Computes the effect size for each control-test group pairing in `idx`

.
The resampling bootstrap distribution of the effect size is then subjected
to Bias-corrected and accelerated bootstrap (BCa) correction.

The following effect sizes `mean_diff`

, `median_diff`

, `cohens_d`

, `hedges_g`

and `cliffs_delta`

are used for most plot types.

## Usage

```
mean_diff(dabest_obj, perm_count = 5000)
median_diff(dabest_obj, perm_count = 5000)
cohens_d(dabest_obj, perm_count = 5000)
hedges_g(dabest_obj, perm_count = 5000)
cliffs_delta(dabest_obj, perm_count = 5000)
cohens_h(dabest_obj, perm_count = 5000)
```

## Arguments

- dabest_obj
A dabest_obj created by loading in dataset along with other specified parameters with the

`load()`

function.- perm_count
The number of reshuffles of control and test labels to be performed for each p-value.

## Value

Returns a `dabest_effectsize_obj`

list with 22 elements. The following are the elements contained within:

`raw_data`

The tidy dataset passed to`load()`

that was cleaned and altered for plotting.`idx`

The list of control-test groupings as initially passed to`load()`

.`delta_x_labels`

Vector containing labels for the x-axis of the delta plot.`delta_y_labels`

String label for the y-axis of the delta plot.`Ns`

List of labels for x-axis of the raw plot.`raw_y_labels`

Vector containing labels for the y-axis of the raw plot.`is_paired`

Boolean value determining if it is a paired plot.`is_colour`

Boolean value determining if there is a colour column for the plot.`paired`

Paired ("sequential" or "baseline") as initially passed to`load()`

.`resamples`

The number of resamples to be used to generate the effect size bootstraps.`control_summary`

Numeric value for plotting of control summary lines for float_contrast =`TRUE`

.`test_summary`

Numeric value for plotting of control summary lines for float_contrast =`TRUE`

.`ylim`

Vector containing the y limits for the raw plot.`enquo_x`

Quosure of x as initially passed to`load()`

.`enquo_y`

Quosure of y as initially passed to`load()`

.`enquo_id_col`

Quosure of id_col as initially passed to`load()`

.`enquo_colour`

Quosure of colour as initially passed to`load()`

.`proportional`

Boolean value as initially passed to`load()`

.`minimeta`

Boolean value as initially passed to`load()`

.`delta`

Boolean value as initially passed to`load()`

.`proportional_data`

List of calculations related to the plotting of proportion plots.`boot_result`

List containing values related to the calculation of the effect sizes, bootstrapping and BCa correction.`baseline_ec_boot_result`

List containing values related to the calculation of the effect sizes, bootstrapping and BCa correction for the baseline error curve.`permtest_pvals`

List containing values related to the calculations of permutation t tests and the corresponding p values, and p values for different types of effect sizes and different statistical tests.

## Details

The plot types listed under here are limited to use only the following effect sizes.

Proportion plots offers only

`mean_diff`

and`cohens_h`

.Mini-Meta Delta plots offers only

`mean_diff`

.

The other plots are able to use all given basic effect sizes as listed in the Description.

## Examples

```
# Loading of the dataset
data(non_proportional_data)
# Applying effect size to the dabest object
dabest_obj <- load(non_proportional_data,
x = Group, y = Measurement,
idx = c("Control 1", "Test 1")
)
dabest_obj.mean_diff <- mean_diff(dabest_obj)
# Printing dabest effectsize object
print(dabest_obj.mean_diff)
#> DABESTR v2023.9.12
#> ==================
#>
#> Good morning!
#> The current time is 06:03 AM on Tuesday December 12, 2023.
#>
#> The unpaired mean difference between Test 1 and Control 1 is 0.585 [95%CI 0.307, 0.869].
#> The p-value of the two-sided permutation t-test is 0.0022, calculated for legacy purposes only.
#>
#> 5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
#> Any p-value reported is the probability of observing the effect size (or greater),
#> assuming the null hypothesis of zero difference is true.
#> For each p-value, 5000 reshuffles of the control and test labels were performed.
#>
```