Learn R Programming

lawstat (version 3.2)

brunner.munzel.test: The Brunner--Munzel Test for Stochastic Equality

Description

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.

Usage

brunner.munzel.test(x, y, alternative = c("two.sided", "greater",
"less"), alpha = 0.05)

Arguments

x

the numeric vector of data values from the sample 1.

y

the numeric vector of data values from the sample 2.

alpha

significance level, default is 0.05 for 95% confidence 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.

Value

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., \(P(X < Y) + 0.5 * P(X =Y )\)

References

Brunner, E. and Munzel, U. (2000). The nonparametric Behrens-Fisher problem: asymptotic theory and a small-sample approximation. Biometrical Journal 42: 17--25.

Neubert, K. and Brunner, E. (2007). A Studentized permutation test for the non-parametric Behrens-Fisher problem. Computational Statistics and Data Analysis 51: 5192--5204.

Reiczigel, J., Zakarias, I., and Rozsa, L. (2005). A bootstrap test of stochastic equality of two populations. The American Statistician 59: 1--6.

See Also

wilcox.test, pwilcox

Examples

Run this code
# NOT RUN {
## Pain score on the third day after surgery for 14 patients under
## the treatment Y and 11 patients under the treatment N
## (see Brunner and Munzel, 2000; Neubert and Brunner, 2007).

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
# }

Run the code above in your browser using DataLab