[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
     Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1    0  0    0     0          0     NaN    NaN
o1 > 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m0b

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o2 ~ 1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
     Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o2 > 1    0  0    0     0          0     NaN    NaN
o2 > 2    0  0    0     0          0     NaN    NaN
o2 > 3    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o2_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m1a

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
   Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C1    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1    0  0    0     0          0     NaN    NaN
o1 > 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m1b

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o2 ~ C1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
   Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C1    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o2 > 1    0  0    0     0          0     NaN    NaN
o2 > 2    0  0    0     0          0     NaN    NaN
o2 > 3    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o2_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m1c

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ c1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
   Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
c1    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1    0  0    0     0          0     NaN    NaN
o1 > 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m1d

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o2 ~ c1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
   Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
c1    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o2 > 1    0  0    0     0          0     NaN    NaN
o2 > 2    0  0    0     0          0     NaN    NaN
o2 > 3    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o2_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m2a

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ C2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
   Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1    0  0    0     0          0     NaN    NaN
o1 > 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m2b

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o2 ~ C2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
   Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C2    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o2 > 1    0  0    0     0          0     NaN    NaN
o2 > 2    0  0    0     0          0     NaN    NaN
o2 > 3    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o2_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m2c

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ c2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
   Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
c2    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1    0  0    0     0          0     NaN    NaN
o1 > 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m2d

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
   Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
c2    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o2 > 1    0  0    0     0          0     NaN    NaN
o2 > 2    0  0    0     0          0     NaN    NaN
o2 > 3    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o2_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m3a

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = c1 ~ o1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
o1.L           0  0    0     0          0     NaN    NaN
o1.Q           0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_c1_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
         Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_c1    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 1:10
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m3b

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = c1 ~ o2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
o22            0  0    0     0          0     NaN    NaN
o23            0  0    0     0          0     NaN    NaN
o24            0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_c1_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
         Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_c1    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m4a

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ M2 + o2 * abs(C1 - C2) + log(C1) + (1 | 
    id), data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)


Posterior summary:
                 Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22                 0  0    0     0          0     NaN    NaN
M23                 0  0    0     0          0     NaN    NaN
M24                 0  0    0     0          0     NaN    NaN
abs(C1 - C2)        0  0    0     0          0     NaN    NaN
log(C1)             0  0    0     0          0     NaN    NaN
o22                 0  0    0     0          0     NaN    NaN
o23                 0  0    0     0          0     NaN    NaN
o24                 0  0    0     0          0     NaN    NaN
o22:abs(C1 - C2)    0  0    0     0          0     NaN    NaN
o23:abs(C1 - C2)    0  0    0     0          0     NaN    NaN
o24:abs(C1 - C2)    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1    0  0    0     0          0     NaN    NaN
o1 > 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m4b

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ ifelse(as.numeric(o2) > as.numeric(M1), 
    1, 0) * abs(C1 - C2) + log(C1) + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
                                                           Mean SD 2.5% 97.5%
abs(C1 - C2)                                                  0  0    0     0
log(C1)                                                       0  0    0     0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0)                 0  0    0     0
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2)    0  0    0     0
                                                           tail-prob. GR-crit
abs(C1 - C2)                                                        0     NaN
log(C1)                                                             0     NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0)                       0     NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2)          0     NaN
                                                           MCE/SD
abs(C1 - C2)                                                  NaN
log(C1)                                                       NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0)                 NaN
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2)    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1    0  0    0     0          0     NaN    NaN
o1 > 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m4c

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ time + c1 + C1 + B2 + (c1 * time | id), 
    data = longDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)


Posterior summary:
     Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C1      0  0    0     0          0     NaN    NaN
B21     0  0    0     0          0     NaN    NaN
time    0  0    0     0          0     NaN    NaN
c1      0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1    0  0    0     0          0     NaN    NaN
o1 > 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN
D_o1_id[1,2]    0  0    0     0          0     NaN    NaN
D_o1_id[2,2]    0  0    0     0                NaN    NaN
D_o1_id[1,3]    0  0    0     0          0     NaN    NaN
D_o1_id[2,3]    0  0    0     0          0     NaN    NaN
D_o1_id[3,3]    0  0    0     0                NaN    NaN
D_o1_id[1,4]    0  0    0     0          0     NaN    NaN
D_o1_id[2,4]    0  0    0     0          0     NaN    NaN
D_o1_id[3,4]    0  0    0     0          0     NaN    NaN
D_o1_id[4,4]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m4d

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ C1 * time + I(time^2) + b2 * c1, data = longDF, 
    random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)


Posterior summary:
          Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C1           0  0    0     0          0     NaN    NaN
time         0  0    0     0          0     NaN    NaN
I(time^2)    0  0    0     0          0     NaN    NaN
b21          0  0    0     0          0     NaN    NaN
c1           0  0    0     0          0     NaN    NaN
C1:time      0  0    0     0          0     NaN    NaN
b21:c1       0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1    0  0    0     0          0     NaN    NaN
o1 > 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN
D_o1_id[1,2]    0  0    0     0          0     NaN    NaN
D_o1_id[2,2]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m4e

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ C1 + log(time) + I(time^2) + p1, data = longDF, 
    random = ~1 | id, n.adapt = 5, n.iter = 10, shrinkage = "ridge", 
    seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
          Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
C1           0  0    0     0          0     NaN    NaN
log(time)    0  0    0     0          0     NaN    NaN
I(time^2)    0  0    0     0          0     NaN    NaN
p1           0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1    0  0    0     0          0     NaN    NaN
o1 > 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m5a

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), 
    nonprop = list(o1 = ~C1 + C2 + b2), seed = 2020, warn = FALSE, 
    mess = FALSE)


Posterior summary:
         Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O22         0  0    0     0          0     NaN    NaN
O23         0  0    0     0          0     NaN    NaN
o12: C1     0  0    0     0          0     NaN    NaN
o12: C2     0  0    0     0          0     NaN    NaN
o13: C1     0  0    0     0          0     NaN    NaN
o13: C2     0  0    0     0          0     NaN    NaN
o12: b21    0  0    0     0          0     NaN    NaN
o13: b21    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1    0  0    0     0          0     NaN    NaN
o1 > 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m5b

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), 
    nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE, 
    mess = FALSE)


Posterior summary:
        Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22        0  0    0     0          0     NaN    NaN
M23        0  0    0     0          0     NaN    NaN
M24        0  0    0     0          0     NaN    NaN
O22        0  0    0     0          0     NaN    NaN
O23        0  0    0     0          0     NaN    NaN
o12: C2    0  0    0     0          0     NaN    NaN
o13: C2    0  0    0     0          0     NaN    NaN
c1:C2      0  0    0     0          0     NaN    NaN
o12: c1    0  0    0     0          0     NaN    NaN
o13: c1    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1    0  0    0     0          0     NaN    NaN
o1 > 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m5c

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), 
    nonprop = list(o1 = ~c1 * C2), seed = 2020, warn = FALSE, 
    mess = FALSE)


Posterior summary:
           Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22           0  0    0     0          0     NaN    NaN
M23           0  0    0     0          0     NaN    NaN
M24           0  0    0     0          0     NaN    NaN
O22           0  0    0     0          0     NaN    NaN
O23           0  0    0     0          0     NaN    NaN
o12: C2       0  0    0     0          0     NaN    NaN
o13: C2       0  0    0     0          0     NaN    NaN
o12: c1       0  0    0     0          0     NaN    NaN
o12: c1:C2    0  0    0     0          0     NaN    NaN
o13: c1       0  0    0     0          0     NaN    NaN
o13: c1:C2    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1    0  0    0     0          0     NaN    NaN
o1 > 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m5d

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), 
    nonprop = list(o1 = ~c1 + C2), seed = 2020, warn = FALSE, 
    mess = FALSE)


Posterior summary:
        Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22        0  0    0     0          0     NaN    NaN
M23        0  0    0     0          0     NaN    NaN
M24        0  0    0     0          0     NaN    NaN
O22        0  0    0     0          0     NaN    NaN
O23        0  0    0     0          0     NaN    NaN
M22:C2     0  0    0     0          0     NaN    NaN
M23:C2     0  0    0     0          0     NaN    NaN
M24:C2     0  0    0     0          0     NaN    NaN
o12: C2    0  0    0     0          0     NaN    NaN
o13: C2    0  0    0     0          0     NaN    NaN
o12: c1    0  0    0     0          0     NaN    NaN
o13: c1    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1    0  0    0     0          0     NaN    NaN
o1 > 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m5e

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), 
    nonprop = ~c1 + M2 * C2 + O2, seed = 2020, warn = FALSE, 
    mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o12: M22       0  0    0     0          0     NaN    NaN
o12: M23       0  0    0     0          0     NaN    NaN
o12: M24       0  0    0     0          0     NaN    NaN
o12: C2        0  0    0     0          0     NaN    NaN
o12: O22       0  0    0     0          0     NaN    NaN
o12: O23       0  0    0     0          0     NaN    NaN
o12: M22:C2    0  0    0     0          0     NaN    NaN
o12: M23:C2    0  0    0     0          0     NaN    NaN
o12: M24:C2    0  0    0     0          0     NaN    NaN
o13: M22       0  0    0     0          0     NaN    NaN
o13: M23       0  0    0     0          0     NaN    NaN
o13: M24       0  0    0     0          0     NaN    NaN
o13: C2        0  0    0     0          0     NaN    NaN
o13: O22       0  0    0     0          0     NaN    NaN
o13: O23       0  0    0     0          0     NaN    NaN
o13: M22:C2    0  0    0     0          0     NaN    NaN
o13: M23:C2    0  0    0     0          0     NaN    NaN
o13: M24:C2    0  0    0     0          0     NaN    NaN
o12: c1        0  0    0     0          0     NaN    NaN
o13: c1        0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 > 1    0  0    0     0          0     NaN    NaN
o1 > 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m6a

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ C1 + C2 + b2 + O2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), 
    nonprop = list(o1 = ~C1 + C2 + b2), rev = "o1", seed = 2020, 
    warn = FALSE, mess = FALSE)


Posterior summary:
         Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O22         0  0    0     0          0     NaN    NaN
O23         0  0    0     0          0     NaN    NaN
o12: C1     0  0    0     0          0     NaN    NaN
o12: C2     0  0    0     0          0     NaN    NaN
o13: C1     0  0    0     0          0     NaN    NaN
o13: C2     0  0    0     0          0     NaN    NaN
o12: b21    0  0    0     0          0     NaN    NaN
o13: b21    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 = 1    0  0    0     0          0     NaN    NaN
o1 = 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m6b

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), 
    nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE, 
    mess = FALSE)


Posterior summary:
        Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22        0  0    0     0          0     NaN    NaN
M23        0  0    0     0          0     NaN    NaN
M24        0  0    0     0          0     NaN    NaN
O22        0  0    0     0          0     NaN    NaN
O23        0  0    0     0          0     NaN    NaN
o12: C2    0  0    0     0          0     NaN    NaN
o13: C2    0  0    0     0          0     NaN    NaN
c1:C2      0  0    0     0          0     NaN    NaN
o12: c1    0  0    0     0          0     NaN    NaN
o13: c1    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 = 1    0  0    0     0          0     NaN    NaN
o1 = 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m6c

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ c1 * C2 + M2 + O2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), 
    nonprop = list(o1 = ~c1 * C2), rev = "o1", seed = 2020, warn = FALSE, 
    mess = FALSE)


Posterior summary:
           Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22           0  0    0     0          0     NaN    NaN
M23           0  0    0     0          0     NaN    NaN
M24           0  0    0     0          0     NaN    NaN
O22           0  0    0     0          0     NaN    NaN
O23           0  0    0     0          0     NaN    NaN
o12: C2       0  0    0     0          0     NaN    NaN
o13: C2       0  0    0     0          0     NaN    NaN
o12: c1       0  0    0     0          0     NaN    NaN
o12: c1:C2    0  0    0     0          0     NaN    NaN
o13: c1       0  0    0     0          0     NaN    NaN
o13: c1:C2    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 = 1    0  0    0     0          0     NaN    NaN
o1 = 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m6d

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), 
    nonprop = list(o1 = ~c1 + C2), rev = "o1", seed = 2020, warn = FALSE, 
    mess = FALSE)


Posterior summary:
        Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22        0  0    0     0          0     NaN    NaN
M23        0  0    0     0          0     NaN    NaN
M24        0  0    0     0          0     NaN    NaN
O22        0  0    0     0          0     NaN    NaN
O23        0  0    0     0          0     NaN    NaN
M22:C2     0  0    0     0          0     NaN    NaN
M23:C2     0  0    0     0          0     NaN    NaN
M24:C2     0  0    0     0          0     NaN    NaN
o12: C2    0  0    0     0          0     NaN    NaN
o13: C2    0  0    0     0          0     NaN    NaN
o12: c1    0  0    0     0          0     NaN    NaN
o13: c1    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 = 1    0  0    0     0          0     NaN    NaN
o1 = 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m6e

Bayesian cumulative logit mixed model fitted with JointAI

Call:
clmm_imp(fixed = o1 ~ c1 + M2 * C2 + O2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, monitor_params = list(other = "p_o1"), 
    nonprop = ~c1 + M2 * C2 + O2, rev = "o1", seed = 2020, warn = FALSE, 
    mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o12: M22       0  0    0     0          0     NaN    NaN
o12: M23       0  0    0     0          0     NaN    NaN
o12: M24       0  0    0     0          0     NaN    NaN
o12: C2        0  0    0     0          0     NaN    NaN
o12: O22       0  0    0     0          0     NaN    NaN
o12: O23       0  0    0     0          0     NaN    NaN
o12: M22:C2    0  0    0     0          0     NaN    NaN
o12: M23:C2    0  0    0     0          0     NaN    NaN
o12: M24:C2    0  0    0     0          0     NaN    NaN
o13: M22       0  0    0     0          0     NaN    NaN
o13: M23       0  0    0     0          0     NaN    NaN
o13: M24       0  0    0     0          0     NaN    NaN
o13: C2        0  0    0     0          0     NaN    NaN
o13: O22       0  0    0     0          0     NaN    NaN
o13: O23       0  0    0     0          0     NaN    NaN
o13: M22:C2    0  0    0     0          0     NaN    NaN
o13: M23:C2    0  0    0     0          0     NaN    NaN
o13: M24:C2    0  0    0     0          0     NaN    NaN
o12: c1        0  0    0     0          0     NaN    NaN
o13: c1        0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
o1 = 1    0  0    0     0          0     NaN    NaN
o1 = 2    0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
             Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_o1_id[1,1]    0  0    0     0                NaN    NaN



MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m7a

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ C1 + o1 + o2 + x + time, data = longDF, random = ~1 | 
    id, n.adapt = 5, n.iter = 10, seed = 2020)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
C1             0  0    0     0          0     NaN    NaN
o1.L           0  0    0     0          0     NaN    NaN
o1.Q           0  0    0     0          0     NaN    NaN
o22            0  0    0     0          0     NaN    NaN
o23            0  0    0     0          0     NaN    NaN
o24            0  0    0     0          0     NaN    NaN
x2             0  0    0     0          0     NaN    NaN
x3             0  0    0     0          0     NaN    NaN
x4             0  0    0     0          0     NaN    NaN
time           0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

$m7b

Bayesian linear mixed model fitted with JointAI

Call:
lme_imp(fixed = y ~ o2 + o1 + c2 + b2, data = longDF, random = ~1 | 
    id, n.adapt = 5, n.iter = 10, seed = 2020)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
o22            0  0    0     0          0     NaN    NaN
o23            0  0    0     0          0     NaN    NaN
o24            0  0    0     0          0     NaN    NaN
o1.L           0  0    0     0          0     NaN    NaN
o1.Q           0  0    0     0          0     NaN    NaN
c2             0  0    0     0          0     NaN    NaN
b21            0  0    0     0          0     NaN    NaN


Posterior summary of random effects covariance matrix:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_y_id[1,1]    0  0    0     0                NaN    NaN


Posterior summary of residual std. deviation:
        Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_y    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 329 
Number of groups:
 - id: 100

