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 unordered @ 2 Removing 0 of 10 Level 2 units for length. Calculating initial values for chain 1 ; K3 Simulation Stan unordered @ 2 number of iterations= 18 number of iterations= 23 number of iterations= 13 number of iterations= 9 number of iterations= 26 number of iterations= 55 number of iterations= 31 number of iterations= 14 number of iterations= 32 number of iterations= 61 Calculating initial values for chain 2 ; K3 Simulation Stan unordered @ 2 number of iterations= 38 number of iterations= 18 One of the variances is going to zero; trying new starting values. number of iterations= 40 number of iterations= 11 number of iterations= 280 number of iterations= 21 number of iterations= 8 number of iterations= 11 number of iterations= 51 number of iterations= 62 Calculating initial values for chain 3 ; K3 Simulation Stan unordered @ 2 number of iterations= 24 number of iterations= 19 number of iterations= 36 number of iterations= 66 number of iterations= 62 number of iterations= 66 number of iterations= 6 number of iterations= 20 number of iterations= 13 number of iterations= 18 **************** Running Model for K3 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. 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: 24.4041 seconds (Warm-up) 94.3454 seconds (Sampling) 118.75 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 / 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. 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: 24.5327 seconds (Warm-up) 121.312 seconds (Sampling) 145.845 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): 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: 45.8838 seconds (Warm-up) 122.494 seconds (Sampling) 168.377 seconds (Total) Labeling components for level 2 model K3 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 K3 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 -668.34507 7.53457 0.06152 0.15278 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -684.2 -673.2 -667.9 -663.2 -654.4 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.04 1.12 lp__ 2370.984 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 8.54057 3.51604 0.02871 0.03856 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 3.492 6.034 7.932 10.365 17.096 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.11 1.31 alphaN 9601.821 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 alpha0[1] 0.5286 0.1174 0.0009588 0.00122 alpha0[2] 0.4714 0.1174 0.0009588 0.00122 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.3014 0.4318 0.5502 0.6192 0.7181 alpha0[2] 0.2819 0.3808 0.4498 0.5682 0.6986 Potential scale reduction factors: Point est. Upper C.I. [1,] 2.49 4.36 alpha0[1] alpha0[2] 3565.493 3565.493 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] -0.7756 0.1513 0.001235 0.005295 mu0[2] 0.2511 0.3204 0.002616 0.004746 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.0436 -0.87028 -0.7886 -0.6955 -0.4327 mu0[2] -0.3959 0.04235 0.2696 0.4629 0.8403 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.06 1.13 mu0[2] 1.48 2.22 Multivariate psrf 1.41 mu0[1] mu0[2] 1286.775 3343.177 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] 0.3592 0.1601 0.001307 0.005791 beta0[2] 0.6356 0.2637 0.002153 0.008038 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1702 0.2583 0.3258 0.4155 0.7606 beta0[2] 0.2411 0.4433 0.6045 0.7780 1.2643 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.10 1.21 beta0[2] 1.15 1.45 Multivariate psrf 1.16 beta0[1] beta0[2] 1163.263 2065.977 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] 1.4920 0.7211 0.005888 0.011073 tau0[2] -0.2286 0.5448 0.004448 0.008606 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.07724 1.0397 1.5484 1.9952 2.7494 tau0[2] -1.17876 -0.6054 -0.2659 0.1183 0.9173 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.25 1.68 tau0[2] 1.50 2.26 Multivariate psrf 1.54 tau0[1] tau0[2] 3659.764 2809.311 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 gamma0[1] 2.312 0.7076 0.005777 0.01087 gamma0[2] 1.380 0.4471 0.003651 0.01153 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.2804 1.805 2.193 2.695 4.019 gamma0[2] 0.6802 1.076 1.326 1.616 2.419 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.37 1.98 gamma0[2] 1.11 1.33 Multivariate psrf 1.38 gamma0[1] gamma0[2] 3303.017 2009.035 Chains of length 5000 for K3 Simulation Stan unordered @ 2 did not converge in run 1 . Maximum Rhat value = 1.539461 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -669.4850168 7.4204099 0.1049404 0.2477404 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -669.1539402 7.3174229 0.1034840 0.2748414 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -666.3962617 7.4814551 0.1058038 0.2704936 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 7.93526327 3.01302388 0.04261059 0.04698810 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 7.82710911 3.00438677 0.04248845 0.05006849 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 9.85933329 4.04633494 0.05722382 0.09308981 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.5975825 0.07074109 0.00100043 0.002155319 alpha0[2] 0.4024175 0.07074109 0.00100043 0.002155319 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.5903409 0.0723308 0.001022912 0.001943714 alpha0[2] 0.4096591 0.0723308 0.001022912 0.001943714 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3979101 0.0740664 0.001047457 0.002229692 alpha0[2] 0.6020899 0.0740664 0.001047457 0.002229692 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.7967967 0.1259355 0.001780997 0.005131653 mu0[2] 0.3902347 0.2618566 0.003703212 0.008813690 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.7859900 0.1315816 0.001860845 0.005840358 mu0[2] 0.3583322 0.2701271 0.003820174 0.008625850 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.743903857 0.1842524 0.002605722 0.013851975 mu0[2] 0.004660464 0.2736212 0.003869588 0.007112953 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3371323 0.1185454 0.001676485 0.005161532 beta0[2] 0.5592072 0.2516725 0.003559187 0.013031164 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3453006 0.1256892 0.001777513 0.005939671 beta0[2] 0.5833201 0.2640835 0.003734705 0.019446164 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3950470 0.2123680 0.003003337 0.015488970 beta0[2] 0.7641568 0.2247336 0.003178213 0.005786115 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.6987943 0.6206503 0.008777321 0.02014306 tau0[2] -0.4464614 0.4463283 0.006312035 0.01396499 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.7121812 0.6439846 0.009107317 0.02030460 tau0[2] -0.4340977 0.4286970 0.006062691 0.01518137 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.064879 0.6975579 0.009864958 0.01689611 tau0[2] 0.194619 0.4885702 0.006909426 0.01552439 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.078348 0.5559264 0.007861987 0.01677798 gamma0[2] 1.305895 0.4176748 0.005906814 0.01935467 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.066731 0.5581509 0.007893446 0.01640625 gamma0[2] 1.268487 0.4196040 0.005934097 0.02586515 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.790791 0.7332232 0.010369342 0.02265223 gamma0[2] 1.566533 0.4433427 0.006269812 0.01239104 **************** Running Model for K3 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: 38.4782 seconds (Warm-up) 191.235 seconds (Sampling) 229.713 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 2). Iteration: 1 / 12000 [ 0%] (Warmup) Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): 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: 48.8114 seconds (Warm-up) 196.008 seconds (Sampling) 244.819 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 3). Iteration: 1 / 12000 [ 0%] (Warmup) Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Iteration: 1200 / 12000 [ 10%] (Warmup) Iteration: 2400 / 12000 [ 20%] (Sampling) Iteration: 3600 / 12000 [ 30%] (Sampling) Iteration: 4800 / 12000 [ 40%] (Sampling) Iteration: 6000 / 12000 [ 50%] (Sampling) Iteration: 7200 / 12000 [ 60%] (Sampling) Iteration: 8400 / 12000 [ 70%] (Sampling) Iteration: 9600 / 12000 [ 80%] (Sampling) Iteration: 10800 / 12000 [ 90%] (Sampling) Iteration: 12000 / 12000 [100%] (Sampling) Elapsed Time: 37.9542 seconds (Warm-up) 188.18 seconds (Sampling) 226.134 seconds (Total) Labeling components for level 2 model K3 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 K3 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 -669.5167 7.4652 0.0431 0.1021 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -685.4 -674.3 -669.1 -664.3 -656.1 Potential scale reduction factors: Point est. Upper C.I. lp__ 1 1 lp__ 5364.858 Iterations = 1:10000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 10000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE 7.88169 3.05090 0.01761 0.02005 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 3.353 5.678 7.410 9.545 15.082 Potential scale reduction factors: Point est. Upper C.I. alphaN 1 1 alphaN 23163.3 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] 0.5735 0.0951 0.0004483 0.008048 alpha0[2] 0.4265 0.0951 0.0004483 0.008048 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.3426 0.5250 0.5855 0.6379 0.7284 alpha0[2] 0.2716 0.3621 0.4145 0.4750 0.6574 Potential scale reduction factors: Point est. Upper C.I. [1,] 1.19 1.56 alpha0[1] alpha0[2] 5536.045 5536.045 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] -0.7868 0.1351 0.0006367 0.002671 mu0[2] 0.3439 0.2878 0.0013566 0.009842 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.0329 -0.8714 -0.7951 -0.7121 -0.4918 mu0[2] -0.2775 0.1762 0.3573 0.5239 0.8893 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.01 1.02 mu0[2] 1.06 1.18 Multivariate psrf 1.05 mu0[1] mu0[2] 3251.622 5169.461 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] 0.3415 0.1302 0.0006138 0.002413 beta0[2] 0.5791 0.2544 0.0011991 0.005808 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1756 0.2556 0.3160 0.3965 0.6477 beta0[2] 0.2298 0.3943 0.5341 0.7117 1.1967 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.03 1.04 beta0[2] 1.02 1.06 Multivariate psrf 1.02 beta0[1] beta0[2] 3685.449 2091.255 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] 1.6306 0.6672 0.003145 0.01380 tau0[2] -0.3625 0.4858 0.002290 0.01545 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] 0.1602 1.2376 1.6811 2.0851 2.8037 tau0[2] -1.2306 -0.6876 -0.3964 -0.0716 0.6761 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.03 1.10 tau0[2] 1.05 1.16 Multivariate psrf 1.06 tau0[1] tau0[2] 6442.187 5108.294 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] 2.154 0.6315 0.002977 0.015792 gamma0[2] 1.330 0.4316 0.002035 0.008401 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.262 1.710 2.041 2.469 3.685 gamma0[2] 0.657 1.032 1.274 1.560 2.333 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.05 1.14 gamma0[2] 1.01 1.03 Multivariate psrf 1.04 gamma0[1] gamma0[2] 6681.47 2949.66 Calculating model fit indexes for K3 Simulation Stan unordered @ 2 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -617.13184 43.00801 1320.27970 43.00801 1320.27970 lppd lppd.bayes pDIC DIC pDICalt DICalt -638.63584 -666.14869 -55.02569 1222.24600 63.05641 1458.41019 Analaysis complete for K3 Simulation Stan unordered @ 2 > proc.time() user system elapsed 1296.312 4.154 1301.277