assoc: Association and Correlation Measures

In exams.forge: Support for Compiling Examination Tasks using the 'exams' Package

Association and Correlation Measures

Description

Compute association and correlation measures for categorical and ordinal data.

The following measures are implemented:

• nom.cc: Corrected contingency coefficient for nominal data.

• nom.cramer: Cramer's V (or Phi) for nominal data.

• ord.spearman: Spearman's rank correlation for ordinal data.

• ord.kendall: Kendall's rank correlation for ordinal data.

Usage

nom.cc(tab, correct = FALSE)

nom.cramer(tab, ...)

ord.spearman(tab, ...)

ord.kendall(tab, ...)

cc_coef(tab, correct = FALSE)

cramer_vf(tab, ...)

cramer_coef(tab, ...)

kendall_corr(tab, ...)

spearman_corr(tab, ...)

rs_corr(tab, ...)

Arguments

tab	A contingency table (matrix or table) with absolute frequencies.
correct	Logical, whether to apply a correction (default: FALSE). Only used for nom.cc.
...	Additional parameters passed to correlation functions.

Details

These functions provide common measures of association:

• Nominal data: nom.cc, nom.cramer.

• Ordinal data: ord.spearman, ord.kendall.

Value

A numeric value representing the association or correlation measure.

Examples

# Create a random contingency table
tab <- matrix(round(10 * runif(15)), ncol = 5)

# Nominal association
nom.cc(tab)
nom.cc(tab, correct = TRUE)
nom.cramer(tab)

# Ordinal correlation
ord.spearman(tab)
ord.kendall(tab)

# Using aliases
cc_coef(tab)
cramer_vf(tab)
spearman_corr(tab)
kendall_corr(tab)
rs_corr(tab)

exams.forge documentation built on Aug. 21, 2025, 5:41 p.m.