lawstat (version 2.4.1)

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

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.

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

References

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.

See Also

wilcox.test, pwilcox

Examples

## 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