Select Variable Subsets with Low Correlation (Data Frame Interface)

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

df: A data frame. Only numeric columns are used.
threshold: A numeric value in (0, 1). Maximum allowed absolute correlation. Defaults to 0.7.
method: Character. Selection algorithm to use. One of "els" or "bron-kerbosch". If not specified, the function chooses automatically: "els" when force_in is provided, otherwise "bron-kerbosch".
force_in: Optional character vector or numeric indices of columns to force into all subsets.
cor_method: Character string indicating which correlation method to use. One of "pearson" (default), "spearman", "kendall", "bicor", "distance", or "maximal".
...: Additional arguments passed to MatSelect(), e.g., use_pivot.

Value

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

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.

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)
#> The following variables were excluded from the correlation analysis:
#>   - label: unordered factor (excluded)
#> CorrCombo object
#> -----------------
#>   Method:      bron-kerbosch
#>   Correlation: pearson
#>   Threshold:   0.800
#>   Subsets:     4 valid combinations
#>   Data Rows:   100 used in correlation
#>   Pivot:       TRUE
#> 
#> Top combinations:
#>   No.  Variables                          Avg    Max    Size
#>   ------------------------------------------------------------
#>   [ 1] V1, V2, V3, V4, V7, V9, ...       0.075  0.241    17
#>   [ 2] V1, V2, V3, V4, V6, V9, ...       0.075  0.259    17
#>   [ 3] V1, V2, V3, V4, V8, V9, ...       0.075  0.269    17
#>   [ 4] V1, V2, V3, V4, V5, V9, ...       0.076  0.288    17

# Try Bron–Kerbosch with pivoting
corrSelect(df, threshold = 0.6, method = "bron-kerbosch", use_pivot = TRUE)
#> The following variables were excluded from the correlation analysis:
#>   - label: unordered factor (excluded)
#> CorrCombo object
#> -----------------
#>   Method:      bron-kerbosch
#>   Correlation: pearson
#>   Threshold:   0.600
#>   Subsets:     4 valid combinations
#>   Data Rows:   100 used in correlation
#>   Pivot:       TRUE
#> 
#> Top combinations:
#>   No.  Variables                          Avg    Max    Size
#>   ------------------------------------------------------------
#>   [ 1] V1, V2, V3, V4, V7, V9, ...       0.075  0.241    17
#>   [ 2] V1, V2, V3, V4, V6, V9, ...       0.075  0.259    17
#>   [ 3] V1, V2, V3, V4, V8, V9, ...       0.075  0.269    17
#>   [ 4] V1, V2, V3, V4, V5, V9, ...       0.076  0.288    17

# Force in a specific variable and use Spearman correlation
corrSelect(df, threshold = 0.6, force_in = "V10", cor_method = "spearman")
#> The following variables were excluded from the correlation analysis:
#>   - label: unordered factor (excluded)
#> CorrCombo object
#> -----------------
#>   Method:      els
#>   Correlation: spearman
#>   Threshold:   0.600
#>   Subsets:     4 valid combinations
#>   Data Rows:   100 used in correlation
#>   Forced-in:   V10
#> 
#> Top combinations:
#>   No.  Variables                          Avg    Max    Size
#>   ------------------------------------------------------------
#>   [ 1] V1, V2, V3, V4, V7, V9, ...       0.076  0.239    17
#>   [ 2] V1, V2, V3, V4, V5, V9, ...       0.076  0.269    17
#>   [ 3] V1, V2, V3, V4, V8, V9, ...       0.076  0.246    17
#>   [ 4] V1, V2, V3, V4, V6, V9, ...       0.076  0.252    17