Rstanで重回帰分析

data {

  int<lower=1> J; // number of data

  int<lower=1> K; // number of covariate

  real y[J];

  matrix[J,K] x;

}

parameters {

  real<lower=0> sd0;

  vector[K] beta;

  real beta0;

}

model {

  for(j in 1:J){

    increment_log_prob(normal_log(y[j], x[j] * beta + beta0, sd0));

  }

}

library(rstan)

n <- 100

x1 <- rnorm(n, mean=1, sd=0.1)

x2 <- rnorm(n, mean=1, sd=0.1)

beta1 <- 1

beta2 <- 2

beta0 <- 3

y <- beta1 * x1 + beta2 * x2 + beta0

data.stan <- list(J = n,

                  K = 2,

                  y = y,

                  x = matrix(c(x1, x2), ncol=2))



fit <- stan(file = 'linear_regression.stan', data = data.stan, 

            iter = 1000, chains = 4)

> fit

Inference for Stan model: linear_regression.

4 chains, each with iter=1000; warmup=500; thin=1; 

post-warmup draws per chain=500, total post-warmup draws=2000.



           mean se_mean    sd    2.5%     25%     50%     75%   97.5% n_eff  Rhat

sd0        0.00    0.00   0.0    0.00    0.00    0.00    0.00    0.00     2  5.75

beta[1]    1.00    0.00   0.0    1.00    1.00    1.00    1.00    1.00     2  6.20

beta[2]    2.00    0.00   0.0    2.00    2.00    2.00    2.00    2.00     2  4.98

beta0      3.00    0.00   0.0    3.00    3.00    3.00    3.00    3.00     2  6.53

lp__    2244.37   95.91 136.4 2073.04 2104.57 2223.56 2344.35 2440.01     2 17.83



Samples were drawn using NUTS(diag_e) at Wed Dec 10 22:45:14 2014.

For each parameter, n_eff is a crude measure of effective sample size,

and Rhat is the potential scale reduction factor on split chains (at 

convergence, Rhat=1).