msaeRB (version 0.2.1)

est_saeRB: EBLUPs Ratio Benchmarking based on a Univariate Fay-Herriot (Model 1)

Description

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

Usage

est_saeRB(
  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.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

# NOT RUN {
## load dataset
data(datamsaeRB)

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

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

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

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

# }