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 @ 3 Removing 0 of 10 Level 2 units for length. Calculating initial values for chain 1 ; K2 Simulation Stan @ 3 number of iterations= 41 number of iterations= 71 number of iterations= 73 number of iterations= 20 number of iterations= 121 number of iterations= 65 number of iterations= 210 number of iterations= 93 number of iterations= 47 number of iterations= 289 Calculating initial values for chain 2 ; K2 Simulation Stan @ 3 number of iterations= 103 number of iterations= 258 number of iterations= 74 number of iterations= 11 number of iterations= 141 number of iterations= 51 number of iterations= 44 number of iterations= 129 number of iterations= 172 WARNING! NOT CONVERGENT! number of iterations= 1000 Calculating initial values for chain 3 ; K2 Simulation Stan @ 3 number of iterations= 60 number of iterations= 61 number of iterations= 106 number of iterations= 70 number of iterations= 65 One of the variances is going to zero; trying new starting values. number of iterations= 111 number of iterations= 355 number of iterations= 110 number of iterations= 96 number of iterations= 82 **************** Running Model for K2 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): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Iteration: 1 / 6000 [ 0%] (Warmup) Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Location parameter is -inf:0, but must be finite! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. 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: 54.7063 seconds (Warm-up) 162.837 seconds (Sampling) 217.543 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. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Iteration: 1 / 6000 [ 0%] (Warmup) Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): 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: 33.1838 seconds (Warm-up) 91.3965 seconds (Sampling) 124.58 seconds (Total) SAMPLING FOR MODEL 'hierModel1pmu' NOW (CHAIN 3). 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: 38.6433 seconds (Warm-up) 83.6171 seconds (Sampling) 122.26 seconds (Total) **************** Convergence diagnostics for K2 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 -616.90327 9.24901 0.07552 0.20293 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -635.6 -622.8 -616.7 -610.6 -599.5 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.03 1.1 lp__ 2228.509 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 11.74569 4.64443 0.03792 0.06374 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 4.632 8.343 11.058 14.442 22.849 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.17 1.5 alphaN 4664.014 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] 0.65428 0.07232 0.0005905 0.002332 alpha0[2] 0.32295 0.09188 0.0007502 0.009095 alpha0[3] 0.02277 0.06050 0.0004940 0.012434 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.489841 0.612380 0.661624 0.70442 0.7750 alpha0[2] 0.035065 0.278997 0.324964 0.37397 0.4962 alpha0[3] 0.002502 0.005623 0.008636 0.01369 0.2759 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.32 1.89 alpha0[3] 1.12 1.22 Multivariate psrf 1.35 alpha0[1] alpha0[2] alpha0[3] 2027.0675 212.8034 965.5541 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] -0.4352 0.1908 0.001558 0.005279 mu0[2] 0.3938 0.3476 0.002838 0.010621 mu0[3] 750.3917 618.0924 5.046703 17.719431 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -0.8063 -0.5593 -0.4369 -0.3150 -0.04642 mu0[2] -0.2902 0.1661 0.3940 0.6175 1.09616 mu0[3] 0.6146 250.7303 618.2789 1113.1552 2224.02169 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.03 1.10 mu0[2] 1.00 1.01 mu0[3] 1.00 1.01 Multivariate psrf 1.03 mu0[1] mu0[2] mu0[3] 1292.650 1313.728 2372.005 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] 0.5286 0.1810 0.001478 0.004036 beta0[2] 0.9537 0.3956 0.003230 0.011408 beta0[3] 1.6385 2.2674 0.018514 0.035370 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.2590 0.4056 0.5013 0.618 0.9706 beta0[2] 0.4062 0.7176 0.8981 1.120 1.8121 beta0[3] 0.1393 0.5175 0.9771 1.867 7.3599 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.25 1.71 beta0[2] 1.08 1.22 beta0[3] 1.00 1.01 Multivariate psrf 1.24 beta0[1] beta0[2] beta0[3] 1860.169 2116.745 5065.290 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] 1.700500 0.4706 0.003842 0.01037 tau0[2] -0.225216 0.5554 0.004535 0.01353 tau0[3] -0.004045 0.9799 0.008000 0.01163 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] 0.6405 1.3985 1.76881 2.0464 2.4453 tau0[2] -1.2042 -0.6154 -0.27100 0.1296 0.9572 tau0[3] -1.9405 -0.6578 -0.02289 0.6657 1.8985 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.50 2.27 tau0[2] 1.27 1.72 tau0[3] 1.00 1.00 Multivariate psrf 1.5 tau0[1] tau0[2] tau0[3] 1451.765 1410.462 7537.831 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] 1.077 0.3708 0.003027 0.008633 gamma0[2] 1.516 0.5598 0.004571 0.010537 gamma0[3] 1.544 1.7999 0.014696 0.021594 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.5305 0.8141 1.022 1.275 1.965 gamma0[2] 0.7034 1.1359 1.447 1.815 2.726 gamma0[3] 0.1493 0.5276 1.025 1.857 6.447 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.26 1.70 gamma0[2] 1.36 1.97 gamma0[3] 1.00 1.00 Multivariate psrf 1.41 gamma0[1] gamma0[2] gamma0[3] 1578.009 2418.842 7371.810 Chains of length 5000 for K2 Simulation Stan @ 3 did not converge in run 1 . Maximum Rhat value = 1.500775 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -618.8337392 8.6235249 0.1219551 0.2802326 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -616.6608490 9.6542644 0.1365319 0.4320431 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -615.2152302 9.0807890 0.1284217 0.3246824 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 9.39132623 3.50855349 0.04961844 0.08707815 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 12.74866409 4.76002211 0.06731688 0.11932297 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 13.09708797 4.62342279 0.06538507 0.12145042 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.60414685 0.073152595 0.0010345339 0.0064374248 alpha0[2] 0.38405641 0.072474411 0.0010249430 0.0055955194 alpha0[3] 0.01179674 0.007572558 0.0001070921 0.0002460749 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.67486994 0.05906851 0.0008353549 0.002230079 alpha0[2] 0.29431694 0.09220737 0.0013040091 0.022368150 alpha0[3] 0.03081311 0.07947353 0.0011239254 0.032207017 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.68381556 0.05515651 0.0007800308 0.001594649 alpha0[2] 0.29047766 0.07725097 0.0010924937 0.014585970 alpha0[3] 0.02570678 0.06644120 0.0009396205 0.018819587 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.4055814 0.1645975 0.002327761 0.009578312 mu0[2] 0.3704790 0.3541478 0.005008407 0.012870145 mu0[3] 792.7399395 612.4829587 8.661817069 14.501616380 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.4762345 0.1905329 0.002694542 0.008846279 mu0[2] 0.4163456 0.3440717 0.004865909 0.021004101 mu0[3] 731.7724344 629.9561135 8.908924794 37.085877974 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.4239316 0.2076146 0.002936113 0.00898897 mu0[2] 0.3946041 0.3431056 0.004852246 0.02021067 mu0[3] 726.6625817 609.5766504 8.620715662 35.21569117 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.4234489 0.1379542 0.001950967 0.004992587 beta0[2] 1.0778366 0.3036333 0.004294023 0.008932143 beta0[3] 1.7550530 2.4101777 0.034085059 0.080121899 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5600416 0.1629638 0.002304656 0.006312096 beta0[2] 0.8915064 0.4688058 0.006629915 0.030067594 beta0[3] 1.5505919 2.1517665 0.030430573 0.043052176 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.6022388 0.1876224 0.002653382 0.009046912 beta0[2] 0.8917650 0.3668137 0.005187529 0.013688447 beta0[3] 1.6097398 2.2281534 0.031510848 0.054649108 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.33441629 0.4324825 0.006116226 0.02013401 tau0[2] 0.10991837 0.5250031 0.007424665 0.02131315 tau0[3] -0.01878726 0.9647650 0.013643838 0.01836852 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.887171039 0.3687892 0.005215467 0.01594312 tau0[2] -0.351037678 0.4936012 0.006980576 0.02512151 tau0[3] -0.004700982 1.0194279 0.014416888 0.02393185 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.87991142 0.3745667 0.005297173 0.01755423 tau0[2] -0.43452793 0.4840642 0.006845702 0.02368712 tau0[3] 0.01135202 0.9540683 0.013492563 0.01752229 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.297257 0.3683151 0.005208762 0.01555522 gamma0[2] 1.900244 0.4648613 0.006574131 0.01371363 gamma0[3] 1.510736 1.7422044 0.024638490 0.03357847 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 0.9770094 0.3224569 0.004560229 0.01646520 gamma0[2] 1.3503573 0.5648158 0.007987701 0.02335230 gamma0[3] 1.5276221 1.8303536 0.025885108 0.03363579 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 0.9574084 0.316166 0.004471262 0.01255827 gamma0[2] 1.2967900 0.426614 0.006033233 0.01630261 gamma0[3] 1.5931879 1.825005 0.025809465 0.04402258 **************** Running Model for K2 Simulation Stan @ 3 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): 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: 71.4647 seconds (Warm-up) 181.165 seconds (Sampling) 252.629 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. 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: 84.0357 seconds (Warm-up) 403.151 seconds (Sampling) 487.187 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: 301.369 seconds (Warm-up) 2021.6 seconds (Sampling) 2322.97 seconds (Total) **************** Convergence diagnostics for K2 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 -615.80144 10.61333 0.06128 0.46102 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -640.0 -621.9 -615.1 -608.6 -597.3 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.04 1.04 lp__ 2804.275 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 12.69819 4.57339 0.02640 0.06184 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 5.524 9.328 12.145 15.397 23.127 Potential scale reduction factors: Point est. Upper C.I. alphaN 1 1.01 alphaN 6227.72 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] 0.5448 0.24296 0.0011453 0.058188 alpha0[2] 0.2900 0.09986 0.0004708 0.008096 alpha0[3] 0.1652 0.27998 0.0013199 0.061597 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.016664 0.53569 0.65065 0.70072 0.7731 alpha0[2] 0.004537 0.25433 0.30205 0.34783 0.4544 alpha0[3] 0.002594 0.00608 0.01031 0.05775 0.7501 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.29 1.84 alpha0[3] 2.33 10.69 Multivariate psrf 1.97 alpha0[1] alpha0[2] alpha0[3] 2319.2149 278.5203 597.1767 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] -0.5428 0.3980 0.001876 0.01856 mu0[2] 0.2701 0.4349 0.002050 0.01891 mu0[3] 588.1057 627.6123 2.958593 30.40172 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.7885 -0.62988 -0.4734 -0.3343 -0.02218 mu0[2] -0.6317 -0.00784 0.3127 0.5708 1.04056 mu0[3] -0.3952 1.09488 413.0339 955.4273 2136.59631 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.31 2.32 mu0[2] 1.40 2.07 mu0[3] 1.20 1.59 Multivariate psrf 1.38 mu0[1] mu0[2] mu0[3] 2059.628 2749.733 6849.357 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] 0.6990 0.5401 0.002546 0.01530 beta0[2] 0.9691 0.5220 0.002461 0.01001 beta0[3] 1.4238 1.9417 0.009153 0.02095 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.2626 0.4473 0.5614 0.7412 2.139 beta0[2] 0.3613 0.6818 0.8820 1.1420 2.055 beta0[3] 0.1588 0.5340 0.8484 1.5386 6.398 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.40 3.41 beta0[2] 1.13 1.27 beta0[3] 1.05 1.11 Multivariate psrf 1.18 beta0[1] beta0[2] beta0[3] 1864.179 2602.255 11275.039 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] 1.4131 0.9309 0.004388 0.05302 tau0[2] -0.3135 0.5374 0.002533 0.01302 tau0[3] 0.4259 1.2092 0.005700 0.08472 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.9086 1.1087 1.7431 2.04669 2.4647 tau0[2] -1.2523 -0.6615 -0.3627 -0.01542 0.8856 tau0[3] -1.8733 -0.4645 0.3588 1.49670 2.3755 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.74 4.20 tau0[2] 1.03 1.08 tau0[3] 1.40 2.04 Multivariate psrf 1.56 tau0[1] tau0[2] tau0[3] 1861.952 2474.679 9495.331 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] 1.016 0.4615 0.002176 0.01200 gamma0[2] 1.390 0.8278 0.003902 0.01419 gamma0[3] 1.447 1.8516 0.008728 0.02052 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.3034 0.7291 0.9476 1.219 2.091 gamma0[2] 0.5481 1.0428 1.3008 1.631 2.588 gamma0[3] 0.1625 0.5846 0.9517 1.602 5.985 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.09 1.13 gamma0[2] 1.17 1.28 gamma0[3] 1.06 1.11 Multivariate psrf 1.02 gamma0[1] gamma0[2] gamma0[3] 2341.909 6279.223 8854.010 Chains of length 10000 for K2 Simulation Stan @ 3 did not converge in run 2 . Maximum Rhat value = 1.972195 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -615.97819995 8.91044794 0.08910448 0.22735039 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -616.04919364 9.22216639 0.09222166 0.26945585 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -615.3769384 13.1618204 0.1316182 1.3373764 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 12.77296361 4.71093794 0.04710938 0.09585567 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 12.87561199 4.72771541 0.04727715 0.09008455 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 12.44598011 4.25521306 0.04255213 0.13081307 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.6525694 0.07136083 0.0005826587 0.0059031173 alpha0[2] 0.3363899 0.07128616 0.0005820491 0.0056035567 alpha0[3] 0.0110407 0.01340217 0.0001094283 0.0005603334 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.67748220 0.05758057 0.0004701434 0.001235965 alpha0[2] 0.29995016 0.07957014 0.0006496875 0.008393907 alpha0[3] 0.02256764 0.06144165 0.0005016690 0.013045762 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3042637 0.2856623 0.0023324230 0.17445877 alpha0[2] 0.2337403 0.1144133 0.0009341808 0.02209254 alpha0[3] 0.4619960 0.3146805 0.0025693555 0.18432846 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.4436153 0.1898187 0.001549863 0.006617940 mu0[2] 0.4152932 0.3360061 0.002743478 0.007755385 mu0[3] 772.8330686 606.3313025 4.950674354 8.062633881 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.4566731 0.1934187 0.001579257 0.005776539 mu0[2] 0.4296984 0.3342739 0.002729335 0.011779383 mu0[3] 749.3009132 626.9857731 5.119317400 18.381638660 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.72796957 0.5917181 0.004831358 0.05498668 mu0[2] -0.03477057 0.4509837 0.003682267 0.05496194 mu0[3] 242.18322642 491.1341229 4.010093322 88.96905102 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5325043 0.1823982 0.001489275 0.01164460 beta0[2] 0.9338560 0.3267865 0.002668201 0.01415525 beta0[3] 1.6303719 2.1317562 0.017405717 0.03170827 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5767401 0.1716487 0.001401506 0.004823483 beta0[2] 0.8888083 0.3984057 0.003252969 0.011899512 beta0[3] 1.5965568 2.1649699 0.017676906 0.028212898 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.9876878 0.8285666 0.006765218 0.04413400 beta0[2] 1.0847002 0.7286582 0.005949469 0.02366161 beta0[3] 1.0443680 1.3651416 0.011146334 0.04637391 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.71442152 0.4680509 0.003821619 0.04076671 tau0[2] -0.22497322 0.5264410 0.004298373 0.03014197 tau0[3] -0.01118338 0.9997611 0.008163015 0.01282850 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.905436247 0.3562722 0.002908950 0.008704216 tau0[2] -0.404593922 0.4610164 0.003764183 0.012348575 tau0[3] -0.004750611 1.0245137 0.008365120 0.017562609 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 0.6195065 1.1361960 0.009277001 0.15350104 tau0[2] -0.3108872 0.6003984 0.004902232 0.02155522 tau0[3] 1.2935190 1.0990339 0.008973574 0.25321392 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.069036 0.3731665 0.003046891 0.02383261 gamma0[2] 1.524313 0.5014835 0.004094595 0.03641082 gamma0[3] 1.604820 2.2661896 0.018503361 0.04150893 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 0.9549033 0.3191863 0.002606145 0.008231889 gamma0[2] 1.3189583 0.4729709 0.003861791 0.011772953 gamma0[3] 1.5829681 1.9354811 0.015803137 0.027668427 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.023550 0.625569 0.005107749 0.02568275 gamma0[2] 1.327654 1.246424 0.010177011 0.01863128 gamma0[3] 1.154187 1.128898 0.009217414 0.03606149 MCMC run did not converge, proceeding anyway. Calculating model fit indexes for K2 Simulation Stan @ 3 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -529.86252 36.51976 1132.76457 36.51976 1132.76457 lppd lppd.bayes pDIC DIC pDICalt DICalt -548.12240 -1150.68045 -1205.11610 -108.87130 57.83958 2417.04007 Analaysis complete for K2 Simulation Stan @ 3 > proc.time() user system elapsed 3637.362 7.599 3649.740