lawstat (version 3.6)

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

Description

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
)

Value

A list of class "htest" with 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., \(P(X < Y) + 0.5 P(X =Y )\).

Arguments

x

the numeric vector of data values from the sample 1.

y

the numeric vector of data values from the sample 2.

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.

alpha

significance level, default is 0.05 for 95% confidence interval.

Author

Wallace Hui, Yulia R. Gel, Joseph L. Gastwirth, Weiwen Miao. This function was updated with the help of Dr. Ian Fellows.

Details

There exist discrepancies with Brunner_Munzel_2000;textuallawstat because there is a typo in the paper. The corrected version is in Neubert_Brunner_2007;textuallawstat (e.g., compare the estimates for the case study on pain scores). The current function follows Neubert_Brunner_2007;textuallawstat.

References

See Also

wilcox.test, pwilcox

Examples

## 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