Title: | Small Area Estimation with Zero-Inflated Model |
---|---|
Description: | This function produces empirical best linier unbiased predictions (EBLUPs) for Zero-Inflated data and its Relative Standard Error. Small Area Estimation with Zero-Inflated Model (SAE-ZIP) is a model developed for Zero-Inflated data that can lead us to overdispersion situation. To handle this kind of situation, this model is created. The model in this package is based on Small Area Estimation with Zero-Inflated Poisson model proposed by Dian Christien Arisona (2018)<https://repository.ipb.ac.id/handle/123456789/92308>. For the data sample itself, we use combination method between Roberto Benavent and Domingo Morales (2015)<doi:10.1016/j.csda.2015.07.013> and Sabine Krieg, Harm Jan Boonstra and Marc Smeets (2016)<doi:10.1515/jos-2016-0051>. |
Authors: | Fadheel Wisnu Utomo [aut, trl, cre], Ika Yuni Wulansari [aut, ths] |
Maintainer: | Fadheel Wisnu Utomo <[email protected]> |
License: | GPL-3 |
Version: | 1.0.2 |
Built: | 2025-03-01 05:06:02 UTC |
Source: | https://github.com/dheel/zipsae |
A Dataset which is generate with Zero-Inflated Poisson method for Small Area Estimation purpose
This data is generated based on Zero-Inflated Poisson with EBLUP based model
dataSAEZIP
dataSAEZIP
A data frame with 300 rows and 3 variables:
Direct Estimation of y
Auxiliary variable of x1
Sampling Variance of y
This function produce EBLUPs for Zero-Inflated data and its Relative Standard Error. Small Area Estimation with Zero-Inflated Model (SAE-ZIP) is a model developed for Zero-Inflated data that can lead us to overdispersion situation. To handle this kind of situation, this model is created. The model in this package is based on Small Area Estimation with Zero-Inflated Poisson model proposed by Dian Christien Arisona (2018). For the data sample itself, we use combination method between Roberto Benavent and Domingo Morales (2015) and Sabine Krieg, Harm Jan Boonstra and Marc Smeets (2016).
zipsae(data, vardir, formula, PRECISION = 1e-04, MAXITER = 100)
zipsae(data, vardir, formula, PRECISION = 1e-04, MAXITER = 100)
data |
The data frame with vardir, response, and explanatory variables included with Zero-Inflated situation also. |
vardir |
Sampling variances of direct estimations, if it is included in data frame so it is the vector with the name of sampling variances.if it is not, it is a data frame of sampling variance in order : |
formula |
List of formula that describe the fitted model |
PRECISION |
Limit of Fisher-scoring convergence tolerance. We set the default in |
MAXITER |
Maximum number of iterations in Fisher-scoring algorithm. We set the default in |
This function returns a list of the following objects:
estimate |
A Vector with a list of EBLUP with Zero-Inflated Poisson model |
dispersion |
A list containing the following objects: |
rse : A dataframe with the values of relative square errors of estimation
coefficient |
A list containing the following objects: |
a : EBLUP estimator for Non-Zero data
b : EBLUP estimator for Complete Data
##load the dataset in package data(dataSAEZIP) ##Extract the vardir (sampling error) dataSAEZIP$vardir -> sError ##Compute the data with SAE ZIP model formula = (y~x1) zipsae(data = dataSAEZIP, vardir = sError, formula) -> saezip saezip$estimate #to see the result of Small Area Estimation with Zero-Inflated Model saezip$dispersion$rse #to see the relative standard error from the estimation saezip$coefficient$a #to see the a coefficient which is gained from the non-zero compilation data saezip$coefficient$b #to see the b coefficient which is gained from the complete compilation data. head(saezip)
##load the dataset in package data(dataSAEZIP) ##Extract the vardir (sampling error) dataSAEZIP$vardir -> sError ##Compute the data with SAE ZIP model formula = (y~x1) zipsae(data = dataSAEZIP, vardir = sError, formula) -> saezip saezip$estimate #to see the result of Small Area Estimation with Zero-Inflated Model saezip$dispersion$rse #to see the relative standard error from the estimation saezip$coefficient$a #to see the a coefficient which is gained from the non-zero compilation data saezip$coefficient$b #to see the b coefficient which is gained from the complete compilation data. head(saezip)