eblupNSFH1: EBLUP under nonstationary Fay-Herriot model for sample area

In NSAE: Nonstationary Small Area Estimation

EBLUP under nonstationary Fay-Herriot model for sample area

Description

This function gives the EBLUP and the estimate of mean squared error (mse) based on a nonstationary Fay-Herriot model for sample area.

Usage

eblupNSFH1(
  formula,
  vardir,
  lat,
  long,
  method = "REML",
  MAXITER,
  PRECISION,
  data
)

Arguments

formula	an object of class list of formula, describe the model to be fitted
vardir	a vector of sampling variances of direct estimators for each small area
lat	a vector of latitude for each small area
long	a vector of longitude for each small area
method	type of fitting method, default is "REML" methods
MAXITER	number of iterations allowed in the algorithm. Default is 100 iterations
PRECISION	convergence tolerance limit for the Fisher-scoring algorithm. Default value is 1e-04
data	a data frame comprising the variables named in formula, vardir, lat and long

Value

The function returns a list with the following objects:

eblup

a vector with the values of the estimators for each small area

mse

a vector of the mean squared error estimates for each small area

sample

a matrix consist of area code, eblup, mse, SE and CV

fit

a list containing the following objects:

• estcoef : a data frame with the estimated model coefficients in the first column (beta),their asymptotic standard errors in the second column (std.error), the t statistics in the third column (tvalue) and the p-values of the significance of each coefficient in last column (pvalue)

• refvar : estimated random effects variance

• spatialcorr : spatial correlation parameter

• randomeffect : a data frame with the values of the random effect estimators

• goodness : goodness of fit statistics

Examples

# Load data set
data(paddysample)
# Fit nonstationary Fay-Herriot model using sample part of paddy data
result <- eblupNSFH1(y ~ x1+x2, var, latitude, longitude, "REML", 100, 1e-04,paddysample)
result

NSAE documentation built on May 28, 2022, 1:08 a.m.