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_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.
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).
subset_list
A list of character vectors, each representing a subset of variable names.
avg_corr
Numeric vector: average correlation of each subset.
min_corr
Numeric vector: minimum correlation of each subset.
max_corr
Numeric vector: maximum correlation of each subset.
names
Character vector of variable names in the original matrix.
threshold
Numeric threshold used for correlation filtering.
forced_in
Character vector of variables that were forced into all subsets.
search_type
Character: the search algorithm used (e.g., "els", "bron-kerbosch").
cor_method
Character: the correlation method used.
n_rows_used
Integer: 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