printfcts.md
In JointAI: Joint Analysis and Imputation of Incomplete Data

lme model

Code
  list_models(mymod)
Output
  Linear mixed model for "y" 
     family: gaussian 
     link: identity 
  * Predictor variables:
    (Intercept), C1, B21, O22, O23, c1, c2, time 
  * Regression coefficients:
    beta[1:8] (normal prior(s) with mean 0 and precision 1e-04) 
  * Precision of  "y" :
    tau_y (Gamma prior with shape parameter 0.01 and rate parameter 0.01)


  Linear mixed model for "c2" 
     family: gaussian 
     link: identity 
  * Predictor variables:
    (Intercept), C1, B21, O22, O23, c1, time 
  * Regression coefficients:
    alpha[1:7] (normal prior(s) with mean 0 and precision 1e-04) 
  * Precision of  "c2" :
    tau_c2 (Gamma prior with shape parameter 0.01 and rate parameter 0.01)


  Linear mixed model for "c1" 
     family: gaussian 
     link: identity 
  * Predictor variables:
    (Intercept), C1, B21, O22, O23, time 
  * Regression coefficients:
    alpha[8:13] (normal prior(s) with mean 0 and precision 1e-04) 
  * Precision of  "c1" :
    tau_c1 (Gamma prior with shape parameter 0.01 and rate parameter 0.01)


  Linear mixed model for "time" 
     family: gaussian 
     link: identity 
  * Predictor variables:
    (Intercept), C1, B21, O22, O23 
  * Regression coefficients:
    alpha[14:18] (normal prior(s) with mean 0 and precision 1e-04) 
  * Precision of  "time" :
    tau_time (Gamma prior with shape parameter 0.01 and rate parameter 0.01)


  Cumulative logit model for "O2" 
  * Reference category: "1"
  * Predictor variables:
    C1, B21 
  * Regression coefficients:
    alpha[19:20] (normal prior(s) with mean 0 and precision 1e-04) 
  * Intercepts:
    - 1: gamma_O2[1] (normal prior with mean 0 and precision 1e-04)
    - 2: gamma_O2[2] = gamma_O2[1] + exp(delta_O2[1])
  * Increments:
    delta_O2[1] (normal prior(s) with mean 0 and precision 1e-04)


  Binomial model for "B2" 
     family: binomial 
     link: logit 
  * Reference category: "0"
  * Predictor variables:
    (Intercept), C1 
  * Regression coefficients:
    alpha[21:22] (normal prior(s) with mean 0 and precision 1e-04)

Code
  parameters(mymod)
Output
     outcome outcat     varname        coef
  1        y   <NA> (Intercept)     beta[1]
  2        y   <NA>          C1     beta[2]
  3        y   <NA>         B21     beta[3]
  4        y   <NA>         O22     beta[4]
  5        y   <NA>         O23     beta[5]
  6        y   <NA>          c1     beta[6]
  7        y   <NA>          c2     beta[7]
  8        y   <NA>        time     beta[8]
  9        y   <NA>        <NA>     sigma_y
  10       y   <NA>        <NA> D_y_id[1,1]
  11       y   <NA>        <NA> D_y_id[1,2]
  12       y   <NA>        <NA> D_y_id[2,2]

mlogitmm

Code
  list_models(mmod)
Output
  Multinomial logit mixed model for "x" 
  * Reference category: "1"
  * Predictor variables:
    (Intercept), C1, B21, O21, O22, p1, c2, y, time, y:time 
  * Regression coefficients:
    x2: beta[1:5]
    x3: beta[6:10]
    x2: beta[11:15]
    x3: beta[16:20] (normal prior(s) with mean 0 and precision 1e-04)


  Linear mixed model for "c2" 
     family: gaussian 
     link: identity 
  * Predictor variables:
    (Intercept), C1, B21, O21, O22, p1, y, time 
  * Regression coefficients:
    alpha[1:8] (normal prior(s) with mean 0 and precision 1e-04) 
  * Precision of  "c2" :
    tau_c2 (Gamma prior with shape parameter 0.01 and rate parameter 0.01)


  Poisson mixed model for "p1" 
     family: poisson 
     link: log 
  * Predictor variables:
    (Intercept), C1, B21, O21, O22, y, time 
  * Regression coefficients:
    alpha[9:15] (normal prior(s) with mean 0 and precision 1e-04)


  Linear mixed model for "y" 
     family: gaussian 
     link: identity 
  * Predictor variables:
    (Intercept), C1, B21, O21, O22, time 
  * Regression coefficients:
    alpha[16:21] (normal prior(s) with mean 0 and precision 1e-04) 
  * Precision of  "y" :
    tau_y (Gamma prior with shape parameter 0.01 and rate parameter 0.01)


  Linear mixed model for "time" 
     family: gaussian 
     link: identity 
  * Predictor variables:
    (Intercept), C1, B21, O21, O22 
  * Regression coefficients:
    alpha[22:26] (normal prior(s) with mean 0 and precision 1e-04) 
  * Precision of  "time" :
    tau_time (Gamma prior with shape parameter 0.01 and rate parameter 0.01)


  Cumulative logit model for "O2" 
  * Reference category: "3"
  * Predictor variables:
    C1, B21 
  * Regression coefficients:
    alpha[27:28] (normal prior(s) with mean 0 and precision 1e-04) 
  * Intercepts:
    - 1: gamma_O2[1] (normal prior with mean 0 and precision 1e-04)
    - 2: gamma_O2[2] = gamma_O2[1] + exp(delta_O2[1])
  * Increments:
    delta_O2[1] (normal prior(s) with mean 0 and precision 1e-04)


  Binomial model for "B2" 
     family: binomial 
     link: logit 
  * Reference category: "0"
  * Predictor variables:
    (Intercept), C1 
  * Regression coefficients:
    alpha[29:30] (normal prior(s) with mean 0 and precision 1e-04)

Code
  parameters(mmod)
Output
     outcome outcat     varname        coef
  1        x     x2 (Intercept)     beta[1]
  2        x     x2          C1     beta[2]
  3        x     x2         B21     beta[3]
  4        x     x2         O21     beta[4]
  5        x     x2         O22     beta[5]
  6        x     x3 (Intercept)     beta[6]
  7        x     x3          C1     beta[7]
  8        x     x3         B21     beta[8]
  9        x     x3         O21     beta[9]
  10       x     x3         O22    beta[10]
  11       x     x2          p1    beta[11]
  12       x     x2          c2    beta[12]
  13       x     x2           y    beta[13]
  14       x     x2        time    beta[14]
  15       x     x2      y:time    beta[15]
  16       x     x3          p1    beta[16]
  17       x     x3          c2    beta[17]
  18       x     x3           y    beta[18]
  19       x     x3        time    beta[19]
  20       x     x3      y:time    beta[20]
  21       x   <NA>        <NA> D_x_id[1,1]
  22       x   <NA>        <NA> D_x_id[1,2]
  23       x   <NA>        <NA> D_x_id[2,2]