Inference for Bugs model at "/home/kubo/ecoasZP/winbugs/model.bug.txt", fit using winbugs, 3 chains, each with 25000 iterations (first 15000 discarded), n.thin = 50 n.sims = 600 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff beta.f[1] 0.055 0.480 -0.867 -0.278 0.062 0.380 0.987 1.033 62 beta.f[2] -0.021 0.164 -0.327 -0.126 -0.023 0.093 0.290 1.020 180 beta.f[3] 1.333 0.432 0.590 1.012 1.332 1.593 2.271 1.103 24 beta.f[4] -0.003 0.001 -0.006 -0.004 -0.003 -0.002 -0.001 1.042 86 beta.z[1] 2.821 0.218 2.399 2.684 2.826 2.960 3.259 1.003 600 beta.z[2] 0.288 0.058 0.182 0.248 0.288 0.324 0.405 1.003 440 beta.c[1] 0.408 0.141 0.135 0.313 0.410 0.499 0.705 1.000 600 beta.c[2] 0.223 0.036 0.151 0.198 0.224 0.244 0.299 1.005 320 beta.c[3] 0.007 0.010 -0.011 0.001 0.007 0.014 0.026 1.013 170 beta.c[4] 2.418 0.612 1.165 2.007 2.436 2.854 3.603 1.041 65 tau.err[1] 523.062 572.038 18.506 140.974 301.543 683.623 2100.260 1.207 14 tau.err[2] 701.570 652.394 63.270 243.697 471.594 973.121 2450.274 1.027 82 tau.err[3] 64.271 228.543 0.000 0.002 0.139 2.423 743.798 1.954 5 tau.err[4] 193.991 440.380 2.852 14.459 42.725 176.546 1308.693 1.044 48 tau.err[5] 105.002 312.700 0.035 0.088 0.548 33.762 1053.859 1.079 31 vc[1,1] 2.539 2.407 0.351 1.024 1.828 3.107 9.320 1.073 38 vc[1,2] -0.609 1.016 -2.418 -1.310 -0.615 -0.030 1.501 1.024 130 vc[1,3] 0.980 0.708 -0.005 0.419 0.859 1.380 2.502 1.024 140 vc[2,1] -0.609 1.016 -2.418 -1.310 -0.615 -0.030 1.501 1.024 130 vc[2,2] 3.602 0.937 1.636 3.007 3.689 4.255 5.404 1.028 89 vc[2,3] 0.046 0.524 -1.062 -0.333 0.142 0.444 0.898 1.044 50 vc[3,1] 0.980 0.708 -0.005 0.419 0.859 1.380 2.502 1.024 140 vc[3,2] 0.046 0.524 -1.062 -0.333 0.142 0.444 0.898 1.044 50 vc[3,3] 0.993 0.452 0.236 0.658 0.961 1.337 1.933 1.000 600 rho[1,1] 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 1 rho[1,2] -0.264 0.373 -0.896 -0.557 -0.260 -0.014 0.491 1.007 300 rho[1,3] 0.627 0.248 -0.007 0.510 0.690 0.813 0.921 1.020 190 rho[2,1] -0.264 0.373 -0.896 -0.557 -0.260 -0.014 0.491 1.007 300 rho[2,2] 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 1 rho[2,3] -0.031 0.349 -0.808 -0.230 0.072 0.209 0.490 1.051 45 rho[3,1] 0.627 0.248 -0.007 0.510 0.690 0.813 0.921 1.020 190 rho[3,2] -0.031 0.349 -0.808 -0.230 0.072 0.209 0.490 1.051 45 rho[3,3] 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 1 deviance -499.639 127.197 -720.577 -587.925 -510.700 -424.075 -212.713 1.081 37 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). pD = 262.9 and DIC = -236.8 (using the rule, pD = Dbar-Dhat) DIC is an estimate of expected predictive error (lower deviance is better).