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

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)


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

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O1 > 1    0  0    0     0          0     NaN    NaN
O1 > 2    0  0    0     0          0     NaN    NaN
O1 > 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

$m0b

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O2 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)


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

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O2 > 1    0  0    0     0          0     NaN    NaN
O2 > 2    0  0    0     0          0     NaN    NaN
O2 > 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

$m1a

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)


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

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O1 > 1    0  0    0     0          0     NaN    NaN
O1 > 2    0  0    0     0          0     NaN    NaN
O1 > 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

$m1b

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O2 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)


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

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O2 > 1    0  0    0     0          0     NaN    NaN
O2 > 2    0  0    0     0          0     NaN    NaN
O2 > 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

$m2a

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O1 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)


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

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O1 > 1    0  0    0     0          0     NaN    NaN
O1 > 2    0  0    0     0          0     NaN    NaN
O1 > 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

$m2b

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)


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

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O2 > 1    0  0    0     0          0     NaN    NaN
O2 > 2    0  0    0     0          0     NaN    NaN
O2 > 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

$m3a

Bayesian linear model fitted with JointAI

Call:
lm_imp(formula = C1 ~ O1, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
O1.L           0  0    0     0          0     NaN    NaN
O1.Q           0  0    0     0          0     NaN    NaN
O1.C           0  0    0     0          0     NaN    NaN

Posterior summary of residual std. deviation:
         Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_C1    0  0    0     0     NaN    NaN


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

Number of observations: 100 

$m3b

Bayesian linear model fitted with JointAI

Call:
lm_imp(formula = C1 ~ O2, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)    0  0    0     0          0     NaN    NaN
O22            0  0    0     0          0     NaN    NaN
O23            0  0    0     0          0     NaN    NaN
O24            0  0    0     0          0     NaN    NaN

Posterior summary of residual std. deviation:
         Mean SD 2.5% 97.5% GR-crit MCE/SD
sigma_C1    0  0    0     0     NaN    NaN


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

Number of observations: 100 

$m4a

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O1 ~ M2 + O2 * abs(C1 - C2) + log(C1), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
                 Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22                 0  0    0     0          0     NaN    NaN
M23                 0  0    0     0          0     NaN    NaN
M24                 0  0    0     0          0     NaN    NaN
O22                 0  0    0     0          0     NaN    NaN
O23                 0  0    0     0          0     NaN    NaN
O24                 0  0    0     0          0     NaN    NaN
abs(C1 - C2)        0  0    0     0          0     NaN    NaN
log(C1)             0  0    0     0          0     NaN    NaN
O22:abs(C1 - C2)    0  0    0     0          0     NaN    NaN
O23:abs(C1 - C2)    0  0    0     0          0     NaN    NaN
O24:abs(C1 - C2)    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O1 > 1    0  0    0     0          0     NaN    NaN
O1 > 2    0  0    0     0          0     NaN    NaN
O1 > 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

$m4b

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O1 ~ ifelse(as.numeric(O2) > as.numeric(M1), 
    1, 0) * abs(C1 - C2) + log(C1), data = wideDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
                                                           Mean SD 2.5% 97.5%
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0)                 0  0    0     0
abs(C1 - C2)                                                  0  0    0     0
log(C1)                                                       0  0    0     0
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2)    0  0    0     0
                                                           tail-prob. GR-crit
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0)                       0     NaN
abs(C1 - C2)                                                        0     NaN
log(C1)                                                             0     NaN
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2)          0     NaN
                                                           MCE/SD
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0)                 NaN
abs(C1 - C2)                                                  NaN
log(C1)                                                       NaN
ifelse(as.numeric(O2) > as.numeric(M1), 1, 0):abs(C1 - C2)    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O1 > 1    0  0    0     0          0     NaN    NaN
O1 > 2    0  0    0     0          0     NaN    NaN
O1 > 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

$m5a

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, n.adapt = 5, 
    n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + 
        C2), seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
        Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22        0  0    0     0          0     NaN    NaN
M23        0  0    0     0          0     NaN    NaN
M24        0  0    0     0          0     NaN    NaN
O22        0  0    0     0          0     NaN    NaN
O23        0  0    0     0          0     NaN    NaN
O24        0  0    0     0          0     NaN    NaN
O12: C1    0  0    0     0          0     NaN    NaN
O12: C2    0  0    0     0          0     NaN    NaN
O13: C1    0  0    0     0          0     NaN    NaN
O13: C2    0  0    0     0          0     NaN    NaN
O14: C1    0  0    0     0          0     NaN    NaN
O14: C2    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O1 > 1    0  0    0     0          0     NaN    NaN
O1 > 2    0  0    0     0          0     NaN    NaN
O1 > 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

$m5b

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, 
    n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + 
        C2), seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
        Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22        0  0    0     0          0     NaN    NaN
M23        0  0    0     0          0     NaN    NaN
M24        0  0    0     0          0     NaN    NaN
O22        0  0    0     0          0     NaN    NaN
O23        0  0    0     0          0     NaN    NaN
O24        0  0    0     0          0     NaN    NaN
C1:C2      0  0    0     0          0     NaN    NaN
O12: C1    0  0    0     0          0     NaN    NaN
O12: C2    0  0    0     0          0     NaN    NaN
O13: C1    0  0    0     0          0     NaN    NaN
O13: C2    0  0    0     0          0     NaN    NaN
O14: C1    0  0    0     0          0     NaN    NaN
O14: C2    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O1 > 1    0  0    0     0          0     NaN    NaN
O1 > 2    0  0    0     0          0     NaN    NaN
O1 > 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

$m5c

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, 
    n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 * 
        C2), seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
           Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22           0  0    0     0          0     NaN    NaN
M23           0  0    0     0          0     NaN    NaN
M24           0  0    0     0          0     NaN    NaN
O22           0  0    0     0          0     NaN    NaN
O23           0  0    0     0          0     NaN    NaN
O24           0  0    0     0          0     NaN    NaN
O12: C1       0  0    0     0          0     NaN    NaN
O12: C2       0  0    0     0          0     NaN    NaN
O12: C1:C2    0  0    0     0          0     NaN    NaN
O13: C1       0  0    0     0          0     NaN    NaN
O13: C2       0  0    0     0          0     NaN    NaN
O13: C1:C2    0  0    0     0          0     NaN    NaN
O14: C1       0  0    0     0          0     NaN    NaN
O14: C2       0  0    0     0          0     NaN    NaN
O14: C1:C2    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O1 > 1    0  0    0     0          0     NaN    NaN
O1 > 2    0  0    0     0          0     NaN    NaN
O1 > 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

$m5d

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, 
    n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + 
        C2), seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
        Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22        0  0    0     0          0     NaN    NaN
M23        0  0    0     0          0     NaN    NaN
M24        0  0    0     0          0     NaN    NaN
O22        0  0    0     0          0     NaN    NaN
O23        0  0    0     0          0     NaN    NaN
O24        0  0    0     0          0     NaN    NaN
M22:C2     0  0    0     0          0     NaN    NaN
M23:C2     0  0    0     0          0     NaN    NaN
M24:C2     0  0    0     0          0     NaN    NaN
O12: C1    0  0    0     0          0     NaN    NaN
O12: C2    0  0    0     0          0     NaN    NaN
O13: C1    0  0    0     0          0     NaN    NaN
O13: C2    0  0    0     0          0     NaN    NaN
O14: C1    0  0    0     0          0     NaN    NaN
O14: C2    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O1 > 1    0  0    0     0          0     NaN    NaN
O1 > 2    0  0    0     0          0     NaN    NaN
O1 > 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

$m5e

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, 
    n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = ~C1 + 
        M2 * C2 + O2, seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O12: C1        0  0    0     0          0     NaN    NaN
O12: M22       0  0    0     0          0     NaN    NaN
O12: M23       0  0    0     0          0     NaN    NaN
O12: M24       0  0    0     0          0     NaN    NaN
O12: C2        0  0    0     0          0     NaN    NaN
O12: O22       0  0    0     0          0     NaN    NaN
O12: O23       0  0    0     0          0     NaN    NaN
O12: O24       0  0    0     0          0     NaN    NaN
O12: M22:C2    0  0    0     0          0     NaN    NaN
O12: M23:C2    0  0    0     0          0     NaN    NaN
O12: M24:C2    0  0    0     0          0     NaN    NaN
O13: C1        0  0    0     0          0     NaN    NaN
O13: M22       0  0    0     0          0     NaN    NaN
O13: M23       0  0    0     0          0     NaN    NaN
O13: M24       0  0    0     0          0     NaN    NaN
O13: C2        0  0    0     0          0     NaN    NaN
O13: O22       0  0    0     0          0     NaN    NaN
O13: O23       0  0    0     0          0     NaN    NaN
O13: O24       0  0    0     0          0     NaN    NaN
O13: M22:C2    0  0    0     0          0     NaN    NaN
O13: M23:C2    0  0    0     0          0     NaN    NaN
O13: M24:C2    0  0    0     0          0     NaN    NaN
O14: C1        0  0    0     0          0     NaN    NaN
O14: M22       0  0    0     0          0     NaN    NaN
O14: M23       0  0    0     0          0     NaN    NaN
O14: M24       0  0    0     0          0     NaN    NaN
O14: C2        0  0    0     0          0     NaN    NaN
O14: O22       0  0    0     0          0     NaN    NaN
O14: O23       0  0    0     0          0     NaN    NaN
O14: O24       0  0    0     0          0     NaN    NaN
O14: M22:C2    0  0    0     0          0     NaN    NaN
O14: M23:C2    0  0    0     0          0     NaN    NaN
O14: M24:C2    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O1 > 1    0  0    0     0          0     NaN    NaN
O1 > 2    0  0    0     0          0     NaN    NaN
O1 > 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

$m6a

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O1 ~ C1 + C2 + M2 + O2, data = wideDF, n.adapt = 5, 
    n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + 
        C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
        Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22        0  0    0     0          0     NaN    NaN
M23        0  0    0     0          0     NaN    NaN
M24        0  0    0     0          0     NaN    NaN
O22        0  0    0     0          0     NaN    NaN
O23        0  0    0     0          0     NaN    NaN
O24        0  0    0     0          0     NaN    NaN
O12: C1    0  0    0     0          0     NaN    NaN
O12: C2    0  0    0     0          0     NaN    NaN
O13: C1    0  0    0     0          0     NaN    NaN
O13: C2    0  0    0     0          0     NaN    NaN
O14: C1    0  0    0     0          0     NaN    NaN
O14: C2    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O1 = 1    0  0    0     0          0     NaN    NaN
O1 = 2    0  0    0     0          0     NaN    NaN
O1 = 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

$m6b

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, 
    n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + 
        C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
        Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22        0  0    0     0          0     NaN    NaN
M23        0  0    0     0          0     NaN    NaN
M24        0  0    0     0          0     NaN    NaN
O22        0  0    0     0          0     NaN    NaN
O23        0  0    0     0          0     NaN    NaN
O24        0  0    0     0          0     NaN    NaN
C1:C2      0  0    0     0          0     NaN    NaN
O12: C1    0  0    0     0          0     NaN    NaN
O12: C2    0  0    0     0          0     NaN    NaN
O13: C1    0  0    0     0          0     NaN    NaN
O13: C2    0  0    0     0          0     NaN    NaN
O14: C1    0  0    0     0          0     NaN    NaN
O14: C2    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O1 = 1    0  0    0     0          0     NaN    NaN
O1 = 2    0  0    0     0          0     NaN    NaN
O1 = 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

$m6c

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O1 ~ C1 * C2 + M2 + O2, data = wideDF, n.adapt = 5, 
    n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 * 
        C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
           Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22           0  0    0     0          0     NaN    NaN
M23           0  0    0     0          0     NaN    NaN
M24           0  0    0     0          0     NaN    NaN
O22           0  0    0     0          0     NaN    NaN
O23           0  0    0     0          0     NaN    NaN
O24           0  0    0     0          0     NaN    NaN
O12: C1       0  0    0     0          0     NaN    NaN
O12: C2       0  0    0     0          0     NaN    NaN
O12: C1:C2    0  0    0     0          0     NaN    NaN
O13: C1       0  0    0     0          0     NaN    NaN
O13: C2       0  0    0     0          0     NaN    NaN
O13: C1:C2    0  0    0     0          0     NaN    NaN
O14: C1       0  0    0     0          0     NaN    NaN
O14: C2       0  0    0     0          0     NaN    NaN
O14: C1:C2    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O1 = 1    0  0    0     0          0     NaN    NaN
O1 = 2    0  0    0     0          0     NaN    NaN
O1 = 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

$m6d

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, 
    n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = list(O1 = ~C1 + 
        C2), rev = "O1", seed = 2020, warn = FALSE, mess = FALSE)


Posterior summary:
        Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
M22        0  0    0     0          0     NaN    NaN
M23        0  0    0     0          0     NaN    NaN
M24        0  0    0     0          0     NaN    NaN
O22        0  0    0     0          0     NaN    NaN
O23        0  0    0     0          0     NaN    NaN
O24        0  0    0     0          0     NaN    NaN
M22:C2     0  0    0     0          0     NaN    NaN
M23:C2     0  0    0     0          0     NaN    NaN
M24:C2     0  0    0     0          0     NaN    NaN
O12: C1    0  0    0     0          0     NaN    NaN
O12: C2    0  0    0     0          0     NaN    NaN
O13: C1    0  0    0     0          0     NaN    NaN
O13: C2    0  0    0     0          0     NaN    NaN
O14: C1    0  0    0     0          0     NaN    NaN
O14: C2    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O1 = 1    0  0    0     0          0     NaN    NaN
O1 = 2    0  0    0     0          0     NaN    NaN
O1 = 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

$m6e

Bayesian cumulative logit model fitted with JointAI

Call:
clm_imp(formula = O1 ~ C1 + M2 * C2 + O2, data = wideDF, n.adapt = 5, 
    n.iter = 10, monitor_params = list(other = "p_O1"), nonprop = ~C1 + 
        M2 * C2 + O2, rev = "O1", seed = 2020, warn = FALSE, 
    mess = FALSE)


Posterior summary:
            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O12: C1        0  0    0     0          0     NaN    NaN
O12: M22       0  0    0     0          0     NaN    NaN
O12: M23       0  0    0     0          0     NaN    NaN
O12: M24       0  0    0     0          0     NaN    NaN
O12: C2        0  0    0     0          0     NaN    NaN
O12: O22       0  0    0     0          0     NaN    NaN
O12: O23       0  0    0     0          0     NaN    NaN
O12: O24       0  0    0     0          0     NaN    NaN
O12: M22:C2    0  0    0     0          0     NaN    NaN
O12: M23:C2    0  0    0     0          0     NaN    NaN
O12: M24:C2    0  0    0     0          0     NaN    NaN
O13: C1        0  0    0     0          0     NaN    NaN
O13: M22       0  0    0     0          0     NaN    NaN
O13: M23       0  0    0     0          0     NaN    NaN
O13: M24       0  0    0     0          0     NaN    NaN
O13: C2        0  0    0     0          0     NaN    NaN
O13: O22       0  0    0     0          0     NaN    NaN
O13: O23       0  0    0     0          0     NaN    NaN
O13: O24       0  0    0     0          0     NaN    NaN
O13: M22:C2    0  0    0     0          0     NaN    NaN
O13: M23:C2    0  0    0     0          0     NaN    NaN
O13: M24:C2    0  0    0     0          0     NaN    NaN
O14: C1        0  0    0     0          0     NaN    NaN
O14: M22       0  0    0     0          0     NaN    NaN
O14: M23       0  0    0     0          0     NaN    NaN
O14: M24       0  0    0     0          0     NaN    NaN
O14: C2        0  0    0     0          0     NaN    NaN
O14: O22       0  0    0     0          0     NaN    NaN
O14: O23       0  0    0     0          0     NaN    NaN
O14: O24       0  0    0     0          0     NaN    NaN
O14: M22:C2    0  0    0     0          0     NaN    NaN
O14: M23:C2    0  0    0     0          0     NaN    NaN
O14: M24:C2    0  0    0     0          0     NaN    NaN

Posterior summary of the intercepts:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
O1 = 1    0  0    0     0          0     NaN    NaN
O1 = 2    0  0    0     0          0     NaN    NaN
O1 = 3    0  0    0     0          0     NaN    NaN


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

Number of observations: 100 

