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Pearson's contingency coefficient

Source: R/assoc.R
contingency_coef.Rd

contingency_coef() computes Pearson's contingency coefficient C for a two-way contingency table.

Usage

contingency_coef(
  x,
  detail = FALSE,
  conf_level = 0.95,
  digits = 3L,
  .include_se = FALSE
)

Arguments

x

A contingency table (of class table).

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.

Value

Same structure as cramer_v(): a scalar when detail = FALSE, a named vector when detail = TRUE. The p-value tests the null hypothesis of no association (Pearson chi-squared test). CI values are NA because no standard asymptotic SE exists for C.

Details

The contingency coefficient is \(C = \sqrt{\chi^2 / (\chi^2 + n)}\). It ranges from 0 (independence) to a maximum that depends on the table dimensions. No standard asymptotic standard error exists, so the confidence interval is not computed.

See also

cramer_v(), assoc_measures()

Examples

tab <- table(sochealth$smoking, sochealth$education)
contingency_coef(tab)
#> [1] 0.1344361