CorrCombo S4 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.

An S4 class that stores the result of correlation-based subset selection.

Usage

## S4 method for signature 'CorrCombo'
show(object)

Arguments

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.

See Also

corrSelect, MatSelect, corrSubset

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)
))

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

Value

An object of class CorrCombo, containing all valid subsets and their correlation statistics.

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 variable 1 into every subset (with warning if too correlated)
res4 <- MatSelect(cmat, threshold = 0.5, force_in = 1)

Coerce CorrCombo to a Data Frame

Description

Converts a CorrCombo object into a data frame of variable combinations.

Usage

## S3 method for class 'CorrCombo'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

Value

A data frame where each row corresponds to a subset of variables. Columns are named VarName01, VarName02, ..., up to the size of the largest subset. Subsets shorter than the maximum length are padded with NA.

See Also

CorrCombo

Examples

set.seed(1)
mat <- matrix(rnorm(100), ncol = 10)
colnames(mat) <- paste0("V", 1:10)
res <- corrSelect(cor(mat), threshold = 0.5)
as.data.frame(res)

Select Variable Subsets with Low Association (Mixed-Type Data Frame Interface)

Description

Identifies combinations of variables of any common data type (numeric, ordered factors, or unordered) factors—whose pair-wise association does not exceed a user-supplied threshold. The routine wraps MatSelect() and handles all pre-processing (type conversion, missing-row removal, constant-column checks) for typical data-frame/tibble/data-table inputs.

Usage

assocSelect(
  df,
  threshold = 0.7,
  method = NULL,
  force_in = NULL,
  method_num_num = c("pearson", "spearman", "kendall", "bicor", "distance", "maximal"),
  method_num_ord = c("spearman", "kendall"),
  method_ord_ord = c("spearman", "kendall"),
  ...
)

Arguments

Details

A single call can therefore screen a data set that mixes continuous and categorical features and return every subset whose internal associations are “sufficiently low” under the metric(s) you choose.

Rows containing NA are dropped with a warning; constant columns are treated as having zero association with every other variable.

The default association measure for each variable-type combination is:

numeric – numeric

method_num_num (default "pearson")

numeric – ordered

method_num_ord

numeric – unordered

"eta" (ANOVA \eta^{2})

ordered – ordered

method_ord_ord

ordered – unordered

"cramersv"

unordered – unordered

"cramersv"

All association measures are rescaled to [0,1] before thresholding. External packages are required for "bicor" (WGCNA), "distance" (energy), and "maximal" (minerva); an informative error is thrown if they are missing.

Value

A CorrCombo S4 object containing:

• all valid subsets,

• their summary association statistics,

• metadata (algorithm used, rows kept, forced-in variables, etc.).

The object’s show() method prints the association metrics that were actually used for this data set.

See Also

corrSelect(), MatSelect(), corrSubset()

Examples

df <- data.frame(
  height = rnorm(15, 170, 10),
  weight = rnorm(15, 70, 12),
  group  = factor(rep(LETTERS[1:3], each = 5)),
  score  = ordered(sample(c("low","med","high"), 15, TRUE))
)

## keep every subset whose internal associations <= 0.6
assocSelect(df, threshold = 0.6)

## use Kendall for all rank-based comparisons and force 'height' to appear
assocSelect(df,
            threshold       = 0.5,
            method_num_num  = "kendall",
            method_num_ord  = "kendall",
            method_ord_ord  = "kendall",
            force_in        = "height")

Select Variable Subsets with Low Correlation (Data Frame Interface)

Description

Identifies combinations of numeric variables in a data frame such that all pairwise absolute correlations fall below a specified threshold. This function is a wrapper around MatSelect() and accepts data frames, tibbles, or data tables with automatic preprocessing.

Usage

corrSelect(
  df,
  threshold = 0.7,
  method = NULL,
  force_in = NULL,
  cor_method = c("pearson", "spearman", "kendall", "bicor", "distance", "maximal"),
  ...
)

Arguments

Details

Only numeric columns are used for correlation analysis. Non‐numeric columns (factors, characters, logicals, etc.) are ignored, and their names and types are printed to inform the user. These can be optionally reattached later using corrSubset() with keepExtra = TRUE.

Rows with missing values are removed before computing correlations. A warning is issued if any rows are dropped.

The cor_method controls how the correlation matrix is computed:

• "pearson": Standard linear correlation.

• "spearman": Rank-based monotonic correlation.

• "kendall": Kendall's tau.

• "bicor": Biweight midcorrelation (WGCNA::bicor).

• "distance": Distance correlation (energy::dcor).

• "maximal": Maximal information coefficient (minerva::mine).

For "bicor", "distance", and "maximal", the corresponding package must be installed.

Value

An object of class CorrCombo, containing selected subsets and correlation statistics.

See Also

assocSelect(), MatSelect(), corrSubset()

Examples

set.seed(42)
n <- 100

# Create 20 variables: 5 blocks of correlated variables + some noise
block1 <- matrix(rnorm(n * 4), ncol = 4)
block2 <- matrix(rnorm(n), ncol = 1)
block2 <- matrix(rep(block2, 4), ncol = 4) + matrix(rnorm(n * 4, sd = 0.1), ncol = 4)
block3 <- matrix(rnorm(n * 4), ncol = 4)
block4 <- matrix(rnorm(n * 4), ncol = 4)
block5 <- matrix(rnorm(n * 4), ncol = 4)

df <- as.data.frame(cbind(block1, block2, block3, block4, block5))
colnames(df) <- paste0("V", 1:20)

# Add a non-numeric column to be ignored
df$label <- factor(sample(c("A", "B"), n, replace = TRUE))

# Basic usage
corrSelect(df, threshold = 0.8)

# Try Bron–Kerbosch with pivoting
corrSelect(df, threshold = 0.6, method = "bron-kerbosch", use_pivot = TRUE)

# Force in a specific variable and use Spearman correlation
corrSelect(df, threshold = 0.6, force_in = "V10", cor_method = "spearman")

Extract Variable Subsets from a CorrCombo Object

Description

Extracts one or more variable subsets from a CorrCombo object as data frames. Typically used after corrSelect or MatSelect to obtain filtered versions of the original dataset containing only low‐correlation variable combinations.

Usage

corrSubset(res, df, which = "best", keepExtra = FALSE)

Arguments

Value

A data frame if a single subset is extracted, or a list of data frames if multiple subsets are extracted. Each data frame contains the selected variables (and optionally extras).

Note

A warning is issued if any rows contain missing values in the selected variables.

See Also

corrSelect, MatSelect, CorrCombo

Examples

# Simulate input data
set.seed(123)
df <- as.data.frame(matrix(rnorm(100), nrow = 10))
colnames(df) <- paste0("V", 1:10)

# Compute correlation matrix
cmat <- cor(df)

# Select subsets using corrSelect
res <- corrSelect(cmat, threshold = 0.5)

# Extract the best subset (default)
corrSubset(res, df)

# Extract the second-best subset
corrSubset(res, df, which = 2)

# Extract the first three subsets
corrSubset(res, df, which = 1:3)

# Extract all subsets
corrSubset(res, df, which = "all")

# Extract best subset and retain additional numeric column
df$CopyV1 <- df$V1
corrSubset(res, df, which = 1, keepExtra = TRUE)