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] 9.741839 0.6202458 8.554475 9.325023 9.746682 10.151450 10.931580
#> mu[2,1] 5.784420 0.7669829 4.174867 5.277436 5.826264 6.301858 7.197337
#> mu[3,1] 6.833434 0.5775605 5.661658 6.473097 6.831333 7.224195 7.955092
#> mu[4,1] 10.606610 0.6449334 9.335370 10.167511 10.606367 11.019123 11.890223
#> mu[5,1] 8.826495 0.5955508 7.646089 8.435416 8.824201 9.212493 9.991693
#> mu[6,1] 7.292662 0.5834467 6.034807 6.916271 7.311626 7.696020 8.396765
#> mu[7,1] 7.002753 0.7399025 5.560736 6.476138 7.010654 7.483124 8.472674
#> mu[8,1] 9.798457 0.6482561 8.535174 9.343831 9.802286 10.231168 11.083758
#> mu[9,1] 5.337739 0.6381254 4.146500 4.894602 5.339610 5.777923 6.606633
#> mu[10,1] 6.180391 0.6500427 4.916330 5.732644 6.189837 6.622495 7.434051
#> mu[11,1] 4.812115 0.5723821 3.704590 4.412633 4.807441 5.198342 5.979691
#> mu[12,1] 7.234092 0.5771617 6.053827 6.856033 7.231937 7.624167 8.332102
#> mu[13,1] 8.375923 0.6760842 7.037926 7.937243 8.385126 8.836838 9.671233
#> mu[14,1] 7.653414 0.5007838 6.693626 7.314376 7.642925 8.007585 8.623352
#> mu[15,1] 7.854009 0.5719025 6.770810 7.463627 7.851588 8.229646 9.010081
#> mu[16,1] 4.084637 0.5666490 2.988672 3.713045 4.098420 4.467589 5.183124
#> mu[17,1] 4.788675 0.6831552 3.410549 4.321240 4.784837 5.247887 6.116602
#> mu[18,1] 4.952307 0.6025147 3.797099 4.554260 4.941065 5.348384 6.158747
#> mu[19,1] 8.057966 0.5904431 6.863598 7.673270 8.066308 8.461709 9.218860
#> mu[20,1] 10.204644 0.6586947 8.978652 9.759132 10.181082 10.649594 11.571437
#> mu[1,2] 7.647056 0.6870529 6.268057 7.200776 7.672959 8.108289 9.039065
#> mu[2,2] 5.222089 0.6225841 3.992376 4.814016 5.250899 5.623723 6.416192
#> mu[3,2] 6.377505 0.6203773 5.167956 5.943811 6.367907 6.806000 7.659485
#> mu[4,2] 5.540160 0.5738784 4.371172 5.169935 5.544654 5.918179 6.654765
#> mu[5,2] 11.342922 0.5044430 10.390306 10.999694 11.345940 11.676307 12.343929
#> mu[6,2] 6.823425 0.6607466 5.534803 6.374414 6.815741 7.272603 8.136619
#> mu[7,2] 4.910094 0.7073583 3.573304 4.419841 4.885654 5.427628 6.236250
#> mu[8,2] 6.689205 0.7607084 5.186798 6.175815 6.695755 7.195435 8.195542
#> mu[9,2] 7.190791 0.6319521 5.959025 6.758025 7.184704 7.611038 8.450640
#> mu[10,2] 9.044671 0.7464970 7.535735 8.559145 9.056679 9.547897 10.506201
#> mu[11,2] 8.317973 0.6097930 7.156237 7.911912 8.314500 8.707120 9.499244
#> mu[12,2] 6.202465 0.6231247 4.961882 5.779055 6.194749 6.624100 7.398044
#> mu[13,2] 8.697059 0.5889092 7.520232 8.299223 8.690902 9.106084 9.870267
#> mu[14,2] 7.659511 0.6166259 6.398453 7.259875 7.651209 8.076654 8.896612
#> mu[15,2] 10.008278 0.6226446 8.806343 9.583068 10.002047 10.420862 11.216493
#> mu[16,2] 8.045865 0.5389620 6.968461 7.686137 8.033365 8.419021 9.083094
#> mu[17,2] 10.026269 0.5791227 8.898817 9.636508 10.023259 10.414615 11.152671
#> mu[18,2] 7.756478 0.5912138 6.606637 7.356090 7.756985 8.166684 8.951995
#> mu[19,2] 8.896976 0.6414044 7.663882 8.471171 8.899801 9.325152 10.138759
#> mu[20,2] 8.505275 0.5900782 7.327505 8.103123 8.511623 8.902627 9.636752
#> mu[1,3] 10.463777 0.4979735 9.516567 10.127883 10.444819 10.797164 11.465765
#> mu[2,3] 8.308894 0.5730274 7.205726 7.915044 8.306913 8.712194 9.421888
#> mu[3,3] 7.297266 0.5373918 6.251551 6.928210 7.302797 7.659203 8.369020
#> mu[4,3] 5.670619 0.6515246 4.471981 5.237810 5.660277 6.101475 6.983081
#> mu[5,3] 8.739207 0.6783365 7.380324 8.306976 8.727057 9.190912 10.097131
#> mu[6,3] 8.326946 0.6068866 7.130793 7.927302 8.330520 8.730958 9.494836
#> mu[7,3] 4.915647 0.6381523 3.671964 4.481749 4.924502 5.323452 6.190698
#> mu[8,3] 10.252285 0.5859985 9.135531 9.849669 10.247670 10.638045 11.405574
#> mu[9,3] 9.707471 0.6385461 8.425524 9.279268 9.706781 10.142662 11.008090
#> mu[10,3] 8.943322 0.6874234 7.599958 8.476048 8.950316 9.404650 10.298633
#> mu[11,3] 8.010829 0.6355028 6.756148 7.587969 8.029508 8.440465 9.267415
#> mu[12,3] 8.186298 0.6932502 6.836035 7.731587 8.186918 8.655538 9.487327
#> mu[13,3] 8.811474 0.7207384 7.402890 8.331512 8.800468 9.306086 10.216466
#> mu[14,3] 8.789901 0.6644813 7.510244 8.337490 8.804346 9.235012 10.081122
#> mu[15,3] 8.418618 0.6375802 7.171603 7.978759 8.416512 8.855417 9.637558
#> mu[16,3] 3.840521 0.6119024 2.606023 3.438393 3.865851 4.247149 5.000575
#> mu[17,3] 9.595180 0.5710353 8.396258 9.217468 9.622462 9.965425 10.662230
#> mu[18,3] 5.871301 0.6561212 4.600318 5.443418 5.886784 6.318467 7.175506
#> mu[19,3] 8.131881 0.4771496 7.216237 7.800638 8.129903 8.461915 9.046658
#> mu[20,3] 5.528586 0.6989829 4.181057 5.055743 5.536906 5.982018 6.931620
#> mu[1,4] 6.315642 0.5494646 5.260518 5.939853 6.300373 6.691158 7.362296
#> mu[2,4] 5.059348 0.6334947 3.777366 4.635772 5.063329 5.482616 6.301496
#> mu[3,4] 7.889229 0.6612283 6.604872 7.444825 7.875631 8.344590 9.160105
#> mu[4,4] 7.557047 0.5727449 6.429986 7.178957 7.564791 7.947285 8.637189
#> mu[5,4] 8.352081 0.6902227 7.011870 7.883717 8.367073 8.833879 9.649200
#> mu[6,4] 7.325979 0.6930734 5.923900 6.852733 7.354538 7.789444 8.677824
#> mu[7,4] 8.665464 0.5811230 7.538089 8.279183 8.667624 9.051079 9.798051
#> mu[8,4] 6.658494 0.6367422 5.402157 6.236250 6.665918 7.081684 7.915252
#> mu[9,4] 4.462531 0.6527338 3.228670 4.034171 4.441300 4.913374 5.751043
#> mu[10,4] 7.611514 0.6169655 6.341137 7.211442 7.617683 8.002330 8.834176
#> mu[11,4] 6.140704 0.5553467 5.024631 5.769161 6.148629 6.522605 7.201242
#> mu[12,4] 7.457102 0.6244227 6.242284 7.028501 7.464589 7.885470 8.682966
#> mu[13,4] 9.541980 0.5593490 8.484762 9.165138 9.530785 9.902450 10.648247
#> mu[14,4] 8.304119 0.4990056 7.320759 7.967113 8.302541 8.647704 9.273260
#> mu[15,4] 9.972787 0.7451469 8.521064 9.466458 9.962541 10.462270 11.449964
#> mu[16,4] 2.984284 0.5588522 1.873632 2.608820 3.005638 3.349396 4.107695
#> mu[17,4] 6.278812 0.6459580 4.980837 5.852285 6.300338 6.699948 7.563774
#> mu[18,4] 3.665760 0.5649335 2.587828 3.283378 3.667452 4.048360 4.798466
#> mu[19,4] 7.996029 0.6008137 6.861532 7.574407 7.987558 8.398943 9.189161
#> mu[20,4] 6.768376 0.6123010 5.592608 6.334278 6.768711 7.190481 7.937137
#> mu[1,5] 8.024193 0.6668391 6.785990 7.584910 8.035926 8.473224 9.312520
#> mu[2,5] 8.019152 0.6184314 6.840517 7.600548 8.009808 8.430838 9.301994
#> mu[3,5] 3.899570 0.6017088 2.681116 3.511906 3.904505 4.294766 5.041059
#> mu[4,5] 7.501428 0.5989298 6.333601 7.102500 7.509760 7.907246 8.720009
#> mu[5,5] 8.341686 0.5675706 7.238005 7.965179 8.341497 8.709594 9.495836
#> mu[6,5] 10.955171 0.5889729 9.825883 10.558122 10.939309 11.342989 12.137315
#> mu[7,5] 8.155806 0.7728081 6.678244 7.646446 8.171872 8.654540 9.663698
#> mu[8,5] 8.216300 0.6943835 6.941004 7.749169 8.195693 8.671179 9.627771
#> mu[9,5] 4.881358 0.5014078 3.934482 4.539594 4.879266 5.208732 5.863881
#> mu[10,5] 7.354155 0.5896299 6.184005 6.946273 7.338401 7.774014 8.495989
#> mu[11,5] 5.408518 0.5691351 4.273276 5.032654 5.429462 5.789250 6.550464
#> mu[12,5] 9.474026 0.6548884 8.215947 9.030170 9.442902 9.910495 10.800454
#> mu[13,5] 11.192047 0.7884469 9.716437 10.638868 11.167308 11.707144 12.803152
#> mu[14,5] 10.090446 0.5563525 9.001099 9.703694 10.100468 10.456299 11.174711
#> mu[15,5] 7.563713 0.5435459 6.486133 7.195713 7.561552 7.918155 8.650917
#> mu[16,5] 6.356204 0.6743140 5.042991 5.908633 6.341328 6.812832 7.675486
#> mu[17,5] 7.718129 0.7161006 6.266500 7.263775 7.733802 8.168672 9.172368
#> mu[18,5] 7.438865 0.6662133 6.114538 6.977819 7.452675 7.877363 8.761825
#> mu[19,5] 9.628319 0.5868765 8.501983 9.214922 9.624864 10.047279 10.743036
#> mu[20,5] 8.969609 0.6486362 7.715099 8.535345 8.960963 9.414809 10.246984y_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. : 2.984
#> 1st Qu.: 6.144 1st Qu.: 6.306
#> Median : 7.684 Median : 7.737
#> Mean : 7.562 Mean : 7.563
#> 3rd Qu.: 8.822 3rd Qu.: 8.752
#> Max. :12.835 Max. :11.343
MSE_dir=dataPanel[,4]
MSE=as.data.frame(cbind(MSE_dir, MSE_HB))
summary(MSE)
#> MSE_dir MSE_HB
#> Min. :0.3133 Min. :0.2277
#> 1st Qu.:0.4971 1st Qu.:0.3335
#> Median :0.6294 Median :0.3848
#> Mean :0.6800 Mean :0.3923
#> 3rd Qu.:0.7749 3rd Qu.:0.4367
#> Max. :1.6929 Max. :0.6216
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. :0.2277
#> 1st Qu.:0.4971 1st Qu.:0.3335
#> Median :0.6294 Median :0.3848
#> Mean :0.6800 Mean :0.3923
#> 3rd Qu.:0.7749 3rd Qu.:0.4367
#> Max. :1.6929 Max. :0.6216