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 K3 Simulation Stan @ 2 Removing 0 of 10 Level 2 units for length. Calculating initial values for chain 1 ; K3 Simulation Stan @ 2 number of iterations= 40 number of iterations= 15 number of iterations= 12 number of iterations= 17 number of iterations= 51 number of iterations= 22 number of iterations= 30 number of iterations= 30 number of iterations= 35 number of iterations= 81 Calculating initial values for chain 2 ; K3 Simulation Stan @ 2 number of iterations= 44 number of iterations= 20 number of iterations= 39 number of iterations= 9 number of iterations= 16 number of iterations= 27 number of iterations= 11 number of iterations= 14 number of iterations= 25 number of iterations= 57 Calculating initial values for chain 3 ; K3 Simulation Stan @ 2 number of iterations= 48 number of iterations= 22 number of iterations= 18 number of iterations= 11 number of iterations= 15 number of iterations= 26 number of iterations= 8 number of iterations= 14 number of iterations= 11 number of iterations= 88 **************** Running Model for K3 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). 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: 42.3213 seconds (Warm-up) 234.985 seconds (Sampling) 277.306 seconds (Total) SAMPLING FOR MODEL 'hierModel1pmu' 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: 79.9257 seconds (Warm-up) 410.947 seconds (Sampling) 490.872 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: 29.5894 seconds (Warm-up) 107.956 seconds (Sampling) 137.546 seconds (Total) **************** Convergence diagnostics for K3 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 -668.50659 7.70687 0.06293 0.15149 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -684.7 -673.4 -668.1 -663.2 -654.5 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.04 1.13 lp__ 2681.672 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 8.68358 3.70405 0.03024 0.11179 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 3.452 6.015 8.013 10.615 17.655 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.08 1.24 alphaN 6614.419 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] 0.5024 0.1005 0.0008205 0.003227 alpha0[2] 0.4976 0.1005 0.0008205 0.003227 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.3265 0.4267 0.4952 0.5757 0.7021 alpha0[2] 0.2979 0.4243 0.5048 0.5733 0.6735 Potential scale reduction factors: Point est. Upper C.I. [1,] 1.85 3.01 alpha0[1] alpha0[2] 1884.488 1884.488 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] -0.7028 0.2243 0.001831 0.004415 mu0[2] 0.1146 0.3527 0.002880 0.005432 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.037 -0.8573 -0.7534 -0.5791 -0.1785 mu0[2] -0.506 -0.1594 0.1122 0.3747 0.7883 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.85 3.53 mu0[2] 1.97 3.33 Multivariate psrf 2.35 mu0[1] mu0[2] 1939.189 3063.008 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] 0.4918 0.3178 0.002595 0.003481 beta0[2] 0.6563 0.2415 0.001972 0.005576 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1632 0.2612 0.3681 0.6627 1.294 beta0[2] 0.2724 0.4931 0.6244 0.7823 1.217 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 2.63 7.09 beta0[2] 1.20 1.55 Multivariate psrf 2.23 beta0[1] beta0[2] 2453.804 2226.792 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] 1.27404 0.7956 0.006496 0.010974 tau0[2] 0.09448 0.6810 0.005560 0.008361 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.3573 0.7433 1.32230 1.8454 2.678 tau0[2] -1.0680 -0.4050 0.03318 0.5477 1.507 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.50 2.27 tau0[2] 1.96 3.31 Multivariate psrf 1.98 tau0[1] tau0[2] 3783.213 3461.751 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] 2.433 0.7037 0.005746 0.012459 gamma0[2] 1.631 0.5415 0.004422 0.008894 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.3683 1.937 2.332 2.810 4.072 gamma0[2] 0.7802 1.255 1.557 1.924 2.883 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.18 1.54 gamma0[2] 1.44 2.14 Multivariate psrf 1.48 gamma0[1] gamma0[2] 3351.385 2949.812 Chains of length 5000 for K3 Simulation Stan @ 2 did not converge in run 1 . Maximum Rhat value = 2.354443 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -666.5219511 7.7346853 0.1093850 0.3127035 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -669.1471385 7.4154457 0.1048702 0.2253691 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -669.8506834 7.5657363 0.1069957 0.2407940 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 9.90760023 4.23647194 0.05991276 0.32551230 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 8.24014093 3.33170960 0.04711749 0.05958854 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 7.90300656 3.13255205 0.04430098 0.05437531 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4503504 0.07894972 0.001116518 0.009050348 alpha0[2] 0.5496496 0.07894972 0.001116518 0.009050348 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4599506 0.07355779 0.001040264 0.002513642 alpha0[2] 0.5400494 0.07355779 0.001040264 0.002513642 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.5968173 0.07230736 0.00102258 0.002343443 alpha0[2] 0.4031827 0.07230736 0.00102258 0.002343443 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.8146695 0.1318566 0.001864735 0.01016692 mu0[2] 0.1492864 0.2853408 0.004035328 0.01321153 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.4954977 0.2322452 0.003284443 0.006535911 mu0[2] -0.2024235 0.2166317 0.003063635 0.005497017 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.7983641 0.1221776 0.001727852 0.005418919 mu0[2] 0.3969557 0.2518123 0.003561164 0.007795357 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.2963951 0.1146257 0.001621053 0.004433066 beta0[2] 0.7588324 0.2299570 0.003252084 0.007926117 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.8507857 0.2901687 0.004103605 0.008117056 beta0[2] 0.6719917 0.2010698 0.002843556 0.006154506 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3283116 0.1091935 0.001544230 0.004848025 beta0[2] 0.5380237 0.2385469 0.003373563 0.013383113 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.453676269 0.6987445 0.009881739 0.01928585 tau0[2] 0.004398738 0.4747158 0.006713495 0.01312508 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 0.6676798 0.6671874 0.009435454 0.01679537 tau0[2] 0.7112333 0.5593046 0.007909762 0.01599113 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.7007611 0.6193218 0.008758533 0.02073064 tau0[2] -0.4321837 0.4326488 0.006118578 0.01418508 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.556795 0.7035144 0.009949196 0.02861290 gamma0[2] 1.611852 0.4438284 0.006276681 0.01442597 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.676622 0.6720263 0.009503887 0.01637771 gamma0[2] 1.984878 0.5288740 0.007479408 0.01374529 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.065590 0.5741240 0.008119339 0.01760799 gamma0[2] 1.294868 0.4057203 0.005737752 0.01774412 **************** Running Model for K3 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. 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: 127.447 seconds (Warm-up) 612.559 seconds (Sampling) 740.006 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): 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): 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): 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: 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: 42.3098 seconds (Warm-up) 211.798 seconds (Sampling) 254.108 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. 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: 55.4009 seconds (Warm-up) 196.423 seconds (Sampling) 251.824 seconds (Total) **************** Convergence diagnostics for K3 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 -668.92689 7.48913 0.04324 0.10457 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -684.6 -673.7 -668.6 -663.7 -655.3 Potential scale reduction factors: Point est. Upper C.I. lp__ 1 1.01 lp__ 5116.838 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 8.31313 3.42089 0.01975 0.06569 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 3.409 5.870 7.712 10.085 16.760 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.04 1.12 alphaN 12908.74 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] 0.5278 0.1047 0.0004937 0.00456 alpha0[2] 0.4722 0.1047 0.0004937 0.00456 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.3283 0.4490 0.5331 0.6073 0.7156 alpha0[2] 0.2844 0.3927 0.4669 0.5510 0.6717 Potential scale reduction factors: Point est. Upper C.I. [1,] 1.67 2.7 alpha0[1] alpha0[2] 3527.842 3527.842 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] -0.6899 0.2271 0.001070 0.01439 mu0[2] 0.1739 0.3592 0.001693 0.02253 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.0275 -0.8479 -0.7425 -0.5602 -0.1616 mu0[2] -0.4889 -0.1084 0.2024 0.4347 0.8337 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.23 1.70 mu0[2] 1.39 2.06 Multivariate psrf 1.41 mu0[1] mu0[2] 1389.292 2604.843 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] 0.4671 0.2646 0.001247 0.022736 beta0[2] 0.6525 0.2702 0.001274 0.004994 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1782 0.2781 0.3828 0.5956 1.137 beta0[2] 0.2512 0.4608 0.6159 0.7981 1.287 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.20 1.67 beta0[2] 1.24 1.67 Multivariate psrf 1.28 beta0[1] beta0[2] 1809.862 2525.322 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] 1.35665 0.7845 0.003698 0.03071 tau0[2] -0.02628 0.6998 0.003299 0.04187 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.2945 0.8367 1.4217 1.9234 2.719 tau0[2] -1.1519 -0.5395 -0.1387 0.4354 1.491 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.16 1.49 tau0[2] 1.38 2.08 Multivariate psrf 1.36 tau0[1] tau0[2] 3484.525 2695.819 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] 2.336 0.6997 0.003298 0.01345 gamma0[2] 1.552 0.5548 0.002615 0.01899 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.3198 1.835 2.221 2.711 4.012 gamma0[2] 0.6999 1.154 1.477 1.862 2.847 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.15 1.44 gamma0[2] 1.24 1.69 Multivariate psrf 1.3 gamma0[1] gamma0[2] 4877.724 1758.763 Chains of length 10000 for K3 Simulation Stan @ 2 did not converge in run 2 . Maximum Rhat value = 1.409082 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -668.3530054 7.5105395 0.0751054 0.1847400 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -669.06715236 7.45181583 0.07451816 0.17924816 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -669.3605216 7.4697903 0.0746979 0.1792934 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 9.09987905 3.88527061 0.03885271 0.18907624 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 7.94657980 3.10249766 0.03102498 0.04120535 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 7.89294275 3.07555025 0.03075550 0.03724898 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.441029 0.07720824 0.0006304027 0.005314804 alpha0[2] 0.558971 0.07720824 0.0006304027 0.005314804 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.5462861 0.09572316 0.0007815763 0.01254049 alpha0[2] 0.4537139 0.09572316 0.0007815763 0.01254049 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.5961082 0.07239617 0.0005911122 0.001268247 alpha0[2] 0.4038918 0.07239617 0.0005911122 0.001268247 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.58040256 0.2576623 0.002103804 0.03551093 mu0[2] -0.05352472 0.2952195 0.002410457 0.02163623 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.6951760 0.2215040 0.001808572 0.02426717 mu0[2] 0.1834305 0.3639201 0.002971395 0.06381931 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.7940251 0.1279865 0.001045005 0.003615192 mu0[2] 0.3916932 0.2611224 0.002132056 0.005345586 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5588113 0.2731506 0.002230265 0.036097788 beta0[2] 0.8096988 0.2546085 0.002078869 0.007614505 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5075331 0.3085964 0.002519679 0.057808131 beta0[2] 0.5961256 0.2392990 0.001953868 0.009505018 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3350308 0.1129694 0.0009223917 0.002720690 beta0[2] 0.5516336 0.2426226 0.0019810053 0.008724076 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.0127961 0.7623734 0.006224753 0.04525596 tau0[2] 0.4125677 0.6287483 0.005133708 0.05281527 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.3604863 0.8031853 0.006557980 0.07946814 tau0[2] -0.0484316 0.7213242 0.005889588 0.11365403 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.6966808 0.6215431 0.005074878 0.011165780 tau0[2] -0.4429628 0.4321946 0.003528854 0.008620103 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.644581 0.7117665 0.005811549 0.02185995 gamma0[2] 1.849847 0.5207158 0.004251627 0.02297749 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.293502 0.6745625 0.005507779 0.03239973 gamma0[2] 1.519974 0.5657003 0.004618924 0.05061980 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.070681 0.5827614 0.004758227 0.01001776 gamma0[2] 1.286863 0.4150853 0.003389158 0.01240136 MCMC run did not converge, proceeding anyway. Calculating model fit indexes for K3 Simulation Stan @ 2 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -613.50782 49.67124 1326.35811 49.67124 1326.35811 lppd lppd.bayes pDIC DIC pDICalt DICalt -638.34344 -765.50985 -254.33282 1022.35406 63.65554 1658.33077 Analaysis complete for K3 Simulation Stan @ 2 > proc.time() user system elapsed 2266.200 8.587 2280.847