| eblupNSFH2 {NSAE} | R Documentation |
EBLUP under nonstationary Fay-Herriot model for sample and non-sample area
Description
This function gives the EBLUP and the estimate of mean squared error (mse) based on a nonstationary Fay-Herriot model for both sample and non-sample area.
Usage
eblupNSFH2(
formula,
vardir,
lat,
long,
indicator,
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 |
indicator |
a vector indicating the sample and non-sample 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 sample area
- eblup.out
a vector with the values of the estimators for each non-sample area
- mse
a vector of the mean squared error estimates for each sample area
- mse.out
a vector of the mean squared error estimates for each non-sample area
- sample
a matrix consist of area code, eblup, mse, SE and CV for sample area
- nonsample
a matrix consist of area code, eblup, mse, SE and CV for non-sample area
- 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 : estimated 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(paddy)
# Fit nonstationary Fay-Herriot model using sample and non-sample part of paddy data
result <- eblupNSFH2(y ~ x1+x2, var, latitude, longitude, indicator , "REML", 100, 1e-04,paddy)
result