est_msaeRBns: EBLUPs Ratio Benchmarking for Non Sampled Area based on a...
In msaeRB: Ratio Benchmarking for Multivariate Small Area Estimation

Description Usage Arguments Value Examples

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

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

`formula`	an object of class list of formula describe the fitted models
`vardir`	matrix containing sampling variances of direct estimators. The order is: `var1, cov12, ..., cov1r, var2, cov23, ..., cov2r, ..., cov(r-1)(r), var(r)`
`weight`	matrix containing proportion of units in small areas. The order is: `w1, w2, ..., w(r)`
`cluster`	matrix containing cluster of auxiliary variables. The order is: `c1, c2, ..., c(r)`
`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

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

## load dataset
data(datamsaeRBns)

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

## Using parameter 'data'
Fo = list(f1 = Y1 ~ X1 + X2,
          f2 = Y2 ~ X1 + X2,
          f3 = Y3 ~ X1 + X2)
vardir = c("v1", "v12", "v13", "v2", "v23", "v3")
weight = c("w1", "w2", "w3")
cluster = c("c1", "c2", "c3")

est_msae = est_msaeRBns(Fo, vardir, weight, cluster, data = datamsaeRBns)

## Without parameter 'data'
Fo = list(f1 = datamsaeRBns$Y1 ~ datamsaeRBns$X1 + datamsaeRBns$X2,
          f2 = datamsaeRBns$Y2 ~ datamsaeRBns$X1 + datamsaeRBns$X2,
          f3 = datamsaeRBns$Y3 ~ datamsaeRBns$X1 + datamsaeRBns$X2)
vardir = datamsaeRBns[, c("v1", "v12", "v13", "v2", "v23", "v3")]
weight = datamsaeRBns[, c("w1", "w2", "w3")]
cluster = datamsaeRBns[, c("c1", "c2", "c3")]

est_msae = est_msaeRBns(Fo, vardir, weight, cluster)

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