In easystats/see: Model Visualisation Toolbox for 'easystats' and 'ggplot2'

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5,
  warning = FALSE,
  message = FALSE
)

pkgs <- c("effectsize", "ggplot2", "poorman")
successfully_loaded <- vapply(pkgs, requireNamespace, FUN.VALUE = logical(1L), quietly = TRUE)
can_evaluate <- all(successfully_loaded)

if (can_evaluate) {
  knitr::opts_chunk$set(eval = TRUE)
  vapply(pkgs, require, FUN.VALUE = logical(1L), quietly = TRUE, character.only = TRUE)
} else {
  knitr::opts_chunk$set(eval = FALSE)
}

This vignette can be referred to by citing the package:

citation("see")

Introduction

The effectsize package in easystats provides 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.

For more, see: https://easystats.github.io/effectsize/

Setup

library(effectsize)
library(see)
data(mtcars)
data(iris)

Effect size tables

(related function documentation)

aov(mpg ~ factor(am) * factor(cyl), data = mtcars) %>%
  eta_squared() %>%
  plot()


t_to_d(t = c(1, -1.3, -3, 2.3), df_error = c(40, 35, 40, 85)) %>%
  plot()

Equivalence tests

(related function documentation)

aov(mpg ~ factor(am) * factor(cyl), data = mtcars) %>%
  eta_squared() %>%
  equivalence_test(range = 0.3) %>%
  plot()


t_to_d(t = c(1, -1.3, -3, 2.3), df_error = c(40, 35, 40, 85)) %>%
  equivalence_test(range = 1) %>%
  plot()