RRTCS (version 0.0.4)

ChaudhuriChristofides: Chaudhuri-Christofides model

Description

Computes the randomized response estimation, its variance estimation and its confidence interval through the Chaudhuri-Christofides model. The function can also return the transformed variable. The Chaudhuri-Christofides model can be seen in Chaudhuri and Christofides (2013, page 97).

Usage

ChaudhuriChristofides(z,mu,sigma,pi,type=c("total","mean"),cl,N=NULL,pij=NULL)

Arguments

z

vector of the observed variable; its length is equal to \(n\) (the sample size)

mu

vector with the means of the scramble variables

sigma

vector with the standard deviations of the scramble variables

pi

vector of the first-order inclusion probabilities

type

the estimator type: total or mean

cl

confidence level

N

size of the population. By default it is NULL

pij

matrix of the second-order inclusion probabilities. By default it is NULL

Value

Point and confidence estimates of the sensitive characteristics using the Chaudhuri-Christofides model. The transformed variable is also reported, if required.

Details

The randomized response given by the person \(i\) is \(z_i=y_iS_1+S_2\) where \(S_1,S_2\) are scramble variables, whose mean \(\mu\) and standard deviation \(\sigma\) are known.

References

Chaudhuri, A., and Christofides, T.C. (2013) Indirect Questioning in Sample Surveys. Springer-Verlag Berlin Heidelberg.

See Also

ChaudhuriChristofidesData

ChaudhuriChristofidesDatapij

ResamplingVariance

Examples

# NOT RUN {
N=417
data(ChaudhuriChristofidesData)
dat=with(ChaudhuriChristofidesData,data.frame(z,Pi))
mu=c(6,6)
sigma=sqrt(c(10,10))
cl=0.95
data(ChaudhuriChristofidesDatapij)
ChaudhuriChristofides(dat$z,mu,sigma,dat$Pi,"mean",cl,pij=ChaudhuriChristofidesDatapij)
# }