CorrCombo: CorrCombo Class

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

CorrCombo Class

Description

Holds the result of corrSelect or MatSelect: a list of valid variable combinations and their correlation statistics.

This class stores all subsets of variables that meet the specified correlation constraint, along with metadata such as the algorithm used, correlation method(s), variables forced into every subset, and summary statistics for each combination.

Usage

CorrCombo(
  subset_list = list(),
  avg_corr = numeric(0),
  min_corr = numeric(0),
  max_corr = numeric(0),
  var_names = character(0),
  threshold = numeric(0),
  forced_in = character(0),
  search_type = character(0),
  cor_method = character(0),
  n_rows_used = integer(0)
)

## S3 method for class 'CorrCombo'
print(x, ...)

## S3 method for class 'CorrCombo'
summary(object, ...)

## S3 method for class 'summary.CorrCombo'
print(x, ...)

Arguments

subset_list	A list of character vectors. Each vector is a valid subset (variable names).
avg_corr	A numeric vector. Average absolute correlation within each subset.
min_corr	A numeric vector. Minimum pairwise absolute correlation in each subset.
max_corr	A numeric vector. Maximum pairwise absolute correlation within each subset.
var_names	Character vector of all variable names used for decoding.
threshold	Numeric scalar. The correlation threshold used during selection.
forced_in	Character vector. Variable names forced into each subset. Defaults to character().
search_type	Character string. One of "els" or "bron-kerbosch".
cor_method	Character string. Correlation method or "mixed". Defaults to character().
n_rows_used	Integer. Number of rows used for computing the correlation matrix. NA for matrix input.
x	A summary.CorrCombo object to be printed.
...	Additional arguments (ignored).
object	A CorrCombo object to summarize.

Details

Properties:

subset_list

A list of character vectors. Each vector is a valid subset (variable names).

avg_corr

A numeric vector. Average absolute correlation within each subset.

min_corr

A numeric vector. Minimum pairwise absolute correlation in each subset.

max_corr

A numeric vector. Maximum pairwise absolute correlation within each subset.

var_names

Character vector of all variable names used for decoding.

threshold

Numeric scalar. The correlation threshold used during selection.

forced_in

Character vector. Variable names that were forced into each subset.

search_type

Character string. One of "els" or "bron-kerbosch".

cor_method

Character string. Either a single method (e.g. "pearson") or "mixed" if multiple methods used.

n_rows_used

Integer. Number of rows used for computing the correlation matrix (after removing missing values). NA when constructed from a matrix directly (e.g. via MatSelect), since a matrix input has no associated row count.

See Also

corrSelect, MatSelect, corrSubset

Examples

print(CorrCombo(
  subset_list = list(c("A", "B"), c("A", "C")),
  avg_corr = c(0.2, 0.3),
  min_corr = c(0.1, 0.2),
  max_corr = c(0.3, 0.4),
  var_names = c("A", "B", "C"),
  threshold = 0.5,
  forced_in = character(),
  search_type = "els",
  cor_method = "mixed",
  n_rows_used = 5L
))

corrselect documentation built on July 19, 2026, 1:06 a.m.