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 unordered @ 4 Removing 0 of 10 Level 2 units for length. Calculating initial values for chain 1 ; K4 Simulation Stan unordered @ 4 number of iterations= 474 number of iterations= 49 number of iterations= 71 number of iterations= 49 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= 83 number of iterations= 32 number of iterations= 89 number of iterations= 180 number of iterations= 87 number of iterations= 51 Calculating initial values for chain 2 ; K4 Simulation Stan unordered @ 4 WARNING! NOT CONVERGENT! number of iterations= 1000 One of the variances is going to zero; trying new starting values. number of iterations= 62 number of iterations= 51 number of iterations= 30 number of iterations= 194 number of iterations= 142 number of iterations= 172 number of iterations= 160 number of iterations= 30 number of iterations= 35 Calculating initial values for chain 3 ; K4 Simulation Stan unordered @ 4 number of iterations= 166 number of iterations= 52 number of iterations= 263 One of the variances is going to zero; trying new starting values. number of iterations= 36 number of iterations= 179 number of iterations= 87 number of iterations= 105 number of iterations= 181 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= 31 One of the variances is going to zero; trying new starting values. number of iterations= 630 **************** Running Model for K4 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): 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: 358.404 seconds (Warm-up) 2226.38 seconds (Sampling) 2584.79 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. 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: 310.948 seconds (Warm-up) 1442.43 seconds (Sampling) 1753.38 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 3). Iteration: 1 / 6000 [ 0%] (Warmup) Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. 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: 398.022 seconds (Warm-up) 1415.85 seconds (Sampling) 1813.87 seconds (Total) Labeling components for level 2 model K4 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 K4 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 -594.4221 15.1096 0.1234 0.8188 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -629.4 -603.2 -593.1 -583.8 -568.7 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.02 1.04 lp__ 591.8056 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 13.00312 4.64807 0.03795 0.12088 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 5.668 9.608 12.355 15.819 23.725 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.01 1.04 alphaN 1560.63 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] 0.2426 0.07182 0.0005864 0.002583 alpha0[2] 0.2998 0.09568 0.0007812 0.002281 alpha0[3] 0.3159 0.10875 0.0008880 0.004338 alpha0[4] 0.1416 0.08666 0.0007075 0.003194 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.11668 0.19339 0.2373 0.2858 0.3982 alpha0[2] 0.09846 0.23670 0.3023 0.3629 0.4850 alpha0[3] 0.08837 0.24760 0.3221 0.3888 0.5216 alpha0[4] 0.02411 0.08031 0.1238 0.1823 0.3697 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.01 1.03 alpha0[3] 1.01 1.02 alpha0[4] 1.00 1.02 Multivariate psrf 1.01 alpha0[1] alpha0[2] alpha0[3] alpha0[4] 770.2370 2070.2678 1151.5375 819.8292 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] -0.99848 0.2251 0.001838 0.007463 mu0[2] -0.39445 0.2248 0.001835 0.007233 mu0[3] -0.02553 0.2136 0.001744 0.005058 mu0[4] 0.92246 0.5889 0.004808 0.022638 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.32685 -1.1223 -1.01302 -0.88283 -0.559584 mu0[2] -0.86804 -0.5473 -0.38081 -0.22801 -0.005907 mu0[3] -0.42510 -0.1529 -0.03386 0.08452 0.449517 mu0[4] -0.01798 0.5249 0.85900 1.24041 2.264548 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.21 1.64 mu0[2] 1.13 1.39 mu0[3] 1.05 1.16 mu0[4] 1.01 1.02 Multivariate psrf 1.28 mu0[1] mu0[2] mu0[3] mu0[4] 1209.201 1553.095 1745.384 1000.377 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] 0.3966 0.2632 0.002149 0.011393 beta0[2] 0.5285 0.2965 0.002421 0.005125 beta0[3] 0.5148 0.3204 0.002616 0.007432 beta0[4] 0.9799 0.4463 0.003644 0.009268 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1345 0.2547 0.3414 0.4737 0.9039 beta0[2] 0.1409 0.3478 0.4777 0.6393 1.2543 beta0[3] 0.1124 0.2938 0.4493 0.6556 1.2998 beta0[4] 0.2889 0.6854 0.9009 1.1951 2.0274 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.05 1.08 beta0[2] 1.04 1.13 beta0[3] 1.08 1.26 beta0[4] 1.00 1.01 Multivariate psrf 1.11 beta0[1] beta0[2] beta0[3] beta0[4] 1009.035 3512.544 1832.264 2923.039 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] 2.0374 1.0931 0.008925 0.02846 tau0[2] 1.2877 0.9735 0.007949 0.02109 tau0[3] 1.1038 0.8704 0.007107 0.01741 tau0[4] 0.9232 0.9243 0.007547 0.04248 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.07249 1.2931 2.0273 2.748 4.222 tau0[2] -0.57061 0.6260 1.2683 1.933 3.288 tau0[3] -0.60784 0.5231 1.1134 1.659 2.848 tau0[4] -0.85962 0.3008 0.9225 1.550 2.692 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.08 1.25 tau0[2] 1.02 1.07 tau0[3] 1.01 1.02 tau0[4] 1.01 1.03 Multivariate psrf 1.08 tau0[1] tau0[2] tau0[3] tau0[4] 1554.2814 3241.9889 3552.8438 489.4361 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] 2.899 1.095 0.008944 0.02441 gamma0[2] 3.191 1.230 0.010044 0.03867 gamma0[3] 2.940 1.188 0.009698 0.02927 gamma0[4] 2.688 1.310 0.010693 0.07142 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.1075 2.154 2.788 3.485 5.433 gamma0[2] 1.0638 2.332 3.098 3.940 5.848 gamma0[3] 0.7259 2.164 2.863 3.642 5.541 gamma0[4] 0.5047 1.846 2.541 3.370 5.674 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.01 1.05 gamma0[2] 1.03 1.10 gamma0[3] 1.02 1.07 gamma0[4] 1.01 1.03 Multivariate psrf 1.05 gamma0[1] gamma0[2] gamma0[3] gamma0[4] 2123.1585 1958.9876 1659.4820 665.2096 Chains of length 5000 for K4 Simulation Stan unordered @ 4 did not converge in run 1 . Maximum Rhat value = 1.275921 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -594.0874585 14.2365319 0.2013350 0.8237171 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -596.2607935 17.0710041 0.2414205 2.1322379 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -592.9181956 13.6064460 0.1924242 0.8991479 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 12.86328038 4.51633668 0.06387065 0.16972291 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 12.52394954 4.53994046 0.06420445 0.22574807 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 13.62213682 4.81470727 0.06809024 0.22744909 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2464575 0.06359016 0.0008993007 0.004055477 alpha0[2] 0.3102565 0.09828854 0.0013900099 0.003028572 alpha0[3] 0.3058226 0.10367723 0.0014662174 0.003812445 alpha0[4] 0.1374634 0.08293950 0.0011729416 0.004188847 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2387672 0.06927077 0.0009796366 0.004250303 alpha0[2] 0.2978621 0.10104750 0.0014290274 0.005072304 alpha0[3] 0.3250763 0.10828523 0.0015313844 0.006237865 alpha0[4] 0.1382944 0.09024307 0.0012762298 0.005716295 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2426857 0.08127089 0.001149344 0.005052340 alpha0[2] 0.2912275 0.08606726 0.001217175 0.003456102 alpha0[3] 0.3168968 0.11325138 0.001601616 0.010766113 alpha0[4] 0.1491899 0.08615220 0.001218376 0.006447299 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -1.047946048 0.1608761 0.002275132 0.006416893 mu0[2] -0.331556016 0.1971510 0.002788135 0.006162658 mu0[3] -0.006808196 0.2103235 0.002974423 0.009213684 mu0[4] 0.957454896 0.5824613 0.008237247 0.023715389 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -1.06917078 0.2596352 0.003671797 0.012056155 mu0[2] -0.35627035 0.2330483 0.003295801 0.017682148 mu0[3] 0.01461838 0.1847480 0.002612731 0.007221888 mu0[4] 0.93443797 0.6266892 0.008862723 0.053675240 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.87832350 0.1918107 0.002712614 0.017741795 mu0[2] -0.49551546 0.2068608 0.002925453 0.010964410 mu0[3] -0.08438623 0.2304856 0.003259559 0.009655028 mu0[4] 0.87548329 0.5521304 0.007808303 0.034186457 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3745511 0.1848853 0.002614674 0.007270237 beta0[2] 0.4830135 0.2968309 0.004197822 0.007144108 beta0[3] 0.5125977 0.3134187 0.004432410 0.010912701 beta0[4] 0.9640275 0.4516997 0.006387998 0.011504197 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3850668 0.3296810 0.004662393 0.01990240 beta0[2] 0.5006488 0.3269474 0.004623734 0.01068008 beta0[3] 0.4112185 0.3185436 0.004504887 0.01316572 beta0[4] 0.9762779 0.4676687 0.006613834 0.01509831 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.4302412 0.2514712 0.003556340 0.026817303 beta0[2] 0.6019027 0.2460064 0.003479056 0.008442542 beta0[3] 0.6204917 0.2939172 0.004156616 0.014307676 beta0[4] 0.9993703 0.4172836 0.005901282 0.020314493 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 2.297340 1.1226589 0.01587679 0.04263709 tau0[2] 1.111068 0.8960477 0.01267203 0.02141499 tau0[3] 1.115691 0.8591959 0.01215087 0.01804730 tau0[4] 0.819573 0.9177718 0.01297925 0.06368833 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 2.1700231 1.0697152 0.01512806 0.06156743 tau0[2] 1.3210559 1.0352409 0.01464052 0.05205748 tau0[3] 1.0199894 0.9116438 0.01289259 0.03753210 tau0[4] 0.9447753 0.9409582 0.01330716 0.07524017 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.644883 0.9699887 0.01371771 0.04102239 tau0[2] 1.430891 0.9572318 0.01353730 0.02891744 tau0[3] 1.175606 0.8315193 0.01175946 0.03151114 tau0[4] 1.005307 0.9043548 0.01278951 0.08076658 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.824207 1.140322 0.01612659 0.04262943 gamma0[2] 3.055546 1.184389 0.01674979 0.03827015 gamma0[3] 3.030959 1.230502 0.01740193 0.04900527 gamma0[4] 2.667675 1.252047 0.01770662 0.07360435 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.799038 1.047780 0.01481785 0.03464428 gamma0[2] 3.042845 1.149920 0.01626232 0.03970775 gamma0[3] 3.081904 1.150480 0.01627025 0.04653741 gamma0[4] 2.833490 1.332122 0.01883906 0.07384235 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 3.073634 1.075195 0.01520555 0.04842837 gamma0[2] 3.475713 1.301369 0.01840413 0.10207367 gamma0[3] 2.707347 1.145797 0.01620401 0.05605401 gamma0[4] 2.562812 1.329022 0.01879521 0.18719084 **************** Running Model for K4 Simulation Stan unordered @ 4 Attempt 2 SAMPLING FOR MODEL 'hierModel1p' 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. 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: 571.487 seconds (Warm-up) 2379.9 seconds (Sampling) 2951.39 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 2). Iteration: 1 / 12000 [ 0%] (Warmup) Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. 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: 1213.87 seconds (Warm-up) 7251.41 seconds (Sampling) 8465.28 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' 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): 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: 541.933 seconds (Warm-up) 2078.74 seconds (Sampling) 2620.67 seconds (Total) Labeling components for level 2 model K4 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 K4 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 -639.9452 106.2564 0.6135 38.4466 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -901.1 -613.2 -598.1 -587.2 -570.2 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.75 7.35 lp__ 715.2895 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 11.01187 6.20698 0.03584 0.94527 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 5.000e-13 8.047e+00 1.156e+01 1.485e+01 2.294e+01 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.34 2 alphaN 2627.517 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] 0.2427 0.1027 0.0004842 0.001825 alpha0[2] 0.2961 0.1267 0.0005973 0.002742 alpha0[3] 0.3066 0.1334 0.0006290 0.002935 alpha0[4] 0.1547 0.1179 0.0005559 0.003064 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.03474 0.1884 0.2356 0.2873 0.4687 alpha0[2] 0.03155 0.2220 0.2992 0.3721 0.5305 alpha0[3] 0.02541 0.2277 0.3173 0.3904 0.5393 alpha0[4] 0.01664 0.0805 0.1275 0.1921 0.4643 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.06 1.08 alpha0[3] 1.05 1.08 alpha0[4] 1.10 1.15 Multivariate psrf 1.01 alpha0[1] alpha0[2] alpha0[3] alpha0[4] 2877.958 2506.805 1997.476 1561.278 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] -0.9781 0.2539 0.001197 0.008254 mu0[2] -0.3927 0.2306 0.001087 0.005993 mu0[3] -0.0175 0.2231 0.001052 0.005117 mu0[4] 0.8659 0.6168 0.002908 0.014919 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1.34474 -1.1191 -0.9992 -0.85220 -0.464882 mu0[2] -0.87867 -0.5444 -0.3794 -0.22504 0.005647 mu0[3] -0.43668 -0.1515 -0.0304 0.09818 0.472145 mu0[4] -0.06594 0.4539 0.7971 1.18856 2.247071 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.07 1.09 mu0[2] 1.01 1.03 mu0[3] 1.01 1.02 mu0[4] 1.01 1.01 Multivariate psrf 1.01 mu0[1] mu0[2] mu0[3] mu0[4] 985.3547 1611.0715 2074.1375 1886.0871 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] 0.4593 0.4380 0.002065 0.015361 beta0[2] 0.5475 0.3159 0.001489 0.006262 beta0[3] 0.5256 0.3721 0.001754 0.011123 beta0[4] 1.0276 0.5358 0.002526 0.014092 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.1472 0.2695 0.3679 0.5355 1.212 beta0[2] 0.1564 0.3494 0.4882 0.6578 1.385 beta0[3] 0.1147 0.2769 0.4309 0.6668 1.427 beta0[4] 0.2804 0.6985 0.9239 1.2316 2.273 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.22 1.59 beta0[2] 1.01 1.02 beta0[3] 1.02 1.03 beta0[4] 1.05 1.09 Multivariate psrf 1.05 beta0[1] beta0[2] beta0[3] beta0[4] 938.251 2870.624 1256.387 2594.507 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] 2.0222 1.1583 0.005460 0.03369 tau0[2] 1.2168 0.9990 0.004709 0.02338 tau0[3] 1.0615 0.9005 0.004245 0.01455 tau0[4] 0.9691 0.9243 0.004357 0.02444 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -0.2348 1.2346 1.994 2.796 4.317 tau0[2] -0.6175 0.5567 1.174 1.830 3.376 tau0[3] -0.7051 0.4625 1.058 1.667 2.791 tau0[4] -0.8406 0.3460 0.982 1.581 2.767 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.01 1.02 tau0[2] 1.02 1.07 tau0[3] 1.01 1.02 tau0[4] 1.00 1.00 Multivariate psrf 1.02 tau0[1] tau0[2] tau0[3] tau0[4] 1240.275 2460.063 4071.155 1574.196 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] 2.919 1.186 0.005590 0.02326 gamma0[2] 3.051 1.262 0.005948 0.02931 gamma0[3] 3.170 1.337 0.006304 0.03177 gamma0[4] 2.731 1.522 0.007173 0.06292 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 1.0103 2.132 2.787 3.540 5.722 gamma0[2] 0.8109 2.212 2.929 3.747 5.774 gamma0[3] 0.6659 2.326 3.098 3.914 6.092 gamma0[4] 0.4317 1.727 2.546 3.473 6.307 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.00 1.00 gamma0[2] 1.00 1.01 gamma0[3] 1.02 1.06 gamma0[4] 1.07 1.20 Multivariate psrf 1.06 gamma0[1] gamma0[2] gamma0[3] gamma0[4] 2895.5990 2212.3376 1779.2170 646.8887 Chains of length 10000 for K4 Simulation Stan unordered @ 4 did not converge in run 2 . Maximum Rhat value = 1.74912 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -597.3176715 14.2088534 0.1420885 0.7631259 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -728.194828 147.541476 1.475415 115.334634 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -594.3232151 14.6807584 0.1468076 0.7663549 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 12.79908134 4.37901209 0.04379012 0.11626749 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 7.14118641 7.36205124 0.07362051 2.83052217 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 13.09534565 4.43762205 0.04437622 0.12798417 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2432315 0.06964409 0.0005686416 0.002640811 alpha0[2] 0.3072791 0.11028607 0.0009004820 0.005532360 alpha0[3] 0.3025468 0.11235232 0.0009173529 0.004194813 alpha0[4] 0.1469426 0.09276201 0.0007573987 0.005569183 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2451233 0.1464771 0.001195981 0.003605861 alpha0[2] 0.2859970 0.1637139 0.001336718 0.005440108 alpha0[3] 0.2955169 0.1707057 0.001393806 0.006006165 alpha0[4] 0.1733628 0.1599959 0.001306361 0.006587756 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.2397066 0.07299338 0.0005959885 0.003163713 alpha0[2] 0.2949024 0.09474973 0.0007736283 0.002730901 alpha0[3] 0.3217322 0.10618559 0.0008670017 0.004885945 alpha0[4] 0.1436588 0.08354100 0.0006821094 0.003171186 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -0.999103317 0.1958189 0.001598855 0.012568624 mu0[2] -0.376286343 0.2256045 0.001842053 0.009515132 mu0[3] 0.003524969 0.2241466 0.001830150 0.009913350 mu0[4] 0.850353804 0.6011116 0.004908056 0.025508686 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -0.96679571 0.3383808 0.002762867 0.013934419 mu0[2] -0.37812042 0.2337106 0.001908239 0.008763203 mu0[3] -0.02913759 0.2286313 0.001866767 0.009523107 mu0[4] 0.86422333 0.6962258 0.005684659 0.031707335 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -0.96836571 0.1998033 0.001631387 0.016157602 mu0[2] -0.42363799 0.2294290 0.001873280 0.012485956 mu0[3] -0.02688807 0.2146981 0.001753002 0.006835605 mu0[4] 0.88317215 0.5428446 0.004432308 0.018634433 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.3978794 0.2224737 0.001816490 0.01186360 beta0[2] 0.5332106 0.3213313 0.002623659 0.01291954 beta0[3] 0.5211854 0.3526221 0.002879147 0.01527335 beta0[4] 0.9948339 0.4993756 0.004077385 0.02733883 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5725705 0.6757305 0.005517316 0.04282713 beta0[2] 0.5671457 0.3472635 0.002835395 0.01136796 beta0[3] 0.5485410 0.4348082 0.003550194 0.02540290 beta0[4] 1.1156211 0.6516991 0.005321101 0.03070778 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.4073594 0.2241389 0.001830086 0.012198014 beta0[2] 0.5422904 0.2737510 0.002235168 0.007537986 beta0[3] 0.5069455 0.3180275 0.002596684 0.015329792 beta0[4] 0.9724668 0.4188305 0.003419737 0.009843383 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 2.1376012 1.1507188 0.009395580 0.06078782 tau0[2] 1.1918799 0.9046076 0.007386090 0.02404197 tau0[3] 1.1442851 0.8816650 0.007198765 0.02950213 tau0[4] 0.9578046 0.8976024 0.007328893 0.03815629 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 1.9125098 1.1873506 0.009694677 0.06482228 tau0[2] 1.0702179 1.0336900 0.008440044 0.04039568 tau0[3] 0.9824859 0.9419140 0.007690696 0.02250798 tau0[4] 0.9530142 0.9564791 0.007809620 0.03618648 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 2.0165444 1.1249590 0.009185252 0.04812557 tau0[2] 1.3884008 1.0275412 0.008389839 0.05206290 tau0[3] 1.0576312 0.8689961 0.007095324 0.02300576 tau0[4] 0.9964885 0.9173123 0.007489823 0.05110771 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.899573 1.196006 0.009765345 0.04965582 gamma0[2] 2.953779 1.209953 0.009879228 0.03922445 gamma0[3] 3.080639 1.254214 0.010240618 0.06008149 gamma0[4] 2.526278 1.287371 0.010511338 0.07134787 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.948040 1.241847 0.01013964 0.03596083 gamma0[2] 3.087884 1.287368 0.01051132 0.04338680 gamma0[3] 3.378867 1.512019 0.01234558 0.05554683 gamma0[4] 3.184058 1.757051 0.01434626 0.13100060 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.910089 1.115967 0.009111829 0.03330254 gamma0[2] 3.111575 1.280755 0.010457322 0.06566813 gamma0[3] 3.049226 1.199633 0.009794959 0.04887389 gamma0[4] 2.484051 1.375747 0.011232928 0.11568591 MCMC run did not converge, proceeding anyway. Calculating model fit indexes for K4 Simulation Stan unordered @ 4 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -464.91953 93.45236 1116.74379 93.45236 1116.74379 lppd lppd.bayes pDIC DIC pDICalt DICalt -511.6457 -2375.9286 -3728.5658 -2705.2744 211.0526 5173.9625 Analaysis complete for K4 Simulation Stan unordered @ 4 > proc.time() user system elapsed 20435.960 42.045 20491.257