compute_nmi: Normalized mutual information

In rolescry: Name-Blind Variable-Role Detection by Data Signature

Normalized mutual information

Description

Computes the normalized mutual information (NMI) between two discrete variables, a name-blind, information-theoretic measure of association in [0, 1]. NMI is the mutual information divided by the smaller of the two marginal Shannon entropies; it is 0 for independent variables and 1 for a perfect (deterministic) association, and unlike a raw chi-squared it is comparable across variables with different numbers of levels.

Usage

compute_nmi(x, y = NULL)

Arguments

x	Either a two-way contingency table / matrix of counts, or a vector (factor, character, or numeric) of the first variable.
y	Optional. If x is a vector, the second variable's vector; a contingency table is formed via table(x, y) on complete cases. Ignored when x is already a table/matrix.

Value

A single numeric in [0, 1]. Returns 0 for degenerate input (fewer than two rows/columns, zero total, or near-zero marginal entropy).

Examples

set.seed(1)
g <- sample(c("A", "B", "C"), 200, replace = TRUE)
y <- ifelse(g == "A", "yes", sample(c("yes", "no"), 200, replace = TRUE))
compute_nmi(g, y)            # > 0: g carries information about y
compute_nmi(g, sample(g))    # ~0: shuffled -> independent

rolescry documentation built on Oct. 21, 2026, 5:14 p.m.