saehb.zinb
Small Area Estimation under Zero Inflated Negative Binomial Model using Hierarchical Bayesian Method
Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
✓DOI references
Found 10 DOI reference(s) in README -
○Academic publication links
-
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (15.0%) to scientific vocabulary
Keywords
bayes
hierarchical
sae
Last synced: 6 months ago
·
JSON representation
Repository
Small Area Estimation under Zero Inflated Negative Binomial Model using Hierarchical Bayesian Method
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
bayes
hierarchical
sae
Created over 3 years ago
· Last pushed over 3 years ago
Metadata Files
Readme
README.Rmd
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# saeHB.zinb
We designed this package to provide function and dataset for area level of Small Area Estimation under Zero Inflated Negative Binomial Model using Hierarchical Bayesian (HB) Method. This package provides model using Univariate Zero Inflated Negative Binomial Distribution for variable of interest. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean, the variation of mean, and the quantile of mean. For the reference, see Rao, J.N.K & Molina (2015).
## Author
Hayun , Azka Ubaidillah
## Maintaner
Hayun <221810327@stis.ac.id>
## Installation
You can install the development version of saeHB.zinb from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("hayunbuto/saeHB.zinb")
```
## Function
* `ZinbHB()` This function gives small area estimator under Zero Inflated Negative Binomial Model and is implemented to variable of interest (y) that assumed to be a Zero Inflated Negative Binomial Distribution. The range of data is (y >= 0)
## Example
This is a basic example of using `ZinbHB()` function to make an estimate based on synthetic data in this package
```{r example}
library(saeHB.zinb)
## For data without any non-sampled area
data(dataZINB) # Load dataset
## For data with non-sampled area use dataHNBNs
## Fitting model
result <- ZinbHB(y ~ x1 + x2, data=dataZINB)
```
Small Area mean Estimates
```r
result$Est
```
Estimated model coefficient
```r
result$coefficient
```
Estimated random effect variances
```r
result$refVar
```
## References
* Desjardins, C. D. (2013). Evaluating the performance of two competing models of school suspension under simulation the zero-inflated negative binomial and the negative binomial hurdle (thesis). Minnesota (US): Minnesota University.
* Emille E. O. Ishida, Joseph M. Hilbe, and Rafael S. de Souza (2017). Bayesian Models for Astrophysical Data: Using R, JAGS, Python, and Stan. Cambridge : Cambridge University Press.
* Garray, A. M., Hashimoto, E. M., Ortega, E. M. M., dan Lachos, V. H. (2011). On Estimation and Influence Diagnostics For Zero Inflated Negative Binomial Regression Models. Computational Statistics and Data Analysis, 55 (3), p.1304-1318.
* Hilbe, J. M. (2011). Negative Binomial Regression 2nd Edition. New York : Cambridge University Press.
* Nadhiroh, I. M. (2009). Zero-Inflated Negative Binomial Models in Small Area Estimation. Bogor: Bogor Agricultural University.
* Ntzoufras, I. (2009). Bayesian Modelling Using WinBUGS. New Jersey : John Wiley & Sons, Inc.
* Rao, J.N.K & Molina. (2015). Small Area Estimation 2nd Edition. New Jersey : John Wiley & Sons, Inc.
* S. Krieg, H. J. Boonstra, and M. Smeets. (2016). Small-area estimation with zero-inflated data – a simulation study. J. Off. Stat., vol. 32, no. 4, pp. 963–986, 2016.
GitHub Events
Total
Last Year
Issues and Pull Requests
Last synced: over 2 years ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- cran 140 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 2
- Total maintainers: 1
cran.r-project.org: saeHB.zinb
Small Area Estimation using Hierarchical Bayesian under Zero Inflated Negative Binomial Distribution
- Homepage: https://github.com/hayunbuto/saeHB.zinb
- Documentation: http://cran.r-project.org/web/packages/saeHB.zinb/saeHB.zinb.pdf
- License: GPL-3
- Status: removed
-
Latest release: 0.1.1
published over 3 years ago
Rankings
Forks count: 28.8%
Dependent packages count: 29.8%
Stargazers count: 35.2%
Dependent repos count: 35.5%
Average: 43.6%
Downloads: 88.6%
Maintainers (1)
Last synced:
over 2 years ago
Dependencies
DESCRIPTION
cran
- R >= 2.10 depends
- coda * imports
- grDevices * imports
- graphics * imports
- rjags * imports
- stats * imports
- stringr * imports
- knitr * suggests
- rmarkdown * suggests
- saeHB * suggests