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 @ 4 Removing 0 of 10 Level 2 units for length. Calculating initial values for chain 1 ; K2 Simulation Stan @ 4 number of iterations= 48 number of iterations= 290 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= 58 number of iterations= 82 number of iterations= 201 number of iterations= 513 number of iterations= 179 WARNING! NOT CONVERGENT! number of iterations= 1000 number of iterations= 116 number of iterations= 182 Calculating initial values for chain 2 ; K2 Simulation Stan @ 4 number of iterations= 824 number of iterations= 74 One of the variances is going to zero; trying new starting values. number of iterations= 60 One of the variances is going to zero; trying new starting values. number of iterations= 80 number of iterations= 122 One of the variances is going to zero; trying new starting values. number of iterations= 60 number of iterations= 92 number of iterations= 40 number of iterations= 77 number of iterations= 621 Calculating initial values for chain 3 ; K2 Simulation Stan @ 4 number of iterations= 32 number of iterations= 87 number of iterations= 155 number of iterations= 306 number of iterations= 106 WARNING! NOT CONVERGENT! number of iterations= 1000 number of iterations= 44 number of iterations= 117 number of iterations= 29 number of iterations= 224 **************** Running Model for K2 Simulation Stan @ 4 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. 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: 52.9039 seconds (Warm-up) 98.7862 seconds (Sampling) 151.69 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): 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: 75.5839 seconds (Warm-up) 140.632 seconds (Sampling) 216.216 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. 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: 244.757 seconds (Warm-up) 805.033 seconds (Sampling) 1049.79 seconds (Total) **************** Convergence diagnostics for K2 Simulation Stan @ 4 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 -674.3132 13.1442 0.1073 0.6182 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -700.2 -683.1 -674.4 -665.3 -648.6 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.02 1.06 lp__ 494.5668 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 12.81294 4.59049 0.03748 0.07764 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 5.570 9.499 12.199 15.498 23.283 Potential scale reduction factors: Point est. Upper C.I. alphaN 1 1 alphaN 3902.191 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] 0.552450 0.185011 1.511e-03 0.0009666 alpha0[2] 0.436709 0.184806 1.509e-03 0.0009589 alpha0[3] 0.002744 0.004607 3.762e-05 0.0002082 alpha0[4] 0.008098 0.005548 4.530e-05 0.0001112 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 2.263e-01 0.3377010 0.641510 0.696879 0.77028 alpha0[2] 2.186e-01 0.2922723 0.347498 0.650806 0.76257 alpha0[3] 3.168e-06 0.0002909 0.001264 0.003532 0.01359 alpha0[4] 1.867e-03 0.0042738 0.006741 0.010293 0.02236 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 5.87 11.08 alpha0[3] 1.06 1.07 alpha0[4] 1.00 1.00 Multivariate psrf 4.61 alpha0[1] alpha0[2] alpha0[3] alpha0[4] 4319.9488 4338.3535 593.0763 2522.7799 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] -0.3962 0.2407 0.001965 0.006106 mu0[2] 0.2451 0.3912 0.003194 0.007547 mu0[3] 463.5998 389.1577 3.177459 4.985783 mu0[4] 1121.6501 604.4273 4.935128 7.260103 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -0.8579 -0.5457 -0.4063 -0.2494 0.1049 mu0[2] -0.4670 -0.0445 0.2478 0.5174 0.9991 mu0[3] 7.6186 158.7446 371.0158 668.2281 1449.2063 mu0[4] 183.8128 663.0228 1049.2237 1488.0218 2475.7245 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.11 1.27 mu0[2] 1.73 2.79 mu0[3] 1.00 1.00 mu0[4] 1.00 1.00 Multivariate psrf 1.64 mu0[1] mu0[2] mu0[3] mu0[4] 1477.217 1599.603 6107.353 7189.118 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] 0.7579 0.3577 0.002921 0.006932 beta0[2] 0.8389 0.2871 0.002344 0.006508 beta0[3] 1.6269 2.1724 0.017738 0.032877 beta0[4] 1.6825 2.1517 0.017569 0.031530 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.3605 0.5095 0.6436 0.9020 1.701 beta0[2] 0.4189 0.6331 0.7973 0.9938 1.521 beta0[3] 0.1401 0.4941 0.9863 1.9203 7.019 beta0[4] 0.1411 0.5280 1.0188 2.0314 7.209 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.86 4.12 beta0[2] 1.07 1.24 beta0[3] 1.00 1.01 beta0[4] 1.01 1.01 Multivariate psrf 1.56 beta0[1] beta0[2] beta0[3] beta0[4] 1383.370 1824.948 4500.215 4789.056 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] 1.138577 1.1834 0.009662 0.007846 tau0[2] 0.336688 1.1915 0.009729 0.009231 tau0[3] -0.003716 1.0000 0.008165 0.015560 tau0[4] 0.003264 0.9994 0.008160 0.015180 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -1.040 -0.1956 1.7307056 2.0649 2.474 tau0[2] -1.198 -0.5914 -0.1919454 1.7282 2.374 tau0[3] -1.961 -0.6791 -0.0001141 0.6662 1.931 tau0[4] -1.959 -0.6553 0.0055562 0.6730 2.001 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 5.63 10.77 tau0[2] 5.27 10.07 tau0[3] 1.00 1.01 tau0[4] 1.00 1.00 Multivariate psrf 5.8 tau0[1] tau0[2] tau0[3] tau0[4] 3238.399 1910.282 4172.032 4371.992 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] 1.047 0.3747 0.003059 0.007030 gamma0[2] 1.180 0.3962 0.003235 0.008046 gamma0[3] 1.668 2.1001 0.017147 0.031924 gamma0[4] 1.627 2.0370 0.016632 0.029789 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.5097 0.7819 0.980 1.242 1.942 gamma0[2] 0.5632 0.9014 1.130 1.396 2.087 gamma0[3] 0.1387 0.5143 1.028 1.990 7.401 gamma0[4] 0.1523 0.5172 1.007 1.953 6.844 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.30 1.82 gamma0[2] 1.28 1.78 gamma0[3] 1.00 1.00 gamma0[4] 1.00 1.00 Multivariate psrf 1.41 gamma0[1] gamma0[2] gamma0[3] gamma0[4] 3005.550 2118.180 4444.389 4888.377 Chains of length 5000 for K2 Simulation Stan @ 4 did not converge in run 1 . Maximum Rhat value = 5.800048 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -674.2569723 13.4137704 0.1896994 0.9278811 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -672.2765010 12.4846273 0.1765593 0.9167371 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -676.4061492 13.1926717 0.1865726 1.3185027 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 12.83374427 4.57960047 0.06476533 0.13223672 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 12.69428938 4.62103648 0.06535132 0.15997568 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 12.91078373 4.56897267 0.06461503 0.10570116 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.67930680 0.055381140 7.832076e-04 0.0017996347 alpha0[2] 0.31031051 0.054624072 7.725010e-04 0.0017682766 alpha0[3] 0.00241888 0.003508492 4.961757e-05 0.0002193602 alpha0[4] 0.00796381 0.005439412 7.692491e-05 0.0001840593 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.675078565 0.056400509 7.976236e-04 0.0019955130 alpha0[2] 0.313819954 0.055989846 7.918160e-04 0.0019862858 alpha0[3] 0.003057470 0.003871770 5.475510e-05 0.0002722979 alpha0[4] 0.008044011 0.005449484 7.706734e-05 0.0001800732 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.302963259 0.055320489 7.823499e-04 0.0010903679 alpha0[2] 0.685996396 0.055801297 7.891495e-04 0.0010965666 alpha0[3] 0.002755446 0.006015114 8.506655e-05 0.0005175351 alpha0[4] 0.008284899 0.005745932 8.125975e-05 0.0002121433 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.4319077 0.1908130 0.002698504 0.00986653 mu0[2] 0.4205647 0.3149278 0.004453752 0.01405244 mu0[3] 461.5900218 382.7040089 5.412251998 8.77192395 mu0[4] 1124.7917889 609.1373165 8.614502544 12.83300968 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.4356740 0.1944138 0.002749426 0.01114038 mu0[2] 0.4210457 0.3244247 0.004588058 0.01327800 mu0[3] 460.4175427 384.4478917 5.436914225 8.69181523 mu0[4] 1121.0746508 592.9386795 8.385419222 13.70369528 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.3211448 0.3019191 0.004269781 0.01068342 mu0[2] -0.1062422 0.2635503 0.003727164 0.01178196 mu0[3] 468.7918054 400.1100088 5.658410008 8.43966991 mu0[4] 1119.0837305 611.1480035 8.642937952 11.04103587 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5954966 0.1806084 0.002554189 0.01046529 beta0[2] 0.8734796 0.2937786 0.004154657 0.01140899 beta0[3] 1.6037568 1.9577197 0.027686337 0.05024200 beta0[4] 1.6032500 2.1161904 0.029927452 0.04721177 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5914398 0.1657177 0.002343602 0.007590309 beta0[2] 0.9027694 0.3014028 0.004262479 0.011233909 beta0[3] 1.6571411 2.3188827 0.032793953 0.066222073 beta0[4] 1.6814930 1.9829982 0.028043830 0.055201508 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 1.0868414 0.4019563 0.005684521 0.01628989 beta0[2] 0.7403177 0.2348764 0.003321654 0.01117111 beta0[3] 1.6196684 2.2245711 0.031460186 0.05309091 beta0[4] 1.7626470 2.3384263 0.033070341 0.06059100 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.912797565 0.3356203 0.004746387 0.01453506 tau0[2] -0.464190622 0.4197369 0.005935976 0.01767247 tau0[3] 0.020399586 1.0575090 0.014955435 0.02979558 tau0[4] 0.003445619 1.0065228 0.014234382 0.02615135 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.953931287 0.3533305 0.004996848 0.01630069 tau0[2] -0.452316222 0.4208296 0.005951430 0.01749722 tau0[3] 0.014261252 0.9567092 0.013529912 0.02493963 tau0[4] -0.001285598 0.9798490 0.013857158 0.02763125 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] -0.450996385 0.4151564 0.005871198 0.008780815 tau0[2] 1.926572221 0.3374849 0.004772757 0.012180192 tau0[3] -0.045808805 0.9820251 0.013887932 0.025871681 tau0[4] 0.007632066 1.0115891 0.014306030 0.025032046 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 0.9327397 0.3036258 0.004293918 0.01297499 gamma0[2] 1.3220612 0.3864582 0.005465345 0.01615479 gamma0[3] 1.6812983 2.0831048 0.029459551 0.04869741 gamma0[4] 1.6474440 2.1653347 0.030622457 0.05374380 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 0.9261453 0.3117819 0.004409262 0.01418540 gamma0[2] 1.2864139 0.3717495 0.005257332 0.01498124 gamma0[3] 1.6488250 2.0700104 0.029274368 0.05750266 gamma0[4] 1.6161817 1.9024228 0.026904321 0.05600435 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.2829907 0.3853507 0.005449682 0.008671354 gamma0[2] 0.9320026 0.3008375 0.004254484 0.009864017 gamma0[3] 1.6731282 2.1465610 0.030356957 0.059112001 gamma0[4] 1.6171836 2.0350162 0.028779475 0.044289837 **************** Running Model for K2 Simulation Stan @ 4 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: 1750.99 seconds (Warm-up) 8601.4 seconds (Sampling) 10352.4 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. 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: 540.132 seconds (Warm-up) 8251.2 seconds (Sampling) 8791.33 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 / 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: 78.5767 seconds (Warm-up) 180.066 seconds (Sampling) 258.642 seconds (Total) **************** Convergence diagnostics for K2 Simulation Stan @ 4 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 -671.75575 13.36268 0.07715 0.77170 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -697.6 -680.9 -671.7 -662.6 -645.8 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.06 1.18 lp__ 505.4226 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 13.26811 4.77595 0.02757 0.30731 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 5.818 9.796 12.564 16.019 24.345 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.01 1.05 alphaN 1840.291 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] 0.33654 0.3196 0.0015068 0.11017 alpha0[2] 0.36822 0.2556 0.0012047 0.04305 alpha0[3] 0.22622 0.2847 0.0013420 0.07638 alpha0[4] 0.06902 0.1157 0.0005452 0.02217 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 4.632e-05 0.002983 0.300263 0.67102 0.7604 alpha0[2] 3.371e-03 0.231570 0.327037 0.64667 0.7575 alpha0[3] 1.005e-05 0.001069 0.007299 0.36779 0.7482 alpha0[4] 2.105e-03 0.004974 0.008506 0.02056 0.3380 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 2.19 3.80 alpha0[3] 1.94 5.64 alpha0[4] 2.31 12.05 Multivariate psrf 2.08 alpha0[1] alpha0[2] alpha0[3] alpha0[4] 10.13063 16.75293 232.20709 1959.45336 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] -72.30 131.84 0.6215 19.008 mu0[2] -20.44 45.37 0.2139 2.888 mu0[3] 253.68 368.14 1.7354 22.937 mu0[4] 806.46 700.81 3.3036 59.558 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -446.7054 -48.7846 -0.6725 -0.4039 -0.03548 mu0[2] -159.3932 -0.6649 -0.1445 0.3884 0.97450 mu0[3] -0.8641 0.1002 47.9281 411.1185 1253.91391 mu0[4] 0.1539 117.0156 731.7750 1270.0497 2364.52895 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 2.04 9.73 mu0[2] 1.89 9.07 mu0[3] 1.22 1.61 mu0[4] 1.30 1.79 Multivariate psrf 1.69 mu0[1] mu0[2] mu0[3] mu0[4] 568.4325 175.9734 2728.1280 4241.2666 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] 1.112 1.342 0.006324 0.06755 beta0[2] 1.194 2.308 0.010879 0.17366 beta0[3] 1.229 1.643 0.007744 0.02579 beta0[4] 1.424 1.768 0.008332 0.02309 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.2654 0.5060 0.6837 1.161 4.652 beta0[2] 0.3055 0.5672 0.7665 1.061 4.967 beta0[3] 0.1827 0.5288 0.7588 1.255 5.308 beta0[4] 0.1536 0.5807 0.8833 1.563 6.012 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.13 1.33 beta0[2] 1.35 3.19 beta0[3] 1.08 1.19 beta0[4] 1.05 1.11 Multivariate psrf 1.11 beta0[1] beta0[2] beta0[3] beta0[4] 904.1065 923.6564 5362.3396 6508.4727 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] 0.6936 1.3154 0.006201 0.18783 tau0[2] 0.4354 1.1975 0.005645 0.17886 tau0[3] 0.3274 1.1435 0.005391 0.08813 tau0[4] -0.1186 0.9338 0.004402 0.02325 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -1.830 -0.4437 0.8948 1.9103 2.419 tau0[2] -1.391 -0.5340 0.0220 1.7348 2.356 tau0[3] -1.681 -0.5305 0.1213 1.3595 2.237 tau0[4] -1.869 -0.7532 -0.2012 0.4748 1.859 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.08 1.24 tau0[2] 1.30 1.84 tau0[3] 1.56 2.48 tau0[4] 1.06 1.17 Multivariate psrf 1.62 tau0[1] tau0[2] tau0[3] tau0[4] 96.24070 58.59246 3634.08058 3590.44111 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] 1.372 1.609 0.007584 0.08866 gamma0[2] 1.365 1.466 0.006910 0.08968 gamma0[3] 1.436 1.666 0.007854 0.02336 gamma0[4] 1.546 1.895 0.008934 0.02566 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.2394 0.7130 0.9862 1.420 5.306 gamma0[2] 0.3983 0.8579 1.1196 1.450 4.023 gamma0[3] 0.1849 0.6818 1.0629 1.537 5.707 gamma0[4] 0.1621 0.6393 1.0700 1.688 6.324 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.13 1.26 gamma0[2] 1.31 2.44 gamma0[3] 1.09 1.16 gamma0[4] 1.04 1.06 Multivariate psrf 1.07 gamma0[1] gamma0[2] gamma0[3] gamma0[4] 1315.7815 748.8638 5077.1154 6049.4053 Chains of length 10000 for K2 Simulation Stan @ 4 did not converge in run 2 . Maximum Rhat value = 2.08237 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -667.7359292 11.0141369 0.1101414 1.6757731 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -674.1633230 14.4857752 0.1448578 1.4322361 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -673.3679838 13.4168985 0.1341690 0.7072024 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 13.88733108 5.04513946 0.05045139 0.89317458 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 13.29476163 4.67186470 0.04671865 0.18716386 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 12.62224399 4.51136795 0.04511368 0.13103622 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2291625 0.3199222 0.002612154 0.23725647 alpha0[2] 0.1093868 0.1456333 0.001189091 0.08229417 alpha0[3] 0.4707714 0.3327635 0.002717002 0.22364760 alpha0[4] 0.1906794 0.1336847 0.001091531 0.06651192 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.228915785 0.317366958 2.591290e-03 0.2177837291 alpha0[2] 0.558027047 0.181050916 1.478275e-03 0.0667548812 alpha0[3] 0.204915983 0.149287116 1.218924e-03 0.0498523843 alpha0[4] 0.008141186 0.005514201 4.502326e-05 0.0001905171 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.551531118 0.184712288 1.508170e-03 0.0742494452 alpha0[2] 0.437236323 0.184801700 1.508900e-03 0.0738302485 alpha0[3] 0.002978388 0.005173425 4.224084e-05 0.0003479820 alpha0[4] 0.008254172 0.005690444 4.646228e-05 0.0001702097 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -199.58664 165.3585 1.350147 56.657460 mu0[2] -61.40981 60.4643 0.493689 8.664103 mu0[3] 153.44893 310.3110 2.533679 48.126253 mu0[4] 375.26601 636.0656 5.193454 177.493265 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -16.9005880 19.0437218 0.155491337 6.4575584 mu0[2] -0.1695999 0.4815129 0.003931537 0.1020687 mu0[3] 153.7844775 310.2825849 2.533446697 48.5864815 mu0[4] 917.8709475 623.6029753 5.091696972 16.9851374 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.4091818 0.2373381 0.001937857 0.01010895 mu0[2] 0.2743169 0.4118929 0.003363092 0.04019362 mu0[3] 453.8184532 392.3536665 3.203554272 7.62784204 mu0[4] 1126.2449893 615.9278744 5.029030035 11.47622061 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 1.3444176 1.388749 0.01133909 0.18745711 beta0[2] 2.0585026 3.832110 0.03128904 0.52034245 beta0[3] 0.9347612 1.230077 0.01004354 0.05666976 beta0[4] 1.0778187 1.287596 0.01051318 0.04575936 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 1.2451735 1.7705871 0.014456783 0.06333311 beta0[2] 0.6919059 0.2724427 0.002224485 0.02351215 beta0[3] 1.1352910 1.4107809 0.011518978 0.03968331 beta0[4] 1.5802648 1.8092095 0.014772134 0.03872392 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.7459213 0.3602508 0.002941435 0.04380175 beta0[2] 0.8320802 0.2980043 0.002433195 0.01099091 beta0[3] 1.6179894 2.0847847 0.017022195 0.03461534 beta0[4] 1.6125929 2.0644107 0.016855843 0.03472725 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 0.3141294 1.4128334 0.011535737 0.35246649 tau0[2] -0.1921426 0.8256558 0.006741451 0.13391054 tau0[3] 1.2383297 1.0861183 0.008868118 0.26183916 tau0[4] -0.3776200 0.7089556 0.005788598 0.06086673 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 0.65882270 1.2332141 0.010069151 0.14503939 tau0[2] 1.12388704 1.1748367 0.009592501 0.38613476 tau0[3] -0.25945469 0.6889799 0.005625498 0.03201405 tau0[4] 0.05761935 0.9791842 0.007995005 0.02397919 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.107893905 1.1649915 0.009512115 0.41504217 tau0[2] 0.374497749 1.1697674 0.009551111 0.34769075 tau0[3] 0.003271385 0.9946561 0.008121333 0.01771176 tau0[4] -0.035656288 1.0243760 0.008363995 0.02423043 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.655102 2.190843 0.01788816 0.25968652 gamma0[2] 1.827360 2.412038 0.01969421 0.26736242 gamma0[3] 1.177967 1.280683 0.01045673 0.04552245 gamma0[4] 1.329459 1.295446 0.01057727 0.03669560 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.397539 1.6242709 0.013262116 0.05300566 gamma0[2] 1.069620 0.3657226 0.002986112 0.02158343 gamma0[3] 1.446526 1.2546714 0.010244349 0.03251421 gamma0[4] 1.651347 2.1512756 0.017565091 0.05731381 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.063180 0.3887349 0.003174007 0.02223731 gamma0[2] 1.198003 0.4077543 0.003329300 0.02090759 gamma0[3] 1.683414 2.2328173 0.018230877 0.04222204 gamma0[4] 1.657323 2.0975253 0.017126222 0.03600266 MCMC run did not converge, proceeding anyway. Calculating model fit indexes for K2 Simulation Stan @ 4 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -530.86391 35.82058 1133.36898 35.82058 1133.36898 lppd lppd.bayes pDIC DIC pDICalt DICalt -548.77420 -6595.20315 -12092.85790 -10995.30950 59.73829 13309.88289 Analaysis complete for K2 Simulation Stan @ 4 > proc.time() user system elapsed 20929.120 40.526 20991.224