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# effectsize: Indices of Effect Size <img src="man/figures/logo.png" align="right" width="120" />
[](https://doi.org/10.21105/joss.02815)
[](https://cran.r-project.org/package=effectsize/)
[](https://cran.r-project.org/package=effectsize/)
***Significant is just not enough!***
The goal of this package is to provide utilities to work with indices of
effect size and standardized parameters, allowing computation and
conversion of indices such as Cohen’s *d*, *r*, odds-ratios, etc.
## Installation
[](https://cran.r-project.org/package=effectsize/)
[](https://easystats.r-universe.dev/)
[](https://github.com/easystats/effectsize/actions)
[](https://github.com/easystats/effectsize/actions/)
[](https://app.codecov.io/gh/easystats/effectsize?branch=main/)
Run the following to install the stable release of **effectsize** from
CRAN:
``` r
install.packages("effectsize")
```
Or you can install the latest development version from
[*R-universe*](https://easystats.r-universe.dev):
``` r
install.packages("effectsize", repos = "https://easystats.r-universe.dev/")
```
<!-- Or from *GitHub*: -->
<!-- ```{r, warning=FALSE, message=FALSE, eval=FALSE} -->
<!-- if (!require("remotes")) install.packages("remotes") -->
<!-- remotes::install_github("easystats/effectsize") -->
<!-- ``` -->
## Documentation
[](https://easystats.github.io/effectsize/)
[](https://easystats.github.io/blog/posts/)
[](https://easystats.github.io/effectsize/reference/index.html)
Click on the buttons above to access the package
[**documentation**](https://easystats.github.io/effectsize/) and the
[**easystats blog**](https://easystats.github.io/blog/posts/), and
check-out these vignettes:
- **Effect Sizes**
- [**Standardized
Differences**](https://easystats.github.io/effectsize/articles/standardized_differences.html)
- [**For Contingency
Tables**](https://easystats.github.io/effectsize/articles/xtabs.html)
- [**ANOVA Effect
Sizes**](https://easystats.github.io/effectsize/articles/anovaES.html)
- **Effect Sizes Conversion**
- [**Between Effect
Sizes**](https://easystats.github.io/effectsize/articles/convert_r_d_OR.html)
- [**Between Probabilities and Odds and Risk
Ratios**](https://easystats.github.io/effectsize/articles/convert_p_OR_RR.html)
- [**Effect Size from Test
Statistics**](https://easystats.github.io/effectsize/articles/from_test_statistics.html)
- [**Plotting Functions for the ‘effectsize’
Package**](https://easystats.github.io/see/articles/effectsize.html)
- [**Automated Interpretation of Indices of Effect
Size**](https://easystats.github.io/effectsize/articles/interpret.html)
# Features
This package is focused on indices of effect size. Check out the package
website for [**a full list of features and functions** provided by
`effectsize`](https://easystats.github.io/effectsize/reference/index.html).
``` r
library(effectsize)
options(es.use_symbols = TRUE) # get nice symbols when printing! (On Windows, requires R >= 4.2.0)
```
> **Tip:**
>
> **Instead of `library(effectsize)`, use `library(easystats)`.** **This
> will make all features of the easystats-ecosystem available.**
>
> **To stay updated, use `easystats::install_latest()`.**
## Effect Size Computation
The package provides functions to compute indices of effect size.
### Standardized Differences (Cohen’s *d*, Hedges’ *g*, Glass’ *delta*)
``` r
cohens_d(mpg ~ am, data = mtcars)
## Cohen's d | 95% CI
## --------------------------
## -1.48 | [-2.27, -0.67]
##
## - Estimated using pooled SD.
hedges_g(mpg ~ am, data = mtcars)
## Hedges' g | 95% CI
## --------------------------
## -1.44 | [-2.21, -0.65]
##
## - Estimated using pooled SD.
glass_delta(mpg ~ am, data = mtcars)
## Glass' Δ (adj.) | 95% CI
## --------------------------------
## -1.10 | [-1.80, -0.37]
```
`effectsize` also provides effect sizes for *paired standardized
differences*, *rank tests*, *common language effect sizes* and more…
### Contingency Tables
``` r
# Dependence
phi(mtcars$am, mtcars$vs)
## ϕ (adj.) | 95% CI
## -----------------------
## 0.00 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].
cramers_v(mtcars$am, mtcars$cyl)
## Cramer's V (adj.) | 95% CI
## --------------------------------
## 0.46 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].
# Goodness-of-fit
fei(table(mtcars$cyl), p = c(0.1, 0.3, 0.6))
## פ | 95% CI
## -------------------
## 0.27 | [0.17, 1.00]
##
## - Adjusted for uniform expected probabilities.
## - One-sided CIs: upper bound fixed at [1.00].
```
### ANOVAs (Eta<sup>2</sup>, Omega<sup>2</sup>, …)
``` r
model <- aov(mpg ~ factor(gear), data = mtcars)
eta_squared(model)
## # Effect Size for ANOVA
##
## Parameter | η² | 95% CI
## ----------------------------------
## factor(gear) | 0.43 | [0.18, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].
omega_squared(model)
## # Effect Size for ANOVA
##
## Parameter | ω² | 95% CI
## ----------------------------------
## factor(gear) | 0.38 | [0.14, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].
epsilon_squared(model)
## # Effect Size for ANOVA
##
## Parameter | ε² | 95% CI
## ----------------------------------
## factor(gear) | 0.39 | [0.14, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].
```
And more…
<!-- ### Regression Models (Standardized Parameters) -->
<!-- Importantly, `effectsize` also provides [advanced methods](https://easystats.github.io/effectsize/articles/standardize_parameters.html) to compute standardized parameters for regression models. -->
<!-- ```{r beta, warning=FALSE, message=FALSE} -->
<!-- m <- lm(rating ~ complaints + privileges + advance, data = attitude) -->
<!-- standardize_parameters(m) -->
<!-- ``` -->
<!-- Also, models can be re-fit with standardized data: -->
<!-- ```{r std-model, warning=FALSE, message=FALSE} -->
<!-- standardize(m) -->
<!-- ``` -->
## Effect Size Conversion
The package also provides ways of converting between different effect
sizes.
``` r
d_to_r(d = 0.2)
## [1] 0.0995
oddsratio_to_riskratio(2.6, p0 = 0.4)
## [1] 1.59
```
And for recovering effect sizes from test statistics.
``` r
F_to_d(15, df = 1, df_error = 60)
## d | 95% CI
## -------------------
## 1.00 | [0.46, 1.53]
F_to_r(15, df = 1, df_error = 60)
## r | 95% CI
## -------------------
## 0.45 | [0.22, 0.61]
F_to_eta2(15, df = 1, df_error = 60)
## η² (partial) | 95% CI
## ---------------------------
## 0.20 | [0.07, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].
```
## Effect Size Interpretation
The package allows for an automated interpretation of different indices.
``` r
interpret_r(r = 0.3)
## [1] "large"
## (Rules: funder2019)
```
Different sets of “rules of thumb” are implemented ([**guidelines are
detailed
here**](https://easystats.github.io/effectsize/articles/interpret.html))
and can be easily changed.
``` r
interpret_cohens_d(d = 0.45, rules = "cohen1988")
## [1] "small"
## (Rules: cohen1988)
interpret_cohens_d(d = 0.45, rules = "gignac2016")
## [1] "moderate"
## (Rules: gignac2016)
```
### Citation
In order to cite this package, please use the following citation:
- Ben-Shachar M, Lüdecke D, Makowski D (2020). effectsize: Estimation of
Effect Size Indices and Standardized Parameters. *Journal of Open
Source Software*, *5*(56), 2815. doi: 10.21105/joss.02815
Corresponding BibTeX entry:
@Article{,
title = {{e}ffectsize: Estimation of Effect Size Indices and Standardized Parameters},
author = {Mattan S. Ben-Shachar and Daniel Lüdecke and Dominique Makowski},
year = {2020},
journal = {Journal of Open Source Software},
volume = {5},
number = {56},
pages = {2815},
publisher = {The Open Journal},
doi = {10.21105/joss.02815},
url = {https://doi.org/10.21105/joss.02815}
}
# Contributing and Support
If you have any questions regarding the the functionality of the
package, you may either contact us via email or also [file an
issue](https://github.com/easystats/effectsize/issues/). Anyone wishing
to contribute to the package by adding functions, features, or in
another way, please follow [this
guide](https://github.com/easystats/effectsize/blob/main/.github/CONTRIBUTING.md/)
and our [code of
conduct](https://github.com/easystats/effectsize/blob/main/.github/CODE_OF_CONDUCT.md/).
|
