Loading required package: Rcpp Loading required package: inline Attaching package: ‘inline’ The following object is masked from ‘package:Rcpp’: registerPlugin rstan (Version 2.2.0, packaged: 2014-02-14 04:29:17 UTC, GitRev: 52d7b230aaa0) Loading required package: lattice Attaching package: ‘coda’ The following object is masked from ‘package:rstan’: traceplot Loading required package: boot Attaching package: ‘boot’ The following object is masked from ‘package:lattice’: melanoma Loading required package: MASS Loading required package: segmented mixtools package, version 1.0.1, Released January 2014 This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772. **************** Cleaning data for K3 Simulation Stan unordered @ 4 Removing 0 of 10 Level 2 units for length. Calculating initial values for chain 1 ; K3 Simulation Stan unordered @ 4 number of iterations= 114 number of iterations= 81 number of iterations= 110 number of iterations= 57 number of iterations= 78 number of iterations= 184 number of iterations= 46 number of iterations= 52 One of the variances is going to zero; trying new starting values. One of the variances is going to zero; trying new starting values. One of the variances is going to zero; trying new starting values. One of the variances is going to zero; trying new starting values. One of the variances is going to zero; trying new starting values. number of iterations= 150 number of iterations= 197 Calculating initial values for chain 2 ; K3 Simulation Stan unordered @ 4 number of iterations= 57 One of the variances is going to zero; trying new starting values. number of iterations= 64 number of iterations= 49 number of iterations= 337 WARNING! NOT CONVERGENT! number of iterations= 1000 One of the variances is going to zero; trying new starting values. number of iterations= 171 number of iterations= 50 number of iterations= 63 One of the variances is going to zero; trying new starting values. number of iterations= 92 number of iterations= 176 Calculating initial values for chain 3 ; K3 Simulation Stan unordered @ 4 number of iterations= 39 number of iterations= 49 number of iterations= 37 number of iterations= 73 number of iterations= 52 number of iterations= 55 number of iterations= 24 number of iterations= 34 number of iterations= 240 One of the variances is going to zero; trying new starting values. number of iterations= 189 **************** Running Model for K3 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: 250.116 seconds (Warm-up) 1454.99 seconds (Sampling) 1705.1 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): 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: 119.542 seconds (Warm-up) 499.049 seconds (Sampling) 618.591 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: 223.655 seconds (Warm-up) 599.536 seconds (Sampling) 823.191 seconds (Total) Labeling components for level 2 model K3 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 K3 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 -675.9128 17.4463 0.1424 2.3536 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -705.2 -688.0 -677.5 -665.7 -630.8 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.11 1.32 lp__ 250.8176 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 13.0280 4.2989 0.0351 0.1143 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 6.175 10.128 12.359 15.340 23.276 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.02 1.06 alphaN 1635.803 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] 0.2150 0.21253 0.001735 0.027417 alpha0[2] 0.4140 0.07017 0.000573 0.002397 alpha0[3] 0.2829 0.13699 0.001119 0.012346 alpha0[4] 0.0881 0.08830 0.000721 0.005899 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 1.141e-05 0.001041 0.2859 0.4186 0.5235 alpha0[2] 2.760e-01 0.372993 0.4140 0.4644 0.5342 alpha0[3] 5.964e-02 0.160416 0.2827 0.4042 0.5255 alpha0[4] 4.814e-06 0.001732 0.0951 0.1517 0.2697 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.02 1.05 alpha0[3] 1.09 1.29 alpha0[4] 1.50 2.32 Multivariate psrf 1.39 alpha0[1] alpha0[2] alpha0[3] alpha0[4] 643.3163 1403.2684 310.9293 1107.8585 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] -202.8852 371.4041 3.032502 41.26347 mu0[2] -0.5627 0.4481 0.003659 0.05692 mu0[3] 0.5407 0.7837 0.006399 0.05764 mu0[4] 262.2086 416.5487 3.401106 41.87552 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1386.8059 -299.4062 -1.1937 -1.0029 -0.809 mu0[2] -1.1429 -0.9929 -0.6412 -0.1535 0.199 mu0[3] -0.5029 -0.1678 0.3514 1.1983 2.058 mu0[4] 0.4688 1.3162 1.9663 493.9032 1448.106 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.35 2.86 mu0[2] 1.16 1.49 mu0[3] 1.31 1.90 mu0[4] 1.35 2.70 Multivariate psrf 1.37 mu0[1] mu0[2] mu0[3] mu0[4] 1710.0256 552.9410 532.9614 1344.0873 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] 0.8796 1.4676 0.011983 0.04847 beta0[2] 0.4714 0.2692 0.002198 0.02169 beta0[3] 0.6922 0.3794 0.003098 0.03599 beta0[4] 1.1294 1.4305 0.011680 0.03197 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.08919 0.2301 0.3554 0.9811 4.490 beta0[2] 0.13715 0.2641 0.4189 0.6295 1.053 beta0[3] 0.20185 0.4617 0.5910 0.8163 1.842 beta0[4] 0.15453 0.4180 0.7677 1.2970 4.446 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.06 1.17 beta0[2] 1.16 1.46 beta0[3] 1.05 1.10 beta0[4] 1.11 1.26 Multivariate psrf 1.16 beta0[1] beta0[2] beta0[3] beta0[4] 3880.5992 917.6283 1714.7481 4123.7875 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] 0.8039 1.3433 0.010968 0.11744 tau0[2] 1.8123 0.9741 0.007954 0.07940 tau0[3] 0.5144 1.1447 0.009346 0.09654 tau0[4] -0.0661 0.9070 0.007406 0.04365 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -1.6909 -0.2145 1.0043 1.9113 2.972 tau0[2] -0.3168 1.1898 1.9136 2.4826 3.492 tau0[3] -1.2322 -0.4978 0.3730 1.5212 2.720 tau0[4] -1.6008 -0.6656 -0.1712 0.4136 2.128 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.14 1.41 tau0[2] 1.18 1.52 tau0[3] 1.21 1.62 tau0[4] 1.01 1.02 Multivariate psrf 1.39 tau0[1] tau0[2] tau0[3] tau0[4] 463.0131 933.3050 689.5296 1609.3127 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] 1.921 1.5355 0.012538 0.05042 gamma0[2] 2.056 0.6741 0.005504 0.03439 gamma0[3] 1.708 0.7731 0.006312 0.06590 gamma0[4] 1.520 1.3783 0.011253 0.04263 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.2077 0.9794 1.791 2.448 4.975 gamma0[2] 0.8920 1.6081 1.937 2.472 3.509 gamma0[3] 0.5170 1.1201 1.650 2.155 3.387 gamma0[4] 0.2045 0.7755 1.271 1.902 4.527 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.07 1.13 gamma0[2] 1.07 1.23 gamma0[3] 1.15 1.44 gamma0[4] 1.06 1.07 Multivariate psrf 1.18 gamma0[1] gamma0[2] gamma0[3] gamma0[4] 2459.009 1925.654 954.269 5416.895 Chains of length 5000 for K3 Simulation Stan unordered @ 4 did not converge in run 1 . Maximum Rhat value = 1.393958 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -676.5965991 14.7188155 0.2081555 1.1928115 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -670.0844673 21.0242798 0.2973282 6.8012896 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -681.0574823 13.9163411 0.1968068 1.4751600 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 13.74052859 4.65072802 0.06577123 0.15651753 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 12.42985997 3.93090686 0.05559142 0.23774185 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 12.91354513 4.18202210 0.05914272 0.19097999 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.20809490 0.21999074 0.003111139 0.008841764 alpha0[2] 0.41599776 0.07221147 0.001021224 0.003167776 alpha0[3] 0.29440067 0.14788319 0.002091384 0.009277248 alpha0[4] 0.08150667 0.08499773 0.001202049 0.002632375 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.1052811 0.18222073 0.0025769902 0.071381224 alpha0[2] 0.4223736 0.07527443 0.0010645412 0.006047993 alpha0[3] 0.3208569 0.13993135 0.0019789281 0.031525718 alpha0[4] 0.1514884 0.06823210 0.0009649476 0.010260257 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.33155582 0.16806464 0.0023767929 0.03990001 alpha0[2] 0.40372975 0.06094214 0.0008618521 0.00225755 alpha0[3] 0.23340972 0.10409362 0.0014721062 0.01708269 alpha0[4] 0.03130471 0.06501243 0.0009194146 0.01417638 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -367.8618254 568.9777652 8.04656072 13.81562559 mu0[2] -0.5657016 0.4707040 0.00665676 0.02101742 mu0[3] 0.6016917 0.8399888 0.01187924 0.03890261 mu0[4] 371.4711962 569.5718849 8.05496284 16.18131956 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -187.91888579 140.3915398 1.985436196 116.1520306 mu0[2] -0.75543496 0.4276688 0.006048150 0.1610882 mu0[3] 0.07098125 0.5272082 0.007455849 0.1395577 mu0[4] 1.47920103 1.1503122 0.016267871 0.1155635 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -52.8749075 143.1029486 2.023781307 40.52040141 mu0[2] -0.3670207 0.3500405 0.004950321 0.05258183 mu0[3] 0.9495234 0.6839017 0.009671831 0.09441269 mu0[4] 413.6752567 305.4727342 4.320036836 124.58002341 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.9974988 1.6407859 0.023204217 0.028376875 beta0[2] 0.4394281 0.2477881 0.003504252 0.008500134 beta0[3] 0.6429674 0.3197755 0.004522309 0.011733259 beta0[4] 1.1740018 1.6472196 0.023295203 0.028849162 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 1.1392732 1.5955977 0.022565159 0.07357698 beta0[2] 0.3749569 0.2681504 0.003792219 0.05292935 beta0[3] 0.7605074 0.4595487 0.006499001 0.10677941 beta0[4] 0.7641736 0.5873025 0.008305711 0.06908038 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5019980 1.0000181 0.014142391 0.12215586 beta0[2] 0.5998058 0.2394445 0.003386257 0.03686959 beta0[3] 0.6731755 0.3331877 0.004711985 0.01079041 beta0[4] 1.4500773 1.6861578 0.023845873 0.05993902 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 0.95971690 1.4027483 0.01983786 0.07302892 tau0[2] 1.69110753 0.8731972 0.01234887 0.02969832 tau0[3] 0.60649042 0.9868331 0.01395593 0.03929522 tau0[4] -0.07288147 0.8831987 0.01249032 0.03265051 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 0.2310066 1.4217200 0.02010616 0.3268812 tau0[2] 1.4441683 0.9284557 0.01313035 0.1942472 tau0[3] 1.0045521 1.2506612 0.01768702 0.2376376 tau0[4] -0.1395591 0.9267857 0.01310673 0.1228119 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.22091586 0.9477663 0.01340344 0.10933345 tau0[2] 2.30166710 0.9126119 0.01290628 0.13460536 tau0[3] -0.06789404 0.8975918 0.01269387 0.16081593 tau0[4] 0.01412592 0.9041814 0.01278706 0.03156272 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.925016 1.3941819 0.019716710 0.03110377 gamma0[2] 2.147319 0.6270365 0.008867635 0.01531902 gamma0[3] 1.797826 0.6693591 0.009466168 0.02288091 gamma0[4] 1.555846 1.5225123 0.021531575 0.02861164 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.676745 1.9539418 0.027632910 0.1281151 gamma0[2] 2.200771 0.6601335 0.009335698 0.0901266 gamma0[3] 1.960069 0.8741353 0.012362140 0.1827015 gamma0[4] 1.446741 0.7384682 0.010443518 0.1201080 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.161286 1.0933528 0.015462343 0.07417906 gamma0[2] 1.819239 0.6700170 0.009475471 0.04778707 gamma0[3] 1.365375 0.6260289 0.008853386 0.07201040 gamma0[4] 1.557747 1.6816769 0.023782503 0.03331873 **************** Running Model for K3 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. 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: 159.16 seconds (Warm-up) 650.276 seconds (Sampling) 809.436 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. 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: 269.031 seconds (Warm-up) 2830.48 seconds (Sampling) 3099.51 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 3). Iteration: 1 / 12000 [ 0%] (Warmup) 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: 347.904 seconds (Warm-up) 759.595 seconds (Sampling) 1107.5 seconds (Total) Labeling components for level 2 model K3 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 K3 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 -671.5661 16.8186 0.0971 0.5437 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -703.4 -683.1 -672.0 -660.6 -637.1 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.44 2.13 lp__ 1128.48 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 13.62510 4.68763 0.02706 0.04755 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 6.34 10.24 13.00 16.36 24.36 Potential scale reduction factors: Point est. Upper C.I. alphaN 1 1.02 alphaN 9852.753 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] 0.2551 0.20659 0.0009739 0.009052 alpha0[2] 0.4115 0.07131 0.0003362 0.001148 alpha0[3] 0.2591 0.13509 0.0006368 0.005106 alpha0[4] 0.0743 0.08377 0.0003949 0.003438 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 1.532e-05 0.002013 0.35545 0.4301 0.5278 alpha0[2] 2.698e-01 0.371759 0.41381 0.4582 0.5355 alpha0[3] 6.216e-02 0.144838 0.21997 0.3868 0.5098 alpha0[4] 7.144e-06 0.002164 0.01955 0.1373 0.2549 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1.02 1.05 alpha0[3] 1.06 1.20 alpha0[4] 1.10 1.29 Multivariate psrf 1.09 alpha0[1] alpha0[2] alpha0[3] alpha0[4] 485.7975 4199.2461 726.5346 545.4920 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] -236.0528 459.6008 2.166579 16.41139 mu0[2] -0.5079 0.4213 0.001986 0.01646 mu0[3] 0.6806 0.8102 0.003819 0.03000 mu0[4] 293.6103 484.0075 2.281633 16.86650 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1655.5624 -290.99477 -1.0857 -0.9612 -0.6931 mu0[2] -1.1289 -0.93802 -0.4391 -0.1564 0.2047 mu0[3] -0.4879 -0.08641 0.6667 1.3852 2.1102 mu0[4] 0.5200 1.53163 5.0446 428.0713 1710.7871 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.05 1.15 mu0[2] 1.05 1.15 mu0[3] 1.06 1.19 mu0[4] 1.07 1.20 Multivariate psrf 1.11 mu0[1] mu0[2] mu0[3] mu0[4] 1385.5012 1087.0057 699.5596 814.8556 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] 0.8030 1.4332 0.006756 0.024023 beta0[2] 0.5041 0.2602 0.001227 0.008678 beta0[3] 0.6876 0.3722 0.001755 0.011410 beta0[4] 1.1876 1.5635 0.007370 0.019075 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.09318 0.2304 0.3437 0.7861 4.292 beta0[2] 0.15269 0.2999 0.4766 0.6454 1.068 beta0[3] 0.18245 0.4477 0.6045 0.8427 1.647 beta0[4] 0.14620 0.4362 0.7686 1.3262 5.047 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.05 1.11 beta0[2] 1.06 1.19 beta0[3] 1.01 1.01 beta0[4] 1.02 1.03 Multivariate psrf 1.06 beta0[1] beta0[2] beta0[3] beta0[4] 4362.5113 970.7956 3692.7893 6743.3521 Iterations = 1:15000 Thinning interval = 1 Number of chains = 3 Sample size per chain = 15000 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE tau0[1] 1.02711 1.2554 0.005918 0.04294 tau0[2] 1.87335 0.9484 0.004471 0.03503 tau0[3] 0.41310 1.0555 0.004976 0.04181 tau0[4] -0.03617 0.8910 0.004200 0.01496 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -1.6180 0.1788 1.1863 1.9826 3.066 tau0[2] -0.1253 1.2353 1.9094 2.5541 3.586 tau0[3] -1.2002 -0.4569 0.2522 1.2354 2.541 tau0[4] -1.6511 -0.6291 -0.1138 0.5051 1.933 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1.04 1.11 tau0[2] 1.07 1.23 tau0[3] 1.06 1.18 tau0[4] 1.00 1.01 Multivariate psrf 1.11 tau0[1] tau0[2] tau0[3] tau0[4] 1834.0247 707.0575 616.9451 9186.0470 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] 1.965 1.3772 0.006492 0.01996 gamma0[2] 2.009 0.6809 0.003210 0.01605 gamma0[3] 1.638 0.7447 0.003510 0.02522 gamma0[4] 1.550 1.5397 0.007258 0.01434 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.2249 1.2886 1.867 2.424 4.687 gamma0[2] 0.8282 1.5639 1.925 2.389 3.541 gamma0[3] 0.4505 1.0722 1.584 2.084 3.321 gamma0[4] 0.1826 0.7207 1.242 1.908 5.211 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1.04 1.06 gamma0[2] 1.04 1.11 gamma0[3] 1.07 1.21 gamma0[4] 1.01 1.01 Multivariate psrf 1.08 gamma0[1] gamma0[2] gamma0[3] gamma0[4] 6426.9046 1802.0084 896.8496 15306.1836 Chains of length 10000 for K3 Simulation Stan unordered @ 4 did not converge in run 2 . Maximum Rhat value = 1.442184 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -659.0399289 14.3660186 0.1436602 1.3320460 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -678.5238655 13.9993087 0.1399931 0.5473171 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -677.1344982 14.4892459 0.1448925 0.7658575 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 13.21372752 4.62912133 0.04629121 0.08869587 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 13.72482985 4.69640288 0.04696403 0.08192177 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 13.93675306 4.70794780 0.04707948 0.07595074 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.32127971 0.16929616 0.0013822974 0.013745933 alpha0[2] 0.40057298 0.07743617 0.0006322637 0.001968189 alpha0[3] 0.22042094 0.12438703 0.0010156158 0.008408586 alpha0[4] 0.05772636 0.07000866 0.0005716183 0.005620018 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.1771004 0.21267369 0.0017364734 0.018031971 alpha0[2] 0.4194439 0.06973498 0.0005693837 0.002376526 alpha0[3] 0.2972791 0.13847070 0.0011306085 0.010642913 alpha0[4] 0.1061766 0.08962995 0.0007318254 0.006329042 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.26693765 0.20869404 0.0017039797 0.014944626 alpha0[2] 0.41435127 0.06485301 0.0005295226 0.001532744 alpha0[3] 0.25972052 0.13095777 0.0010692657 0.007118636 alpha0[4] 0.05899056 0.08120205 0.0006630120 0.005894721 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -123.2974530 371.2216359 3.031011965 31.89720502 mu0[2] -0.4416190 0.3467434 0.002831148 0.01344846 mu0[3] 0.8309234 0.7446733 0.006080232 0.05426443 mu0[4] 180.8541987 361.9652205 2.955433650 25.55419890 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -334.6489987 499.6780895 4.079854517 15.35419708 mu0[2] -0.6209807 0.4589501 0.003747312 0.04043257 mu0[3] 0.4239407 0.7945858 0.006487766 0.05161249 mu0[4] 268.8580071 517.5396182 4.225693288 26.12851280 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -250.2119342 472.8701417 3.860968540 34.21725269 mu0[2] -0.4611635 0.4270556 0.003486894 0.02493223 mu0[3] 0.7868746 0.8267251 0.006750182 0.04992381 mu0[4] 431.1188415 521.2654583 4.256114645 34.99420670 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 0.5702372 1.0416178 0.008504774 0.041128377 beta0[2] 0.5730075 0.2585392 0.002110964 0.011622627 beta0[3] 0.7030499 0.4026118 0.003287312 0.009633387 beta0[4] 1.2394031 1.6782245 0.013702645 0.035482925 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 1.0523501 1.7158474 0.014009836 0.03179848 beta0[2] 0.4296215 0.2559723 0.002090005 0.01786950 beta0[3] 0.6966714 0.3752849 0.003064189 0.03189150 beta0[4] 1.0218894 1.2851084 0.010492866 0.02644231 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 0.7862996 1.4200138 0.011594364 0.049912161 beta0[2] 0.5096172 0.2459145 0.002007884 0.014944128 beta0[3] 0.6632260 0.3344335 0.002730638 0.007866025 beta0[4] 1.3015097 1.6801783 0.013718599 0.036283181 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.16174870 1.0257463 0.008375184 0.02980168 tau0[2] 2.17272437 0.9873687 0.008061832 0.05630522 tau0[3] 0.19271773 1.0029319 0.008188904 0.06787947 tau0[4] 0.01286301 0.9189768 0.007503414 0.01632353 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 0.75266416 1.4233616 0.011621699 0.11205711 tau0[2] 1.59881401 0.8614592 0.007033785 0.05874234 tau0[3] 0.72463501 1.1146782 0.009101309 0.08210359 tau0[4] -0.09088307 0.8763952 0.007155737 0.04010700 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 1.16691397 1.2398915 0.010123672 0.05612676 tau0[2] 1.84850825 0.9032180 0.007374744 0.06649899 tau0[3] 0.32194137 0.9697234 0.007917758 0.06623973 tau0[4] -0.03049322 0.8739839 0.007136049 0.01174309 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.060536 0.9658848 0.007886416 0.01776182 gamma0[2] 1.860781 0.7213590 0.005889872 0.03064877 gamma0[3] 1.410246 0.7077415 0.005778685 0.03672340 gamma0[4] 1.542660 1.6175385 0.013207147 0.02163042 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.827353 1.6824124 0.013736840 0.04951921 gamma0[2] 2.148856 0.6374149 0.005204471 0.02853103 gamma0[3] 1.840956 0.7657390 0.006252233 0.05359678 gamma0[4] 1.513164 1.2977592 0.010596159 0.03218613 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 2.006647 1.3773930 0.011246367 0.02862469 gamma0[2] 2.018172 0.6500389 0.005307546 0.02375310 gamma0[3] 1.661736 0.6948531 0.005673452 0.03876411 gamma0[4] 1.594489 1.6759269 0.013683886 0.01863489 MCMC run did not converge, proceeding anyway. Calculating model fit indexes for K3 Simulation Stan unordered @ 4 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -533.31769 60.92595 1188.48728 60.92595 1188.48728 lppd lppd.bayes pDIC DIC pDICalt DICalt -563.7807 -1038.3655 -949.1697 178.3916 118.9610 2314.6530 Analaysis complete for K3 Simulation Stan unordered @ 4 > proc.time() user system elapsed 8452.004 22.207 8480.419