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

 Bayesian linear model for "y" 


Coefficients:
(Intercept) 
          0 


Residual standard deviation:
sigma_y 
      0 

Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "identity"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear model for "y" 


Coefficients:
(Intercept) 
          0 


Residual standard deviation:
sigma_y 
      0 

Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "log"), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear model for "y" 


Coefficients:
(Intercept) 
          0 


Residual standard deviation:
sigma_y 
      0 

Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "inverse"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear model for "y" 


Coefficients:
(Intercept) 
          0 


Residual standard deviation:
sigma_y 
      0 

Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "logit"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept) 
          0 

Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "probit"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept) 
          0 

Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "log"), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept) 
          0 

Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "cloglog"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept) 
          0 

Call:
glm_imp(formula = L1 ~ 1, family = Gamma(link = "inverse"), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma model for "L1" 


Coefficients:
(Intercept) 
          0 


Residual standard deviation:
sigma_L1 
       0 

Call:
glm_imp(formula = L1 ~ 1, family = Gamma(link = "log"), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma model for "L1" 


Coefficients:
(Intercept) 
          0 


Residual standard deviation:
sigma_L1 
       0 

Call:
glm_imp(formula = P1 ~ 1, family = poisson(link = "log"), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian poisson model for "P1" 


Coefficients:
(Intercept) 
          0 

Call:
glm_imp(formula = P1 ~ 1, family = poisson(link = "identity"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian poisson model for "P1" 


Coefficients:
(Intercept) 
          0 

Call:
lognorm_imp(formula = L1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian log-normal model for "L1" 


Coefficients:
(Intercept) 
          0 


Residual standard deviation:
sigma_L1 
       0 

Call:
betareg_imp(formula = Be1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian beta model for "Be1" 


Coefficients:
(Intercept) 
          0 

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

 Bayesian linear model for "y" 


Coefficients:
(Intercept)          C1 
          0           0 


Residual standard deviation:
sigma_y 
      0 

Call:
glm_imp(formula = B1 ~ C1, family = binomial(), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept)          C1 
          0           0 

Call:
glm_imp(formula = L1 ~ C1, family = Gamma(), data = wideDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma model for "L1" 


Coefficients:
(Intercept)          C1 
          0           0 


Residual standard deviation:
sigma_L1 
       0 

Call:
glm_imp(formula = P1 ~ C1, family = poisson(), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian poisson model for "P1" 


Coefficients:
(Intercept)          C1 
          0           0 

Call:
lognorm_imp(formula = L1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian log-normal model for "L1" 


Coefficients:
(Intercept)          C1 
          0           0 


Residual standard deviation:
sigma_L1 
       0 

Call:
betareg_imp(formula = Be1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian beta model for "Be1" 


Coefficients:
(Intercept)          C1 
          0           0 

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

 Bayesian linear model for "y" 


Coefficients:
(Intercept)          C2 
          0           0 


Residual standard deviation:
sigma_y 
      0 

Call:
glm_imp(formula = B2 ~ C2, family = binomial(), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial model for "B2" 


Coefficients:
(Intercept)          C2 
          0           0 

Call:
glm_imp(formula = L1mis ~ C2, family = Gamma(), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma model for "L1mis" 


Coefficients:
(Intercept)          C2 
          0           0 


Residual standard deviation:
sigma_L1mis 
          0 

Call:
glm_imp(formula = P2 ~ C2, family = poisson(), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian poisson model for "P2" 


Coefficients:
(Intercept)          C2 
          0           0 

Call:
lognorm_imp(formula = L1mis ~ C2, data = wideDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian log-normal model for "L1mis" 


Coefficients:
(Intercept)          C2 
          0           0 


Residual standard deviation:
sigma_L1mis 
          0 

Call:
betareg_imp(formula = Be2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian beta model for "Be2" 


Coefficients:
(Intercept)          C2 
          0           0 

Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF, 
    n.adapt = 5, n.iter = 10, models = c(P2 = "glm_poisson_log", 
        L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear model for "C1" 


Coefficients:
(Intercept)          C2         B21          P2       L1mis         Be2 
          0           0           0           0           0           0 


Residual standard deviation:
sigma_C1 
       0 

Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5, 
    n.iter = 10, models = c(C2 = "glm_gaussian_inverse", P2 = "glm_poisson_identity", 
        B2 = "glm_binomial_probit", L1mis = "lognorm"), seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear model for "C1" 


Coefficients:
(Intercept)          C2         B21          P2       L1mis 
          0           0           0           0           0 


Residual standard deviation:
sigma_C1 
       0 

Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5, 
    n.iter = 10, models = c(C2 = "glm_gaussian_log", P2 = "glm_poisson_identity", 
        L1mis = "glm_gamma_log", B2 = "glm_binomial_log"), seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear model for "C1" 


Coefficients:
(Intercept)          C2         B21          P2       L1mis 
          0           0           0           0           0 


Residual standard deviation:
sigma_C1 
       0 

Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF, 
    n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", 
        P2 = "glm_poisson_identity", L1mis = "glm_gamma_log", 
        B2 = "glm_binomial_log"), trunc = list(Be2 = c(0, 1)), 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear model for "C1" 


Coefficients:
(Intercept)          C2         B21          P2       L1mis         Be2 
          0           0           0           0           0           0 


Residual standard deviation:
sigma_C1 
       0 

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

 Bayesian linear model for "y" 


Coefficients:
     (Intercept)              M22              M23              M24 
               0                0                0                0 
             O22              O23              O24     abs(C1 - C2) 
               0                0                0                0 
         log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) 
               0                0                0                0 


Residual standard deviation:
sigma_y 
      0 

Call:
glm_imp(formula = B1 ~ L1mis + abs(C1 - C2) + log(Be2), family = binomial(), 
    data = wideDF, n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", 
        L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
 (Intercept)        L1mis abs(C1 - C2)     log(Be2) 
           0            0            0            0 

Call:
lm_imp(formula = y ~ C2 + B2 + B1 + O1, data = wideDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear model for "y" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 


Residual standard deviation:
sigma_y 
      0 

Call:
glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "log"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear model for "y" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 


Residual standard deviation:
sigma_y 
      0 

Call:
glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "inverse"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear model for "y" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 


Residual standard deviation:
sigma_y 
      0 

Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "logit"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept)          C2         B21          C1        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 

Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "probit"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept)          C2         B21          C1        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 

Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "log"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept)          C2         B21          C1        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 

Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "cloglog"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept)          C2         B21          C1        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 

Call:
glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "inverse"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian Gamma model for "L1" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 


Residual standard deviation:
sigma_L1 
       0 

Call:
glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "log"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian Gamma model for "L1" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 


Residual standard deviation:
sigma_L1 
       0 

Call:
glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "log"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian poisson model for "P1" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 

Call:
glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "identity"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian poisson model for "P1" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 

Call:
lognorm_imp(formula = L1 ~ C2 + B2 + B1 + O1, data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian log-normal model for "L1" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 


Residual standard deviation:
sigma_L1 
       0 

Call:
betareg_imp(formula = Be1 ~ C2 + B2 + B1 + O1, data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian beta model for "Be1" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 

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

 Bayesian linear model for "y" 


Coefficients:
     (Intercept)              M22              M23              M24 
               0                0                0                0 
             O22              O23              O24     abs(C1 - C2) 
               0                0                0                0 
         log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) 
               0                0                0                0 


Residual standard deviation:
sigma_y 
      0 

Call:
glm_imp(formula = B1 ~ M2 + O2 * abs(C1 - C2) + log(C1), family = "binomial", 
    data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
     (Intercept)              M22              M23              M24 
               0                0                0                0 
             O22              O23              O24     abs(C1 - C2) 
               0                0                0                0 
         log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) 
               0                0                0                0 

Call:
glm_imp(formula = C1 ~ M2 + O2 * abs(y - C2), family = Gamma(link = "log"), 
    data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian Gamma model for "C1" 


Coefficients:
    (Intercept)             M22             M23             M24             O22 
              0               0               0               0               0 
            O23             O24     abs(y - C2) O22:abs(y - C2) O23:abs(y - C2) 
              0               0               0               0               0 
O24:abs(y - C2) 
              0 


Residual standard deviation:
sigma_C1 
       0 

Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat), 
    data = NHANES, n.adapt = 5, n.iter = 5, trunc = list(bili = c(1e-05, 
        1e+10)), seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear model for "SBP" 


Coefficients:
 (Intercept)          age genderfemale    log(bili)   exp(creat) 
           0            0            0            0            0 


Residual standard deviation:
sigma_SBP 
        0 

Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat), 
    data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "lognorm", 
        creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear model for "SBP" 


Coefficients:
 (Intercept)          age genderfemale    log(bili)   exp(creat) 
           0            0            0            0            0 


Residual standard deviation:
sigma_SBP 
        0 

Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat), 
    data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "glm_gamma_inverse", 
        creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear model for "SBP" 


Coefficients:
 (Intercept)          age genderfemale    log(bili)   exp(creat) 
           0            0            0            0            0 


Residual standard deviation:
sigma_SBP 
        0 

Call:
lm_imp(formula = SBP ~ ns(age, df = 2) + gender + I(bili^2) + 
    I(bili^3), data = NHANES, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear model for "SBP" 


Coefficients:
     (Intercept) ns(age, df = 2)1 ns(age, df = 2)2     genderfemale 
               0                0                0                0 
       I(bili^2)        I(bili^3) 
               0                0 


Residual standard deviation:
sigma_SBP 
        0 
$m0a1

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

 Bayesian linear model for "y" 


Coefficients:
(Intercept) 
          0 


Residual standard deviation:
sigma_y 
      0 

$m0a2

Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "identity"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear model for "y" 


Coefficients:
(Intercept) 
          0 


Residual standard deviation:
sigma_y 
      0 

$m0a3

Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "log"), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear model for "y" 


Coefficients:
(Intercept) 
          0 


Residual standard deviation:
sigma_y 
      0 

$m0a4

Call:
glm_imp(formula = y ~ 1, family = gaussian(link = "inverse"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear model for "y" 


Coefficients:
(Intercept) 
          0 


Residual standard deviation:
sigma_y 
      0 

$m0b1

Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "logit"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept) 
          0 

$m0b2

Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "probit"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept) 
          0 

$m0b3

Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "log"), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept) 
          0 

$m0b4

Call:
glm_imp(formula = B1 ~ 1, family = binomial(link = "cloglog"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept) 
          0 

$m0c1

Call:
glm_imp(formula = L1 ~ 1, family = Gamma(link = "inverse"), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma model for "L1" 


Coefficients:
(Intercept) 
          0 


Residual standard deviation:
sigma_L1 
       0 

$m0c2

Call:
glm_imp(formula = L1 ~ 1, family = Gamma(link = "log"), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma model for "L1" 


Coefficients:
(Intercept) 
          0 


Residual standard deviation:
sigma_L1 
       0 

$m0d1

Call:
glm_imp(formula = P1 ~ 1, family = poisson(link = "log"), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian poisson model for "P1" 


Coefficients:
(Intercept) 
          0 

$m0d2

Call:
glm_imp(formula = P1 ~ 1, family = poisson(link = "identity"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian poisson model for "P1" 


Coefficients:
(Intercept) 
          0 

$m0e1

Call:
lognorm_imp(formula = L1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian log-normal model for "L1" 


Coefficients:
(Intercept) 
          0 


Residual standard deviation:
sigma_L1 
       0 

$m0f1

Call:
betareg_imp(formula = Be1 ~ 1, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian beta model for "Be1" 


Coefficients:
(Intercept) 
          0 

$m1a

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

 Bayesian linear model for "y" 


Coefficients:
(Intercept)          C1 
          0           0 


Residual standard deviation:
sigma_y 
      0 

$m1b

Call:
glm_imp(formula = B1 ~ C1, family = binomial(), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept)          C1 
          0           0 

$m1c

Call:
glm_imp(formula = L1 ~ C1, family = Gamma(), data = wideDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma model for "L1" 


Coefficients:
(Intercept)          C1 
          0           0 


Residual standard deviation:
sigma_L1 
       0 

$m1d

Call:
glm_imp(formula = P1 ~ C1, family = poisson(), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian poisson model for "P1" 


Coefficients:
(Intercept)          C1 
          0           0 

$m1e

Call:
lognorm_imp(formula = L1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian log-normal model for "L1" 


Coefficients:
(Intercept)          C1 
          0           0 


Residual standard deviation:
sigma_L1 
       0 

$m1f

Call:
betareg_imp(formula = Be1 ~ C1, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian beta model for "Be1" 


Coefficients:
(Intercept)          C1 
          0           0 

$m2a

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

 Bayesian linear model for "y" 


Coefficients:
(Intercept)          C2 
          0           0 


Residual standard deviation:
sigma_y 
      0 

$m2b

Call:
glm_imp(formula = B2 ~ C2, family = binomial(), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian binomial model for "B2" 


Coefficients:
(Intercept)          C2 
          0           0 

$m2c

Call:
glm_imp(formula = L1mis ~ C2, family = Gamma(), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian Gamma model for "L1mis" 


Coefficients:
(Intercept)          C2 
          0           0 


Residual standard deviation:
sigma_L1mis 
          0 

$m2d

Call:
glm_imp(formula = P2 ~ C2, family = poisson(), data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian poisson model for "P2" 


Coefficients:
(Intercept)          C2 
          0           0 

$m2e

Call:
lognorm_imp(formula = L1mis ~ C2, data = wideDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian log-normal model for "L1mis" 


Coefficients:
(Intercept)          C2 
          0           0 


Residual standard deviation:
sigma_L1mis 
          0 

$m2f

Call:
betareg_imp(formula = Be2 ~ C2, data = wideDF, n.adapt = 5, n.iter = 10, 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian beta model for "Be2" 


Coefficients:
(Intercept)          C2 
          0           0 

$m3a

Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF, 
    n.adapt = 5, n.iter = 10, models = c(P2 = "glm_poisson_log", 
        L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear model for "C1" 


Coefficients:
(Intercept)          C2         B21          P2       L1mis         Be2 
          0           0           0           0           0           0 


Residual standard deviation:
sigma_C1 
       0 

$m3b

Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5, 
    n.iter = 10, models = c(C2 = "glm_gaussian_inverse", P2 = "glm_poisson_identity", 
        B2 = "glm_binomial_probit", L1mis = "lognorm"), seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear model for "C1" 


Coefficients:
(Intercept)          C2         B21          P2       L1mis 
          0           0           0           0           0 


Residual standard deviation:
sigma_C1 
       0 

$m3c

Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis, data = wideDF, n.adapt = 5, 
    n.iter = 10, models = c(C2 = "glm_gaussian_log", P2 = "glm_poisson_identity", 
        L1mis = "glm_gamma_log", B2 = "glm_binomial_log"), seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear model for "C1" 


Coefficients:
(Intercept)          C2         B21          P2       L1mis 
          0           0           0           0           0 


Residual standard deviation:
sigma_C1 
       0 

$m3d

Call:
lm_imp(formula = C1 ~ C2 + B2 + P2 + L1mis + Be2, data = wideDF, 
    n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", 
        P2 = "glm_poisson_identity", L1mis = "glm_gamma_log", 
        B2 = "glm_binomial_log"), trunc = list(Be2 = c(0, 1)), 
    seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear model for "C1" 


Coefficients:
(Intercept)          C2         B21          P2       L1mis         Be2 
          0           0           0           0           0           0 


Residual standard deviation:
sigma_C1 
       0 

$m4a

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

 Bayesian linear model for "y" 


Coefficients:
     (Intercept)              M22              M23              M24 
               0                0                0                0 
             O22              O23              O24     abs(C1 - C2) 
               0                0                0                0 
         log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) 
               0                0                0                0 


Residual standard deviation:
sigma_y 
      0 

$m4b

Call:
glm_imp(formula = B1 ~ L1mis + abs(C1 - C2) + log(Be2), family = binomial(), 
    data = wideDF, n.adapt = 5, n.iter = 10, models = c(C2 = "glm_gaussian_log", 
        L1mis = "glm_gamma_inverse", Be2 = "beta"), seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
 (Intercept)        L1mis abs(C1 - C2)     log(Be2) 
           0            0            0            0 

$m5a1

Call:
lm_imp(formula = y ~ C2 + B2 + B1 + O1, data = wideDF, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear model for "y" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 


Residual standard deviation:
sigma_y 
      0 

$m5a2

Call:
glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "log"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear model for "y" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 


Residual standard deviation:
sigma_y 
      0 

$m5a3

Call:
glm_imp(formula = y ~ C2 + B2 + B1 + O1, family = gaussian(link = "inverse"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian linear model for "y" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 


Residual standard deviation:
sigma_y 
      0 

$m5b1

Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "logit"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept)          C2         B21          C1        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 

$m5b2

Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "probit"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept)          C2         B21          C1        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 

$m5b3

Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "log"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept)          C2         B21          C1        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 

$m5b4

Call:
glm_imp(formula = B1 ~ C2 + B2 + C1 + O1, family = binomial(link = "cloglog"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
(Intercept)          C2         B21          C1        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 

$m5c1

Call:
glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "inverse"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian Gamma model for "L1" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 


Residual standard deviation:
sigma_L1 
       0 

$m5c2

Call:
glm_imp(formula = L1 ~ C2 + B2 + B1 + O1, family = Gamma(link = "log"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian Gamma model for "L1" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 


Residual standard deviation:
sigma_L1 
       0 

$m5d1

Call:
glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "log"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian poisson model for "P1" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 

$m5d2

Call:
glm_imp(formula = P1 ~ C2 + B2 + B1 + O1, family = poisson(link = "identity"), 
    data = wideDF, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian poisson model for "P1" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 

$m5e1

Call:
lognorm_imp(formula = L1 ~ C2 + B2 + B1 + O1, data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian log-normal model for "L1" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 


Residual standard deviation:
sigma_L1 
       0 

$m5f1

Call:
betareg_imp(formula = Be1 ~ C2 + B2 + B1 + O1, data = wideDF, 
    n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian beta model for "Be1" 


Coefficients:
(Intercept)          C2         B21         B11        O1.L        O1.Q 
          0           0           0           0           0           0 
       O1.C 
          0 

$m6a

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

 Bayesian linear model for "y" 


Coefficients:
     (Intercept)              M22              M23              M24 
               0                0                0                0 
             O22              O23              O24     abs(C1 - C2) 
               0                0                0                0 
         log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) 
               0                0                0                0 


Residual standard deviation:
sigma_y 
      0 

$m6b

Call:
glm_imp(formula = B1 ~ M2 + O2 * abs(C1 - C2) + log(C1), family = "binomial", 
    data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian binomial model for "B1" 


Coefficients:
     (Intercept)              M22              M23              M24 
               0                0                0                0 
             O22              O23              O24     abs(C1 - C2) 
               0                0                0                0 
         log(C1) O22:abs(C1 - C2) O23:abs(C1 - C2) O24:abs(C1 - C2) 
               0                0                0                0 

$m6c

Call:
glm_imp(formula = C1 ~ M2 + O2 * abs(y - C2), family = Gamma(link = "log"), 
    data = wideDF, n.adapt = 5, n.iter = 5, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian Gamma model for "C1" 


Coefficients:
    (Intercept)             M22             M23             M24             O22 
              0               0               0               0               0 
            O23             O24     abs(y - C2) O22:abs(y - C2) O23:abs(y - C2) 
              0               0               0               0               0 
O24:abs(y - C2) 
              0 


Residual standard deviation:
sigma_C1 
       0 

$m6d

Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat), 
    data = NHANES, n.adapt = 5, n.iter = 5, trunc = list(bili = c(1e-05, 
        1e+10)), seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear model for "SBP" 


Coefficients:
 (Intercept)          age genderfemale    log(bili)   exp(creat) 
           0            0            0            0            0 


Residual standard deviation:
sigma_SBP 
        0 

$m6e

Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat), 
    data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "lognorm", 
        creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear model for "SBP" 


Coefficients:
 (Intercept)          age genderfemale    log(bili)   exp(creat) 
           0            0            0            0            0 


Residual standard deviation:
sigma_SBP 
        0 

$m6f

Call:
lm_imp(formula = SBP ~ age + gender + log(bili) + exp(creat), 
    data = NHANES, n.adapt = 5, n.iter = 5, models = c(bili = "glm_gamma_inverse", 
        creat = "lm"), seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian linear model for "SBP" 


Coefficients:
 (Intercept)          age genderfemale    log(bili)   exp(creat) 
           0            0            0            0            0 


Residual standard deviation:
sigma_SBP 
        0 

$mod7a

Call:
lm_imp(formula = SBP ~ ns(age, df = 2) + gender + I(bili^2) + 
    I(bili^3), data = NHANES, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)

 Bayesian linear model for "SBP" 


Coefficients:
     (Intercept) ns(age, df = 2)1 ns(age, df = 2)2     genderfemale 
               0                0                0                0 
       I(bili^2)        I(bili^3) 
               0                0 


Residual standard deviation:
sigma_SBP 
        0 

