# effsize_t_parametric

##### Calculating Cohen's *d* or Hedge's *g* (for between-/within- or one
sample designs).

Calculating Cohen's *d* or Hedge's *g* (for between-/within- or one
sample designs).

- Keywords
- internal

##### Usage

```
effsize_t_parametric(formula = NULL, data = NULL, mu = 0,
paired = FALSE, hedges.correction = TRUE, conf.level = 0.95,
var.equal = FALSE, noncentral = TRUE, tobject = NULL, ...)
```

##### Arguments

- formula
This function only accepts the variables in

`formula`

format e.g.`sleep_rem ~ vore`

or`~ vore`

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

**not**be accepted.- mu
If conducting a single sample test against a mean (Default:

`0`

).- 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`

.- hedges.correction
Logical indicating whether to apply Hedges correction, 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.- noncentral
Logical indicating whether to use non-central

*t*-distributions for computing the confidence intervals (Default:`TRUE`

).- tobject
Object with the

*t*-test specification.- ...
Additional arguments.

##### Details

This function is a rewrite of functionality provided in `lsr::cohensD`

and
`effsize::cohen.d`

.

References-

Cooper, Harris, Hedges, Larry V., Valentine, Jeffrey C., The Handbook of Research Synthesis and Meta-Analysis, 2009.

Cumming, G., Finch, S., A Primer On The Understanding, Use, And Calculation Of Confidence Intervals That Are Based On Central And Noncentral Distributions, Educational and Psychological Measurement, Vol. 61 No. 4, August 2001 532-574.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.) Hillsdale, NJ: Lawrence Erlbaum Associates.

David C. Howell (2010). Confidence Intervals on Effect Size

##### Examples

```
# NOT RUN {
#---------------- two-sample test ------------------------------------
# creating a smaller dataset
msleep_short <- dplyr::filter(
.data = ggplot2::msleep,
vore %in% c("carni", "herbi")
)
# with defaults
tobj1 <- t.test(
formula = sleep_rem ~ vore,
data = msleep_short
)
ggstatsplot:::effsize_t_parametric(
formula = sleep_rem ~ vore,
data = msleep_short,
tobject = tobj1
)
# changing defaults
tobj2 <- t.test(
formula = sleep_rem ~ vore,
data = msleep_short,
mu = 1,
paired = FALSE,
conf.level = .99
)
ggstatsplot:::effsize_t_parametric(
formula = sleep_rem ~ vore,
data = msleep_short,
mu = 1, # ignored in this case
paired = FALSE,
hedges.correction = TRUE,
conf.level = .99,
noncentral = FALSE,
tobject = tobj2
)
#---------------- one-sample test ------------------------------------
tobj3 <- t.test(
x = msleep_short$sleep_rem,
mu = 2,
conf.level = .90
)
ggstatsplot:::effsize_t_parametric(
formula = ~sleep_rem,
data = msleep_short,
mu = 2,
hedges.correction = TRUE,
conf.level = .90,
noncentral = TRUE,
tobject = tobj3
)
# }
```

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