mse_saeRBns: 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 univariate non sampled area in small area estimation

Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```mse_saeRBns( 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 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` `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``` ```## load dataset data(datamsaeRBns) # Compute MSE EBLUP and Ratio Benchmark ## Using parameter 'data' mse_sae = mse_saeRBns(Y1 ~ X1 + X2, v1, w1, c1, data = datamsaeRBns) ## Without parameter 'data' mse_sae = mse_saeRBns(datamsaeRBns\$Y1 ~ datamsaeRBns\$X1 + datamsaeRBns\$X2, datamsaeRBns\$v1, datamsaeRBns\$w1, datamsaeRBns\$c1) ## Return mse_sae\$pbmse.eblupRB # to see the MSE Ratio Benchmark estimators ```

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