effectsize.BFBayesFactor {effectsize}

Effect Sizes

Description

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

Usage

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

effectsize(model, ...)

## S3 method for class 'aov'
effectsize(model, type = NULL, ...)

## S3 method for class 'htest'
effectsize(model, type = NULL, verbose = TRUE, ...)

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 0.95 (⁠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 off warnings.

...

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".

    • For a Paired t-test: depending on type: "rm_rm", "rm_av", "rm_b", "rm_d", "rm_z".

  • 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", "riskratio", "arr", or "nnt".

  • 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", "arr", or "nnt".

  • 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.

Value

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

Plotting with see

The see package contains relevant plotting functions. See the plotting vignette in the see package.

See Also

vignette(package = "effectsize")

Examples

## 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)

Tt <- t.test(
  x = c(1.83, 0.50, 1.62, 2.48, 1.68, 1.88, 1.55, 3.06, 1.30),
  y = c(0.878, 0.647, 0.598, 2.05, 1.06, 1.29, 1.06, 3.14, 1.29),
  paired = TRUE
)
effectsize(Tt, type = "rm_b")

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", exact = FALSE)
effectsize(Wt)
effectsize(Wt, type = "u2")

## Models and Anova Tables
## -----------------------
fit <- lm(mpg ~ factor(cyl) * wt + hp, data = mtcars)
effectsize(fit, method = "basic")

anova_table <- anova(fit)
effectsize(anova_table)
effectsize(anova_table, type = "epsilon")


## Bayesian Hypothesis Testing
## ---------------------------
bf_prop <- BayesFactor::proportionBF(3, 7, p = 0.3)
effectsize(bf_prop)

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

data(RCT_table)
bf_xtab <- BayesFactor::contingencyTableBF(RCT_table, sampleType = "poisson", fixedMargin = "cols")
effectsize(bf_xtab)
effectsize(bf_xtab, type = "oddsratio")
effectsize(bf_xtab, type = "arr")

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