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zipsae

Small Area Estimation with Zero-Inflated Model

https://github.com/dheel/zipsae

Science Score: 23.0%

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Small Area Estimation with Zero-Inflated Model

Basic Info
  • Host: GitHub
  • Owner: dheel
  • Language: R
  • Default Branch: main
  • Size: 20.5 KB
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  • Stars: 1
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Created about 5 years ago · Last pushed over 1 year ago
Metadata Files
Readme

README.Rmd 

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# 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)](https://repository.ipb.ac.id/handle/123456789/92308). For the data sample itself, we use combination method between [Roberto Benavent and Domingo Morales (2015)](https://www.sciencedirect.com/science/article/pii/S016794731500170X) and [Sabine Krieg, Harm Jan Boonstra and Marc Smeets (2016)](https://www.researchgate.net/publication/310818136_Small-Area_Estimation_with_Zero-Inflated_Data_-_a_Simulation_Study).

## 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](https://CRAN.R-project.org) or find on my github repository [Github](https://github.com/dheel/zipsae)

## Example

```{r}
##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$estimate)
## saezip$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

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Packages

  • Total packages: 1
  • Total downloads:
    • cran 270 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
  • Total maintainers: 1
cran.r-project.org: zipsae

Small Area Estimation with Zero-Inflated Model

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 270 Last month
Rankings
Forks count: 28.8%
Dependent packages count: 29.8%
Average: 31.6%
Stargazers count: 31.7%
Downloads: 32.4%
Dependent repos count: 35.5%
Maintainers (1)
221709671@stis.ac.id
Last synced: 12 months ago

Dependencies

DESCRIPTION cran
  • R >= 2.10 depends
  • stats * imports