RSSampling (version 1.0)

con.Mrss: Selecting a ranked set sample (classical or modified) with a concomitant variable

Description

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

Usage

con.Mrss(X,Y,m,r=1,type="r",sets=FALSE,concomitant=FALSE,p)

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. "r" for traditional RSS, "p" for Percentile RSS, "m" for Median RSS, "bg" for Balanced Groups RSS, "e" for Extreme RSS. (By default = "r")

sets

logical; if TRUE, ranked set samples are given with ranked sets (see rankedsets)

concomitant

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

p

Value of percentile for Percentile RSS method

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

Details

X and Y must be vectors and also they should be in same length. Value of percentile (p) must be between 0 and 1.

References

McIntyre, G. A. (1952). A method for unbiased selective sampling, using ranked sets. Australian Journal of Agricultural Research, 3(4), 385-390.

Samawi, H. M., Ahmed, M. S., & Abu-Dayyeh, W. (1996). Estimating the population mean using extreme ranked set sampling. Biometrical Journal, 38(5), 577-586.

Muttlak, H. A. (1997). Median ranked set sampling. Journal of Applied Statistical Sciences, 6(4), 245-255.

Muttlak, H. A. (2003). Modified ranked set sampling methods. Pakistan Journal Of Statistics, 19(3), 315-324.

Jemain, A. A., Al-Omari, A., & Ibrahim, K. (2008). Some variations of ranked set sampling. Electronic Journal of Applied Statistical Analysis, 1(1), 1-15.

See Also

Mrss, Rrss, Drss, con.Rrss

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[,3])

## Selecting modified ranked set samples
con.Mrss(xx, xy, m=5, r=3, type="r", concomitant=TRUE, sets=TRUE)
con.Mrss(xx, xy, m=4, r=7, type="m", concomitant=TRUE, sets=TRUE)
con.Mrss(xx, xy, m=5, r=2, type="e", concomitant=TRUE, sets=TRUE)
con.Mrss(xx, xy, m=8, r=3, type="p", concomitant=TRUE, sets=TRUE, p=0.25)
con.Mrss(xx, xy, m=6, r=5, type="bg", concomitant=TRUE, sets=TRUE)

# }