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 unordered @ 4 Removing 0 of 10 Level 2 units for length. Calculating initial values for chain 1 ; K2 Simulation Stan unordered @ 4 WARNING! NOT CONVERGENT! number of iterations= 1000 number of iterations= 234 number of iterations= 57 number of iterations= 52 One of the variances is going to zero; trying new starting values. number of iterations= 61 number of iterations= 167 number of iterations= 123 number of iterations= 297 number of iterations= 149 number of iterations= 165 Calculating initial values for chain 2 ; K2 Simulation Stan unordered @ 4 number of iterations= 175 number of iterations= 27 One of the variances is going to zero; trying new starting values. number of iterations= 35 number of iterations= 85 number of iterations= 34 number of iterations= 26 number of iterations= 119 number of iterations= 265 number of iterations= 69 number of iterations= 150 Calculating initial values for chain 3 ; K2 Simulation Stan unordered @ 4 number of iterations= 88 number of iterations= 72 number of iterations= 226 number of iterations= 90 One of the variances is going to zero; trying new starting values. number of iterations= 65 number of iterations= 95 number of iterations= 122 number of iterations= 361 number of iterations= 141 WARNING! NOT CONVERGENT! number of iterations= 1000 **************** Running Model for K2 Simulation Stan unordered @ 4 Attempt 1 TRANSLATING MODEL 'hierModel1p' FROM Stan CODE TO C++ CODE NOW. COMPILING THE C++ CODE FOR MODEL 'hierModel1p' NOW. SAMPLING FOR MODEL 'hierModel1p' 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. 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: 401.886 seconds (Warm-up) 2276.26 seconds (Sampling) 2678.14 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' 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: 416.147 seconds (Warm-up) 2502.4 seconds (Sampling) 2918.54 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 3). Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Iteration: 1 / 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: 377.875 seconds (Warm-up) 3502.38 seconds (Sampling) 3880.25 seconds (Total) Labeling components for level 2 model K2 Simulation Stan unordered @ 4 Labeling components for alpha0 Labeling components for mu0 Labeling components for beta0 Labeling components for tau0 Labeling components for gamma0 Labeling components for pi Labeling components for mu Labeling components for sigma **************** Convergence diagnostics for K2 Simulation Stan unordered @ 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 -694.0182 16.2542 0.1327 0.5050 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -725.7 -705.4 -694.0 -682.8 -662.1 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.3 1.8 lp__ 820.2741 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 12.01830 4.60107 0.03757 0.06750 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 4.892 8.673 11.379 14.617 22.712 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.02 1.06 alphaN 5035.992 Iterations = 1:5000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 5000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE alpha0[1] 0.17214 0.2919 0.002384 0.003628 alpha0[2] 0.41536 0.2846 0.002324 0.003378 alpha0[3] 0.32850 0.2460 0.002008 0.002777 alpha0[4] 0.08399 0.1442 0.001177 0.001603 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 7.136e-06 0.0007096 0.003720 0.3040 0.7478 alpha0[2] 5.344e-05 0.1610746 0.423003 0.6793 0.7655 alpha0[3] 6.235e-05 0.0175812 0.312278 0.5537 0.7487 alpha0[4] 7.455e-06 0.0007476 0.004024 0.1680 0.3963 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1 1 alpha0[3] 1 1 alpha0[4] 1 1 Multivariate psrf 1 alpha0[1] alpha0[2] alpha0[3] alpha0[4] 7705.308 7228.386 9395.406 8621.552 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] -681.1 675.5 5.515 7.883 mu0[2] -117.1 280.3 2.289 3.341 mu0[3] 114.8 270.6 2.209 3.206 mu0[4] 681.5 666.8 5.444 7.556 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -2.244e+03 -1112.6247 -535.5740 -0.86340 -0.2008 mu0[2] -1.009e+03 -1.1751 -0.4573 -0.05433 0.8336 mu0[3] -6.908e-01 -0.1754 0.4384 4.84930 973.0961 mu0[4] -3.068e-03 1.3553 546.8330 1104.19887 2244.8054 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1 1 mu0[2] 1 1 mu0[3] 1 1 mu0[4] 1 1 Multivariate psrf 1 mu0[1] mu0[2] mu0[3] mu0[4] 8424.690 8328.615 7921.375 8562.604 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] 1.3765 1.794 0.014648 0.01975 beta0[2] 0.9233 1.094 0.008931 0.01194 beta0[3] 0.9761 1.032 0.008423 0.01058 beta0[4] 1.4625 1.879 0.015345 0.02160 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1641 0.4894 0.7450 1.5407 6.219 beta0[2] 0.2601 0.4990 0.6564 0.9275 3.484 beta0[3] 0.2677 0.5490 0.7461 1.0298 3.540 beta0[4] 0.1615 0.5675 0.9080 1.6076 6.229 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1 1.00 beta0[2] 1 1.00 beta0[3] 1 1.01 beta0[4] 1 1.00 Multivariate psrf 1 beta0[1] beta0[2] beta0[3] beta0[4] 8626.052 8839.211 9599.468 7898.166 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] 0.4794 1.2002 0.009800 0.01380 tau0[2] 0.8326 1.2262 0.010012 0.01495 tau0[3] 0.2907 1.1423 0.009327 0.01322 tau0[4] -0.0868 0.9245 0.007548 0.01161 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -1.825 -0.4175 0.43746 1.5646 2.377 tau0[2] -1.324 -0.3216 1.21574 1.9457 2.438 tau0[3] -1.390 -0.5642 -0.09629 1.4420 2.355 tau0[4] -1.888 -0.6771 -0.16456 0.4878 1.882 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1 1 tau0[2] 1 1 tau0[3] 1 1 tau0[4] 1 1 Multivariate psrf 1 tau0[1] tau0[2] tau0[3] tau0[4] 8033.576 6819.298 7859.244 7104.094 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] 1.452 1.715 0.014006 0.02053 gamma0[2] 1.226 1.097 0.008959 0.01156 gamma0[3] 1.315 1.053 0.008600 0.01252 gamma0[4] 1.575 1.833 0.014963 0.02217 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.1604 0.5882 0.9436 1.591 6.206 gamma0[2] 0.2743 0.7525 1.0030 1.349 3.840 gamma0[3] 0.2647 0.8481 1.1384 1.488 3.696 gamma0[4] 0.1614 0.6484 1.1496 1.771 6.375 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1 1.01 gamma0[2] 1 1.00 gamma0[3] 1 1.00 gamma0[4] 1 1.00 Multivariate psrf 1 gamma0[1] gamma0[2] gamma0[3] gamma0[4] 7903.896 9330.834 8307.012 8120.973 Chains of length 5000 for K2 Simulation Stan unordered @ 4 did not converge in run 1 . Maximum Rhat value = 1.295223 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -699.9734527 14.9634316 0.2116149 0.8787325 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -698.4804412 15.0959186 0.2134885 0.8836672 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -683.6006645 13.3031498 0.1881349 0.8614668 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 11.41478139 4.56369091 0.06454034 0.12200844 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 11.83103330 4.56113073 0.06450413 0.09205943 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 12.80907976 4.56750616 0.06459429 0.13284582 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.16474055 0.2873125 0.004063213 0.005086047 alpha0[2] 0.41584855 0.2848592 0.004028517 0.005472754 alpha0[3] 0.33593047 0.2464683 0.003485588 0.003909125 alpha0[4] 0.08348043 0.1442000 0.002039296 0.002445747 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.16677099 0.2902193 0.004104320 0.005153792 alpha0[2] 0.41997455 0.2867648 0.004055467 0.005669105 alpha0[3] 0.32901048 0.2434698 0.003443183 0.003933213 alpha0[4] 0.08424398 0.1484186 0.002098956 0.002598058 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.18490719 0.2978352 0.004212026 0.008126746 alpha0[2] 0.41026000 0.2822177 0.003991161 0.006373968 alpha0[3] 0.32057333 0.2477477 0.003503682 0.006216199 alpha0[4] 0.08425948 0.1398455 0.001977715 0.003223699 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -677.1203 654.1340 9.250851 11.434502 mu0[2] -110.9015 263.4861 3.726256 4.426110 mu0[3] 109.6355 261.1053 3.692587 4.567578 mu0[4] 677.2728 660.1701 9.336215 11.296353 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -697.1011 685.8354 9.699177 11.483894 mu0[2] -117.8375 283.7980 4.013510 4.972950 mu0[3] 111.7298 270.4369 3.824556 4.938054 mu0[4] 687.0421 671.2485 9.492887 11.551146 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -668.9698 685.7394 9.697820 17.222312 mu0[2] -122.5326 292.8299 4.141241 7.491231 mu0[3] 123.1161 279.6685 3.955110 6.876132 mu0[4] 680.0833 668.9457 9.460320 15.898609 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 1.4059368 1.808406 0.02557473 0.03413601 beta0[2] 0.9269678 1.153733 0.01631624 0.01896391 beta0[3] 0.9572346 1.026062 0.01451071 0.01789219 beta0[4] 1.4343900 1.954420 0.02763967 0.03659547 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 1.3668985 1.736973 0.02456451 0.02902892 beta0[2] 0.9135764 1.018072 0.01439771 0.01848041 beta0[3] 1.0096988 1.109226 0.01568682 0.01790536 beta0[4] 1.4834715 1.871709 0.02646996 0.03315097 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 1.3567790 1.8350088 0.02595094 0.03877856 beta0[2] 0.9294455 1.1055961 0.01563549 0.02410838 beta0[3] 0.9612281 0.9530602 0.01347831 0.01916471 beta0[4] 1.4696958 1.8093840 0.02558855 0.04197428 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 0.44810302 1.2004735 0.01697726 0.02137271 tau0[2] 0.84440962 1.2135907 0.01716276 0.02523445 tau0[3] 0.31053221 1.1400095 0.01612217 0.02095716 tau0[4] -0.07028733 0.9411123 0.01330934 0.01805258 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 0.46397739 1.2114175 0.01713203 0.02187929 tau0[2] 0.86198412 1.2431832 0.01758127 0.02442554 tau0[3] 0.26442832 1.1643549 0.01646647 0.02095190 tau0[4] -0.06700568 0.9267072 0.01310562 0.01682655 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 0.5261246 1.1874196 0.01679265 0.02788698 tau0[2] 0.7913667 1.2208091 0.01726485 0.02789076 tau0[3] 0.2970954 1.1219689 0.01586704 0.02635834 tau0[4] -0.1231015 0.9043468 0.01278939 0.02458393 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.447638 1.632465 0.02308654 0.03062744 gamma0[2] 1.246421 1.159913 0.01640365 0.01972086 gamma0[3] 1.330488 1.072727 0.01517065 0.01968107 gamma0[4] 1.579229 1.717939 0.02429532 0.03013798 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.427942 1.6298719 0.02304987 0.02809781 gamma0[2] 1.212100 1.0367116 0.01466132 0.01745695 gamma0[3] 1.284160 0.9800244 0.01385964 0.01599869 gamma0[4] 1.559497 1.8637271 0.02635708 0.03192294 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.479436 1.872586 0.02648237 0.04544218 gamma0[2] 1.219261 1.091542 0.01543674 0.02254243 gamma0[3] 1.328969 1.102766 0.01559547 0.02770984 gamma0[4] 1.586710 1.910698 0.02702135 0.04995008 **************** Running Model for K2 Simulation Stan unordered @ 4 Attempt 2 SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 1). Iteration: 1 / 12000 [ 0%] (Warmup) Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. 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: 804.96 seconds (Warm-up) 4677.01 seconds (Sampling) 5481.97 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' 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. 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: 465.733 seconds (Warm-up) 4349.9 seconds (Sampling) 4815.63 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 3). Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Iteration: 1 / 12000 [ 0%] (Warmup) Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Iteration: 1200 / 12000 [ 10%] (Warmup) Iteration: 2400 / 12000 [ 20%] (Sampling) Iteration: 3600 / 12000 [ 30%] (Sampling) Iteration: 4800 / 12000 [ 40%] (Sampling) Iteration: 6000 / 12000 [ 50%] (Sampling) Iteration: 7200 / 12000 [ 60%] (Sampling) Iteration: 8400 / 12000 [ 70%] (Sampling) Iteration: 9600 / 12000 [ 80%] (Sampling) Iteration: 10800 / 12000 [ 90%] (Sampling) Iteration: 12000 / 12000 [100%] (Sampling) Elapsed Time: 832.089 seconds (Warm-up) 2514.11 seconds (Sampling) 3346.2 seconds (Total) Labeling components for level 2 model K2 Simulation Stan unordered @ 4 Labeling components for alpha0 Labeling components for mu0 Labeling components for beta0 Labeling components for tau0 Labeling components for gamma0 Labeling components for pi Labeling components for mu Labeling components for sigma **************** Convergence diagnostics for K2 Simulation Stan unordered @ 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 -694.85465 16.31856 0.09422 0.38825 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -726.4 -706.4 -694.7 -683.1 -663.8 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.26 1.73 lp__ 1436.338 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 11.86409 4.51480 0.02607 0.05087 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 4.814 8.616 11.317 14.436 22.416 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.01 1.04 alphaN 9917.276 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] 0.16921 0.2899 0.001367 0.002073 alpha0[2] 0.41761 0.2842 0.001340 0.002026 alpha0[3] 0.32935 0.2442 0.001151 0.001712 alpha0[4] 0.08383 0.1447 0.000682 0.001020 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 6.568e-06 0.0006756 0.003624 0.03656 0.7466 alpha0[2] 5.330e-05 0.1887902 0.455527 0.67871 0.7678 alpha0[3] 5.842e-05 0.0322011 0.313726 0.53609 0.7486 alpha0[4] 6.393e-06 0.0007157 0.003929 0.03406 0.4010 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1 1 alpha0[3] 1 1 alpha0[4] 1 1 Multivariate psrf 1 alpha0[1] alpha0[2] alpha0[3] alpha0[4] 20785.81 20259.04 22329.92 21849.21 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] -688.0 676.7 3.190 6.332 mu0[2] -114.7 277.0 1.306 1.949 mu0[3] 115.8 277.7 1.309 1.891 mu0[4] 673.9 667.4 3.146 4.695 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -2.255e+03 -1123.7641 -543.1255 -1.15641 -0.1973 mu0[2] -1.002e+03 -0.9744 -0.4564 -0.06332 0.8358 mu0[3] -7.000e-01 -0.1638 0.4296 1.45380 998.7315 mu0[4] 1.869e-03 1.4445 528.0950 1098.65841 2236.8552 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1 1 mu0[2] 1 1 mu0[3] 1 1 mu0[4] 1 1 Multivariate psrf 1 mu0[1] mu0[2] mu0[3] mu0[4] 18485.66 21115.83 22526.44 22291.71 Iterations = 1:15000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 15000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE beta0[1] 1.3904 1.891 0.008914 0.012938 beta0[2] 0.9185 1.157 0.005456 0.008056 beta0[3] 0.9864 1.095 0.005162 0.006992 beta0[4] 1.4618 1.937 0.009131 0.013457 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1618 0.4862 0.7486 1.5469 6.302 beta0[2] 0.2674 0.4952 0.6519 0.9241 3.451 beta0[3] 0.2549 0.5451 0.7471 1.0342 3.607 beta0[4] 0.1601 0.5590 0.9033 1.5860 6.351 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1 1 beta0[2] 1 1 beta0[3] 1 1 beta0[4] 1 1 Multivariate psrf 1 beta0[1] beta0[2] beta0[3] beta0[4] 22497.98 23482.85 25898.07 22432.45 Iterations = 1:15000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 15000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE tau0[1] 0.45467 1.2083 0.005696 0.008944 tau0[2] 0.84518 1.2218 0.005760 0.008668 tau0[3] 0.28357 1.1426 0.005386 0.008191 tau0[4] -0.08627 0.9243 0.004357 0.006839 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -1.829 -0.4593 0.4004 1.5530 2.375 tau0[2] -1.332 -0.3106 1.2555 1.9474 2.428 tau0[3] -1.392 -0.5727 -0.1024 1.4292 2.351 tau0[4] -1.870 -0.6767 -0.1665 0.4859 1.872 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1 1 tau0[2] 1 1 tau0[3] 1 1 tau0[4] 1 1 Multivariate psrf 1 tau0[1] tau0[2] tau0[3] tau0[4] 21192.31 20293.49 20360.99 19592.56 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] 1.465 1.916 0.009033 0.012891 gamma0[2] 1.230 1.121 0.005287 0.007926 gamma0[3] 1.307 1.087 0.005123 0.007156 gamma0[4] 1.603 1.942 0.009153 0.014200 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.1607 0.5848 0.942 1.579 6.266 gamma0[2] 0.2714 0.7529 1.013 1.357 3.807 gamma0[3] 0.2679 0.8473 1.132 1.480 3.639 gamma0[4] 0.1617 0.6526 1.148 1.784 6.452 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1 1 gamma0[2] 1 1 gamma0[3] 1 1 gamma0[4] 1 1 Multivariate psrf 1 gamma0[1] gamma0[2] gamma0[3] gamma0[4] 22561.65 22829.95 23582.11 20643.29 Chains of length 10000 for K2 Simulation Stan unordered @ 4 did not converge in run 2 . Maximum Rhat value = 1.259743 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -699.9004760 15.7131878 0.1571319 0.7361935 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -684.8601610 12.9342601 0.1293426 0.6175082 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -699.8033044 15.3259872 0.1532599 0.6582779 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 11.56691162 4.46481866 0.04464819 0.06846355 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 12.48831143 4.56907700 0.04569077 0.11769624 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 11.53706030 4.44503084 0.04445031 0.06893986 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.16819303 0.2901222 0.002368838 0.003013375 alpha0[2] 0.41307814 0.2868440 0.002342072 0.003221106 alpha0[3] 0.33383119 0.2480695 0.002025479 0.002404641 alpha0[4] 0.08489763 0.1456801 0.001189473 0.001483888 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.16834358 0.2879551 0.002351144 0.004054941 alpha0[2] 0.41884460 0.2825396 0.002306926 0.003911660 alpha0[3] 0.32778086 0.2410813 0.001968421 0.003447272 alpha0[4] 0.08503097 0.1463415 0.001194873 0.002107786 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.17108209 0.2916546 0.002381350 0.003624949 alpha0[2] 0.42090461 0.2831661 0.002312042 0.003357798 alpha0[3] 0.32645146 0.2433942 0.001987306 0.002952328 alpha0[4] 0.08156184 0.1419747 0.001159219 0.001651476 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -682.6208 665.4788 5.433612 6.741219 mu0[2] -118.3849 279.8693 2.285123 2.905162 mu0[3] 117.1309 279.5490 2.282508 2.860648 mu0[4] 676.4233 668.6217 5.459274 6.493927 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -703.3152 690.8007 5.640364 15.778622 mu0[2] -111.7206 272.1789 2.222331 3.674080 mu0[3] 112.8423 275.9143 2.252831 3.671659 mu0[4] 657.7078 661.9646 5.404918 9.499021 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -678.1157 673.2926 5.497411 8.150690 mu0[2] -114.1155 278.8972 2.277186 3.500632 mu0[3] 117.4068 277.6757 2.267212 3.243353 mu0[4] 687.6756 671.3786 5.481783 8.121172 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 1.3818204 1.862497 0.015207222 0.01927357 beta0[2] 0.9263908 1.176185 0.009603514 0.01147571 beta0[3] 0.9842440 1.082061 0.008834995 0.01068584 beta0[4] 1.4570376 1.926729 0.015731680 0.02009283 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 1.3853460 1.817140 0.014836889 0.02531631 beta0[2] 0.9236072 1.206693 0.009852604 0.01760697 beta0[3] 1.0043318 1.156642 0.009443943 0.01440181 beta0[4] 1.4750282 1.977984 0.016150175 0.02804930 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 1.4038852 1.988988 0.016240017 0.02222686 beta0[2] 0.9055208 1.085894 0.008866290 0.01193298 beta0[3] 0.9706958 1.043082 0.008516726 0.01087832 beta0[4] 1.4533130 1.905371 0.015557287 0.02095905 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 0.47929291 1.1993256 0.009792452 0.01212448 tau0[2] 0.82929702 1.2177537 0.009942917 0.01367506 tau0[3] 0.29735615 1.1552600 0.009432658 0.01250896 tau0[4] -0.09946094 0.9293136 0.007587814 0.01011932 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 0.41438728 1.2239854 0.009993799 0.01949113 tau0[2] 0.85555072 1.2258788 0.010009258 0.01640051 tau0[3] 0.28897799 1.1407089 0.009313849 0.01610975 tau0[4] -0.06233101 0.9253981 0.007555844 0.01390462 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 0.4703429 1.200341 0.009800746 0.01389361 tau0[2] 0.8506807 1.221661 0.009974819 0.01484026 tau0[3] 0.2643864 1.131669 0.009240040 0.01370562 tau0[4] -0.0970151 0.917746 0.007493365 0.01118750 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.487389 2.079731 0.016980929 0.02143225 gamma0[2] 1.241809 1.141854 0.009323202 0.01141300 gamma0[3] 1.306733 1.108538 0.009051177 0.01124927 gamma0[4] 1.573853 1.918526 0.015664699 0.01993605 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.446272 1.794346 0.014650773 0.02405901 gamma0[2] 1.225000 1.104964 0.009021996 0.01703384 gamma0[3] 1.301756 1.047741 0.008554769 0.01309198 gamma0[4] 1.621584 1.940742 0.015846093 0.02949120 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.460564 1.862510 0.015207328 0.02138830 gamma0[2] 1.222831 1.117208 0.009121962 0.01203856 gamma0[3] 1.313015 1.102958 0.009005615 0.01276424 gamma0[4] 1.613653 1.964963 0.016043856 0.02339959 MCMC run did not converge, proceeding anyway. Calculating model fit indexes for K2 Simulation Stan unordered @ 4 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -530.32462 35.50933 1131.66791 35.50933 1131.66791 lppd lppd.bayes pDIC DIC pDICalt DICalt -548.07929 -8118.14290 -15140.12723 -14043.96865 57.87596 16352.03774 Analaysis complete for K2 Simulation Stan unordered @ 4 > proc.time() user system elapsed 23337.92 45.11 23401.55