deffCR: Chen-Rust design effect

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/deffCR.R

Description

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

Usage

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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

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.

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

Author(s)

Richard Valliant, Jill A. Dever, Frauke Kreuter

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. (2013, chap. 14). Practical Tools for Designing and Weighting Survey Samples. New York: Springer.

See Also

deff, deffH, deffK, deffS

Examples

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require(sampling)
require(reshape)
data(MDarea.pop)
Ni <- table(MDarea.pop$TRACT)
m <- 10
probi <- m*Ni / sum(Ni)
    # select sample of clusters
set.seed(-780087528)
sam <- sampling::cluster(data=MDarea.pop, clustername="TRACT", size=m, method="systematic",
                pik=probi, description=TRUE)
    # extract data for the sample clusters
samclus <- getdata(MDarea.pop, sam)
samclus <- rename(samclus, c(Prob = "pi1"))
    # treat sample clusters as strata and select srswor from each
nbar <- 4
s <- sampling::strata(data = as.data.frame(samclus), stratanames = "TRACT",
            size = rep(nbar,m), method="srswor")
    # extracts the observed data
samdat <- getdata(samclus,s)
samdat <- rename(samdat, c(Prob = "pi2"))
    # add a fake stratum ID
H <- 2
nh <- m * nbar / H
stratum <- NULL
for (h in 1:H){
    stratum <- c(stratum, rep(h,nh))
}
wt <- 1/(samdat$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)

Example output

Loading required package: sampling
Loading required package: reshape
Number of selected clusters: 10 
Number of units in the population and number of selected units: 403997 67098 
$`strata components`
     stratum   deff.w    deff.c    deff.s
[1,]       1 1.409053 1.4344129 0.7461304
[2,]       2 1.295517 0.3680934 0.1701147

$`overall deff`
[1] 1.589175

PracTools documentation built on Aug. 4, 2020, 1:06 a.m.