brunner.munzel.test {lawstat} | R Documentation |
This function performs the Brunner-Munzel test for stochastic equality of two samples, which is also known as the Generalized Wilcoxon Test. NAs from the data are omitted.
brunner.munzel.test(x, y, alternative = c("two.sided", "greater", "less"), alpha=0.05)
x |
the numeric vector of data values from the sample 1. |
y |
the numeric vector of data values from the sample 2. |
alpha |
confidence level, default is 0.05 for 95 interval. |
alternative |
a character string specifying the alternative hypothesis, must be one of 'two.sided' (default), 'greater' or 'less'. User can specify just the initial letter. |
A list containing the following components:
statistic |
the Brunner-Munzel test statistic. |
parameter |
the degrees of freedom. |
conf.int |
the confidence interval. |
p.value |
the p-value of the test. |
data.name |
a character string giving the name of the data. |
estimate |
an estimate of the effect size, i.e. P(X<Y)+.5*P(X=Y) |
Wallace Hui, Yulia R. Gel, Joseph L. Gastwirth, Weiwen Miao This function was updated with the help of Dr. Ian Fellows
Brunner, E. and Munzel, U. (2000) The Nonparametric
Behrens-Fisher Problem: Asymptotic Theory and a Small-Sample
Approximation, Biometrical Journal 42, 17-25.
Reiczigel, J., Zakarias, I. and Rozsa, L. (2005) A Bootstrap Test of Stochastic Equality of Two Populations, The American Statistician 59, 1-6.
wilcox.test
, pwilcox
## Pain score on the third day after surgery for 14 patients under ## the treatment \emph{Y} and 11 patients under the treatment \emph{N} ## (see Brunner and Munzel (2000)) Y<-c(1,2,1,1,1,1,1,1,1,1,2,4,1,1) N<-c(3,3,4,3,1,2,3,1,1,5,4) brunner.munzel.test(Y, N) ## Brunner-Munzel Test ## data: Y and N ## Brunner-Munzel Test Statistic = 3.1375, df = 17.683, p-value = 0.005786 ## 95 percent confidence interval: ## 0.5952169 0.9827052 ## sample estimates: ## P(X<Y)+.5*P(X=Y) ## 0.788961