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 K2 Simulation Stan unordered @ 2 Removing 0 of 10 Level 2 units for length. Calculating initial values for chain 1 ; K2 Simulation Stan unordered @ 2 number of iterations= 24 number of iterations= 58 number of iterations= 13 number of iterations= 13 number of iterations= 35 number of iterations= 22 number of iterations= 32 number of iterations= 138 number of iterations= 40 number of iterations= 126 Calculating initial values for chain 2 ; K2 Simulation Stan unordered @ 2 number of iterations= 17 number of iterations= 24 number of iterations= 12 number of iterations= 21 number of iterations= 30 number of iterations= 24 number of iterations= 36 number of iterations= 86 number of iterations= 49 number of iterations= 184 Calculating initial values for chain 3 ; K2 Simulation Stan unordered @ 2 number of iterations= 23 number of iterations= 51 number of iterations= 9 number of iterations= 16 number of iterations= 30 number of iterations= 34 number of iterations= 44 number of iterations= 34 number of iterations= 72 number of iterations= 204 **************** Running Model for K2 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). 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) 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: 41.1383 seconds (Warm-up) 178.922 seconds (Sampling) 220.06 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. 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: 27.4355 seconds (Warm-up) 115.24 seconds (Sampling) 142.676 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' 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): 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: 30.5365 seconds (Warm-up) 114.684 seconds (Sampling) 145.221 seconds (Total) Labeling components for level 2 model K2 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 K2 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 -574.73515 7.45653 0.06088 0.12908 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -590.0 -579.6 -574.4 -569.5 -560.9 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.07 1.22 lp__ 3135.804 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 10.77633 4.61181 0.03766 0.03764 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 3.991 7.385 10.067 13.405 21.563 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.28 1.83 alphaN 12267.69 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] 0.6295 0.1089 0.0008893 0.001354 alpha0[2] 0.3705 0.1089 0.0008893 0.001354 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.3396 0.5798 0.6536 0.7050 0.7791 alpha0[2] 0.2209 0.2950 0.3464 0.4202 0.6604 Potential scale reduction factors: Point est. Upper C.I. [1,] 1.55 2.38 alpha0[1] alpha0[2] 4623.813 4623.813 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] -0.4104 0.2207 0.001802 0.004066 mu0[2] 0.3421 0.3545 0.002894 0.005199 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -0.8520 -0.54683 -0.4091 -0.2711 0.01589 mu0[2] -0.3237 0.09681 0.3370 0.5752 1.05888 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.07 1.22 mu0[2] 1.13 1.40 Multivariate psrf 1.2 mu0[1] mu0[2] 2939.381 4203.414 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] 0.6119 0.2579 0.002106 0.006788 beta0[2] 0.9074 0.3262 0.002664 0.004372 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.2995 0.4496 0.5524 0.6995 1.301 beta0[2] 0.4024 0.6780 0.8626 1.0871 1.669 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.08 1.09 beta0[2] 1.04 1.12 Multivariate psrf 1.04 beta0[1] beta0[2] 1571.307 5387.901 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] 1.490 0.7346 0.005998 0.008584 tau0[2] -0.068 0.7047 0.005754 0.008545 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.2716 1.0262 1.7069 2.0340 2.472 tau0[2] -1.1651 -0.5736 -0.1961 0.3575 1.531 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.96 3.25 tau0[2] 1.61 2.49 Multivariate psrf 1.85 tau0[1] tau0[2] 4214.542 4567.821 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] 1.205 0.5189 0.004237 0.005489 gamma0[2] 1.497 0.4978 0.004064 0.005932 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.5170 0.8117 1.087 1.510 2.446 gamma0[2] 0.7594 1.1365 1.419 1.772 2.684 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 2.00 3.51 gamma0[2] 1.47 2.20 Multivariate psrf 2.01 gamma0[1] gamma0[2] 4701.232 5018.613 Chains of length 5000 for K2 Simulation Stan unordered @ 2 did not converge in run 1 . Maximum Rhat value = 2.007357 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -577.3095141 7.0860069 0.1002113 0.2184938 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -573.1191250 7.2771663 0.1029147 0.2232174 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -573.7768068 7.3139407 0.1034347 0.2288758 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 7.86022343 2.99408779 0.04234280 0.04507091 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 12.26398836 4.69746028 0.06643212 0.07501192 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 12.20478989 4.47484298 0.06328384 0.07135315 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.5420824 0.09913068 0.001401919 0.002114628 alpha0[2] 0.4579176 0.09913068 0.001401919 0.002114628 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.6714547 0.08786958 0.001242664 0.002662874 alpha0[2] 0.3285453 0.08786958 0.001242664 0.002662874 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.6748336 0.08117219 0.001147948 0.002219655 alpha0[2] 0.3251664 0.08117219 0.001147948 0.002219655 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.3347376 0.2221080 0.003141081 0.006008231 mu0[2] 0.1782721 0.3304774 0.004673657 0.008343429 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.4484690 0.2127792 0.003009153 0.007286365 mu0[2] 0.4188806 0.3416634 0.004831851 0.009030689 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.4478623 0.2071688 0.002929809 0.007720606 mu0[2] 0.4290048 0.3328188 0.004706769 0.009595121 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.6282562 0.3507086 0.004959769 0.016648887 beta0[2] 0.9863294 0.3567479 0.005045178 0.008075879 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.6075728 0.2013407 0.002847388 0.008953279 beta0[2] 0.8754908 0.3052465 0.004316838 0.007082158 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5997622 0.1887787 0.002669734 0.007570148 beta0[2] 0.8605039 0.2990646 0.004229412 0.007526558 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 0.7707995 0.5414969 0.007657922 0.01178535 tau0[2] 0.5228510 0.5635869 0.007970322 0.01280152 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.852165 0.5436608 0.007688525 0.01689146 tau0[2] -0.364620 0.5813636 0.008221724 0.01572542 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.8462214 0.5050597 0.007142622 0.01545788 tau0[2] -0.3622337 0.5572731 0.007881032 0.01568158 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.715818 0.4585482 0.006484851 0.009242219 gamma0[2] 1.870852 0.4853539 0.006863941 0.010647471 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 0.9503732 0.3246089 0.004590663 0.009153076 gamma0[2] 1.3120362 0.3851553 0.005446919 0.010248160 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 0.9473521 0.3164172 0.004474814 0.010097768 gamma0[2] 1.3089119 0.3875224 0.005480394 0.009912896 **************** Running Model for K2 Simulation Stan unordered @ 2 Attempt 2 SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 1). 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: 75.0122 seconds (Warm-up) 388.777 seconds (Sampling) 463.79 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' 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. 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: 70.5347 seconds (Warm-up) 356.689 seconds (Sampling) 427.224 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. 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: 57.1756 seconds (Warm-up) 243.812 seconds (Sampling) 300.988 seconds (Total) Labeling components for level 2 model K2 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 K2 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 -575.71879 7.47499 0.04316 0.08846 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -591.3 -580.6 -575.4 -570.5 -562.0 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.09 1.27 lp__ 6639.605 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 9.89860 4.13253 0.02386 0.02989 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 3.925 6.930 9.188 12.178 19.762 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.23 1.65 alphaN 16095.56 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] 0.6044 0.1183 0.0005575 0.004853 alpha0[2] 0.3956 0.1183 0.0005575 0.004853 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.3180 0.5457 0.6311 0.6892 0.7715 alpha0[2] 0.2285 0.3108 0.3689 0.4543 0.6820 Potential scale reduction factors: Point est. Upper C.I. [1,] 1.26 1.8 alpha0[1] alpha0[2] 5206.666 5206.666 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] -0.3901 0.2141 0.001009 0.003924 mu0[2] 0.3097 0.3564 0.001680 0.007408 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -0.8116 -0.52349 -0.3930 -0.2580 0.03849 mu0[2] -0.3297 0.05458 0.2952 0.5464 1.04490 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.04 1.14 mu0[2] 1.06 1.21 Multivariate psrf 1.11 mu0[1] mu0[2] 3679.609 6103.893 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] 0.595 0.2834 0.001336 0.005705 beta0[2] 0.923 0.3400 0.001603 0.003616 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.2270 0.4124 0.5379 0.7079 1.303 beta0[2] 0.3843 0.6963 0.8810 1.0985 1.701 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.08 1.21 beta0[2] 1.08 1.24 Multivariate psrf 1.1 beta0[1] beta0[2] 3207.51 12415.91 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] 1.32639 0.7552 0.003560 0.04034 tau0[2] 0.07579 0.7329 0.003455 0.03007 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.3874 0.8546 1.46695 1.9168 2.413 tau0[2] -1.1175 -0.4683 -0.02347 0.5571 1.671 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.37 2.09 tau0[2] 1.26 1.74 Multivariate psrf 1.34 tau0[1] tau0[2] 3854.970 5047.934 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] 1.301 0.5248 0.002474 0.028142 gamma0[2] 1.590 0.5145 0.002426 0.009394 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.5501 0.9019 1.221 1.606 2.533 gamma0[2] 0.7986 1.2245 1.524 1.874 2.785 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.34 2.02 gamma0[2] 1.28 1.79 Multivariate psrf 1.43 gamma0[1] gamma0[2] 3084.622 7391.543 Chains of length 10000 for K2 Simulation Stan unordered @ 2 did not converge in run 2 . Maximum Rhat value = 1.425074 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -575.78916835 7.05515078 0.07055151 0.14601532 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -578.16652448 7.21595892 0.07215959 0.15277746 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -573.20067121 7.31122708 0.07311227 0.16053549 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 8.91949955 3.40276517 0.03402765 0.05125371 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 8.60125046 3.32704798 0.03327048 0.05145891 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 12.17505822 4.55675900 0.04556759 0.05259012 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.5821438 0.09676243 0.0007900619 0.003460255 alpha0[2] 0.4178562 0.09676243 0.0007900619 0.003460255 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.5575665 0.1351951 0.001103863 0.01408629 alpha0[2] 0.4424335 0.1351951 0.001103863 0.01408629 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.6735813 0.08273507 0.000675529 0.001256947 alpha0[2] 0.3264187 0.08273507 0.000675529 0.001256947 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.3829841 0.1908319 0.001558136 0.006348481 mu0[2] 0.2942050 0.3688440 0.003011599 0.011387975 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.3454608 0.2310855 0.001886805 0.008813862 mu0[2] 0.2137572 0.3343730 0.002730144 0.018456303 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.4417576 0.2074386 0.001693729 0.004538981 mu0[2] 0.4211720 0.3337028 0.002724672 0.004853827 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5162511 0.3199040 0.002612005 0.012815630 beta0[2] 1.0410761 0.3705548 0.003025568 0.009085763 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.6614347 0.2944041 0.00240380 0.010264492 beta0[2] 0.8557493 0.3107900 0.00253759 0.004132651 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.6072717 0.2028982 0.001656656 0.004833976 beta0[2] 0.8722009 0.3031309 0.002475053 0.004245935 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.0890423 0.5721961 0.004671962 0.02840394 tau0[2] 0.2862738 0.6224416 0.005082215 0.01890831 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.0415526 0.8404799 0.006862489 0.11731385 tau0[2] 0.3099175 0.7757431 0.006333916 0.08773328 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.8485761 0.5165901 0.004217941 0.008862286 tau0[2] -0.3688261 0.5703061 0.004656530 0.009148531 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.461585 0.4544372 0.003710464 0.024020454 gamma0[2] 1.868168 0.4981511 0.004067386 0.008094949 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.492188 0.5760161 0.004703152 0.08070294 gamma0[2] 1.595514 0.4884949 0.003988544 0.02612867 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 0.9480637 0.3176824 0.002593866 0.006141432 gamma0[2] 1.3069148 0.3872459 0.003161850 0.006787498 MCMC run did not converge, proceeding anyway. Calculating model fit indexes for K2 Simulation Stan unordered @ 2 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -529.53733 36.38424 1131.84315 36.38424 1131.84315 lppd lppd.bayes pDIC DIC pDICalt DICalt -547.72945 -680.23874 -265.01856 830.44034 57.09036 1474.65820 Analaysis complete for K2 Simulation Stan unordered @ 2 > proc.time() user system elapsed 1879.254 6.356 1890.839