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 @ 2 Removing 0 of 10 Level 2 units for length. Calculating initial values for chain 1 ; K4 Simulation Stan @ 2 number of iterations= 91 number of iterations= 37 number of iterations= 22 number of iterations= 14 number of iterations= 27 number of iterations= 12 number of iterations= 81 number of iterations= 95 number of iterations= 51 number of iterations= 20 Calculating initial values for chain 2 ; K4 Simulation Stan @ 2 number of iterations= 112 number of iterations= 59 number of iterations= 22 number of iterations= 9 number of iterations= 34 number of iterations= 30 number of iterations= 17 number of iterations= 101 number of iterations= 26 number of iterations= 50 Calculating initial values for chain 3 ; K4 Simulation Stan @ 2 number of iterations= 92 number of iterations= 34 number of iterations= 5 number of iterations= 9 number of iterations= 23 number of iterations= 19 number of iterations= 40 number of iterations= 117 number of iterations= 17 number of iterations= 49 **************** Running Model for K4 Simulation Stan @ 2 Attempt 1 TRANSLATING MODEL 'hierModel1pmu' FROM Stan CODE TO C++ CODE NOW. COMPILING THE C++ CODE FOR MODEL 'hierModel1pmu' NOW. SAMPLING FOR MODEL 'hierModel1pmu' NOW (CHAIN 1). 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. 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: 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): Location parameter is -inf:0, but must be finite! 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: 54.3959 seconds (Warm-up) 318.203 seconds (Sampling) 372.599 seconds (Total) SAMPLING FOR MODEL 'hierModel1pmu' NOW (CHAIN 2). 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. 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. 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. 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: 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: 64.7428 seconds (Warm-up) 381.72 seconds (Sampling) 446.463 seconds (Total) SAMPLING FOR MODEL 'hierModel1pmu' NOW (CHAIN 3). 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: 114.957 seconds (Warm-up) 638.538 seconds (Sampling) 753.495 seconds (Total) **************** Convergence diagnostics for K4 Simulation Stan @ 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 -624.87645 9.18337 0.07498 0.39900 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -644.3 -630.7 -623.9 -618.2 -609.2 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.18 1.54 lp__ 1329.14 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 7.49133 2.96381 0.02420 0.07607 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 3.102 5.397 7.076 9.078 14.605 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.16 1.45 alphaN 5953.091 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] 0.4431 0.1047 0.0008548 0.004642 alpha0[2] 0.5569 0.1047 0.0008548 0.004642 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.2735 0.3624 0.4286 0.5138 0.6494 alpha0[2] 0.3506 0.4862 0.5714 0.6376 0.7265 Potential scale reduction factors: Point est. Upper C.I. [1,] 2.16 3.79 alpha0[1] alpha0[2] 5976.927 5976.927 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] -0.59065 0.2079 0.001698 0.007048 mu0[2] 0.02638 0.2491 0.002034 0.011552 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.0005 -0.7294 -0.59672 -0.4495 -0.2015 mu0[2] -0.3869 -0.1392 -0.02077 0.1702 0.5880 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.13 1.40 mu0[2] 1.20 1.64 Multivariate psrf 1.18 mu0[1] mu0[2] 2415.906 4137.949 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] 0.6222 0.1728 0.001411 0.007403 beta0[2] 0.6682 0.4347 0.003550 0.016753 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.3835 0.4963 0.5913 0.7084 1.075 beta0[2] 0.1098 0.2505 0.6639 1.0281 1.504 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.02 1.06 beta0[2] 2.44 5.45 Multivariate psrf 2.15 beta0[1] beta0[2] 3155.203 2684.145 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] 2.36466 0.8961 0.007317 0.02476 tau0[2] -0.02887 0.7051 0.005757 0.02409 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] 0.5306 1.8009 2.340 2.9962 4.056 tau0[2] -1.2146 -0.5199 -0.131 0.4265 1.402 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.06 1.19 tau0[2] 1.57 2.50 Multivariate psrf 1.48 tau0[1] tau0[2] 3108.132 1727.951 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] 2.795 0.9780 0.007985 0.02328 gamma0[2] 2.170 0.8824 0.007205 0.02729 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.463 2.109 2.614 3.249 5.249 gamma0[2] 1.048 1.490 1.949 2.693 4.296 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.23 1.67 gamma0[2] 2.20 4.19 Multivariate psrf 2 gamma0[1] gamma0[2] 3005.144 2234.302 Chains of length 5000 for K4 Simulation Stan @ 2 did not converge in run 1 . Maximum Rhat value = 2.145662 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -625.9582771 8.1459970 0.1152018 0.8050955 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -628.3669200 9.8005444 0.1386006 0.8562547 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -620.3041485 7.5074819 0.1061718 0.2267968 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 6.74295965 2.36524827 0.03344966 0.20452026 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 6.87142561 2.68321509 0.03794639 0.08982508 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 8.85960230 3.27793247 0.04635697 0.04669896 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.5455795 0.08353774 0.001181402 0.01373085 alpha0[2] 0.4544205 0.08353774 0.001181402 0.01373085 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4229725 0.06845649 0.000968121 0.002154795 alpha0[2] 0.5770275 0.06845649 0.000968121 0.002154795 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3608016 0.05946059 0.0008408997 0.0008467953 alpha0[2] 0.6391984 0.05946059 0.0008408997 0.0008467953 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.50510034 0.1864871 0.002637327 0.01942226 mu0[2] 0.05470267 0.2633457 0.003724271 0.03076210 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.6706764 0.2039249 0.002883934 0.00592485 mu0[2] 0.1200977 0.2798208 0.003957263 0.01584472 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.5961860 0.1989297 0.002813291 0.005894183 mu0[2] -0.0956616 0.1184018 0.001674454 0.001932777 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.6024048 0.1775048 0.002510296 0.02134881 beta0[2] 1.0853130 0.2506200 0.003544302 0.01560906 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.6122599 0.1611660 0.002279231 0.003912074 beta0[2] 0.6690658 0.3767122 0.005327515 0.047712621 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.6518761 0.1751973 0.002477664 0.004701065 beta0[2] 0.2503562 0.1169726 0.001654242 0.002405799 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 2.0968680 0.6953914 0.009834319 0.06564509 tau0[2] 0.5260247 0.6974906 0.009864007 0.06555289 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 2.4992910 0.8680558 0.012276163 0.02294820 tau0[2] -0.1974001 0.5647646 0.007986978 0.02750211 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 2.4978356 1.0319132 0.014593456 0.02608427 tau0[2] -0.4152197 0.4477601 0.006332284 0.01296337 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.388569 0.637489 0.009015456 0.05848236 gamma0[2] 3.069878 0.767677 0.010856592 0.07061796 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.701111 0.9086400 0.012850111 0.02275724 gamma0[2] 1.891540 0.5711756 0.008077643 0.04047424 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.296381 1.1009734 0.015570115 0.030628522 gamma0[2] 1.550057 0.3854903 0.005451656 0.008804508 **************** Running Model for K4 Simulation Stan @ 2 Attempt 2 SAMPLING FOR MODEL 'hierModel1pmu' NOW (CHAIN 1). 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. 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. 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. 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: 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. 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: 175.002 seconds (Warm-up) 1101.52 seconds (Sampling) 1276.52 seconds (Total) SAMPLING FOR MODEL 'hierModel1pmu' 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): Location parameter is inf:0, but must be finite! 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: 155.893 seconds (Warm-up) 633.843 seconds (Sampling) 789.735 seconds (Total) SAMPLING FOR MODEL 'hierModel1pmu' 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): Location parameter is inf:0, but must be finite! 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. 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: 184.761 seconds (Warm-up) 997.101 seconds (Sampling) 1181.86 seconds (Total) **************** Convergence diagnostics for K4 Simulation Stan @ 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 -628.21934 10.23257 0.05908 0.59070 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -651.6 -634.4 -627.0 -620.9 -611.5 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.07 1.21 lp__ 481.5866 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 6.53041 2.52682 0.01459 0.03815 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 2.825 4.729 6.134 7.884 12.594 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.01 1.02 alphaN 5182.677 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] 0.4815 0.09703 0.0004574 0.005857 alpha0[2] 0.5185 0.09703 0.0004574 0.005857 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.2994 0.4109 0.4806 0.5526 0.6621 alpha0[2] 0.3379 0.4474 0.5194 0.5891 0.7006 Potential scale reduction factors: Point est. Upper C.I. [1,] 1.36 1.95 alpha0[1] alpha0[2] 292.2304 292.2304 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] -0.56344 0.2551 0.001203 0.005945 mu0[2] -0.08602 0.3348 0.001578 0.016577 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.0585 -0.7332 -0.57714 -0.3946 -0.05796 mu0[2] -0.6909 -0.3129 -0.09836 0.1156 0.62438 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.07 1.22 mu0[2] 1.20 1.56 Multivariate psrf 1.3 mu0[1] mu0[2] 2682.6330 385.4282 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] 0.7957 0.3835 0.001808 0.02072 beta0[2] 0.7118 0.3378 0.001592 0.02643 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.2219 0.5231 0.6986 1.0105 1.718 beta0[2] 0.1386 0.4936 0.6970 0.9153 1.420 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.16 1.53 beta0[2] 1.12 1.35 Multivariate psrf 1.24 beta0[1] beta0[2] 3647.8030 486.6866 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] 1.2887 1.337 0.006301 0.1356 tau0[2] 0.9756 1.278 0.006025 0.1155 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.9311 0.12742 1.2937 2.363 3.683 tau0[2] -1.0305 -0.08952 0.7915 2.017 3.429 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.37 2.04 tau0[2] 1.44 2.29 Multivariate psrf 1.43 tau0[1] tau0[2] 1453.7888 293.5135 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] 2.529 0.8996 0.004241 0.03354 gamma0[2] 2.610 0.9272 0.004371 0.05200 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.240 1.896 2.378 2.987 4.731 gamma0[2] 1.186 1.946 2.504 3.125 4.720 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.13 1.39 gamma0[2] 1.19 1.53 Multivariate psrf 1.23 gamma0[1] gamma0[2] 1233.9406 864.9167 Chains of length 10000 for K4 Simulation Stan @ 2 did not converge in run 2 . Maximum Rhat value = 1.426634 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -628.18600685 9.13533628 0.09135336 0.54487909 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -631.0476490 11.4478540 0.1144785 1.5015667 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -625.42435329 9.15510182 0.09155102 0.76728031 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 6.48840524 2.50396247 0.02503962 0.06076181 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 6.76374237 2.68924856 0.02689249 0.08461439 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 6.33908364 2.35817801 0.02358178 0.04740753 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.5431969 0.08020483 0.0006548697 0.005959741 alpha0[2] 0.4568031 0.08020483 0.0006548697 0.005959741 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4739761 0.08923549 0.0007286047 0.01119485 alpha0[2] 0.5260239 0.08923549 0.0007286047 0.01119485 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4271999 0.08389409 0.0006849924 0.01216239 alpha0[2] 0.5728001 0.08389409 0.0006849924 0.01216239 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.5017740 0.2716723 0.002218195 0.01202284 mu0[2] -0.1985727 0.3254479 0.002657271 0.03123471 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.5442611 0.2629915 0.002147317 0.01221380 mu0[2] -0.1540860 0.3129262 0.002555032 0.03237222 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.6442741 0.2040017 0.001665667 0.004935593 mu0[2] 0.0945962 0.2871699 0.002344733 0.021203758 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.8866960 0.4377503 0.003574216 0.04454804 beta0[2] 0.8431684 0.2995793 0.002446054 0.02421993 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.8796550 0.4183168 0.003415543 0.04323789 beta0[2] 0.6827412 0.2752221 0.002247179 0.01572054 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.6208482 0.1696688 0.001385340 0.002885439 beta0[2] 0.6095850 0.3850015 0.003143524 0.073833297 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 0.7537777 1.146729 0.009363002 0.2455675 tau0[2] 1.5209711 1.112900 0.009086794 0.1445735 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 0.8850751 1.375205 0.01122850 0.3235095 tau0[2] 1.3832969 1.343075 0.01096616 0.3113455 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 2.22737871 0.9077182 0.007411488 0.02414014 tau0[2] 0.02266423 0.6970973 0.005691776 0.04747012 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.172401 0.6289408 0.005135280 0.03733983 gamma0[2] 3.088850 0.9187184 0.007501305 0.04371056 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.571974 0.9339002 0.007625263 0.08726567 gamma0[2] 2.300557 0.7690081 0.006278925 0.03917459 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.843560 0.9656404 0.007884421 0.03340333 gamma0[2] 2.439165 0.8887053 0.007256249 0.14454187 MCMC run did not converge, proceeding anyway. Calculating model fit indexes for K4 Simulation Stan @ 2 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -552.76924 77.30772 1260.15393 77.30772 1260.15393 lppd lppd.bayes pDIC DIC pDICalt DICalt -591.4231 -903.3256 -623.8049 559.0413 162.8156 2132.2824 Analaysis complete for K4 Simulation Stan @ 2 > proc.time() user system elapsed 4911.361 11.045 4933.581