# subtitle_contingency_tab

##### Subtitle for categorical tests

Making text subtitle for contingency analysis (Pearson's chi-square test for independence for between-subjects design or McNemar's test for within-subjects design) or goodness of fit test for a single categorical variable.

##### Usage

```
subtitle_contingency_tab(data, main, condition = NULL, counts = NULL,
ratio = NULL, nboot = 100, paired = FALSE, stat.title = NULL,
legend.title = NULL, conf.level = 0.95, conf.type = "norm",
simulate.p.value = FALSE, B = 2000, bias.correct = FALSE, k = 2,
messages = TRUE, ...)
```subtitle_onesample_proptest(data, main, condition = NULL,
counts = NULL, ratio = NULL, nboot = 100, paired = FALSE,
stat.title = NULL, legend.title = NULL, conf.level = 0.95,
conf.type = "norm", simulate.p.value = FALSE, B = 2000,
bias.correct = FALSE, k = 2, 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.- main
The variable to use as the

**rows**in the contingency table.- condition
The variable to use as the

**columns**in the contingency table. Default is`NULL`

. If`NULL`

, one-sample proportion test (a goodness of fit test) will be run for the`main`

variable. Otherwise an appropriate association test will be run.- counts
A string naming a variable in data containing counts, or

`NULL`

if each row represents a single observation (Default).- ratio
A vector of proportions: the expected proportions for the proportion test (should sum to 1). Default is

`NULL`

, which means the null is equal theoretical proportions across the levels of the nominal variable. This means if there are two levels this will be`ratio = c(0.5,0.5)`

or if there are four levels this will be`ratio = c(0.25,0.25,0.25,0.25)`

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

`100`

).- paired
Logical indicating whether data came from a within-subjects or repeated measures design study (Default:

`FALSE`

). If`TRUE`

, McNemar's test subtitle will be returned. If`FALSE`

, Pearson's chi-square test will be returned.- stat.title
Title for the effect being investigated with the chi-square test. The default is

`NULL`

, i.e. no title will be added to describe the effect being shown. An example of a`stat.title`

argument will be something like`"main x condition"`

or`"interaction"`

.- legend.title
Title text for the legend.

- 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`

.- simulate.p.value
a logical indicating whether to compute p-values by Monte Carlo simulation.

- B
an integer specifying the number of replicates used in the Monte Carlo test.

- bias.correct
If

`TRUE`

, a bias correction will be applied to Cramer's*V*.- k
Number of digits after decimal point (should be an integer) (Default:

`k = 2`

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

`TRUE`

).- ...
Additional arguments (currently ignored).

##### Details

For more details about how the effect sizes and their confidence
intervals were computed, see documentation in `?rcompanion::cramerV`

,
`?rcompanion::cramerVFit`

, and `?rcompanion::cohenG`

.

##### See Also

##### Examples

```
# NOT RUN {
# ------------------------ association tests -----------------------------
set.seed(123)
# without counts data
ggstatsplot::subtitle_contingency_tab(
data = mtcars,
main = am,
condition = cyl,
nboot = 15
)
# with counts data
# in case of no variation, a `NULL` will be returned.
library(jmv)
as.data.frame(HairEyeColor) %>%
dplyr::filter(.data = ., Sex == "Male") %>%
subtitle_contingency_tab(
data = .,
main = Hair,
condition = Sex,
counts = Freq
)
# ------------------------ goodness of fit tests ---------------------------
# for reproducibility
set.seed(123)
# with counts
subtitle_contingency_tab(
data = as.data.frame(HairEyeColor),
main = Eye,
counts = Freq,
ratio = c(0.2, 0.2, 0.3, 0.3)
)
# in case of no variation, only sample size will be shown
subtitle_contingency_tab(
data = cbind.data.frame(x = rep("a", 10)),
main = x
)
# }
```

*Documentation reproduced from package ggstatsplot, version 0.0.12, License: GPL-3 | file LICENSE*