Created
December 6, 2020 02:42
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hierarchical model Stan
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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) |
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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); | |
} |
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