Inference for Bugs model at "/home/kubo/yazawa/winbugs/model.bug.txt", fit using winbugs, 3 chains, each with 12000 iterations (first 10000 discarded), n.thin = 10 n.sims = 600 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff coefbase[1] 30.4 1.3 28.0 29.6 30.4 31.3 33.4 1.0 200 coefbase[2] 0.1 0.0 0.1 0.1 0.1 0.1 0.1 1.4 9 coefdamage -0.5 0.0 -0.6 -0.5 -0.5 -0.4 -0.4 1.0 600 coefspc[1,1] -1.1 4.5 -8.7 -4.5 -1.5 1.7 8.0 1.0 240 coefspc[1,2] -3.9 4.0 -10.9 -6.6 -4.3 -1.2 5.0 1.0 600 coefspc[1,3] -3.8 4.5 -11.7 -6.8 -4.1 -1.3 6.1 1.0 110 coefspc[1,4] 1.4 3.7 -4.8 -1.3 1.1 3.7 9.3 1.0 76 coefspc[1,5] -0.8 3.9 -7.4 -3.4 -1.1 1.5 7.6 1.0 100 coefspc[1,6] -2.6 5.4 -12.9 -6.2 -2.7 0.8 8.6 1.0 110 coefspc[1,7] -0.2 2.1 -4.2 -1.6 -0.2 1.2 4.1 1.0 600 coefspc[1,8] -1.8 2.1 -5.6 -3.3 -1.9 -0.3 2.6 1.0 600 coefspc[1,9] -4.4 3.2 -10.2 -6.7 -4.6 -2.3 2.0 1.0 600 coefspc[1,10] 8.4 3.5 2.4 6.0 8.3 10.6 15.5 1.0 210 coefspc[1,11] -1.4 5.1 -10.4 -5.2 -1.4 2.1 9.7 1.0 180 coefspc[1,12] -4.6 4.9 -12.9 -8.0 -5.1 -1.8 5.9 1.0 89 coefspc[1,13] -1.1 4.8 -10.0 -4.3 -1.4 1.8 9.2 1.0 260 coefspc[1,14] 4.7 3.4 -1.1 2.2 4.4 7.1 11.3 1.0 95 coefspc[1,15] -1.2 3.4 -7.5 -3.8 -1.3 1.0 5.7 1.0 180 coefspc[1,16] -1.6 5.1 -10.6 -5.1 -1.8 1.6 9.4 1.0 270 coefspc[1,17] -0.3 5.0 -9.6 -3.9 -0.6 3.1 10.2 1.0 350 coefspc[1,18] -3.3 3.8 -9.8 -6.0 -3.4 -0.8 5.1 1.0 340 coefspc[1,19] -3.1 4.5 -11.1 -6.2 -3.5 -0.7 6.0 1.0 110 coefspc[1,20] 5.7 3.9 -1.4 3.0 5.5 8.2 13.5 1.0 290 coefspc[1,21] -1.2 5.0 -9.7 -5.1 -1.5 2.2 9.0 1.1 31 coefspc[1,22] 6.1 3.4 0.1 3.6 5.9 8.6 12.4 1.0 320 coefspc[1,23] -1.2 2.9 -6.4 -3.3 -1.5 0.4 5.2 1.0 140 coefspc[1,24] 0.9 4.0 -6.0 -1.9 0.6 3.7 8.8 1.0 600 coefspc[1,25] 12.3 2.9 6.9 10.0 12.2 14.1 18.2 1.0 600 coefspc[1,26] -1.6 3.9 -8.1 -4.5 -1.8 1.1 6.3 1.0 53 coefspc[1,27] -0.5 5.0 -9.6 -4.2 -0.8 2.6 9.6 1.0 210 coefspc[1,28] -0.6 5.0 -10.3 -4.0 -0.6 2.7 9.8 1.0 150 coefspc[1,29] -2.2 5.8 -11.2 -6.4 -3.0 1.2 11.3 1.0 600 coefspc[1,30] -2.2 3.8 -9.6 -4.9 -2.6 0.4 6.0 1.0 490 coefspc[1,31] -2.1 5.2 -11.6 -5.7 -2.2 1.2 8.5 1.0 130 coefspc[1,32] 4.0 3.2 -1.5 1.7 3.8 6.1 10.4 1.0 140 coefspc[1,33] 1.2 4.3 -7.0 -1.6 1.0 3.7 9.9 1.0 49 coefspc[1,34] -1.4 4.5 -9.3 -4.6 -1.7 1.6 8.0 1.0 600 coefspc[1,35] -1.1 4.7 -9.5 -4.4 -1.1 2.2 8.6 1.0 270 coefspc[1,36] -0.9 4.9 -9.3 -4.3 -1.1 2.2 9.6 1.1 24 coefspc[1,37] 1.0 4.9 -7.8 -2.4 0.7 4.4 10.9 1.0 600 coefspc[1,38] -0.9 5.1 -9.6 -4.6 -1.2 2.3 9.5 1.0 220 coefspc[1,39] 0.4 4.9 -9.1 -3.1 0.4 3.6 10.5 1.0 220 coefspc[1,40] 6.6 3.4 0.4 4.3 6.5 8.9 13.9 1.0 94 coefspc[1,41] -1.5 5.0 -10.6 -4.9 -1.7 1.7 8.9 1.0 600 coefspc[1,42] -0.2 5.2 -9.7 -3.7 -0.5 3.1 10.7 1.0 130 coefspc[1,43] 3.9 2.9 -1.5 1.9 3.6 5.7 10.4 1.0 440 coefspc[1,44] -1.6 5.0 -11.2 -4.8 -1.7 1.9 7.7 1.0 100 coefspc[1,45] -3.5 3.5 -9.7 -5.9 -3.6 -1.3 3.7 1.0 600 coefspc[1,46] -0.4 3.8 -7.1 -3.0 -0.5 2.1 7.7 1.0 240 coefspc[1,47] 5.2 3.4 -0.5 2.7 4.8 7.3 12.8 1.0 600 coefspc[1,48] 6.3 2.9 1.2 4.2 6.2 8.1 12.3 1.0 600 coefspc[1,49] 8.3 2.7 3.0 6.4 8.4 10.1 13.3 1.0 600 coefspc[1,50] -7.8 4.9 -16.0 -11.8 -8.4 -4.4 2.4 1.0 370 coefspc[2,1] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 230 coefspc[2,2] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 220 coefspc[2,3] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.1 37 coefspc[2,4] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.2 17 coefspc[2,5] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 55 coefspc[2,6] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 180 coefspc[2,7] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 46 coefspc[2,8] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 96 coefspc[2,9] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 68 coefspc[2,10] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.1 47 coefspc[2,11] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 79 coefspc[2,12] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 56 coefspc[2,13] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 350 coefspc[2,14] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 56 coefspc[2,15] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 110 coefspc[2,16] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 130 coefspc[2,17] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 92 coefspc[2,18] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 99 coefspc[2,19] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 66 coefspc[2,20] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 180 coefspc[2,21] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.2 18 coefspc[2,22] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 73 coefspc[2,23] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 68 coefspc[2,24] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 76 coefspc[2,25] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.2 18 coefspc[2,26] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.1 34 coefspc[2,27] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 320 coefspc[2,28] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.1 45 coefspc[2,29] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 210 coefspc[2,30] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 110 coefspc[2,31] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 310 coefspc[2,32] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 600 coefspc[2,33] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 58 coefspc[2,34] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 130 coefspc[2,35] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 95 coefspc[2,36] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 76 coefspc[2,37] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 120 coefspc[2,38] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 47 coefspc[2,39] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.1 39 coefspc[2,40] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 180 coefspc[2,41] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 79 coefspc[2,42] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 500 coefspc[2,43] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 130 coefspc[2,44] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 600 coefspc[2,45] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 250 coefspc[2,46] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 68 coefspc[2,47] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 52 coefspc[2,48] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 49 coefspc[2,49] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.1 22 coefspc[2,50] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 130 tau.coefspc[1] 0.0 0.0 0.0 0.0 0.0 0.0 0.1 1.0 500 tau.coefspc[2] 1532.4 339.7 934.2 1294.7 1508.5 1759.0 2254.4 1.0 110 tau.derr 190.4 109.0 63.0 114.8 156.9 231.4 480.5 1.2 14 tau.herr 2.8 0.1 2.5 2.7 2.7 2.8 3.1 1.0 210 deviance 3336.2 16.5 3303.0 3326.0 3336.0 3347.0 3372.0 1.1 44 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 = 68.0 and DIC = 3404.2 (using the rule, pD = Dbar-Dhat) DIC is an estimate of expected predictive error (lower deviance is better).