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 @ 3 Removing 0 of 10 Level 2 units for length. Calculating initial values for chain 1 ; K3 Simulation Stan @ 3 number of iterations= 80 One of the variances is going to zero; trying new starting values. number of iterations= 106 number of iterations= 15 number of iterations= 13 number of iterations= 44 number of iterations= 98 number of iterations= 19 number of iterations= 45 number of iterations= 50 number of iterations= 214 Calculating initial values for chain 2 ; K3 Simulation Stan @ 3 number of iterations= 45 number of iterations= 41 number of iterations= 18 number of iterations= 6 number of iterations= 82 number of iterations= 134 number of iterations= 9 number of iterations= 15 number of iterations= 100 number of iterations= 129 Calculating initial values for chain 3 ; K3 Simulation Stan @ 3 number of iterations= 36 number of iterations= 40 number of iterations= 15 number of iterations= 14 number of iterations= 78 number of iterations= 65 number of iterations= 21 number of iterations= 18 One of the variances is going to zero; trying new starting values. number of iterations= 52 One of the variances is going to zero; trying new starting values. number of iterations= 231 **************** Running Model for K3 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). 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 / 6000 [ 0%] (Warmup) Iteration: 600 / 6000 [ 10%] (Warmup) Iteration: 1200 / 6000 [ 20%] (Sampling) Iteration: 1800 / 6000 [ 30%] (Sampling) Iteration: 2400 / 6000 [ 40%] (Sampling) Iteration: 3000 / 6000 [ 50%] (Sampling) Iteration: 3600 / 6000 [ 60%] (Sampling) Iteration: 4200 / 6000 [ 70%] (Sampling) Iteration: 4800 / 6000 [ 80%] (Sampling) Iteration: 5400 / 6000 [ 90%] (Sampling) Iteration: 6000 / 6000 [100%] (Sampling) Elapsed Time: 74.5489 seconds (Warm-up) 252.175 seconds (Sampling) 326.724 seconds (Total) SAMPLING FOR MODEL 'hierModel1pmu' NOW (CHAIN 2). Iteration: 1 / 6000 [ 0%] (Warmup) Iteration: 600 / 6000 [ 10%] (Warmup) Iteration: 1200 / 6000 [ 20%] (Sampling) Iteration: 1800 / 6000 [ 30%] (Sampling) Iteration: 2400 / 6000 [ 40%] (Sampling) Iteration: 3000 / 6000 [ 50%] (Sampling) Iteration: 3600 / 6000 [ 60%] (Sampling) Iteration: 4200 / 6000 [ 70%] (Sampling) Iteration: 4800 / 6000 [ 80%] (Sampling) Iteration: 5400 / 6000 [ 90%] (Sampling) Iteration: 6000 / 6000 [100%] (Sampling) Elapsed Time: 64.879 seconds (Warm-up) 255.287 seconds (Sampling) 320.166 seconds (Total) SAMPLING FOR MODEL 'hierModel1pmu' NOW (CHAIN 3). Iteration: 1 / 6000 [ 0%] (Warmup) Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Iteration: 600 / 6000 [ 10%] (Warmup) Iteration: 1200 / 6000 [ 20%] (Sampling) Iteration: 1800 / 6000 [ 30%] (Sampling) Iteration: 2400 / 6000 [ 40%] (Sampling) Iteration: 3000 / 6000 [ 50%] (Sampling) Iteration: 3600 / 6000 [ 60%] (Sampling) Iteration: 4200 / 6000 [ 70%] (Sampling) Iteration: 4800 / 6000 [ 80%] (Sampling) Iteration: 5400 / 6000 [ 90%] (Sampling) Iteration: 6000 / 6000 [100%] (Sampling) Elapsed Time: 102.069 seconds (Warm-up) 298.274 seconds (Sampling) 400.343 seconds (Total) **************** Convergence diagnostics for K3 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 -615.3808 10.2141 0.0834 0.2842 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -636.7 -622.0 -615.0 -608.2 -596.6 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.01 1.05 lp__ 1345.625 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 14.11577 4.72858 0.03861 0.09734 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 6.522 10.712 13.648 17.006 24.738 Potential scale reduction factors: Point est. Upper C.I. alphaN 1 1 alphaN 4873.785 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] 0.4298 0.05955 0.0004862 0.002044 alpha0[2] 0.3492 0.10350 0.0008451 0.002010 alpha0[3] 0.2210 0.10470 0.0008548 0.003518 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.31468 0.3892 0.4288 0.4719 0.5429 alpha0[2] 0.14430 0.2722 0.3646 0.4285 0.5230 alpha0[3] 0.08175 0.1379 0.1931 0.3006 0.4511 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 2.45 4.29 alpha0[3] 2.66 4.95 Multivariate psrf 2.45 alpha0[1] alpha0[2] alpha0[3] 2718.3297 1274.3372 732.8193 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] -0.95779 0.1156 0.0009437 0.003823 mu0[2] -0.06511 0.2730 0.0022294 0.007185 mu0[3] 1.03025 0.6412 0.0052354 0.022165 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.17644 -1.0291 -0.96017 -0.89845 -0.7031 mu0[2] -0.57966 -0.2406 -0.08255 0.09747 0.5241 mu0[3] -0.05125 0.4962 0.97317 1.56897 2.1790 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.01 1.03 mu0[2] 1.16 1.46 mu0[3] 1.90 3.29 Multivariate psrf 1.77 mu0[1] mu0[2] mu0[3] 1988.4690 1475.6531 647.2042 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] 0.2980 0.1039 0.0008486 0.003114 beta0[2] 0.7401 0.3592 0.0029331 0.013951 beta0[3] 0.8399 0.4567 0.0037293 0.043196 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1672 0.2272 0.2760 0.3412 0.5644 beta0[2] 0.2966 0.5002 0.6506 0.8733 1.6955 beta0[3] 0.1808 0.5050 0.7735 1.0781 2.0695 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.03 1.06 beta0[2] 1.38 2.27 beta0[3] 1.14 1.41 Multivariate psrf 1.41 beta0[1] beta0[2] beta0[3] 1607.2624 708.9946 1085.1953 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] 2.1965 0.6966 0.005688 0.01392 tau0[2] 0.6766 1.0772 0.008796 0.02371 tau0[3] 0.4999 1.1673 0.009531 0.04292 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] 0.6353 1.7802 2.2683 2.665 3.397 tau0[2] -1.1967 -0.3030 0.8804 1.494 2.508 tau0[3] -1.2239 -0.4357 0.2603 1.388 3.025 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.02 1.08 tau0[2] 2.63 4.80 tau0[3] 2.39 4.18 Multivariate psrf 2.53 tau0[1] tau0[2] tau0[3] 2675.5510 796.4061 402.2246 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] 2.070 0.6110 0.004989 0.01096 gamma0[2] 1.756 0.7463 0.006094 0.01916 gamma0[3] 1.650 0.6968 0.005689 0.02528 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.1701 1.638 1.980 2.372 3.559 gamma0[2] 0.4943 1.178 1.754 2.209 3.352 gamma0[3] 0.5339 1.133 1.584 2.083 3.110 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.01 1.04 gamma0[2] 1.72 2.77 gamma0[3] 1.10 1.32 Multivariate psrf 1.63 gamma0[1] gamma0[2] gamma0[3] 3080.493 1491.832 1005.013 Chains of length 5000 for K3 Simulation Stan @ 3 did not converge in run 1 . Maximum Rhat value = 2.525736 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -614.6997461 10.2375702 0.1447811 0.4444697 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -614.3730918 10.1807338 0.1439773 0.4522660 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -617.0694391 10.0117797 0.1415879 0.5701287 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 14.10153161 4.75933880 0.06730721 0.09184688 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 14.18402677 4.73275176 0.06693122 0.11008573 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 14.06175628 4.69354522 0.06637675 0.25439718 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4327235 0.05865395 0.0008294921 0.001663087 alpha0[2] 0.4078666 0.06525369 0.0009228265 0.002622620 alpha0[3] 0.1594098 0.05229883 0.0007396172 0.002649748 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4318184 0.05864072 0.0008293049 0.001590570 alpha0[2] 0.4050937 0.06750813 0.0009547091 0.003110881 alpha0[3] 0.1630879 0.05516212 0.0007801102 0.003282437 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4248607 0.06102260 0.0008629899 0.005683987 alpha0[2] 0.2347021 0.06039842 0.0008541627 0.004448401 alpha0[3] 0.3404373 0.07545538 0.0010671003 0.009673544 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.9527801 0.1281862 0.001812827 0.01012992 mu0[2] -0.1524815 0.2326326 0.003289922 0.01244901 mu0[3] 1.3680274 0.5112623 0.007230340 0.02777684 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.9714472 0.1090692 0.001542472 0.003105930 mu0[2] -0.1052938 0.2391028 0.003381425 0.008701415 mu0[3] 1.3105190 0.5348396 0.007563774 0.032849857 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.94915239 0.1070926 0.001514518 0.004389725 mu0[2] 0.06244009 0.2947522 0.004168426 0.015294910 mu0[3] 0.41220661 0.3337963 0.004720593 0.050703884 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3132234 0.1186878 0.001678500 0.007799696 beta0[2] 0.6319589 0.2209902 0.003125273 0.011247653 beta0[3] 0.7150879 0.3974669 0.005621031 0.017244906 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.2951956 0.09834386 0.001390792 0.003923847 beta0[2] 0.6172658 0.23577093 0.003334305 0.018303788 beta0[3] 0.7591810 0.40497149 0.005727162 0.017440634 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.2854513 0.09087191 0.001285123 0.003323616 beta0[2] 0.9711492 0.45010427 0.006365436 0.035917240 beta0[3] 1.0453637 0.48951737 0.006922821 0.127244573 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 2.1020252 0.7156697 0.010121097 0.02327730 tau0[2] 1.3238692 0.6748438 0.009543732 0.03356419 tau0[3] -0.1494828 0.6855985 0.009695828 0.04528356 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 2.1501349 0.6921526 0.009788516 0.02008369 tau0[2] 1.2582522 0.7110959 0.010056415 0.04334210 tau0[3] -0.1250894 0.7222762 0.010214528 0.07099585 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 2.3372757 0.6586761 0.009315087 0.02827836 tau0[2] -0.5523271 0.5027854 0.007110460 0.04533901 tau0[3] 1.7743482 0.8121246 0.011485176 0.09741503 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.114600 0.6216787 0.008791864 0.01961877 gamma0[2] 2.110429 0.6184014 0.008745516 0.02144579 gamma0[3] 1.505118 0.6609387 0.009347085 0.03019738 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.112345 0.6346428 0.008975205 0.01853600 gamma0[2] 2.068653 0.5956013 0.008423074 0.02563512 gamma0[3] 1.505978 0.6604796 0.009340592 0.03286893 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.982021 0.5652139 0.007993331 0.01879770 gamma0[2] 1.087439 0.5134695 0.007261555 0.04676946 gamma0[3] 1.937588 0.6776580 0.009583532 0.06130784 **************** Running Model for K3 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) 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: 234.998 seconds (Warm-up) 1199.52 seconds (Sampling) 1434.52 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): 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: 114.626 seconds (Warm-up) 599.245 seconds (Sampling) 713.87 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): 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: 231.106 seconds (Warm-up) 1065.89 seconds (Sampling) 1296.99 seconds (Total) **************** Convergence diagnostics for K3 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 -618.35959 10.06720 0.05812 0.15122 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -639.1 -625.0 -618.0 -611.4 -599.5 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.02 1.06 lp__ 4479.284 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 13.19983 4.68457 0.02705 0.04432 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 6.039 9.797 12.539 15.866 24.004 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.05 1.14 alphaN 11250.17 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] 0.4146 0.06672 0.0003145 0.001380 alpha0[2] 0.3224 0.10765 0.0005074 0.003513 alpha0[3] 0.2629 0.11087 0.0005227 0.004039 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.2800 0.3709 0.4152 0.4602 0.5402 alpha0[2] 0.1353 0.2343 0.3222 0.4089 0.5167 alpha0[3] 0.0915 0.1679 0.2512 0.3522 0.4755 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 2.04 3.67 alpha0[3] 2.16 3.88 Multivariate psrf 2.02 alpha0[1] alpha0[2] alpha0[3] 3769.7929 2345.3166 748.9126 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] -0.8661 0.2158 0.001017 0.008523 mu0[2] -0.1861 0.2948 0.001390 0.011616 mu0[3] 0.7105 0.7450 0.003512 0.031126 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.1921 -1.0142 -0.9111 -0.74658 -0.3557 mu0[2] -0.7225 -0.3900 -0.1998 -0.00359 0.4390 mu0[3] -0.5040 0.1232 0.6530 1.31075 2.0864 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.29 1.89 mu0[2] 1.04 1.10 mu0[3] 2.11 3.69 Multivariate psrf 1.9 mu0[1] mu0[2] mu0[3] 708.0751 1762.0233 1047.0721 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] 0.4531 0.2911 0.001372 0.01711 beta0[2] 0.9774 0.5131 0.002419 0.02093 beta0[3] 0.7464 0.3942 0.001858 0.01650 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1376 0.2556 0.3555 0.5641 1.239 beta0[2] 0.3504 0.5764 0.8420 1.2840 2.195 beta0[3] 0.1852 0.4650 0.6773 0.9513 1.739 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.55 3.20 beta0[2] 1.50 2.57 beta0[3] 1.02 1.07 Multivariate psrf 1.6 beta0[1] beta0[2] beta0[3] 636.3964 1088.2233 2115.5665 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] 1.8337 0.8249 0.003889 0.03317 tau0[2] 0.8880 1.2204 0.005753 0.03418 tau0[3] 0.6851 1.3141 0.006195 0.03064 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] 0.1499 1.2735 1.8727 2.432 3.306 tau0[2] -1.1340 -0.2048 0.9688 1.804 3.207 tau0[3] -1.1457 -0.3793 0.3628 1.674 3.416 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.09 1.29 tau0[2] 2.47 4.54 tau0[3] 2.74 5.06 Multivariate psrf 2.68 tau0[1] tau0[2] tau0[3] 1351.1142 638.2430 773.6592 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] 2.142 0.6512 0.003070 0.01060 gamma0[2] 1.709 0.7256 0.003420 0.01308 gamma0[3] 1.714 0.7445 0.003509 0.02246 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.1531 1.691 2.040 2.481 3.704 gamma0[2] 0.5409 1.159 1.671 2.150 3.309 gamma0[3] 0.5546 1.163 1.648 2.154 3.383 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.02 1.07 gamma0[2] 1.49 2.26 gamma0[3] 1.18 1.51 Multivariate psrf 1.39 gamma0[1] gamma0[2] gamma0[3] 4441.750 2156.227 1129.175 Chains of length 10000 for K3 Simulation Stan @ 3 did not converge in run 2 . Maximum Rhat value = 2.679751 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -616.87257973 9.86862153 0.09868622 0.24407508 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -618.2206425 10.0265232 0.1002652 0.2925833 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -619.9855528 10.0633018 0.1006330 0.2462448 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 12.38382903 4.29792224 0.04297922 0.06266620 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 12.76597343 4.42285422 0.04422854 0.07698768 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 14.44967988 5.03883458 0.05038835 0.08844176 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4307318 0.06214223 0.0005073892 0.001249141 alpha0[2] 0.3269450 0.09437275 0.0007705503 0.010186044 alpha0[3] 0.2423231 0.09091027 0.0007422792 0.010659855 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4109631 0.06562408 0.0005358184 0.002264130 alpha0[2] 0.4142747 0.06554230 0.0005351506 0.001805686 alpha0[3] 0.1747623 0.05842346 0.0004770256 0.002673655 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.4022065 0.06898324 0.0005632458 0.003234530 alpha0[2] 0.2260899 0.06184055 0.0005049259 0.002014924 alpha0[3] 0.3717036 0.07190291 0.0005870848 0.005101019 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.8419582 0.1789619 0.001461218 0.015213021 mu0[2] -0.1264572 0.2899643 0.002367549 0.008548126 mu0[3] 0.8099946 0.5955414 0.004862575 0.066703730 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.9896233 0.1345330 0.001098457 0.00672439 mu0[2] -0.2212912 0.2336789 0.001907980 0.01046780 mu0[3] 1.3192509 0.5038575 0.004113979 0.01657846 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.766575710 0.2527576 0.002063758 0.01941873 mu0[2] -0.210549418 0.3414611 0.002788018 0.03211913 mu0[3] 0.002301572 0.4176785 0.003410331 0.06320569 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3987099 0.1597156 0.001304072 0.01040154 beta0[2] 1.0422431 0.4663760 0.003807944 0.05061970 beta0[3] 0.7232412 0.3856127 0.003148514 0.02023488 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.2955750 0.1403985 0.001146349 0.007554534 beta0[2] 0.6045170 0.1941132 0.001584928 0.006947720 beta0[3] 0.6969012 0.3813678 0.003113855 0.009336132 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.6649479 0.3693145 0.003015440 0.04969430 beta0[2] 1.2854227 0.5444259 0.004445219 0.03647846 beta0[3] 0.8189399 0.4046130 0.003303652 0.04420056 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 2.155079 0.7299257 0.005959819 0.02370415 tau0[2] 1.203922 0.7776658 0.006349614 0.05011495 tau0[3] 0.172177 0.7424911 0.006062414 0.05165699 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.6567877 0.7660810 0.006255025 0.04430107 tau0[2] 1.8723925 0.8873238 0.007244969 0.08299053 tau0[3] -0.2960937 0.6111830 0.004990288 0.03045384 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.6891945 0.8754117 0.007147707 0.08589842 tau0[2] -0.4123768 0.5624813 0.004592641 0.03342838 tau0[3] 2.1792383 0.8930591 0.007291797 0.06967445 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.029474 0.6358119 0.005191383 0.01483262 gamma0[2] 2.056068 0.6429651 0.005249788 0.02175087 gamma0[3] 1.957611 0.7126107 0.005818442 0.04949755 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.241060 0.6459719 0.005274339 0.01502936 gamma0[2] 1.923458 0.6368660 0.005199989 0.02506719 gamma0[3] 1.329131 0.6537853 0.005338135 0.02832043 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.155691 0.6543404 0.005342667 0.02378341 gamma0[2] 1.147582 0.5279032 0.004310312 0.02091752 gamma0[3] 1.854648 0.7072771 0.005774893 0.03586158 MCMC run did not converge, proceeding anyway. Calculating model fit indexes for K3 Simulation Stan @ 3 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -533.63236 60.77037 1188.80546 60.77037 1188.80546 lppd lppd.bayes pDIC DIC pDICalt DICalt -564.0175 -806.8203 -485.6055 642.4296 112.8658 1839.3722 Analaysis complete for K3 Simulation Stan @ 3 > proc.time() user system elapsed 4608.480 13.117 4628.606