# brunner.munzel.test: Brunner-Munzel Test for Stochastic Equality In vlyubchich/lawstat: Tools for Biostatistics, Public Policy, and Law

 brunner.munzel.test R Documentation

## 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. `NA`s 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. `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.

### Details

There exist discrepancies with \insertCiteBrunner_Munzel_2000;textuallawstat because there is a typo in the paper. The corrected version is in \insertCiteNeubert_Brunner_2007;textuallawstat (e.g., compare the estimates for the case study on pain scores). The current function follows \insertCiteNeubert_Brunner_2007;textuallawstat.

### 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 )`.

### Author(s)

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

### References

\insertAllCited

`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<Y)+.5*P(X=Y)
##        0.788961

``````

vlyubchich/lawstat documentation built on April 17, 2023, 12:47 a.m.