Inference for Bugs model at "../Mi model JAGS simp1.txt", fit using jags,
3 chains, each with 3e+05 iterations (first 150000 discarded), n.thin = 300
n.sims = 1500 iterations saved
mu.vect sd.vect 2.5% 25% 50% 75% 97.5% Rhat n.eff
Loc[1] 1.784 0.106 1.581 1.714 1.787 1.856 1.992 1.007 310
Loc[2] 1.992 0.114 1.762 1.917 1.992 2.069 2.215 1.004 560
alpha[1] 1.386 18.611 -46.595 0.356 1.894 3.274 42.430 1.003 820
alpha[2] -0.188 0.115 -0.408 -0.267 -0.189 -0.110 0.039 1.007 310
alpha[3] 0.050 0.317 -0.578 -0.162 0.055 0.264 0.662 1.004 550
alpha[4] 0.032 0.231 -0.442 -0.112 0.029 0.182 0.496 1.009 230
alpha[5] -0.373 0.142 -0.652 -0.469 -0.371 -0.278 -0.097 1.001 1500
alpha[6] 0.097 0.331 -0.545 -0.117 0.093 0.320 0.772 1.003 650
alpha[7] 0.098 0.240 -0.377 -0.064 0.094 0.254 0.583 1.007 300
alpha[8] 0.041 0.192 -0.331 -0.086 0.038 0.169 0.419 1.001 1500
alpha[9] 0.289 0.762 -1.202 -0.211 0.293 0.789 1.815 1.010 210
alpha[10] -0.202 0.120 -0.439 -0.280 -0.201 -0.121 0.025 1.000 1500
alpha[11] 0.073 0.334 -0.586 -0.148 0.062 0.304 0.723 1.001 1500
alpha[12] 0.216 0.779 -1.389 -0.292 0.209 0.738 1.764 1.010 210
alpha[13] 0.193 0.373 -0.506 -0.058 0.200 0.438 0.892 1.001 1500
delta -2.487 0.641 -3.651 -2.942 -2.524 -2.059 -1.175 1.001 1500
e.obs[1] 0.388 0.160 0.074 0.277 0.391 0.496 0.703 1.000 1500
e.obs[2] -0.062 0.154 -0.379 -0.159 -0.064 0.036 0.254 1.003 680
e.obs[3] 0.770 0.121 0.546 0.685 0.771 0.853 1.013 1.003 660
e.obs[4] -0.270 0.224 -0.703 -0.420 -0.276 -0.120 0.172 1.007 280
e.obs[5] 0.854 0.159 0.555 0.745 0.857 0.959 1.173 1.003 800
e.obs[6] -0.796 0.249 -1.299 -0.965 -0.795 -0.632 -0.310 1.001 1500
e.obs[7] 0.074 0.145 -0.205 -0.026 0.074 0.171 0.356 1.001 1500
e.obs[8] -0.045 0.229 -0.475 -0.207 -0.047 0.116 0.401 1.006 400
e.obs[9] 0.673 0.160 0.346 0.566 0.673 0.777 0.976 1.001 1500
e.obs[10] -0.009 0.192 -0.399 -0.128 -0.004 0.113 0.349 1.001 1500
e.obs[11] -0.189 0.129 -0.448 -0.276 -0.187 -0.102 0.057 1.001 1500
e.obs[12] 0.082 0.219 -0.326 -0.061 0.077 0.218 0.543 1.005 1200
e.obs[13] -0.073 0.158 -0.376 -0.172 -0.078 0.026 0.239 1.001 1500
e.obs[14] -0.368 0.233 -0.849 -0.520 -0.369 -0.216 0.097 1.003 790
e.obs[15] 0.400 0.297 -0.198 0.198 0.403 0.609 0.962 1.006 380
e.obs[16] -0.111 0.195 -0.493 -0.236 -0.107 0.017 0.237 1.003 790
e.obs[17] -0.921 0.188 -1.301 -1.040 -0.920 -0.794 -0.566 1.003 670
e.obs[18] -0.498 0.190 -0.889 -0.614 -0.496 -0.378 -0.125 1.001 1500
e.obs[19] -0.216 0.245 -0.711 -0.383 -0.215 -0.058 0.258 1.001 1500
e.obs[20] -0.035 0.194 -0.410 -0.163 -0.030 0.093 0.348 1.001 1500
e.obs[21] -0.103 0.275 -0.645 -0.285 -0.105 0.082 0.433 1.004 560
e.obs[22] -0.221 0.305 -0.795 -0.427 -0.217 -0.016 0.362 1.002 1100
e.obs[23] -0.092 0.225 -0.518 -0.246 -0.095 0.063 0.343 1.003 650
e.obs[24] -0.111 0.332 -0.762 -0.324 -0.124 0.115 0.554 1.005 370
e.obs[25] -0.040 0.203 -0.436 -0.164 -0.042 0.098 0.353 1.009 260
e.obs[26] -0.103 0.198 -0.492 -0.228 -0.098 0.023 0.281 1.002 870
e.obs[27] 0.069 0.111 -0.146 0.001 0.066 0.138 0.293 1.001 1500
e.obs[28] 0.655 0.116 0.428 0.578 0.652 0.734 0.884 1.003 1500
e.obs[29] -0.172 0.171 -0.514 -0.292 -0.165 -0.055 0.152 1.001 1400
e.obs[30] 0.064 0.199 -0.351 -0.065 0.063 0.203 0.443 1.004 520
e.obs[31] -0.345 0.139 -0.618 -0.433 -0.348 -0.259 -0.062 1.000 1500
e.obs[32] -0.267 0.185 -0.625 -0.390 -0.264 -0.140 0.089 1.001 1500
e.obs[33] -0.507 0.161 -0.829 -0.620 -0.502 -0.392 -0.204 1.002 1200
e.obs[34] -0.895 0.186 -1.252 -1.018 -0.893 -0.769 -0.540 1.001 1500
e.obs[35] -1.337 0.175 -1.671 -1.453 -1.334 -1.219 -1.001 1.001 1500
e.obs[36] -0.462 0.122 -0.698 -0.545 -0.462 -0.380 -0.219 1.000 1500
e.obs[37] 0.127 0.115 -0.101 0.055 0.124 0.201 0.351 1.000 1500
e.obs[38] -0.491 0.125 -0.742 -0.575 -0.495 -0.405 -0.240 1.000 1500
e.obs[39] 0.091 0.126 -0.160 0.012 0.090 0.170 0.348 1.000 1500
e.obs[40] 0.076 0.176 -0.270 -0.043 0.079 0.191 0.421 1.000 1500
e.obs[41] -0.103 0.210 -0.503 -0.244 -0.107 0.036 0.298 1.000 1500
e.obs[42] 0.215 0.091 0.040 0.158 0.212 0.272 0.398 1.001 1500
e.obs[43] 0.071 0.074 -0.063 0.031 0.062 0.108 0.240 1.003 830
e.obs[44] -0.003 0.049 -0.105 -0.016 0.001 0.015 0.084 1.014 500
e.obs[45] -0.008 0.040 -0.082 -0.026 -0.009 0.008 0.065 1.010 1500
e.obs[46] 0.040 0.087 -0.128 -0.016 0.039 0.098 0.215 1.001 1500
e.obs[47] -0.161 0.158 -0.484 -0.260 -0.156 -0.060 0.160 1.001 1500
e.obs[48] 0.022 0.243 -0.443 -0.149 0.032 0.182 0.498 1.000 1500
e.obs[49] 1.713 0.314 1.098 1.486 1.723 1.925 2.307 1.001 1500
e.obs[50] -0.121 0.118 -0.344 -0.199 -0.123 -0.043 0.118 1.003 1200
e.obs[51] -0.551 0.106 -0.766 -0.621 -0.551 -0.481 -0.349 1.002 950
e.obs[52] 0.031 0.106 -0.194 -0.037 0.032 0.100 0.237 1.002 1100
e.obs[53] 0.709 0.221 0.215 0.581 0.718 0.856 1.105 1.004 660
e.obs[54] 0.149 0.248 -0.373 -0.005 0.155 0.312 0.609 1.002 1400
e.obs[55] 0.538 0.168 0.205 0.426 0.545 0.644 0.859 1.001 1500
e.obs[56] 0.859 0.141 0.587 0.761 0.853 0.953 1.138 1.002 1100
e.obs[57] 0.230 0.095 0.043 0.169 0.230 0.295 0.412 1.003 800
e.obs[58] -0.511 0.136 -0.791 -0.600 -0.507 -0.423 -0.257 1.001 1500
e.obs[59] 0.169 0.139 -0.126 0.083 0.172 0.262 0.423 1.001 1400
e.obs[60] -0.820 0.140 -1.081 -0.912 -0.823 -0.727 -0.532 1.003 1100
e.obs[61] 0.136 0.137 -0.128 0.047 0.131 0.226 0.409 1.002 1200
e.obs[62] -0.217 0.171 -0.536 -0.337 -0.224 -0.105 0.136 1.002 1400
e.obs[63] -0.087 0.191 -0.448 -0.219 -0.098 0.038 0.301 1.002 1500
e.obs[64] 0.270 0.200 -0.113 0.132 0.260 0.405 0.680 1.002 1500
e.obs[65] -0.024 0.114 -0.233 -0.103 -0.025 0.053 0.201 1.002 1500
e.obs[66] -0.136 0.140 -0.398 -0.232 -0.143 -0.047 0.153 1.003 1200
e.obs[67] 0.201 0.203 -0.209 0.062 0.207 0.333 0.591 1.001 1500
e.obs[68] 0.081 0.110 -0.150 0.008 0.081 0.154 0.300 1.001 1500
mu[1] 1.615 0.099 1.419 1.548 1.613 1.684 1.810 1.001 1500
mu[2] 1.682 0.096 1.486 1.621 1.683 1.742 1.879 1.003 690
mu[3] 1.678 0.075 1.527 1.627 1.677 1.731 1.818 1.003 750
mu[4] 1.781 0.139 1.506 1.687 1.785 1.874 2.050 1.007 270
mu[5] 1.899 0.099 1.701 1.834 1.897 1.967 2.085 1.003 750
mu[6] 1.700 0.155 1.397 1.598 1.699 1.805 2.012 1.001 1500
mu[7] 1.684 0.090 1.509 1.623 1.684 1.746 1.857 1.001 1500
mu[8] 1.471 0.143 1.194 1.371 1.472 1.572 1.738 1.005 450
mu[9] 1.700 0.099 1.511 1.635 1.700 1.766 1.903 1.000 1500
mu[10] 1.451 0.119 1.229 1.376 1.448 1.525 1.694 1.001 1500
mu[11] 1.676 0.080 1.523 1.622 1.675 1.730 1.837 1.001 1500
mu[12] 2.092 0.136 1.806 2.007 2.095 2.181 2.346 1.006 1000
mu[13] 1.865 0.098 1.672 1.804 1.869 1.927 2.054 1.002 1500
mu[14] 1.949 0.145 1.660 1.854 1.949 2.043 2.248 1.003 790
mu[15] 1.786 0.185 1.437 1.656 1.784 1.911 2.158 1.005 430
mu[16] 1.752 0.121 1.535 1.672 1.749 1.829 1.989 1.002 840
mu[17] 1.900 0.117 1.679 1.821 1.899 1.974 2.136 1.003 720
mu[18] 1.912 0.118 1.680 1.837 1.911 1.984 2.155 1.001 1500
mu[19] 1.887 0.153 1.593 1.789 1.887 1.992 2.196 1.001 1500
mu[20] 1.936 0.120 1.698 1.857 1.933 2.016 2.169 1.001 1500
mu[21] 2.106 0.171 1.773 1.991 2.107 2.220 2.443 1.004 570
mu[22] 2.178 0.189 1.816 2.051 2.176 2.307 2.536 1.002 1100
mu[23] 2.060 0.140 1.789 1.964 2.062 2.156 2.325 1.003 660
mu[24] 2.038 0.206 1.625 1.898 2.047 2.171 2.444 1.005 370
mu[25] 2.154 0.126 1.910 2.068 2.155 2.231 2.400 1.008 270
mu[26] 1.875 0.123 1.637 1.797 1.872 1.953 2.117 1.002 910
mu[27] 1.536 0.069 1.396 1.493 1.537 1.578 1.669 1.000 1500
mu[28] 1.647 0.072 1.504 1.597 1.648 1.694 1.788 1.000 1500
mu[29] 1.828 0.106 1.627 1.756 1.824 1.903 2.041 1.002 1300
mu[30] 1.948 0.124 1.712 1.862 1.949 2.028 2.206 1.004 520
mu[31] 1.496 0.086 1.320 1.443 1.498 1.551 1.666 1.000 1500
mu[32] 1.747 0.115 1.526 1.669 1.746 1.824 1.970 1.001 1500
mu[33] 1.746 0.100 1.557 1.674 1.742 1.816 1.946 1.002 1100
mu[34] 1.757 0.116 1.536 1.678 1.755 1.833 1.979 1.001 1500
mu[35] 1.725 0.109 1.517 1.653 1.724 1.798 1.934 1.001 1500
mu[36] 1.632 0.076 1.481 1.581 1.632 1.684 1.779 1.000 1500
mu[37] 1.525 0.071 1.385 1.479 1.527 1.569 1.667 1.000 1500
mu[38] 1.612 0.078 1.456 1.558 1.614 1.664 1.768 1.000 1500
mu[39] 1.535 0.078 1.375 1.485 1.535 1.584 1.691 1.000 1500
mu[40] 1.407 0.110 1.193 1.336 1.406 1.481 1.623 1.000 1500
mu[41] 1.340 0.131 1.091 1.254 1.343 1.428 1.589 1.000 1500
mu[42] 1.590 0.057 1.477 1.555 1.593 1.626 1.699 1.001 1500
mu[43] 2.076 0.046 1.971 2.053 2.082 2.101 2.159 1.003 830
mu[44] 1.692 0.030 1.638 1.681 1.690 1.700 1.756 1.011 500
mu[45] 1.808 0.025 1.763 1.798 1.809 1.819 1.854 1.009 1500
mu[46] 2.077 0.054 1.968 2.041 2.078 2.112 2.182 1.001 1500
mu[47] 2.505 0.098 2.305 2.442 2.501 2.566 2.706 1.001 1500
mu[48] 1.989 0.151 1.693 1.889 1.983 2.095 2.278 1.000 1500
mu[49] 2.244 0.195 1.875 2.113 2.238 2.385 2.627 1.001 1500
mu[50] 1.697 0.073 1.549 1.649 1.699 1.746 1.836 1.003 1100
mu[51] 1.880 0.066 1.754 1.837 1.880 1.923 2.014 1.002 950
mu[52] 1.815 0.066 1.687 1.772 1.814 1.857 1.955 1.002 1100
mu[53] 2.083 0.138 1.837 1.992 2.077 2.163 2.390 1.003 710
mu[54] 1.752 0.154 1.466 1.651 1.748 1.847 2.076 1.002 1500
mu[55] 1.524 0.105 1.325 1.458 1.519 1.594 1.731 1.001 1500
mu[56] 1.766 0.088 1.592 1.707 1.770 1.826 1.935 1.002 1000
mu[57] 1.929 0.059 1.816 1.888 1.929 1.967 2.045 1.003 790
mu[58] 2.178 0.085 2.020 2.123 2.176 2.234 2.352 1.001 1500
mu[59] 2.415 0.086 2.257 2.358 2.413 2.469 2.599 1.002 1300
mu[60] 1.709 0.087 1.530 1.652 1.712 1.767 1.872 1.003 1100
mu[61] 1.729 0.085 1.559 1.673 1.732 1.784 1.893 1.003 1100
mu[62] 1.613 0.106 1.393 1.543 1.617 1.688 1.811 1.003 1300
mu[63] 1.566 0.119 1.325 1.488 1.573 1.648 1.790 1.003 1400
mu[64] 1.581 0.124 1.326 1.497 1.587 1.667 1.819 1.003 1400
mu[65] 1.782 0.071 1.642 1.735 1.783 1.831 1.912 1.002 1500
mu[66] 1.688 0.087 1.509 1.633 1.693 1.748 1.851 1.003 1100
mu[67] 2.136 0.126 1.894 2.054 2.133 2.223 2.391 1.001 1500
mu[68] 1.692 0.068 1.556 1.647 1.692 1.738 1.836 1.002 1400
p.val[1] 0.460 0.842 0.000 0.000 0.000 0.000 2.000 1.000 1500
p.val[2] 1.895 0.447 0.000 2.000 2.000 2.000 2.000 1.020 470
p.val[3] 0.837 0.987 0.000 0.000 0.000 2.000 2.000 1.003 730
p.val[4] 0.903 0.996 0.000 0.000 0.000 2.000 2.000 1.005 380
p.val[5] 1.991 0.136 2.000 2.000 2.000 2.000 2.000 1.009 1500
p.val[6] 0.789 0.978 0.000 0.000 0.000 2.000 2.000 1.002 960
p.val[7] 0.681 0.948 0.000 0.000 0.000 2.000 2.000 1.005 430
p.val[8] 0.835 0.987 0.000 0.000 0.000 2.000 2.000 1.001 1400
p.val[9] 0.701 0.955 0.000 0.000 0.000 2.000 2.000 1.008 260
p.val[10] 1.920 0.392 0.000 2.000 2.000 2.000 2.000 1.002 1500
p.val[11] 0.849 0.989 0.000 0.000 0.000 2.000 2.000 1.001 1500
p.val[12] 0.779 0.976 0.000 0.000 0.000 2.000 2.000 1.008 270
p.val[13] 0.592 0.913 0.000 0.000 0.000 2.000 2.000 1.000 1500
tau 15.381 3.251 9.644 12.967 15.318 17.418 22.285 1.001 1500
deviance 8.338 7.855 -4.519 2.718 7.645 13.050 25.116 1.000 1500
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 = 30.9 and DIC = 39.2
DIC is an estimate of expected predictive error (lower deviance is better).