Inference for Bugs model at "/home/kubo/nina/model2008/model.bug.txt", fit using WinBUGS, 3 chains, each with 40000 iterations (first 20000 discarded), n.thin = 100 n.sims = 600 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff wf[1] 169.217 73.149 75.629 134.625 160.700 190.925 328.822 1.006 600 wf[2] 244.064 131.329 72.366 159.225 215.300 295.925 557.717 1.016 600 wf[3] 253.005 107.491 122.292 185.500 222.600 287.675 537.713 1.014 280 wf[4] 717.947 224.523 309.627 567.775 702.600 859.300 1170.025 1.051 79 wf[5] 1129.025 304.430 517.771 933.325 1133.000 1318.999 1756.050 1.078 62 wf[6] 1754.850 431.677 840.562 1490.745 1766.500 2037.999 2533.225 1.067 48 wf[7] 1875.032 522.999 934.232 1558.500 1828.000 2110.496 3066.548 1.045 77 wf[8] 5.795 2.035 2.925 4.651 5.480 6.548 10.348 1.016 140 wf[9] 14.180 4.221 7.361 11.640 13.685 16.340 23.897 1.004 600 wf[10] 8.273 4.028 4.398 5.723 7.076 9.561 19.422 1.040 83 wf[11] 32.749 10.007 16.373 26.645 32.050 37.655 55.982 1.015 190 wf[12] 54.257 15.332 33.699 44.847 51.545 60.422 87.587 1.021 170 wf[13] 73.405 22.309 40.714 59.370 68.600 81.910 130.445 1.022 120 wf[14] 357.627 115.109 113.759 287.900 364.350 428.200 592.517 1.049 59 wf[15] 9.367 2.741 4.482 7.749 9.082 10.765 15.394 1.004 600 wf[16] 3.964 1.498 2.016 3.003 3.537 4.554 8.087 1.300 11 wf[17] 12.547 4.328 6.698 9.895 11.600 14.090 23.623 1.091 32 wf[18] 33.157 10.888 19.309 26.592 30.885 36.805 65.337 1.010 210 wf[19] 78.638 20.182 41.090 64.195 77.435 91.162 119.500 1.012 200 wf[20] 123.117 33.139 62.210 102.725 121.300 139.350 199.617 1.020 300 wf[21] 159.955 46.877 78.278 130.875 155.450 181.449 270.512 1.011 280 wf[22] 2.735 1.199 1.241 2.054 2.474 3.065 6.034 1.027 160 wf[23] 13.553 4.436 6.490 10.762 13.065 15.700 24.571 1.026 78 wf[24] 16.631 4.708 8.958 13.775 16.275 18.832 28.467 1.011 160 wf[25] 42.289 11.409 25.025 35.007 40.070 47.162 71.644 1.019 190 wf[26] 63.910 18.192 39.374 51.960 59.615 70.387 110.507 1.014 600 wf[27] 106.652 43.337 48.155 76.225 96.730 130.850 218.307 1.025 86 wf[28] 143.360 41.758 74.663 116.875 140.450 164.425 232.755 1.019 230 log.wf[1] 5.065 0.358 4.325 4.902 5.079 5.252 5.795 1.006 600 log.wf[2] 5.370 0.511 4.282 5.070 5.372 5.690 6.324 1.018 560 log.wf[3] 5.462 0.366 4.807 5.223 5.405 5.662 6.287 1.012 280 log.wf[4] 6.525 0.331 5.736 6.341 6.555 6.756 7.064 1.060 74 log.wf[5] 6.987 0.307 6.249 6.839 7.032 7.185 7.471 1.088 59 log.wf[6] 7.434 0.284 6.734 7.307 7.476 7.619 7.837 1.073 48 log.wf[7] 7.498 0.285 6.840 7.352 7.511 7.655 8.028 1.052 74 log.wf[8] 1.708 0.309 1.073 1.537 1.701 1.879 2.337 1.011 170 log.wf[9] 2.610 0.292 1.996 2.455 2.616 2.794 3.173 1.004 600 log.wf[10] 2.028 0.388 1.481 1.745 1.957 2.257 2.967 1.030 86 log.wf[11] 3.446 0.291 2.796 3.282 3.467 3.628 4.025 1.010 220 log.wf[12] 3.961 0.250 3.517 3.803 3.942 4.101 4.472 1.018 180 log.wf[13] 4.256 0.278 3.707 4.084 4.228 4.406 4.871 1.019 120 log.wf[14] 5.816 0.383 4.735 5.663 5.898 6.060 6.384 1.053 59 log.wf[15] 2.194 0.301 1.500 2.048 2.206 2.376 2.734 1.003 600 log.wf[16] 1.316 0.341 0.701 1.100 1.263 1.516 2.090 1.304 11 log.wf[17] 2.480 0.306 1.902 2.292 2.451 2.646 3.163 1.090 32 log.wf[18] 3.458 0.285 2.961 3.281 3.430 3.605 4.179 1.008 210 log.wf[19] 4.330 0.269 3.716 4.162 4.349 4.512 4.784 1.014 190 log.wf[20] 4.777 0.275 4.130 4.632 4.798 4.937 5.296 1.023 300 log.wf[21] 5.034 0.289 4.361 4.874 5.046 5.201 5.601 1.011 270 log.wf[22] 0.934 0.364 0.216 0.720 0.906 1.120 1.797 1.027 160 log.wf[23] 2.555 0.325 1.871 2.376 2.570 2.754 3.202 1.023 85 log.wf[24] 2.773 0.280 2.193 2.623 2.790 2.936 3.348 1.011 170 log.wf[25] 3.712 0.250 3.220 3.556 3.690 3.853 4.271 1.016 200 log.wf[26] 4.123 0.256 3.673 3.951 4.087 4.254 4.705 1.012 600 log.wf[27] 4.593 0.390 3.875 4.334 4.572 4.873 5.386 1.023 89 log.wf[28] 4.923 0.294 4.313 4.761 4.945 5.103 5.450 1.021 210 log.d.wf[1] 4.990 0.604 3.667 4.615 5.036 5.395 6.002 1.002 530 log.d.wf[2] 3.030 1.881 -0.978 1.780 3.172 4.489 5.959 1.011 410 log.d.wf[3] 2.672 1.756 -1.084 1.489 2.761 3.991 5.461 1.012 150 log.d.wf[4] 4.909 1.879 0.271 3.958 5.611 6.224 6.989 1.009 290 log.d.wf[5] 3.348 2.065 -0.828 1.853 3.437 5.017 6.692 1.001 600 log.d.wf[6] 2.497 1.726 -1.015 1.348 2.584 3.612 5.787 1.008 220 log.d.wf[7] 2.349 1.656 -1.083 1.233 2.407 3.492 5.429 1.000 600 log.d.wf[8] 1.518 0.543 0.358 1.192 1.515 1.871 2.510 1.009 600 log.d.wf[9] 0.955 1.171 -1.926 0.441 1.180 1.716 2.642 1.008 250 log.d.wf[10] 0.087 1.213 -2.613 -0.657 0.236 0.960 2.196 1.006 350 log.d.wf[11] 2.844 0.709 1.097 2.514 2.916 3.287 4.037 1.000 600 log.d.wf[12] 1.716 1.327 -1.328 1.055 1.905 2.657 3.599 1.001 600 log.d.wf[13] 1.838 1.461 -1.413 0.996 1.980 2.952 4.032 1.006 310 log.d.wf[14] 5.272 0.862 2.758 4.966 5.460 5.817 6.384 1.046 88 log.d.wf[15] 1.751 0.533 0.741 1.377 1.748 2.114 2.791 1.009 210 log.d.wf[16] 2.119 0.798 0.243 1.746 2.212 2.615 3.338 1.142 20 log.d.wf[17] 1.738 0.985 -0.561 1.295 1.880 2.371 3.142 1.007 370 log.d.wf[18] 2.227 1.207 -0.956 1.745 2.417 3.026 3.893 1.012 520 log.d.wf[19] 3.345 1.177 0.151 2.901 3.608 4.094 4.848 1.016 180 log.d.wf[20] 2.437 1.549 -1.005 1.524 2.648 3.576 4.784 1.003 540 log.d.wf[21] 2.247 1.692 -1.577 1.227 2.493 3.407 4.903 0.999 600 log.d.wf[22] 0.731 0.506 -0.211 0.376 0.700 1.045 1.752 1.006 600 log.d.wf[23] 2.004 0.710 0.407 1.626 2.072 2.468 3.165 1.007 500 log.d.wf[24] 3.317 0.558 2.147 2.984 3.329 3.677 4.362 1.006 310 log.d.wf[25] 2.358 1.238 -0.774 1.725 2.611 3.244 4.147 1.008 600 log.d.wf[26] 1.934 1.463 -1.129 1.051 2.181 2.894 4.198 1.018 200 log.d.wf[27] 2.608 1.525 -0.847 1.708 2.782 3.730 5.006 1.016 130 log.d.wf[28] 2.629 1.602 -0.918 1.748 2.852 3.780 5.013 1.020 130 mean.log.d.wf 2.503 0.480 1.538 2.209 2.522 2.793 3.419 1.008 280 log.ion[1,1] 3.670 0.646 2.300 3.273 3.754 4.125 4.730 1.021 110 log.ion[1,2] 1.003 1.628 -2.521 0.016 1.294 2.159 3.532 1.052 44 log.ion[2,1] 4.088 0.522 2.909 3.770 4.115 4.415 5.039 1.018 540 log.ion[2,2] 1.818 1.529 -1.569 0.969 2.191 2.902 3.882 1.050 53 log.ion[3,1] 4.472 0.387 3.825 4.213 4.422 4.728 5.324 1.014 220 log.ion[3,2] 2.534 1.003 -0.293 2.164 2.716 3.156 3.917 1.022 160 log.ion[4,1] 5.457 0.352 4.610 5.258 5.496 5.702 6.056 1.040 95 log.ion[4,2] 3.602 0.816 1.321 3.270 3.752 4.110 4.681 1.018 600 log.ion[5,1] 5.774 0.320 4.943 5.638 5.815 5.983 6.283 1.070 71 log.ion[5,2] 3.759 0.813 1.689 3.385 3.899 4.315 4.857 1.004 600 log.ion[6,1] 5.839 0.307 5.103 5.691 5.871 6.040 6.308 1.065 62 log.ion[6,2] 3.767 0.894 1.559 3.402 3.941 4.368 4.961 1.005 600 log.ion[7,1] 5.852 0.304 5.114 5.707 5.866 6.049 6.343 1.052 74 log.ion[7,2] 3.776 0.917 1.529 3.335 3.970 4.430 5.019 1.006 370 log.ion[8,1] -0.168 0.577 -1.376 -0.517 -0.131 0.199 0.890 1.006 480 log.ion[8,2] -4.746 2.078 -9.817 -5.903 -4.330 -3.229 -1.695 1.029 92 log.ion[9,1] 0.497 0.360 -0.202 0.264 0.484 0.729 1.236 1.023 100 log.ion[9,2] -3.771 1.933 -8.704 -4.774 -3.325 -2.401 -0.970 1.029 100 log.ion[10,1] 0.678 0.393 0.087 0.410 0.602 0.900 1.610 1.037 110 log.ion[10,2] -2.985 1.892 -7.895 -3.928 -2.537 -1.748 -0.436 1.030 140 log.ion[11,1] 2.071 0.318 1.450 1.875 2.078 2.269 2.651 1.010 180 log.ion[11,2] -1.297 1.690 -5.855 -1.945 -0.810 -0.222 0.712 1.040 180 log.ion[12,1] 2.342 0.273 1.847 2.170 2.339 2.514 2.885 1.010 250 log.ion[12,2] -1.107 1.706 -5.797 -1.726 -0.640 0.009 0.988 1.018 300 log.ion[13,1] 2.631 0.314 2.068 2.415 2.607 2.819 3.300 1.024 86 log.ion[13,2] -0.734 1.747 -5.419 -1.468 -0.322 0.420 1.471 1.016 410 log.ion[14,1] 4.401 0.424 3.319 4.204 4.444 4.692 5.054 1.027 90 log.ion[14,2] 0.951 1.782 -3.371 0.122 1.334 2.190 3.327 1.022 540 log.ion[15,1] 0.357 0.389 -0.438 0.116 0.371 0.628 1.071 1.006 290 log.ion[15,2] -5.453 3.146 -14.471 -6.617 -4.739 -3.462 -1.827 1.098 94 log.ion[16,1] 1.740 0.341 1.086 1.507 1.691 1.941 2.525 1.302 11 log.ion[16,2] -4.141 2.859 -10.901 -4.794 -3.341 -2.511 -1.350 1.133 110 log.ion[17,1] 2.180 0.310 1.592 1.980 2.149 2.356 2.874 1.095 30 log.ion[17,2] -2.836 2.810 -9.162 -3.446 -2.111 -1.288 -0.135 1.160 45 log.ion[18,1] 2.680 0.290 2.162 2.505 2.651 2.841 3.391 1.007 220 log.ion[18,2] -2.087 2.845 -8.434 -2.919 -1.386 -0.452 0.722 1.159 41 log.ion[19,1] 3.511 0.281 2.908 3.341 3.534 3.697 4.001 1.013 180 log.ion[19,2] -1.563 2.809 -7.972 -2.252 -0.868 0.045 1.270 1.153 41 log.ion[20,1] 3.797 0.281 3.157 3.630 3.813 3.966 4.364 1.021 340 log.ion[20,2] -1.208 2.823 -7.292 -1.977 -0.535 0.385 1.653 1.148 46 log.ion[21,1] 4.000 0.416 3.262 3.749 3.988 4.253 4.786 1.002 600 log.ion[21,2] -0.910 2.869 -7.231 -1.796 -0.279 0.728 2.360 1.135 54 log.ion[22,1] -0.783 0.433 -1.642 -1.045 -0.798 -0.549 0.114 1.017 310 log.ion[22,2] -3.294 1.154 -6.003 -4.004 -3.106 -2.444 -1.574 1.025 120 log.ion[23,1] 0.967 0.397 0.152 0.726 0.966 1.231 1.766 1.018 110 log.ion[23,2] -1.090 0.899 -3.193 -1.618 -0.968 -0.462 0.365 1.039 53 log.ion[24,1] 2.605 0.284 1.997 2.425 2.625 2.782 3.182 1.011 170 log.ion[24,2] 2.506 0.284 1.927 2.348 2.519 2.672 3.088 1.013 140 log.ion[25,1] 3.046 0.263 2.536 2.877 3.030 3.201 3.623 1.013 190 log.ion[25,2] 2.435 0.277 1.914 2.255 2.418 2.599 3.066 1.010 270 log.ion[26,1] 3.269 0.267 2.801 3.099 3.242 3.412 3.863 1.008 440 log.ion[26,2] 2.358 0.337 1.633 2.147 2.351 2.576 3.021 1.000 600 log.ion[27,1] 3.642 0.377 2.932 3.386 3.622 3.914 4.333 1.028 74 log.ion[27,2] 2.496 0.472 1.458 2.194 2.513 2.816 3.342 1.009 230 log.ion[28,1] 3.944 0.300 3.319 3.750 3.960 4.143 4.515 1.022 170 log.ion[28,2] 2.862 0.404 1.984 2.618 2.861 3.133 3.572 1.009 340 alpha[1] -1.030 0.573 -2.020 -1.310 -1.024 -0.709 -0.078 1.065 79 alpha[2] -2.604 2.918 -7.765 -4.376 -3.050 -0.942 4.770 1.133 30 beta1[1,1] -0.451 0.701 -1.708 -0.861 -0.407 -0.080 0.667 1.047 120 beta1[1,2] -0.126 1.114 -2.146 -0.392 -0.034 0.275 1.653 1.055 600 beta1[1,3] 0.160 1.420 -2.025 -0.223 0.061 0.516 2.932 1.103 600 beta1[1,4] 0.372 1.653 -1.615 -0.191 0.119 0.593 3.810 1.165 87 beta1[1,5] 0.240 1.467 -2.361 -0.236 0.082 0.581 3.214 1.081 600 beta1[1,6] 0.121 1.527 -2.438 -0.277 0.062 0.454 2.451 1.136 350 beta1[2,1] -1.839 3.341 -7.799 -3.451 -1.680 -0.144 6.672 1.141 400 beta1[2,2] -6.329 11.787 -47.331 -6.665 -1.456 0.093 2.853 1.250 20 beta1[2,3] 0.340 8.213 -20.261 -1.155 0.107 2.394 22.702 1.055 390 beta1[2,4] -2.339 7.215 -20.266 -4.021 -0.148 0.820 8.706 1.117 35 beta1[2,5] 4.272 11.218 -7.437 -0.227 0.682 4.179 39.309 1.245 27 beta1[2,6] 3.577 13.343 -10.993 -0.788 0.383 3.659 43.625 1.236 42 beta2[1,1] 0.120 0.332 -0.511 -0.077 0.076 0.268 0.903 1.010 600 beta2[1,2] -0.185 0.319 -0.955 -0.337 -0.126 -0.004 0.373 1.010 600 beta2[1,3] 0.110 0.293 -0.445 -0.049 0.067 0.233 0.814 1.000 600 beta2[1,4] -0.028 0.295 -0.700 -0.162 -0.011 0.134 0.558 1.005 600 beta2[2,1] 0.894 2.057 -3.455 -0.022 0.765 1.779 5.817 1.114 23 beta2[2,2] -1.204 2.300 -7.524 -1.722 -0.685 -0.003 1.463 1.185 37 beta2[2,3] -2.148 2.686 -9.687 -2.639 -1.332 -0.501 0.583 1.001 600 beta2[2,4] 1.226 1.987 -3.335 0.286 1.112 2.054 5.680 1.105 26 tau.beta[1,1] 13.988 38.479 0.056 0.728 2.603 10.672 95.905 1.009 320 tau.beta[1,2] 33.155 55.196 0.660 4.590 13.345 38.497 181.467 1.003 600 tau.beta[2,1] 4.438 20.326 0.001 0.014 0.079 0.768 37.533 1.084 28 tau.beta[2,2] 3.755 17.038 0.009 0.104 0.328 1.064 37.175 1.000 600 tau.li[1] 53.738 64.833 4.837 14.317 28.125 67.507 250.941 1.012 160 tau.li[2] 11.056 20.530 0.554 1.856 4.495 11.317 65.472 1.040 52 tau[1] 3.703 1.446 1.657 2.702 3.435 4.457 7.263 1.005 290 tau[2] 0.368 0.182 0.136 0.241 0.336 0.454 0.816 1.014 170 tau[3] 626.597 218.113 295.067 475.799 595.400 745.800 1170.075 1.015 130 tau[4] 21.333 31.239 2.031 5.589 11.675 22.307 113.430 1.019 110 deviance -184.855 29.885 -242.025 -204.750 -184.700 -164.600 -127.890 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 = 57.1 and DIC = -127.7 DIC is an estimate of expected predictive error (lower deviance is better).