Inference for Bugs model at "single_component_gsi_exclude_high_stress_unconfined.jags", fit using jags,
 3 chains, each with 4e+06 iterations (first 10000 discarded), n.thin = 20
 n.sims = 598500 iterations saved
         mu.vect sd.vect    2.5%     25%     50%     75%   97.5%  Rhat  n.eff
A_log      0.397   0.232  -0.058   0.240   0.397   0.553   0.851 1.001  73000
Q         36.061   1.140  33.828  35.290  36.063  36.833  38.291 1.001  67000
n          3.072   0.038   2.998   3.047   3.072   3.098   3.146 1.001 350000
deviance 500.580   2.877 496.163 498.600 500.151 502.077 507.491 1.001 160000

For each parameter, n.eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor (at convergence, Rhat=1).

DIC info (using the rule, pD = var(deviance)/2)
pD = 4.1 and DIC = 504.7
DIC is an estimate of expected predictive error (lower deviance is better).

Iterations = 10001:3999981
Thinning interval = 20 
Number of chains = 3 
Sample size per chain = 199500 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

             Mean      SD  Naive SE Time-series SE
A_log      0.3965 0.23222 3.002e-04      1.553e-03
deviance 500.5795 2.87663 3.718e-03      8.057e-03
n          3.0721 0.03776 4.881e-05      9.959e-05
Q         36.0613 1.13994 1.474e-03      7.615e-03

2. Quantiles for each variable:

              2.5%      25%      50%      75%    97.5%
A_log     -0.05842   0.2396   0.3967   0.5535   0.8508
deviance 496.16312 498.5999 500.1509 502.0772 507.4910
n          2.99800   3.0467   3.0721   3.0976   3.1463
Q         33.82776  35.2902  36.0626  36.8327  38.2905

Potential scale reduction factors:

         Point est. Upper C.I.
A_log             1          1
deviance          1          1
n                 1          1
Q                 1          1

Multivariate psrf

1
