goodman_kruskal_tau: Goodman-Kruskal's Tau

Description

goodman_kruskal_tau() computes Goodman-Kruskal's Tau, a proportional reduction in error (PRE) measure for nominal variables.

Usage

goodman_kruskal_tau(
  x,
  direction = c("row", "column"),
  detail = FALSE,
  conf_level = 0.95,
  digits = 3L,
  .include_se = FALSE
)

Value

Same structure as cramer_v(): a scalar when detail = FALSE, a named vector when detail = TRUE. The p-value tests H0: tau = 0 (Wald z-test).

Arguments

x: A contingency table (of class table).
direction: Direction of prediction: "row" (default, column predicts row) or "column" (row predicts column).
detail: Logical. If FALSE (default), return the estimate as a numeric scalar. If TRUE, return a named numeric vector including confidence interval and p-value.
conf_level: A number between 0 and 1 giving the confidence level (default 0.95). Only used when detail = TRUE. Set to NULL to omit the confidence interval.
digits: Number of decimal places used when printing the result (default 3). Only affects the detail = TRUE output.
.include_se: Internal parameter; do not use.

Details

Unlike lambda_gk(), Goodman-Kruskal's Tau uses all cell frequencies rather than only the modal categories, making it more sensitive to association patterns where lambda may be zero. Standard error formulas follow the DescTools implementations (Signorell et al., 2024); see cramer_v() for full references.

Examples

Run this code

tab <- table(sochealth$smoking, sochealth$education)
goodman_kruskal_tau(tab)
goodman_kruskal_tau(tab, direction = "column", detail = TRUE)