Inference for Bugs model at "/home/kubo/Andrew_strep/model.bug.txt", fit using WinBUGS, 3 chains, each with 3000 iterations (first 1000 discarded), n.thin = 10 n.sims = 600 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff beta[1] 4.308 1.124 2.117 3.498 4.359 5.012 6.455 1.000 600 beta[2] 1.455 0.915 -0.220 0.830 1.413 2.081 3.372 1.002 600 beta[3] 1.229 0.337 0.559 1.017 1.232 1.446 1.896 1.002 600 beta[4] 1.727 0.310 1.105 1.517 1.734 1.942 2.292 1.004 470 beta[5] 2.596 0.299 2.046 2.394 2.601 2.800 3.184 0.999 600 beta[6] -6.576 1.663 -9.787 -7.612 -6.617 -5.507 -3.102 1.001 600 beta[7] -3.986 1.306 -6.647 -4.854 -3.942 -3.111 -1.382 1.007 240 beta[8] -1.570 1.417 -4.249 -2.534 -1.645 -0.651 1.312 1.000 600 beta[9] 1.355 1.613 -1.909 0.280 1.420 2.485 4.234 1.056 67 r[1] -2.626 1.506 -5.499 -3.628 -2.577 -1.680 0.345 1.011 200 r[2] 4.725 1.096 2.715 3.983 4.786 5.482 6.931 1.044 230 r[3] 1.932 1.221 -0.363 1.174 1.977 2.726 4.273 1.003 600 r[4] -0.489 1.519 -3.464 -1.350 -0.429 0.571 2.252 1.049 69 r[5] -2.575 1.163 -4.565 -3.423 -2.621 -1.817 -0.240 1.008 210 r[6] -4.192 1.369 -7.178 -5.073 -4.090 -3.316 -1.563 1.010 600 r[7] 2.138 1.811 -1.400 1.020 2.135 3.285 5.703 1.003 600 r[8] -4.397 1.301 -6.923 -5.211 -4.412 -3.462 -1.828 1.001 600 r[9] 2.323 1.549 -0.592 1.302 2.351 3.372 5.408 0.999 600 r[10] -1.765 1.392 -4.448 -2.725 -1.779 -0.770 0.943 0.999 600 r[11] 0.524 1.306 -2.194 -0.265 0.546 1.384 3.012 1.000 600 r[12] 2.009 1.298 -0.423 1.107 2.025 2.957 4.423 1.003 460 r[13] 0.044 1.443 -2.709 -0.960 0.019 0.972 2.756 1.004 500 r[14] 1.158 1.198 -1.297 0.435 1.223 1.920 3.550 1.000 600 r[15] -0.009 1.961 -3.663 -1.328 -0.070 1.300 3.902 1.001 600 r[16] -3.989 1.309 -6.592 -4.831 -4.017 -3.182 -1.328 1.001 600 r[17] -0.744 1.347 -3.188 -1.661 -0.688 0.190 1.857 1.010 180 r[18] 1.031 1.278 -1.528 0.221 1.018 1.827 3.597 1.003 600 r[19] -0.197 1.176 -2.375 -0.992 -0.170 0.508 2.121 1.002 600 r[20] 0.334 1.398 -2.228 -0.592 0.250 1.296 2.980 1.008 220 r[21] 4.425 1.634 1.256 3.328 4.441 5.647 7.673 1.001 600 r[22] 4.006 1.800 0.465 2.758 4.034 5.290 7.263 1.001 600 r[23] -3.399 2.250 -8.204 -4.673 -3.317 -1.821 0.431 0.999 600 r[24] -2.429 2.467 -7.395 -4.118 -2.332 -0.689 2.154 0.999 600 r[25] -4.395 2.183 -9.143 -5.749 -4.153 -2.962 -0.403 1.016 130 r[26] -0.163 1.737 -3.839 -1.317 -0.160 1.096 3.313 1.008 230 r[27] 0.112 1.526 -2.985 -0.848 0.058 1.169 3.091 1.004 450 r[28] 2.464 1.317 -0.185 1.526 2.447 3.398 4.931 1.005 500 r[29] 3.751 1.109 1.673 3.024 3.786 4.463 5.871 1.001 600 r[30] 1.250 1.180 -1.007 0.391 1.256 2.006 3.510 1.000 600 r[31] 0.296 1.400 -2.615 -0.551 0.315 1.130 3.291 1.060 58 r[32] -6.777 1.857 -10.972 -7.809 -6.660 -5.521 -3.517 1.009 210 r[33] 0.898 1.391 -1.871 -0.068 0.952 1.888 3.536 1.047 84 r[34] 0.029 1.186 -2.385 -0.797 0.079 0.830 2.123 1.000 600 r[35] 3.764 1.091 1.830 2.971 3.768 4.506 6.075 1.026 310 r[36] -0.507 1.384 -3.338 -1.353 -0.490 0.306 2.408 1.053 64 r[37] 1.438 1.200 -0.931 0.646 1.446 2.246 3.558 1.000 600 r[38] -1.288 1.492 -4.612 -2.221 -1.274 -0.274 1.531 1.046 78 r[39] 0.335 1.292 -2.269 -0.433 0.329 1.258 2.725 1.003 600 r[40] 1.091 1.401 -1.838 0.219 1.070 2.006 3.992 1.048 87 r[41] -1.627 1.143 -3.894 -2.306 -1.572 -0.854 0.389 1.002 510 r[42] -2.229 1.099 -4.329 -2.934 -2.238 -1.471 -0.283 1.008 390 r[43] 3.170 1.202 0.763 2.382 3.199 3.972 5.394 1.002 600 r[44] -8.740 1.751 -12.681 -9.752 -8.638 -7.525 -5.793 1.001 600 r[45] 0.252 1.178 -1.921 -0.604 0.260 1.037 2.573 1.004 370 r[46] 1.059 1.903 -2.722 -0.240 1.004 2.423 4.997 1.003 430 r[47] 3.031 1.052 1.075 2.277 3.086 3.709 5.050 1.005 330 r[48] 1.169 1.281 -1.374 0.417 1.243 2.024 3.524 1.009 600 r[49] 0.409 1.383 -2.383 -0.436 0.465 1.251 3.044 1.051 61 r[50] 1.090 1.293 -1.542 0.290 1.124 1.936 3.700 1.015 440 tau[1] 0.256 0.025 0.211 0.239 0.257 0.272 0.307 1.010 240 tau[2] 0.105 0.028 0.058 0.086 0.102 0.121 0.168 1.001 600 deviance 3214.845 29.477 3156.975 3195.000 3215.500 3234.000 3276.000 1.006 340 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 = 365.5 and DIC = 3580.3 DIC is an estimate of expected predictive error (lower deviance is better).