RaoYuAr1: Small Area Estimation using Hierarchical Bayesian under...

In Veliatrimarliana/saeHB.panel: Small Area Estimation using Hierarchical Bayesian Method for Rao Yu Model

Small Area Estimation using Hierarchical Bayesian under Rao-Yu Model

Description

This function is implemented to variable of interest ydi

Usage

RaoYuAr1(
  formula,
  area,
  period,
  vardir,
  iter.update = 3,
  iter.mcmc = 2000,
  thin = 1,
  burn.in = 1000,
  tau.e = 1,
  tau.v = 1,
  data
)

Arguments

formula	Formula that describe the fitted model
area	Number of areas (domain) of the data
period	Number of periods (subdomains) for each area of the data
vardir	Sampling variances of direct estimations
iter.update	Number of updates with default 3
iter.mcmc	Number of total iterations per chain with default 2000
thin	Thinning rate, must be a positive integer with default 1
burn.in	Number of iterations to discard at the beginning with default 1000
tau.e	Variance of area-by-time effect of variable interest with default 1
tau.v	Variance of random area effect of variable interest with default 1
data	The data frame

Value

This function returns a list of the following objects:

Est	A vector with the values of Small Area mean Estimates using Hierarchical bayesian method
refVar	Estimated random effect variances
coefficient	A dataframe with the estimated model coefficient
alpha	Parameter dispersion of Generalized Poisson distribution
plot	Trace, Density, Autocorrelation Function Plot of MCMC samples

Examples

##For data without any non-sampled area
data(dataAr1)     # Load dataset
formula = ydi ~ xdi1 + xdi2
area = max(dataAr1[, "area"])
period = max(dataAr1[,"period"])
vardir = dataAr1[,"vardir"]

result <- RaoYuAr1(formula, area, period, vardir, data = dataAr1)
result$Est
result$refVar
result$coefficient
result$plot
## For data with non-sampled area use dataAr1Ns

Veliatrimarliana/saeHB.panel documentation built on April 4, 2022, 11:50 a.m.