Inference for Bugs model at "/home/kubo/cao_alpinia/fruit_seed/winbugsS/model.bug.txt", fit using WinBUGS, 3 chains, each with 42000 iterations (first 2000 discarded), n.thin = 100 n.sims = 1200 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff beta[1] 2.999 0.073 2.855 2.950 3.001 3.049 3.144 1.002 1200 beta[2] -0.010 0.007 -0.023 -0.014 -0.010 -0.005 0.003 1.013 160 beta[3] -0.017 0.012 -0.040 -0.025 -0.017 -0.010 0.005 1.000 1200 beta[4] 0.064 0.020 0.024 0.050 0.064 0.077 0.104 1.001 1200 beta[5] 0.143 0.102 -0.051 0.073 0.140 0.208 0.342 1.007 290 beta[6] 0.461 0.104 0.262 0.390 0.461 0.529 0.665 1.010 620 beta[7] -0.003 0.001 -0.004 -0.003 -0.003 -0.002 -0.001 1.000 1200 beta[8] 0.004 0.002 0.000 0.003 0.004 0.005 0.008 1.001 1200 beta[9] 0.007 0.007 -0.008 0.002 0.007 0.011 0.020 1.053 42 beta[10] 0.011 0.007 -0.003 0.006 0.011 0.016 0.024 1.001 1200 beta[11] -0.152 0.137 -0.428 -0.236 -0.154 -0.062 0.117 1.007 390 tau[1] 159.400 43.230 98.529 131.100 152.300 179.300 261.902 1.002 1100 tau[2] 7.202 1.033 5.416 6.452 7.152 7.877 9.354 1.000 1200 re[1] -0.260 0.120 -0.490 -0.341 -0.260 -0.179 -0.027 1.010 200 re[2] -0.023 0.091 -0.207 -0.080 -0.022 0.034 0.157 1.016 150 re[3] -0.007 0.129 -0.248 -0.093 -0.010 0.069 0.275 1.039 57 re[4] 0.144 0.088 -0.028 0.083 0.143 0.205 0.309 1.001 1200 re[5] -0.174 0.105 -0.372 -0.245 -0.172 -0.105 0.037 1.015 140 re[6] 0.134 0.127 -0.108 0.044 0.136 0.215 0.385 1.010 190 re[7] 0.063 0.137 -0.199 -0.033 0.061 0.149 0.344 1.015 140 re[8] -0.187 0.126 -0.452 -0.268 -0.183 -0.103 0.057 1.016 140 re[9] -0.003 0.132 -0.266 -0.089 -0.004 0.083 0.267 1.015 130 re[10] 0.139 0.081 -0.020 0.086 0.140 0.190 0.291 1.003 750 re[11] -0.080 0.101 -0.272 -0.149 -0.084 -0.011 0.124 1.016 150 re[12] 0.222 0.087 0.050 0.164 0.219 0.279 0.402 1.000 1200 re[13] -0.248 0.126 -0.489 -0.332 -0.244 -0.167 0.001 1.009 240 re[14] 0.225 0.159 -0.084 0.118 0.223 0.326 0.543 1.017 130 re[15] -0.046 0.173 -0.375 -0.165 -0.049 0.059 0.338 1.053 45 re[16] -0.023 0.109 -0.232 -0.096 -0.028 0.049 0.200 1.013 160 re[17] 0.134 0.095 -0.044 0.068 0.131 0.198 0.327 1.013 180 re[18] -0.939 0.151 -1.231 -1.040 -0.939 -0.854 -0.613 1.039 58 re[19] 0.429 0.104 0.231 0.359 0.427 0.499 0.641 1.010 190 re[20] -0.008 0.093 -0.190 -0.071 -0.009 0.054 0.179 1.012 170 re[21] -0.065 0.086 -0.234 -0.123 -0.068 -0.007 0.095 1.019 120 re[22] -0.487 0.098 -0.672 -0.553 -0.487 -0.421 -0.288 1.005 390 re[23] 0.157 0.100 -0.046 0.090 0.155 0.223 0.352 1.006 340 re[24] -0.028 0.085 -0.197 -0.090 -0.026 0.030 0.131 1.012 170 re[25] 0.182 0.103 -0.022 0.114 0.185 0.248 0.385 1.010 250 re[26] 0.012 0.089 -0.163 -0.047 0.008 0.069 0.196 1.003 1200 re[27] -0.603 0.097 -0.786 -0.672 -0.603 -0.542 -0.409 1.014 150 re[28] 0.177 0.086 0.017 0.118 0.178 0.233 0.354 1.020 160 re[29] 0.066 0.090 -0.107 0.004 0.066 0.126 0.236 1.009 220 re[30] -0.134 0.079 -0.287 -0.190 -0.141 -0.079 0.021 1.002 1200 re[31] 0.083 0.090 -0.085 0.022 0.080 0.142 0.254 1.002 1100 re[32] 0.063 0.127 -0.185 -0.020 0.060 0.140 0.337 1.039 58 re[33] -0.099 0.089 -0.267 -0.161 -0.099 -0.044 0.080 1.012 180 re[34] 0.143 0.094 -0.043 0.083 0.143 0.206 0.317 1.000 1200 re[35] 0.014 0.091 -0.167 -0.048 0.014 0.074 0.188 1.009 210 re[36] 0.040 0.089 -0.127 -0.020 0.040 0.100 0.211 1.002 1000 re[37] 0.098 0.091 -0.077 0.035 0.100 0.158 0.276 1.004 1000 re[38] -0.162 0.089 -0.328 -0.222 -0.163 -0.101 0.012 1.012 280 re[39] -0.044 0.110 -0.269 -0.113 -0.042 0.033 0.159 1.011 180 re[40] -0.058 0.111 -0.271 -0.133 -0.063 0.020 0.162 1.014 160 re[41] 0.062 0.106 -0.149 -0.013 0.064 0.133 0.268 1.006 440 re[42] 0.197 0.096 0.004 0.129 0.197 0.263 0.387 1.013 190 re[43] 0.042 0.125 -0.206 -0.044 0.040 0.125 0.279 1.013 180 re[44] 0.101 0.091 -0.073 0.036 0.098 0.165 0.272 1.006 650 re[45] 0.161 0.088 -0.001 0.099 0.158 0.219 0.337 1.023 110 re[46] -0.202 0.098 -0.381 -0.271 -0.202 -0.135 -0.013 1.001 1200 re[47] 0.004 0.113 -0.225 -0.069 0.004 0.080 0.231 1.014 150 re[48] 0.070 0.107 -0.135 -0.001 0.072 0.138 0.281 1.015 180 re[49] -0.309 0.095 -0.491 -0.374 -0.311 -0.242 -0.128 1.011 190 re[50] -0.261 0.133 -0.514 -0.352 -0.262 -0.172 -0.009 1.011 190 re[51] 0.094 0.123 -0.147 0.005 0.094 0.173 0.340 1.008 280 re[52] 0.138 0.118 -0.093 0.063 0.142 0.214 0.356 1.009 260 re[53] 0.068 0.095 -0.129 0.007 0.067 0.132 0.258 1.016 190 re[54] 0.136 0.101 -0.066 0.070 0.134 0.204 0.337 1.014 180 re[55] 0.180 0.092 -0.008 0.118 0.182 0.243 0.359 1.008 250 re[56] 0.081 0.083 -0.073 0.027 0.079 0.138 0.246 1.019 190 re[57] -0.130 0.112 -0.353 -0.202 -0.129 -0.051 0.083 1.011 190 re[58] 0.320 0.092 0.143 0.260 0.319 0.381 0.500 1.012 170 re[59] 0.500 0.090 0.319 0.444 0.499 0.564 0.675 1.002 1200 re[60] -0.006 0.105 -0.207 -0.075 -0.010 0.065 0.201 1.007 310 re[61] 0.014 0.094 -0.176 -0.046 0.015 0.080 0.200 1.001 1200 re[62] 0.346 0.095 0.166 0.283 0.345 0.409 0.534 1.000 1200 re[63] -0.384 0.138 -0.674 -0.469 -0.390 -0.293 -0.118 1.008 260 re[64] 0.540 0.086 0.374 0.483 0.535 0.596 0.716 1.002 1200 re[65] -0.339 0.166 -0.686 -0.445 -0.332 -0.226 -0.024 1.019 110 re[66] -0.253 0.097 -0.444 -0.318 -0.255 -0.190 -0.062 1.003 770 re[67] -0.256 0.129 -0.512 -0.342 -0.260 -0.170 0.008 1.002 1100 re[68] 0.123 0.091 -0.045 0.059 0.120 0.181 0.296 1.001 1200 re[69] -0.111 0.123 -0.350 -0.198 -0.114 -0.032 0.134 1.004 460 re[70] 0.709 0.112 0.488 0.634 0.711 0.787 0.926 1.006 310 re[71] -0.086 0.126 -0.328 -0.167 -0.086 -0.007 0.157 1.003 670 re[72] 0.098 0.092 -0.082 0.039 0.095 0.161 0.277 1.004 1200 re[73] -0.046 0.086 -0.198 -0.108 -0.050 0.013 0.125 1.000 1200 re[74] 0.518 0.085 0.354 0.458 0.517 0.574 0.687 1.001 1200 re[75] 0.277 0.098 0.089 0.209 0.278 0.344 0.475 1.004 610 re[76] 0.127 0.157 -0.181 0.021 0.131 0.229 0.436 1.018 110 re[77] 0.122 0.164 -0.201 0.008 0.128 0.240 0.419 1.023 88 re[78] -0.401 0.132 -0.672 -0.488 -0.400 -0.314 -0.158 1.004 520 re[79] 0.083 0.092 -0.089 0.024 0.084 0.145 0.275 1.001 1200 re[80] 0.042 0.084 -0.122 -0.014 0.043 0.098 0.209 1.002 970 re[81] -0.359 0.097 -0.544 -0.422 -0.361 -0.298 -0.165 1.006 310 re[82] 0.097 0.083 -0.056 0.039 0.095 0.154 0.263 1.001 1200 re[83] -0.097 0.109 -0.312 -0.164 -0.100 -0.023 0.115 1.004 490 re[84] 0.138 0.100 -0.062 0.069 0.142 0.205 0.323 1.009 240 re[85] 0.472 0.153 0.162 0.361 0.477 0.582 0.758 1.024 100 re[86] 0.052 0.087 -0.106 -0.010 0.049 0.112 0.221 1.000 1200 re[87] -1.416 0.125 -1.657 -1.496 -1.414 -1.332 -1.170 1.001 1200 re[88] 0.249 0.109 0.040 0.178 0.245 0.327 0.461 1.009 320 re[89] -0.912 0.157 -1.220 -1.020 -0.911 -0.803 -0.614 1.005 420 re[90] 0.621 0.089 0.448 0.562 0.622 0.682 0.786 1.000 1200 re[91] -0.839 0.120 -1.081 -0.919 -0.839 -0.760 -0.608 1.004 860 re[92] 0.255 0.103 0.053 0.187 0.251 0.326 0.458 1.001 1200 re[93] 0.051 0.128 -0.210 -0.035 0.054 0.135 0.303 1.011 210 re[94] -0.060 0.091 -0.240 -0.125 -0.057 0.004 0.112 1.003 580 re[95] 0.217 0.098 0.020 0.149 0.219 0.283 0.416 1.006 550 re[96] 0.183 0.094 -0.006 0.121 0.182 0.244 0.367 1.002 1200 re[97] 0.563 0.120 0.336 0.481 0.565 0.645 0.792 1.008 650 re[98] 0.653 0.084 0.502 0.591 0.648 0.708 0.822 1.001 1200 re[99] 0.323 0.122 0.088 0.241 0.324 0.401 0.564 1.006 370 re[100] 0.190 0.088 0.020 0.132 0.190 0.252 0.363 1.003 630 re[101] -0.333 0.105 -0.536 -0.405 -0.333 -0.262 -0.128 1.001 1200 re[102] -1.551 0.137 -1.838 -1.639 -1.547 -1.455 -1.294 1.005 420 re[103] -0.690 0.145 -0.975 -0.789 -0.686 -0.590 -0.426 1.003 890 re[104] -0.035 0.105 -0.251 -0.110 -0.034 0.037 0.167 1.003 650 re[105] 0.361 0.088 0.192 0.302 0.361 0.422 0.536 1.000 1200 re[106] -0.387 0.106 -0.592 -0.458 -0.389 -0.315 -0.180 1.002 920 re[107] -0.536 0.107 -0.746 -0.607 -0.535 -0.465 -0.333 1.000 1200 re[108] 0.282 0.119 0.049 0.199 0.286 0.363 0.511 1.006 410 re[109] 0.268 0.133 0.012 0.183 0.265 0.357 0.521 1.006 320 re[110] 0.404 0.085 0.237 0.345 0.408 0.463 0.569 1.003 570 re[111] 0.179 0.084 0.015 0.123 0.179 0.236 0.349 1.004 430 re[112] 0.268 0.102 0.074 0.196 0.269 0.338 0.460 1.008 270 re[113] 0.420 0.085 0.258 0.359 0.419 0.476 0.584 1.001 1200 re[114] -0.205 0.106 -0.421 -0.275 -0.206 -0.136 -0.002 1.002 910 deviance 10944.908 61.006 10820.000 10900.000 10950.000 10990.000 11060.000 1.006 310 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 = 363.5 and DIC = 11308.4 DIC is an estimate of expected predictive error (lower deviance is better).