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 @ 3 Removing 0 of 10 essays for length. Calculating initial values for chain 1 ; K2 Simulation JAGS @ 3 number of iterations= 481 number of iterations= 47 number of iterations= 122 number of iterations= 25 number of iterations= 85 number of iterations= 127 number of iterations= 128 number of iterations= 91 number of iterations= 59 number of iterations= 887 Calculating initial values for chain 2 ; K2 Simulation JAGS @ 3 number of iterations= 50 number of iterations= 54 number of iterations= 46 WARNING! NOT CONVERGENT! number of iterations= 1000 number of iterations= 141 number of iterations= 54 number of iterations= 77 number of iterations= 44 number of iterations= 103 number of iterations= 355 Calculating initial values for chain 3 ; K2 Simulation JAGS @ 3 number of iterations= 44 number of iterations= 131 number of iterations= 84 number of iterations= 24 One of the variances is going to zero; trying new starting values. number of iterations= 185 One of the variances is going to zero; trying new starting values. WARNING! NOT CONVERGENT! number of iterations= 1000 One of the variances is going to zero; trying new starting values. number of iterations= 65 number of iterations= 113 number of iterations= 22 number of iterations= 195 Loading Model for K2 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: 832 Initializing model Burn in iterations for K2 Simulation JAGS @ 3 **************** Learning hyperparameters for K2 Simulation JAGS @ 3 Attempt 1 Labeling components for level 2 model K2 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 K2 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 1.096e+03 1.074e+01 8.772e-02 2.024e-01 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 1077 1088 1095 1102 1119 Potential scale reduction factors: Point est. Upper C.I. deviance 1 1 deviance 2911.029 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 12.60806 4.63128 0.03781 0.17725 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 5.345 9.303 11.989 15.247 23.333 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.01 1.03 alphaN 727.8449 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] 0.4263 0.3195 0.002609 0.02894 alpha0[2] 0.4365 0.2042 0.001667 0.01280 alpha0[3] 0.1372 0.1535 0.001254 0.01889 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.005110 0.018052 0.62893 0.6888 0.7605 alpha0[2] 0.014471 0.294529 0.35559 0.6554 0.7592 alpha0[3] 0.003735 0.009002 0.01736 0.2910 0.4064 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.07 1.21 alpha0[3] 1.20 1.61 Multivariate psrf 1.18 alpha0[1] alpha0[2] alpha0[3] 137.56960 306.42230 70.85452 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] -2.23540 5.4221 0.044271 0.79886 mu0[2] 0.06004 0.5698 0.004652 0.04647 mu0[3] 18.50750 25.7812 0.210502 7.28437 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -16.048762 -2.0679 -0.6590 -0.4363 -0.1864 mu0[2] -0.818202 -0.4651 0.1021 0.5290 1.0764 mu0[3] 0.007764 0.5710 3.0535 27.5992 87.2763 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.34 3.00 mu0[2] 1.09 1.29 mu0[3] 1.95 5.47 Multivariate psrf 1.64 mu0[1] mu0[2] mu0[3] 124.77567 140.09587 18.19171 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] 1.1951 1.1305 0.009231 0.07757 tau0[2] 0.4780 1.2036 0.009828 0.09762 tau0[3] -0.1334 0.8225 0.006715 0.01529 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -1.375 0.3302 1.67136 2.044 2.479 tau0[2] -1.183 -0.5265 -0.02409 1.792 2.408 tau0[3] -1.736 -0.6248 -0.19728 0.321 1.707 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.08 1.26 tau0[2] 1.15 1.46 tau0[3] 1.01 1.04 Multivariate psrf 1.13 tau0[1] tau0[2] tau0[3] 221.6329 154.5376 3368.0877 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] 0.8157 0.7153 0.005840 0.03842 beta0[2] 0.8058 0.5050 0.004123 0.02070 beta0[3] 1.3641 1.5813 0.012911 0.09459 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.2160 0.4799 0.6056 0.8344 2.884 beta0[2] 0.3517 0.5471 0.7137 0.9461 1.710 beta0[3] 0.1770 0.5772 0.8606 1.3858 6.359 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.06 1.11 beta0[2] 1.08 1.14 beta0[3] 1.06 1.12 Multivariate psrf 1.03 beta0[1] beta0[2] beta0[3] 353.0947 633.0091 349.4810 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] 1.032 0.8535 0.006969 0.03306 gamma0[2] 1.165 0.4418 0.003607 0.01770 gamma0[3] 1.404 1.1809 0.009642 0.04516 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.2001 0.6503 0.8652 1.136 3.187 gamma0[2] 0.5007 0.8659 1.1052 1.390 2.181 gamma0[3] 0.1718 0.7506 1.1825 1.674 4.514 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.07 1.09 gamma0[2] 1.04 1.10 gamma0[3] 1.06 1.06 Multivariate psrf 1.03 gamma0[1] gamma0[2] gamma0[3] 990.7111 674.4951 901.7929 Chains of length 5000 for K2 Simulation JAGS @ 3 did not converge in run 1 . Maximum Rhat value = 1.637895 . deviance [[ 1 ]] Mean SD Naive SE Time-series SE 1096.0917210 10.7613860 0.1521890 0.3744557 deviance [[ 2 ]] Mean SD Naive SE Time-series SE 1095.3626120 10.8439431 0.1533565 0.3696389 deviance [[ 3 ]] Mean SD Naive SE Time-series SE 1095.5015184 10.6103479 0.1500530 0.3032173 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 12.87529369 4.49996934 0.06363918 0.25152660 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 12.88378561 4.68140124 0.06620501 0.35649796 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 12.06510800 4.66340724 0.06595054 0.30399353 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.57302644 0.2425063 0.003429556 0.06280650 alpha0[2] 0.37217611 0.1571818 0.002222886 0.02639972 alpha0[3] 0.05479745 0.1031134 0.001458244 0.04084077 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3765337 0.3244076 0.004587816 0.03843332 alpha0[2] 0.4567871 0.2209419 0.003124590 0.01634130 alpha0[3] 0.1666793 0.1510884 0.002136712 0.02802270 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3293864 0.3299530 0.004666240 0.04601624 alpha0[2] 0.4804448 0.2122715 0.003001972 0.02260636 alpha0[3] 0.1901688 0.1637407 0.002315643 0.02751642 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.9603237 1.1693972 0.016537773 0.18208935 mu0[2] 0.2804359 0.5524247 0.007812464 0.07501413 mu0[3] 42.8613500 30.1971268 0.427051862 21.14605517 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -1.351609175 1.4289662 0.020208634 0.17097674 mu0[2] -0.006707582 0.5402998 0.007640993 0.08396755 mu0[3] 7.729577471 11.1017705 0.157002744 4.95238582 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -4.39426965 8.8164235 0.124683056 2.38354265 mu0[2] -0.09360928 0.5481222 0.007751619 0.08220495 mu0[3] 4.93157257 8.0867731 0.114364242 2.42427428 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.60141196 0.8694915 0.01229647 0.15622817 tau0[2] -0.10239051 0.9346273 0.01321763 0.19654680 tau0[3] -0.03990519 0.9058434 0.01281056 0.02103337 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.0110331 1.1478723 0.01623337 0.12323707 tau0[2] 0.6884408 1.2007701 0.01698145 0.13432683 tau0[3] -0.1468836 0.7922629 0.01120429 0.02575222 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 0.9728691 1.229929 0.01739382 0.12066194 tau0[2] 0.8479552 1.230201 0.01739767 0.17057112 tau0[3] -0.2134449 0.752377 0.01064022 0.03160108 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.7077907 0.4737237 0.006699464 0.04953104 beta0[2] 0.8804034 0.3414654 0.004829050 0.02300996 beta0[3] 1.6203131 1.9732857 0.027906474 0.22563926 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.8185186 0.7062508 0.009987894 0.05598051 beta0[2] 0.8034398 0.4383990 0.006199898 0.02857591 beta0[3] 1.1419444 1.0727289 0.015170678 0.08212919 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.9208645 0.8882806 0.012562185 0.08775633 beta0[2] 0.7335662 0.6675525 0.009440618 0.05011643 beta0[3] 1.3299612 1.5301500 0.021639589 0.15121253 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 0.9631038 0.6516765 0.009216097 0.05956959 gamma0[2] 1.2561560 0.3765442 0.005325138 0.02226314 gamma0[3] 1.4478440 1.5211387 0.021512150 0.11236156 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 0.9972363 0.7154858 0.010118497 0.02813119 gamma0[2] 1.1413776 0.5087043 0.007194164 0.03937307 gamma0[3] 1.4230516 1.0865763 0.015366509 0.06451482 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.136068 1.1100319 0.015698222 0.07414232 gamma0[2] 1.097758 0.4142635 0.005858571 0.02783462 gamma0[3] 1.340329 0.8265204 0.011688763 0.03963517 **************** Learning hyperparameters for K2 Simulation JAGS @ 3 Attempt 2 Labeling components for level 2 model K2 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 K2 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 1.096e+03 1.077e+01 5.078e-02 1.318e-01 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 1077 1088 1095 1103 1119 Potential scale reduction factors: Point est. Upper C.I. deviance 1 1.01 deviance 7332.946 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 12.655 4.667 0.022 0.103 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 5.332 9.296 12.039 15.346 23.368 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.02 1.06 alphaN 2044.207 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] 0.3975 0.3275 0.0015437 0.019764 alpha0[2] 0.4582 0.1987 0.0009365 0.008562 alpha0[3] 0.1443 0.1615 0.0007613 0.009923 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.002883 0.009865 0.61364 0.6866 0.7651 alpha0[2] 0.136260 0.299319 0.36932 0.6629 0.7634 alpha0[3] 0.003248 0.008401 0.01697 0.3018 0.4229 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.41 2.16 alpha0[3] 1.64 2.98 Multivariate psrf 1.57 alpha0[1] alpha0[2] alpha0[3] 169.7768 397.9362 153.7230 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] -29.63092 58.6892 0.276664 17.36632 mu0[2] 0.03669 0.5356 0.002525 0.02789 mu0[3] 31.32962 36.8093 0.173521 6.53310 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -203.73650 -7.0589 -0.67600 -0.4135 -0.05444 mu0[2] -0.80647 -0.4425 0.04745 0.4722 1.02936 mu0[3] -0.08994 0.5195 5.71978 65.9940 105.09669 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.92 8.45 mu0[2] 1.43 2.13 mu0[3] 2.04 4.18 Multivariate psrf 1.98 mu0[1] mu0[2] mu0[3] 95.03356 372.20713 23.38549 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] 1.0933 1.1705 0.005518 0.04228 tau0[2] 0.5605 1.2238 0.005769 0.05916 tau0[3] -0.1543 0.8396 0.003958 0.01147 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -1.490 0.1669 1.58972 2.0091 2.462 tau0[2] -1.153 -0.5118 0.06412 1.8619 2.414 tau0[3] -1.704 -0.6712 -0.24931 0.2959 1.803 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.33 1.97 tau0[2] 1.48 2.35 tau0[3] 1.05 1.12 Multivariate psrf 1.38 tau0[1] tau0[2] tau0[3] 571.7806 327.9011 7902.2642 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] 0.9464 1.0717 0.005052 0.03760 beta0[2] 0.7773 0.4206 0.001983 0.01166 beta0[3] 1.2490 1.3581 0.006402 0.03944 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.2108 0.4779 0.6247 0.9270 3.899 beta0[2] 0.3492 0.5281 0.7002 0.9271 1.641 beta0[3] 0.1782 0.5776 0.8458 1.3095 5.317 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.20 1.61 beta0[2] 1.06 1.16 beta0[3] 1.09 1.21 Multivariate psrf 1.11 beta0[1] beta0[2] beta0[3] 708.9895 1350.8962 1251.3032 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] 1.262 1.7309 0.008160 0.09575 gamma0[2] 1.163 0.4353 0.002052 0.01151 gamma0[3] 1.437 1.2858 0.006061 0.03106 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.2070 0.6718 0.9031 1.244 4.844 gamma0[2] 0.5349 0.8691 1.0990 1.376 2.183 gamma0[3] 0.1799 0.7692 1.1878 1.664 4.947 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.20 1.46 gamma0[2] 1.05 1.16 gamma0[3] 1.09 1.10 Multivariate psrf 1.07 gamma0[1] gamma0[2] gamma0[3] 818.9787 1627.1234 2151.3709 Chains of length 10000 for K2 Simulation JAGS @ 3 did not converge in run 2 . Maximum Rhat value = 1.980178 . deviance [[ 1 ]] Mean SD Naive SE Time-series SE 1096.8864658 10.8098426 0.0882620 0.2777167 deviance [[ 2 ]] Mean SD Naive SE Time-series SE 1.095832e+03 1.071119e+01 8.745648e-02 1.994798e-01 deviance [[ 3 ]] Mean SD Naive SE Time-series SE 1.095742e+03 1.075572e+01 8.782013e-02 1.985461e-01 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 13.37650087 4.76577109 0.03891236 0.16970726 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 12.6051198 4.6403007 0.0378879 0.1913371 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 11.98272623 4.48504629 0.03662025 0.17360522 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.63072297 0.16421329 0.0013407959 0.02318722 alpha0[2] 0.34360282 0.11932424 0.0009742784 0.01068229 alpha0[3] 0.02567421 0.06319062 0.0005159492 0.01509416 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.1425020 0.2653465 0.002166545 0.03991940 alpha0[2] 0.5915506 0.1809111 0.001477133 0.01737452 alpha0[3] 0.2659473 0.1306131 0.001066451 0.01638047 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4191341 0.3231772 0.002638731 0.03720883 alpha0[2] 0.4395631 0.2004242 0.001636457 0.01561521 alpha0[3] 0.1413028 0.1682892 0.001374076 0.01974866 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.5882409 0.7477078 0.006105009 0.08541953 mu0[2] 0.3753722 0.4225549 0.003450147 0.02858916 mu0[3] 66.1324277 28.9836650 0.236650634 10.88165920 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -83.4324094 76.4143838 0.623920831 52.01459426 mu0[2] -0.3155548 0.4321263 0.003528296 0.06082327 mu0[3] 2.9243212 7.2929663 0.059546821 2.10238054 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -4.87211712 11.9435161 0.097518401 2.96245484 mu0[2] 0.05024985 0.5064205 0.004134906 0.04983244 mu0[3] 24.93210979 33.3550370 0.272342737 16.16483643 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.73799950 0.6993940 0.005710528 0.05807723 tau0[2] -0.22846183 0.7641313 0.006239106 0.06363129 tau0[3] -0.01054734 0.9656760 0.007884711 0.01286038 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 0.3920931 1.1601995 0.009472990 0.07821690 tau0[2] 1.4154446 0.9966480 0.008137597 0.10876894 tau0[3] -0.3263239 0.6321401 0.005161402 0.02319753 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.1498114 1.1682168 0.009538450 0.08124807 tau0[2] 0.4943984 1.2481437 0.010191050 0.12498249 tau0[3] -0.1261470 0.8552748 0.006983289 0.02192803 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.6360886 0.3196939 0.002610290 0.02731301 beta0[2] 0.8704996 0.3257594 0.002659815 0.01445784 beta0[3] 1.5185189 1.7012736 0.013890840 0.09117536 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 1.3157190 1.4912725 0.012176189 0.09658850 beta0[2] 0.6906847 0.3748640 0.003060752 0.02373276 beta0[3] 0.9430309 0.6996608 0.005712706 0.03200291 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.8874991 0.9397846 0.007673309 0.05145275 beta0[2] 0.7706406 0.5176193 0.004226344 0.02124299 beta0[3] 1.2853239 1.4080140 0.011496386 0.06828534 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 0.9669074 0.4553824 0.003718182 0.02304607 gamma0[2] 1.2736545 0.3852198 0.003145307 0.01576605 gamma0[3] 1.5208333 1.7244137 0.014079779 0.08061854 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.643899 2.6238974 0.021424033 0.27768096 gamma0[2] 1.054029 0.4455914 0.003638238 0.02600076 gamma0[3] 1.360632 0.7126172 0.005818495 0.02797469 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.173959 1.2866974 0.010505840 0.06985858 gamma0[2] 1.160291 0.4443235 0.003627886 0.01637321 gamma0[3] 1.430389 1.2105484 0.009884086 0.03744865 MCMC run did not converge, proceeding anyway. Learning parameters for K2 Simulation JAGS @ 3 Labeling components for K2 Simulation JAGS @ 3 Labeling components for pi Labeling components for mu Labeling components for tau Calculating model fit indexes for K2 Simulation JAGS @ 3 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -530.41461 35.52201 1131.87323 35.52201 1131.87323 lppd lppd.bayes pDIC DIC pDICalt DICalt -548.17561 -1217.63197 -1338.91272 -242.56150 55.72694 2546.71783 Analaysis complete for K2 Simulation JAGS @ 3 > proc.time() user system elapsed 1483.978 2.038 1486.622