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 @ 3 Removing 0 of 10 Level 2 units for length. Calculating initial values for chain 1 ; K2 Simulation Stan unordered @ 3 number of iterations= 178 number of iterations= 105 number of iterations= 63 number of iterations= 23 number of iterations= 148 number of iterations= 85 number of iterations= 125 number of iterations= 123 number of iterations= 57 number of iterations= 135 Calculating initial values for chain 2 ; K2 Simulation Stan unordered @ 3 number of iterations= 21 number of iterations= 39 number of iterations= 40 number of iterations= 90 One of the variances is going to zero; trying new starting values. number of iterations= 429 number of iterations= 399 number of iterations= 89 number of iterations= 342 One of the variances is going to zero; trying new starting values. number of iterations= 65 number of iterations= 239 Calculating initial values for chain 3 ; K2 Simulation Stan unordered @ 3 number of iterations= 228 number of iterations= 68 number of iterations= 28 number of iterations= 22 number of iterations= 54 number of iterations= 160 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= 105 number of iterations= 27 number of iterations= 77 number of iterations= 490 **************** Running Model for K2 Simulation Stan unordered @ 3 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). Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Informational Message: The current Metropolis proposal is about to be rejected becuase of the following issue: Error in function stan::prob::normal_log(N4stan5agrad3varE): Scale parameter is 0:0, but must be > 0! If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. Iteration: 1 / 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: 39.1137 seconds (Warm-up) 103.89 seconds (Sampling) 143.003 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. 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: 156.666 seconds (Warm-up) 909.309 seconds (Sampling) 1065.97 seconds (Total) SAMPLING FOR MODEL 'hierModel1p' NOW (CHAIN 3). Iteration: 1 / 6000 [ 0%] (Warmup) 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: 81.13 seconds (Warm-up) 372.436 seconds (Sampling) 453.566 seconds (Total) Labeling components for level 2 model K2 Simulation Stan unordered @ 3 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 @ 3 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 -626.45417 12.16534 0.09933 0.27286 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -653.7 -633.8 -625.0 -617.8 -606.1 Potential scale reduction factors: Point est. Upper C.I. lp__ 1.53 2.4 lp__ 1768.511 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 12.68222 4.70499 0.03842 0.06653 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 5.457 9.293 12.043 15.364 23.586 Potential scale reduction factors: Point est. Upper C.I. alphaN 1.01 1.05 alphaN 5788.468 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] 0.3538 0.3361 0.002744 0.008981 alpha0[2] 0.4874 0.1923 0.001570 0.004582 alpha0[3] 0.1588 0.1699 0.001387 0.004856 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 0.0001078 0.006103 0.54872 0.6834 0.7679 alpha0[2] 0.2230031 0.307212 0.41099 0.6794 0.7649 alpha0[3] 0.0001204 0.005748 0.01898 0.3098 0.4359 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1 1.01 alpha0[3] 1 1.01 Multivariate psrf 1 alpha0[1] alpha0[2] alpha0[3] 5742.720 5678.290 5667.145 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] -3.837e+02 579.9006 4.734868 15.59475 mu0[2] 9.974e-03 0.5125 0.004185 0.01266 mu0[3] 4.286e+02 595.3951 4.861380 16.90118 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1945.7479 -649.7620 -0.77347 -0.4224 -0.07573 mu0[2] -0.7579 -0.4312 -0.05114 0.4344 0.98522 mu0[3] -0.1322 0.4368 65.50332 739.0015 1973.55596 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1.00 1.01 mu0[2] 1.00 1.00 mu0[3] 1.01 1.02 Multivariate psrf 1.01 mu0[1] mu0[2] mu0[3] 5044.471 4962.571 5475.460 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] 1.0940 1.5604 0.012741 0.020103 beta0[2] 0.7554 0.3053 0.002493 0.006542 beta0[3] 1.3082 1.8140 0.014811 0.024715 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.2053 0.4861 0.6423 1.0370 5.093 beta0[2] 0.3630 0.5399 0.6868 0.9025 1.513 beta0[3] 0.1877 0.6149 0.8788 1.2943 5.429 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1.02 1.02 beta0[2] 1.01 1.02 beta0[3] 1.00 1.00 Multivariate psrf 1.01 beta0[1] beta0[2] beta0[3] 6279.221 4107.535 6443.278 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] 0.9649 1.2154 0.009924 0.02724 tau0[2] 0.6994 1.2494 0.010201 0.03055 tau0[3] -0.1716 0.8502 0.006941 0.01203 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -1.578 -0.03676 1.4356 1.9801 2.438 tau0[2] -1.092 -0.47359 0.3115 1.9520 2.445 tau0[3] -1.686 -0.69339 -0.2973 0.2645 1.834 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1 1.01 tau0[2] 1 1.01 tau0[3] 1 1.01 Multivariate psrf 1 tau0[1] tau0[2] tau0[3] 5170.127 5446.444 5894.494 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] 1.263 1.4779 0.012067 0.020630 gamma0[2] 1.124 0.4002 0.003268 0.008095 gamma0[3] 1.510 1.5945 0.013019 0.019409 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.1924 0.6545 0.9046 1.288 5.191 gamma0[2] 0.5444 0.8367 1.0572 1.333 2.063 gamma0[3] 0.1999 0.8247 1.1807 1.632 5.642 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1 1.00 gamma0[2] 1 1.01 gamma0[3] 1 1.00 Multivariate psrf 1 gamma0[1] gamma0[2] gamma0[3] 5564.591 3163.982 6866.896 Chains of length 5000 for K2 Simulation Stan unordered @ 3 did not converge in run 1 . Maximum Rhat value = 1.533833 . lp__ [[ 1 ]] Mean SD Naive SE Time-series SE -621.6246072 8.9151355 0.1260791 0.3340413 lp__ [[ 2 ]] Mean SD Naive SE Time-series SE -636.0242835 12.1468445 0.1717823 0.6701436 lp__ [[ 3 ]] Mean SD Naive SE Time-series SE -621.7136227 8.9223597 0.1261812 0.3307571 alphaN [[ 1 ]] Mean SD Naive SE Time-series SE 13.08352622 4.87299499 0.06891456 0.13770381 alphaN [[ 2 ]] Mean SD Naive SE Time-series SE 11.95098153 4.47971594 0.06335275 0.08179062 alphaN [[ 3 ]] Mean SD Naive SE Time-series SE 13.01214002 4.66914811 0.06603173 0.11912097 alpha0 [[ 1 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3837939 0.3308312 0.004678660 0.02486376 alpha0[2] 0.4719892 0.1915444 0.002708846 0.01242893 alpha0[3] 0.1442169 0.1686652 0.002385285 0.01359515 alpha0 [[ 2 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3357846 0.3393300 0.004798851 0.005265574 alpha0[2] 0.4988769 0.1916191 0.002709903 0.003041425 alpha0[3] 0.1653385 0.1718210 0.002429916 0.002679378 alpha0 [[ 3 ]] Mean SD Naive SE Time-series SE alpha0[1] 0.3417390 0.3361844 0.004754365 0.008944233 alpha0[2] 0.4913605 0.1927117 0.002725355 0.005023920 alpha0[3] 0.1669006 0.1683730 0.002381154 0.004498462 mu0 [[ 1 ]] Mean SD Naive SE Time-series SE mu0[1] -345.85977209 560.3958924 7.925194713 43.2817858 mu0[2] 0.03625215 0.5090579 0.007199166 0.0340128 mu0[3] 488.40576764 623.0128545 8.810732284 47.4347637 mu0 [[ 2 ]] Mean SD Naive SE Time-series SE mu0[1] -3.942538e+02 570.3687515 8.066232239 10.172246655 mu0[2] -6.006614e-03 0.5090478 0.007199023 0.008651506 mu0[3] 3.867747e+02 571.2404281 8.078559608 9.209728337 mu0 [[ 3 ]] Mean SD Naive SE Time-series SE mu0[1] -4.111347e+02 606.0760063 8.571209079 14.55944063 mu0[2] -3.225251e-04 0.5184632 0.007332176 0.01451124 mu0[3] 4.107641e+02 586.1072795 8.288808637 15.36144879 beta0 [[ 1 ]] Mean SD Naive SE Time-series SE beta0[1] 1.0143954 1.289552 0.018237012 0.03506338 beta0[2] 0.7839551 0.324338 0.004586831 0.01635184 beta0[3] 1.3599662 1.833474 0.025929237 0.05284078 beta0 [[ 2 ]] Mean SD Naive SE Time-series SE beta0[1] 1.1676809 1.7758063 0.025113694 0.031516778 beta0[2] 0.7381897 0.2938624 0.004155842 0.005654361 beta0[3] 1.2658680 1.7300712 0.024466902 0.030212877 beta0 [[ 3 ]] Mean SD Naive SE Time-series SE beta0[1] 1.0999513 1.5740414 0.022260306 0.037606541 beta0[2] 0.7440001 0.2948565 0.004169901 0.009263552 beta0[3] 1.2988483 1.8745631 0.026510325 0.042338797 tau0 [[ 1 ]] Mean SD Naive SE Time-series SE tau0[1] 1.0326113 1.1973413 0.01693296 0.07167162 tau0[2] 0.6145742 1.2375882 0.01750214 0.08293790 tau0[3] -0.1196239 0.8821361 0.01247529 0.02422939 tau0 [[ 2 ]] Mean SD Naive SE Time-series SE tau0[1] 0.9277727 1.2198393 0.01725113 0.02047473 tau0[2] 0.7484544 1.2560435 0.01776314 0.02034619 tau0[3] -0.2086033 0.8319608 0.01176570 0.01477045 tau0 [[ 3 ]] Mean SD Naive SE Time-series SE tau0[1] 0.9343030 1.2263111 0.01734266 0.03348121 tau0[2] 0.7350542 1.2503050 0.01768198 0.03326477 tau0[3] -0.1865530 0.8330385 0.01178094 0.02229295 gamma0 [[ 1 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.266076 1.4093809 0.01993166 0.03894845 gamma0[2] 1.139980 0.3967046 0.00561025 0.01849192 gamma0[3] 1.521667 1.6113639 0.02278813 0.03392819 gamma0 [[ 2 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.277590 1.537551 0.021744250 0.029395611 gamma0[2] 1.101727 0.391691 0.005539347 0.009840554 gamma0[3] 1.506125 1.568002 0.022174900 0.030467233 gamma0 [[ 3 ]] Mean SD Naive SE Time-series SE gamma0[1] 1.245165 1.4839525 0.020986257 0.03807111 gamma0[2] 1.130043 0.4110672 0.005813369 0.01228618 gamma0[3] 1.502996 1.6040988 0.022685383 0.03620880 **************** Running Model for K2 Simulation Stan unordered @ 3 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: 286.099 seconds (Warm-up) 1876.36 seconds (Sampling) 2162.46 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. 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: 372.874 seconds (Warm-up) 1102.84 seconds (Sampling) 1475.71 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. 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: 411.845 seconds (Warm-up) 1965.21 seconds (Sampling) 2377.06 seconds (Total) Labeling components for level 2 model K2 Simulation Stan unordered @ 3 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 @ 3 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 -636.20169 11.91971 0.06882 0.28484 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% -658.9 -644.4 -636.4 -628.1 -612.8 Potential scale reduction factors: Point est. Upper C.I. lp__ 1 1 lp__ 1762.844 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 11.99901 4.48889 0.02592 0.03454 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% 4.983 8.751 11.402 14.573 22.416 Potential scale reduction factors: Point est. Upper C.I. alphaN 1 1 alphaN 17663.52 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] 0.3439 0.3386 0.0015961 0.003441 alpha0[2] 0.4938 0.1917 0.0009038 0.001865 alpha0[3] 0.1623 0.1716 0.0008090 0.001694 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% alpha0[1] 1.884e-05 0.00223 0.3310 0.6826 0.7709 alpha0[2] 2.233e-01 0.31088 0.4454 0.6809 0.7688 alpha0[3] 1.588e-05 0.00208 0.0194 0.3148 0.4350 Potential scale reduction factors: Point est. Upper C.I. alpha0[2] 1 1 alpha0[3] 1 1 Multivariate psrf 1 alpha0[1] alpha0[2] alpha0[3] 19264.47 18648.12 19448.91 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] -3.942e+02 584.0791 2.753375 5.734847 mu0[2] -4.986e-03 0.5131 0.002419 0.005198 mu0[3] 4.068e+02 584.2231 2.754054 6.180515 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% mu0[1] -1952.9623 -669.1413 -0.86444 -0.4390 -0.1022 mu0[2] -0.7657 -0.4436 -0.09628 0.4256 0.9766 mu0[3] -0.1376 0.4259 24.25069 683.9593 1954.3151 Potential scale reduction factors: Point est. Upper C.I. mu0[1] 1 1 mu0[2] 1 1 mu0[3] 1 1 Multivariate psrf 1 mu0[1] mu0[2] mu0[3] 19952.94 14990.51 19487.41 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] 1.1214 1.6342 0.007704 0.010176 beta0[2] 0.7463 0.3021 0.001424 0.002902 beta0[3] 1.2698 1.6311 0.007689 0.010852 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% beta0[1] 0.2016 0.4830 0.6440 1.0653 5.149 beta0[2] 0.3647 0.5332 0.6783 0.8881 1.493 beta0[3] 0.1898 0.6088 0.8637 1.2770 5.263 Potential scale reduction factors: Point est. Upper C.I. beta0[1] 1 1 beta0[2] 1 1 beta0[3] 1 1 Multivariate psrf 1 beta0[1] beta0[2] beta0[3] 26175.53 12324.62 22943.01 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] 0.9391 1.2214 0.005758 0.011159 tau0[2] 0.7242 1.2419 0.005854 0.012230 tau0[3] -0.1763 0.8432 0.003975 0.005808 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% tau0[1] -1.633 -0.04936 1.3794 1.9645 2.442 tau0[2] -1.101 -0.45974 0.5072 1.9475 2.436 tau0[3] -1.658 -0.69375 -0.3023 0.2447 1.862 Potential scale reduction factors: Point est. Upper C.I. tau0[1] 1 1 tau0[2] 1 1 tau0[3] 1 1 Multivariate psrf 1 tau0[1] tau0[2] tau0[3] 18745.57 18078.46 21507.48 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] 1.288 1.5237 0.007183 0.009929 gamma0[2] 1.132 0.4033 0.001901 0.004339 gamma0[3] 1.495 1.5331 0.007227 0.009808 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% gamma0[1] 0.1943 0.6593 0.9166 1.323 5.160 gamma0[2] 0.5382 0.8424 1.0703 1.353 2.069 gamma0[3] 0.1934 0.8376 1.1906 1.630 5.426 Potential scale reduction factors: Point est. Upper C.I. gamma0[1] 1 1 gamma0[2] 1 1 gamma0[3] 1 1 Multivariate psrf 1 gamma0[1] gamma0[2] gamma0[3] 23874.145 8883.564 24473.462 Calculating model fit indexes for K2 Simulation Stan unordered @ 3 lppd pWAIC1 WAIC1 pWAIC2 WAIC2 -529.96243 35.50992 1130.94470 35.50992 1130.94470 lppd lppd.bayes pDIC DIC pDICalt DICalt -547.71739 -1129.40042 -1163.36606 -67.93129 56.48489 2371.77063 Analaysis complete for K2 Simulation Stan unordered @ 3 > proc.time() user system elapsed 7874.573 16.569 7895.417