MannWhitneyWilc-help: Mann-Whitney-Wilcoxon test with RSS

In RSSampling: Ranked Set Sampling

Description

In this function, we introduce the RSS version of the Mann-Whitney-Wilcoxon (MWW) test.

Usage

  mwwutestrss(X,Y,m,r,l,n,delta0=0,alpha=0.05,lambda=0.5,alternative="two.sided")

Arguments

X	First obtained ranked set sample
Y	Second obtained ranked set sample
m	Set size which was used while sampling X
r	Cycles size which was used while sampling X
l	Set size which was used while sampling Y
n	Cycles size which was used while sampling Y
delta0	The median value of difference in the null hypothesis. (By Default = 0)
alpha	The significance level (by default = 0.05).
lambda	constant in the variance formula of the test statistic, see Chen et. al.(2003)
alternative	Character string defining the alternative hypothesis, one of "two.sided", "less" or "greater" (by default = "two.sided")

Details

The test statistics and an approximate confidence intervals are constructed by using the normal approximation. Also note that, we assume that the ranking mechanism in the RSS is consistent. For more details please refer to Chen et. al.(2003, pg. 115-124).

There should be two datasets to compare as "X" and "Y", respectively.

Value

medianX	median value of the first sample
medianY	median value of the second sample
MWW.test.mwwUrss	The value of the Mann-Whitney-Wilcoxon test statistic
C.I.	the confidence interval of the Mann-Whitney-Wilcoxon test statistic
z.test	the z statistic for test
p.value	the p value for the test

References

Chen, Z., Bai Z., Sinha B. K. (2003). Ranked Set Sampling: Theory and Application. New York: Springer.

Examples

library("LearnBayes")
mu=c(1,1.2,2)
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[,2])
samplerss=con.rss(xx,xy,m=3,r=12,concomitant=TRUE)
sample.x=as.numeric(samplerss$sample.x)
sample.y=as.numeric(samplerss$sample.y)
mwwutestrss(sample.x,sample.y,m=3,r=12,l=3,n=12,delta0=0)
  

RSSampling documentation built on May 2, 2019, 4:28 a.m.