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

In rakaikmana/hurdlenb: Small Area Estimastion using Hierarchical Bayesian Method under Hurdle Negative Binomial Distribution

Description

This function is implemented to variable of interest (y) that assumed to be a Hurdle Negative Binomial Distribution. The range of data is (0 < y < ∞). This model can be used to handle overdispersion and excess zero in data.

Usage

HurdleNB(
  formula,
  iter.update = 3,
  iter.mcmc = 2000,
  coef.nonzero,
  var.coef.nonzero,
  coef.zero,
  var.coef.zero,
  thin = 2,
  burn.in = 1000,
  tau.u = 1,
  tau.v = 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 2000
coef.nonzero	Optional argument for the mean of the prior distribution of the model coefficients for variable of interest (y) which value is zero count
var.coef.nonzero	Optional argument of variance of coefficient non-zero
coef.zero	Optional argument for the mean of the prior distribution of the model coefficients for variable of interest (y) which value is zero count
var.coef.zero	Optional argument for variance of coefficient zero
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.u	Variance of random effect area for non-zero count of variable interest with default 1
tau.v	Variance of random effect area for zero count 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
plot	Trace, Density, Autocorrelation Function Plot of MCMC samples

Examples

##For data without any non-sampled area
data(dataHNB)     # Load dataset

result <- HurdleNB(y ~ x1 + x2, data = dataHNB)

result$Est          # Small Area mean Estimates
result$refVar       # Estimated random effect variances
result$coefficient  # Estimated model coefficient

# Load library 'coda' to execute the plot
# autocorr.plot(result$plot[[3]])    # Generate ACF Plot
# plot(result$plot[[3]])             # Generate Density and Trace plot

## For data with non-sampled area use dataHNBNs

rakaikmana/hurdlenb documentation built on Feb. 14, 2022, 12:49 a.m.