PracTools (version 1.2.5)

deffCR: Chen-Rust design effect

Description

Chen-Rust design effect for stratified, clustered, two-stage samples

Usage

deffCR(w, strvar=NULL, clvar=NULL, Wh=NULL, nest=FALSE, y)

Arguments

w

vector of weights for a sample

strvar

vector of stratum identifiers; equal in length to that of w.

clvar

vector of cluster identifiers; equal in length to that of w.

Wh

vector of the proportions of elements that are in each stratum; length is number of strata.

nest

Are cluster IDs numbered within strata (TRUE or FALSE)? If TRUE, cluster IDs can be restarted within strata, e.g., 1,2,3,1,2,3,...

y

vector of the sample values of an analysis variable

Value

A list with components:

strata components

Matrix with deff's due to weighting, clustering, and stratification for each stratum

overall deff

Design effect for full sample accounting for weighting, clustering, and stratification

Details

The Chen-Rust deff accounts for stratification, clustering, and unequal weights, but does not account for the use of any auxiliary data in the estimator of a mean. The Chen-Rust deff returned here is appropriate for stratified, two-stage sampling.

References

Chen, S. and Rust, K. (2017). An Extension of Kish's Formula for Design Effects to Two- and Three-Stage Designs with Stratification. Journal of Survey Statistics and Methodology, 5(2), 111-130.

Valliant, R., Dever, J., Kreuter, F. (2018, chap. 14). Practical Tools for Designing and Weighting Survey Samples, 2nd edition. New York: Springer.

See Also

deff, deffH, deffK, deffS

Examples

# NOT RUN {
require(sampling)
require(reshape)
data(MDarea.pop)
Ni <- table(MDarea.pop$TRACT)
m <- 20
probi <- m*Ni / sum(Ni)
    # select sample of clusters
set.seed(-780087528)
sam <- sampling::cluster(data=MDarea.pop, clustername="TRACT", size=m, method="systematic",
                pik=probi, description=TRUE)
    # extract data for the sample clusters
samclus <- getdata(MDarea.pop, sam)
samclus <- rename(samclus, c(Prob = "pi1"))
    # treat sample clusters as strata and select srswor from each
nbar <- 8
s <- sampling::strata(data = as.data.frame(samclus), stratanames = "TRACT",
            size = rep(nbar,m), method="srswor")
    # extracts the observed data
samdat <- getdata(samclus,s)
samdat <- rename(samdat, c(Prob = "pi2"))
    # add a fake stratum ID
H <- 2
nh <- m * nbar / H
stratum <- NULL
for (h in 1:H){
    stratum <- c(stratum, rep(h,nh))
}
wt <- 1/(samdat$pi1*samdat$pi2) * runif(m*nbar)
samdat <- cbind(subset(samdat, select = -c(Stratum)), stratum, wt)
deffCR(w = samdat$wt, strvar = samdat$stratum, clvar = samdat$TRACT, Wh=NULL, y=samdat$y2)
# }