teststat_MRSS {RSStest}

Median Ranked Set Sampling Test

Description

This function tests for the difference of two population means using ranked set sampling given in Özdemir, Ebegil and Gökpınar (2019).

Usage

teststat_MRSS(
  x1,
  x2,
  alpha = 0.05,
  alternative = "two-tailed",
  tn = 2000,
  table = TRUE
)

Arguments

x1

A (non-empty) numeric matrix (m1xr1) of median ranked set sample for Group 1 with set size m1 and cycle size r1.

x2

A (non-empty) numeric matrix (m2xr2) of median ranked set sample for Group 2 with set size m2 and cycle size r2.

alpha

A scalar value of the significance level for hypothesis testing used in the table. Default is 0.05.

alternative

A character string specifying the alternative hypothesis, must be one of "two-sided", "right" or "left". Can be abbreviated. Default is "two-sided".

tn

A scalar value of the number of repetitions of Monte Carlo simulation. Default is 2000.

table

A logical value that shows table gives the results of the hypothesis test are printed out. Default is TRUE.

Value

If table is TRUE the hypothesis test results table includes sample sizes, test statistics, p values and test results are printed out.

References

MacEachern, S. N., Öztürk, Ö., Wolfe, A. D. (2002). A new ranked set sample estimator of variance. Journal of the Royal Statistical Society: Series B., 64, Part 2 177–188.

Özturk, Ö., Balakrishnan N (2009) Exact two-sample nonparametric test for quantile difference between two populations based on ranked set samples. Ann Inst Stat Math 61(1):235–249

Özdemir, Y. A., Ebegil, M., & Gökpinar, F. (2017). A test statistic based on ranked set sampling for two normal means. Communications in Statistics-Simulation and Computation, 46(10), 8077-8085.

Özdemir, Y. A., Ebegil, M., & Gökpinar, F. (2019). A test statistic for two normal means with median ranked set sampling. Iranian Journal of Science and Technology, Transactions A: Science, 43(3), 1109-1126.

See Also

datagen_MRSS, datagen_RSS, teststat_RSS

Examples

x1=matrix(c(1,2.3, 3.4,4.5,5.6,4 ),nrow=3)
x2=matrix(c(2,3.2, 4.2,6.5,4.6,6 ),nrow=3)
teststat_MRSS(x1,x2,tn=1000)