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Estimate an effect size

Source: R/effect_size_stats.R
effect_size.Rd

Quantify the magnitude of a group difference or association without adding the full hypothesis-test output produced by compare_groups(). For directional two-group measures, the Contrast column names the grouping variable and reports first group minus second group. Factor order is therefore meaningful. Cramer's V and omnibus measures have no direction.

Usage

effect_size(
  data,
  variable,
  group,
  method = c("auto", "hedges_g", "rank_biserial", "omega_squared", "epsilon_squared",
    "cramers_v"),
  paired = FALSE,
  id = NULL,
  conf.level = 0.95,
  interpretation = FALSE,
  digits = 2,
  format = c("table", "tibble")
)

Arguments

data

A data frame.

variable

Outcome or response variable.

group

Grouping variable.

method

Effect-size method: "auto", "hedges_g", "rank_biserial", "omega_squared", "epsilon_squared", or "cramers_v".

paired

Logical; whether the two-group comparison is paired.

id

Pair or participant identifier required when paired = TRUE.

conf.level

Confidence level for supported intervals.

interpretation

Logical; display a conventional magnitude label. These labels are generic teaching aids and are not clinical importance thresholds.

digits

Number of decimal places.

format

Output format: "table" (default) or a plain console "tibble".

Value

A publication-ready gt_effect object containing summary, table, inputs, method, assumptions, diagnostics, denominators, and notes.

Details

The default method = "auto" selects one measure from the outcome and comparison structure:

  • Hedges' g for two-group parametric comparisons

  • rank-biserial correlation for two-group rank comparisons

  • omega-squared for comparisons involving more than two continuous groups

  • epsilon-squared when a multi-group rank method is requested

  • Cramer's V for categorical associations

Risk ratios, odds ratios, and risk differences are intentionally not duplicated here; use crosstabs() for those epidemiological measures.

Examples

effect_size(mtcars, variable = mpg, group = am)

effect_size(
  mtcars,
  variable = mpg,
  group = am,
  method = "hedges_g",
  interpretation = TRUE
)