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 K3 Simulation JAGS @ 3 Removing 0 of 10 essays for length. Calculating initial values for chain 1 ; K3 Simulation JAGS @ 3 number of iterations= 58 number of iterations= 45 number of iterations= 26 number of iterations= 12 number of iterations= 36 number of iterations= 108 number of iterations= 22 number of iterations= 18 One of the variances is going to zero; trying new starting values. One of the variances is going to zero; trying new starting values. One of the variances is going to zero; trying new starting values. number of iterations= 48 number of iterations= 81 Calculating initial values for chain 2 ; K3 Simulation JAGS @ 3 number of iterations= 40 number of iterations= 76 number of iterations= 21 number of iterations= 22 number of iterations= 75 number of iterations= 200 number of iterations= 28 number of iterations= 18 One of the variances is going to zero; trying new starting values. One of the variances is going to zero; trying new starting values. number of iterations= 36 number of iterations= 119 Calculating initial values for chain 3 ; K3 Simulation JAGS @ 3 number of iterations= 67 number of iterations= 47 number of iterations= 26 number of iterations= 11 number of iterations= 55 number of iterations= 203 number of iterations= 25 number of iterations= 22 number of iterations= 40 One of the variances is going to zero; trying new starting values. One of the variances is going to zero; trying new starting values. One of the variances is going to zero; trying new starting values. One of the variances is going to zero; trying new starting values. number of iterations= 136 Loading Model for K3 Simulation JAGS @ 3 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: 836 Initializing model Burn in iterations for K3 Simulation JAGS @ 3 **************** Learning hyperparameters for K3 Simulation JAGS @ 3 Attempt 1 Labeling components for level 2 model K3 Simulation JAGS @ 3 Labeling components for alpha0 Labeling components for mu0 Labeling components for tau0 Labeling components for beta0 Labeling components for gamma0 **************** Convergence diagnostics for K3 Simulation JAGS @ 3 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 1130.2675 15.1771 0.1239 0.5580 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 1101 1120 1130 1140 1161 Potential scale reduction factors: Point est. Upper C.I. deviance 1.02 1.06 deviance 789.3857 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 13.22369 4.70493 0.03842 0.18032 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 5.854 9.750 12.682 15.975 23.996 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.04 1.12 alphaN 700.1714 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] 0.4124 0.06624 0.0005408 0.003069 alpha0[2] 0.4037 0.06256 0.0005108 0.002901 alpha0[3] 0.1838 0.06429 0.0005249 0.004762 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.28166 0.3682 0.4138 0.4574 0.5416 alpha0[2] 0.27737 0.3651 0.4045 0.4457 0.5245 alpha0[3] 0.08555 0.1362 0.1758 0.2234 0.3235 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.01 1.04 alpha0[3] 1.16 1.50 Multivariate psrf 1.19 alpha0[1] alpha0[2] alpha0[3] 480.9787 469.1441 182.0763 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] -0.9794 0.1229 0.001004 0.01907 mu0[2] -0.1968 0.2949 0.002408 0.09798 mu0[3] 1.2619 0.4946 0.004039 0.03956 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.2129 -1.0638 -0.9815 -0.90892 -0.6938 mu0[2] -0.7163 -0.3833 -0.2438 -0.05038 0.6293 mu0[3] 0.2859 0.9058 1.2890 1.61649 2.1845 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.13 1.36 mu0[2] 1.13 1.39 mu0[3] 1.07 1.22 Multivariate psrf 1.1 mu0[1] mu0[2] mu0[3] 52.14560 23.43098 190.82771 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] 1.8052 0.7060 0.005765 0.04991 tau0[2] 1.5995 0.8701 0.007104 0.05951 tau0[3] -0.3584 0.6506 0.005313 0.03521 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] 0.3938 1.3259 1.8146 2.3036 3.126 tau0[2] -0.1599 1.0350 1.5628 2.1512 3.369 tau0[3] -1.3319 -0.7848 -0.4654 -0.0389 1.105 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.15 1.46 tau0[2] 1.33 1.94 tau0[3] 1.02 1.04 Multivariate psrf 1.32 tau0[1] tau0[2] tau0[3] 188.5830 197.0092 340.2337 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] 0.2840 0.1364 0.001114 0.01704 beta0[2] 0.6635 0.2501 0.002042 0.04848 beta0[3] 0.6906 0.3705 0.003025 0.01853 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.08435 0.1936 0.2599 0.3433 0.6373 beta0[2] 0.33614 0.4783 0.6208 0.7823 1.2732 beta0[3] 0.14267 0.4058 0.6475 0.9098 1.5344 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.09 1.25 beta0[2] 1.07 1.16 beta0[3] 1.01 1.04 Multivariate psrf 1.07 beta0[1] beta0[2] beta0[3] 64.70167 47.81062 416.69362 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] 2.193 0.5906 0.004822 0.03991 gamma0[2] 2.021 0.6461 0.005276 0.04374 gamma0[3] 1.255 0.5919 0.004833 0.03164 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.2422 1.7679 2.124 2.545 3.502 gamma0[2] 0.9248 1.5884 1.935 2.381 3.552 gamma0[3] 0.4331 0.8089 1.135 1.598 2.666 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.00 1.02 gamma0[2] 1.07 1.21 gamma0[3] 1.07 1.20 Multivariate psrf 1.06 gamma0[1] gamma0[2] gamma0[3] 219.1404 227.7979 456.3769 Chains of length 5000 for K3 Simulation JAGS @ 3 did not converge in run 1 . Maximum Rhat value = 1.316539 . deviance [[ 1 ]] Mean SD Naive SE Time-series SE 1127.7451218 16.2819437 0.2302615 1.0927572 deviance [[ 2 ]] Mean SD Naive SE Time-series SE 1131.7416090 14.1119806 0.1995735 0.7416113 deviance [[ 3 ]] Mean SD Naive SE Time-series SE 1131.3159188 14.7391513 0.2084431 1.0287151 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 14.36315424 4.82401231 0.06822184 0.26508729 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 12.30829410 4.40669907 0.06232014 0.33725687 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 12.99962011 4.64122778 0.06563687 0.32960278 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4335544 0.05566330 0.0007871979 0.003567841 alpha0[2] 0.4094619 0.05874868 0.0008308318 0.004966605 alpha0[3] 0.1569836 0.04864693 0.0006879715 0.005649269 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3801606 0.06404296 0.0009057042 0.005612293 alpha0[2] 0.4071991 0.06476704 0.0009159442 0.004634034 alpha0[3] 0.2126403 0.06189809 0.0008753711 0.007522935 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4235395 0.06596980 0.0009329538 0.006366846 alpha0[2] 0.3945631 0.06297590 0.0008906138 0.005442611 alpha0[3] 0.1818974 0.06818326 0.0009642569 0.010750522 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.9540219 0.07595278 0.001074134 0.01420477 mu0[2] -0.1680282 0.28196694 0.003987615 0.10311103 mu0[3] 1.4171039 0.49497589 0.007000016 0.07067101 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -1.0253766 0.1458487 0.002062612 0.04153654 mu0[2] -0.1134908 0.3573167 0.005053221 0.27094243 mu0[3] 1.1396979 0.3962788 0.005604228 0.03901175 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.9589425 0.1229471 0.001738735 0.03669520 mu0[2] -0.3087490 0.1831065 0.002589517 0.04856790 mu0[3] 1.2287882 0.5403400 0.007641562 0.08698339 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 2.0617384 0.6077602 0.008595027 0.07071173 tau0[2] 1.2253195 0.5865888 0.008295619 0.06333377 tau0[3] -0.2689053 0.6706396 0.009484275 0.06608537 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.4731136 0.6475079 0.009157145 0.07847426 tau0[2] 2.1472170 0.9531786 0.013479981 0.13898099 tau0[3] -0.4097702 0.6936938 0.009810312 0.06313172 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.8808437 0.7247197 0.010249084 0.10613193 tau0[2] 1.4259699 0.7407810 0.010476225 0.09246028 tau0[3] -0.3964773 0.5719500 0.008088594 0.05298714 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3021026 0.1015695 0.001436410 0.02069045 beta0[2] 0.7214678 0.2713229 0.003837086 0.06973718 beta0[3] 0.6897068 0.3809009 0.005386752 0.03689821 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.2417289 0.1648579 0.002331442 0.03848515 beta0[2] 0.6429155 0.2900658 0.004102150 0.12400530 beta0[3] 0.7360138 0.3653281 0.005166519 0.03155311 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3082100 0.1250350 0.001768262 0.02650531 beta0[2] 0.6260639 0.1571931 0.002223046 0.03013936 beta0[3] 0.6462211 0.3596563 0.005086308 0.02710439 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.242248 0.5765736 0.008153982 0.06487547 gamma0[2] 1.957153 0.5314464 0.007515786 0.05887246 gamma0[3] 1.435225 0.6662719 0.009422508 0.07793473 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.152227 0.5775385 0.008167628 0.06998115 gamma0[2] 1.884847 0.7262700 0.010271008 0.09793890 gamma0[3] 1.147218 0.5317992 0.007520777 0.04015454 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.185355 0.6136113 0.008677775 0.07229149 gamma0[2] 2.221822 0.6162850 0.008715586 0.06449113 gamma0[3] 1.181776 0.5242866 0.007414532 0.03636224 **************** Learning hyperparameters for K3 Simulation JAGS @ 3 Attempt 2 Labeling components for level 2 model K3 Simulation JAGS @ 3 Labeling components for alpha0 Labeling components for mu0 Labeling components for tau0 Labeling components for beta0 Labeling components for gamma0 **************** Convergence diagnostics for K3 Simulation JAGS @ 3 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 1.128e+03 1.508e+01 7.108e-02 3.464e-01 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 1099 1118 1128 1138 1159 Potential scale reduction factors: Point est. Upper C.I. deviance 1 1.01 deviance 1943.44 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 13.58637 4.71172 0.02221 0.10668 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 6.216 10.143 13.011 16.337 24.436 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.01 1.05 alphaN 2021.455 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] 0.4244 0.06350 0.0002993 0.001983 alpha0[2] 0.4053 0.06439 0.0003035 0.002044 alpha0[3] 0.1703 0.05844 0.0002755 0.002967 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.29810 0.3828 0.4245 0.4658 0.5495 alpha0[2] 0.27508 0.3653 0.4079 0.4487 0.5273 alpha0[3] 0.08231 0.1282 0.1624 0.2033 0.3035 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.00 1.01 alpha0[3] 1.06 1.19 Multivariate psrf 1.09 alpha0[1] alpha0[2] alpha0[3] 1018.2530 1017.4179 381.8008 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] -0.9714 0.1276 0.0006014 0.01174 mu0[2] -0.1657 0.2598 0.0012248 0.04600 mu0[3] 1.3464 0.4987 0.0023511 0.02272 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.2128 -1.0455 -0.9744 -0.90452 -0.6891 mu0[2] -0.6284 -0.3340 -0.1799 -0.01586 0.4575 mu0[3] 0.3500 0.9939 1.3816 1.71114 2.2279 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.03 1.05 mu0[2] 1.05 1.11 mu0[3] 1.01 1.03 Multivariate psrf 1.03 mu0[1] mu0[2] mu0[3] 122.91035 63.96853 488.19580 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] 1.8564 0.7515 0.003543 0.03077 tau0[2] 1.6314 0.8522 0.004017 0.03301 tau0[3] -0.2723 0.6576 0.003100 0.01997 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] 0.28339 1.3628 1.8975 2.3801 3.212 tau0[2] -0.06808 1.0811 1.5995 2.1766 3.357 tau0[3] -1.32202 -0.7279 -0.3588 0.1039 1.168 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.24 1.67 tau0[2] 1.40 2.06 tau0[3] 1.00 1.01 Multivariate psrf 1.41 tau0[1] tau0[2] tau0[3] 506.7608 539.4520 1082.1354 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] 0.3136 0.1302 0.0006138 0.009314 beta0[2] 0.6424 0.2179 0.0010272 0.021704 beta0[3] 0.7009 0.3876 0.0018271 0.012416 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1172 0.2276 0.2872 0.3729 0.6503 beta0[2] 0.3273 0.4834 0.6071 0.7587 1.1745 beta0[3] 0.1468 0.4078 0.6439 0.9190 1.6181 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.05 1.10 beta0[2] 1.02 1.05 beta0[3] 1.00 1.01 Multivariate psrf 1.03 beta0[1] beta0[2] beta0[3] 203.6043 114.7371 972.3317 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] 2.200 0.6549 0.003087 0.02796 gamma0[2] 2.021 0.6300 0.002970 0.02335 gamma0[3] 1.366 0.6264 0.002953 0.02075 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.2422 1.7410 2.096 2.537 3.748 gamma0[2] 0.9405 1.6065 1.950 2.359 3.515 gamma0[3] 0.4628 0.8933 1.270 1.726 2.847 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.01 1.02 gamma0[2] 1.06 1.16 gamma0[3] 1.01 1.04 Multivariate psrf 1.05 gamma0[1] gamma0[2] gamma0[3] 549.1687 743.8726 902.8145 Chains of length 10000 for K3 Simulation JAGS @ 3 did not converge in run 2 . Maximum Rhat value = 1.405777 . deviance [[ 1 ]] Mean SD Naive SE Time-series SE 1127.4954824 15.4542888 0.1261837 0.5736121 deviance [[ 2 ]] Mean SD Naive SE Time-series SE 1128.9599656 14.5944278 0.1191630 0.5476858 deviance [[ 3 ]] Mean SD Naive SE Time-series SE 1127.6362361 15.1298077 0.1235344 0.6716209 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 14.07159636 4.73312781 0.03864583 0.15860365 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 12.82506307 4.51685797 0.03687999 0.18371456 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 13.86245589 4.78622145 0.03907933 0.20861076 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4367328 0.05785650 0.0004723964 0.002768910 alpha0[2] 0.4029121 0.06492203 0.0005300861 0.003604149 alpha0[3] 0.1603551 0.05218343 0.0004260759 0.004199171 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4028556 0.06357068 0.0005190524 0.003654542 alpha0[2] 0.4085273 0.06429975 0.0005250053 0.003156750 alpha0[3] 0.1886171 0.06191571 0.0005055397 0.005730107 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4336124 0.06328987 0.0005167596 0.003788627 alpha0[2] 0.4044622 0.06382106 0.0005210967 0.003827390 alpha0[3] 0.1619253 0.05642157 0.0004606802 0.005364651 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.9821030 0.1250300 0.001020866 0.02221223 mu0[2] -0.1180056 0.2274284 0.001856945 0.04220905 mu0[3] 1.3718500 0.5176467 0.004226568 0.04178508 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.9594961 0.1510957 0.001233691 0.02327006 mu0[2] -0.2024295 0.3151162 0.002572913 0.12487216 mu0[3] 1.2891507 0.4641349 0.003789646 0.03392308 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.9725698 0.1005170 0.000820718 0.01435003 mu0[2] -0.1766214 0.2185122 0.001784144 0.04088209 mu0[3] 1.3783314 0.5078945 0.004146941 0.04181527 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 2.1073507 0.6367803 0.005199289 0.04678706 tau0[2] 1.2580236 0.6227199 0.005084487 0.04242678 tau0[3] -0.2676004 0.6533291 0.005334410 0.03453748 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.4153840 0.6973948 0.005694205 0.05154950 tau0[2] 2.2224515 0.8755004 0.007148431 0.07260326 tau0[3] -0.3107044 0.6655167 0.005433921 0.03470246 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 2.0464572 0.7133920 0.005824821 0.06061832 tau0[2] 1.4136147 0.6986948 0.005704819 0.05227983 tau0[3] -0.2386277 0.6518954 0.005322703 0.03452554 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3266518 0.1197120 0.0009774446 0.01732401 beta0[2] 0.6354588 0.2307596 0.0018841444 0.03273217 beta0[3] 0.7001572 0.3774333 0.0030817297 0.02055704 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3223612 0.1607098 0.001312190 0.01904392 beta0[2] 0.6734923 0.2201397 0.001797433 0.04767596 beta0[3] 0.7309196 0.3879313 0.003167445 0.02194428 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.2918523 0.09990496 0.0008157206 0.01085932 beta0[2] 0.6181035 0.19782939 0.0016152702 0.02992095 beta0[3] 0.6715388 0.39493757 0.0032246518 0.02198448 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.190465 0.6581756 0.005373981 0.04959063 gamma0[2] 2.066567 0.5693434 0.004648669 0.03444804 gamma0[3] 1.435104 0.6551424 0.005349215 0.03605345 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.262342 0.6449818 0.005266254 0.04544942 gamma0[2] 1.854139 0.7102102 0.005798842 0.05061004 gamma0[3] 1.273821 0.5954546 0.004861866 0.03431424 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.147761 0.6563749 0.005359279 0.05010235 gamma0[2] 2.142876 0.5635483 0.004601353 0.03405849 gamma0[3] 1.388767 0.6161172 0.005030576 0.03739281 MCMC run did not converge, proceeding anyway. Learning parameters for K3 Simulation JAGS @ 3 Labeling components for K3 Simulation JAGS @ 3 Labeling components for pi Labeling components for mu Labeling components for tau Calculating model fit indexes for K3 Simulation JAGS @ 3 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -534.79202 60.29588 1190.17580 60.29588 1190.17580 lppd lppd.bayes pDIC DIC pDICalt DICalt -564.9400 -666.5776 -203.2752 926.6047 107.3182 1547.7916 Analaysis complete for K3 Simulation JAGS @ 3 > proc.time() user system elapsed 1934.176 5.391 1943.593