RSSampling (version 1.0)

Rrss: Selecting a robust ranked set sample

Description

The Rrss function samples from a target population by using robust ranked set sampling methods.

Usage

Rrss(X,m,r=1,type="l",sets=FALSE,alpha)

Arguments

X

A vector of 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 samples are given with ranked sets (see rankedsets)

alpha

Coefficient of the method

Value

sets

the ranked sets where ranked set sample is chosen from

sample

the obtained ranked set sample of X

Details

Target population X must be a vector. Coefficient of the method must be between 0 and 0.5.

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

con.Mrss, Rrss, Drss

Examples

# NOT RUN {
 data=rexp(10000)
 ## Selecting L-ranked set sample
 Rrss(data, m=8, r=3, sets=TRUE, alpha=0.2)
  ## Selecting Truncation-based ranked set sample
 Rrss(data, m=8, r=3, type="tb", sets=TRUE, alpha=0.1)
  ## Selecting Robust extreme ranked set sample
 Rrss(data, m=8, r=3, type="re", sets=TRUE, alpha=0.4)
# }