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

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept) 
          0 


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



Residual standard deviation:
sigma_y 
      0 

Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "identity"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept) 
          0 


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



Residual standard deviation:
sigma_y 
      0 

Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "log"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept) 
          0 


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



Residual standard deviation:
sigma_y 
      0 

Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "inverse"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept) 
          0 


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



Residual standard deviation:
sigma_y 
      0 

Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "logit"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 b1
                        (Intercept)
         b1 (Intercept)           0


Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "probit"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 b1
                        (Intercept)
         b1 (Intercept)           0


Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "log"), 
    n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 b1
                        (Intercept)
         b1 (Intercept)           0


Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "cloglog"), 
    n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 b1
                        (Intercept)
         b1 (Intercept)           0


Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "inverse"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma mixed model for "L1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 L1
                        (Intercept)
         L1 (Intercept)           0



Residual standard deviation:
sigma_L1 
       0 

Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "log"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma mixed model for "L1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 L1
                        (Intercept)
         L1 (Intercept)           0



Residual standard deviation:
sigma_L1 
       0 

Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "log"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian poisson mixed model for "p1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 p1
                        (Intercept)
         p1 (Intercept)           0


Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "identity"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian poisson mixed model for "p1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 p1
                        (Intercept)
         p1 (Intercept)           0


Call:
lognormmm_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian log-normal mixed model for "L1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 L1
                        (Intercept)
         L1 (Intercept)           0



Residual standard deviation:
sigma_L1 
       0 

Call:
betamm_imp(fixed = Be1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian beta mixed model for "Be1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                Be1
                        (Intercept)
        Be1 (Intercept)           0


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

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1 
          0           0 


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



Residual standard deviation:
sigma_y 
      0 

Call:
glme_imp(fixed = b1 ~ C1 + (1 | id), data = longDF, family = binomial(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b1" 

Fixed effects:
(Intercept)          C1 
          0           0 


Random effects covariance matrix:
$id
                                 b1
                        (Intercept)
         b1 (Intercept)           0


Call:
glme_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, family = Gamma(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma mixed model for "L1" 

Fixed effects:
(Intercept)          C1 
          0           0 


Random effects covariance matrix:
$id
                                 L1
                        (Intercept)
         L1 (Intercept)           0



Residual standard deviation:
sigma_L1 
       0 

Call:
glme_imp(fixed = p1 ~ C1 + (1 | id), data = longDF, family = poisson(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian poisson mixed model for "p1" 

Fixed effects:
(Intercept)          C1 
          0           0 


Random effects covariance matrix:
$id
                                 p1
                        (Intercept)
         p1 (Intercept)           0


Call:
lognormmm_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian log-normal mixed model for "L1" 

Fixed effects:
(Intercept)          C1 
          0           0 


Random effects covariance matrix:
$id
                                 L1
                        (Intercept)
         L1 (Intercept)           0



Residual standard deviation:
sigma_L1 
       0 

Call:
betamm_imp(fixed = Be1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian beta mixed model for "Be1" 

Fixed effects:
(Intercept)          C1 
          0           0 


Random effects covariance matrix:
$id
                                Be1
                        (Intercept)
        Be1 (Intercept)           0


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

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          c2 
          0           0 


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



Residual standard deviation:
sigma_y 
      0 

Call:
glme_imp(fixed = b2 ~ c2 + (1 | id), data = longDF, family = binomial(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b2" 

Fixed effects:
(Intercept)          c2 
          0           0 


Random effects covariance matrix:
$id
                                 b2
                        (Intercept)
         b2 (Intercept)           0


Call:
glme_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, family = Gamma(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma mixed model for "L1mis" 

Fixed effects:
(Intercept)          c2 
          0           0 


Random effects covariance matrix:
$id
                              L1mis
                        (Intercept)
      L1mis (Intercept)           0



Residual standard deviation:
sigma_L1mis 
          0 

Call:
glme_imp(fixed = p2 ~ c2 + (1 | id), data = longDF, family = poisson(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian poisson mixed model for "p2" 

Fixed effects:
(Intercept)          c2 
          0           0 


Random effects covariance matrix:
$id
                                 p2
                        (Intercept)
         p2 (Intercept)           0


Call:
lognormmm_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian log-normal mixed model for "L1mis" 

Fixed effects:
(Intercept)          c2 
          0           0 


Random effects covariance matrix:
$id
                              L1mis
                        (Intercept)
      L1mis (Intercept)           0



Residual standard deviation:
sigma_L1mis 
          0 

Call:
betamm_imp(fixed = Be2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian beta mixed model for "Be2" 

Fixed effects:
(Intercept)          c2 
          0           0 


Random effects covariance matrix:
$id
                                Be2
                        (Intercept)
        Be2 (Intercept)           0


Call:
lme_imp(fixed = y ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
C2 
 0 


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



Residual standard deviation:
sigma_y 
      0 

Call:
glme_imp(fixed = b2 ~ 0 + C2 + (1 | id), data = longDF, family = binomial(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b2" 

Fixed effects:
C2 
 0 


Random effects covariance matrix:
$id
                                 b2
                        (Intercept)
         b2 (Intercept)           0


Call:
glme_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, family = Gamma(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma mixed model for "L1mis" 

Fixed effects:
C2 
 0 


Random effects covariance matrix:
$id
                              L1mis
                        (Intercept)
      L1mis (Intercept)           0



Residual standard deviation:
sigma_L1mis 
          0 

Call:
glme_imp(fixed = p2 ~ 0 + C2 + (1 | id), data = longDF, family = poisson(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian poisson mixed model for "p2" 

Fixed effects:
C2 
 0 


Random effects covariance matrix:
$id
                                 p2
                        (Intercept)
         p2 (Intercept)           0


Call:
lognormmm_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian log-normal mixed model for "L1mis" 

Fixed effects:
C2 
 0 


Random effects covariance matrix:
$id
                              L1mis
                        (Intercept)
      L1mis (Intercept)           0



Residual standard deviation:
sigma_L1mis 
          0 

Call:
betamm_imp(fixed = Be2 ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian beta mixed model for "Be2" 

Fixed effects:
C2 
 0 


Random effects covariance matrix:
$id
                                Be2
                        (Intercept)
        Be2 (Intercept)           0


Call:
lme_imp(fixed = c1 ~ c2 + B2 + p2 + L1mis + Be2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, models = c(p2 = "glmm_poisson_log", 
        L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "c1" 

Fixed effects:
(Intercept)         B21          c2          p2       L1mis         Be2 
          0           0           0           0           0           0 


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



Residual standard deviation:
sigma_c1 
       0 

Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_inverse", 
        p2 = "glmm_poisson_identity", b2 = "glmm_binomial_probit", 
        L1mis = "glmm_lognorm"), seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "c1" 

Fixed effects:
(Intercept)          c2         b21          p2       L1mis 
          0           0           0           0           0 


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



Residual standard deviation:
sigma_c1 
       0 

Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", 
        p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", 
        b2 = "glmm_binomial_log"), no_model = "time", seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "c1" 

Fixed effects:
(Intercept)          c2         b21          p2       L1mis 
          0           0           0           0           0 


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



Residual standard deviation:
sigma_c1 
       0 

Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + Be2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", 
        p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", 
        b2 = "glmm_binomial_log"), shrinkage = "ridge", seed = 2020, 
    warn = FALSE, mess = FALSE, trunc = list(Be2 = c(0, 1)))

 Bayesian linear mixed model for "c1" 

Fixed effects:
(Intercept)          c2         b21          p2       L1mis         Be2 
          0           0           0           0           0           0 


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



Residual standard deviation:
sigma_c1 
       0 

Call:
lme_imp(fixed = y ~ M2 + o2 * abs(C1 - c2) + log(C1) + time + 
    I(time^2) + (time | id), data = longDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
     (Intercept)              M22              M23              M24 
               0                0                0                0 
         log(C1)              o22              o23              o24 
               0                0                0                0 
    abs(C1 - c2)             time        I(time^2) o22:abs(C1 - c2) 
               0                0                0                0 
o23:abs(C1 - c2) o24:abs(C1 - c2) 
               0                0 


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



Residual standard deviation:
sigma_y 
      0 

Call:
glme_imp(fixed = b1 ~ L1mis + abs(c1 - C2) + log(Be2) + time + 
    (time + I(time^2) | id), data = longDF, family = binomial(), 
    n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", 
        L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), shrinkage = "ridge", 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b1" 

Fixed effects:
 (Intercept)        L1mis abs(c1 - C2)     log(Be2)         time 
           0            0            0            0            0 


Random effects covariance matrix:
$id
                                 b1          b1          b1
                        (Intercept)        time   I(time^2)
         b1 (Intercept)           0           0           0
         b1        time           0           0           0
         b1   I(time^2)           0           0           0


Call:
lme_imp(fixed = y ~ b2 + C1 + C2 + time + (0 + time | id), data = longDF, 
    n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1          C2         b21        time 
          0           0           0           0           0 


Random effects covariance matrix:
$id
             y
          time
   y time    0



Residual standard deviation:
sigma_y 
      0 

Call:
glme_imp(fixed = b1 ~ c1 + C2 + B1 + time + (0 + time + I(time^2) | 
    id), data = longDF, family = binomial(), n.adapt = 5, n.iter = 10, 
    shrinkage = "ridge", seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b1" 

Fixed effects:
(Intercept)          C2         B11          c1        time 
          0           0           0           0           0 


Random effects covariance matrix:
$id
                           b1        b1
                         time I(time^2)
       b1      time         0         0
       b1 I(time^2)         0         0


Call:
lme_imp(fixed = y ~ ns(time, df = 2), data = longDF, random = ~ns(time, 
    df = 2) | id, n.iter = 10, seed = 2020, adapt = 5)

 Bayesian linear mixed model for "y" 

Fixed effects:
      (Intercept) ns(time, df = 2)1 ns(time, df = 2)2 
                0                 0                 0 


Random effects covariance matrix:
$id
                                                    y                 y                 y
                                          (Intercept) ns(time, df = 2)1 ns(time, df = 2)2
                y       (Intercept)                 0                 0                 0
                y ns(time, df = 2)1                 0                 0                 0
                y ns(time, df = 2)2                 0                 0                 0



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ bs(time, df = 3), data = longDF, random = ~bs(time, 
    df = 3) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
      (Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3 
                0                 0                 0                 0 


Random effects covariance matrix:
$id
                                                    y                 y                 y                 y
                                          (Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3
                y       (Intercept)                 0                 0                 0                 0
                y bs(time, df = 3)1                 0                 0                 0                 0
                y bs(time, df = 3)2                 0                 0                 0                 0
                y bs(time, df = 3)3                 0                 0                 0                 0



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ C1 + c1 + ns(time, df = 3), data = longDF, 
    random = ~ns(time, df = 3) | id, n.iter = 10, seed = 2020, 
    nadapt = 5)

 Bayesian linear mixed model for "y" 

Fixed effects:
      (Intercept)                C1                c1 ns(time, df = 3)1 
                0                 0                 0                 0 
ns(time, df = 3)2 ns(time, df = 3)3 
                0                 0 


Random effects covariance matrix:
$id
                                                    y                 y                 y                 y
                                          (Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
                y       (Intercept)                 0                 0                 0                 0
                y ns(time, df = 3)1                 0                 0                 0                 0
                y ns(time, df = 3)2                 0                 0                 0                 0
                y ns(time, df = 3)3                 0                 0                 0                 0



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, 
    random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
      (Intercept)                C1                C2                c1 
                0                 0                 0                 0 
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 
                0                 0                 0 


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



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, 
    random = ~ns(time, df = 3) | id, n.adapt = 5, n.iter = 10, 
    no_model = "time", seed = 2020)

 Bayesian linear mixed model for "y" 

Fixed effects:
      (Intercept)                C1                C2                c1 
                0                 0                 0                 0 
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 
                0                 0                 0 


Random effects covariance matrix:
$id
                                                    y                 y                 y                 y
                                          (Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
                y       (Intercept)                 0                 0                 0                 0
                y ns(time, df = 3)1                 0                 0                 0                 0
                y ns(time, df = 3)2                 0                 0                 0                 0
                y ns(time, df = 3)3                 0                 0                 0                 0



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, 
    random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
      (Intercept)                C1                C2                c1 
                0                 0                 0                 0 
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 
                0                 0                 0 


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



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + 
    c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          c1          c2        time 
          0           0           0           0 


Random effects covariance matrix:
$id
                                  y           y           y
                        (Intercept)        time          c2
          y (Intercept)           0           0           0
          y        time           0           0           0
          y          c2           0           0           0



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + 
    c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          c1          c2        time 
          0           0           0           0 


Random effects covariance matrix:
$id
                                  y           y           y
                        (Intercept)        time          c2
          y (Intercept)           0           0           0
          y        time           0           0           0
          y          c2           0           0           0



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + 
    c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)         B21          c1          c2        time      B21:c1 
          0           0           0           0           0           0 


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



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + 
    c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)         B21          c1          c2        time      B21:c1 
          0           0           0           0           0           0 


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



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, 
    random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c1          c2        time 
          0           0           0           0           0           0 
     B21:c1 
          0 


Random effects covariance matrix:
$id
                                  y           y           y
                        (Intercept)        time          c2
          y (Intercept)           0           0           0
          y        time           0           0           0
          y          c2           0           0           0



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, 
    random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c1          c2        time 
          0           0           0           0           0           0 
     B21:c1 
          0 


Random effects covariance matrix:
$id
                                  y           y           y
                        (Intercept)        time          c2
          y (Intercept)           0           0           0
          y        time           0           0           0
          y          c2           0           0           0



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, 
    random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = c("time", 
        "c1"), seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c1          c2        time 
          0           0           0           0           0           0 
     B21:c1 
          0 


Random effects covariance matrix:
$id
                                  y           y           y
                        (Intercept)        time          c2
          y (Intercept)           0           0           0
          y        time           0           0           0
          y          c2           0           0           0



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, 
    random = ~time + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c2          c1        time 
          0           0           0           0           0           0 
     B21:c2 
          0 


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



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, 
    random = ~time + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c2          c1        time 
          0           0           0           0           0           0 
     B21:c2 
          0 


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



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, 
    random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c2          c1        time 
          0           0           0           0           0           0 
     B21:c2 
          0 


Random effects covariance matrix:
$id
                                  y           y           y
                        (Intercept)        time          c2
          y (Intercept)           0           0           0
          y        time           0           0           0
          y          c2           0           0           0



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, 
    random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c2          c1        time 
          0           0           0           0           0           0 
     B21:c2 
          0 


Random effects covariance matrix:
$id
                                  y           y           y
                        (Intercept)        time          c2
          y (Intercept)           0           0           0
          y        time           0           0           0
          y          c2           0           0           0



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ C1 + B2 * c1 * time, data = longDF, random = ~time + 
    I(time^2) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c1        time      B21:c1 
          0           0           0           0           0           0 
   B21:time     c1:time B21:c1:time 
          0           0           0 


Random effects covariance matrix:
$id
                                  y           y           y
                        (Intercept)        time   I(time^2)
          y (Intercept)           0           0           0
          y        time           0           0           0
          y   I(time^2)           0           0           0



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ c1 * b1 + o1, data = longDF, random = ~b1 | 
    id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          c1         b11        o1.L        o1.Q      c1:b11 
          0           0           0           0           0           0 


Random effects covariance matrix:
$id
                                  y           y
                        (Intercept)         b11
          y (Intercept)           0           0
          y         b11           0           0



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ c1 + C1 * time + b1 + B2, data = longDF, 
    random = ~C1 * time | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c1        time         b11 
          0           0           0           0           0           0 
    C1:time 
          0 


Random effects covariance matrix:
$id
                                  y           y           y           y
                        (Intercept)          C1        time     C1:time
          y (Intercept)           0           0           0           0
          y          C1           0           0           0           0
          y        time           0           0           0           0
          y     C1:time           0           0           0           0



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ c1 + b1 + time + (1 | id) + (1 | o1), data = longDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          c1         b11        time 
          0           0           0           0 


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

$o1
                                  y
                        (Intercept)
          y (Intercept)           0



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + time + (time | id), data = longDF, 
    n.adapt = 5, n.iter = 10, monitor_params = c(analysis_random = TRUE), 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1          C2         B11        time 
          0           0           0           0           0 


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



Residual standard deviation:
sigma_y 
      0 

Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, monitor_params = c(analysis_random = TRUE), 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1          C2         B11 
          0           0           0           0 


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



Residual standard deviation:
sigma_y 
      0 
$m0a1

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

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept) 
          0 


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



Residual standard deviation:
sigma_y 
      0 

$m0a2

Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "identity"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept) 
          0 


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



Residual standard deviation:
sigma_y 
      0 

$m0a3

Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "log"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept) 
          0 


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



Residual standard deviation:
sigma_y 
      0 

$m0a4

Call:
glme_imp(fixed = y ~ 1 + (1 | id), data = longDF, family = gaussian(link = "inverse"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept) 
          0 


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



Residual standard deviation:
sigma_y 
      0 

$m0b1

Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "logit"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 b1
                        (Intercept)
         b1 (Intercept)           0


$m0b2

Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "probit"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 b1
                        (Intercept)
         b1 (Intercept)           0


$m0b3

Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "log"), 
    n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 b1
                        (Intercept)
         b1 (Intercept)           0


$m0b4

Call:
glme_imp(fixed = b1 ~ 1 + (1 | id), data = longDF, family = binomial(link = "cloglog"), 
    n.adapt = 50, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 b1
                        (Intercept)
         b1 (Intercept)           0


$m0c1

Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "inverse"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma mixed model for "L1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 L1
                        (Intercept)
         L1 (Intercept)           0



Residual standard deviation:
sigma_L1 
       0 

$m0c2

Call:
glme_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, family = Gamma(link = "log"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma mixed model for "L1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 L1
                        (Intercept)
         L1 (Intercept)           0



Residual standard deviation:
sigma_L1 
       0 

$m0d1

Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "log"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian poisson mixed model for "p1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 p1
                        (Intercept)
         p1 (Intercept)           0


$m0d2

Call:
glme_imp(fixed = p1 ~ 1 + (1 | id), data = longDF, family = poisson(link = "identity"), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian poisson mixed model for "p1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 p1
                        (Intercept)
         p1 (Intercept)           0


$m0e1

Call:
lognormmm_imp(fixed = L1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian log-normal mixed model for "L1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                 L1
                        (Intercept)
         L1 (Intercept)           0



Residual standard deviation:
sigma_L1 
       0 

$m0f1

Call:
betamm_imp(fixed = Be1 ~ 1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian beta mixed model for "Be1" 

Fixed effects:
(Intercept) 
          0 


Random effects covariance matrix:
$id
                                Be1
                        (Intercept)
        Be1 (Intercept)           0


$m1a

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

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1 
          0           0 


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



Residual standard deviation:
sigma_y 
      0 

$m1b

Call:
glme_imp(fixed = b1 ~ C1 + (1 | id), data = longDF, family = binomial(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b1" 

Fixed effects:
(Intercept)          C1 
          0           0 


Random effects covariance matrix:
$id
                                 b1
                        (Intercept)
         b1 (Intercept)           0


$m1c

Call:
glme_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, family = Gamma(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma mixed model for "L1" 

Fixed effects:
(Intercept)          C1 
          0           0 


Random effects covariance matrix:
$id
                                 L1
                        (Intercept)
         L1 (Intercept)           0



Residual standard deviation:
sigma_L1 
       0 

$m1d

Call:
glme_imp(fixed = p1 ~ C1 + (1 | id), data = longDF, family = poisson(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian poisson mixed model for "p1" 

Fixed effects:
(Intercept)          C1 
          0           0 


Random effects covariance matrix:
$id
                                 p1
                        (Intercept)
         p1 (Intercept)           0


$m1e

Call:
lognormmm_imp(fixed = L1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian log-normal mixed model for "L1" 

Fixed effects:
(Intercept)          C1 
          0           0 


Random effects covariance matrix:
$id
                                 L1
                        (Intercept)
         L1 (Intercept)           0



Residual standard deviation:
sigma_L1 
       0 

$m1f

Call:
betamm_imp(fixed = Be1 ~ C1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian beta mixed model for "Be1" 

Fixed effects:
(Intercept)          C1 
          0           0 


Random effects covariance matrix:
$id
                                Be1
                        (Intercept)
        Be1 (Intercept)           0


$m2a

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

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          c2 
          0           0 


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



Residual standard deviation:
sigma_y 
      0 

$m2b

Call:
glme_imp(fixed = b2 ~ c2 + (1 | id), data = longDF, family = binomial(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b2" 

Fixed effects:
(Intercept)          c2 
          0           0 


Random effects covariance matrix:
$id
                                 b2
                        (Intercept)
         b2 (Intercept)           0


$m2c

Call:
glme_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, family = Gamma(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma mixed model for "L1mis" 

Fixed effects:
(Intercept)          c2 
          0           0 


Random effects covariance matrix:
$id
                              L1mis
                        (Intercept)
      L1mis (Intercept)           0



Residual standard deviation:
sigma_L1mis 
          0 

$m2d

Call:
glme_imp(fixed = p2 ~ c2 + (1 | id), data = longDF, family = poisson(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian poisson mixed model for "p2" 

Fixed effects:
(Intercept)          c2 
          0           0 


Random effects covariance matrix:
$id
                                 p2
                        (Intercept)
         p2 (Intercept)           0


$m2e

Call:
lognormmm_imp(fixed = L1mis ~ c2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian log-normal mixed model for "L1mis" 

Fixed effects:
(Intercept)          c2 
          0           0 


Random effects covariance matrix:
$id
                              L1mis
                        (Intercept)
      L1mis (Intercept)           0



Residual standard deviation:
sigma_L1mis 
          0 

$m2f

Call:
betamm_imp(fixed = Be2 ~ c2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian beta mixed model for "Be2" 

Fixed effects:
(Intercept)          c2 
          0           0 


Random effects covariance matrix:
$id
                                Be2
                        (Intercept)
        Be2 (Intercept)           0


$m3a

Call:
lme_imp(fixed = y ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
C2 
 0 


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



Residual standard deviation:
sigma_y 
      0 

$m3b

Call:
glme_imp(fixed = b2 ~ 0 + C2 + (1 | id), data = longDF, family = binomial(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b2" 

Fixed effects:
C2 
 0 


Random effects covariance matrix:
$id
                                 b2
                        (Intercept)
         b2 (Intercept)           0


$m3c

Call:
glme_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, family = Gamma(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma mixed model for "L1mis" 

Fixed effects:
C2 
 0 


Random effects covariance matrix:
$id
                              L1mis
                        (Intercept)
      L1mis (Intercept)           0



Residual standard deviation:
sigma_L1mis 
          0 

$m3d

Call:
glme_imp(fixed = p2 ~ 0 + C2 + (1 | id), data = longDF, family = poisson(), 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian poisson mixed model for "p2" 

Fixed effects:
C2 
 0 


Random effects covariance matrix:
$id
                                 p2
                        (Intercept)
         p2 (Intercept)           0


$m3e

Call:
lognormmm_imp(fixed = L1mis ~ 0 + C2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian log-normal mixed model for "L1mis" 

Fixed effects:
C2 
 0 


Random effects covariance matrix:
$id
                              L1mis
                        (Intercept)
      L1mis (Intercept)           0



Residual standard deviation:
sigma_L1mis 
          0 

$m3f

Call:
betamm_imp(fixed = Be2 ~ 0 + C2 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian beta mixed model for "Be2" 

Fixed effects:
C2 
 0 


Random effects covariance matrix:
$id
                                Be2
                        (Intercept)
        Be2 (Intercept)           0


$m4a

Call:
lme_imp(fixed = c1 ~ c2 + B2 + p2 + L1mis + Be2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, models = c(p2 = "glmm_poisson_log", 
        L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "c1" 

Fixed effects:
(Intercept)         B21          c2          p2       L1mis         Be2 
          0           0           0           0           0           0 


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



Residual standard deviation:
sigma_c1 
       0 

$m4b

Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_inverse", 
        p2 = "glmm_poisson_identity", b2 = "glmm_binomial_probit", 
        L1mis = "glmm_lognorm"), seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "c1" 

Fixed effects:
(Intercept)          c2         b21          p2       L1mis 
          0           0           0           0           0 


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



Residual standard deviation:
sigma_c1 
       0 

$m4c

Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", 
        p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", 
        b2 = "glmm_binomial_log"), no_model = "time", seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "c1" 

Fixed effects:
(Intercept)          c2         b21          p2       L1mis 
          0           0           0           0           0 


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



Residual standard deviation:
sigma_c1 
       0 

$m4d

Call:
lme_imp(fixed = c1 ~ c2 + b2 + p2 + L1mis + Be2 + (1 | id), data = longDF, 
    n.adapt = 5, n.iter = 10, models = c(c2 = "glmm_gaussian_log", 
        p2 = "glmm_poisson_identity", L1mis = "glmm_gamma_log", 
        b2 = "glmm_binomial_log"), shrinkage = "ridge", seed = 2020, 
    warn = FALSE, mess = FALSE, trunc = list(Be2 = c(0, 1)))

 Bayesian linear mixed model for "c1" 

Fixed effects:
(Intercept)          c2         b21          p2       L1mis         Be2 
          0           0           0           0           0           0 


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



Residual standard deviation:
sigma_c1 
       0 

$m5a

Call:
lme_imp(fixed = y ~ M2 + o2 * abs(C1 - c2) + log(C1) + time + 
    I(time^2) + (time | id), data = longDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
     (Intercept)              M22              M23              M24 
               0                0                0                0 
         log(C1)              o22              o23              o24 
               0                0                0                0 
    abs(C1 - c2)             time        I(time^2) o22:abs(C1 - c2) 
               0                0                0                0 
o23:abs(C1 - c2) o24:abs(C1 - c2) 
               0                0 


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



Residual standard deviation:
sigma_y 
      0 

$m5b

Call:
glme_imp(fixed = b1 ~ L1mis + abs(c1 - C2) + log(Be2) + time + 
    (time + I(time^2) | id), data = longDF, family = binomial(), 
    n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", 
        L1mis = "glmm_gamma_inverse", Be2 = "glmm_beta"), shrinkage = "ridge", 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b1" 

Fixed effects:
 (Intercept)        L1mis abs(c1 - C2)     log(Be2)         time 
           0            0            0            0            0 


Random effects covariance matrix:
$id
                                 b1          b1          b1
                        (Intercept)        time   I(time^2)
         b1 (Intercept)           0           0           0
         b1        time           0           0           0
         b1   I(time^2)           0           0           0


$m6a

Call:
lme_imp(fixed = y ~ b2 + C1 + C2 + time + (0 + time | id), data = longDF, 
    n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1          C2         b21        time 
          0           0           0           0           0 


Random effects covariance matrix:
$id
             y
          time
   y time    0



Residual standard deviation:
sigma_y 
      0 

$m6b

Call:
glme_imp(fixed = b1 ~ c1 + C2 + B1 + time + (0 + time + I(time^2) | 
    id), data = longDF, family = binomial(), n.adapt = 5, n.iter = 10, 
    shrinkage = "ridge", seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial mixed model for "b1" 

Fixed effects:
(Intercept)          C2         B11          c1        time 
          0           0           0           0           0 


Random effects covariance matrix:
$id
                           b1        b1
                         time I(time^2)
       b1      time         0         0
       b1 I(time^2)         0         0


$m7a

Call:
lme_imp(fixed = y ~ ns(time, df = 2), data = longDF, random = ~ns(time, 
    df = 2) | id, n.iter = 10, seed = 2020, adapt = 5)

 Bayesian linear mixed model for "y" 

Fixed effects:
      (Intercept) ns(time, df = 2)1 ns(time, df = 2)2 
                0                 0                 0 


Random effects covariance matrix:
$id
                                                    y                 y                 y
                                          (Intercept) ns(time, df = 2)1 ns(time, df = 2)2
                y       (Intercept)                 0                 0                 0
                y ns(time, df = 2)1                 0                 0                 0
                y ns(time, df = 2)2                 0                 0                 0



Residual standard deviation:
sigma_y 
      0 

$m7b

Call:
lme_imp(fixed = y ~ bs(time, df = 3), data = longDF, random = ~bs(time, 
    df = 3) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
      (Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3 
                0                 0                 0                 0 


Random effects covariance matrix:
$id
                                                    y                 y                 y                 y
                                          (Intercept) bs(time, df = 3)1 bs(time, df = 3)2 bs(time, df = 3)3
                y       (Intercept)                 0                 0                 0                 0
                y bs(time, df = 3)1                 0                 0                 0                 0
                y bs(time, df = 3)2                 0                 0                 0                 0
                y bs(time, df = 3)3                 0                 0                 0                 0



Residual standard deviation:
sigma_y 
      0 

$m7c

Call:
lme_imp(fixed = y ~ C1 + c1 + ns(time, df = 3), data = longDF, 
    random = ~ns(time, df = 3) | id, n.iter = 10, seed = 2020, 
    nadapt = 5)

 Bayesian linear mixed model for "y" 

Fixed effects:
      (Intercept)                C1                c1 ns(time, df = 3)1 
                0                 0                 0                 0 
ns(time, df = 3)2 ns(time, df = 3)3 
                0                 0 


Random effects covariance matrix:
$id
                                                    y                 y                 y                 y
                                          (Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
                y       (Intercept)                 0                 0                 0                 0
                y ns(time, df = 3)1                 0                 0                 0                 0
                y ns(time, df = 3)2                 0                 0                 0                 0
                y ns(time, df = 3)3                 0                 0                 0                 0



Residual standard deviation:
sigma_y 
      0 

$m7d

Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, 
    random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
      (Intercept)                C1                C2                c1 
                0                 0                 0                 0 
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 
                0                 0                 0 


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



Residual standard deviation:
sigma_y 
      0 

$m7e

Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, 
    random = ~ns(time, df = 3) | id, n.adapt = 5, n.iter = 10, 
    no_model = "time", seed = 2020)

 Bayesian linear mixed model for "y" 

Fixed effects:
      (Intercept)                C1                C2                c1 
                0                 0                 0                 0 
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 
                0                 0                 0 


Random effects covariance matrix:
$id
                                                    y                 y                 y                 y
                                          (Intercept) ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3
                y       (Intercept)                 0                 0                 0                 0
                y ns(time, df = 3)1                 0                 0                 0                 0
                y ns(time, df = 3)2                 0                 0                 0                 0
                y ns(time, df = 3)3                 0                 0                 0                 0



Residual standard deviation:
sigma_y 
      0 

$m7f

Call:
lme_imp(fixed = y ~ C1 + C2 + c1 + ns(time, df = 3), data = longDF, 
    random = ~time | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
      (Intercept)                C1                C2                c1 
                0                 0                 0                 0 
ns(time, df = 3)1 ns(time, df = 3)2 ns(time, df = 3)3 
                0                 0                 0 


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



Residual standard deviation:
sigma_y 
      0 

$m8a

Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + 
    c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          c1          c2        time 
          0           0           0           0 


Random effects covariance matrix:
$id
                                  y           y           y
                        (Intercept)        time          c2
          y (Intercept)           0           0           0
          y        time           0           0           0
          y          c2           0           0           0



Residual standard deviation:
sigma_y 
      0 

$m8b

Call:
lme_imp(fixed = y ~ c1 + c2 + time, data = longDF, random = ~time + 
    c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          c1          c2        time 
          0           0           0           0 


Random effects covariance matrix:
$id
                                  y           y           y
                        (Intercept)        time          c2
          y (Intercept)           0           0           0
          y        time           0           0           0
          y          c2           0           0           0



Residual standard deviation:
sigma_y 
      0 

$m8c

Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + 
    c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)         B21          c1          c2        time      B21:c1 
          0           0           0           0           0           0 


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



Residual standard deviation:
sigma_y 
      0 

$m8d

Call:
lme_imp(fixed = y ~ B2 * c1 + c2 + time, data = longDF, random = ~time + 
    c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)         B21          c1          c2        time      B21:c1 
          0           0           0           0           0           0 


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



Residual standard deviation:
sigma_y 
      0 

$m8e

Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, 
    random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c1          c2        time 
          0           0           0           0           0           0 
     B21:c1 
          0 


Random effects covariance matrix:
$id
                                  y           y           y
                        (Intercept)        time          c2
          y (Intercept)           0           0           0
          y        time           0           0           0
          y          c2           0           0           0



Residual standard deviation:
sigma_y 
      0 

$m8f

Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, 
    random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = "time", 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c1          c2        time 
          0           0           0           0           0           0 
     B21:c1 
          0 


Random effects covariance matrix:
$id
                                  y           y           y
                        (Intercept)        time          c2
          y (Intercept)           0           0           0
          y        time           0           0           0
          y          c2           0           0           0



Residual standard deviation:
sigma_y 
      0 

$m8g

Call:
lme_imp(fixed = y ~ C1 + B2 * c1 + c2 + time, data = longDF, 
    random = ~time + c2 | id, n.adapt = 5, n.iter = 10, no_model = c("time", 
        "c1"), seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c1          c2        time 
          0           0           0           0           0           0 
     B21:c1 
          0 


Random effects covariance matrix:
$id
                                  y           y           y
                        (Intercept)        time          c2
          y (Intercept)           0           0           0
          y        time           0           0           0
          y          c2           0           0           0



Residual standard deviation:
sigma_y 
      0 

$m8h

Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, 
    random = ~time + c1 | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c2          c1        time 
          0           0           0           0           0           0 
     B21:c2 
          0 


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



Residual standard deviation:
sigma_y 
      0 

$m8i

Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, 
    random = ~time + c1 | id, n.adapt = 5, n.iter = 10, no_model = "time", 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c2          c1        time 
          0           0           0           0           0           0 
     B21:c2 
          0 


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



Residual standard deviation:
sigma_y 
      0 

$m8j

Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, 
    random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c2          c1        time 
          0           0           0           0           0           0 
     B21:c2 
          0 


Random effects covariance matrix:
$id
                                  y           y           y
                        (Intercept)        time          c2
          y (Intercept)           0           0           0
          y        time           0           0           0
          y          c2           0           0           0



Residual standard deviation:
sigma_y 
      0 

$m8k

Call:
lme_imp(fixed = y ~ C1 + B2 * c2 + c1 + time, data = longDF, 
    random = ~time + c2 | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c2          c1        time 
          0           0           0           0           0           0 
     B21:c2 
          0 


Random effects covariance matrix:
$id
                                  y           y           y
                        (Intercept)        time          c2
          y (Intercept)           0           0           0
          y        time           0           0           0
          y          c2           0           0           0



Residual standard deviation:
sigma_y 
      0 

$m8l

Call:
lme_imp(fixed = y ~ C1 + B2 * c1 * time, data = longDF, random = ~time + 
    I(time^2) | id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c1        time      B21:c1 
          0           0           0           0           0           0 
   B21:time     c1:time B21:c1:time 
          0           0           0 


Random effects covariance matrix:
$id
                                  y           y           y
                        (Intercept)        time   I(time^2)
          y (Intercept)           0           0           0
          y        time           0           0           0
          y   I(time^2)           0           0           0



Residual standard deviation:
sigma_y 
      0 

$m8m

Call:
lme_imp(fixed = y ~ c1 * b1 + o1, data = longDF, random = ~b1 | 
    id, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          c1         b11        o1.L        o1.Q      c1:b11 
          0           0           0           0           0           0 


Random effects covariance matrix:
$id
                                  y           y
                        (Intercept)         b11
          y (Intercept)           0           0
          y         b11           0           0



Residual standard deviation:
sigma_y 
      0 

$m8n

Call:
lme_imp(fixed = y ~ c1 + C1 * time + b1 + B2, data = longDF, 
    random = ~C1 * time | id, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1         B21          c1        time         b11 
          0           0           0           0           0           0 
    C1:time 
          0 


Random effects covariance matrix:
$id
                                  y           y           y           y
                        (Intercept)          C1        time     C1:time
          y (Intercept)           0           0           0           0
          y          C1           0           0           0           0
          y        time           0           0           0           0
          y     C1:time           0           0           0           0



Residual standard deviation:
sigma_y 
      0 

$m9a

Call:
lme_imp(fixed = y ~ c1 + b1 + time + (1 | id) + (1 | o1), data = longDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          c1         b11        time 
          0           0           0           0 


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

$o1
                                  y
                        (Intercept)
          y (Intercept)           0



Residual standard deviation:
sigma_y 
      0 

$m9b

Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + time + (time | id), data = longDF, 
    n.adapt = 5, n.iter = 10, monitor_params = c(analysis_random = TRUE), 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1          C2         B11        time 
          0           0           0           0           0 


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



Residual standard deviation:
sigma_y 
      0 

$m9c

Call:
lme_imp(fixed = y ~ C1 + C2 + B1 + (1 | id), data = longDF, n.adapt = 5, 
    n.iter = 10, monitor_params = c(analysis_random = TRUE), 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear mixed model for "y" 

Fixed effects:
(Intercept)          C1          C2         B11 
          0           0           0           0 


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



Residual standard deviation:
sigma_y 
      0 

