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] 0.74 0.02 0.74 0.22 1.24 1348 1
## beta[2] -0.60 0.01 0.33 -0.81 -0.37 2295 1
## beta[3] 0.17 0.00 0.13 0.08 0.26 1598 1
## beta[4] 0.57 0.00 0.16 0.46 0.69 1671 1
## beta[5] -0.22 0.00 0.18 -0.34 -0.11 1847 1
## sigma_unscaled 0.10 0.00 0.01 0.09 0.10 2271 1
## sigma 0.48 0.00 0.05 0.44 0.51 2271 1
## lp__ -48.00 0.05 1.79 -49.01 -46.66 1518 1
##
## Samples were drawn using NUTS(diag_e) at Mon Jun 27 15:11:23 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 0.9 0.9 1