Bài tập ANOVA Bayes !!!
Lê Minh Thuận UMP
2023-11-16
gọi thư viện
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## cutreelibrary(tidyverse)
library(cluster)
library(dplyr)
thuan = read.csv("E:\\OneDrive - UMP\\R - lenh 2016\\bvlvt02112022.csv", header=T)
attach(thuan)library(FactoMineR)
pca.ome <- PCA(thuan, graph = T)## Warning in PCA(thuan, graph = T): Missing values are imputed by the mean of the
## variable: you should use the imputePCA function of the missMDA package
get_eig(pca.ome)## eigenvalue variance.percent cumulative.variance.percent
## Dim.1 1.572658e+01 3.083643e+01 30.83643
## Dim.2 6.091225e+00 1.194358e+01 42.78001
## Dim.3 3.526289e+00 6.914292e+00 49.69430
## Dim.4 2.941474e+00 5.767596e+00 55.46189
## Dim.5 2.437581e+00 4.779570e+00 60.24146
## Dim.6 2.216688e+00 4.346447e+00 64.58791
## Dim.7 1.742316e+00 3.416306e+00 68.00422
## Dim.8 1.488365e+00 2.918364e+00 70.92258
## Dim.9 1.198734e+00 2.350458e+00 73.27304
## Dim.10 1.090694e+00 2.138616e+00 75.41165
## Dim.11 1.053070e+00 2.064843e+00 77.47650
## Dim.12 8.981883e-01 1.761154e+00 79.23765
## Dim.13 8.374660e-01 1.642090e+00 80.87974
## Dim.14 7.852821e-01 1.539769e+00 82.41951
## Dim.15 7.619128e-01 1.493947e+00 83.91346
## Dim.16 6.670315e-01 1.307905e+00 85.22136
## Dim.17 6.443879e-01 1.263506e+00 86.48487
## Dim.18 6.194712e-01 1.214649e+00 87.69952
## Dim.19 4.608157e-01 9.035603e-01 88.60308
## Dim.20 4.550068e-01 8.921701e-01 89.49525
## Dim.21 4.434991e-01 8.696060e-01 90.36485
## Dim.22 4.006276e-01 7.855444e-01 91.15040
## Dim.23 3.852650e-01 7.554215e-01 91.90582
## Dim.24 3.452121e-01 6.768865e-01 92.58270
## Dim.25 3.203328e-01 6.281035e-01 93.21081
## Dim.26 3.057978e-01 5.996035e-01 93.81041
## Dim.27 2.782583e-01 5.456044e-01 94.35602
## Dim.28 2.680467e-01 5.255818e-01 94.88160
## Dim.29 2.512086e-01 4.925660e-01 95.37416
## Dim.30 2.464986e-01 4.833306e-01 95.85749
## Dim.31 2.289479e-01 4.489174e-01 96.30641
## Dim.32 2.163304e-01 4.241772e-01 96.73059
## Dim.33 1.848280e-01 3.624079e-01 97.09300
## Dim.34 1.810496e-01 3.549992e-01 97.44800
## Dim.35 1.675115e-01 3.284539e-01 97.77645
## Dim.36 1.561930e-01 3.062608e-01 98.08271
## Dim.37 1.397313e-01 2.739829e-01 98.35669
## Dim.38 1.388412e-01 2.722376e-01 98.62893
## Dim.39 1.279358e-01 2.508546e-01 98.87979
## Dim.40 1.220846e-01 2.393816e-01 99.11917
## Dim.41 1.123400e-01 2.202746e-01 99.33944
## Dim.42 9.981139e-02 1.957086e-01 99.53515
## Dim.43 8.557182e-02 1.677879e-01 99.70294
## Dim.44 8.148942e-02 1.597832e-01 99.86272
## Dim.45 7.001167e-02 1.372778e-01 100.00000
## Dim.46 4.502314e-27 8.828066e-27 100.00000
## Dim.47 3.101521e-30 6.081413e-30 100.00000
## Dim.48 8.201051e-31 1.608049e-30 100.00000
## Dim.49 5.223314e-31 1.024179e-30 100.00000
## Dim.50 1.996670e-31 3.915040e-31 100.00000
## Dim.51 1.061922e-31 2.082199e-31 100.00000fviz_screeplot(pca.ome, addlabels=T, ylim = c(0, 50))
var <- get_pca_var(pca.ome)
var## Principal Component Analysis Results for variables
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for the variables"
## 2 "$cor" "Correlations between variables and dimensions"
## 3 "$cos2" "Cos2 for the variables"
## 4 "$contrib" "contributions of the variables"head(var$coord)## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## id -0.04880622 0.06766462 -0.002381938 -0.02978331 -0.004095345
## gioi -0.12570133 -0.08699425 0.125153165 -0.11087317 0.050399689
## thang 0.03992797 -0.02473749 -0.069547437 0.10578645 -0.070718875
## nam 0.04701565 -0.14160881 0.197359673 -0.05939562 0.126814987
## thamnien -0.01230854 -0.22820993 0.206052905 -0.04712547 0.068243945
## a3 -0.13030023 0.01699582 0.243556920 -0.40215270 0.312276865head(var$contrib)## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## id 0.0151466300 0.075165519 0.0001608951 0.0301565 0.0006880533
## gioi 0.1004720981 0.124244296 0.4441869689 0.4179149 0.1042069607
## thang 0.0101372532 0.010046308 0.1371653452 0.3804478 0.2051689828
## nam 0.0140556405 0.329212213 1.1045845751 0.1199344 0.6597542320
## thamnien 0.0009633377 0.854996705 1.2040364190 0.0754999 0.1910597804
## a3 0.1079583237 0.004742196 1.6822211327 5.4981552 4.0005586319fviz_pca_var(pca.ome, col.var = "blue")
fviz_pca_var(pca.ome, col.var="contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = T )
fviz_contrib(pca.ome, choice = "var", axes = 1, top = 10)
fviz_contrib(pca.ome, choice = "var", axes = 2, top = 10)
ind <- get_pca_ind(pca.ome)
ind## Principal Component Analysis Results for individuals
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for the individuals"
## 2 "$cos2" "Cos2 for the individuals"
## 3 "$contrib" "contributions of the individuals"head(ind$coord)## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## 1 -4.8026068 -0.5049304 -0.11961131 1.0568665 1.4066833
## 2 -4.8026869 -0.5047519 -0.11961957 1.0567534 1.4066662
## 3 0.0742801 1.2296056 1.48707116 -0.3760771 -0.9909968
## 4 -7.8936346 0.3570179 -0.84545357 0.1515572 -0.1694290
## 5 -3.3204308 -1.1039399 0.05008929 1.2022377 -3.2076433
## 6 -2.2520869 -1.0708266 0.05830314 -1.6595463 0.8614978Đọc
fviz_pca_ind(pca.ome, col.ind = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = T)
fviz_pca_biplot(pca.ome, repel = TRUE)
fviz_pca_ind(pca.ome, label = "none", palette = c("#00AFBB", "#E7B800", "#FC4E07"),addEllipses = T)
goi thư viện
library(lavaan)## This is lavaan 0.6-16
## lavaan is FREE software! Please report any bugs.library(lavaanPlot)
library(OpenMx)m1a <- ' UWES = ~ uwes1+ uwes2+ uwes3+ uwes4+ uwes5+ uwes6+ uwes7+ uwes8+ uwes9
GJS = ~ gjs1+ gjs2+ gjs3+ gjs4+ gjs5+ gjs6+ gjs7+ gjs8+ gjs9+ gjs10
WSS = ~ wss1+ wss2+ wss3+ wss4+ wss5+ wss6+ wss7+ wss8
BRCS = ~ brcs1+ brcs2+ brcs3+ brcs4
BRS = ~ brs1+ brs2+ brs3+ brs4+ brs5+ brs6
CH = ~ gioi*congviec + gioi*thamnien + congviec*thamnien '
m1a.1 <- cfa(m1a, data=thuan, std.lv=T)
summary(m1a.1, std=T) ## lavaan 0.6.16 ended normally after 54 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 93
## Number of equality constraints 1
##
## Number of observations 532
##
## Model Test User Model:
##
## Test statistic 4576.818
## Degrees of freedom 688
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## UWES =~
## uwes1 1.257 0.050 25.195 0.000 1.257 0.868
## uwes2 1.188 0.045 26.685 0.000 1.188 0.899
## uwes3 1.293 0.046 28.268 0.000 1.293 0.929
## uwes4 1.342 0.048 28.133 0.000 1.342 0.926
## uwes5 1.433 0.055 26.104 0.000 1.433 0.887
## uwes6 1.276 0.049 26.267 0.000 1.276 0.890
## uwes7 1.166 0.050 23.375 0.000 1.166 0.828
## uwes8 1.164 0.053 21.799 0.000 1.164 0.791
## uwes9 1.169 0.061 19.090 0.000 1.169 0.720
## GJS =~
## gjs1 0.618 0.031 19.813 0.000 0.618 0.744
## gjs2 0.354 0.028 12.667 0.000 0.354 0.522
## gjs3 0.681 0.030 22.789 0.000 0.681 0.818
## gjs4 0.819 0.034 24.091 0.000 0.819 0.848
## gjs5 0.849 0.034 25.249 0.000 0.849 0.873
## gjs6 0.830 0.035 23.637 0.000 0.830 0.838
## gjs7 0.884 0.046 19.128 0.000 0.884 0.725
## gjs8 0.666 0.034 19.842 0.000 0.666 0.745
## gjs9 0.488 0.029 17.112 0.000 0.488 0.667
## gjs10 0.715 0.029 24.782 0.000 0.715 0.863
## WSS =~
## wss1 0.780 0.034 22.869 0.000 0.780 0.827
## wss2 0.926 0.039 23.738 0.000 0.926 0.848
## wss3 0.848 0.036 23.859 0.000 0.848 0.850
## wss4 0.816 0.039 20.987 0.000 0.816 0.782
## wss5 0.885 0.039 22.521 0.000 0.885 0.819
## wss6 0.361 0.043 8.391 0.000 0.361 0.366
## wss7 0.204 0.043 4.756 0.000 0.204 0.212
## wss8 0.277 0.039 7.044 0.000 0.277 0.310
## BRCS =~
## brcs1 0.573 0.026 22.154 0.000 0.573 0.814
## brcs2 0.562 0.026 22.031 0.000 0.562 0.811
## brcs3 0.607 0.024 25.053 0.000 0.607 0.881
## brcs4 0.617 0.027 22.849 0.000 0.617 0.830
## BRS =~
## brs1 0.235 0.032 7.380 0.000 0.235 0.324
## brs2 0.598 0.034 17.658 0.000 0.598 0.691
## brs3 0.502 0.035 14.274 0.000 0.502 0.584
## brs4 0.976 0.038 25.640 0.000 0.976 0.891
## brs5 0.688 0.035 19.678 0.000 0.688 0.747
## brs6 0.961 0.036 26.342 0.000 0.961 0.906
## CH =~
## congvic (gioi) 0.864 0.143 6.025 0.000 0.864 0.754
## thamnin (gioi) 0.864 0.143 6.025 0.000 0.864 0.171
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## UWES ~~
## GJS 0.669 0.026 25.867 0.000 0.669 0.669
## WSS -0.250 0.043 -5.772 0.000 -0.250 -0.250
## BRCS 0.545 0.033 16.279 0.000 0.545 0.545
## BRS 0.225 0.044 5.146 0.000 0.225 0.225
## CH 0.068 0.059 1.156 0.248 0.068 0.068
## GJS ~~
## WSS -0.227 0.044 -5.120 0.000 -0.227 -0.227
## BRCS 0.569 0.033 17.300 0.000 0.569 0.569
## BRS 0.251 0.044 5.737 0.000 0.251 0.251
## CH 0.100 0.060 1.655 0.098 0.100 0.100
## WSS ~~
## BRCS -0.071 0.047 -1.490 0.136 -0.071 -0.071
## BRS 0.290 0.044 6.644 0.000 0.290 0.290
## CH -0.176 0.065 -2.725 0.006 -0.176 -0.176
## BRCS ~~
## BRS 0.348 0.042 8.208 0.000 0.348 0.348
## CH 0.155 0.064 2.421 0.015 0.155 0.155
## BRS ~~
## CH 0.173 0.065 2.684 0.007 0.173 0.173
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .uwes1 0.516 0.035 14.784 0.000 0.516 0.246
## .uwes2 0.336 0.024 14.211 0.000 0.336 0.192
## .uwes3 0.266 0.020 13.158 0.000 0.266 0.137
## .uwes4 0.298 0.022 13.277 0.000 0.298 0.142
## .uwes5 0.556 0.038 14.467 0.000 0.556 0.213
## .uwes6 0.426 0.030 14.400 0.000 0.426 0.207
## .uwes7 0.623 0.041 15.225 0.000 0.623 0.314
## .uwes8 0.813 0.053 15.482 0.000 0.813 0.375
## .uwes9 1.271 0.081 15.777 0.000 1.271 0.482
## .gjs1 0.308 0.020 15.233 0.000 0.308 0.447
## .gjs2 0.334 0.021 15.985 0.000 0.334 0.727
## .gjs3 0.229 0.016 14.541 0.000 0.229 0.330
## .gjs4 0.262 0.019 14.073 0.000 0.262 0.281
## .gjs5 0.225 0.017 13.509 0.000 0.225 0.238
## .gjs6 0.293 0.021 14.253 0.000 0.293 0.298
## .gjs7 0.704 0.046 15.346 0.000 0.704 0.474
## .gjs8 0.356 0.023 15.228 0.000 0.356 0.446
## .gjs9 0.297 0.019 15.613 0.000 0.297 0.555
## .gjs10 0.175 0.013 13.758 0.000 0.175 0.255
## .wss1 0.280 0.021 13.163 0.000 0.280 0.315
## .wss2 0.336 0.027 12.607 0.000 0.336 0.282
## .wss3 0.276 0.022 12.520 0.000 0.276 0.277
## .wss4 0.424 0.030 14.049 0.000 0.424 0.389
## .wss5 0.384 0.029 13.355 0.000 0.384 0.329
## .wss6 0.843 0.052 16.091 0.000 0.843 0.866
## .wss7 0.877 0.054 16.243 0.000 0.877 0.955
## .wss8 0.722 0.045 16.159 0.000 0.722 0.904
## .brcs1 0.168 0.013 13.063 0.000 0.168 0.338
## .brcs2 0.165 0.013 13.135 0.000 0.165 0.343
## .brcs3 0.107 0.010 10.554 0.000 0.107 0.224
## .brcs4 0.171 0.014 12.609 0.000 0.171 0.311
## .brs1 0.472 0.029 16.144 0.000 0.472 0.895
## .brs2 0.392 0.026 15.000 0.000 0.392 0.523
## .brs3 0.487 0.031 15.573 0.000 0.487 0.659
## .brs4 0.246 0.024 10.257 0.000 0.246 0.205
## .brs5 0.374 0.026 14.474 0.000 0.374 0.441
## .brs6 0.201 0.022 9.265 0.000 0.201 0.178
## .congviec 0.566 0.245 2.306 0.021 0.566 0.431
## .thamnien 24.862 1.544 16.106 0.000 24.862 0.971
## UWES 1.000 1.000 1.000
## GJS 1.000 1.000 1.000
## WSS 1.000 1.000 1.000
## BRCS 1.000 1.000 1.000
## BRS 1.000 1.000 1.000
## CH 1.000 1.000 1.000 library(semPlot)
fit <- sem(m1a, data = thuan)
lavaanPlot(model = fit, coefs = T, stand = T, covs = T,
edge_options = list(color="red"),
node_options = list(color="blue"),
sig = .05, digits = 2,
stars = c("regress","latent", "covs"))### Phân tích khác
library(BayesFactor)## Loading required package: coda## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:OpenMx':
##
## %&%, expm## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack## ************
## Welcome to BayesFactor 0.9.12-4.5. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
##
## Type BFManual() to open the manual.
## ************bf = ttestBF(formula = uwes_tol ~ gioi, data=thuan)
bf## Bayes factor analysis
## --------------
## [1] Alt., r=0.707 : 0.2293691 ±0.08%
##
## Against denominator:
## Null, mu1-mu2 = 0
## ---
## Bayes factor type: BFindepSample, JZSmcmc = posterior(bf, iter =10)
summary(mcmc)##
## Iterations = 1:10
## Thinning interval = 1
## Number of chains = 1
## Sample size per chain = 10
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## mu 40.1111 0.49901 0.15780 0.16700
## beta (1 - 2) 1.2952 0.42674 0.13495 0.08089
## sig2 130.7485 6.84357 2.16413 2.16413
## delta 0.1131 0.03686 0.01166 0.00642
## g 3.3530 9.24151 2.92242 2.92242
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## mu 39.20097 39.82513 40.3437 40.4032 40.642
## beta (1 - 2) 0.68218 1.08987 1.1813 1.4806 2.013
## sig2 120.45701 127.62974 129.7631 137.1886 139.417
## delta 0.06091 0.09555 0.1034 0.1318 0.176
## g 0.06145 0.15187 0.3440 0.8540 23.254table(mcmc [, 2] <0) # nam nho hon nu##
## FALSE
## 10# hoi qui tuyen tinh
library(rstanarm)## Loading required package: Rcpp## This is rstanarm version 2.26.1## - See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!## - Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.## - For execution on a local, multicore CPU with excess RAM we recommend calling## options(mc.cores = parallel::detectCores())library(BAS)
library(broom)
library(GGally)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2m1 = stan_glm(uwes_tol ~ congviec, data=thuan)## Found more than one class "family" in cache; using the first, from namespace 'lme4'## Also defined by 'MatrixModels'## Found more than one class "family" in cache; using the first, from namespace 'lme4'## Also defined by 'MatrixModels'## Found more than one class "family" in cache; using the first, from namespace 'lme4'## Also defined by 'MatrixModels'## Found more than one class "family" in cache; using the first, from namespace 'lme4'## Also defined by 'MatrixModels'##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 0.002253 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 22.53 seconds.
## Chain 1: Adjust your expectations accordingly!
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## Chain 1: 0.234 seconds (Sampling)
## Chain 1: 0.409 seconds (Total)
## Chain 1:
##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
## Chain 2:
## Chain 2: Gradient evaluation took 4.1e-05 seconds
## Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.41 seconds.
## Chain 2: Adjust your expectations accordingly!
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## Chain 2:
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## Chain 2:
##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
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## Chain 4:m1## stan_glm
## family: gaussian [identity]
## formula: uwes_tol ~ congviec
## observations: 532
## predictors: 2
## ------
## Median MAD_SD
## (Intercept) 39.1 1.1
## congviec 0.5 0.4
##
## Auxiliary parameter(s):
## Median MAD_SD
## sigma 11.5 0.4
##
## ------
## * For help interpreting the printed output see ?print.stanreg
## * For info on the priors used see ?prior_summary.stanregsummary(m1)##
## Model Info:
## function: stan_glm
## family: gaussian [identity]
## formula: uwes_tol ~ congviec
## algorithm: sampling
## sample: 4000 (posterior sample size)
## priors: see help('prior_summary')
## observations: 532
## predictors: 2
##
## Estimates:
## mean sd 10% 50% 90%
## (Intercept) 39.1 1.1 37.6 39.1 40.5
## congviec 0.5 0.4 -0.1 0.5 1.0
## sigma 11.5 0.4 11.1 11.5 12.0
##
## Fit Diagnostics:
## mean sd 10% 50% 90%
## mean_PPD 40.2 0.7 39.2 40.2 41.1
##
## The mean_ppd is the sample average posterior predictive distribution of the outcome variable (for details see help('summary.stanreg')).
##
## MCMC diagnostics
## mcse Rhat n_eff
## (Intercept) 0.0 1.0 3965
## congviec 0.0 1.0 3750
## sigma 0.0 1.0 3751
## mean_PPD 0.0 1.0 3913
## log-posterior 0.0 1.0 1627
##
## For each parameter, mcse is Monte Carlo standard error, n_eff is a crude measure of effective sample size, and Rhat is the potential scale reduction factor on split chains (at convergence Rhat=1).prior_summary(m1)## Priors for model 'm1'
## ------
## Intercept (after predictors centered)
## Specified prior:
## ~ normal(location = 40, scale = 2.5)
## Adjusted prior:
## ~ normal(location = 40, scale = 29)
##
## Coefficients
## Specified prior:
## ~ normal(location = 0, scale = 2.5)
## Adjusted prior:
## ~ normal(location = 0, scale = 25)
##
## Auxiliary (sigma)
## Specified prior:
## ~ exponential(rate = 1)
## Adjusted prior:
## ~ exponential(rate = 0.087)
## ------
## See help('prior_summary.stanreg') for more detailspost.r2 = bayes_R2(m1)
summary(post.r2)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000000 0.0006088 0.0023688 0.0041031 0.0058689 0.0441478## Da bien cho outcom dịnh luong
m.da = stan_glm(uwes_tol ~ congviec + gioi*thamnien*congviec + tuoi, data=thuan,
prior = default_prior_coef(family),
prior_intercept = default_prior_intercept(family),
prior_aux = exponential(autoscale = T),
prior_PD = T,
adapt_delta = NULL,
QR = F)## Found more than one class "family" in cache; using the first, from namespace 'lme4'## Also defined by 'MatrixModels'## Found more than one class "family" in cache; using the first, from namespace 'lme4'## Also defined by 'MatrixModels'## Found more than one class "family" in cache; using the first, from namespace 'lme4'## Also defined by 'MatrixModels'## Found more than one class "family" in cache; using the first, from namespace 'lme4'## Also defined by 'MatrixModels'##
## SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
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## Chain 4:summary(m.da)##
## Model Info:
## function: stan_glm
## family: gaussian [identity]
## formula: uwes_tol ~ congviec + gioi * thamnien * congviec + tuoi
## algorithm: sampling
## sample: 4000 (posterior sample size)
## priors: see help('prior_summary')
## observations: 532
## predictors: 9
##
## Estimates:
## mean sd 10% 50% 90%
## (Intercept) 42.9 191.5 -204.6 43.1 281.2
## congviec -0.1 25.8 -33.5 -0.5 33.3
## gioi -0.6 57.7 -75.3 -0.2 73.6
## thamnien 0.2 5.8 -7.1 0.1 7.4
## tuoi 0.0 4.4 -5.6 -0.1 5.4
## gioi:thamnien 0.0 3.1 -4.0 0.0 4.0
## congviec:gioi -0.2 13.8 -17.9 -0.3 17.1
## congviec:thamnien 0.0 1.4 -1.8 0.0 1.8
## congviec:gioi:thamnien 0.0 0.7 -0.9 0.0 0.9
## sigma 11.7 12.1 1.2 7.9 26.9
##
## MCMC diagnostics
## mcse Rhat n_eff
## (Intercept) 2.7 1.0 5067
## congviec 0.4 1.0 4864
## gioi 0.8 1.0 4657
## thamnien 0.1 1.0 5131
## tuoi 0.1 1.0 5007
## gioi:thamnien 0.0 1.0 5026
## congviec:gioi 0.2 1.0 4965
## congviec:thamnien 0.0 1.0 5015
## congviec:gioi:thamnien 0.0 1.0 4705
## sigma 0.2 1.0 6435
## log-posterior 0.1 1.0 1946
##
## For each parameter, mcse is Monte Carlo standard error, n_eff is a crude measure of effective sample size, and Rhat is the potential scale reduction factor on split chains (at convergence Rhat=1).prior_summary(m.da)## Priors for model 'm.da'
## ------
## Intercept (after predictors centered)
## Specified prior:
## ~ normal(location = 40, scale = 2.5)
## Adjusted prior:
## ~ normal(location = 40, scale = 29)
##
## Coefficients
## Specified prior:
## ~ normal(location = [0,0,0,...], scale = [2.5,2.5,2.5,...])
## Adjusted prior:
## ~ normal(location = [0,0,0,...], scale = [25.08,58.51, 5.73,...])
##
## Auxiliary (sigma)
## Specified prior:
## ~ exponential(rate = 1)
## Adjusted prior:
## ~ exponential(rate = 0.087)
## ------
## See help('prior_summary.stanreg') for more detailspost.r2.m.da = bayes_R2(m.da)
summary(post.r2.m.da)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2001 0.9489 0.9885 0.9461 0.9980 1.0000#exp(coefficients(m.da))
# exp(confint(m.da))
# Tương tự có thể dùng cách khác
library(MCMCpack)## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## select## ##
## ## Markov Chain Monte Carlo Package (MCMCpack)## ## Copyright (C) 2003-2023 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park## ##
## ## Support provided by the U.S. National Science Foundation## ## (Grants SES-0350646 and SES-0350613)
## ####
## Attaching package: 'MCMCpack'## The following object is masked from 'package:OpenMx':
##
## vechlibrary(broom)
posterior = MCMCregress(formula= uwes_tol ~ congviec, b0=0, B0=0.01,
data=thuan, verbose=10)##
##
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## beta =
## 39.75928
## 0.52134
## sigma2 = 131.70690
##
##
## MCMCregress iteration 761 of 11000
## beta =
## 41.26304
## -0.11435
## sigma2 = 120.94362
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##
## MCMCregress iteration 771 of 11000
## beta =
## 39.78486
## -0.00494
## sigma2 = 144.55643
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##
## MCMCregress iteration 781 of 11000
## beta =
## 37.19253
## 0.89705
## sigma2 = 128.05924
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## MCMCregress iteration 791 of 11000
## beta =
## 39.17227
## 0.59941
## sigma2 = 130.92452
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## MCMCregress iteration 801 of 11000
## beta =
## 35.96489
## 1.42963
## sigma2 = 137.19070
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## MCMCregress iteration 811 of 11000
## beta =
## 39.72507
## 0.57422
## sigma2 = 134.27447
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## MCMCregress iteration 821 of 11000
## beta =
## 38.16305
## 0.70897
## sigma2 = 129.83460
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## MCMCregress iteration 831 of 11000
## beta =
## 40.34922
## 0.57369
## sigma2 = 120.68201
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##
## MCMCregress iteration 841 of 11000
## beta =
## 37.40128
## 1.05913
## sigma2 = 131.67798
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## MCMCregress iteration 851 of 11000
## beta =
## 38.39178
## 0.54557
## sigma2 = 134.07171
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## MCMCregress iteration 861 of 11000
## beta =
## 37.06474
## 1.25433
## sigma2 = 139.95301
##
##
## MCMCregress iteration 871 of 11000
## beta =
## 38.34501
## 0.98172
## sigma2 = 132.00199
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## MCMCregress iteration 881 of 11000
## beta =
## 39.75708
## 0.48151
## sigma2 = 140.86694
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##
## MCMCregress iteration 891 of 11000
## beta =
## 40.48641
## 0.00570
## sigma2 = 119.37332
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## MCMCregress iteration 901 of 11000
## beta =
## 41.30087
## -0.04424
## sigma2 = 142.43019
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## MCMCregress iteration 911 of 11000
## beta =
## 39.27044
## 0.56932
## sigma2 = 152.60481
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## MCMCregress iteration 921 of 11000
## beta =
## 38.81117
## 0.73244
## sigma2 = 126.77390
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## MCMCregress iteration 931 of 11000
## beta =
## 37.62781
## 0.91160
## sigma2 = 123.41498
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## MCMCregress iteration 941 of 11000
## beta =
## 37.83309
## 1.22585
## sigma2 = 129.41708
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## MCMCregress iteration 951 of 11000
## beta =
## 39.68103
## 0.26496
## sigma2 = 141.74696
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## MCMCregress iteration 961 of 11000
## beta =
## 39.02745
## 0.37268
## sigma2 = 133.78579
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## MCMCregress iteration 971 of 11000
## beta =
## 39.64571
## 0.14641
## sigma2 = 129.99150
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## MCMCregress iteration 981 of 11000
## beta =
## 38.67649
## 0.56418
## sigma2 = 116.54102
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## MCMCregress iteration 991 of 11000
## beta =
## 39.15344
## 0.52137
## sigma2 = 129.24121
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## MCMCregress iteration 1001 of 11000
## beta =
## 39.50623
## -0.22457
## sigma2 = 133.13164
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## MCMCregress iteration 1011 of 11000
## beta =
## 37.49115
## 0.96900
## sigma2 = 125.03844
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## MCMCregress iteration 1021 of 11000
## beta =
## 38.48769
## 0.69573
## sigma2 = 131.62856
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## MCMCregress iteration 1031 of 11000
## beta =
## 38.43881
## 0.48604
## sigma2 = 136.11033
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## MCMCregress iteration 1041 of 11000
## beta =
## 38.36714
## 0.78372
## sigma2 = 137.45906
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## MCMCregress iteration 1051 of 11000
## beta =
## 38.30277
## 0.76350
## sigma2 = 125.59302
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## MCMCregress iteration 1061 of 11000
## beta =
## 36.82119
## 0.96587
## sigma2 = 136.85259
##
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## MCMCregress iteration 1071 of 11000
## beta =
## 38.33397
## 0.57302
## sigma2 = 125.96008
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## MCMCregress iteration 1081 of 11000
## beta =
## 39.58601
## 0.44253
## sigma2 = 131.43219
##
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## MCMCregress iteration 1091 of 11000
## beta =
## 37.64593
## 1.53163
## sigma2 = 140.39828
##
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## MCMCregress iteration 1101 of 11000
## beta =
## 39.29842
## 0.41848
## sigma2 = 143.89614
##
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## MCMCregress iteration 1111 of 11000
## beta =
## 39.11533
## 0.36825
## sigma2 = 128.15188
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## MCMCregress iteration 1121 of 11000
## beta =
## 39.41513
## 0.08492
## sigma2 = 135.67256
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## MCMCregress iteration 1131 of 11000
## beta =
## 36.90828
## 1.25245
## sigma2 = 126.96162
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## MCMCregress iteration 1141 of 11000
## beta =
## 38.34075
## 0.54554
## sigma2 = 142.17433
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## MCMCregress iteration 1151 of 11000
## beta =
## 37.18912
## 1.04335
## sigma2 = 123.03683
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## MCMCregress iteration 1161 of 11000
## beta =
## 39.25496
## 0.58902
## sigma2 = 122.19891
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## MCMCregress iteration 1171 of 11000
## beta =
## 37.87551
## 1.05406
## sigma2 = 137.73188
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## MCMCregress iteration 1181 of 11000
## beta =
## 38.87686
## 0.80209
## sigma2 = 122.38451
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## MCMCregress iteration 1191 of 11000
## beta =
## 37.95526
## 0.92923
## sigma2 = 131.86457
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## MCMCregress iteration 1201 of 11000
## beta =
## 35.99306
## 1.88014
## sigma2 = 142.92453
##
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## MCMCregress iteration 1211 of 11000
## beta =
## 37.12370
## 1.02269
## sigma2 = 132.70822
##
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## MCMCregress iteration 1221 of 11000
## beta =
## 39.21482
## 0.51930
## sigma2 = 118.59560
##
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## MCMCregress iteration 1231 of 11000
## beta =
## 37.41081
## 1.22712
## sigma2 = 122.87236
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## MCMCregress iteration 1241 of 11000
## beta =
## 37.80150
## 1.02328
## sigma2 = 129.99485
##
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## MCMCregress iteration 1251 of 11000
## beta =
## 40.24198
## 0.11857
## sigma2 = 135.61487
##
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## MCMCregress iteration 1261 of 11000
## beta =
## 39.07815
## 0.21329
## sigma2 = 129.40673
##
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## MCMCregress iteration 1271 of 11000
## beta =
## 40.33634
## -0.27790
## sigma2 = 135.02237
##
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## MCMCregress iteration 1281 of 11000
## beta =
## 37.80626
## 1.03660
## sigma2 = 119.10507
##
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## MCMCregress iteration 1291 of 11000
## beta =
## 39.33488
## -0.07501
## sigma2 = 135.65729
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## MCMCregress iteration 1301 of 11000
## beta =
## 40.11513
## 0.05110
## sigma2 = 145.10500
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## MCMCregress iteration 1311 of 11000
## beta =
## 35.46880
## 1.64444
## sigma2 = 123.41603
##
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## MCMCregress iteration 1321 of 11000
## beta =
## 37.94134
## 0.82867
## sigma2 = 124.77659
##
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## MCMCregress iteration 1331 of 11000
## beta =
## 37.90313
## 0.93942
## sigma2 = 134.25354
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## MCMCregress iteration 1341 of 11000
## beta =
## 38.21845
## 0.79771
## sigma2 = 137.37548
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## MCMCregress iteration 1351 of 11000
## beta =
## 39.15306
## 0.28469
## sigma2 = 146.91814
##
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## MCMCregress iteration 1361 of 11000
## beta =
## 39.73260
## 0.32274
## sigma2 = 122.16805
##
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## MCMCregress iteration 1371 of 11000
## beta =
## 39.89364
## 0.20375
## sigma2 = 127.06388
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## MCMCregress iteration 1381 of 11000
## beta =
## 38.07182
## 0.84393
## sigma2 = 125.80307
##
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## MCMCregress iteration 1391 of 11000
## beta =
## 38.34319
## 0.42309
## sigma2 = 132.62828
##
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## MCMCregress iteration 1401 of 11000
## beta =
## 38.77507
## 0.51877
## sigma2 = 132.60558
##
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## MCMCregress iteration 1411 of 11000
## beta =
## 38.41159
## 0.64267
## sigma2 = 133.26620
##
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## MCMCregress iteration 1421 of 11000
## beta =
## 37.92690
## 0.72095
## sigma2 = 129.10131
##
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## MCMCregress iteration 1431 of 11000
## beta =
## 39.24876
## 0.00131
## sigma2 = 127.09465
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## MCMCregress iteration 1441 of 11000
## beta =
## 37.70569
## 1.05135
## sigma2 = 133.30763
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## MCMCregress iteration 1451 of 11000
## beta =
## 38.55133
## 0.43922
## sigma2 = 141.73574
##
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## MCMCregress iteration 1461 of 11000
## beta =
## 38.74321
## 0.60275
## sigma2 = 120.96288
##
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## MCMCregress iteration 1471 of 11000
## beta =
## 39.52580
## -0.05905
## sigma2 = 131.65821
##
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## MCMCregress iteration 1481 of 11000
## beta =
## 38.78894
## 0.54174
## sigma2 = 134.14076
##
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## MCMCregress iteration 1491 of 11000
## beta =
## 37.77151
## 0.85696
## sigma2 = 119.35726
##
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## MCMCregress iteration 1501 of 11000
## beta =
## 37.81239
## 1.06035
## sigma2 = 123.64710
##
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## MCMCregress iteration 1511 of 11000
## beta =
## 41.79296
## -0.81118
## sigma2 = 130.93304
##
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## MCMCregress iteration 1521 of 11000
## beta =
## 38.49496
## 0.55011
## sigma2 = 115.05278
##
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## MCMCregress iteration 1531 of 11000
## beta =
## 37.16952
## 0.92577
## sigma2 = 135.92992
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## MCMCregress iteration 1541 of 11000
## beta =
## 38.96128
## 0.42330
## sigma2 = 123.38644
##
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## MCMCregress iteration 1551 of 11000
## beta =
## 39.00225
## 0.63750
## sigma2 = 143.67720
##
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## MCMCregress iteration 1561 of 11000
## beta =
## 38.09013
## 0.96675
## sigma2 = 135.22284
##
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## MCMCregress iteration 1571 of 11000
## beta =
## 37.91040
## 0.66665
## sigma2 = 129.00351
##
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## MCMCregress iteration 1581 of 11000
## beta =
## 38.81695
## 0.52968
## sigma2 = 137.46096
##
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## MCMCregress iteration 1591 of 11000
## beta =
## 38.04795
## 0.84959
## sigma2 = 132.43822
##
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## MCMCregress iteration 1601 of 11000
## beta =
## 37.85692
## 0.65339
## sigma2 = 132.15723
##
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## MCMCregress iteration 1611 of 11000
## beta =
## 37.15486
## 1.42620
## sigma2 = 121.52382
##
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## MCMCregress iteration 1621 of 11000
## beta =
## 37.84601
## 0.91423
## sigma2 = 118.21874
##
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## MCMCregress iteration 1631 of 11000
## beta =
## 40.77478
## 0.08202
## sigma2 = 140.33167
##
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## MCMCregress iteration 1641 of 11000
## beta =
## 39.18061
## 0.57345
## sigma2 = 122.35921
##
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## MCMCregress iteration 1651 of 11000
## beta =
## 38.85010
## 0.32723
## sigma2 = 133.92970
##
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## MCMCregress iteration 1661 of 11000
## beta =
## 39.70137
## -0.00475
## sigma2 = 137.33746
##
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## MCMCregress iteration 1671 of 11000
## beta =
## 37.36571
## 1.01476
## sigma2 = 134.32605
##
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## MCMCregress iteration 1681 of 11000
## beta =
## 36.81313
## 0.95764
## sigma2 = 127.40520
##
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## MCMCregress iteration 1691 of 11000
## beta =
## 39.15817
## 0.42848
## sigma2 = 138.75822
##
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## MCMCregress iteration 1701 of 11000
## beta =
## 37.76032
## 0.77264
## sigma2 = 120.12268
##
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## MCMCregress iteration 1711 of 11000
## beta =
## 37.99743
## 0.56541
## sigma2 = 135.90500
##
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## MCMCregress iteration 1721 of 11000
## beta =
## 38.18878
## 0.77517
## sigma2 = 131.86724
##
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## MCMCregress iteration 1731 of 11000
## beta =
## 39.71321
## 0.34877
## sigma2 = 117.68795
##
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## MCMCregress iteration 1741 of 11000
## beta =
## 37.47380
## 0.73390
## sigma2 = 133.79396
##
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## MCMCregress iteration 1751 of 11000
## beta =
## 38.77859
## 0.50288
## sigma2 = 126.65472
##
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## MCMCregress iteration 1761 of 11000
## beta =
## 39.03062
## 0.33035
## sigma2 = 140.92389
##
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## MCMCregress iteration 1771 of 11000
## beta =
## 38.26516
## 0.81700
## sigma2 = 126.68253
##
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## MCMCregress iteration 1781 of 11000
## beta =
## 37.22884
## 1.28701
## sigma2 = 147.28096
##
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## MCMCregress iteration 1791 of 11000
## beta =
## 39.05472
## 0.34080
## sigma2 = 132.46123
##
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## MCMCregress iteration 1801 of 11000
## beta =
## 39.31119
## 0.20386
## sigma2 = 133.09013
##
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## MCMCregress iteration 1811 of 11000
## beta =
## 37.25195
## 1.41919
## sigma2 = 130.46178
##
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## MCMCregress iteration 1821 of 11000
## beta =
## 37.21566
## 1.22342
## sigma2 = 126.92582
##
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## MCMCregress iteration 1831 of 11000
## beta =
## 39.48053
## 0.41031
## sigma2 = 146.82104
##
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## MCMCregress iteration 1841 of 11000
## beta =
## 38.81918
## 0.58162
## sigma2 = 130.47731
##
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## MCMCregress iteration 1851 of 11000
## beta =
## 36.58730
## 1.00054
## sigma2 = 131.93643
##
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## MCMCregress iteration 1861 of 11000
## beta =
## 38.37008
## 0.89149
## sigma2 = 142.24997
##
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## MCMCregress iteration 1871 of 11000
## beta =
## 38.50095
## 0.68459
## sigma2 = 146.82653
##
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## MCMCregress iteration 1881 of 11000
## beta =
## 38.31844
## 0.89533
## sigma2 = 122.94543
##
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## MCMCregress iteration 1891 of 11000
## beta =
## 38.72889
## 0.78058
## sigma2 = 141.05784
##
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## MCMCregress iteration 1901 of 11000
## beta =
## 37.81523
## 0.81678
## sigma2 = 129.01271
##
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## MCMCregress iteration 1911 of 11000
## beta =
## 37.58876
## 0.80774
## sigma2 = 123.83752
##
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## MCMCregress iteration 1921 of 11000
## beta =
## 39.42157
## 0.36896
## sigma2 = 129.32430
##
##
## MCMCregress iteration 1931 of 11000
## beta =
## 38.51493
## 0.72860
## sigma2 = 143.67092
##
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## MCMCregress iteration 1941 of 11000
## beta =
## 39.49322
## 0.44487
## sigma2 = 124.00525
##
##
## MCMCregress iteration 1951 of 11000
## beta =
## 37.61075
## 0.67867
## sigma2 = 123.79095
##
##
## MCMCregress iteration 1961 of 11000
## beta =
## 38.13231
## 0.81309
## sigma2 = 123.98272
##
##
## MCMCregress iteration 1971 of 11000
## beta =
## 37.74561
## 0.87624
## sigma2 = 131.27354
##
##
## MCMCregress iteration 1981 of 11000
## beta =
## 40.07345
## 0.03629
## sigma2 = 132.70083
##
##
## MCMCregress iteration 1991 of 11000
## beta =
## 40.11257
## 0.19567
## sigma2 = 139.36015
##
##
## MCMCregress iteration 2001 of 11000
## beta =
## 37.60384
## 1.05476
## sigma2 = 141.20417
##
##
## MCMCregress iteration 2011 of 11000
## beta =
## 39.35994
## 0.55171
## sigma2 = 146.62665
##
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## MCMCregress iteration 2021 of 11000
## beta =
## 39.65173
## 0.23835
## sigma2 = 130.94320
##
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## MCMCregress iteration 2031 of 11000
## beta =
## 38.31874
## 0.92134
## sigma2 = 136.54383
##
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## MCMCregress iteration 2041 of 11000
## beta =
## 39.10286
## 0.55831
## sigma2 = 145.49820
##
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## MCMCregress iteration 2051 of 11000
## beta =
## 36.79581
## 1.00828
## sigma2 = 138.83442
##
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## MCMCregress iteration 2061 of 11000
## beta =
## 39.14723
## 0.40900
## sigma2 = 134.21202
##
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## MCMCregress iteration 2071 of 11000
## beta =
## 38.66939
## 0.83683
## sigma2 = 146.85167
##
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## MCMCregress iteration 2081 of 11000
## beta =
## 38.10940
## 0.96762
## sigma2 = 143.66511
##
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## MCMCregress iteration 2091 of 11000
## beta =
## 38.29739
## 0.72068
## sigma2 = 136.89393
##
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## MCMCregress iteration 2101 of 11000
## beta =
## 37.04538
## 1.49620
## sigma2 = 154.80561
##
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## MCMCregress iteration 2111 of 11000
## beta =
## 38.30096
## 0.80312
## sigma2 = 133.01999
##
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## MCMCregress iteration 2121 of 11000
## beta =
## 41.36077
## -0.67722
## sigma2 = 135.62048
##
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## MCMCregress iteration 2131 of 11000
## beta =
## 36.86037
## 1.48916
## sigma2 = 121.11987
##
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## MCMCregress iteration 2141 of 11000
## beta =
## 37.89083
## 1.28021
## sigma2 = 134.99865
##
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## MCMCregress iteration 2151 of 11000
## beta =
## 38.43011
## 0.86981
## sigma2 = 134.07405
##
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## MCMCregress iteration 2161 of 11000
## beta =
## 38.95592
## 0.49439
## sigma2 = 130.41277
##
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## MCMCregress iteration 2171 of 11000
## beta =
## 38.45198
## 1.03149
## sigma2 = 131.53542
##
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## MCMCregress iteration 2181 of 11000
## beta =
## 38.10693
## 0.89821
## sigma2 = 121.32851
##
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## MCMCregress iteration 2191 of 11000
## beta =
## 38.01985
## 0.86253
## sigma2 = 139.29581
##
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## MCMCregress iteration 2201 of 11000
## beta =
## 37.90996
## 0.77296
## sigma2 = 136.82314
##
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## MCMCregress iteration 2211 of 11000
## beta =
## 38.75925
## 0.59659
## sigma2 = 124.38679
##
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## MCMCregress iteration 2221 of 11000
## beta =
## 37.83500
## 1.02317
## sigma2 = 125.49062
##
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## MCMCregress iteration 2231 of 11000
## beta =
## 37.12943
## 1.27492
## sigma2 = 122.77898
##
##
## MCMCregress iteration 2241 of 11000
## beta =
## 39.02967
## 0.52465
## sigma2 = 133.41075
##
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## MCMCregress iteration 2251 of 11000
## beta =
## 39.06321
## 0.92943
## sigma2 = 134.64734
##
##
## MCMCregress iteration 2261 of 11000
## beta =
## 39.21901
## 0.32174
## sigma2 = 137.75997
##
##
## MCMCregress iteration 2271 of 11000
## beta =
## 38.64254
## 0.58937
## sigma2 = 127.01297
##
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## MCMCregress iteration 2281 of 11000
## beta =
## 38.39761
## 0.75218
## sigma2 = 133.20148
##
##
## MCMCregress iteration 2291 of 11000
## beta =
## 39.36769
## 0.76692
## sigma2 = 146.65696
##
##
## MCMCregress iteration 2301 of 11000
## beta =
## 39.65500
## 0.33333
## sigma2 = 148.38847
##
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## MCMCregress iteration 2311 of 11000
## beta =
## 39.36287
## 0.22234
## sigma2 = 135.71541
##
##
## MCMCregress iteration 2321 of 11000
## beta =
## 37.16332
## 1.03861
## sigma2 = 114.69326
##
##
## MCMCregress iteration 2331 of 11000
## beta =
## 36.87338
## 1.11257
## sigma2 = 139.38591
##
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## MCMCregress iteration 2341 of 11000
## beta =
## 39.34741
## 0.10853
## sigma2 = 140.95608
##
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## MCMCregress iteration 2351 of 11000
## beta =
## 38.80726
## 0.61491
## sigma2 = 153.87810
##
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## MCMCregress iteration 2361 of 11000
## beta =
## 38.18175
## 0.68120
## sigma2 = 129.58959
##
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## MCMCregress iteration 2371 of 11000
## beta =
## 39.40714
## 0.34715
## sigma2 = 140.06999
##
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## MCMCregress iteration 2381 of 11000
## beta =
## 40.75209
## 0.06478
## sigma2 = 140.52713
##
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## MCMCregress iteration 2391 of 11000
## beta =
## 38.52391
## 0.80919
## sigma2 = 121.96867
##
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## MCMCregress iteration 2401 of 11000
## beta =
## 38.05394
## 0.62799
## sigma2 = 128.86422
##
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## MCMCregress iteration 2411 of 11000
## beta =
## 38.81457
## 0.57989
## sigma2 = 153.80114
##
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## MCMCregress iteration 2421 of 11000
## beta =
## 37.82580
## 0.90595
## sigma2 = 148.91471
##
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## MCMCregress iteration 2431 of 11000
## beta =
## 37.84372
## 1.05529
## sigma2 = 128.69915
##
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## MCMCregress iteration 2441 of 11000
## beta =
## 38.77773
## 0.45067
## sigma2 = 112.88032
##
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## MCMCregress iteration 2451 of 11000
## beta =
## 40.30035
## -0.13301
## sigma2 = 136.90765
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## MCMCregress iteration 2461 of 11000
## beta =
## 39.13895
## 0.25787
## sigma2 = 134.40133
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## MCMCregress iteration 2471 of 11000
## beta =
## 37.57564
## 1.18641
## sigma2 = 148.35909
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## MCMCregress iteration 2481 of 11000
## beta =
## 40.88239
## 0.01057
## sigma2 = 140.73062
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## MCMCregress iteration 2491 of 11000
## beta =
## 39.08188
## -0.08675
## sigma2 = 134.23536
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## MCMCregress iteration 2501 of 11000
## beta =
## 39.65186
## 0.65728
## sigma2 = 143.94436
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## MCMCregress iteration 2511 of 11000
## beta =
## 39.10545
## 0.58275
## sigma2 = 129.82355
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## MCMCregress iteration 2521 of 11000
## beta =
## 39.50510
## 0.52435
## sigma2 = 137.79940
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## MCMCregress iteration 2531 of 11000
## beta =
## 37.44608
## 1.26268
## sigma2 = 135.86642
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## MCMCregress iteration 2541 of 11000
## beta =
## 38.82072
## 0.51082
## sigma2 = 131.62028
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## MCMCregress iteration 2551 of 11000
## beta =
## 37.23786
## 1.07220
## sigma2 = 127.12560
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## MCMCregress iteration 2561 of 11000
## beta =
## 38.42179
## 0.84531
## sigma2 = 140.51165
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## MCMCregress iteration 2571 of 11000
## beta =
## 37.91588
## 0.88837
## sigma2 = 131.18586
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## MCMCregress iteration 2581 of 11000
## beta =
## 39.96491
## 0.14552
## sigma2 = 136.18621
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## MCMCregress iteration 2591 of 11000
## beta =
## 37.62852
## 0.94003
## sigma2 = 137.64566
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## MCMCregress iteration 2601 of 11000
## beta =
## 37.29939
## 1.00279
## sigma2 = 130.62010
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## MCMCregress iteration 2611 of 11000
## beta =
## 38.97842
## 0.71021
## sigma2 = 133.76386
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## MCMCregress iteration 2621 of 11000
## beta =
## 38.40059
## 0.79792
## sigma2 = 137.88396
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## MCMCregress iteration 2631 of 11000
## beta =
## 38.26668
## 0.42965
## sigma2 = 129.81774
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## MCMCregress iteration 2641 of 11000
## beta =
## 38.56139
## 0.48048
## sigma2 = 128.87570
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## MCMCregress iteration 2651 of 11000
## beta =
## 38.82174
## 0.10898
## sigma2 = 122.56096
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## MCMCregress iteration 2661 of 11000
## beta =
## 39.25735
## 0.81574
## sigma2 = 136.39687
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## MCMCregress iteration 2671 of 11000
## beta =
## 39.71256
## 0.41829
## sigma2 = 126.27557
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## MCMCregress iteration 2681 of 11000
## beta =
## 39.55764
## 0.46328
## sigma2 = 146.05961
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## MCMCregress iteration 2691 of 11000
## beta =
## 40.20223
## -0.13049
## sigma2 = 143.11932
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## MCMCregress iteration 2701 of 11000
## beta =
## 38.96805
## 0.49905
## sigma2 = 123.63305
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## MCMCregress iteration 2711 of 11000
## beta =
## 39.34745
## 0.20866
## sigma2 = 144.47167
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## MCMCregress iteration 2721 of 11000
## beta =
## 37.39656
## 1.10014
## sigma2 = 132.51045
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## MCMCregress iteration 2731 of 11000
## beta =
## 38.61295
## 0.43421
## sigma2 = 145.22632
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## MCMCregress iteration 2741 of 11000
## beta =
## 36.11595
## 1.49858
## sigma2 = 131.52878
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## MCMCregress iteration 2751 of 11000
## beta =
## 39.49294
## 0.42986
## sigma2 = 138.06493
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## MCMCregress iteration 2761 of 11000
## beta =
## 38.54898
## 0.79159
## sigma2 = 137.64147
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## MCMCregress iteration 2771 of 11000
## beta =
## 39.82360
## 0.09414
## sigma2 = 128.31118
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## MCMCregress iteration 2781 of 11000
## beta =
## 38.84347
## 0.53783
## sigma2 = 137.80193
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## MCMCregress iteration 2791 of 11000
## beta =
## 38.73256
## 0.52681
## sigma2 = 124.72445
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## MCMCregress iteration 2801 of 11000
## beta =
## 39.12888
## 0.54311
## sigma2 = 133.74091
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## MCMCregress iteration 2811 of 11000
## beta =
## 38.41611
## 0.91468
## sigma2 = 133.80988
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## MCMCregress iteration 2821 of 11000
## beta =
## 38.01369
## 0.59280
## sigma2 = 140.81525
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## MCMCregress iteration 2831 of 11000
## beta =
## 36.83263
## 1.48052
## sigma2 = 130.70297
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## MCMCregress iteration 2841 of 11000
## beta =
## 38.99309
## 0.60359
## sigma2 = 127.13253
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## MCMCregress iteration 2851 of 11000
## beta =
## 36.86782
## 1.10244
## sigma2 = 134.47538
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## MCMCregress iteration 2861 of 11000
## beta =
## 38.42494
## 0.78161
## sigma2 = 123.06630
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## MCMCregress iteration 2871 of 11000
## beta =
## 39.58557
## 0.62119
## sigma2 = 133.46761
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## MCMCregress iteration 2881 of 11000
## beta =
## 40.37444
## -0.05062
## sigma2 = 138.57658
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## MCMCregress iteration 2891 of 11000
## beta =
## 37.74441
## 0.71383
## sigma2 = 132.03627
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## MCMCregress iteration 2901 of 11000
## beta =
## 37.41777
## 0.92167
## sigma2 = 140.10368
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## MCMCregress iteration 2911 of 11000
## beta =
## 37.74871
## 1.01844
## sigma2 = 127.35026
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## MCMCregress iteration 2921 of 11000
## beta =
## 38.43063
## 0.86981
## sigma2 = 146.49347
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## MCMCregress iteration 2931 of 11000
## beta =
## 37.57384
## 0.88137
## sigma2 = 126.90943
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## MCMCregress iteration 2941 of 11000
## beta =
## 37.36226
## 1.26610
## sigma2 = 127.84477
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## MCMCregress iteration 2951 of 11000
## beta =
## 37.91540
## 0.64457
## sigma2 = 142.71192
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## MCMCregress iteration 2961 of 11000
## beta =
## 38.06125
## 0.79889
## sigma2 = 138.61806
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## MCMCregress iteration 2971 of 11000
## beta =
## 39.69989
## -0.14277
## sigma2 = 130.40348
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## MCMCregress iteration 2981 of 11000
## beta =
## 37.92425
## 0.69028
## sigma2 = 144.90924
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## MCMCregress iteration 2991 of 11000
## beta =
## 39.73304
## -0.14674
## sigma2 = 142.19990
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## MCMCregress iteration 3001 of 11000
## beta =
## 37.18171
## 0.99984
## sigma2 = 133.32028
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## MCMCregress iteration 3011 of 11000
## beta =
## 37.01096
## 1.26768
## sigma2 = 148.71071
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## MCMCregress iteration 3021 of 11000
## beta =
## 38.23809
## 0.53269
## sigma2 = 132.80897
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## MCMCregress iteration 3031 of 11000
## beta =
## 39.01099
## 0.33315
## sigma2 = 146.12386
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## MCMCregress iteration 3041 of 11000
## beta =
## 38.99931
## 0.70230
## sigma2 = 149.06655
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## MCMCregress iteration 3051 of 11000
## beta =
## 38.60359
## 0.58530
## sigma2 = 133.01013
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## MCMCregress iteration 3061 of 11000
## beta =
## 36.65826
## 1.34227
## sigma2 = 135.99528
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## MCMCregress iteration 3071 of 11000
## beta =
## 36.12365
## 1.00761
## sigma2 = 149.60224
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## MCMCregress iteration 3081 of 11000
## beta =
## 38.87592
## 0.74419
## sigma2 = 138.28555
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## MCMCregress iteration 3091 of 11000
## beta =
## 39.39197
## -0.03245
## sigma2 = 133.64590
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## MCMCregress iteration 3101 of 11000
## beta =
## 39.26984
## 0.14354
## sigma2 = 129.15921
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## MCMCregress iteration 3111 of 11000
## beta =
## 40.60388
## -0.31683
## sigma2 = 134.69071
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## MCMCregress iteration 3121 of 11000
## beta =
## 38.07693
## 0.56210
## sigma2 = 143.29141
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## MCMCregress iteration 3131 of 11000
## beta =
## 38.78759
## 0.54675
## sigma2 = 122.27705
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## MCMCregress iteration 3141 of 11000
## beta =
## 39.96436
## 0.11468
## sigma2 = 145.50374
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## MCMCregress iteration 3151 of 11000
## beta =
## 39.61048
## 0.09093
## sigma2 = 138.45694
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## beta =
## 38.70723
## 0.50113
## sigma2 = 131.38515
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## beta =
## 38.21902
## 0.47184
## sigma2 = 133.11060
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## MCMCregress iteration 3181 of 11000
## beta =
## 39.05681
## 0.58960
## sigma2 = 124.49051
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## beta =
## 38.79648
## 0.65964
## sigma2 = 126.17682
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## MCMCregress iteration 3201 of 11000
## beta =
## 40.68633
## -0.07113
## sigma2 = 151.61139
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## MCMCregress iteration 3211 of 11000
## beta =
## 37.96304
## 0.80050
## sigma2 = 128.99904
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## beta =
## 37.88679
## 1.19618
## sigma2 = 128.03303
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## MCMCregress iteration 3231 of 11000
## beta =
## 38.04095
## 0.94553
## sigma2 = 125.31542
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## MCMCregress iteration 3241 of 11000
## beta =
## 36.11943
## 1.55083
## sigma2 = 129.30185
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## MCMCregress iteration 3251 of 11000
## beta =
## 40.55439
## 0.32917
## sigma2 = 126.51475
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## MCMCregress iteration 3261 of 11000
## beta =
## 38.19320
## 0.62753
## sigma2 = 122.48889
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## MCMCregress iteration 3271 of 11000
## beta =
## 39.41650
## 0.23797
## sigma2 = 125.19070
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## MCMCregress iteration 3281 of 11000
## beta =
## 39.27323
## 0.69792
## sigma2 = 126.83005
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## MCMCregress iteration 3291 of 11000
## beta =
## 39.86167
## 0.53187
## sigma2 = 135.29611
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## MCMCregress iteration 3301 of 11000
## beta =
## 39.15611
## 0.56286
## sigma2 = 130.67370
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## MCMCregress iteration 3311 of 11000
## beta =
## 39.87544
## 0.56383
## sigma2 = 131.11324
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## MCMCregress iteration 3321 of 11000
## beta =
## 36.97229
## 1.29377
## sigma2 = 146.39390
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## MCMCregress iteration 3331 of 11000
## beta =
## 38.67895
## 0.46127
## sigma2 = 118.52748
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## MCMCregress iteration 3341 of 11000
## beta =
## 37.68720
## 0.91919
## sigma2 = 124.12543
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## MCMCregress iteration 3351 of 11000
## beta =
## 36.90616
## 1.29624
## sigma2 = 139.79918
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## MCMCregress iteration 3361 of 11000
## beta =
## 38.50391
## 0.70183
## sigma2 = 125.01181
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## MCMCregress iteration 3371 of 11000
## beta =
## 38.99835
## 0.20028
## sigma2 = 138.56839
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## MCMCregress iteration 3381 of 11000
## beta =
## 37.75987
## 0.81430
## sigma2 = 132.76973
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## MCMCregress iteration 3391 of 11000
## beta =
## 36.98803
## 1.42473
## sigma2 = 129.61107
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## MCMCregress iteration 3401 of 11000
## beta =
## 40.98736
## -0.41534
## sigma2 = 143.82446
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## MCMCregress iteration 3411 of 11000
## beta =
## 38.52121
## 0.87611
## sigma2 = 139.81692
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## MCMCregress iteration 3421 of 11000
## beta =
## 36.95282
## 1.04386
## sigma2 = 123.92754
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## MCMCregress iteration 3431 of 11000
## beta =
## 38.89571
## 0.34877
## sigma2 = 131.21897
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## MCMCregress iteration 3441 of 11000
## beta =
## 37.48882
## 1.04497
## sigma2 = 130.12543
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## MCMCregress iteration 3451 of 11000
## beta =
## 40.08730
## 0.05668
## sigma2 = 136.34429
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## MCMCregress iteration 3461 of 11000
## beta =
## 38.73902
## 0.35136
## sigma2 = 126.36164
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## beta =
## 39.90786
## 0.21789
## sigma2 = 134.68700
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## MCMCregress iteration 3481 of 11000
## beta =
## 40.10501
## -0.06199
## sigma2 = 130.78007
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## MCMCregress iteration 3491 of 11000
## beta =
## 38.43903
## 0.62856
## sigma2 = 129.01675
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## MCMCregress iteration 3501 of 11000
## beta =
## 39.41884
## 0.76040
## sigma2 = 136.40219
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## MCMCregress iteration 3511 of 11000
## beta =
## 35.81645
## 2.13901
## sigma2 = 140.53644
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## MCMCregress iteration 3521 of 11000
## beta =
## 38.24694
## 1.05554
## sigma2 = 139.88649
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## MCMCregress iteration 3531 of 11000
## beta =
## 38.73115
## 0.29997
## sigma2 = 148.21601
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## MCMCregress iteration 3541 of 11000
## beta =
## 39.90109
## 0.29441
## sigma2 = 126.90389
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## MCMCregress iteration 3551 of 11000
## beta =
## 39.19016
## 0.71326
## sigma2 = 133.38471
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## MCMCregress iteration 3561 of 11000
## beta =
## 38.95972
## 0.42478
## sigma2 = 128.37324
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## MCMCregress iteration 3571 of 11000
## beta =
## 37.86511
## 0.72899
## sigma2 = 141.98186
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## MCMCregress iteration 3581 of 11000
## beta =
## 39.67866
## 0.36131
## sigma2 = 130.73509
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## MCMCregress iteration 3591 of 11000
## beta =
## 39.21555
## 0.54524
## sigma2 = 129.86921
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## MCMCregress iteration 3601 of 11000
## beta =
## 39.43516
## 0.41425
## sigma2 = 128.67233
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## beta =
## 38.63430
## 0.80252
## sigma2 = 130.04777
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## MCMCregress iteration 3621 of 11000
## beta =
## 36.23457
## 1.05777
## sigma2 = 137.84980
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## MCMCregress iteration 3631 of 11000
## beta =
## 37.42383
## 1.00452
## sigma2 = 132.20509
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## MCMCregress iteration 3641 of 11000
## beta =
## 39.00714
## 0.52465
## sigma2 = 126.17905
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## MCMCregress iteration 3651 of 11000
## beta =
## 37.39352
## 0.90454
## sigma2 = 136.89923
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## beta =
## 39.44694
## 0.16697
## sigma2 = 127.44900
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## MCMCregress iteration 3671 of 11000
## beta =
## 37.70466
## 0.82732
## sigma2 = 149.90892
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## MCMCregress iteration 3681 of 11000
## beta =
## 39.51307
## 0.59096
## sigma2 = 140.94350
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## MCMCregress iteration 3691 of 11000
## beta =
## 39.25296
## 0.34615
## sigma2 = 122.67656
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## MCMCregress iteration 3701 of 11000
## beta =
## 38.61424
## 0.78644
## sigma2 = 129.36622
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## MCMCregress iteration 3711 of 11000
## beta =
## 38.16168
## 0.97834
## sigma2 = 140.94639
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## MCMCregress iteration 3721 of 11000
## beta =
## 38.03043
## 0.88887
## sigma2 = 141.70791
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## MCMCregress iteration 3731 of 11000
## beta =
## 38.95013
## 0.18638
## sigma2 = 142.06035
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## MCMCregress iteration 3741 of 11000
## beta =
## 37.33539
## 1.36753
## sigma2 = 126.62758
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## MCMCregress iteration 3751 of 11000
## beta =
## 37.55213
## 0.88756
## sigma2 = 148.25167
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## MCMCregress iteration 3761 of 11000
## beta =
## 39.03354
## 0.76874
## sigma2 = 124.71996
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## MCMCregress iteration 3771 of 11000
## beta =
## 39.46550
## 0.69541
## sigma2 = 142.53356
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## MCMCregress iteration 3781 of 11000
## beta =
## 36.07524
## 1.52310
## sigma2 = 132.89026
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## MCMCregress iteration 3791 of 11000
## beta =
## 40.48108
## 0.26157
## sigma2 = 137.93048
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## MCMCregress iteration 3801 of 11000
## beta =
## 37.91919
## 0.69509
## sigma2 = 136.93480
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## beta =
## 36.80500
## 1.18739
## sigma2 = 131.95745
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## MCMCregress iteration 3821 of 11000
## beta =
## 39.14611
## 0.51143
## sigma2 = 117.64715
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## beta =
## 41.05681
## -0.13067
## sigma2 = 117.68831
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## MCMCregress iteration 3841 of 11000
## beta =
## 39.02680
## 0.56273
## sigma2 = 128.01279
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## MCMCregress iteration 3851 of 11000
## beta =
## 37.72240
## 0.55787
## sigma2 = 125.17343
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## MCMCregress iteration 3861 of 11000
## beta =
## 40.30188
## -0.14120
## sigma2 = 136.48525
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## MCMCregress iteration 3871 of 11000
## beta =
## 37.24281
## 1.19237
## sigma2 = 138.27359
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## MCMCregress iteration 3881 of 11000
## beta =
## 40.33941
## 0.05757
## sigma2 = 121.57483
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## MCMCregress iteration 3891 of 11000
## beta =
## 40.81947
## -0.14108
## sigma2 = 129.84243
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## MCMCregress iteration 3901 of 11000
## beta =
## 38.48439
## 0.49018
## sigma2 = 147.19211
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## MCMCregress iteration 3911 of 11000
## beta =
## 37.86495
## 0.91546
## sigma2 = 132.73351
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## MCMCregress iteration 3921 of 11000
## beta =
## 41.05127
## -0.25116
## sigma2 = 135.52443
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## beta =
## 38.78209
## 0.57852
## sigma2 = 138.38601
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## MCMCregress iteration 3941 of 11000
## beta =
## 39.63219
## 0.12521
## sigma2 = 120.28876
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## MCMCregress iteration 3951 of 11000
## beta =
## 41.31752
## -0.16121
## sigma2 = 140.54827
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## beta =
## 38.74514
## 0.65196
## sigma2 = 132.52192
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## MCMCregress iteration 3971 of 11000
## beta =
## 37.95885
## 1.05900
## sigma2 = 138.30168
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## MCMCregress iteration 3981 of 11000
## beta =
## 40.21692
## -0.27555
## sigma2 = 128.90689
##
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## MCMCregress iteration 3991 of 11000
## beta =
## 38.11100
## 0.64891
## sigma2 = 134.80383
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## MCMCregress iteration 4001 of 11000
## beta =
## 39.60998
## 0.46928
## sigma2 = 133.66264
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## MCMCregress iteration 4011 of 11000
## beta =
## 39.72036
## 0.36632
## sigma2 = 137.15031
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## MCMCregress iteration 4021 of 11000
## beta =
## 38.53684
## 0.41400
## sigma2 = 120.47248
##
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## MCMCregress iteration 4031 of 11000
## beta =
## 37.95884
## 0.72706
## sigma2 = 128.28045
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## MCMCregress iteration 4041 of 11000
## beta =
## 38.13061
## 0.94040
## sigma2 = 115.51323
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## MCMCregress iteration 4051 of 11000
## beta =
## 38.11594
## 1.34195
## sigma2 = 144.93121
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## MCMCregress iteration 4061 of 11000
## beta =
## 38.84455
## 0.61657
## sigma2 = 143.90157
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## MCMCregress iteration 4071 of 11000
## beta =
## 39.60390
## 0.21928
## sigma2 = 128.00365
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## MCMCregress iteration 4081 of 11000
## beta =
## 37.17444
## 0.92712
## sigma2 = 122.21342
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## MCMCregress iteration 4091 of 11000
## beta =
## 37.06821
## 1.12956
## sigma2 = 133.26787
##
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## MCMCregress iteration 4101 of 11000
## beta =
## 37.98621
## 0.77136
## sigma2 = 140.57772
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## MCMCregress iteration 4111 of 11000
## beta =
## 39.73024
## 0.53111
## sigma2 = 135.58767
##
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## MCMCregress iteration 4121 of 11000
## beta =
## 38.46250
## 0.74863
## sigma2 = 129.58167
##
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## MCMCregress iteration 4131 of 11000
## beta =
## 37.40115
## 0.88411
## sigma2 = 136.68546
##
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## MCMCregress iteration 4141 of 11000
## beta =
## 35.98756
## 1.84773
## sigma2 = 125.64610
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##
## MCMCregress iteration 4151 of 11000
## beta =
## 39.82625
## 0.07781
## sigma2 = 128.98832
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##
## MCMCregress iteration 4161 of 11000
## beta =
## 37.15084
## 0.85020
## sigma2 = 129.97317
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## MCMCregress iteration 4171 of 11000
## beta =
## 40.59384
## 0.08054
## sigma2 = 120.19516
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## MCMCregress iteration 4181 of 11000
## beta =
## 38.68456
## 0.86066
## sigma2 = 142.74854
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## MCMCregress iteration 4191 of 11000
## beta =
## 39.65302
## 0.17949
## sigma2 = 132.10675
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## MCMCregress iteration 4201 of 11000
## beta =
## 39.18183
## 0.43343
## sigma2 = 129.08924
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## MCMCregress iteration 4211 of 11000
## beta =
## 39.24560
## 0.17749
## sigma2 = 127.69987
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## MCMCregress iteration 4221 of 11000
## beta =
## 40.43877
## 0.44220
## sigma2 = 129.85173
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## MCMCregress iteration 4231 of 11000
## beta =
## 40.22599
## -0.25011
## sigma2 = 133.44466
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## MCMCregress iteration 4241 of 11000
## beta =
## 39.82612
## 0.10656
## sigma2 = 146.24587
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## MCMCregress iteration 4251 of 11000
## beta =
## 38.56085
## 0.43069
## sigma2 = 141.02686
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## MCMCregress iteration 4261 of 11000
## beta =
## 38.24101
## 0.69078
## sigma2 = 133.08792
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##
## MCMCregress iteration 4271 of 11000
## beta =
## 38.04817
## 0.68286
## sigma2 = 131.42212
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## MCMCregress iteration 4281 of 11000
## beta =
## 37.82141
## 0.86530
## sigma2 = 134.14765
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## MCMCregress iteration 4291 of 11000
## beta =
## 38.12040
## 0.91829
## sigma2 = 126.91875
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## MCMCregress iteration 4301 of 11000
## beta =
## 39.53724
## 0.49181
## sigma2 = 126.60792
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## MCMCregress iteration 4311 of 11000
## beta =
## 39.59863
## 0.47986
## sigma2 = 140.00470
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##
## MCMCregress iteration 4321 of 11000
## beta =
## 40.26096
## 0.17693
## sigma2 = 141.04616
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## MCMCregress iteration 4331 of 11000
## beta =
## 38.06922
## 0.67359
## sigma2 = 123.60866
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## MCMCregress iteration 4341 of 11000
## beta =
## 40.12032
## 0.16193
## sigma2 = 122.82774
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## MCMCregress iteration 4351 of 11000
## beta =
## 39.23726
## 0.35551
## sigma2 = 116.57000
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## MCMCregress iteration 4361 of 11000
## beta =
## 39.97549
## -0.06564
## sigma2 = 127.36280
##
##
## MCMCregress iteration 4371 of 11000
## beta =
## 38.17580
## 0.74090
## sigma2 = 134.32938
##
##
## MCMCregress iteration 4381 of 11000
## beta =
## 36.57519
## 1.19317
## sigma2 = 128.40988
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## MCMCregress iteration 4391 of 11000
## beta =
## 36.79496
## 1.33668
## sigma2 = 128.47451
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## MCMCregress iteration 4401 of 11000
## beta =
## 39.52158
## 0.56790
## sigma2 = 130.14511
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## MCMCregress iteration 4411 of 11000
## beta =
## 38.96150
## 0.66770
## sigma2 = 140.93311
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## MCMCregress iteration 4421 of 11000
## beta =
## 38.75175
## 0.77359
## sigma2 = 137.02506
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## MCMCregress iteration 4431 of 11000
## beta =
## 38.06572
## 0.93776
## sigma2 = 140.76862
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## MCMCregress iteration 4441 of 11000
## beta =
## 40.08045
## 0.06319
## sigma2 = 127.11306
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## MCMCregress iteration 4451 of 11000
## beta =
## 38.42267
## 0.72981
## sigma2 = 141.10365
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## MCMCregress iteration 4461 of 11000
## beta =
## 37.64529
## 0.91510
## sigma2 = 135.24574
##
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## MCMCregress iteration 4471 of 11000
## beta =
## 37.55676
## 1.14977
## sigma2 = 142.22506
##
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## MCMCregress iteration 4481 of 11000
## beta =
## 38.52371
## 0.49500
## sigma2 = 130.17305
##
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## MCMCregress iteration 4491 of 11000
## beta =
## 37.56870
## 1.05464
## sigma2 = 116.73998
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## MCMCregress iteration 4501 of 11000
## beta =
## 37.89599
## 0.74026
## sigma2 = 137.75408
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## MCMCregress iteration 4511 of 11000
## beta =
## 40.83430
## -0.21695
## sigma2 = 133.81206
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## MCMCregress iteration 4521 of 11000
## beta =
## 40.53326
## 0.23553
## sigma2 = 130.73950
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## MCMCregress iteration 4531 of 11000
## beta =
## 37.74264
## 1.29418
## sigma2 = 123.12199
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## MCMCregress iteration 4541 of 11000
## beta =
## 39.55411
## 0.45254
## sigma2 = 131.52763
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## MCMCregress iteration 4551 of 11000
## beta =
## 39.82683
## 0.49005
## sigma2 = 134.31328
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## MCMCregress iteration 4561 of 11000
## beta =
## 37.27426
## 1.11856
## sigma2 = 136.33586
##
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## MCMCregress iteration 4571 of 11000
## beta =
## 40.07673
## 0.09193
## sigma2 = 138.27552
##
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## MCMCregress iteration 4581 of 11000
## beta =
## 38.65941
## 0.83964
## sigma2 = 127.87058
##
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## MCMCregress iteration 4591 of 11000
## beta =
## 39.03411
## 0.68856
## sigma2 = 112.79364
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## MCMCregress iteration 4601 of 11000
## beta =
## 39.62804
## 0.54436
## sigma2 = 132.39824
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## MCMCregress iteration 4611 of 11000
## beta =
## 39.39363
## -0.12825
## sigma2 = 125.22059
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## MCMCregress iteration 4621 of 11000
## beta =
## 37.93723
## 0.82585
## sigma2 = 131.77431
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##
## MCMCregress iteration 4631 of 11000
## beta =
## 38.20107
## 0.44048
## sigma2 = 135.07914
##
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## MCMCregress iteration 4641 of 11000
## beta =
## 39.61578
## 0.18011
## sigma2 = 125.43391
##
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## MCMCregress iteration 4651 of 11000
## beta =
## 39.12624
## 0.39327
## sigma2 = 124.42105
##
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## MCMCregress iteration 4661 of 11000
## beta =
## 39.12070
## 0.45610
## sigma2 = 128.41404
##
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## MCMCregress iteration 4671 of 11000
## beta =
## 38.57816
## 0.81776
## sigma2 = 128.86319
##
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## MCMCregress iteration 4681 of 11000
## beta =
## 41.45622
## -0.28947
## sigma2 = 120.09371
##
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## MCMCregress iteration 4691 of 11000
## beta =
## 39.34863
## 0.31169
## sigma2 = 139.39648
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## MCMCregress iteration 4701 of 11000
## beta =
## 36.37592
## 0.98063
## sigma2 = 123.74137
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## MCMCregress iteration 4711 of 11000
## beta =
## 39.03384
## 0.66038
## sigma2 = 138.21303
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## MCMCregress iteration 4721 of 11000
## beta =
## 36.78347
## 1.33788
## sigma2 = 133.83970
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## MCMCregress iteration 4731 of 11000
## beta =
## 38.80760
## 0.34745
## sigma2 = 132.16122
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## MCMCregress iteration 4741 of 11000
## beta =
## 39.46641
## 0.44882
## sigma2 = 123.47798
##
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## MCMCregress iteration 4751 of 11000
## beta =
## 37.42375
## 1.13584
## sigma2 = 131.50197
##
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## MCMCregress iteration 4761 of 11000
## beta =
## 38.34588
## 0.07145
## sigma2 = 119.12901
##
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## MCMCregress iteration 4771 of 11000
## beta =
## 38.03218
## 1.02975
## sigma2 = 136.06011
##
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## MCMCregress iteration 4781 of 11000
## beta =
## 37.57668
## 0.79923
## sigma2 = 129.32952
##
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## MCMCregress iteration 4791 of 11000
## beta =
## 39.20991
## 0.42114
## sigma2 = 138.29831
##
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## MCMCregress iteration 4801 of 11000
## beta =
## 38.88346
## 0.37481
## sigma2 = 136.67030
##
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## MCMCregress iteration 4811 of 11000
## beta =
## 37.54705
## 1.00561
## sigma2 = 118.82509
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## MCMCregress iteration 4821 of 11000
## beta =
## 36.97022
## 1.02441
## sigma2 = 131.03873
##
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## MCMCregress iteration 4831 of 11000
## beta =
## 38.85595
## 0.94794
## sigma2 = 141.65914
##
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## MCMCregress iteration 4841 of 11000
## beta =
## 39.41698
## 0.17311
## sigma2 = 129.50204
##
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## MCMCregress iteration 4851 of 11000
## beta =
## 37.92362
## 0.60387
## sigma2 = 135.19949
##
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## MCMCregress iteration 4861 of 11000
## beta =
## 38.62588
## 0.42846
## sigma2 = 132.72866
##
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## MCMCregress iteration 4871 of 11000
## beta =
## 37.13659
## 1.31421
## sigma2 = 127.78363
##
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## MCMCregress iteration 4881 of 11000
## beta =
## 37.88970
## 0.94204
## sigma2 = 134.36277
##
##
## MCMCregress iteration 4891 of 11000
## beta =
## 37.99808
## 0.66702
## sigma2 = 134.07968
##
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## MCMCregress iteration 4901 of 11000
## beta =
## 38.55114
## 0.62187
## sigma2 = 135.02981
##
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## MCMCregress iteration 4911 of 11000
## beta =
## 37.58976
## 0.83847
## sigma2 = 134.60095
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## MCMCregress iteration 4921 of 11000
## beta =
## 39.68736
## 0.06993
## sigma2 = 128.15852
##
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## MCMCregress iteration 4931 of 11000
## beta =
## 38.26696
## 1.02181
## sigma2 = 133.59876
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## MCMCregress iteration 4941 of 11000
## beta =
## 41.17466
## -0.37565
## sigma2 = 145.13604
##
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## MCMCregress iteration 4951 of 11000
## beta =
## 36.79148
## 1.54806
## sigma2 = 126.71352
##
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## MCMCregress iteration 4961 of 11000
## beta =
## 37.13706
## 0.96926
## sigma2 = 129.80403
##
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## MCMCregress iteration 4971 of 11000
## beta =
## 39.35085
## 0.33492
## sigma2 = 123.64676
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## MCMCregress iteration 4981 of 11000
## beta =
## 37.80859
## 1.12306
## sigma2 = 136.56252
##
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## MCMCregress iteration 4991 of 11000
## beta =
## 39.48026
## 0.27458
## sigma2 = 144.03227
##
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## MCMCregress iteration 5001 of 11000
## beta =
## 37.15596
## 1.05132
## sigma2 = 132.41853
##
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## MCMCregress iteration 5011 of 11000
## beta =
## 38.41038
## 0.51597
## sigma2 = 128.02229
##
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## MCMCregress iteration 5021 of 11000
## beta =
## 38.96820
## 0.55972
## sigma2 = 121.43016
##
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## MCMCregress iteration 5031 of 11000
## beta =
## 36.05073
## 1.38839
## sigma2 = 117.82315
##
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## MCMCregress iteration 5041 of 11000
## beta =
## 41.21262
## -0.29354
## sigma2 = 142.94781
##
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## MCMCregress iteration 5051 of 11000
## beta =
## 41.23966
## -0.71786
## sigma2 = 132.80309
##
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## MCMCregress iteration 5061 of 11000
## beta =
## 37.64325
## 0.76566
## sigma2 = 137.83054
##
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## MCMCregress iteration 5071 of 11000
## beta =
## 37.50162
## 0.71164
## sigma2 = 144.64990
##
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## MCMCregress iteration 5081 of 11000
## beta =
## 39.22644
## 0.51589
## sigma2 = 136.02770
##
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## MCMCregress iteration 5091 of 11000
## beta =
## 39.45106
## 0.55188
## sigma2 = 129.24853
##
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## MCMCregress iteration 5101 of 11000
## beta =
## 37.07729
## 1.15577
## sigma2 = 120.30236
##
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## MCMCregress iteration 5111 of 11000
## beta =
## 39.78402
## 0.38190
## sigma2 = 140.33158
##
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## MCMCregress iteration 5121 of 11000
## beta =
## 39.04898
## 0.35310
## sigma2 = 141.13466
##
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## MCMCregress iteration 5131 of 11000
## beta =
## 38.63099
## 0.51381
## sigma2 = 138.36988
##
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## MCMCregress iteration 5141 of 11000
## beta =
## 38.66443
## 0.65651
## sigma2 = 135.54856
##
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## MCMCregress iteration 5151 of 11000
## beta =
## 41.27575
## -0.43438
## sigma2 = 131.72371
##
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## MCMCregress iteration 5161 of 11000
## beta =
## 36.39199
## 1.23977
## sigma2 = 152.38839
##
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## MCMCregress iteration 5171 of 11000
## beta =
## 36.86022
## 1.36736
## sigma2 = 154.31152
##
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## MCMCregress iteration 5181 of 11000
## beta =
## 38.50899
## 0.93920
## sigma2 = 122.53406
##
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## MCMCregress iteration 5191 of 11000
## beta =
## 37.96461
## 0.67220
## sigma2 = 136.13358
##
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## MCMCregress iteration 5201 of 11000
## beta =
## 39.66230
## 0.27253
## sigma2 = 128.87164
##
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## MCMCregress iteration 5211 of 11000
## beta =
## 38.57111
## 0.88451
## sigma2 = 124.09817
##
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## MCMCregress iteration 5221 of 11000
## beta =
## 36.88858
## 1.01532
## sigma2 = 125.53128
##
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## MCMCregress iteration 5231 of 11000
## beta =
## 40.57677
## -0.12060
## sigma2 = 138.01809
##
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## MCMCregress iteration 5241 of 11000
## beta =
## 37.09207
## 1.45006
## sigma2 = 126.41746
##
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## MCMCregress iteration 5251 of 11000
## beta =
## 39.72309
## 0.15689
## sigma2 = 146.96617
##
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## MCMCregress iteration 5261 of 11000
## beta =
## 36.15203
## 1.65976
## sigma2 = 124.07318
##
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## MCMCregress iteration 5271 of 11000
## beta =
## 38.68686
## 0.56417
## sigma2 = 131.80021
##
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## MCMCregress iteration 5281 of 11000
## beta =
## 39.46198
## 0.27634
## sigma2 = 133.84824
##
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## MCMCregress iteration 5291 of 11000
## beta =
## 40.29870
## 0.29492
## sigma2 = 133.63679
##
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## MCMCregress iteration 5301 of 11000
## beta =
## 38.75028
## 0.19553
## sigma2 = 134.25564
##
##
## MCMCregress iteration 5311 of 11000
## beta =
## 38.61268
## 0.49981
## sigma2 = 136.23082
##
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## MCMCregress iteration 5321 of 11000
## beta =
## 36.96896
## 1.03523
## sigma2 = 140.66412
##
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## MCMCregress iteration 5331 of 11000
## beta =
## 39.30430
## 0.14900
## sigma2 = 121.02532
##
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## MCMCregress iteration 5341 of 11000
## beta =
## 38.76376
## 0.60126
## sigma2 = 147.40899
##
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## MCMCregress iteration 5351 of 11000
## beta =
## 38.76882
## 0.29198
## sigma2 = 133.56222
##
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## MCMCregress iteration 5361 of 11000
## beta =
## 38.79708
## 0.33063
## sigma2 = 144.24524
##
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## MCMCregress iteration 5371 of 11000
## beta =
## 38.32997
## 0.58106
## sigma2 = 127.24226
##
##
## MCMCregress iteration 5381 of 11000
## beta =
## 39.28274
## 0.40944
## sigma2 = 141.11525
##
##
## MCMCregress iteration 5391 of 11000
## beta =
## 35.52192
## 1.64295
## sigma2 = 125.45774
##
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## MCMCregress iteration 5401 of 11000
## beta =
## 38.82495
## 0.78083
## sigma2 = 125.22096
##
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## MCMCregress iteration 5411 of 11000
## beta =
## 39.75787
## 0.55696
## sigma2 = 128.05429
##
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## MCMCregress iteration 5421 of 11000
## beta =
## 39.56825
## 0.12301
## sigma2 = 133.50936
##
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## MCMCregress iteration 5431 of 11000
## beta =
## 39.95646
## 0.00538
## sigma2 = 140.03067
##
##
## MCMCregress iteration 5441 of 11000
## beta =
## 37.36230
## 0.95386
## sigma2 = 138.40681
##
##
## MCMCregress iteration 5451 of 11000
## beta =
## 39.75392
## 0.44340
## sigma2 = 131.59447
##
##
## MCMCregress iteration 5461 of 11000
## beta =
## 39.77716
## 0.00073
## sigma2 = 139.02816
##
##
## MCMCregress iteration 5471 of 11000
## beta =
## 37.14813
## 1.10778
## sigma2 = 147.16683
##
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## MCMCregress iteration 5481 of 11000
## beta =
## 38.87217
## 0.49701
## sigma2 = 140.13412
##
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## MCMCregress iteration 5491 of 11000
## beta =
## 37.73287
## 0.88055
## sigma2 = 119.45907
##
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## MCMCregress iteration 5501 of 11000
## beta =
## 38.69572
## 0.80045
## sigma2 = 137.13218
##
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## MCMCregress iteration 5511 of 11000
## beta =
## 37.91382
## 1.16182
## sigma2 = 128.93355
##
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## MCMCregress iteration 5521 of 11000
## beta =
## 38.85137
## 0.53579
## sigma2 = 126.50980
##
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## MCMCregress iteration 5531 of 11000
## beta =
## 38.00135
## 0.76712
## sigma2 = 130.23025
##
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## MCMCregress iteration 5541 of 11000
## beta =
## 39.26373
## 0.21957
## sigma2 = 130.48289
##
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## MCMCregress iteration 5551 of 11000
## beta =
## 36.89853
## 1.59899
## sigma2 = 139.35789
##
##
## MCMCregress iteration 5561 of 11000
## beta =
## 37.68537
## 1.10232
## sigma2 = 141.51792
##
##
## MCMCregress iteration 5571 of 11000
## beta =
## 38.57344
## 0.72848
## sigma2 = 123.31908
##
##
## MCMCregress iteration 5581 of 11000
## beta =
## 38.96909
## 0.44671
## sigma2 = 126.40414
##
##
## MCMCregress iteration 5591 of 11000
## beta =
## 37.64403
## 1.23501
## sigma2 = 137.59077
##
##
## MCMCregress iteration 5601 of 11000
## beta =
## 39.41363
## 0.48076
## sigma2 = 154.67743
##
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## MCMCregress iteration 5611 of 11000
## beta =
## 37.03922
## 1.29652
## sigma2 = 123.21217
##
##
## MCMCregress iteration 5621 of 11000
## beta =
## 36.57029
## 1.28894
## sigma2 = 131.07659
##
##
## MCMCregress iteration 5631 of 11000
## beta =
## 35.83592
## 1.60282
## sigma2 = 148.14663
##
##
## MCMCregress iteration 5641 of 11000
## beta =
## 37.98378
## 0.76427
## sigma2 = 129.39103
##
##
## MCMCregress iteration 5651 of 11000
## beta =
## 38.39253
## 0.85742
## sigma2 = 125.82435
##
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## MCMCregress iteration 5661 of 11000
## beta =
## 39.49203
## 0.33589
## sigma2 = 143.08190
##
##
## MCMCregress iteration 5671 of 11000
## beta =
## 39.60410
## 0.39799
## sigma2 = 130.24068
##
##
## MCMCregress iteration 5681 of 11000
## beta =
## 37.89249
## 1.36093
## sigma2 = 141.90921
##
##
## MCMCregress iteration 5691 of 11000
## beta =
## 39.98699
## -0.07582
## sigma2 = 125.23956
##
##
## MCMCregress iteration 5701 of 11000
## beta =
## 39.97421
## -0.15743
## sigma2 = 120.50374
##
##
## MCMCregress iteration 5711 of 11000
## beta =
## 37.72722
## 0.76748
## sigma2 = 122.33287
##
##
## MCMCregress iteration 5721 of 11000
## beta =
## 38.72342
## 0.87815
## sigma2 = 120.67888
##
##
## MCMCregress iteration 5731 of 11000
## beta =
## 39.63318
## 0.55875
## sigma2 = 125.71162
##
##
## MCMCregress iteration 5741 of 11000
## beta =
## 35.82128
## 1.72555
## sigma2 = 137.95595
##
##
## MCMCregress iteration 5751 of 11000
## beta =
## 36.53259
## 1.31662
## sigma2 = 129.34068
##
##
## MCMCregress iteration 5761 of 11000
## beta =
## 37.79867
## 0.74117
## sigma2 = 132.31277
##
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## MCMCregress iteration 5771 of 11000
## beta =
## 37.74812
## 0.72018
## sigma2 = 128.42482
##
##
## MCMCregress iteration 5781 of 11000
## beta =
## 36.70137
## 1.56683
## sigma2 = 122.00792
##
##
## MCMCregress iteration 5791 of 11000
## beta =
## 38.62311
## 0.22627
## sigma2 = 126.83153
##
##
## MCMCregress iteration 5801 of 11000
## beta =
## 37.56811
## 1.40054
## sigma2 = 143.97392
##
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## MCMCregress iteration 5811 of 11000
## beta =
## 37.39905
## 0.70700
## sigma2 = 122.81561
##
##
## MCMCregress iteration 5821 of 11000
## beta =
## 36.89774
## 1.14223
## sigma2 = 138.49813
##
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## MCMCregress iteration 5831 of 11000
## beta =
## 38.11718
## 0.88529
## sigma2 = 136.24192
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## MCMCregress iteration 5841 of 11000
## beta =
## 39.71094
## 0.15784
## sigma2 = 116.73892
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## MCMCregress iteration 5851 of 11000
## beta =
## 38.51112
## 0.57810
## sigma2 = 145.89011
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## MCMCregress iteration 5861 of 11000
## beta =
## 40.24515
## 0.21505
## sigma2 = 139.57933
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## MCMCregress iteration 5871 of 11000
## beta =
## 38.84120
## 0.16562
## sigma2 = 149.96826
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## MCMCregress iteration 5881 of 11000
## beta =
## 38.62906
## 0.37388
## sigma2 = 121.50610
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## MCMCregress iteration 5891 of 11000
## beta =
## 38.61812
## 0.62921
## sigma2 = 139.03466
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## MCMCregress iteration 5901 of 11000
## beta =
## 36.77653
## 0.98250
## sigma2 = 130.73443
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## MCMCregress iteration 5911 of 11000
## beta =
## 38.69628
## 0.64353
## sigma2 = 139.38993
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## MCMCregress iteration 5921 of 11000
## beta =
## 38.94915
## 0.56110
## sigma2 = 151.37851
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## MCMCregress iteration 5931 of 11000
## beta =
## 36.76636
## 1.42088
## sigma2 = 132.54905
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## MCMCregress iteration 5941 of 11000
## beta =
## 40.02325
## 0.02351
## sigma2 = 133.09042
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## MCMCregress iteration 5951 of 11000
## beta =
## 38.39187
## 0.56816
## sigma2 = 139.21538
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## MCMCregress iteration 5961 of 11000
## beta =
## 39.61185
## 0.25148
## sigma2 = 123.02131
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## MCMCregress iteration 5971 of 11000
## beta =
## 39.05724
## 0.74268
## sigma2 = 121.83345
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## MCMCregress iteration 5981 of 11000
## beta =
## 37.78521
## 0.79786
## sigma2 = 119.63761
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## MCMCregress iteration 5991 of 11000
## beta =
## 38.26764
## 0.77252
## sigma2 = 135.06496
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## MCMCregress iteration 6001 of 11000
## beta =
## 38.55985
## 0.83312
## sigma2 = 128.95288
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## MCMCregress iteration 6011 of 11000
## beta =
## 40.53149
## 0.03453
## sigma2 = 122.03453
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## MCMCregress iteration 6021 of 11000
## beta =
## 37.57383
## 1.06610
## sigma2 = 139.47386
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## MCMCregress iteration 6031 of 11000
## beta =
## 38.82787
## 0.48115
## sigma2 = 129.58273
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## MCMCregress iteration 6041 of 11000
## beta =
## 38.69771
## 0.71026
## sigma2 = 138.72922
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## MCMCregress iteration 6051 of 11000
## beta =
## 38.06506
## 0.65112
## sigma2 = 136.55922
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## MCMCregress iteration 6061 of 11000
## beta =
## 37.21530
## 1.32453
## sigma2 = 139.30901
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## MCMCregress iteration 6071 of 11000
## beta =
## 41.39445
## -0.31596
## sigma2 = 120.37251
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## MCMCregress iteration 6081 of 11000
## beta =
## 38.26696
## 0.56276
## sigma2 = 138.29162
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## MCMCregress iteration 6091 of 11000
## beta =
## 37.62441
## 0.84342
## sigma2 = 137.36228
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## MCMCregress iteration 6101 of 11000
## beta =
## 37.52863
## 0.85422
## sigma2 = 109.41597
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## MCMCregress iteration 6111 of 11000
## beta =
## 40.59219
## -0.10161
## sigma2 = 135.78266
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## MCMCregress iteration 6121 of 11000
## beta =
## 39.67830
## 0.42936
## sigma2 = 137.32912
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## MCMCregress iteration 6131 of 11000
## beta =
## 38.87469
## 0.51236
## sigma2 = 129.71829
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## MCMCregress iteration 6141 of 11000
## beta =
## 40.81123
## 0.02130
## sigma2 = 143.03843
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## MCMCregress iteration 6151 of 11000
## beta =
## 39.34566
## 0.56376
## sigma2 = 132.86434
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## MCMCregress iteration 6161 of 11000
## beta =
## 37.11542
## 1.24368
## sigma2 = 125.87944
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## MCMCregress iteration 6171 of 11000
## beta =
## 37.13635
## 1.11072
## sigma2 = 145.70585
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## MCMCregress iteration 6181 of 11000
## beta =
## 36.97446
## 1.23267
## sigma2 = 130.79188
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## MCMCregress iteration 6191 of 11000
## beta =
## 38.88178
## 0.31282
## sigma2 = 127.94105
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## MCMCregress iteration 6201 of 11000
## beta =
## 39.10918
## 0.83030
## sigma2 = 127.56029
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## MCMCregress iteration 6211 of 11000
## beta =
## 41.49100
## -0.26878
## sigma2 = 129.76213
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## MCMCregress iteration 6221 of 11000
## beta =
## 36.34827
## 1.31997
## sigma2 = 126.04842
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## MCMCregress iteration 6231 of 11000
## beta =
## 39.05603
## 0.00530
## sigma2 = 137.98050
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## MCMCregress iteration 6241 of 11000
## beta =
## 36.59183
## 1.24213
## sigma2 = 124.21057
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## MCMCregress iteration 6251 of 11000
## beta =
## 38.79514
## 0.51772
## sigma2 = 137.04735
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## MCMCregress iteration 6261 of 11000
## beta =
## 37.80434
## 1.00405
## sigma2 = 118.32409
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## MCMCregress iteration 6271 of 11000
## beta =
## 37.92026
## 0.59116
## sigma2 = 141.36401
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## MCMCregress iteration 6281 of 11000
## beta =
## 38.97902
## 0.73569
## sigma2 = 136.20526
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## MCMCregress iteration 6291 of 11000
## beta =
## 39.30171
## 0.56981
## sigma2 = 145.76378
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## MCMCregress iteration 6301 of 11000
## beta =
## 37.67626
## 0.78634
## sigma2 = 137.19115
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## MCMCregress iteration 6311 of 11000
## beta =
## 37.45433
## 1.69075
## sigma2 = 148.02910
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## MCMCregress iteration 6321 of 11000
## beta =
## 38.48852
## 0.79325
## sigma2 = 132.24184
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## MCMCregress iteration 6331 of 11000
## beta =
## 39.47274
## -0.06769
## sigma2 = 125.09891
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## MCMCregress iteration 6341 of 11000
## beta =
## 39.00947
## 0.38886
## sigma2 = 124.77339
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## MCMCregress iteration 6351 of 11000
## beta =
## 39.68266
## 0.26341
## sigma2 = 135.40515
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## MCMCregress iteration 6361 of 11000
## beta =
## 38.03057
## 1.01519
## sigma2 = 143.92350
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## MCMCregress iteration 6371 of 11000
## beta =
## 38.94420
## 0.48420
## sigma2 = 143.96981
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## MCMCregress iteration 6381 of 11000
## beta =
## 37.46399
## 0.84884
## sigma2 = 131.34031
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## MCMCregress iteration 6391 of 11000
## beta =
## 39.08587
## 0.55186
## sigma2 = 152.44012
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## MCMCregress iteration 6401 of 11000
## beta =
## 38.39437
## 0.84916
## sigma2 = 152.73056
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## MCMCregress iteration 6411 of 11000
## beta =
## 36.68385
## 1.31362
## sigma2 = 137.03213
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## MCMCregress iteration 6421 of 11000
## beta =
## 37.21224
## 0.95857
## sigma2 = 155.29528
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## MCMCregress iteration 6431 of 11000
## beta =
## 37.54177
## 1.21705
## sigma2 = 127.63016
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## MCMCregress iteration 6441 of 11000
## beta =
## 39.10149
## 0.22148
## sigma2 = 128.89955
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## MCMCregress iteration 6451 of 11000
## beta =
## 39.24137
## 0.32871
## sigma2 = 125.74454
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## MCMCregress iteration 6461 of 11000
## beta =
## 40.82885
## -0.19539
## sigma2 = 130.55111
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## MCMCregress iteration 6471 of 11000
## beta =
## 38.58785
## 0.83067
## sigma2 = 127.75889
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## MCMCregress iteration 6481 of 11000
## beta =
## 37.57480
## 1.11787
## sigma2 = 137.27010
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## MCMCregress iteration 6491 of 11000
## beta =
## 38.86612
## 0.77679
## sigma2 = 129.69475
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## MCMCregress iteration 6501 of 11000
## beta =
## 38.85859
## 0.52152
## sigma2 = 128.49846
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## MCMCregress iteration 6511 of 11000
## beta =
## 37.72621
## 1.28431
## sigma2 = 128.94868
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## MCMCregress iteration 6521 of 11000
## beta =
## 38.00783
## 0.87174
## sigma2 = 141.05916
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## MCMCregress iteration 6531 of 11000
## beta =
## 37.95984
## 1.06321
## sigma2 = 137.20009
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## MCMCregress iteration 6541 of 11000
## beta =
## 38.12381
## 0.77496
## sigma2 = 135.40254
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## MCMCregress iteration 6551 of 11000
## beta =
## 39.63357
## 0.18045
## sigma2 = 116.84534
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## MCMCregress iteration 6561 of 11000
## beta =
## 38.42800
## 0.58734
## sigma2 = 148.90229
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## MCMCregress iteration 6571 of 11000
## beta =
## 38.31154
## 0.65095
## sigma2 = 135.09797
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## MCMCregress iteration 6581 of 11000
## beta =
## 37.45367
## 0.97424
## sigma2 = 140.04752
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## MCMCregress iteration 6591 of 11000
## beta =
## 37.03591
## 1.25776
## sigma2 = 131.75248
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## MCMCregress iteration 6601 of 11000
## beta =
## 40.10340
## -0.11358
## sigma2 = 126.56371
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## MCMCregress iteration 6611 of 11000
## beta =
## 38.30365
## 0.87312
## sigma2 = 121.55557
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## MCMCregress iteration 6621 of 11000
## beta =
## 38.81631
## 0.40756
## sigma2 = 135.27206
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## MCMCregress iteration 6631 of 11000
## beta =
## 38.51642
## 0.40634
## sigma2 = 116.86233
##
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## MCMCregress iteration 6641 of 11000
## beta =
## 38.08333
## 0.76403
## sigma2 = 139.14294
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## MCMCregress iteration 6651 of 11000
## beta =
## 38.63666
## 0.94192
## sigma2 = 138.64516
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## MCMCregress iteration 6661 of 11000
## beta =
## 39.87808
## 0.16124
## sigma2 = 135.53838
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## MCMCregress iteration 6671 of 11000
## beta =
## 39.49259
## 0.34752
## sigma2 = 135.94966
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## MCMCregress iteration 6681 of 11000
## beta =
## 38.64766
## 0.69563
## sigma2 = 145.29898
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## MCMCregress iteration 6691 of 11000
## beta =
## 37.83383
## 0.95008
## sigma2 = 134.87956
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## MCMCregress iteration 6701 of 11000
## beta =
## 38.63450
## 0.75700
## sigma2 = 117.22446
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## MCMCregress iteration 6711 of 11000
## beta =
## 38.84070
## 0.39998
## sigma2 = 125.54697
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## MCMCregress iteration 6721 of 11000
## beta =
## 36.37623
## 1.80480
## sigma2 = 139.36699
##
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## MCMCregress iteration 6731 of 11000
## beta =
## 37.45880
## 1.28782
## sigma2 = 141.07194
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## MCMCregress iteration 6741 of 11000
## beta =
## 37.93402
## 0.78613
## sigma2 = 140.30334
##
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## MCMCregress iteration 6751 of 11000
## beta =
## 37.13302
## 0.77691
## sigma2 = 133.43373
##
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## MCMCregress iteration 6761 of 11000
## beta =
## 39.40056
## 0.74542
## sigma2 = 128.76316
##
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## MCMCregress iteration 6771 of 11000
## beta =
## 39.74322
## 0.22602
## sigma2 = 136.90235
##
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## MCMCregress iteration 6781 of 11000
## beta =
## 39.12483
## 0.51327
## sigma2 = 119.42207
##
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## MCMCregress iteration 6791 of 11000
## beta =
## 38.52611
## 0.62142
## sigma2 = 115.92001
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## MCMCregress iteration 6801 of 11000
## beta =
## 37.77955
## 1.01190
## sigma2 = 138.92759
##
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## MCMCregress iteration 6811 of 11000
## beta =
## 37.20441
## 1.36328
## sigma2 = 132.83998
##
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## MCMCregress iteration 6821 of 11000
## beta =
## 36.91322
## 1.22188
## sigma2 = 133.41111
##
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## MCMCregress iteration 6831 of 11000
## beta =
## 38.60744
## 0.72713
## sigma2 = 138.55412
##
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## MCMCregress iteration 6841 of 11000
## beta =
## 39.06323
## 0.44201
## sigma2 = 128.41612
##
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## MCMCregress iteration 6851 of 11000
## beta =
## 36.53364
## 1.10531
## sigma2 = 144.46973
##
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## MCMCregress iteration 6861 of 11000
## beta =
## 36.41146
## 1.32453
## sigma2 = 139.35227
##
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## MCMCregress iteration 6871 of 11000
## beta =
## 37.22763
## 1.22081
## sigma2 = 145.74882
##
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## MCMCregress iteration 6881 of 11000
## beta =
## 39.16350
## 0.42048
## sigma2 = 131.66456
##
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## MCMCregress iteration 6891 of 11000
## beta =
## 38.74589
## 0.69849
## sigma2 = 136.17864
##
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## MCMCregress iteration 6901 of 11000
## beta =
## 39.36396
## 0.54540
## sigma2 = 153.40552
##
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## MCMCregress iteration 6911 of 11000
## beta =
## 38.53859
## 0.38797
## sigma2 = 133.24302
##
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## MCMCregress iteration 6921 of 11000
## beta =
## 38.69920
## 0.57600
## sigma2 = 135.59928
##
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## MCMCregress iteration 6931 of 11000
## beta =
## 38.64447
## 0.58231
## sigma2 = 133.82622
##
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## MCMCregress iteration 6941 of 11000
## beta =
## 37.62721
## 0.90379
## sigma2 = 131.51572
##
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## MCMCregress iteration 6951 of 11000
## beta =
## 37.54608
## 0.79337
## sigma2 = 143.74774
##
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## MCMCregress iteration 6961 of 11000
## beta =
## 39.45334
## 0.26824
## sigma2 = 142.38777
##
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## MCMCregress iteration 6971 of 11000
## beta =
## 38.92510
## 0.33558
## sigma2 = 122.75110
##
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## MCMCregress iteration 6981 of 11000
## beta =
## 38.66704
## 0.09708
## sigma2 = 131.32124
##
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## MCMCregress iteration 6991 of 11000
## beta =
## 38.49236
## 0.67147
## sigma2 = 128.25134
##
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## MCMCregress iteration 7001 of 11000
## beta =
## 38.57418
## 0.66724
## sigma2 = 124.96532
##
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## MCMCregress iteration 7011 of 11000
## beta =
## 38.15055
## 0.88320
## sigma2 = 144.04530
##
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## MCMCregress iteration 7021 of 11000
## beta =
## 37.27506
## 1.31816
## sigma2 = 132.04225
##
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## MCMCregress iteration 7031 of 11000
## beta =
## 38.94649
## 0.57071
## sigma2 = 127.81636
##
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## MCMCregress iteration 7041 of 11000
## beta =
## 37.76306
## 1.00399
## sigma2 = 137.84649
##
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## MCMCregress iteration 7051 of 11000
## beta =
## 38.34498
## 0.84664
## sigma2 = 133.54989
##
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## MCMCregress iteration 7061 of 11000
## beta =
## 39.06526
## 0.42725
## sigma2 = 142.87793
##
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## MCMCregress iteration 7071 of 11000
## beta =
## 39.14239
## 0.51246
## sigma2 = 137.79261
##
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## MCMCregress iteration 7081 of 11000
## beta =
## 39.14392
## 0.57231
## sigma2 = 138.68738
##
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## MCMCregress iteration 7091 of 11000
## beta =
## 38.02329
## 0.98691
## sigma2 = 134.30472
##
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## MCMCregress iteration 7101 of 11000
## beta =
## 36.96729
## 1.23621
## sigma2 = 132.88614
##
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## MCMCregress iteration 7111 of 11000
## beta =
## 37.39301
## 1.10464
## sigma2 = 127.64705
##
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## MCMCregress iteration 7121 of 11000
## beta =
## 36.29492
## 1.45614
## sigma2 = 119.67324
##
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## MCMCregress iteration 7131 of 11000
## beta =
## 37.37347
## 0.83807
## sigma2 = 141.41646
##
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## MCMCregress iteration 7141 of 11000
## beta =
## 40.33094
## 0.10251
## sigma2 = 131.35966
##
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## MCMCregress iteration 7151 of 11000
## beta =
## 38.70461
## 0.55992
## sigma2 = 135.82070
##
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## MCMCregress iteration 7161 of 11000
## beta =
## 39.26665
## 0.31145
## sigma2 = 134.42628
##
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## MCMCregress iteration 7171 of 11000
## beta =
## 38.24698
## 0.95329
## sigma2 = 132.41292
##
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## MCMCregress iteration 7181 of 11000
## beta =
## 39.08092
## 0.62468
## sigma2 = 131.85367
##
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## MCMCregress iteration 7191 of 11000
## beta =
## 39.05903
## 0.69593
## sigma2 = 132.26773
##
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## MCMCregress iteration 7201 of 11000
## beta =
## 36.64801
## 1.29208
## sigma2 = 128.39449
##
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## MCMCregress iteration 7211 of 11000
## beta =
## 37.80115
## 0.83663
## sigma2 = 133.73734
##
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## MCMCregress iteration 7221 of 11000
## beta =
## 38.37260
## 0.76485
## sigma2 = 118.93287
##
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## MCMCregress iteration 7231 of 11000
## beta =
## 37.25701
## 1.02900
## sigma2 = 125.47352
##
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## MCMCregress iteration 7241 of 11000
## beta =
## 38.90416
## 0.62599
## sigma2 = 116.63405
##
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## MCMCregress iteration 7251 of 11000
## beta =
## 39.90474
## 0.09122
## sigma2 = 133.62919
##
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## MCMCregress iteration 7261 of 11000
## beta =
## 38.16952
## 0.79329
## sigma2 = 125.73708
##
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## MCMCregress iteration 7271 of 11000
## beta =
## 38.20653
## 0.52456
## sigma2 = 142.39840
##
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## MCMCregress iteration 7281 of 11000
## beta =
## 40.65846
## -0.15071
## sigma2 = 118.95648
##
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## MCMCregress iteration 7291 of 11000
## beta =
## 38.04939
## 0.76683
## sigma2 = 137.15725
##
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## MCMCregress iteration 7301 of 11000
## beta =
## 38.66304
## 0.84823
## sigma2 = 135.66282
##
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## MCMCregress iteration 7311 of 11000
## beta =
## 37.19638
## 1.05668
## sigma2 = 139.53388
##
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## MCMCregress iteration 7321 of 11000
## beta =
## 39.14273
## 0.72675
## sigma2 = 135.25309
##
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## MCMCregress iteration 7331 of 11000
## beta =
## 36.53609
## 1.31478
## sigma2 = 139.43745
##
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## MCMCregress iteration 7341 of 11000
## beta =
## 38.58694
## 0.66133
## sigma2 = 137.62439
##
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## MCMCregress iteration 7351 of 11000
## beta =
## 39.05372
## 0.15246
## sigma2 = 133.91822
##
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## MCMCregress iteration 7361 of 11000
## beta =
## 38.33794
## 0.66815
## sigma2 = 153.70712
##
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## MCMCregress iteration 7371 of 11000
## beta =
## 37.86717
## 1.12886
## sigma2 = 134.78670
##
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## MCMCregress iteration 7381 of 11000
## beta =
## 39.51256
## 0.41885
## sigma2 = 139.76007
##
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## MCMCregress iteration 7391 of 11000
## beta =
## 39.53065
## 0.35227
## sigma2 = 133.64969
##
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## MCMCregress iteration 7401 of 11000
## beta =
## 38.41002
## 0.53931
## sigma2 = 130.69975
##
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## MCMCregress iteration 7411 of 11000
## beta =
## 39.99194
## -0.36215
## sigma2 = 145.83495
##
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## MCMCregress iteration 7421 of 11000
## beta =
## 37.25347
## 1.08125
## sigma2 = 138.60200
##
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## MCMCregress iteration 7431 of 11000
## beta =
## 38.27130
## 0.66232
## sigma2 = 141.53338
##
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## MCMCregress iteration 7441 of 11000
## beta =
## 39.40465
## 0.79881
## sigma2 = 138.39418
##
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## MCMCregress iteration 7451 of 11000
## beta =
## 40.53945
## -0.14597
## sigma2 = 131.75720
##
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## MCMCregress iteration 7461 of 11000
## beta =
## 36.70603
## 1.20124
## sigma2 = 132.86129
##
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## MCMCregress iteration 7471 of 11000
## beta =
## 39.22137
## 0.53382
## sigma2 = 138.46746
##
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## MCMCregress iteration 7481 of 11000
## beta =
## 38.25479
## 0.95462
## sigma2 = 141.40230
##
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## MCMCregress iteration 7491 of 11000
## beta =
## 37.65377
## 0.75417
## sigma2 = 140.72967
##
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## MCMCregress iteration 7501 of 11000
## beta =
## 37.25060
## 1.11938
## sigma2 = 126.39571
##
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## MCMCregress iteration 7511 of 11000
## beta =
## 38.58396
## 0.66565
## sigma2 = 117.78346
##
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## MCMCregress iteration 7521 of 11000
## beta =
## 37.83286
## 0.50030
## sigma2 = 127.28215
##
##
## MCMCregress iteration 7531 of 11000
## beta =
## 37.50998
## 0.86166
## sigma2 = 143.90538
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## MCMCregress iteration 7541 of 11000
## beta =
## 38.84782
## 0.69805
## sigma2 = 129.68519
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## MCMCregress iteration 7551 of 11000
## beta =
## 39.55526
## 0.41595
## sigma2 = 126.18625
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## MCMCregress iteration 7561 of 11000
## beta =
## 38.66737
## 0.72154
## sigma2 = 124.80570
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## MCMCregress iteration 7571 of 11000
## beta =
## 39.97214
## 0.44595
## sigma2 = 142.62149
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## MCMCregress iteration 7581 of 11000
## beta =
## 37.23525
## 1.03319
## sigma2 = 125.22525
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##
## MCMCregress iteration 7591 of 11000
## beta =
## 40.39551
## -0.06755
## sigma2 = 139.20691
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## MCMCregress iteration 7601 of 11000
## beta =
## 38.63159
## 0.65269
## sigma2 = 140.74905
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## MCMCregress iteration 7611 of 11000
## beta =
## 38.42913
## 0.45537
## sigma2 = 140.57449
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## MCMCregress iteration 7621 of 11000
## beta =
## 36.34554
## 1.51257
## sigma2 = 146.97345
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## MCMCregress iteration 7631 of 11000
## beta =
## 39.30485
## 0.15789
## sigma2 = 140.58201
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##
## MCMCregress iteration 7641 of 11000
## beta =
## 39.22974
## 0.32157
## sigma2 = 126.49909
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## MCMCregress iteration 7651 of 11000
## beta =
## 38.91787
## 0.79757
## sigma2 = 115.75143
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## MCMCregress iteration 7661 of 11000
## beta =
## 38.25794
## 0.38855
## sigma2 = 135.68307
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## MCMCregress iteration 7671 of 11000
## beta =
## 38.26630
## 0.96254
## sigma2 = 140.51364
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## MCMCregress iteration 7681 of 11000
## beta =
## 37.43425
## 1.13771
## sigma2 = 140.02131
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## MCMCregress iteration 7691 of 11000
## beta =
## 37.93607
## 0.74254
## sigma2 = 139.25139
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## MCMCregress iteration 7701 of 11000
## beta =
## 38.59019
## 0.18947
## sigma2 = 144.77135
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## MCMCregress iteration 7711 of 11000
## beta =
## 38.85370
## 0.35516
## sigma2 = 130.48786
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## MCMCregress iteration 7721 of 11000
## beta =
## 38.10525
## 1.14981
## sigma2 = 130.14108
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##
## MCMCregress iteration 7731 of 11000
## beta =
## 38.87123
## 0.41833
## sigma2 = 134.80169
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## MCMCregress iteration 7741 of 11000
## beta =
## 39.80230
## -0.18748
## sigma2 = 134.16615
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## MCMCregress iteration 7751 of 11000
## beta =
## 38.28869
## 1.09140
## sigma2 = 138.49335
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## MCMCregress iteration 7761 of 11000
## beta =
## 40.03219
## 0.08582
## sigma2 = 128.06586
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## MCMCregress iteration 7771 of 11000
## beta =
## 38.46227
## 0.90734
## sigma2 = 141.28956
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## MCMCregress iteration 7781 of 11000
## beta =
## 38.08149
## 0.67276
## sigma2 = 135.47125
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## MCMCregress iteration 7791 of 11000
## beta =
## 37.96311
## 0.43073
## sigma2 = 131.33288
##
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## MCMCregress iteration 7801 of 11000
## beta =
## 39.86468
## 0.22877
## sigma2 = 130.52219
##
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## MCMCregress iteration 7811 of 11000
## beta =
## 40.71386
## -0.07410
## sigma2 = 115.92652
##
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## MCMCregress iteration 7821 of 11000
## beta =
## 36.42520
## 1.56896
## sigma2 = 143.26419
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## MCMCregress iteration 7831 of 11000
## beta =
## 37.79585
## 1.22223
## sigma2 = 115.69441
##
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## MCMCregress iteration 7841 of 11000
## beta =
## 39.14834
## 0.21466
## sigma2 = 130.20026
##
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## MCMCregress iteration 7851 of 11000
## beta =
## 36.78161
## 1.54618
## sigma2 = 135.80218
##
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## MCMCregress iteration 7861 of 11000
## beta =
## 39.23484
## 0.39462
## sigma2 = 147.10097
##
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## MCMCregress iteration 7871 of 11000
## beta =
## 38.96706
## 0.26315
## sigma2 = 135.89572
##
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## MCMCregress iteration 7881 of 11000
## beta =
## 37.77708
## 0.94831
## sigma2 = 129.92972
##
##
## MCMCregress iteration 7891 of 11000
## beta =
## 38.96965
## 0.40012
## sigma2 = 139.77461
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## MCMCregress iteration 7901 of 11000
## beta =
## 38.86237
## 0.90997
## sigma2 = 131.55349
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## MCMCregress iteration 7911 of 11000
## beta =
## 38.82799
## 0.99634
## sigma2 = 121.06560
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## MCMCregress iteration 7921 of 11000
## beta =
## 39.48186
## 0.72254
## sigma2 = 131.36281
##
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## MCMCregress iteration 7931 of 11000
## beta =
## 38.85582
## 0.63426
## sigma2 = 144.55073
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## MCMCregress iteration 7941 of 11000
## beta =
## 39.23265
## 0.50012
## sigma2 = 138.43700
##
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## MCMCregress iteration 7951 of 11000
## beta =
## 36.56499
## 1.42361
## sigma2 = 127.92867
##
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## MCMCregress iteration 7961 of 11000
## beta =
## 38.09561
## 1.01108
## sigma2 = 129.95342
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## MCMCregress iteration 7971 of 11000
## beta =
## 38.98737
## 0.39151
## sigma2 = 136.29435
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## MCMCregress iteration 7981 of 11000
## beta =
## 38.73397
## 0.74975
## sigma2 = 132.58110
##
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## MCMCregress iteration 7991 of 11000
## beta =
## 38.76908
## 0.36272
## sigma2 = 131.13582
##
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## MCMCregress iteration 8001 of 11000
## beta =
## 37.45724
## 0.77866
## sigma2 = 135.31086
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## MCMCregress iteration 8011 of 11000
## beta =
## 40.55960
## 0.22761
## sigma2 = 153.10198
##
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## MCMCregress iteration 8021 of 11000
## beta =
## 37.04325
## 1.01786
## sigma2 = 134.65803
##
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## MCMCregress iteration 8031 of 11000
## beta =
## 37.59755
## 1.03752
## sigma2 = 139.59284
##
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## MCMCregress iteration 8041 of 11000
## beta =
## 39.20264
## 0.51666
## sigma2 = 130.76121
##
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## MCMCregress iteration 8051 of 11000
## beta =
## 40.19860
## -0.33338
## sigma2 = 142.93339
##
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## MCMCregress iteration 8061 of 11000
## beta =
## 39.21584
## 0.33386
## sigma2 = 133.53229
##
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## MCMCregress iteration 8071 of 11000
## beta =
## 38.43343
## 0.70228
## sigma2 = 131.87334
##
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## MCMCregress iteration 8081 of 11000
## beta =
## 37.72676
## 0.98768
## sigma2 = 146.86458
##
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## MCMCregress iteration 8091 of 11000
## beta =
## 38.35589
## 0.90455
## sigma2 = 139.49935
##
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## MCMCregress iteration 8101 of 11000
## beta =
## 40.91103
## -0.52689
## sigma2 = 123.21363
##
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## MCMCregress iteration 8111 of 11000
## beta =
## 40.91618
## 0.14821
## sigma2 = 132.88019
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## MCMCregress iteration 8121 of 11000
## beta =
## 37.44812
## 0.95007
## sigma2 = 137.76602
##
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## MCMCregress iteration 8131 of 11000
## beta =
## 37.97667
## 0.83349
## sigma2 = 132.32315
##
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## MCMCregress iteration 8141 of 11000
## beta =
## 41.09184
## -0.25113
## sigma2 = 140.75105
##
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## MCMCregress iteration 8151 of 11000
## beta =
## 37.59745
## 1.31955
## sigma2 = 130.81667
##
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## MCMCregress iteration 8161 of 11000
## beta =
## 38.05766
## 0.79317
## sigma2 = 124.77793
##
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## MCMCregress iteration 8171 of 11000
## beta =
## 37.19035
## 1.03216
## sigma2 = 130.46395
##
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## MCMCregress iteration 8181 of 11000
## beta =
## 38.32652
## 1.05654
## sigma2 = 130.71264
##
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## MCMCregress iteration 8191 of 11000
## beta =
## 39.40926
## 0.63155
## sigma2 = 133.35432
##
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## MCMCregress iteration 8201 of 11000
## beta =
## 38.55942
## 1.12167
## sigma2 = 133.25400
##
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## MCMCregress iteration 8211 of 11000
## beta =
## 39.91466
## -0.11285
## sigma2 = 128.93802
##
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## MCMCregress iteration 8221 of 11000
## beta =
## 39.66208
## 0.15903
## sigma2 = 132.55950
##
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## MCMCregress iteration 8231 of 11000
## beta =
## 39.07558
## 0.45935
## sigma2 = 132.67152
##
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## MCMCregress iteration 8241 of 11000
## beta =
## 38.64808
## 0.23413
## sigma2 = 143.69132
##
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## MCMCregress iteration 8251 of 11000
## beta =
## 38.34187
## 0.81926
## sigma2 = 137.94867
##
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## MCMCregress iteration 8261 of 11000
## beta =
## 40.26720
## -0.11545
## sigma2 = 127.27795
##
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## MCMCregress iteration 8271 of 11000
## beta =
## 39.33661
## 0.36974
## sigma2 = 135.78647
##
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## MCMCregress iteration 8281 of 11000
## beta =
## 39.51620
## 0.02221
## sigma2 = 126.46209
##
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## MCMCregress iteration 8291 of 11000
## beta =
## 39.61945
## 0.36154
## sigma2 = 125.90316
##
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## MCMCregress iteration 8301 of 11000
## beta =
## 36.82218
## 1.20667
## sigma2 = 127.00649
##
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## MCMCregress iteration 8311 of 11000
## beta =
## 40.09989
## 0.07279
## sigma2 = 148.79798
##
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## MCMCregress iteration 8321 of 11000
## beta =
## 37.78523
## 1.22479
## sigma2 = 142.87474
##
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## MCMCregress iteration 8331 of 11000
## beta =
## 39.67424
## 0.31755
## sigma2 = 138.46243
##
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## MCMCregress iteration 8341 of 11000
## beta =
## 39.78195
## 0.21818
## sigma2 = 140.62158
##
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## MCMCregress iteration 8351 of 11000
## beta =
## 39.40998
## 0.29056
## sigma2 = 132.26628
##
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## MCMCregress iteration 8361 of 11000
## beta =
## 39.93676
## 0.15617
## sigma2 = 119.27390
##
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## MCMCregress iteration 8371 of 11000
## beta =
## 34.96739
## 1.90729
## sigma2 = 134.77356
##
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## MCMCregress iteration 8381 of 11000
## beta =
## 35.12608
## 2.08986
## sigma2 = 137.50970
##
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## MCMCregress iteration 8391 of 11000
## beta =
## 38.39392
## 0.60553
## sigma2 = 138.93889
##
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## MCMCregress iteration 8401 of 11000
## beta =
## 39.13069
## 0.24551
## sigma2 = 133.79228
##
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## MCMCregress iteration 8411 of 11000
## beta =
## 36.94337
## 0.99914
## sigma2 = 148.41299
##
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## MCMCregress iteration 8421 of 11000
## beta =
## 36.95985
## 1.34297
## sigma2 = 127.06690
##
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## MCMCregress iteration 8431 of 11000
## beta =
## 38.96809
## 0.66597
## sigma2 = 140.63327
##
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## MCMCregress iteration 8441 of 11000
## beta =
## 39.53635
## 0.06277
## sigma2 = 123.95693
##
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## MCMCregress iteration 8451 of 11000
## beta =
## 37.10246
## 1.39972
## sigma2 = 162.61532
##
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## MCMCregress iteration 8461 of 11000
## beta =
## 38.94390
## 0.58174
## sigma2 = 135.59035
##
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## MCMCregress iteration 8471 of 11000
## beta =
## 37.69734
## 1.27900
## sigma2 = 149.84624
##
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## MCMCregress iteration 8481 of 11000
## beta =
## 38.12126
## 0.58216
## sigma2 = 123.06038
##
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## MCMCregress iteration 8491 of 11000
## beta =
## 37.84674
## 0.87864
## sigma2 = 138.43553
##
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## MCMCregress iteration 8501 of 11000
## beta =
## 38.85001
## 0.51032
## sigma2 = 125.32921
##
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## MCMCregress iteration 8511 of 11000
## beta =
## 39.79136
## -0.06714
## sigma2 = 112.30914
##
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## MCMCregress iteration 8521 of 11000
## beta =
## 38.62599
## 0.55972
## sigma2 = 132.79198
##
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## MCMCregress iteration 8531 of 11000
## beta =
## 39.03966
## 0.12402
## sigma2 = 120.74975
##
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## MCMCregress iteration 8541 of 11000
## beta =
## 37.41527
## 1.09282
## sigma2 = 128.45430
##
##
## MCMCregress iteration 8551 of 11000
## beta =
## 35.83229
## 1.74460
## sigma2 = 139.99444
##
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## MCMCregress iteration 8561 of 11000
## beta =
## 38.33316
## 0.85411
## sigma2 = 130.92853
##
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## MCMCregress iteration 8571 of 11000
## beta =
## 38.28920
## 0.82243
## sigma2 = 122.26048
##
##
## MCMCregress iteration 8581 of 11000
## beta =
## 39.97455
## 0.44547
## sigma2 = 140.78338
##
##
## MCMCregress iteration 8591 of 11000
## beta =
## 37.85912
## 0.82532
## sigma2 = 123.12822
##
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## MCMCregress iteration 8601 of 11000
## beta =
## 40.92292
## 0.13010
## sigma2 = 126.29133
##
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## MCMCregress iteration 8611 of 11000
## beta =
## 38.96665
## 0.45415
## sigma2 = 122.45716
##
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## MCMCregress iteration 8621 of 11000
## beta =
## 38.30725
## 1.24974
## sigma2 = 130.21234
##
##
## MCMCregress iteration 8631 of 11000
## beta =
## 39.01956
## 0.45704
## sigma2 = 132.22919
##
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## MCMCregress iteration 8641 of 11000
## beta =
## 37.95518
## 0.79620
## sigma2 = 142.08714
##
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## MCMCregress iteration 8651 of 11000
## beta =
## 39.89795
## 0.27495
## sigma2 = 119.74042
##
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## MCMCregress iteration 8661 of 11000
## beta =
## 38.97573
## 0.48586
## sigma2 = 134.65370
##
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## MCMCregress iteration 8671 of 11000
## beta =
## 37.06848
## 1.21746
## sigma2 = 126.28529
##
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## MCMCregress iteration 8681 of 11000
## beta =
## 36.92321
## 1.06017
## sigma2 = 139.02643
##
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## MCMCregress iteration 8691 of 11000
## beta =
## 39.58088
## -0.04660
## sigma2 = 125.65533
##
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## MCMCregress iteration 8701 of 11000
## beta =
## 38.14387
## 0.57860
## sigma2 = 127.22370
##
##
## MCMCregress iteration 8711 of 11000
## beta =
## 38.66102
## 0.35348
## sigma2 = 132.33400
##
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## MCMCregress iteration 8721 of 11000
## beta =
## 39.68470
## 0.03644
## sigma2 = 133.26107
##
##
## MCMCregress iteration 8731 of 11000
## beta =
## 36.57741
## 1.18867
## sigma2 = 133.42372
##
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## MCMCregress iteration 8741 of 11000
## beta =
## 39.06376
## 0.05657
## sigma2 = 136.86691
##
##
## MCMCregress iteration 8751 of 11000
## beta =
## 36.60089
## 1.55027
## sigma2 = 132.41685
##
##
## MCMCregress iteration 8761 of 11000
## beta =
## 39.78082
## 0.43149
## sigma2 = 126.92335
##
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## MCMCregress iteration 8771 of 11000
## beta =
## 37.29867
## 1.22857
## sigma2 = 138.95265
##
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## MCMCregress iteration 8781 of 11000
## beta =
## 39.24966
## 0.37408
## sigma2 = 118.44029
##
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## MCMCregress iteration 8791 of 11000
## beta =
## 39.04952
## 0.64023
## sigma2 = 116.89196
##
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## MCMCregress iteration 8801 of 11000
## beta =
## 38.73966
## 0.40745
## sigma2 = 131.69253
##
##
## MCMCregress iteration 8811 of 11000
## beta =
## 39.93507
## 0.60777
## sigma2 = 122.26526
##
##
## MCMCregress iteration 8821 of 11000
## beta =
## 36.99841
## 1.16467
## sigma2 = 160.89480
##
##
## MCMCregress iteration 8831 of 11000
## beta =
## 39.47171
## 0.63318
## sigma2 = 126.63497
##
##
## MCMCregress iteration 8841 of 11000
## beta =
## 37.43788
## 1.17974
## sigma2 = 135.46177
##
##
## MCMCregress iteration 8851 of 11000
## beta =
## 38.31048
## 0.70886
## sigma2 = 138.16530
##
##
## MCMCregress iteration 8861 of 11000
## beta =
## 38.30059
## 1.02725
## sigma2 = 139.19621
##
##
## MCMCregress iteration 8871 of 11000
## beta =
## 37.40122
## 0.72296
## sigma2 = 130.48678
##
##
## MCMCregress iteration 8881 of 11000
## beta =
## 39.06690
## 0.40783
## sigma2 = 117.69512
##
##
## MCMCregress iteration 8891 of 11000
## beta =
## 38.66063
## 0.35186
## sigma2 = 133.28332
##
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## MCMCregress iteration 8901 of 11000
## beta =
## 38.98628
## 0.84418
## sigma2 = 139.10260
##
##
## MCMCregress iteration 8911 of 11000
## beta =
## 39.39431
## 0.37339
## sigma2 = 131.86489
##
##
## MCMCregress iteration 8921 of 11000
## beta =
## 38.59778
## 0.93219
## sigma2 = 131.67946
##
##
## MCMCregress iteration 8931 of 11000
## beta =
## 37.46775
## 0.89861
## sigma2 = 124.26239
##
##
## MCMCregress iteration 8941 of 11000
## beta =
## 37.34671
## 1.30456
## sigma2 = 129.82971
##
##
## MCMCregress iteration 8951 of 11000
## beta =
## 38.53589
## 0.24680
## sigma2 = 127.84798
##
##
## MCMCregress iteration 8961 of 11000
## beta =
## 38.10731
## 0.77768
## sigma2 = 135.67055
##
##
## MCMCregress iteration 8971 of 11000
## beta =
## 38.73847
## 0.54634
## sigma2 = 137.89149
##
##
## MCMCregress iteration 8981 of 11000
## beta =
## 38.71139
## 0.87543
## sigma2 = 129.07782
##
##
## MCMCregress iteration 8991 of 11000
## beta =
## 38.24897
## 0.55275
## sigma2 = 137.00169
##
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## MCMCregress iteration 9001 of 11000
## beta =
## 38.02473
## 0.48307
## sigma2 = 142.06941
##
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## MCMCregress iteration 9011 of 11000
## beta =
## 37.66692
## 0.87734
## sigma2 = 137.94012
##
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## MCMCregress iteration 9021 of 11000
## beta =
## 39.88940
## 0.07696
## sigma2 = 135.97949
##
##
## MCMCregress iteration 9031 of 11000
## beta =
## 39.08563
## 0.66467
## sigma2 = 136.11979
##
##
## MCMCregress iteration 9041 of 11000
## beta =
## 39.17553
## 0.27946
## sigma2 = 130.21203
##
##
## MCMCregress iteration 9051 of 11000
## beta =
## 39.59992
## 0.27493
## sigma2 = 127.88371
##
##
## MCMCregress iteration 9061 of 11000
## beta =
## 38.88194
## 0.65085
## sigma2 = 121.24702
##
##
## MCMCregress iteration 9071 of 11000
## beta =
## 38.17678
## 0.70766
## sigma2 = 123.81583
##
##
## MCMCregress iteration 9081 of 11000
## beta =
## 36.41916
## 1.36372
## sigma2 = 133.56038
##
##
## MCMCregress iteration 9091 of 11000
## beta =
## 38.27944
## 0.81041
## sigma2 = 137.70053
##
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## MCMCregress iteration 9101 of 11000
## beta =
## 39.73718
## 0.51567
## sigma2 = 125.29583
##
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## MCMCregress iteration 9111 of 11000
## beta =
## 37.87485
## 0.64720
## sigma2 = 123.92272
##
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## MCMCregress iteration 9121 of 11000
## beta =
## 39.18184
## 0.64760
## sigma2 = 131.50826
##
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## MCMCregress iteration 9131 of 11000
## beta =
## 37.46106
## 1.24563
## sigma2 = 122.03801
##
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## MCMCregress iteration 9141 of 11000
## beta =
## 37.49607
## 0.87125
## sigma2 = 125.19142
##
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## MCMCregress iteration 9151 of 11000
## beta =
## 38.24321
## 0.59200
## sigma2 = 148.59749
##
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## MCMCregress iteration 9161 of 11000
## beta =
## 37.78579
## 1.23723
## sigma2 = 142.32892
##
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## MCMCregress iteration 9171 of 11000
## beta =
## 38.52707
## 0.90736
## sigma2 = 140.20570
##
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## MCMCregress iteration 9181 of 11000
## beta =
## 39.95321
## 0.10305
## sigma2 = 133.15899
##
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## MCMCregress iteration 9191 of 11000
## beta =
## 37.65864
## 0.70075
## sigma2 = 124.37526
##
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## MCMCregress iteration 9201 of 11000
## beta =
## 38.24529
## 0.90275
## sigma2 = 146.92745
##
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## MCMCregress iteration 9211 of 11000
## beta =
## 37.10669
## 1.37255
## sigma2 = 147.12121
##
##
## MCMCregress iteration 9221 of 11000
## beta =
## 40.01673
## 0.29436
## sigma2 = 142.31762
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## MCMCregress iteration 9231 of 11000
## beta =
## 37.26603
## 1.02512
## sigma2 = 129.68145
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## MCMCregress iteration 9241 of 11000
## beta =
## 37.67099
## 0.95979
## sigma2 = 133.31514
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## MCMCregress iteration 9251 of 11000
## beta =
## 38.17319
## 0.56232
## sigma2 = 139.46619
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## MCMCregress iteration 9261 of 11000
## beta =
## 38.50914
## 0.69669
## sigma2 = 144.47135
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## MCMCregress iteration 9271 of 11000
## beta =
## 38.37852
## 0.81700
## sigma2 = 145.65634
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## MCMCregress iteration 9281 of 11000
## beta =
## 38.45848
## 1.22069
## sigma2 = 125.77285
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## MCMCregress iteration 9291 of 11000
## beta =
## 39.20865
## 0.51901
## sigma2 = 140.64151
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## MCMCregress iteration 9301 of 11000
## beta =
## 39.71277
## 0.30475
## sigma2 = 141.11769
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## MCMCregress iteration 9311 of 11000
## beta =
## 38.66330
## 0.83575
## sigma2 = 133.46035
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##
## MCMCregress iteration 9321 of 11000
## beta =
## 37.25805
## 1.17268
## sigma2 = 132.22074
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## MCMCregress iteration 9331 of 11000
## beta =
## 37.52301
## 1.13308
## sigma2 = 132.46038
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## MCMCregress iteration 9341 of 11000
## beta =
## 39.05702
## 0.77397
## sigma2 = 131.08893
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## MCMCregress iteration 9351 of 11000
## beta =
## 37.02239
## 1.13894
## sigma2 = 127.81834
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## MCMCregress iteration 9361 of 11000
## beta =
## 38.18725
## 0.84167
## sigma2 = 141.02205
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## MCMCregress iteration 9371 of 11000
## beta =
## 36.25010
## 1.46113
## sigma2 = 125.79446
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## MCMCregress iteration 9381 of 11000
## beta =
## 38.05231
## 1.04367
## sigma2 = 115.43265
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## MCMCregress iteration 9391 of 11000
## beta =
## 39.01242
## 0.57825
## sigma2 = 131.09214
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## MCMCregress iteration 9401 of 11000
## beta =
## 39.87900
## -0.00187
## sigma2 = 128.70482
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##
## MCMCregress iteration 9411 of 11000
## beta =
## 37.30860
## 1.36394
## sigma2 = 131.75549
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## MCMCregress iteration 9421 of 11000
## beta =
## 38.65514
## 0.41469
## sigma2 = 145.99098
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## MCMCregress iteration 9431 of 11000
## beta =
## 37.88137
## 0.75913
## sigma2 = 127.72226
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##
## MCMCregress iteration 9441 of 11000
## beta =
## 39.17051
## 0.44290
## sigma2 = 122.27613
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## MCMCregress iteration 9451 of 11000
## beta =
## 38.93403
## 0.64284
## sigma2 = 135.92234
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## MCMCregress iteration 9461 of 11000
## beta =
## 38.92986
## 0.29808
## sigma2 = 125.74726
##
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## MCMCregress iteration 9471 of 11000
## beta =
## 39.28359
## 0.55299
## sigma2 = 128.30815
##
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## MCMCregress iteration 9481 of 11000
## beta =
## 37.73280
## 0.63693
## sigma2 = 134.04341
##
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## MCMCregress iteration 9491 of 11000
## beta =
## 39.03310
## 0.59500
## sigma2 = 130.71428
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##
## MCMCregress iteration 9501 of 11000
## beta =
## 37.55732
## 0.88561
## sigma2 = 130.86182
##
##
## MCMCregress iteration 9511 of 11000
## beta =
## 37.22036
## 1.14208
## sigma2 = 129.43137
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##
## MCMCregress iteration 9521 of 11000
## beta =
## 39.31727
## 0.22646
## sigma2 = 133.74349
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## MCMCregress iteration 9531 of 11000
## beta =
## 38.75153
## 0.37013
## sigma2 = 134.01970
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## MCMCregress iteration 9541 of 11000
## beta =
## 37.97690
## 0.50422
## sigma2 = 150.68954
##
##
## MCMCregress iteration 9551 of 11000
## beta =
## 39.85222
## 0.20823
## sigma2 = 130.78552
##
##
## MCMCregress iteration 9561 of 11000
## beta =
## 38.56250
## 0.28314
## sigma2 = 134.49974
##
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## MCMCregress iteration 9571 of 11000
## beta =
## 38.51429
## 0.51435
## sigma2 = 149.48251
##
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## MCMCregress iteration 9581 of 11000
## beta =
## 37.77605
## 1.18311
## sigma2 = 130.63587
##
##
## MCMCregress iteration 9591 of 11000
## beta =
## 36.05409
## 1.55002
## sigma2 = 140.80132
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## MCMCregress iteration 9601 of 11000
## beta =
## 39.46079
## 0.45251
## sigma2 = 128.31633
##
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## MCMCregress iteration 9611 of 11000
## beta =
## 38.39282
## 0.89045
## sigma2 = 132.53181
##
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## MCMCregress iteration 9621 of 11000
## beta =
## 38.04489
## 0.97551
## sigma2 = 135.98523
##
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## MCMCregress iteration 9631 of 11000
## beta =
## 38.05197
## 0.78948
## sigma2 = 145.46198
##
##
## MCMCregress iteration 9641 of 11000
## beta =
## 40.68576
## -0.12340
## sigma2 = 129.69517
##
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## MCMCregress iteration 9651 of 11000
## beta =
## 37.74493
## 1.25213
## sigma2 = 136.23071
##
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## MCMCregress iteration 9661 of 11000
## beta =
## 39.76858
## -0.14931
## sigma2 = 134.61637
##
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## MCMCregress iteration 9671 of 11000
## beta =
## 38.19865
## 0.44906
## sigma2 = 121.09620
##
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## MCMCregress iteration 9681 of 11000
## beta =
## 39.32190
## 0.40906
## sigma2 = 126.17589
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## MCMCregress iteration 9691 of 11000
## beta =
## 38.53954
## 0.84242
## sigma2 = 124.63752
##
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## MCMCregress iteration 9701 of 11000
## beta =
## 37.37832
## 0.98532
## sigma2 = 129.76288
##
##
## MCMCregress iteration 9711 of 11000
## beta =
## 39.14391
## 0.42605
## sigma2 = 131.42056
##
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## MCMCregress iteration 9721 of 11000
## beta =
## 38.26881
## 0.68525
## sigma2 = 138.84838
##
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## MCMCregress iteration 9731 of 11000
## beta =
## 38.27314
## 0.61286
## sigma2 = 134.75892
##
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## MCMCregress iteration 9741 of 11000
## beta =
## 39.09935
## 0.61833
## sigma2 = 145.89610
##
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## MCMCregress iteration 9751 of 11000
## beta =
## 38.79187
## 0.68219
## sigma2 = 127.04556
##
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## MCMCregress iteration 9761 of 11000
## beta =
## 37.10156
## 1.11429
## sigma2 = 128.55298
##
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## MCMCregress iteration 9771 of 11000
## beta =
## 38.56521
## 0.58355
## sigma2 = 131.51810
##
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## MCMCregress iteration 9781 of 11000
## beta =
## 37.55355
## 0.99145
## sigma2 = 162.82102
##
##
## MCMCregress iteration 9791 of 11000
## beta =
## 39.85959
## 0.02395
## sigma2 = 130.16390
##
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## MCMCregress iteration 9801 of 11000
## beta =
## 37.83573
## 1.07737
## sigma2 = 121.40861
##
##
## MCMCregress iteration 9811 of 11000
## beta =
## 40.78750
## -0.41501
## sigma2 = 127.83004
##
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## MCMCregress iteration 9821 of 11000
## beta =
## 35.94980
## 1.38181
## sigma2 = 129.18135
##
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## MCMCregress iteration 9831 of 11000
## beta =
## 39.27091
## 0.83479
## sigma2 = 143.38805
##
##
## MCMCregress iteration 9841 of 11000
## beta =
## 39.15836
## 0.47769
## sigma2 = 141.97146
##
##
## MCMCregress iteration 9851 of 11000
## beta =
## 37.28911
## 1.42312
## sigma2 = 130.23473
##
##
## MCMCregress iteration 9861 of 11000
## beta =
## 37.78280
## 0.64256
## sigma2 = 129.59908
##
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## MCMCregress iteration 9871 of 11000
## beta =
## 38.46249
## 0.61656
## sigma2 = 130.74922
##
##
## MCMCregress iteration 9881 of 11000
## beta =
## 39.77415
## 0.39734
## sigma2 = 135.33543
##
##
## MCMCregress iteration 9891 of 11000
## beta =
## 36.96320
## 1.09055
## sigma2 = 122.86848
##
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## MCMCregress iteration 9901 of 11000
## beta =
## 38.16421
## 0.58476
## sigma2 = 133.62685
##
##
## MCMCregress iteration 9911 of 11000
## beta =
## 38.23367
## 0.85945
## sigma2 = 137.20151
##
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## MCMCregress iteration 9921 of 11000
## beta =
## 37.79066
## 1.05653
## sigma2 = 129.76817
##
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## MCMCregress iteration 9931 of 11000
## beta =
## 38.56977
## 0.77260
## sigma2 = 147.37413
##
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## MCMCregress iteration 9941 of 11000
## beta =
## 39.40534
## 0.49708
## sigma2 = 138.40222
##
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## MCMCregress iteration 9951 of 11000
## beta =
## 37.96092
## 0.88042
## sigma2 = 139.73061
##
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## MCMCregress iteration 9961 of 11000
## beta =
## 38.90287
## 0.84257
## sigma2 = 124.52150
##
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## MCMCregress iteration 9971 of 11000
## beta =
## 40.00369
## 0.50630
## sigma2 = 125.24078
##
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## MCMCregress iteration 9981 of 11000
## beta =
## 40.08552
## 0.17443
## sigma2 = 140.22584
##
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## MCMCregress iteration 9991 of 11000
## beta =
## 39.10105
## 0.59007
## sigma2 = 123.62620
##
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## MCMCregress iteration 10001 of 11000
## beta =
## 39.76575
## 0.09375
## sigma2 = 141.88600
##
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## MCMCregress iteration 10011 of 11000
## beta =
## 41.11425
## -0.30844
## sigma2 = 149.05810
##
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## MCMCregress iteration 10021 of 11000
## beta =
## 38.59270
## 0.57965
## sigma2 = 127.46191
##
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## MCMCregress iteration 10031 of 11000
## beta =
## 38.91261
## 0.39800
## sigma2 = 142.87238
##
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## MCMCregress iteration 10041 of 11000
## beta =
## 39.22577
## 0.27560
## sigma2 = 138.54038
##
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## MCMCregress iteration 10051 of 11000
## beta =
## 38.28463
## 0.48983
## sigma2 = 135.33981
##
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## MCMCregress iteration 10061 of 11000
## beta =
## 37.69899
## 0.93208
## sigma2 = 130.54471
##
##
## MCMCregress iteration 10071 of 11000
## beta =
## 38.50223
## 0.71419
## sigma2 = 127.82217
##
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## MCMCregress iteration 10081 of 11000
## beta =
## 37.95019
## 1.32236
## sigma2 = 122.41878
##
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## MCMCregress iteration 10091 of 11000
## beta =
## 38.72307
## 0.77133
## sigma2 = 142.58163
##
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## MCMCregress iteration 10101 of 11000
## beta =
## 38.50598
## 0.72287
## sigma2 = 139.14341
##
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## MCMCregress iteration 10111 of 11000
## beta =
## 39.82232
## 0.39397
## sigma2 = 141.87479
##
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## MCMCregress iteration 10121 of 11000
## beta =
## 39.72384
## 0.27544
## sigma2 = 136.80409
##
##
## MCMCregress iteration 10131 of 11000
## beta =
## 37.75628
## 0.64988
## sigma2 = 135.51166
##
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## MCMCregress iteration 10141 of 11000
## beta =
## 38.45435
## 0.28073
## sigma2 = 132.60433
##
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## MCMCregress iteration 10151 of 11000
## beta =
## 38.94691
## 0.26531
## sigma2 = 134.16606
##
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## MCMCregress iteration 10161 of 11000
## beta =
## 37.23353
## 1.38029
## sigma2 = 142.72515
##
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## MCMCregress iteration 10171 of 11000
## beta =
## 37.00044
## 1.04513
## sigma2 = 134.35088
##
##
## MCMCregress iteration 10181 of 11000
## beta =
## 37.46628
## 1.08607
## sigma2 = 132.03779
##
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## MCMCregress iteration 10191 of 11000
## beta =
## 39.19829
## 0.65555
## sigma2 = 131.23317
##
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## MCMCregress iteration 10201 of 11000
## beta =
## 39.69246
## 0.02587
## sigma2 = 126.03746
##
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## MCMCregress iteration 10211 of 11000
## beta =
## 39.50263
## -0.07824
## sigma2 = 128.47664
##
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## MCMCregress iteration 10221 of 11000
## beta =
## 38.42777
## 0.64865
## sigma2 = 141.81714
##
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## MCMCregress iteration 10231 of 11000
## beta =
## 38.81683
## 0.50362
## sigma2 = 129.34436
##
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## MCMCregress iteration 10241 of 11000
## beta =
## 38.08409
## 0.77764
## sigma2 = 135.07010
##
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## MCMCregress iteration 10251 of 11000
## beta =
## 38.59002
## 0.36825
## sigma2 = 136.64103
##
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## MCMCregress iteration 10261 of 11000
## beta =
## 39.54700
## 0.41741
## sigma2 = 135.91150
##
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## MCMCregress iteration 10271 of 11000
## beta =
## 38.93891
## 0.22238
## sigma2 = 147.81662
##
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## MCMCregress iteration 10281 of 11000
## beta =
## 36.77314
## 1.06870
## sigma2 = 132.69489
##
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## MCMCregress iteration 10291 of 11000
## beta =
## 40.58122
## -0.22175
## sigma2 = 125.70969
##
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## MCMCregress iteration 10301 of 11000
## beta =
## 37.67283
## 0.78029
## sigma2 = 123.36979
##
##
## MCMCregress iteration 10311 of 11000
## beta =
## 38.58365
## 0.70318
## sigma2 = 143.76267
##
##
## MCMCregress iteration 10321 of 11000
## beta =
## 39.77981
## 0.31577
## sigma2 = 121.85095
##
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## MCMCregress iteration 10331 of 11000
## beta =
## 38.16730
## 0.61439
## sigma2 = 129.63834
##
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## MCMCregress iteration 10341 of 11000
## beta =
## 40.35801
## 0.02995
## sigma2 = 133.06988
##
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## MCMCregress iteration 10351 of 11000
## beta =
## 37.45940
## 0.93600
## sigma2 = 139.37637
##
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## MCMCregress iteration 10361 of 11000
## beta =
## 39.07269
## 0.33374
## sigma2 = 115.51074
##
##
## MCMCregress iteration 10371 of 11000
## beta =
## 39.64096
## 0.48616
## sigma2 = 127.28064
##
##
## MCMCregress iteration 10381 of 11000
## beta =
## 38.78194
## 0.21382
## sigma2 = 136.19705
##
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## MCMCregress iteration 10391 of 11000
## beta =
## 39.57891
## 0.54275
## sigma2 = 135.24572
##
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## MCMCregress iteration 10401 of 11000
## beta =
## 37.46282
## 0.76909
## sigma2 = 136.18094
##
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## MCMCregress iteration 10411 of 11000
## beta =
## 38.53841
## 0.88735
## sigma2 = 139.60645
##
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## MCMCregress iteration 10421 of 11000
## beta =
## 40.44766
## 0.03991
## sigma2 = 143.74028
##
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## MCMCregress iteration 10431 of 11000
## beta =
## 39.50803
## 0.37020
## sigma2 = 131.41948
##
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## MCMCregress iteration 10441 of 11000
## beta =
## 38.36085
## 0.93792
## sigma2 = 133.39007
##
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## MCMCregress iteration 10451 of 11000
## beta =
## 38.02156
## 0.71153
## sigma2 = 117.82118
##
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## MCMCregress iteration 10461 of 11000
## beta =
## 40.67877
## 0.06655
## sigma2 = 130.41065
##
##
## MCMCregress iteration 10471 of 11000
## beta =
## 38.67601
## 0.79557
## sigma2 = 117.77008
##
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## MCMCregress iteration 10481 of 11000
## beta =
## 37.42186
## 0.64612
## sigma2 = 151.90835
##
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## MCMCregress iteration 10491 of 11000
## beta =
## 38.58524
## 0.58562
## sigma2 = 127.47170
##
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## MCMCregress iteration 10501 of 11000
## beta =
## 38.05282
## 0.91198
## sigma2 = 142.11678
##
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## MCMCregress iteration 10511 of 11000
## beta =
## 37.76698
## 0.60661
## sigma2 = 133.04517
##
##
## MCMCregress iteration 10521 of 11000
## beta =
## 38.08722
## 0.47563
## sigma2 = 136.80500
##
##
## MCMCregress iteration 10531 of 11000
## beta =
## 39.07660
## 0.39475
## sigma2 = 130.17235
##
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## MCMCregress iteration 10541 of 11000
## beta =
## 38.79121
## 0.34870
## sigma2 = 129.19188
##
##
## MCMCregress iteration 10551 of 11000
## beta =
## 36.96843
## 0.96229
## sigma2 = 136.87840
##
##
## MCMCregress iteration 10561 of 11000
## beta =
## 37.60981
## 1.14446
## sigma2 = 134.47584
##
##
## MCMCregress iteration 10571 of 11000
## beta =
## 36.07294
## 1.75519
## sigma2 = 149.10486
##
##
## MCMCregress iteration 10581 of 11000
## beta =
## 38.91174
## 0.43344
## sigma2 = 122.87050
##
##
## MCMCregress iteration 10591 of 11000
## beta =
## 38.52900
## 0.97843
## sigma2 = 130.21418
##
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## MCMCregress iteration 10601 of 11000
## beta =
## 39.42976
## 0.28336
## sigma2 = 111.65502
##
##
## MCMCregress iteration 10611 of 11000
## beta =
## 39.11843
## 0.52429
## sigma2 = 123.22294
##
##
## MCMCregress iteration 10621 of 11000
## beta =
## 40.29229
## -0.06928
## sigma2 = 121.99847
##
##
## MCMCregress iteration 10631 of 11000
## beta =
## 38.67114
## 1.05728
## sigma2 = 134.70814
##
##
## MCMCregress iteration 10641 of 11000
## beta =
## 38.84161
## 0.54834
## sigma2 = 138.25316
##
##
## MCMCregress iteration 10651 of 11000
## beta =
## 38.05180
## 0.84808
## sigma2 = 138.71274
##
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## MCMCregress iteration 10661 of 11000
## beta =
## 39.82863
## 0.41544
## sigma2 = 126.39843
##
##
## MCMCregress iteration 10671 of 11000
## beta =
## 40.46840
## -0.11834
## sigma2 = 138.51172
##
##
## MCMCregress iteration 10681 of 11000
## beta =
## 38.21153
## 1.22134
## sigma2 = 126.61821
##
##
## MCMCregress iteration 10691 of 11000
## beta =
## 39.71723
## 0.09731
## sigma2 = 133.14957
##
##
## MCMCregress iteration 10701 of 11000
## beta =
## 36.16148
## 1.58739
## sigma2 = 143.99364
##
##
## MCMCregress iteration 10711 of 11000
## beta =
## 38.40656
## 0.41974
## sigma2 = 126.81092
##
##
## MCMCregress iteration 10721 of 11000
## beta =
## 38.82340
## 0.64726
## sigma2 = 131.52120
##
##
## MCMCregress iteration 10731 of 11000
## beta =
## 37.89373
## 0.96667
## sigma2 = 124.50003
##
##
## MCMCregress iteration 10741 of 11000
## beta =
## 37.55989
## 0.82760
## sigma2 = 128.84470
##
##
## MCMCregress iteration 10751 of 11000
## beta =
## 39.37078
## 0.20898
## sigma2 = 140.67033
##
##
## MCMCregress iteration 10761 of 11000
## beta =
## 38.84146
## 0.71447
## sigma2 = 121.77453
##
##
## MCMCregress iteration 10771 of 11000
## beta =
## 38.75080
## 1.00771
## sigma2 = 129.68508
##
##
## MCMCregress iteration 10781 of 11000
## beta =
## 37.46667
## 0.65264
## sigma2 = 136.63050
##
##
## MCMCregress iteration 10791 of 11000
## beta =
## 37.36068
## 0.83134
## sigma2 = 121.62215
##
##
## MCMCregress iteration 10801 of 11000
## beta =
## 38.82190
## 0.73728
## sigma2 = 133.91706
##
##
## MCMCregress iteration 10811 of 11000
## beta =
## 38.28748
## 0.72061
## sigma2 = 133.61592
##
##
## MCMCregress iteration 10821 of 11000
## beta =
## 38.34277
## 0.55097
## sigma2 = 126.73940
##
##
## MCMCregress iteration 10831 of 11000
## beta =
## 38.54855
## 0.57745
## sigma2 = 137.34656
##
##
## MCMCregress iteration 10841 of 11000
## beta =
## 38.65406
## 0.72098
## sigma2 = 137.57573
##
##
## MCMCregress iteration 10851 of 11000
## beta =
## 39.51947
## 0.03700
## sigma2 = 131.60430
##
##
## MCMCregress iteration 10861 of 11000
## beta =
## 37.59779
## 0.88532
## sigma2 = 134.48487
##
##
## MCMCregress iteration 10871 of 11000
## beta =
## 38.43772
## 0.41710
## sigma2 = 129.98655
##
##
## MCMCregress iteration 10881 of 11000
## beta =
## 39.55639
## 0.47286
## sigma2 = 134.02380
##
##
## MCMCregress iteration 10891 of 11000
## beta =
## 38.15379
## 0.73680
## sigma2 = 131.85296
##
##
## MCMCregress iteration 10901 of 11000
## beta =
## 39.83996
## 0.13125
## sigma2 = 130.02210
##
##
## MCMCregress iteration 10911 of 11000
## beta =
## 38.20015
## 0.82186
## sigma2 = 133.90803
##
##
## MCMCregress iteration 10921 of 11000
## beta =
## 40.22437
## 0.00831
## sigma2 = 134.35696
##
##
## MCMCregress iteration 10931 of 11000
## beta =
## 38.61245
## 0.82235
## sigma2 = 128.19427
##
##
## MCMCregress iteration 10941 of 11000
## beta =
## 39.01061
## 0.56156
## sigma2 = 141.48396
##
##
## MCMCregress iteration 10951 of 11000
## beta =
## 38.41508
## 0.94816
## sigma2 = 142.71731
##
##
## MCMCregress iteration 10961 of 11000
## beta =
## 40.69073
## -0.22467
## sigma2 = 127.33435
##
##
## MCMCregress iteration 10971 of 11000
## beta =
## 37.60150
## 0.81172
## sigma2 = 129.53709
##
##
## MCMCregress iteration 10981 of 11000
## beta =
## 38.49362
## 0.86493
## sigma2 = 120.96099
##
##
## MCMCregress iteration 10991 of 11000
## beta =
## 37.15306
## 1.07951
## sigma2 = 137.79680plot(posterior, col= "red")
raftery.diag(posterior)##
## Quantile (q) = 0.025
## Accuracy (r) = +/- 0.005
## Probability (s) = 0.95
##
## Burn-in Total Lower bound Dependence
## (M) (N) (Nmin) factor (I)
## (Intercept) 2 3710 3746 0.990
## congviec 2 3636 3746 0.971
## sigma2 2 3802 3746 1.010summary(posterior) ##
## Iterations = 1001:11000
## Thinning interval = 1
## Number of chains = 1
## 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
## (Intercept) 38.5685 1.1081 0.011081 0.011081
## congviec 0.6426 0.4364 0.004364 0.004364
## sigma2 133.6371 8.3022 0.083022 0.083022
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## (Intercept) 36.3920 37.8219 38.5772 39.3126 40.752
## congviec -0.2028 0.3517 0.6388 0.9274 1.499
## sigma2 118.2541 127.8499 133.3116 139.1302 150.690apply(posterior, 2, quantile, probs=c(0.025, 0.5, 0.975))## (Intercept) congviec sigma2
## 2.5% 36.39197 -0.2028191 118.2541
## 50% 38.57717 0.6387640 133.3116
## 97.5% 40.75189 1.4985906 150.6900library(nnet)
require(brms)## Loading required package: brms## Loading 'brms' package (version 2.20.4). Useful instructions
## can be found by typing help('brms'). A more detailed introduction
## to the package is available through vignette('brms_overview').##
## Attaching package: 'brms'## The following objects are masked from 'package:MCMCpack':
##
## ddirichlet, rdirichlet## The following objects are masked from 'package:rstanarm':
##
## dirichlet, exponential, get_y, lasso, ngrps## The following object is masked from 'package:stats':
##
## arm.2 = multinom(uwes_tol ~ congviec, data=thuan)## # weights: 129 (84 variable)
## initial value 2000.958462
## iter 10 value 1790.232977
## iter 20 value 1732.824691
## iter 30 value 1703.693118
## iter 40 value 1698.904538
## iter 50 value 1698.099745
## iter 60 value 1696.769044
## iter 70 value 1696.596122
## iter 80 value 1696.569898
## final value 1696.569837
## convergedsummary(m.2)## Call:
## multinom(formula = uwes_tol ~ congviec, data = thuan)
##
## Coefficients:
## (Intercept) congviec
## 4 -12.415962 11.92546
## 9 -12.793798 11.59141
## 10 36.474126 -36.47803
## 14 -12.793657 11.59134
## 15 -16.857851 13.34498
## 18 36.478099 -36.47406
## 19 -12.793755 11.59150
## 20 -13.148145 12.06081
## 21 -14.098698 12.40738
## 22 -10.790713 11.09445
## 23 -11.958709 11.85146
## 24 -11.696678 11.59215
## 25 -13.059031 12.18313
## 26 -9.923890 10.72781
## 27 -9.925637 11.50991
## 28 -12.423205 12.13572
## 29 -12.793846 11.59142
## 30 -13.906590 12.56766
## 31 -14.800612 12.40989
## 32 -9.077899 10.53196
## 33 -9.494699 10.21977
## 34 -10.674790 11.32248
## 35 -11.186001 11.59216
## 36 -10.779266 11.88378
## 37 -13.906590 12.56765
## 38 -13.148142 12.06080
## 39 42.716244 -42.02292
## 40 -12.366618 12.18351
## 41 -12.415964 11.92546
## 42 -11.003298 11.59206
## 43 -9.981584 11.32248
## 44 -11.757348 12.00484
## 45 -10.615646 12.20892
## 46 -11.171489 11.74780
## 47 -9.657097 11.24237
## 48 -12.049120 12.06060
## 49 -11.173713 11.74867
## 50 -11.729698 12.13562
## 51 -13.183312 12.40767
## 52 -9.820029 11.52003
## 53 -12.415963 11.92546
## 54 -10.508843 12.06064
##
## Std. Errors:
## (Intercept) congviec
## 4 1.0263387 0.5187729
## 9 1.7029334 0.8281014
## 10 0.5005233 0.5005233
## 14 1.7029445 0.8281278
## 15 1.6701554 0.5127741
## 18 0.4995301 0.4995301
## 19 1.7027531 0.8280026
## 20 1.2426016 0.5594275
## 21 1.3403124 0.5317962
## 22 1.0703094 0.6617628
## 23 0.9059589 0.4935835
## 24 1.0289216 0.5669590
## 25 1.0566020 0.4948242
## 26 0.9761889 0.6670539
## 27 0.5695060 0.4237333
## 28 0.8463756 0.4517729
## 29 1.7029676 0.8281144
## 30 1.0444659 0.4573424
## 31 1.8586796 0.6542475
## 32 0.7917886 0.5878757
## 33 1.1631273 0.8524724
## 34 0.8460814 0.5333055
## 35 0.8309852 0.4986750
## 36 0.5785583 0.4092300
## 37 1.0444699 0.4573433
## 38 1.2426159 0.5594335
## 39 0.4330285 0.4330285
## 40 0.7936886 0.4373673
## 41 1.0263419 0.5187740
## 42 0.7735567 0.4800753
## 43 0.6536432 0.4613450
## 44 0.7326760 0.4362950
## 45 0.4729905 0.3825030
## 46 0.7266969 0.4531362
## 47 0.6191693 0.4542058
## 48 0.7805169 0.4430451
## 49 0.7267884 0.4530952
## 50 0.6554005 0.4134776
## 51 0.8986786 0.4424588
## 52 0.5496932 0.4176981
## 53 1.0263405 0.5187736
## 54 0.4918504 0.3878595
##
## Residual Deviance: 3393.14
## AIC: 3561.14#exp(coefficients(m.2)) #exp(confint(m.2))
library(bayesplot)## This is bayesplot version 1.10.0## - Online documentation and vignettes at mc-stan.org/bayesplot## - bayesplot theme set to bayesplot::theme_default()## * Does _not_ affect other ggplot2 plots## * See ?bayesplot_theme_set for details on theme setting##
## Attaching package: 'bayesplot'## The following object is masked from 'package:brms':
##
## rhatlibrary(ggplot2)
p = ggplot(thuan, aes(x = uwes_tol, group=congviec))
p + geom_density(aes(fill = congviec), alpha=0.5) + theme_light(base_size = 12)
library(rstan)## Warning: package 'rstan' was built under R version 4.3.2## Loading required package: StanHeaders##
## rstan version 2.32.3 (Stan version 2.26.1)## For execution on a local, multicore CPU with excess RAM we recommend calling
## options(mc.cores = parallel::detectCores()).
## To avoid recompilation of unchanged Stan programs, we recommend calling
## rstan_options(auto_write = TRUE)
## For within-chain threading using `reduce_sum()` or `map_rect()` Stan functions,
## change `threads_per_chain` option:
## rstan_options(threads_per_chain = 1)## Do not specify '-march=native' in 'LOCAL_CPPFLAGS' or a Makevars file##
## Attaching package: 'rstan'## The following object is masked from 'package:coda':
##
## traceplot## The following object is masked from 'package:tidyr':
##
## extractlibrary(shinystan)## Loading required package: shiny##
## This is shinystan version 2.6.0library(rstanarm)
library(psych)##
## Attaching package: 'psych'## The following object is masked from 'package:rstan':
##
## lookup## The following object is masked from 'package:brms':
##
## cs## The following object is masked from 'package:rstanarm':
##
## logit## The following object is masked from 'package:OpenMx':
##
## tr## The following object is masked from 'package:lavaan':
##
## cor2cov## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(brms)
set.seed(123)# Có Intercept
# family = "poisson", prior = prior)
prior0 = get_prior(uwes_tol ~ congviec + gioi + congviec*gioi, family = gaussian, data=thuan)## Found more than one class "family" in cache; using the first, from namespace 'lme4'## Also defined by 'MatrixModels'bf.1 = brm(data=thuan, uwes1 ~ congviec + gioi + congviec*gioi,
prior0, family = gaussian, chains = 2, iter = 10)## Found more than one class "family" in cache; using the first, from namespace 'lme4'
## Also defined by 'MatrixModels'## Compiling Stan program...## Start sampling##
## SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 3.4e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.34 seconds.
## Chain 1: Adjust your expectations accordingly!
## Chain 1:
## Chain 1:
## Chain 1: WARNING: No variance estimation is
## Chain 1: performed for num_warmup < 20
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## Chain 1:
##
## SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
## Chain 2:
## Chain 2: Gradient evaluation took 9e-06 seconds
## Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
## Chain 2: Adjust your expectations accordingly!
## Chain 2:
## Chain 2:
## Chain 2: WARNING: No variance estimation is
## Chain 2: performed for num_warmup < 20
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## Chain 2:
## Chain 2: Elapsed Time: 0 seconds (Warm-up)
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## Chain 2: 0 seconds (Total)
## Chain 2:## Warning: There were 5 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problems## Warning: The largest R-hat is NA, indicating chains have not mixed.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#r-hatsummary(bf.1)## Warning: Parts of the model have not converged (some Rhats are > 1.05). Be
## careful when analysing the results! We recommend running more iterations and/or
## setting stronger priors.## Warning: There were 5 divergent transitions after warmup. Increasing
## adapt_delta above 0.8 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: uwes1 ~ congviec + gioi + congviec * gioi
## Data: thuan (Number of observations: 532)
## Draws: 2 chains, each with iter = 10; warmup = 5; thin = 1;
## total post-warmup draws = 10
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -0.06 2.23 -2.18 2.06 NA NA NA
## congviec 0.77 1.02 -0.20 1.74 NA NA NA
## gioi -0.75 1.14 -1.84 0.34 NA NA NA
## congviec:gioi 0.12 1.12 -0.94 1.18 NA NA NA
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 4.41 1.12 3.35 5.47 Inf NA NA
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).bf.1$fit## Inference for Stan model: anon_model.
## 2 chains, each with iter=10; warmup=5; thin=1;
## post-warmup draws per chain=5, total post-warmup draws=10.
##
## mean se_mean sd 2.5% 25% 50% 75%
## b_Intercept -0.06 NaN 2.23 -2.18 -2.18 -0.06 2.06
## b_congviec 0.77 NaN 1.02 -0.20 -0.20 0.77 1.74
## b_gioi -0.75 NaN 1.14 -1.84 -1.84 -0.75 0.34
## b_congviec:gioi 0.12 NaN 1.12 -0.94 -0.94 0.12 1.18
## sigma 4.41 NaN 1.12 3.35 3.35 4.41 5.47
## lprior -9.71 NaN 0.14 -9.85 -9.85 -9.71 -9.58
## lp__ -1547.87 NaN 111.59 -1653.73 -1653.73 -1547.87 -1442.01
## 97.5% n_eff Rhat
## b_Intercept 2.06 NaN Inf
## b_congviec 1.74 NaN Inf
## b_gioi 0.34 NaN Inf
## b_congviec:gioi 1.18 NaN Inf
## sigma 5.47 NaN Inf
## lprior -9.58 NaN Inf
## lp__ -1442.01 NaN Inf
##
## Samples were drawn using NUTS(diag_e) at Thu Nov 16 19:32:40 2023.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).Không Intercept
prior1 = get_prior(uwes_tol ~ congviec - 1, family = gaussian, data=thuan)## Found more than one class "family" in cache; using the first, from namespace 'lme4'## Also defined by 'MatrixModels'bf.2 = brm(data = thuan, uwes_tol ~ congviec-1, prior1,
family = gaussian, chains = 2, iter = 10)## Found more than one class "family" in cache; using the first, from namespace 'lme4'
## Also defined by 'MatrixModels'## Compiling Stan program...## Start sampling##
## SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 9.9e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.99 seconds.
## Chain 1: Adjust your expectations accordingly!
## Chain 1:
## Chain 1:
## Chain 1: WARNING: No variance estimation is
## Chain 1: performed for num_warmup < 20
## Chain 1:
## Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
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## Chain 1:
## Chain 1: Elapsed Time: 0.001 seconds (Warm-up)
## Chain 1: 0.002 seconds (Sampling)
## Chain 1: 0.003 seconds (Total)
## Chain 1:
##
## SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
## Chain 2:
## Chain 2: Gradient evaluation took 2.3e-05 seconds
## Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
## Chain 2: Adjust your expectations accordingly!
## Chain 2:
## Chain 2:
## Chain 2: WARNING: No variance estimation is
## Chain 2: performed for num_warmup < 20
## Chain 2:
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## Chain 2:
## Chain 2: Elapsed Time: 0.001 seconds (Warm-up)
## Chain 2: 0.001 seconds (Sampling)
## Chain 2: 0.002 seconds (Total)
## Chain 2:## Warning: There were 10 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problems## Warning: The largest R-hat is NA, indicating chains have not mixed.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#r-hatsummary(bf.2)## Warning: Parts of the model have not converged (some Rhats are > 1.05). Be
## careful when analysing the results! We recommend running more iterations and/or
## setting stronger priors.## Warning: There were 10 divergent transitions after warmup. Increasing
## adapt_delta above 0.8 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: uwes_tol ~ congviec - 1
## Data: thuan (Number of observations: 532)
## Draws: 2 chains, each with iter = 10; warmup = 5; thin = 1;
## total post-warmup draws = 10
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## congviec 1.12 0.64 0.51 1.73 Inf NA NA
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.45 0.01 0.44 0.46 Inf NA NA
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).Vẽ biểu đồ
bf.2$fit## Inference for Stan model: anon_model.
## 2 chains, each with iter=10; warmup=5; thin=1;
## post-warmup draws per chain=5, total post-warmup draws=10.
##
## mean se_mean sd 2.5% 25% 50%
## b_congviec 1.12 NaN 0.64 0.51 0.51 1.12
## sigma 0.45 0.01 0.01 0.44 0.44 0.45
## lprior -2.79 NaN 0.00 -2.79 -2.79 -2.79
## lp__ -2038169.10 NaN 27677.51 -2064426.30 -2064426.30 -2038169.10
## 75% 97.5% n_eff Rhat
## b_congviec 1.73 1.73 NaN Inf
## sigma 0.46 0.46 1 Inf
## lprior -2.79 -2.79 NaN Inf
## lp__ -2011911.91 -2011911.91 NaN Inf
##
## Samples were drawn using NUTS(diag_e) at Thu Nov 16 19:34:20 2023.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).pairs(bf.2)
plot(bf.2, ignore_prior = T, theme = ggplot2::theme())
marginal_effects(bf.2, probs=c(0.05,0.95), conditions=congviec)## Warning: Method 'marginal_effects' is deprecated. Please use
## 'conditional_effects' instead.## Warning: Argument 'probs' is deprecated. Please use 'prob' instead.## Warning: The following variables in 'conditions' are not part of the model:
## 'conditions'
kiểm tra bằng phương pháp khác
# launch_shinystan(bf.1, rstudio = getOption("shinystan.rstudio"))Kiểm tra giả thuyết
#hypothesis(bf.2, "congviecAA > congviecCC", digits = 4)
#hypothesis(bf.2, "congviecBB > congviecCC", digits = 4)
#hypothesis(bf.2, "congviecEE > congviecCC", digits = 4)