mseFH.mprop: 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 Multivariate Fay Herriot model with Additive Logistic Transformation

Description

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

Usage

mseFH.mprop(
  formula,
  vardir,
  MAXITER = 100,
  PRECISION = 1e-04,
  L = 1000,
  B = 400,
  data
)

Arguments

formula	an object of class formula that describe the fitted model.
vardir	sampling variances of direct estimations. If data is defined, it is a vector containing names of sampling variance columns. If data is not defined, it should be a data frame of sampling variances of direct estimators. The order is var1, var2, \dots, var(q-1), cov12, \dots, cov1(q-1), cov23, \dots, cov(q-2)(q-1).
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: 400.
data	optional data frame containing the variables named in formula and vardir.

Value

The function returns a list with the following objects:

est	a list containing the following objects:

• PC : data frame containing transformed EBLUP estimators using inverse alr for each category.

• EBP : data frame containing Empirical Best Predictor using Monte Carlo for each category.

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

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

• refvar : estimated covariance matrix of random effects.

components

a list containing the following objects:

• random.effects : data frame containing estimated random effect values of the fitted model for each category.

• residuals : data frame containing residuals of the fitted model for each category.

mse	a list containing estimated MSE of the estimators.

• PC : estimated MSE of plugin (PC) estimators for each category.

• EBP : estimated MSE of EBP estimators for each category.

Examples

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

## If data is defined
Fo = list(Y1 ~ X1,
          Y2 ~ X2,
          Y3 ~ X3)
vardir = c("v1", "v2", "v3", "v12", "v13", "v23")
MSE.data <- mseFH.mprop(Fo, vardir, data = datasaem, B = 10)

## If data is undefined
Fo = list(datasaem$Y1 ~ datasaem$X1,
          datasaem$Y2 ~ datasaem$X2,
          datasaem$Y3 ~ datasaem$X3)
vardir = datasaem[, c("v1", "v2", "v3", "v12", "v13", "v23")]
MSE <- mseFH.mprop(Fo, vardir, B = 10)

## See the estimators
MSE$mse

## NOTE:
## B = 10 is just for examples.
## Please choose a proper number for Bootstrap iterations in real calculation.

## End(Not run)

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