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 @ 2 Removing 0 of 10 Level 2 units for length. Calculating initial values for chain 1 ; K2 Simulation Stan @ 2 number of iterations= 22 number of iterations= 61 number of iterations= 8 number of iterations= 17 number of iterations= 40 number of iterations= 25 number of iterations= 36 number of iterations= 151 number of iterations= 34 number of iterations= 256 Calculating initial values for chain 2 ; K2 Simulation Stan @ 2 number of iterations= 14 number of iterations= 45 number of iterations= 15 number of iterations= 12 number of iterations= 21 number of iterations= 19 number of iterations= 58 number of iterations= 62 number of iterations= 36 number of iterations= 252 Calculating initial values for chain 3 ; K2 Simulation Stan @ 2 number of iterations= 25 number of iterations= 57 number of iterations= 12 number of iterations= 20 number of iterations= 21 number of iterations= 15 number of iterations= 22 number of iterations= 83 number of iterations= 42 number of iterations= 187 **************** Running Model for K2 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) 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: 37.0489 seconds (Warm-up) 199.037 seconds (Sampling) 236.086 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): 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: 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: 52.8206 seconds (Warm-up) 253.047 seconds (Sampling) 305.867 seconds (Total) SAMPLING FOR MODEL 'hierModel1pmu' 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): 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: 63.9793 seconds (Warm-up) 248.606 seconds (Sampling) 312.585 seconds (Total) **************** Convergence diagnostics for K2 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 -577.33533 7.25190 0.05921 0.12663 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -592.5 -582.1 -577.0 -572.3 -564.1 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.01 1.05 lp__ 3265.384 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 9.04359 3.42198 0.02794 0.03239 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 3.870 6.557 8.561 10.950 17.155 Potential scale reduction factors: Point est. Upper C.I. alphaN 1 1 alphaN 11195.71 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] 0.4455 0.1402 0.001145 0.001692 alpha0[2] 0.5545 0.1402 0.001145 0.001692 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.2457 0.3350 0.4017 0.5749 0.7147 alpha0[2] 0.2853 0.4251 0.5983 0.6650 0.7543 Potential scale reduction factors: Point est. Upper C.I. [1,] 3.46 6.33 alpha0[1] alpha0[2] 2127.22 2127.22 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] -0.41901 0.2405 0.001963 0.006387 mu0[2] -0.01296 0.3830 0.003127 0.006726 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -0.9654 -0.5443 -0.4066 -0.2725 0.02772 mu0[2] -0.5264 -0.2877 -0.1144 0.1977 0.88301 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.05 1.1 mu0[2] 2.06 4.0 Multivariate psrf 1.85 mu0[1] mu0[2] 1782.891 1707.883 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] 1.0035 0.5303 0.004330 0.008643 beta0[2] 0.7121 0.3610 0.002947 0.005660 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.2637 0.5127 0.9999 1.3464 2.155 beta0[2] 0.2752 0.4364 0.6050 0.9221 1.589 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 2.23 4.44 beta0[2] 2.27 4.39 Multivariate psrf 2.49 beta0[1] beta0[2] 1632.270 2464.948 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] 0.4594 0.8047 0.006570 0.008947 tau0[2] 0.9525 0.7890 0.006442 0.007825 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.9197 -0.1584 0.3522 1.140 1.930 tau0[2] -0.7432 0.3672 1.1709 1.547 2.087 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 2.46 4.36 tau0[2] 2.64 4.85 Multivariate psrf 2.87 tau0[1] tau0[2] 3270.207 3715.666 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] 1.667 0.5178 0.004228 0.006361 gamma0[2] 1.467 0.5039 0.004114 0.005744 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.8524 1.307 1.607 1.952 2.853 gamma0[2] 0.7649 1.097 1.376 1.738 2.687 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.42 2.12 gamma0[2] 1.66 2.67 Multivariate psrf 1.77 gamma0[1] gamma0[2] 4763.175 4853.063 Chains of length 5000 for K2 Simulation Stan @ 2 did not converge in run 1 . Maximum Rhat value = 2.868914 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -577.6941243 7.2212367 0.1021237 0.2195861 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -578.1209520 7.1913917 0.1017016 0.2063260 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -576.1909078 7.2022779 0.1018556 0.2313609 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 9.08013381 3.38760139 0.04790792 0.05421648 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 9.11230298 3.43702604 0.04860689 0.05541466 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 8.93833923 3.43923886 0.04863818 0.05859895 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3625913 0.06630119 0.0009376404 0.003279857 alpha0[2] 0.6374087 0.06630119 0.0009376404 0.003279857 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3543315 0.06668857 0.0009431188 0.001837559 alpha0[2] 0.6456685 0.06668857 0.0009431188 0.001837559 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.6195232 0.06833163 0.0009663552 0.003410264 alpha0[2] 0.3804768 0.06833163 0.0009663552 0.003410264 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.4535631 0.2728028 0.003858014 0.01522902 mu0[2] -0.2145025 0.2131837 0.003014872 0.01367983 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.4246457 0.2617340 0.003701478 0.007987862 mu0[2] -0.1947507 0.2020126 0.002856890 0.009158761 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.3788146 0.1664891 0.002354511 0.008448273 mu0[2] 0.3703636 0.3649355 0.005160968 0.011666202 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 1.3026870 0.4319900 0.006109261 0.02004071 beta0[2] 0.5120942 0.1913744 0.002706442 0.01209386 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 1.2676006 0.3955391 0.005593768 0.01293892 beta0[2] 0.5302928 0.1871388 0.002646543 0.00930654 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.4401964 0.1551673 0.002194397 0.010162769 beta0[2] 1.0940282 0.3167226 0.004479134 0.007444158 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 0.01993099 0.5368266 0.007591875 0.01865746 tau0[2] 1.37140961 0.4187772 0.005922404 0.01153834 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 0.006684378 0.5292068 0.007484114 0.01443241 tau0[2] 1.430712731 0.4101798 0.005800818 0.01133216 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.35160139 0.4244246 0.006002271 0.01280972 tau0[2] 0.05538401 0.5612976 0.007937947 0.01701507 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.866843 0.4684134 0.006624366 0.01114791 gamma0[2] 1.268219 0.3584330 0.005069008 0.00920849 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.849072 0.4818742 0.006814731 0.011364364 gamma0[2] 1.236726 0.3552269 0.005023667 0.008808134 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.283699 0.3645151 0.005155023 0.01052391 gamma0[2] 1.896117 0.4799643 0.006787721 0.01160164 **************** Running Model for K2 Simulation Stan @ 2 Attempt 2 SAMPLING FOR MODEL 'hierModel1pmu' 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: 63.5264 seconds (Warm-up) 553.402 seconds (Sampling) 616.928 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. Iteration: 1 / 12000 [ 0%] (Warmup) Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Location parameter is -inf:0, but must be finite! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): 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: 34.1966 seconds (Warm-up) 213.531 seconds (Sampling) 247.728 seconds (Total) SAMPLING FOR MODEL 'hierModel1pmu' NOW (CHAIN 3). Iteration: 1 / 12000 [ 0%] (Warmup) Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Location parameter is inf:0, but must be finite! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Iteration: 1200 / 12000 [ 10%] (Warmup) Iteration: 2400 / 12000 [ 20%] (Sampling) Iteration: 3600 / 12000 [ 30%] (Sampling) Iteration: 4800 / 12000 [ 40%] (Sampling) Iteration: 6000 / 12000 [ 50%] (Sampling) Iteration: 7200 / 12000 [ 60%] (Sampling) Iteration: 8400 / 12000 [ 70%] (Sampling) Iteration: 9600 / 12000 [ 80%] (Sampling) Iteration: 10800 / 12000 [ 90%] (Sampling) Iteration: 12000 / 12000 [100%] (Sampling) Elapsed Time: 33.2876 seconds (Warm-up) 204.603 seconds (Sampling) 237.891 seconds (Total) **************** Convergence diagnostics for K2 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 -575.38015 7.77049 0.04486 0.09493 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -591.7 -580.4 -575.0 -569.9 -561.2 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.14 1.4 lp__ 5994.514 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 10.86397 4.54946 0.02627 0.03565 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 4.085 7.504 10.192 13.506 21.467 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.2 1.61 alphaN 13692.84 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] 0.5437 0.1575 0.0007426 0.02679 alpha0[2] 0.4563 0.1575 0.0007426 0.02679 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.2693 0.3950 0.5871 0.6824 0.7701 alpha0[2] 0.2299 0.3176 0.4129 0.6050 0.7307 Potential scale reduction factors: Point est. Upper C.I. [1,] 1.96 4.4 alpha0[1] alpha0[2] 308.1776 308.1776 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] -0.4314 0.2323 0.001095 0.003409 mu0[2] 0.1564 0.4161 0.001962 0.022644 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -0.9199 -0.5666 -0.4280 -0.2881 0.01963 mu0[2] -0.4852 -0.1897 0.1145 0.4671 0.98546 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.02 1.03 mu0[2] 1.59 2.69 Multivariate psrf 1.51 mu0[1] mu0[2] 4662.417 3906.750 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] 0.8649 0.4599 0.002168 0.025330 beta0[2] 0.7474 0.3305 0.001558 0.009227 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.3117 0.5080 0.7299 1.1396 1.965 beta0[2] 0.2982 0.4976 0.6895 0.9308 1.532 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.73 3.08 beta0[2] 1.37 2.06 Multivariate psrf 1.68 beta0[1] beta0[2] 1707.867 1722.587 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] 1.0391 0.9796 0.004618 0.1108 tau0[2] 0.4125 0.9453 0.004456 0.1074 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.7825 0.1733 1.2536 1.91 2.414 tau0[2] -1.0901 -0.4181 0.2871 1.30 1.957 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.93 3.87 tau0[2] 1.95 4.09 Multivariate psrf 1.89 tau0[1] tau0[2] 1540.750 1061.253 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] 1.440 0.6342 0.002990 0.03132 gamma0[2] 1.343 0.4395 0.002072 0.01404 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.5576 0.9224 1.354 1.845 2.858 gamma0[2] 0.7274 1.0331 1.259 1.563 2.410 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.74 2.89 gamma0[2] 1.13 1.38 Multivariate psrf 1.66 gamma0[1] gamma0[2] 1753.539 2556.746 Chains of length 10000 for K2 Simulation Stan @ 2 did not converge in run 2 . Maximum Rhat value = 1.890688 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -579.00578228 7.36613114 0.07366131 0.15927300 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -573.58825248 7.34748135 0.07347481 0.17157201 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -573.54641936 7.29297244 0.07292972 0.16219996 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 8.34095721 3.31628944 0.03316289 0.05847974 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 12.07401752 4.55866168 0.04558662 0.06654666 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 12.17694597 4.55665818 0.04556658 0.05991297 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3974804 0.07945355 0.0006487355 0.007038434 alpha0[2] 0.6025196 0.07945355 0.0006487355 0.007038434 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.5711789 0.1651748 0.001348647 0.07991098 alpha0[2] 0.4288211 0.1651748 0.001348647 0.07991098 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.6625031 0.0678406 0.0005539162 0.005106801 alpha0[2] 0.3374969 0.0678406 0.0005539162 0.005106801 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.4189026 0.2753112 0.002247907 0.007122156 mu0[2] -0.1766915 0.2185276 0.001784270 0.007371229 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.4468624 0.2211800 0.001805927 0.005363361 mu0[2] 0.2323948 0.4195163 0.003425336 0.067251774 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.4285789 0.1918605 0.001566534 0.005008370 mu0[2] 0.4136085 0.3359876 0.002743327 0.006145593 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 1.2453670 0.4118725 0.003362925 0.011113483 beta0[2] 0.5451712 0.1999946 0.001632949 0.006786678 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.8100443 0.4215225 0.003441717 0.07443412 beta0[2] 0.7520636 0.3175499 0.002592784 0.02369908 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5392851 0.1829559 0.001493829 0.01052256 beta0[2] 0.9450376 0.3270072 0.002670002 0.01258985 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 0.1229123 0.5686514 0.004643019 0.01508434 tau0[2] 1.3006372 0.4174027 0.003408079 0.01522986 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.265593 0.9877029 0.008064560 0.3291221 tau0[2] 0.208635 0.9631053 0.007863722 0.3204143 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.7288183 0.4623955 0.003775443 0.04396874 tau0[2] -0.2718274 0.5294902 0.004323269 0.03051514 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.996249 0.5121791 0.004181925 0.01365688 gamma0[2] 1.201679 0.3563775 0.002909810 0.01079371 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.264044 0.5682987 0.004640139 0.09054951 gamma0[2] 1.307582 0.3887723 0.003174313 0.01076206 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.058336 0.3680647 0.003005236 0.02098451 gamma0[2] 1.518798 0.4991994 0.004075946 0.03926386 MCMC run did not converge, proceeding anyway. Calculating model fit indexes for K2 Simulation Stan @ 2 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -529.14057 36.33796 1130.95706 36.33796 1130.95706 lppd lppd.bayes pDIC DIC pDICalt DICalt -547.30955 -730.99981 -367.38052 727.23859 55.23876 1572.47713 Analaysis complete for K2 Simulation Stan @ 2 > proc.time() user system elapsed 2046.392 4.955 2054.922