saeFH.uprop {sae.prop}R Documentation

EBLUPs based on a Univariate Fay Herriot model with Additive Logistic Transformation

Description

This function gives the transformed EBLUP and Empirical Best Predictor (EBP) based on a univariate Fay-Herriot model.

Usage

saeFH.uprop(formula, vardir, MAXITER = 100, PRECISION = 1e-04, L = 1000, data)

Arguments

formula

an object of class formula that describe the fitted model.

vardir

vector containing the sampling variances of direct estimators for each domain. The values must be sorted as the variables in formula.

MAXITER

maximum number of iterations allowed in the Fisher-scoring algorithm, Default: 100.

PRECISION

convergence tolerance limit for the Fisher-scoring algorithm, Default: 1e-4.

L

number of Monte Carlo iterations in calculating Empirical Best Predictor (EBP), Default: 1000.

data

optional data frame containing the variables named in formula and vardir.

Value

The function returns a list with the following objects:

est

a data frame containing values of the estimators for each domains.

fit

a list containing the following objects (model is fitted using REML):

components

a data frame containing the following columns:

Examples

## Not run: 
## Load dataset
data(datasaeu)

## If data is defined
Fo = y ~ x1 + x2
vardir = "vardir"
model.data <- saeFH.uprop(Fo, vardir, data = datasaeu)

## If data is undefined
Fo = datasaeu$y ~ datasaeu$x1 + datasaeu$x2
vardir = datasaeu$vardir
model <- saeFH.uprop(Fo, vardir)

## See the estimators
model$est

## End(Not run)


[Package sae.prop version 0.1.2 Index]