Loading required package: Rcpp Loading required package: inline Attaching package: ‘inline’ The following object is masked from ‘package:Rcpp’: registerPlugin rstan (Version 2.2.0, packaged: 2014-02-14 04:29:17 UTC, GitRev: 52d7b230aaa0) Loading required package: lattice Attaching package: ‘coda’ The following object is masked from ‘package:rstan’: traceplot Loading required package: boot Attaching package: ‘boot’ The following object is masked from ‘package:lattice’: melanoma Loading required package: MASS Loading required package: segmented mixtools package, version 1.0.1, Released January 2014 This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772. **************** Cleaning data for K4 Simulation Stan @ 4 Removing 0 of 10 Level 2 units for length. Calculating initial values for chain 1 ; K4 Simulation Stan @ 4 WARNING! NOT CONVERGENT! number of iterations= 1000 number of iterations= 59 number of iterations= 18 number of iterations= 46 One of the variances is going to zero; trying new starting values. number of iterations= 61 number of iterations= 417 number of iterations= 55 number of iterations= 486 number of iterations= 62 number of iterations= 282 Calculating initial values for chain 2 ; K4 Simulation Stan @ 4 number of iterations= 143 number of iterations= 46 number of iterations= 63 One of the variances is going to zero; trying new starting values. number of iterations= 71 One of the variances is going to zero; trying new starting values. number of iterations= 63 number of iterations= 44 number of iterations= 113 number of iterations= 149 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= 104 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= 43 Calculating initial values for chain 3 ; K4 Simulation Stan @ 4 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= 82 number of iterations= 40 number of iterations= 14 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. number of iterations= 242 One of the variances is going to zero; trying new starting values. number of iterations= 62 number of iterations= 215 number of iterations= 331 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= 21 number of iterations= 26 **************** Running Model for K4 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): 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: 694.672 seconds (Warm-up) 4351.88 seconds (Sampling) 5046.55 seconds (Total) SAMPLING FOR MODEL 'hierModel1pmu' NOW (CHAIN 2). Iteration: 1 / 6000 [ 0%] (Warmup) Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. 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: 486.313 seconds (Warm-up) 1841.88 seconds (Sampling) 2328.2 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 / 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: 863.101 seconds (Warm-up) 1778.86 seconds (Sampling) 2641.96 seconds (Total) **************** Convergence diagnostics for K4 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 -603.4829 15.4341 0.1260 0.9249 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -633.4 -614.0 -604.2 -592.5 -573.7 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.16 1.47 lp__ 452.2571 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 12.98457 4.54829 0.03714 0.27151 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 5.629 9.431 12.389 17.230 22.224 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.05 1.17 alphaN 1518.327 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] 0.2562 0.07404 0.0006045 0.004216 alpha0[2] 0.3621 0.10832 0.0008844 0.007223 alpha0[3] 0.1766 0.12294 0.0010038 0.011776 alpha0[4] 0.2050 0.11867 0.0009690 0.005030 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.09882 0.20638 0.2579 0.3259 0.3832 alpha0[2] 0.12392 0.29010 0.3708 0.4584 0.5296 alpha0[3] 0.01331 0.07779 0.1634 0.2618 0.4402 alpha0[4] 0.01035 0.11866 0.1822 0.2876 0.4569 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.10 1.30 alpha0[3] 1.26 1.81 alpha0[4] 2.20 3.96 Multivariate psrf 1.89 alpha0[1] alpha0[2] alpha0[3] alpha0[4] 240.8491 359.6417 205.3532 428.7757 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] -0.856906 0.2773 0.002264 0.019318 mu0[2] -0.324938 0.2142 0.001749 0.007797 mu0[3] -0.002492 0.2831 0.002311 0.018312 mu0[4] 38.384797 210.6373 1.719846 39.051859 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.3304 -1.0633 -0.8997 -0.6543 -0.40858 mu0[2] -0.7929 -0.4693 -0.2719 -0.1966 0.03933 mu0[3] -0.4416 -0.1893 -0.0560 0.1305 0.70362 mu0[4] -0.1026 0.1704 0.3312 0.7512 637.13752 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.80 3.00 mu0[2] 1.54 2.36 mu0[3] 1.28 1.79 mu0[4] 1.35 3.21 Multivariate psrf 1.68 mu0[1] mu0[2] mu0[3] mu0[4] 413.7450 765.3558 748.0670 561.4112 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] 0.4945 0.2607 0.002129 0.015927 beta0[2] 0.4311 0.2302 0.001879 0.009613 beta0[3] 0.8678 0.5202 0.004248 0.022669 beta0[4] 0.8718 0.7108 0.005804 0.015801 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.19452 0.3384 0.4653 0.5642 1.154 beta0[2] 0.08619 0.2974 0.3805 0.5452 0.980 beta0[3] 0.24608 0.4304 0.7813 1.1550 2.094 beta0[4] 0.14155 0.3961 0.8322 1.2165 2.073 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.20 1.66 beta0[2] 1.33 1.89 beta0[3] 1.88 3.20 beta0[4] 1.47 3.92 Multivariate psrf 1.81 beta0[1] beta0[2] beta0[3] beta0[4] 219.913 682.335 1027.128 1459.379 Iterations = 1:5000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 5000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE tau0[1] 2.0504 1.1105 0.009067 0.05510 tau0[2] 0.8923 0.8409 0.006866 0.02621 tau0[3] 0.8591 1.1053 0.009025 0.10131 tau0[4] 1.3042 0.9482 0.007742 0.04632 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.1870 1.43625 1.9060 2.758 4.365 tau0[2] -0.7614 0.38917 0.8669 1.346 2.751 tau0[3] -0.8281 0.04515 0.9201 1.644 2.945 tau0[4] -0.7373 0.68706 1.3694 2.093 2.927 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.13 1.37 tau0[2] 1.27 1.74 tau0[3] 1.29 1.80 tau0[4] 1.24 1.69 Multivariate psrf 1.36 tau0[1] tau0[2] tau0[3] tau0[4] 755.3812 1738.0484 353.7135 1377.1758 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] 3.427 1.350 0.011019 0.04593 gamma0[2] 2.872 1.022 0.008342 0.03227 gamma0[3] 3.646 2.823 0.023046 0.96973 gamma0[4] 2.447 1.240 0.010127 0.06321 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.1386 2.429 3.285 4.655 6.033 gamma0[2] 1.3755 2.082 2.696 3.462 5.281 gamma0[3] 0.4380 1.846 2.748 4.047 10.132 gamma0[4] 0.3743 1.449 2.378 3.194 5.169 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.43 2.14 gamma0[2] 1.56 2.41 gamma0[3] 1.43 3.25 gamma0[4] 1.37 2.12 Multivariate psrf 1.61 gamma0[1] gamma0[2] gamma0[3] gamma0[4] 792.7695 1046.1925 541.9472 1238.0119 Chains of length 5000 for K4 Simulation Stan @ 4 did not converge in run 1 . Maximum Rhat value = 1.887192 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -595.9830278 13.9307700 0.1970108 0.7641239 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -607.6949289 12.3881821 0.1751954 1.7060322 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -606.7708325 16.8041903 0.2376471 2.0505337 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 12.90523714 4.58493990 0.06484084 0.15267543 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 14.22603559 4.40229027 0.06225779 0.77977951 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 11.82243156 4.33227061 0.06126756 0.17917626 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2047642 0.07232374 0.001022812 0.006507499 alpha0[2] 0.3222934 0.09253181 0.001308597 0.006869185 alpha0[3] 0.1490416 0.07279921 0.001029536 0.006361231 alpha0[4] 0.3239008 0.09231931 0.001305592 0.006895175 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2974160 0.05151827 0.0007285783 0.007933943 alpha0[2] 0.3921226 0.12119264 0.0017139227 0.018810788 alpha0[3] 0.1341388 0.14533699 0.0020553754 0.031493831 alpha0[4] 0.1763227 0.05683287 0.0008037382 0.003975750 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2664519 0.06412237 0.0009068273 0.007396036 alpha0[2] 0.3719990 0.09679523 0.0013688913 0.008279544 alpha0[3] 0.2466332 0.10704223 0.0015138057 0.014691286 alpha0[4] 0.1149159 0.08615855 0.0012184660 0.012819913 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.94657011 0.2393284 0.003384614 0.018387227 mu0[2] -0.48269912 0.2001232 0.002830169 0.009265745 mu0[3] -0.09109109 0.2257280 0.003192275 0.009141483 mu0[4] 0.14858398 0.1987895 0.002811308 0.010387650 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.60398235 0.2166234 0.003063518 0.05383548 mu0[2] -0.30239752 0.1676676 0.002371177 0.01849141 mu0[3] -0.08420266 0.2248676 0.003180108 0.02260144 mu0[4] 0.49878954 0.3819498 0.005401586 0.02799711 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -1.0201641 0.1669477 0.002360997 0.01105305 mu0[2] -0.1897185 0.1607709 0.002273644 0.01092245 mu0[3] 0.1678164 0.3088106 0.004367241 0.04922917 mu0[4] 114.5070167 352.7436476 4.988548505 117.15557426 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5587584 0.3247115 0.004592114 0.03158550 beta0[2] 0.5457563 0.2199659 0.003110787 0.01016970 beta0[3] 1.3378488 0.4657360 0.006586501 0.01561798 beta0[4] 0.3679485 0.2020080 0.002856825 0.01151936 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5615171 0.2215372 0.003133010 0.02971493 beta0[2] 0.4636138 0.2006160 0.002837139 0.02132057 beta0[3] 0.5056164 0.3143053 0.004444948 0.03164400 beta0[4] 1.2251804 0.3172085 0.004486005 0.01238799 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3632820 0.1536251 0.002172587 0.02006104 beta0[2] 0.2840451 0.1856608 0.002625641 0.01654262 beta0[3] 0.7600268 0.3640222 0.005148051 0.05813360 beta0[4] 1.0221982 0.9864011 0.013949818 0.04428270 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.697418 1.2147270 0.01717883 0.14466969 tau0[2] 1.389110 0.8852928 0.01251993 0.04711325 tau0[3] 0.979543 0.9800020 0.01385932 0.07531982 tau0[4] 1.114081 0.8274156 0.01170142 0.02627679 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.9586447 0.8723685 0.012337154 0.04145797 tau0[2] 0.6452372 0.5935742 0.008394407 0.05901858 tau0[3] 0.2027284 1.0889537 0.015400130 0.28649703 tau0[4] 1.8482093 0.7307483 0.010334341 0.04050412 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 2.4950901 1.0642089 0.01505019 0.06839536 tau0[2] 0.6425833 0.7845057 0.01109459 0.02189508 tau0[3] 1.3951209 0.8870083 0.01254419 0.06803546 tau0[4] 0.9501643 1.0106035 0.01429209 0.13029124 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.359944 1.3376952 0.01891787 0.09825060 gamma0[2] 2.538415 0.8581393 0.01213592 0.05219829 gamma0[3] 3.246294 1.1332974 0.01602725 0.05289405 gamma0[4] 3.305920 0.8970535 0.01268625 0.02752193 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 4.326306 1.0371310 0.01466725 0.08313532 gamma0[2] 2.382404 0.7329059 0.01036485 0.07366003 gamma0[3] 5.315481 4.0684270 0.05753625 2.90550005 gamma0[4] 1.929825 0.8644675 0.01222542 0.08364902 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.595598 1.0462384 0.01479605 0.04922363 gamma0[2] 3.695615 0.9103623 0.01287447 0.03499014 gamma0[3] 2.377253 1.2284338 0.01737268 0.13655884 gamma0[4] 2.104499 1.3932577 0.01970364 0.16793163 **************** Running Model for K4 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. 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: 1047.68 seconds (Warm-up) 7672.46 seconds (Sampling) 8720.14 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 / 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: 689.685 seconds (Warm-up) 2493.02 seconds (Sampling) 3182.7 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. 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. 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: 983.054 seconds (Warm-up) 4683.3 seconds (Sampling) 5666.36 seconds (Total) **************** Convergence diagnostics for K4 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 -598.86914 15.17475 0.08761 0.62498 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -632.7 -607.5 -598.0 -588.6 -571.9 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.05 1.14 lp__ 938.9734 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 12.78810 4.33565 0.02503 0.07673 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 5.885 9.619 12.390 15.156 22.766 Potential scale reduction factors: Point est. Upper C.I. alphaN 1 1 alphaN 3630.996 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] 0.2569 0.06989 0.0003295 0.003212 alpha0[2] 0.3006 0.12002 0.0005658 0.006879 alpha0[3] 0.2627 0.13477 0.0006353 0.009714 alpha0[4] 0.1798 0.11522 0.0005432 0.008069 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.12730 0.20851 0.2523 0.3062 0.4018 alpha0[2] 0.06761 0.21331 0.3000 0.3878 0.5144 alpha0[3] 0.01453 0.15622 0.2792 0.3686 0.4900 alpha0[4] 0.02038 0.09762 0.1592 0.2275 0.4772 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.01 1.04 alpha0[3] 1.17 1.52 alpha0[4] 1.44 2.47 Multivariate psrf 1.31 alpha0[1] alpha0[2] alpha0[3] alpha0[4] 623.7334 339.7328 252.0688 394.4707 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] -0.90512 0.2501 0.001179 0.01962 mu0[2] -0.38177 0.2139 0.001008 0.00892 mu0[3] -0.01386 0.2335 0.001101 0.01002 mu0[4] 13.33380 122.8932 0.579324 14.82977 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.3015 -1.0782 -0.95581 -0.74467 -0.408869 mu0[2] -0.8137 -0.5393 -0.36940 -0.21662 0.001941 mu0[3] -0.4288 -0.1688 -0.02904 0.09828 0.529781 mu0[4] -0.1114 0.2916 0.62237 1.04466 2.507862 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.17 1.53 mu0[2] 1.05 1.16 mu0[3] 1.06 1.17 mu0[4] 1.31 2.11 Multivariate psrf 1.21 mu0[1] mu0[2] mu0[3] mu0[4] 489.2258 845.6269 751.8916 366.7453 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] 0.4603 0.2282 0.001076 0.00856 beta0[2] 0.5202 0.3134 0.001477 0.01476 beta0[3] 0.6207 0.5004 0.002359 0.02339 beta0[4] 0.9603 0.5638 0.002658 0.01727 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1811 0.3053 0.4211 0.5559 1.010 beta0[2] 0.1208 0.3510 0.4539 0.6280 1.285 beta0[3] 0.1241 0.2793 0.4575 0.8355 1.837 beta0[4] 0.2034 0.6459 0.9128 1.2084 2.053 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.26 1.75 beta0[2] 1.16 1.47 beta0[3] 1.56 2.90 beta0[4] 1.10 1.29 Multivariate psrf 1.55 beta0[1] beta0[2] beta0[3] beta0[4] 736.5245 567.4154 334.6630 2498.8333 Iterations = 1:15000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 15000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE tau0[1] 2.0792 1.1048 0.005208 0.02636 tau0[2] 1.0814 0.8799 0.004148 0.02652 tau0[3] 0.8808 0.9565 0.004509 0.04458 tau0[4] 1.1493 0.9656 0.004552 0.04424 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.05898 1.3928 2.0152 2.777 4.388 tau0[2] -0.61259 0.5124 1.0342 1.635 2.911 tau0[3] -0.81986 0.2634 0.8791 1.534 2.757 tau0[4] -0.72963 0.4788 1.1514 1.856 3.034 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.07 1.22 tau0[2] 1.04 1.13 tau0[3] 1.05 1.15 tau0[4] 1.02 1.05 Multivariate psrf 1.08 tau0[1] tau0[2] tau0[3] tau0[4] 1805.2252 1081.9637 764.7044 616.3971 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] 3.223 1.281 0.006038 0.04752 gamma0[2] 2.716 1.135 0.005351 0.05794 gamma0[3] 3.337 1.916 0.009034 0.30745 gamma0[4] 2.545 1.296 0.006111 0.06460 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.1245 2.329 3.053 4.020 5.990 gamma0[2] 0.8143 2.006 2.601 3.370 5.240 gamma0[3] 0.5860 2.282 3.023 3.912 9.807 gamma0[4] 0.4500 1.577 2.391 3.263 5.337 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.12 1.37 gamma0[2] 1.05 1.18 gamma0[3] 1.21 1.67 gamma0[4] 1.01 1.03 Multivariate psrf 1.15 gamma0[1] gamma0[2] gamma0[3] gamma0[4] 2047.0201 419.4391 279.4477 435.9003 Chains of length 10000 for K4 Simulation Stan @ 4 did not converge in run 2 . Maximum Rhat value = 1.551392 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -602.689881 17.252496 0.172525 1.597904 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -597.2685680 13.2786838 0.1327868 0.7681825 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -596.6489760 13.9571462 0.1395715 0.6099543 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 12.72728886 4.47021792 0.04470218 0.11529542 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 12.75882392 4.11481280 0.04114813 0.16324519 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 12.87817507 4.41249487 0.04412495 0.11422065 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2582272 0.08259812 0.0006744108 0.007693535 alpha0[2] 0.2852243 0.12651672 0.0010330047 0.011908922 alpha0[3] 0.1989140 0.12013184 0.0009808724 0.013139078 alpha0[4] 0.2576344 0.14222898 0.0011612947 0.023002467 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2595049 0.06078985 0.0004963471 0.004626116 alpha0[2] 0.3128040 0.12057873 0.0009845212 0.014330748 alpha0[3] 0.2728971 0.14834896 0.0012112642 0.024348295 alpha0[4] 0.1547939 0.06694405 0.0005465959 0.005340882 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2528662 0.06411897 0.0005235292 0.003499039 alpha0[2] 0.3038181 0.11080272 0.0009047004 0.008869191 alpha0[3] 0.3164168 0.10486235 0.0008561975 0.009149630 alpha0[4] 0.1268989 0.07509109 0.0006131162 0.005321318 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.79958105 0.2522985 0.002060009 0.02003253 mu0[2] -0.44148215 0.2106985 0.001720346 0.00879174 mu0[3] -0.06899034 0.2596870 0.002120335 0.01243859 mu0[4] 0.44149855 0.6637080 0.005419153 0.05287131 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.8978062 0.2663908 0.002175072 0.05449504 mu0[2] -0.3636470 0.1903543 0.001554236 0.01736562 mu0[3] -0.0152131 0.1831658 0.001495543 0.01230615 mu0[4] 0.7838886 0.5251927 0.004288180 0.03843062 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -1.01796743 0.1704578 0.001391782 0.009733137 mu0[2] -0.34017593 0.2257396 0.001843156 0.018362791 mu0[3] 0.04262247 0.2374550 0.001938812 0.024428752 mu0[4] 38.77602000 210.5669978 1.719272337 44.489272540 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5883599 0.2533361 0.002068481 0.01237172 beta0[2] 0.6438302 0.4037815 0.003296862 0.03600452 beta0[3] 1.0056453 0.5949020 0.004857355 0.04964292 beta0[4] 0.7699223 0.5404064 0.004412400 0.04682967 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.4269566 0.1972599 0.001610620 0.01955975 beta0[2] 0.4588461 0.2119677 0.001730709 0.01185289 beta0[3] 0.3858924 0.2527092 0.002063362 0.02157595 beta0[4] 1.0965138 0.3929647 0.003208544 0.01485786 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3654575 0.1633492 0.001333741 0.01112718 beta0[2] 0.4577827 0.2523422 0.002060366 0.02286139 beta0[3] 0.4704415 0.3278067 0.002676531 0.04466174 beta0[4] 1.0143382 0.6704709 0.005474372 0.01642564 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.7148189 1.0924913 0.008920154 0.05542634 tau0[2] 1.3025155 0.9330357 0.007618204 0.04486320 tau0[3] 0.8369255 0.9310341 0.007601862 0.05267304 tau0[4] 1.1178820 0.9178144 0.007493923 0.05425348 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 2.1747518 1.0007065 0.008170734 0.03987721 tau0[2] 0.9572594 0.7606360 0.006210567 0.04812336 tau0[3] 0.6697186 0.9758223 0.007967556 0.11384321 tau0[4] 1.2988028 1.0020028 0.008181318 0.10403959 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 2.347882 1.1193551 0.009139497 0.03990145 tau0[2] 0.984444 0.8944688 0.007303307 0.04474550 tau0[3] 1.135806 0.9023383 0.007367561 0.04636222 tau0[4] 1.031289 0.9558414 0.007804413 0.06202953 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.704517 1.255476 0.010250917 0.05913502 gamma0[2] 2.621015 1.117786 0.009126688 0.08179787 gamma0[3] 2.739146 1.242644 0.010146150 0.09866014 gamma0[4] 2.669540 1.188385 0.009703123 0.09792199 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.244965 1.284162 0.01048514 0.1265645 gamma0[2] 2.472072 1.046753 0.00854670 0.1232677 gamma0[3] 4.072485 2.663904 0.02175069 0.9099238 gamma0[4] 2.575804 1.376066 0.01123553 0.1378350 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.719856 1.100842 0.008988337 0.02848701 gamma0[2] 3.054776 1.156377 0.009441780 0.09125108 gamma0[3] 3.198615 1.208352 0.009866155 0.11405763 gamma0[4] 2.389994 1.302028 0.010631012 0.09472161 MCMC run did not converge, proceeding anyway. Calculating model fit indexes for K4 Simulation Stan @ 4 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -462.20639 97.17959 1118.77196 97.17959 1118.77196 lppd lppd.bayes pDIC DIC pDICalt DICalt -510.7962 -1417.8550 -1814.1177 -792.5253 191.1029 3217.9160 Analaysis complete for K4 Simulation Stan @ 4 > proc.time() user system elapsed 27683.441 50.221 27736.722