Rrss {RSSampling}

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

Details

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

Value

sets

the ranked sets where ranked set sample is chosen from

sample

the obtained ranked set sample of X

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

 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)