library(saeHB.panel)
data("dataPanel")area = max(dataPanel[,2])
period = max(dataPanel[,3])
vardir = dataPanel[,4]
result=Panel(ydi~xdi1+xdi2,area=area, period=period, vardir=vardir ,iter.mcmc = 10000,thin=5,burn.in = 1000,data=dataPanel)
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 100
#> Unobserved stochastic nodes: 125
#> Total graph size: 1045
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 100
#> Unobserved stochastic nodes: 125
#> Total graph size: 1045
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 100
#> Unobserved stochastic nodes: 125
#> Total graph size: 1045
#>
#> Initializing modelresult$Est
#> MEAN SD 2.5% 25% 50% 75% 97.5%
#> mu[1,1] 9.752136 0.6124659 8.609981 9.323923 9.736431 10.164796 10.963274
#> mu[2,1] 5.801203 0.7309269 4.367575 5.325107 5.795131 6.279402 7.274722
#> mu[3,1] 6.858141 0.5687987 5.797578 6.485633 6.845016 7.218864 7.985506
#> mu[4,1] 10.581704 0.6432533 9.333173 10.149241 10.585915 11.002166 11.829277
#> mu[5,1] 8.798723 0.6010973 7.584619 8.410607 8.805575 9.202984 9.989943
#> mu[6,1] 7.298191 0.5729346 6.165684 6.910407 7.319978 7.684776 8.371344
#> mu[7,1] 7.045174 0.7349421 5.604417 6.554623 7.033682 7.534953 8.533297
#> mu[8,1] 9.768334 0.6295971 8.512256 9.352599 9.774879 10.184547 11.041561
#> mu[9,1] 5.322008 0.6378247 4.062486 4.904209 5.315432 5.753291 6.559807
#> mu[10,1] 6.193670 0.6510411 4.940262 5.771715 6.201241 6.619997 7.495059
#> mu[11,1] 4.793837 0.5450891 3.735246 4.428616 4.785507 5.158345 5.907330
#> mu[12,1] 7.251186 0.5617443 6.104048 6.876969 7.264450 7.635493 8.325727
#> mu[13,1] 8.359093 0.6621224 7.042852 7.929061 8.354730 8.828422 9.641971
#> mu[14,1] 7.645729 0.5138700 6.602750 7.298795 7.642554 7.994811 8.652333
#> mu[15,1] 7.859000 0.5611347 6.773308 7.488308 7.846521 8.238432 8.934363
#> mu[16,1] 4.128804 0.5621926 3.009283 3.759037 4.132867 4.503353 5.234996
#> mu[17,1] 4.774395 0.7167687 3.404219 4.276326 4.761153 5.287048 6.118600
#> mu[18,1] 4.985327 0.6080561 3.755835 4.609342 4.977473 5.374385 6.213731
#> mu[19,1] 8.041941 0.5741725 6.894807 7.666357 8.035900 8.427448 9.178695
#> mu[20,1] 10.208848 0.6243478 9.011281 9.782419 10.208382 10.613351 11.406783
#> mu[1,2] 7.642568 0.6912977 6.303438 7.181564 7.658748 8.111204 9.020948
#> mu[2,2] 5.232766 0.6368340 3.964604 4.796478 5.232430 5.650177 6.490149
#> mu[3,2] 6.396484 0.6162629 5.196491 5.981919 6.406532 6.801545 7.610228
#> mu[4,2] 5.515802 0.5752741 4.392696 5.138365 5.531801 5.903169 6.596848
#> mu[5,2] 11.352661 0.5122295 10.359392 11.011682 11.352621 11.688131 12.344208
#> mu[6,2] 6.859733 0.6603307 5.575311 6.413047 6.882296 7.305056 8.164053
#> mu[7,2] 4.928224 0.6976758 3.479781 4.452407 4.938737 5.427567 6.221512
#> mu[8,2] 6.667404 0.7609844 5.185700 6.128314 6.689247 7.213081 8.140416
#> mu[9,2] 7.173016 0.6191522 5.967218 6.772633 7.173454 7.588003 8.404412
#> mu[10,2] 9.046880 0.7425420 7.608971 8.557280 9.037421 9.539712 10.513685
#> mu[11,2] 8.342091 0.5994254 7.207054 7.935187 8.331874 8.734188 9.543230
#> mu[12,2] 6.197042 0.6166748 4.932807 5.787667 6.206670 6.605289 7.401428
#> mu[13,2] 8.648147 0.6222672 7.401633 8.231000 8.634532 9.073373 9.840293
#> mu[14,2] 7.640987 0.6202007 6.444048 7.221241 7.646882 8.053025 8.866613
#> mu[15,2] 10.023599 0.6104519 8.806940 9.606923 10.021618 10.448493 11.189938
#> mu[16,2] 8.046220 0.5203763 7.035455 7.714451 8.040082 8.380397 9.048101
#> mu[17,2] 10.026864 0.5919834 8.918618 9.619610 10.007817 10.432482 11.212877
#> mu[18,2] 7.733113 0.5805454 6.564761 7.348397 7.730044 8.140158 8.855878
#> mu[19,2] 8.845267 0.6512973 7.609042 8.409485 8.851275 9.283214 10.112193
#> mu[20,2] 8.532086 0.5950901 7.325853 8.131277 8.540279 8.930683 9.658758
#> mu[1,3] 10.458987 0.5049596 9.467370 10.112045 10.452075 10.797381 11.495039
#> mu[2,3] 8.306386 0.5697999 7.156087 7.929692 8.300141 8.696994 9.403502
#> mu[3,3] 7.311878 0.5324306 6.272986 6.948223 7.302157 7.668975 8.339722
#> mu[4,3] 5.672559 0.6722076 4.413225 5.205020 5.659222 6.127819 7.039520
#> mu[5,3] 8.722088 0.6625361 7.429211 8.263213 8.738841 9.165897 10.032074
#> mu[6,3] 8.340975 0.5854464 7.183491 7.944676 8.330770 8.731775 9.487852
#> mu[7,3] 4.897288 0.6522877 3.658184 4.456548 4.895338 5.353783 6.161907
#> mu[8,3] 10.264373 0.5753236 9.118180 9.888915 10.265643 10.655508 11.368562
#> mu[9,3] 9.683942 0.6370450 8.447277 9.244335 9.690344 10.104347 10.925345
#> mu[10,3] 8.959605 0.6941541 7.541456 8.502210 8.956843 9.410482 10.304983
#> mu[11,3] 8.045272 0.6230408 6.706032 7.647451 8.064941 8.468146 9.203010
#> mu[12,3] 8.141284 0.6797666 6.814727 7.672620 8.143289 8.613034 9.441586
#> mu[13,3] 8.767303 0.7212964 7.350859 8.276934 8.755779 9.261225 10.199863
#> mu[14,3] 8.765207 0.6563833 7.459108 8.325459 8.784806 9.190914 10.006986
#> mu[15,3] 8.410449 0.6376941 7.195252 7.975103 8.400425 8.834605 9.645621
#> mu[16,3] 3.853502 0.6257575 2.630678 3.448299 3.861271 4.272773 5.055215
#> mu[17,3] 9.599412 0.5733406 8.420623 9.225125 9.617020 9.984542 10.698477
#> mu[18,3] 5.874432 0.6802476 4.532222 5.429343 5.854807 6.317597 7.240923
#> mu[19,3] 8.103983 0.4839114 7.173966 7.776756 8.098452 8.417259 9.066292
#> mu[20,3] 5.541383 0.6975923 4.159091 5.074002 5.559757 6.032522 6.852095
#> mu[1,4] 6.325169 0.5450947 5.258529 5.951589 6.318683 6.688744 7.348998
#> mu[2,4] 5.047717 0.6524987 3.781742 4.604759 5.051208 5.482710 6.297373
#> mu[3,4] 7.901430 0.6462716 6.624330 7.466352 7.914908 8.336323 9.142926
#> mu[4,4] 7.506377 0.5898328 6.399958 7.109784 7.495731 7.926312 8.626216
#> mu[5,4] 8.337158 0.6852664 6.989692 7.879843 8.331808 8.814511 9.682981
#> mu[6,4] 7.339889 0.6739121 5.999174 6.898068 7.350481 7.811748 8.587974
#> mu[7,4] 8.673375 0.5785137 7.522647 8.288404 8.684442 9.064596 9.805344
#> mu[8,4] 6.668995 0.6269314 5.456354 6.257542 6.661650 7.074012 7.900555
#> mu[9,4] 4.488113 0.6686146 3.143271 4.048468 4.502463 4.948500 5.781034
#> mu[10,4] 7.619808 0.6013100 6.451413 7.204990 7.619711 8.029753 8.808889
#> mu[11,4] 6.135479 0.5692275 5.032192 5.746322 6.139935 6.497904 7.288834
#> mu[12,4] 7.478214 0.5835217 6.415812 7.076661 7.465343 7.873691 8.645476
#> mu[13,4] 9.560203 0.5575599 8.422277 9.194787 9.557173 9.945789 10.662804
#> mu[14,4] 8.301249 0.4844674 7.356745 7.968952 8.293394 8.607174 9.251752
#> mu[15,4] 9.977907 0.7405957 8.576440 9.490480 9.949042 10.453777 11.437915
#> mu[16,4] 2.988291 0.5645520 1.862218 2.602842 3.003986 3.375541 4.043800
#> mu[17,4] 6.278503 0.6532120 4.988435 5.828989 6.296636 6.726917 7.551022
#> mu[18,4] 3.697684 0.5439708 2.598984 3.338845 3.701209 4.069050 4.760269
#> mu[19,4] 7.986930 0.5746981 6.815116 7.604943 7.974848 8.368753 9.120709
#> mu[20,4] 6.782302 0.6003881 5.632427 6.378508 6.780111 7.180121 7.954862
#> mu[1,5] 8.073887 0.6637309 6.703122 7.659978 8.097506 8.514359 9.345665
#> mu[2,5] 8.011364 0.6422046 6.780716 7.576589 7.991577 8.454892 9.251322
#> mu[3,5] 3.926218 0.5983334 2.762718 3.527956 3.932377 4.335054 5.063799
#> mu[4,5] 7.472436 0.5919003 6.299083 7.084743 7.465183 7.852019 8.676658
#> mu[5,5] 8.345445 0.5530180 7.254370 7.978404 8.362646 8.718580 9.426851
#> mu[6,5] 10.961103 0.6028754 9.810578 10.556025 10.948844 11.359035 12.170743
#> mu[7,5] 8.146307 0.8039913 6.597035 7.626960 8.136255 8.651029 9.729764
#> mu[8,5] 8.177322 0.6861243 6.837553 7.713267 8.178094 8.641857 9.549685
#> mu[9,5] 4.899703 0.4980033 3.940790 4.557847 4.896511 5.243789 5.895168
#> mu[10,5] 7.353186 0.5816796 6.207749 6.963996 7.363834 7.748520 8.456249
#> mu[11,5] 5.440817 0.5470165 4.350330 5.075553 5.455526 5.808367 6.506125
#> mu[12,5] 9.479360 0.6420196 8.279229 9.013894 9.473832 9.926340 10.734548
#> mu[13,5] 11.166559 0.7608682 9.671523 10.627655 11.168319 11.669159 12.676869
#> mu[14,5] 10.096374 0.5638302 9.019645 9.717334 10.095405 10.481477 11.215986
#> mu[15,5] 7.601468 0.5407422 6.527761 7.226207 7.603056 7.958274 8.636101
#> mu[16,5] 6.389745 0.6884731 5.119944 5.894932 6.403136 6.880268 7.735017
#> mu[17,5] 7.715552 0.7230206 6.309554 7.258293 7.742355 8.191344 9.151261
#> mu[18,5] 7.472576 0.6605719 6.201693 7.005696 7.477455 7.926210 8.751628
#> mu[19,5] 9.619481 0.6043400 8.419692 9.219359 9.616381 10.026470 10.812570
#> mu[20,5] 8.977355 0.6379558 7.738110 8.533683 8.974069 9.411419 10.176824result$coefficient
#> Mean SD 2.5% 25% 50% 75% 97.5%
#> b[0] -0.1270417 0.2984369 -0.721951 -0.3245736 -0.1324369 0.07419669 0.4568618
#> b[1] 2.1831679 0.1799421 1.830949 2.0629472 2.1820772 2.30181042 2.5397202
#> b[2] 2.2730059 0.1032029 2.070343 2.2045506 2.2710199 2.34258539 2.4762505result$refvar
#> NULLMSE_HB=result$Est$SD^2
summary(MSE_HB)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.2342 0.3294 0.3818 0.3890 0.4369 0.6464RSE_HB=sqrt(MSE_HB)/result$Est$MEAN*100
summary(RSE_HB)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 4.512 6.946 7.921 8.799 10.081 18.892y_dir=dataPanel[,1]
y_HB=result$Est$MEAN
y=as.data.frame(cbind(y_dir,y_HB))
summary(y)
#> y_dir y_HB
#> Min. : 2.555 Min. : 2.988
#> 1st Qu.: 6.144 1st Qu.: 6.314
#> Median : 7.684 Median : 7.724
#> Mean : 7.562 Mean : 7.565
#> 3rd Qu.: 8.822 3rd Qu.: 8.733
#> Max. :12.835 Max. :11.353
MSE_dir=dataPanel[,4]
MSE=as.data.frame(cbind(MSE_dir, MSE_HB))
summary(MSE)
#> MSE_dir MSE_HB
#> Min. :0.3133 Min. :0.2342
#> 1st Qu.:0.4971 1st Qu.:0.3294
#> Median :0.6294 Median :0.3818
#> Mean :0.6800 Mean :0.3890
#> 3rd Qu.:0.7749 3rd Qu.:0.4369
#> Max. :1.6929 Max. :0.6464
RSE_dir=sqrt(MSE_dir)/y_dir*100
RSE=as.data.frame(cbind(MSE_dir, MSE_HB))
summary(RSE)
#> MSE_dir MSE_HB
#> Min. :0.3133 Min. :0.2342
#> 1st Qu.:0.4971 1st Qu.:0.3294
#> Median :0.6294 Median :0.3818
#> Mean :0.6800 Mean :0.3890
#> 3rd Qu.:0.7749 3rd Qu.:0.4369
#> Max. :1.6929 Max. :0.6464