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).
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 [email protected]
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")
devtools::install_github("DianRahmawatiSalis/saeHB.panel.beta")
#> Skipping install of 'saeHB.panel.beta' from a github remote, the SHA1 (fe67bb61) has not changed since last install.
#> Use `force = TRUE` to force installation
This is a basic example which shows you how to solve a common problem:
library(saeHB.panel.beta)
data("dataPanelbeta")
dataPanelbeta <- dataPanelbeta[1:25,] #for the example only use part of the dataset
formula <- ydi~xdi1+xdi2
area <- max(dataPanelbeta[,2])
period <- max(dataPanelbeta[,3])
result<-Panel.beta(formula,area=area, period=period ,iter.mcmc = 10000,thin=5,burn.in = 1000,data=dataPanelbeta)
#> 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
result$Est
#> MEAN SD 2.5% 25% 50% 75% 97.5%
#> mu[1,1] 0.9745402 0.02043167 0.9242318 0.9671592 0.9798158 0.9874685 0.9962959
#> mu[2,1] 0.9529850 0.03378111 0.8687035 0.9391639 0.9606148 0.9762169 0.9920275
#> mu[3,1] 0.9416476 0.04334522 0.8257088 0.9271068 0.9515223 0.9695644 0.9886169
#> mu[4,1] 0.9707650 0.02343253 0.9100078 0.9631909 0.9768535 0.9858918 0.9956301
#> mu[5,1] 0.9392371 0.05230504 0.7937674 0.9226590 0.9552521 0.9731984 0.9904665
#> mu[1,2] 0.9730519 0.02075604 0.9151144 0.9651103 0.9784012 0.9872296 0.9955669
#> mu[2,2] 0.9644632 0.02716892 0.8916524 0.9553131 0.9715651 0.9825200 0.9941719
#> mu[3,2] 0.9190929 0.05909138 0.7582093 0.8974757 0.9337880 0.9586956 0.9846804
#> mu[4,2] 0.9806928 0.01626167 0.9376644 0.9753268 0.9851461 0.9914809 0.9977165
#> mu[5,2] 0.9414686 0.04347486 0.8300576 0.9253633 0.9528456 0.9703039 0.9899214
#> mu[1,3] 0.9727516 0.02296491 0.9084448 0.9653176 0.9785098 0.9877710 0.9961441
#> mu[2,3] 0.8650717 0.08258513 0.6512432 0.8283691 0.8813968 0.9243896 0.9664624
#> mu[3,3] 0.9547238 0.03083291 0.8773217 0.9424085 0.9616815 0.9760577 0.9921776
#> mu[4,3] 0.9604722 0.02744216 0.8909641 0.9491892 0.9671605 0.9790931 0.9939556
#> mu[5,3] 0.9185326 0.05607900 0.7741958 0.8964084 0.9307007 0.9567351 0.9842448
#> mu[1,4] 0.9584364 0.03167149 0.8737031 0.9456576 0.9671949 0.9791662 0.9929711
#> mu[2,4] 0.9360060 0.04438870 0.8225671 0.9184701 0.9461601 0.9665405 0.9865605
#> mu[3,4] 0.9350573 0.04267082 0.8231444 0.9169837 0.9452816 0.9656088 0.9877537
#> mu[4,4] 0.9774635 0.01875693 0.9297391 0.9713515 0.9826057 0.9896004 0.9971276
#> mu[5,4] 0.8457488 0.10996071 0.5617364 0.8037121 0.8748879 0.9238268 0.9705472
#> mu[1,5] 0.9700137 0.02447969 0.9072987 0.9622983 0.9762001 0.9854658 0.9954380
#> mu[2,5] 0.8870202 0.07028000 0.7023668 0.8552202 0.9036043 0.9369899 0.9772847
#> mu[3,5] 0.9605815 0.03019414 0.8833168 0.9502352 0.9675795 0.9797505 0.9936401
#> mu[4,5] 0.9366477 0.04333110 0.8299269 0.9170241 0.9469230 0.9665555 0.9886723
#> mu[5,5] 0.8613721 0.08544666 0.6459957 0.8197358 0.8803638 0.9229781 0.9675223
Extract coefficient estimation
result$coefficient
#> Mean SD 2.5% 25% 50% 75% 97.5%
#> b[0] 1.932909 0.3961688 1.1738158 1.6717925 1.933390 2.195974 2.704684
#> b[1] 1.188665 0.5270641 0.1656371 0.8391137 1.179424 1.531993 2.244406
#> b[2] 1.206062 0.4730080 0.3134756 0.8882160 1.197545 1.528039 2.165126
Extract area random effect variance
result$refVar
#> [1] 0.5076331
Extract MSE
MSE_HB<-result$Est$SD^2
summary(MSE_HB)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.0002644 0.0005993 0.0011412 0.0023440 0.0027358 0.0120914
Extract RSE
RSE_HB<-sqrt(MSE_HB)/result$Est$MEAN*100
summary(RSE_HB)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 1.658 2.524 3.545 4.626 5.569 13.002
Extract convergence diagnostic using geweke test
result$convergence.test
#> b[0] b[1] b[2]
#> Z-score 0.7387093 0.1672595 1.760278