Inference for Bugs model at "/home/kubo/public_html/stat/2008/g/fig/model.bug.txt", fit using WinBUGS, 3 chains, each with 700 iterations (first 100 discarded), n.thin = 3 n.sims = 600 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff beta 0.081 0.335 -0.588 -0.143 0.098 0.302 0.709 1.029 89 sigma 2.997 0.367 2.363 2.734 2.963 3.245 3.736 0.999 600 r[1] -3.852 1.649 -7.567 -4.950 -3.596 -2.630 -1.314 1.003 490 r[2] -1.248 0.866 -3.054 -1.782 -1.206 -0.675 0.425 1.014 140 r[3] 1.912 1.091 -0.047 1.150 1.867 2.565 4.258 1.002 600 r[4] 3.852 1.815 1.225 2.438 3.672 5.029 8.209 1.006 440 r[5] -2.084 1.079 -4.589 -2.710 -2.048 -1.304 -0.293 1.001 600 r[6] 1.985 1.116 0.004 1.229 1.888 2.636 4.253 1.012 230 r[7] 3.599 1.769 0.641 2.428 3.381 4.656 7.467 1.000 600 r[8] 3.785 1.729 0.994 2.598 3.575 4.742 7.905 1.008 420 r[9] -2.149 1.099 -4.423 -2.772 -2.067 -1.372 -0.396 1.000 600 r[10] -2.188 1.026 -4.330 -2.892 -2.116 -1.433 -0.374 1.008 440 r[11] -0.061 0.797 -1.600 -0.588 -0.043 0.485 1.543 1.000 600 r[12] -3.822 1.663 -7.616 -4.797 -3.660 -2.605 -1.312 1.002 520 r[13] -2.057 1.046 -4.166 -2.712 -1.968 -1.355 -0.152 1.009 210 r[14] -0.072 0.778 -1.577 -0.580 -0.065 0.412 1.448 1.009 230 r[15] 1.897 1.117 -0.130 1.169 1.858 2.447 4.338 1.006 290 r[16] 3.616 1.795 0.801 2.379 3.428 4.642 8.079 1.005 600 r[17] 2.041 1.085 0.160 1.285 1.984 2.726 4.334 1.002 600 r[18] -3.873 1.706 -7.806 -4.883 -3.678 -2.648 -1.120 1.002 570 r[19] -1.197 0.901 -2.945 -1.811 -1.176 -0.589 0.559 1.015 140 r[20] -1.247 0.908 -3.228 -1.783 -1.216 -0.627 0.356 1.004 600 r[21] -2.092 1.097 -4.690 -2.713 -1.990 -1.350 -0.247 1.001 600 r[22] -2.135 1.107 -4.387 -2.793 -2.024 -1.418 -0.189 1.009 200 r[23] 0.405 0.791 -1.151 -0.105 0.373 0.888 2.047 0.999 600 r[24] 2.029 1.139 0.043 1.246 1.966 2.762 4.393 1.002 600 r[25] 3.698 1.685 0.989 2.500 3.499 4.658 7.425 1.002 600 r[26] -0.591 0.821 -2.216 -1.125 -0.604 -0.039 1.067 1.004 490 r[27] 3.824 1.804 0.981 2.505 3.484 5.025 7.783 1.001 600 r[28] -0.014 0.780 -1.607 -0.518 0.013 0.484 1.586 1.000 600 r[29] 1.076 0.893 -0.581 0.474 1.028 1.619 2.990 0.999 600 r[30] -3.826 1.747 -7.895 -4.800 -3.522 -2.579 -1.163 1.003 600 r[31] 3.783 1.798 1.006 2.469 3.580 4.833 7.955 1.020 170 r[32] 1.958 1.075 0.102 1.210 1.804 2.648 4.249 1.000 600 r[33] 3.764 1.754 0.990 2.522 3.567 4.765 8.051 1.007 250 r[34] -3.784 1.700 -7.587 -4.754 -3.498 -2.577 -1.029 1.005 480 r[35] -1.234 0.859 -3.051 -1.770 -1.180 -0.614 0.291 0.999 600 r[36] 1.126 0.938 -0.616 0.503 1.081 1.739 2.972 1.008 220 r[37] 1.950 1.074 0.089 1.224 1.868 2.564 4.380 1.004 370 r[38] 3.707 1.754 1.019 2.487 3.478 4.662 7.816 1.017 600 r[39] -1.231 0.849 -2.955 -1.777 -1.195 -0.626 0.317 1.004 360 r[40] -2.071 1.092 -4.424 -2.715 -1.917 -1.327 -0.032 1.003 600 r[41] -2.118 1.067 -4.632 -2.756 -1.962 -1.372 -0.304 1.002 600 r[42] -3.789 1.677 -7.437 -4.705 -3.585 -2.558 -1.278 1.002 600 r[43] -3.857 1.590 -7.445 -4.853 -3.731 -2.736 -1.244 1.013 140 r[44] 1.937 1.090 0.025 1.176 1.838 2.522 4.462 1.001 600 r[45] 3.763 1.896 0.919 2.376 3.452 4.863 7.957 1.001 600 r[46] 0.470 0.764 -1.031 -0.054 0.448 0.939 1.963 1.005 390 r[47] 1.889 1.077 -0.015 1.158 1.848 2.469 4.152 1.010 230 r[48] -1.244 0.895 -3.173 -1.825 -1.163 -0.653 0.391 1.008 230 r[49] 3.630 1.658 1.101 2.494 3.467 4.412 7.742 1.008 470 r[50] -2.115 1.082 -4.410 -2.735 -2.058 -1.344 -0.201 1.004 380 r[51] 0.436 0.793 -1.106 -0.090 0.440 0.908 1.982 1.011 170 r[52] 1.920 1.116 0.019 1.153 1.850 2.596 4.124 1.006 290 r[53] -0.559 0.773 -2.201 -1.038 -0.546 -0.034 0.892 1.000 600 r[54] 3.665 1.617 1.214 2.474 3.456 4.529 7.429 1.000 600 r[55] -3.871 1.703 -7.800 -4.849 -3.689 -2.656 -1.146 0.999 600 r[56] 3.745 1.721 0.916 2.586 3.572 4.699 7.572 1.000 600 r[57] 1.007 0.884 -0.700 0.436 0.991 1.505 2.866 1.006 600 r[58] -0.642 0.813 -2.312 -1.171 -0.640 -0.105 0.922 1.007 250 r[59] -1.211 0.890 -3.167 -1.728 -1.160 -0.605 0.467 1.003 600 r[60] -3.859 1.744 -7.882 -5.008 -3.587 -2.587 -1.222 1.003 450 r[61] -3.852 1.810 -7.901 -4.796 -3.632 -2.593 -1.005 1.003 600 r[62] -2.053 1.081 -4.440 -2.618 -1.958 -1.392 -0.057 1.005 600 r[63] -1.232 0.877 -3.124 -1.822 -1.151 -0.637 0.257 1.004 560 r[64] 3.714 1.663 1.040 2.513 3.513 4.682 7.522 1.008 230 r[65] 1.979 1.039 0.162 1.298 1.954 2.602 4.290 1.000 600 r[66] 2.002 1.138 0.144 1.252 1.806 2.690 4.442 1.000 600 r[67] 1.931 1.119 -0.016 1.165 1.841 2.612 4.214 1.004 530 r[68] 3.742 1.774 1.069 2.476 3.544 4.812 7.760 1.008 600 r[69] -3.812 1.686 -7.514 -4.671 -3.625 -2.607 -1.135 1.003 600 r[70] -3.924 1.817 -8.086 -4.907 -3.692 -2.628 -1.047 1.000 600 r[71] -3.836 1.709 -7.614 -4.884 -3.630 -2.649 -1.004 1.006 420 r[72] 0.471 0.801 -1.144 -0.082 0.476 0.966 2.058 1.009 230 r[73] -2.087 1.162 -4.785 -2.737 -1.945 -1.253 -0.261 1.005 510 r[74] -3.938 1.896 -8.464 -4.985 -3.604 -2.602 -0.914 1.003 600 r[75] -3.815 1.777 -7.785 -4.914 -3.529 -2.577 -1.072 0.999 600 r[76] -3.864 1.788 -7.894 -4.991 -3.553 -2.555 -1.134 1.007 270 r[77] 3.623 1.772 1.130 2.266 3.363 4.661 8.023 0.999 600 r[78] -2.145 1.092 -4.512 -2.777 -2.038 -1.391 -0.240 1.006 330 r[79] 3.805 1.858 0.914 2.524 3.559 4.859 8.091 1.012 220 r[80] -0.092 0.793 -1.696 -0.575 -0.128 0.464 1.446 1.001 600 r[81] 1.953 1.110 0.051 1.244 1.802 2.599 4.508 1.000 600 r[82] -1.251 0.944 -3.201 -1.817 -1.235 -0.570 0.369 1.002 600 r[83] -2.123 1.084 -4.693 -2.704 -2.032 -1.391 -0.198 1.011 180 r[84] -0.065 0.788 -1.621 -0.586 -0.070 0.478 1.499 1.000 600 r[85] 1.934 1.078 -0.050 1.183 1.817 2.636 4.248 1.003 600 r[86] -3.784 1.729 -7.786 -4.782 -3.675 -2.510 -0.947 1.000 600 r[87] 3.594 1.736 0.933 2.279 3.281 4.643 7.459 1.002 590 r[88] -2.037 1.098 -4.402 -2.666 -1.998 -1.286 -0.220 1.007 560 r[89] 3.728 1.712 0.973 2.538 3.505 4.655 7.494 1.003 600 r[90] 1.951 1.084 0.124 1.195 1.820 2.651 4.334 1.002 600 r[91] 1.203 0.937 -0.401 0.581 1.151 1.748 3.214 1.000 600 r[92] -1.285 0.905 -3.136 -1.849 -1.250 -0.722 0.403 1.002 600 r[93] 3.676 1.737 0.875 2.463 3.439 4.600 7.559 1.011 240 r[94] 1.147 0.888 -0.422 0.520 1.086 1.733 3.126 1.000 600 r[95] 1.092 0.929 -0.642 0.462 1.036 1.734 2.925 1.000 600 r[96] -2.109 1.120 -4.751 -2.778 -2.026 -1.322 -0.179 1.002 600 r[97] -3.929 1.836 -8.266 -4.948 -3.671 -2.532 -1.237 0.999 600 r[98] -0.054 0.806 -1.538 -0.593 -0.112 0.458 1.715 1.000 600 r[99] 2.002 1.096 0.095 1.237 1.884 2.718 4.476 1.006 280 r[100] -3.851 1.677 -7.745 -4.722 -3.705 -2.672 -1.097 1.001 600 tau 0.116 0.029 0.072 0.095 0.114 0.134 0.179 0.999 600 q[1] 0.050 0.059 0.001 0.008 0.029 0.071 0.221 1.001 600 q[2] 0.264 0.138 0.057 0.158 0.241 0.358 0.581 1.004 380 q[3] 0.842 0.115 0.542 0.771 0.875 0.927 0.988 1.004 580 q[4] 0.949 0.065 0.759 0.928 0.975 0.994 1.000 1.035 210 q[5] 0.157 0.117 0.014 0.066 0.126 0.220 0.446 1.003 600 q[6] 0.850 0.115 0.566 0.791 0.884 0.937 0.985 1.010 320 q[7] 0.939 0.082 0.710 0.923 0.970 0.991 1.000 1.039 450 q[8] 0.950 0.066 0.767 0.936 0.974 0.992 1.000 1.016 350 q[9] 0.149 0.111 0.010 0.064 0.126 0.213 0.422 0.999 600 q[10] 0.141 0.103 0.017 0.062 0.114 0.199 0.411 1.005 600 q[11] 0.505 0.164 0.191 0.383 0.503 0.629 0.805 1.000 600 q[12] 0.052 0.060 0.001 0.009 0.029 0.078 0.212 1.000 600 q[13] 0.157 0.115 0.018 0.068 0.133 0.213 0.472 1.008 250 q[14] 0.502 0.159 0.211 0.392 0.503 0.611 0.802 1.001 600 q[15] 0.841 0.118 0.541 0.786 0.872 0.928 0.989 1.015 390 q[16] 0.940 0.077 0.712 0.913 0.971 0.990 1.000 1.029 360 q[17] 0.855 0.114 0.584 0.792 0.887 0.941 0.988 1.000 600 q[18] 0.052 0.065 0.000 0.008 0.028 0.076 0.228 1.001 600 q[19] 0.273 0.142 0.060 0.168 0.255 0.363 0.586 1.003 480 q[20] 0.266 0.140 0.043 0.163 0.244 0.360 0.578 1.005 600 q[21] 0.156 0.115 0.012 0.068 0.132 0.214 0.436 1.003 440 q[22] 0.151 0.116 0.012 0.064 0.127 0.204 0.451 1.002 560 q[23] 0.606 0.154 0.299 0.505 0.617 0.709 0.889 1.004 600 q[24] 0.852 0.118 0.591 0.788 0.885 0.943 0.990 1.015 190 q[25] 0.948 0.067 0.752 0.932 0.975 0.991 1.000 1.002 600 q[26] 0.388 0.158 0.128 0.269 0.384 0.493 0.709 0.999 600 q[27] 0.950 0.062 0.778 0.929 0.973 0.993 1.000 1.015 600 q[28] 0.515 0.160 0.208 0.401 0.517 0.631 0.827 1.003 570 q[29] 0.733 0.140 0.430 0.640 0.752 0.844 0.948 1.007 430 q[30] 0.055 0.067 0.000 0.009 0.031 0.072 0.229 1.002 600 q[31] 0.947 0.067 0.738 0.929 0.975 0.993 1.000 1.010 290 q[32] 0.848 0.113 0.578 0.793 0.868 0.936 0.987 1.002 600 q[33] 0.948 0.068 0.754 0.929 0.975 0.992 1.000 1.012 320 q[34] 0.057 0.073 0.001 0.009 0.029 0.075 0.270 1.002 600 q[35] 0.266 0.139 0.056 0.164 0.245 0.359 0.577 1.004 380 q[36] 0.739 0.146 0.413 0.641 0.768 0.849 0.957 1.001 600 q[37] 0.848 0.112 0.560 0.795 0.872 0.931 0.987 1.007 600 q[38] 0.947 0.066 0.772 0.928 0.972 0.992 1.000 1.015 600 q[39] 0.266 0.138 0.050 0.163 0.245 0.359 0.578 1.004 400 q[40] 0.158 0.115 0.014 0.068 0.138 0.216 0.439 1.003 600 q[41] 0.152 0.110 0.013 0.067 0.129 0.212 0.420 1.006 600 q[42] 0.054 0.066 0.001 0.009 0.030 0.077 0.233 1.002 600 q[43] 0.050 0.063 0.001 0.008 0.026 0.067 0.235 1.008 220 q[44] 0.843 0.120 0.530 0.786 0.870 0.932 0.989 1.001 600 q[45] 0.945 0.072 0.748 0.925 0.971 0.992 1.000 1.000 600 q[46] 0.620 0.152 0.321 0.519 0.629 0.738 0.882 1.002 600 q[47] 0.840 0.117 0.569 0.777 0.868 0.931 0.984 1.001 600 q[48] 0.265 0.140 0.058 0.157 0.243 0.360 0.556 1.001 600 q[49] 0.947 0.062 0.780 0.926 0.970 0.991 1.000 1.000 600 q[50] 0.153 0.114 0.014 0.064 0.127 0.215 0.437 1.000 600 q[51] 0.612 0.156 0.292 0.504 0.621 0.723 0.883 1.006 370 q[52] 0.842 0.120 0.548 0.779 0.868 0.935 0.986 1.001 600 q[53] 0.395 0.157 0.120 0.284 0.386 0.503 0.725 1.002 600 q[54] 0.950 0.057 0.806 0.930 0.968 0.991 1.000 1.019 600 q[55] 0.052 0.063 0.001 0.009 0.027 0.070 0.239 1.000 600 q[56] 0.948 0.069 0.748 0.937 0.973 0.992 1.000 1.004 600 q[57] 0.720 0.147 0.395 0.628 0.744 0.832 0.945 1.000 600 q[58] 0.378 0.160 0.110 0.259 0.365 0.490 0.703 0.999 600 q[59] 0.274 0.149 0.044 0.161 0.245 0.370 0.597 1.010 530 q[60] 0.055 0.070 0.000 0.007 0.029 0.075 0.254 1.001 600 q[61] 0.056 0.074 0.000 0.008 0.028 0.072 0.266 1.002 600 q[62] 0.160 0.115 0.012 0.074 0.136 0.221 0.445 1.013 600 q[63] 0.268 0.141 0.047 0.158 0.253 0.358 0.579 1.002 600 q[64] 0.948 0.064 0.759 0.933 0.973 0.991 1.000 1.013 290 q[65] 0.852 0.111 0.557 0.799 0.880 0.930 0.988 1.001 600 q[66] 0.850 0.114 0.562 0.789 0.875 0.938 0.990 1.004 370 q[67] 0.843 0.119 0.529 0.779 0.873 0.936 0.985 1.002 600 q[68] 0.947 0.066 0.770 0.926 0.974 0.993 1.000 1.024 240 q[69] 0.055 0.069 0.001 0.010 0.029 0.074 0.255 1.002 600 q[70] 0.054 0.071 0.000 0.008 0.027 0.077 0.237 1.002 600 q[71] 0.053 0.065 0.001 0.009 0.029 0.073 0.246 1.011 220 q[72] 0.620 0.154 0.281 0.525 0.626 0.732 0.885 1.002 600 q[73] 0.160 0.116 0.009 0.068 0.131 0.226 0.426 1.001 600 q[74] 0.056 0.074 0.000 0.007 0.028 0.077 0.280 1.002 600 q[75] 0.056 0.067 0.000 0.009 0.031 0.077 0.241 1.001 600 q[76] 0.055 0.067 0.000 0.008 0.029 0.076 0.259 1.014 160 q[77] 0.942 0.070 0.747 0.915 0.968 0.991 1.000 1.031 600 q[78] 0.150 0.114 0.012 0.063 0.120 0.208 0.436 1.002 600 q[79] 0.945 0.074 0.740 0.927 0.975 0.992 1.000 1.047 170 q[80] 0.497 0.164 0.184 0.380 0.501 0.610 0.807 1.002 600 q[81] 0.846 0.112 0.560 0.785 0.872 0.929 0.989 1.006 600 q[82] 0.268 0.150 0.043 0.148 0.247 0.372 0.605 1.002 540 q[83] 0.151 0.110 0.011 0.069 0.124 0.209 0.409 1.015 230 q[84] 0.503 0.161 0.204 0.389 0.499 0.615 0.812 1.004 400 q[85] 0.846 0.113 0.558 0.786 0.866 0.937 0.985 1.006 600 q[86] 0.058 0.076 0.000 0.009 0.028 0.079 0.271 0.999 600 q[87] 0.940 0.073 0.738 0.918 0.969 0.992 1.000 0.999 600 q[88] 0.162 0.120 0.012 0.071 0.135 0.230 0.447 1.019 170 q[89] 0.948 0.066 0.759 0.928 0.972 0.991 1.000 1.003 600 q[90] 0.848 0.109 0.578 0.786 0.872 0.935 0.989 1.002 600 q[91] 0.752 0.140 0.456 0.662 0.771 0.856 0.959 1.008 600 q[92] 0.261 0.143 0.040 0.143 0.248 0.345 0.593 1.000 600 q[93] 0.944 0.071 0.715 0.924 0.973 0.991 1.000 1.009 600 q[94] 0.744 0.138 0.420 0.654 0.763 0.850 0.957 1.001 600 q[95] 0.735 0.145 0.388 0.641 0.756 0.847 0.944 1.004 370 q[96] 0.154 0.114 0.010 0.068 0.126 0.217 0.434 1.002 600 q[97] 0.053 0.065 0.000 0.008 0.030 0.072 0.223 1.000 600 q[98] 0.504 0.163 0.207 0.392 0.503 0.614 0.817 1.003 420 q[99] 0.852 0.111 0.587 0.800 0.876 0.942 0.988 1.002 600 q[100] 0.054 0.070 0.000 0.009 0.027 0.069 0.257 1.002 600 deviance 222.978 14.462 197.200 212.600 222.200 232.300 252.717 1.013 140 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 = 78.0 and DIC = 301.0 DIC is an estimate of expected predictive error (lower deviance is better).