Inference for Bugs model at "three_component_new_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_GBS_cold_log  -0.946   0.741  -2.442  -1.432  -0.933  -0.439   0.458 1.001 240000
A_GBS_warm_log  22.714   1.979  19.023  21.344  22.655  24.015  26.771 1.002   2500
A_dis_log        5.033   1.010   2.937   4.384   5.067   5.721   6.932 1.002   1800
Q_GBS_cold      51.504   3.327  44.891  49.296  51.531  53.753  57.912 1.001 600000
Q_GBS_warm     181.834   9.856 162.525 175.189 181.854 188.479 201.183 1.002   3000
Q_dis           61.928   4.873  52.013  58.750  62.038  65.226  71.261 1.002   1700
n_GBS_cold       1.892   0.146   1.597   1.796   1.895   1.991   2.170 1.001  46000
n_GBS_warm       2.515   0.259   1.989   2.344   2.521   2.693   3.002 1.001   6900
n_dis            3.625   0.113   3.432   3.547   3.615   3.690   3.876 1.001 140000
p_cold           1.198   0.097   1.015   1.132   1.195   1.261   1.397 1.001  79000
p_warm           1.925   0.367   1.155   1.684   1.948   2.187   2.582 1.001   4500
deviance       -17.928   6.738 -30.211 -22.597 -18.249 -13.629  -3.744 1.001  16000

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 = 22.7 and DIC = 4.8
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_dis_log        5.0329 1.01024 0.0013058      0.0172828
A_GBS_cold_log  -0.9461 0.74107 0.0009579      0.0066172
A_GBS_warm_log  22.7139 1.97946 0.0025587      0.0323004
deviance       -17.9284 6.73842 0.0087102      0.0264298
n_dis            3.6247 0.11279 0.0001458      0.0005451
n_GBS_cold       1.8920 0.14604 0.0001888      0.0009540
n_GBS_warm       2.5149 0.25869 0.0003344      0.0024110
p_cold           1.1980 0.09705 0.0001254      0.0004994
p_warm           1.9251 0.36680 0.0004741      0.0042033
Q_dis           61.9285 4.87257 0.0062983      0.0824604
Q_GBS_cold      51.5035 3.32736 0.0043010      0.0301510
Q_GBS_warm     181.8340 9.85556 0.0127394      0.1574426

2. Quantiles for each variable:

                  2.5%     25%      50%      75%    97.5%
A_dis_log        2.937   4.384   5.0674   5.7205   6.9321
A_GBS_cold_log  -2.442  -1.432  -0.9333  -0.4394   0.4584
A_GBS_warm_log  19.023  21.344  22.6549  24.0148  26.7711
deviance       -30.211 -22.597 -18.2492 -13.6288  -3.7444
n_dis            3.432   3.547   3.6149   3.6901   3.8761
n_GBS_cold       1.597   1.796   1.8950   1.9913   2.1700
n_GBS_warm       1.989   2.344   2.5212   2.6931   3.0022
p_cold           1.015   1.132   1.1952   1.2611   1.3966
p_warm           1.155   1.684   1.9477   2.1868   2.5816
Q_dis           52.013  58.750  62.0383  65.2261  71.2614
Q_GBS_cold      44.891  49.296  51.5307  53.7527  57.9124
Q_GBS_warm     162.525 175.189 181.8540 188.4788 201.1828

Potential scale reduction factors:

               Point est. Upper C.I.
A_dis_log               1       1.01
A_GBS_cold_log          1       1.00
A_GBS_warm_log          1       1.00
deviance                1       1.00
n_dis                   1       1.00
n_GBS_cold              1       1.00
n_GBS_warm              1       1.00
p_cold                  1       1.00
p_warm                  1       1.00
Q_dis                   1       1.01
Q_GBS_cold              1       1.00
Q_GBS_warm              1       1.00

Multivariate psrf

1
