Inference for Bugs model at "/home/kubo/miyata/winbugs/model.bug.txt", fit using winbugs, 3 chains, each with 60000 iterations (first 50000 discarded), n.thin = 25 n.sims = 1200 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff coefbase[1] 0.5 0.2 0.1 0.3 0.5 0.6 0.9 1.4 9 coefbase[2] 0.0 0.1 -0.1 0.0 0.0 0.1 0.2 1.1 33 coefbase[3] -0.6 0.1 -0.7 -0.6 -0.6 -0.5 -0.4 1.1 48 coefbase[4] -2.0 0.0 -2.1 -2.0 -2.0 -1.9 -1.9 1.0 990 coefbase[5] -0.1 0.0 -0.1 -0.1 -0.1 -0.1 0.0 1.0 420 coefspc[1,1] -0.4 0.2 -0.8 -0.5 -0.4 -0.2 0.1 1.3 11 coefspc[1,2] -0.2 0.1 -0.4 -0.3 -0.2 -0.1 0.0 1.0 80 coefspc[1,3] -0.1 0.1 -0.3 -0.2 -0.1 0.0 0.1 1.0 730 coefspc[1,4] 0.1 0.1 0.0 0.0 0.1 0.1 0.2 1.0 1200 coefspc[1,5] 0.0 0.0 -0.1 0.0 0.0 0.0 0.1 1.0 1100 coefspc[2,1] -0.8 0.2 -1.2 -1.0 -0.8 -0.7 -0.4 1.3 11 coefspc[2,2] -0.2 0.1 -0.4 -0.3 -0.3 -0.2 -0.1 1.1 46 coefspc[2,3] -0.1 0.1 -0.3 -0.2 -0.1 -0.1 0.0 1.1 45 coefspc[2,4] 0.2 0.1 0.1 0.2 0.2 0.2 0.3 1.0 1200 coefspc[2,5] 0.0 0.0 0.0 0.0 0.0 0.1 0.1 1.0 520 coefspc[3,1] 0.0 0.2 -0.4 -0.1 0.0 0.1 0.4 1.3 11 coefspc[3,2] -0.1 0.1 -0.3 -0.2 -0.1 0.0 0.1 1.1 37 coefspc[3,3] 0.2 0.1 0.0 0.2 0.2 0.3 0.4 1.0 140 coefspc[3,4] 0.0 0.1 -0.1 0.0 0.0 0.0 0.1 1.0 1200 coefspc[3,5] 0.0 0.0 -0.1 -0.1 0.0 0.0 0.0 1.0 1200 coefspc[4,1] -0.4 0.2 -0.8 -0.5 -0.3 -0.2 0.1 1.2 14 coefspc[4,2] 0.3 0.1 0.1 0.2 0.3 0.3 0.5 1.1 38 coefspc[4,3] -0.1 0.1 -0.3 -0.2 -0.1 0.0 0.1 1.0 1200 coefspc[4,4] 0.1 0.1 0.0 0.1 0.1 0.2 0.3 1.0 1200 coefspc[4,5] 0.1 0.0 0.0 0.0 0.1 0.1 0.1 1.0 170 coefspc[5,1] 0.2 0.2 -0.2 0.1 0.2 0.4 0.6 1.2 12 coefspc[5,2] 0.3 0.1 0.1 0.2 0.3 0.4 0.5 1.1 43 coefspc[5,3] 0.0 0.1 -0.2 0.0 0.0 0.1 0.2 1.0 500 coefspc[5,4] 0.0 0.1 -0.1 -0.1 0.0 0.0 0.1 1.0 1200 coefspc[5,5] 0.0 0.0 0.0 0.0 0.0 0.1 0.1 1.0 450 coefspc[6,1] 0.4 0.2 0.1 0.3 0.4 0.6 0.9 1.4 10 coefspc[6,2] 0.2 0.1 0.0 0.1 0.2 0.2 0.4 1.0 110 coefspc[6,3] 0.2 0.1 0.0 0.1 0.2 0.3 0.4 1.0 110 coefspc[6,4] 0.0 0.1 -0.1 0.0 0.0 0.0 0.1 1.0 950 coefspc[6,5] 0.0 0.0 -0.1 0.0 0.0 0.0 0.1 1.0 390 coefspc[7,1] 1.7 0.2 1.2 1.5 1.6 1.8 2.1 1.3 11 coefspc[7,2] 0.0 0.1 -0.2 -0.1 -0.1 0.0 0.2 1.1 46 coefspc[7,3] 0.2 0.1 -0.1 0.1 0.2 0.2 0.3 1.0 120 coefspc[7,4] 0.0 0.1 -0.1 -0.1 0.0 0.0 0.1 1.0 1200 coefspc[7,5] 0.0 0.0 -0.1 0.0 0.0 0.0 0.0 1.0 1200 coefspc[8,1] -0.3 0.2 -0.7 -0.4 -0.3 -0.1 0.2 1.4 9 coefspc[8,2] 0.0 0.1 -0.1 0.0 0.0 0.1 0.2 1.1 38 coefspc[8,3] 0.0 0.1 -0.2 -0.1 0.0 0.0 0.1 1.1 56 coefspc[8,4] 0.2 0.1 0.1 0.1 0.2 0.2 0.3 1.0 1200 coefspc[8,5] -0.1 0.0 -0.1 -0.1 -0.1 0.0 0.0 1.0 390 coefspc[9,1] -0.1 0.2 -0.5 -0.3 -0.1 0.0 0.3 1.3 10 coefspc[9,2] 0.0 0.1 -0.2 0.0 0.0 0.1 0.2 1.1 48 coefspc[9,3] -0.3 0.1 -0.5 -0.4 -0.3 -0.3 -0.1 1.0 74 coefspc[9,4] 0.0 0.1 -0.1 0.0 0.0 0.1 0.1 1.0 1200 coefspc[9,5] 0.0 0.0 -0.1 0.0 0.0 0.0 0.1 1.0 160 coefspc[10,1] 0.2 0.2 -0.2 0.0 0.2 0.3 0.6 1.3 10 coefspc[10,2] 0.3 0.1 0.1 0.3 0.3 0.4 0.6 1.1 48 coefspc[10,3] -0.2 0.1 -0.4 -0.3 -0.2 -0.1 0.0 1.0 190 coefspc[10,4] -0.1 0.1 -0.2 -0.2 -0.1 -0.1 0.0 1.0 1200 coefspc[10,5] 0.0 0.0 0.0 0.0 0.0 0.1 0.1 1.0 430 coefspc[11,1] -0.6 0.2 -1.0 -0.7 -0.6 -0.4 -0.1 1.2 14 coefspc[11,2] 0.3 0.1 0.1 0.2 0.3 0.4 0.5 1.1 46 coefspc[11,3] -0.1 0.1 -0.3 -0.1 -0.1 0.0 0.2 1.0 220 coefspc[11,4] -0.2 0.1 -0.3 -0.2 -0.2 -0.1 -0.1 1.0 1200 coefspc[11,5] 0.0 0.0 -0.1 0.0 0.0 0.0 0.1 1.0 120 coefspc[12,1] -0.1 0.2 -0.5 -0.3 -0.1 0.0 0.3 1.3 10 coefspc[12,2] -0.2 0.1 -0.4 -0.2 -0.2 -0.1 0.0 1.1 44 coefspc[12,3] 0.3 0.1 0.1 0.2 0.3 0.3 0.5 1.0 110 coefspc[12,4] 0.0 0.1 -0.1 -0.1 0.0 0.0 0.1 1.0 1200 coefspc[12,5] -0.1 0.0 -0.1 -0.1 0.0 0.0 0.0 1.0 1200 coefspc[13,1] 0.2 0.2 -0.2 0.0 0.2 0.3 0.7 1.2 17 coefspc[13,2] -0.1 0.2 -0.3 -0.2 -0.1 0.0 0.3 1.3 14 coefspc[13,3] 0.1 0.1 0.0 0.1 0.1 0.2 0.3 1.1 31 coefspc[13,4] -0.2 0.1 -0.3 -0.2 -0.2 -0.1 0.0 1.0 210 coefspc[13,5] 0.0 0.1 -0.1 0.0 0.0 0.0 0.1 1.0 50 tau.coefspc[1] 2.6 1.2 0.9 1.8 2.5 3.3 5.3 1.0 190 tau.coefspc[2] 20.0 9.7 6.3 13.2 18.1 25.0 43.5 1.0 60 tau.coefspc[3] 25.4 12.2 8.9 16.7 23.1 31.1 57.9 1.0 1100 tau.coefspc[4] 57.9 25.3 19.3 39.1 54.2 71.7 115.6 1.0 1200 tau.coefspc[5] 268.0 125.9 83.0 177.6 244.9 336.1 564.6 1.0 1200 tau.werr 29.0 2.7 24.0 27.2 28.8 30.6 34.7 1.0 170 tau.herr 1.0 0.2 0.5 0.8 0.9 1.1 1.5 1.0 78 tau.aerr 68.3 22.6 44.6 55.8 63.5 74.1 125.8 1.1 43 deviance -1180.0 116.0 -1438.0 -1243.0 -1172.0 -1107.8 -972.9 1.1 57 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 = 771.7 and DIC = -408.3 (using the rule, pD = Dbar-Dhat) DIC is an estimate of expected predictive error (lower deviance is better).