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] 9.723664 0.6112683 8.519237 9.321458 9.723963 10.121754 10.902824
#> mu[2,1] 5.824378 0.7370342 4.352147 5.332510 5.833372 6.321688 7.231439
#> mu[3,1] 6.815908 0.5821256 5.663756 6.429555 6.808731 7.203492 7.969172
#> mu[4,1] 10.590095 0.6510799 9.296393 10.160081 10.589323 11.036723 11.850398
#> mu[5,1] 8.780990 0.5957041 7.628728 8.382668 8.769358 9.177463 9.983150
#> mu[6,1] 7.312822 0.5848041 6.135273 6.924183 7.317597 7.691126 8.483604
#> mu[7,1] 7.004231 0.7310222 5.633671 6.516327 6.998994 7.485621 8.537911
#> mu[8,1] 9.819480 0.6238810 8.595401 9.403240 9.794675 10.223385 11.124157
#> mu[9,1] 5.325415 0.6251330 4.111398 4.903640 5.330906 5.745828 6.504141
#> mu[10,1] 6.207977 0.6245519 4.992967 5.791071 6.211634 6.615252 7.453498
#> mu[11,1] 4.807990 0.5662404 3.715806 4.430966 4.794851 5.184537 5.906351
#> mu[12,1] 7.222976 0.5720793 6.135496 6.844048 7.226307 7.604056 8.322878
#> mu[13,1] 8.389722 0.6869435 7.014833 7.915478 8.410808 8.833539 9.685506
#> mu[14,1] 7.676289 0.5121864 6.690655 7.333118 7.674745 8.016929 8.681777
#> mu[15,1] 7.830787 0.5526308 6.728377 7.451308 7.834586 8.210294 8.931682
#> mu[16,1] 4.132527 0.5732968 3.055176 3.744244 4.136387 4.517915 5.254847
#> mu[17,1] 4.783350 0.7071572 3.423340 4.318023 4.780401 5.258751 6.180894
#> mu[18,1] 4.983317 0.5872418 3.842927 4.601201 4.987284 5.379662 6.189906
#> mu[19,1] 8.043452 0.5801370 6.856964 7.651372 8.055795 8.434031 9.137956
#> mu[20,1] 10.205536 0.6143436 9.014525 9.795076 10.188993 10.609499 11.447757
#> mu[1,2] 7.660061 0.7072955 6.267338 7.184103 7.662266 8.172843 8.961283
#> mu[2,2] 5.270999 0.6320368 4.067395 4.835846 5.254451 5.690318 6.537684
#> mu[3,2] 6.367752 0.6173705 5.190114 5.955485 6.363856 6.796363 7.547035
#> mu[4,2] 5.555584 0.5851628 4.421839 5.162145 5.561468 5.952279 6.671918
#> mu[5,2] 11.344160 0.4968724 10.360852 11.014166 11.344639 11.687901 12.316508
#> mu[6,2] 6.871077 0.6717882 5.561198 6.421755 6.865768 7.334782 8.119361
#> mu[7,2] 4.933847 0.6781324 3.564545 4.477675 4.940009 5.390080 6.245080
#> mu[8,2] 6.708216 0.7331424 5.226005 6.206694 6.708365 7.204056 8.116983
#> mu[9,2] 7.148629 0.5993317 5.974890 6.754039 7.142702 7.550329 8.321716
#> mu[10,2] 9.007252 0.7450769 7.551385 8.502379 9.022620 9.492366 10.450616
#> mu[11,2] 8.350478 0.6097577 7.151381 7.940001 8.326217 8.740063 9.573839
#> mu[12,2] 6.217860 0.6295121 4.962143 5.805513 6.224244 6.648592 7.415252
#> mu[13,2] 8.675853 0.6024213 7.506726 8.271488 8.651330 9.079678 9.887152
#> mu[14,2] 7.657496 0.6006967 6.502124 7.267581 7.651874 8.082446 8.824177
#> mu[15,2] 10.024940 0.6142552 8.843214 9.609506 10.015115 10.441897 11.236585
#> mu[16,2] 8.069702 0.5355338 7.000386 7.711750 8.086954 8.423014 9.125184
#> mu[17,2] 10.043996 0.5815221 8.911889 9.645521 10.032941 10.428995 11.154816
#> mu[18,2] 7.760132 0.6009561 6.557454 7.373451 7.744159 8.148111 9.013164
#> mu[19,2] 8.868768 0.6398147 7.590060 8.417839 8.906214 9.307409 10.089767
#> mu[20,2] 8.518729 0.6042617 7.370842 8.099080 8.517670 8.949048 9.692913
#> mu[1,3] 10.444070 0.4742824 9.523596 10.132695 10.438038 10.773100 11.355856
#> mu[2,3] 8.322540 0.5672308 7.228911 7.939477 8.321173 8.705900 9.461820
#> mu[3,3] 7.311571 0.5240995 6.268318 6.963908 7.302629 7.667139 8.329109
#> mu[4,3] 5.692836 0.6710246 4.421067 5.234945 5.677704 6.138136 7.037556
#> mu[5,3] 8.763416 0.6596917 7.496006 8.341682 8.762801 9.197092 10.045593
#> mu[6,3] 8.311265 0.5876544 7.161635 7.899908 8.304205 8.698240 9.512510
#> mu[7,3] 4.903631 0.6507921 3.687597 4.464670 4.892417 5.331092 6.184750
#> mu[8,3] 10.255640 0.5914390 9.095465 9.863346 10.261791 10.665373 11.386051
#> mu[9,3] 9.680864 0.6401407 8.489026 9.244855 9.657616 10.105475 10.922580
#> mu[10,3] 8.942932 0.6795378 7.627600 8.488854 8.965792 9.372830 10.245604
#> mu[11,3] 8.052336 0.6255678 6.812950 7.628491 8.051892 8.463773 9.279332
#> mu[12,3] 8.163758 0.6532113 6.885346 7.713385 8.154918 8.577364 9.464104
#> mu[13,3] 8.797629 0.7065410 7.419285 8.323583 8.794629 9.267117 10.156398
#> mu[14,3] 8.782331 0.6801421 7.479690 8.319022 8.784433 9.249158 10.118945
#> mu[15,3] 8.401010 0.6516857 7.139610 7.974280 8.405712 8.843775 9.675377
#> mu[16,3] 3.856752 0.6211611 2.592554 3.440126 3.871200 4.281211 5.061365
#> mu[17,3] 9.603444 0.5780370 8.490815 9.208203 9.605191 9.998606 10.784178
#> mu[18,3] 5.853854 0.6666648 4.589160 5.406236 5.836070 6.307192 7.177898
#> mu[19,3] 8.118223 0.5004519 7.126442 7.782896 8.125901 8.431517 9.073440
#> mu[20,3] 5.548978 0.6928870 4.204999 5.101191 5.546985 6.015382 6.934608
#> mu[1,4] 6.307638 0.5502357 5.228121 5.960137 6.320300 6.681583 7.362888
#> mu[2,4] 5.055940 0.6379053 3.795108 4.608257 5.063022 5.502261 6.283973
#> mu[3,4] 7.855844 0.6424762 6.608786 7.407455 7.834220 8.294747 9.105642
#> mu[4,4] 7.520935 0.5777881 6.381165 7.135348 7.523011 7.909710 8.628249
#> mu[5,4] 8.350863 0.6759438 6.946480 7.934922 8.380970 8.797645 9.675952
#> mu[6,4] 7.349164 0.6613551 6.014976 6.916005 7.366083 7.801738 8.617050
#> mu[7,4] 8.646165 0.5884358 7.481880 8.270558 8.646824 9.045117 9.846381
#> mu[8,4] 6.652079 0.6244246 5.430617 6.244517 6.632527 7.066455 7.878876
#> mu[9,4] 4.428591 0.6582414 3.120819 4.010011 4.440374 4.885103 5.660897
#> mu[10,4] 7.614100 0.6202826 6.388716 7.189227 7.616787 8.040298 8.790105
#> mu[11,4] 6.163492 0.5788441 5.013363 5.762958 6.152919 6.574612 7.266919
#> mu[12,4] 7.466970 0.6256517 6.210090 7.041119 7.460352 7.892557 8.711986
#> mu[13,4] 9.535206 0.5588244 8.457552 9.160040 9.532458 9.920991 10.590782
#> mu[14,4] 8.296072 0.4980407 7.290190 7.977801 8.311928 8.636527 9.253296
#> mu[15,4] 9.953425 0.7164040 8.514811 9.461042 9.978649 10.422505 11.320593
#> mu[16,4] 2.983086 0.5534105 1.858102 2.621045 2.988797 3.369939 4.054142
#> mu[17,4] 6.276827 0.6669271 4.870442 5.832721 6.292868 6.709708 7.531141
#> mu[18,4] 3.735839 0.5543445 2.624819 3.381018 3.727663 4.106237 4.797388
#> mu[19,4] 8.001697 0.5707169 6.897731 7.620168 7.998497 8.376493 9.127311
#> mu[20,4] 6.775277 0.5784845 5.607494 6.391509 6.779883 7.167046 7.898787
#> mu[1,5] 8.062443 0.6646692 6.722568 7.593758 8.071152 8.511549 9.325938
#> mu[2,5] 8.019477 0.6296378 6.793029 7.592715 8.019078 8.440330 9.260784
#> mu[3,5] 3.860854 0.6040372 2.694874 3.458674 3.871058 4.273807 5.033185
#> mu[4,5] 7.476997 0.5854649 6.337232 7.086933 7.478811 7.851277 8.675719
#> mu[5,5] 8.347181 0.5646373 7.251462 7.960013 8.336706 8.722126 9.459833
#> mu[6,5] 10.946546 0.5741231 9.836332 10.569693 10.949200 11.322153 12.084922
#> mu[7,5] 8.143672 0.8088549 6.571693 7.591012 8.148061 8.674360 9.773569
#> mu[8,5] 8.207947 0.6879587 6.925124 7.737622 8.210001 8.675839 9.568076
#> mu[9,5] 4.865666 0.4983981 3.890788 4.539992 4.872970 5.197896 5.843914
#> mu[10,5] 7.350802 0.5874846 6.267151 6.941086 7.325019 7.742479 8.548045
#> mu[11,5] 5.414444 0.5632643 4.277490 5.047429 5.429458 5.786030 6.515971
#> mu[12,5] 9.428212 0.6449558 8.100790 8.997264 9.423168 9.865019 10.692593
#> mu[13,5] 11.172276 0.7588405 9.728960 10.656834 11.135891 11.691277 12.668190
#> mu[14,5] 10.102035 0.5607841 8.986322 9.710931 10.129960 10.457043 11.262044
#> mu[15,5] 7.580093 0.5311053 6.519942 7.211625 7.592544 7.945263 8.598978
#> mu[16,5] 6.364961 0.6518116 5.067106 5.937461 6.381132 6.792452 7.656423
#> mu[17,5] 7.706045 0.7202027 6.290485 7.231585 7.722190 8.162355 9.142800
#> mu[18,5] 7.453765 0.6567782 6.150417 7.002764 7.420774 7.892382 8.771227
#> mu[19,5] 9.610056 0.6000195 8.472619 9.212438 9.598813 10.020619 10.775081
#> mu[20,5] 8.968127 0.6431050 7.665416 8.562608 8.968379 9.384505 10.253525y_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. : 2.983
#> 1st Qu.: 6.144 1st Qu.: 6.300
#> Median : 7.684 Median : 7.733
#> Mean : 7.562 Mean : 7.564
#> 3rd Qu.: 8.822 3rd Qu.: 8.768
#> Max. :12.835 Max. :11.344
MSE_dir=dataPanel[,4]
MSE=as.data.frame(cbind(MSE_dir, MSE_HB))
summary(MSE)
#> MSE_dir MSE_HB
#> Min. :0.3133 Min. :0.2249
#> 1st Qu.:0.4971 1st Qu.:0.3345
#> Median :0.6294 Median :0.3793
#> Mean :0.6800 Mean :0.3871
#> 3rd Qu.:0.7749 3rd Qu.:0.4338
#> Max. :1.6929 Max. :0.6542
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. :0.2249
#> 1st Qu.:0.4971 1st Qu.:0.3345
#> Median :0.6294 Median :0.3793
#> Mean :0.6800 Mean :0.3871
#> 3rd Qu.:0.7749 3rd Qu.:0.4338
#> Max. :1.6929 Max. :0.6542