# subtitle_mann_nonparametric

##### Making text subtitle for the Mann-Whitney *U*-test
(between-subjects designs).

Making text subtitle for the Mann-Whitney *U*-test
(between-subjects designs).

##### Usage

```
subtitle_mann_nonparametric(data, x, y, paired = FALSE, k = 2,
conf.level = 0.95, conf.type = "norm", nboot = 100,
stat.title = NULL, messages = TRUE, ...)
```subtitle_t_nonparametric(data, x, y, paired = FALSE, k = 2,
conf.level = 0.95, conf.type = "norm", nboot = 100,
stat.title = NULL, messages = TRUE, ...)

##### Arguments

- data
A dataframe (or a tibble) from which variables specified are to be taken. A matrix or tables will

**not**be accepted.- x
The grouping variable from the dataframe

`data`

.- y
The response (a.k.a. outcome or dependent) variable from the dataframe

`data`

.- paired
Logical that decides whether the design is repeated measures/within-subjects (in which case one-way Friedman Rank Sum Test will be carried out) or between-subjects (in which case one-way Kruskal<U+2013>Wallis H test will be carried out). The default is

`FALSE`

.- k
Number of digits after decimal point (should be an integer) (Default:

`k = 2`

).- conf.level
Scalar between 0 and 1. If unspecified, the defaults return

`95%`

lower and upper confidence intervals (`0.95`

).- conf.type
A vector of character strings representing the type of intervals required. The value should be any subset of the values

`"norm"`

,`"basic"`

,`"perc"`

,`"bca"`

. For more, see`?boot::boot.ci`

.- nboot
Number of bootstrap samples for computing confidence interval for the effect size (Default:

`100`

).- stat.title
A character describing the test being run, which will be added as a prefix in the subtitle. The default is

`NULL`

. An example of a`stat.title`

argument will be something like`"Student's t-test: "`

.- messages
Decides whether messages references, notes, and warnings are to be displayed (Default:

`TRUE`

).- ...
Additional arguments.

##### Details

Two-sample Wilcoxon test, also known as Mann-Whitney test, is carried out.

For the two independent samples case, the Mann-Whitney *U*-test is
calculated and *W* is reported from *stats::wilcox.test*. For the paired
samples case the Wilcoxon signed rank test is run and *V* is reported.

Since there is no single commonly accepted method for reporting effect size
for these tests we are computing and reporting *r* (computed as
\(Z/\sqrt{N}\)) along with the confidence intervals associated with the
estimate. Note that *N* here corresponds to total *sample size* for
independent/between-subjects designs, and to total number of *pairs* (and
**not** *observations*) for repeated measures/within-subjects designs.

*Note:* The *stats::wilcox.test* function does not follow the
same convention as *stats::t.test*. The sign of the *V* test statistic
will always be positive since it is **the sum of the positive signed ranks**.
Therefore *V* will vary in magnitude but not significance based solely
on the order of the grouping variable. Consider manually
reordering your factor levels if appropriate as shown in the second example
below.

##### Examples

```
# NOT RUN {
set.seed(123)
# -------------- between-subjects design ------------------------
# simple function call
ggstatsplot::subtitle_mann_nonparametric(
data = sleep,
x = group,
y = extra
)
# creating a smaller dataset
msleep_short <- dplyr::filter(
.data = ggplot2::msleep,
vore %in% c("carni", "herbi")
)
# modifying few things
ggstatsplot::subtitle_mann_nonparametric(
data = msleep_short,
x = vore,
y = sleep_rem,
nboot = 200,
conf.level = 0.99,
conf.type = "bca"
)
# The order of the grouping factor matters when computing *V*
# Changing default alphabeical order manually
msleep_short$vore <- factor(msleep_short$vore,
levels = c("herbi", "carni")
)
# note the change in the reported *V* value but the identical
# value for *p* and the reversed effect size
ggstatsplot::subtitle_mann_nonparametric(
data = msleep_short,
x = vore,
y = sleep_rem
)
# -------------- within-subjects design ------------------------
# using dataset included in the package
ggstatsplot::subtitle_mann_nonparametric(
data = VR_dilemma,
x = modality,
y = score,
paired = TRUE,
conf.level = 0.90,
conf.type = "perc",
nboot = 200,
k = 5
)
# }
```

*Documentation reproduced from package ggstatsplot, version 0.0.11, License: GPL-3 | file LICENSE*