Perform the Baumgartner-Weiss-Schindler hypothesis test.
Perform the Baumgartner-Weiss-Schindler hypothesis test.
bws_test(x, y, method = c("default", "BWS", "Neuhauser", "B1", "B2", "B3",
"B4", "B5"), alternative = c("two.sided", "greater", "less"))x |
a vector of the first sample. |
y |
a vector of the first sample. |
method |
a character string specifying the test statistic to use. should be one of the following:
Only Neuhauser's test supports one-sided alternatives. |
alternative |
a character string specifying the alternative hypothesis,
must be one of “two.sided” (default), “greater” or
“less”. You can specify just the initial letter.
“greater” corresponds to testing whether the survival function
of |
Object of class htest, a list of the test statistic,
the p-value, and the method noted.
The code will happily compute Murakami's B_3 through B_5 for large sample sizes, even though nominal coverage is not achieved. A warning will be thrown. User assumes all risk relying on results from this function.
Steven E. Pav shabbychef@gmail.com
W. Baumgartner, P. Weiss, H. Schindler, 'A nonparametric test for the general two-sample problem', Biometrics 54, no. 3 (Sep., 1998): pp. 1129-1135. http://doai.io/10.2307/2533862
# under the null set.seed(123) x <- rnorm(100) y <- rnorm(100) hval <- bws_test(x,y) # under the alternative set.seed(123) x <- rnorm(100) y <- rnorm(100,mean=1.0) hval <- bws_test(x,y) show(hval) stopifnot(hval$p.value < 0.05) # under the alternative with a one sided test. set.seed(123) x <- rnorm(100) y <- rnorm(100,mean=0.7) hval <- bws_test(x,y,alternative='less') show(hval) stopifnot(hval$p.value < 0.01) hval <- bws_test(x,y,alternative='greater') stopifnot(hval$p.value > 0.99) hval <- bws_test(x,y,alternative='two.sided') stopifnot(hval$p.value < 0.05)
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