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 @ 3 Removing 0 of 10 Level 2 units for length. Calculating initial values for chain 1 ; K4 Simulation Stan unordered @ 3 number of iterations= 443 number of iterations= 95 One of the variances is going to zero; trying new starting values. number of iterations= 25 number of iterations= 57 number of iterations= 110 number of iterations= 70 number of iterations= 148 number of iterations= 104 number of iterations= 11 number of iterations= 230 Calculating initial values for chain 2 ; K4 Simulation Stan unordered @ 3 number of iterations= 50 number of iterations= 72 number of iterations= 69 number of iterations= 38 One of the variances is going to zero; trying new starting values. One of the variances is going to zero; trying new starting values. number of iterations= 95 number of iterations= 43 number of iterations= 90 number of iterations= 115 number of iterations= 24 number of iterations= 16 Calculating initial values for chain 3 ; K4 Simulation Stan unordered @ 3 number of iterations= 107 number of iterations= 71 number of iterations= 7 One of the variances is going to zero; trying new starting values. number of iterations= 76 number of iterations= 50 number of iterations= 30 number of iterations= 37 number of iterations= 41 number of iterations= 16 number of iterations= 16 **************** Running Model for K4 Simulation Stan unordered @ 3 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) 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: 305.324 seconds (Warm-up) 1386.28 seconds (Sampling) 1691.6 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: 187.99 seconds (Warm-up) 1341.16 seconds (Sampling) 1529.15 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 3). 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. 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: 131.215 seconds (Warm-up) 479.021 seconds (Sampling) 610.236 seconds (Total) Labeling components for level 2 model K4 Simulation Stan unordered @ 3 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 @ 3 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 -582.81080 10.16321 0.08298 0.23738 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -603.9 -589.4 -582.4 -575.9 -563.7 Potential scale reduction factors: Point est. Upper C.I. lp__ 1 1 lp__ 1912.311 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 11.69755 4.10404 0.03351 0.07651 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 5.254 8.721 11.118 14.132 20.752 Potential scale reduction factors: Point est. Upper C.I. alphaN 1 1.01 alphaN 4058.726 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] 0.3165 0.08766 0.0007157 0.002511 alpha0[2] 0.4398 0.12084 0.0009867 0.001970 alpha0[3] 0.2437 0.12064 0.0009850 0.003897 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.16545 0.2585 0.3098 0.3634 0.5240 alpha0[2] 0.16791 0.3751 0.4681 0.5248 0.6234 alpha0[3] 0.06916 0.1591 0.2170 0.2985 0.5441 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.29 1.80 alpha0[3] 1.28 1.76 Multivariate psrf 1.27 alpha0[1] alpha0[2] alpha0[3] 1440.964 3316.122 1302.007 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] -0.8725 0.2762 0.002256 0.004599 mu0[2] -0.1408 0.1715 0.001400 0.002881 mu0[3] 0.5273 0.4929 0.004024 0.014062 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.2643 -1.0616 -0.9480 -0.7145 -0.2428 mu0[2] -0.5543 -0.2241 -0.1171 -0.0365 0.1479 mu0[3] -0.1644 0.1316 0.4599 0.8421 1.6212 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.84 3.78 mu0[2] 1.20 1.57 mu0[3] 1.47 2.35 Multivariate psrf 1.96 mu0[1] mu0[2] mu0[3] 1865.605 3212.326 1450.212 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] 0.5271 0.3914 0.003195 0.007445 beta0[2] 0.4551 0.3840 0.003135 0.006186 beta0[3] 0.8280 0.4101 0.003348 0.009112 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1766 0.2740 0.3746 0.5954 1.575 beta0[2] 0.1184 0.2110 0.3098 0.5125 1.515 beta0[3] 0.1680 0.5446 0.8007 1.0537 1.757 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.89 5.29 beta0[2] 1.34 2.00 beta0[3] 1.13 1.38 Multivariate psrf 1.94 beta0[1] beta0[2] beta0[3] 1665.555 3368.260 3404.668 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] 1.8687 1.1198 0.009143 0.04554 tau0[2] 1.2365 0.9405 0.007679 0.01763 tau0[3] 0.6822 0.8937 0.007297 0.01632 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.08356 1.10164 1.7947 2.551 4.481 tau0[2] -0.44855 0.61535 1.1967 1.797 3.184 tau0[3] -0.89225 0.08564 0.6186 1.205 2.576 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.04 1.04 tau0[2] 1.04 1.04 tau0[3] 1.13 1.37 Multivariate psrf 1.09 tau0[1] tau0[2] tau0[3] 1701.809 3172.626 3427.755 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] 2.856 1.111 0.009069 0.02826 gamma0[2] 3.050 1.041 0.008499 0.02710 gamma0[3] 2.939 1.037 0.008470 0.03028 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.9523 2.110 2.772 3.486 5.313 gamma0[2] 1.1569 2.414 2.978 3.634 5.303 gamma0[3] 1.3677 2.197 2.787 3.509 5.387 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.10 1.28 gamma0[2] 1.11 1.30 gamma0[3] 1.10 1.30 Multivariate psrf 1.18 gamma0[1] gamma0[2] gamma0[3] 1907.935 3590.993 1997.312 Chains of length 5000 for K4 Simulation Stan unordered @ 3 did not converge in run 1 . Maximum Rhat value = 1.963599 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -582.9134391 10.5167089 0.1487287 0.4206115 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -583.0827268 9.8288973 0.1390016 0.3482137 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -582.4362469 10.1232656 0.1431646 0.4571360 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 11.38475797 4.21215138 0.05956882 0.18368410 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 11.81818442 4.03668289 0.05708732 0.08936001 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 11.88969363 4.04348787 0.05718355 0.10467321 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3164754 0.1235835 0.001747735 0.006376388 alpha0[2] 0.3659423 0.1305752 0.001846612 0.003947642 alpha0[3] 0.3175823 0.1223733 0.001730620 0.009885753 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3162167 0.06206207 0.0008776902 0.002360716 alpha0[2] 0.4758082 0.09357322 0.0013233252 0.002422077 alpha0[3] 0.2079751 0.09845299 0.0013923356 0.003650708 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3168264 0.06268954 0.0008865639 0.003240793 alpha0[2] 0.4775649 0.09914044 0.0014020575 0.003670595 alpha0[3] 0.2056087 0.10396781 0.0014703268 0.005064136 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.6197882 0.3021681 0.004273303 0.010304699 mu0[2] -0.2236286 0.2051019 0.002900579 0.006397527 mu0[3] 0.1495719 0.2576102 0.003643158 0.010219272 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.9975568 0.1457473 0.002061177 0.006864935 mu0[2] -0.1058233 0.1357942 0.001920419 0.004465259 mu0[3] 0.7014704 0.4487846 0.006346773 0.017836315 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -1.00025902 0.1434122 0.002028155 0.006087480 mu0[2] -0.09309735 0.1318928 0.001865246 0.003716153 mu0[3] 0.73094347 0.4965272 0.007021955 0.036838965 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.8879605 0.4693926 0.006638213 0.02045178 beta0[2] 0.7008780 0.4421844 0.006253432 0.01387236 beta0[3] 0.6489060 0.4376137 0.006188793 0.02260743 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3454885 0.1492514 0.002110734 0.007014730 beta0[2] 0.3380904 0.2838544 0.004014308 0.010200171 beta0[3] 0.9121400 0.3578877 0.005061296 0.007513551 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3478628 0.1468137 0.002076259 0.005604989 beta0[2] 0.3263799 0.2750230 0.003889412 0.006923752 beta0[3] 0.9228265 0.3699097 0.005231313 0.013408021 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.885585 1.408949 0.01992555 0.12708907 tau0[2] 1.254354 1.171950 0.01657387 0.03987123 tau0[3] 1.050424 1.015591 0.01436262 0.03114613 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.8494498 0.9275733 0.01311787 0.02886149 tau0[2] 1.2485235 0.7988424 0.01129734 0.02074641 tau0[3] 0.4765291 0.7536522 0.01065825 0.01759682 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.8709403 0.9571781 0.01353654 0.04096343 tau0[2] 1.2067693 0.8006633 0.01132309 0.02785842 tau0[3] 0.5196096 0.7698434 0.01088723 0.03343331 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.453088 1.260045 0.01781973 0.06898760 gamma0[2] 2.697033 1.283139 0.01814633 0.07581345 gamma0[3] 3.321098 1.190197 0.01683193 0.05265114 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.065063 0.9390153 0.01327968 0.03154992 gamma0[2] 3.219008 0.8614757 0.01218311 0.02184030 gamma0[3] 2.700682 0.8637910 0.01221585 0.02382171 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.051297 0.9937814 0.01405419 0.03787366 gamma0[2] 3.235440 0.8214725 0.01161738 0.01964434 gamma0[3] 2.794975 0.9178975 0.01298103 0.07009613 **************** Running Model for K4 Simulation Stan unordered @ 3 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: 501.633 seconds (Warm-up) 2601.31 seconds (Sampling) 3102.94 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) 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: 303.015 seconds (Warm-up) 1488.05 seconds (Sampling) 1791.06 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 3). 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. 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: 508.605 seconds (Warm-up) 2518.98 seconds (Sampling) 3027.58 seconds (Total) Labeling components for level 2 model K4 Simulation Stan unordered @ 3 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 @ 3 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 -583.10026 10.64689 0.06147 0.18176 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -605.0 -590.1 -582.8 -575.8 -563.1 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.02 1.07 lp__ 3413.035 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 11.42372 4.22269 0.02438 0.04400 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 5.074 8.334 10.798 13.845 21.348 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.1 1.31 alphaN 8809.726 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] 0.3153 0.08469 0.0003992 0.001753 alpha0[2] 0.4415 0.12723 0.0005998 0.002346 alpha0[3] 0.2431 0.12925 0.0006093 0.003481 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.16964 0.2606 0.3086 0.3592 0.5255 alpha0[2] 0.15805 0.3665 0.4686 0.5325 0.6358 alpha0[3] 0.06049 0.1501 0.2140 0.3045 0.5509 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.16 1.46 alpha0[3] 1.20 1.58 Multivariate psrf 1.18 alpha0[1] alpha0[2] alpha0[3] 2692.313 3637.300 2147.236 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] -0.8287 0.2661 0.0012545 0.008666 mu0[2] -0.1673 0.1916 0.0009033 0.004762 mu0[3] 0.5129 0.4887 0.0023036 0.012133 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.2447 -1.0215 -0.8726 -0.6531 -0.2604 mu0[2] -0.6047 -0.2738 -0.1386 -0.0408 0.1558 mu0[3] -0.1673 0.1244 0.4396 0.8157 1.6220 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.25 1.74 mu0[2] 1.09 1.29 mu0[3] 1.28 1.77 Multivariate psrf 1.28 mu0[1] mu0[2] mu0[3] 892.6973 3487.4503 1757.9352 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] 0.5560 0.3487 0.001644 0.010419 beta0[2] 0.4870 0.3803 0.001793 0.007051 beta0[3] 0.8701 0.4304 0.002029 0.006857 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1832 0.3234 0.4614 0.6614 1.492 beta0[2] 0.1045 0.2278 0.3631 0.5885 1.514 beta0[3] 0.1644 0.5849 0.8513 1.1120 1.820 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.40 2.39 beta0[2] 1.12 1.33 beta0[3] 1.06 1.19 Multivariate psrf 1.33 beta0[1] beta0[2] beta0[3] 798.5832 3026.0917 8885.6645 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] 1.9330 1.1517 0.005429 0.02411 tau0[2] 1.0464 0.9573 0.004513 0.01427 tau0[3] 0.8593 0.9258 0.004364 0.01890 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.1683 1.1413 1.8801 2.663 4.445 tau0[2] -0.6241 0.4207 0.9852 1.573 3.223 tau0[3] -0.8207 0.2341 0.8151 1.437 2.718 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.02 1.02 tau0[2] 1.03 1.05 tau0[3] 1.05 1.17 Multivariate psrf 1.05 tau0[1] tau0[2] tau0[3] 2682.110 4727.623 4456.962 Iterations = 1:15000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 15000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE gamma0[1] 3.012 1.169 0.005511 0.02534 gamma0[2] 2.905 1.087 0.005123 0.03190 gamma0[3] 2.816 1.069 0.005039 0.02095 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.077 2.218 2.895 3.668 5.646 gamma0[2] 1.159 2.126 2.832 3.548 5.297 gamma0[3] 1.035 2.087 2.693 3.409 5.265 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.09 1.28 gamma0[2] 1.12 1.35 gamma0[3] 1.09 1.28 Multivariate psrf 1.13 gamma0[1] gamma0[2] gamma0[3] 2743.066 3890.250 2614.157 Chains of length 10000 for K4 Simulation Stan unordered @ 3 did not converge in run 2 . Maximum Rhat value = 1.328712 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -584.0770840 10.3931415 0.1039314 0.2944043 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -584.1117925 10.1904807 0.1019048 0.3002416 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -581.1118899 11.0587656 0.1105877 0.3471499 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 11.18392042 4.01188003 0.04011880 0.07632639 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 10.10626949 3.53644749 0.03536447 0.06016419 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 12.98098101 4.54713939 0.04547139 0.08931660 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3098057 0.1017374 0.0008306826 0.004141229 alpha0[2] 0.3874394 0.1339231 0.0010934771 0.005776315 alpha0[3] 0.3027549 0.1356551 0.0011076190 0.009144290 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3114743 0.06529169 0.0005331044 0.002039753 alpha0[2] 0.4437539 0.10936873 0.0008929920 0.002727936 alpha0[3] 0.2447718 0.11474043 0.0009368517 0.003489682 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3247648 0.0822712 0.0006717416 0.002521993 alpha0[2] 0.4934224 0.1142011 0.0009324482 0.002956174 alpha0[3] 0.1818129 0.1060001 0.0008654872 0.003644205 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.7019131 0.2960950 0.002417606 0.014026492 mu0[2] -0.1977941 0.2032640 0.001659644 0.005063704 mu0[3] 0.2418023 0.3756614 0.003067262 0.027774147 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.96740959 0.1703706 0.001391070 0.010045326 mu0[2] -0.09281924 0.1570345 0.001282181 0.003857224 mu0[3] 0.53958943 0.4145720 0.003384966 0.015734017 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.8168134 0.2455510 0.002004915 0.01944719 mu0[2] -0.2112726 0.1891128 0.001544099 0.01278868 mu0[3] 0.7572933 0.5191252 0.004238639 0.01749239 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.7744950 0.4372183 0.003569873 0.02298454 beta0[2] 0.6182944 0.4465814 0.003646322 0.01467122 beta0[3] 0.7414250 0.4721171 0.003854820 0.01863846 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3784527 0.1895138 0.001547373 0.011975799 beta0[2] 0.3759840 0.3541362 0.002891510 0.008900957 beta0[3] 0.9117243 0.3989419 0.003257347 0.005798828 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5151552 0.2384724 0.001947119 0.017470895 beta0[2] 0.4667303 0.2811353 0.002295460 0.012369191 beta0[3] 0.9571579 0.3846397 0.003140570 0.006491389 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.9053659 1.348195 0.011007964 0.05577210 tau0[2] 1.1375684 1.141380 0.009319331 0.02327623 tau0[3] 0.9834399 0.999754 0.008162957 0.02007321 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.9820529 0.9864577 0.008054393 0.03469424 tau0[2] 0.9527367 0.8696061 0.007100304 0.02834826 tau0[3] 1.0098361 0.9023483 0.007367643 0.04918664 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.9115567 1.0885692 0.008888130 0.03031611 tau0[2] 1.0487666 0.8205638 0.006699875 0.02207237 tau0[3] 0.5845043 0.8025686 0.006552945 0.01981903 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.654743 1.167558 0.009533070 0.05086688 gamma0[2] 2.855538 1.096394 0.008952018 0.04672678 gamma0[3] 3.167512 1.087778 0.008881671 0.03387777 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.974823 0.9749689 0.007960588 0.02406488 gamma0[2] 3.339393 0.9886380 0.008072196 0.01751518 gamma0[3] 2.454681 1.0365719 0.008463574 0.04364226 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.405515 1.2257344 0.010008079 0.05112477 gamma0[2] 2.521242 1.0125484 0.008267423 0.08167413 gamma0[3] 2.825049 0.9570914 0.007814619 0.02998183 MCMC run did not converge, proceeding anyway. Calculating model fit indexes for K4 Simulation Stan unordered @ 3 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -487.44715 76.61888 1128.13207 76.61888 1128.13207 lppd lppd.bayes pDIC DIC pDICalt DICalt -525.75659 -1074.29044 -1097.06770 -45.55451 116.72941 2382.03970 Analaysis complete for K4 Simulation Stan unordered @ 3 > proc.time() user system elapsed 11936.969 21.449 11963.992