credpath

# linc_ig = alpha_g + beta_g * educ_i for individual i in cluster g
# want to estimate alpha_g and beta_g for each cluster

male_fit <- map2stan(                     # create output called male_fit
  alist(
    linc ~ dnorm( mu , sigma ),           # likelihood: log(income) has normal distribution
    mu <- alpha_attract[ornt] +           # mean mu is linearly related to alpha_clusterName + beta_clusterName * education
          beta_attract[ornt]*educ,
    c(alpha_attract,beta_attract)[ornt] ~ dmvnorm2( c(alpha,beta) , sigma_attract , Rho ),    # alpha_clusterName and beta_clusterName are jointly normaly distributed with means alpha and beta and standard deviations sigma_clusterName and correlation Rho 

credpath

// trying out a censoring thing


data 
{
  int<lower=2> K;             // there are K categories for data y
  int<lower=1> N;             // N obersevations
  int<lower=1,upper=K> y[N];  // outcome variable
  int<lower=1,upper=11> x[N]; // predictor variable
}

credpath

# trying out a rounding thing


functions
{
  int income_rounding(real log_income)    // function to transform latent log income into NSFG categories
  {
    real inc;
    inc = exp(log_income);
    

credpath

# This is the stan code to implement the model

data 
{
  int<lower=1> N;         // number of observations
  int<lower=1> G;         // number of clusters
  vector[N] y;            // the predicted variable (income)
  vector[N] x;            // the predictor variable (education)
}