Inference for Bugs model at "single_component_exclude_high_stress_unconfined.jags", fit using jags,
 3 chains, each with 4e+05 iterations (first 10000 discarded), n.thin = 10
 n.sims = 117000 iterations saved
         mu.vect sd.vect    2.5%     25%     50%     75%   97.5%  Rhat n.eff
A_log     11.950   0.954  10.139  11.281  11.948  12.588  13.855 1.006   420
Q         90.423   4.813  81.284  87.050  90.418  93.646 100.010 1.006   420
n          3.529   0.069   3.394   3.482   3.529   3.575   3.665 1.002  1400
deviance 172.895   4.072 166.288 169.997 172.389 175.296 182.129 1.003   880

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 = 8.3 and DIC = 181.2
DIC is an estimate of expected predictive error (lower deviance is better).

Iterations = 10001:399991
Thinning interval = 10 
Number of chains = 3 
Sample size per chain = 39000 

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

            Mean      SD  Naive SE Time-series SE
A_log     11.950 0.95384 0.0027886        0.05912
deviance 172.895 4.07158 0.0119034        0.16684
n          3.529 0.06907 0.0002019        0.00200
Q         90.423 4.81345 0.0140722        0.30048

2. Quantiles for each variable:

            2.5%     25%     50%     75%   97.5%
A_log     10.139  11.281  11.948  12.588  13.855
deviance 166.288 169.997 172.389 175.296 182.129
n          3.394   3.482   3.529   3.575   3.665
Q         81.284  87.050  90.418  93.646 100.010

Potential scale reduction factors:

         Point est. Upper C.I.
A_log             1       1.02
deviance          1       1.01
n                 1       1.00
Q                 1       1.02

Multivariate psrf

1
