# mse_msaeRBns: Parametric Bootstrap Mean Squared Error Estimators of Ratio... In msaeRB: Ratio Benchmarking for Multivariate Small Area Estimation

## Description

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

## Usage

 1 2 3 4 5 6 7 8 9 10 11 mse_msaeRBns( formula, vardir, weight, cluster, samevar = FALSE, B = 1000, 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.eblupRB parametric bootstrap mean squared error estimates of the ratio benchmark running.time time for running function

## Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 ## load dataset data(datamsaeRBns) # Compute MSE EBLUP and Ratio 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_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 = , c("v1", "v12", "v13", "v2", "v23", "v3")] weight = , c("w1", "w2", "w3")] cluster = , c("c1", "c2", "c3")] mse_msae = mse_msaeRBns(Fo, vardir, weight, cluster) ## Return mse_msae\$pbmse.eblupRB # to see the MSE of Ratio Benchmark

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