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

In saeHB: Small Area Estimation using Hierarchical Bayesian Method

Small Area Estimation using Hierarchical Bayesian under Weibull Distribution

Description

This function is implemented to variable of interest (y) that assumed to be a Weibull Distribution. The range of data is (y > 0

Usage

Weibull(
  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

Examples

## Data Generation
set.seed(123)
m <- 30
x <- runif(m, 0, 1)
b0 <- b1 <- 0.5
u <- rnorm(m, 0, 1)
Mu <- exp(b0 + b1 * x + u)
k <- rgamma(m, 2, 1)
lambda <- Mu / gamma(1 + 1 / k)
y <- rweibull(m, k, lambda)
MU <- lambda * gamma(1 + 1 / k)
vardir <- lambda^2 * (gamma(1 + 2 / k) - (gamma(1 + 1 / k))^2)
dataWeibull <- as.data.frame(cbind(y, x, vardir))
dataWeibullNs <- dataWeibull
dataWeibullNs$y[c(3, 14, 22, 29, 30)] <- NA
dataWeibullNs$vardir[c(3, 14, 22, 29, 30)] <- NA


## Compute Fitted Model
## y ~ x


## For data without any nonsampled area

formula <- y ~ x
var.coef <- c(1, 1)
coef <- c(0, 0)


## Using parameter coef and var.coef
saeHBWeibull <- Weibull(
  formula, coef = coef, var.coef = var.coef,
 iter.update = 10, data = dataWeibull
)

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

## Do not using parameter coef and var.coef
saeHBWeibull <- Weibull(formula, data = dataWeibull)



## For data with nonsampled area use dataWeibullNs

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