Description Usage Arguments Value Note Author(s) References See Also Examples

View source: R/brunner.munzel.test.R

This function performs 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.

1 2 | ```
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` |
significance level, default is 0.05 for 95% confidence interval. |

`alternative` |
a character string specifying the alternative
hypothesis, must be one of |

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 |

`data.name` |
a character string giving the name of the data. |

`estimate` |
an estimate of the effect size, i.e., |

There exist discrepancies with Brunner and Munzel (2000) because there is a typo in the paper. The corrected version is in Neubert and Brunner (2007) (e.g., compare the estimates for the case study on pain scores). The current R function follows Neubert and Brunner (2007).

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.

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
## 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
``` |

```
Loading required package: Hmisc
Loading required package: lattice
Loading required package: survival
Loading required package: Formula
Loading required package: ggplot2
Attaching package: 'Hmisc'
The following objects are masked from 'package:base':
format.pval, round.POSIXt, trunc.POSIXt, units
Loading required package: Kendall
Loading required package: mvtnorm
Loading required package: VGAM
Loading required package: stats4
Loading required package: splines
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
```

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