MatSelect: Select Variable Subsets with Low Correlation or Association...

In corrselect: Correlation-Based and Model-Based Predictor Pruning

Select Variable Subsets with Low Correlation or Association (Matrix Interface)

Description

Identifies all maximal subsets of variables from a symmetric matrix (typically a correlation matrix) such that all pairwise absolute values stay below a specified threshold. Implements exact algorithms such as Eppstein–Löffler–Strash (ELS) and Bron–Kerbosch (with or without pivoting).

Usage

MatSelect(mat, threshold = 0.7, method = NULL, force_in = NULL, ...)

Arguments

mat	A numeric, symmetric matrix with 1s on the diagonal (e.g. correlation matrix). Column names (if present) are used to label output variables.
threshold	A numeric scalar in (0, 1]. Maximum allowed absolute pairwise value. Defaults to 0.7.
method	Character. Selection algorithm to use. One of "els" or "bron-kerbosch". If not specified, the function chooses automatically: "els" when force_in is provided, otherwise "bron-kerbosch".
force_in	Optional integer vector of 1-based column indices to force into every subset. If the forced variables are themselves mutually correlated beyond threshold, MatSelect() warns (naming the offending pair) but still forces them into every returned subset – unlike corrPrune(), which treats this condition as infeasible and errors instead.
...	Additional arguments passed to the backend. The only supported argument is use_pivot (logical), for enabling pivoting in Bron–Kerbosch (ignored by ELS); any other named argument is an error.

Value

An object of class CorrCombo, containing all valid subsets and their correlation statistics. If every variable is pairwise correlated above threshold, the only valid maximal subsets are single variables; these are returned with min_corr/max_corr set to NA (there is no pair to summarize).

Subsets are ordered by decreasing size, then increasing average absolute correlation. Subsets tying on both keep the order the search enumerated them in, which is itself determined by mat and threshold, so the ordering is reproducible across platforms. It is this ordering that corrSubset(which = "best") and print() read.

Examples

set.seed(42)
mat <- matrix(rnorm(100), ncol = 10)
colnames(mat) <- paste0("V", 1:10)
cmat <- cor(mat)

# Default method (Bron-Kerbosch)
res1 <- MatSelect(cmat, threshold = 0.5)

# Bron–Kerbosch without pivot
res2 <- MatSelect(cmat, threshold = 0.5, method = "bron-kerbosch", use_pivot = FALSE)

# Bron–Kerbosch with pivoting
res3 <- MatSelect(cmat, threshold = 0.5, method = "bron-kerbosch", use_pivot = TRUE)

# Force variables 1 and 2 into every subset (warns if they are mutually
# correlated beyond the threshold; both are still forced in regardless)
res4 <- MatSelect(cmat, threshold = 0.5, force_in = c(1, 2))

corrselect documentation built on Aug. 23, 2026, 1:06 a.m.