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

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2 
     0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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

 Bayesian cumulative logit mixed model for "o2" 

Fixed effects:
o2 > 1 o2 > 2 o2 > 3 
     0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2     C1 
     0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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

 Bayesian cumulative logit mixed model for "o2" 

Fixed effects:
o2 > 1 o2 > 2 o2 > 3     C1 
     0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2     c1 
     0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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

 Bayesian cumulative logit mixed model for "o2" 

Fixed effects:
o2 > 1 o2 > 2 o2 > 3     c1 
     0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2     C2 
     0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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

 Bayesian cumulative logit mixed model for "o2" 

Fixed effects:
o2 > 1 o2 > 2 o2 > 3     C2 
     0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2     c2 
     0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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

 Bayesian cumulative logit mixed model for "o2" 

Fixed effects:
o2 > 1 o2 > 2 o2 > 3     c2 
     0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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

 Bayesian linear mixed model for "c1" 

Fixed effects:
(Intercept)        o1.L        o1.Q 
          0           0           0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0



Residual standard deviation:
sigma_c1 
       0 

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

 Bayesian linear mixed model for "c1" 

Fixed effects:
(Intercept)         o22         o23         o24 
          0           0           0           0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0



Residual standard deviation:
sigma_c1 
       0 

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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
          o1 > 1           o1 > 2              M22              M23 
               0                0                0                0 
             M24     abs(C1 - C2)          log(C1)              o22 
               0                0                0                0 
             o23              o24 o22:abs(C1 - C2) o23:abs(C1 - C2) 
               0                0                0                0 
o24:abs(C1 - C2) 
               0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
                                                    o1 > 1 
                                                         0 
                                                    o1 > 2 
                                                         0 
                                              abs(C1 - C2) 
                                                         0 
                                                   log(C1) 
                                                         0 
             ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 
                                                         0 
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 
                                                         0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2     C1    B21   time     c1 
     0      0      0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept) c1 time c1:time
(Intercept)           0  0    0       0
c1                    0  0    0       0
time                  0  0    0       0
c1:time               0  0    0       0


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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
   o1 > 1    o1 > 2        C1      time I(time^2)       b21        c1   C1:time 
        0         0         0         0         0         0         0         0 
   b21:c1 
        0 


Random effects covariance matrix:
$id
            (Intercept) time
(Intercept)           0    0
time                  0    0


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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
   o1 > 1    o1 > 2        C1 log(time) I(time^2)        p1 
        0         0         0         0         0         0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2    O22    O23     C1     C2     C1     C2    b21    b21 
     0      0      0      0      0      0      0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2    M22    M23    M24    O22    O23  c1:C2     C2     C2     c1 
     0      0      0      0      0      0      0      0      0      0      0 
    c1 
     0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2    M22    M23    M24    O22    O23     C2     C2     c1  c1:C2 
     0      0      0      0      0      0      0      0      0      0      0 
    c1  c1:C2 
     0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2    M22    M23    M24    O22    O23 M22:C2 M23:C2 M24:C2     C2 
     0      0      0      0      0      0      0      0      0      0      0 
    C2     c1     c1 
     0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2    M22    M23    M24     C2    O22    O23 M22:C2 M23:C2 M24:C2 
     0      0      0      0      0      0      0      0      0      0      0 
   M22    M23    M24     C2    O22    O23 M22:C2 M23:C2 M24:C2     c1     c1 
     0      0      0      0      0      0      0      0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 = 1 o1 = 2    O22    O23     C1     C2     C1     C2    b21    b21 
     0      0      0      0      0      0      0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 = 1 o1 = 2    M22    M23    M24    O22    O23  c1:C2     C2     C2     c1 
     0      0      0      0      0      0      0      0      0      0      0 
    c1 
     0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 = 1 o1 = 2    M22    M23    M24    O22    O23     C2     C2     c1  c1:C2 
     0      0      0      0      0      0      0      0      0      0      0 
    c1  c1:C2 
     0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 = 1 o1 = 2    M22    M23    M24    O22    O23 M22:C2 M23:C2 M24:C2     C2 
     0      0      0      0      0      0      0      0      0      0      0 
    C2     c1     c1 
     0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 = 1 o1 = 2    M22    M23    M24     C2    O22    O23 M22:C2 M23:C2 M24:C2 
     0      0      0      0      0      0      0      0      0      0      0 
   M22    M23    M24     C2    O22    O23 M22:C2 M23:C2 M24:C2     c1     c1 
     0      0      0      0      0      0      0      0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


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

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1        o1.L        o1.Q         o22         o23 
          0           0           0           0           0           0 
        o24          x2          x3          x4        time 
          0           0           0           0           0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0



Residual standard deviation:
sigma_y 
      0 

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

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)         o22         o23         o24        o1.L        o1.Q 
          0           0           0           0           0           0 
         c2         b21 
          0           0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0



Residual standard deviation:
sigma_y 
      0 
$m0a

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

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2 
     0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m0b

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

 Bayesian cumulative logit mixed model for "o2" 

Fixed effects:
o2 > 1 o2 > 2 o2 > 3 
     0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m1a

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

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2     C1 
     0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m1b

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

 Bayesian cumulative logit mixed model for "o2" 

Fixed effects:
o2 > 1 o2 > 2 o2 > 3     C1 
     0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m1c

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

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2     c1 
     0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m1d

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

 Bayesian cumulative logit mixed model for "o2" 

Fixed effects:
o2 > 1 o2 > 2 o2 > 3     c1 
     0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m2a

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

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2     C2 
     0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m2b

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

 Bayesian cumulative logit mixed model for "o2" 

Fixed effects:
o2 > 1 o2 > 2 o2 > 3     C2 
     0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m2c

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

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2     c2 
     0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m2d

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

 Bayesian cumulative logit mixed model for "o2" 

Fixed effects:
o2 > 1 o2 > 2 o2 > 3     c2 
     0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m3a

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

 Bayesian linear mixed model for "c1" 

Fixed effects:
(Intercept)        o1.L        o1.Q 
          0           0           0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0



Residual standard deviation:
sigma_c1 
       0 

$m3b

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

 Bayesian linear mixed model for "c1" 

Fixed effects:
(Intercept)         o22         o23         o24 
          0           0           0           0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0



Residual standard deviation:
sigma_c1 
       0 

$m4a

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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
          o1 > 1           o1 > 2              M22              M23 
               0                0                0                0 
             M24     abs(C1 - C2)          log(C1)              o22 
               0                0                0                0 
             o23              o24 o22:abs(C1 - C2) o23:abs(C1 - C2) 
               0                0                0                0 
o24:abs(C1 - C2) 
               0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m4b

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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
                                                    o1 > 1 
                                                         0 
                                                    o1 > 2 
                                                         0 
                                              abs(C1 - C2) 
                                                         0 
                                                   log(C1) 
                                                         0 
             ifelse(as.numeric(o2) > as.numeric(M1), 1, 0) 
                                                         0 
ifelse(as.numeric(o2) > as.numeric(M1), 1, 0):abs(C1 - C2) 
                                                         0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m4c

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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2     C1    B21   time     c1 
     0      0      0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept) c1 time c1:time
(Intercept)           0  0    0       0
c1                    0  0    0       0
time                  0  0    0       0
c1:time               0  0    0       0


$m4d

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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
   o1 > 1    o1 > 2        C1      time I(time^2)       b21        c1   C1:time 
        0         0         0         0         0         0         0         0 
   b21:c1 
        0 


Random effects covariance matrix:
$id
            (Intercept) time
(Intercept)           0    0
time                  0    0


$m4e

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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
   o1 > 1    o1 > 2        C1 log(time) I(time^2)        p1 
        0         0         0         0         0         0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m5a

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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2    O22    O23     C1     C2     C1     C2    b21    b21 
     0      0      0      0      0      0      0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m5b

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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2    M22    M23    M24    O22    O23  c1:C2     C2     C2     c1 
     0      0      0      0      0      0      0      0      0      0      0 
    c1 
     0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m5c

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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2    M22    M23    M24    O22    O23     C2     C2     c1  c1:C2 
     0      0      0      0      0      0      0      0      0      0      0 
    c1  c1:C2 
     0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m5d

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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2    M22    M23    M24    O22    O23 M22:C2 M23:C2 M24:C2     C2 
     0      0      0      0      0      0      0      0      0      0      0 
    C2     c1     c1 
     0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m5e

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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 > 1 o1 > 2    M22    M23    M24     C2    O22    O23 M22:C2 M23:C2 M24:C2 
     0      0      0      0      0      0      0      0      0      0      0 
   M22    M23    M24     C2    O22    O23 M22:C2 M23:C2 M24:C2     c1     c1 
     0      0      0      0      0      0      0      0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m6a

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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 = 1 o1 = 2    O22    O23     C1     C2     C1     C2    b21    b21 
     0      0      0      0      0      0      0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m6b

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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 = 1 o1 = 2    M22    M23    M24    O22    O23  c1:C2     C2     C2     c1 
     0      0      0      0      0      0      0      0      0      0      0 
    c1 
     0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m6c

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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 = 1 o1 = 2    M22    M23    M24    O22    O23     C2     C2     c1  c1:C2 
     0      0      0      0      0      0      0      0      0      0      0 
    c1  c1:C2 
     0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m6d

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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 = 1 o1 = 2    M22    M23    M24    O22    O23 M22:C2 M23:C2 M24:C2     C2 
     0      0      0      0      0      0      0      0      0      0      0 
    C2     c1     c1 
     0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m6e

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)

 Bayesian cumulative logit mixed model for "o1" 

Fixed effects:
o1 = 1 o1 = 2    M22    M23    M24     C2    O22    O23 M22:C2 M23:C2 M24:C2 
     0      0      0      0      0      0      0      0      0      0      0 
   M22    M23    M24     C2    O22    O23 M22:C2 M23:C2 M24:C2     c1     c1 
     0      0      0      0      0      0      0      0      0      0      0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0


$m7a

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

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1        o1.L        o1.Q         o22         o23 
          0           0           0           0           0           0 
        o24          x2          x3          x4        time 
          0           0           0           0           0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0



Residual standard deviation:
sigma_y 
      0 

$m7b

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

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)         o22         o23         o24        o1.L        o1.Q 
          0           0           0           0           0           0 
         c2         b21 
          0           0 


Random effects covariance matrix:
$id
            (Intercept)
(Intercept)           0



Residual standard deviation:
sigma_y 
      0 

