mseFH.uprop: Parametric Bootstrap Mean Squared Error of EBLUPs based on a...

In sae.prop: Small Area Estimation using Fay-Herriot Models with Additive Logistic Transformation

Parametric Bootstrap Mean Squared Error of EBLUPs based on a Univariate Fay Herriot model with Additive Logistic Transformation

Description

This function gives the MSE of transformed EBLUP and Empirical Best Predictor (EBP) based on a univariate Fay-Herriot model with modified parametric bootstrap approach proposed by Butar & Lahiri.

Usage

mseFH.uprop(
  formula,
  vardir,
  MAXITER = 100,
  PRECISION = 1e-04,
  L = 1000,
  B = 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.
B	number of Bootstrap iterations in calculating MSE, 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.

• PC : transformed EBLUP estimators using inverse alr.

• EBP : Empirical Best Predictor using Monte Carlo.

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

• convergence : a logical value equal to TRUE if Fisher-scoring algorithm converges in less than MAXITER iterations.

• iterations : number of iterations performed by the Fisher-scoring algorithm.

• estcoef : a data frame that contains the estimated model coefficients, standard errors, t-statistics, and p-values of each coefficient.

• refvar : estimated random effects variance.

components

a data frame containing the following columns:

• random.effects : estimated random effect values of the fitted model.

• residuals : residuals of the fitted model.

mse	a data frame containing estimated MSE of the estimators.

• PC : estimated MSE of plugin (PC) estimators.

• EBP : estimated MSE of EBP estimators.

Examples

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

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

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

## See the estimators
MSE$mse

## End(Not run)

sae.prop documentation built on Oct. 15, 2023, 5:06 p.m.