# subtitle_t_parametric

##### Making text subtitle for the t-test (between-/within-subjects designs).

Making text subtitle for the t-test (between-/within-subjects designs).

##### Usage

```
subtitle_t_parametric(data, x, y, paired = FALSE, effsize.type = "g",
effsize.noncentral = TRUE, conf.level = 0.95, var.equal = FALSE,
k = 2, ...)
```

##### 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
a logical indicating whether you want a paired t-test.

- effsize.type
Type of effect size needed for

*parametric*tests. The argument can be`"biased"`

(`"d"`

for Cohen's*d*for**t-test**;`"partial_eta"`

for partial eta-squared for**anova**) or`"unbiased"`

(`"g"`

Hedge's*g*for**t-test**;`"partial_omega"`

for partial omega-squared for**anova**)).- effsize.noncentral
Logical indicating whether to use non-central

*t*-distributions for computing the confidence interval for Cohen's*d*or Hedge's*g*(Default:`TRUE`

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

`95%`

lower and upper confidence intervals (`0.95`

).- var.equal
a logical variable indicating whether to treat the variances in the samples as equal. If

`TRUE`

, then a simple F test for the equality of means in a one-way analysis of variance is performed. If`FALSE`

, an approximate method of Welch (1951) is used, which generalizes the commonly known 2-sample Welch test to the case of arbitrarily many samples.- k
Number of digits after decimal point (should be an integer) (Default:

`k = 2`

).- ...
Additional arguments.

##### Details

Cohen's *d* is calculated in the traditional fashion as the
difference between means or mean minus *mu* divided by the estimated
standardized deviation. By default Hedge's correction is applied
(*N*-3)/(*N*-2.25) to produce *g*. For independent samples *t*-test, there
are two possibilities implemented. If the *t*-test did not make a
homogeneity of variance assumption, (the Welch test), the variance term
will mirror the Welch test, otherwise a pooled and weighted estimate is
used. If a paired samples *t*-test was requested, then effect size desired is
based on the standard deviation of the differences.

The computation of the confidence intervals defaults to a use of
non-central Student-*t* distributions (`effsize.noncentral = TRUE`

);
otherwise a central distribution is used.

When computing confidence intervals the variance of the effect size *d* or *g* is
computed using the conversion formula reported in Cooper et al. (2009)

`((n1+n2)/(n1*n2) + .5*d^2/df) * ((n1+n2)/df)`

(independent samples)`sqrt(((1 / n) + (d^2 / n)) * 2 * (1 - r))`

(paired case)

##### See Also

subtitle_t_parametric

##### Examples

```
# NOT RUN {
# creating a smaller dataset
msleep_short <- dplyr::filter(
.data = ggplot2::msleep,
vore %in% c("carni", "herbi")
)
# with defaults
subtitle_t_parametric(
data = msleep_short,
x = vore,
y = sleep_rem
)
# changing defaults
subtitle_t_parametric(
data = msleep_short,
x = vore,
y = sleep_rem,
var.equal = TRUE,
k = 2,
effsize.type = "d"
)
# }
```

*Documentation reproduced from package ggstatsplot, version 0.0.10, License: GPL-3 | file LICENSE*