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Computes, interpret and formats the effect sizes of a variety of models and statistical tests (see list of supported objects in report()).

Usage

report_effectsize(x, ...)

Arguments

x

The R object that you want to report (see list of of supported objects above).

...

Arguments passed to or from other methods.

Value

An object of class report_effectsize().

Examples

library(report)

# h-tests
report_effectsize(t.test(iris$Sepal.Width, iris$Sepal.Length))
#> Effect sizes were labelled following Cohen's (1988) recommendations. 
#> 
#> large (Cohen's d = -4.21, 95% CI [-4.66, -3.76])

# ANOVAs
report_effectsize(aov(Sepal.Length ~ Species, data = iris))
#> For one-way between subjects designs, partial eta squared is equivalent to eta squared.
#> Returning eta squared.
#> Effect sizes were labelled following Field's (2013) recommendations. 
#> 
#> large (Eta2 = 0.62, 95% CI [0.54, 1.00])

# GLMs
report_effectsize(lm(Sepal.Length ~ Petal.Length * Species, data = iris))
#> Effect sizes were labelled following Cohen's (1988) recommendations. 
#> 
#> small (Std. beta = 0.49, 95% CI [-1.03, 2.01])
#> large (Std. beta = 1.16, 95% CI [-0.01, 2.32])
#> large (Std. beta = -0.88, 95% CI [-2.41, 0.65])
#> large (Std. beta = -1.75, 95% CI [-3.32, -0.18])
#> medium (Std. beta = 0.61, 95% CI [-0.63, 1.85])
#> large (Std. beta = 0.97, 95% CI [-0.26, 2.19])
report_effectsize(glm(vs ~ disp, data = mtcars, family = "binomial"))
#> Effect sizes were labelled following Chen's (2010) recommendations. 
#> 
#> small (Std. beta = -0.85, 95% CI [-2.42, 0.27])
#> large (Std. beta = -2.68, 95% CI [-4.90, -1.27])
# \donttest{
# Mixed models
if (require("lme4")) {
  model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
  report_effectsize(model)
}
#> Effect sizes were labelled following Cohen's (1988) recommendations. 
#> 
#> very small (Std. beta = -1.46e-13, 95% CI [-1.49, 1.49])
#> large (Std. beta = 1.89, 95% CI [1.63, 2.16])

# Bayesian models
if (require("rstanarm")) {
  model <- stan_glm(Sepal.Length ~ Species, data = iris, refresh = 0, iter = 600)
  report_effectsize(model, effectsize_method = "basic")
}
#> Effect sizes were labelled following Cohen's (1988) recommendations. 
#> 
#> very small (Std. beta = 0.00, 95% CI [0.00, 0.00])
#> medium (Std. beta = 0.53, 95% CI [0.40, 0.64])
#> large (Std. beta = 0.90, 95% CI [0.78, 1.02])
# }