Loading required package: coda Loading required package: lattice Linked to JAGS 3.4.0 Loaded modules: basemod,bugs Loading required package: boot Attaching package: ‘boot’ The following object is masked from ‘package:lattice’: melanoma Loading required package: MASS Loading required package: segmented mixtools package, version 1.0.1, Released January 2014 This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772. **************** Cleaning data for K2 Simulation JAGS @ 4 Removing 0 of 10 essays for length. Calculating initial values for chain 1 ; K2 Simulation JAGS @ 4 number of iterations= 34 number of iterations= 165 number of iterations= 44 number of iterations= 596 number of iterations= 109 number of iterations= 128 number of iterations= 172 number of iterations= 422 number of iterations= 67 WARNING! NOT CONVERGENT! number of iterations= 1000 Calculating initial values for chain 2 ; K2 Simulation JAGS @ 4 number of iterations= 482 number of iterations= 69 number of iterations= 48 number of iterations= 138 number of iterations= 97 One of the variances is going to zero; trying new starting values. number of iterations= 274 number of iterations= 139 number of iterations= 140 number of iterations= 54 number of iterations= 116 Calculating initial values for chain 3 ; K2 Simulation JAGS @ 4 number of iterations= 832 number of iterations= 248 One of the variances is going to zero; trying new starting values. number of iterations= 32 number of iterations= 68 number of iterations= 93 One of the variances is going to zero; trying new starting values. number of iterations= 200 number of iterations= 44 number of iterations= 182 number of iterations= 68 number of iterations= 264 Loading Model for K2 Simulation JAGS @ 4 module mix loaded module dic loaded Compiling data graph Resolving undeclared variables Allocating nodes Initializing Reading data back into data table Compiling model graph Resolving undeclared variables Allocating nodes Graph Size: 921 Initializing model Burn in iterations for K2 Simulation JAGS @ 4 **************** Learning hyperparameters for K2 Simulation JAGS @ 4 Attempt 1 Labeling components for level 2 model K2 Simulation JAGS @ 4 Labeling components for alpha0 Labeling components for mu0 Labeling components for tau0 Labeling components for beta0 Labeling components for gamma0 **************** Convergence diagnostics for K2 Simulation JAGS @ 4 Run Number 1 Iterations = 1:5000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 5000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE 1.097e+03 1.164e+01 9.505e-02 2.211e-01 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 1076 1088 1096 1104 1121 Potential scale reduction factors: Point est. Upper C.I. deviance 1 1.01 deviance 2903.478 Iterations = 1:5000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 5000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE 12.58757 4.72338 0.03857 0.28893 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 5.249 9.138 11.969 15.370 23.271 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.01 1.03 alphaN 271.3296 Iterations = 1:5000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 5000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE alpha0[1] 0.2079 0.2841 0.0023200 0.018566 alpha0[2] 0.4270 0.2928 0.0023903 0.014859 alpha0[3] 0.2606 0.2302 0.0018793 0.013833 alpha0[4] 0.1045 0.1157 0.0009449 0.007968 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.003988 0.01734 0.03934 0.5784 0.7437 alpha0[2] 0.010168 0.07634 0.59942 0.6892 0.7731 alpha0[3] 0.006499 0.06014 0.21667 0.3331 0.7494 alpha0[4] 0.003087 0.01304 0.04430 0.1965 0.3478 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.01 1.02 alpha0[3] 1.01 1.03 alpha0[4] 1.11 1.34 Multivariate psrf 1.11 alpha0[1] alpha0[2] alpha0[3] alpha0[4] 233.5748 454.1105 288.2044 197.1883 Iterations = 1:5000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 5000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE mu0[1] -2.3500 4.8114 0.03928 0.50889 mu0[2] -0.4429 0.6479 0.00529 0.04737 mu0[3] 1.1137 4.1824 0.03415 0.48196 mu0[4] 18.6498 42.0964 0.34372 11.57455 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -10.9105 -2.68190 -1.3564 -0.5560 -0.1319 mu0[2] -2.2917 -0.60347 -0.3823 -0.1676 0.6277 mu0[3] -0.5440 -0.07387 0.4133 0.7767 15.9592 mu0[4] 0.1986 0.83570 1.3973 6.6709 162.8855 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.28 1.99 mu0[2] 1.04 1.11 mu0[3] 1.35 3.18 mu0[4] 1.69 7.30 Multivariate psrf 1.5 mu0[1] mu0[2] mu0[3] mu0[4] 114.56376 188.17420 250.38827 55.92288 Iterations = 1:5000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 5000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE tau0[1] 0.499928 1.1441 0.009341 0.04604 tau0[2] 1.008025 1.1919 0.009731 0.05186 tau0[3] 0.367610 1.0132 0.008273 0.04292 tau0[4] 0.007166 0.8773 0.007163 0.02398 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -1.475 -0.38051 0.33496 1.5950 2.415 tau0[2] -1.262 -0.09035 1.53700 2.0047 2.517 tau0[3] -1.283 -0.38735 0.15085 1.1562 2.281 tau0[4] -1.692 -0.54548 -0.04706 0.5335 1.834 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.00 1.01 tau0[2] 1.00 1.00 tau0[3] 1.03 1.11 tau0[4] 1.01 1.02 Multivariate psrf 1.04 tau0[1] tau0[2] tau0[3] tau0[4] 632.5156 539.7275 559.6375 2425.0644 Iterations = 1:5000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 5000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE beta0[1] 1.0106 1.0728 0.008760 0.05801 beta0[2] 0.7787 0.6543 0.005342 0.02019 beta0[3] 0.8490 0.6954 0.005678 0.02559 beta0[4] 1.2215 1.5316 0.012505 0.07361 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1538 0.4628 0.6586 1.1442 4.030 beta0[2] 0.2339 0.4662 0.5962 0.8543 2.344 beta0[3] 0.1858 0.4939 0.6901 1.0017 2.440 beta0[4] 0.1583 0.5064 0.8204 1.3436 4.865 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.07 1.10 beta0[2] 1.03 1.03 beta0[3] 1.04 1.04 beta0[4] 1.15 1.32 Multivariate psrf 1.04 beta0[1] beta0[2] beta0[3] beta0[4] 435.3767 1143.1190 744.9886 450.3377 Iterations = 1:5000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 5000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE gamma0[1] 1.123 1.2537 0.010237 0.05967 gamma0[2] 1.041 0.6646 0.005426 0.01887 gamma0[3] 1.207 0.8882 0.007253 0.03102 gamma0[4] 1.366 1.1845 0.009671 0.03829 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.1353 0.5162 0.8259 1.283 4.282 gamma0[2] 0.2168 0.6955 0.9311 1.227 2.550 gamma0[3] 0.1947 0.7438 1.0747 1.438 3.174 gamma0[4] 0.1632 0.6560 1.1547 1.679 4.348 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.08 1.11 gamma0[2] 1.06 1.09 gamma0[3] 1.02 1.05 gamma0[4] 1.09 1.13 Multivariate psrf 1.03 gamma0[1] gamma0[2] gamma0[3] gamma0[4] 651.7233 1423.7520 873.6342 937.7263 Chains of length 5000 for K2 Simulation JAGS @ 4 did not converge in run 1 . Maximum Rhat value = 1.501287 . deviance [[ 1 ]] Mean SD Naive SE Time-series SE 1095.9654936 11.9074500 0.1683968 0.4138250 deviance [[ 2 ]] Mean SD Naive SE Time-series SE 1096.5582799 11.2769805 0.1594806 0.3176378 deviance [[ 3 ]] Mean SD Naive SE Time-series SE 1097.2807269 11.6965053 0.1654136 0.4096878 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 12.59391929 4.62024440 0.06534012 0.44289834 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 13.10038324 4.76907772 0.06744494 0.51920437 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 12.0684158 4.7240248 0.0668078 0.5344264 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.1788737 0.2648048 0.003744905 0.03191502 alpha0[2] 0.4026241 0.3057209 0.004323547 0.03295255 alpha0[3] 0.2717870 0.2461163 0.003480610 0.02811468 alpha0[4] 0.1467152 0.1153907 0.001631871 0.01509625 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2120588 0.2955643 0.004179910 0.03121183 alpha0[2] 0.4507880 0.2788408 0.003943405 0.01940578 alpha0[3] 0.2752506 0.2180306 0.003083418 0.02263528 alpha0[4] 0.0619026 0.1133351 0.001602801 0.01442630 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2327134 0.2885987 0.004081402 0.03331168 alpha0[2] 0.4276310 0.2911589 0.004117608 0.02290605 alpha0[3] 0.2346388 0.2232242 0.003156867 0.02048103 alpha0[4] 0.1050168 0.1021058 0.001443994 0.01163274 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -1.8802685 1.5107355 0.021365026 0.19573891 mu0[2] -0.5805002 0.5691715 0.008049300 0.07782004 mu0[3] 0.2083637 0.5175969 0.007319926 0.05202892 mu0[4] 1.3806146 1.1526372 0.016300752 0.19682071 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -3.5937438 7.8859685 0.11152444 1.48125747 mu0[2] -0.3428269 0.7586614 0.01072909 0.07850501 mu0[3] 2.6923096 6.9341650 0.09806390 1.44396754 mu0[4] 52.0002271 60.2831048 0.85253184 34.71362752 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -1.5758879 1.6186427 0.022891064 0.31353947 mu0[2] -0.4055041 0.5740082 0.008117702 0.08932940 mu0[3] 0.4405652 0.6059248 0.008569070 0.05348158 mu0[4] 2.5685479 3.4976286 0.049463937 0.81075044 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 0.41552609 1.0795031 0.01526648 0.06854653 tau0[2] 0.99609291 1.1552041 0.01633705 0.08767320 tau0[3] 0.57467131 1.0179381 0.01439582 0.08376329 tau0[4] 0.06272797 0.8255209 0.01167463 0.05029557 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 0.55660277 1.1913020 0.01684755 0.08234465 tau0[2] 0.99012698 1.2162280 0.01720006 0.08256035 tau0[3] 0.14908364 0.9850418 0.01393059 0.07214694 tau0[4] -0.06423991 0.9348885 0.01322132 0.02196771 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 0.52765571 1.1539683 0.01631958 0.08718214 tau0[2] 1.03785432 1.2029471 0.01701224 0.09851614 tau0[3] 0.37907652 0.9915375 0.01402246 0.06602316 tau0[4] 0.02301072 0.8634622 0.01221120 0.04651878 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.9099207 0.7926891 0.011210317 0.05326788 beta0[2] 0.7822991 0.5981553 0.008459193 0.04074375 beta0[3] 0.8197704 0.5646744 0.007985702 0.03380448 beta0[4] 0.9619354 1.0378650 0.014677628 0.09575245 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 1.1451693 1.3936901 0.01970975 0.14214374 beta0[2] 0.7858815 0.8059086 0.01139727 0.03148160 beta0[3] 0.8620044 0.8644087 0.01222459 0.05423122 beta0[4] 1.5648188 2.1935544 0.03102154 0.18619250 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.9768447 0.9237586 0.013063919 0.08511352 beta0[2] 0.7678629 0.5262854 0.007442800 0.03189531 beta0[3] 0.8651376 0.6193446 0.008758856 0.04254691 beta0[4] 1.1378156 0.9783887 0.013836506 0.07023367 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.103860 1.1093198 0.01568815 0.06684671 gamma0[2] 1.098150 0.8645544 0.01222665 0.04634284 gamma0[3] 1.151813 0.9160825 0.01295536 0.05467161 gamma0[4] 1.270210 0.8266844 0.01169108 0.05104797 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.275114 1.6575416 0.02344118 0.15796895 gamma0[2] 1.060680 0.5828264 0.00824241 0.02291288 gamma0[3] 1.337340 0.9362336 0.01324034 0.04580416 gamma0[4] 1.523404 1.6104926 0.02277581 0.08979735 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 0.9901310 0.8348225 0.011806173 0.05116409 gamma0[2] 0.9637629 0.4779843 0.006759719 0.02308543 gamma0[3] 1.1316771 0.7910916 0.011187724 0.05978772 gamma0[4] 1.3041115 0.9458498 0.013376336 0.05028436 **************** Learning hyperparameters for K2 Simulation JAGS @ 4 Attempt 2 Labeling components for level 2 model K2 Simulation JAGS @ 4 Labeling components for alpha0 Labeling components for mu0 Labeling components for tau0 Labeling components for beta0 Labeling components for gamma0 **************** Convergence diagnostics for K2 Simulation JAGS @ 4 Run Number 2 Iterations = 1:15000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 15000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE 1.097e+03 1.135e+01 5.349e-02 1.461e-01 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 1076 1089 1096 1104 1120 Potential scale reduction factors: Point est. Upper C.I. deviance 1 1 deviance 6221.589 Iterations = 1:15000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 15000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE 12.43723 4.79150 0.02259 0.15975 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 4.991 8.988 11.771 15.184 23.525 Potential scale reduction factors: Point est. Upper C.I. alphaN 1 1.01 alphaN 923.54 Iterations = 1:15000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 15000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE alpha0[1] 0.27475 0.3118 0.0014698 0.017780 alpha0[2] 0.40879 0.2748 0.0012953 0.009013 alpha0[3] 0.23582 0.2211 0.0010425 0.012299 alpha0[4] 0.08065 0.1147 0.0005407 0.007602 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.002496 0.014115 0.05865 0.6525 0.7638 alpha0[2] 0.004739 0.136378 0.37151 0.6752 0.7661 alpha0[3] 0.002323 0.039333 0.20361 0.3165 0.7353 alpha0[4] 0.001856 0.005728 0.01412 0.1378 0.3526 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.01 1.02 alpha0[3] 1.01 1.04 alpha0[4] 1.04 1.14 Multivariate psrf 1.05 alpha0[1] alpha0[2] alpha0[3] alpha0[4] 362.4348 925.6085 484.3666 224.4974 Iterations = 1:15000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 15000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE mu0[1] -5.7188 15.752 0.074257 2.0167 mu0[2] -0.5059 1.803 0.008501 0.1227 mu0[3] 3.2140 8.011 0.037764 0.8072 mu0[4] 25.6225 40.960 0.193089 8.4219 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -57.7746 -2.82980 -0.9806 -0.46678 -0.1071 mu0[2] -3.5196 -0.55613 -0.3279 0.08647 0.7941 mu0[3] -0.5928 0.07234 0.5451 1.03858 31.1341 mu0[4] 0.2084 0.99254 4.4035 34.55011 161.5500 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.32 2.62 mu0[2] 1.25 1.46 mu0[3] 1.56 6.00 mu0[4] 1.47 3.21 Multivariate psrf 1.56 mu0[1] mu0[2] mu0[3] mu0[4] 136.64046 265.99232 287.43650 18.96137 Iterations = 1:15000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 15000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE tau0[1] 0.68827 1.1779 0.005553 0.04750 tau0[2] 0.82814 1.2024 0.005668 0.04130 tau0[3] 0.25334 1.0214 0.004815 0.03428 tau0[4] -0.02328 0.9167 0.004321 0.01350 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -1.471 -0.2689 0.63499 1.8242 2.420 tau0[2] -1.263 -0.2793 1.06560 1.9440 2.460 tau0[3] -1.475 -0.4706 0.06247 0.9384 2.255 tau0[4] -1.830 -0.6070 -0.06308 0.5521 1.855 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.03 1.10 tau0[2] 1.04 1.12 tau0[3] 1.01 1.04 tau0[4] 1.00 1.01 Multivariate psrf 1.05 tau0[1] tau0[2] tau0[3] tau0[4] 857.1873 946.4263 878.0266 6465.6669 Iterations = 1:15000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 15000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE beta0[1] 1.0420 1.2451 0.005869 0.05613 beta0[2] 0.8392 0.9620 0.004535 0.06441 beta0[3] 0.9478 0.9586 0.004519 0.02475 beta0[4] 1.3391 1.5530 0.007321 0.05559 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1720 0.4713 0.6588 1.1214 4.293 beta0[2] 0.2532 0.4831 0.6394 0.9158 2.360 beta0[3] 0.1803 0.4999 0.7210 1.0673 3.220 beta0[4] 0.1517 0.5198 0.8634 1.5316 5.457 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.05 1.08 beta0[2] 1.18 1.28 beta0[3] 1.18 1.37 beta0[4] 1.06 1.11 Multivariate psrf 1.04 beta0[1] beta0[2] beta0[3] beta0[4] 1007.913 1398.424 1874.311 1349.569 Iterations = 1:15000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 15000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE gamma0[1] 1.092 1.0798 0.005090 0.02614 gamma0[2] 1.080 0.6788 0.003200 0.01663 gamma0[3] 1.217 0.8541 0.004026 0.01753 gamma0[4] 1.443 1.3513 0.006370 0.03541 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.1500 0.5685 0.8517 1.239 3.923 gamma0[2] 0.2321 0.7208 0.9750 1.295 2.431 gamma0[3] 0.1854 0.7363 1.0918 1.483 3.250 gamma0[4] 0.1553 0.6188 1.1385 1.733 5.339 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.04 1.05 gamma0[2] 1.02 1.05 gamma0[3] 1.05 1.08 gamma0[4] 1.00 1.00 Multivariate psrf 1.03 gamma0[1] gamma0[2] gamma0[3] gamma0[4] 1745.742 2132.356 2407.807 1759.279 Chains of length 10000 for K2 Simulation JAGS @ 4 did not converge in run 2 . Maximum Rhat value = 1.563583 . deviance [[ 1 ]] Mean SD Naive SE Time-series SE 1.096676e+03 1.159850e+01 9.470135e-02 2.870489e-01 deviance [[ 2 ]] Mean SD Naive SE Time-series SE 1.096438e+03 1.098240e+01 8.967095e-02 2.301291e-01 deviance [[ 3 ]] Mean SD Naive SE Time-series SE 1.096567e+03 1.144967e+01 9.348615e-02 2.381486e-01 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 12.46088719 4.75453014 0.03882058 0.26749539 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 12.72462918 4.91144610 0.04010179 0.25457525 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 12.12616372 4.68728062 0.03827149 0.30549973 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.27332266 0.3000006 0.0024494949 0.02369315 alpha0[2] 0.40129688 0.2863117 0.0023377255 0.01532829 alpha0[3] 0.24178144 0.2013985 0.0016444116 0.01342168 alpha0[4] 0.08359902 0.1078845 0.0008808735 0.01429473 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.34400561 0.3333924 0.0027221375 0.04109298 alpha0[2] 0.39629770 0.2446936 0.0019979152 0.01480195 alpha0[3] 0.20719593 0.2337559 0.0019086092 0.03051351 alpha0[4] 0.05250077 0.1127973 0.0009209861 0.01276054 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2069073 0.2848009 0.0023253896 0.02439566 alpha0[2] 0.4287756 0.2899368 0.0023673237 0.01664445 alpha0[3] 0.2584754 0.2239563 0.0018285955 0.01581571 alpha0[4] 0.1058417 0.1169140 0.0009545986 0.01236508 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -1.3353873 1.3249786 0.010818405 0.12792680 mu0[2] -0.3103402 0.5052684 0.004125499 0.04854017 mu0[3] 0.4781099 0.5671380 0.004630662 0.04901013 mu0[4] 18.9117141 25.8945691 0.211428271 18.02845301 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -11.6897969 25.146179 0.20531769 5.514386 mu0[2] -0.7308497 2.976814 0.02430558 0.356290 mu0[3] 8.7217584 12.087272 0.09869216 2.420177 mu0[4] 52.2530096 55.942262 0.45676666 17.338899 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -4.1310995 7.2776273 0.059421578 2.48572100 mu0[2] -0.4765473 0.7413908 0.006053431 0.07910782 mu0[3] 0.4420168 0.7798032 0.006367066 0.06876855 mu0[4] 5.7028809 9.0736404 0.074085963 3.56207491 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 0.64733274 1.1287111 0.009215888 0.06000647 tau0[2] 0.91024160 1.1772691 0.009612362 0.05844951 tau0[3] 0.29278778 0.9525054 0.007777174 0.05687846 tau0[4] 0.03509326 0.8959225 0.007315177 0.02580306 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 0.93770410 1.2163813 0.009931712 0.11571527 tau0[2] 0.52577712 1.2036089 0.009827425 0.09062501 tau0[3] 0.11547682 1.0577955 0.008636864 0.06038617 tau0[4] -0.04564966 0.9546723 0.007794866 0.01472628 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 0.47978775 1.1408685 0.009315152 0.05755807 tau0[2] 1.04841242 1.1646863 0.009509624 0.06101679 tau0[3] 0.35174744 1.0360414 0.008459242 0.06076145 tau0[4] -0.05928525 0.8953424 0.007310440 0.02753350 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.8988028 0.7371156 0.006018523 0.03175824 beta0[2] 0.7936803 0.5607859 0.004578798 0.02453485 beta0[3] 0.8451024 0.5907416 0.004823385 0.02281807 beta0[4] 1.3470241 1.6110099 0.013153841 0.14286383 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 1.0755519 1.405207 0.01147347 0.07265744 beta0[2] 0.9706388 1.470541 0.01200692 0.19080500 beta0[3] 1.1644493 1.419920 0.01159360 0.06761864 beta0[4] 1.5553949 1.897725 0.01549486 0.07646927 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 1.1515359 1.4489477 0.011830609 0.14853620 beta0[2] 0.7533618 0.5222459 0.004264120 0.01827559 beta0[3] 0.8339191 0.5669117 0.004628815 0.02052218 beta0[4] 1.1150034 0.9707799 0.007926385 0.03941788 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.031323 0.9261474 0.007561962 0.03871326 gamma0[2] 1.042857 0.6651194 0.005430677 0.01991178 gamma0[3] 1.219783 0.7783855 0.006355491 0.03239400 gamma0[4] 1.456091 1.3685569 0.011174220 0.07342207 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.199045 1.3187243 0.010767339 0.05719494 gamma0[2] 1.186063 0.6395947 0.005222269 0.02470999 gamma0[3] 1.325626 1.0504450 0.008576848 0.02901892 gamma0[4] 1.440890 1.4069052 0.011487333 0.04457779 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.045857 0.9403853 0.007678214 0.03711954 gamma0[2] 1.009622 0.7163529 0.005848997 0.03847857 gamma0[3] 1.106270 0.6747646 0.005509430 0.02958787 gamma0[4] 1.431833 1.2749055 0.010409560 0.06252482 MCMC run did not converge, proceeding anyway. Learning parameters for K2 Simulation JAGS @ 4 Labeling components for K2 Simulation JAGS @ 4 Labeling components for pi Labeling components for mu Labeling components for tau Calculating model fit indexes for K2 Simulation JAGS @ 4 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -529.39286 38.20577 1135.19726 38.20577 1135.19726 lppd lppd.bayes pDIC DIC pDICalt DICalt -548.49575 -1349.91100 -1602.83050 -505.83901 61.30465 2822.43129 Analaysis complete for K2 Simulation JAGS @ 4 > proc.time() user system elapsed 3069.435 5.357 3076.775