library(saeHB.panel.beta)
data("dataPanelbeta")dataPanelbeta = dataPanelbeta[1:25,]
area = max(dataPanelbeta[,2])
period = max(dataPanelbeta[,3])
result=Panel.beta(ydi~xdi1+xdi2,area=area, period=period ,iter.mcmc = 10000,thin=5,burn.in = 1000,data=dataPanelbeta)
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 25
#> Unobserved stochastic nodes: 62
#> Total graph size: 359
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 25
#> Unobserved stochastic nodes: 62
#> Total graph size: 359
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 25
#> Unobserved stochastic nodes: 62
#> Total graph size: 359
#>
#> Initializing modelresult$Est
#> MEAN SD 2.5% 25% 50% 75% 97.5%
#> mu[1,1] 0.9719535 0.02061787 0.9166499 0.9644096 0.9773811 0.9857204 0.9952012
#> mu[2,1] 0.9514280 0.03222725 0.8611312 0.9378029 0.9591268 0.9739508 0.9904320
#> mu[3,1] 0.9390208 0.04455595 0.8228649 0.9199625 0.9508659 0.9695210 0.9884628
#> mu[4,1] 0.9678398 0.02438212 0.9021842 0.9595408 0.9740868 0.9839907 0.9941866
#> mu[5,1] 0.9382578 0.05156252 0.7925702 0.9217405 0.9526406 0.9716127 0.9894025
#> mu[1,2] 0.9715688 0.01976605 0.9194820 0.9628219 0.9767548 0.9853496 0.9946669
#> mu[2,2] 0.9618195 0.02944097 0.8886327 0.9509297 0.9691897 0.9811544 0.9937639
#> mu[3,2] 0.9189901 0.05577968 0.7710505 0.8978962 0.9331747 0.9572892 0.9825825
#> mu[4,2] 0.9780266 0.01907898 0.9286450 0.9719153 0.9834606 0.9903756 0.9965821
#> mu[5,2] 0.9381488 0.04447410 0.8139635 0.9216262 0.9490601 0.9682362 0.9877690
#> mu[1,3] 0.9711196 0.02282323 0.9087227 0.9641149 0.9773839 0.9859197 0.9950974
#> mu[2,3] 0.8661055 0.07812405 0.6721167 0.8283691 0.8825579 0.9224848 0.9659853
#> mu[3,3] 0.9509133 0.03681342 0.8556804 0.9380567 0.9598577 0.9743866 0.9913904
#> mu[4,3] 0.9572476 0.03054124 0.8757072 0.9442599 0.9651752 0.9782601 0.9921975
#> mu[5,3] 0.9168006 0.05590522 0.7684558 0.8957907 0.9312979 0.9554758 0.9829260
#> mu[1,4] 0.9554054 0.03032574 0.8740965 0.9423437 0.9633072 0.9770979 0.9914717
#> mu[2,4] 0.9335967 0.04619315 0.8110946 0.9149314 0.9449527 0.9661885 0.9864045
#> mu[3,4] 0.9314238 0.04360424 0.8187981 0.9110693 0.9422314 0.9620262 0.9848933
#> mu[4,4] 0.9755893 0.01986152 0.9278304 0.9686696 0.9810486 0.9889795 0.9962874
#> mu[5,4] 0.8535058 0.09944652 0.5880184 0.8132333 0.8811233 0.9213305 0.9675989
#> mu[1,5] 0.9681095 0.02333712 0.9019573 0.9600936 0.9738826 0.9838491 0.9939025
#> mu[2,5] 0.8862782 0.07099672 0.7145739 0.8509536 0.9026111 0.9357349 0.9750832
#> mu[3,5] 0.9569566 0.03083234 0.8773421 0.9448862 0.9646435 0.9780894 0.9929445
#> mu[4,5] 0.9313902 0.04622079 0.8192289 0.9104698 0.9425658 0.9625906 0.9875092
#> mu[5,5] 0.8661052 0.08213003 0.6479326 0.8299681 0.8856596 0.9244538 0.9675885result$coefficient
#> Mean SD 2.5% 25% 50% 75% 97.5%
#> b[0] 1.950274 0.3860334 1.2120171 1.6912668 1.956273 2.214548 2.690693
#> b[1] 1.169191 0.5144580 0.1648285 0.8122012 1.171475 1.516812 2.191292
#> b[2] 1.143227 0.4596883 0.2441420 0.8352869 1.138786 1.459374 2.062806result$refvar
#> NULLMSE_HB=result$Est$SD^2
summary(MSE_HB)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.0003640 0.0005945 0.0013552 0.0022443 0.0026587 0.0098896RSE_HB=sqrt(MSE_HB)/result$Est$MEAN*100
summary(RSE_HB)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 1.951 2.519 3.871 4.609 5.496 11.652y_dir=dataPanelbeta[,1]
y_HB=result$Est$MEAN
y=as.data.frame(cbind(y_dir,y_HB))
summary(y)
#> y_dir y_HB
#> Min. :0.3836 Min. :0.8535
#> 1st Qu.:0.9702 1st Qu.:0.9314
#> Median :1.0000 Median :0.9509
#> Mean :0.9423 Mean :0.9383
#> 3rd Qu.:1.0000 3rd Qu.:0.9678
#> Max. :1.0000 Max. :0.9780
MSE_dir=dataPanelbeta[,4]
MSE=as.data.frame(cbind(MSE_dir, MSE_HB))
summary(MSE)
#> MSE_dir MSE_HB
#> Min. :0.0004401 Min. :0.0003640
#> 1st Qu.:0.0036464 1st Qu.:0.0005945
#> Median :0.0228563 Median :0.0013552
#> Mean :0.0256965 Mean :0.0022443
#> 3rd Qu.:0.0428368 3rd Qu.:0.0026587
#> Max. :0.0887137 Max. :0.0098896
RSE_dir=sqrt(MSE_dir)/y_dir*100
RSE=as.data.frame(cbind(MSE_dir, MSE_HB))
summary(RSE)
#> MSE_dir MSE_HB
#> Min. :0.0004401 Min. :0.0003640
#> 1st Qu.:0.0036464 1st Qu.:0.0005945
#> Median :0.0228563 Median :0.0013552
#> Mean :0.0256965 Mean :0.0022443
#> 3rd Qu.:0.0428368 3rd Qu.:0.0026587
#> Max. :0.0887137 Max. :0.0098896