Effect Size
This function tries to return the best effect-size measure for the provided input model. See details.
effectsize(model, ...) ## S3 method for class 'BFBayesFactor' effectsize(model, type = NULL, verbose = TRUE, ...) ## S3 method for class 'aov' effectsize(model, type = NULL, ...) ## S3 method for class 'htest' effectsize(model, type = NULL, verbose = TRUE, ...)
model |
An object of class |
... |
Arguments passed to or from other methods. See details. |
type |
The effect size of interest. See details. |
verbose |
Toggle warnings and messages on or off. |
For an object of class htest, data is extracted via insight::get_data(), and passed to the relevant function according to:
A t-test depending on type: "cohens_d" (default), "hedges_g".
A correlation test returns r.
A Chi-squared tests of independence or goodness-of-fit, depending on type: "cramers_v" (default), "phi" or "cohens_w", "cohens_h", "oddsratio", or "riskratio".
A One-way ANOVA test, depending on type: "eta" (default), "omega" or "epsilon" -squared, "f", or "f2".
A McNemar test returns Cohen's g.
A Fisher's Exact test (in the 2x2 case) returns Odds ratio.
A Wilcoxon test returns rank biserial correlation.
A Kruskal-Wallis test returns rank Epsilon squared.
A Friedman test returns Kendall's W.
For an object of class BFBayesFactor, using bayestestR::describe_posterior(),
A t-test returns Cohen's d.
A correlation test returns r.
A contingency table test, depending on type: "cramers_v" (default), "phi" or "cohens_w", "cohens_h", "oddsratio", or "riskratio".
Objects of class anova, aov, or aovlist, depending on type: "eta" (default), "omega" or "epsilon" -squared, "f", or "f2".
Other objects are passed to standardize_parameters().
For statistical models it is recommended to directly use the listed functions, for the full range of options they provide.
A data frame with the effect size (depending on input) and and its
CIs (CI_low and CI_high).
Other effect size indices:
cohens_d(),
eta_squared(),
phi(),
rank_biserial(),
standardize_parameters()
## Hypothesis Testing
## ------------------
contingency_table <- as.table(rbind(c(762, 327, 468), c(484, 239, 477), c(484, 239, 477)))
Xsq <- chisq.test(contingency_table)
effectsize(Xsq)
effectsize(Xsq, type = "phi")
Ts <- t.test(1:10, y = c(7:20))
effectsize(Ts)
Aov <- oneway.test(extra ~ group, data = sleep, var.equal = TRUE)
effectsize(Aov)
effectsize(Aov, type = "omega")
## Bayesian Hypothesis Testing
## ---------------------------
if (require(BayesFactor)) {
bf1 <- ttestBF(mtcars$mpg[mtcars$am == 1], mtcars$mpg[mtcars$am == 0])
effectsize(bf1, test = NULL)
bf2 <- correlationBF(attitude$rating, attitude$complaints)
effectsize(bf2, test = NULL)
data(raceDolls)
bf3 <- contingencyTableBF(raceDolls, sampleType = "poisson", fixedMargin = "cols")
effectsize(bf3, test = NULL)
effectsize(bf3, type = "oddsratio", test = NULL)
}
## Models and Anova Tables
## -----------------------
fit <- lm(mpg ~ factor(cyl) * wt + hp, data = mtcars)
effectsize(fit)
anova_table <- anova(fit)
effectsize(anova_table)
effectsize(anova_table, type = "epsilon")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.