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 @ 3 Removing 0 of 10 essays for length. Calculating initial values for chain 1 ; K4 Simulation JAGS @ 3 WARNING! NOT CONVERGENT! number of iterations= 1000 number of iterations= 68 number of iterations= 36 number of iterations= 64 One of the variances is going to zero; trying new starting values. number of iterations= 89 number of iterations= 39 number of iterations= 50 number of iterations= 161 number of iterations= 59 number of iterations= 389 Calculating initial values for chain 2 ; K4 Simulation JAGS @ 3 One of the variances is going to zero; trying new starting values. number of iterations= 137 number of iterations= 28 number of iterations= 11 number of iterations= 22 number of iterations= 60 number of iterations= 8 number of iterations= 43 One of the variances is going to zero; trying new starting values. number of iterations= 170 number of iterations= 35 number of iterations= 127 Calculating initial values for chain 3 ; K4 Simulation JAGS @ 3 number of iterations= 187 number of iterations= 108 number of iterations= 29 One of the variances is going to zero; trying new starting values. number of iterations= 62 number of iterations= 38 number of iterations= 11 number of iterations= 67 number of iterations= 136 number of iterations= 49 number of iterations= 72 Loading Model for K4 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: 816 Initializing model Burn in iterations for K4 Simulation JAGS @ 3 **************** Learning hyperparameters for K4 Simulation JAGS @ 3 Attempt 1 Labeling components for level 2 model K4 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 K4 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 1050.7969 15.0029 0.1225 0.4335 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 1023 1041 1050 1061 1082 Potential scale reduction factors: Point est. Upper C.I. deviance 1.02 1.05 deviance 1365.058 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 11.5309 4.2378 0.0346 0.1361 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 5.127 8.504 10.941 13.887 21.553 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.11 1.34 alphaN 909.6965 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] 0.3405 0.09716 0.0007933 0.006790 alpha0[2] 0.4243 0.10464 0.0008544 0.005627 alpha0[3] 0.2352 0.09993 0.0008159 0.008133 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.18296 0.2679 0.3274 0.4020 0.5551 alpha0[2] 0.20657 0.3561 0.4234 0.4994 0.6121 alpha0[3] 0.07846 0.1633 0.2178 0.2931 0.4594 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.65 2.64 alpha0[3] 1.43 2.12 Multivariate psrf 1.58 alpha0[1] alpha0[2] alpha0[3] 177.2775 247.2115 124.2582 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] -0.7774 0.2093 0.001709 0.03509 mu0[2] -0.1802 0.1698 0.001386 0.04815 mu0[3] 0.6359 0.4056 0.003312 0.05681 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.08535 -0.9368 -0.7993 -0.65175 -0.2703 mu0[2] -0.49983 -0.2937 -0.1858 -0.06687 0.2095 mu0[3] -0.01026 0.3209 0.6032 0.88664 1.6558 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 2.09 4.38 mu0[2] 1.37 2.22 mu0[3] 1.47 2.33 Multivariate psrf 1.87 mu0[1] mu0[2] mu0[3] 23.11475 14.61582 37.74805 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] 1.396 0.9550 0.007797 0.07874 tau0[2] 1.324 0.8268 0.006751 0.06984 tau0[3] 1.002 0.8723 0.007123 0.04855 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.4176 0.7655 1.3676 1.973 3.568 tau0[2] -0.1859 0.7710 1.2974 1.836 3.111 tau0[3] -0.6993 0.4182 0.9929 1.585 2.696 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.20 1.58 tau0[2] 1.06 1.20 tau0[3] 1.28 1.79 Multivariate psrf 1.3 tau0[1] tau0[2] tau0[3] 137.2330 136.7264 274.8342 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] 0.6294 0.2399 0.001959 0.02579 beta0[2] 0.5267 0.1344 0.001098 0.01705 beta0[3] 0.8868 0.2897 0.002365 0.04373 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.3054 0.4306 0.5480 0.8147 1.0809 beta0[2] 0.3156 0.4380 0.5060 0.6027 0.8598 beta0[3] 0.4415 0.6938 0.8476 1.0143 1.6337 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 3.22 6.08 beta0[2] 1.07 1.16 beta0[3] 1.09 1.24 Multivariate psrf 2.65 beta0[1] beta0[2] beta0[3] 39.77744 62.29935 50.29869 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] 3.330 1.134 0.009262 0.08893 gamma0[2] 3.253 1.060 0.008653 0.09068 gamma0[3] 2.336 0.873 0.007128 0.05661 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.567 2.573 3.128 3.914 6.111 gamma0[2] 1.714 2.446 3.092 3.915 5.494 gamma0[3] 1.004 1.778 2.202 2.709 4.602 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.53 2.51 gamma0[2] 1.51 2.51 gamma0[3] 1.08 1.23 Multivariate psrf 1.56 gamma0[1] gamma0[2] gamma0[3] 161.4418 126.4901 253.0411 Chains of length 5000 for K4 Simulation JAGS @ 3 did not converge in run 1 . Maximum Rhat value = 2.645222 . deviance [[ 1 ]] Mean SD Naive SE Time-series SE 1048.4002676 14.5826441 0.2062297 0.5506625 deviance [[ 2 ]] Mean SD Naive SE Time-series SE 1052.8303042 14.7468162 0.2085515 0.8788269 deviance [[ 3 ]] Mean SD Naive SE Time-series SE 1051.1599894 15.3367674 0.2168946 0.7845033 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 13.1036648 4.5169133 0.0638788 0.2223924 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 10.05442157 3.49586483 0.04943899 0.24856233 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 11.43460125 4.07377389 0.05761186 0.23544596 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2792644 0.05383550 0.000761349 0.005433584 alpha0[2] 0.5132529 0.08533573 0.001206829 0.011978487 alpha0[3] 0.2074827 0.08710015 0.001231782 0.017288966 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3216929 0.08459447 0.001196346 0.014718344 alpha0[2] 0.3705612 0.09402849 0.001329764 0.009844806 alpha0[3] 0.3077459 0.09386425 0.001327441 0.011720577 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4204256 0.08819225 0.0012472268 0.012992471 alpha0[2] 0.3891729 0.06852022 0.0009690223 0.006679392 alpha0[3] 0.1904015 0.07433263 0.0010512221 0.012611144 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.9788330 0.08369602 0.001183640 0.03867005 mu0[2] -0.1580136 0.19649200 0.002778817 0.07655126 mu0[3] 0.6215570 0.27813703 0.003933452 0.07105152 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.6174583 0.20398065 0.002884722 0.09213438 mu0[2] -0.0910538 0.08759921 0.001238840 0.03402263 mu0[3] 0.3811285 0.29281467 0.004141025 0.09420087 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.7359414 0.1219606 0.001724783 0.03315821 mu0[2] -0.2915425 0.1391273 0.001967558 0.11766426 mu0[3] 0.9049356 0.4393146 0.006212846 0.12297533 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.552132 0.8851436 0.012517821 0.12711905 tau0[2] 1.069411 0.6522513 0.009224226 0.09188487 tau0[3] 1.163983 0.8901410 0.012588495 0.09850262 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 0.8801427 0.8081883 0.01142951 0.10639405 tau0[2] 1.4005374 0.8673281 0.01226587 0.12803000 tau0[3] 1.3773339 0.7345296 0.01038782 0.08584357 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.7561322 0.9377112 0.013261239 0.16829260 tau0[2] 1.5023089 0.8780372 0.012417321 0.13806567 tau0[3] 0.4658381 0.7047129 0.009966145 0.06434291 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.9217153 0.1181313 0.001670628 0.06362755 beta0[2] 0.5169925 0.1379763 0.001951280 0.02448354 beta0[3] 0.9761319 0.3070185 0.004341897 0.09484114 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.4919097 0.1431045 0.002023803 0.03877730 beta0[2] 0.5077033 0.1651327 0.002335329 0.03809833 beta0[3] 0.8245702 0.1930216 0.002729738 0.03973550 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.4746862 0.09943513 0.001406225 0.02086504 beta0[2] 0.5553276 0.08149744 0.001152548 0.02377126 beta0[3] 0.8595854 0.32808019 0.004639755 0.08145578 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 4.118672 1.2258983 0.017336820 0.23131881 gamma0[2] 2.432997 0.5974195 0.008448788 0.08036439 gamma0[3] 2.343860 0.7618205 0.010773769 0.07188291 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.288246 0.7889564 0.01115753 0.09296561 gamma0[2] 3.786194 1.1454405 0.01619897 0.23096785 gamma0[3] 2.074017 0.8014637 0.01133441 0.08857336 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.583355 0.7442030 0.01052462 0.09502513 gamma0[2] 3.539573 0.8136813 0.01150719 0.11915404 gamma0[3] 2.588667 0.9651083 0.01364869 0.12581181 **************** Learning hyperparameters for K4 Simulation JAGS @ 3 Attempt 2 Labeling components for level 2 model K4 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 K4 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 1.051e+03 1.540e+01 7.259e-02 3.121e-01 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 1022 1040 1050 1061 1083 Potential scale reduction factors: Point est. Upper C.I. deviance 1.01 1.05 deviance 2596.146 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 11.67876 4.26428 0.02010 0.09408 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 5.233 8.589 11.058 14.071 21.756 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.11 1.34 alphaN 1903.659 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] 0.3616 0.09889 0.0004662 0.003951 alpha0[2] 0.4235 0.10407 0.0004906 0.003801 alpha0[3] 0.2150 0.08522 0.0004017 0.006134 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.19798 0.2857 0.3513 0.4316 0.5633 alpha0[2] 0.21305 0.3509 0.4262 0.5000 0.6108 alpha0[3] 0.08311 0.1534 0.2025 0.2610 0.4185 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.72 2.77 alpha0[3] 1.04 1.12 Multivariate psrf 1.83 alpha0[1] alpha0[2] alpha0[3] 492.5023 485.1594 233.1285 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] -0.8653 0.2516 0.0011860 0.03708 mu0[2] -0.2035 0.1658 0.0007815 0.03235 mu0[3] 0.6179 0.3633 0.0017127 0.03424 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.374461 -1.0134 -0.8470 -0.69117 -0.4130 mu0[2] -0.494088 -0.3280 -0.2161 -0.07794 0.1019 mu0[3] -0.007429 0.3673 0.5881 0.84172 1.4389 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 2.05 3.50 mu0[2] 1.22 1.65 mu0[3] 1.04 1.12 Multivariate psrf 1.8 mu0[1] mu0[2] mu0[3] 46.47476 30.79330 120.97411 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] 1.4630 0.9890 0.004662 0.05726 tau0[2] 1.3227 0.8519 0.004016 0.04236 tau0[3] 0.8443 0.8465 0.003990 0.03301 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.3157 0.7917 1.397 2.057 3.615 tau0[2] -0.2764 0.7322 1.297 1.885 3.055 tau0[3] -0.8133 0.2819 0.830 1.398 2.528 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.09 1.28 tau0[2] 1.15 1.45 tau0[3] 1.07 1.21 Multivariate psrf 1.18 tau0[1] tau0[2] tau0[3] 287.3397 355.7899 654.1331 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] 0.6946 0.3222 0.0015187 0.07984 beta0[2] 0.5267 0.1873 0.0008831 0.02851 beta0[3] 0.9102 0.2904 0.0013688 0.02634 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.3071 0.4560 0.6050 0.8582 1.539 beta0[2] 0.2809 0.3983 0.4808 0.6108 1.011 beta0[3] 0.4702 0.7061 0.8675 1.0642 1.616 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 2.13 4.34 beta0[2] 1.09 1.29 beta0[3] 1.03 1.10 Multivariate psrf 1.82 beta0[1] beta0[2] beta0[3] 61.24236 124.04963 123.60188 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] 3.230 1.1736 0.005532 0.06775 gamma0[2] 3.253 1.0429 0.004916 0.04690 gamma0[3] 2.523 0.9982 0.004705 0.04290 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.5864 2.462 3.034 3.766 6.001 gamma0[2] 1.6732 2.483 3.129 3.871 5.583 gamma0[3] 0.8726 1.881 2.385 3.027 4.891 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.32 1.99 gamma0[2] 1.54 2.47 gamma0[3] 1.16 1.47 Multivariate psrf 1.5 gamma0[1] gamma0[2] gamma0[3] 381.2276 386.8637 563.3629 Chains of length 10000 for K4 Simulation JAGS @ 3 did not converge in run 2 . Maximum Rhat value = 1.831788 . deviance [[ 1 ]] Mean SD Naive SE Time-series SE 1048.9725430 14.9547731 0.1221052 0.4396257 deviance [[ 2 ]] Mean SD Naive SE Time-series SE 1053.2408464 15.4837582 0.1264244 0.6319698 deviance [[ 3 ]] Mean SD Naive SE Time-series SE 1049.9947006 15.4295894 0.1259821 0.5327848 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 13.32982453 4.55301685 0.03717523 0.15627798 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 10.30820061 3.59455376 0.02934941 0.15678588 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 11.39824692 4.02745669 0.03288405 0.17506733 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2811968 0.05354796 0.0004372172 0.003222012 alpha0[2] 0.5114057 0.07793765 0.0006363582 0.005329947 alpha0[3] 0.2073975 0.07549619 0.0006164238 0.006699729 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3599026 0.07551915 0.0006166113 0.006686068 alpha0[2] 0.4058648 0.08879888 0.0007250398 0.008020059 alpha0[3] 0.2342326 0.09712108 0.0007929903 0.013458258 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4435907 0.08704421 0.0007107130 0.009241199 alpha0[2] 0.3531532 0.07445355 0.0006079107 0.006104640 alpha0[3] 0.2032561 0.07803853 0.0006371819 0.010612721 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -1.1187302 0.1693434 0.001382683 0.09461358 mu0[2] -0.2410310 0.1806756 0.001475210 0.03898050 mu0[3] 0.6039086 0.3217032 0.002626695 0.04349712 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.7270683 0.1994880 0.001628812 0.05021059 mu0[2] -0.1102553 0.1389903 0.001134851 0.06133309 mu0[3] 0.5537907 0.3373687 0.002754604 0.05679356 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.7501544 0.1575449 0.001286349 0.03005012 mu0[2] -0.2592117 0.1314528 0.001073307 0.06433885 mu0[3] 0.6961476 0.4102359 0.003349562 0.07369590 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.8344479 1.0640476 0.008687913 0.11479784 tau0[2] 0.9943874 0.7003333 0.005718197 0.06823443 tau0[3] 0.8636085 0.8431974 0.006884678 0.05511060 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.253040 0.8773773 0.007163755 0.08117806 tau0[2] 1.275288 0.8242980 0.006730365 0.07251582 tau0[3] 1.082438 0.8352716 0.006819964 0.06641281 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.3014828 0.9078211 0.007412328 0.09869173 tau0[2] 1.6985114 0.8694543 0.007099065 0.07896443 tau0[3] 0.5867348 0.7858432 0.006416383 0.04856252 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 1.0176396 0.3061178 0.002499441 0.23523279 beta0[2] 0.4820066 0.1715205 0.001400459 0.01740804 beta0[3] 0.9199751 0.2703557 0.002207445 0.03804697 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.4786211 0.1305654 0.001066062 0.02208517 beta0[2] 0.4987800 0.1761586 0.001438329 0.03914290 beta0[3] 0.8500517 0.2794497 0.002281697 0.04490725 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5875076 0.1953417 0.001594959 0.03938610 beta0[2] 0.5991889 0.1918231 0.001566229 0.07402261 beta0[3] 0.9606203 0.3090569 0.002523439 0.05270494 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.882432 1.4163655 0.011564575 0.18108175 gamma0[2] 2.429048 0.6128524 0.005003919 0.04727908 gamma0[3] 2.471159 0.8535692 0.006969363 0.05126312 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.165388 0.8800993 0.007185980 0.07153548 gamma0[2] 3.805578 1.0282500 0.008395626 0.10708414 gamma0[3] 2.132831 0.8785481 0.007173315 0.06898110 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.641131 0.7581669 0.006190407 0.05837487 gamma0[2] 3.525647 0.8783577 0.007171760 0.07805381 gamma0[3] 2.966138 1.0664518 0.008707542 0.09580572 MCMC run did not converge, proceeding anyway. Learning parameters for K4 Simulation JAGS @ 3 Labeling components for K4 Simulation JAGS @ 3 Labeling components for pi Labeling components for mu Labeling components for tau Calculating model fit indexes for K4 Simulation JAGS @ 3 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -488.58116 73.39105 1123.94441 73.39105 1123.94441 lppd lppd.bayes pDIC DIC pDICalt DICalt -525.2767 -776.2748 -501.9963 548.5571 103.7087 1759.9671 Analaysis complete for K4 Simulation JAGS @ 3 > proc.time() user system elapsed 1885.899 4.874 1893.971