Inference for Bugs model at "../Mi model JAGS simp3.txt", fit using jags,
3 chains, each with 4e+05 iterations (first 2e+05 discarded), n.thin = 300
n.sims = 1998 iterations saved
mu.vect sd.vect 2.5% 25% 50% 75% 97.5% Rhat n.eff
Loc[1] 1.765 0.085 1.593 1.708 1.766 1.820 1.924 1.001 2000
Loc[2] 1.975 0.088 1.797 1.919 1.977 2.036 2.140 1.001 2000
alpha[1] 1.771 19.247 -42.218 0.500 1.858 3.533 42.383 1.002 2000
alpha[2] -0.165 0.090 -0.341 -0.223 -0.167 -0.107 0.010 1.000 2000
alpha[3] -0.031 0.220 -0.496 -0.175 -0.032 0.109 0.397 1.000 2000
alpha[4] 0.050 0.161 -0.251 -0.065 0.053 0.155 0.368 1.002 1100
alpha[5] -0.430 0.102 -0.634 -0.497 -0.429 -0.363 -0.227 1.001 2000
alpha[6] 0.179 0.226 -0.259 0.030 0.176 0.321 0.659 1.001 2000
alpha[7] 0.098 0.166 -0.223 -0.011 0.102 0.208 0.414 1.003 1100
alpha[8] 0.132 0.125 -0.120 0.055 0.135 0.217 0.372 1.001 2000
alpha[9] 0.457 0.142 0.150 0.367 0.464 0.552 0.715 1.000 2000
alpha[10] -0.212 0.111 -0.429 -0.285 -0.213 -0.139 0.014 1.001 2000
alpha[11] 0.194 0.148 -0.106 0.097 0.196 0.292 0.471 1.001 1900
delta -2.514 0.629 -3.601 -2.974 -2.567 -2.114 -1.195 1.003 750
e.obs[1] 0.403 0.133 0.155 0.311 0.401 0.490 0.666 1.003 710
e.obs[2] -0.046 0.132 -0.315 -0.129 -0.046 0.039 0.218 1.001 2000
e.obs[3] 0.793 0.111 0.576 0.717 0.791 0.869 1.014 1.005 510
e.obs[4] -0.228 0.167 -0.546 -0.339 -0.231 -0.120 0.109 1.001 2000
e.obs[5] 0.802 0.141 0.519 0.706 0.805 0.898 1.069 1.001 2000
e.obs[6] -0.808 0.161 -1.112 -0.917 -0.812 -0.703 -0.485 1.004 490
e.obs[7] 0.093 0.128 -0.155 0.006 0.090 0.180 0.347 1.004 520
e.obs[8] 0.059 0.185 -0.304 -0.066 0.063 0.175 0.432 1.003 1800
e.obs[9] 0.675 0.130 0.424 0.585 0.674 0.763 0.940 1.005 450
e.obs[10] -0.043 0.179 -0.422 -0.151 -0.036 0.068 0.299 1.006 570
e.obs[11] -0.185 0.113 -0.396 -0.265 -0.186 -0.108 0.039 1.003 690
e.obs[12] 0.080 0.202 -0.319 -0.053 0.075 0.205 0.493 1.001 2000
e.obs[13] -0.064 0.151 -0.362 -0.163 -0.065 0.040 0.226 1.000 2000
e.obs[14] -0.468 0.192 -0.845 -0.596 -0.461 -0.337 -0.103 1.001 2000
e.obs[15] 0.518 0.241 0.064 0.352 0.509 0.683 0.995 1.002 1200
e.obs[16] -0.099 0.172 -0.439 -0.207 -0.095 0.015 0.222 1.001 2000
e.obs[17] -0.882 0.183 -1.223 -1.005 -0.890 -0.757 -0.523 1.003 810
e.obs[18] -0.477 0.188 -0.834 -0.606 -0.484 -0.350 -0.108 1.003 700
e.obs[19] -0.211 0.182 -0.545 -0.333 -0.216 -0.096 0.158 1.004 540
e.obs[20] -0.040 0.179 -0.373 -0.160 -0.045 0.079 0.310 1.003 650
e.obs[21] -0.220 0.217 -0.626 -0.367 -0.220 -0.075 0.217 1.001 2000
e.obs[22] -0.333 0.224 -0.763 -0.487 -0.331 -0.187 0.110 1.001 2000
e.obs[23] -0.173 0.191 -0.527 -0.303 -0.178 -0.042 0.213 1.002 1300
e.obs[24] -0.098 0.213 -0.488 -0.246 -0.102 0.044 0.312 1.001 2000
e.obs[25] -0.026 0.168 -0.367 -0.131 -0.023 0.085 0.292 1.001 2000
e.obs[26] -0.137 0.190 -0.524 -0.258 -0.130 -0.009 0.211 1.000 2000
e.obs[27] 0.060 0.112 -0.153 -0.015 0.059 0.132 0.286 1.002 1500
e.obs[28] 0.671 0.113 0.453 0.595 0.668 0.747 0.893 1.002 1100
e.obs[29] -0.118 0.153 -0.421 -0.220 -0.121 -0.019 0.190 1.001 1600
e.obs[30] 0.107 0.186 -0.265 -0.015 0.108 0.235 0.456 1.001 2000
e.obs[31] -0.355 0.140 -0.624 -0.448 -0.355 -0.265 -0.068 1.001 1800
e.obs[32] -0.207 0.165 -0.521 -0.319 -0.209 -0.098 0.129 1.002 1300
e.obs[33] -0.461 0.148 -0.757 -0.559 -0.464 -0.365 -0.157 1.001 1700
e.obs[34] -0.835 0.166 -1.153 -0.948 -0.837 -0.727 -0.499 1.002 1300
e.obs[35] -1.282 0.158 -1.584 -1.389 -1.284 -1.178 -0.959 1.002 1400
e.obs[36] -0.440 0.120 -0.671 -0.521 -0.442 -0.362 -0.210 1.002 1300
e.obs[37] 0.116 0.117 -0.107 0.038 0.116 0.190 0.349 1.002 1400
e.obs[38] -0.475 0.122 -0.709 -0.557 -0.476 -0.391 -0.233 1.002 1300
e.obs[39] 0.091 0.127 -0.149 0.004 0.090 0.174 0.341 1.001 1700
e.obs[40] 0.040 0.171 -0.277 -0.080 0.035 0.154 0.400 1.001 1600
e.obs[41] -0.094 0.210 -0.499 -0.236 -0.097 0.037 0.331 1.001 2000
e.obs[42] 0.199 0.091 0.027 0.139 0.195 0.259 0.386 1.002 1100
e.obs[43] 0.074 0.073 -0.052 0.033 0.067 0.110 0.246 1.002 2000
e.obs[44] -0.005 0.053 -0.104 -0.016 0.000 0.015 0.067 1.030 2000
e.obs[45] -0.008 0.042 -0.077 -0.025 -0.009 0.009 0.071 1.021 2000
e.obs[46] 0.046 0.082 -0.115 -0.008 0.046 0.100 0.204 1.002 1500
e.obs[47] -0.167 0.147 -0.471 -0.262 -0.164 -0.071 0.120 1.003 1900
e.obs[48] 0.094 0.213 -0.324 -0.044 0.087 0.238 0.507 1.001 1800
e.obs[49] 1.820 0.271 1.301 1.639 1.814 2.007 2.337 1.002 1500
e.obs[50] -0.141 0.113 -0.366 -0.215 -0.138 -0.068 0.086 1.002 1500
e.obs[51] -0.553 0.105 -0.764 -0.622 -0.554 -0.484 -0.349 1.001 2000
e.obs[52] 0.019 0.105 -0.190 -0.049 0.018 0.092 0.216 1.002 1500
e.obs[53] 0.697 0.215 0.252 0.560 0.708 0.837 1.103 1.001 2000
e.obs[54] 0.109 0.239 -0.372 -0.048 0.114 0.275 0.539 1.001 2000
e.obs[55] 0.488 0.157 0.185 0.381 0.492 0.597 0.785 1.003 890
e.obs[56] 0.861 0.133 0.603 0.770 0.860 0.950 1.127 1.001 2000
e.obs[57] 0.218 0.091 0.041 0.157 0.218 0.279 0.394 1.001 1700
e.obs[58] -0.500 0.136 -0.781 -0.586 -0.497 -0.413 -0.235 1.001 2000
e.obs[59] 0.179 0.135 -0.091 0.089 0.180 0.268 0.444 1.001 2000
e.obs[60] -0.832 0.134 -1.088 -0.922 -0.830 -0.745 -0.570 1.001 2000
e.obs[61] 0.129 0.131 -0.122 0.041 0.129 0.218 0.384 1.001 2000
e.obs[62] -0.257 0.160 -0.567 -0.364 -0.258 -0.152 0.066 1.001 1700
e.obs[63] -0.134 0.179 -0.482 -0.256 -0.137 -0.017 0.224 1.002 1500
e.obs[64] 0.226 0.189 -0.141 0.097 0.224 0.351 0.607 1.001 1700
e.obs[65] -0.018 0.110 -0.222 -0.094 -0.019 0.054 0.204 1.001 2000
e.obs[66] -0.154 0.134 -0.414 -0.244 -0.155 -0.068 0.107 1.001 2000
e.obs[67] 0.260 0.181 -0.082 0.133 0.254 0.381 0.619 1.001 2000
e.obs[68] 0.058 0.106 -0.148 -0.012 0.056 0.132 0.267 1.002 1000
mu[1] 1.605 0.083 1.442 1.552 1.606 1.662 1.760 1.003 750
mu[2] 1.672 0.082 1.508 1.619 1.673 1.724 1.839 1.001 2000
mu[3] 1.664 0.069 1.526 1.617 1.665 1.711 1.799 1.004 580
mu[4] 1.754 0.104 1.545 1.687 1.757 1.824 1.952 1.001 2000
mu[5] 1.931 0.088 1.765 1.872 1.930 1.991 2.108 1.001 2000
mu[6] 1.707 0.100 1.506 1.642 1.710 1.775 1.896 1.004 510
mu[7] 1.672 0.079 1.515 1.618 1.674 1.726 1.827 1.004 540
mu[8] 1.406 0.115 1.174 1.334 1.403 1.483 1.632 1.002 1900
mu[9] 1.698 0.081 1.534 1.644 1.699 1.754 1.854 1.004 530
mu[10] 1.473 0.111 1.260 1.404 1.469 1.540 1.708 1.004 630
mu[11] 1.673 0.070 1.534 1.626 1.674 1.723 1.805 1.003 710
mu[12] 2.093 0.125 1.837 2.016 2.096 2.176 2.341 1.001 2000
mu[13] 1.860 0.094 1.680 1.796 1.861 1.921 2.045 1.000 2000
mu[14] 2.011 0.120 1.784 1.930 2.007 2.091 2.246 1.001 2000
mu[15] 1.713 0.150 1.416 1.610 1.718 1.816 1.995 1.002 1100
mu[16] 1.744 0.107 1.545 1.673 1.742 1.811 1.956 1.001 2000
mu[17] 1.876 0.114 1.652 1.798 1.881 1.952 2.088 1.003 810
mu[18] 1.899 0.117 1.670 1.820 1.903 1.979 2.121 1.003 710
mu[19] 1.884 0.113 1.655 1.813 1.888 1.960 2.092 1.004 560
mu[20] 1.939 0.111 1.722 1.865 1.942 2.014 2.146 1.003 670
mu[21] 2.179 0.135 1.907 2.089 2.179 2.270 2.431 1.001 2000
mu[22] 2.248 0.140 1.973 2.157 2.247 2.344 2.515 1.001 2000
mu[23] 2.110 0.119 1.870 2.029 2.113 2.191 2.330 1.002 1300
mu[24] 2.031 0.133 1.776 1.942 2.033 2.123 2.273 1.001 2000
mu[25] 2.145 0.104 1.948 2.076 2.143 2.211 2.357 1.001 2000
mu[26] 1.897 0.118 1.680 1.817 1.892 1.972 2.138 1.000 2000
mu[27] 1.541 0.070 1.401 1.496 1.542 1.588 1.673 1.002 1500
mu[28] 1.637 0.070 1.498 1.589 1.638 1.683 1.772 1.002 1300
mu[29] 1.795 0.095 1.604 1.734 1.797 1.858 1.984 1.001 1700
mu[30] 1.922 0.116 1.704 1.842 1.921 1.997 2.153 1.000 2000
mu[31] 1.503 0.087 1.324 1.446 1.503 1.560 1.670 1.001 1800
mu[32] 1.710 0.103 1.501 1.642 1.711 1.780 1.905 1.002 1400
mu[33] 1.717 0.092 1.528 1.657 1.719 1.778 1.901 1.001 1700
mu[34] 1.719 0.103 1.510 1.652 1.720 1.789 1.917 1.002 1400
mu[35] 1.692 0.098 1.491 1.627 1.693 1.758 1.879 1.002 1400
mu[36] 1.618 0.074 1.475 1.570 1.620 1.669 1.762 1.002 1300
mu[37] 1.532 0.073 1.387 1.486 1.532 1.581 1.670 1.002 1400
mu[38] 1.602 0.076 1.451 1.550 1.602 1.653 1.748 1.002 1300
mu[39] 1.535 0.079 1.379 1.483 1.535 1.589 1.684 1.001 1700
mu[40] 1.430 0.106 1.206 1.359 1.433 1.505 1.627 1.001 1700
mu[41] 1.335 0.130 1.071 1.254 1.337 1.423 1.587 1.001 1900
mu[42] 1.600 0.057 1.484 1.563 1.603 1.637 1.707 1.002 1200
mu[43] 2.074 0.045 1.967 2.052 2.079 2.100 2.153 1.002 2000
mu[44] 1.693 0.033 1.648 1.681 1.690 1.700 1.755 1.024 2000
mu[45] 1.808 0.026 1.759 1.797 1.808 1.818 1.851 1.017 2000
mu[46] 2.074 0.051 1.975 2.040 2.073 2.107 2.173 1.002 1400
mu[47] 2.509 0.091 2.330 2.449 2.507 2.568 2.698 1.003 1700
mu[48] 1.945 0.132 1.688 1.855 1.949 2.030 2.204 1.001 1900
mu[49] 2.178 0.168 1.856 2.062 2.181 2.290 2.501 1.001 1900
mu[50] 1.710 0.070 1.569 1.664 1.708 1.756 1.850 1.002 1400
mu[51] 1.882 0.065 1.754 1.839 1.882 1.924 2.012 1.001 2000
mu[52] 1.822 0.066 1.700 1.777 1.823 1.865 1.952 1.002 1500
mu[53] 2.091 0.134 1.838 2.004 2.084 2.176 2.368 1.001 2000
mu[54] 1.776 0.149 1.509 1.674 1.773 1.874 2.076 1.001 2000
mu[55] 1.555 0.098 1.370 1.488 1.553 1.622 1.744 1.002 900
mu[56] 1.764 0.082 1.599 1.709 1.765 1.821 1.925 1.001 2000
mu[57] 1.936 0.056 1.827 1.898 1.936 1.974 2.046 1.001 1800
mu[58] 2.171 0.085 2.007 2.117 2.169 2.224 2.346 1.001 2000
mu[59] 2.409 0.084 2.244 2.353 2.408 2.465 2.577 1.001 1900
mu[60] 1.717 0.083 1.554 1.663 1.716 1.773 1.876 1.001 2000
mu[61] 1.733 0.082 1.575 1.678 1.733 1.788 1.889 1.001 2000
mu[62] 1.638 0.099 1.437 1.572 1.639 1.705 1.831 1.001 1600
mu[63] 1.595 0.111 1.373 1.523 1.597 1.671 1.812 1.002 1400
mu[64] 1.609 0.117 1.372 1.531 1.610 1.689 1.837 1.001 1600
mu[65] 1.778 0.069 1.640 1.734 1.779 1.826 1.905 1.001 2000
mu[66] 1.700 0.084 1.537 1.646 1.700 1.755 1.861 1.001 2000
mu[67] 2.100 0.113 1.876 2.024 2.103 2.178 2.312 1.001 2000
mu[68] 1.706 0.066 1.577 1.661 1.708 1.750 1.835 1.002 1000
p.val[1] 0.457 0.840 0.000 0.000 0.000 0.000 2.000 1.003 760
p.val[2] 1.921 0.390 0.000 2.000 2.000 2.000 2.000 1.000 2000
p.val[3] 1.108 0.994 0.000 0.000 2.000 2.000 2.000 1.001 1900
p.val[4] 0.751 0.969 0.000 0.000 0.000 2.000 2.000 1.001 1600
p.val[5] 2.000 0.000 2.000 2.000 2.000 2.000 2.000 1.000 1
p.val[6] 0.416 0.812 0.000 0.000 0.000 0.000 2.000 1.000 2000
p.val[7] 0.541 0.888 0.000 0.000 0.000 2.000 2.000 1.002 1200
p.val[8] 0.282 0.697 0.000 0.000 0.000 0.000 2.000 1.001 2000
p.val[9] 0.008 0.126 0.000 0.000 0.000 0.000 0.000 1.153 640
p.val[10] 1.934 0.358 0.000 2.000 2.000 2.000 2.000 1.004 2000
p.val[11] 0.186 0.581 0.000 0.000 0.000 0.000 2.000 1.002 2000
tau 15.822 3.116 10.328 13.663 15.668 17.755 22.440 1.002 1500
deviance 6.336 7.497 -5.591 0.798 5.547 10.898 23.760 1.004 500
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 = var(deviance)/2)
pD = 28.0 and DIC = 34.4
DIC is an estimate of expected predictive error (lower deviance is better).