mse_msaeOBns: Parametric Bootstrap Mean Squared Error Estimators of Optimum...
In yas-q/msaeOB: Optimum Benchmarking for Multivariate Small Area Estimation

mse_msaeOBns

R Documentation

Parametric Bootstrap Mean Squared Error Estimators of Optimum Benchmarking for Multivariate Non Sampled Area in Small Area Estimation

Description

Calculates the parametric bootstrap mean squared error estimates of optimum benchmarking for multivariate non sampled area in small area estimation

Usage

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

Arguments

`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`
`B`	number of bootstrap. Default is 1000
`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

`mse.eblup`	estimated mean squared errors of the EBLUPs for the small domains based on Prasad Rao
`pbmse.eblupOB`	parametric bootstrap mean squared error estimates of the optimum benchmark
`running.time`	time for running function

Examples


## load dataset
data(datamsaeOBns)

# Compute MSE EBLUP and Optimum Benchmark
# This is the long running example
## 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")

mse_msae = mse_msaeOBns(Fo, vardir, weight, cluster, data = datamsaeOBns)

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

mse_msae = mse_msaeOBns(Fo, vardir, weight, cluster)

## Return
mse_msae$pbmse.eblupOB # to see the MSE of Optimum Benchmark

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