\begin{Schunk}
\begin{Sinput}
 params <- c("b0","b1","sigmay","sigmax","sigmaeta","phi")
 print(fit1,pars=params)
\end{Sinput}
\begin{Soutput}
Inference for Stan model: sem1b.
2 chains, each with iter=2000; warmup=1000; thin=1; 
post-warmup draws per chain=1000, total post-warmup draws=2000.

             mean se_mean   sd  2.5%   25%   50%   75% 97.5% n_eff Rhat
Intercept    0.01    0.00 0.06 -0.10 -0.03  0.01  0.04  0.12   242 1.00
Voter        0.19    0.00 0.05  0.09  0.16  0.19  0.23  0.30   378 1.00
Sentiment   -0.16    0.00 0.05 -0.27 -0.20 -0.16 -0.12 -0.06   406 1.00
Voter^2      0.04    0.00 0.07 -0.10  0.00  0.04  0.09  0.18   213 1.00
VoterXSent   0.16    0.01 0.12 -0.06  0.08  0.16  0.24  0.40   195 1.00
Sentiment^2 -0.03    0.00 0.07 -0.17 -0.07 -0.02  0.02  0.11   196 1.01
sigmay[1]    1.00    0.00 0.03  0.95  0.98  1.00  1.02  1.06   719 1.00
sigmay[2]    1.00    0.00 0.03  0.95  0.98  1.00  1.02  1.06   519 1.00
sigmax[1]    1.01    0.00 0.03  0.95  0.99  1.01  1.03  1.08  1238 1.00
sigmax[2]    1.05    0.00 0.03  0.99  1.03  1.05  1.07  1.11  1309 1.00
sigmax[3]    1.04    0.00 0.03  0.98  1.02  1.04  1.06  1.10  1604 1.00
sigmax[4]    0.98    0.00 0.03  0.92  0.96  0.98  1.00  1.04  1274 1.01
sigmaeta     0.51    0.00 0.04  0.41  0.49  0.51  0.54  0.59    90 1.03
phi[1,1]     0.93    0.00 0.07  0.80  0.88  0.92  0.97  1.06   780 1.00
phi[1,2]     0.54    0.00 0.05  0.45  0.51  0.54  0.57  0.63   740 1.01
phi[2,1]     0.54    0.00 0.05  0.45  0.51  0.54  0.57  0.63   740 1.01
phi[2,2]     0.88    0.00 0.06  0.75  0.84  0.88  0.92  1.01   564 1.01

Samples were drawn using NUTS(diag_e) at Thu May 17 12:50:44 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}
\begin{Sinput}
 
\end{Sinput}
\end{Schunk}
