Several functions are provided for small area estimation at the area level using the hierarchical bayesian (HB) method with panel data under beta distribution for variable interest. This package also provides a dataset produced by data generation. The ‘rjags’ package is employed to obtain parameter estimates. Model-based estimators involve the HB estimators, which include the mean and the variation of the mean. For the reference, see Rao and Molina (2015, ISBN:978-1-118-73578-7).
Dian Rahmawati Salis, Azka Ubaidillah
Dian Rahmawati Salis dianrahmawatisalis03@gmail.com
RaoYuAr1.beta()
This function gives estimation of y
using Hierarchical Bayesian Rao Yu Model under Beta distributionPanel.beta()
This function gives estimation of y using
Hierarchical Bayesian Rao Yu Model under Beta distribution when rho =
0You can install the development version of saeHB.panel.beta from GitHub with:
# install.packages("devtools")
::install_github("DianRahmawatiSalis/saeHB.panel.beta")
devtools#> Downloading GitHub repo DianRahmawatiSalis/saeHB.panel.beta@HEAD
#> rlang (1.0.6 -> 1.1.1) [CRAN]
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#> tibble (3.1.8 -> 3.2.1) [CRAN]
#> rjags (4-13 -> 4-14 ) [CRAN]
#> dplyr (1.1.0 -> 1.1.2) [CRAN]
#> Installing 6 packages: rlang, cli, vctrs, tibble, rjags, dplyr
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This is a basic example which shows you how to solve a common problem:
library(saeHB.panel.beta)
data("dataPanelbeta")
<- dataPanelbeta[1:25,] #for the example only use part of the dataset
dataPanelbeta <- ydi~xdi1+xdi2
formula <- max(dataPanelbeta[,2])
area <- max(dataPanelbeta[,3])
period <-Panel.beta(formula,area=area, period=period ,iter.mcmc = 10000,thin=5,burn.in = 1000,data=dataPanelbeta)
result#> 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 model
Extract area mean estimation
$Est
result#> MEAN SD 2.5% 25% 50% 75% 97.5%
#> mu[1,1] 0.9717543 0.02049596 0.9190452 0.9647537 0.9771319 0.9855882 0.9944996
#> mu[2,1] 0.9510178 0.03288811 0.8726664 0.9375707 0.9584745 0.9742376 0.9896039
#> mu[3,1] 0.9423424 0.04229746 0.8342821 0.9257270 0.9531654 0.9709367 0.9884046
#> mu[4,1] 0.9685811 0.02368207 0.9106474 0.9610285 0.9744508 0.9837632 0.9934777
#> mu[5,1] 0.9404871 0.04833029 0.8108703 0.9242932 0.9548771 0.9729015 0.9889299
#> mu[1,2] 0.9712634 0.02151995 0.9176578 0.9634467 0.9766530 0.9855381 0.9940899
#> mu[2,2] 0.9623792 0.02698444 0.8903765 0.9515899 0.9698480 0.9802553 0.9930563
#> mu[3,2] 0.9199737 0.05673965 0.7725415 0.8974167 0.9355057 0.9591863 0.9829062
#> mu[4,2] 0.9785373 0.01764545 0.9313410 0.9731022 0.9830868 0.9898914 0.9964443
#> mu[5,2] 0.9380432 0.04478322 0.8197203 0.9205959 0.9494996 0.9685290 0.9868380
#> mu[1,3] 0.9706437 0.02230411 0.9062298 0.9628493 0.9761609 0.9859619 0.9954731
#> mu[2,3] 0.8655381 0.07748549 0.6717318 0.8303173 0.8821522 0.9202044 0.9650687
#> mu[3,3] 0.9514076 0.03298669 0.8613422 0.9374960 0.9599521 0.9751765 0.9904608
#> mu[4,3] 0.9581918 0.02852203 0.8840658 0.9465256 0.9652434 0.9774888 0.9910127
#> mu[5,3] 0.9168573 0.05704092 0.7595892 0.8949962 0.9311539 0.9560012 0.9824765
#> mu[1,4] 0.9551522 0.02996238 0.8751646 0.9425921 0.9623560 0.9763032 0.9909293
#> mu[2,4] 0.9342440 0.04342608 0.8190859 0.9170173 0.9451454 0.9635272 0.9851920
#> mu[3,4] 0.9334419 0.04256771 0.8268696 0.9137840 0.9447703 0.9633846 0.9844740
#> mu[4,4] 0.9757125 0.02018988 0.9194181 0.9701157 0.9809731 0.9882532 0.9956180
#> mu[5,4] 0.8548727 0.09644667 0.5998419 0.8110210 0.8829172 0.9249182 0.9678862
#> mu[1,5] 0.9682527 0.02270194 0.9080987 0.9594752 0.9742140 0.9836890 0.9938743
#> mu[2,5] 0.8867797 0.07030962 0.7065786 0.8544728 0.9037534 0.9359809 0.9757281
#> mu[3,5] 0.9575245 0.02978026 0.8859338 0.9445934 0.9654878 0.9779015 0.9917703
#> mu[4,5] 0.9300983 0.04640135 0.8169823 0.9101969 0.9413470 0.9620436 0.9852102
#> mu[5,5] 0.8637439 0.08470990 0.6407232 0.8247754 0.8853290 0.9257943 0.9666170
Extract coefficient estimation
$coefficient
result#> Mean SD 2.5% 25% 50% 75% 97.5%
#> b[0] 1.945340 0.3914832 1.1681623 1.6812227 1.951651 2.214936 2.712910
#> b[1] 1.169466 0.5381308 0.1030402 0.8199215 1.161090 1.528560 2.200846
#> b[2] 1.137731 0.4537707 0.2633356 0.8324130 1.125769 1.435226 2.020636
Extract area random effect variance
$refVar
result#> [1] 0.4626572
Extract MSE
<-result$Est$SD^2
MSE_HBsummary(MSE_HB)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.0003114 0.0005608 0.0010881 0.0021821 0.0023358 0.0093020
Extract RSE
<-sqrt(MSE_HB)/result$Est$MEAN*100
RSE_HBsummary(RSE_HB)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 1.803 2.445 3.467 4.528 5.139 11.282
Extract convergence diagnostic using geweke test
$covergence.test
result#> NULL