In arinams/saeHB.spatial: Small Area Estimation Hierarchical Bayes For Spatial Model

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saeHB.spatial

Provides several functions and datasets for area level of Small Area Estimation under Spatial Model using Hierarchical Bayesian (HB) Method. Model-based estimators include the HB estimators based on a Spatial Fay-Herriot model with univariate normal distribution for variable of interest.The 'rjags' package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) .

Author

Arina Mana Sikana, Azka Ubaidillah

Maintaner

Arina Mana Sikana [e-mail]

Function

• sar.normal() This function gives small area estimator under Spatial SAR Model and is implemented to variable of interest (y) that assumed to be a Normal Distribution. The range of data is (-∞ < y < ∞)

Installation

You can install the development version of saeHB.spatial from GitHub with:

# install.packages("devtools")
devtools::install_github("arinams/saeHB.spatial")

Example

This is a basic example of using sar.normal() function to make an estimate based on synthetic data in this package

library(saeHB.spatial)

## For data without any non-sampled area
data(sp.norm)       # Load dataset
data(prox.mat)      # Load proximity Matrix

## For data with non-sampled area use sp.normNs

## Fitting model
result <- sar.normal(y ~ x1 + x2, "vardir", prox.mat, data = sp.norm)

Small Area mean Estimates

result$Est

Estimated model coefficient

result$coefficient

Estimated random effect variances

result$refVar

References

• Rao, J.N.K & Molina. (2015). Small Area Estimation 2nd Edition. New Jersey: John Wiley and Sons, Inc. .
• J. Kubacki and A. Jedrzejczak. (2016). Small Area Estimation of Income Under Spatial SAR Model. Statistics in Transition New Series, Vol. 17, No. 3, pp. 365–390. .
• H. C. Chung and G. S. Datta. (2020). Bayesian Hierarchical Spatial Models for Small Area Estimation. Research Report Series. Washington, D.C.: U.S. Census Bureau.

arinams/saeHB.spatial documentation built on Nov. 27, 2024, 12:30 a.m.