# mean_diff: Compute Effect Size(s) In dabestr: Data Analysis using Bootstrap-Coupled Estimation

## Description

For each pair of observations in a `dabest` object, a desired effect size can be computed. Currently there are five effect sizes available:

• The mean difference, given by `mean_diff()`.

• The median difference, given by `median_diff()`.

• Cohen's d, given by `cohens_d()`.

• Hedges' g, given by `hedges_g()`.

• Cliff's delta, given by `cliffs_delta()`.

## Usage

 ```1 2 3 4 5 6 7 8 9``` ```mean_diff(x, ci = 95, reps = 5000, seed = 12345) median_diff(x, ci = 95, reps = 5000, seed = 12345) cohens_d(x, ci = 95, reps = 5000, seed = 12345) hedges_g(x, ci = 95, reps = 5000, seed = 12345) cliffs_delta(x, ci = 95, reps = 5000, seed = 12345) ```

## Arguments

 `x` A `dabest` object, generated by the dabest() function. `ci` float, default 95. The level of the confidence intervals produced. The default `ci = 95` produces 95% CIs. `reps` integer, default 5000. The number of bootstrap resamples that will be generated. `seed` integer, default 12345. This specifies the seed used to set the random number generator. Setting a seed ensures that the bootstrap confidence intervals for the same data will remain stable over separate runs/calls of this function. See set.seed for more details.

## Value

A `dabest_effsize` object with 10 elements.

`data`

The dataset passed to dabest(), as a `tibble`.

`x` and `y`

The columns in `data` used to plot the x and y axes, respectively, as supplied to dabest(). These are quoted variables for tidy evaluation during the computation of effect sizes.

`idx`

The vector of control-test groupings as initially passed to dabest().

`is.paired`

Whether or not the experiment consists of paired (aka repeated) observations. Originally supplied to dabest().

`id.column`

If `is.paired` is `TRUE`, the column in `data` that indicates the pairing of observations. As passed to dabest().

`effect.size`

The effect size being computed. One of the following: ```c("mean_diff", "median_diff", "cohens_d", "hedges_g", "cliffs_delta")```.

`.data.name`

The variable name of the dataset passed to dabest().

`summary`

A tibble with a row for the mean or median of each group in the `x` column of `data`, as indicated in `idx`.

`result`

A tibble with the following 15 columns:

control_group, test_group

The name of the control group and test group respectively.

control_size, test_size

The number of observations in the control group and test group respectively.

effect_size

The effect size used.

paired

Is the difference paired (`TRUE`) or not (`FALSE`)?

difference

The effect size of the difference between the two groups.

variable

The variable whose difference is being computed, ie. the column supplied to `y`.

ci

The `ci` passed to this function.

bca_ci_low, bca_ci_high

The lower and upper limits of the Bias Corrected and Accelerated bootstrap confidence interval.

pct_ci_low, pct_ci_high

The lower and upper limits of the percentile bootstrap confidence interval.

bootstraps

The vector of bootstrap resamples generated.

• Generating estimation plots after effect size computation.

• The mathematical definitions and equations used to compute each effect size.

• The ` effsize ` package, which is used under the hood to compute Cohen's d, Hedges' g, and Cliff's delta.

• The ` boot() ` and ` boot.ci() ` functions from the `boot` package, which generate the (nonparametric) bootstrapped resamples used to compute the confidence intervals.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```# Loading data for unpaired (two independent groups) analysis. petal_widths <- dabest(iris, Species, Petal.Width, idx = c("setosa", "versicolor"), paired = FALSE) # Compute the mean difference. mean_diff(petal_widths) # Plotting the mean differences. mean_diff(petal_widths) %>% plot() ```

dabestr documentation built on July 13, 2020, 9:07 a.m.