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 @ 3 Removing 0 of 10 Level 2 units for length. Calculating initial values for chain 1 ; K4 Simulation Stan @ 3 number of iterations= 492 number of iterations= 105 number of iterations= 16 number of iterations= 54 number of iterations= 151 number of iterations= 23 number of iterations= 73 number of iterations= 51 number of iterations= 251 number of iterations= 59 Calculating initial values for chain 2 ; K4 Simulation Stan @ 3 number of iterations= 888 number of iterations= 34 number of iterations= 17 One of the variances is going to zero; trying new starting values. number of iterations= 33 One of the variances is going to zero; trying new starting values. 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= 106 number of iterations= 30 number of iterations= 62 number of iterations= 36 number of iterations= 10 number of iterations= 43 Calculating initial values for chain 3 ; K4 Simulation Stan @ 3 number of iterations= 939 number of iterations= 286 number of iterations= 110 One of the variances is going to zero; trying new starting values. number of iterations= 75 number of iterations= 79 number of iterations= 36 number of iterations= 106 number of iterations= 72 number of iterations= 20 One of the variances is going to zero; trying new starting values. number of iterations= 37 **************** Running Model for K4 Simulation Stan @ 3 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): 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: 442.882 seconds (Warm-up) 1865.57 seconds (Sampling) 2308.46 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. 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: 357.356 seconds (Warm-up) 1460.63 seconds (Sampling) 1817.98 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): 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: 363.85 seconds (Warm-up) 1812.29 seconds (Sampling) 2176.14 seconds (Total) **************** Convergence diagnostics for K4 Simulation Stan @ 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 -586.61863 10.83272 0.08845 0.25079 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -609.0 -593.8 -586.1 -579.0 -566.9 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.1 1.3 lp__ 1904.311 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 10.82096 3.95528 0.03229 0.08354 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 4.813 8.007 10.222 13.007 20.184 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.02 1.08 alphaN 2284.803 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] 0.3306 0.08918 0.0007282 0.002729 alpha0[2] 0.3603 0.14688 0.0011993 0.004727 alpha0[3] 0.3091 0.12068 0.0009854 0.005796 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.16786 0.2685 0.3244 0.3894 0.5189 alpha0[2] 0.08746 0.2299 0.3875 0.4783 0.5935 alpha0[3] 0.10534 0.2118 0.2991 0.4029 0.5410 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 2.79 5.02 alpha0[3] 1.59 2.50 Multivariate psrf 2.53 alpha0[1] alpha0[2] alpha0[3] 959.6428 423.1568 356.1217 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] -0.7373 0.2853 0.002330 0.010174 mu0[2] -0.2011 0.1941 0.001585 0.007376 mu0[3] 0.2190 0.3992 0.003260 0.007320 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.2421 -0.95384 -0.7351 -0.52697 -0.2006 mu0[2] -0.6188 -0.32309 -0.1782 -0.07225 0.1494 mu0[3] -0.3437 -0.06117 0.1267 0.42560 1.1757 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.60 2.60 mu0[2] 1.08 1.22 mu0[3] 1.96 3.74 Multivariate psrf 1.98 mu0[1] mu0[2] mu0[3] 600.5044 1194.1660 2068.8029 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] 0.7330 0.4114 0.003359 0.007647 beta0[2] 0.6715 0.5359 0.004375 0.014940 beta0[3] 0.7692 0.3200 0.002613 0.008209 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.2052 0.4136 0.6200 1.0057 1.705 beta0[2] 0.0796 0.2636 0.4659 1.0327 1.941 beta0[3] 0.3372 0.5375 0.7072 0.9341 1.547 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 2.80 5.79 beta0[2] 3.00 7.31 beta0[3] 1.44 2.21 Multivariate psrf 2.72 beta0[1] beta0[2] beta0[3] 1196.795 1050.255 1525.162 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] 2.2339 1.3845 0.011305 0.06750 tau0[2] 0.9125 0.9218 0.007527 0.02700 tau0[3] 1.0276 0.8166 0.006668 0.04436 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.1406 1.2122 2.0752 3.173 5.040 tau0[2] -0.8426 0.2803 0.8935 1.527 2.736 tau0[3] -0.5287 0.4635 1.0132 1.587 2.572 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.55 2.47 tau0[2] 1.18 1.51 tau0[3] 1.01 1.05 Multivariate psrf 1.45 tau0[1] tau0[2] tau0[3] 434.7937 1095.5275 823.0031 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] 2.706 1.1921 0.009734 0.03920 gamma0[2] 3.271 0.9582 0.007824 0.03059 gamma0[3] 2.290 1.2218 0.009976 0.03622 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.5288 1.890 2.681 3.441 5.197 gamma0[2] 1.7808 2.620 3.127 3.776 5.597 gamma0[3] 0.3447 1.351 2.178 3.105 4.905 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.33 1.91 gamma0[2] 1.09 1.28 gamma0[3] 2.19 3.84 Multivariate psrf 1.9 gamma0[1] gamma0[2] gamma0[3] 1250.099 1161.287 1389.453 Chains of length 5000 for K4 Simulation Stan @ 3 did not converge in run 1 . Maximum Rhat value = 2.71697 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -586.0862279 10.6175451 0.1501548 0.4101859 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -590.6469583 10.5777768 0.1495924 0.5285890 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -583.1227171 9.9363259 0.1405209 0.3441002 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 11.10773343 4.06016929 0.05741946 0.15637004 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 10.03224405 3.71836038 0.05258556 0.15070560 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 11.32290876 3.95871343 0.05598466 0.12506940 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4089121 0.07375610 0.001043069 0.004724980 alpha0[2] 0.1902515 0.07645487 0.001081235 0.005306567 alpha0[3] 0.4008364 0.09048430 0.001279641 0.006600381 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2734411 0.06601982 0.0009336612 0.006139492 alpha0[2] 0.4276621 0.08996097 0.0012722402 0.007110239 alpha0[3] 0.2988967 0.11265414 0.0015931702 0.013439860 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3094871 0.06493329 0.0009182955 0.002650134 alpha0[2] 0.4629534 0.08239994 0.0011653112 0.011060944 alpha0[3] 0.2275595 0.08745766 0.0012368380 0.008837853 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.66041200 0.2043907 0.002890521 0.011544335 mu0[2] -0.25977065 0.2288581 0.003236543 0.007471172 mu0[3] -0.04096288 0.2144650 0.003032993 0.006795252 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.5810926 0.2848173 0.004027925 0.022633910 mu0[2] -0.1735737 0.1552213 0.002195160 0.014691607 mu0[3] 0.1015227 0.2316714 0.003276328 0.009212077 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.9702519 0.1918614 0.002713329 0.01691250 mu0[2] -0.1699697 0.1772805 0.002507124 0.01476255 mu0[3] 0.5965191 0.3932266 0.005561064 0.01874100 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.6091962 0.1838628 0.002600213 0.007072958 beta0[2] 1.3067248 0.4220378 0.005968516 0.013565499 beta0[3] 0.5837944 0.1915439 0.002708840 0.006912944 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 1.1949175 0.3151705 0.004457184 0.01642709 beta0[2] 0.3078521 0.2007631 0.002839219 0.02883997 beta0[3] 0.7373067 0.2855133 0.004037767 0.01892378 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3948593 0.1776977 0.002513025 0.01436874 beta0[2] 0.4000729 0.1834535 0.002594425 0.03151110 beta0[3] 0.9866263 0.3260430 0.004610944 0.01416515 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 2.1712125 1.0579689 0.01496194 0.13794054 tau0[2] 0.7529328 0.8627805 0.01220156 0.04491965 tau0[3] 0.9758877 0.8268219 0.01169303 0.03226014 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 3.2237824 1.3959801 0.01974214 0.13749041 tau0[2] 0.5988377 0.8746017 0.01236874 0.05570008 tau0[3] 1.1568017 0.8054940 0.01139141 0.10252313 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.3067785 0.9163688 0.01295941 0.05547342 tau0[2] 1.3858197 0.8321470 0.01176834 0.03797981 tau0[3] 0.9499639 0.8019112 0.01134074 0.07848791 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.526946 0.9078066 0.01283832 0.05781827 gamma0[2] 2.906733 0.9276777 0.01311934 0.06205186 gamma0[3] 3.456201 0.9036322 0.01277929 0.02743459 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.135676 1.2592222 0.017808091 0.09676964 gamma0[2] 3.512610 0.9230928 0.013054503 0.05785333 gamma0[3] 1.248425 0.6644318 0.009396485 0.04474300 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.454886 0.9674601 0.01368195 0.03349540 gamma0[2] 3.392831 0.9145817 0.01293414 0.03499044 gamma0[3] 2.166222 0.8721128 0.01233354 0.09513272 **************** Running Model for K4 Simulation Stan @ 3 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. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Iteration: 1 / 12000 [ 0%] (Warmup) Iteration: 1200 / 12000 [ 10%] (Warmup) Iteration: 2400 / 12000 [ 20%] (Sampling) Iteration: 3600 / 12000 [ 30%] (Sampling) Iteration: 4800 / 12000 [ 40%] (Sampling) Iteration: 6000 / 12000 [ 50%] (Sampling) Iteration: 7200 / 12000 [ 60%] (Sampling) Iteration: 8400 / 12000 [ 70%] (Sampling) Iteration: 9600 / 12000 [ 80%] (Sampling) Iteration: 10800 / 12000 [ 90%] (Sampling) Iteration: 12000 / 12000 [100%] (Sampling) Elapsed Time: 576.212 seconds (Warm-up) 4541.69 seconds (Sampling) 5117.91 seconds (Total) SAMPLING FOR MODEL 'hierModel1pmu' 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): 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: 732.21 seconds (Warm-up) 5451.65 seconds (Sampling) 6183.86 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. 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: 1074.22 seconds (Warm-up) 7519.55 seconds (Sampling) 8593.76 seconds (Total) **************** Convergence diagnostics for K4 Simulation Stan @ 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 -585.3418 10.3572 0.0598 0.1498 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -606.7 -592.1 -585.0 -578.2 -566.0 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.01 1.02 lp__ 4793.048 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 10.87149 3.89124 0.02247 0.05473 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 4.879 8.058 10.311 13.102 19.947 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.01 1.03 alphaN 5255.846 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] 0.2922 0.08784 0.0004141 0.005070 alpha0[2] 0.2922 0.12521 0.0005902 0.009522 alpha0[3] 0.4156 0.12445 0.0005867 0.012089 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.12161 0.2344 0.2907 0.3475 0.4733 alpha0[2] 0.09234 0.1939 0.2770 0.3807 0.5523 alpha0[3] 0.14204 0.3383 0.4420 0.5067 0.6063 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.52 2.46 alpha0[3] 1.14 1.41 Multivariate psrf 1.49 alpha0[1] alpha0[2] alpha0[3] 613.0977 443.2926 243.6493 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] -0.86551 0.2756 0.001299 0.01557 mu0[2] -0.28758 0.2606 0.001229 0.01586 mu0[3] 0.07953 0.2789 0.001315 0.02272 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.3215 -1.05517 -0.91100 -0.6867 -0.2769 mu0[2] -0.8989 -0.44093 -0.24435 -0.1033 0.1282 mu0[3] -0.2807 -0.08264 0.01616 0.1536 0.8831 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.01 1.02 mu0[2] 1.18 1.52 mu0[3] 1.25 1.87 Multivariate psrf 1.4 mu0[1] mu0[2] mu0[3] 440.8591 3748.7610 2146.9261 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] 0.7307 0.5504 0.002595 0.06133 beta0[2] 0.9055 0.5125 0.002416 0.04237 beta0[3] 0.4474 0.3189 0.001503 0.03824 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1805 0.3143 0.5017 1.0747 2.070 beta0[2] 0.1375 0.4945 0.8843 1.2195 2.035 beta0[3] 0.0954 0.2166 0.3445 0.5994 1.258 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.47 2.57 beta0[2] 1.53 2.72 beta0[3] 1.09 1.21 Multivariate psrf 1.54 beta0[1] beta0[2] beta0[3] 133.4279 1455.1566 107.8260 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] 2.0712 1.1775 0.005551 0.04587 tau0[2] 1.0687 1.0097 0.004760 0.03109 tau0[3] 0.9348 0.8268 0.003898 0.01976 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.07593 1.2576 1.9978 2.811 4.650 tau0[2] -0.75251 0.3664 1.0080 1.715 3.208 tau0[3] -0.69430 0.3776 0.9366 1.499 2.517 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.07 1.22 tau0[2] 1.31 1.83 tau0[3] 1.07 1.22 Multivariate psrf 1.29 tau0[1] tau0[2] tau0[3] 802.4667 1085.7764 1783.9225 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] 2.733 1.103 0.005200 0.03746 gamma0[2] 2.969 0.995 0.004691 0.02950 gamma0[3] 2.994 1.110 0.005233 0.08007 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.7144 1.998 2.658 3.378 5.158 gamma0[2] 1.3583 2.292 2.855 3.513 5.289 gamma0[3] 0.6711 2.384 3.003 3.638 5.278 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.01 1.01 gamma0[2] 1.06 1.21 gamma0[3] 1.14 1.42 Multivariate psrf 1.14 gamma0[1] gamma0[2] gamma0[3] 1533.257 1250.110 1888.695 Chains of length 10000 for K4 Simulation Stan @ 3 did not converge in run 2 . Maximum Rhat value = 1.53923 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -585.4782046 10.1116692 0.1011167 0.2528456 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -584.3507942 10.3367387 0.1033674 0.2470035 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -586.1964738 10.5360701 0.1053607 0.2774160 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 10.85648230 3.88258538 0.03882585 0.10815343 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 11.29333445 4.01488751 0.04014888 0.08460499 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 10.46464535 3.72702965 0.03727030 0.08999264 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3344855 0.08575757 0.0007002077 0.009927662 alpha0[2] 0.2112054 0.07796660 0.0006365947 0.004086877 alpha0[3] 0.4543091 0.08707241 0.0007109432 0.006345574 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2896233 0.06607486 0.0005394989 0.00303723 alpha0[2] 0.2818784 0.12924581 0.0010552876 0.02496518 alpha0[3] 0.4284983 0.12515656 0.0010218991 0.02262577 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2525065 0.08976472 0.0007329259 0.01111689 alpha0[2] 0.3834879 0.09618096 0.0007853142 0.01326828 alpha0[3] 0.3640056 0.13745900 0.0011223480 0.02762236 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.881378395 0.2375652 0.001939711 0.029204651 mu0[2] -0.230803561 0.2269100 0.001852712 0.004680946 mu0[3] -0.005661516 0.1689071 0.001379120 0.004449674 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.84861955 0.2801230 0.002287195 0.031928276 mu0[2] -0.21568399 0.2077727 0.001696457 0.005632295 mu0[3] 0.03748505 0.1740395 0.001421026 0.006706750 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.8665246 0.3041592 0.002483450 0.01762791 mu0[2] -0.4162396 0.2901607 0.002369152 0.04702418 mu0[3] 0.2067693 0.3864641 0.003155466 0.06767450 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.4475502 0.2005173 0.001637217 0.02038308 beta0[2] 1.2414984 0.4016181 0.003279198 0.01093543 beta0[3] 0.3865975 0.2183473 0.001782798 0.02965738 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.6316308 0.4539037 0.003706108 0.11768415 beta0[2] 0.9446456 0.5668667 0.004628448 0.12430214 beta0[3] 0.4294922 0.2903976 0.002371087 0.04750236 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 1.1130073 0.6530905 0.005332462 0.13995222 beta0[2] 0.5304209 0.2239231 0.001828324 0.02421168 beta0[3] 0.5261434 0.4035675 0.003295115 0.10011466 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 2.1111555 1.0230336 0.008353034 0.04872310 tau0[2] 0.8503927 0.8846572 0.007223196 0.06236972 tau0[3] 0.9930079 0.8223202 0.006714217 0.02789263 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 2.3877691 1.2747082 0.010407949 0.10227212 tau0[2] 0.6461794 0.8202419 0.006697247 0.03278474 tau0[3] 1.1489743 0.7876977 0.006431525 0.03715325 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.7148121 1.1221199 0.009162070 0.07813463 tau0[2] 1.7096676 0.9829294 0.008025585 0.06111910 tau0[3] 0.6622709 0.7942497 0.006485021 0.03682215 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.736453 0.9691451 0.007913037 0.03050154 gamma0[2] 2.636576 0.9582729 0.007824265 0.05992678 gamma0[3] 3.456042 0.8878996 0.007249670 0.02109166 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.672375 1.1327917 0.009249206 0.06091605 gamma0[2] 3.154587 0.9741163 0.007953626 0.05310419 gamma0[3] 2.600079 1.2398771 0.010123554 0.21249349 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.790248 1.1922309 0.009734524 0.08939071 gamma0[2] 3.116507 0.9677494 0.007901641 0.03772900 gamma0[3] 2.925608 0.9988337 0.008155443 0.10998402 MCMC run did not converge, proceeding anyway. Calculating model fit indexes for K4 Simulation Stan @ 3 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -487.59582 74.91662 1125.02489 74.91662 1125.02489 lppd lppd.bayes pDIC DIC pDICalt DICalt -525.0541 -1279.9872 -1509.8662 -459.7579 117.5237 2795.0219 Analaysis complete for K4 Simulation Stan @ 3 > proc.time() user system elapsed 26293.392 44.029 26351.329