BWStest (version 0.2.3)

murakami_cdf: Murakami test statistic distribution.

Description

Estimates the CDF of the Murakami test statistics via permutations.

Usage

murakami_cdf(B, n1, n2, flavor = 0L, lower_tail = TRUE)

Value

a vector of the same size as B of the CDF under the null.

Arguments

B

the Murakami test statistic or a vector of the same.

n1

number of elements in the first sample.

n2

number of elements in the second sample.

flavor

the 'flavor' of the test statistic. See murakami_stat.

lower_tail

boolean, when TRUE returns the CDF, $\mathrm{\Psi }$, otherwise compute the upper tail, $1-\mathrm{\Psi }$, which is potentially more useful for hypothesis tests.

Author

Steven E. Pav

Details

Given the Murakami test statistic $B_j$ for $0\le j\le 5$, computes the CDF under the null that the two samples come from the same distribution. The CDF is computed by permutation test and memoization.

References

W. Baumgartner, P. Weiss, H. Schindler, 'A nonparametric test for the general two-sample problem', Biometrics 54, no. 3 (Sep., 1998): pp. 1129-1135. tools:::Rd_expr_doi("10.2307/2533862")

M. Neuhauser, 'Exact tests based on the Baumgartner-Weiss-Schindler Statistic--a survey', Statistical Papers 46, no. 1 (2005): pp. 1-30. tools:::Rd_expr_doi("10.1007/BF02762032")

M. Neuhauser, 'One-sided two-sample and trend tests based on a modified Baumgartner-Weiss-Schindler statistic', J. Nonparametric Statistics 13, no. 5 (2001): pp 729-739. tools:::Rd_expr_doi("10.1080/10485250108832874")

H. Murakami, 'K-sample rank test based on modified Baumgartner statistic and its power comparison', J. Jpn. Comp. Statist. 19, no. 1 (2006): pp. 1-13. tools:::Rd_expr_doi("10.5183/jjscs1988.19.1")

H. Murakami, 'Modified Baumgartner Statistics for the two-sample and multisample problems: a numerical comparison', J. Stat. Comp. and Sim. 82, no. 5 (2012): pp. 711-728. tools:::Rd_expr_doi("10.1080/00949655.2010.551516")

H. Murakami, 'Lepage type statistic based on the modified Baumgartner statistic', Comp. Stat. & Data Analysis 51 (2007): pp 5061-5067. tools:::Rd_expr_doi("10.1016/j.csda.2006.04.026")

See Also

murakami_stat.

Examples

# basic usage:
xv <- seq(0,4,length.out=101)
yv <- murakami_cdf(xv, n1=8, n2=6, flavor=1L)
plot(xv,yv)
zv <- bws_cdf(xv)
lines(xv,zv,col='red')

# check under the null:
# \donttest{
flavor <- 1L
n1 <- 8
n2 <- 8
set.seed(1234)
Bvals <- replicate(2000,murakami_stat(rnorm(n1),rnorm(n2),flavor))
# should be uniform:
plot(ecdf(murakami_cdf(Bvals,n1,n2,flavor)))
# }