NA -0.089747520 1
88 1 -0.083699898 1
89 1 -0.044061996 1
90 1 -0.209291697 1
91 1 0.639036426 1
92 NA 0.094698299 1
93 1 -0.055510622 1
94 1 -0.421318463 1
95 1 0.125295503 1
96 1 0.213084904 1
97 NA -0.161914659 1
98 1 -0.034767685 1
99 0 -0.320681689 1
100 NA 0.058192962 1
$m2b$spM_lvlone
center scale
B2 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_binom
[1] 0
$m2b$tau_reg_binom
[1] 1e-04
$m2c
$m2c$M_lvlone
L1mis C2 (Intercept)
1 0.9364352 0.144065882 1
2 0.8943541 0.032778478 1
3 0.2868460 0.343008492 1
4 0.9068418 -0.361887858 1
5 0.7621346 -0.389600647 1
6 NA -0.205306841 1
7 NA 0.079434830 1
8 0.7593154 -0.331246757 1
9 0.5863705 -0.329638800 1
10 0.7342586 0.167597533 1
11 0.7218028 0.860207989 1
12 NA 0.022730640 1
13 0.7200126 0.217171172 1
14 0.5289014 -0.403002412 1
15 0.7322482 0.087369742 1
16 0.7462471 -0.183870429 1
17 0.9119922 -0.194577002 1
18 NA -0.349718516 1
19 NA -0.508781244 1
20 NA 0.494883111 1
21 0.7288999 0.258041067 1
22 0.7160420 -0.922621989 1
23 NA 0.431254949 1
24 0.7210413 -0.294218881 1
25 0.7816086 -0.425548895 1
26 0.6747483 0.057176054 1
27 0.4746725 0.289090158 1
28 0.9270652 -0.473079489 1
29 0.5306249 -0.385664863 1
30 0.8913764 -0.154780107 1
31 NA 0.100536296 1
32 0.4610800 0.634791958 1
33 0.7183814 -0.387252617 1
34 0.6375974 -0.181741088 1
35 0.9202563 -0.311562695 1
36 0.7263222 -0.044115907 1
37 NA -0.657409991 1
38 NA 0.159577214 1
39 0.7945509 -0.460416933 1
40 0.6355032 NA 1
41 0.9939049 -0.248909867 1
42 1.0690739 -0.609021545 1
43 0.7009106 0.025471883 1
44 0.7595403 0.066648592 1
45 0.8356414 -0.276108719 1
46 0.4929132 -0.179737577 1
47 NA 0.181190937 1
48 0.5363034 -0.453871693 1
49 0.8494053 0.448629602 1
50 0.6292812 -0.529811821 1
51 0.9561312 -0.028304571 1
52 0.9735411 -0.520318482 1
53 0.7156259 0.171317619 1
54 0.5184434 0.432732046 1
55 0.7948965 -0.346286005 1
56 0.5191792 -0.469375653 1
57 0.9233108 0.031021711 1
58 0.8025356 -0.118837515 1
59 0.8546624 0.507769984 1
60 0.8639819 0.271797031 1
61 0.7521237 -0.124442204 1
62 0.5590215 0.277677389 1
63 0.5972103 -0.102893730 1
64 0.6071272 NA 1
65 0.8837829 -0.678303052 1
66 0.7775301 0.478880037 1
67 NA -0.428028760 1
68 0.7857549 0.048119185 1
69 0.9119262 0.216932805 1
70 0.5816103 -0.234575269 1
71 0.4886093 0.006827078 1
72 NA -0.456055171 1
73 NA 0.346486708 1
74 0.7328840 0.205092215 1
75 0.7946099 -0.136596858 1
76 0.7734810 -0.500179043 1
77 0.5296147 0.527352086 1
78 0.7723288 0.022742250 1
79 0.8079308 NA 1
80 NA -0.002032440 1
81 NA -0.154246160 1
82 NA 0.140201825 1
83 0.4544158 -0.141417121 1
84 0.6482660 NA 1
85 0.7272109 -0.021285339 1
86 NA -0.010196306 1
87 0.6768061 -0.089747520 1
88 0.8115758 -0.083699898 1
89 NA -0.044061996 1
90 0.6408465 -0.209291697 1
91 0.5917453 0.639036426 1
92 0.7224845 0.094698299 1
93 0.4501596 -0.055510622 1
94 0.5190455 -0.421318463 1
95 0.7305821 0.125295503 1
96 0.9696445 0.213084904 1
97 0.7087457 -0.161914659 1
98 0.9964080 -0.034767685 1
99 NA -0.320681689 1
100 0.9296776 0.058192962 1
$m2c$spM_lvlone
center scale
L1mis 0.72862466 0.1577261
C2 -0.06490582 0.3331735
(Intercept) NA NA
$m2c$mu_reg_norm
[1] 0
$m2c$tau_reg_norm
[1] 1e-04
$m2c$shape_tau_norm
[1] 0.01
$m2c$rate_tau_norm
[1] 0.01
$m2c$mu_reg_gamma
[1] 0
$m2c$tau_reg_gamma
[1] 1e-04
$m2c$shape_tau_gamma
[1] 0.01
$m2c$rate_tau_gamma
[1] 0.01
$m2d
$m2d$M_lvlone
P2 C2 (Intercept)
1 0 0.144065882 1
2 NA 0.032778478 1
3 1 0.343008492 1
4 1 -0.361887858 1
5 0 -0.389600647 1
6 1 -0.205306841 1
7 1 0.079434830 1
8 0 -0.331246757 1
9 2 -0.329638800 1
10 NA 0.167597533 1
11 3 0.860207989 1
12 0 0.022730640 1
13 5 0.217171172 1
14 0 -0.403002412 1
15 1 0.087369742 1
16 4 -0.183870429 1
17 NA -0.194577002 1
18 1 -0.349718516 1
19 NA -0.508781244 1
20 3 0.494883111 1
21 3 0.258041067 1
22 NA -0.922621989 1
23 6 0.431254949 1
24 4 -0.294218881 1
25 NA -0.425548895 1
26 1 0.057176054 1
27 1 0.289090158 1
28 2 -0.473079489 1
29 2 -0.385664863 1
30 NA -0.154780107 1
31 5 0.100536296 1
32 2 0.634791958 1
33 0 -0.387252617 1
34 2 -0.181741088 1
35 NA -0.311562695 1
36 2 -0.044115907 1
37 4 -0.657409991 1
38 2 0.159577214 1
39 2 -0.460416933 1
40 NA NA 1
41 2 -0.248909867 1
42 6 -0.609021545 1
43 1 0.025471883 1
44 2 0.066648592 1
45 1 -0.276108719 1
46 2 -0.179737577 1
47 NA 0.181190937 1
48 2 -0.453871693 1
49 NA 0.448629602 1
50 2 -0.529811821 1
51 2 -0.028304571 1
52 1 -0.520318482 1
53 0 0.171317619 1
54 3 0.432732046 1
55 1 -0.346286005 1
56 6 -0.469375653 1
57 NA 0.031021711 1
58 7 -0.118837515 1
59 1 0.507769984 1
60 2 0.271797031 1
61 NA -0.124442204 1
62 2 0.277677389 1
63 2 -0.102893730 1
64 1 NA 1
65 0 -0.678303052 1
66 2 0.478880037 1
67 NA -0.428028760 1
68 NA 0.048119185 1
69 3 0.216932805 1
70 1 -0.234575269 1
71 NA 0.006827078 1
72 NA -0.456055171 1
73 3 0.346486708 1
74 2 0.205092215 1
75 1 -0.136596858 1
76 3 -0.500179043 1
77 2 0.527352086 1
78 2 0.022742250 1
79 0 NA 1
80 1 -0.002032440 1
81 2 -0.154246160 1
82 1 0.140201825 1
83 NA -0.141417121 1
84 NA NA 1
85 5 -0.021285339 1
86 0 -0.010196306 1
87 NA -0.089747520 1
88 2 -0.083699898 1
89 1 -0.044061996 1
90 3 -0.209291697 1
91 2 0.639036426 1
92 6 0.094698299 1
93 0 -0.055510622 1
94 4 -0.421318463 1
95 3 0.125295503 1
96 3 0.213084904 1
97 3 -0.161914659 1
98 3 -0.034767685 1
99 5 -0.320681689 1
100 2 0.058192962 1
$m2d$spM_lvlone
center scale
P2 2.15000000 1.6466306
C2 -0.06490582 0.3331735
(Intercept) NA NA
$m2d$mu_reg_norm
[1] 0
$m2d$tau_reg_norm
[1] 1e-04
$m2d$shape_tau_norm
[1] 0.01
$m2d$rate_tau_norm
[1] 0.01
$m2d$mu_reg_poisson
[1] 0
$m2d$tau_reg_poisson
[1] 1e-04
$m2e
$m2e$M_lvlone
L1mis C2 (Intercept)
1 0.9364352 0.144065882 1
2 0.8943541 0.032778478 1
3 0.2868460 0.343008492 1
4 0.9068418 -0.361887858 1
5 0.7621346 -0.389600647 1
6 NA -0.205306841 1
7 NA 0.079434830 1
8 0.7593154 -0.331246757 1
9 0.5863705 -0.329638800 1
10 0.7342586 0.167597533 1
11 0.7218028 0.860207989 1
12 NA 0.022730640 1
13 0.7200126 0.217171172 1
14 0.5289014 -0.403002412 1
15 0.7322482 0.087369742 1
16 0.7462471 -0.183870429 1
17 0.9119922 -0.194577002 1
18 NA -0.349718516 1
19 NA -0.508781244 1
20 NA 0.494883111 1
21 0.7288999 0.258041067 1
22 0.7160420 -0.922621989 1
23 NA 0.431254949 1
24 0.7210413 -0.294218881 1
25 0.7816086 -0.425548895 1
26 0.6747483 0.057176054 1
27 0.4746725 0.289090158 1
28 0.9270652 -0.473079489 1
29 0.5306249 -0.385664863 1
30 0.8913764 -0.154780107 1
31 NA 0.100536296 1
32 0.4610800 0.634791958 1
33 0.7183814 -0.387252617 1
34 0.6375974 -0.181741088 1
35 0.9202563 -0.311562695 1
36 0.7263222 -0.044115907 1
37 NA -0.657409991 1
38 NA 0.159577214 1
39 0.7945509 -0.460416933 1
40 0.6355032 NA 1
41 0.9939049 -0.248909867 1
42 1.0690739 -0.609021545 1
43 0.7009106 0.025471883 1
44 0.7595403 0.066648592 1
45 0.8356414 -0.276108719 1
46 0.4929132 -0.179737577 1
47 NA 0.181190937 1
48 0.5363034 -0.453871693 1
49 0.8494053 0.448629602 1
50 0.6292812 -0.529811821 1
51 0.9561312 -0.028304571 1
52 0.9735411 -0.520318482 1
53 0.7156259 0.171317619 1
54 0.5184434 0.432732046 1
55 0.7948965 -0.346286005 1
56 0.5191792 -0.469375653 1
57 0.9233108 0.031021711 1
58 0.8025356 -0.118837515 1
59 0.8546624 0.507769984 1
60 0.8639819 0.271797031 1
61 0.7521237 -0.124442204 1
62 0.5590215 0.277677389 1
63 0.5972103 -0.102893730 1
64 0.6071272 NA 1
65 0.8837829 -0.678303052 1
66 0.7775301 0.478880037 1
67 NA -0.428028760 1
68 0.7857549 0.048119185 1
69 0.9119262 0.216932805 1
70 0.5816103 -0.234575269 1
71 0.4886093 0.006827078 1
72 NA -0.456055171 1
73 NA 0.346486708 1
74 0.7328840 0.205092215 1
75 0.7946099 -0.136596858 1
76 0.7734810 -0.500179043 1
77 0.5296147 0.527352086 1
78 0.7723288 0.022742250 1
79 0.8079308 NA 1
80 NA -0.002032440 1
81 NA -0.154246160 1
82 NA 0.140201825 1
83 0.4544158 -0.141417121 1
84 0.6482660 NA 1
85 0.7272109 -0.021285339 1
86 NA -0.010196306 1
87 0.6768061 -0.089747520 1
88 0.8115758 -0.083699898 1
89 NA -0.044061996 1
90 0.6408465 -0.209291697 1
91 0.5917453 0.639036426 1
92 0.7224845 0.094698299 1
93 0.4501596 -0.055510622 1
94 0.5190455 -0.421318463 1
95 0.7305821 0.125295503 1
96 0.9696445 0.213084904 1
97 0.7087457 -0.161914659 1
98 0.9964080 -0.034767685 1
99 NA -0.320681689 1
100 0.9296776 0.058192962 1
$m2e$spM_lvlone
center scale
L1mis 0.72862466 0.1577261
C2 -0.06490582 0.3331735
(Intercept) NA NA
$m2e$mu_reg_norm
[1] 0
$m2e$tau_reg_norm
[1] 1e-04
$m2e$shape_tau_norm
[1] 0.01
$m2e$rate_tau_norm
[1] 0.01
$m2f
$m2f$M_lvlone
Be2 C2 (Intercept)
1 0.13821330 0.144065882 1
2 NA 0.032778478 1
3 0.85221266 0.343008492 1
4 0.61517266 -0.361887858 1
5 0.56718424 -0.389600647 1
6 0.16127199 -0.205306841 1
7 NA 0.079434830 1
8 0.51062047 -0.331246757 1
9 0.29560086 -0.329638800 1
10 0.43261394 0.167597533 1
11 0.54537238 0.860207989 1
12 0.36458613 0.022730640 1
13 0.84543642 0.217171172 1
14 0.88041616 -0.403002412 1
15 0.47940969 0.087369742 1
16 0.25520352 -0.183870429 1
17 0.53793620 -0.194577002 1
18 0.41924865 -0.349718516 1
19 0.19038933 -0.508781244 1
20 NA 0.494883111 1
21 0.26763985 0.258041067 1
22 NA -0.922621989 1
23 NA 0.431254949 1
24 0.39688480 -0.294218881 1
25 0.20117762 -0.425548895 1
26 0.56039795 0.057176054 1
27 0.69959156 0.289090158 1
28 0.16198957 -0.473079489 1
29 0.73477348 -0.385664863 1
30 NA -0.154780107 1
31 0.69439759 0.100536296 1
32 NA 0.634791958 1
33 NA -0.387252617 1
34 0.68680241 -0.181741088 1
35 0.20563215 -0.311562695 1
36 0.39312999 -0.044115907 1
37 0.33592359 -0.657409991 1
38 0.80799798 0.159577214 1
39 0.70399665 -0.460416933 1
40 0.14770504 NA 1
41 0.32976608 -0.248909867 1
42 0.57875125 -0.609021545 1
43 0.69765999 0.025471883 1
44 0.92706981 0.066648592 1
45 0.59881110 -0.276108719 1
46 NA -0.179737577 1
47 0.57021551 0.181190937 1
48 0.31297307 -0.453871693 1
49 0.45752036 0.448629602 1
50 0.76707228 -0.529811821 1
51 0.79670238 -0.028304571 1
52 0.31851588 -0.520318482 1
53 0.27413726 0.171317619 1
54 0.87099655 0.432732046 1
55 0.14767954 -0.346286005 1
56 0.72225832 -0.469375653 1
57 0.91165899 0.031021711 1
58 NA -0.118837515 1
59 0.74875442 0.507769984 1
60 0.57086552 0.271797031 1
61 0.17368573 -0.124442204 1
62 NA 0.277677389 1
63 0.60538003 -0.102893730 1
64 NA NA 1
65 0.44987490 -0.678303052 1
66 0.71105443 0.478880037 1
67 0.09500493 -0.428028760 1
68 0.37292542 0.048119185 1
69 0.41025328 0.216932805 1
70 0.87473911 -0.234575269 1
71 0.57325664 0.006827078 1
72 0.76227946 -0.456055171 1
73 0.56061854 0.346486708 1
74 0.61145842 0.205092215 1
75 NA -0.136596858 1
76 0.23795025 -0.500179043 1
77 0.28135640 0.527352086 1
78 NA 0.022742250 1
79 0.43010097 NA 1
80 0.30775746 -0.002032440 1
81 0.43379094 -0.154246160 1
82 0.70103825 0.140201825 1
83 0.19501290 -0.141417121 1
84 0.42336380 NA 1
85 NA -0.021285339 1
86 0.49004839 -0.010196306 1
87 NA -0.089747520 1
88 0.71840773 -0.083699898 1
89 0.81565945 -0.044061996 1
90 0.83308857 -0.209291697 1
91 0.56239647 0.639036426 1
92 NA 0.094698299 1
93 NA -0.055510622 1
94 NA -0.421318463 1
95 0.73286310 0.125295503 1
96 0.39788846 0.213084904 1
97 NA -0.161914659 1
98 0.81066470 -0.034767685 1
99 0.40892733 -0.320681689 1
100 0.76834275 0.058192962 1
$m2f$spM_lvlone
center scale
Be2 0.51799407 0.2297468
C2 -0.06490582 0.3331735
(Intercept) NA NA
$m2f$mu_reg_norm
[1] 0
$m2f$tau_reg_norm
[1] 1e-04
$m2f$shape_tau_norm
[1] 0.01
$m2f$rate_tau_norm
[1] 0.01
$m2f$mu_reg_beta
[1] 0
$m2f$tau_reg_beta
[1] 1e-04
$m2f$shape_tau_beta
[1] 0.01
$m2f$rate_tau_beta
[1] 0.01
$m3a
$m3a$M_lvlone
C1 B2 P2 L1mis Be2 C2 (Intercept) B21
1 1.410531 1 0 0.9364352 0.13821330 0.144065882 1 NA
2 1.434183 1 NA 0.8943541 NA 0.032778478 1 NA
3 1.430994 1 1 0.2868460 0.85221266 0.343008492 1 NA
4 1.453096 1 1 0.9068418 0.61517266 -0.361887858 1 NA
5 1.438344 1 0 0.7621346 0.56718424 -0.389600647 1 NA
6 1.453207 NA 1 NA 0.16127199 -0.205306841 1 NA
7 1.425176 1 1 NA NA 0.079434830 1 NA
8 1.437908 1 0 0.7593154 0.51062047 -0.331246757 1 NA
9 1.416911 1 2 0.5863705 0.29560086 -0.329638800 1 NA
10 1.448638 NA NA 0.7342586 0.43261394 0.167597533 1 NA
11 1.428375 1 3 0.7218028 0.54537238 0.860207989 1 NA
12 1.450130 1 0 NA 0.36458613 0.022730640 1 NA
13 1.420545 1 5 0.7200126 0.84543642 0.217171172 1 NA
14 1.423005 1 0 0.5289014 0.88041616 -0.403002412 1 NA
15 1.435902 1 1 0.7322482 0.47940969 0.087369742 1 NA
16 1.423901 1 4 0.7462471 0.25520352 -0.183870429 1 NA
17 1.457208 1 NA 0.9119922 0.53793620 -0.194577002 1 NA
18 1.414280 1 1 NA 0.41924865 -0.349718516 1 NA
19 1.443383 NA NA NA 0.19038933 -0.508781244 1 NA
20 1.434954 NA 3 NA NA 0.494883111 1 NA
21 1.429499 1 3 0.7288999 0.26763985 0.258041067 1 NA
22 1.441897 NA NA 0.7160420 NA -0.922621989 1 NA
23 1.423713 NA 6 NA NA 0.431254949 1 NA
24 1.435395 1 4 0.7210413 0.39688480 -0.294218881 1 NA
25 1.425944 NA NA 0.7816086 0.20117762 -0.425548895 1 NA
26 1.437115 NA 1 0.6747483 0.56039795 0.057176054 1 NA
27 1.441326 1 1 0.4746725 0.69959156 0.289090158 1 NA
28 1.422953 1 2 0.9270652 0.16198957 -0.473079489 1 NA
29 1.437797 1 2 0.5306249 0.73477348 -0.385664863 1 NA
30 1.472121 1 NA 0.8913764 NA -0.154780107 1 NA
31 1.421782 NA 5 NA 0.69439759 0.100536296 1 NA
32 1.457672 1 2 0.4610800 NA 0.634791958 1 NA
33 1.430842 1 0 0.7183814 NA -0.387252617 1 NA
34 1.431523 0 2 0.6375974 0.68680241 -0.181741088 1 NA
35 1.421395 1 NA 0.9202563 0.20563215 -0.311562695 1 NA
36 1.434496 1 2 0.7263222 0.39312999 -0.044115907 1 NA
37 1.425383 1 4 NA 0.33592359 -0.657409991 1 NA
38 1.421802 NA 2 NA 0.80799798 0.159577214 1 NA
39 1.430094 1 2 0.7945509 0.70399665 -0.460416933 1 NA
40 1.447621 NA NA 0.6355032 0.14770504 NA 1 NA
41 1.434797 1 2 0.9939049 0.32976608 -0.248909867 1 NA
42 1.446091 1 6 1.0690739 0.57875125 -0.609021545 1 NA
43 1.445306 1 1 0.7009106 0.69765999 0.025471883 1 NA
44 1.448783 1 2 0.7595403 0.92706981 0.066648592 1 NA
45 1.450617 1 1 0.8356414 0.59881110 -0.276108719 1 NA
46 1.415055 1 2 0.4929132 NA -0.179737577 1 NA
47 1.436590 0 NA NA 0.57021551 0.181190937 1 NA
48 1.433938 1 2 0.5363034 0.31297307 -0.453871693 1 NA
49 1.414941 0 NA 0.8494053 0.45752036 0.448629602 1 NA
50 1.421807 1 2 0.6292812 0.76707228 -0.529811821 1 NA
51 1.453203 1 2 0.9561312 0.79670238 -0.028304571 1 NA
52 1.452129 1 1 0.9735411 0.31851588 -0.520318482 1 NA
53 1.431510 1 0 0.7156259 0.27413726 0.171317619 1 NA
54 1.430082 1 3 0.5184434 0.87099655 0.432732046 1 NA
55 1.443492 1 1 0.7948965 0.14767954 -0.346286005 1 NA
56 1.436460 1 6 0.5191792 0.72225832 -0.469375653 1 NA
57 1.418119 1 NA 0.9233108 0.91165899 0.031021711 1 NA
58 1.434971 NA 7 0.8025356 NA -0.118837515 1 NA
59 1.445599 1 1 0.8546624 0.74875442 0.507769984 1 NA
60 1.437097 NA 2 0.8639819 0.57086552 0.271797031 1 NA
61 1.428360 1 NA 0.7521237 0.17368573 -0.124442204 1 NA
62 1.440550 1 2 0.5590215 NA 0.277677389 1 NA
63 1.443014 1 2 0.5972103 0.60538003 -0.102893730 1 NA
64 1.424298 1 1 0.6071272 NA NA 1 NA
65 1.448823 1 0 0.8837829 0.44987490 -0.678303052 1 NA
66 1.425834 0 2 0.7775301 0.71105443 0.478880037 1 NA
67 1.427102 1 NA NA 0.09500493 -0.428028760 1 NA
68 1.414240 1 NA 0.7857549 0.37292542 0.048119185 1 NA
69 1.456218 NA 3 0.9119262 0.41025328 0.216932805 1 NA
70 1.470594 1 1 0.5816103 0.87473911 -0.234575269 1 NA
71 1.425058 1 NA 0.4886093 0.57325664 0.006827078 1 NA
72 1.432371 1 NA NA 0.76227946 -0.456055171 1 NA
73 1.441656 1 3 NA 0.56061854 0.346486708 1 NA
74 1.434952 1 2 0.7328840 0.61145842 0.205092215 1 NA
75 1.402860 1 1 0.7946099 NA -0.136596858 1 NA
76 1.453363 1 3 0.7734810 0.23795025 -0.500179043 1 NA
77 1.432909 1 2 0.5296147 0.28135640 0.527352086 1 NA
78 1.435103 1 2 0.7723288 NA 0.022742250 1 NA
79 1.434462 1 0 0.8079308 0.43010097 NA 1 NA
80 1.434661 1 1 NA 0.30775746 -0.002032440 1 NA
81 1.445881 0 2 NA 0.43379094 -0.154246160 1 NA
82 1.442548 NA 1 NA 0.70103825 0.140201825 1 NA
83 1.430097 1 NA 0.4544158 0.19501290 -0.141417121 1 NA
84 1.430119 1 NA 0.6482660 0.42336380 NA 1 NA
85 1.430315 1 5 0.7272109 NA -0.021285339 1 NA
86 1.437584 NA 0 NA 0.49004839 -0.010196306 1 NA
87 1.409738 NA NA 0.6768061 NA -0.089747520 1 NA
88 1.422388 1 2 0.8115758 0.71840773 -0.083699898 1 NA
89 1.422509 1 1 NA 0.81565945 -0.044061996 1 NA
90 1.439432 1 3 0.6408465 0.83308857 -0.209291697 1 NA
91 1.430175 1 2 0.5917453 0.56239647 0.639036426 1 NA
92 1.418002 NA 6 0.7224845 NA 0.094698299 1 NA
93 1.423812 1 0 0.4501596 NA -0.055510622 1 NA
94 1.423473 1 4 0.5190455 NA -0.421318463 1 NA
95 1.434412 1 3 0.7305821 0.73286310 0.125295503 1 NA
96 1.450844 1 3 0.9696445 0.39788846 0.213084904 1 NA
97 1.433371 NA 3 0.7087457 NA -0.161914659 1 NA
98 1.444378 1 3 0.9964080 0.81066470 -0.034767685 1 NA
99 1.422523 0 5 NA 0.40892733 -0.320681689 1 NA
100 1.410394 NA 2 0.9296776 0.76834275 0.058192962 1 NA
$m3a$spM_lvlone
center scale
C1 1.43410054 0.01299651
B2 NA NA
P2 2.15000000 1.64663062
L1mis 0.72862466 0.15772614
Be2 0.51799407 0.22974678
C2 -0.06490582 0.33317347
(Intercept) NA NA
B21 NA NA
$m3a$mu_reg_norm
[1] 0
$m3a$tau_reg_norm
[1] 1e-04
$m3a$shape_tau_norm
[1] 0.01
$m3a$rate_tau_norm
[1] 0.01
$m3a$mu_reg_gamma
[1] 0
$m3a$tau_reg_gamma
[1] 1e-04
$m3a$shape_tau_gamma
[1] 0.01
$m3a$rate_tau_gamma
[1] 0.01
$m3a$mu_reg_beta
[1] 0
$m3a$tau_reg_beta
[1] 1e-04
$m3a$shape_tau_beta
[1] 0.01
$m3a$rate_tau_beta
[1] 0.01
$m3a$mu_reg_binom
[1] 0
$m3a$tau_reg_binom
[1] 1e-04
$m3a$mu_reg_poisson
[1] 0
$m3a$tau_reg_poisson
[1] 1e-04
$m3b
$m3b$M_lvlone
C1 B2 P2 L1mis C2 (Intercept) B21
1 1.410531 1 0 0.9364352 0.144065882 1 NA
2 1.434183 1 NA 0.8943541 0.032778478 1 NA
3 1.430994 1 1 0.2868460 0.343008492 1 NA
4 1.453096 1 1 0.9068418 -0.361887858 1 NA
5 1.438344 1 0 0.7621346 -0.389600647 1 NA
6 1.453207 NA 1 NA -0.205306841 1 NA
7 1.425176 1 1 NA 0.079434830 1 NA
8 1.437908 1 0 0.7593154 -0.331246757 1 NA
9 1.416911 1 2 0.5863705 -0.329638800 1 NA
10 1.448638 NA NA 0.7342586 0.167597533 1 NA
11 1.428375 1 3 0.7218028 0.860207989 1 NA
12 1.450130 1 0 NA 0.022730640 1 NA
13 1.420545 1 5 0.7200126 0.217171172 1 NA
14 1.423005 1 0 0.5289014 -0.403002412 1 NA
15 1.435902 1 1 0.7322482 0.087369742 1 NA
16 1.423901 1 4 0.7462471 -0.183870429 1 NA
17 1.457208 1 NA 0.9119922 -0.194577002 1 NA
18 1.414280 1 1 NA -0.349718516 1 NA
19 1.443383 NA NA NA -0.508781244 1 NA
20 1.434954 NA 3 NA 0.494883111 1 NA
21 1.429499 1 3 0.7288999 0.258041067 1 NA
22 1.441897 NA NA 0.7160420 -0.922621989 1 NA
23 1.423713 NA 6 NA 0.431254949 1 NA
24 1.435395 1 4 0.7210413 -0.294218881 1 NA
25 1.425944 NA NA 0.7816086 -0.425548895 1 NA
26 1.437115 NA 1 0.6747483 0.057176054 1 NA
27 1.441326 1 1 0.4746725 0.289090158 1 NA
28 1.422953 1 2 0.9270652 -0.473079489 1 NA
29 1.437797 1 2 0.5306249 -0.385664863 1 NA
30 1.472121 1 NA 0.8913764 -0.154780107 1 NA
31 1.421782 NA 5 NA 0.100536296 1 NA
32 1.457672 1 2 0.4610800 0.634791958 1 NA
33 1.430842 1 0 0.7183814 -0.387252617 1 NA
34 1.431523 0 2 0.6375974 -0.181741088 1 NA
35 1.421395 1 NA 0.9202563 -0.311562695 1 NA
36 1.434496 1 2 0.7263222 -0.044115907 1 NA
37 1.425383 1 4 NA -0.657409991 1 NA
38 1.421802 NA 2 NA 0.159577214 1 NA
39 1.430094 1 2 0.7945509 -0.460416933 1 NA
40 1.447621 NA NA 0.6355032 NA 1 NA
41 1.434797 1 2 0.9939049 -0.248909867 1 NA
42 1.446091 1 6 1.0690739 -0.609021545 1 NA
43 1.445306 1 1 0.7009106 0.025471883 1 NA
44 1.448783 1 2 0.7595403 0.066648592 1 NA
45 1.450617 1 1 0.8356414 -0.276108719 1 NA
46 1.415055 1 2 0.4929132 -0.179737577 1 NA
47 1.436590 0 NA NA 0.181190937 1 NA
48 1.433938 1 2 0.5363034 -0.453871693 1 NA
49 1.414941 0 NA 0.8494053 0.448629602 1 NA
50 1.421807 1 2 0.6292812 -0.529811821 1 NA
51 1.453203 1 2 0.9561312 -0.028304571 1 NA
52 1.452129 1 1 0.9735411 -0.520318482 1 NA
53 1.431510 1 0 0.7156259 0.171317619 1 NA
54 1.430082 1 3 0.5184434 0.432732046 1 NA
55 1.443492 1 1 0.7948965 -0.346286005 1 NA
56 1.436460 1 6 0.5191792 -0.469375653 1 NA
57 1.418119 1 NA 0.9233108 0.031021711 1 NA
58 1.434971 NA 7 0.8025356 -0.118837515 1 NA
59 1.445599 1 1 0.8546624 0.507769984 1 NA
60 1.437097 NA 2 0.8639819 0.271797031 1 NA
61 1.428360 1 NA 0.7521237 -0.124442204 1 NA
62 1.440550 1 2 0.5590215 0.277677389 1 NA
63 1.443014 1 2 0.5972103 -0.102893730 1 NA
64 1.424298 1 1 0.6071272 NA 1 NA
65 1.448823 1 0 0.8837829 -0.678303052 1 NA
66 1.425834 0 2 0.7775301 0.478880037 1 NA
67 1.427102 1 NA NA -0.428028760 1 NA
68 1.414240 1 NA 0.7857549 0.048119185 1 NA
69 1.456218 NA 3 0.9119262 0.216932805 1 NA
70 1.470594 1 1 0.5816103 -0.234575269 1 NA
71 1.425058 1 NA 0.4886093 0.006827078 1 NA
72 1.432371 1 NA NA -0.456055171 1 NA
73 1.441656 1 3 NA 0.346486708 1 NA
74 1.434952 1 2 0.7328840 0.205092215 1 NA
75 1.402860 1 1 0.7946099 -0.136596858 1 NA
76 1.453363 1 3 0.7734810 -0.500179043 1 NA
77 1.432909 1 2 0.5296147 0.527352086 1 NA
78 1.435103 1 2 0.7723288 0.022742250 1 NA
79 1.434462 1 0 0.8079308 NA 1 NA
80 1.434661 1 1 NA -0.002032440 1 NA
81 1.445881 0 2 NA -0.154246160 1 NA
82 1.442548 NA 1 NA 0.140201825 1 NA
83 1.430097 1 NA 0.4544158 -0.141417121 1 NA
84 1.430119 1 NA 0.6482660 NA 1 NA
85 1.430315 1 5 0.7272109 -0.021285339 1 NA
86 1.437584 NA 0 NA -0.010196306 1 NA
87 1.409738 NA NA 0.6768061 -0.089747520 1 NA
88 1.422388 1 2 0.8115758 -0.083699898 1 NA
89 1.422509 1 1 NA -0.044061996 1 NA
90 1.439432 1 3 0.6408465 -0.209291697 1 NA
91 1.430175 1 2 0.5917453 0.639036426 1 NA
92 1.418002 NA 6 0.7224845 0.094698299 1 NA
93 1.423812 1 0 0.4501596 -0.055510622 1 NA
94 1.423473 1 4 0.5190455 -0.421318463 1 NA
95 1.434412 1 3 0.7305821 0.125295503 1 NA
96 1.450844 1 3 0.9696445 0.213084904 1 NA
97 1.433371 NA 3 0.7087457 -0.161914659 1 NA
98 1.444378 1 3 0.9964080 -0.034767685 1 NA
99 1.422523 0 5 NA -0.320681689 1 NA
100 1.410394 NA 2 0.9296776 0.058192962 1 NA
$m3b$spM_lvlone
center scale
C1 1.43410054 0.01299651
B2 NA NA
P2 2.15000000 1.64663062
L1mis 0.72862466 0.15772614
C2 -0.06490582 0.33317347
(Intercept) NA NA
B21 NA NA
$m3b$mu_reg_norm
[1] 0
$m3b$tau_reg_norm
[1] 1e-04
$m3b$shape_tau_norm
[1] 0.01
$m3b$rate_tau_norm
[1] 0.01
$m3b$mu_reg_binom
[1] 0
$m3b$tau_reg_binom
[1] 1e-04
$m3b$mu_reg_poisson
[1] 0
$m3b$tau_reg_poisson
[1] 1e-04
$m3c
$m3c$M_lvlone
C1 B2 P2 L1mis C2 (Intercept) B21
1 1.410531 1 0 0.9364352 0.144065882 1 NA
2 1.434183 1 NA 0.8943541 0.032778478 1 NA
3 1.430994 1 1 0.2868460 0.343008492 1 NA
4 1.453096 1 1 0.9068418 -0.361887858 1 NA
5 1.438344 1 0 0.7621346 -0.389600647 1 NA
6 1.453207 NA 1 NA -0.205306841 1 NA
7 1.425176 1 1 NA 0.079434830 1 NA
8 1.437908 1 0 0.7593154 -0.331246757 1 NA
9 1.416911 1 2 0.5863705 -0.329638800 1 NA
10 1.448638 NA NA 0.7342586 0.167597533 1 NA
11 1.428375 1 3 0.7218028 0.860207989 1 NA
12 1.450130 1 0 NA 0.022730640 1 NA
13 1.420545 1 5 0.7200126 0.217171172 1 NA
14 1.423005 1 0 0.5289014 -0.403002412 1 NA
15 1.435902 1 1 0.7322482 0.087369742 1 NA
16 1.423901 1 4 0.7462471 -0.183870429 1 NA
17 1.457208 1 NA 0.9119922 -0.194577002 1 NA
18 1.414280 1 1 NA -0.349718516 1 NA
19 1.443383 NA NA NA -0.508781244 1 NA
20 1.434954 NA 3 NA 0.494883111 1 NA
21 1.429499 1 3 0.7288999 0.258041067 1 NA
22 1.441897 NA NA 0.7160420 -0.922621989 1 NA
23 1.423713 NA 6 NA 0.431254949 1 NA
24 1.435395 1 4 0.7210413 -0.294218881 1 NA
25 1.425944 NA NA 0.7816086 -0.425548895 1 NA
26 1.437115 NA 1 0.6747483 0.057176054 1 NA
27 1.441326 1 1 0.4746725 0.289090158 1 NA
28 1.422953 1 2 0.9270652 -0.473079489 1 NA
29 1.437797 1 2 0.5306249 -0.385664863 1 NA
30 1.472121 1 NA 0.8913764 -0.154780107 1 NA
31 1.421782 NA 5 NA 0.100536296 1 NA
32 1.457672 1 2 0.4610800 0.634791958 1 NA
33 1.430842 1 0 0.7183814 -0.387252617 1 NA
34 1.431523 0 2 0.6375974 -0.181741088 1 NA
35 1.421395 1 NA 0.9202563 -0.311562695 1 NA
36 1.434496 1 2 0.7263222 -0.044115907 1 NA
37 1.425383 1 4 NA -0.657409991 1 NA
38 1.421802 NA 2 NA 0.159577214 1 NA
39 1.430094 1 2 0.7945509 -0.460416933 1 NA
40 1.447621 NA NA 0.6355032 NA 1 NA
41 1.434797 1 2 0.9939049 -0.248909867 1 NA
42 1.446091 1 6 1.0690739 -0.609021545 1 NA
43 1.445306 1 1 0.7009106 0.025471883 1 NA
44 1.448783 1 2 0.7595403 0.066648592 1 NA
45 1.450617 1 1 0.8356414 -0.276108719 1 NA
46 1.415055 1 2 0.4929132 -0.179737577 1 NA
47 1.436590 0 NA NA 0.181190937 1 NA
48 1.433938 1 2 0.5363034 -0.453871693 1 NA
49 1.414941 0 NA 0.8494053 0.448629602 1 NA
50 1.421807 1 2 0.6292812 -0.529811821 1 NA
51 1.453203 1 2 0.9561312 -0.028304571 1 NA
52 1.452129 1 1 0.9735411 -0.520318482 1 NA
53 1.431510 1 0 0.7156259 0.171317619 1 NA
54 1.430082 1 3 0.5184434 0.432732046 1 NA
55 1.443492 1 1 0.7948965 -0.346286005 1 NA
56 1.436460 1 6 0.5191792 -0.469375653 1 NA
57 1.418119 1 NA 0.9233108 0.031021711 1 NA
58 1.434971 NA 7 0.8025356 -0.118837515 1 NA
59 1.445599 1 1 0.8546624 0.507769984 1 NA
60 1.437097 NA 2 0.8639819 0.271797031 1 NA
61 1.428360 1 NA 0.7521237 -0.124442204 1 NA
62 1.440550 1 2 0.5590215 0.277677389 1 NA
63 1.443014 1 2 0.5972103 -0.102893730 1 NA
64 1.424298 1 1 0.6071272 NA 1 NA
65 1.448823 1 0 0.8837829 -0.678303052 1 NA
66 1.425834 0 2 0.7775301 0.478880037 1 NA
67 1.427102 1 NA NA -0.428028760 1 NA
68 1.414240 1 NA 0.7857549 0.048119185 1 NA
69 1.456218 NA 3 0.9119262 0.216932805 1 NA
70 1.470594 1 1 0.5816103 -0.234575269 1 NA
71 1.425058 1 NA 0.4886093 0.006827078 1 NA
72 1.432371 1 NA NA -0.456055171 1 NA
73 1.441656 1 3 NA 0.346486708 1 NA
74 1.434952 1 2 0.7328840 0.205092215 1 NA
75 1.402860 1 1 0.7946099 -0.136596858 1 NA
76 1.453363 1 3 0.7734810 -0.500179043 1 NA
77 1.432909 1 2 0.5296147 0.527352086 1 NA
78 1.435103 1 2 0.7723288 0.022742250 1 NA
79 1.434462 1 0 0.8079308 NA 1 NA
80 1.434661 1 1 NA -0.002032440 1 NA
81 1.445881 0 2 NA -0.154246160 1 NA
82 1.442548 NA 1 NA 0.140201825 1 NA
83 1.430097 1 NA 0.4544158 -0.141417121 1 NA
84 1.430119 1 NA 0.6482660 NA 1 NA
85 1.430315 1 5 0.7272109 -0.021285339 1 NA
86 1.437584 NA 0 NA -0.010196306 1 NA
87 1.409738 NA NA 0.6768061 -0.089747520 1 NA
88 1.422388 1 2 0.8115758 -0.083699898 1 NA
89 1.422509 1 1 NA -0.044061996 1 NA
90 1.439432 1 3 0.6408465 -0.209291697 1 NA
91 1.430175 1 2 0.5917453 0.639036426 1 NA
92 1.418002 NA 6 0.7224845 0.094698299 1 NA
93 1.423812 1 0 0.4501596 -0.055510622 1 NA
94 1.423473 1 4 0.5190455 -0.421318463 1 NA
95 1.434412 1 3 0.7305821 0.125295503 1 NA
96 1.450844 1 3 0.9696445 0.213084904 1 NA
97 1.433371 NA 3 0.7087457 -0.161914659 1 NA
98 1.444378 1 3 0.9964080 -0.034767685 1 NA
99 1.422523 0 5 NA -0.320681689 1 NA
100 1.410394 NA 2 0.9296776 0.058192962 1 NA
$m3c$spM_lvlone
center scale
C1 1.43410054 0.01299651
B2 NA NA
P2 2.15000000 1.64663062
L1mis 0.72862466 0.15772614
C2 -0.06490582 0.33317347
(Intercept) NA NA
B21 NA NA
$m3c$mu_reg_norm
[1] 0
$m3c$tau_reg_norm
[1] 1e-04
$m3c$shape_tau_norm
[1] 0.01
$m3c$rate_tau_norm
[1] 0.01
$m3c$mu_reg_gamma
[1] 0
$m3c$tau_reg_gamma
[1] 1e-04
$m3c$shape_tau_gamma
[1] 0.01
$m3c$rate_tau_gamma
[1] 0.01
$m3c$mu_reg_binom
[1] 0
$m3c$tau_reg_binom
[1] 1e-04
$m3c$mu_reg_poisson
[1] 0
$m3c$tau_reg_poisson
[1] 1e-04
$m3d
$m3d$M_lvlone
C1 B2 P2 L1mis Be2 C2 (Intercept) B21
1 1.410531 1 0 0.9364352 0.13821330 0.144065882 1 NA
2 1.434183 1 NA 0.8943541 NA 0.032778478 1 NA
3 1.430994 1 1 0.2868460 0.85221266 0.343008492 1 NA
4 1.453096 1 1 0.9068418 0.61517266 -0.361887858 1 NA
5 1.438344 1 0 0.7621346 0.56718424 -0.389600647 1 NA
6 1.453207 NA 1 NA 0.16127199 -0.205306841 1 NA
7 1.425176 1 1 NA NA 0.079434830 1 NA
8 1.437908 1 0 0.7593154 0.51062047 -0.331246757 1 NA
9 1.416911 1 2 0.5863705 0.29560086 -0.329638800 1 NA
10 1.448638 NA NA 0.7342586 0.43261394 0.167597533 1 NA
11 1.428375 1 3 0.7218028 0.54537238 0.860207989 1 NA
12 1.450130 1 0 NA 0.36458613 0.022730640 1 NA
13 1.420545 1 5 0.7200126 0.84543642 0.217171172 1 NA
14 1.423005 1 0 0.5289014 0.88041616 -0.403002412 1 NA
15 1.435902 1 1 0.7322482 0.47940969 0.087369742 1 NA
16 1.423901 1 4 0.7462471 0.25520352 -0.183870429 1 NA
17 1.457208 1 NA 0.9119922 0.53793620 -0.194577002 1 NA
18 1.414280 1 1 NA 0.41924865 -0.349718516 1 NA
19 1.443383 NA NA NA 0.19038933 -0.508781244 1 NA
20 1.434954 NA 3 NA NA 0.494883111 1 NA
21 1.429499 1 3 0.7288999 0.26763985 0.258041067 1 NA
22 1.441897 NA NA 0.7160420 NA -0.922621989 1 NA
23 1.423713 NA 6 NA NA 0.431254949 1 NA
24 1.435395 1 4 0.7210413 0.39688480 -0.294218881 1 NA
25 1.425944 NA NA 0.7816086 0.20117762 -0.425548895 1 NA
26 1.437115 NA 1 0.6747483 0.56039795 0.057176054 1 NA
27 1.441326 1 1 0.4746725 0.69959156 0.289090158 1 NA
28 1.422953 1 2 0.9270652 0.16198957 -0.473079489 1 NA
29 1.437797 1 2 0.5306249 0.73477348 -0.385664863 1 NA
30 1.472121 1 NA 0.8913764 NA -0.154780107 1 NA
31 1.421782 NA 5 NA 0.69439759 0.100536296 1 NA
32 1.457672 1 2 0.4610800 NA 0.634791958 1 NA
33 1.430842 1 0 0.7183814 NA -0.387252617 1 NA
34 1.431523 0 2 0.6375974 0.68680241 -0.181741088 1 NA
35 1.421395 1 NA 0.9202563 0.20563215 -0.311562695 1 NA
36 1.434496 1 2 0.7263222 0.39312999 -0.044115907 1 NA
37 1.425383 1 4 NA 0.33592359 -0.657409991 1 NA
38 1.421802 NA 2 NA 0.80799798 0.159577214 1 NA
39 1.430094 1 2 0.7945509 0.70399665 -0.460416933 1 NA
40 1.447621 NA NA 0.6355032 0.14770504 NA 1 NA
41 1.434797 1 2 0.9939049 0.32976608 -0.248909867 1 NA
42 1.446091 1 6 1.0690739 0.57875125 -0.609021545 1 NA
43 1.445306 1 1 0.7009106 0.69765999 0.025471883 1 NA
44 1.448783 1 2 0.7595403 0.92706981 0.066648592 1 NA
45 1.450617 1 1 0.8356414 0.59881110 -0.276108719 1 NA
46 1.415055 1 2 0.4929132 NA -0.179737577 1 NA
47 1.436590 0 NA NA 0.57021551 0.181190937 1 NA
48 1.433938 1 2 0.5363034 0.31297307 -0.453871693 1 NA
49 1.414941 0 NA 0.8494053 0.45752036 0.448629602 1 NA
50 1.421807 1 2 0.6292812 0.76707228 -0.529811821 1 NA
51 1.453203 1 2 0.9561312 0.79670238 -0.028304571 1 NA
52 1.452129 1 1 0.9735411 0.31851588 -0.520318482 1 NA
53 1.431510 1 0 0.7156259 0.27413726 0.171317619 1 NA
54 1.430082 1 3 0.5184434 0.87099655 0.432732046 1 NA
55 1.443492 1 1 0.7948965 0.14767954 -0.346286005 1 NA
56 1.436460 1 6 0.5191792 0.72225832 -0.469375653 1 NA
57 1.418119 1 NA 0.9233108 0.91165899 0.031021711 1 NA
58 1.434971 NA 7 0.8025356 NA -0.118837515 1 NA
59 1.445599 1 1 0.8546624 0.74875442 0.507769984 1 NA
60 1.437097 NA 2 0.8639819 0.57086552 0.271797031 1 NA
61 1.428360 1 NA 0.7521237 0.17368573 -0.124442204 1 NA
62 1.440550 1 2 0.5590215 NA 0.277677389 1 NA
63 1.443014 1 2 0.5972103 0.60538003 -0.102893730 1 NA
64 1.424298 1 1 0.6071272 NA NA 1 NA
65 1.448823 1 0 0.8837829 0.44987490 -0.678303052 1 NA
66 1.425834 0 2 0.7775301 0.71105443 0.478880037 1 NA
67 1.427102 1 NA NA 0.09500493 -0.428028760 1 NA
68 1.414240 1 NA 0.7857549 0.37292542 0.048119185 1 NA
69 1.456218 NA 3 0.9119262 0.41025328 0.216932805 1 NA
70 1.470594 1 1 0.5816103 0.87473911 -0.234575269 1 NA
71 1.425058 1 NA 0.4886093 0.57325664 0.006827078 1 NA
72 1.432371 1 NA NA 0.76227946 -0.456055171 1 NA
73 1.441656 1 3 NA 0.56061854 0.346486708 1 NA
74 1.434952 1 2 0.7328840 0.61145842 0.205092215 1 NA
75 1.402860 1 1 0.7946099 NA -0.136596858 1 NA
76 1.453363 1 3 0.7734810 0.23795025 -0.500179043 1 NA
77 1.432909 1 2 0.5296147 0.28135640 0.527352086 1 NA
78 1.435103 1 2 0.7723288 NA 0.022742250 1 NA
79 1.434462 1 0 0.8079308 0.43010097 NA 1 NA
80 1.434661 1 1 NA 0.30775746 -0.002032440 1 NA
81 1.445881 0 2 NA 0.43379094 -0.154246160 1 NA
82 1.442548 NA 1 NA 0.70103825 0.140201825 1 NA
83 1.430097 1 NA 0.4544158 0.19501290 -0.141417121 1 NA
84 1.430119 1 NA 0.6482660 0.42336380 NA 1 NA
85 1.430315 1 5 0.7272109 NA -0.021285339 1 NA
86 1.437584 NA 0 NA 0.49004839 -0.010196306 1 NA
87 1.409738 NA NA 0.6768061 NA -0.089747520 1 NA
88 1.422388 1 2 0.8115758 0.71840773 -0.083699898 1 NA
89 1.422509 1 1 NA 0.81565945 -0.044061996 1 NA
90 1.439432 1 3 0.6408465 0.83308857 -0.209291697 1 NA
91 1.430175 1 2 0.5917453 0.56239647 0.639036426 1 NA
92 1.418002 NA 6 0.7224845 NA 0.094698299 1 NA
93 1.423812 1 0 0.4501596 NA -0.055510622 1 NA
94 1.423473 1 4 0.5190455 NA -0.421318463 1 NA
95 1.434412 1 3 0.7305821 0.73286310 0.125295503 1 NA
96 1.450844 1 3 0.9696445 0.39788846 0.213084904 1 NA
97 1.433371 NA 3 0.7087457 NA -0.161914659 1 NA
98 1.444378 1 3 0.9964080 0.81066470 -0.034767685 1 NA
99 1.422523 0 5 NA 0.40892733 -0.320681689 1 NA
100 1.410394 NA 2 0.9296776 0.76834275 0.058192962 1 NA
$m3d$spM_lvlone
center scale
C1 1.43410054 0.01299651
B2 NA NA
P2 2.15000000 1.64663062
L1mis 0.72862466 0.15772614
Be2 0.51799407 0.22974678
C2 -0.06490582 0.33317347
(Intercept) NA NA
B21 NA NA
$m3d$mu_reg_norm
[1] 0
$m3d$tau_reg_norm
[1] 1e-04
$m3d$shape_tau_norm
[1] 0.01
$m3d$rate_tau_norm
[1] 0.01
$m3d$mu_reg_gamma
[1] 0
$m3d$tau_reg_gamma
[1] 1e-04
$m3d$shape_tau_gamma
[1] 0.01
$m3d$rate_tau_gamma
[1] 0.01
$m3d$mu_reg_binom
[1] 0
$m3d$tau_reg_binom
[1] 1e-04
$m3d$mu_reg_poisson
[1] 0
$m3d$tau_reg_poisson
[1] 1e-04
$m4a
$m4a$M_lvlone
y C2 M2 O2 (Intercept) M22 M23 M24 O22 O23 O24
1 -4.76915977 0.144065882 4 4 1 NA NA NA NA NA NA
2 -2.69277172 0.032778478 1 4 1 NA NA NA NA NA NA
3 -1.17551547 0.343008492 3 4 1 NA NA NA NA NA NA
4 -4.57464473 -0.361887858 3 1 1 NA NA NA NA NA NA
5 -2.20260004 -0.389600647 4 2 1 NA NA NA NA NA NA
6 -3.48995315 -0.205306841 4 3 1 NA NA NA NA NA NA
7 -0.44987258 0.079434830 1 4 1 NA NA NA NA NA NA
8 -2.29588848 -0.331246757 1 2 1 NA NA NA NA NA NA
9 -4.49135812 -0.329638800 2 4 1 NA NA NA NA NA NA
10 -5.52545368 0.167597533 2 3 1 NA NA NA NA NA NA
11 -4.16286741 0.860207989 3 2 1 NA NA NA NA NA NA
12 -2.93455761 0.022730640 3 1 1 NA NA NA NA NA NA
13 -0.04202496 0.217171172 2 1 1 NA NA NA NA NA NA
14 -1.63149775 -0.403002412 3 1 1 NA NA NA NA NA NA
15 -0.97786151 0.087369742 2 4 1 NA NA NA NA NA NA
16 -1.79100431 -0.183870429 1 3 1 NA NA NA NA NA NA
17 -6.26520032 -0.194577002 4 3 1 NA NA NA NA NA NA
18 -1.36028709 -0.349718516 2 1 1 NA NA NA NA NA NA
19 -1.15396597 -0.508781244 3 3 1 NA NA NA NA NA NA
20 -3.21707239 0.494883111 3 1 1 NA NA NA NA NA NA
21 -1.59389898 0.258041067 2 3 1 NA NA NA NA NA NA
22 -5.50335066 -0.922621989 2 3 1 NA NA NA NA NA NA
23 0.57290123 0.431254949 3 2 1 NA NA NA NA NA NA
24 -8.22270323 -0.294218881 3 3 1 NA NA NA NA NA NA
25 -1.41364158 -0.425548895 2 2 1 NA NA NA NA NA NA
26 -6.28031574 0.057176054 2 2 1 NA NA NA NA NA NA
27 -3.15624425 0.289090158 1 1 1 NA NA NA NA NA NA
28 -3.55693639 -0.473079489 3 4 1 NA NA NA NA NA NA
29 -1.11821124 -0.385664863 4 3 1 NA NA NA NA NA NA
30 -2.82834175 -0.154780107 2 3 1 NA NA NA NA NA NA
31 -3.72259860 0.100536296 NA 2 1 NA NA NA NA NA NA
32 -1.75256656 0.634791958 4 2 1 NA NA NA NA NA NA
33 -5.55044409 -0.387252617 4 1 1 NA NA NA NA NA NA
34 -7.45068147 -0.181741088 4 1 1 NA NA NA NA NA NA
35 -0.97491919 -0.311562695 2 4 1 NA NA NA NA NA NA
36 -2.98356481 -0.044115907 1 3 1 NA NA NA NA NA NA
37 -1.86039471 -0.657409991 3 3 1 NA NA NA NA NA NA
38 -7.28754607 0.159577214 4 1 1 NA NA NA NA NA NA
39 -8.66234796 -0.460416933 3 2 1 NA NA NA NA NA NA
40 -4.16291375 NA 3 3 1 NA NA NA NA NA NA
41 -3.48250771 -0.248909867 1 3 1 NA NA NA NA NA NA
42 -7.27930410 -0.609021545 4 3 1 NA NA NA NA NA NA
43 -6.12866190 0.025471883 1 3 1 NA NA NA NA NA NA
44 -4.96880803 0.066648592 2 4 1 NA NA NA NA NA NA
45 -4.76746713 -0.276108719 2 4 1 NA NA NA NA NA NA
46 -1.91249177 -0.179737577 1 1 1 NA NA NA NA NA NA
47 -0.61884029 0.181190937 4 4 1 NA NA NA NA NA NA
48 -0.20496175 -0.453871693 2 4 1 NA NA NA NA NA NA
49 -7.12636055 0.448629602 4 1 1 NA NA NA NA NA NA
50 -6.23103837 -0.529811821 1 2 1 NA NA NA NA NA NA
51 -3.32561065 -0.028304571 4 1 1 NA NA NA NA NA NA
52 -2.95942339 -0.520318482 4 3 1 NA NA NA NA NA NA
53 -4.44915114 0.171317619 4 2 1 NA NA NA NA NA NA
54 -0.81566463 0.432732046 3 1 1 NA NA NA NA NA NA
55 -6.50029573 -0.346286005 3 2 1 NA NA NA NA NA NA
56 -2.74718050 -0.469375653 3 3 1 NA NA NA NA NA NA
57 -6.35015663 0.031021711 2 NA 1 NA NA NA NA NA NA
58 -2.69505883 -0.118837515 3 4 1 NA NA NA NA NA NA
59 -1.55660833 0.507769984 3 4 1 NA NA NA NA NA NA
60 -3.76240209 0.271797031 4 3 1 NA NA NA NA NA NA
61 -3.92885797 -0.124442204 2 4 1 NA NA NA NA NA NA
62 -1.72044748 0.277677389 2 1 1 NA NA NA NA NA NA
63 -0.56602625 -0.102893730 1 4 1 NA NA NA NA NA NA
64 -4.42235015 NA 2 4 1 NA NA NA NA NA NA
65 -2.39122287 -0.678303052 2 4 1 NA NA NA NA NA NA
66 -0.81807247 0.478880037 3 1 1 NA NA NA NA NA NA
67 -6.48196782 -0.428028760 2 3 1 NA NA NA NA NA NA
68 -1.37306273 0.048119185 4 3 1 NA NA NA NA NA NA
69 -4.99886487 0.216932805 NA 4 1 NA NA NA NA NA NA
70 -5.82288217 -0.234575269 1 1 1 NA NA NA NA NA NA
71 -2.68234219 0.006827078 2 4 1 NA NA NA NA NA NA
72 -3.96170442 -0.456055171 3 4 1 NA NA NA NA NA NA
73 -7.19573667 0.346486708 4 2 1 NA NA NA NA NA NA
74 -5.08799713 0.205092215 4 4 1 NA NA NA NA NA NA
75 -1.32967262 -0.136596858 1 3 1 NA NA NA NA NA NA
76 -2.56532332 -0.500179043 4 2 1 NA NA NA NA NA NA
77 -3.21002900 0.527352086 NA 2 1 NA NA NA NA NA NA
78 -3.40559790 0.022742250 2 3 1 NA NA NA NA NA NA
79 -4.56223913 NA 2 2 1 NA NA NA NA NA NA
80 -2.04250454 -0.002032440 2 1 1 NA NA NA NA NA NA
81 -2.20378059 -0.154246160 4 4 1 NA NA NA NA NA NA
82 -3.37471317 0.140201825 3 2 1 NA NA NA NA NA NA
83 -0.95345385 -0.141417121 3 4 1 NA NA NA NA NA NA
84 -4.89337660 NA 1 1 1 NA NA NA NA NA NA
85 -9.82258463 -0.021285339 2 1 1 NA NA NA NA NA NA
86 -4.51800734 -0.010196306 1 2 1 NA NA NA NA NA NA
87 -0.18662049 -0.089747520 3 3 1 NA NA NA NA NA NA
88 -2.87120881 -0.083699898 1 3 1 NA NA NA NA NA NA
89 1.29290150 -0.044061996 2 2 1 NA NA NA NA NA NA
90 -1.39497744 -0.209291697 1 4 1 NA NA NA NA NA NA
91 1.14575040 0.639036426 3 2 1 NA NA NA NA NA NA
92 0.92801246 0.094698299 1 1 1 NA NA NA NA NA NA
93 -2.59938157 -0.055510622 4 NA 1 NA NA NA NA NA NA
94 -3.26905923 -0.421318463 4 3 1 NA NA NA NA NA NA
95 -3.26861434 0.125295503 1 1 1 NA NA NA NA NA NA
96 -5.71017484 0.213084904 4 3 1 NA NA NA NA NA NA
97 -3.76781806 -0.161914659 4 2 1 NA NA NA NA NA NA
98 -2.02677390 -0.034767685 3 2 1 NA NA NA NA NA NA
99 -2.96199765 -0.320681689 3 4 1 NA NA NA NA NA NA
100 -4.81129496 0.058192962 4 3 1 NA NA NA NA NA NA
abs(C1 - C2) log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
1 NA 0.3439662 NA NA NA
2 NA 0.3605954 NA NA NA
3 NA 0.3583696 NA NA NA
4 NA 0.3736964 NA NA NA
5 NA 0.3634928 NA NA NA
6 NA 0.3737730 NA NA NA
7 NA 0.3542952 NA NA NA
8 NA 0.3631892 NA NA NA
9 NA 0.3484794 NA NA NA
10 NA 0.3706241 NA NA NA
11 NA 0.3565373 NA NA NA
12 NA 0.3716534 NA NA NA
13 NA 0.3510408 NA NA NA
14 NA 0.3527707 NA NA NA
15 NA 0.3617934 NA NA NA
16 NA 0.3534000 NA NA NA
17 NA 0.3765220 NA NA NA
18 NA 0.3466206 NA NA NA
19 NA 0.3669896 NA NA NA
20 NA 0.3611331 NA NA NA
21 NA 0.3573242 NA NA NA
22 NA 0.3659595 NA NA NA
23 NA 0.3532680 NA NA NA
24 NA 0.3614400 NA NA NA
25 NA 0.3548341 NA NA NA
26 NA 0.3626380 NA NA NA
27 NA 0.3655634 NA NA NA
28 NA 0.3527344 NA NA NA
29 NA 0.3631120 NA NA NA
30 NA 0.3867045 NA NA NA
31 NA 0.3519109 NA NA NA
32 NA 0.3768405 NA NA NA
33 NA 0.3582630 NA NA NA
34 NA 0.3587390 NA NA NA
35 NA 0.3516387 NA NA NA
36 NA 0.3608133 NA NA NA
37 NA 0.3544406 NA NA NA
38 NA 0.3519254 NA NA NA
39 NA 0.3577404 NA NA NA
40 NA 0.3699214 NA NA NA
41 NA 0.3610235 NA NA NA
42 NA 0.3688639 NA NA NA
43 NA 0.3683210 NA NA NA
44 NA 0.3707242 NA NA NA
45 NA 0.3719890 NA NA NA
46 NA 0.3471687 NA NA NA
47 NA 0.3622725 NA NA NA
48 NA 0.3604242 NA NA NA
49 NA 0.3470878 NA NA NA
50 NA 0.3519288 NA NA NA
51 NA 0.3737703 NA NA NA
52 NA 0.3730309 NA NA NA
53 NA 0.3587298 NA NA NA
54 NA 0.3577317 NA NA NA
55 NA 0.3670651 NA NA NA
56 NA 0.3621821 NA NA NA
57 NA 0.3493310 NA NA NA
58 NA 0.3611449 NA NA NA
59 NA 0.3685236 NA NA NA
60 NA 0.3626252 NA NA NA
61 NA 0.3565271 NA NA NA
62 NA 0.3650248 NA NA NA
63 NA 0.3667342 NA NA NA
64 NA 0.3536790 NA NA NA
65 NA 0.3707512 NA NA NA
66 NA 0.3547570 NA NA NA
67 NA 0.3556460 NA NA NA
68 NA 0.3465922 NA NA NA
69 NA 0.3758430 NA NA NA
70 NA 0.3856661 NA NA NA
71 NA 0.3542125 NA NA NA
72 NA 0.3593309 NA NA NA
73 NA 0.3657925 NA NA NA
74 NA 0.3611311 NA NA NA
75 NA 0.3385130 NA NA NA
76 NA 0.3738804 NA NA NA
77 NA 0.3597065 NA NA NA
78 NA 0.3612366 NA NA NA
79 NA 0.3607899 NA NA NA
80 NA 0.3609283 NA NA NA
81 NA 0.3687189 NA NA NA
82 NA 0.3664112 NA NA NA
83 NA 0.3577425 NA NA NA
84 NA 0.3577579 NA NA NA
85 NA 0.3578947 NA NA NA
86 NA 0.3629637 NA NA NA
87 NA 0.3434041 NA NA NA
88 NA 0.3523374 NA NA NA
89 NA 0.3524220 NA NA NA
90 NA 0.3642486 NA NA NA
91 NA 0.3577968 NA NA NA
92 NA 0.3492491 NA NA NA
93 NA 0.3533376 NA NA NA
94 NA 0.3530999 NA NA NA
95 NA 0.3607553 NA NA NA
96 NA 0.3721453 NA NA NA
97 NA 0.3600291 NA NA NA
98 NA 0.3676785 NA NA NA
99 NA 0.3524318 NA NA NA
100 NA 0.3438689 NA NA NA
C1
1 1.410531
2 1.434183
3 1.430994
4 1.453096
5 1.438344
6 1.453207
7 1.425176
8 1.437908
9 1.416911
10 1.448638
11 1.428375
12 1.450130
13 1.420545
14 1.423005
15 1.435902
16 1.423901
17 1.457208
18 1.414280
19 1.443383
20 1.434954
21 1.429499
22 1.441897
23 1.423713
24 1.435395
25 1.425944
26 1.437115
27 1.441326
28 1.422953
29 1.437797
30 1.472121
31 1.421782
32 1.457672
33 1.430842
34 1.431523
35 1.421395
36 1.434496
37 1.425383
38 1.421802
39 1.430094
40 1.447621
41 1.434797
42 1.446091
43 1.445306
44 1.448783
45 1.450617
46 1.415055
47 1.436590
48 1.433938
49 1.414941
50 1.421807
51 1.453203
52 1.452129
53 1.431510
54 1.430082
55 1.443492
56 1.436460
57 1.418119
58 1.434971
59 1.445599
60 1.437097
61 1.428360
62 1.440550
63 1.443014
64 1.424298
65 1.448823
66 1.425834
67 1.427102
68 1.414240
69 1.456218
70 1.470594
71 1.425058
72 1.432371
73 1.441656
74 1.434952
75 1.402860
76 1.453363
77 1.432909
78 1.435103
79 1.434462
80 1.434661
81 1.445881
82 1.442548
83 1.430097
84 1.430119
85 1.430315
86 1.437584
87 1.409738
88 1.422388
89 1.422509
90 1.439432
91 1.430175
92 1.418002
93 1.423812
94 1.423473
95 1.434412
96 1.450844
97 1.433371
98 1.444378
99 1.422523
100 1.410394
$m4a$spM_lvlone
center scale
y -3.34428345 2.276495066
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
B1 L1mis Be2 C2 (Intercept) abs(C1 - C2) log(Be2)
1 1 0.9364352 0.13821330 0.144065882 1 NA NA
2 1 0.8943541 NA 0.032778478 1 NA NA
3 1 0.2868460 0.85221266 0.343008492 1 NA NA
4 1 0.9068418 0.61517266 -0.361887858 1 NA NA
5 1 0.7621346 0.56718424 -0.389600647 1 NA NA
6 1 NA 0.16127199 -0.205306841 1 NA NA
7 0 NA NA 0.079434830 1 NA NA
8 0 0.7593154 0.51062047 -0.331246757 1 NA NA
9 1 0.5863705 0.29560086 -0.329638800 1 NA NA
10 1 0.7342586 0.43261394 0.167597533 1 NA NA
11 1 0.7218028 0.54537238 0.860207989 1 NA NA
12 0 NA 0.36458613 0.022730640 1 NA NA
13 1 0.7200126 0.84543642 0.217171172 1 NA NA
14 0 0.5289014 0.88041616 -0.403002412 1 NA NA
15 1 0.7322482 0.47940969 0.087369742 1 NA NA
16 1 0.7462471 0.25520352 -0.183870429 1 NA NA
17 1 0.9119922 0.53793620 -0.194577002 1 NA NA
18 1 NA 0.41924865 -0.349718516 1 NA NA
19 1 NA 0.19038933 -0.508781244 1 NA NA
20 1 NA NA 0.494883111 1 NA NA
21 1 0.7288999 0.26763985 0.258041067 1 NA NA
22 1 0.7160420 NA -0.922621989 1 NA NA
23 1 NA NA 0.431254949 1 NA NA
24 1 0.7210413 0.39688480 -0.294218881 1 NA NA
25 0 0.7816086 0.20117762 -0.425548895 1 NA NA
26 1 0.6747483 0.56039795 0.057176054 1 NA NA
27 1 0.4746725 0.69959156 0.289090158 1 NA NA
28 1 0.9270652 0.16198957 -0.473079489 1 NA NA
29 1 0.5306249 0.73477348 -0.385664863 1 NA NA
30 0 0.8913764 NA -0.154780107 1 NA NA
31 0 NA 0.69439759 0.100536296 1 NA NA
32 1 0.4610800 NA 0.634791958 1 NA NA
33 1 0.7183814 NA -0.387252617 1 NA NA
34 1 0.6375974 0.68680241 -0.181741088 1 NA NA
35 1 0.9202563 0.20563215 -0.311562695 1 NA NA
36 0 0.7263222 0.39312999 -0.044115907 1 NA NA
37 1 NA 0.33592359 -0.657409991 1 NA NA
38 1 NA 0.80799798 0.159577214 1 NA NA
39 1 0.7945509 0.70399665 -0.460416933 1 NA NA
40 1 0.6355032 0.14770504 NA 1 NA NA
41 1 0.9939049 0.32976608 -0.248909867 1 NA NA
42 1 1.0690739 0.57875125 -0.609021545 1 NA NA
43 1 0.7009106 0.69765999 0.025471883 1 NA NA
44 1 0.7595403 0.92706981 0.066648592 1 NA NA
45 1 0.8356414 0.59881110 -0.276108719 1 NA NA
46 1 0.4929132 NA -0.179737577 1 NA NA
47 0 NA 0.57021551 0.181190937 1 NA NA
48 1 0.5363034 0.31297307 -0.453871693 1 NA NA
49 1 0.8494053 0.45752036 0.448629602 1 NA NA
50 0 0.6292812 0.76707228 -0.529811821 1 NA NA
51 1 0.9561312 0.79670238 -0.028304571 1 NA NA
52 1 0.9735411 0.31851588 -0.520318482 1 NA NA
53 1 0.7156259 0.27413726 0.171317619 1 NA NA
54 1 0.5184434 0.87099655 0.432732046 1 NA NA
55 0 0.7948965 0.14767954 -0.346286005 1 NA NA
56 1 0.5191792 0.72225832 -0.469375653 1 NA NA
57 1 0.9233108 0.91165899 0.031021711 1 NA NA
58 1 0.8025356 NA -0.118837515 1 NA NA
59 1 0.8546624 0.74875442 0.507769984 1 NA NA
60 0 0.8639819 0.57086552 0.271797031 1 NA NA
61 1 0.7521237 0.17368573 -0.124442204 1 NA NA
62 1 0.5590215 NA 0.277677389 1 NA NA
63 0 0.5972103 0.60538003 -0.102893730 1 NA NA
64 1 0.6071272 NA NA 1 NA NA
65 1 0.8837829 0.44987490 -0.678303052 1 NA NA
66 0 0.7775301 0.71105443 0.478880037 1 NA NA
67 0 NA 0.09500493 -0.428028760 1 NA NA
68 1 0.7857549 0.37292542 0.048119185 1 NA NA
69 0 0.9119262 0.41025328 0.216932805 1 NA NA
70 0 0.5816103 0.87473911 -0.234575269 1 NA NA
71 1 0.4886093 0.57325664 0.006827078 1 NA NA
72 1 NA 0.76227946 -0.456055171 1 NA NA
73 0 NA 0.56061854 0.346486708 1 NA NA
74 1 0.7328840 0.61145842 0.205092215 1 NA NA
75 1 0.7946099 NA -0.136596858 1 NA NA
76 0 0.7734810 0.23795025 -0.500179043 1 NA NA
77 0 0.5296147 0.28135640 0.527352086 1 NA NA
78 0 0.7723288 NA 0.022742250 1 NA NA
79 1 0.8079308 0.43010097 NA 1 NA NA
80 1 NA 0.30775746 -0.002032440 1 NA NA
81 1 NA 0.43379094 -0.154246160 1 NA NA
82 1 NA 0.70103825 0.140201825 1 NA NA
83 1 0.4544158 0.19501290 -0.141417121 1 NA NA
84 1 0.6482660 0.42336380 NA 1 NA NA
85 1 0.7272109 NA -0.021285339 1 NA NA
86 1 NA 0.49004839 -0.010196306 1 NA NA
87 1 0.6768061 NA -0.089747520 1 NA NA
88 0 0.8115758 0.71840773 -0.083699898 1 NA NA
89 1 NA 0.81565945 -0.044061996 1 NA NA
90 1 0.6408465 0.83308857 -0.209291697 1 NA NA
91 1 0.5917453 0.56239647 0.639036426 1 NA NA
92 1 0.7224845 NA 0.094698299 1 NA NA
93 1 0.4501596 NA -0.055510622 1 NA NA
94 1 0.5190455 NA -0.421318463 1 NA NA
95 1 0.7305821 0.73286310 0.125295503 1 NA NA
96 1 0.9696445 0.39788846 0.213084904 1 NA NA
97 1 0.7087457 NA -0.161914659 1 NA NA
98 1 0.9964080 0.81066470 -0.034767685 1 NA NA
99 1 NA 0.40892733 -0.320681689 1 NA NA
100 1 0.9296776 0.76834275 0.058192962 1 NA NA
C1
1 1.410531
2 1.434183
3 1.430994
4 1.453096
5 1.438344
6 1.453207
7 1.425176
8 1.437908
9 1.416911
10 1.448638
11 1.428375
12 1.450130
13 1.420545
14 1.423005
15 1.435902
16 1.423901
17 1.457208
18 1.414280
19 1.443383
20 1.434954
21 1.429499
22 1.441897
23 1.423713
24 1.435395
25 1.425944
26 1.437115
27 1.441326
28 1.422953
29 1.437797
30 1.472121
31 1.421782
32 1.457672
33 1.430842
34 1.431523
35 1.421395
36 1.434496
37 1.425383
38 1.421802
39 1.430094
40 1.447621
41 1.434797
42 1.446091
43 1.445306
44 1.448783
45 1.450617
46 1.415055
47 1.436590
48 1.433938
49 1.414941
50 1.421807
51 1.453203
52 1.452129
53 1.431510
54 1.430082
55 1.443492
56 1.436460
57 1.418119
58 1.434971
59 1.445599
60 1.437097
61 1.428360
62 1.440550
63 1.443014
64 1.424298
65 1.448823
66 1.425834
67 1.427102
68 1.414240
69 1.456218
70 1.470594
71 1.425058
72 1.432371
73 1.441656
74 1.434952
75 1.402860
76 1.453363
77 1.432909
78 1.435103
79 1.434462
80 1.434661
81 1.445881
82 1.442548
83 1.430097
84 1.430119
85 1.430315
86 1.437584
87 1.409738
88 1.422388
89 1.422509
90 1.439432
91 1.430175
92 1.418002
93 1.423812
94 1.423473
95 1.434412
96 1.450844
97 1.433371
98 1.444378
99 1.422523
100 1.410394
$m4b$spM_lvlone
center scale
B1 NA NA
L1mis 0.72862466 0.15772614
Be2 0.51799407 0.22974678
C2 -0.06490582 0.33317347
(Intercept) NA NA
abs(C1 - C2) 1.49900534 0.33421418
log(Be2) -0.78337703 0.54590062
C1 1.43410054 0.01299651
$m4b$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_gamma
[1] 0
$m4b$tau_reg_gamma
[1] 1e-04
$m4b$shape_tau_gamma
[1] 0.01
$m4b$rate_tau_gamma
[1] 0.01
$m4b$mu_reg_beta
[1] 0
$m4b$tau_reg_beta
[1] 1e-04
$m4b$shape_tau_beta
[1] 0.01
$m4b$rate_tau_beta
[1] 0.01
$m4b$mu_reg_binom
[1] 0
$m4b$tau_reg_binom
[1] 1e-04
$m5a1
$m5a1$M_lvlone
y B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 -4.76915977 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 -2.69277172 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 -1.17551547 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 -4.57464473 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 -2.20260004 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 -3.48995315 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 -0.44987258 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 -2.29588848 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 -4.49135812 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 -5.52545368 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 -4.16286741 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 -2.93455761 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 -0.04202496 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 -1.63149775 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 -0.97786151 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 -1.79100431 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 -6.26520032 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 -1.36028709 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 -1.15396597 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 -3.21707239 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 -1.59389898 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 -5.50335066 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 0.57290123 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 -8.22270323 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 -1.41364158 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 -6.28031574 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 -3.15624425 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 -3.55693639 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 -1.11821124 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 -2.82834175 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 -3.72259860 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 -1.75256656 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 -5.55044409 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 -7.45068147 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 -0.97491919 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 -2.98356481 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 -1.86039471 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 -7.28754607 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 -8.66234796 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 -4.16291375 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 -3.48250771 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 -7.27930410 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 -6.12866190 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 -4.96880803 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 -4.76746713 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 -1.91249177 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 -0.61884029 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 -0.20496175 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 -7.12636055 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 -6.23103837 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 -3.32561065 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 -2.95942339 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 -4.44915114 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 -0.81566463 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 -6.50029573 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 -2.74718050 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 -6.35015663 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 -2.69505883 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 -1.55660833 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 -3.76240209 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 -3.92885797 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 -1.72044748 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 -0.56602625 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 -4.42235015 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 -2.39122287 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 -0.81807247 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 -6.48196782 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 -1.37306273 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 -4.99886487 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 -5.82288217 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 -2.68234219 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 -3.96170442 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 -7.19573667 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 -5.08799713 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 -1.32967262 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 -2.56532332 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 -3.21002900 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 -3.40559790 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 -4.56223913 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 -2.04250454 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 -2.20378059 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 -3.37471317 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 -0.95345385 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 -4.89337660 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 -9.82258463 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 -4.51800734 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 -0.18662049 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 -2.87120881 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 1.29290150 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 -1.39497744 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 1.14575040 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 0.92801246 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 -2.59938157 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 -3.26905923 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 -3.26861434 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 -5.71017484 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 -3.76781806 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 -2.02677390 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 -2.96199765 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 -4.81129496 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5a1$spM_lvlone
center scale
y -3.34428345 2.2764951
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5a1$mu_reg_norm
[1] 0
$m5a1$tau_reg_norm
[1] 1e-04
$m5a1$shape_tau_norm
[1] 0.01
$m5a1$rate_tau_norm
[1] 0.01
$m5a1$mu_reg_binom
[1] 0
$m5a1$tau_reg_binom
[1] 1e-04
$m5a2
$m5a2$M_lvlone
y B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 -4.76915977 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 -2.69277172 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 -1.17551547 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 -4.57464473 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 -2.20260004 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 -3.48995315 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 -0.44987258 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 -2.29588848 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 -4.49135812 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 -5.52545368 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 -4.16286741 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 -2.93455761 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 -0.04202496 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 -1.63149775 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 -0.97786151 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 -1.79100431 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 -6.26520032 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 -1.36028709 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 -1.15396597 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 -3.21707239 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 -1.59389898 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 -5.50335066 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 0.57290123 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 -8.22270323 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 -1.41364158 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 -6.28031574 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 -3.15624425 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 -3.55693639 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 -1.11821124 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 -2.82834175 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 -3.72259860 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 -1.75256656 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 -5.55044409 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 -7.45068147 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 -0.97491919 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 -2.98356481 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 -1.86039471 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 -7.28754607 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 -8.66234796 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 -4.16291375 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 -3.48250771 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 -7.27930410 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 -6.12866190 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 -4.96880803 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 -4.76746713 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 -1.91249177 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 -0.61884029 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 -0.20496175 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 -7.12636055 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 -6.23103837 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 -3.32561065 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 -2.95942339 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 -4.44915114 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 -0.81566463 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 -6.50029573 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 -2.74718050 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 -6.35015663 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 -2.69505883 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 -1.55660833 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 -3.76240209 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 -3.92885797 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 -1.72044748 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 -0.56602625 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 -4.42235015 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 -2.39122287 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 -0.81807247 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 -6.48196782 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 -1.37306273 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 -4.99886487 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 -5.82288217 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 -2.68234219 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 -3.96170442 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 -7.19573667 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 -5.08799713 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 -1.32967262 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 -2.56532332 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 -3.21002900 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 -3.40559790 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 -4.56223913 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 -2.04250454 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 -2.20378059 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 -3.37471317 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 -0.95345385 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 -4.89337660 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 -9.82258463 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 -4.51800734 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 -0.18662049 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 -2.87120881 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 1.29290150 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 -1.39497744 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 1.14575040 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 0.92801246 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 -2.59938157 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 -3.26905923 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 -3.26861434 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 -5.71017484 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 -3.76781806 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 -2.02677390 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 -2.96199765 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 -4.81129496 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5a2$spM_lvlone
center scale
y -3.34428345 2.2764951
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5a2$mu_reg_norm
[1] 0
$m5a2$tau_reg_norm
[1] 1e-04
$m5a2$shape_tau_norm
[1] 0.01
$m5a2$rate_tau_norm
[1] 0.01
$m5a2$mu_reg_binom
[1] 0
$m5a2$tau_reg_binom
[1] 1e-04
$m5a3
$m5a3$M_lvlone
y B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 -4.76915977 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 -2.69277172 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 -1.17551547 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 -4.57464473 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 -2.20260004 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 -3.48995315 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 -0.44987258 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 -2.29588848 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 -4.49135812 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 -5.52545368 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 -4.16286741 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 -2.93455761 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 -0.04202496 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 -1.63149775 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 -0.97786151 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 -1.79100431 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 -6.26520032 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 -1.36028709 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 -1.15396597 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 -3.21707239 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 -1.59389898 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 -5.50335066 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 0.57290123 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 -8.22270323 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 -1.41364158 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 -6.28031574 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 -3.15624425 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 -3.55693639 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 -1.11821124 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 -2.82834175 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 -3.72259860 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 -1.75256656 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 -5.55044409 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 -7.45068147 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 -0.97491919 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 -2.98356481 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 -1.86039471 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 -7.28754607 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 -8.66234796 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 -4.16291375 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 -3.48250771 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 -7.27930410 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 -6.12866190 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 -4.96880803 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 -4.76746713 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 -1.91249177 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 -0.61884029 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 -0.20496175 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 -7.12636055 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 -6.23103837 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 -3.32561065 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 -2.95942339 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 -4.44915114 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 -0.81566463 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 -6.50029573 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 -2.74718050 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 -6.35015663 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 -2.69505883 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 -1.55660833 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 -3.76240209 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 -3.92885797 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 -1.72044748 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 -0.56602625 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 -4.42235015 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 -2.39122287 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 -0.81807247 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 -6.48196782 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 -1.37306273 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 -4.99886487 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 -5.82288217 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 -2.68234219 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 -3.96170442 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 -7.19573667 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 -5.08799713 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 -1.32967262 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 -2.56532332 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 -3.21002900 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 -3.40559790 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 -4.56223913 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 -2.04250454 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 -2.20378059 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 -3.37471317 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 -0.95345385 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 -4.89337660 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 -9.82258463 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 -4.51800734 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 -0.18662049 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 -2.87120881 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 1.29290150 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 -1.39497744 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 1.14575040 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 0.92801246 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 -2.59938157 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 -3.26905923 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 -3.26861434 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 -5.71017484 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 -3.76781806 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 -2.02677390 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 -2.96199765 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 -4.81129496 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5a3$spM_lvlone
center scale
y -3.34428345 2.2764951
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5a3$mu_reg_norm
[1] 0
$m5a3$tau_reg_norm
[1] 1e-04
$m5a3$shape_tau_norm
[1] 0.01
$m5a3$rate_tau_norm
[1] 0.01
$m5a3$mu_reg_binom
[1] 0
$m5a3$tau_reg_binom
[1] 1e-04
$m5b1
$m5b1$M_lvlone
B1 B2 C2 (Intercept) B21 C1 O1.L O1.Q O1.C
1 1 1 0.144065882 1 NA 1.410531 -0.2236068 -0.5 0.6708204
2 1 1 0.032778478 1 NA 1.434183 0.6708204 0.5 0.2236068
3 1 1 0.343008492 1 NA 1.430994 0.2236068 -0.5 -0.6708204
4 1 1 -0.361887858 1 NA 1.453096 -0.2236068 -0.5 0.6708204
5 1 1 -0.389600647 1 NA 1.438344 0.2236068 -0.5 -0.6708204
6 1 NA -0.205306841 1 NA 1.453207 -0.6708204 0.5 -0.2236068
7 0 1 0.079434830 1 NA 1.425176 0.2236068 -0.5 -0.6708204
8 0 1 -0.331246757 1 NA 1.437908 0.6708204 0.5 0.2236068
9 1 1 -0.329638800 1 NA 1.416911 0.6708204 0.5 0.2236068
10 1 NA 0.167597533 1 NA 1.448638 -0.2236068 -0.5 0.6708204
11 1 1 0.860207989 1 NA 1.428375 -0.6708204 0.5 -0.2236068
12 0 1 0.022730640 1 NA 1.450130 0.2236068 -0.5 -0.6708204
13 1 1 0.217171172 1 NA 1.420545 0.2236068 -0.5 -0.6708204
14 0 1 -0.403002412 1 NA 1.423005 -0.6708204 0.5 -0.2236068
15 1 1 0.087369742 1 NA 1.435902 -0.6708204 0.5 -0.2236068
16 1 1 -0.183870429 1 NA 1.423901 0.6708204 0.5 0.2236068
17 1 1 -0.194577002 1 NA 1.457208 -0.2236068 -0.5 0.6708204
18 1 1 -0.349718516 1 NA 1.414280 0.2236068 -0.5 -0.6708204
19 1 NA -0.508781244 1 NA 1.443383 0.6708204 0.5 0.2236068
20 1 NA 0.494883111 1 NA 1.434954 -0.6708204 0.5 -0.2236068
21 1 1 0.258041067 1 NA 1.429499 0.2236068 -0.5 -0.6708204
22 1 NA -0.922621989 1 NA 1.441897 0.6708204 0.5 0.2236068
23 1 NA 0.431254949 1 NA 1.423713 0.6708204 0.5 0.2236068
24 1 1 -0.294218881 1 NA 1.435395 -0.2236068 -0.5 0.6708204
25 0 NA -0.425548895 1 NA 1.425944 -0.6708204 0.5 -0.2236068
26 1 NA 0.057176054 1 NA 1.437115 0.2236068 -0.5 -0.6708204
27 1 1 0.289090158 1 NA 1.441326 0.6708204 0.5 0.2236068
28 1 1 -0.473079489 1 NA 1.422953 -0.6708204 0.5 -0.2236068
29 1 1 -0.385664863 1 NA 1.437797 0.6708204 0.5 0.2236068
30 0 1 -0.154780107 1 NA 1.472121 0.6708204 0.5 0.2236068
31 0 NA 0.100536296 1 NA 1.421782 -0.2236068 -0.5 0.6708204
32 1 1 0.634791958 1 NA 1.457672 0.2236068 -0.5 -0.6708204
33 1 1 -0.387252617 1 NA 1.430842 0.2236068 -0.5 -0.6708204
34 1 0 -0.181741088 1 NA 1.431523 -0.6708204 0.5 -0.2236068
35 1 1 -0.311562695 1 NA 1.421395 -0.6708204 0.5 -0.2236068
36 0 1 -0.044115907 1 NA 1.434496 0.6708204 0.5 0.2236068
37 1 1 -0.657409991 1 NA 1.425383 0.6708204 0.5 0.2236068
38 1 NA 0.159577214 1 NA 1.421802 0.6708204 0.5 0.2236068
39 1 1 -0.460416933 1 NA 1.430094 -0.6708204 0.5 -0.2236068
40 1 NA NA 1 NA 1.447621 -0.2236068 -0.5 0.6708204
41 1 1 -0.248909867 1 NA 1.434797 -0.6708204 0.5 -0.2236068
42 1 1 -0.609021545 1 NA 1.446091 -0.6708204 0.5 -0.2236068
43 1 1 0.025471883 1 NA 1.445306 -0.2236068 -0.5 0.6708204
44 1 1 0.066648592 1 NA 1.448783 -0.2236068 -0.5 0.6708204
45 1 1 -0.276108719 1 NA 1.450617 -0.6708204 0.5 -0.2236068
46 1 1 -0.179737577 1 NA 1.415055 -0.6708204 0.5 -0.2236068
47 0 0 0.181190937 1 NA 1.436590 0.6708204 0.5 0.2236068
48 1 1 -0.453871693 1 NA 1.433938 0.6708204 0.5 0.2236068
49 1 0 0.448629602 1 NA 1.414941 -0.2236068 -0.5 0.6708204
50 0 1 -0.529811821 1 NA 1.421807 -0.2236068 -0.5 0.6708204
51 1 1 -0.028304571 1 NA 1.453203 -0.6708204 0.5 -0.2236068
52 1 1 -0.520318482 1 NA 1.452129 0.2236068 -0.5 -0.6708204
53 1 1 0.171317619 1 NA 1.431510 -0.6708204 0.5 -0.2236068
54 1 1 0.432732046 1 NA 1.430082 0.2236068 -0.5 -0.6708204
55 0 1 -0.346286005 1 NA 1.443492 -0.2236068 -0.5 0.6708204
56 1 1 -0.469375653 1 NA 1.436460 0.6708204 0.5 0.2236068
57 1 1 0.031021711 1 NA 1.418119 -0.2236068 -0.5 0.6708204
58 1 NA -0.118837515 1 NA 1.434971 -0.6708204 0.5 -0.2236068
59 1 1 0.507769984 1 NA 1.445599 -0.6708204 0.5 -0.2236068
60 0 NA 0.271797031 1 NA 1.437097 0.6708204 0.5 0.2236068
61 1 1 -0.124442204 1 NA 1.428360 -0.2236068 -0.5 0.6708204
62 1 1 0.277677389 1 NA 1.440550 0.6708204 0.5 0.2236068
63 0 1 -0.102893730 1 NA 1.443014 0.2236068 -0.5 -0.6708204
64 1 1 NA 1 NA 1.424298 -0.2236068 -0.5 0.6708204
65 1 1 -0.678303052 1 NA 1.448823 0.2236068 -0.5 -0.6708204
66 0 0 0.478880037 1 NA 1.425834 0.2236068 -0.5 -0.6708204
67 0 1 -0.428028760 1 NA 1.427102 -0.2236068 -0.5 0.6708204
68 1 1 0.048119185 1 NA 1.414240 -0.6708204 0.5 -0.2236068
69 0 NA 0.216932805 1 NA 1.456218 -0.6708204 0.5 -0.2236068
70 0 1 -0.234575269 1 NA 1.470594 -0.6708204 0.5 -0.2236068
71 1 1 0.006827078 1 NA 1.425058 -0.6708204 0.5 -0.2236068
72 1 1 -0.456055171 1 NA 1.432371 0.2236068 -0.5 -0.6708204
73 0 1 0.346486708 1 NA 1.441656 -0.2236068 -0.5 0.6708204
74 1 1 0.205092215 1 NA 1.434952 -0.2236068 -0.5 0.6708204
75 1 1 -0.136596858 1 NA 1.402860 0.2236068 -0.5 -0.6708204
76 0 1 -0.500179043 1 NA 1.453363 0.2236068 -0.5 -0.6708204
77 0 1 0.527352086 1 NA 1.432909 0.6708204 0.5 0.2236068
78 0 1 0.022742250 1 NA 1.435103 0.2236068 -0.5 -0.6708204
79 1 1 NA 1 NA 1.434462 -0.2236068 -0.5 0.6708204
80 1 1 -0.002032440 1 NA 1.434661 -0.2236068 -0.5 0.6708204
81 1 0 -0.154246160 1 NA 1.445881 0.2236068 -0.5 -0.6708204
82 1 NA 0.140201825 1 NA 1.442548 -0.6708204 0.5 -0.2236068
83 1 1 -0.141417121 1 NA 1.430097 0.2236068 -0.5 -0.6708204
84 1 1 NA 1 NA 1.430119 -0.2236068 -0.5 0.6708204
85 1 1 -0.021285339 1 NA 1.430315 -0.2236068 -0.5 0.6708204
86 1 NA -0.010196306 1 NA 1.437584 0.6708204 0.5 0.2236068
87 1 NA -0.089747520 1 NA 1.409738 0.2236068 -0.5 -0.6708204
88 0 1 -0.083699898 1 NA 1.422388 -0.2236068 -0.5 0.6708204
89 1 1 -0.044061996 1 NA 1.422509 0.2236068 -0.5 -0.6708204
90 1 1 -0.209291697 1 NA 1.439432 0.2236068 -0.5 -0.6708204
91 1 1 0.639036426 1 NA 1.430175 0.6708204 0.5 0.2236068
92 1 NA 0.094698299 1 NA 1.418002 -0.6708204 0.5 -0.2236068
93 1 1 -0.055510622 1 NA 1.423812 0.6708204 0.5 0.2236068
94 1 1 -0.421318463 1 NA 1.423473 -0.6708204 0.5 -0.2236068
95 1 1 0.125295503 1 NA 1.434412 -0.6708204 0.5 -0.2236068
96 1 1 0.213084904 1 NA 1.450844 0.2236068 -0.5 -0.6708204
97 1 NA -0.161914659 1 NA 1.433371 -0.6708204 0.5 -0.2236068
98 1 1 -0.034767685 1 NA 1.444378 0.2236068 -0.5 -0.6708204
99 1 0 -0.320681689 1 NA 1.422523 0.2236068 -0.5 -0.6708204
100 1 NA 0.058192962 1 NA 1.410394 0.2236068 -0.5 -0.6708204
$m5b1$spM_lvlone
center scale
B1 NA NA
B2 NA NA
C2 -0.06490582 0.33317347
(Intercept) NA NA
B21 NA NA
C1 1.43410054 0.01299651
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5b1$mu_reg_norm
[1] 0
$m5b1$tau_reg_norm
[1] 1e-04
$m5b1$shape_tau_norm
[1] 0.01
$m5b1$rate_tau_norm
[1] 0.01
$m5b1$mu_reg_binom
[1] 0
$m5b1$tau_reg_binom
[1] 1e-04
$m5b2
$m5b2$M_lvlone
B1 B2 C2 (Intercept) B21 C1 O1.L O1.Q O1.C
1 1 1 0.144065882 1 NA 1.410531 -0.2236068 -0.5 0.6708204
2 1 1 0.032778478 1 NA 1.434183 0.6708204 0.5 0.2236068
3 1 1 0.343008492 1 NA 1.430994 0.2236068 -0.5 -0.6708204
4 1 1 -0.361887858 1 NA 1.453096 -0.2236068 -0.5 0.6708204
5 1 1 -0.389600647 1 NA 1.438344 0.2236068 -0.5 -0.6708204
6 1 NA -0.205306841 1 NA 1.453207 -0.6708204 0.5 -0.2236068
7 0 1 0.079434830 1 NA 1.425176 0.2236068 -0.5 -0.6708204
8 0 1 -0.331246757 1 NA 1.437908 0.6708204 0.5 0.2236068
9 1 1 -0.329638800 1 NA 1.416911 0.6708204 0.5 0.2236068
10 1 NA 0.167597533 1 NA 1.448638 -0.2236068 -0.5 0.6708204
11 1 1 0.860207989 1 NA 1.428375 -0.6708204 0.5 -0.2236068
12 0 1 0.022730640 1 NA 1.450130 0.2236068 -0.5 -0.6708204
13 1 1 0.217171172 1 NA 1.420545 0.2236068 -0.5 -0.6708204
14 0 1 -0.403002412 1 NA 1.423005 -0.6708204 0.5 -0.2236068
15 1 1 0.087369742 1 NA 1.435902 -0.6708204 0.5 -0.2236068
16 1 1 -0.183870429 1 NA 1.423901 0.6708204 0.5 0.2236068
17 1 1 -0.194577002 1 NA 1.457208 -0.2236068 -0.5 0.6708204
18 1 1 -0.349718516 1 NA 1.414280 0.2236068 -0.5 -0.6708204
19 1 NA -0.508781244 1 NA 1.443383 0.6708204 0.5 0.2236068
20 1 NA 0.494883111 1 NA 1.434954 -0.6708204 0.5 -0.2236068
21 1 1 0.258041067 1 NA 1.429499 0.2236068 -0.5 -0.6708204
22 1 NA -0.922621989 1 NA 1.441897 0.6708204 0.5 0.2236068
23 1 NA 0.431254949 1 NA 1.423713 0.6708204 0.5 0.2236068
24 1 1 -0.294218881 1 NA 1.435395 -0.2236068 -0.5 0.6708204
25 0 NA -0.425548895 1 NA 1.425944 -0.6708204 0.5 -0.2236068
26 1 NA 0.057176054 1 NA 1.437115 0.2236068 -0.5 -0.6708204
27 1 1 0.289090158 1 NA 1.441326 0.6708204 0.5 0.2236068
28 1 1 -0.473079489 1 NA 1.422953 -0.6708204 0.5 -0.2236068
29 1 1 -0.385664863 1 NA 1.437797 0.6708204 0.5 0.2236068
30 0 1 -0.154780107 1 NA 1.472121 0.6708204 0.5 0.2236068
31 0 NA 0.100536296 1 NA 1.421782 -0.2236068 -0.5 0.6708204
32 1 1 0.634791958 1 NA 1.457672 0.2236068 -0.5 -0.6708204
33 1 1 -0.387252617 1 NA 1.430842 0.2236068 -0.5 -0.6708204
34 1 0 -0.181741088 1 NA 1.431523 -0.6708204 0.5 -0.2236068
35 1 1 -0.311562695 1 NA 1.421395 -0.6708204 0.5 -0.2236068
36 0 1 -0.044115907 1 NA 1.434496 0.6708204 0.5 0.2236068
37 1 1 -0.657409991 1 NA 1.425383 0.6708204 0.5 0.2236068
38 1 NA 0.159577214 1 NA 1.421802 0.6708204 0.5 0.2236068
39 1 1 -0.460416933 1 NA 1.430094 -0.6708204 0.5 -0.2236068
40 1 NA NA 1 NA 1.447621 -0.2236068 -0.5 0.6708204
41 1 1 -0.248909867 1 NA 1.434797 -0.6708204 0.5 -0.2236068
42 1 1 -0.609021545 1 NA 1.446091 -0.6708204 0.5 -0.2236068
43 1 1 0.025471883 1 NA 1.445306 -0.2236068 -0.5 0.6708204
44 1 1 0.066648592 1 NA 1.448783 -0.2236068 -0.5 0.6708204
45 1 1 -0.276108719 1 NA 1.450617 -0.6708204 0.5 -0.2236068
46 1 1 -0.179737577 1 NA 1.415055 -0.6708204 0.5 -0.2236068
47 0 0 0.181190937 1 NA 1.436590 0.6708204 0.5 0.2236068
48 1 1 -0.453871693 1 NA 1.433938 0.6708204 0.5 0.2236068
49 1 0 0.448629602 1 NA 1.414941 -0.2236068 -0.5 0.6708204
50 0 1 -0.529811821 1 NA 1.421807 -0.2236068 -0.5 0.6708204
51 1 1 -0.028304571 1 NA 1.453203 -0.6708204 0.5 -0.2236068
52 1 1 -0.520318482 1 NA 1.452129 0.2236068 -0.5 -0.6708204
53 1 1 0.171317619 1 NA 1.431510 -0.6708204 0.5 -0.2236068
54 1 1 0.432732046 1 NA 1.430082 0.2236068 -0.5 -0.6708204
55 0 1 -0.346286005 1 NA 1.443492 -0.2236068 -0.5 0.6708204
56 1 1 -0.469375653 1 NA 1.436460 0.6708204 0.5 0.2236068
57 1 1 0.031021711 1 NA 1.418119 -0.2236068 -0.5 0.6708204
58 1 NA -0.118837515 1 NA 1.434971 -0.6708204 0.5 -0.2236068
59 1 1 0.507769984 1 NA 1.445599 -0.6708204 0.5 -0.2236068
60 0 NA 0.271797031 1 NA 1.437097 0.6708204 0.5 0.2236068
61 1 1 -0.124442204 1 NA 1.428360 -0.2236068 -0.5 0.6708204
62 1 1 0.277677389 1 NA 1.440550 0.6708204 0.5 0.2236068
63 0 1 -0.102893730 1 NA 1.443014 0.2236068 -0.5 -0.6708204
64 1 1 NA 1 NA 1.424298 -0.2236068 -0.5 0.6708204
65 1 1 -0.678303052 1 NA 1.448823 0.2236068 -0.5 -0.6708204
66 0 0 0.478880037 1 NA 1.425834 0.2236068 -0.5 -0.6708204
67 0 1 -0.428028760 1 NA 1.427102 -0.2236068 -0.5 0.6708204
68 1 1 0.048119185 1 NA 1.414240 -0.6708204 0.5 -0.2236068
69 0 NA 0.216932805 1 NA 1.456218 -0.6708204 0.5 -0.2236068
70 0 1 -0.234575269 1 NA 1.470594 -0.6708204 0.5 -0.2236068
71 1 1 0.006827078 1 NA 1.425058 -0.6708204 0.5 -0.2236068
72 1 1 -0.456055171 1 NA 1.432371 0.2236068 -0.5 -0.6708204
73 0 1 0.346486708 1 NA 1.441656 -0.2236068 -0.5 0.6708204
74 1 1 0.205092215 1 NA 1.434952 -0.2236068 -0.5 0.6708204
75 1 1 -0.136596858 1 NA 1.402860 0.2236068 -0.5 -0.6708204
76 0 1 -0.500179043 1 NA 1.453363 0.2236068 -0.5 -0.6708204
77 0 1 0.527352086 1 NA 1.432909 0.6708204 0.5 0.2236068
78 0 1 0.022742250 1 NA 1.435103 0.2236068 -0.5 -0.6708204
79 1 1 NA 1 NA 1.434462 -0.2236068 -0.5 0.6708204
80 1 1 -0.002032440 1 NA 1.434661 -0.2236068 -0.5 0.6708204
81 1 0 -0.154246160 1 NA 1.445881 0.2236068 -0.5 -0.6708204
82 1 NA 0.140201825 1 NA 1.442548 -0.6708204 0.5 -0.2236068
83 1 1 -0.141417121 1 NA 1.430097 0.2236068 -0.5 -0.6708204
84 1 1 NA 1 NA 1.430119 -0.2236068 -0.5 0.6708204
85 1 1 -0.021285339 1 NA 1.430315 -0.2236068 -0.5 0.6708204
86 1 NA -0.010196306 1 NA 1.437584 0.6708204 0.5 0.2236068
87 1 NA -0.089747520 1 NA 1.409738 0.2236068 -0.5 -0.6708204
88 0 1 -0.083699898 1 NA 1.422388 -0.2236068 -0.5 0.6708204
89 1 1 -0.044061996 1 NA 1.422509 0.2236068 -0.5 -0.6708204
90 1 1 -0.209291697 1 NA 1.439432 0.2236068 -0.5 -0.6708204
91 1 1 0.639036426 1 NA 1.430175 0.6708204 0.5 0.2236068
92 1 NA 0.094698299 1 NA 1.418002 -0.6708204 0.5 -0.2236068
93 1 1 -0.055510622 1 NA 1.423812 0.6708204 0.5 0.2236068
94 1 1 -0.421318463 1 NA 1.423473 -0.6708204 0.5 -0.2236068
95 1 1 0.125295503 1 NA 1.434412 -0.6708204 0.5 -0.2236068
96 1 1 0.213084904 1 NA 1.450844 0.2236068 -0.5 -0.6708204
97 1 NA -0.161914659 1 NA 1.433371 -0.6708204 0.5 -0.2236068
98 1 1 -0.034767685 1 NA 1.444378 0.2236068 -0.5 -0.6708204
99 1 0 -0.320681689 1 NA 1.422523 0.2236068 -0.5 -0.6708204
100 1 NA 0.058192962 1 NA 1.410394 0.2236068 -0.5 -0.6708204
$m5b2$spM_lvlone
center scale
B1 NA NA
B2 NA NA
C2 -0.06490582 0.33317347
(Intercept) NA NA
B21 NA NA
C1 1.43410054 0.01299651
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5b2$mu_reg_norm
[1] 0
$m5b2$tau_reg_norm
[1] 1e-04
$m5b2$shape_tau_norm
[1] 0.01
$m5b2$rate_tau_norm
[1] 0.01
$m5b2$mu_reg_binom
[1] 0
$m5b2$tau_reg_binom
[1] 1e-04
$m5b3
$m5b3$M_lvlone
B1 B2 C2 (Intercept) B21 C1 O1.L O1.Q O1.C
1 1 1 0.144065882 1 NA 1.410531 -0.2236068 -0.5 0.6708204
2 1 1 0.032778478 1 NA 1.434183 0.6708204 0.5 0.2236068
3 1 1 0.343008492 1 NA 1.430994 0.2236068 -0.5 -0.6708204
4 1 1 -0.361887858 1 NA 1.453096 -0.2236068 -0.5 0.6708204
5 1 1 -0.389600647 1 NA 1.438344 0.2236068 -0.5 -0.6708204
6 1 NA -0.205306841 1 NA 1.453207 -0.6708204 0.5 -0.2236068
7 0 1 0.079434830 1 NA 1.425176 0.2236068 -0.5 -0.6708204
8 0 1 -0.331246757 1 NA 1.437908 0.6708204 0.5 0.2236068
9 1 1 -0.329638800 1 NA 1.416911 0.6708204 0.5 0.2236068
10 1 NA 0.167597533 1 NA 1.448638 -0.2236068 -0.5 0.6708204
11 1 1 0.860207989 1 NA 1.428375 -0.6708204 0.5 -0.2236068
12 0 1 0.022730640 1 NA 1.450130 0.2236068 -0.5 -0.6708204
13 1 1 0.217171172 1 NA 1.420545 0.2236068 -0.5 -0.6708204
14 0 1 -0.403002412 1 NA 1.423005 -0.6708204 0.5 -0.2236068
15 1 1 0.087369742 1 NA 1.435902 -0.6708204 0.5 -0.2236068
16 1 1 -0.183870429 1 NA 1.423901 0.6708204 0.5 0.2236068
17 1 1 -0.194577002 1 NA 1.457208 -0.2236068 -0.5 0.6708204
18 1 1 -0.349718516 1 NA 1.414280 0.2236068 -0.5 -0.6708204
19 1 NA -0.508781244 1 NA 1.443383 0.6708204 0.5 0.2236068
20 1 NA 0.494883111 1 NA 1.434954 -0.6708204 0.5 -0.2236068
21 1 1 0.258041067 1 NA 1.429499 0.2236068 -0.5 -0.6708204
22 1 NA -0.922621989 1 NA 1.441897 0.6708204 0.5 0.2236068
23 1 NA 0.431254949 1 NA 1.423713 0.6708204 0.5 0.2236068
24 1 1 -0.294218881 1 NA 1.435395 -0.2236068 -0.5 0.6708204
25 0 NA -0.425548895 1 NA 1.425944 -0.6708204 0.5 -0.2236068
26 1 NA 0.057176054 1 NA 1.437115 0.2236068 -0.5 -0.6708204
27 1 1 0.289090158 1 NA 1.441326 0.6708204 0.5 0.2236068
28 1 1 -0.473079489 1 NA 1.422953 -0.6708204 0.5 -0.2236068
29 1 1 -0.385664863 1 NA 1.437797 0.6708204 0.5 0.2236068
30 0 1 -0.154780107 1 NA 1.472121 0.6708204 0.5 0.2236068
31 0 NA 0.100536296 1 NA 1.421782 -0.2236068 -0.5 0.6708204
32 1 1 0.634791958 1 NA 1.457672 0.2236068 -0.5 -0.6708204
33 1 1 -0.387252617 1 NA 1.430842 0.2236068 -0.5 -0.6708204
34 1 0 -0.181741088 1 NA 1.431523 -0.6708204 0.5 -0.2236068
35 1 1 -0.311562695 1 NA 1.421395 -0.6708204 0.5 -0.2236068
36 0 1 -0.044115907 1 NA 1.434496 0.6708204 0.5 0.2236068
37 1 1 -0.657409991 1 NA 1.425383 0.6708204 0.5 0.2236068
38 1 NA 0.159577214 1 NA 1.421802 0.6708204 0.5 0.2236068
39 1 1 -0.460416933 1 NA 1.430094 -0.6708204 0.5 -0.2236068
40 1 NA NA 1 NA 1.447621 -0.2236068 -0.5 0.6708204
41 1 1 -0.248909867 1 NA 1.434797 -0.6708204 0.5 -0.2236068
42 1 1 -0.609021545 1 NA 1.446091 -0.6708204 0.5 -0.2236068
43 1 1 0.025471883 1 NA 1.445306 -0.2236068 -0.5 0.6708204
44 1 1 0.066648592 1 NA 1.448783 -0.2236068 -0.5 0.6708204
45 1 1 -0.276108719 1 NA 1.450617 -0.6708204 0.5 -0.2236068
46 1 1 -0.179737577 1 NA 1.415055 -0.6708204 0.5 -0.2236068
47 0 0 0.181190937 1 NA 1.436590 0.6708204 0.5 0.2236068
48 1 1 -0.453871693 1 NA 1.433938 0.6708204 0.5 0.2236068
49 1 0 0.448629602 1 NA 1.414941 -0.2236068 -0.5 0.6708204
50 0 1 -0.529811821 1 NA 1.421807 -0.2236068 -0.5 0.6708204
51 1 1 -0.028304571 1 NA 1.453203 -0.6708204 0.5 -0.2236068
52 1 1 -0.520318482 1 NA 1.452129 0.2236068 -0.5 -0.6708204
53 1 1 0.171317619 1 NA 1.431510 -0.6708204 0.5 -0.2236068
54 1 1 0.432732046 1 NA 1.430082 0.2236068 -0.5 -0.6708204
55 0 1 -0.346286005 1 NA 1.443492 -0.2236068 -0.5 0.6708204
56 1 1 -0.469375653 1 NA 1.436460 0.6708204 0.5 0.2236068
57 1 1 0.031021711 1 NA 1.418119 -0.2236068 -0.5 0.6708204
58 1 NA -0.118837515 1 NA 1.434971 -0.6708204 0.5 -0.2236068
59 1 1 0.507769984 1 NA 1.445599 -0.6708204 0.5 -0.2236068
60 0 NA 0.271797031 1 NA 1.437097 0.6708204 0.5 0.2236068
61 1 1 -0.124442204 1 NA 1.428360 -0.2236068 -0.5 0.6708204
62 1 1 0.277677389 1 NA 1.440550 0.6708204 0.5 0.2236068
63 0 1 -0.102893730 1 NA 1.443014 0.2236068 -0.5 -0.6708204
64 1 1 NA 1 NA 1.424298 -0.2236068 -0.5 0.6708204
65 1 1 -0.678303052 1 NA 1.448823 0.2236068 -0.5 -0.6708204
66 0 0 0.478880037 1 NA 1.425834 0.2236068 -0.5 -0.6708204
67 0 1 -0.428028760 1 NA 1.427102 -0.2236068 -0.5 0.6708204
68 1 1 0.048119185 1 NA 1.414240 -0.6708204 0.5 -0.2236068
69 0 NA 0.216932805 1 NA 1.456218 -0.6708204 0.5 -0.2236068
70 0 1 -0.234575269 1 NA 1.470594 -0.6708204 0.5 -0.2236068
71 1 1 0.006827078 1 NA 1.425058 -0.6708204 0.5 -0.2236068
72 1 1 -0.456055171 1 NA 1.432371 0.2236068 -0.5 -0.6708204
73 0 1 0.346486708 1 NA 1.441656 -0.2236068 -0.5 0.6708204
74 1 1 0.205092215 1 NA 1.434952 -0.2236068 -0.5 0.6708204
75 1 1 -0.136596858 1 NA 1.402860 0.2236068 -0.5 -0.6708204
76 0 1 -0.500179043 1 NA 1.453363 0.2236068 -0.5 -0.6708204
77 0 1 0.527352086 1 NA 1.432909 0.6708204 0.5 0.2236068
78 0 1 0.022742250 1 NA 1.435103 0.2236068 -0.5 -0.6708204
79 1 1 NA 1 NA 1.434462 -0.2236068 -0.5 0.6708204
80 1 1 -0.002032440 1 NA 1.434661 -0.2236068 -0.5 0.6708204
81 1 0 -0.154246160 1 NA 1.445881 0.2236068 -0.5 -0.6708204
82 1 NA 0.140201825 1 NA 1.442548 -0.6708204 0.5 -0.2236068
83 1 1 -0.141417121 1 NA 1.430097 0.2236068 -0.5 -0.6708204
84 1 1 NA 1 NA 1.430119 -0.2236068 -0.5 0.6708204
85 1 1 -0.021285339 1 NA 1.430315 -0.2236068 -0.5 0.6708204
86 1 NA -0.010196306 1 NA 1.437584 0.6708204 0.5 0.2236068
87 1 NA -0.089747520 1 NA 1.409738 0.2236068 -0.5 -0.6708204
88 0 1 -0.083699898 1 NA 1.422388 -0.2236068 -0.5 0.6708204
89 1 1 -0.044061996 1 NA 1.422509 0.2236068 -0.5 -0.6708204
90 1 1 -0.209291697 1 NA 1.439432 0.2236068 -0.5 -0.6708204
91 1 1 0.639036426 1 NA 1.430175 0.6708204 0.5 0.2236068
92 1 NA 0.094698299 1 NA 1.418002 -0.6708204 0.5 -0.2236068
93 1 1 -0.055510622 1 NA 1.423812 0.6708204 0.5 0.2236068
94 1 1 -0.421318463 1 NA 1.423473 -0.6708204 0.5 -0.2236068
95 1 1 0.125295503 1 NA 1.434412 -0.6708204 0.5 -0.2236068
96 1 1 0.213084904 1 NA 1.450844 0.2236068 -0.5 -0.6708204
97 1 NA -0.161914659 1 NA 1.433371 -0.6708204 0.5 -0.2236068
98 1 1 -0.034767685 1 NA 1.444378 0.2236068 -0.5 -0.6708204
99 1 0 -0.320681689 1 NA 1.422523 0.2236068 -0.5 -0.6708204
100 1 NA 0.058192962 1 NA 1.410394 0.2236068 -0.5 -0.6708204
$m5b3$spM_lvlone
center scale
B1 NA NA
B2 NA NA
C2 -0.06490582 0.33317347
(Intercept) NA NA
B21 NA NA
C1 1.43410054 0.01299651
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5b3$mu_reg_norm
[1] 0
$m5b3$tau_reg_norm
[1] 1e-04
$m5b3$shape_tau_norm
[1] 0.01
$m5b3$rate_tau_norm
[1] 0.01
$m5b3$mu_reg_binom
[1] 0
$m5b3$tau_reg_binom
[1] 1e-04
$m5b4
$m5b4$M_lvlone
B1 B2 C2 (Intercept) B21 C1 O1.L O1.Q O1.C
1 1 1 0.144065882 1 NA 1.410531 -0.2236068 -0.5 0.6708204
2 1 1 0.032778478 1 NA 1.434183 0.6708204 0.5 0.2236068
3 1 1 0.343008492 1 NA 1.430994 0.2236068 -0.5 -0.6708204
4 1 1 -0.361887858 1 NA 1.453096 -0.2236068 -0.5 0.6708204
5 1 1 -0.389600647 1 NA 1.438344 0.2236068 -0.5 -0.6708204
6 1 NA -0.205306841 1 NA 1.453207 -0.6708204 0.5 -0.2236068
7 0 1 0.079434830 1 NA 1.425176 0.2236068 -0.5 -0.6708204
8 0 1 -0.331246757 1 NA 1.437908 0.6708204 0.5 0.2236068
9 1 1 -0.329638800 1 NA 1.416911 0.6708204 0.5 0.2236068
10 1 NA 0.167597533 1 NA 1.448638 -0.2236068 -0.5 0.6708204
11 1 1 0.860207989 1 NA 1.428375 -0.6708204 0.5 -0.2236068
12 0 1 0.022730640 1 NA 1.450130 0.2236068 -0.5 -0.6708204
13 1 1 0.217171172 1 NA 1.420545 0.2236068 -0.5 -0.6708204
14 0 1 -0.403002412 1 NA 1.423005 -0.6708204 0.5 -0.2236068
15 1 1 0.087369742 1 NA 1.435902 -0.6708204 0.5 -0.2236068
16 1 1 -0.183870429 1 NA 1.423901 0.6708204 0.5 0.2236068
17 1 1 -0.194577002 1 NA 1.457208 -0.2236068 -0.5 0.6708204
18 1 1 -0.349718516 1 NA 1.414280 0.2236068 -0.5 -0.6708204
19 1 NA -0.508781244 1 NA 1.443383 0.6708204 0.5 0.2236068
20 1 NA 0.494883111 1 NA 1.434954 -0.6708204 0.5 -0.2236068
21 1 1 0.258041067 1 NA 1.429499 0.2236068 -0.5 -0.6708204
22 1 NA -0.922621989 1 NA 1.441897 0.6708204 0.5 0.2236068
23 1 NA 0.431254949 1 NA 1.423713 0.6708204 0.5 0.2236068
24 1 1 -0.294218881 1 NA 1.435395 -0.2236068 -0.5 0.6708204
25 0 NA -0.425548895 1 NA 1.425944 -0.6708204 0.5 -0.2236068
26 1 NA 0.057176054 1 NA 1.437115 0.2236068 -0.5 -0.6708204
27 1 1 0.289090158 1 NA 1.441326 0.6708204 0.5 0.2236068
28 1 1 -0.473079489 1 NA 1.422953 -0.6708204 0.5 -0.2236068
29 1 1 -0.385664863 1 NA 1.437797 0.6708204 0.5 0.2236068
30 0 1 -0.154780107 1 NA 1.472121 0.6708204 0.5 0.2236068
31 0 NA 0.100536296 1 NA 1.421782 -0.2236068 -0.5 0.6708204
32 1 1 0.634791958 1 NA 1.457672 0.2236068 -0.5 -0.6708204
33 1 1 -0.387252617 1 NA 1.430842 0.2236068 -0.5 -0.6708204
34 1 0 -0.181741088 1 NA 1.431523 -0.6708204 0.5 -0.2236068
35 1 1 -0.311562695 1 NA 1.421395 -0.6708204 0.5 -0.2236068
36 0 1 -0.044115907 1 NA 1.434496 0.6708204 0.5 0.2236068
37 1 1 -0.657409991 1 NA 1.425383 0.6708204 0.5 0.2236068
38 1 NA 0.159577214 1 NA 1.421802 0.6708204 0.5 0.2236068
39 1 1 -0.460416933 1 NA 1.430094 -0.6708204 0.5 -0.2236068
40 1 NA NA 1 NA 1.447621 -0.2236068 -0.5 0.6708204
41 1 1 -0.248909867 1 NA 1.434797 -0.6708204 0.5 -0.2236068
42 1 1 -0.609021545 1 NA 1.446091 -0.6708204 0.5 -0.2236068
43 1 1 0.025471883 1 NA 1.445306 -0.2236068 -0.5 0.6708204
44 1 1 0.066648592 1 NA 1.448783 -0.2236068 -0.5 0.6708204
45 1 1 -0.276108719 1 NA 1.450617 -0.6708204 0.5 -0.2236068
46 1 1 -0.179737577 1 NA 1.415055 -0.6708204 0.5 -0.2236068
47 0 0 0.181190937 1 NA 1.436590 0.6708204 0.5 0.2236068
48 1 1 -0.453871693 1 NA 1.433938 0.6708204 0.5 0.2236068
49 1 0 0.448629602 1 NA 1.414941 -0.2236068 -0.5 0.6708204
50 0 1 -0.529811821 1 NA 1.421807 -0.2236068 -0.5 0.6708204
51 1 1 -0.028304571 1 NA 1.453203 -0.6708204 0.5 -0.2236068
52 1 1 -0.520318482 1 NA 1.452129 0.2236068 -0.5 -0.6708204
53 1 1 0.171317619 1 NA 1.431510 -0.6708204 0.5 -0.2236068
54 1 1 0.432732046 1 NA 1.430082 0.2236068 -0.5 -0.6708204
55 0 1 -0.346286005 1 NA 1.443492 -0.2236068 -0.5 0.6708204
56 1 1 -0.469375653 1 NA 1.436460 0.6708204 0.5 0.2236068
57 1 1 0.031021711 1 NA 1.418119 -0.2236068 -0.5 0.6708204
58 1 NA -0.118837515 1 NA 1.434971 -0.6708204 0.5 -0.2236068
59 1 1 0.507769984 1 NA 1.445599 -0.6708204 0.5 -0.2236068
60 0 NA 0.271797031 1 NA 1.437097 0.6708204 0.5 0.2236068
61 1 1 -0.124442204 1 NA 1.428360 -0.2236068 -0.5 0.6708204
62 1 1 0.277677389 1 NA 1.440550 0.6708204 0.5 0.2236068
63 0 1 -0.102893730 1 NA 1.443014 0.2236068 -0.5 -0.6708204
64 1 1 NA 1 NA 1.424298 -0.2236068 -0.5 0.6708204
65 1 1 -0.678303052 1 NA 1.448823 0.2236068 -0.5 -0.6708204
66 0 0 0.478880037 1 NA 1.425834 0.2236068 -0.5 -0.6708204
67 0 1 -0.428028760 1 NA 1.427102 -0.2236068 -0.5 0.6708204
68 1 1 0.048119185 1 NA 1.414240 -0.6708204 0.5 -0.2236068
69 0 NA 0.216932805 1 NA 1.456218 -0.6708204 0.5 -0.2236068
70 0 1 -0.234575269 1 NA 1.470594 -0.6708204 0.5 -0.2236068
71 1 1 0.006827078 1 NA 1.425058 -0.6708204 0.5 -0.2236068
72 1 1 -0.456055171 1 NA 1.432371 0.2236068 -0.5 -0.6708204
73 0 1 0.346486708 1 NA 1.441656 -0.2236068 -0.5 0.6708204
74 1 1 0.205092215 1 NA 1.434952 -0.2236068 -0.5 0.6708204
75 1 1 -0.136596858 1 NA 1.402860 0.2236068 -0.5 -0.6708204
76 0 1 -0.500179043 1 NA 1.453363 0.2236068 -0.5 -0.6708204
77 0 1 0.527352086 1 NA 1.432909 0.6708204 0.5 0.2236068
78 0 1 0.022742250 1 NA 1.435103 0.2236068 -0.5 -0.6708204
79 1 1 NA 1 NA 1.434462 -0.2236068 -0.5 0.6708204
80 1 1 -0.002032440 1 NA 1.434661 -0.2236068 -0.5 0.6708204
81 1 0 -0.154246160 1 NA 1.445881 0.2236068 -0.5 -0.6708204
82 1 NA 0.140201825 1 NA 1.442548 -0.6708204 0.5 -0.2236068
83 1 1 -0.141417121 1 NA 1.430097 0.2236068 -0.5 -0.6708204
84 1 1 NA 1 NA 1.430119 -0.2236068 -0.5 0.6708204
85 1 1 -0.021285339 1 NA 1.430315 -0.2236068 -0.5 0.6708204
86 1 NA -0.010196306 1 NA 1.437584 0.6708204 0.5 0.2236068
87 1 NA -0.089747520 1 NA 1.409738 0.2236068 -0.5 -0.6708204
88 0 1 -0.083699898 1 NA 1.422388 -0.2236068 -0.5 0.6708204
89 1 1 -0.044061996 1 NA 1.422509 0.2236068 -0.5 -0.6708204
90 1 1 -0.209291697 1 NA 1.439432 0.2236068 -0.5 -0.6708204
91 1 1 0.639036426 1 NA 1.430175 0.6708204 0.5 0.2236068
92 1 NA 0.094698299 1 NA 1.418002 -0.6708204 0.5 -0.2236068
93 1 1 -0.055510622 1 NA 1.423812 0.6708204 0.5 0.2236068
94 1 1 -0.421318463 1 NA 1.423473 -0.6708204 0.5 -0.2236068
95 1 1 0.125295503 1 NA 1.434412 -0.6708204 0.5 -0.2236068
96 1 1 0.213084904 1 NA 1.450844 0.2236068 -0.5 -0.6708204
97 1 NA -0.161914659 1 NA 1.433371 -0.6708204 0.5 -0.2236068
98 1 1 -0.034767685 1 NA 1.444378 0.2236068 -0.5 -0.6708204
99 1 0 -0.320681689 1 NA 1.422523 0.2236068 -0.5 -0.6708204
100 1 NA 0.058192962 1 NA 1.410394 0.2236068 -0.5 -0.6708204
$m5b4$spM_lvlone
center scale
B1 NA NA
B2 NA NA
C2 -0.06490582 0.33317347
(Intercept) NA NA
B21 NA NA
C1 1.43410054 0.01299651
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5b4$mu_reg_norm
[1] 0
$m5b4$tau_reg_norm
[1] 1e-04
$m5b4$shape_tau_norm
[1] 0.01
$m5b4$rate_tau_norm
[1] 0.01
$m5b4$mu_reg_binom
[1] 0
$m5b4$tau_reg_binom
[1] 1e-04
$m5c1
$m5c1$M_lvlone
L1 B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 0.9364352 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 0.8943541 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 0.2868460 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 0.9068418 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 0.7621346 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 0.5858621 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 0.7194403 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 0.7593154 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 0.5863705 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 0.7342586 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 0.7218028 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 0.7241254 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 0.7200126 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 0.5289014 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 0.7322482 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 0.7462471 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 0.9119922 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 0.6262513 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 0.4587835 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 0.7173364 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 0.7288999 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 0.7160420 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 0.5795514 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 0.7210413 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 0.7816086 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 0.6747483 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 0.4746725 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 0.9270652 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 0.5306249 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 0.8913764 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 0.8090308 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 0.4610800 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 0.7183814 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 0.6375974 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 0.9202563 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 0.7263222 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 1.0638781 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 0.6053893 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 0.7945509 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 0.6355032 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 0.9939049 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 1.0690739 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 0.7009106 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 0.7595403 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 0.8356414 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 0.4929132 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 0.5298192 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 0.5363034 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 0.8494053 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 0.6292812 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 0.9561312 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 0.9735411 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 0.7156259 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 0.5184434 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 0.7948965 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 0.5191792 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 0.9233108 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 0.8025356 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 0.8546624 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 0.8639819 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 0.7521237 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 0.5590215 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 0.5972103 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 0.6071272 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 0.8837829 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 0.7775301 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 0.6756191 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 0.7857549 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 0.9119262 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 0.5816103 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 0.4886093 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 0.8292467 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 0.6767456 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 0.7328840 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 0.7946099 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 0.7734810 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 0.5296147 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 0.7723288 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 0.8079308 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 0.5214822 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 0.6264777 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 0.8332107 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 0.4544158 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 0.6482660 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 0.7272109 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 0.7302426 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 0.6768061 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 0.8115758 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 0.9775567 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 0.6408465 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 0.5917453 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 0.7224845 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 0.4501596 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 0.5190455 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 0.7305821 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 0.9696445 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 0.7087457 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 0.9964080 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 0.9084899 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 0.9296776 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5c1$spM_lvlone
center scale
L1 0.72488512 0.1569229
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5c1$mu_reg_norm
[1] 0
$m5c1$tau_reg_norm
[1] 1e-04
$m5c1$shape_tau_norm
[1] 0.01
$m5c1$rate_tau_norm
[1] 0.01
$m5c1$mu_reg_gamma
[1] 0
$m5c1$tau_reg_gamma
[1] 1e-04
$m5c1$shape_tau_gamma
[1] 0.01
$m5c1$rate_tau_gamma
[1] 0.01
$m5c1$mu_reg_binom
[1] 0
$m5c1$tau_reg_binom
[1] 1e-04
$m5c2
$m5c2$M_lvlone
L1 B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 0.9364352 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 0.8943541 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 0.2868460 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 0.9068418 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 0.7621346 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 0.5858621 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 0.7194403 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 0.7593154 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 0.5863705 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 0.7342586 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 0.7218028 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 0.7241254 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 0.7200126 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 0.5289014 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 0.7322482 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 0.7462471 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 0.9119922 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 0.6262513 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 0.4587835 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 0.7173364 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 0.7288999 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 0.7160420 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 0.5795514 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 0.7210413 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 0.7816086 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 0.6747483 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 0.4746725 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 0.9270652 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 0.5306249 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 0.8913764 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 0.8090308 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 0.4610800 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 0.7183814 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 0.6375974 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 0.9202563 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 0.7263222 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 1.0638781 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 0.6053893 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 0.7945509 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 0.6355032 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 0.9939049 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 1.0690739 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 0.7009106 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 0.7595403 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 0.8356414 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 0.4929132 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 0.5298192 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 0.5363034 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 0.8494053 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 0.6292812 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 0.9561312 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 0.9735411 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 0.7156259 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 0.5184434 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 0.7948965 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 0.5191792 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 0.9233108 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 0.8025356 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 0.8546624 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 0.8639819 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 0.7521237 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 0.5590215 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 0.5972103 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 0.6071272 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 0.8837829 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 0.7775301 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 0.6756191 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 0.7857549 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 0.9119262 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 0.5816103 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 0.4886093 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 0.8292467 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 0.6767456 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 0.7328840 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 0.7946099 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 0.7734810 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 0.5296147 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 0.7723288 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 0.8079308 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 0.5214822 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 0.6264777 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 0.8332107 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 0.4544158 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 0.6482660 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 0.7272109 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 0.7302426 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 0.6768061 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 0.8115758 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 0.9775567 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 0.6408465 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 0.5917453 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 0.7224845 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 0.4501596 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 0.5190455 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 0.7305821 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 0.9696445 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 0.7087457 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 0.9964080 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 0.9084899 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 0.9296776 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5c2$spM_lvlone
center scale
L1 0.72488512 0.1569229
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5c2$mu_reg_norm
[1] 0
$m5c2$tau_reg_norm
[1] 1e-04
$m5c2$shape_tau_norm
[1] 0.01
$m5c2$rate_tau_norm
[1] 0.01
$m5c2$mu_reg_gamma
[1] 0
$m5c2$tau_reg_gamma
[1] 1e-04
$m5c2$shape_tau_gamma
[1] 0.01
$m5c2$rate_tau_gamma
[1] 0.01
$m5c2$mu_reg_binom
[1] 0
$m5c2$tau_reg_binom
[1] 1e-04
$m5d1
$m5d1$M_lvlone
P1 B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 1 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 3 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 3 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 3 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 5 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 3 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 0 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 2 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 4 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 3 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 4 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 3 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 2 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 6 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 2 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 5 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 2 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 2 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 1 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 2 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 2 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 2 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 1 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 0 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 2 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 4 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 3 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 5 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 5 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 0 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 3 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 2 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 2 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 3 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 1 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 4 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 2 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 2 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 8 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 4 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 3 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 3 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 2 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 3 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 2 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 3 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 4 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 3 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 2 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 4 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 1 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 2 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 4 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 3 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 1 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 3 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 3 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 4 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 1 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 5 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 5 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 0 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 2 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 0 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 2 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 4 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 2 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 3 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 1 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 3 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 1 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 5 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 0 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 4 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 1 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 3 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 2 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 1 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 2 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 4 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 6 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 3 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 1 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 3 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 1 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 5 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 2 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 2 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 1 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 5 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 1 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 5 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 1 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 1 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 1 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 3 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 2 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 0 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 2 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 4 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5d1$spM_lvlone
center scale
P1 2.61000000 1.5627934
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5d1$mu_reg_norm
[1] 0
$m5d1$tau_reg_norm
[1] 1e-04
$m5d1$shape_tau_norm
[1] 0.01
$m5d1$rate_tau_norm
[1] 0.01
$m5d1$mu_reg_binom
[1] 0
$m5d1$tau_reg_binom
[1] 1e-04
$m5d1$mu_reg_poisson
[1] 0
$m5d1$tau_reg_poisson
[1] 1e-04
$m5d2
$m5d2$M_lvlone
P1 B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 1 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 3 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 3 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 3 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 5 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 3 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 0 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 2 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 4 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 3 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 4 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 3 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 2 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 6 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 2 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 5 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 2 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 2 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 1 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 2 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 2 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 2 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 1 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 0 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 2 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 4 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 3 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 5 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 5 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 0 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 3 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 2 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 2 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 3 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 1 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 4 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 2 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 2 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 8 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 4 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 3 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 3 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 2 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 3 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 2 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 3 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 4 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 3 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 2 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 4 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 1 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 2 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 4 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 3 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 1 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 3 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 3 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 4 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 1 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 5 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 5 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 0 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 2 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 0 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 2 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 4 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 2 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 3 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 1 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 3 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 1 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 5 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 0 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 4 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 1 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 3 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 2 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 1 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 2 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 4 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 6 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 3 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 1 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 3 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 1 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 5 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 2 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 2 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 1 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 5 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 1 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 5 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 1 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 1 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 1 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 3 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 2 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 0 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 2 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 4 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5d2$spM_lvlone
center scale
P1 2.61000000 1.5627934
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5d2$mu_reg_norm
[1] 0
$m5d2$tau_reg_norm
[1] 1e-04
$m5d2$shape_tau_norm
[1] 0.01
$m5d2$rate_tau_norm
[1] 0.01
$m5d2$mu_reg_binom
[1] 0
$m5d2$tau_reg_binom
[1] 1e-04
$m5d2$mu_reg_poisson
[1] 0
$m5d2$tau_reg_poisson
[1] 1e-04
$m5e1
$m5e1$M_lvlone
L1 B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 0.9364352 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 0.8943541 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 0.2868460 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 0.9068418 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 0.7621346 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 0.5858621 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 0.7194403 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 0.7593154 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 0.5863705 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 0.7342586 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 0.7218028 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 0.7241254 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 0.7200126 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 0.5289014 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 0.7322482 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 0.7462471 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 0.9119922 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 0.6262513 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 0.4587835 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 0.7173364 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 0.7288999 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 0.7160420 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 0.5795514 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 0.7210413 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 0.7816086 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 0.6747483 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 0.4746725 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 0.9270652 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 0.5306249 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 0.8913764 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 0.8090308 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 0.4610800 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 0.7183814 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 0.6375974 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 0.9202563 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 0.7263222 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 1.0638781 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 0.6053893 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 0.7945509 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 0.6355032 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 0.9939049 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 1.0690739 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 0.7009106 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 0.7595403 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 0.8356414 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 0.4929132 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 0.5298192 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 0.5363034 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 0.8494053 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 0.6292812 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 0.9561312 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 0.9735411 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 0.7156259 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 0.5184434 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 0.7948965 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 0.5191792 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 0.9233108 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 0.8025356 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 0.8546624 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 0.8639819 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 0.7521237 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 0.5590215 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 0.5972103 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 0.6071272 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 0.8837829 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 0.7775301 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 0.6756191 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 0.7857549 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 0.9119262 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 0.5816103 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 0.4886093 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 0.8292467 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 0.6767456 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 0.7328840 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 0.7946099 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 0.7734810 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 0.5296147 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 0.7723288 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 0.8079308 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 0.5214822 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 0.6264777 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 0.8332107 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 0.4544158 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 0.6482660 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 0.7272109 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 0.7302426 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 0.6768061 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 0.8115758 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 0.9775567 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 0.6408465 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 0.5917453 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 0.7224845 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 0.4501596 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 0.5190455 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 0.7305821 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 0.9696445 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 0.7087457 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 0.9964080 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 0.9084899 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 0.9296776 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5e1$spM_lvlone
center scale
L1 0.72488512 0.1569229
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5e1$mu_reg_norm
[1] 0
$m5e1$tau_reg_norm
[1] 1e-04
$m5e1$shape_tau_norm
[1] 0.01
$m5e1$rate_tau_norm
[1] 0.01
$m5e1$mu_reg_binom
[1] 0
$m5e1$tau_reg_binom
[1] 1e-04
$m5f1
$m5f1$M_lvlone
Be1 B2 C2 (Intercept) B21 B11 O1.L O1.Q O1.C
1 0.69649948 1 0.144065882 1 NA 1 -0.2236068 -0.5 0.6708204
2 0.56085128 1 0.032778478 1 NA 1 0.6708204 0.5 0.2236068
3 0.35796663 1 0.343008492 1 NA 1 0.2236068 -0.5 -0.6708204
4 0.53961336 1 -0.361887858 1 NA 1 -0.2236068 -0.5 0.6708204
5 0.06191042 1 -0.389600647 1 NA 1 0.2236068 -0.5 -0.6708204
6 0.51256785 NA -0.205306841 1 NA 1 -0.6708204 0.5 -0.2236068
7 0.13154723 1 0.079434830 1 NA 0 0.2236068 -0.5 -0.6708204
8 0.35032766 1 -0.331246757 1 NA 0 0.6708204 0.5 0.2236068
9 0.21796890 1 -0.329638800 1 NA 1 0.6708204 0.5 0.2236068
10 0.10476230 NA 0.167597533 1 NA 1 -0.2236068 -0.5 0.6708204
11 0.66083800 1 0.860207989 1 NA 1 -0.6708204 0.5 -0.2236068
12 0.66884267 1 0.022730640 1 NA 0 0.2236068 -0.5 -0.6708204
13 0.69840279 1 0.217171172 1 NA 1 0.2236068 -0.5 -0.6708204
14 0.50398472 1 -0.403002412 1 NA 0 -0.6708204 0.5 -0.2236068
15 0.52807655 1 0.087369742 1 NA 1 -0.6708204 0.5 -0.2236068
16 0.40135087 1 -0.183870429 1 NA 1 0.6708204 0.5 0.2236068
17 0.45554802 1 -0.194577002 1 NA 1 -0.2236068 -0.5 0.6708204
18 0.68717635 1 -0.349718516 1 NA 1 0.2236068 -0.5 -0.6708204
19 0.35880655 NA -0.508781244 1 NA 1 0.6708204 0.5 0.2236068
20 0.36341035 NA 0.494883111 1 NA 1 -0.6708204 0.5 -0.2236068
21 0.71468563 1 0.258041067 1 NA 1 0.2236068 -0.5 -0.6708204
22 0.44558172 NA -0.922621989 1 NA 1 0.6708204 0.5 0.2236068
23 0.33262526 NA 0.431254949 1 NA 1 0.6708204 0.5 0.2236068
24 0.66812751 1 -0.294218881 1 NA 1 -0.2236068 -0.5 0.6708204
25 0.23180310 NA -0.425548895 1 NA 0 -0.6708204 0.5 -0.2236068
26 0.37786624 NA 0.057176054 1 NA 1 0.2236068 -0.5 -0.6708204
27 0.88834598 1 0.289090158 1 NA 1 0.6708204 0.5 0.2236068
28 0.46487057 1 -0.473079489 1 NA 1 -0.6708204 0.5 -0.2236068
29 0.47018802 1 -0.385664863 1 NA 1 0.6708204 0.5 0.2236068
30 0.91617346 1 -0.154780107 1 NA 0 0.6708204 0.5 0.2236068
31 0.67589111 NA 0.100536296 1 NA 0 -0.2236068 -0.5 0.6708204
32 0.61623852 1 0.634791958 1 NA 1 0.2236068 -0.5 -0.6708204
33 0.44182889 1 -0.387252617 1 NA 1 0.2236068 -0.5 -0.6708204
34 0.29868153 0 -0.181741088 1 NA 1 -0.6708204 0.5 -0.2236068
35 0.44235110 1 -0.311562695 1 NA 1 -0.6708204 0.5 -0.2236068
36 0.72557250 1 -0.044115907 1 NA 0 0.6708204 0.5 0.2236068
37 0.74809277 1 -0.657409991 1 NA 1 0.6708204 0.5 0.2236068
38 0.26452559 NA 0.159577214 1 NA 1 0.6708204 0.5 0.2236068
39 0.41597215 1 -0.460416933 1 NA 1 -0.6708204 0.5 -0.2236068
40 0.29080530 NA NA 1 NA 1 -0.2236068 -0.5 0.6708204
41 0.80342568 1 -0.248909867 1 NA 1 -0.6708204 0.5 -0.2236068
42 0.76614332 1 -0.609021545 1 NA 1 -0.6708204 0.5 -0.2236068
43 0.29734466 1 0.025471883 1 NA 1 -0.2236068 -0.5 0.6708204
44 0.42809509 1 0.066648592 1 NA 1 -0.2236068 -0.5 0.6708204
45 0.12861202 1 -0.276108719 1 NA 1 -0.6708204 0.5 -0.2236068
46 0.44369392 1 -0.179737577 1 NA 1 -0.6708204 0.5 -0.2236068
47 0.35290028 0 0.181190937 1 NA 0 0.6708204 0.5 0.2236068
48 0.88288407 1 -0.453871693 1 NA 1 0.6708204 0.5 0.2236068
49 0.37880332 0 0.448629602 1 NA 1 -0.2236068 -0.5 0.6708204
50 0.60663793 1 -0.529811821 1 NA 0 -0.2236068 -0.5 0.6708204
51 0.15505292 1 -0.028304571 1 NA 1 -0.6708204 0.5 -0.2236068
52 0.65796074 1 -0.520318482 1 NA 1 0.2236068 -0.5 -0.6708204
53 0.63416487 1 0.171317619 1 NA 1 -0.6708204 0.5 -0.2236068
54 0.83040459 1 0.432732046 1 NA 1 0.2236068 -0.5 -0.6708204
55 0.64947589 1 -0.346286005 1 NA 0 -0.2236068 -0.5 0.6708204
56 0.67541381 1 -0.469375653 1 NA 1 0.6708204 0.5 0.2236068
57 0.53637356 1 0.031021711 1 NA 1 -0.2236068 -0.5 0.6708204
58 0.39157422 NA -0.118837515 1 NA 1 -0.6708204 0.5 -0.2236068
59 0.88168026 1 0.507769984 1 NA 1 -0.6708204 0.5 -0.2236068
60 0.32582606 NA 0.271797031 1 NA 0 0.6708204 0.5 0.2236068
61 0.64492753 1 -0.124442204 1 NA 1 -0.2236068 -0.5 0.6708204
62 0.34804110 1 0.277677389 1 NA 1 0.6708204 0.5 0.2236068
63 0.49241010 1 -0.102893730 1 NA 0 0.2236068 -0.5 -0.6708204
64 0.43387493 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
65 0.21806182 1 -0.678303052 1 NA 1 0.2236068 -0.5 -0.6708204
66 0.60021691 0 0.478880037 1 NA 0 0.2236068 -0.5 -0.6708204
67 0.30567313 1 -0.428028760 1 NA 0 -0.2236068 -0.5 0.6708204
68 0.22476988 1 0.048119185 1 NA 1 -0.6708204 0.5 -0.2236068
69 0.23155216 NA 0.216932805 1 NA 0 -0.6708204 0.5 -0.2236068
70 0.29610794 1 -0.234575269 1 NA 0 -0.6708204 0.5 -0.2236068
71 0.83435168 1 0.006827078 1 NA 1 -0.6708204 0.5 -0.2236068
72 0.65543408 1 -0.456055171 1 NA 1 0.2236068 -0.5 -0.6708204
73 0.59684715 1 0.346486708 1 NA 0 -0.2236068 -0.5 0.6708204
74 0.80640183 1 0.205092215 1 NA 1 -0.2236068 -0.5 0.6708204
75 0.52288624 1 -0.136596858 1 NA 1 0.2236068 -0.5 -0.6708204
76 0.41546840 1 -0.500179043 1 NA 0 0.2236068 -0.5 -0.6708204
77 0.44756212 1 0.527352086 1 NA 0 0.6708204 0.5 0.2236068
78 0.68093413 1 0.022742250 1 NA 0 0.2236068 -0.5 -0.6708204
79 0.29261828 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
80 0.21008516 1 -0.002032440 1 NA 1 -0.2236068 -0.5 0.6708204
81 0.44710869 0 -0.154246160 1 NA 1 0.2236068 -0.5 -0.6708204
82 0.70470991 NA 0.140201825 1 NA 1 -0.6708204 0.5 -0.2236068
83 0.31300581 1 -0.141417121 1 NA 1 0.2236068 -0.5 -0.6708204
84 0.44774544 1 NA 1 NA 1 -0.2236068 -0.5 0.6708204
85 0.68031201 1 -0.021285339 1 NA 1 -0.2236068 -0.5 0.6708204
86 0.44456865 NA -0.010196306 1 NA 1 0.6708204 0.5 0.2236068
87 0.79031803 NA -0.089747520 1 NA 1 0.2236068 -0.5 -0.6708204
88 0.22231438 1 -0.083699898 1 NA 0 -0.2236068 -0.5 0.6708204
89 0.30114327 1 -0.044061996 1 NA 1 0.2236068 -0.5 -0.6708204
90 0.45339193 1 -0.209291697 1 NA 1 0.2236068 -0.5 -0.6708204
91 0.35526875 1 0.639036426 1 NA 1 0.6708204 0.5 0.2236068
92 0.68684691 NA 0.094698299 1 NA 1 -0.6708204 0.5 -0.2236068
93 0.81430167 1 -0.055510622 1 NA 1 0.6708204 0.5 0.2236068
94 0.60104343 1 -0.421318463 1 NA 1 -0.6708204 0.5 -0.2236068
95 0.82012448 1 0.125295503 1 NA 1 -0.6708204 0.5 -0.2236068
96 0.55669948 1 0.213084904 1 NA 1 0.2236068 -0.5 -0.6708204
97 0.76622465 NA -0.161914659 1 NA 1 -0.6708204 0.5 -0.2236068
98 0.50112270 1 -0.034767685 1 NA 1 0.2236068 -0.5 -0.6708204
99 0.53468983 0 -0.320681689 1 NA 1 0.2236068 -0.5 -0.6708204
100 0.58249327 NA 0.058192962 1 NA 1 0.2236068 -0.5 -0.6708204
$m5f1$spM_lvlone
center scale
Be1 0.50398804 0.2049899
B2 NA NA
C2 -0.06490582 0.3331735
(Intercept) NA NA
B21 NA NA
B11 NA NA
O1.L NA NA
O1.Q NA NA
O1.C NA NA
$m5f1$mu_reg_norm
[1] 0
$m5f1$tau_reg_norm
[1] 1e-04
$m5f1$shape_tau_norm
[1] 0.01
$m5f1$rate_tau_norm
[1] 0.01
$m5f1$mu_reg_beta
[1] 0
$m5f1$tau_reg_beta
[1] 1e-04
$m5f1$shape_tau_beta
[1] 0.01
$m5f1$rate_tau_beta
[1] 0.01
$m5f1$mu_reg_binom
[1] 0
$m5f1$tau_reg_binom
[1] 1e-04
$m6a
$m6a$M_lvlone
y C2 M2 O2 (Intercept) M22 M23 M24 O22 O23 O24
1 -4.76915977 0.144065882 4 4 1 NA NA NA NA NA NA
2 -2.69277172 0.032778478 1 4 1 NA NA NA NA NA NA
3 -1.17551547 0.343008492 3 4 1 NA NA NA NA NA NA
4 -4.57464473 -0.361887858 3 1 1 NA NA NA NA NA NA
5 -2.20260004 -0.389600647 4 2 1 NA NA NA NA NA NA
6 -3.48995315 -0.205306841 4 3 1 NA NA NA NA NA NA
7 -0.44987258 0.079434830 1 4 1 NA NA NA NA NA NA
8 -2.29588848 -0.331246757 1 2 1 NA NA NA NA NA NA
9 -4.49135812 -0.329638800 2 4 1 NA NA NA NA NA NA
10 -5.52545368 0.167597533 2 3 1 NA NA NA NA NA NA
11 -4.16286741 0.860207989 3 2 1 NA NA NA NA NA NA
12 -2.93455761 0.022730640 3 1 1 NA NA NA NA NA NA
13 -0.04202496 0.217171172 2 1 1 NA NA NA NA NA NA
14 -1.63149775 -0.403002412 3 1 1 NA NA NA NA NA NA
15 -0.97786151 0.087369742 2 4 1 NA NA NA NA NA NA
16 -1.79100431 -0.183870429 1 3 1 NA NA NA NA NA NA
17 -6.26520032 -0.194577002 4 3 1 NA NA NA NA NA NA
18 -1.36028709 -0.349718516 2 1 1 NA NA NA NA NA NA
19 -1.15396597 -0.508781244 3 3 1 NA NA NA NA NA NA
20 -3.21707239 0.494883111 3 1 1 NA NA NA NA NA NA
21 -1.59389898 0.258041067 2 3 1 NA NA NA NA NA NA
22 -5.50335066 -0.922621989 2 3 1 NA NA NA NA NA NA
23 0.57290123 0.431254949 3 2 1 NA NA NA NA NA NA
24 -8.22270323 -0.294218881 3 3 1 NA NA NA NA NA NA
25 -1.41364158 -0.425548895 2 2 1 NA NA NA NA NA NA
26 -6.28031574 0.057176054 2 2 1 NA NA NA NA NA NA
27 -3.15624425 0.289090158 1 1 1 NA NA NA NA NA NA
28 -3.55693639 -0.473079489 3 4 1 NA NA NA NA NA NA
29 -1.11821124 -0.385664863 4 3 1 NA NA NA NA NA NA
30 -2.82834175 -0.154780107 2 3 1 NA NA NA NA NA NA
31 -3.72259860 0.100536296 NA 2 1 NA NA NA NA NA NA
32 -1.75256656 0.634791958 4 2 1 NA NA NA NA NA NA
33 -5.55044409 -0.387252617 4 1 1 NA NA NA NA NA NA
34 -7.45068147 -0.181741088 4 1 1 NA NA NA NA NA NA
35 -0.97491919 -0.311562695 2 4 1 NA NA NA NA NA NA
36 -2.98356481 -0.044115907 1 3 1 NA NA NA NA NA NA
37 -1.86039471 -0.657409991 3 3 1 NA NA NA NA NA NA
38 -7.28754607 0.159577214 4 1 1 NA NA NA NA NA NA
39 -8.66234796 -0.460416933 3 2 1 NA NA NA NA NA NA
40 -4.16291375 NA 3 3 1 NA NA NA NA NA NA
41 -3.48250771 -0.248909867 1 3 1 NA NA NA NA NA NA
42 -7.27930410 -0.609021545 4 3 1 NA NA NA NA NA NA
43 -6.12866190 0.025471883 1 3 1 NA NA NA NA NA NA
44 -4.96880803 0.066648592 2 4 1 NA NA NA NA NA NA
45 -4.76746713 -0.276108719 2 4 1 NA NA NA NA NA NA
46 -1.91249177 -0.179737577 1 1 1 NA NA NA NA NA NA
47 -0.61884029 0.181190937 4 4 1 NA NA NA NA NA NA
48 -0.20496175 -0.453871693 2 4 1 NA NA NA NA NA NA
49 -7.12636055 0.448629602 4 1 1 NA NA NA NA NA NA
50 -6.23103837 -0.529811821 1 2 1 NA NA NA NA NA NA
51 -3.32561065 -0.028304571 4 1 1 NA NA NA NA NA NA
52 -2.95942339 -0.520318482 4 3 1 NA NA NA NA NA NA
53 -4.44915114 0.171317619 4 2 1 NA NA NA NA NA NA
54 -0.81566463 0.432732046 3 1 1 NA NA NA NA NA NA
55 -6.50029573 -0.346286005 3 2 1 NA NA NA NA NA NA
56 -2.74718050 -0.469375653 3 3 1 NA NA NA NA NA NA
57 -6.35015663 0.031021711 2 NA 1 NA NA NA NA NA NA
58 -2.69505883 -0.118837515 3 4 1 NA NA NA NA NA NA
59 -1.55660833 0.507769984 3 4 1 NA NA NA NA NA NA
60 -3.76240209 0.271797031 4 3 1 NA NA NA NA NA NA
61 -3.92885797 -0.124442204 2 4 1 NA NA NA NA NA NA
62 -1.72044748 0.277677389 2 1 1 NA NA NA NA NA NA
63 -0.56602625 -0.102893730 1 4 1 NA NA NA NA NA NA
64 -4.42235015 NA 2 4 1 NA NA NA NA NA NA
65 -2.39122287 -0.678303052 2 4 1 NA NA NA NA NA NA
66 -0.81807247 0.478880037 3 1 1 NA NA NA NA NA NA
67 -6.48196782 -0.428028760 2 3 1 NA NA NA NA NA NA
68 -1.37306273 0.048119185 4 3 1 NA NA NA NA NA NA
69 -4.99886487 0.216932805 NA 4 1 NA NA NA NA NA NA
70 -5.82288217 -0.234575269 1 1 1 NA NA NA NA NA NA
71 -2.68234219 0.006827078 2 4 1 NA NA NA NA NA NA
72 -3.96170442 -0.456055171 3 4 1 NA NA NA NA NA NA
73 -7.19573667 0.346486708 4 2 1 NA NA NA NA NA NA
74 -5.08799713 0.205092215 4 4 1 NA NA NA NA NA NA
75 -1.32967262 -0.136596858 1 3 1 NA NA NA NA NA NA
76 -2.56532332 -0.500179043 4 2 1 NA NA NA NA NA NA
77 -3.21002900 0.527352086 NA 2 1 NA NA NA NA NA NA
78 -3.40559790 0.022742250 2 3 1 NA NA NA NA NA NA
79 -4.56223913 NA 2 2 1 NA NA NA NA NA NA
80 -2.04250454 -0.002032440 2 1 1 NA NA NA NA NA NA
81 -2.20378059 -0.154246160 4 4 1 NA NA NA NA NA NA
82 -3.37471317 0.140201825 3 2 1 NA NA NA NA NA NA
83 -0.95345385 -0.141417121 3 4 1 NA NA NA NA NA NA
84 -4.89337660 NA 1 1 1 NA NA NA NA NA NA
85 -9.82258463 -0.021285339 2 1 1 NA NA NA NA NA NA
86 -4.51800734 -0.010196306 1 2 1 NA NA NA NA NA NA
87 -0.18662049 -0.089747520 3 3 1 NA NA NA NA NA NA
88 -2.87120881 -0.083699898 1 3 1 NA NA NA NA NA NA
89 1.29290150 -0.044061996 2 2 1 NA NA NA NA NA NA
90 -1.39497744 -0.209291697 1 4 1 NA NA NA NA NA NA
91 1.14575040 0.639036426 3 2 1 NA NA NA NA NA NA
92 0.92801246 0.094698299 1 1 1 NA NA NA NA NA NA
93 -2.59938157 -0.055510622 4 NA 1 NA NA NA NA NA NA
94 -3.26905923 -0.421318463 4 3 1 NA NA NA NA NA NA
95 -3.26861434 0.125295503 1 1 1 NA NA NA NA NA NA
96 -5.71017484 0.213084904 4 3 1 NA NA NA NA NA NA
97 -3.76781806 -0.161914659 4 2 1 NA NA NA NA NA NA
98 -2.02677390 -0.034767685 3 2 1 NA NA NA NA NA NA
99 -2.96199765 -0.320681689 3 4 1 NA NA NA NA NA NA
100 -4.81129496 0.058192962 4 3 1 NA NA NA NA NA NA
abs(C1 - C2) log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2)
1 NA 0.3439662 NA NA NA
2 NA 0.3605954 NA NA NA
3 NA 0.3583696 NA NA NA
4 NA 0.3736964 NA NA NA
5 NA 0.3634928 NA NA NA
6 NA 0.3737730 NA NA NA
7 NA 0.3542952 NA NA NA
8 NA 0.3631892 NA NA NA
9 NA 0.3484794 NA NA NA
10 NA 0.3706241 NA NA NA
11 NA 0.3565373 NA NA NA
12 NA 0.3716534 NA NA NA
13 NA 0.3510408 NA NA NA
14 NA 0.3527707 NA NA NA
15 NA 0.3617934 NA NA NA
16 NA 0.3534000 NA NA NA
17 NA 0.3765220 NA NA NA
18 NA 0.3466206 NA NA NA
19 NA 0.3669896 NA NA NA
20 NA 0.3611331 NA NA NA
21 NA 0.3573242 NA NA NA
22 NA 0.3659595 NA NA NA
23 NA 0.3532680 NA NA NA
24 NA 0.3614400 NA NA NA
25 NA 0.3548341 NA NA NA
26 NA 0.3626380 NA NA NA
27 NA 0.3655634 NA NA NA
28 NA 0.3527344 NA NA NA
29 NA 0.3631120 NA NA NA
30 NA 0.3867045 NA NA NA
31 NA 0.3519109 NA NA NA
32 NA 0.3768405 NA NA NA
33 NA 0.3582630 NA NA NA
34 NA 0.3587390 NA NA NA
35 NA 0.3516387 NA NA NA
36 NA 0.3608133 NA NA NA
37 NA 0.3544406 NA NA NA
38 NA 0.3519254 NA NA NA
39 NA 0.3577404 NA NA NA
40 NA 0.3699214 NA NA NA
41 NA 0.3610235 NA NA NA
42 NA 0.3688639 NA NA NA
43 NA 0.3683210 NA NA NA
44 NA 0.3707242 NA NA NA
45 NA 0.3719890 NA NA NA
46 NA 0.3471687 NA NA NA
47 NA 0.3622725 NA NA NA
48 NA 0.3604242 NA NA NA
49 NA 0.3470878 NA NA NA
50 NA 0.3519288 NA NA NA
51 NA 0.3737703 NA NA NA
52 NA 0.3730309 NA NA NA
53 NA 0.3587298 NA NA NA
54 NA 0.3577317 NA NA NA
55 NA 0.3670651 NA NA NA
56 NA 0.3621821 NA NA NA
57 NA 0.3493310 NA NA NA
58 NA 0.3611449 NA NA NA
59 NA 0.3685236 NA NA NA
60 NA 0.3626252 NA NA NA
61 NA 0.3565271 NA NA NA
62 NA 0.3650248 NA NA NA
63 NA 0.3667342 NA NA NA
64 NA 0.3536790 NA NA NA
65 NA 0.3707512 NA NA NA
66 NA 0.3547570 NA NA NA
67 NA 0.3556460 NA NA NA
68 NA 0.3465922 NA NA NA
69 NA 0.3758430 NA NA NA
70 NA 0.3856661 NA NA NA
71 NA 0.3542125 NA NA NA
72 NA 0.3593309 NA NA NA
73 NA 0.3657925 NA NA NA
74 NA 0.3611311 NA NA NA
75 NA 0.3385130 NA NA NA
76 NA 0.3738804 NA NA NA
77 NA 0.3597065 NA NA NA
78 NA 0.3612366 NA NA NA
79 NA 0.3607899 NA NA NA
80 NA 0.3609283 NA NA NA
81 NA 0.3687189 NA NA NA
82 NA 0.3664112 NA NA NA
83 NA 0.3577425 NA NA NA
84 NA 0.3577579 NA NA NA
85 NA 0.3578947 NA NA NA
86 NA 0.3629637 NA NA NA
87 NA 0.3434041 NA NA NA
88 NA 0.3523374 NA NA NA
89 NA 0.3524220 NA NA NA
90 NA 0.3642486 NA NA NA
91 NA 0.3577968 NA NA NA
92 NA 0.3492491 NA NA NA
93 NA 0.3533376 NA NA NA
94 NA 0.3530999 NA NA NA
95 NA 0.3607553 NA NA NA
96 NA 0.3721453 NA NA NA
97 NA 0.3600291 NA NA NA
98 NA 0.3676785 NA NA NA
99 NA 0.3524318 NA NA NA
100 NA 0.3438689 NA NA NA
C1
1 1.410531
2 1.434183
3 1.430994
4 1.453096
5 1.438344
6 1.453207
7 1.425176
8 1.437908
9 1.416911
10 1.448638
11 1.428375
12 1.450130
13 1.420545
14 1.423005
15 1.435902
16 1.423901
17 1.457208
18 1.414280
19 1.443383
20 1.434954
21 1.429499
22 1.441897
23 1.423713
24 1.435395
25 1.425944
26 1.437115
27 1.441326
28 1.422953
29 1.437797
30 1.472121
31 1.421782
32 1.457672
33 1.430842
34 1.431523
35 1.421395
36 1.434496
37 1.425383
38 1.421802
39 1.430094
40 1.447621
41 1.434797
42 1.446091
43 1.445306
44 1.448783
45 1.450617
46 1.415055
47 1.436590
48 1.433938
49 1.414941
50 1.421807
51 1.453203
52 1.452129
53 1.431510
54 1.430082
55 1.443492
56 1.436460
57 1.418119
58 1.434971
59 1.445599
60 1.437097
61 1.428360
62 1.440550
63 1.443014
64 1.424298
65 1.448823
66 1.425834
67 1.427102
68 1.414240
69 1.456218
70 1.470594
71 1.425058
72 1.432371
73 1.441656
74 1.434952
75 1.402860
76 1.453363
77 1.432909
78 1.435103
79 1.434462
80 1.434661
81 1.445881
82 1.442548
83 1.430097
84 1.430119
85 1.430315
86 1.437584
87 1.409738
88 1.422388
89 1.422509
90 1.439432
91 1.430175
92 1.418002
93 1.423812
94 1.423473
95 1.434412
96 1.450844
97 1.433371
98 1.444378
99 1.422523
100 1.410394
$m6a$spM_lvlone
center scale
y -3.34428345 2.276495066
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
$m6a$mu_reg_norm
[1] 0
$m6a$tau_reg_norm
[1] 1e-04
$m6a$shape_tau_norm
[1] 0.01
$m6a$rate_tau_norm
[1] 0.01
$m6a$mu_reg_multinomial
[1] 0
$m6a$tau_reg_multinomial
[1] 1e-04
$m6a$mu_reg_ordinal
[1] 0
$m6a$tau_reg_ordinal
[1] 1e-04
$m6a$mu_delta_ordinal
[1] 0
$m6a$tau_delta_ordinal
[1] 1e-04
$m6b
$m6b$M_lvlone
B1 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 1 0.032778478 1 4 1 NA NA NA NA NA NA NA
3 1 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 1 -0.389600647 4 2 1 NA NA NA NA NA NA NA
6 1 -0.205306841 4 3 1 NA NA NA NA NA NA NA
7 0 0.079434830 1 4 1 NA NA NA NA NA NA NA
8 0 -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 1 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 0 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 0 -0.403002412 3 1 1 NA NA NA NA NA NA NA
15 1 0.087369742 2 4 1 NA NA NA NA NA NA NA
16 1 -0.183870429 1 3 1 NA NA NA NA NA NA NA
17 1 -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 1 -0.508781244 3 3 1 NA NA NA NA NA NA NA
20 1 0.494883111 3 1 1 NA NA NA NA NA NA NA
21 1 0.258041067 2 3 1 NA NA NA NA NA NA NA
22 1 -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 1 -0.294218881 3 3 1 NA NA NA NA NA NA NA
25 0 -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 1 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 1 -0.385664863 4 3 1 NA NA NA NA NA NA NA
30 0 -0.154780107 2 3 1 NA NA NA NA NA NA NA
31 0 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 1 -0.387252617 4 1 1 NA NA NA NA NA NA NA
34 1 -0.181741088 4 1 1 NA NA NA NA NA NA NA
35 1 -0.311562695 2 4 1 NA NA NA NA NA NA NA
36 0 -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 1 0.159577214 4 1 1 NA NA NA NA NA NA NA
39 1 -0.460416933 3 2 1 NA NA NA NA NA NA NA
40 1 NA 3 3 1 NA NA NA NA NA NA NA
41 1 -0.248909867 1 3 1 NA NA NA NA NA NA NA
42 1 -0.609021545 4 3 1 NA NA NA NA NA NA NA
43 1 0.025471883 1 3 1 NA NA NA NA NA NA NA
44 1 0.066648592 2 4 1 NA NA NA NA NA NA NA
45 1 -0.276108719 2 4 1 NA NA NA NA NA NA NA
46 1 -0.179737577 1 1 1 NA NA NA NA NA NA NA
47 0 0.181190937 4 4 1 NA NA NA NA NA NA NA
48 1 -0.453871693 2 4 1 NA NA NA NA NA NA NA
49 1 0.448629602 4 1 1 NA NA NA NA NA NA NA
50 0 -0.529811821 1 2 1 NA NA NA NA NA NA NA
51 1 -0.028304571 4 1 1 NA NA NA NA NA NA NA
52 1 -0.520318482 4 3 1 NA NA NA NA NA NA NA
53 1 0.171317619 4 2 1 NA NA NA NA NA NA NA
54 1 0.432732046 3 1 1 NA NA NA NA NA NA NA
55 0 -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 1 0.031021711 2 NA 1 NA NA NA NA NA NA NA
58 1 -0.118837515 3 4 1 NA NA NA NA NA NA NA
59 1 0.507769984 3 4 1 NA NA NA NA NA NA NA
60 0 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 0 -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 1 -0.678303052 2 4 1 NA NA NA NA NA NA NA
66 0 0.478880037 3 1 1 NA NA NA NA NA NA NA
67 0 -0.428028760 2 3 1 NA NA NA NA NA NA NA
68 1 0.048119185 4 3 1 NA NA NA NA NA NA NA
69 0 0.216932805 NA 4 1 NA NA NA NA NA NA NA
70 0 -0.234575269 1 1 1 NA NA NA NA NA NA NA
71 1 0.006827078 2 4 1 NA NA NA NA NA NA NA
72 1 -0.456055171 3 4 1 NA NA NA NA NA NA NA
73 0 0.346486708 4 2 1 NA NA NA NA NA NA NA
74 1 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 0 -0.500179043 4 2 1 NA NA NA NA NA NA NA
77 0 0.527352086 NA 2 1 NA NA NA NA NA NA NA
78 0 0.022742250 2 3 1 NA NA NA NA NA NA NA
79 1 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 1 0.140201825 3 2 1 NA NA NA NA NA NA NA
83 1 -0.141417121 3 4 1 NA NA NA NA NA NA NA
84 1 NA 1 1 1 NA NA NA NA NA NA NA
85 1 -0.021285339 2 1 1 NA NA NA NA NA NA NA
86 1 -0.010196306 1 2 1 NA NA NA NA NA NA NA
87 1 -0.089747520 3 3 1 NA NA NA NA NA NA NA
88 0 -0.083699898 1 3 1 NA NA NA NA NA NA NA
89 1 -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 1 -0.421318463 4 3 1 NA NA NA NA NA NA NA
95 1 0.125295503 1 1 1 NA NA NA NA NA NA NA
96 1 0.213084904 4 3 1 NA NA NA NA NA NA NA
97 1 -0.161914659 4 2 1 NA NA NA NA NA NA NA
98 1 -0.034767685 3 2 1 NA NA NA NA NA NA NA
99 1 -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
$m6b$spM_lvlone
center scale
B1 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
$m6b$mu_reg_norm
[1] 0
$m6b$tau_reg_norm
[1] 1e-04
$m6b$shape_tau_norm
[1] 0.01
$m6b$rate_tau_norm
[1] 0.01
$m6b$mu_reg_binom
[1] 0
$m6b$tau_reg_binom
[1] 1e-04
$m6b$mu_reg_multinomial
[1] 0
$m6b$tau_reg_multinomial
[1] 1e-04
$m6b$mu_reg_ordinal
[1] 0
$m6b$tau_reg_ordinal
[1] 1e-04
$m6b$mu_delta_ordinal
[1] 0
$m6b$tau_delta_ordinal
[1] 1e-04
$m6c
$m6c$M_lvlone
C1 C2 M2 O2 (Intercept) M22 M23 M24 O22 O23 O24 abs(y - C2)
1 1.410531 0.144065882 4 4 1 NA NA NA NA NA NA NA
2 1.434183 0.032778478 1 4 1 NA NA NA NA NA NA NA
3 1.430994 0.343008492 3 4 1 NA NA NA NA NA NA NA
4 1.453096 -0.361887858 3 1 1 NA NA NA NA NA NA NA
5 1.438344 -0.389600647 4 2 1 NA NA NA NA NA NA NA
6 1.453207 -0.205306841 4 3 1 NA NA NA NA NA NA NA
7 1.425176 0.079434830 1 4 1 NA NA NA NA NA NA NA
8 1.437908 -0.331246757 1 2 1 NA NA NA NA NA NA NA
9 1.416911 -0.329638800 2 4 1 NA NA NA NA NA NA NA
10 1.448638 0.167597533 2 3 1 NA NA NA NA NA NA NA
11 1.428375 0.860207989 3 2 1 NA NA NA NA NA NA NA
12 1.450130 0.022730640 3 1 1 NA NA NA NA NA NA NA
13 1.420545 0.217171172 2 1 1 NA NA NA NA NA NA NA
14 1.423005 -0.403002412 3 1 1 NA NA NA NA NA NA NA
15 1.435902 0.087369742 2 4 1 NA NA NA NA NA NA NA
16 1.423901 -0.183870429 1 3 1 NA NA NA NA NA NA NA
17 1.457208 -0.194577002 4 3 1 NA NA NA NA NA NA NA
18 1.414280 -0.349718516 2 1 1 NA NA NA NA NA NA NA
19 1.443383 -0.508781244 3 3 1 NA NA NA NA NA NA NA
20 1.434954 0.494883111 3 1 1 NA NA NA NA NA NA NA
21 1.429499 0.258041067 2 3 1 NA NA NA NA NA NA NA
22 1.441897 -0.922621989 2 3 1 NA NA NA NA NA NA NA
23 1.423713 0.431254949 3 2 1 NA NA NA NA NA NA NA
24 1.435395 -0.294218881 3 3 1 NA NA NA NA NA NA NA
25 1.425944 -0.425548895 2 2 1 NA NA NA NA NA NA NA
26 1.437115 0.057176054 2 2 1 NA NA NA NA NA NA NA
27 1.441326 0.289090158 1 1 1 NA NA NA NA NA NA NA
28 1.422953 -0.473079489 3 4 1 NA NA NA NA NA NA NA
29 1.437797 -0.385664863 4 3 1 NA NA NA NA NA NA NA
30 1.472121 -0.154780107 2 3 1 NA NA NA NA NA NA NA
31 1.421782 0.100536296 NA 2 1 NA NA NA NA NA NA NA
32 1.457672 0.634791958 4 2 1 NA NA NA NA NA NA NA
33 1.430842 -0.387252617 4 1 1 NA NA NA NA NA NA NA
34 1.431523 -0.181741088 4 1 1 NA NA NA NA NA NA NA
35 1.421395 -0.311562695 2 4 1 NA NA NA NA NA NA NA
36 1.434496 -0.044115907 1 3 1 NA NA NA NA NA NA NA
37 1.425383 -0.657409991 3 3 1 NA NA NA NA NA NA NA
38 1.421802 0.159577214 4 1 1 NA NA NA NA NA NA NA
39 1.430094 -0.460416933 3 2 1 NA NA NA NA NA NA NA
40 1.447621 NA 3 3 1 NA NA NA NA NA NA NA
41 1.434797 -0.248909867 1 3 1 NA NA NA NA NA NA NA
42 1.446091 -0.609021545 4 3 1 NA NA NA NA NA NA NA
43 1.445306 0.025471883 1 3 1 NA NA NA NA NA NA NA
44 1.448783 0.066648592 2 4 1 NA NA NA NA NA NA NA
45 1.450617 -0.276108719 2 4 1 NA NA NA NA NA NA NA
46 1.415055 -0.179737577 1 1 1 NA NA NA NA NA NA NA
47 1.436590 0.181190937 4 4 1 NA NA NA NA NA NA NA
48 1.433938 -0.453871693 2 4 1 NA NA NA NA NA NA NA
49 1.414941 0.448629602 4 1 1 NA NA NA NA NA NA NA
50 1.421807 -0.529811821 1 2 1 NA NA NA NA NA NA NA
51 1.453203 -0.028304571 4 1 1 NA NA NA NA NA NA NA
52 1.452129 -0.520318482 4 3 1 NA NA NA NA NA NA NA
53 1.431510 0.171317619 4 2 1 NA NA NA NA NA NA NA
54 1.430082 0.432732046 3 1 1 NA NA NA NA NA NA NA
55 1.443492 -0.346286005 3 2 1 NA NA NA NA NA NA NA
56 1.436460 -0.469375653 3 3 1 NA NA NA NA NA NA NA
57 1.418119 0.031021711 2 NA 1 NA NA NA NA NA NA NA
58 1.434971 -0.118837515 3 4 1 NA NA NA NA NA NA NA
59 1.445599 0.507769984 3 4 1 NA NA NA NA NA NA NA
60 1.437097 0.271797031 4 3 1 NA NA NA NA NA NA NA
61 1.428360 -0.124442204 2 4 1 NA NA NA NA NA NA NA
62 1.440550 0.277677389 2 1 1 NA NA NA NA NA NA NA
63 1.443014 -0.102893730 1 4 1 NA NA NA NA NA NA NA
64 1.424298 NA 2 4 1 NA NA NA NA NA NA NA
65 1.448823 -0.678303052 2 4 1 NA NA NA NA NA NA NA
66 1.425834 0.478880037 3 1 1 NA NA NA NA NA NA NA
67 1.427102 -0.428028760 2 3 1 NA NA NA NA NA NA NA
68 1.414240 0.048119185 4 3 1 NA NA NA NA NA NA NA
69 1.456218 0.216932805 NA 4 1 NA NA NA NA NA NA NA
70 1.470594 -0.234575269 1 1 1 NA NA NA NA NA NA NA
71 1.425058 0.006827078 2 4 1 NA NA NA NA NA NA NA
72 1.432371 -0.456055171 3 4 1 NA NA NA NA NA NA NA
73 1.441656 0.346486708 4 2 1 NA NA NA NA NA NA NA
74 1.434952 0.205092215 4 4 1 NA NA NA NA NA NA NA
75 1.402860 -0.136596858 1 3 1 NA NA NA NA NA NA NA
76 1.453363 -0.500179043 4 2 1 NA NA NA NA NA NA NA
77 1.432909 0.527352086 NA 2 1 NA NA NA NA NA NA NA
78 1.435103 0.022742250 2 3 1 NA NA NA NA NA NA NA
79 1.434462 NA 2 2 1 NA NA NA NA NA NA NA
80 1.434661 -0.002032440 2 1 1 NA NA NA NA NA NA NA
81 1.445881 -0.154246160 4 4 1 NA NA NA NA NA NA NA
82 1.442548 0.140201825 3 2 1 NA NA NA NA NA NA NA
83 1.430097 -0.141417121 3 4 1 NA NA NA NA NA NA NA
84 1.430119 NA 1 1 1 NA NA NA NA NA NA NA
85 1.430315 -0.021285339 2 1 1 NA NA NA NA NA NA NA
86 1.437584 -0.010196306 1 2 1 NA NA NA NA NA NA NA
87 1.409738 -0.089747520 3 3 1 NA NA NA NA NA NA NA
88 1.422388 -0.083699898 1 3 1 NA NA NA NA NA NA NA
89 1.422509 -0.044061996 2 2 1 NA NA NA NA NA NA NA
90 1.439432 -0.209291697 1 4 1 NA NA NA NA NA NA NA
91 1.430175 0.639036426 3 2 1 NA NA NA NA NA NA NA
92 1.418002 0.094698299 1 1 1 NA NA NA NA NA NA NA
93 1.423812 -0.055510622 4 NA 1 NA NA NA NA NA NA NA
94 1.423473 -0.421318463 4 3 1 NA NA NA NA NA NA NA
95 1.434412 0.125295503 1 1 1 NA NA NA NA NA NA NA
96 1.450844 0.213084904 4 3 1 NA NA NA NA NA NA NA
97 1.433371 -0.161914659 4 2 1 NA NA NA NA NA NA NA
98 1.444378 -0.034767685 3 2 1 NA NA NA NA NA NA NA
99 1.422523 -0.320681689 3 4 1 NA NA NA NA NA NA NA
100 1.410394 0.058192962 4 3 1 NA NA NA NA NA NA NA
O22:abs(y - C2) O23:abs(y - C2) O24:abs(y - C2) y
1 NA NA NA -4.76915977
2 NA NA NA -2.69277172
3 NA NA NA -1.17551547
4 NA NA NA -4.57464473
5 NA NA NA -2.20260004
6 NA NA NA -3.48995315
7 NA NA NA -0.44987258
8 NA NA NA -2.29588848
9 NA NA NA -4.49135812
10 NA NA NA -5.52545368
11 NA NA NA -4.16286741
12 NA NA NA -2.93455761
13 NA NA NA -0.04202496
14 NA NA NA -1.63149775
15 NA NA NA -0.97786151
16 NA NA NA -1.79100431
17 NA NA NA -6.26520032
18 NA NA NA -1.36028709
19 NA NA NA -1.15396597
20 NA NA NA -3.21707239
21 NA NA NA -1.59389898
22 NA NA NA -5.50335066
23 NA NA NA 0.57290123
24 NA NA NA -8.22270323
25 NA NA NA -1.41364158
26 NA NA NA -6.28031574
27 NA NA NA -3.15624425
28 NA NA NA -3.55693639
29 NA NA NA -1.11821124
30 NA NA NA -2.82834175
31 NA NA NA -3.72259860
32 NA NA NA -1.75256656
33 NA NA NA -5.55044409
34 NA NA NA -7.45068147
35 NA NA NA -0.97491919
36 NA NA NA -2.98356481
37 NA NA NA -1.86039471
38 NA NA NA -7.28754607
39 NA NA NA -8.66234796
40 NA NA NA -4.16291375
41 NA NA NA -3.48250771
42 NA NA NA -7.27930410
43 NA NA NA -6.12866190
44 NA NA NA -4.96880803
45 NA NA NA -4.76746713
46 NA NA NA -1.91249177
47 NA NA NA -0.61884029
48 NA NA NA -0.20496175
49 NA NA NA -7.12636055
50 NA NA NA -6.23103837
51 NA NA NA -3.32561065
52 NA NA NA -2.95942339
53 NA NA NA -4.44915114
54 NA NA NA -0.81566463
55 NA NA NA -6.50029573
56 NA NA NA -2.74718050
57 NA NA NA -6.35015663
58 NA NA NA -2.69505883
59 NA NA NA -1.55660833
60 NA NA NA -3.76240209
61 NA NA NA -3.92885797
62 NA NA NA -1.72044748
63 NA NA NA -0.56602625
64 NA NA NA -4.42235015
65 NA NA NA -2.39122287
66 NA NA NA -0.81807247
67 NA NA NA -6.48196782
68 NA NA NA -1.37306273
69 NA NA NA -4.99886487
70 NA NA NA -5.82288217
71 NA NA NA -2.68234219
72 NA NA NA -3.96170442
73 NA NA NA -7.19573667
74 NA NA NA -5.08799713
75 NA NA NA -1.32967262
76 NA NA NA -2.56532332
77 NA NA NA -3.21002900
78 NA NA NA -3.40559790
79 NA NA NA -4.56223913
80 NA NA NA -2.04250454
81 NA NA NA -2.20378059
82 NA NA NA -3.37471317
83 NA NA NA -0.95345385
84 NA NA NA -4.89337660
85 NA NA NA -9.82258463
86 NA NA NA -4.51800734
87 NA NA NA -0.18662049
88 NA NA NA -2.87120881
89 NA NA NA 1.29290150
90 NA NA NA -1.39497744
91 NA NA NA 1.14575040
92 NA NA NA 0.92801246
93 NA NA NA -2.59938157
94 NA NA NA -3.26905923
95 NA NA NA -3.26861434
96 NA NA NA -5.71017484
97 NA NA NA -3.76781806
98 NA NA NA -2.02677390
99 NA NA NA -2.96199765
100 NA NA NA -4.81129496
$m6c$spM_lvlone
center scale
C1 1.43410054 0.01299651
C2 -0.06490582 0.33317347
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(y - C2) 3.29470420 2.19275349
O22:abs(y - C2) 0.80813977 1.84992792
O23:abs(y - C2) 0.98554111 1.92203764
O24:abs(y - C2) 0.67287100 1.40175060
y -3.34428345 2.27649507
$m6c$mu_reg_norm
[1] 0
$m6c$tau_reg_norm
[1] 1e-04
$m6c$shape_tau_norm
[1] 0.01
$m6c$rate_tau_norm
[1] 0.01
$m6c$mu_reg_gamma
[1] 0
$m6c$tau_reg_gamma
[1] 1e-04
$m6c$shape_tau_gamma
[1] 0.01
$m6c$rate_tau_gamma
[1] 0.01
$m6c$mu_reg_multinomial
[1] 0
$m6c$tau_reg_multinomial
[1] 1e-04
$m6c$mu_reg_ordinal
[1] 0
$m6c$tau_reg_ordinal
[1] 1e-04
$m6c$mu_delta_ordinal
[1] 0
$m6c$tau_delta_ordinal
[1] 1e-04
$m6d
$m6d$M_lvlone
SBP bili creat (Intercept) age genderfemale log(bili) exp(creat)
10 108.00000 0.9 1.10 1 35 0 NA NA
14 105.33333 1.0 0.77 1 38 0 NA NA
41 110.00000 0.9 1.14 1 78 1 NA NA
77 106.00000 0.7 0.99 1 23 0 NA NA
91 114.66667 0.6 0.90 1 40 0 NA NA
105 139.33333 1.2 0.88 1 54 0 NA NA
114 124.00000 0.3 0.68 1 31 1 NA NA
135 100.00000 0.5 0.66 1 27 1 NA NA
149 114.66667 0.4 1.05 1 37 0 NA NA
154 156.66667 NA NA 1 50 1 NA NA
155 127.33333 0.8 0.98 1 63 0 NA NA
176 106.66667 0.6 0.67 1 26 1 NA NA
215 114.00000 0.9 0.74 1 35 1 NA NA
220 126.00000 NA NA 1 44 0 NA NA
224 86.00000 1.0 0.76 1 34 1 NA NA
226 117.33333 0.6 0.93 1 60 0 NA NA
264 128.00000 0.6 0.79 1 24 0 NA NA
282 113.33333 NA NA 1 48 0 NA NA
286 117.33333 0.4 0.57 1 68 1 NA NA
300 115.33333 0.7 0.83 1 37 0 NA NA
301 126.66667 0.6 0.77 1 35 0 NA NA
311 110.00000 0.6 0.72 1 59 0 NA NA
317 124.66667 0.6 0.76 1 20 1 NA NA
337 111.33333 0.9 0.91 1 71 1 NA NA
383 153.33333 NA NA 1 53 0 NA NA
391 115.33333 0.8 0.91 1 23 0 NA NA
392 126.66667 1.0 0.83 1 32 0 NA NA
420 98.00000 0.8 0.66 1 36 1 NA NA
422 166.66667 0.6 1.22 1 48 0 NA NA
461 124.66667 1.0 0.99 1 56 0 NA NA
475 112.66667 0.5 0.64 1 40 1 NA NA
483 106.66667 0.6 0.88 1 27 0 NA NA
501 112.66667 0.8 0.95 1 23 0 NA NA
533 110.66667 0.8 0.72 1 44 1 NA NA
538 127.33333 0.4 0.73 1 62 1 NA NA
550 134.00000 1.2 0.73 1 59 1 NA NA
557 135.33333 0.7 1.11 1 54 1 NA NA
589 128.66667 0.7 0.84 1 44 0 NA NA
598 118.66667 0.6 0.68 1 62 1 NA NA
621 120.66667 0.5 0.75 1 31 0 NA NA
631 116.66667 0.7 0.68 1 61 1 NA NA
637 118.66667 0.6 0.79 1 45 1 NA NA
650 111.33333 0.5 0.80 1 41 1 NA NA
673 135.33333 0.4 0.73 1 49 1 NA NA
696 140.66667 1.1 0.81 1 38 0 NA NA
703 106.00000 0.7 0.95 1 36 0 NA NA
704 124.66667 0.4 0.66 1 58 0 NA NA
726 112.66667 0.6 1.05 1 21 1 NA NA
739 107.33333 0.4 0.78 1 50 0 NA NA
747 105.33333 0.8 0.62 1 35 1 NA NA
755 115.33333 0.5 0.47 1 37 1 NA NA
756 123.33333 1.4 0.95 1 37 0 NA NA
766 117.33333 0.8 0.84 1 47 0 NA NA
777 124.00000 0.7 1.00 1 31 0 NA NA
793 109.33333 0.6 0.92 1 42 1 NA NA
818 127.33333 1.0 0.79 1 38 0 NA NA
850 98.66667 1.0 0.75 1 31 0 NA NA
862 108.66667 1.1 0.93 1 43 0 NA NA
866 108.00000 0.5 0.69 1 35 1 NA NA
867 109.33333 0.7 0.80 1 23 1 NA NA
887 160.66667 0.7 0.64 1 54 1 NA NA
894 138.66667 0.7 0.61 1 45 1 NA NA
913 99.33333 0.9 0.72 1 27 1 NA NA
974 114.00000 0.6 0.58 1 23 0 NA NA
976 137.33333 0.8 1.07 1 57 0 NA NA
980 117.33333 0.5 0.69 1 41 1 NA NA
1028 118.66667 0.8 0.74 1 30 0 NA NA
1039 124.66667 1.0 1.07 1 58 0 NA NA
1040 112.00000 0.7 0.97 1 29 1 NA NA
1046 110.66667 1.0 0.62 1 43 1 NA NA
1055 112.00000 1.0 0.69 1 35 0 NA NA
1092 114.66667 0.5 0.68 1 37 1 NA NA
1108 108.66667 0.7 1.03 1 21 0 NA NA
1150 141.33333 1.1 1.15 1 71 0 NA NA
1153 122.00000 0.4 0.94 1 26 0 NA NA
1165 98.00000 1.1 0.92 1 45 0 NA NA
1174 116.66667 0.7 0.84 1 63 1 NA NA
1212 124.66667 0.5 1.35 1 61 0 NA NA
1231 134.66667 0.9 1.10 1 56 0 NA NA
1245 130.00000 NA NA 1 66 1 NA NA
1247 108.66667 0.6 0.82 1 52 1 NA NA
1273 126.66667 0.4 1.00 1 42 0 NA NA
1278 103.33333 0.6 0.69 1 29 0 NA NA
1299 112.00000 0.7 1.10 1 39 0 NA NA
1346 99.33333 0.5 0.77 1 23 1 NA NA
1352 102.00000 0.4 1.04 1 46 1 NA NA
1360 103.00000 0.5 1.02 1 42 0 NA NA
1397 106.66667 0.7 0.66 1 31 1 NA NA
1399 106.66667 0.5 1.15 1 33 1 NA NA
1410 167.33333 0.6 0.72 1 70 1 NA NA
1439 130.00000 1.1 0.69 1 44 0 NA NA
1481 93.33333 0.7 0.77 1 58 1 NA NA
1494 120.66667 0.7 1.05 1 70 0 NA NA
1499 130.00000 0.8 1.29 1 38 0 NA NA
1509 111.33333 1.1 0.88 1 73 0 NA NA
1512 127.33333 0.4 0.77 1 47 1 NA NA
1520 120.00000 0.8 0.71 1 56 1 NA NA
1560 144.00000 0.8 1.08 1 32 0 NA NA
1602 118.00000 0.5 1.15 1 28 0 NA NA
1608 140.66667 0.7 0.89 1 58 0 NA NA
1619 122.00000 0.8 0.90 1 34 0 NA NA
1642 128.66667 0.8 1.18 1 30 0 NA NA
1648 100.00000 1.0 0.73 1 33 1 NA NA
1663 124.00000 0.4 0.96 1 51 0 NA NA
1671 140.66667 0.5 0.86 1 74 1 NA NA
1691 122.00000 1.0 1.12 1 56 0 NA NA
1701 119.33333 0.7 0.77 1 56 1 NA NA
1726 154.66667 0.6 1.12 1 31 0 NA NA
1733 106.66667 0.5 0.93 1 38 1 NA NA
1743 114.66667 0.5 1.13 1 74 0 NA NA
1753 118.66667 1.4 0.85 1 42 1 NA NA
1761 112.66667 0.6 0.68 1 47 1 NA NA
1765 125.33333 0.6 0.99 1 49 0 NA NA
1766 114.00000 1.2 0.98 1 61 0 NA NA
1795 177.33333 0.8 0.63 1 65 1 NA NA
1804 122.66667 0.8 1.01 1 43 0 NA NA
1809 116.00000 0.7 0.79 1 26 0 NA NA
1813 96.66667 NA NA 1 36 0 NA NA
1858 97.33333 1.1 0.83 1 43 1 NA NA
1878 122.00000 0.6 0.96 1 51 0 NA NA
1889 128.00000 0.7 0.98 1 34 0 NA NA
1933 104.66667 1.2 0.52 1 77 1 NA NA
1940 110.66667 0.7 0.83 1 48 1 NA NA
1988 136.00000 0.7 0.64 1 62 1 NA NA
1993 116.66667 0.7 0.72 1 45 1 NA NA
1997 123.33333 0.5 1.01 1 56 0 NA NA
2005 122.00000 0.6 0.93 1 78 1 NA NA
2032 126.66667 0.7 0.77 1 20 0 NA NA
2034 116.00000 0.6 0.98 1 25 0 NA NA
2036 122.00000 0.4 0.67 1 52 1 NA NA
2054 111.33333 0.7 0.64 1 43 1 NA NA
2086 124.66667 0.3 0.56 1 47 1 NA NA
2122 141.33333 0.7 0.68 1 71 1 NA NA
2124 115.33333 0.5 0.96 1 27 0 NA NA
2133 134.66667 0.5 1.38 1 60 0 NA NA
2163 128.66667 0.5 0.64 1 53 1 NA NA
2174 148.66667 0.6 0.85 1 55 1 NA NA
2175 125.33333 1.0 0.72 1 64 0 NA NA
2195 109.33333 1.3 0.85 1 42 1 NA NA
2197 94.00000 0.7 0.87 1 22 0 NA NA
2202 118.66667 0.7 0.88 1 20 0 NA NA
2222 140.66667 0.6 0.66 1 75 1 NA NA
2231 104.00000 0.8 0.83 1 32 0 NA NA
2248 107.33333 0.5 0.82 1 29 1 NA NA
2260 142.00000 0.5 0.76 1 45 1 NA NA
2265 93.33333 0.6 0.56 1 40 1 NA NA
2268 110.00000 0.8 0.82 1 61 1 NA NA
2306 106.66667 0.9 0.95 1 32 0 NA NA
2313 138.00000 0.6 0.86 1 48 1 NA NA
2333 126.00000 0.7 1.06 1 70 0 NA NA
2337 124.00000 0.4 0.50 1 43 1 NA NA
2351 136.00000 0.6 1.03 1 33 0 NA NA
2375 98.66667 1.0 0.82 1 34 0 NA NA
2378 134.66667 0.6 0.77 1 25 0 NA NA
2385 101.33333 0.5 0.74 1 48 1 NA NA
2401 114.66667 0.7 0.84 1 69 1 NA NA
2417 122.66667 0.7 0.68 1 68 1 NA NA
2428 140.66667 0.6 0.74 1 65 0 NA NA
2431 115.33333 0.6 0.69 1 22 1 NA NA
2440 116.66667 0.4 0.65 1 44 1 NA NA
2446 132.00000 0.5 0.73 1 30 0 NA NA
2453 127.33333 0.7 0.80 1 60 0 NA NA
2460 94.66667 0.5 0.65 1 22 1 NA NA
2475 116.00000 0.8 0.92 1 39 0 NA NA
2491 102.66667 0.7 0.64 1 43 1 NA NA
2493 114.00000 0.5 0.83 1 46 1 NA NA
2519 116.00000 0.8 0.73 1 38 0 NA NA
2549 115.33333 0.8 0.85 1 36 0 NA NA
2551 111.33333 0.8 0.58 1 68 1 NA NA
2552 86.00000 0.6 0.69 1 36 1 NA NA
2554 112.66667 0.9 0.89 1 21 0 NA NA
2562 93.33333 NA NA 1 62 0 NA NA
2590 98.66667 1.1 0.84 1 23 1 NA NA
2615 125.33333 1.2 0.91 1 22 0 NA NA
2618 145.33333 1.1 0.82 1 37 0 NA NA
2631 106.00000 1.1 0.65 1 37 1 NA NA
2648 116.66667 0.8 1.12 1 43 0 NA NA
2661 141.33333 0.5 0.94 1 35 0 NA NA
2672 126.66667 0.9 0.84 1 29 0 NA NA
2676 111.33333 NA NA 1 41 0 NA NA
2681 102.66667 0.9 0.79 1 21 1 NA NA
2718 111.33333 0.7 0.95 1 20 0 NA NA
2733 142.66667 0.6 0.80 1 53 1 NA NA
2752 98.66667 1.0 1.01 1 24 0 NA NA
2763 124.00000 0.8 0.94 1 28 0 NA NA
2764 129.33333 1.0 1.08 1 27 0 NA NA
$m6d$spM_lvlone
center scale
SBP 119.2956989 15.3559299
bili 0.7207865 0.2266570
creat 0.8437640 0.1711968
(Intercept) NA NA
age 43.5107527 15.0631963
genderfemale NA NA
log(bili) -0.3758477 0.3135642
exp(creat) 2.3601663 0.4232889
$m6d$mu_reg_norm
[1] 0
$m6d$tau_reg_norm
[1] 1e-04
$m6d$shape_tau_norm
[1] 0.01
$m6d$rate_tau_norm
[1] 0.01
$m6e
$m6e$M_lvlone
SBP bili creat (Intercept) age genderfemale log(bili) exp(creat)
10 108.00000 0.9 1.10 1 35 0 NA NA
14 105.33333 1.0 0.77 1 38 0 NA NA
41 110.00000 0.9 1.14 1 78 1 NA NA
77 106.00000 0.7 0.99 1 23 0 NA NA
91 114.66667 0.6 0.90 1 40 0 NA NA
105 139.33333 1.2 0.88 1 54 0 NA NA
114 124.00000 0.3 0.68 1 31 1 NA NA
135 100.00000 0.5 0.66 1 27 1 NA NA
149 114.66667 0.4 1.05 1 37 0 NA NA
154 156.66667 NA NA 1 50 1 NA NA
155 127.33333 0.8 0.98 1 63 0 NA NA
176 106.66667 0.6 0.67 1 26 1 NA NA
215 114.00000 0.9 0.74 1 35 1 NA NA
220 126.00000 NA NA 1 44 0 NA NA
224 86.00000 1.0 0.76 1 34 1 NA NA
226 117.33333 0.6 0.93 1 60 0 NA NA
264 128.00000 0.6 0.79 1 24 0 NA NA
282 113.33333 NA NA 1 48 0 NA NA
286 117.33333 0.4 0.57 1 68 1 NA NA
300 115.33333 0.7 0.83 1 37 0 NA NA
301 126.66667 0.6 0.77 1 35 0 NA NA
311 110.00000 0.6 0.72 1 59 0 NA NA
317 124.66667 0.6 0.76 1 20 1 NA NA
337 111.33333 0.9 0.91 1 71 1 NA NA
383 153.33333 NA NA 1 53 0 NA NA
391 115.33333 0.8 0.91 1 23 0 NA NA
392 126.66667 1.0 0.83 1 32 0 NA NA
420 98.00000 0.8 0.66 1 36 1 NA NA
422 166.66667 0.6 1.22 1 48 0 NA NA
461 124.66667 1.0 0.99 1 56 0 NA NA
475 112.66667 0.5 0.64 1 40 1 NA NA
483 106.66667 0.6 0.88 1 27 0 NA NA
501 112.66667 0.8 0.95 1 23 0 NA NA
533 110.66667 0.8 0.72 1 44 1 NA NA
538 127.33333 0.4 0.73 1 62 1 NA NA
550 134.00000 1.2 0.73 1 59 1 NA NA
557 135.33333 0.7 1.11 1 54 1 NA NA
589 128.66667 0.7 0.84 1 44 0 NA NA
598 118.66667 0.6 0.68 1 62 1 NA NA
621 120.66667 0.5 0.75 1 31 0 NA NA
631 116.66667 0.7 0.68 1 61 1 NA NA
637 118.66667 0.6 0.79 1 45 1 NA NA
650 111.33333 0.5 0.80 1 41 1 NA NA
673 135.33333 0.4 0.73 1 49 1 NA NA
696 140.66667 1.1 0.81 1 38 0 NA NA
703 106.00000 0.7 0.95 1 36 0 NA NA
704 124.66667 0.4 0.66 1 58 0 NA NA
726 112.66667 0.6 1.05 1 21 1 NA NA
739 107.33333 0.4 0.78 1 50 0 NA NA
747 105.33333 0.8 0.62 1 35 1 NA NA
755 115.33333 0.5 0.47 1 37 1 NA NA
756 123.33333 1.4 0.95 1 37 0 NA NA
766 117.33333 0.8 0.84 1 47 0 NA NA
777 124.00000 0.7 1.00 1 31 0 NA NA
793 109.33333 0.6 0.92 1 42 1 NA NA
818 127.33333 1.0 0.79 1 38 0 NA NA
850 98.66667 1.0 0.75 1 31 0 NA NA
862 108.66667 1.1 0.93 1 43 0 NA NA
866 108.00000 0.5 0.69 1 35 1 NA NA
867 109.33333 0.7 0.80 1 23 1 NA NA
887 160.66667 0.7 0.64 1 54 1 NA NA
894 138.66667 0.7 0.61 1 45 1 NA NA
913 99.33333 0.9 0.72 1 27 1 NA NA
974 114.00000 0.6 0.58 1 23 0 NA NA
976 137.33333 0.8 1.07 1 57 0 NA NA
980 117.33333 0.5 0.69 1 41 1 NA NA
1028 118.66667 0.8 0.74 1 30 0 NA NA
1039 124.66667 1.0 1.07 1 58 0 NA NA
1040 112.00000 0.7 0.97 1 29 1 NA NA
1046 110.66667 1.0 0.62 1 43 1 NA NA
1055 112.00000 1.0 0.69 1 35 0 NA NA
1092 114.66667 0.5 0.68 1 37 1 NA NA
1108 108.66667 0.7 1.03 1 21 0 NA NA
1150 141.33333 1.1 1.15 1 71 0 NA NA
1153 122.00000 0.4 0.94 1 26 0 NA NA
1165 98.00000 1.1 0.92 1 45 0 NA NA
1174 116.66667 0.7 0.84 1 63 1 NA NA
1212 124.66667 0.5 1.35 1 61 0 NA NA
1231 134.66667 0.9 1.10 1 56 0 NA NA
1245 130.00000 NA NA 1 66 1 NA NA
1247 108.66667 0.6 0.82 1 52 1 NA NA
1273 126.66667 0.4 1.00 1 42 0 NA NA
1278 103.33333 0.6 0.69 1 29 0 NA NA
1299 112.00000 0.7 1.10 1 39 0 NA NA
1346 99.33333 0.5 0.77 1 23 1 NA NA
1352 102.00000 0.4 1.04 1 46 1 NA NA
1360 103.00000 0.5 1.02 1 42 0 NA NA
1397 106.66667 0.7 0.66 1 31 1 NA NA
1399 106.66667 0.5 1.15 1 33 1 NA NA
1410 167.33333 0.6 0.72 1 70 1 NA NA
1439 130.00000 1.1 0.69 1 44 0 NA NA
1481 93.33333 0.7 0.77 1 58 1 NA NA
1494 120.66667 0.7 1.05 1 70 0 NA NA
1499 130.00000 0.8 1.29 1 38 0 NA NA
1509 111.33333 1.1 0.88 1 73 0 NA NA
1512 127.33333 0.4 0.77 1 47 1 NA NA
1520 120.00000 0.8 0.71 1 56 1 NA NA
1560 144.00000 0.8 1.08 1 32 0 NA NA
1602 118.00000 0.5 1.15 1 28 0 NA NA
1608 140.66667 0.7 0.89 1 58 0 NA NA
1619 122.00000 0.8 0.90 1 34 0 NA NA
1642 128.66667 0.8 1.18 1 30 0 NA NA
1648 100.00000 1.0 0.73 1 33 1 NA NA
1663 124.00000 0.4 0.96 1 51 0 NA NA
1671 140.66667 0.5 0.86 1 74 1 NA NA
1691 122.00000 1.0 1.12 1 56 0 NA NA
1701 119.33333 0.7 0.77 1 56 1 NA NA
1726 154.66667 0.6 1.12 1 31 0 NA NA
1733 106.66667 0.5 0.93 1 38 1 NA NA
1743 114.66667 0.5 1.13 1 74 0 NA NA
1753 118.66667 1.4 0.85 1 42 1 NA NA
1761 112.66667 0.6 0.68 1 47 1 NA NA
1765 125.33333 0.6 0.99 1 49 0 NA NA
1766 114.00000 1.2 0.98 1 61 0 NA NA
1795 177.33333 0.8 0.63 1 65 1 NA NA
1804 122.66667 0.8 1.01 1 43 0 NA NA
1809 116.00000 0.7 0.79 1 26 0 NA NA
1813 96.66667 NA NA 1 36 0 NA NA
1858 97.33333 1.1 0.83 1 43 1 NA NA
1878 122.00000 0.6 0.96 1 51 0 NA NA
1889 128.00000 0.7 0.98 1 34 0 NA NA
1933 104.66667 1.2 0.52 1 77 1 NA NA
1940 110.66667 0.7 0.83 1 48 1 NA NA
1988 136.00000 0.7 0.64 1 62 1 NA NA
1993 116.66667 0.7 0.72 1 45 1 NA NA
1997 123.33333 0.5 1.01 1 56 0 NA NA
2005 122.00000 0.6 0.93 1 78 1 NA NA
2032 126.66667 0.7 0.77 1 20 0 NA NA
2034 116.00000 0.6 0.98 1 25 0 NA NA
2036 122.00000 0.4 0.67 1 52 1 NA NA
2054 111.33333 0.7 0.64 1 43 1 NA NA
2086 124.66667 0.3 0.56 1 47 1 NA NA
2122 141.33333 0.7 0.68 1 71 1 NA NA
2124 115.33333 0.5 0.96 1 27 0 NA NA
2133 134.66667 0.5 1.38 1 60 0 NA NA
2163 128.66667 0.5 0.64 1 53 1 NA NA
2174 148.66667 0.6 0.85 1 55 1 NA NA
2175 125.33333 1.0 0.72 1 64 0 NA NA
2195 109.33333 1.3 0.85 1 42 1 NA NA
2197 94.00000 0.7 0.87 1 22 0 NA NA
2202 118.66667 0.7 0.88 1 20 0 NA NA
2222 140.66667 0.6 0.66 1 75 1 NA NA
2231 104.00000 0.8 0.83 1 32 0 NA NA
2248 107.33333 0.5 0.82 1 29 1 NA NA
2260 142.00000 0.5 0.76 1 45 1 NA NA
2265 93.33333 0.6 0.56 1 40 1 NA NA
2268 110.00000 0.8 0.82 1 61 1 NA NA
2306 106.66667 0.9 0.95 1 32 0 NA NA
2313 138.00000 0.6 0.86 1 48 1 NA NA
2333 126.00000 0.7 1.06 1 70 0 NA NA
2337 124.00000 0.4 0.50 1 43 1 NA NA
2351 136.00000 0.6 1.03 1 33 0 NA NA
2375 98.66667 1.0 0.82 1 34 0 NA NA
2378 134.66667 0.6 0.77 1 25 0 NA NA
2385 101.33333 0.5 0.74 1 48 1 NA NA
2401 114.66667 0.7 0.84 1 69 1 NA NA
2417 122.66667 0.7 0.68 1 68 1 NA NA
2428 140.66667 0.6 0.74 1 65 0 NA NA
2431 115.33333 0.6 0.69 1 22 1 NA NA
2440 116.66667 0.4 0.65 1 44 1 NA NA
2446 132.00000 0.5 0.73 1 30 0 NA NA
2453 127.33333 0.7 0.80 1 60 0 NA NA
2460 94.66667 0.5 0.65 1 22 1 NA NA
2475 116.00000 0.8 0.92 1 39 0 NA NA
2491 102.66667 0.7 0.64 1 43 1 NA NA
2493 114.00000 0.5 0.83 1 46 1 NA NA
2519 116.00000 0.8 0.73 1 38 0 NA NA
2549 115.33333 0.8 0.85 1 36 0 NA NA
2551 111.33333 0.8 0.58 1 68 1 NA NA
2552 86.00000 0.6 0.69 1 36 1 NA NA
2554 112.66667 0.9 0.89 1 21 0 NA NA
2562 93.33333 NA NA 1 62 0 NA NA
2590 98.66667 1.1 0.84 1 23 1 NA NA
2615 125.33333 1.2 0.91 1 22 0 NA NA
2618 145.33333 1.1 0.82 1 37 0 NA NA
2631 106.00000 1.1 0.65 1 37 1 NA NA
2648 116.66667 0.8 1.12 1 43 0 NA NA
2661 141.33333 0.5 0.94 1 35 0 NA NA
2672 126.66667 0.9 0.84 1 29 0 NA NA
2676 111.33333 NA NA 1 41 0 NA NA
2681 102.66667 0.9 0.79 1 21 1 NA NA
2718 111.33333 0.7 0.95 1 20 0 NA NA
2733 142.66667 0.6 0.80 1 53 1 NA NA
2752 98.66667 1.0 1.01 1 24 0 NA NA
2763 124.00000 0.8 0.94 1 28 0 NA NA
2764 129.33333 1.0 1.08 1 27 0 NA NA
$m6e$spM_lvlone
center scale
SBP 119.2956989 15.3559299
bili 0.7207865 0.2266570
creat 0.8437640 0.1711968
(Intercept) NA NA
age 43.5107527 15.0631963
genderfemale NA NA
log(bili) -0.3758477 0.3135642
exp(creat) 2.3601663 0.4232889
$m6e$mu_reg_norm
[1] 0
$m6e$tau_reg_norm
[1] 1e-04
$m6e$shape_tau_norm
[1] 0.01
$m6e$rate_tau_norm
[1] 0.01
$m6f
$m6f$M_lvlone
SBP bili creat (Intercept) age genderfemale log(bili) exp(creat)
10 108.00000 0.9 1.10 1 35 0 NA NA
14 105.33333 1.0 0.77 1 38 0 NA NA
41 110.00000 0.9 1.14 1 78 1 NA NA
77 106.00000 0.7 0.99 1 23 0 NA NA
91 114.66667 0.6 0.90 1 40 0 NA NA
105 139.33333 1.2 0.88 1 54 0 NA NA
114 124.00000 0.3 0.68 1 31 1 NA NA
135 100.00000 0.5 0.66 1 27 1 NA NA
149 114.66667 0.4 1.05 1 37 0 NA NA
154 156.66667 NA NA 1 50 1 NA NA
155 127.33333 0.8 0.98 1 63 0 NA NA
176 106.66667 0.6 0.67 1 26 1 NA NA
215 114.00000 0.9 0.74 1 35 1 NA NA
220 126.00000 NA NA 1 44 0 NA NA
224 86.00000 1.0 0.76 1 34 1 NA NA
226 117.33333 0.6 0.93 1 60 0 NA NA
264 128.00000 0.6 0.79 1 24 0 NA NA
282 113.33333 NA NA 1 48 0 NA NA
286 117.33333 0.4 0.57 1 68 1 NA NA
300 115.33333 0.7 0.83 1 37 0 NA NA
301 126.66667 0.6 0.77 1 35 0 NA NA
311 110.00000 0.6 0.72 1 59 0 NA NA
317 124.66667 0.6 0.76 1 20 1 NA NA
337 111.33333 0.9 0.91 1 71 1 NA NA
383 153.33333 NA NA 1 53 0 NA NA
391 115.33333 0.8 0.91 1 23 0 NA NA
392 126.66667 1.0 0.83 1 32 0 NA NA
420 98.00000 0.8 0.66 1 36 1 NA NA
422 166.66667 0.6 1.22 1 48 0 NA NA
461 124.66667 1.0 0.99 1 56 0 NA NA
475 112.66667 0.5 0.64 1 40 1 NA NA
483 106.66667 0.6 0.88 1 27 0 NA NA
501 112.66667 0.8 0.95 1 23 0 NA NA
533 110.66667 0.8 0.72 1 44 1 NA NA
538 127.33333 0.4 0.73 1 62 1 NA NA
550 134.00000 1.2 0.73 1 59 1 NA NA
557 135.33333 0.7 1.11 1 54 1 NA NA
589 128.66667 0.7 0.84 1 44 0 NA NA
598 118.66667 0.6 0.68 1 62 1 NA NA
621 120.66667 0.5 0.75 1 31 0 NA NA
631 116.66667 0.7 0.68 1 61 1 NA NA
637 118.66667 0.6 0.79 1 45 1 NA NA
650 111.33333 0.5 0.80 1 41 1 NA NA
673 135.33333 0.4 0.73 1 49 1 NA NA
696 140.66667 1.1 0.81 1 38 0 NA NA
703 106.00000 0.7 0.95 1 36 0 NA NA
704 124.66667 0.4 0.66 1 58 0 NA NA
726 112.66667 0.6 1.05 1 21 1 NA NA
739 107.33333 0.4 0.78 1 50 0 NA NA
747 105.33333 0.8 0.62 1 35 1 NA NA
755 115.33333 0.5 0.47 1 37 1 NA NA
756 123.33333 1.4 0.95 1 37 0 NA NA
766 117.33333 0.8 0.84 1 47 0 NA NA
777 124.00000 0.7 1.00 1 31 0 NA NA
793 109.33333 0.6 0.92 1 42 1 NA NA
818 127.33333 1.0 0.79 1 38 0 NA NA
850 98.66667 1.0 0.75 1 31 0 NA NA
862 108.66667 1.1 0.93 1 43 0 NA NA
866 108.00000 0.5 0.69 1 35 1 NA NA
867 109.33333 0.7 0.80 1 23 1 NA NA
887 160.66667 0.7 0.64 1 54 1 NA NA
894 138.66667 0.7 0.61 1 45 1 NA NA
913 99.33333 0.9 0.72 1 27 1 NA NA
974 114.00000 0.6 0.58 1 23 0 NA NA
976 137.33333 0.8 1.07 1 57 0 NA NA
980 117.33333 0.5 0.69 1 41 1 NA NA
1028 118.66667 0.8 0.74 1 30 0 NA NA
1039 124.66667 1.0 1.07 1 58 0 NA NA
1040 112.00000 0.7 0.97 1 29 1 NA NA
1046 110.66667 1.0 0.62 1 43 1 NA NA
1055 112.00000 1.0 0.69 1 35 0 NA NA
1092 114.66667 0.5 0.68 1 37 1 NA NA
1108 108.66667 0.7 1.03 1 21 0 NA NA
1150 141.33333 1.1 1.15 1 71 0 NA NA
1153 122.00000 0.4 0.94 1 26 0 NA NA
1165 98.00000 1.1 0.92 1 45 0 NA NA
1174 116.66667 0.7 0.84 1 63 1 NA NA
1212 124.66667 0.5 1.35 1 61 0 NA NA
1231 134.66667 0.9 1.10 1 56 0 NA NA
1245 130.00000 NA NA 1 66 1 NA NA
1247 108.66667 0.6 0.82 1 52 1 NA NA
1273 126.66667 0.4 1.00 1 42 0 NA NA
1278 103.33333 0.6 0.69 1 29 0 NA NA
1299 112.00000 0.7 1.10 1 39 0 NA NA
1346 99.33333 0.5 0.77 1 23 1 NA NA
1352 102.00000 0.4 1.04 1 46 1 NA NA
1360 103.00000 0.5 1.02 1 42 0 NA NA
1397 106.66667 0.7 0.66 1 31 1 NA NA
1399 106.66667 0.5 1.15 1 33 1 NA NA
1410 167.33333 0.6 0.72 1 70 1 NA NA
1439 130.00000 1.1 0.69 1 44 0 NA NA
1481 93.33333 0.7 0.77 1 58 1 NA NA
1494 120.66667 0.7 1.05 1 70 0 NA NA
1499 130.00000 0.8 1.29 1 38 0 NA NA
1509 111.33333 1.1 0.88 1 73 0 NA NA
1512 127.33333 0.4 0.77 1 47 1 NA NA
1520 120.00000 0.8 0.71 1 56 1 NA NA
1560 144.00000 0.8 1.08 1 32 0 NA NA
1602 118.00000 0.5 1.15 1 28 0 NA NA
1608 140.66667 0.7 0.89 1 58 0 NA NA
1619 122.00000 0.8 0.90 1 34 0 NA NA
1642 128.66667 0.8 1.18 1 30 0 NA NA
1648 100.00000 1.0 0.73 1 33 1 NA NA
1663 124.00000 0.4 0.96 1 51 0 NA NA
1671 140.66667 0.5 0.86 1 74 1 NA NA
1691 122.00000 1.0 1.12 1 56 0 NA NA
1701 119.33333 0.7 0.77 1 56 1 NA NA
1726 154.66667 0.6 1.12 1 31 0 NA NA
1733 106.66667 0.5 0.93 1 38 1 NA NA
1743 114.66667 0.5 1.13 1 74 0 NA NA
1753 118.66667 1.4 0.85 1 42 1 NA NA
1761 112.66667 0.6 0.68 1 47 1 NA NA
1765 125.33333 0.6 0.99 1 49 0 NA NA
1766 114.00000 1.2 0.98 1 61 0 NA NA
1795 177.33333 0.8 0.63 1 65 1 NA NA
1804 122.66667 0.8 1.01 1 43 0 NA NA
1809 116.00000 0.7 0.79 1 26 0 NA NA
1813 96.66667 NA NA 1 36 0 NA NA
1858 97.33333 1.1 0.83 1 43 1 NA NA
1878 122.00000 0.6 0.96 1 51 0 NA NA
1889 128.00000 0.7 0.98 1 34 0 NA NA
1933 104.66667 1.2 0.52 1 77 1 NA NA
1940 110.66667 0.7 0.83 1 48 1 NA NA
1988 136.00000 0.7 0.64 1 62 1 NA NA
1993 116.66667 0.7 0.72 1 45 1 NA NA
1997 123.33333 0.5 1.01 1 56 0 NA NA
2005 122.00000 0.6 0.93 1 78 1 NA NA
2032 126.66667 0.7 0.77 1 20 0 NA NA
2034 116.00000 0.6 0.98 1 25 0 NA NA
2036 122.00000 0.4 0.67 1 52 1 NA NA
2054 111.33333 0.7 0.64 1 43 1 NA NA
2086 124.66667 0.3 0.56 1 47 1 NA NA
2122 141.33333 0.7 0.68 1 71 1 NA NA
2124 115.33333 0.5 0.96 1 27 0 NA NA
2133 134.66667 0.5 1.38 1 60 0 NA NA
2163 128.66667 0.5 0.64 1 53 1 NA NA
2174 148.66667 0.6 0.85 1 55 1 NA NA
2175 125.33333 1.0 0.72 1 64 0 NA NA
2195 109.33333 1.3 0.85 1 42 1 NA NA
2197 94.00000 0.7 0.87 1 22 0 NA NA
2202 118.66667 0.7 0.88 1 20 0 NA NA
2222 140.66667 0.6 0.66 1 75 1 NA NA
2231 104.00000 0.8 0.83 1 32 0 NA NA
2248 107.33333 0.5 0.82 1 29 1 NA NA
2260 142.00000 0.5 0.76 1 45 1 NA NA
2265 93.33333 0.6 0.56 1 40 1 NA NA
2268 110.00000 0.8 0.82 1 61 1 NA NA
2306 106.66667 0.9 0.95 1 32 0 NA NA
2313 138.00000 0.6 0.86 1 48 1 NA NA
2333 126.00000 0.7 1.06 1 70 0 NA NA
2337 124.00000 0.4 0.50 1 43 1 NA NA
2351 136.00000 0.6 1.03 1 33 0 NA NA
2375 98.66667 1.0 0.82 1 34 0 NA NA
2378 134.66667 0.6 0.77 1 25 0 NA NA
2385 101.33333 0.5 0.74 1 48 1 NA NA
2401 114.66667 0.7 0.84 1 69 1 NA NA
2417 122.66667 0.7 0.68 1 68 1 NA NA
2428 140.66667 0.6 0.74 1 65 0 NA NA
2431 115.33333 0.6 0.69 1 22 1 NA NA
2440 116.66667 0.4 0.65 1 44 1 NA NA
2446 132.00000 0.5 0.73 1 30 0 NA NA
2453 127.33333 0.7 0.80 1 60 0 NA NA
2460 94.66667 0.5 0.65 1 22 1 NA NA
2475 116.00000 0.8 0.92 1 39 0 NA NA
2491 102.66667 0.7 0.64 1 43 1 NA NA
2493 114.00000 0.5 0.83 1 46 1 NA NA
2519 116.00000 0.8 0.73 1 38 0 NA NA
2549 115.33333 0.8 0.85 1 36 0 NA NA
2551 111.33333 0.8 0.58 1 68 1 NA NA
2552 86.00000 0.6 0.69 1 36 1 NA NA
2554 112.66667 0.9 0.89 1 21 0 NA NA
2562 93.33333 NA NA 1 62 0 NA NA
2590 98.66667 1.1 0.84 1 23 1 NA NA
2615 125.33333 1.2 0.91 1 22 0 NA NA
2618 145.33333 1.1 0.82 1 37 0 NA NA
2631 106.00000 1.1 0.65 1 37 1 NA NA
2648 116.66667 0.8 1.12 1 43 0 NA NA
2661 141.33333 0.5 0.94 1 35 0 NA NA
2672 126.66667 0.9 0.84 1 29 0 NA NA
2676 111.33333 NA NA 1 41 0 NA NA
2681 102.66667 0.9 0.79 1 21 1 NA NA
2718 111.33333 0.7 0.95 1 20 0 NA NA
2733 142.66667 0.6 0.80 1 53 1 NA NA
2752 98.66667 1.0 1.01 1 24 0 NA NA
2763 124.00000 0.8 0.94 1 28 0 NA NA
2764 129.33333 1.0 1.08 1 27 0 NA NA
$m6f$spM_lvlone
center scale
SBP 119.2956989 15.3559299
bili 0.7207865 0.2266570
creat 0.8437640 0.1711968
(Intercept) NA NA
age 43.5107527 15.0631963
genderfemale NA NA
log(bili) -0.3758477 0.3135642
exp(creat) 2.3601663 0.4232889
$m6f$mu_reg_norm
[1] 0
$m6f$tau_reg_norm
[1] 1e-04
$m6f$shape_tau_norm
[1] 0.01
$m6f$rate_tau_norm
[1] 0.01
$m6f$mu_reg_gamma
[1] 0
$m6f$tau_reg_gamma
[1] 1e-04
$m6f$shape_tau_gamma
[1] 0.01
$m6f$rate_tau_gamma
[1] 0.01
$mod7a
$mod7a$M_lvlone
SBP bili (Intercept) ns(age, df = 2)1 ns(age, df = 2)2 genderfemale
10 108.00000 0.9 1 0.40123555 -0.219432742 0
14 105.33333 1.0 1 0.46083535 -0.235722523 0
41 110.00000 0.9 1 0.31290629 0.806930162 1
77 106.00000 0.7 1 0.08775523 -0.053921737 0
91 114.66667 0.6 1 0.49442764 -0.238374584 0
105 139.33333 1.2 1 0.57496216 -0.050288463 0
114 124.00000 0.3 1 0.30749447 -0.178633788 1
135 100.00000 0.5 1 0.20151795 -0.121481193 1
149 114.66667 0.4 1 0.44212741 -0.231841236 0
154 156.66667 NA 1 0.57740015 -0.138040409 1
155 127.33333 0.8 1 0.51397109 0.221341024 0
176 106.66667 0.6 1 0.17363342 -0.105334677 1
215 114.00000 0.9 1 0.40123555 -0.219432742 1
220 126.00000 NA 1 0.54452072 -0.220834542 0
224 86.00000 1.0 1 0.37919068 -0.211091372 1
226 117.33333 0.6 1 0.54156624 0.121087888 0
264 128.00000 0.6 1 0.11668254 -0.071462030 0
282 113.33333 NA 1 0.57165173 -0.172603893 0
286 117.33333 0.4 1 0.45580036 0.404707513 1
300 115.33333 0.7 1 0.44212741 -0.231841236 0
301 126.66667 0.6 1 0.40123555 -0.219432742 0
311 110.00000 0.6 1 0.54929184 0.089638669 0
317 124.66667 0.6 1 0.00000000 0.000000000 1
337 111.33333 0.9 1 0.41545995 0.521995691 1
383 153.33333 NA 1 0.57720730 -0.074412613 0
391 115.33333 0.8 1 0.08775523 -0.053921737 0
392 126.66667 1.0 1 0.33225062 -0.190598976 0
420 98.00000 0.8 1 0.42223764 -0.226380337 1
422 166.66667 0.6 1 0.57165173 -0.172603893 0
461 124.66667 1.0 1 0.56745550 0.001991464 0
475 112.66667 0.5 1 0.49442764 -0.238374584 1
483 106.66667 0.6 1 0.20151795 -0.121481193 0
501 112.66667 0.8 1 0.08775523 -0.053921737 0
533 110.66667 0.8 1 0.54452072 -0.220834542 1
538 127.33333 0.4 1 0.52386338 0.186995848 1
550 134.00000 1.2 1 0.54929184 0.089638669 1
557 135.33333 0.7 1 0.57496216 -0.050288463 1
589 128.66667 0.7 1 0.54452072 -0.220834542 0
598 118.66667 0.6 1 0.52386338 0.186995848 1
621 120.66667 0.5 1 0.30749447 -0.178633788 0
631 116.66667 0.7 1 0.53307593 0.153559208 1
637 118.66667 0.6 1 0.55336396 -0.211530878 1
650 111.33333 0.5 1 0.50917296 -0.236959521 1
673 135.33333 0.4 1 0.57514196 -0.156145511 1
696 140.66667 1.1 1 0.46083535 -0.235722523 0
703 106.00000 0.7 1 0.42223764 -0.226380337 0
704 124.66667 0.4 1 0.55621024 0.059268337 0
726 112.66667 0.6 1 0.02934443 -0.018097803 1
739 107.33333 0.4 1 0.57740015 -0.138040409 0
747 105.33333 0.8 1 0.40123555 -0.219432742 1
755 115.33333 0.5 1 0.44212741 -0.231841236 1
756 123.33333 1.4 1 0.44212741 -0.231841236 0
766 117.33333 0.8 1 0.56688698 -0.187358770 0
777 124.00000 0.7 1 0.30749447 -0.178633788 0
793 109.33333 0.6 1 0.52245837 -0.233593171 1
818 127.33333 1.0 1 0.46083535 -0.235722523 0
850 98.66667 1.0 1 0.30749447 -0.178633788 0
862 108.66667 1.1 1 0.53423302 -0.228207567 0
866 108.00000 0.5 1 0.40123555 -0.219432742 1
867 109.33333 0.7 1 0.08775523 -0.053921737 1
887 160.66667 0.7 1 0.57496216 -0.050288463 1
894 138.66667 0.7 1 0.55336396 -0.211530878 1
913 99.33333 0.9 1 0.20151795 -0.121481193 1
974 114.00000 0.6 1 0.08775523 -0.053921737 0
976 137.33333 0.8 1 0.56227896 0.030033674 0
980 117.33333 0.5 1 0.50917296 -0.236959521 1
1028 118.66667 0.8 1 0.28197362 -0.165646497 0
1039 124.66667 1.0 1 0.55621024 0.059268337 0
1040 112.00000 0.7 1 0.25575756 -0.151730022 1
1046 110.66667 1.0 1 0.53423302 -0.228207567 1
1055 112.00000 1.0 1 0.40123555 -0.219432742 0
1092 114.66667 0.5 1 0.44212741 -0.231841236 1
1108 108.66667 0.7 1 0.02934443 -0.018097803 0
1150 141.33333 1.1 1 0.41545995 0.521995691 0
1153 122.00000 0.4 1 0.17363342 -0.105334677 0
1165 98.00000 1.1 1 0.55336396 -0.211530878 0
1174 116.66667 0.7 1 0.51397109 0.221341024 1
1212 124.66667 0.5 1 0.53307593 0.153559208 0
1231 134.66667 0.9 1 0.56745550 0.001991464 0
1245 130.00000 NA 1 0.48064057 0.329259929 1
1247 108.66667 0.6 1 0.57839033 -0.097117177 1
1273 126.66667 0.4 1 0.52245837 -0.233593171 0
1278 103.33333 0.6 1 0.25575756 -0.151730022 0
1299 112.00000 0.7 1 0.47829193 -0.237931278 0
1346 99.33333 0.5 1 0.08775523 -0.053921737 1
1352 102.00000 0.4 1 0.56080521 -0.200353360 1
1360 103.00000 0.5 1 0.52245837 -0.233593171 0
1397 106.66667 0.7 1 0.30749447 -0.178633788 1
1399 106.66667 0.5 1 0.35617252 -0.201449144 1
1410 167.33333 0.6 1 0.42926079 0.482426436 1
1439 130.00000 1.1 1 0.54452072 -0.220834542 0
1481 93.33333 0.7 1 0.55621024 0.059268337 1
1494 120.66667 0.7 1 0.42926079 0.482426436 0
1499 130.00000 0.8 1 0.46083535 -0.235722523 0
1509 111.33333 1.1 1 0.38700859 0.602269871 0
1512 127.33333 0.4 1 0.56688698 -0.187358770 1
1520 120.00000 0.8 1 0.56745550 0.001991464 1
1560 144.00000 0.8 1 0.33225062 -0.190598976 0
1602 118.00000 0.5 1 0.22891583 -0.136977281 0
1608 140.66667 0.7 1 0.55621024 0.059268337 0
1619 122.00000 0.8 1 0.37919068 -0.211091372 0
1642 128.66667 0.8 1 0.28197362 -0.165646497 0
1648 100.00000 1.0 1 0.35617252 -0.201449144 1
1663 124.00000 0.4 1 0.57846877 -0.118345370 0
1671 140.66667 0.5 1 0.37244303 0.642861228 1
1691 122.00000 1.0 1 0.56745550 0.001991464 0
1701 119.33333 0.7 1 0.56745550 0.001991464 1
1726 154.66667 0.6 1 0.30749447 -0.178633788 0
1733 106.66667 0.5 1 0.46083535 -0.235722523 1
1743 114.66667 0.5 1 0.37244303 0.642861228 0
1753 118.66667 1.4 1 0.52245837 -0.233593171 1
1761 112.66667 0.6 1 0.56688698 -0.187358770 1
1765 125.33333 0.6 1 0.57514196 -0.156145511 0
1766 114.00000 1.2 1 0.53307593 0.153559208 0
1795 177.33333 0.8 1 0.49231720 0.292529847 1
1804 122.66667 0.8 1 0.53423302 -0.228207567 0
1809 116.00000 0.7 1 0.17363342 -0.105334677 0
1813 96.66667 NA 1 0.42223764 -0.226380337 0
1858 97.33333 1.1 1 0.53423302 -0.228207567 1
1878 122.00000 0.6 1 0.57846877 -0.118345370 0
1889 128.00000 0.7 1 0.37919068 -0.211091372 0
1933 104.66667 1.2 1 0.32789669 0.765770970 1
1940 110.66667 0.7 1 0.57165173 -0.172603893 1
1988 136.00000 0.7 1 0.52386338 0.186995848 1
1993 116.66667 0.7 1 0.55336396 -0.211530878 1
1997 123.33333 0.5 1 0.56745550 0.001991464 0
2005 122.00000 0.6 1 0.31290629 0.806930162 1
2032 126.66667 0.7 1 0.00000000 0.000000000 0
2034 116.00000 0.6 1 0.14533178 -0.088630649 0
2036 122.00000 0.4 1 0.57839033 -0.097117177 1
2054 111.33333 0.7 1 0.53423302 -0.228207567 1
2086 124.66667 0.3 1 0.56688698 -0.187358770 1
2122 141.33333 0.7 1 0.41545995 0.521995691 1
2124 115.33333 0.5 1 0.20151795 -0.121481193 0
2133 134.66667 0.5 1 0.54156624 0.121087888 0
2163 128.66667 0.5 1 0.57720730 -0.074412613 1
2174 148.66667 0.6 1 0.57169740 -0.024801509 1
2175 125.33333 1.0 1 0.50344153 0.256537951 0
2195 109.33333 1.3 1 0.52245837 -0.233593171 1
2197 94.00000 0.7 1 0.05861935 -0.036102689 0
2202 118.66667 0.7 1 0.00000000 0.000000000 0
2222 140.66667 0.6 1 0.35770754 0.683679720 1
2231 104.00000 0.8 1 0.33225062 -0.190598976 0
2248 107.33333 0.5 1 0.25575756 -0.151730022 1
2260 142.00000 0.5 1 0.55336396 -0.211530878 1
2265 93.33333 0.6 1 0.49442764 -0.238374584 1
2268 110.00000 0.8 1 0.53307593 0.153559208 1
2306 106.66667 0.9 1 0.33225062 -0.190598976 0
2313 138.00000 0.6 1 0.57165173 -0.172603893 1
2333 126.00000 0.7 1 0.42926079 0.482426436 0
2337 124.00000 0.4 1 0.53423302 -0.228207567 1
2351 136.00000 0.6 1 0.35617252 -0.201449144 0
2375 98.66667 1.0 1 0.37919068 -0.211091372 0
2378 134.66667 0.6 1 0.14533178 -0.088630649 0
2385 101.33333 0.5 1 0.57165173 -0.172603893 1
2401 114.66667 0.7 1 0.44272175 0.443311449 1
2417 122.66667 0.7 1 0.45580036 0.404707513 1
2428 140.66667 0.6 1 0.49231720 0.292529847 0
2431 115.33333 0.6 1 0.05861935 -0.036102689 1
2440 116.66667 0.4 1 0.54452072 -0.220834542 1
2446 132.00000 0.5 1 0.28197362 -0.165646497 0
2453 127.33333 0.7 1 0.54156624 0.121087888 0
2460 94.66667 0.5 1 0.05861935 -0.036102689 1
2475 116.00000 0.8 1 0.47829193 -0.237931278 0
2491 102.66667 0.7 1 0.53423302 -0.228207567 1
2493 114.00000 0.5 1 0.56080521 -0.200353360 1
2519 116.00000 0.8 1 0.46083535 -0.235722523 0
2549 115.33333 0.8 1 0.42223764 -0.226380337 0
2551 111.33333 0.8 1 0.45580036 0.404707513 1
2552 86.00000 0.6 1 0.42223764 -0.226380337 1
2554 112.66667 0.9 1 0.02934443 -0.018097803 0
2562 93.33333 NA 1 0.52386338 0.186995848 0
2590 98.66667 1.1 1 0.08775523 -0.053921737 1
2615 125.33333 1.2 1 0.05861935 -0.036102689 0
2618 145.33333 1.1 1 0.44212741 -0.231841236 0
2631 106.00000 1.1 1 0.44212741 -0.231841236 1
2648 116.66667 0.8 1 0.53423302 -0.228207567 0
2661 141.33333 0.5 1 0.40123555 -0.219432742 0
2672 126.66667 0.9 1 0.25575756 -0.151730022 0
2676 111.33333 NA 1 0.50917296 -0.236959521 0
2681 102.66667 0.9 1 0.02934443 -0.018097803 1
2718 111.33333 0.7 1 0.00000000 0.000000000 0
2733 142.66667 0.6 1 0.57720730 -0.074412613 1
2752 98.66667 1.0 1 0.11668254 -0.071462030 0
2763 124.00000 0.8 1 0.22891583 -0.136977281 0
2764 129.33333 1.0 1 0.20151795 -0.121481193 0
I(bili^2) I(bili^3) age
10 NA NA 35
14 NA NA 38
41 NA NA 78
77 NA NA 23
91 NA NA 40
105 NA NA 54
114 NA NA 31
135 NA NA 27
149 NA NA 37
154 NA NA 50
155 NA NA 63
176 NA NA 26
215 NA NA 35
220 NA NA 44
224 NA NA 34
226 NA NA 60
264 NA NA 24
282 NA NA 48
286 NA NA 68
300 NA NA 37
301 NA NA 35
311 NA NA 59
317 NA NA 20
337 NA NA 71
383 NA NA 53
391 NA NA 23
392 NA NA 32
420 NA NA 36
422 NA NA 48
461 NA NA 56
475 NA NA 40
483 NA NA 27
501 NA NA 23
533 NA NA 44
538 NA NA 62
550 NA NA 59
557 NA NA 54
589 NA NA 44
598 NA NA 62
621 NA NA 31
631 NA NA 61
637 NA NA 45
650 NA NA 41
673 NA NA 49
696 NA NA 38
703 NA NA 36
704 NA NA 58
726 NA NA 21
739 NA NA 50
747 NA NA 35
755 NA NA 37
756 NA NA 37
766 NA NA 47
777 NA NA 31
793 NA NA 42
818 NA NA 38
850 NA NA 31
862 NA NA 43
866 NA NA 35
867 NA NA 23
887 NA NA 54
894 NA NA 45
913 NA NA 27
974 NA NA 23
976 NA NA 57
980 NA NA 41
1028 NA NA 30
1039 NA NA 58
1040 NA NA 29
1046 NA NA 43
1055 NA NA 35
1092 NA NA 37
1108 NA NA 21
1150 NA NA 71
1153 NA NA 26
1165 NA NA 45
1174 NA NA 63
1212 NA NA 61
1231 NA NA 56
1245 NA NA 66
1247 NA NA 52
1273 NA NA 42
1278 NA NA 29
1299 NA NA 39
1346 NA NA 23
1352 NA NA 46
1360 NA NA 42
1397 NA NA 31
1399 NA NA 33
1410 NA NA 70
1439 NA NA 44
1481 NA NA 58
1494 NA NA 70
1499 NA NA 38
1509 NA NA 73
1512 NA NA 47
1520 NA NA 56
1560 NA NA 32
1602 NA NA 28
1608 NA NA 58
1619 NA NA 34
1642 NA NA 30
1648 NA NA 33
1663 NA NA 51
1671 NA NA 74
1691 NA NA 56
1701 NA NA 56
1726 NA NA 31
1733 NA NA 38
1743 NA NA 74
1753 NA NA 42
1761 NA NA 47
1765 NA NA 49
1766 NA NA 61
1795 NA NA 65
1804 NA NA 43
1809 NA NA 26
1813 NA NA 36
1858 NA NA 43
1878 NA NA 51
1889 NA NA 34
1933 NA NA 77
1940 NA NA 48
1988 NA NA 62
1993 NA NA 45
1997 NA NA 56
2005 NA NA 78
2032 NA NA 20
2034 NA NA 25
2036 NA NA 52
2054 NA NA 43
2086 NA NA 47
2122 NA NA 71
2124 NA NA 27
2133 NA NA 60
2163 NA NA 53
2174 NA NA 55
2175 NA NA 64
2195 NA NA 42
2197 NA NA 22
2202 NA NA 20
2222 NA NA 75
2231 NA NA 32
2248 NA NA 29
2260 NA NA 45
2265 NA NA 40
2268 NA NA 61
2306 NA NA 32
2313 NA NA 48
2333 NA NA 70
2337 NA NA 43
2351 NA NA 33
2375 NA NA 34
2378 NA NA 25
2385 NA NA 48
2401 NA NA 69
2417 NA NA 68
2428 NA NA 65
2431 NA NA 22
2440 NA NA 44
2446 NA NA 30
2453 NA NA 60
2460 NA NA 22
2475 NA NA 39
2491 NA NA 43
2493 NA NA 46
2519 NA NA 38
2549 NA NA 36
2551 NA NA 68
2552 NA NA 36
2554 NA NA 21
2562 NA NA 62
2590 NA NA 23
2615 NA NA 22
2618 NA NA 37
2631 NA NA 37
2648 NA NA 43
2661 NA NA 35
2672 NA NA 29
2676 NA NA 41
2681 NA NA 21
2718 NA NA 20
2733 NA NA 53
2752 NA NA 24
2763 NA NA 28
2764 NA NA 27
$mod7a$spM_lvlone
center scale
SBP 119.29569892 15.3559299
bili 0.72078652 0.2266570
(Intercept) NA NA
ns(age, df = 2)1 0.40886544 0.1673890
ns(age, df = 2)2 -0.04985511 0.2381012
genderfemale NA NA
I(bili^2) 0.57061798 0.3661097
I(bili^3) 0.49253371 0.4876694
age 43.51075269 15.0631963
$mod7a$mu_reg_norm
[1] 0
$mod7a$tau_reg_norm
[1] 1e-04
$mod7a$shape_tau_norm
[1] 0.01
$mod7a$rate_tau_norm
[1] 0.01
jagsmodel remains the same
Code
lapply(models, "[[", "jagsmodel")
Output
$m0a1
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
}
$m0a2
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
}
$m0a3
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
log(mu_y[i]) <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
}
$m0a4
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- 1/max(1e-10, inv_mu_y[i])
inv_mu_y[i] <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for y
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
}
$m0b1
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
logit(mu_B1[i]) <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for B1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
}
$m0b2
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
probit(mu_B1[i]) <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for B1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
}
$m0b3
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
log(mu_B1[i]) <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for B1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
}
$m0b4
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
cloglog(mu_B1[i]) <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for B1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
}
$m0c1
model {
# Gamma model for L1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i])
shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2)
rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2)
mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i])
inv_mu_L1[i] <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for L1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1 <- sqrt(1/tau_L1)
}
$m0c2
model {
# Gamma model for L1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i])
shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2)
rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2)
log(mu_L1[i]) <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for L1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1 <- sqrt(1/tau_L1)
}
$m0d1
model {
# Poisson model for P1 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i]))
log(mu_P1[i]) <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for P1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
}
$m0d2
model {
# Poisson model for P1 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i]))
mu_P1[i] <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for P1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
}
$m0e1
model {
# Log-normal model for L1 -------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1)
mu_L1[i] <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for L1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1 <- sqrt(1/tau_L1)
}
$m0f1
model {
# Beta model for Be1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15)
shape1_Be1[i] <- mu_Be1[i] * tau_Be1
shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1
logit(mu_Be1[i]) <- M_lvlone[i, 2] * beta[1]
}
# Priors for the model for Be1
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta)
}
$m1a
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- M_lvlone[i, 2] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2]
}
# Priors for the model for y
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
}
$m1b
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
logit(mu_B1[i]) <- M_lvlone[i, 2] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2]
}
# Priors for the model for B1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
}
$m1c
model {
# Gamma model for L1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i])
shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2)
rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2)
mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i])
inv_mu_L1[i] <- M_lvlone[i, 2] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2]
}
# Priors for the model for L1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1 <- sqrt(1/tau_L1)
}
$m1d
model {
# Poisson model for P1 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i]))
log(mu_P1[i]) <- M_lvlone[i, 2] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2]
}
# Priors for the model for P1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
}
$m1e
model {
# Log-normal model for L1 -------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1)
mu_L1[i] <- M_lvlone[i, 2] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2]
}
# Priors for the model for L1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1 <- sqrt(1/tau_L1)
}
$m1f
model {
# Beta model for Be1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15)
shape1_Be1[i] <- mu_Be1[i] * tau_Be1
shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1
logit(mu_Be1[i]) <- M_lvlone[i, 2] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2]
}
# Priors for the model for Be1
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta)
}
$m2a
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2]
}
# Priors for the model for y
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
# 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 {
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2]
}
# Priors for the model for B2
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# 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)
}
$m2c
model {
# Gamma model for L1mis ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i])
inv_mu_L1mis[i] <- M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2]
}
# Priors for the model for L1mis
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
# 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)
}
$m2d
model {
# Poisson model for P2 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P2[i]))
log(mu_P2[i]) <- M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2]
}
# Priors for the model for P2
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
# 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)
}
$m2e
model {
# Log-normal model for L1mis ----------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dlnorm(mu_L1mis[i], tau_L1mis)
mu_L1mis[i] <- M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2]
}
# Priors for the model for L1mis
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1mis <- sqrt(1/tau_L1mis)
# 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)
}
$m2f
model {
# Beta model for Be2 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15)
shape1_Be2[i] <- mu_Be2[i] * tau_Be2
shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2
logit(mu_Be2[i]) <- M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[2]
}
# Priors for the model for Be2
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta)
# 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, 7] * beta[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[2] +
M_lvlone[i, 8] * beta[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[6]
}
# Priors for the model for C1
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C1 <- sqrt(1/tau_C1)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 7] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[5]
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Poisson model for P2 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i]))
log(mu_P2[i]) <- M_lvlone[i, 7] * alpha[6] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[7] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[8] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9]
}
# Priors for the model for P2
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
# Gamma model for L1mis ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i])
inv_mu_L1mis[i] <- M_lvlone[i, 7] * alpha[10] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[11] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[12]
}
# Priors for the model for L1mis
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
# Beta model for Be2 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 5] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15)
shape1_Be2[i] <- mu_Be2[i] * tau_Be2
shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2
logit(mu_Be2[i]) <- M_lvlone[i, 7] * alpha[13] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[14]
}
# Priors for the model for Be2
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta)
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 6] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 7] * alpha[15]
}
# Priors for the model for C2
for (k in 15:15) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m3b
model {
# 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, 6] * beta[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] +
M_lvlone[i, 7] * beta[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5]
}
# Priors for the model for C1
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C1 <- sqrt(1/tau_C1)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
probit(mu_B2[i]) <- M_lvlone[i, 6] * alpha[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Poisson model for P2 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i]))
mu_P2[i] <- M_lvlone[i, 6] * alpha[5] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[7]
}
# Priors for the model for P2
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
# Log-normal model for L1mis ----------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dlnorm(mu_L1mis[i], tau_L1mis)
mu_L1mis[i] <- M_lvlone[i, 6] * alpha[8] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9]
}
# Priors for the model for L1mis
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1mis ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1mis <- sqrt(1/tau_L1mis)
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 5] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- 1/max(1e-10, inv_mu_C2[i])
inv_mu_C2[i] <- M_lvlone[i, 6] * alpha[10]
}
# Priors for the model for C2
for (k in 10:10) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m3c
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, 6] * beta[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] +
M_lvlone[i, 7] * beta[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5]
}
# Priors for the model for C1
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C1 <- sqrt(1/tau_C1)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
log(mu_B2[i]) <- M_lvlone[i, 6] * alpha[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4]
M_lvlone[i, 7] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Poisson model for P2 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i]))
mu_P2[i] <- M_lvlone[i, 6] * alpha[5] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[7]
}
# Priors for the model for P2
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
# Gamma model for L1mis ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
log(mu_L1mis[i]) <- M_lvlone[i, 6] * alpha[8] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9]
}
# Priors for the model for L1mis
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 5] ~ dnorm(mu_C2[i], tau_C2)
log(mu_C2[i]) <- M_lvlone[i, 6] * alpha[10]
}
# Priors for the model for C2
for (k in 10:10) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m3d
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, 7] * beta[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[2] +
M_lvlone[i, 8] * beta[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[4] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[5] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[6]
}
# Priors for the model for C1
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C1 <- sqrt(1/tau_C1)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
log(mu_B2[i]) <- M_lvlone[i, 7] * alpha[1] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[2] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[3] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[4] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[5]
M_lvlone[i, 8] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:5) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Poisson model for P2 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dpois(max(1e-10, mu_P2[i]))
mu_P2[i] <- M_lvlone[i, 7] * alpha[6] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[7] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[8] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[9]
}
# Priors for the model for P2
for (k in 6:9) {
alpha[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
# Gamma model for L1mis ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
log(mu_L1mis[i]) <- M_lvlone[i, 7] * alpha[10] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[11] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[12]
}
# Priors for the model for L1mis
for (k in 10:12) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
# Normal model for Be2 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 5] ~ dnorm(mu_Be2[i], tau_Be2)T(0, 1)
mu_Be2[i] <- M_lvlone[i, 7] * alpha[13] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[14]
}
# Priors for the model for Be2
for (k in 13:14) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_Be2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_Be2 <- sqrt(1/tau_Be2)
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 6] ~ dnorm(mu_C2[i], tau_C2)
log(mu_C2[i]) <- M_lvlone[i, 7] * alpha[15]
}
# Priors for the model for C2
for (k in 15:15) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m4a
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- 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]
}
# Priors for the model for y
for (k in 1:12) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
# 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 {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
logit(mu_B1[i]) <- M_lvlone[i, 5] * beta[1] +
(M_lvlone[i, 2] - spM_lvlone[2, 1])/spM_lvlone[2, 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]
}
# Priors for the model for B1
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Gamma model for L1mis ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dgamma(shape_L1mis[i], rate_L1mis[i])
shape_L1mis[i] <- pow(mu_L1mis[i], 2) / pow(sigma_L1mis, 2)
rate_L1mis[i] <- mu_L1mis[i] / pow(sigma_L1mis, 2)
mu_L1mis[i] <- 1/max(1e-10, inv_mu_L1mis[i])
inv_mu_L1mis[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * alpha[2] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4]
}
# Priors for the model for L1mis
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1mis ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1mis <- sqrt(1/tau_L1mis)
# Beta model for Be2 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dbeta(shape1_Be2[i], shape2_Be2[i])T(1e-15, 1 - 1e-15)
shape1_Be2[i] <- mu_Be2[i] * tau_Be2
shape2_Be2[i] <- (1 - mu_Be2[i]) * tau_Be2
logit(mu_Be2[i]) <- M_lvlone[i, 5] * alpha[5] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * alpha[6] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[7]
M_lvlone[i, 7] <- log(M_lvlone[i, 3])
}
# Priors for the model for Be2
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be2 ~ dgamma(shape_tau_beta, rate_tau_beta)
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 4] ~ dnorm(mu_C2[i], tau_C2)
log(mu_C2[i]) <- M_lvlone[i, 5] * alpha[8] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * alpha[9]
M_lvlone[i, 6] <- abs(M_lvlone[i, 8] - M_lvlone[i, 4])
}
# Priors for the model for C2
for (k in 8:9) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5a1
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5a2
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
log(mu_y[i]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5a3
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- 1/max(1e-10, inv_mu_y[i])
inv_mu_y[i] <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for y
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5b1
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
logit(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for B1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] +
M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] +
M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5b2
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
probit(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for B1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] +
M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] +
M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5b3
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
log(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for B1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] +
M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] +
M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5b4
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
cloglog(mu_B1[i]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for B1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[3] +
M_lvlone[i, 7] * alpha[4] + M_lvlone[i, 8] * alpha[5] +
M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] +
(M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5c1
model {
# Gamma model for L1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i])
shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2)
rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2)
mu_L1[i] <- 1/max(1e-10, inv_mu_L1[i])
inv_mu_L1[i] <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for L1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1 <- sqrt(1/tau_L1)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5c2
model {
# Gamma model for L1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dgamma(shape_L1[i], rate_L1[i])
shape_L1[i] <- pow(mu_L1[i], 2) / pow(sigma_L1, 2)
rate_L1[i] <- mu_L1[i] / pow(sigma_L1, 2)
log(mu_L1[i]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for L1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_L1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_L1 <- sqrt(1/tau_L1)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5d1
model {
# Poisson model for P1 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i]))
log(mu_P1[i]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for P1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5d2
model {
# Poisson model for P1 ----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dpois(max(1e-10, mu_P1[i]))
mu_P1[i] <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for P1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_poisson, tau_reg_poisson)
}
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5e1
model {
# Log-normal model for L1 -------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dlnorm(mu_L1[i], tau_L1)
mu_L1[i] <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for L1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_L1 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_L1 <- sqrt(1/tau_L1)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m5f1
model {
# Beta model for Be1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbeta(shape1_Be1[i], shape2_Be1[i])T(1e-15, 1 - 1e-15)
shape1_Be1[i] <- mu_Be1[i] * tau_Be1
shape2_Be1[i] <- (1 - mu_Be1[i]) * tau_Be1
logit(mu_Be1[i]) <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[2] +
M_lvlone[i, 5] * beta[3] + M_lvlone[i, 6] * beta[4] +
M_lvlone[i, 7] * beta[5] + M_lvlone[i, 8] * beta[6] +
M_lvlone[i, 9] * beta[7]
}
# Priors for the model for Be1
for (k in 1:7) {
beta[k] ~ dnorm(mu_reg_beta, tau_reg_beta)
}
tau_Be1 ~ dgamma(shape_tau_beta, rate_tau_beta)
# Binomial model for B2 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 2] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B2[i])))
logit(mu_B2[i]) <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] + M_lvlone[i, 7] * alpha[4] +
M_lvlone[i, 8] * alpha[5] + M_lvlone[i, 9] * alpha[6]
M_lvlone[i, 5] <- ifelse(M_lvlone[i, 2] == 1, 1, 0)
}
# Priors for the model for B2
for (k in 1:6) {
alpha[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# Normal model for C2 -----------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 3] ~ dnorm(mu_C2[i], tau_C2)
mu_C2[i] <- M_lvlone[i, 4] * alpha[7] + M_lvlone[i, 6] * alpha[8] +
M_lvlone[i, 7] * alpha[9] + M_lvlone[i, 8] * alpha[10] +
M_lvlone[i, 9] * alpha[11]
}
# Priors for the model for C2
for (k in 7:11) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_C2 ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_C2 <- sqrt(1/tau_C2)
}
$m6a
model {
# Normal model for y ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dnorm(mu_y[i], tau_y)
mu_y[i] <- 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]
}
# Priors for the model for y
for (k in 1:12) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_y ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_y <- sqrt(1/tau_y)
# 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]
}
}
$m6b
model {
# Binomial model for B1 ---------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dbern(max(1e-16, min(1 - 1e-16, mu_B1[i])))
logit(mu_B1[i]) <- 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]
}
# Priors for the model for B1
for (k in 1:12) {
beta[k] ~ dnorm(mu_reg_binom, tau_reg_binom)
}
# 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]
}
}
$m6c
model {
# Gamma model for C1 ------------------------------------------------------------
for (i in 1:100) {
M_lvlone[i, 1] ~ dgamma(shape_C1[i], rate_C1[i])
shape_C1[i] <- pow(mu_C1[i], 2) / pow(sigma_C1, 2)
rate_C1[i] <- mu_C1[i] / pow(sigma_C1, 2)
log(mu_C1[i]) <- 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]
}
# Priors for the model for C1
for (k in 1:11) {
beta[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_C1 ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_C1 <- sqrt(1/tau_C1)
# 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, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 2] * alpha[8]
M_lvlone[i, 12] <- abs(M_lvlone[i, 16] - 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, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 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, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 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, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 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, 16] - spM_lvlone[16, 1])/spM_lvlone[16, 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, 13] <- M_lvlone[i, 9] * M_lvlone[i, 12]
M_lvlone[i, 14] <- M_lvlone[i, 10] * M_lvlone[i, 12]
M_lvlone[i, 15] <- M_lvlone[i, 11] * M_lvlone[i, 12]
}
}
$m6d
model {
# Normal model for SBP ----------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP)
mu_SBP[i] <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] +
M_lvlone[i, 6] * 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]
}
# Priors for the model for SBP
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_SBP <- sqrt(1/tau_SBP)
# Normal model for bili ---------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 2] ~ dnorm(mu_bili[i], tau_bili)T(1e-05, 1e+10)
mu_bili[i] <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4]
M_lvlone[i, 7] <- log(M_lvlone[i, 2])
}
# Priors for the model for bili
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_bili ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_bili <- sqrt(1/tau_bili)
# Normal model for creat --------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 3] ~ dnorm(mu_creat[i], tau_creat)
mu_creat[i] <- M_lvlone[i, 4] * alpha[5] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] +
M_lvlone[i, 6] * alpha[7]
M_lvlone[i, 8] <- exp(M_lvlone[i, 3])
}
# Priors for the model for creat
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_creat ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_creat <- sqrt(1/tau_creat)
}
$m6e
model {
# Normal model for SBP ----------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP)
mu_SBP[i] <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] +
M_lvlone[i, 6] * 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]
}
# Priors for the model for SBP
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_SBP <- sqrt(1/tau_SBP)
# Log-normal model for bili -----------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 2] ~ dlnorm(mu_bili[i], tau_bili)
mu_bili[i] <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4]
M_lvlone[i, 7] <- log(M_lvlone[i, 2])
}
# Priors for the model for bili
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_bili ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_bili <- sqrt(1/tau_bili)
# Normal model for creat --------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 3] ~ dnorm(mu_creat[i], tau_creat)
mu_creat[i] <- M_lvlone[i, 4] * alpha[5] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] +
M_lvlone[i, 6] * alpha[7]
M_lvlone[i, 8] <- exp(M_lvlone[i, 3])
}
# Priors for the model for creat
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_creat ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_creat <- sqrt(1/tau_creat)
}
$m6f
model {
# Normal model for SBP ----------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP)
mu_SBP[i] <- M_lvlone[i, 4] * beta[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[2] +
M_lvlone[i, 6] * 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]
}
# Priors for the model for SBP
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_SBP <- sqrt(1/tau_SBP)
# Gamma model for bili ----------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 2] ~ dgamma(shape_bili[i], rate_bili[i])
shape_bili[i] <- pow(mu_bili[i], 2) / pow(sigma_bili, 2)
rate_bili[i] <- mu_bili[i] / pow(sigma_bili, 2)
mu_bili[i] <- 1/max(1e-10, inv_mu_bili[i])
inv_mu_bili[i] <- M_lvlone[i, 4] * alpha[1] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] +
(M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * alpha[4]
M_lvlone[i, 7] <- log(M_lvlone[i, 2])
}
# Priors for the model for bili
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_gamma, tau_reg_gamma)
}
tau_bili ~ dgamma(shape_tau_gamma, rate_tau_gamma)
sigma_bili <- sqrt(1/tau_bili)
# Normal model for creat --------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 3] ~ dnorm(mu_creat[i], tau_creat)
mu_creat[i] <- M_lvlone[i, 4] * alpha[5] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * alpha[6] +
M_lvlone[i, 6] * alpha[7]
M_lvlone[i, 8] <- exp(M_lvlone[i, 3])
}
# Priors for the model for creat
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_creat ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_creat <- sqrt(1/tau_creat)
}
$mod7a
model {
# Normal model for SBP ----------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 1] ~ dnorm(mu_SBP[i], tau_SBP)
mu_SBP[i] <- M_lvlone[i, 3] * beta[1] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[2] +
(M_lvlone[i, 5] - spM_lvlone[5, 1])/spM_lvlone[5, 2] * beta[3] +
M_lvlone[i, 6] * beta[4] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[5] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[6]
}
# Priors for the model for SBP
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_SBP ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_SBP <- sqrt(1/tau_SBP)
# Normal model for bili ---------------------------------------------------------
for (i in 1:186) {
M_lvlone[i, 2] ~ dnorm(mu_bili[i], tau_bili)
mu_bili[i] <- M_lvlone[i, 3] * alpha[1] +
(M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3]
M_lvlone[i, 7] <- M_lvlone[i, 2]^2
M_lvlone[i, 8] <- M_lvlone[i, 2]^3
}
# Priors for the model for bili
for (k in 1:3) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_bili ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_bili <- sqrt(1/tau_bili)
}
GRcrit and MCerror give same result
Code
lapply(models0, GR_crit, multivariate = FALSE)
Output
$m0a1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_y NaN NaN
$m0a2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_y NaN NaN
$m0a3
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_y NaN NaN
$m0a4
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_y NaN NaN
$m0b1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
$m0b2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
$m0b3
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
$m0b4
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
$m0c1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_L1 NaN NaN
$m0c2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_L1 NaN NaN
$m0d1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
$m0d2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
$m0e1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
sigma_L1 NaN NaN
$m0f1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
tau_Be1 NaN NaN
$m1a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
sigma_y NaN NaN
$m1b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
$m1c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
sigma_L1 NaN NaN
$m1d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
$m1e
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
sigma_L1 NaN NaN
$m1f
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C1 NaN NaN
tau_Be1 NaN NaN
$m2a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
sigma_y NaN NaN
$m2b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
$m2c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
sigma_L1mis NaN NaN
$m2d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
$m2e
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
sigma_L1mis NaN NaN
$m2f
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
tau_Be2 NaN NaN
$m3a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
P2 NaN NaN
L1mis NaN NaN
Be2 NaN NaN
sigma_C1 NaN NaN
$m3b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
P2 NaN NaN
L1mis NaN NaN
sigma_C1 NaN NaN
$m3c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
P2 NaN NaN
L1mis NaN NaN
sigma_C1 NaN NaN
$m3d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
P2 NaN NaN
L1mis NaN NaN
Be2 NaN NaN
sigma_C1 NaN NaN
$m4a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
O24 NaN NaN
abs(C1 - C2) NaN NaN
log(C1) NaN NaN
O22:abs(C1 - C2) NaN NaN
O23:abs(C1 - C2) NaN NaN
O24:abs(C1 - C2) NaN NaN
sigma_y NaN NaN
$m4b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
L1mis NaN NaN
abs(C1 - C2) NaN NaN
log(Be2) NaN NaN
$m5a1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
sigma_y NaN NaN
$m5a2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
sigma_y NaN NaN
$m5a3
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
sigma_y NaN NaN
$m5b1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
C1 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
$m5b2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
C1 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
$m5b3
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
C1 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
$m5b4
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
C1 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
$m5c1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
sigma_L1 NaN NaN
$m5c2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
sigma_L1 NaN NaN
$m5d1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
$m5d2
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
$m5e1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
sigma_L1 NaN NaN
$m5f1
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
C2 NaN NaN
B21 NaN NaN
B11 NaN NaN
O1.L NaN NaN
O1.Q NaN NaN
O1.C NaN NaN
tau_Be1 NaN NaN
$m6a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
O24 NaN NaN
abs(C1 - C2) NaN NaN
log(C1) NaN NaN
O22:abs(C1 - C2) NaN NaN
O23:abs(C1 - C2) NaN NaN
O24:abs(C1 - C2) NaN NaN
sigma_y NaN NaN
$m6b
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
O24 NaN NaN
abs(C1 - C2) NaN NaN
log(C1) NaN NaN
O22:abs(C1 - C2) NaN NaN
O23:abs(C1 - C2) NaN NaN
O24:abs(C1 - C2) NaN NaN
$m6c
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
M22 NaN NaN
M23 NaN NaN
M24 NaN NaN
O22 NaN NaN
O23 NaN NaN
O24 NaN NaN
abs(y - C2) NaN NaN
O22:abs(y - C2) NaN NaN
O23:abs(y - C2) NaN NaN
O24:abs(y - C2) NaN NaN
sigma_C1 NaN NaN
$m6d
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
age NaN NaN
genderfemale NaN NaN
log(bili) NaN NaN
exp(creat) NaN NaN
sigma_SBP NaN NaN
$m6e
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
age NaN NaN
genderfemale NaN NaN
log(bili) NaN NaN
exp(creat) NaN NaN
sigma_SBP NaN NaN
$m6f
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
age NaN NaN
genderfemale NaN NaN
log(bili) NaN NaN
exp(creat) NaN NaN
sigma_SBP NaN NaN
$mod7a
Potential scale reduction factors:
Point est. Upper C.I.
(Intercept) NaN NaN
ns(age, df = 2)1 NaN NaN
ns(age, df = 2)2 NaN NaN
genderfemale NaN NaN
I(bili^2) NaN NaN
I(bili^3) NaN NaN
sigma_SBP NaN NaN
Code
lapply(models0, MC_error)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_y 0 0 0 NaN
$m0a2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_y 0 0 0 NaN
$m0a3
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_y 0 0 0 NaN
$m0a4
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_y 0 0 0 NaN
$m0b1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
$m0b2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
$m0b3
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
$m0b4
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
$m0c1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_L1 0 0 0 NaN
$m0c2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_L1 0 0 0 NaN
$m0d1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
$m0d2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
$m0e1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
sigma_L1 0 0 0 NaN
$m0f1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
tau_Be1 0 0 0 NaN
$m1a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
sigma_y 0 0 0 NaN
$m1b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
$m1c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
sigma_L1 0 0 0 NaN
$m1d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
$m1e
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
sigma_L1 0 0 0 NaN
$m1f
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C1 0 0 0 NaN
tau_Be1 0 0 0 NaN
$m2a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
sigma_y 0 0 0 NaN
$m2b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
$m2c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
sigma_L1mis 0 0 0 NaN
$m2d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
$m2e
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
sigma_L1mis 0 0 0 NaN
$m2f
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
tau_Be2 0 0 0 NaN
$m3a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
P2 0 0 0 NaN
L1mis 0 0 0 NaN
Be2 0 0 0 NaN
sigma_C1 0 0 0 NaN
$m3b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
P2 0 0 0 NaN
L1mis 0 0 0 NaN
sigma_C1 0 0 0 NaN
$m3c
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
P2 0 0 0 NaN
L1mis 0 0 0 NaN
sigma_C1 0 0 0 NaN
$m3d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
P2 0 0 0 NaN
L1mis 0 0 0 NaN
Be2 0 0 0 NaN
sigma_C1 0 0 0 NaN
$m4a
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
O22 0 0 0 NaN
O23 0 0 0 NaN
O24 0 0 0 NaN
abs(C1 - C2) 0 0 0 NaN
log(C1) 0 0 0 NaN
O22:abs(C1 - C2) 0 0 0 NaN
O23:abs(C1 - C2) 0 0 0 NaN
O24:abs(C1 - C2) 0 0 0 NaN
sigma_y 0 0 0 NaN
$m4b
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
L1mis 0 0 0 NaN
abs(C1 - C2) 0 0 0 NaN
log(Be2) 0 0 0 NaN
$m5a1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
sigma_y 0 0 0 NaN
$m5a2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
sigma_y 0 0 0 NaN
$m5a3
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
sigma_y 0 0 0 NaN
$m5b1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
C1 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
$m5b2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
C1 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
$m5b3
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
C1 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
$m5b4
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
C1 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
$m5c1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
sigma_L1 0 0 0 NaN
$m5c2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
sigma_L1 0 0 0 NaN
$m5d1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
$m5d2
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
$m5e1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
sigma_L1 0 0 0 NaN
$m5f1
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
C2 0 0 0 NaN
B21 0 0 0 NaN
B11 0 0 0 NaN
O1.L 0 0 0 NaN
O1.Q 0 0 0 NaN
O1.C 0 0 0 NaN
tau_Be1 0 0 0 NaN
$m6a
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
O22 0 0 0 NaN
O23 0 0 0 NaN
O24 0 0 0 NaN
abs(C1 - C2) 0 0 0 NaN
log(C1) 0 0 0 NaN
O22:abs(C1 - C2) 0 0 0 NaN
O23:abs(C1 - C2) 0 0 0 NaN
O24:abs(C1 - C2) 0 0 0 NaN
sigma_y 0 0 0 NaN
$m6b
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
O22 0 0 0 NaN
O23 0 0 0 NaN
O24 0 0 0 NaN
abs(C1 - C2) 0 0 0 NaN
log(C1) 0 0 0 NaN
O22:abs(C1 - C2) 0 0 0 NaN
O23:abs(C1 - C2) 0 0 0 NaN
O24:abs(C1 - C2) 0 0 0 NaN
$m6c
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
O22 0 0 0 NaN
O23 0 0 0 NaN
O24 0 0 0 NaN
abs(y - C2) 0 0 0 NaN
O22:abs(y - C2) 0 0 0 NaN
O23:abs(y - C2) 0 0 0 NaN
O24:abs(y - C2) 0 0 0 NaN
sigma_C1 0 0 0 NaN
$m6d
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
age 0 0 0 NaN
genderfemale 0 0 0 NaN
log(bili) 0 0 0 NaN
exp(creat) 0 0 0 NaN
sigma_SBP 0 0 0 NaN
$m6e
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
age 0 0 0 NaN
genderfemale 0 0 0 NaN
log(bili) 0 0 0 NaN
exp(creat) 0 0 0 NaN
sigma_SBP 0 0 0 NaN
$m6f
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
age 0 0 0 NaN
genderfemale 0 0 0 NaN
log(bili) 0 0 0 NaN
exp(creat) 0 0 0 NaN
sigma_SBP 0 0 0 NaN
$mod7a
est MCSE SD MCSE/SD
(Intercept) 0 0 0 NaN
ns(age, df = 2)1 0 0 0 NaN
ns(age, df = 2)2 0 0 0 NaN
genderfemale 0 0 0 NaN
I(bili^2) 0 0 0 NaN
I(bili^3) 0 0 0 NaN
sigma_SBP 0 0 0 NaN
summary output remained the same
Code
lapply(models0, print)
Output
Call:
lm_imp(formula = y ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "identity"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "log"), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "inverse"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "logit"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept)
0
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "probit"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept)
0
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "log"), data = wideDF,
n.adapt = 150, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept)
0
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "cloglog"),
data = wideDF, n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept)
0
Call:
glm_imp(formula = L1 ~ 1, family = Gamma(link = "inverse"), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_L1
0
Call:
glm_imp(formula = L1 ~ 1, family = Gamma(link = "log"), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_L1
0
Call:
glm_imp(formula = P1 ~ 1, family = poisson(link = "log"), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept)
0
Call:
glm_imp(formula = P1 ~ 1, family = poisson(link = "identity"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept)
0
Call:
lognorm_imp(formula = L1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal model for "L1"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_L1
0
Call:
betareg_imp(formula = Be1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta model for "Be1"
Coefficients:
(Intercept)
0
Call:
lm_imp(formula = y ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C1
0 0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = B1 ~ C1, family = binomial(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C1
0 0
Call:
glm_imp(formula = L1 ~ C1, family = Gamma(), data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept) C1
0 0
Residual standard deviation:
sigma_L1
0
Call:
glm_imp(formula = P1 ~ C1, family = poisson(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept) C1
0 0
Call:
lognorm_imp(formula = L1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal model for "L1"
Coefficients:
(Intercept) C1
0 0
Residual standard deviation:
sigma_L1
0
Call:
betareg_imp(formula = Be1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta model for "Be1"
Coefficients:
(Intercept) C1
0 0
Call:
lm_imp(formula = y ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C2
0 0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = B2 ~ C2, family = binomial(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial model for "B2"
Coefficients:
(Intercept) C2
0 0
Call:
glm_imp(formula = L1mis ~ C2, family = Gamma(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma model for "L1mis"
Coefficients:
(Intercept) C2
0 0
Residual standard deviation:
sigma_L1mis
0
Call:
glm_imp(formula = P2 ~ C2, family = poisson(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson model for "P2"
Coefficients:
(Intercept) C2
0 0
Call:
lognorm_imp(formula = L1mis ~ C2, data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal model for "L1mis"
Coefficients:
(Intercept) C2
0 0
Residual standard deviation:
sigma_L1mis
0
Call:
betareg_imp(formula = Be2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta model for "Be2"
Coefficients:
(Intercept) C2
0 0
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF,
n.adapt = 5, n.iter = 10, models = c(P2 = "glm_poisson_log",
L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) C2 B21 P2 L1mis Be2
0 0 0 0 0 0
Residual standard deviation:
sigma_C1
0
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5,
n.iter = 10, models = c(C2 = "glm_gaussian_inverse", P2 = "glm_poisson_identity",
B2 = "glm_binomial_probit", L1mis = "lognorm"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) C2 B21 P2 L1mis
0 0 0 0 0
Residual standard deviation:
sigma_C1
0
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5,
n.iter = 10, models = c(C2 = "glm_gaussian_log", P2 = "glm_poisson_identity",
L1mis = "glm_gamma_log", B2 = "glm_binomial_log"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) C2 B21 P2 L1mis
0 0 0 0 0
Residual standard deviation:
sigma_C1
0
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF,
n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
P2 = "glm_poisson_identity", L1mis = "glm_gamma_log",
B2 = "glm_binomial_log"), seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(Be2 = c(0, 1)))
Bayesian linear model for "C1"
Coefficients:
(Intercept) C2 B21 P2 L1mis Be2
0 0 0 0 0 0
Residual standard deviation:
sigma_C1
0
Call:
lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
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
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = B1 ~ L1mis + abs(C1 - C2) + log(Be2), family = binomial(),
data = wideDF, n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) L1mis abs(C1 - C2) log(Be2)
0 0 0 0
Call:
lm_imp(formula = y ~ C2 + B2 + B1 + O1, data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "inverse"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "logit"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "probit"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "cloglog"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Call:
glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "inverse"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_L1
0
Call:
glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_L1
0
Call:
glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Call:
glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "identity"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Call:
lognorm_imp(formula = L1 ~ C2 + B2 + B1 + O1, data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal model for "L1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_L1
0
Call:
betareg_imp(formula = Be1 ~ C2 + B2 + B1 + O1, data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta model for "Be1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Call:
lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
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
Residual standard deviation:
sigma_y
0
Call:
glm_imp(formula = B1 ~ M2 + O2 * abs(C1 - C2) + log(C1), family = "binomial",
data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
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
Call:
glm_imp(formula = C1 ~ M2 + O2 * abs(y - C2), family = Gamma(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian Gamma model for "C1"
Coefficients:
(Intercept) M22 M23 M24 O22
0 0 0 0 0
O23 O24 abs(y - C2) O22:abs(y - C2) O23:abs(y - C2)
0 0 0 0 0
O24:abs(y - C2)
0
Residual standard deviation:
sigma_C1
0
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(bili = c(1e-05, 1e+10)))
Bayesian linear model for "SBP"
Coefficients:
(Intercept) age genderfemale log(bili) exp(creat)
0 0 0 0 0
Residual standard deviation:
sigma_SBP
0
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "lognorm",
creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "SBP"
Coefficients:
(Intercept) age genderfemale log(bili) exp(creat)
0 0 0 0 0
Residual standard deviation:
sigma_SBP
0
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "glm_gamma_inverse",
creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "SBP"
Coefficients:
(Intercept) age genderfemale log(bili) exp(creat)
0 0 0 0 0
Residual standard deviation:
sigma_SBP
0
Call:
lm_imp(formula = SBP ~ ns(age, df = 2) + gender + I(bili^2) +
I(bili^3), data = NHANES, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear model for "SBP"
Coefficients:
(Intercept) ns(age, df = 2)1 ns(age, df = 2)2 genderfemale
0 0 0 0
I(bili^2) I(bili^3)
0 0
Residual standard deviation:
sigma_SBP
0
$m0a1
Call:
lm_imp(formula = y ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_y
0
$m0a2
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "identity"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_y
0
$m0a3
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "log"), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_y
0
$m0a4
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "inverse"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_y
0
$m0b1
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "logit"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept)
0
$m0b2
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "probit"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept)
0
$m0b3
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "log"), data = wideDF,
n.adapt = 150, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept)
0
$m0b4
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "cloglog"),
data = wideDF, n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept)
0
$m0c1
Call:
glm_imp(formula = L1 ~ 1, family = Gamma(link = "inverse"), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_L1
0
$m0c2
Call:
glm_imp(formula = L1 ~ 1, family = Gamma(link = "log"), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_L1
0
$m0d1
Call:
glm_imp(formula = P1 ~ 1, family = poisson(link = "log"), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept)
0
$m0d2
Call:
glm_imp(formula = P1 ~ 1, family = poisson(link = "identity"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept)
0
$m0e1
Call:
lognorm_imp(formula = L1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal model for "L1"
Coefficients:
(Intercept)
0
Residual standard deviation:
sigma_L1
0
$m0f1
Call:
betareg_imp(formula = Be1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta model for "Be1"
Coefficients:
(Intercept)
0
$m1a
Call:
lm_imp(formula = y ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C1
0 0
Residual standard deviation:
sigma_y
0
$m1b
Call:
glm_imp(formula = B1 ~ C1, family = binomial(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C1
0 0
$m1c
Call:
glm_imp(formula = L1 ~ C1, family = Gamma(), data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept) C1
0 0
Residual standard deviation:
sigma_L1
0
$m1d
Call:
glm_imp(formula = P1 ~ C1, family = poisson(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept) C1
0 0
$m1e
Call:
lognorm_imp(formula = L1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal model for "L1"
Coefficients:
(Intercept) C1
0 0
Residual standard deviation:
sigma_L1
0
$m1f
Call:
betareg_imp(formula = Be1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta model for "Be1"
Coefficients:
(Intercept) C1
0 0
$m2a
Call:
lm_imp(formula = y ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C2
0 0
Residual standard deviation:
sigma_y
0
$m2b
Call:
glm_imp(formula = B2 ~ C2, family = binomial(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian binomial model for "B2"
Coefficients:
(Intercept) C2
0 0
$m2c
Call:
glm_imp(formula = L1mis ~ C2, family = Gamma(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian Gamma model for "L1mis"
Coefficients:
(Intercept) C2
0 0
Residual standard deviation:
sigma_L1mis
0
$m2d
Call:
glm_imp(formula = P2 ~ C2, family = poisson(), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian poisson model for "P2"
Coefficients:
(Intercept) C2
0 0
$m2e
Call:
lognorm_imp(formula = L1mis ~ C2, data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal model for "L1mis"
Coefficients:
(Intercept) C2
0 0
Residual standard deviation:
sigma_L1mis
0
$m2f
Call:
betareg_imp(formula = Be2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10,
seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta model for "Be2"
Coefficients:
(Intercept) C2
0 0
$m3a
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF,
n.adapt = 5, n.iter = 10, models = c(P2 = "glm_poisson_log",
L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) C2 B21 P2 L1mis Be2
0 0 0 0 0 0
Residual standard deviation:
sigma_C1
0
$m3b
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5,
n.iter = 10, models = c(C2 = "glm_gaussian_inverse", P2 = "glm_poisson_identity",
B2 = "glm_binomial_probit", L1mis = "lognorm"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) C2 B21 P2 L1mis
0 0 0 0 0
Residual standard deviation:
sigma_C1
0
$m3c
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5,
n.iter = 10, models = c(C2 = "glm_gaussian_log", P2 = "glm_poisson_identity",
L1mis = "glm_gamma_log", B2 = "glm_binomial_log"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear model for "C1"
Coefficients:
(Intercept) C2 B21 P2 L1mis
0 0 0 0 0
Residual standard deviation:
sigma_C1
0
$m3d
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF,
n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
P2 = "glm_poisson_identity", L1mis = "glm_gamma_log",
B2 = "glm_binomial_log"), seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(Be2 = c(0, 1)))
Bayesian linear model for "C1"
Coefficients:
(Intercept) C2 B21 P2 L1mis Be2
0 0 0 0 0 0
Residual standard deviation:
sigma_C1
0
$m4a
Call:
lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
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
Residual standard deviation:
sigma_y
0
$m4b
Call:
glm_imp(formula = B1 ~ L1mis + abs(C1 - C2) + log(Be2), family = binomial(),
data = wideDF, n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) L1mis abs(C1 - C2) log(Be2)
0 0 0 0
$m5a1
Call:
lm_imp(formula = y ~ C2 + B2 + B1 + O1, data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_y
0
$m5a2
Call:
glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_y
0
$m5a3
Call:
glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "inverse"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian linear model for "y"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_y
0
$m5b1
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "logit"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5b2
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "probit"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5b3
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5b4
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "cloglog"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
Coefficients:
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5c1
Call:
glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "inverse"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_L1
0
$m5c2
Call:
glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian Gamma model for "L1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_L1
0
$m5d1
Call:
glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5d2
Call:
glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "identity"),
data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian poisson model for "P1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5e1
Call:
lognorm_imp(formula = L1 ~ C2 + B2 + B1 + O1, data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian log-normal model for "L1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
Residual standard deviation:
sigma_L1
0
$m5f1
Call:
betareg_imp(formula = Be1 ~ C2 + B2 + B1 + O1, data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian beta model for "Be1"
Coefficients:
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m6a
Call:
lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "y"
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
Residual standard deviation:
sigma_y
0
$m6b
Call:
glm_imp(formula = B1 ~ M2 + O2 * abs(C1 - C2) + log(C1), family = "binomial",
data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian binomial model for "B1"
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
$m6c
Call:
glm_imp(formula = C1 ~ M2 + O2 * abs(y - C2), family = Gamma(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian Gamma model for "C1"
Coefficients:
(Intercept) M22 M23 M24 O22
0 0 0 0 0
O23 O24 abs(y - C2) O22:abs(y - C2) O23:abs(y - C2)
0 0 0 0 0
O24:abs(y - C2)
0
Residual standard deviation:
sigma_C1
0
$m6d
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(bili = c(1e-05, 1e+10)))
Bayesian linear model for "SBP"
Coefficients:
(Intercept) age genderfemale log(bili) exp(creat)
0 0 0 0 0
Residual standard deviation:
sigma_SBP
0
$m6e
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "lognorm",
creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "SBP"
Coefficients:
(Intercept) age genderfemale log(bili) exp(creat)
0 0 0 0 0
Residual standard deviation:
sigma_SBP
0
$m6f
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "glm_gamma_inverse",
creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE)
Bayesian linear model for "SBP"
Coefficients:
(Intercept) age genderfemale log(bili) exp(creat)
0 0 0 0 0
Residual standard deviation:
sigma_SBP
0
$mod7a
Call:
lm_imp(formula = SBP ~ ns(age, df = 2) + gender + I(bili^2) +
I(bili^3), data = NHANES, n.adapt = 5, n.iter = 10, seed = 2020,
warn = FALSE, mess = FALSE)
Bayesian linear model for "SBP"
Coefficients:
(Intercept) ns(age, df = 2)1 ns(age, df = 2)2 genderfemale
0 0 0 0
I(bili^2) I(bili^3)
0 0
Residual standard deviation:
sigma_SBP
0
Code
lapply(models0, coef)
Output
$m0a1
$m0a1$y
(Intercept) sigma_y
0 0
$m0a2
$m0a2$y
(Intercept) sigma_y
0 0
$m0a3
$m0a3$y
(Intercept) sigma_y
0 0
$m0a4
$m0a4$y
(Intercept) sigma_y
0 0
$m0b1
$m0b1$B1
(Intercept)
0
$m0b2
$m0b2$B1
(Intercept)
0
$m0b3
$m0b3$B1
(Intercept)
0
$m0b4
$m0b4$B1
(Intercept)
0
$m0c1
$m0c1$L1
(Intercept) sigma_L1
0 0
$m0c2
$m0c2$L1
(Intercept) sigma_L1
0 0
$m0d1
$m0d1$P1
(Intercept)
0
$m0d2
$m0d2$P1
(Intercept)
0
$m0e1
$m0e1$L1
(Intercept) sigma_L1
0 0
$m0f1
$m0f1$Be1
(Intercept) tau_Be1
0 0
$m1a
$m1a$y
(Intercept) C1 sigma_y
0 0 0
$m1b
$m1b$B1
(Intercept) C1
0 0
$m1c
$m1c$L1
(Intercept) C1 sigma_L1
0 0 0
$m1d
$m1d$P1
(Intercept) C1
0 0
$m1e
$m1e$L1
(Intercept) C1 sigma_L1
0 0 0
$m1f
$m1f$Be1
(Intercept) C1 tau_Be1
0 0 0
$m2a
$m2a$y
(Intercept) C2 sigma_y
0 0 0
$m2b
$m2b$B2
(Intercept) C2
0 0
$m2c
$m2c$L1mis
(Intercept) C2 sigma_L1mis
0 0 0
$m2d
$m2d$P2
(Intercept) C2
0 0
$m2e
$m2e$L1mis
(Intercept) C2 sigma_L1mis
0 0 0
$m2f
$m2f$Be2
(Intercept) C2 tau_Be2
0 0 0
$m3a
$m3a$C1
(Intercept) C2 B21 P2 L1mis Be2
0 0 0 0 0 0
sigma_C1
0
$m3b
$m3b$C1
(Intercept) C2 B21 P2 L1mis sigma_C1
0 0 0 0 0 0
$m3c
$m3c$C1
(Intercept) C2 B21 P2 L1mis sigma_C1
0 0 0 0 0 0
$m3d
$m3d$C1
(Intercept) C2 B21 P2 L1mis Be2
0 0 0 0 0 0
sigma_C1
0
$m4a
$m4a$y
(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
sigma_y
0
$m4b
$m4b$B1
(Intercept) L1mis abs(C1 - C2) log(Be2)
0 0 0 0
$m5a1
$m5a1$y
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C sigma_y
0 0
$m5a2
$m5a2$y
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C sigma_y
0 0
$m5a3
$m5a3$y
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C sigma_y
0 0
$m5b1
$m5b1$B1
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5b2
$m5b2$B1
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5b3
$m5b3$B1
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5b4
$m5b4$B1
(Intercept) C2 B21 C1 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5c1
$m5c1$L1
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C sigma_L1
0 0
$m5c2
$m5c2$L1
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C sigma_L1
0 0
$m5d1
$m5d1$P1
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5d2
$m5d2$P1
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C
0
$m5e1
$m5e1$L1
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C sigma_L1
0 0
$m5f1
$m5f1$Be1
(Intercept) C2 B21 B11 O1.L O1.Q
0 0 0 0 0 0
O1.C tau_Be1
0 0
$m6a
$m6a$y
(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
sigma_y
0
$m6b
$m6b$B1
(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
$m6c
$m6c$C1
(Intercept) M22 M23 M24 O22
0 0 0 0 0
O23 O24 abs(y - C2) O22:abs(y - C2) O23:abs(y - C2)
0 0 0 0 0
O24:abs(y - C2) sigma_C1
0 0
$m6d
$m6d$SBP
(Intercept) age genderfemale log(bili) exp(creat) sigma_SBP
0 0 0 0 0 0
$m6e
$m6e$SBP
(Intercept) age genderfemale log(bili) exp(creat) sigma_SBP
0 0 0 0 0 0
$m6f
$m6f$SBP
(Intercept) age genderfemale log(bili) exp(creat) sigma_SBP
0 0 0 0 0 0
$mod7a
$mod7a$SBP
(Intercept) ns(age, df = 2)1 ns(age, df = 2)2 genderfemale
0 0 0 0
I(bili^2) I(bili^3) sigma_SBP
0 0 0
Code
lapply(models0, confint)
Output
$m0a1
$m0a1$y
2.5% 97.5%
(Intercept) 0 0
sigma_y 0 0
$m0a2
$m0a2$y
2.5% 97.5%
(Intercept) 0 0
sigma_y 0 0
$m0a3
$m0a3$y
2.5% 97.5%
(Intercept) 0 0
sigma_y 0 0
$m0a4
$m0a4$y
2.5% 97.5%
(Intercept) 0 0
sigma_y 0 0
$m0b1
$m0b1$B1
2.5% 97.5%
(Intercept) 0 0
$m0b2
$m0b2$B1
2.5% 97.5%
(Intercept) 0 0
$m0b3
$m0b3$B1
2.5% 97.5%
(Intercept) 0 0
$m0b4
$m0b4$B1
2.5% 97.5%
(Intercept) 0 0
$m0c1
$m0c1$L1
2.5% 97.5%
(Intercept) 0 0
sigma_L1 0 0
$m0c2
$m0c2$L1
2.5% 97.5%
(Intercept) 0 0
sigma_L1 0 0
$m0d1
$m0d1$P1
2.5% 97.5%
(Intercept) 0 0
$m0d2
$m0d2$P1
2.5% 97.5%
(Intercept) 0 0
$m0e1
$m0e1$L1
2.5% 97.5%
(Intercept) 0 0
sigma_L1 0 0
$m0f1
$m0f1$Be1
2.5% 97.5%
(Intercept) 0 0
tau_Be1 0 0
$m1a
$m1a$y
2.5% 97.5%
(Intercept) 0 0
C1 0 0
sigma_y 0 0
$m1b
$m1b$B1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
$m1c
$m1c$L1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
sigma_L1 0 0
$m1d
$m1d$P1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
$m1e
$m1e$L1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
sigma_L1 0 0
$m1f
$m1f$Be1
2.5% 97.5%
(Intercept) 0 0
C1 0 0
tau_Be1 0 0
$m2a
$m2a$y
2.5% 97.5%
(Intercept) 0 0
C2 0 0
sigma_y 0 0
$m2b
$m2b$B2
2.5% 97.5%
(Intercept) 0 0
C2 0 0
$m2c
$m2c$L1mis
2.5% 97.5%
(Intercept) 0 0
C2 0 0
sigma_L1mis 0 0
$m2d
$m2d$P2
2.5% 97.5%
(Intercept) 0 0
C2 0 0
$m2e
$m2e$L1mis
2.5% 97.5%
(Intercept) 0 0
C2 0 0
sigma_L1mis 0 0
$m2f
$m2f$Be2
2.5% 97.5%
(Intercept) 0 0
C2 0 0
tau_Be2 0 0
$m3a
$m3a$C1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
P2 0 0
L1mis 0 0
Be2 0 0
sigma_C1 0 0
$m3b
$m3b$C1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
P2 0 0
L1mis 0 0
sigma_C1 0 0
$m3c
$m3c$C1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
P2 0 0
L1mis 0 0
sigma_C1 0 0
$m3d
$m3d$C1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
P2 0 0
L1mis 0 0
Be2 0 0
sigma_C1 0 0
$m4a
$m4a$y
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
sigma_y 0 0
$m4b
$m4b$B1
2.5% 97.5%
(Intercept) 0 0
L1mis 0 0
abs(C1 - C2) 0 0
log(Be2) 0 0
$m5a1
$m5a1$y
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
sigma_y 0 0
$m5a2
$m5a2$y
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
sigma_y 0 0
$m5a3
$m5a3$y
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
sigma_y 0 0
$m5b1
$m5b1$B1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
C1 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
$m5b2
$m5b2$B1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
C1 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
$m5b3
$m5b3$B1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
C1 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
$m5b4
$m5b4$B1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
C1 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
$m5c1
$m5c1$L1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
sigma_L1 0 0
$m5c2
$m5c2$L1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
sigma_L1 0 0
$m5d1
$m5d1$P1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
$m5d2
$m5d2$P1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
$m5e1
$m5e1$L1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
sigma_L1 0 0
$m5f1
$m5f1$Be1
2.5% 97.5%
(Intercept) 0 0
C2 0 0
B21 0 0
B11 0 0
O1.L 0 0
O1.Q 0 0
O1.C 0 0
tau_Be1 0 0
$m6a
$m6a$y
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
sigma_y 0 0
$m6b
$m6b$B1
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
$m6c
$m6c$C1
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(y - C2) 0 0
O22:abs(y - C2) 0 0
O23:abs(y - C2) 0 0
O24:abs(y - C2) 0 0
sigma_C1 0 0
$m6d
$m6d$SBP
2.5% 97.5%
(Intercept) 0 0
age 0 0
genderfemale 0 0
log(bili) 0 0
exp(creat) 0 0
sigma_SBP 0 0
$m6e
$m6e$SBP
2.5% 97.5%
(Intercept) 0 0
age 0 0
genderfemale 0 0
log(bili) 0 0
exp(creat) 0 0
sigma_SBP 0 0
$m6f
$m6f$SBP
2.5% 97.5%
(Intercept) 0 0
age 0 0
genderfemale 0 0
log(bili) 0 0
exp(creat) 0 0
sigma_SBP 0 0
$mod7a
$mod7a$SBP
2.5% 97.5%
(Intercept) 0 0
ns(age, df = 2)1 0 0
ns(age, df = 2)2 0 0
genderfemale 0 0
I(bili^2) 0 0
I(bili^3) 0 0
sigma_SBP 0 0
Code
lapply(models0, summary, missinfo = TRUE)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a1
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = y ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
y 0 0
$m0a2
Bayesian linear model fitted with JointAI
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "identity"),
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
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
y 0 0
$m0a3
Bayesian linear model fitted with JointAI
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "log"), 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
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
y 0 0
$m0a4
Bayesian linear model fitted with JointAI
Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "inverse"),
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
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
y 0 0
$m0b1
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "logit"),
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
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
B1 0 0
$m0b2
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "probit"),
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
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
B1 0 0
$m0b3
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "log"), data = wideDF,
n.adapt = 150, 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
MCMC settings:
Iterations = 151:160
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
B1 0 0
$m0b4
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "cloglog"),
data = wideDF, n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 51:60
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
B1 0 0
$m0c1
Bayesian Gamma model fitted with JointAI
Call:
glm_imp(formula = L1 ~ 1, family = Gamma(link = "inverse"), 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
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
L1 0 0
$m0c2
Bayesian Gamma model fitted with JointAI
Call:
glm_imp(formula = L1 ~ 1, family = Gamma(link = "log"), 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
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
L1 0 0
$m0d1
Bayesian poisson model fitted with JointAI
Call:
glm_imp(formula = P1 ~ 1, family = poisson(link = "log"), 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
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
P1 0 0
$m0d2
Bayesian poisson model fitted with JointAI
Call:
glm_imp(formula = P1 ~ 1, family = poisson(link = "identity"),
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
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
P1 0 0
$m0e1
Bayesian log-normal model fitted with JointAI
Call:
lognorm_imp(formula = L1 ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
L1 0 0
$m0f1
Bayesian beta model fitted with JointAI
Call:
betareg_imp(formula = Be1 ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be1 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
Be1 0 0
$m1a
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = y ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
y 0 0
C1 0 0
$m1b
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ C1, family = binomial(), 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
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
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
B1 0 0
C1 0 0
$m1c
Bayesian Gamma model fitted with JointAI
Call:
glm_imp(formula = L1 ~ C1, family = Gamma(), 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
C1 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
L1 0 0
C1 0 0
$m1d
Bayesian poisson model fitted with JointAI
Call:
glm_imp(formula = P1 ~ C1, family = poisson(), 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
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
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
P1 0 0
C1 0 0
$m1e
Bayesian log-normal model fitted with JointAI
Call:
lognorm_imp(formula = L1 ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
L1 0 0
C1 0 0
$m1f
Bayesian beta model fitted with JointAI
Call:
betareg_imp(formula = Be1 ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be1 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 100 100
Number and proportion of missing values:
# NA % NA
Be1 0 0
C1 0 0
$m2a
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = y ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 1:10
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 96 96
Number and proportion of missing values:
# NA % NA
y 0 0
C2 4 4
$m2b
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B2 ~ C2, family = binomial(), 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
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
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
C2 4 4
B2 20 20
$m2c
Bayesian Gamma model fitted with JointAI
Call:
glm_imp(formula = L1mis ~ C2, family = Gamma(), 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
C2 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1mis 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 76 76
Number and proportion of missing values:
# NA % NA
C2 4 4
L1mis 20 20
$m2d
Bayesian poisson model fitted with JointAI
Call:
glm_imp(formula = P2 ~ C2, family = poisson(), 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
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
Number and proportion of complete cases:
# %
lvlone 78 78
Number and proportion of missing values:
# NA % NA
C2 4 4
P2 20 20
$m2e
Bayesian log-normal model fitted with JointAI
Call:
lognorm_imp(formula = L1mis ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1mis 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 76 76
Number and proportion of missing values:
# NA % NA
C2 4 4
L1mis 20 20
$m2f
Bayesian beta model fitted with JointAI
Call:
betareg_imp(formula = Be2 ~ 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
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be2 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
C2 4 4
Be2 20 20
$m3a
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF,
n.adapt = 5, n.iter = 10, models = c(P2 = "glm_poisson_log",
L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
P2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Be2 0 0 0 0 0 NaN NaN
Posterior summary of 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
Number and proportion of complete cases:
# %
lvlone 46 46
Number and proportion of missing values:
# NA % NA
C1 0 0
C2 4 4
B2 20 20
P2 20 20
L1mis 20 20
Be2 20 20
$m3b
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5,
n.iter = 10, models = c(C2 = "glm_gaussian_inverse", P2 = "glm_poisson_identity",
B2 = "glm_binomial_probit", L1mis = "lognorm"), seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
P2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Posterior summary of 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
Number and proportion of complete cases:
# %
lvlone 55 55
Number and proportion of missing values:
# NA % NA
C1 0 0
C2 4 4
B2 20 20
P2 20 20
L1mis 20 20
$m3c
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5,
n.iter = 10, models = c(C2 = "glm_gaussian_log", P2 = "glm_poisson_identity",
L1mis = "glm_gamma_log", B2 = "glm_binomial_log"), seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
P2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Posterior summary of 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
Number and proportion of complete cases:
# %
lvlone 55 55
Number and proportion of missing values:
# NA % NA
C1 0 0
C2 4 4
B2 20 20
P2 20 20
L1mis 20 20
$m3d
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF,
n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
P2 = "glm_poisson_identity", L1mis = "glm_gamma_log",
B2 = "glm_binomial_log"), seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(Be2 = c(0, 1)))
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
P2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Be2 0 0 0 0 0 NaN NaN
Posterior summary of 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
Number and proportion of complete cases:
# %
lvlone 46 46
Number and proportion of missing values:
# NA % NA
C1 0 0
C2 4 4
B2 20 20
P2 20 20
L1mis 20 20
Be2 20 20
$m4a
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(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
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 91 91
Number and proportion of missing values:
# NA % NA
y 0 0
C1 0 0
O2 2 2
M2 3 3
C2 4 4
$m4b
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ L1mis + abs(C1 - C2) + log(Be2), family = binomial(),
data = wideDF, n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log",
L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020,
warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(Be2) 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 60 60
Number and proportion of missing values:
# NA % NA
B1 0 0
C1 0 0
C2 4 4
L1mis 20 20
Be2 20 20
$m5a1
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = y ~ C2 + B2 + B1 + O1, data = wideDF, n.adapt = 5,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
y 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5a2
Bayesian linear model fitted with JointAI
Call:
glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "log"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
y 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5a3
Bayesian linear model fitted with JointAI
Call:
glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "inverse"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
y 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5b1
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "logit"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 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
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
B1 0 0
C1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5b2
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "probit"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 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
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
B1 0 0
C1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5b3
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "log"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 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
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
B1 0 0
C1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5b4
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "cloglog"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 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
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
B1 0 0
C1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5c1
Bayesian Gamma model fitted with JointAI
Call:
glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "inverse"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
L1 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5c2
Bayesian Gamma model fitted with JointAI
Call:
glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "log"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
L1 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5d1
Bayesian poisson model fitted with JointAI
Call:
glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "log"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 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
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
P1 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5d2
Bayesian poisson model fitted with JointAI
Call:
glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "identity"),
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
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 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
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
P1 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5e1
Bayesian log-normal model fitted with JointAI
Call:
lognorm_imp(formula = L1 ~ C2 + B2 + B1 + O1, data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_L1 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
L1 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m5f1
Bayesian beta model fitted with JointAI
Call:
betareg_imp(formula = Be1 ~ C2 + B2 + B1 + O1, data = wideDF,
n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
tau_Be1 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:15
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 77 77
Number and proportion of missing values:
# NA % NA
Be1 0 0
B1 0 0
O1 0 0
C2 4 4
B2 20 20
$m6a
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = y ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF,
n.adapt = 5, n.iter = 5, 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
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:10
Sample size per chain = 5
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 91 91
Number and proportion of missing values:
# NA % NA
y 0 0
C1 0 0
O2 2 2
M2 3 3
C2 4 4
$m6b
Bayesian binomial model fitted with JointAI
Call:
glm_imp(formula = B1 ~ M2 + O2 * abs(C1 - C2) + log(C1), family = "binomial",
data = wideDF, n.adapt = 5, n.iter = 5, 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
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:10
Sample size per chain = 5
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 91 91
Number and proportion of missing values:
# NA % NA
B1 0 0
C1 0 0
O2 2 2
M2 3 3
C2 4 4
$m6c
Bayesian Gamma model fitted with JointAI
Call:
glm_imp(formula = C1 ~ M2 + O2 * abs(y - C2), family = Gamma(link = "log"),
data = wideDF, n.adapt = 5, n.iter = 5, 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
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(y - C2) 0 0 0 0 0 NaN NaN
O22:abs(y - C2) 0 0 0 0 0 NaN NaN
O23:abs(y - C2) 0 0 0 0 0 NaN NaN
O24:abs(y - C2) 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 = 6:10
Sample size per chain = 5
Thinning interval = 1
Number of chains = 3
Number of observations: 100
Number and proportion of complete cases:
# %
lvlone 91 91
Number and proportion of missing values:
# NA % NA
C1 0 0
y 0 0
O2 2 2
M2 3 3
C2 4 4
$m6d
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(bili = c(1e-05, 1e+10)))
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
genderfemale 0 0 0 0 0 NaN NaN
log(bili) 0 0 0 0 0 NaN NaN
exp(creat) 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_SBP 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:10
Sample size per chain = 5
Thinning interval = 1
Number of chains = 3
Number of observations: 186
Number and proportion of complete cases:
# %
lvlone 178 95.7
Number and proportion of missing values:
# NA % NA
SBP 0 0.0
age 0 0.0
gender 0 0.0
bili 8 4.3
creat 8 4.3
$m6e
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "lognorm",
creat = "lm"), 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
age 0 0 0 0 0 NaN NaN
genderfemale 0 0 0 0 0 NaN NaN
log(bili) 0 0 0 0 0 NaN NaN
exp(creat) 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_SBP 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:10
Sample size per chain = 5
Thinning interval = 1
Number of chains = 3
Number of observations: 186
Number and proportion of complete cases:
# %
lvlone 178 95.7
Number and proportion of missing values:
# NA % NA
SBP 0 0.0
age 0 0.0
gender 0 0.0
bili 8 4.3
creat 8 4.3
$m6f
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat),
data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "glm_gamma_inverse",
creat = "lm"), 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
age 0 0 0 0 0 NaN NaN
genderfemale 0 0 0 0 0 NaN NaN
log(bili) 0 0 0 0 0 NaN NaN
exp(creat) 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_SBP 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 6:10
Sample size per chain = 5
Thinning interval = 1
Number of chains = 3
Number of observations: 186
Number and proportion of complete cases:
# %
lvlone 178 95.7
Number and proportion of missing values:
# NA % NA
SBP 0 0.0
age 0 0.0
gender 0 0.0
bili 8 4.3
creat 8 4.3
$mod7a
Bayesian linear model fitted with JointAI
Call:
lm_imp(formula = SBP ~ ns(age, df = 2) + gender + I(bili^2) +
I(bili^3), data = NHANES, 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
ns(age, df = 2)1 0 0 0 0 0 NaN NaN
ns(age, df = 2)2 0 0 0 0 0 NaN NaN
genderfemale 0 0 0 0 0 NaN NaN
I(bili^2) 0 0 0 0 0 NaN NaN
I(bili^3) 0 0 0 0 0 NaN NaN
Posterior summary of residual std. deviation:
Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_SBP 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: 186
Number and proportion of complete cases:
# %
lvlone 178 95.7
Number and proportion of missing values:
# NA % NA
SBP 0 0.0
age 0 0.0
gender 0 0.0
bili 8 4.3
Code
lapply(models0, function(x) coef(summary(x)))
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a1
$m0a1$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0a2
$m0a2$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0a3
$m0a3$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0a4
$m0a4$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0b1
$m0b1$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0b2
$m0b2$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0b3
$m0b3$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0b4
$m0b4$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0c1
$m0c1$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0c2
$m0c2$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0d1
$m0d1$P1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0d2
$m0d2$P1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0e1
$m0e1$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m0f1
$m0f1$Be1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
$m1a
$m1a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1b
$m1b$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1c
$m1c$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1d
$m1d$P1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1e
$m1e$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m1f
$m1f$Be1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
$m2a
$m2a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
$m2b
$m2b$B2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
$m2c
$m2c$L1mis
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
$m2d
$m2d$P2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
$m2e
$m2e$L1mis
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
$m2f
$m2f$Be2
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
$m3a
$m3a$C1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
P2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Be2 0 0 0 0 0 NaN NaN
$m3b
$m3b$C1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
P2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
$m3c
$m3c$C1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
P2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
$m3d
$m3d$C1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
P2 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
Be2 0 0 0 0 0 NaN NaN
$m4a
$m4a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
$m4b
$m4b$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
L1mis 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(Be2) 0 0 0 0 0 NaN NaN
$m5a1
$m5a1$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5a2
$m5a2$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5a3
$m5a3$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5b1
$m5b1$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5b2
$m5b2$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5b3
$m5b3$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5b4
$m5b4$B1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
C1 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5c1
$m5c1$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5c2
$m5c2$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5d1
$m5d1$P1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5d2
$m5d2$P1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5e1
$m5e1$L1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m5f1
$m5f1$Be1
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
C2 0 0 0 0 0 NaN NaN
B21 0 0 0 0 0 NaN NaN
B11 0 0 0 0 0 NaN NaN
O1.L 0 0 0 0 0 NaN NaN
O1.Q 0 0 0 0 0 NaN NaN
O1.C 0 0 0 0 0 NaN NaN
$m6a
$m6a$y
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
M22 0 0 0 0 0 NaN NaN
M23 0 0 0 0 0 NaN NaN
M24 0 0 0 0 0 NaN NaN
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
$m6b
$m6b$B1
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
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(C1 - C2) 0 0 0 0 0 NaN NaN
log(C1) 0 0 0 0 0 NaN NaN
O22:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O23:abs(C1 - C2) 0 0 0 0 0 NaN NaN
O24:abs(C1 - C2) 0 0 0 0 0 NaN NaN
$m6c
$m6c$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
O22 0 0 0 0 0 NaN NaN
O23 0 0 0 0 0 NaN NaN
O24 0 0 0 0 0 NaN NaN
abs(y - C2) 0 0 0 0 0 NaN NaN
O22:abs(y - C2) 0 0 0 0 0 NaN NaN
O23:abs(y - C2) 0 0 0 0 0 NaN NaN
O24:abs(y - C2) 0 0 0 0 0 NaN NaN
$m6d
$m6d$SBP
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
genderfemale 0 0 0 0 0 NaN NaN
log(bili) 0 0 0 0 0 NaN NaN
exp(creat) 0 0 0 0 0 NaN NaN
$m6e
$m6e$SBP
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
genderfemale 0 0 0 0 0 NaN NaN
log(bili) 0 0 0 0 0 NaN NaN
exp(creat) 0 0 0 0 0 NaN NaN
$m6f
$m6f$SBP
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
genderfemale 0 0 0 0 0 NaN NaN
log(bili) 0 0 0 0 0 NaN NaN
exp(creat) 0 0 0 0 0 NaN NaN
$mod7a
$mod7a$SBP
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept) 0 0 0 0 0 NaN NaN
ns(age, df = 2)1 0 0 0 0 0 NaN NaN
ns(age, df = 2)2 0 0 0 0 0 NaN NaN
genderfemale 0 0 0 0 0 NaN NaN
I(bili^2) 0 0 0 0 0 NaN NaN
I(bili^3) 0 0 0 0 0 NaN NaN
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