CorrCombo Class

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.

Value

CorrCombo() returns an S7 CorrCombo object holding the discovered subsets and their correlation statistics, with properties subset_list, avg_corr, min_corr, max_corr, var_names, threshold, forced_in, search_type, cor_method, and n_rows_used as described above.

print.CorrCombo() returns x, invisibly. Called to print a formatted summary of a CorrCombo object to the console.

summary.CorrCombo() returns a list of class summary.CorrCombo with 10 elements:

n_subsets: Integer. Number of maximal subsets found.
search_type: Character string. One of "els" or "bron-kerbosch".
cor_method: Character string. Correlation method used, or "mixed" if multiple methods were used.
threshold: Numeric scalar. The correlation threshold used during selection.
n_rows_used: Integer. Number of rows used to compute the correlation matrix, or NA for matrix input.
forced_in: Character vector. Variable names forced into every subset.
size_range: Integer vector of length 2 giving the smallest and largest subset sizes found (c(NA, NA) if no subsets were found).
size_median: Numeric scalar. Median subset size across all discovered subsets (NA if none found).
avg_corr_range: Numeric vector of length 2 giving the smallest and largest average absolute correlation across subsets (c(NA, NA) if none found).
n_max_size: Integer. Number of subsets that attain the largest size (0 if none found).

print.summary.CorrCombo() returns x, invisibly. Called to print a formatted summary.CorrCombo object to the console.

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.

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
))

Usage

Arguments

Value

Details

See also

Examples