Inference for Bugs model at "../Mi model JAGS simp4.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] 1.734 0.069 1.599 1.688 1.734 1.780 1.867 1.003 690
Loc[2] 1.942 0.071 1.804 1.896 1.939 1.988 2.085 1.000 2000
alpha[1] 1.109 19.892 -50.888 0.296 1.840 3.239 40.483 1.007 2000
alpha[2] -0.129 0.072 -0.270 -0.175 -0.130 -0.080 0.009 1.001 2000
alpha[3] -0.086 0.197 -0.463 -0.221 -0.085 0.047 0.305 1.001 2000
alpha[4] 0.136 0.059 0.021 0.098 0.135 0.174 0.251 1.000 2000
alpha[5] -0.446 0.099 -0.638 -0.513 -0.446 -0.380 -0.247 1.000 2000
alpha[6] 0.230 0.209 -0.179 0.090 0.232 0.372 0.637 1.001 2000
alpha[7] 0.143 0.126 -0.108 0.062 0.145 0.226 0.380 1.000 2000
alpha[8] 0.473 0.136 0.178 0.391 0.480 0.560 0.728 1.001 2000
alpha[9] -0.218 0.115 -0.428 -0.296 -0.220 -0.142 0.018 1.000 2000
alpha[10] 0.217 0.139 -0.064 0.126 0.218 0.309 0.489 1.001 2000
delta -2.510 0.624 -3.604 -2.954 -2.548 -2.108 -1.160 1.000 2000
e.obs[1] 0.414 0.140 0.148 0.319 0.413 0.504 0.697 1.001 2000
e.obs[2] -0.087 0.108 -0.303 -0.156 -0.087 -0.019 0.125 1.002 1400
e.obs[3] 0.832 0.097 0.644 0.768 0.829 0.895 1.033 1.003 830
e.obs[4] -0.159 0.122 -0.396 -0.241 -0.159 -0.078 0.078 1.003 740
e.obs[5] 0.816 0.138 0.548 0.726 0.814 0.906 1.094 1.001 2000
e.obs[6] -0.752 0.137 -1.013 -0.842 -0.752 -0.664 -0.475 1.001 2000
e.obs[7] 0.143 0.103 -0.050 0.073 0.141 0.210 0.357 1.002 1000
e.obs[8] 0.127 0.149 -0.174 0.032 0.127 0.222 0.411 1.002 950
e.obs[9] 0.721 0.112 0.507 0.647 0.718 0.793 0.954 1.001 1600
e.obs[10] -0.067 0.171 -0.408 -0.172 -0.066 0.043 0.261 1.002 2000
e.obs[11] -0.174 0.117 -0.397 -0.252 -0.175 -0.099 0.064 1.001 1900
e.obs[12] 0.100 0.202 -0.292 -0.036 0.099 0.234 0.490 1.001 1600
e.obs[13] -0.099 0.132 -0.355 -0.184 -0.098 -0.015 0.162 1.001 1900
e.obs[14] -0.462 0.188 -0.829 -0.587 -0.459 -0.336 -0.087 1.000 2000
e.obs[15] 0.643 0.131 0.371 0.562 0.646 0.729 0.894 1.000 2000
e.obs[16] -0.125 0.167 -0.455 -0.234 -0.120 -0.015 0.199 1.001 2000
e.obs[17] -0.787 0.098 -0.989 -0.852 -0.785 -0.724 -0.596 1.001 2000
e.obs[18] -0.380 0.103 -0.584 -0.449 -0.378 -0.313 -0.182 1.001 2000
e.obs[19] -0.130 0.130 -0.388 -0.213 -0.130 -0.041 0.118 1.001 2000
e.obs[20] 0.044 0.114 -0.182 -0.029 0.043 0.122 0.260 1.001 2000
e.obs[21] -0.166 0.191 -0.538 -0.292 -0.162 -0.039 0.207 1.001 2000
e.obs[22] -0.275 0.197 -0.664 -0.401 -0.279 -0.145 0.118 1.001 2000
e.obs[23] -0.112 0.158 -0.418 -0.217 -0.111 -0.009 0.200 1.001 2000
e.obs[24] 0.005 0.129 -0.248 -0.081 0.005 0.091 0.258 1.000 2000
e.obs[25] -0.045 0.159 -0.361 -0.144 -0.046 0.058 0.262 1.000 2000
e.obs[26] -0.121 0.178 -0.510 -0.232 -0.114 0.001 0.197 1.002 1300
e.obs[27] 0.051 0.108 -0.166 -0.020 0.050 0.124 0.266 1.001 2000
e.obs[28] 0.659 0.107 0.438 0.590 0.659 0.731 0.870 1.001 2000
e.obs[29] -0.143 0.146 -0.421 -0.236 -0.145 -0.044 0.141 1.001 2000
e.obs[30] 0.081 0.179 -0.282 -0.037 0.082 0.199 0.418 1.003 910
e.obs[31] -0.368 0.136 -0.632 -0.460 -0.367 -0.279 -0.094 1.001 2000
e.obs[32] -0.235 0.156 -0.552 -0.343 -0.233 -0.131 0.065 1.001 2000
e.obs[33] -0.486 0.139 -0.752 -0.579 -0.483 -0.392 -0.213 1.001 2000
e.obs[34] -0.863 0.157 -1.177 -0.972 -0.861 -0.759 -0.560 1.001 2000
e.obs[35] -1.310 0.149 -1.605 -1.413 -1.306 -1.210 -1.024 1.001 2000
e.obs[36] -0.458 0.112 -0.687 -0.533 -0.457 -0.384 -0.240 1.001 2000
e.obs[37] 0.107 0.112 -0.111 0.033 0.109 0.181 0.336 1.001 2000
e.obs[38] -0.488 0.116 -0.722 -0.562 -0.488 -0.409 -0.256 1.001 2000
e.obs[39] 0.077 0.123 -0.172 -0.005 0.076 0.158 0.321 1.001 2000
e.obs[40] 0.033 0.169 -0.294 -0.084 0.035 0.141 0.381 1.001 2000
e.obs[41] -0.095 0.208 -0.501 -0.233 -0.102 0.036 0.345 1.001 2000
e.obs[42] 0.196 0.087 0.028 0.137 0.196 0.251 0.370 1.001 1700
e.obs[43] 0.077 0.074 -0.054 0.034 0.069 0.111 0.263 1.002 2000
e.obs[44] -0.003 0.039 -0.094 -0.016 0.001 0.015 0.063 1.003 630
e.obs[45] -0.005 0.036 -0.073 -0.022 -0.008 0.011 0.074 1.008 540
e.obs[46] 0.042 0.082 -0.120 -0.010 0.041 0.094 0.213 1.003 690
e.obs[47] -0.170 0.152 -0.461 -0.266 -0.172 -0.074 0.135 1.004 660
e.obs[48] 0.066 0.207 -0.352 -0.065 0.070 0.201 0.472 1.001 2000
e.obs[49] 1.783 0.264 1.263 1.613 1.782 1.959 2.305 1.001 2000
e.obs[50] -0.147 0.110 -0.360 -0.219 -0.146 -0.072 0.061 1.001 2000
e.obs[51] -0.560 0.104 -0.766 -0.627 -0.559 -0.489 -0.361 1.001 2000
e.obs[52] 0.015 0.104 -0.194 -0.051 0.014 0.085 0.215 1.001 2000
e.obs[53] 0.692 0.214 0.226 0.563 0.697 0.834 1.096 1.002 1800
e.obs[54] 0.120 0.241 -0.391 -0.037 0.125 0.278 0.586 1.002 2000
e.obs[55] 0.497 0.156 0.195 0.395 0.500 0.598 0.814 1.000 2000
e.obs[56] 0.850 0.129 0.595 0.764 0.851 0.936 1.106 1.001 2000
e.obs[57] 0.218 0.089 0.042 0.161 0.217 0.278 0.384 1.001 2000
e.obs[58] -0.501 0.138 -0.772 -0.593 -0.502 -0.409 -0.233 1.001 2000
e.obs[59] 0.173 0.133 -0.091 0.090 0.175 0.258 0.439 1.001 2000
e.obs[60] -0.842 0.131 -1.092 -0.931 -0.841 -0.753 -0.597 1.001 2000
e.obs[61] 0.118 0.128 -0.130 0.032 0.117 0.204 0.356 1.001 2000
e.obs[62] -0.262 0.159 -0.565 -0.367 -0.256 -0.157 0.046 1.001 2000
e.obs[63] -0.139 0.178 -0.482 -0.258 -0.135 -0.023 0.208 1.000 2000
e.obs[64] 0.219 0.188 -0.142 0.093 0.225 0.340 0.585 1.000 2000
e.obs[65] -0.031 0.106 -0.245 -0.100 -0.030 0.037 0.167 1.001 2000
e.obs[66] -0.163 0.132 -0.420 -0.252 -0.162 -0.074 0.084 1.001 2000
e.obs[67] 0.236 0.177 -0.109 0.119 0.233 0.353 0.579 1.001 2000
e.obs[68] 0.055 0.104 -0.149 -0.011 0.057 0.124 0.254 1.000 2000
mu[1] 1.598 0.087 1.423 1.543 1.600 1.658 1.764 1.001 2000
mu[2] 1.698 0.067 1.566 1.655 1.698 1.741 1.832 1.002 1400
mu[3] 1.640 0.060 1.514 1.601 1.642 1.679 1.756 1.003 860
mu[4] 1.711 0.076 1.564 1.661 1.711 1.763 1.859 1.003 740
mu[5] 1.923 0.086 1.750 1.867 1.924 1.979 2.090 1.001 2000
mu[6] 1.672 0.085 1.500 1.618 1.672 1.728 1.835 1.001 2000
mu[7] 1.641 0.064 1.508 1.600 1.642 1.685 1.761 1.002 1000
mu[8] 1.364 0.093 1.187 1.304 1.364 1.423 1.551 1.002 950
mu[9] 1.670 0.070 1.525 1.625 1.671 1.716 1.803 1.002 1500
mu[10] 1.487 0.106 1.284 1.419 1.487 1.553 1.699 1.002 2000
mu[11] 1.667 0.073 1.518 1.620 1.667 1.715 1.805 1.001 1800
mu[12] 2.081 0.125 1.838 1.997 2.082 2.165 2.325 1.001 1600
mu[13] 1.882 0.082 1.719 1.829 1.881 1.934 2.041 1.001 1900
mu[14] 2.007 0.117 1.774 1.929 2.005 2.085 2.236 1.000 2000
mu[15] 1.635 0.082 1.479 1.582 1.633 1.685 1.804 1.001 2000
mu[16] 1.760 0.104 1.559 1.692 1.757 1.828 1.965 1.001 2000
mu[17] 1.816 0.061 1.698 1.777 1.816 1.857 1.942 1.001 2000
mu[18] 1.839 0.064 1.716 1.797 1.837 1.881 1.966 1.001 2000
mu[19] 1.834 0.081 1.680 1.779 1.834 1.886 1.995 1.001 2000
mu[20] 1.887 0.071 1.752 1.839 1.888 1.932 2.027 1.001 2000
mu[21] 2.145 0.119 1.914 2.067 2.143 2.224 2.376 1.001 2000
mu[22] 2.212 0.123 1.968 2.131 2.214 2.290 2.454 1.001 2000
mu[23] 2.072 0.098 1.878 2.008 2.072 2.137 2.262 1.001 2000
mu[24] 1.967 0.080 1.809 1.913 1.966 2.020 2.124 1.000 2000
mu[25] 2.157 0.099 1.966 2.093 2.158 2.219 2.353 1.000 2000
mu[26] 1.887 0.111 1.689 1.811 1.882 1.956 2.129 1.002 1200
mu[27] 1.547 0.067 1.413 1.501 1.547 1.591 1.682 1.001 2000
mu[28] 1.644 0.067 1.513 1.599 1.644 1.687 1.781 1.002 2000
mu[29] 1.811 0.091 1.634 1.749 1.812 1.868 1.983 1.002 2000
mu[30] 1.937 0.111 1.728 1.864 1.937 2.011 2.164 1.004 840
mu[31] 1.510 0.085 1.340 1.455 1.510 1.568 1.675 1.001 2000
mu[32] 1.728 0.097 1.541 1.663 1.726 1.795 1.924 1.001 2000
mu[33] 1.732 0.087 1.563 1.674 1.731 1.790 1.898 1.002 1900
mu[34] 1.737 0.097 1.548 1.672 1.735 1.804 1.932 1.001 2000
mu[35] 1.709 0.092 1.531 1.647 1.706 1.773 1.892 1.001 2000
mu[36] 1.630 0.070 1.494 1.584 1.629 1.676 1.772 1.001 2000
mu[37] 1.538 0.070 1.395 1.492 1.536 1.584 1.673 1.001 2000
mu[38] 1.610 0.072 1.466 1.561 1.610 1.656 1.755 1.001 2000
mu[39] 1.543 0.076 1.392 1.493 1.544 1.594 1.698 1.001 2000
mu[40] 1.434 0.105 1.218 1.367 1.433 1.507 1.637 1.001 2000
mu[41] 1.335 0.129 1.062 1.254 1.340 1.421 1.588 1.001 2000
mu[42] 1.603 0.054 1.494 1.568 1.602 1.639 1.707 1.001 1700
mu[43] 2.072 0.046 1.957 2.051 2.078 2.099 2.154 1.002 2000
mu[44] 1.692 0.024 1.651 1.681 1.690 1.700 1.748 1.003 620
mu[45] 1.806 0.022 1.757 1.796 1.808 1.817 1.848 1.009 530
mu[46] 2.076 0.051 1.970 2.044 2.077 2.108 2.177 1.003 700
mu[47] 2.511 0.094 2.321 2.451 2.512 2.570 2.692 1.004 660
mu[48] 1.962 0.129 1.709 1.878 1.959 2.043 2.221 1.001 2000
mu[49] 2.201 0.164 1.876 2.091 2.201 2.306 2.524 1.001 2000
mu[50] 1.714 0.068 1.584 1.667 1.713 1.758 1.846 1.001 2000
mu[51] 1.886 0.065 1.762 1.841 1.885 1.927 2.014 1.001 2000
mu[52] 1.825 0.065 1.700 1.781 1.826 1.866 1.955 1.001 2000
mu[53] 2.094 0.133 1.843 2.006 2.091 2.174 2.384 1.002 1700
mu[54] 1.770 0.150 1.480 1.672 1.767 1.867 2.087 1.003 2000
mu[55] 1.549 0.097 1.352 1.487 1.547 1.613 1.737 1.000 2000
mu[56] 1.771 0.080 1.612 1.717 1.771 1.825 1.929 1.001 2000
mu[57] 1.936 0.055 1.833 1.899 1.937 1.971 2.046 1.001 2000
mu[58] 2.172 0.086 2.005 2.115 2.172 2.229 2.341 1.001 2000
mu[59] 2.412 0.083 2.247 2.359 2.411 2.464 2.577 1.001 2000
mu[60] 1.723 0.082 1.571 1.668 1.723 1.779 1.879 1.001 2000
mu[61] 1.740 0.080 1.592 1.686 1.740 1.793 1.894 1.001 2000
mu[62] 1.641 0.099 1.449 1.576 1.637 1.706 1.829 1.001 2000
mu[63] 1.599 0.111 1.383 1.526 1.596 1.673 1.812 1.000 2000
mu[64] 1.613 0.117 1.386 1.538 1.609 1.692 1.837 1.000 2000
mu[65] 1.787 0.066 1.663 1.745 1.786 1.830 1.920 1.001 2000
mu[66] 1.705 0.082 1.551 1.650 1.705 1.760 1.865 1.001 2000
mu[67] 2.114 0.110 1.901 2.041 2.116 2.187 2.329 1.001 2000
mu[68] 1.708 0.065 1.585 1.666 1.707 1.749 1.835 1.000 2000
p.val[1] 0.468 0.847 0.000 0.000 0.000 0.000 2.000 1.002 1400
p.val[2] 1.931 0.365 0.000 2.000 2.000 2.000 2.000 1.004 2000
p.val[3] 1.350 0.937 0.000 0.000 2.000 2.000 2.000 1.001 2000
p.val[4] 0.019 0.194 0.000 0.000 0.000 0.000 0.000 1.001 2000
p.val[5] 2.000 0.000 2.000 2.000 2.000 2.000 2.000 1.000 1
p.val[6] 0.270 0.684 0.000 0.000 0.000 0.000 2.000 1.000 2000
p.val[7] 0.262 0.675 0.000 0.000 0.000 0.000 2.000 1.000 2000
p.val[8] 0.003 0.077 0.000 0.000 0.000 0.000 0.000 1.134 2000
p.val[9] 1.934 0.358 0.000 2.000 2.000 2.000 2.000 1.013 1100
p.val[10] 0.111 0.458 0.000 0.000 0.000 0.000 2.000 1.004 1900
tau 15.903 3.127 10.532 13.757 15.614 17.839 22.972 1.002 1600
deviance 5.605 7.036 -5.543 0.449 4.695 9.795 22.375 1.001 2000
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 = 24.8 and DIC = 30.4
DIC is an estimate of expected predictive error (lower deviance is better).