effectsize.BFBayesFactor: Effect Sizes

Description

This function tries to return the best effect-size measure for the provided input model. See details.

Usage

# S3 method for BFBayesFactor
effectsize(model, type = NULL, ci = 0.95, test = NULL, verbose = TRUE, ...)
effectsize(model, ...)
# S3 method for aov
effectsize(model, type = NULL, ...)
# S3 method for htest
effectsize(model, type = NULL, verbose = TRUE, ...)

Value

A data frame with the effect size (depending on input) and and its CIs (CI_low and CI_high).

Arguments

model: An object of class htest, or a statistical model. See details.
type: The effect size of interest. See details.
ci: Value or vector of probability of the CI (between 0 and 1) to be estimated. Default to .95 (95%).
test: The indices of effect existence to compute. Character (vector) or list with one or more of these options: "p_direction" (or "pd"), "rope", "p_map", "equivalence_test" (or "equitest"), "bayesfactor" (or "bf") or "all" to compute all tests. For each "test", the corresponding bayestestR function is called (e.g. rope() or p_direction()) and its results included in the summary output.
verbose: Toggle warnings and messages on or off.
...: Arguments passed to or from other methods. See details.

Details

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", or one of "p_superiority", "u1", "u2", "u3", "overlap".
- A Chi-squared tests of independence or Fisher's Exact Test, depending on type: "cramers_v" (default), "tschuprows_t", "phi", "cohens_w", "pearsons_c", "cohens_h", "oddsratio", or "riskratio".
- A Chi-squared tests of goodness-of-fit, depending on type: "fei" (default) "cohens_w", "pearsons_c"
- 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 Wilcoxon test depending on type: returns "rank_biserial" correlation (default) or one of "p_superiority", "vda", "u2", "u3", "overlap".
- A Kruskal-Wallis test depending on type: "epsilon" (default) or "eta".
- A Friedman test returns Kendall's W. (Where applicable, ci and alternative are taken from the htest if not otherwise provided.)
For an object of class BFBayesFactor, using bayestestR::describe_posterior(),
- A t-test depending on type: "cohens_d" (default) or one of "p_superiority", "u1", "u2", "u3", "overlap".
- A correlation test returns r.
- A contingency table test, depending on type: "cramers_v" (default), "phi", "tschuprows_t", "cohens_w", "pearsons_c", "cohens_h", "oddsratio", or "riskratio".
- A proportion test returns p.
Objects of class anova, aov, aovlist or afex_aov, depending on type: "eta" (default), "omega" or "epsilon" -squared, "f", or "f2".
Other objects are passed to parameters::standardize_parameters().

For statistical models it is recommended to directly use the listed functions, for the full range of options they provide.

Examples

Run this code


## Hypothesis Testing
## ------------------
data("Music_preferences")
Xsq <- chisq.test(Music_preferences)
effectsize(Xsq)
effectsize(Xsq, type = "cohens_w")

Tt <- t.test(1:10, y = c(7:20), alternative = "less")
effectsize(Tt)

Aov <- oneway.test(extra ~ group, data = sleep, var.equal = TRUE)
effectsize(Aov)
effectsize(Aov, type = "omega")

Wt <- wilcox.test(1:10, 7:20, mu = -3, alternative = "less")
effectsize(Wt)
effectsize(Wt, type = "u2")

## Bayesian Hypothesis Testing
## ---------------------------
# \donttest{
if (require(BayesFactor)) {
  bf_prop <- proportionBF(3, 7, p = 0.3)
  effectsize(bf_prop)

  bf_corr <- correlationBF(attitude$rating, attitude$complaints)
  effectsize(bf_corr)

  data(raceDolls)
  bf_xtab <- contingencyTableBF(raceDolls, sampleType = "poisson", fixedMargin = "cols")
  effectsize(bf_xtab)
  effectsize(bf_xtab, type = "oddsratio")

  bf_ttest <- ttestBF(sleep$extra[sleep$group == 1],
    sleep$extra[sleep$group == 2],
    paired = TRUE, mu = -1
  )
  effectsize(bf_ttest)
}
# }

## 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")