\begin{Schunk}
\begin{Sinput}
 names(stanm0)[1:ncol(X)]  <- colnames(X)
 stanm0
\end{Sinput}
\begin{Soutput}
Inference for Stan model: OLS_reg.
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   2.5%    25%    50%    75%  97.5% n_eff Rhat
(Intercept)  -0.14    0.00 0.19  -0.52  -0.28  -0.14  -0.01   0.23  2058    1
x1            0.19    0.00 0.10  -0.01   0.12   0.19   0.26   0.39  4000    1
x2           -0.68    0.00 0.10  -0.89  -0.75  -0.68  -0.61  -0.48  4000    1
x3fmed        0.05    0.01 0.26  -0.46  -0.13   0.05   0.23   0.58  2483    1
x3fhi        -0.12    0.01 0.27  -0.64  -0.29  -0.12   0.06   0.41  2533    1
x1:x2        -0.50    0.00 0.09  -0.67  -0.56  -0.50  -0.44  -0.32  4000    1
sigma         1.02    0.00 0.09   0.86   0.95   1.01   1.07   1.21  4000    1
lp__        -38.54    0.05 1.94 -43.28 -39.60 -38.22 -37.11 -35.73  1793    1

Samples were drawn using NUTS(diag_e) at Wed May 30 17:57:07 2018.
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).
\end{Soutput}
\end{Schunk}
