README.md

In arinams/spatial: Small Area Estimation under Spatial SAR Model using Hierarchical Bayesian Method

saeHB.spatial

Provides several functions and datasets for area level of Small Area Estimation under Spatial SAR Model using Hierarchical Bayesian (HB) Method. For the reference, see Rao and Molina (2015) .

Author

Arina Mana Sikana, Azka Ubaidillah

Maintaner

Arina Mana Sikana [e-mail]

Function

• spatial.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 spatial.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 <- spatial.normal(y ~ x1 + x2, "vardir", prox.mat, data = sp.norm)
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 64
#>    Unobserved stochastic nodes: 6
#>    Total graph size: 8989
#> 
#> Initializing model
#> 
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 64
#>    Unobserved stochastic nodes: 6
#>    Total graph size: 8989
#> 
#> Initializing model
#> 
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 64
#>    Unobserved stochastic nodes: 6
#>    Total graph size: 8989
#> 
#> Initializing model

Small Area mean Estimates

result$Est

Estimated model coefficient

result$coefficient

Estimated random effect variances

result$refVar
#>  [1] 3.446660 3.277981 2.942481 2.829723 2.829723 2.942481 3.277981 3.446660
#>  [9] 3.277981 3.004318 2.717549 2.623733 2.623733 2.717549 3.004318 3.277981
#> [17] 2.942481 2.717549 2.482897 2.408138 2.408138 2.482897 2.717549 2.942481
#> [25] 2.829723 2.623733 2.408138 2.341689 2.341689 2.408138 2.623733 2.829723
#> [33] 2.829723 2.623733 2.408138 2.341689 2.341689 2.408138 2.623733 2.829723
#> [41] 2.942481 2.717549 2.482897 2.408138 2.408138 2.482897 2.717549 2.942481
#> [49] 3.277981 3.004318 2.717549 2.623733 2.623733 2.717549 3.004318 3.277981
#> [57] 3.446660 3.277981 2.942481 2.829723 2.829723 2.942481 3.277981 3.446660

References

Rao, J.N.K & Molina. (2015). Small Area Estimation 2nd Edition. New York: John Wiley and Sons, Inc. .

arinams/spatial documentation built on Feb. 14, 2022, 12:44 a.m.