saeHB (version 0.2.3)

PoissonGamma: Small Area Estimation using Hierarchical Bayesian under Poisson Gamma Distribution

Description

This function is implemented to variable of interest \((y)\) that assumed to be a Poisson Distribution which it is parameter \((\lambda)\) is assumed to be a Gamma distribution. The data is a count data, \(y = 1,2,3,...\)

Usage

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

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

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

Examples

# \donttest{
## Load Dataset
library(CARBayesdata)
data(lipdata)
dataPoissonGamma <- lipdata
dataPoissonGammaNs <- lipdata
dataPoissonGammaNs$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)
dat <- dataPoissonGamma


## Using parameter coef and var.coef
saeHBPoissonGamma <- PoissonGamma(formula, coef = c, var.coef = v, iter.update = 10, data = dat)

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

## Do not using parameter coef and var.coef
saeHBPoissonGamma <- PoissonGamma(formula, data = dataPoissonGamma)



## For data with nonsampled area use dataPoissonGammaNs
# }