Inference for Bugs model at "/home/kubo/cao_alpinia/fruit_seed/winbugsS/model.bug.txt", fit using WinBUGS, 3 chains, each with 22000 iterations (first 2000 discarded), n.thin = 50 n.sims = 1200 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff beta[1] 3.010 0.087 2.829 2.955 3.013 3.072 3.160 1.023 230 beta[2] -0.009 0.008 -0.025 -0.014 -0.009 -0.004 0.006 1.017 180 beta[3] -0.020 0.011 -0.041 -0.028 -0.020 -0.013 0.000 1.001 1200 beta[4] 0.070 0.020 0.030 0.057 0.071 0.084 0.110 1.015 1100 beta[5] 0.152 0.118 -0.078 0.068 0.151 0.233 0.389 1.024 94 beta[6] 0.449 0.114 0.256 0.367 0.445 0.521 0.682 1.027 88 beta[7] -0.003 0.001 -0.005 -0.003 -0.003 -0.002 -0.001 1.000 1200 beta[8] 0.004 0.002 0.000 0.003 0.004 0.006 0.008 1.001 1200 beta[9] 0.007 0.008 -0.006 0.002 0.007 0.012 0.024 1.021 120 beta[10] 0.010 0.008 -0.005 0.004 0.010 0.015 0.025 1.033 130 beta[11] -0.153 0.144 -0.475 -0.242 -0.146 -0.050 0.095 1.020 150 tau[1] 218.579 71.666 127.597 172.250 205.400 244.850 387.247 1.002 730 tau[2] 7.066 1.027 5.210 6.355 6.960 7.734 9.192 1.000 1200 re[1] -0.263 0.125 -0.514 -0.349 -0.261 -0.179 -0.026 1.011 260 re[2] -0.024 0.091 -0.195 -0.091 -0.025 0.041 0.152 1.005 440 re[3] -0.013 0.135 -0.273 -0.105 -0.007 0.075 0.256 1.002 740 re[4] 0.138 0.086 -0.022 0.078 0.138 0.193 0.311 1.017 120 re[5] -0.182 0.099 -0.358 -0.249 -0.184 -0.112 0.015 1.002 750 re[6] 0.148 0.145 -0.113 0.045 0.140 0.244 0.450 1.031 70 re[7] 0.081 0.161 -0.211 -0.035 0.079 0.191 0.416 1.035 66 re[8] -0.195 0.129 -0.444 -0.283 -0.195 -0.109 0.060 1.015 150 re[9] 0.016 0.156 -0.268 -0.103 0.007 0.116 0.335 1.030 74 re[10] 0.134 0.077 -0.013 0.083 0.132 0.187 0.286 1.019 110 re[11] -0.069 0.115 -0.277 -0.148 -0.076 0.007 0.170 1.025 93 re[12] 0.214 0.082 0.057 0.160 0.212 0.267 0.384 1.015 130 re[13] -0.255 0.125 -0.487 -0.342 -0.258 -0.169 0.004 1.007 280 re[14] 0.245 0.181 -0.086 0.115 0.234 0.364 0.630 1.034 66 re[15] -0.047 0.184 -0.386 -0.177 -0.047 0.068 0.332 1.001 1200 re[16] -0.010 0.124 -0.249 -0.095 -0.019 0.072 0.250 1.031 74 re[17] 0.142 0.110 -0.068 0.066 0.140 0.211 0.376 1.037 62 re[18] -0.944 0.161 -1.236 -1.059 -0.944 -0.835 -0.622 1.002 880 re[19] 0.423 0.108 0.207 0.352 0.419 0.497 0.641 1.009 340 re[20] -0.016 0.091 -0.190 -0.079 -0.015 0.047 0.160 1.015 150 re[21] -0.058 0.095 -0.238 -0.119 -0.063 0.000 0.137 1.032 70 re[22] -0.491 0.096 -0.675 -0.557 -0.495 -0.429 -0.300 1.019 110 re[23] 0.152 0.101 -0.040 0.082 0.150 0.221 0.345 1.018 120 re[24] -0.026 0.090 -0.190 -0.087 -0.027 0.032 0.154 1.006 380 re[25] 0.174 0.097 -0.012 0.106 0.172 0.243 0.364 1.004 470 re[26] 0.003 0.087 -0.162 -0.060 0.000 0.062 0.180 1.021 100 re[27] -0.607 0.094 -0.797 -0.670 -0.604 -0.538 -0.425 1.004 1100 re[28] 0.183 0.093 0.003 0.125 0.177 0.242 0.379 1.019 110 re[29] 0.128 0.087 -0.040 0.071 0.128 0.185 0.293 1.009 210 re[30] -0.142 0.080 -0.292 -0.196 -0.144 -0.089 0.012 1.022 91 re[31] 0.079 0.087 -0.087 0.022 0.075 0.135 0.253 1.010 200 re[32] 0.060 0.132 -0.191 -0.035 0.059 0.148 0.327 1.004 450 re[33] -0.104 0.087 -0.280 -0.165 -0.102 -0.044 0.061 1.009 220 re[34] 0.138 0.090 -0.042 0.079 0.139 0.200 0.309 1.014 150 re[35] 0.009 0.089 -0.173 -0.051 0.010 0.070 0.179 1.004 450 re[36] 0.034 0.084 -0.129 -0.023 0.035 0.093 0.199 1.011 180 re[37] 0.092 0.085 -0.067 0.036 0.091 0.147 0.269 1.019 110 re[38] -0.167 0.087 -0.331 -0.227 -0.169 -0.106 0.003 1.010 390 re[39] -0.045 0.108 -0.259 -0.118 -0.048 0.027 0.166 1.001 1200 re[40] -0.044 0.129 -0.281 -0.135 -0.052 0.039 0.239 1.032 68 re[41] 0.056 0.102 -0.124 -0.018 0.050 0.124 0.254 1.000 1200 re[42] 0.192 0.092 0.019 0.126 0.192 0.255 0.370 1.005 600 re[43] 0.030 0.123 -0.199 -0.057 0.032 0.115 0.260 1.005 1200 re[44] 0.097 0.089 -0.071 0.032 0.094 0.154 0.275 1.010 290 re[45] 0.162 0.091 -0.014 0.101 0.156 0.220 0.355 1.028 85 re[46] -0.208 0.097 -0.397 -0.274 -0.213 -0.148 -0.018 1.012 170 re[47] -0.006 0.108 -0.205 -0.083 -0.010 0.067 0.208 1.005 1200 re[48] 0.062 0.104 -0.135 -0.010 0.059 0.136 0.265 1.003 1200 re[49] -0.313 0.092 -0.484 -0.379 -0.313 -0.246 -0.133 1.005 520 re[50] -0.270 0.129 -0.518 -0.359 -0.270 -0.185 -0.022 1.010 490 re[51] 0.085 0.118 -0.126 -0.003 0.081 0.169 0.321 1.008 1200 re[52] 0.129 0.117 -0.096 0.049 0.131 0.206 0.354 1.016 160 re[53] 0.068 0.092 -0.104 0.001 0.068 0.128 0.253 1.008 350 re[54] 0.139 0.095 -0.040 0.072 0.137 0.202 0.325 1.004 610 re[55] 0.173 0.088 0.004 0.111 0.176 0.234 0.342 1.014 140 re[56] 0.084 0.088 -0.082 0.027 0.077 0.142 0.275 1.021 97 re[57] -0.136 0.106 -0.324 -0.209 -0.141 -0.069 0.079 1.002 1200 re[58] 0.316 0.090 0.140 0.252 0.316 0.379 0.489 1.024 120 re[59] 0.492 0.083 0.329 0.439 0.493 0.547 0.650 1.017 130 re[60] -0.013 0.103 -0.227 -0.083 -0.012 0.057 0.184 1.017 140 re[61] 0.004 0.097 -0.185 -0.063 0.002 0.072 0.194 1.026 81 re[62] 0.338 0.094 0.162 0.271 0.341 0.404 0.525 1.029 120 re[63] -0.383 0.141 -0.660 -0.484 -0.386 -0.289 -0.110 1.007 370 re[64] 0.528 0.087 0.356 0.469 0.526 0.587 0.696 1.024 89 re[65] -0.332 0.167 -0.648 -0.445 -0.327 -0.217 -0.011 1.011 260 re[66] -0.259 0.101 -0.457 -0.332 -0.259 -0.184 -0.061 1.007 420 re[67] -0.256 0.129 -0.502 -0.345 -0.254 -0.174 -0.003 1.007 270 re[68] 0.113 0.090 -0.060 0.054 0.110 0.178 0.285 1.015 130 re[69] -0.117 0.122 -0.350 -0.198 -0.118 -0.041 0.127 1.009 220 re[70] 0.704 0.110 0.485 0.627 0.702 0.781 0.921 1.008 240 re[71] -0.089 0.133 -0.360 -0.176 -0.090 0.005 0.160 1.011 220 re[72] 0.089 0.094 -0.092 0.021 0.090 0.154 0.267 1.014 150 re[73] -0.059 0.089 -0.233 -0.119 -0.062 0.004 0.110 1.021 100 re[74] 0.505 0.087 0.337 0.445 0.504 0.566 0.675 1.022 110 re[75] 0.277 0.102 0.075 0.205 0.283 0.351 0.466 1.014 180 re[76] 0.424 0.165 0.105 0.317 0.420 0.533 0.781 1.028 75 re[77] 0.081 0.176 -0.240 -0.039 0.066 0.192 0.468 1.031 69 re[78] -0.404 0.131 -0.654 -0.496 -0.408 -0.315 -0.142 1.005 410 re[79] 0.071 0.094 -0.111 0.009 0.066 0.133 0.264 1.016 120 re[80] 0.027 0.086 -0.143 -0.030 0.026 0.087 0.193 1.026 85 re[81] -0.382 0.102 -0.575 -0.450 -0.386 -0.317 -0.169 1.024 89 re[82] 0.090 0.086 -0.081 0.029 0.091 0.147 0.264 1.023 90 re[83] -0.101 0.109 -0.303 -0.176 -0.100 -0.026 0.112 1.006 310 re[84] 0.114 0.104 -0.070 0.048 0.105 0.181 0.328 1.040 57 re[85] 0.437 0.164 0.133 0.325 0.427 0.535 0.805 1.032 67 re[86] 0.037 0.090 -0.135 -0.021 0.036 0.099 0.210 1.026 84 re[87] -1.446 0.132 -1.689 -1.534 -1.449 -1.363 -1.168 1.018 140 re[88] 0.231 0.126 -0.026 0.146 0.235 0.319 0.468 1.008 510 re[89] -0.929 0.170 -1.271 -1.042 -0.923 -0.815 -0.618 1.006 320 re[90] 0.610 0.099 0.433 0.544 0.607 0.668 0.813 1.014 370 re[91] -0.855 0.137 -1.131 -0.949 -0.853 -0.762 -0.604 1.005 1200 re[92] 0.252 0.109 0.053 0.176 0.247 0.323 0.484 1.016 170 re[93] 0.051 0.137 -0.220 -0.037 0.055 0.142 0.319 1.018 180 re[94] -0.070 0.100 -0.258 -0.138 -0.072 -0.006 0.135 1.020 150 re[95] 0.208 0.113 -0.011 0.127 0.208 0.282 0.433 1.010 800 re[96] 0.241 0.108 0.037 0.167 0.238 0.310 0.452 1.011 300 re[97] 0.548 0.140 0.275 0.453 0.548 0.650 0.802 1.011 650 re[98] 0.645 0.095 0.473 0.580 0.643 0.707 0.841 1.016 210 re[99] 0.315 0.135 0.046 0.227 0.319 0.401 0.579 1.024 170 re[100] 0.180 0.103 -0.004 0.109 0.173 0.248 0.389 1.014 280 re[101] -0.348 0.121 -0.574 -0.434 -0.349 -0.265 -0.126 1.010 1200 re[102] -1.568 0.136 -1.834 -1.663 -1.566 -1.476 -1.286 1.007 290 re[103] -0.701 0.161 -1.025 -0.806 -0.695 -0.592 -0.399 1.006 570 re[104] -0.036 0.111 -0.250 -0.111 -0.036 0.036 0.197 1.019 240 re[105] 0.354 0.102 0.166 0.284 0.353 0.417 0.561 1.006 1000 re[106] -0.397 0.118 -0.621 -0.477 -0.398 -0.316 -0.162 1.009 260 re[107] -0.354 0.106 -0.556 -0.426 -0.355 -0.286 -0.142 1.014 260 re[108] 0.282 0.129 0.034 0.198 0.277 0.365 0.540 1.026 150 re[109] 0.268 0.145 -0.018 0.176 0.269 0.363 0.544 1.030 110 re[110] 0.396 0.094 0.224 0.335 0.395 0.451 0.606 1.023 270 re[111] 0.174 0.097 -0.004 0.103 0.175 0.236 0.369 1.020 190 re[112] 0.265 0.112 0.041 0.190 0.266 0.337 0.486 1.032 91 re[113] 0.411 0.094 0.240 0.344 0.409 0.472 0.612 1.011 560 re[114] -0.216 0.119 -0.443 -0.298 -0.215 -0.135 0.017 1.009 560 deviance 10841.967 55.867 10730.000 10810.000 10840.000 10880.000 10950.000 1.001 1200 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 = 308.9 and DIC = 11150.9 DIC is an estimate of expected predictive error (lower deviance is better).