Inference for Bugs model at "/home/kubo/statyaku2007/winbugs/model.bug", fit using winbugs, 3 chains, each with 1000 iterations (first 500 discarded) n.sims = 1500 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff const -0.6 0.4 -1.5 -0.9 -0.6 -0.4 0.1 1.0 96 light -1.7 0.4 -2.4 -2.0 -1.7 -1.5 -1.0 1.0 190 const.spc[1] -0.1 0.7 -1.3 -0.5 -0.1 0.4 1.3 1.0 1500 const.spc[2] -0.6 0.8 -2.1 -1.1 -0.6 -0.1 0.9 1.0 1100 const.spc[3] 0.6 0.6 -0.6 0.2 0.6 1.0 1.8 1.0 670 const.spc[4] 0.0 1.0 -2.1 -0.7 0.0 0.7 1.9 1.0 1500 const.spc[5] 0.9 0.7 -0.3 0.5 0.8 1.3 2.2 1.0 380 const.spc[6] -1.1 0.7 -2.6 -1.6 -1.1 -0.6 0.3 1.0 1100 const.spc[7] -0.4 1.0 -2.5 -1.1 -0.4 0.2 1.4 1.0 1500 const.spc[8] -0.5 0.7 -1.8 -0.9 -0.5 -0.1 0.8 1.0 1500 const.spc[9] 1.8 0.8 0.2 1.2 1.8 2.3 3.5 1.0 160 const.spc[10] -1.0 0.8 -2.7 -1.5 -0.9 -0.4 0.5 1.0 510 const.spc[11] 0.9 0.7 -0.4 0.4 0.9 1.4 2.3 1.0 150 const.spc[12] -0.3 0.6 -1.5 -0.7 -0.3 0.1 0.9 1.0 770 const.spc[13] -0.3 0.6 -1.6 -0.8 -0.4 0.1 0.9 1.0 1300 const.spc[14] -2.6 1.1 -5.1 -3.3 -2.6 -1.9 -0.8 1.0 610 const.spc[15] -0.5 0.7 -1.8 -1.0 -0.5 -0.1 0.7 1.0 270 const.spc[16] 0.1 0.6 -1.1 -0.3 0.1 0.5 1.3 1.0 1500 const.spc[17] -1.4 0.8 -3.0 -1.9 -1.4 -0.9 0.0 1.0 860 const.spc[18] 2.1 1.4 -0.2 1.1 2.0 2.9 5.2 1.0 740 const.spc[19] 1.7 0.9 -0.1 1.0 1.7 2.3 3.5 1.0 1200 const.spc[20] -2.7 1.1 -5.0 -3.4 -2.6 -2.0 -0.9 1.0 810 const.spc[21] 1.5 0.6 0.4 1.1 1.5 1.9 2.7 1.0 610 const.spc[22] 1.7 0.6 0.6 1.3 1.6 2.0 3.0 1.0 260 light.spc[1] 0.4 0.6 -0.4 0.0 0.2 0.6 1.9 1.0 110 light.spc[2] 0.1 0.5 -0.7 -0.1 0.1 0.4 1.3 1.0 260 light.spc[3] -0.1 0.4 -1.2 -0.3 -0.1 0.1 0.7 1.0 1500 light.spc[4] 0.0 0.5 -1.0 -0.2 0.0 0.2 0.9 1.0 1500 light.spc[5] 0.0 0.5 -1.0 -0.2 0.0 0.2 1.0 1.0 1500 light.spc[6] -0.1 0.5 -1.4 -0.3 0.0 0.1 0.9 1.0 1500 light.spc[7] -0.1 0.5 -1.3 -0.3 0.0 0.2 0.9 1.0 450 light.spc[8] 0.0 0.4 -1.0 -0.2 0.0 0.2 0.9 1.0 1400 light.spc[9] 0.0 0.5 -1.1 -0.2 0.0 0.2 1.0 1.0 260 light.spc[10] 0.0 0.5 -1.2 -0.2 0.0 0.2 1.2 1.0 1500 light.spc[11] 0.0 0.5 -1.2 -0.2 0.0 0.2 1.1 1.0 1500 light.spc[12] 0.0 0.5 -1.0 -0.2 0.0 0.2 1.0 1.0 1500 light.spc[13] -0.2 0.5 -1.6 -0.4 -0.1 0.1 0.6 1.0 1500 light.spc[14] -0.1 0.5 -1.2 -0.2 0.0 0.2 0.9 1.0 1200 light.spc[15] 0.1 0.5 -0.9 -0.1 0.1 0.3 1.2 1.0 380 light.spc[16] -0.2 0.5 -1.4 -0.4 -0.1 0.1 0.5 1.0 430 light.spc[17] -0.1 0.5 -1.4 -0.3 -0.1 0.1 0.6 1.0 750 light.spc[18] 0.2 0.6 -0.7 -0.1 0.1 0.4 1.8 1.0 810 light.spc[19] 0.2 0.6 -0.7 -0.1 0.1 0.4 1.6 1.0 250 light.spc[20] 0.0 0.5 -1.1 -0.2 0.0 0.2 1.0 1.0 1500 light.spc[21] -0.1 0.4 -1.1 -0.3 0.0 0.1 0.7 1.0 150 light.spc[22] 0.1 0.4 -0.6 -0.1 0.1 0.3 1.1 1.0 290 tau.const.spc 0.5 0.3 0.2 0.3 0.4 0.6 1.2 1.0 590 tau.const.tree 11.1 20.3 0.7 1.7 3.1 9.0 81.0 1.2 14 deviance 462.1 14.5 432.4 452.3 462.9 472.5 487.7 1.2 19 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 = 37.7 and DIC = 499.7 (using the rule, pD = Dbar-Dhat) DIC is an estimate of expected predictive error (lower deviance is better).