Inference for Bugs model at "/home/kubo/tanabe/yaku/winbugs/model.bug.txt", fit using WinBUGS, 3 chains, each with 9000 iterations (first 3000 discarded), n.thin = 30 n.sims = 600 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff coefbase[1] 0.735 0.109 0.531 0.666 0.737 0.802 0.935 1.016 130 coefbase[2] 0.189 0.110 -0.040 0.121 0.187 0.261 0.420 1.023 180 coefbase[3] -1.921 0.121 -2.189 -1.997 -1.930 -1.838 -1.687 0.999 600 coefbase[4] -1.968 0.186 -2.340 -2.080 -1.971 -1.830 -1.619 1.080 30 coefbase[5] 6.414 0.181 6.041 6.294 6.418 6.538 6.760 1.017 220 coefbase[6] 0.428 0.089 0.246 0.374 0.430 0.484 0.609 0.999 600 coefbase[7] 0.781 0.158 0.463 0.674 0.775 0.897 1.086 1.045 51 coefbase[8] -0.110 0.032 -0.177 -0.131 -0.110 -0.088 -0.049 1.004 510 coefbase[9] 0.008 0.039 -0.065 -0.020 0.007 0.034 0.088 0.999 600 coefbase[10] -0.588 0.038 -0.662 -0.614 -0.588 -0.563 -0.512 1.002 550 coefbase[11] -0.830 0.035 -0.897 -0.852 -0.830 -0.810 -0.762 1.007 240 coefspc[1,1] -0.478 0.136 -0.733 -0.565 -0.482 -0.382 -0.204 1.014 130 coefspc[1,2] -0.288 0.132 -0.547 -0.371 -0.288 -0.203 -0.050 1.021 100 coefspc[1,3] -0.105 0.243 -0.615 -0.263 -0.089 0.050 0.392 1.009 200 coefspc[1,4] 0.379 0.209 -0.041 0.241 0.383 0.518 0.798 1.002 600 coefspc[1,5] -0.407 0.186 -0.786 -0.523 -0.404 -0.287 -0.037 1.013 160 coefspc[1,6] 0.074 0.185 -0.299 -0.043 0.075 0.194 0.447 1.006 600 coefspc[1,7] 0.324 0.132 0.075 0.242 0.327 0.411 0.589 1.018 120 coefspc[1,8] 0.340 0.188 -0.003 0.199 0.345 0.470 0.691 1.002 600 coefspc[1,9] 0.460 0.220 0.036 0.307 0.454 0.599 0.906 1.006 450 coefspc[1,10] -0.125 0.191 -0.524 -0.251 -0.110 -0.001 0.241 1.002 600 coefspc[1,11] -0.206 0.138 -0.499 -0.296 -0.205 -0.118 0.054 1.015 130 coefspc[1,12] 0.355 0.206 -0.054 0.224 0.361 0.498 0.724 1.003 600 coefspc[1,13] 0.141 0.133 -0.120 0.053 0.140 0.224 0.423 1.006 360 coefspc[1,14] 0.090 0.188 -0.286 -0.039 0.090 0.214 0.465 1.002 600 coefspc[1,15] -0.888 0.136 -1.148 -0.981 -0.888 -0.801 -0.621 1.012 150 coefspc[1,16] 0.068 0.213 -0.315 -0.072 0.053 0.220 0.489 1.004 370 coefspc[1,17] 0.322 0.207 -0.056 0.182 0.323 0.471 0.694 1.000 600 coefspc[2,1] -0.380 0.145 -0.681 -0.473 -0.374 -0.287 -0.080 1.025 99 coefspc[2,2] 0.156 0.152 -0.135 0.056 0.156 0.253 0.439 1.005 600 coefspc[2,3] -0.078 0.268 -0.661 -0.254 -0.080 0.107 0.441 1.002 600 coefspc[2,4] -0.139 0.211 -0.559 -0.275 -0.131 0.000 0.264 1.003 600 coefspc[2,5] 0.130 0.195 -0.255 -0.004 0.128 0.268 0.522 1.000 600 coefspc[2,6] -0.021 0.187 -0.388 -0.135 -0.027 0.105 0.360 1.004 370 coefspc[2,7] 0.653 0.139 0.364 0.571 0.654 0.742 0.930 1.006 600 coefspc[2,8] 0.252 0.195 -0.094 0.105 0.258 0.375 0.665 1.001 600 coefspc[2,9] -0.076 0.227 -0.512 -0.223 -0.084 0.062 0.419 1.003 460 coefspc[2,10] -0.258 0.200 -0.646 -0.383 -0.252 -0.129 0.129 1.001 600 coefspc[2,11] 0.126 0.142 -0.138 0.036 0.127 0.208 0.441 1.009 250 coefspc[2,12] -0.060 0.217 -0.484 -0.198 -0.064 0.082 0.363 1.001 600 coefspc[2,13] 0.204 0.135 -0.065 0.119 0.206 0.289 0.485 1.014 190 coefspc[2,14] -0.165 0.202 -0.542 -0.303 -0.171 -0.030 0.242 1.013 150 coefspc[2,15] -0.946 0.150 -1.259 -1.042 -0.940 -0.845 -0.660 1.013 180 coefspc[2,16] 0.258 0.216 -0.178 0.108 0.257 0.390 0.656 1.009 200 coefspc[2,17] 0.360 0.214 -0.028 0.217 0.354 0.504 0.777 1.008 220 coefspc[3,1] -0.233 0.153 -0.521 -0.341 -0.234 -0.129 0.064 1.000 600 coefspc[3,2] -0.882 0.189 -1.253 -1.008 -0.884 -0.747 -0.538 1.001 600 coefspc[3,3] 0.187 0.268 -0.345 0.020 0.181 0.367 0.711 1.001 600 coefspc[3,4] 0.353 0.221 -0.045 0.203 0.334 0.490 0.829 1.003 430 coefspc[3,5] -0.025 0.217 -0.466 -0.163 -0.027 0.115 0.390 1.001 600 coefspc[3,6] 0.248 0.199 -0.137 0.109 0.248 0.376 0.655 0.999 600 coefspc[3,7] -0.378 0.158 -0.701 -0.490 -0.370 -0.274 -0.067 0.999 600 coefspc[3,8] -0.099 0.211 -0.513 -0.224 -0.110 0.044 0.320 1.004 350 coefspc[3,9] 0.160 0.231 -0.312 0.020 0.156 0.303 0.625 1.005 590 coefspc[3,10] 0.096 0.221 -0.331 -0.050 0.090 0.230 0.564 1.000 600 coefspc[3,11] 0.049 0.150 -0.236 -0.052 0.047 0.150 0.341 1.001 600 coefspc[3,12] 0.226 0.226 -0.209 0.072 0.228 0.386 0.658 1.007 250 coefspc[3,13] 0.046 0.152 -0.260 -0.053 0.044 0.143 0.345 0.999 600 coefspc[3,14] 0.008 0.207 -0.405 -0.128 0.002 0.148 0.412 1.001 600 coefspc[3,15] 0.263 0.162 -0.035 0.150 0.267 0.368 0.576 1.004 600 coefspc[3,16] 0.173 0.236 -0.258 0.015 0.172 0.315 0.674 1.005 600 coefspc[3,17] -0.109 0.216 -0.528 -0.254 -0.116 0.051 0.318 0.999 600 coefspc[4,1] -0.227 0.223 -0.638 -0.373 -0.224 -0.072 0.205 1.053 41 coefspc[4,2] -1.192 0.223 -1.620 -1.354 -1.183 -1.053 -0.758 1.060 37 coefspc[4,3] 1.333 0.454 0.496 1.023 1.330 1.649 2.246 1.056 61 coefspc[4,4] -0.588 0.343 -1.222 -0.821 -0.602 -0.351 0.130 1.008 210 coefspc[4,5] 1.690 0.313 1.067 1.473 1.706 1.899 2.240 1.037 66 coefspc[4,6] 0.268 0.290 -0.304 0.070 0.284 0.460 0.884 1.018 160 coefspc[4,7] -0.758 0.214 -1.172 -0.896 -0.765 -0.622 -0.298 1.070 34 coefspc[4,8] 0.162 0.305 -0.442 -0.046 0.175 0.361 0.749 1.018 120 coefspc[4,9] -0.181 0.334 -0.809 -0.421 -0.180 0.042 0.464 1.023 87 coefspc[4,10] -0.141 0.314 -0.742 -0.345 -0.148 0.072 0.527 1.019 110 coefspc[4,11] 0.071 0.213 -0.305 -0.086 0.063 0.206 0.487 1.043 54 coefspc[4,12] 0.926 0.346 0.275 0.683 0.912 1.153 1.621 1.023 160 coefspc[4,13] -1.373 0.211 -1.785 -1.517 -1.382 -1.235 -0.930 1.045 49 coefspc[4,14] 1.556 0.329 0.911 1.341 1.528 1.764 2.220 1.030 76 coefspc[4,15] -0.394 0.218 -0.815 -0.547 -0.407 -0.253 0.057 1.045 53 coefspc[4,16] -0.380 0.334 -1.031 -0.609 -0.380 -0.151 0.253 1.013 300 coefspc[4,17] 0.287 0.353 -0.380 0.048 0.289 0.522 0.983 1.011 160 coefspc[5,1] 1.117 0.238 0.673 0.948 1.104 1.277 1.598 1.015 140 coefspc[5,2] -1.503 0.234 -1.971 -1.653 -1.505 -1.343 -1.058 1.007 600 coefspc[5,3] 0.107 0.456 -0.764 -0.202 0.111 0.405 0.951 1.003 600 coefspc[5,4] 0.483 0.382 -0.229 0.233 0.467 0.754 1.233 1.001 600 coefspc[5,5] 0.174 0.344 -0.496 -0.058 0.189 0.392 0.847 1.000 600 coefspc[5,6] 0.385 0.325 -0.276 0.156 0.386 0.602 1.024 1.010 600 coefspc[5,7] -0.311 0.228 -0.757 -0.472 -0.316 -0.151 0.154 1.013 180 coefspc[5,8] -0.262 0.339 -0.906 -0.479 -0.244 -0.018 0.340 1.005 340 coefspc[5,9] 0.335 0.361 -0.417 0.102 0.334 0.569 1.019 0.999 600 coefspc[5,10] 0.277 0.343 -0.350 0.044 0.287 0.511 0.974 1.001 600 coefspc[5,11] -0.382 0.230 -0.836 -0.533 -0.377 -0.225 0.072 1.005 450 coefspc[5,12] 0.620 0.381 -0.077 0.338 0.616 0.872 1.395 1.004 370 coefspc[5,13] -0.545 0.238 -0.978 -0.692 -0.550 -0.399 -0.087 1.010 330 coefspc[5,14] 0.069 0.372 -0.638 -0.200 0.076 0.334 0.811 0.999 600 coefspc[5,15] -0.630 0.260 -1.114 -0.802 -0.629 -0.458 -0.121 1.005 600 coefspc[5,16] -0.141 0.389 -0.876 -0.400 -0.140 0.130 0.561 1.011 600 coefspc[5,17] 0.293 0.377 -0.466 0.047 0.280 0.526 1.055 0.999 600 coefspc[6,1] 0.148 0.121 -0.075 0.068 0.146 0.224 0.398 0.999 600 coefspc[6,2] -0.558 0.132 -0.826 -0.639 -0.558 -0.469 -0.288 0.999 600 coefspc[6,3] 0.024 0.206 -0.342 -0.124 0.026 0.145 0.440 1.003 600 coefspc[6,4] 0.266 0.190 -0.073 0.128 0.254 0.399 0.637 1.000 600 coefspc[6,5] -0.069 0.166 -0.424 -0.167 -0.069 0.038 0.244 1.006 260 coefspc[6,6] 0.276 0.174 -0.058 0.152 0.272 0.383 0.624 1.002 600 coefspc[6,7] -0.251 0.121 -0.485 -0.337 -0.252 -0.172 -0.023 1.003 600 coefspc[6,8] -0.162 0.172 -0.531 -0.275 -0.162 -0.045 0.159 1.000 600 coefspc[6,9] 0.059 0.180 -0.269 -0.070 0.053 0.175 0.420 1.007 270 coefspc[6,10] 0.296 0.189 -0.032 0.154 0.281 0.427 0.686 1.007 320 coefspc[6,11] 0.020 0.119 -0.187 -0.062 0.016 0.105 0.261 1.000 600 coefspc[6,12] 0.140 0.179 -0.189 0.012 0.138 0.267 0.504 1.002 570 coefspc[6,13] 0.199 0.113 -0.029 0.124 0.201 0.272 0.410 1.003 600 coefspc[6,14] -0.079 0.174 -0.427 -0.182 -0.082 0.033 0.247 1.003 600 coefspc[6,15] 0.007 0.118 -0.235 -0.064 0.006 0.080 0.240 0.999 600 coefspc[6,16] -0.123 0.189 -0.496 -0.249 -0.127 0.003 0.234 1.000 600 coefspc[6,17] -0.115 0.183 -0.505 -0.231 -0.109 0.020 0.209 1.004 400 coefspc[7,1] -0.411 0.189 -0.788 -0.536 -0.412 -0.284 -0.051 1.025 86 coefspc[7,2] -0.962 0.182 -1.327 -1.083 -0.967 -0.833 -0.614 1.036 60 coefspc[7,3] 0.569 0.313 -0.060 0.359 0.600 0.786 1.172 1.014 170 coefspc[7,4] -0.016 0.301 -0.635 -0.214 -0.009 0.196 0.571 1.005 320 coefspc[7,5] 1.000 0.240 0.506 0.844 0.999 1.174 1.428 1.019 110 coefspc[7,6] 0.053 0.237 -0.373 -0.131 0.062 0.221 0.508 1.006 380 coefspc[7,7] -0.566 0.178 -0.910 -0.677 -0.570 -0.448 -0.233 1.034 63 coefspc[7,8] 0.268 0.240 -0.170 0.097 0.269 0.426 0.769 1.018 100 coefspc[7,9] -0.114 0.279 -0.679 -0.296 -0.106 0.055 0.432 1.020 100 coefspc[7,10] 0.200 0.280 -0.341 0.018 0.189 0.390 0.758 1.005 330 coefspc[7,11] -0.134 0.175 -0.485 -0.257 -0.131 -0.014 0.204 1.031 74 coefspc[7,12] 0.923 0.272 0.339 0.745 0.921 1.101 1.467 1.002 600 coefspc[7,13] -1.018 0.184 -1.399 -1.132 -1.016 -0.896 -0.652 1.030 74 coefspc[7,14] 0.921 0.242 0.452 0.760 0.915 1.070 1.393 1.031 110 coefspc[7,15] -0.673 0.180 -1.008 -0.801 -0.672 -0.553 -0.315 1.035 61 coefspc[7,16] -0.047 0.265 -0.553 -0.227 -0.059 0.129 0.461 1.014 220 coefspc[7,17] 0.142 0.272 -0.408 -0.052 0.148 0.337 0.678 1.010 180 log.conv12[1] -0.276 0.034 -0.336 -0.298 -0.276 -0.254 -0.207 1.003 600 log.conv12[2] -0.448 0.034 -0.518 -0.472 -0.448 -0.425 -0.383 1.014 130 tau.coefspc[1] 6.482 2.784 2.486 4.567 6.093 7.956 12.851 1.007 300 tau.coefspc[2] 7.096 2.976 2.740 4.845 6.745 8.669 13.723 1.008 310 tau.coefspc[3] 8.979 4.554 3.064 5.920 7.966 11.022 21.270 1.010 350 tau.coefspc[4] 1.224 0.484 0.515 0.892 1.127 1.483 2.319 1.004 400 tau.coefspc[5] 2.334 0.922 0.866 1.665 2.247 2.839 4.635 1.005 320 tau.coefspc[6] 16.372 9.305 4.876 10.120 13.835 20.037 41.894 1.000 600 tau.coefspc[7] 2.443 0.999 0.953 1.741 2.282 2.999 4.897 1.000 600 tau.err[1] 8.188 0.575 7.109 7.810 8.188 8.586 9.327 1.002 600 tau.err[2] 7.975 0.342 7.378 7.742 7.967 8.202 8.724 1.000 600 tau.w 1.461 0.155 1.173 1.357 1.452 1.560 1.781 1.003 560 deviance 1458.557 30.325 1404.000 1437.000 1457.500 1477.250 1519.050 1.000 600 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 = Dbar-Dhat) pD = 299.7 and DIC = 1758.3 DIC is an estimate of expected predictive error (lower deviance is better).