Loading required package: Rcpp Loading required package: inline Attaching package: ‘inline’ The following object is masked from ‘package:Rcpp’: registerPlugin rstan (Version 2.2.0, packaged: 2014-02-14 04:29:17 UTC, GitRev: 52d7b230aaa0) Loading required package: lattice Attaching package: ‘coda’ The following object is masked from ‘package:rstan’: traceplot 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 Stan unordered @ 2 Removing 0 of 10 Level 2 units for length. Calculating initial values for chain 1 ; K4 Simulation Stan unordered @ 2 number of iterations= 19 One of the variances is going to zero; trying new starting values. number of iterations= 61 number of iterations= 7 number of iterations= 12 number of iterations= 39 number of iterations= 10 number of iterations= 88 number of iterations= 73 number of iterations= 42 number of iterations= 29 Calculating initial values for chain 2 ; K4 Simulation Stan unordered @ 2 number of iterations= 36 number of iterations= 35 number of iterations= 9 number of iterations= 11 number of iterations= 19 number of iterations= 55 number of iterations= 38 number of iterations= 168 number of iterations= 13 number of iterations= 15 Calculating initial values for chain 3 ; K4 Simulation Stan unordered @ 2 number of iterations= 107 number of iterations= 42 number of iterations= 18 number of iterations= 11 number of iterations= 45 number of iterations= 17 number of iterations= 29 number of iterations= 36 number of iterations= 5 number of iterations= 22 **************** Running Model for K4 Simulation Stan unordered @ 2 Attempt 1 TRANSLATING MODEL 'hierModel1p' FROM Stan CODE TO C++ CODE NOW. COMPILING THE C++ CODE FOR MODEL 'hierModel1p' NOW. SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 1). Iteration: 1 / 6000 [ 0%] (Warmup) Iteration: 600 / 6000 [ 10%] (Warmup) Iteration: 1200 / 6000 [ 20%] (Sampling) Iteration: 1800 / 6000 [ 30%] (Sampling) Iteration: 2400 / 6000 [ 40%] (Sampling) Iteration: 3000 / 6000 [ 50%] (Sampling) Iteration: 3600 / 6000 [ 60%] (Sampling) Iteration: 4200 / 6000 [ 70%] (Sampling) Iteration: 4800 / 6000 [ 80%] (Sampling) Iteration: 5400 / 6000 [ 90%] (Sampling) Iteration: 6000 / 6000 [100%] (Sampling) Elapsed Time: 89.0423 seconds (Warm-up) 720.677 seconds (Sampling) 809.719 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 2). Iteration: 1 / 6000 [ 0%] (Warmup) Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Iteration: 600 / 6000 [ 10%] (Warmup) Iteration: 1200 / 6000 [ 20%] (Sampling) Iteration: 1800 / 6000 [ 30%] (Sampling) Iteration: 2400 / 6000 [ 40%] (Sampling) Iteration: 3000 / 6000 [ 50%] (Sampling) Iteration: 3600 / 6000 [ 60%] (Sampling) Iteration: 4200 / 6000 [ 70%] (Sampling) Iteration: 4800 / 6000 [ 80%] (Sampling) Iteration: 5400 / 6000 [ 90%] (Sampling) Iteration: 6000 / 6000 [100%] (Sampling) Elapsed Time: 125.68 seconds (Warm-up) 829.465 seconds (Sampling) 955.145 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 3). Iteration: 1 / 6000 [ 0%] (Warmup) Iteration: 600 / 6000 [ 10%] (Warmup) Iteration: 1200 / 6000 [ 20%] (Sampling) Iteration: 1800 / 6000 [ 30%] (Sampling) Iteration: 2400 / 6000 [ 40%] (Sampling) Iteration: 3000 / 6000 [ 50%] (Sampling) Iteration: 3600 / 6000 [ 60%] (Sampling) Iteration: 4200 / 6000 [ 70%] (Sampling) Iteration: 4800 / 6000 [ 80%] (Sampling) Iteration: 5400 / 6000 [ 90%] (Sampling) Iteration: 6000 / 6000 [100%] (Sampling) Elapsed Time: 89.3034 seconds (Warm-up) 350.061 seconds (Sampling) 439.364 seconds (Total) Labeling components for level 2 model K4 Simulation Stan unordered @ 2 Labeling components for alpha0 Labeling components for mu0 Labeling components for beta0 Labeling components for tau0 Labeling components for gamma0 Labeling components for pi Labeling components for mu Labeling components for sigma **************** Convergence diagnostics for K4 Simulation Stan unordered @ 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 -624.15265 9.32900 0.07617 0.44418 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -645.2 -629.8 -623.2 -617.4 -608.7 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.18 1.53 lp__ 1092.074 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 6.96366 2.71447 0.02216 0.04368 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 2.976 5.020 6.525 8.444 13.468 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.07 1.21 alphaN 5840.498 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] 0.4994 0.1142 0.0009325 0.004598 alpha0[2] 0.5006 0.1142 0.0009325 0.004598 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.3041 0.4135 0.4861 0.5862 0.7204 alpha0[2] 0.2796 0.4138 0.5139 0.5865 0.6959 Potential scale reduction factors: Point est. Upper C.I. [1,] 1.51 2.39 alpha0[1] alpha0[2] 2152.232 2152.232 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] -0.6039 0.2285 0.001866 0.004666 mu0[2] 0.1014 0.3153 0.002574 0.006637 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.045 -0.7501 -0.60913 -0.4567 -0.1511 mu0[2] -0.485 -0.1058 0.08027 0.2993 0.7706 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.09 1.29 mu0[2] 1.06 1.19 Multivariate psrf 1.15 mu0[1] mu0[2] 3242.853 3096.648 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] 0.6584 0.2348 0.001917 0.003571 beta0[2] 0.8518 0.3499 0.002857 0.022218 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.3795 0.5073 0.6011 0.7425 1.290 beta0[2] 0.1657 0.6652 0.8572 1.0534 1.581 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.07 1.18 beta0[2] 1.15 1.43 Multivariate psrf 1.16 beta0[1] beta0[2] 4217.058 2211.993 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] 1.8408 0.9807 0.008007 0.02348 tau0[2] 0.4186 0.8488 0.006931 0.02728 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.08989 1.1559 1.8640 2.5474 3.648 tau0[2] -1.05732 -0.1982 0.3627 0.9836 2.210 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.42 2.12 tau0[2] 1.36 1.93 Multivariate psrf 1.51 tau0[1] tau0[2] 3484.120 3257.698 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] 2.681 0.8212 0.006705 0.01189 gamma0[2] 2.794 0.9625 0.007859 0.05229 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.512 2.104 2.533 3.101 4.682 gamma0[2] 1.223 2.119 2.711 3.351 4.965 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.01 1.02 gamma0[2] 1.55 2.40 Multivariate psrf 1.45 gamma0[1] gamma0[2] 4954.783 3302.764 Chains of length 5000 for K4 Simulation Stan unordered @ 2 did not converge in run 1 . Maximum Rhat value = 1.505137 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -628.4709378 10.0229275 0.1417456 1.1927263 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -620.2642824 7.3774986 0.1043336 0.2667218 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -623.7227318 8.5017962 0.1202336 0.5309614 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 6.78944779 2.59424388 0.03668815 0.08700511 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 7.80649498 2.93312026 0.04148058 0.09038046 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 6.29502662 2.36360009 0.03342635 0.03785645 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4467306 0.08657487 0.001224353 0.01292239 alpha0[2] 0.5532694 0.08657487 0.001224353 0.01292239 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.5862042 0.1204744 0.001703765 0.002897882 alpha0[2] 0.4137958 0.1204744 0.001703765 0.002897882 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4652378 0.07514649 0.001062732 0.003859952 alpha0[2] 0.5347622 0.07514649 0.001062732 0.003859952 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.62851047 0.2237442 0.003164221 0.01129757 mu0[2] 0.08060724 0.2818185 0.003985516 0.01577220 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.51717217 0.2287620 0.003235184 0.005464277 mu0[2] 0.02675726 0.3316289 0.004689941 0.009132892 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.6660592 0.2055367 0.002906729 0.00620295 mu0[2] 0.1968247 0.3062023 0.004330355 0.00801682 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.6386352 0.2035541 0.002878689 0.005986896 beta0[2] 0.7087671 0.3864207 0.005464813 0.061479885 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.7191415 0.2856386 0.00403954 0.007493355 beta0[2] 0.9867406 0.2945389 0.00416541 0.006547941 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.6174707 0.1912505 0.002704691 0.00476934 beta0[2] 0.8599327 0.3041010 0.004300637 0.02490329 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 2.31586005 0.9769588 0.01381628 0.06455484 tau0[2] -0.02991028 0.7592189 0.01073698 0.07753515 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.139484 0.7088807 0.01002509 0.01562474 tau0[2] 0.963060 0.7676622 0.01085638 0.01801847 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 2.0670033 0.8123827 0.011488826 0.02348348 tau0[2] 0.3225174 0.6993365 0.009890111 0.01905683 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.630566 0.8489995 0.01200667 0.02201498 gamma0[2] 2.096307 0.7729980 0.01093184 0.15438985 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.759008 0.8088122 0.01143833 0.01749015 gamma0[2] 3.428003 0.9085033 0.01284818 0.02097684 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.652477 0.7993635 0.011304707 0.02192667 gamma0[2] 2.857843 0.6808895 0.009629231 0.01818485 **************** Running Model for K4 Simulation Stan unordered @ 2 Attempt 2 SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 1). Iteration: 1 / 12000 [ 0%] (Warmup) Iteration: 1200 / 12000 [ 10%] (Warmup) Iteration: 2400 / 12000 [ 20%] (Sampling) Iteration: 3600 / 12000 [ 30%] (Sampling) Iteration: 4800 / 12000 [ 40%] (Sampling) Iteration: 6000 / 12000 [ 50%] (Sampling) Iteration: 7200 / 12000 [ 60%] (Sampling) Iteration: 8400 / 12000 [ 70%] (Sampling) Iteration: 9600 / 12000 [ 80%] (Sampling) Iteration: 10800 / 12000 [ 90%] (Sampling) Iteration: 12000 / 12000 [100%] (Sampling) Elapsed Time: 134.797 seconds (Warm-up) 668.37 seconds (Sampling) 803.167 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 2). Iteration: 1 / 12000 [ 0%] (Warmup) Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Iteration: 1200 / 12000 [ 10%] (Warmup) Iteration: 2400 / 12000 [ 20%] (Sampling) Iteration: 3600 / 12000 [ 30%] (Sampling) Iteration: 4800 / 12000 [ 40%] (Sampling) Iteration: 6000 / 12000 [ 50%] (Sampling) Iteration: 7200 / 12000 [ 60%] (Sampling) Iteration: 8400 / 12000 [ 70%] (Sampling) Iteration: 9600 / 12000 [ 80%] (Sampling) Iteration: 10800 / 12000 [ 90%] (Sampling) Iteration: 12000 / 12000 [100%] (Sampling) Elapsed Time: 122.812 seconds (Warm-up) 667.718 seconds (Sampling) 790.53 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 3). Iteration: 1 / 12000 [ 0%] (Warmup) Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Iteration: 1200 / 12000 [ 10%] (Warmup) Iteration: 2400 / 12000 [ 20%] (Sampling) Iteration: 3600 / 12000 [ 30%] (Sampling) Iteration: 4800 / 12000 [ 40%] (Sampling) Iteration: 6000 / 12000 [ 50%] (Sampling) Iteration: 7200 / 12000 [ 60%] (Sampling) Iteration: 8400 / 12000 [ 70%] (Sampling) Iteration: 9600 / 12000 [ 80%] (Sampling) Iteration: 10800 / 12000 [ 90%] (Sampling) Iteration: 12000 / 12000 [100%] (Sampling) Elapsed Time: 230.648 seconds (Warm-up) 1262 seconds (Sampling) 1492.65 seconds (Total) Labeling components for level 2 model K4 Simulation Stan unordered @ 2 Labeling components for alpha0 Labeling components for mu0 Labeling components for beta0 Labeling components for tau0 Labeling components for gamma0 Labeling components for pi Labeling components for mu Labeling components for sigma **************** Convergence diagnostics for K4 Simulation Stan unordered @ 2 Run Number 2 Iterations = 1:10000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 10000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE -623.48603 9.66138 0.05578 0.24590 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -644.3 -629.7 -622.6 -616.6 -606.7 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.29 1.78 lp__ 2245.581 Iterations = 1:10000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 10000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE 7.93056 3.62107 0.02091 0.04292 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 3.031 5.317 7.154 9.785 16.984 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.5 2.4 alphaN 11256.85 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] 0.4792 0.1298 0.000612 0.006112 alpha0[2] 0.5208 0.1298 0.000612 0.006112 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.2578 0.3868 0.4605 0.5634 0.7444 alpha0[2] 0.2556 0.4366 0.5395 0.6132 0.7422 Potential scale reduction factors: Point est. Upper C.I. [1,] 1.08 1.12 alpha0[1] alpha0[2] 3912.059 3912.059 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] -0.59102 0.2428 0.001145 0.005606 mu0[2] 0.08311 0.3107 0.001465 0.006397 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.0561 -0.7519 -0.5994 -0.4282 -0.1159 mu0[2] -0.4628 -0.1290 0.0513 0.2827 0.7438 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.09 1.26 mu0[2] 1.04 1.12 Multivariate psrf 1.11 mu0[1] mu0[2] 2863.804 3105.224 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] 0.6793 0.2782 0.001312 0.004121 beta0[2] 0.8144 0.3653 0.001722 0.014040 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.3648 0.4984 0.6009 0.772 1.439 beta0[2] 0.1651 0.5802 0.8146 1.030 1.592 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.12 1.30 beta0[2] 1.02 1.06 Multivariate psrf 1.08 beta0[1] beta0[2] 7046.838 1836.361 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] 1.8240 1.012 0.004769 0.04482 tau0[2] 0.4053 0.858 0.004045 0.03840 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.1445 1.0983 1.842 2.5680 3.679 tau0[2] -1.0802 -0.2199 0.344 0.9797 2.218 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.12 1.38 tau0[2] 1.12 1.36 Multivariate psrf 1.17 tau0[1] tau0[2] 805.1189 853.3660 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] 2.738 0.9334 0.004400 0.00975 gamma0[2] 2.673 0.9942 0.004687 0.06207 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.448 2.074 2.556 3.215 5.025 gamma0[2] 1.204 1.935 2.541 3.243 4.999 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.02 1.03 gamma0[2] 1.08 1.23 Multivariate psrf 1.08 gamma0[1] gamma0[2] 9326.020 5407.073 Chains of length 10000 for K4 Simulation Stan unordered @ 2 did not converge in run 2 . Maximum Rhat value = 1.498046 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -623.93093569 8.32699412 0.08326994 0.31076773 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -628.48609122 9.74988780 0.09749888 0.63285968 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -618.04107600 7.79710531 0.07797105 0.21706693 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 6.26556151 2.32855835 0.02328558 0.02531402 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 6.88047379 2.68401905 0.02684019 0.07393291 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 10.64564430 3.93258899 0.03932589 0.10231555 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4647191 0.08208068 0.0006701859 0.006394477 alpha0[2] 0.5352809 0.08208068 0.0006701859 0.006394477 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4769157 0.1192245 0.0009734639 0.01695362 alpha0[2] 0.5230843 0.1192245 0.0009734639 0.01695362 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4959552 0.1706622 0.001393451 0.002806489 alpha0[2] 0.5040448 0.1706622 0.001393451 0.002806489 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.6491654 0.2134894 0.001743134 0.005316740 mu0[2] 0.1542148 0.3089352 0.002522445 0.008942539 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.61947015 0.2225753 0.001817320 0.007417536 mu0[2] 0.07743083 0.2997456 0.002447413 0.007750706 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.50441249 0.2646643 0.002160975 0.01412579 mu0[2] 0.01767237 0.3082104 0.002516528 0.01510902 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.6212220 0.1970342 0.001608778 0.003088976 beta0[2] 0.8143933 0.3307068 0.002700210 0.025120058 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.6547374 0.2285517 0.001866117 0.005322192 beta0[2] 0.7589385 0.3819379 0.003118510 0.032433775 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.7619733 0.3610729 0.002948148 0.010722146 beta0[2] 0.8699990 0.3727970 0.003043875 0.009548326 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 2.097328 0.9101406 0.007431267 0.03829344 tau0[2] 0.234275 0.7705997 0.006291920 0.03685654 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.9984915 1.0584754 0.008642415 0.1025074 tau0[2] 0.2008427 0.8677047 0.007084779 0.1000469 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.376132 0.9034223 0.007376412 0.07816043 tau0[2] 0.780676 0.8061544 0.006582223 0.04365588 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.666493 0.8285305 0.006764923 0.01649822 gamma0[2] 2.648945 0.8405646 0.006863181 0.08943193 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.718606 0.8717375 0.007117707 0.0141580 gamma0[2] 2.394828 1.0112093 0.008256490 0.1627156 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.828710 1.074088 0.008769892 0.01956801 gamma0[2] 2.975527 1.032993 0.008434348 0.01421594 MCMC run did not converge, proceeding anyway. Calculating model fit indexes for K4 Simulation Stan unordered @ 2 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -563.75843 53.34271 1234.20228 53.34271 1234.20228 lppd lppd.bayes pDIC DIC pDICalt DICalt -590.4298 -755.6645 -330.4694 850.3901 127.9249 1767.1788 Analaysis complete for K4 Simulation Stan unordered @ 2 > proc.time() user system elapsed 5447.553 11.461 5464.033