RSSampling (version 1.0)

regRSS: Regression estimator based on ranked set sampling

Description

It obtains the regression estimator for mean of interested population based on ranked set sampling.

Usage

regRSS(X,Y,mu_Y)

Arguments

X

An obtained ranked set sample for interested variable from target population

Y

An obtained ranked set sample for concomitant variable from target population

mu_Y

The known mean for population Y

Value

B

the B coefficient

X_reg

the regression estimate for mean of X based on ranked set sampling

Details

In this code, variable X and Y represents interested and concomitant variable, respectively, please note that notation is vice versa in the reference (Yu&Lam(1997)).

X and Y must be in same length.

References

Yu, P.L.H. and Lam, K. (1997). "Regression Estimator in Ranked Set Sampling". Biometrics, Vol. 53, No. 3, pp. 1070-1080.

Examples

# NOT RUN {
library("LearnBayes")
mu=c(1,12,2)
Sigma <- matrix(c(1,2,0,2,5,0.5,0,0.5,3), 3, 3)
x <- rmnorm(10000, mu, Sigma)
xx=as.numeric(x[,1])
xy=as.numeric(x[,2])
samplerss=con.rss(xx,xy,m=4,r=8,sets=FALSE,concomitant=TRUE)
sample.x=samplerss$sample.x
sample.y=samplerss$sample.y

regRSS(sample.x,sample.y,mu_Y=mean(xy))
# }