hierarchical model Stan

test.R

rm(list=ls())
library(rstan)
rstan_options(auto_write = FALSE)
options(mc.cores = parallel::detectCores())
N=1000
data = list(N=N,x1<-rnorm(N,5,2),x2<-rnorm(N,7,2),x3<-rnorm(N,9,2),y<-x1+x2+x3)
fit <- suppressMessages(stan(file = '~/Desktop/695/test.stan', data = data, iter=11000, warmup=1000, chains=2, seed=483892929, refresh=11000))
print(fit)
plot(fit)

test.stan

data {
  int<lower=0> N;
  vector[N] y;
  vector[N] x1;
  vector[N] x2;
  vector[N] x3;
}

parameters {
  real mu1;
  real mu2;
  real mu3;
  real mu;
  real<lower=0> phi; 
  real<lower=0> sigma;
}

model {
  x1 ~ normal(mu1, sigma);
  x2 ~ normal(mu2, sigma);
  x3 ~ normal(mu3, sigma);
  mu1 ~ normal(mu, phi);
  mu2 ~normal(mu,phi);
  mu3 ~normal(mu,phi);
  y ~ normal(mu*3, sqrt(3)*sigma);
}