sar.normal {saeHB.spatial} | R Documentation |
Small Area Estimation under Spatial Simultaneous Autoregressive (SAR) Model and Normal Distribution using Hierarchical Bayesian Method
Description
This function gives small area estimator under Spatial SAR Model and is implemented to variable of interest (y) that assumed to be a Normal Distribution. The range of data is (-\infty < y < \infty)
.
Usage
sar.normal(
formula,
vardir,
proxmat,
iter.update = 3,
iter.mcmc = 2000,
thin = 1,
burn.in = 1000,
coef,
var.coef,
data
)
Arguments
formula |
formula that describe the fitted model. |
vardir |
sampling variances of direct estimations. |
proxmat |
|
iter.update |
number of updates with default |
iter.mcmc |
number of total iterations per chain with default |
thin |
thinning rate, must be a positive integer with default |
burn.in |
number of iterations to discard at the beginning with default |
coef |
optional vector containing the mean of the prior distribution of the regression model coefficients. |
var.coef |
optional vector containing the variances of the prior distribution of the regression model coefficients. |
data |
the data frame. |
Value
This function returns a list of the following objects:
Est |
A data frame of Small Area mean Estimates using Hierarchical Bayesian Method |
refVar |
Estimated random effect variances |
coefficient |
A data frame with estimated model coefficient |
plot |
Trace, Density, and Autocorrelation Function Plot of MCMC samples |
Examples
## For data without any non-sampled area
data(sp.norm) # Load dataset
data(prox.mat) # Load proximity Matrix
result <- sar.normal(y ~ x1 + x2, "vardir", prox.mat, data = sp.norm)
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 sp.normNs