est_saeOB: EBLUPs Optimum Benchmarking based on a Univariate Fay-Herriot...

In yas-q/msaeOB: Optimum Benchmarking for Multivariate Small Area Estimation

EBLUPs Optimum Benchmarking based on a Univariate Fay-Herriot (Model 1)

Description

This function gives EBLUPs optimum benchmarking based on univariate Fay-Herriot (model 1)

Usage

est_saeOB(
  formula,
  vardir,
  weight,
  samevar = FALSE,
  MAXITER = 100,
  PRECISION = 1e-04,
  data
)

Arguments

formula	an object of class list of formula describe the fitted model
vardir	vector containing sampling variances of direct estimators
weight	vector containing proportion of units in small areas
samevar	logical. If TRUE, the varians is same. Default is FALSE
MAXITER	maximum number of iterations for Fisher-scoring. Default is 100
PRECISION	coverage tolerance limit for the Fisher Scoring algorithm. Default value is 1e-4
data	dataframe containing the variables named in formula, vardir, and weight

Value

This function returns a list with following objects:

eblup

a list containing a value of estimators

• est.eblup : a dataframe containing EBLUP estimators

• est.eblupOB : a dataframe containing optimum benchmark estimators

fit	a list containing following objects:

• method : fitting method, named "REML"

• convergence : logical value of convergence of Fisher Scoring

• iterations : number of iterations of Fisher Scoring algorithm

• estcoef : a data frame containing estimated model coefficients (beta, std. error, t value, p-value)

• refvar : estimated random effect variance

random.effect	a data frame containing values of random effect estimators
agregation	a data frame containing agregation of direct, EBLUP, and optimum benchmark estimation

Examples

## load dataset
data(datamsaeOB)

# Compute EBLUP & Optimum Benchmark using auxiliary variables X1 and X2 for each dependent variable

## Using parameter 'data'
est_sae = est_saeOB(Y1 ~ X1 + X2, v1, w1, data = datamsaeOB)

## Without parameter 'data'
est_sae = est_saeOB(datamsaeOB$Y1 ~ datamsaeOB$X1 + datamsaeOB$X2, datamsaeOB$v1, datamsaeOB$w1)

## Return
est_sae$eblup$est.eblupOB # to see the Optimum Benchmark estimators

yas-q/msaeOB documentation built on June 23, 2022, 7:10 p.m.