The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

zipsae

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). 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).

Authors

Fadheel Wisnu Utomo, Ika Yuni Wulansari

Maintainer

Fadheel Wisnu Utomo 221709671@stis.ac.id

Installation

You can install the released version of zipsae from CRAN or find on my github repository Github

Example

##load the dataset in package
library(zipsae)
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

head(saezip$estimate)
#>           [,1]
#> [1,] 0.2925708
#> [2,] 0.2790501
#> [3,] 0.2772425
#> [4,] 0.2884874
#> [5,] 0.2931530
#> [6,] 0.2970365
## saezip$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$coefficient$lambda   #to see the estimator which is gained from the non-zero compilation data.
## saezip$coefficient$omega   #to see the estimator which is gained from the complete compilation data.

References

• Arisona, D.C. (2018). Kajian Pendugaan Area Kecil pada Data Overdispersi Menggunakan Regresi Zero-Inflated Poisson. Bogor: Bogor Agricultural University.
• Benavent, Roberto & Morales, Domingo. (2015). Multivariate Fay-Herriot models for small area estimation. Computational Statistics and Data Analysis 94 2016 372-390. DOI: 10.1016/j.csda.2015.07.013.
• Rao, J.N.K & Molina. (2015). Small Area Estimation 2nd Edition. New York: John Wiley and Sons, Inc.
• S. Krieg, H. J. Boonstra, and M. Smeets. Small-area estimation with zero-inflated data – a simulation study. J. Off. Stat., vol. 32, no. 4, pp. 963–986, 2016, doi: 10.1515/JOS-2016-0051

These 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.
Health stats visible at Monitor.