print(post, pars = 'y_rep', include = FALSE, digits = 2, probs = c(.25, .75))

## Inference for Stan model: regression.
## 4 chains, each with iter=2000; warmup=1000; thin=1;
## post-warmup draws per chain=1000, total post-warmup draws=4000.
##
## mean se_mean sd 25% 75% n_eff Rhat
## beta[1] 6.44 0.00 0.03 6.42 6.46 2201 1
## beta[2] -0.07 0.00 0.02 -0.08 -0.06 2500 1
## beta[3] 0.02 0.00 0.01 0.01 0.02 2604 1
## beta[4] 0.00 0.00 0.00 0.00 0.00 3428 1
## sigma_unscaled 0.04 0.00 0.00 0.04 0.04 2142 1
## sigma 0.21 0.00 0.01 0.21 0.21 2142 1
## lp__ 84.63 0.04 1.52 83.87 85.75 1686 1
##
## Samples were drawn using NUTS(diag_e) at Mon Jul 11 22:34:07 2016.
## 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).
## The estimated Bayesian Fraction of Missing Information is a measure of
## the efficiency of the sampler with values close to 1 being ideal.
## For each chain, these estimates are
## 1.1 1 1 1.1