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 @ 2 Removing 0 of 10 essays for length. Calculating initial values for chain 1 ; K4 Simulation JAGS @ 2 number of iterations= 133 number of iterations= 56 number of iterations= 17 number of iterations= 12 number of iterations= 13 number of iterations= 28 number of iterations= 69 number of iterations= 83 number of iterations= 13 number of iterations= 26 Calculating initial values for chain 2 ; K4 Simulation JAGS @ 2 number of iterations= 63 number of iterations= 46 number of iterations= 26 number of iterations= 15 number of iterations= 19 number of iterations= 12 number of iterations= 31 number of iterations= 48 number of iterations= 15 number of iterations= 36 Calculating initial values for chain 3 ; K4 Simulation JAGS @ 2 number of iterations= 34 number of iterations= 45 number of iterations= 4 number of iterations= 7 number of iterations= 15 number of iterations= 20 number of iterations= 31 number of iterations= 60 number of iterations= 13 number of iterations= 19 Loading Model for K4 Simulation JAGS @ 2 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: 727 Initializing model Burn in iterations for K4 Simulation JAGS @ 2 **************** Learning hyperparameters for K4 Simulation JAGS @ 2 Attempt 1 Labeling components for level 2 model K4 Simulation JAGS @ 2 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 @ 2 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 1180.6073 15.6920 0.1281 1.7804 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 1156 1169 1178 1190 1216 Potential scale reduction factors: Point est. Upper C.I. deviance 1.23 1.66 deviance 689.4905 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 6.85501 2.64821 0.02162 0.05142 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 3.004 4.972 6.402 8.261 13.292 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.13 1.39 alphaN 2858.961 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] 0.4821 0.1008 0.0008233 0.004158 alpha0[2] 0.5179 0.1008 0.0008233 0.004158 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.2916 0.4078 0.4828 0.5563 0.6711 alpha0[2] 0.3289 0.4437 0.5172 0.5922 0.7084 Potential scale reduction factors: Point est. Upper C.I. [1,] 1.87 3.1 alpha0[1] alpha0[2] 593.2872 593.2872 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] -0.7094 0.2477 0.002023 0.05353 mu0[2] 0.1234 0.3262 0.002664 0.10311 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.1531 -0.8914 -0.72219 -0.5647 -0.1276 mu0[2] -0.3145 -0.1242 0.02217 0.3363 0.8683 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.46 2.22 mu0[2] 1.47 2.83 Multivariate psrf 1.41 mu0[1] mu0[2] 17.53442 15.42580 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] 0.7596 0.9465 0.007729 0.08049 tau0[2] 1.2784 0.8312 0.006786 0.06950 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -1.0233 0.1486 0.7251 1.304 2.805 tau0[2] -0.3166 0.6596 1.2720 1.898 2.849 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.56 2.42 tau0[2] 1.40 2.05 Multivariate psrf 1.65 tau0[1] tau0[2] 92.26136 105.81628 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] 0.5576 0.1287 0.001051 0.01978 beta0[2] 0.8419 0.3092 0.002525 0.05874 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.3357 0.4659 0.5452 0.6388 0.839 beta0[2] 0.2390 0.6825 0.8143 1.0944 1.379 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.04 1.12 beta0[2] 2.01 3.48 Multivariate psrf 1.79 beta0[1] beta0[2] 43.16786 23.62916 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] 3.319 0.8906 0.007272 0.07078 gamma0[2] 2.625 0.7148 0.005836 0.04657 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.992 2.697 3.167 3.792 5.460 gamma0[2] 1.560 2.114 2.499 3.023 4.353 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.11 1.34 gamma0[2] 1.22 1.62 Multivariate psrf 1.26 gamma0[1] gamma0[2] 144.5351 200.8536 Chains of length 5000 for K4 Simulation JAGS @ 2 did not converge in run 1 . Maximum Rhat value = 1.785405 . deviance [[ 1 ]] Mean SD Naive SE Time-series SE 1176.9810807 12.4606065 0.1762196 0.9386711 deviance [[ 2 ]] Mean SD Naive SE Time-series SE 1176.0126959 11.6818571 0.1652064 0.5220834 deviance [[ 3 ]] Mean SD Naive SE Time-series SE 1188.8281176 18.5801302 0.2627627 5.2321441 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 6.45976960 2.28445611 0.03230709 0.07124901 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 6.15307675 2.17231121 0.03072112 0.06254915 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 7.95217643 3.04156579 0.04301424 0.12168444 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.5487037 0.08447732 0.00119469 0.01093026 alpha0[2] 0.4512963 0.08447732 0.00119469 0.01093026 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.5082267 0.06998582 0.000989749 0.004257412 alpha0[2] 0.4917733 0.06998582 0.000989749 0.004257412 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.389283 0.06882317 0.0009733067 0.004241187 alpha0[2] 0.610717 0.06882317 0.0009733067 0.004241187 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.5419482 0.2363192 0.003342058 0.09539066 mu0[2] 0.1141724 0.2139969 0.003026374 0.13932597 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.72746211 0.1595592 0.002256507 0.05838946 mu0[2] -0.07285975 0.1375831 0.001945719 0.04199437 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.8586958 0.2284412 0.003230646 0.1152406 mu0[2] 0.3287390 0.4169553 0.005896639 0.2729806 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 0.3130525 0.7048396 0.009967938 0.1288764 tau0[2] 1.5460827 0.7293165 0.010314092 0.1176962 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 0.4423872 0.7436795 0.01051722 0.1394892 tau0[2] 1.6093854 0.7736848 0.01094155 0.1323033 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.5234616 0.8687096 0.012285409 0.1491453 tau0[2] 0.6797963 0.6345035 0.008973235 0.1100840 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5490167 0.1139254 0.001611148 0.02683838 beta0[2] 1.0883378 0.2499529 0.003534868 0.13775782 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5359483 0.1426422 0.002017265 0.03987777 beta0[2] 0.8765079 0.1666511 0.002356802 0.04320875 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5877763 0.1222969 0.001729540 0.03479438 beta0[2] 0.5609983 0.2360769 0.003338631 0.10101831 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.971319 0.7559876 0.01069128 0.10713914 gamma0[2] 3.019359 0.7206686 0.01019179 0.09043092 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.388096 0.8113124 0.011473690 0.12269346 gamma0[2] 2.495691 0.6594085 0.009325445 0.08177054 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.597388 0.9730730 0.013761331 0.13622969 gamma0[2] 2.360965 0.5802872 0.008206501 0.06823885 **************** Learning hyperparameters for K4 Simulation JAGS @ 2 Attempt 2 Labeling components for level 2 model K4 Simulation JAGS @ 2 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 @ 2 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 1179.282 14.848 0.070 0.923 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 1156 1169 1177 1188 1214 Potential scale reduction factors: Point est. Upper C.I. deviance 1.08 1.23 deviance 792.5194 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 6.62374 2.52179 0.01189 0.03806 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 2.884 4.835 6.233 7.975 12.720 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.05 1.14 alphaN 5345.284 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] 0.4623 0.08615 0.0004061 0.002836 alpha0[2] 0.5377 0.08615 0.0004061 0.002836 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.3034 0.4017 0.459 0.5193 0.6417 alpha0[2] 0.3583 0.4807 0.541 0.5983 0.6966 Potential scale reduction factors: Point est. Upper C.I. [1,] 1.27 1.74 alpha0[1] alpha0[2] 1028.194 1028.194 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] -0.7288 0.2165 0.001021 0.0301 mu0[2] 0.1540 0.3273 0.001543 0.0809 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.1184 -0.88786 -0.7316 -0.5857 -0.2717 mu0[2] -0.4622 -0.09511 0.1683 0.3646 0.8240 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.36 1.98 mu0[2] 1.04 1.08 Multivariate psrf 1.31 mu0[1] mu0[2] 50.50926 17.06396 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] 0.8933 0.7947 0.003746 0.04518 tau0[2] 1.1893 0.7726 0.003642 0.04345 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.7048 0.3878 0.8988 1.391 2.524 tau0[2] -0.3265 0.6622 1.1838 1.728 2.697 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.06 1.20 tau0[2] 1.15 1.44 Multivariate psrf 1.17 tau0[1] tau0[2] 293.2418 281.2557 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] 0.5428 0.1526 0.0007192 0.01510 beta0[2] 0.8213 0.2577 0.0012148 0.03646 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.3125 0.4311 0.5211 0.6265 0.8971 beta0[2] 0.2678 0.6885 0.8005 0.9663 1.3429 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.06 1.21 beta0[2] 1.16 1.47 Multivariate psrf 1.11 beta0[1] beta0[2] 96.2560 61.3485 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] 3.449 0.8989 0.004237 0.04270 gamma0[2] 2.676 0.7751 0.003654 0.03389 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 2.097 2.814 3.296 3.924 5.698 gamma0[2] 1.576 2.136 2.551 3.064 4.534 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.11 1.33 gamma0[2] 1.30 1.83 Multivariate psrf 1.32 gamma0[1] gamma0[2] 410.3854 518.4529 Chains of length 10000 for K4 Simulation JAGS @ 2 did not converge in run 2 . Maximum Rhat value = 1.320644 . deviance [[ 1 ]] Mean SD Naive SE Time-series SE 1176.9042111 12.5894065 0.1027921 0.6623221 deviance [[ 2 ]] Mean SD Naive SE Time-series SE 1177.0475118 12.7202901 0.1038607 0.6476047 deviance [[ 3 ]] Mean SD Naive SE Time-series SE 1183.8953633 17.5845383 0.1435772 2.6096169 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 6.26666560 2.27916867 0.01860933 0.04707797 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 6.33929922 2.27250969 0.01855496 0.05054674 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 7.26525998 2.84608670 0.02323820 0.09090439 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.5004676 0.08379975 0.0006842221 0.005816952 alpha0[2] 0.4995324 0.08379975 0.0006842221 0.005816952 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4749994 0.07860179 0.0006417809 0.005541524 alpha0[2] 0.5250006 0.07860179 0.0006417809 0.005541524 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.411393 0.06967166 0.0005688667 0.002799282 alpha0[2] 0.588607 0.06967166 0.0005688667 0.002799282 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.5792203 0.1836381 0.001499399 0.03723698 mu0[2] 0.1129750 0.2731674 0.002230402 0.11388612 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.7992486 0.1644708 0.001342899 0.03953434 mu0[2] 0.2025424 0.2955294 0.002412987 0.11554897 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.8079211 0.2150389 0.001755785 0.07215729 mu0[2] 0.1463772 0.3941822 0.003218484 0.18049692 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 0.8098606 0.7552995 0.006166995 0.07634855 tau0[2] 1.2506434 0.7497234 0.006121466 0.07571975 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 0.7191781 0.7543935 0.006159597 0.08060508 tau0[2] 1.4766571 0.7742599 0.006321806 0.08285445 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.1509510 0.8070573 0.006589595 0.07773784 tau0[2] 0.8405083 0.6489744 0.005298854 0.06627210 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5504131 0.1612751 0.001316806 0.02835163 beta0[2] 0.9225013 0.2301608 0.001879255 0.06587360 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.4960697 0.1509879 0.001232811 0.02608320 beta0[2] 0.8059411 0.1674592 0.001367299 0.02855046 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5820084 0.1313412 0.001072397 0.02382549 beta0[2] 0.7353309 0.3167985 0.002586649 0.08252093 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.093465 0.7816813 0.006382401 0.06439612 gamma0[2] 3.148769 0.8429037 0.006882280 0.08073291 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.530209 0.8330672 0.006801965 0.07158556 gamma0[2] 2.492639 0.6493025 0.005301532 0.04917319 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.723641 0.9541844 0.007790883 0.08446737 gamma0[2] 2.386532 0.5737381 0.004684552 0.03741941 MCMC run did not converge, proceeding anyway. Learning parameters for K4 Simulation JAGS @ 2 Labeling components for K4 Simulation JAGS @ 2 Labeling components for pi Labeling components for mu Labeling components for tau Calculating model fit indexes for K4 Simulation JAGS @ 2 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -563.3195 50.4766 1227.5922 50.4766 1227.5922 lppd lppd.bayes pDIC DIC pDICalt DICalt -588.55780 -653.47252 -129.82943 1047.28618 75.94964 1458.84433 Analaysis complete for K4 Simulation JAGS @ 2 > proc.time() user system elapsed 665.437 1.522 667.829