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 @ 4 Removing 0 of 10 Level 2 units for length. Calculating initial values for chain 1 ; K3 Simulation Stan @ 4 number of iterations= 214 number of iterations= 108 number of iterations= 146 number of iterations= 70 number of iterations= 78 number of iterations= 49 number of iterations= 39 number of iterations= 31 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. 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. 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. 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. 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= 448 number of iterations= 181 Calculating initial values for chain 2 ; K3 Simulation Stan @ 4 number of iterations= 78 number of iterations= 36 number of iterations= 104 number of iterations= 55 number of iterations= 103 number of iterations= 101 One of the variances is going to zero; trying new starting values. number of iterations= 30 number of iterations= 30 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. 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. 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. 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. 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. 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. 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. Error in normalmixEM(subset, k = K) : Too many tries! 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. 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. 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. 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. 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. One of the variances is going to zero; trying new starting values. number of iterations= 59 number of iterations= 154 Calculating initial values for chain 3 ; K3 Simulation Stan @ 4 number of iterations= 253 One of the variances is going to zero; trying new starting values. number of iterations= 239 number of iterations= 147 One of the variances is going to zero; trying new starting values. number of iterations= 84 One of the variances is going to zero; trying new starting values. number of iterations= 34 number of iterations= 89 number of iterations= 29 number of iterations= 52 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. 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= 68 number of iterations= 162 **************** Running Model for K3 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). 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: 209.246 seconds (Warm-up) 650.556 seconds (Sampling) 859.801 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. 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: 232.007 seconds (Warm-up) 1882.76 seconds (Sampling) 2114.76 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): Location parameter is inf:0, but must be finite! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Iteration: 600 / 6000 [ 10%] (Warmup) Iteration: 1200 / 6000 [ 20%] (Sampling) Iteration: 1800 / 6000 [ 30%] (Sampling) Iteration: 2400 / 6000 [ 40%] (Sampling) Iteration: 3000 / 6000 [ 50%] (Sampling) Iteration: 3600 / 6000 [ 60%] (Sampling) Iteration: 4200 / 6000 [ 70%] (Sampling) Iteration: 4800 / 6000 [ 80%] (Sampling) Iteration: 5400 / 6000 [ 90%] (Sampling) Iteration: 6000 / 6000 [100%] (Sampling) Elapsed Time: 86.1333 seconds (Warm-up) 313.691 seconds (Sampling) 399.824 seconds (Total) **************** Convergence diagnostics for K3 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 -656.6912 12.3926 0.1012 0.3421 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -681.2 -665.0 -656.6 -648.5 -633.0 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.02 1.06 lp__ 1386.117 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 14.56162 4.99173 0.04076 0.07843 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 6.696 10.978 13.874 17.447 26.304 Potential scale reduction factors: Point est. Upper C.I. alphaN 1 1.01 alphaN 4095.706 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] 0.41842 0.06349 0.0005184 0.001350 alpha0[2] 0.37539 0.10291 0.0008402 0.009929 alpha0[3] 0.18474 0.10328 0.0008432 0.012709 alpha0[4] 0.02145 0.03667 0.0002994 0.004706 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.288572 0.377589 0.420026 0.46151 0.5378 alpha0[2] 0.136521 0.325520 0.395468 0.44583 0.5338 alpha0[3] 0.020631 0.116780 0.158286 0.22845 0.4354 alpha0[4] 0.002246 0.005033 0.008089 0.01464 0.1423 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.54 2.66 alpha0[3] 1.53 2.74 alpha0[4] 1.09 1.21 Multivariate psrf 1.4 alpha0[1] alpha0[2] alpha0[3] alpha0[4] 3376.50250 1065.52137 261.72818 59.80118 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] -0.9206 0.1679 0.001371 0.003957 mu0[2] -0.2630 0.3651 0.002981 0.008476 mu0[3] 1.0780 0.6715 0.005483 0.063587 mu0[4] 643.7146 621.8304 5.077224 32.840608 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.2036 -1.0244 -0.9450 -8.406e-01 -0.5143 mu0[2] -0.9018 -0.5669 -0.2331 3.145e-03 0.4262 mu0[3] -0.1499 0.5220 1.1289 1.622e+00 2.2012 mu0[4] 1.2653 91.7664 503.1467 1.011e+03 2142.9958 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.38 2.29 mu0[2] 2.20 4.05 mu0[3] 1.17 1.48 mu0[4] 1.01 1.03 Multivariate psrf 2.16 mu0[1] mu0[2] mu0[3] mu0[4] 3045.2422 1090.9017 270.7364 500.2750 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] 0.4837 0.3350 0.002736 0.005171 beta0[2] 0.6352 0.3362 0.002745 0.017091 beta0[3] 0.7851 0.4816 0.003933 0.017374 beta0[4] 1.4716 1.9871 0.016224 0.025954 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1699 0.2447 0.3287 0.6731 1.324 beta0[2] 0.2446 0.4217 0.5487 0.7477 1.572 beta0[3] 0.1646 0.4600 0.7111 1.0140 1.808 beta0[4] 0.1336 0.4599 0.8712 1.6847 6.617 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 2.89 9.09 beta0[2] 1.38 2.45 beta0[3] 1.02 1.03 beta0[4] 1.01 1.02 Multivariate psrf 2.29 beta0[1] beta0[2] beta0[3] beta0[4] 2337.1957 626.3671 755.7211 6511.4920 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] 1.86839 0.8051 0.006574 0.01952 tau0[2] 1.20624 1.1135 0.009092 0.10212 tau0[3] 0.24800 1.0022 0.008183 0.10461 tau0[4] 0.04256 0.9770 0.007977 0.01170 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] 0.1951 1.3242 1.91444 2.4454 3.319 tau0[2] -1.1169 0.5906 1.34406 1.9575 3.152 tau0[3] -1.2525 -0.4771 0.06723 0.8056 2.679 tau0[4] -1.8575 -0.6113 0.04020 0.6864 1.981 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.30 1.81 tau0[2] 1.77 3.06 tau0[3] 1.33 1.96 tau0[4] 1.00 1.00 Multivariate psrf 1.62 tau0[1] tau0[2] tau0[3] tau0[4] 2515.5039 817.1822 529.1381 7317.6277 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] 2.173 0.6624 0.005408 0.01445 gamma0[2] 1.926 0.7146 0.005835 0.03537 gamma0[3] 1.519 0.7276 0.005941 0.02604 gamma0[4] 1.649 2.2145 0.018081 0.03026 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.1947 1.7128 2.072 2.517 3.806 gamma0[2] 0.6294 1.4816 1.879 2.321 3.499 gamma0[3] 0.3597 1.0135 1.456 1.920 3.097 gamma0[4] 0.1412 0.5332 1.054 1.972 6.849 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.01 1.02 gamma0[2] 1.15 1.43 gamma0[3] 1.05 1.15 gamma0[4] 1.00 1.01 Multivariate psrf 1.14 gamma0[1] gamma0[2] gamma0[3] gamma0[4] 3197.5023 1290.8336 923.2392 5354.3620 Chains of length 5000 for K3 Simulation Stan @ 4 did not converge in run 1 . Maximum Rhat value = 2.294593 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -658.2408454 13.1228871 0.1855856 0.7105515 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -657.3304024 12.1505737 0.1718351 0.5650498 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -654.5022728 11.5475203 0.1633066 0.4784030 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 14.32858648 4.97246864 0.07032133 0.13885270 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 14.53678585 5.04133575 0.07129525 0.12602109 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 14.81948076 4.94975913 0.07000016 0.14212196 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.39916095 0.06958038 0.0009840151 0.003397546 alpha0[2] 0.42317835 0.06399319 0.0009050004 0.002517821 alpha0[3] 0.14680429 0.06916413 0.0009781285 0.005871121 alpha0[4] 0.03085641 0.04705374 0.0006654404 0.010737553 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.42318767 0.05930134 0.0008386476 0.001350337 alpha0[2] 0.29706878 0.11869573 0.0016786111 0.029502913 alpha0[3] 0.26249381 0.12318116 0.0017420447 0.037317700 alpha0[4] 0.01724974 0.02968237 0.0004197721 0.006881719 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.43291652 0.05598547 0.0007917542 0.001745733 alpha0[2] 0.40591179 0.06510029 0.0009206571 0.003241440 alpha0[3] 0.14492313 0.05451875 0.0007710116 0.005150280 alpha0[4] 0.01624856 0.02839761 0.0004016028 0.006055224 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.8146866 0.2146131 0.003035088 0.01108641 mu0[2] -0.6439819 0.1887754 0.002669687 0.01234760 mu0[3] 1.1100186 0.6452609 0.009125367 0.05736043 mu0[4] 568.3491979 628.0527130 8.882006646 75.82389369 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.9712906 0.1036127 0.001465305 0.002479332 mu0[2] -0.0319932 0.3060426 0.004328096 0.020386672 mu0[3] 0.7714477 0.6765265 0.009567529 0.175250906 mu0[4] 667.8037426 626.3100357 8.857361467 36.635884558 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.9758183 0.1046855 0.001480476 0.003443807 mu0[2] -0.1131341 0.2227110 0.003149609 0.008860311 mu0[3] 1.3525446 0.5554728 0.007855572 0.048851970 mu0[4] 694.9907956 603.7946431 8.538945732 51.138039425 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.8763807 0.2952356 0.004175261 0.01488722 beta0[2] 0.4708412 0.1752055 0.002477780 0.01156038 beta0[3] 0.7956581 0.5653745 0.007995602 0.03159226 beta0[4] 1.3293931 1.6944562 0.023963229 0.05046636 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.2885130 0.09710129 0.001373220 0.002944769 beta0[2] 0.8320353 0.44461591 0.006287818 0.048523246 beta0[3] 0.8109071 0.40292508 0.005698221 0.027330961 beta0[4] 1.5407154 2.15844898 0.030525078 0.037139734 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.2862820 0.09421533 0.001332406 0.003219062 beta0[2] 0.6026936 0.20972319 0.002965934 0.011859698 beta0[3] 0.7488120 0.46034045 0.006510197 0.031169938 beta0[4] 1.5446684 2.07033610 0.029278974 0.046218332 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.35621513 0.7526134 0.01064356 0.04928702 tau0[2] 2.05290715 0.7810007 0.01104502 0.05021751 tau0[3] -0.07402335 0.7611649 0.01076450 0.06132685 tau0[4] 0.03431064 0.9591473 0.01356439 0.02169506 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 2.18002854 0.6865654 0.009709501 0.01740534 tau0[2] 0.31463497 1.0841083 0.015331607 0.30098301 tau0[3] 0.86950282 1.1741263 0.016604653 0.30562342 tau0[4] 0.05244959 0.9959761 0.014085228 0.01737169 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 2.06891338 0.7123815 0.010074595 0.02643225 tau0[2] 1.25118176 0.6485796 0.009172301 0.02735058 tau0[3] -0.05147638 0.6897477 0.009754505 0.03633619 tau0[4] 0.04090619 0.9756449 0.013797702 0.02141322 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.218113 0.6741313 0.009533656 0.03415549 gamma0[2] 2.110361 0.6878338 0.009727439 0.02857854 gamma0[3] 1.332090 0.7058916 0.009982815 0.03338700 gamma0[4] 1.628965 2.1041685 0.029757436 0.04988170 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.106670 0.6437458 0.009103941 0.01468432 gamma0[2] 1.595332 0.7418823 0.010491800 0.09958139 gamma0[3] 1.696719 0.7165158 0.010133064 0.05851902 gamma0[4] 1.617763 2.1159017 0.029923369 0.05007754 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.193888 0.6638409 0.009388129 0.02230104 gamma0[2] 2.073106 0.5863079 0.008291646 0.02289055 gamma0[3] 1.528850 0.7143399 0.010102291 0.03954508 gamma0[4] 1.699180 2.4094602 0.034074912 0.05695523 **************** Running Model for K3 Simulation Stan @ 4 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. 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): 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: 1824.55 seconds (Warm-up) 9561.24 seconds (Sampling) 11385.8 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. 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: 203.827 seconds (Warm-up) 866.198 seconds (Sampling) 1070.03 seconds (Total) SAMPLING FOR MODEL 'hierModel1pmu' NOW (CHAIN 3). Iteration: 1 / 12000 [ 0%] (Warmup) Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Location parameter is inf:0, but must be finite! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Iteration: 1200 / 12000 [ 10%] (Warmup) Iteration: 2400 / 12000 [ 20%] (Sampling) Iteration: 3600 / 12000 [ 30%] (Sampling) Iteration: 4800 / 12000 [ 40%] (Sampling) Iteration: 6000 / 12000 [ 50%] (Sampling) Iteration: 7200 / 12000 [ 60%] (Sampling) Iteration: 8400 / 12000 [ 70%] (Sampling) Iteration: 9600 / 12000 [ 80%] (Sampling) Iteration: 10800 / 12000 [ 90%] (Sampling) Iteration: 12000 / 12000 [100%] (Sampling) Elapsed Time: 475.908 seconds (Warm-up) 3837.36 seconds (Sampling) 4313.27 seconds (Total) **************** Convergence diagnostics for K3 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 -659.83877 14.91689 0.08612 0.82229 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -691.2 -669.4 -659.5 -649.9 -631.2 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.15 1.44 lp__ 491.325 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 14.26934 4.69976 0.02713 0.11729 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 6.705 10.920 13.729 16.988 25.168 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.01 1.03 alphaN 5927.439 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] 0.32852 0.17921 0.0008448 0.035151 alpha0[2] 0.35727 0.12881 0.0006072 0.015054 alpha0[3] 0.25844 0.14309 0.0006746 0.018377 alpha0[4] 0.05577 0.06666 0.0003142 0.005589 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.0005164 0.305879 0.39774 0.4495 0.5315 alpha0[2] 0.0135515 0.304968 0.38989 0.4441 0.5346 alpha0[3] 0.0436997 0.134264 0.21898 0.3917 0.5114 alpha0[4] 0.0024550 0.006207 0.01311 0.1048 0.2181 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.39 2.25 alpha0[3] 1.29 1.82 alpha0[4] 1.48 2.40 Multivariate psrf 1.95 alpha0[1] alpha0[2] alpha0[3] alpha0[4] 3287.43170 238.44675 59.51031 156.36005 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] -4.3929 9.476 0.044672 1.28146 mu0[2] -0.4301 0.412 0.001942 0.01736 mu0[3] 0.7261 0.781 0.003682 0.07049 mu0[4] 449.0204 597.409 2.816211 34.43960 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -38.2964 -1.15324 -0.9772 -0.8461 -0.5103 mu0[2] -1.0663 -0.80038 -0.4240 -0.1240 0.3689 mu0[3] -0.4011 0.02334 0.5975 1.4250 2.1341 mu0[4] 0.7096 1.83433 136.8387 760.7281 1990.4196 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.62 6.69 mu0[2] 2.15 3.86 mu0[3] 1.26 1.71 mu0[4] 1.14 1.42 Multivariate psrf 1.89 mu0[1] mu0[2] mu0[3] mu0[4] 3299.4508 321.2835 139.0808 543.9272 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] 0.7518 1.1347 0.005349 0.04449 beta0[2] 0.6138 0.4917 0.002318 0.01509 beta0[3] 0.7260 0.4139 0.001951 0.01106 beta0[4] 1.2482 1.8399 0.008673 0.02764 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1661 0.2708 0.4713 0.7697 3.682 beta0[2] 0.1784 0.3357 0.5035 0.7240 1.751 beta0[3] 0.1901 0.4609 0.6443 0.9013 1.679 beta0[4] 0.1382 0.4264 0.7423 1.3465 5.775 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.48 4.90 beta0[2] 1.36 2.64 beta0[3] 1.02 1.05 beta0[4] 1.08 1.18 Multivariate psrf 1.36 beta0[1] beta0[2] beta0[3] beta0[4] 2199.2039 662.9879 1564.7939 4498.3879 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] 1.61912 1.1709 0.005520 0.03170 tau0[2] 1.15611 1.1227 0.005292 0.07294 tau0[3] 0.59518 1.0324 0.004867 0.09507 tau0[4] -0.01124 0.9074 0.004277 0.01862 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -1.094 0.9361 1.84335 2.465 3.423 tau0[2] -1.139 0.4655 1.27730 1.949 3.096 tau0[3] -1.148 -0.2248 0.54943 1.365 2.654 tau0[4] -1.717 -0.6252 -0.06685 0.579 1.882 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.92 3.36 tau0[2] 1.56 2.46 tau0[3] 1.06 1.20 tau0[4] 1.02 1.05 Multivariate psrf 1.88 tau0[1] tau0[2] tau0[3] tau0[4] 2752.6414 168.3845 118.8046 7724.5052 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] 2.044 1.0528 0.004963 0.03444 gamma0[2] 1.916 0.7608 0.003586 0.03372 gamma0[3] 1.681 0.6987 0.003294 0.02464 gamma0[4] 1.585 1.7951 0.008462 0.01929 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.3691 1.4923 1.925 2.435 4.367 gamma0[2] 0.4938 1.4700 1.885 2.332 3.526 gamma0[3] 0.4567 1.2153 1.667 2.078 3.146 gamma0[4] 0.1654 0.6611 1.186 1.907 5.688 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.14 1.18 gamma0[2] 1.10 1.29 gamma0[3] 1.02 1.07 gamma0[4] 1.03 1.04 Multivariate psrf 1.07 gamma0[1] gamma0[2] gamma0[3] gamma0[4] 4809.4304 478.4122 1521.6437 11396.7768 Chains of length 10000 for K3 Simulation Stan @ 4 did not converge in run 2 . Maximum Rhat value = 1.948589 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -667.0374292 14.0442254 0.1404423 2.0706558 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -656.2285898 12.8287598 0.1282876 0.8270632 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -656.2502852 15.1006215 0.1510062 1.0553821 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 13.74451131 4.35421635 0.04354216 0.32779634 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 14.72545291 4.75370278 0.04753703 0.09308194 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 14.33806014 4.92164736 0.04921647 0.08779087 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.1395139 0.18844523 0.0015386488 0.105421709 alpha0[2] 0.4190957 0.06197226 0.0005060014 0.004516952 alpha0[3] 0.3372644 0.14958605 0.0012213650 0.041090293 alpha0[4] 0.1041260 0.07047149 0.0005753973 0.013690571 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.42000269 0.06084428 0.0004967915 0.002252596 alpha0[2] 0.37779744 0.10554290 0.0008617541 0.017510134 alpha0[3] 0.18247059 0.10212064 0.0008338116 0.018769203 alpha0[4] 0.01972928 0.03340800 0.0002727752 0.003579562 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.42605438 0.05938476 0.0004848746 0.001174921 alpha0[2] 0.27491229 0.15417182 0.0012588077 0.041383156 alpha0[3] 0.25557538 0.12896682 0.0010530097 0.031601681 alpha0[4] 0.04345795 0.05881667 0.0004802361 0.008994678 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -11.3553285 14.0227293 0.114495105 3.84437025 mu0[2] -0.8533093 0.2001869 0.001634519 0.03481637 mu0[3] 0.3079265 0.7039559 0.005747776 0.14785666 mu0[4] 190.4818455 450.4027602 3.677523137 71.58486065 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.8460056 0.1967765 0.001606673 0.01205393 mu0[2] -0.2776459 0.2996131 0.002446331 0.03118218 mu0[3] 1.1276583 0.6623555 0.005408110 0.07193004 mu0[4] 656.7294478 625.8105665 5.109721879 30.32753580 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.9773514 0.1087056 0.0008875779 0.001978216 mu0[2] -0.1592872 0.3219300 0.0026285471 0.023000828 mu0[3] 0.7427630 0.7478209 0.0061059322 0.132990034 mu0[4] 499.8500482 603.0427519 4.9238234505 68.048679351 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 1.4284266 1.7547094 0.014327142 0.12833299 beta0[2] 0.3357924 0.1528899 0.001248341 0.01894672 beta0[3] 0.6661010 0.3713244 0.003031851 0.02157872 beta0[4] 0.8498881 1.0739668 0.008768902 0.05108614 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5373874 0.2367682 0.001933204 0.03660359 beta0[2] 0.6596115 0.3239393 0.002644953 0.02387246 beta0[3] 0.7628627 0.4504468 0.003677882 0.01549586 beta0[4] 1.5393639 2.3142827 0.018896039 0.04780623 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.2894867 0.09915345 0.0008095845 0.002233724 beta0[2] 0.8459675 0.68090202 0.0055595418 0.033475102 beta0[3] 0.7491786 0.40939169 0.0033426692 0.019889218 beta0[4] 1.3553848 1.84161035 0.0150366855 0.044496565 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 0.4993645 1.0734837 0.008764958 0.08914276 tau0[2] 2.0525074 0.7500426 0.006124072 0.09037789 tau0[3] 0.8201988 0.9285716 0.007581755 0.18244707 tau0[4] -0.1382765 0.7667577 0.006260551 0.05095287 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 2.24166792 0.7565116 0.006176891 0.02889174 tau0[2] 0.87856504 0.9157644 0.007477185 0.12644974 tau0[3] 0.26409981 0.9944823 0.008119914 0.13676579 tau0[4] 0.03183455 0.9807325 0.008007647 0.01428578 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 2.11631547 0.7069367 0.005772114 0.01612514 tau0[2] 0.53725869 1.0566751 0.008627716 0.15402799 tau0[3] 0.70124105 1.0838430 0.008849541 0.17131165 tau0[4] 0.07271235 0.9462507 0.007726105 0.01793023 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.967329 1.5564648 0.012708482 0.10055183 gamma0[2] 2.148525 0.6357462 0.005190846 0.06067975 gamma0[3] 1.738853 0.6181002 0.005046767 0.05908356 gamma0[4] 1.468112 1.3177571 0.010759441 0.04198019 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.028831 0.6741999 0.005504819 0.02101984 gamma0[2] 1.941833 0.6886536 0.005622833 0.05115345 gamma0[3] 1.548702 0.7162148 0.005847870 0.02214727 gamma0[4] 1.663869 2.0586038 0.016808430 0.03144290 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.134483 0.6585005 0.005376634 0.01106598 gamma0[2] 1.658596 0.8584900 0.007009542 0.06271130 gamma0[3] 1.756575 0.7370439 0.006017938 0.03850447 gamma0[4] 1.622136 1.9162985 0.015646512 0.02448562 MCMC run did not converge, proceeding anyway. Calculating model fit indexes for K3 Simulation Stan @ 4 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -533.48598 60.58716 1188.14628 60.58716 1188.14628 lppd lppd.bayes pDIC DIC pDICalt DICalt -563.7796 -1663.2336 -2198.9080 -1071.3489 126.2947 3579.0566 Analaysis complete for K3 Simulation Stan @ 4 > proc.time() user system elapsed 20250.534 34.259 20293.792