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 K4 Simulation JAGS @ 4 Removing 0 of 10 essays for length. Calculating initial values for chain 1 ; K4 Simulation JAGS @ 4 WARNING! NOT CONVERGENT! number of iterations= 1000 number of iterations= 306 number of iterations= 18 number of iterations= 47 number of iterations= 105 number of iterations= 297 number of iterations= 52 number of iterations= 55 number of iterations= 32 One of the variances is going to zero; trying new starting values. number of iterations= 29 Calculating initial values for chain 2 ; K4 Simulation JAGS @ 4 number of iterations= 218 One of the variances is going to zero; trying new starting values. number of iterations= 61 number of iterations= 34 One of the variances is going to zero; trying new starting values. number of iterations= 55 number of iterations= 106 number of iterations= 19 number of iterations= 68 number of iterations= 651 number of iterations= 45 number of iterations= 286 Calculating initial values for chain 3 ; K4 Simulation JAGS @ 4 number of iterations= 899 number of iterations= 38 number of iterations= 24 number of iterations= 77 number of iterations= 125 number of iterations= 75 One of the variances is going to zero; trying new starting values. number of iterations= 105 number of iterations= 182 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= 27 number of iterations= 30 Loading Model for K4 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: 905 Initializing model Burn in iterations for K4 Simulation JAGS @ 4 **************** Learning hyperparameters for K4 Simulation JAGS @ 4 Attempt 1 Labeling components for level 2 model K4 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 K4 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 1021.1078 18.9923 0.1551 0.7046 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 984.7 1008.3 1020.9 1033.5 1058.9 Potential scale reduction factors: Point est. Upper C.I. deviance 1.11 1.35 deviance 786.3123 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 12.84163 4.53750 0.03705 0.21478 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 5.701 9.486 12.299 15.610 22.985 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.08 1.24 alphaN 477.0043 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] 0.2402 0.07373 0.0006020 0.005319 alpha0[2] 0.2722 0.12193 0.0009956 0.011759 alpha0[3] 0.3336 0.11398 0.0009307 0.006360 alpha0[4] 0.1540 0.08537 0.0006970 0.006888 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.09779 0.18833 0.2395 0.2909 0.3871 alpha0[2] 0.04260 0.18036 0.2834 0.3588 0.4952 alpha0[3] 0.06849 0.26940 0.3458 0.4149 0.5174 alpha0[4] 0.03403 0.09344 0.1398 0.1969 0.3800 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.19 1.56 alpha0[3] 1.40 2.14 alpha0[4] 1.13 1.31 Multivariate psrf 1.42 alpha0[1] alpha0[2] alpha0[3] alpha0[4] 163.1005 107.0039 239.0085 161.9809 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] -1.02998 0.1290 0.001053 0.02758 mu0[2] -0.42298 0.3391 0.002769 0.06766 mu0[3] -0.01286 0.1633 0.001333 0.02235 mu0[4] 0.66153 0.3747 0.003060 0.03955 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.28555 -1.1176 -1.02857 -0.95175 -0.7777 mu0[2] -1.02183 -0.7290 -0.36082 -0.13934 0.1198 mu0[3] -0.28649 -0.1225 -0.03218 0.06465 0.3968 mu0[4] -0.05331 0.3866 0.66672 0.92322 1.3848 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.07 1.20 mu0[2] 1.86 3.13 mu0[3] 1.45 2.25 mu0[4] 1.14 1.42 Multivariate psrf 1.79 mu0[1] mu0[2] mu0[3] mu0[4] 23.17042 38.67125 38.29458 80.54816 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] 1.870 1.1215 0.009157 0.08716 tau0[2] 1.298 0.9542 0.007791 0.06928 tau0[3] 1.226 0.9350 0.007634 0.07654 tau0[4] 0.976 0.8366 0.006831 0.03732 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.1676 1.0876 1.8115 2.589 4.268 tau0[2] -0.5905 0.6776 1.3080 1.902 3.148 tau0[3] -0.6057 0.5928 1.1938 1.837 3.082 tau0[4] -0.6884 0.4330 0.9844 1.542 2.561 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.09 1.26 tau0[2] 1.06 1.20 tau0[3] 1.03 1.11 tau0[4] 1.03 1.11 Multivariate psrf 1.13 tau0[1] tau0[2] tau0[3] tau0[4] 166.7404 182.6575 161.4883 502.9597 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] 0.3901 0.1337 0.001092 0.02180 beta0[2] 0.5627 0.3565 0.002911 0.04050 beta0[3] 0.5391 0.3886 0.003173 0.05636 beta0[4] 0.8428 0.3326 0.002715 0.02840 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1822 0.2861 0.3746 0.4842 0.6756 beta0[2] 0.1392 0.3162 0.4737 0.7105 1.5492 beta0[3] 0.1069 0.2257 0.4132 0.7599 1.5430 beta0[4] 0.2142 0.6415 0.8307 1.0477 1.5361 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.11 1.33 beta0[2] 1.14 1.36 beta0[3] 1.19 1.56 beta0[4] 1.09 1.19 Multivariate psrf 1.24 beta0[1] beta0[2] beta0[3] beta0[4] 36.68472 80.84769 82.61320 134.49406 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] 3.150 1.166 0.009517 0.11023 gamma0[2] 2.847 1.323 0.010798 0.09632 gamma0[3] 3.244 1.193 0.009740 0.08590 gamma0[4] 2.082 1.197 0.009770 0.07951 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.2505 2.344 3.001 3.805 5.820 gamma0[2] 0.6556 1.946 2.679 3.545 5.995 gamma0[3] 0.6714 2.540 3.209 3.987 5.554 gamma0[4] 0.2728 1.181 1.961 2.787 4.886 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.08 1.19 gamma0[2] 1.04 1.08 gamma0[3] 1.06 1.15 gamma0[4] 1.05 1.08 Multivariate psrf 1.06 gamma0[1] gamma0[2] gamma0[3] gamma0[4] 138.0979 183.2685 219.1483 222.3330 Chains of length 5000 for K4 Simulation JAGS @ 4 did not converge in run 1 . Maximum Rhat value = 1.793433 . deviance [[ 1 ]] Mean SD Naive SE Time-series SE 1018.172640 19.275097 0.272591 1.479545 deviance [[ 2 ]] Mean SD Naive SE Time-series SE 1029.2767821 17.1574073 0.2426424 1.2416765 deviance [[ 3 ]] Mean SD Naive SE Time-series SE 1015.8738507 17.7085987 0.2504374 0.8583315 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 12.29567416 4.56930893 0.06461979 0.44786799 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 11.7811013 4.0684380 0.0575364 0.3641485 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 14.44812553 4.50996506 0.06378054 0.28632990 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2117547 0.07549328 0.0010676362 0.010758779 alpha0[2] 0.2090101 0.10464513 0.0014799056 0.024023300 alpha0[3] 0.4047731 0.07595695 0.0010741935 0.008473116 alpha0[4] 0.1744621 0.05431930 0.0007681909 0.006363972 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2889390 0.06114744 0.0008647554 0.006935102 alpha0[2] 0.2926303 0.13395167 0.0018943628 0.021912538 alpha0[3] 0.2663396 0.12489539 0.0017662875 0.013550993 alpha0[4] 0.1520912 0.11891608 0.0016817274 0.016883467 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2200195 0.05726539 0.0008098549 0.009528039 alpha0[2] 0.3148415 0.09739357 0.0013773531 0.013683471 alpha0[3] 0.3296119 0.08947208 0.0012653263 0.010422109 alpha0[4] 0.1355271 0.06332691 0.0008955777 0.010073683 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -1.06110399 0.1251542 0.001769947 0.04312884 mu0[2] -0.68699429 0.2874849 0.004065650 0.13332099 mu0[3] -0.04632744 0.1076669 0.001522640 0.03760469 mu0[4] 0.79681050 0.2862472 0.004048147 0.05689109 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -1.0347189 0.1491592 0.002109429 0.06258378 mu0[2] -0.1320930 0.2065880 0.002921596 0.03705203 mu0[3] 0.1026876 0.1759834 0.002488780 0.04062486 mu0[4] 0.5017241 0.3614860 0.005112185 0.06622664 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.99411859 0.0985305 0.001393432 0.03271793 mu0[2] -0.44986495 0.2543449 0.003596981 0.14849015 mu0[3] -0.09493128 0.1272743 0.001799930 0.03780893 mu0[4] 0.68604820 0.4052826 0.005731561 0.08035409 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.495392 1.0228112 0.01446473 0.1359059 tau0[2] 1.582581 0.9460068 0.01337856 0.1125596 tau0[3] 1.442628 0.8746345 0.01236920 0.1473733 tau0[4] 1.017927 0.8210669 0.01161164 0.0717471 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 2.0258700 0.8784184 0.01242271 0.10975835 tau0[2] 1.0469734 0.8900113 0.01258666 0.11463562 tau0[3] 1.0930368 0.8596114 0.01215674 0.09766194 tau0[4] 0.7785904 0.8188888 0.01158084 0.05747332 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 2.088491 1.3204320 0.01867373 0.19458084 tau0[2] 1.263799 0.9484206 0.01341269 0.13184616 tau0[3] 1.141351 1.0234524 0.01447380 0.14651790 tau0[4] 1.131405 0.8308362 0.01174980 0.06391296 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3947314 0.1379755 0.001951268 0.04134520 beta0[2] 0.4690384 0.2226977 0.003149422 0.05912708 beta0[3] 0.5074142 0.2778250 0.003929039 0.14143600 beta0[4] 0.8447251 0.2284609 0.003230925 0.04682728 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3388420 0.1233579 0.001744544 0.03014438 beta0[2] 0.6478956 0.4933928 0.006977629 0.09748437 beta0[3] 0.7027788 0.4926803 0.006967552 0.06407445 beta0[4] 0.7743278 0.4278201 0.006050291 0.06007265 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.4366616 0.1208291 0.001708782 0.04072750 beta0[2] 0.5712160 0.2687485 0.003800678 0.04194821 beta0[3] 0.4071546 0.2966426 0.004195161 0.06695166 beta0[4] 0.9094754 0.2957617 0.004182702 0.03819139 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.302160 0.9945259 0.01406472 0.1399339 gamma0[2] 2.738310 1.1438424 0.01617637 0.1640876 gamma0[3] 3.466578 1.0096480 0.01427858 0.1732570 gamma0[4] 1.938500 0.9159059 0.01295287 0.1164127 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.838937 0.9096178 0.01286394 0.1176950 gamma0[2] 3.078961 1.5557736 0.02200196 0.1737678 gamma0[3] 2.967161 1.4317285 0.02024770 0.1234035 gamma0[4] 2.204721 1.5097587 0.02135121 0.1723613 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.308416 1.454306 0.02056699 0.2755496 gamma0[2] 2.723293 1.199182 0.01695900 0.1624148 gamma0[3] 3.298079 1.034820 0.01463457 0.1455008 gamma0[4] 2.102446 1.068455 0.01511024 0.1167821 **************** Learning hyperparameters for K4 Simulation JAGS @ 4 Attempt 2 Labeling components for level 2 model K4 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 K4 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 1018.4959 19.3254 0.0911 0.5337 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 981.4 1005.3 1018.1 1031.3 1057.1 Potential scale reduction factors: Point est. Upper C.I. deviance 1.05 1.15 deviance 1460.94 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 13.56057 4.72838 0.02229 0.13573 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 6.027 10.139 13.011 16.347 24.303 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.05 1.16 alphaN 1235.947 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] 0.2286 0.06949 0.0003276 0.003627 alpha0[2] 0.2843 0.10406 0.0004905 0.007315 alpha0[3] 0.3415 0.09827 0.0004633 0.005309 alpha0[4] 0.1457 0.07820 0.0003686 0.004598 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.10271 0.17828 0.2240 0.2747 0.3745 alpha0[2] 0.07117 0.21329 0.2907 0.3553 0.4783 alpha0[3] 0.10386 0.28747 0.3503 0.4099 0.5072 alpha0[4] 0.03891 0.08983 0.1312 0.1833 0.3507 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.04 1.14 alpha0[3] 1.04 1.10 alpha0[4] 1.05 1.10 Multivariate psrf 1.13 alpha0[1] alpha0[2] alpha0[3] alpha0[4] 328.8752 226.4387 369.3713 284.7887 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] -1.00713 0.1571 0.0007408 0.02300 mu0[2] -0.47465 0.2733 0.0012882 0.03648 mu0[3] -0.04038 0.1865 0.0008790 0.01755 mu0[4] 0.88885 0.6671 0.0031445 0.11003 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.29267 -1.1149 -1.01757 -0.91097 -0.66471 mu0[2] -1.00445 -0.6568 -0.47812 -0.28580 0.05866 mu0[3] -0.45257 -0.1428 -0.02733 0.07485 0.28374 mu0[4] 0.06991 0.5000 0.78014 1.11640 3.21586 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.24 1.66 mu0[2] 1.13 1.37 mu0[3] 1.66 2.91 mu0[4] 1.12 1.13 Multivariate psrf 1.48 mu0[1] mu0[2] mu0[3] mu0[4] 44.52593 60.97380 73.77887 81.98184 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] 1.749 1.1007 0.005189 0.05109 tau0[2] 1.313 0.9296 0.004382 0.04259 tau0[3] 1.178 0.9146 0.004311 0.04343 tau0[4] 1.126 0.8598 0.004053 0.02692 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.2781 0.9877 1.702 2.460 4.063 tau0[2] -0.3982 0.6723 1.276 1.906 3.223 tau0[3] -0.6696 0.5598 1.179 1.786 2.957 tau0[4] -0.5913 0.5578 1.139 1.710 2.777 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.12 1.38 tau0[2] 1.06 1.20 tau0[3] 1.04 1.14 tau0[4] 1.00 1.01 Multivariate psrf 1.17 tau0[1] tau0[2] tau0[3] tau0[4] 440.5277 482.9278 431.2203 1088.5478 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] 0.4046 0.1681 0.0007923 0.01830 beta0[2] 0.5536 0.3049 0.0014373 0.02936 beta0[3] 0.4990 0.2902 0.0013678 0.02274 beta0[4] 0.9488 0.4924 0.0023212 0.06300 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1916 0.2816 0.3652 0.4919 0.8728 beta0[2] 0.1647 0.3624 0.4870 0.6667 1.4662 beta0[3] 0.1463 0.2964 0.4176 0.6367 1.3060 beta0[4] 0.2957 0.6748 0.8644 1.0897 2.4050 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.08 1.27 beta0[2] 1.04 1.06 beta0[3] 1.04 1.05 beta0[4] 1.22 1.64 Multivariate psrf 1.12 beta0[1] beta0[2] beta0[3] beta0[4] 92.81723 158.41885 178.77474 197.40824 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] 3.391 1.211 0.005709 0.06197 gamma0[2] 2.818 1.263 0.005954 0.06284 gamma0[3] 3.320 1.137 0.005360 0.05727 gamma0[4] 2.151 1.208 0.005693 0.05014 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.3948 2.548 3.258 4.072 6.183 gamma0[2] 0.7464 1.971 2.652 3.480 5.841 gamma0[3] 1.1729 2.595 3.273 4.020 5.638 gamma0[4] 0.3268 1.271 1.980 2.834 5.035 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.08 1.26 gamma0[2] 1.05 1.15 gamma0[3] 1.04 1.14 gamma0[4] 1.07 1.21 Multivariate psrf 1.11 gamma0[1] gamma0[2] gamma0[3] gamma0[4] 367.0423 387.7094 383.6584 686.2243 Chains of length 10000 for K4 Simulation JAGS @ 4 did not converge in run 2 . Maximum Rhat value = 1.484349 . deviance [[ 1 ]] Mean SD Naive SE Time-series SE 1018.0207867 19.1733142 0.1565495 0.9700250 deviance [[ 2 ]] Mean SD Naive SE Time-series SE 1023.5123184 19.2454586 0.1571385 1.0852183 deviance [[ 3 ]] Mean SD Naive SE Time-series SE 1013.9546806 18.3425158 0.1497660 0.6672203 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 13.53098453 4.73044531 0.03862392 0.26270646 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 12.36474051 4.34629599 0.03548736 0.24003866 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 14.78599365 4.78284576 0.03905177 0.19793948 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2094922 0.06618550 0.0005404024 0.006400903 alpha0[2] 0.2756115 0.09916212 0.0008096553 0.015660294 alpha0[3] 0.3596572 0.08807489 0.0007191284 0.009789334 alpha0[4] 0.1552391 0.06515009 0.0005319482 0.006763307 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2620177 0.06534885 0.0005335711 0.005785655 alpha0[2] 0.2656471 0.11341176 0.0009260031 0.012289641 alpha0[3] 0.3252038 0.11590662 0.0009463736 0.010786108 alpha0[4] 0.1471315 0.09927242 0.0008105559 0.010121631 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2143042 0.06441847 0.0005259746 0.006630779 alpha0[2] 0.3114942 0.09289067 0.0007584491 0.009234598 alpha0[3] 0.3395789 0.08475639 0.0006920330 0.006444839 alpha0[4] 0.1346227 0.06349706 0.0005184513 0.006487383 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -1.08159421 0.12984931 0.0010602152 0.03208546 mu0[2] -0.56267244 0.27927555 0.0022802753 0.08024491 mu0[3] -0.03130644 0.09818815 0.0008017029 0.02146369 mu0[4] 0.92487395 0.39954296 0.0032622546 0.07274603 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -1.01507127 0.1402664 0.001145270 0.03236567 mu0[2] -0.37597909 0.3080405 0.002515140 0.05011358 mu0[3] 0.09161647 0.1364860 0.001114403 0.02493056 mu0[4] 0.87102260 0.9636081 0.007867827 0.31286444 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.9247245 0.1585853 0.001294843 0.05181852 mu0[2] -0.4852839 0.1831752 0.001495619 0.05503543 mu0[3] -0.1814409 0.1965635 0.001604934 0.04109365 mu0[4] 0.8706568 0.4947911 0.004039953 0.07604941 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.256277 0.9448425 0.007714606 0.06913971 tau0[2] 1.591480 0.9373645 0.007653549 0.08734073 tau0[3] 1.427037 0.8873648 0.007245303 0.07932394 tau0[4] 1.156481 0.7987976 0.006522155 0.03997352 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 2.015509 1.0088614 0.008237319 0.08999275 tau0[2] 1.064970 0.8500261 0.006940434 0.05996273 tau0[3] 1.049281 0.8647654 0.007060780 0.06883239 tau0[4] 1.143274 0.8941603 0.007300788 0.04268596 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.975366 1.1658829 0.009519394 0.10300957 tau0[2] 1.281649 0.9226556 0.007533452 0.07144222 tau0[3] 1.058783 0.9388039 0.007665302 0.07710604 tau0[4] 1.077485 0.8812433 0.007195322 0.05568721 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.4020062 0.1775268 0.001449501 0.03977839 beta0[2] 0.5197943 0.3349582 0.002734922 0.07344258 beta0[3] 0.4731542 0.2545264 0.002078199 0.04654684 beta0[4] 0.8687752 0.2677381 0.002186072 0.03247099 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3512321 0.1461958 0.001193684 0.02150542 beta0[2] 0.5767268 0.3511957 0.002867501 0.04173572 beta0[3] 0.5113107 0.3591019 0.002932055 0.03568781 beta0[4] 1.1122484 0.7258268 0.005926351 0.18409546 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.4604720 0.1608641 0.001313450 0.03114007 beta0[2] 0.5643555 0.2038743 0.001664627 0.02494265 beta0[3] 0.5125798 0.2405001 0.001963675 0.03485010 beta0[4] 0.8653880 0.2979959 0.002433126 0.02792505 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.681961 1.1311912 0.009236138 0.10107027 gamma0[2] 2.674813 1.0683478 0.008723023 0.09585214 gamma0[3] 3.520923 1.0263166 0.008379840 0.10288743 gamma0[4] 1.807850 0.9479314 0.007739828 0.04964861 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.959633 1.028710 0.008399378 0.08880706 gamma0[2] 2.624689 1.348693 0.011012034 0.11629536 gamma0[3] 3.418638 1.242318 0.010143485 0.09664654 gamma0[4] 2.467298 1.364790 0.011143462 0.10283003 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.532108 1.330938 0.010867065 0.1283087 gamma0[2] 3.155018 1.286340 0.010502919 0.1132615 gamma0[3] 3.019260 1.068228 0.008722044 0.0979558 gamma0[4] 2.179335 1.181322 0.009645457 0.0979229 MCMC run did not converge, proceeding anyway. Learning parameters for K4 Simulation JAGS @ 4 Labeling components for K4 Simulation JAGS @ 4 Labeling components for pi Labeling components for mu Labeling components for tau Calculating model fit indexes for K4 Simulation JAGS @ 4 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -462.70674 97.74346 1120.90040 97.74346 1120.90040 lppd lppd.bayes pDIC DIC pDICalt DICalt -511.5785 -808.0197 -592.8824 430.2746 195.9544 2007.9482 Analaysis complete for K4 Simulation JAGS @ 4 > proc.time() user system elapsed 3107.479 4.613 3112.387