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rstan (Version 2.2.0, packaged: 2014-02-14 04:29:17 UTC, GitRev: 52d7b230aaa0)
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Attaching package: ‘boot’
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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 @ 3
Removing 0 of 10 Level 2 units for length.
Calculating initial values for chain 1 ; K3 Simulation Stan unordered @ 3
number of iterations= 51
One of the variances is going to zero; trying new starting values.
number of iterations= 110
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Calculating initial values for chain 2 ; K3 Simulation Stan unordered @ 3
One of the variances is going to zero; trying new starting values.
One of the variances is going to zero; trying new starting values.
One of the variances is going to zero; trying new starting values.
One of the variances is going to zero; trying new starting values.
One of the variances is going to zero; trying new starting values.
One of the variances is going to zero; trying new starting values.
One of the variances is going to zero; trying new starting values.
One of the variances is going to zero; trying new starting values.
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One of the variances is going to zero; trying new starting values.
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Calculating initial values for chain 3 ; K3 Simulation Stan unordered @ 3
number of iterations= 79
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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= 39
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number of iterations= 219
****************
Running Model for K3 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.
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)
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Elapsed Time: 64.805 seconds (Warm-up)
316.959 seconds (Sampling)
381.764 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)
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Elapsed Time: 66.4396 seconds (Warm-up)
289.809 seconds (Sampling)
356.249 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): 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)
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Elapsed Time: 98.8965 seconds (Warm-up)
491.589 seconds (Sampling)
590.486 seconds (Total)
Labeling components for level 2 model K3 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 K3 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
-616.76246 10.47600 0.08554 0.24924
2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
-638.1 -623.7 -616.5 -609.6 -597.0
Potential scale reduction factors:
Point est. Upper C.I.
lp__ 1.04 1.14
lp__
1708.282
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
13.30699 4.58207 0.03741 0.05787
2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
6.04 10.02 12.75 16.00 23.80
Potential scale reduction factors:
Point est. Upper C.I.
alphaN 1.03 1.1
alphaN
6904.41
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] 0.4131 0.06807 0.0005558 0.0010255
alpha0[2] 0.4128 0.06296 0.0005141 0.0008883
alpha0[3] 0.1740 0.05975 0.0004879 0.0012840
2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
alpha0[1] 0.27494 0.3676 0.4147 0.4584 0.5439
alpha0[2] 0.28929 0.3717 0.4123 0.4543 0.5363
alpha0[3] 0.08133 0.1307 0.1655 0.2084 0.3114
Potential scale reduction factors:
Point est. Upper C.I.
alpha0[2] 1.00 1.00
alpha0[3] 1.14 1.41
Multivariate psrf
1.14
alpha0[1] alpha0[2] alpha0[3]
4729.853 5421.687 2114.486
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] -0.8429 0.2159 0.001763 0.004618
mu0[2] -0.3842 0.2157 0.001761 0.003485
mu0[3] 1.3577 0.4991 0.004075 0.009991
2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
mu0[1] -1.2044 -1.0192 -0.8433 -0.6834 -0.43331
mu0[2] -0.7716 -0.5296 -0.3954 -0.2516 0.07141
mu0[3] 0.3115 1.0281 1.3942 1.7160 2.20931
Potential scale reduction factors:
Point est. Upper C.I.
mu0[1] 1.53 2.36
mu0[2] 1.06 1.19
mu0[3] 1.06 1.19
Multivariate psrf
1.5
mu0[1] mu0[2] mu0[3]
2370.169 3800.723 2391.535
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] 0.4807 0.2454 0.002004 0.007443
beta0[2] 0.6345 0.1975 0.001613 0.003411
beta0[3] 0.6890 0.3873 0.003162 0.008481
2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
beta0[1] 0.09231 0.2988 0.4736 0.6329 1.007
beta0[2] 0.35094 0.5012 0.6015 0.7278 1.116
beta0[3] 0.13739 0.3975 0.6249 0.9111 1.578
Potential scale reduction factors:
Point est. Upper C.I.
beta0[1] 1.67 2.64
beta0[2] 1.01 1.01
beta0[3] 1.00 1.00
Multivariate psrf
1.5
beta0[1] beta0[2] beta0[3]
1262.441 4982.193 2393.099
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] 1.538 0.8050 0.006573 0.01813
tau0[2] 2.077 0.9506 0.007762 0.03358
tau0[3] -0.269 0.6127 0.005003 0.01323
2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
tau0[1] 0.04465 0.9917 1.4993 2.03489 3.281
tau0[2] 0.20194 1.3905 2.1236 2.82378 3.697
tau0[3] -1.23803 -0.7040 -0.3562 0.08377 1.124
Potential scale reduction factors:
Point est. Upper C.I.
tau0[1] 1.04 1.14
tau0[2] 1.13 1.40
tau0[3] 1.06 1.19
Multivariate psrf
1.15
tau0[1] tau0[2] tau0[3]
2543.7611 726.1588 2594.2593
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] 2.146 0.6227 0.005084 0.01100
gamma0[2] 1.907 0.6862 0.005603 0.01903
gamma0[3] 1.319 0.6255 0.005107 0.01277
2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
gamma0[1] 1.1709 1.7214 2.056 2.474 3.597
gamma0[2] 0.8329 1.4271 1.821 2.289 3.549
gamma0[3] 0.4506 0.8487 1.203 1.675 2.818
Potential scale reduction factors:
Point est. Upper C.I.
gamma0[1] 1.01 1.02
gamma0[2] 1.06 1.20
gamma0[3] 1.09 1.28
Multivariate psrf
1.13
gamma0[1] gamma0[2] gamma0[3]
3720.001 1391.854 2253.634
Chains of length 5000 for K3 Simulation Stan unordered @ 3 did not converge in run 1 .
Maximum Rhat value = 1.500166 .
lp__ [[ 1 ]]
Mean SD Naive SE Time-series SE
-619.6171151 10.4124597 0.1472544 0.4412830
lp__ [[ 2 ]]
Mean SD Naive SE Time-series SE
-615.7258074 10.3320306 0.1461170 0.4111837
lp__ [[ 3 ]]
Mean SD Naive SE Time-series SE
-614.9444479 10.0787957 0.1425357 0.4419317
alphaN [[ 1 ]]
Mean SD Naive SE Time-series SE
12.24322950 4.33935478 0.06136774 0.12201440
alphaN [[ 2 ]]
Mean SD Naive SE Time-series SE
13.79608517 4.55220496 0.06437790 0.08162934
alphaN [[ 3 ]]
Mean SD Naive SE Time-series SE
13.88164907 4.66266580 0.06594005 0.09270504
alpha0 [[ 1 ]]
Mean SD Naive SE Time-series SE
alpha0[1] 0.3864068 0.06756858 0.0009555640 0.002273420
alpha0[2] 0.4124028 0.06005367 0.0008492871 0.001257084
alpha0[3] 0.2011904 0.06380767 0.0009023767 0.002723708
alpha0 [[ 2 ]]
Mean SD Naive SE Time-series SE
alpha0[1] 0.4270223 0.06536913 0.0009244592 0.001473331
alpha0[2] 0.4133796 0.06661240 0.0009420417 0.001854598
alpha0[3] 0.1595980 0.05438198 0.0007690773 0.002155746
alpha0 [[ 3 ]]
Mean SD Naive SE Time-series SE
alpha0[1] 0.4259131 0.06318920 0.0008936303 0.001458140
alpha0[2] 0.4127637 0.06205467 0.0008775855 0.001442960
alpha0[3] 0.1613232 0.05075000 0.0007177133 0.001664789
mu0 [[ 1 ]]
Mean SD Naive SE Time-series SE
mu0[1] -1.0158385 0.1503759 0.002126636 0.011198902
mu0[2] -0.3166227 0.2120658 0.002999063 0.005979147
mu0[3] 1.1978327 0.4804137 0.006794075 0.017287893
mu0 [[ 2 ]]
Mean SD Naive SE Time-series SE
mu0[1] -0.7474484 0.1900344 0.002687493 0.005485041
mu0[2] -0.4272561 0.2037789 0.002881869 0.006727856
mu0[3] 1.4492850 0.4996708 0.007066412 0.018536323
mu0 [[ 3 ]]
Mean SD Naive SE Time-series SE
mu0[1] -0.7654309 0.1898931 0.002685494 0.006036651
mu0[2] -0.4086797 0.2145530 0.003034237 0.005321812
mu0[3] 1.4259642 0.4779536 0.006759285 0.015997130
beta0 [[ 1 ]]
Mean SD Naive SE Time-series SE
beta0[1] 0.2675990 0.1756948 0.002484700 0.018956721
beta0[2] 0.6224272 0.1832442 0.002591464 0.004002747
beta0[3] 0.6773111 0.3664392 0.005182233 0.011418430
beta0 [[ 2 ]]
Mean SD Naive SE Time-series SE
beta0[1] 0.5994198 0.2090297 0.002956126 0.008127535
beta0[2] 0.6379930 0.2140780 0.003027520 0.008490911
beta0[3] 0.6900275 0.3974373 0.005620612 0.013229169
beta0 [[ 3 ]]
Mean SD Naive SE Time-series SE
beta0[1] 0.5751799 0.1941264 0.002745361 0.008553485
beta0[2] 0.6429961 0.1934423 0.002735687 0.004077446
beta0[3] 0.6996432 0.3968629 0.005612489 0.018490623
tau0 [[ 1 ]]
Mean SD Naive SE Time-series SE
tau0[1] 1.3316453 0.6597754 0.009330634 0.01773809
tau0[2] 2.5119426 0.7927424 0.011211070 0.04889120
tau0[3] -0.4618085 0.5110294 0.007227047 0.01512779
tau0 [[ 2 ]]
Mean SD Naive SE Time-series SE
tau0[1] 1.6728758 0.8450972 0.01195148 0.03152930
tau0[2] 1.8265446 0.9463612 0.01338357 0.06236882
tau0[3] -0.1778415 0.6242233 0.00882785 0.01956274
tau0 [[ 3 ]]
Mean SD Naive SE Time-series SE
tau0[1] 1.6107933 0.8538130 0.012074739 0.04061946
tau0[2] 1.8927543 0.9493713 0.013426138 0.06219372
tau0[3] -0.1673247 0.6478354 0.009161776 0.03106333
gamma0 [[ 1 ]]
Mean SD Naive SE Time-series SE
gamma0[1] 2.214494 0.5929675 0.008385827 0.01408161
gamma0[2] 1.683325 0.6303388 0.008914336 0.03105437
gamma0[3] 1.083274 0.4967780 0.007025503 0.01911896
gamma0 [[ 2 ]]
Mean SD Naive SE Time-series SE
gamma0[1] 2.104871 0.6455945 0.009130085 0.02394333
gamma0[2] 2.020043 0.6763651 0.009565247 0.03999133
gamma0[3] 1.435609 0.6380674 0.009023636 0.02108213
gamma0 [[ 3 ]]
Mean SD Naive SE Time-series SE
gamma0[1] 2.118493 0.6228407 0.008808298 0.01783351
gamma0[2] 2.018809 0.6947689 0.009825516 0.02637675
gamma0[3] 1.439011 0.6606066 0.009342389 0.02566606
****************
Running Model for K3 Simulation Stan unordered @ 3 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.
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)
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Iteration: 10800 / 12000 [ 90%] (Sampling)
Iteration: 12000 / 12000 [100%] (Sampling)
Elapsed Time: 125.786 seconds (Warm-up)
693.024 seconds (Sampling)
818.81 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.
Iteration: 1200 / 12000 [ 10%] (Warmup)
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Elapsed Time: 106.315 seconds (Warm-up)
491.549 seconds (Sampling)
597.864 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.
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.
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: 125.246 seconds (Warm-up)
394.371 seconds (Sampling)
519.617 seconds (Total)
Labeling components for level 2 model K3 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 K3 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
-614.88360 10.11160 0.05838 0.16175
2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
-635.7 -621.5 -614.6 -607.8 -596.2
Potential scale reduction factors:
Point est. Upper C.I.
lp__ 1 1
lp__
4049.295
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
13.57127 4.63606 0.02677 0.04128
2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
6.356 10.185 12.946 16.313 24.280
Potential scale reduction factors:
Point est. Upper C.I.
alphaN 1.03 1.1
alphaN
13032.16
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] 0.4266 0.06367 0.0003002 0.0009676
alpha0[2] 0.3925 0.07516 0.0003543 0.0022299
alpha0[3] 0.1809 0.06923 0.0003264 0.0013408
2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
alpha0[1] 0.29784 0.3851 0.4277 0.4696 0.5493
alpha0[2] 0.22478 0.3478 0.3979 0.4433 0.5273
alpha0[3] 0.08045 0.1305 0.1685 0.2190 0.3484
Potential scale reduction factors:
Point est. Upper C.I.
alpha0[2] 1.13 1.38
alpha0[3] 1.31 1.83
Multivariate psrf
1.25
alpha0[1] alpha0[2] alpha0[3]
7737.125 5525.038 2982.202
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] -0.9234 0.1692 0.0007977 0.008037
mu0[2] -0.1834 0.2849 0.0013431 0.012760
mu0[3] 1.2396 0.5645 0.0026610 0.013238
2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
mu0[1] -1.1907 -1.0352 -0.9512 -0.83744 -0.5177
mu0[2] -0.7154 -0.3845 -0.1872 0.00559 0.3908
mu0[3] 0.1625 0.8171 1.2778 1.67207 2.2052
Potential scale reduction factors:
Point est. Upper C.I.
mu0[1] 1.04 1.14
mu0[2] 1.04 1.14
mu0[3] 1.24 1.67
Multivariate psrf
1.23
mu0[1] mu0[2] mu0[3]
567.6133 493.9351 2508.4469
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] 0.3640 0.1949 0.0009186 0.013598
beta0[2] 0.6528 0.2712 0.0012787 0.004631
beta0[3] 0.7723 0.4225 0.0019919 0.009044
2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
beta0[1] 0.1312 0.2335 0.3066 0.4434 0.8676
beta0[2] 0.2890 0.4775 0.5995 0.7601 1.3472
beta0[3] 0.1581 0.4465 0.7127 1.0291 1.7346
Potential scale reduction factors:
Point est. Upper C.I.
beta0[1] 1.06 1.20
beta0[2] 1.09 1.22
beta0[3] 1.09 1.27
Multivariate psrf
1.16
beta0[1] beta0[2] beta0[3]
462.7261 3480.7477 4057.7878
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] 1.90246 0.7919 0.003733 0.02339
tau0[2] 1.43592 0.9584 0.004518 0.07122
tau0[3] 0.01507 0.8225 0.003877 0.03972
2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
tau0[1] 0.2671 1.3763 1.9443 2.4671 3.328
tau0[2] -0.3916 0.7961 1.3846 2.0377 3.410
tau0[3] -1.2372 -0.5838 -0.1109 0.4884 1.980
Potential scale reduction factors:
Point est. Upper C.I.
tau0[1] 1.00 1.01
tau0[2] 1.03 1.06
tau0[3] 1.15 1.43
Multivariate psrf
1.11
tau0[1] tau0[2] tau0[3]
1665.367 673.160 2213.323
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] 2.144 0.6432 0.003032 0.006814
gamma0[2] 2.029 0.6539 0.003082 0.014159
gamma0[3] 1.566 0.7173 0.003382 0.031854
2. Quantiles for each variable:
2.5% 25% 50% 75% 97.5%
gamma0[1] 1.1888 1.701 2.042 2.471 3.682
gamma0[2] 0.9363 1.596 1.950 2.380 3.563
gamma0[3] 0.4885 1.021 1.491 1.993 3.207
Potential scale reduction factors:
Point est. Upper C.I.
gamma0[1] 1.00 1.00
gamma0[2] 1.00 1.01
gamma0[3] 1.04 1.13
Multivariate psrf
1.04
gamma0[1] gamma0[2] gamma0[3]
9313.606 3476.199 3208.698
Chains of length 10000 for K3 Simulation Stan unordered @ 3 did not converge in run 2 .
Maximum Rhat value = 1.249106 .
lp__ [[ 1 ]]
Mean SD Naive SE Time-series SE
-614.54009201 9.76342202 0.09763422 0.24091651
lp__ [[ 2 ]]
Mean SD Naive SE Time-series SE
-615.1568706 10.1976433 0.1019764 0.2807325
lp__ [[ 3 ]]
Mean SD Naive SE Time-series SE
-614.9538455 10.3556778 0.1035568 0.3140377
alphaN [[ 1 ]]
Mean SD Naive SE Time-series SE
12.48848291 4.22392296 0.04223923 0.05777725
alphaN [[ 2 ]]
Mean SD Naive SE Time-series SE
14.05403403 4.71595976 0.04715960 0.07065654
alphaN [[ 3 ]]
Mean SD Naive SE Time-series SE
14.17129461 4.75771811 0.04757718 0.08367963
alpha0 [[ 1 ]]
Mean SD Naive SE Time-series SE
alpha0[1] 0.4157984 0.06835481 0.0005581147 0.002516967
alpha0[2] 0.3598621 0.08189726 0.0006686883 0.006437694
alpha0[3] 0.2243394 0.07276210 0.0005941001 0.003321494
alpha0 [[ 2 ]]
Mean SD Naive SE Time-series SE
alpha0[1] 0.4324096 0.06055383 0.0004944199 0.001013841
alpha0[2] 0.4075017 0.06644899 0.0005425537 0.001392763
alpha0[3] 0.1600887 0.05659043 0.0004620589 0.001747279
alpha0 [[ 3 ]]
Mean SD Naive SE Time-series SE
alpha0[1] 0.4316260 0.06040175 0.0004931782 0.001031002
alpha0[2] 0.4102505 0.06496288 0.0005304197 0.001169233
alpha0[3] 0.1581235 0.05517291 0.0004504849 0.001447244
mu0 [[ 1 ]]
Mean SD Naive SE Time-series SE
mu0[1] -0.9684290 0.1435294 0.001171913 0.007953424
mu0[2] -0.1093595 0.3105073 0.002535282 0.025598871
mu0[3] 0.9043139 0.5051192 0.004124281 0.033280203
mu0 [[ 2 ]]
Mean SD Naive SE Time-series SE
mu0[1] -0.9021052 0.1776050 0.001450138 0.01678516
mu0[2] -0.2190813 0.2624596 0.002142973 0.02111596
mu0[3] 1.4062046 0.5191605 0.004238928 0.01590489
mu0 [[ 3 ]]
Mean SD Naive SE Time-series SE
mu0[1] -0.8997536 0.1752687 0.001431063 0.01537282
mu0[2] -0.2218870 0.2646149 0.002160572 0.01908135
mu0[3] 1.4084147 0.5124428 0.004184078 0.01471583
beta0 [[ 1 ]]
Mean SD Naive SE Time-series SE
beta0[1] 0.3015564 0.1595321 0.001302574 0.008465941
beta0[2] 0.7238149 0.3454109 0.002820268 0.010763003
beta0[3] 0.9291315 0.4459400 0.003641085 0.023961968
beta0 [[ 2 ]]
Mean SD Naive SE Time-series SE
beta0[1] 0.3921809 0.2080824 0.001698986 0.031102548
beta0[2] 0.6101687 0.2158724 0.001762591 0.006484469
beta0[3] 0.6906967 0.3875210 0.003164096 0.009230504
beta0 [[ 3 ]]
Mean SD Naive SE Time-series SE
beta0[1] 0.3982300 0.1982421 0.001618640 0.025001650
beta0[2] 0.6244083 0.2171456 0.001772987 0.005926321
beta0[3] 0.6971790 0.3869531 0.003159459 0.008765234
tau0 [[ 1 ]]
Mean SD Naive SE Time-series SE
tau0[1] 1.838072 0.8019741 0.006548091 0.05655172
tau0[2] 1.318853 1.1567633 0.009444932 0.20319272
tau0[3] 0.369909 0.9675045 0.007899642 0.11553203
tau0 [[ 2 ]]
Mean SD Naive SE Time-series SE
tau0[1] 1.9337136 0.7724204 0.006306786 0.02674674
tau0[2] 1.4861486 0.8335710 0.006806079 0.04653814
tau0[3] -0.1695221 0.6740956 0.005503968 0.02133918
tau0 [[ 3 ]]
Mean SD Naive SE Time-series SE
tau0[1] 1.9355882 0.7972259 0.006509322 0.03175569
tau0[2] 1.5027457 0.8378214 0.006840783 0.04684103
tau0[3] -0.1551863 0.6710466 0.005479072 0.01982871
gamma0 [[ 1 ]]
Mean SD Naive SE Time-series SE
gamma0[1] 2.147850 0.6430383 0.005250386 0.01108869
gamma0[2] 1.981751 0.6797606 0.005550222 0.03574109
gamma0[3] 1.748650 0.7816572 0.006382204 0.09244888
gamma0 [[ 2 ]]
Mean SD Naive SE Time-series SE
gamma0[1] 2.147847 0.6483881 0.005294067 0.01064753
gamma0[2] 2.054912 0.6374492 0.005204751 0.01570814
gamma0[3] 1.468816 0.6566078 0.005361180 0.01522855
gamma0 [[ 3 ]]
Mean SD Naive SE Time-series SE
gamma0[1] 2.136920 0.6379685 0.005208991 0.01347222
gamma0[2] 2.049150 0.6410611 0.005234242 0.01673343
gamma0[3] 1.481891 0.6721241 0.005487870 0.01880008
MCMC run did not converge, proceeding anyway.
Calculating model fit indexes for K3 Simulation Stan unordered @ 3
lppd pWAIC1 WAIC1 pWAIC2 WAIC2
-533.29001 60.02722 1186.63445 60.02722 1186.63445
lppd lppd.bayes pDIC DIC pDICalt DICalt
-563.3036 -683.5002 -240.3932 886.2141 117.2020 1601.4045
Analaysis complete for K3 Simulation Stan unordered @ 3
> proc.time()
user system elapsed
3473.781 9.698 3485.561