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) 


