Inference for Bugs model at "single_component_gss_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      3.304   0.280  2.753  3.116  3.304  3.493   3.855 1.002   3900
Q         62.803   1.787 59.286 61.607 62.803 64.005  66.319 1.001   4600
n          2.844   0.040  2.765  2.817  2.844  2.871   2.922 1.001 170000
p          0.788   0.040  0.711  0.761  0.788  0.815   0.866 1.001  14000
deviance  87.756   6.328 75.753 83.432 87.613 91.903 100.618 1.001  36000

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 = 20.0 and DIC = 107.8
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     3.3042 0.28038 3.624e-04      0.0025999
deviance 87.7561 6.32840 8.180e-03      0.0139018
n         2.8436 0.04012 5.186e-05      0.0000638
p         0.7881 0.03953 5.110e-05      0.0002763
Q        62.8031 1.78715 2.310e-03      0.0171400

2. Quantiles for each variable:

            2.5%     25%     50%     75%    97.5%
A_log     2.7532  3.1163  3.3042  3.4934   3.8545
deviance 75.7533 83.4317 87.6125 91.9035 100.6184
n         2.7646  2.8166  2.8436  2.8708   2.9222
p         0.7106  0.7615  0.7881  0.8147   0.8656
Q        59.2856 61.6071 62.8028 64.0053  66.3188

Potential scale reduction factors:

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

Multivariate psrf

1
