effectsize (version 0.4.1)

effectsize: Effect Size

Description

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

Usage

effectsize(model, ...)
# S3 method for htest
effectsize(model, type = NULL, ...)
# S3 method for BFBayesFactor
effectsize(model, type = NULL, ...)
# S3 method for aov
effectsize(model, type = NULL, ...)

Arguments

model

An object of class htest, or a statistical model. See details.

...

Arguments passed to or from other methods. See details.

type

The effect size of interest. See details.

Details

• For an object of class htest:

  • A t-test returns Cohen's d via t_to_d().

  • 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", "oddsratio", or "riskratio".

  • A One-way ANOVA test, depending on type: "eta" (default), "omega" or "epsilon" -squared, "f", or "f2".

• 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", "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.

See Also

Other effect size indices: cohens_d(), eta_squared(), phi(), standardize_parameters()

Examples

# NOT RUN {
## 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(iris$Sepal.Length, iris$Sepal.Width)
  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")
# }