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 = datamsaeRBns[, c("v1", "v12", "v13", "v2", "v23", "v3")] weight = datamsaeRBns[, c("w1", "w2", "w3")] cluster = datamsaeRBns[, 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.