con.Rrss {RSSampling}

Selecting a robust ranked set sample with a concomitant variable

Description

The con.Rrss function samples from a target population by using robust ranked set sampling methods. Ranking procedure of X is done by using the concomitant variable Y.

Usage

  con.Rrss(X,Y,m,r=1,type="l",sets=FALSE,concomitant=FALSE,alpha)

Arguments

X

A vector of target population

Y

A vector of concomitant variable from target population

m

Size of units in each set

r

Number of cycles. (By default =1)

type

type of the modified RSS method. "l" for L-RSS, "tb" for truncation-based RSS, "re" for robust extreme RSS. (By default ="l")

sets

logical; if TRUE, ranked set sample is given with ranked sets (see rankedsets)

concomitant

logical; if TRUE, ranked set sample of concomitant variable is given

alpha

Coefficient of the method

Details

X and Y must be vectors and also they should be in same length. Coefficient of the method must be between 0 and 0.5.

Value

corr.coef

the correlation coefficient between X and Y

var.of.interest

the sets of X, which are ranked by Y

concomitant.var.

the ranked sets of Y

sample.x

the obtained ranked set sample of X

sample.y

the obtained ranked set sample of Y

References

Al-Nasser, A. D. (2007). L ranked set sampling: A generalization procedure for robust visual sampling. Communications in Statistics-Simulation and Computation, 36(1), 33-43.

Al-Omari, A. I., & Raqab, M. Z. (2013). Estimation of the population mean and median using truncation-based ranked set samples. Journal of Statistical Computation and Simulation, 83(8), 1453-1471.

Al-Nasser, A. D., & Mustafa, A. B. (2009). Robust extreme ranked set sampling. Journal of Statistical Computation and Simulation 79(7), 859-867.

See Also

Mrss, Rrss, Drss, con.Mrss

Examples

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[,3])

## Selecting robust ranked set samples
con.Rrss(xx,xy,m=8,r=4,type="l", sets=TRUE, concomitant=TRUE, alpha=0.3)
con.Rrss(xx,xy,m=5,r=2,type="re", sets=TRUE, concomitant=TRUE, alpha=0.2)
con.Rrss(xx,xy,m=6,r=3,type="tb", sets=TRUE, concomitant=TRUE, alpha=0.25)