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

In saeHB: Small Area Estimation using Hierarchical Bayesian Method

Small Area Estimation using Hierarchical Bayesian under Poisson Distribution

Description

This function is implemented to variable of interest (y) that assumed to be a Poisson Distribution. The data is a count data, y = 1,2,3,...

Usage

Poisson(
  formula,
  iter.update = 3,
  iter.mcmc = 10000,
  coef,
  var.coef,
  thin = 2,
  burn.in = 2000,
  tau.u = 1,
  data
)

Arguments

formula	Formula that describe the fitted model
iter.update	Number of updates with default 3
iter.mcmc	Number of total iterations per chain with default 10000
coef	a vector contains prior initial value of Coefficient of Regression Model for fixed effect with default vector of 0 with the length of the number of regression coefficients
var.coef	a vector contains prior initial value of variance of Coefficient of Regression Model with default vector of 1 with the length of the number of regression coefficients
thin	Thinning rate, must be a positive integer with default 2
burn.in	Number of iterations to discard at the beginning with default 2000
tau.u	Prior initial value of inverse of Variance of area random effect 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
plot	Trace, Dencity, Autocorrelation Function Plot of MCMC samples

Author(s)

Azka Ubaidillah [aut], Ika Yuni Wulansari [aut], Zaza Yuda Perwira [aut, cre], Jayanti Wulansari [aut, cre], Fauzan Rais Arfizain [aut,cre]

Examples

## Load Dataset
library(CARBayesdata)
data(lipdata)
dataPoisson <- lipdata
dataPoissonNs <- lipdata
dataPoissonNs$observed[c(2, 9, 15, 23, 40)] <- NA


## Compute Fitted Model
# observed ~ pcaff


## For data without any nonsampled area

formula <- observed ~ pcaff
v <- c(1, 1)
c <- c(0, 0)


## Using parameter coef and var.coef
saeHBPoisson <- Poisson(formula, coef = c, var.coef = v, iter.update = 10, data = dataPoisson)

saeHBPoisson$Est # Small Area mean Estimates
saeHBPoisson$refVar # Random effect variance
saeHBPoisson$coefficient # coefficient
# Load Library 'coda' to execute the plot
# autocorr.plot(saeHBPoisson$plot[[3]]) is used to generate ACF Plot
# plot(saeHBPoisson$plot[[3]]) is used to generate Density and trace plot

## Do not using parameter coef and var.coef
saeHBPoisson <- Poisson(formula, data = dataPoisson)



## For data with nonsampled area use dataPoissonNs

saeHB documentation built on Nov. 26, 2025, 5:06 p.m.