# wilcoxe.test: Wilcoxon Exact Test In PASWR2: Probability and Statistics with R, Second Edition

## Description

Performs exact one sample and two sample Wilcoxon tests on vectors of data

## Usage

 ```1 2 3 4 5 6 7 8``` ```wilcoxe.test( x, y = NULL, mu = 0, paired = FALSE, alternative = c("two.sided", "less", "greater"), conf.level = 0.95 ) ```

## Arguments

 `x` is a numeric vector of data values. Non-finite (i.e. infinite or missing) values will be omitted. `y` an optional numeric vector of data values `mu` a number specifying an optional parameter used to form the null hypothesis `paired` a logical indicating whether you want a paired test `alternative` a character string specifying the alternative hypothesis, must be one of `"two.sided"` (default), `"less"`, or `"greater"`. You can specify just the initial letter. `conf.level` confidence level of the interval

## Details

If only `x` is given, or if both `x` and `y` are given and `paired = TRUE`, a Wilcoxon signed rank test of the null hypothesis that the distribution of `x` (in the one sample case) or of `x - y` (in the paired two sample case) is symmetric about `mu` is performed.

Otherwise, if both `x` and `y` are given and `paired = FALSE`, a Wilcoxon rank sum test is done. In this case, the null hypothesis is that the distribution of `x` and `y` differ by a location shift `mu`, and the alternative is that they differ by some other location shift (and the one-sided alternative `"greater"` is that `x` is shifted to the right of `y`).

## Value

A list of class `htest`, containing the following components:

 `statistic` the value of the test statistic with a name describing it `p.value` the p-value for the test `null.value` the location parameter `mu` `alternative` a character string describing the alternative hypothesis `method` the type of test applied `data.name` a character string giving the names of the data `conf.int` a confidence interval for the location parameter `estimate` an estimate of the location parameter

## Note

The function is rather primitive and should only be used for problems with fewer than 19 observations as the memory requirements are rather large.

## Author(s)

Alan T. Arnholt <arnholtat@appstate.edu>

## References

• Gibbons, J.D. and Chakraborti, S. 1992. Nonparametric Statistical Inference. Marcel Dekker Inc., New York.

• Hollander, M. and Wolfe, D.A. 1999. Nonparametric Statistical Methods. New York: John Wiley & Sons.

`wilcox.test`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ``` # Wilcoxon Signed Rank Test PH <- c(7.2, 7.3, 7.3, 7.4) wilcoxe.test(PH, mu = 7.25, alternative = "greater") # Wilcoxon Signed Rank Test (Dependent Samples) with(data = AGGRESSION, wilcoxe.test(violence, noviolence, paired = TRUE, alternative = "greater")) # Wilcoxon Rank Sum Test x <- c(7.2, 7.2, 7.3, 7.3) y <- c(7.3, 7.3, 7.4, 7.4) wilcoxe.test(x, y) rm(PH, x, y) ```

PASWR2 documentation built on Sept. 5, 2021, 5:44 p.m.