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.
An S4 class that stores the result of correlation-based subset selection.
Slots
subset_listA list of character vectors. Each vector is a valid subset (variable names).
avg_corrA numeric vector. Average absolute correlation within each subset.
min_corrA numeric vector. Minimum pairwise absolute correlation in each subset.
max_corrA numeric vector. Maximum pairwise absolute correlation within each subset.
namesCharacter vector of all variable names used for decoding.
thresholdNumeric scalar. The correlation threshold used during selection.
forced_inCharacter vector. Variable names that were forced into each subset.
search_typeCharacter string. One of
"els"or"bron-kerbosch".cor_methodCharacter string. Either a single method (e.g. "pearson") or "mixed" if multiple methods used.
n_rows_usedInteger. Number of rows used for computing the correlation matrix (after removing missing values).
subset_listA list of character vectors, each representing a subset of variable names.
avg_corrNumeric vector: average correlation of each subset.
min_corrNumeric vector: minimum correlation of each subset.
max_corrNumeric vector: maximum correlation of each subset.
namesCharacter vector of variable names in the original matrix.
thresholdNumeric threshold used for correlation filtering.
forced_inCharacter vector of variables that were forced into all subsets.
search_typeCharacter: the search algorithm used (e.g., "els", "bron-kerbosch").
cor_methodCharacter: the correlation method used.
n_rows_usedInteger: number of rows used to compute correlations.
Examples
show(new("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),
names = c("A", "B", "C"),
threshold = 0.5,
forced_in = character(),
search_type = "els",
cor_method = "mixed",
n_rows_used = as.integer(5)
))
#> CorrCombo object
#> -----------------
#> Method: els
#> Correlation: mixed
#> Threshold: 0.500
#> Subsets: 2 valid combinations
#> Data Rows: 5 used in correlation
#>
#> Top combinations:
#> No. Variables Avg Max Size
#> ------------------------------------------------------------
#> [ 1] A, B 0.200 0.300 2
#> [ 2] A, C 0.300 0.400 2