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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%
#> 1 9.735949 0.6121378 8.567735 9.317395 9.737974 10.145544 10.994328
#> 2 7.659038 0.7044743 6.248753 7.182316 7.690047 8.142173 8.960447
#> 3 10.453702 0.4811839 9.522878 10.140783 10.454707 10.768473 11.390893
#> 4 6.297277 0.5450886 5.252422 5.916801 6.288940 6.683777 7.371260
#> 5 8.049112 0.6659218 6.706812 7.599489 8.058002 8.501883 9.344586
#> 6 5.766448 0.7497661 4.223969 5.260277 5.790315 6.260757 7.238622
#> 7 5.198601 0.6505446 3.924509 4.771667 5.198760 5.613655 6.509753
#> 8 8.289370 0.5662593 7.145112 7.907951 8.311635 8.673833 9.413260
#> 9 5.041052 0.6272359 3.807721 4.617709 5.044117 5.472141 6.217980
#> 10 8.025084 0.6337914 6.793501 7.587574 8.040486 8.445983 9.225125
#> 11 6.836335 0.5729557 5.687766 6.451239 6.838707 7.219083 7.953967
#> 12 6.367160 0.6042771 5.171119 5.972091 6.369304 6.781055 7.548945
#> 13 7.321336 0.5351143 6.292801 6.953695 7.337132 7.688628 8.340143
#> 14 7.904344 0.6468338 6.650875 7.478424 7.909097 8.327142 9.199033
#> 15 3.882464 0.5797566 2.755131 3.489755 3.878520 4.288515 4.996382
#> 16 10.612400 0.6364409 9.365214 10.188094 10.612055 11.042238 11.865563
#> 17 5.529372 0.5929085 4.435540 5.126991 5.525258 5.925288 6.722707
#> 18 5.711196 0.6550802 4.457098 5.287509 5.691249 6.159764 7.041247
#> 19 7.537422 0.5920514 6.358459 7.134664 7.541426 7.944604 8.661639
#> 20 7.481153 0.5970112 6.338063 7.072598 7.458780 7.882106 8.675665
#> 21 8.782549 0.6049014 7.633092 8.376638 8.774738 9.175899 9.979196
#> 22 11.348342 0.5012512 10.401823 11.010482 11.366300 11.679545 12.307202
#> 23 8.700078 0.6551946 7.437538 8.242929 8.690697 9.133692 9.975174
#> 24 8.352716 0.6680579 7.015852 7.917489 8.378008 8.806466 9.598347
#> 25 8.311734 0.5693600 7.248545 7.926767 8.328394 8.704065 9.393986
#> 26 7.298087 0.5866371 6.199235 6.895541 7.308114 7.688962 8.409395
#> 27 6.858620 0.6732228 5.520112 6.398891 6.872498 7.309536 8.182303
#> 28 8.344350 0.5671279 7.245632 7.952137 8.340127 8.715310 9.464526
#> 29 7.368477 0.6918846 6.018667 6.902439 7.393045 7.823638 8.726341
#> 30 10.938084 0.5976249 9.789713 10.533218 10.941321 11.342965 12.083861
#> 31 6.973436 0.7437112 5.549925 6.457724 6.967912 7.470393 8.405680
#> 32 4.903108 0.6929095 3.558959 4.439249 4.891664 5.357967 6.270334
#> 33 4.877547 0.6548079 3.578263 4.423984 4.882281 5.321828 6.140086
#> 34 8.658485 0.5687610 7.525848 8.283215 8.657967 9.040515 9.718471
#> 35 8.134487 0.7764808 6.663605 7.612095 8.143926 8.669493 9.628789
#> 36 9.783047 0.6417051 8.506458 9.346193 9.776710 10.222915 11.038353
#> 37 6.657348 0.7388632 5.238824 6.132719 6.633701 7.151968 8.104646
#> 38 10.245847 0.5910248 9.058181 9.842114 10.253098 10.641357 11.363890
#> 39 6.650903 0.6365261 5.418151 6.214801 6.634641 7.097875 7.856829
#> 40 8.194216 0.6814879 6.866800 7.725982 8.199317 8.636156 9.546680
#> 41 5.325361 0.6263146 4.004998 4.905226 5.341808 5.748813 6.571450
#> 42 7.162074 0.6240977 5.968728 6.746705 7.148308 7.581462 8.397846
#> 43 9.670416 0.6181281 8.488326 9.248174 9.660570 10.101743 10.829171
#> 44 4.456775 0.6413931 3.219746 4.027285 4.450801 4.912894 5.670077
#> 45 4.897188 0.4996369 3.943678 4.549180 4.898268 5.239143 5.901005
#> 46 6.202616 0.6509340 4.915031 5.752480 6.193635 6.630903 7.458573
#> 47 9.019272 0.7760212 7.470275 8.519884 9.036367 9.537772 10.513910
#> 48 8.965524 0.6912567 7.669118 8.502673 8.947947 9.435706 10.297405
#> 49 7.600447 0.6114461 6.421201 7.179628 7.600246 8.018466 8.820040
#> 50 7.356035 0.5793126 6.232122 6.951674 7.362215 7.742041 8.536628
#> 51 4.791197 0.5599882 3.668142 4.403974 4.773631 5.177508 5.919847
#> 52 8.323706 0.5935887 7.155002 7.936149 8.340430 8.711490 9.534025
#> 53 8.042837 0.6299469 6.811619 7.621292 8.057902 8.473784 9.262759
#> 54 6.121236 0.5492669 5.094068 5.720720 6.121078 6.484423 7.194599
#> 55 5.409486 0.5460336 4.381885 5.036666 5.403238 5.790154 6.473381
#> 56 7.228431 0.5718314 6.140890 6.842884 7.215396 7.610135 8.333789
#> 57 6.163553 0.6184403 4.999050 5.764833 6.139750 6.581747 7.356166
#> 58 8.171180 0.6691121 6.780232 7.724375 8.173173 8.631082 9.445246
#> 59 7.414350 0.6190073 6.192859 7.009204 7.418229 7.826181 8.595362
#> 60 9.454056 0.6290654 8.205452 9.025744 9.481282 9.886793 10.665682
#> 61 8.355348 0.6888520 6.977730 7.912315 8.354487 8.831335 9.682746
#> 62 8.662812 0.5967155 7.459707 8.272564 8.668659 9.060090 9.823466
#> 63 8.780451 0.7300686 7.316782 8.328851 8.774338 9.257016 10.161279
#> 64 9.539040 0.5585970 8.516799 9.163489 9.539765 9.915545 10.627114
#> 65 11.189206 0.7745236 9.641325 10.657559 11.168768 11.731545 12.737155
#> 66 7.634514 0.5157630 6.629451 7.283474 7.648590 7.967917 8.645451
#> 67 7.625944 0.6135744 6.465175 7.214532 7.635558 8.025929 8.877513
#> 68 8.777241 0.6800049 7.483548 8.305327 8.759944 9.243822 10.123432
#> 69 8.286562 0.4680264 7.316635 7.995572 8.285981 8.585894 9.211598
#> 70 10.074608 0.5651047 8.931933 9.709290 10.096195 10.449529 11.132893
#> 71 7.871299 0.5651614 6.748434 7.507661 7.878314 8.246035 8.981074
#> 72 10.024884 0.6219466 8.818762 9.581831 10.024654 10.449521 11.206561
#> 73 8.403710 0.6288665 7.208001 7.982019 8.390669 8.817939 9.706382
#> 74 9.971874 0.6954261 8.698280 9.497963 9.966099 10.444405 11.348806
#> 75 7.588726 0.5444926 6.541949 7.217258 7.584306 7.948679 8.647397
#> 76 4.106199 0.5695674 2.972178 3.719571 4.120510 4.482569 5.192925
#> 77 8.049372 0.5265914 7.010626 7.697080 8.041270 8.384769 9.092358
#> 78 3.832853 0.6176809 2.585330 3.400187 3.830435 4.233590 5.067853
#> 79 2.981198 0.5632213 1.833809 2.621826 2.997800 3.351580 4.059498
#> 80 6.343089 0.6720520 5.014648 5.890564 6.344433 6.790086 7.674171
#> 81 4.798933 0.6864811 3.477283 4.351462 4.782995 5.245087 6.150123
#> 82 10.036699 0.5832795 8.970668 9.653057 10.026594 10.433085 11.201843
#> 83 9.626379 0.5604178 8.473652 9.261895 9.639458 10.001778 10.692274
#> 84 6.255403 0.6488186 4.886265 5.850816 6.268610 6.678049 7.498141
#> 85 7.701270 0.7224672 6.288136 7.217773 7.703252 8.204897 9.070996
#> 86 4.969774 0.6052871 3.734310 4.580730 4.970488 5.373129 6.158406
#> 87 7.747988 0.5889062 6.604826 7.342356 7.730743 8.144714 8.907811
#> 88 5.870017 0.6465025 4.598935 5.434562 5.882998 6.316882 7.120723
#> 89 3.694486 0.5604431 2.596343 3.324021 3.694337 4.093418 4.752998
#> 90 7.434006 0.6494390 6.154185 7.008632 7.433452 7.857234 8.737611
#> 91 8.057735 0.5884660 6.894449 7.662945 8.062980 8.462046 9.177387
#> 92 8.889384 0.6547184 7.612325 8.447714 8.881262 9.334176 10.177310
#> 93 8.123292 0.4892343 7.203380 7.773962 8.111529 8.459402 9.093083
#> 94 7.988167 0.5763062 6.893634 7.599854 8.004051 8.370387 9.134768
#> 95 9.627186 0.6071962 8.411301 9.214983 9.625628 10.049837 10.768049
#> 96 10.169493 0.6387945 8.919428 9.737970 10.164583 10.587973 11.440235
#> 97 8.515764 0.6056714 7.350825 8.092639 8.516128 8.928916 9.665140
#> 98 5.523847 0.6828150 4.165543 5.083356 5.527076 5.976183 6.826558
#> 99 6.788477 0.5945450 5.598174 6.385088 6.804051 7.202265 7.894870
#> 100 8.953666 0.6516479 7.722518 8.510994 8.934912 9.388486 10.249274result$coefficient
#> Mean SD 2.5% 25% 50% 75%
#> b[0] -0.1863384 0.2747091 -0.7213781 -0.3742546 -0.1952395 0.0004129886
#> b[1] 2.2136857 0.1710081 1.8604432 2.1043023 2.2141018 2.3343981014
#> b[2] 2.2783006 0.1004344 2.0905277 2.2075792 2.2761226 2.3448286080
#> 97.5%
#> b[0] 0.3496634
#> b[1] 2.5270772
#> b[2] 2.4789208result$refvar
#> NULLMSE_HB=result$Est$SD^2
summary(MSE_HB)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.2190 0.3280 0.3818 0.3877 0.4289 0.6029RSE_HB=sqrt(MSE_HB)/result$Est$MEAN*100
summary(RSE_HB)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 4.417 6.888 8.042 8.794 10.121 18.892y_dir=dataPanel[,1]
y_HB=result$Est$MEAN
y=as.data.frame(cbind(y_dir,y_HB))
summary(y)
#> y_dir y_HB
#> Min. : 2.555 Min. : 2.981
#> 1st Qu.: 6.144 1st Qu.: 6.287
#> Median : 7.684 Median : 7.725
#> Mean : 7.562 Mean : 7.557
#> 3rd Qu.: 8.822 3rd Qu.: 8.719
#> Max. :12.835 Max. :11.348
MSE_dir=dataPanel[,4]
MSE=as.data.frame(cbind(MSE_dir, MSE_HB))
summary(MSE)
#> MSE_dir MSE_HB
#> Min. :0.3133 Min. :0.2190
#> 1st Qu.:0.4971 1st Qu.:0.3280
#> Median :0.6294 Median :0.3818
#> Mean :0.6800 Mean :0.3877
#> 3rd Qu.:0.7749 3rd Qu.:0.4289
#> Max. :1.6929 Max. :0.6029
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. :0.2190
#> 1st Qu.:0.4971 1st Qu.:0.3280
#> Median :0.6294 Median :0.3818
#> Mean :0.6800 Mean :0.3877
#> 3rd Qu.:0.7749 3rd Qu.:0.4289
#> Max. :1.6929 Max. :0.6029These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.