est_saeRBns: EBLUPs Ratio Benchmarking for Non Sampled Area based on a...

In msaeRB: Ratio Benchmarking for Multivariate Small Area Estimation

Description

This function gives EBLUPs ratio benchmarking for non sampled area based on univariate Fay-Herriot (model 1)

Usage

est_saeRBns(
  formula,
  vardir,
  weight,
  cluster,
  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
cluster	vector containing cluster of auxiliary variable
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.eblupRB : a dataframe containing ratio benchmark estimators

fit	a list contining 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 ratio benchmark estimation

Examples

## load dataset
data(datamsaeRBns)

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

## Using parameter 'data'
est_sae = est_saeRBns(Y1 ~ X1 + X2, v1, w1, c1, data = datamsaeRBns)

## Without parameter 'data'
est_sae = est_saeRBns(datamsaeRBns$Y1 ~ datamsaeRBns$X1 + datamsaeRBns$X2,
datamsaeRBns$v1, datamsaeRBns$w1, datamsaeRBns$c1)

## Return
est_sae$eblup$est.eblupRB # to see the Ratio Benchmark estimators

msaeRB documentation built on June 13, 2021, 1:06 a.m.