This function performs a Mann-Whitney-U-Test (or Wilcoxon rank sum test,
see `wilcox.test`

and `wilcox_test`

)
for `x`

, for each group indicated by `grp`

. If `grp`

has more than two categories, a comparison between each combination of
two groups is performed.

The function reports U, p and Z-values as well as effect size r
and group-rank-means.

- data
A data frame.

- x
Bare (unquoted) variable name, or a character vector with the variable name.

- grp
Bare (unquoted) name of the cross-classifying variable, where

`x`

is grouped into the categories represented by`grp`

, or a character vector with the variable name.- distribution
Indicates how the null distribution of the test statistic should be computed. May be one of

`"exact"`

,`"approximate"`

or`"asymptotic"`

(default). See`wilcox_test`

for details.- out
Character vector, indicating whether the results should be printed to console (

`out = "txt"`

) or as HTML-table in the viewer-pane (`out = "viewer"`

) or browser (`out = "browser"`

), of if the results should be plotted (`out = "plot"`

, only applies to certain functions). May be abbreviated.- encoding
Character vector, indicating the charset encoding used for variable and value labels. Default is

`"UTF-8"`

. Only used when`out`

is not`"txt"`

.- file
Destination file, if the output should be saved as file. Only used when

`out`

is not`"txt"`

.

(Invisibly) returns a data frame with U, p and Z-values for each group-comparison as well as effect-size r; additionally, group-labels and groups' n's are also included.

This function calls the `wilcox_test`

with formula. If `grp`

has more than two groups, additionally a Kruskal-Wallis-Test (see `kruskal.test`

)
is performed.

Interpretation of effect sizes, as a rule-of-thumb:

small effect >= 0.1

medium effect >= 0.3

large effect >= 0.5

```
data(efc)
# Mann-Whitney-U-Tests for elder's age by elder's dependency.
mwu(efc, e17age, e42dep)
#>
#> # Mann-Whitney-U-Test
#>
#> Groups 1 = independent (n = 65) | 2 = slightly dependent (n = 224):
#> U = 7635.000, W = 5490.000, p = 0.003, Z = -3.020
#> effect-size r = 0.178
#> rank-mean(1) = 117.46
#> rank-mean(2) = 152.99
#>
#> Groups 1 = independent (n = 65) | 3 = moderately dependent (n = 304):
#> U = 8692.000, W = 6547.000, p < .001, Z = -4.273
#> effect-size r = 0.222
#> rank-mean(1) = 133.72
#> rank-mean(3) = 195.96
#>
#> Groups 1 = independent (n = 65) | 4 = severely dependent (n = 297):
#> U = 7905.500, W = 5760.500, p < .001, Z = -5.096
#> effect-size r = 0.268
#> rank-mean(1) = 121.62
#> rank-mean(4) = 194.60
#>
#> Groups 2 = slightly dependent (n = 224) | 3 = moderately dependent (n = 304):
#> U = 54664.500, W = 29464.500, p = 0.008, Z = -2.647
#> effect-size r = 0.115
#> rank-mean(2) = 244.04
#> rank-mean(3) = 279.58
#>
#> Groups 2 = slightly dependent (n = 224) | 4 = severely dependent (n = 297):
#> U = 51007.500, W = 25807.500, p < .001, Z = -4.386
#> effect-size r = 0.192
#> rank-mean(2) = 227.71
#> rank-mean(4) = 286.11
#>
#> Groups 3 = moderately dependent (n = 304) | 4 = severely dependent (n = 297):
#> U = 87819.500, W = 41459.500, p = 0.083, Z = -1.732
#> effect-size r = 0.071
#> rank-mean(3) = 288.88
#> rank-mean(4) = 313.41
#>
#> # Kruskal-Wallis-Test
#>
#> chi-squared = 38.476
#> df = 3
#> p < .001***
```