Correlation Subset Selection with corrselect

Overview

corrselect identifies all maximal subsets of variables whose pairwise correlations stay below a chosen threshold. This process reduces multicollinearity and redundancy before modeling, while preserving interpretability. Unlike greedy or stepwise approaches, corrselect exhaustively searches for all valid subsets using fast, exact algorithms. It is fully model-agnostic, making it suitable as a preprocessing step for regression, clustering, feature selection, and other analyses.

Given a threshold $t \in (0,1)$ , the functions corrSelect() (data-frame interface) and MatSelect() (matrix interface) enumerate all maximal subsets $S$ of variables satisfying:

$\forall i, j \in S,\ i \neq j: \ |r_{ij}| < t$

where $r_{ij}$ denotes the chosen correlation measure between variables $i$ and $j$ . Enumeration relies on two exact graph-theoretic algorithms:

Eppstein–Löffler–Strash (ELS), a degeneracy-ordered backtracking algorithm optimized for sparse graphs.
Bron–Kerbosch (BK), a classical recursive clique-finding method, with optional pivoting to reduce search space.

Results are returned as a CorrCombo S4 object containing each subset’s variable names and summary statistics (avg_corr, min_corr, max_corr). You can then extract subsets from the original data via corrSubset(). Because the procedure does not depend on any downstream model, it cleanly separates “feature curation” from “model fitting” and supports multiple correlation measures (pearson, spearman, kendall, bicor, distance, maximal).

Quick Start (`CorrSelect`)

Simulated numeric example

set.seed(42)
n <- 100
df <- data.frame(
  A = rnorm(n),
  B = rnorm(n),
  C = rnorm(n),
  D = rnorm(n),
  E = rnorm(n)
)
df$F <- df$A * 0.9 + rnorm(n, sd = 0.1)  # strongly correlated with A

Basic selection

res <- corrSelect(df, threshold = 0.7)
res
#> CorrCombo object
#> -----------------
#>   Method:      bron-kerbosch
#>   Correlation: pearson
#>   Threshold:   0.700
#>   Subsets:     2 valid combinations
#>   Data Rows:   100 used in correlation
#>   Pivot:       TRUE
#> 
#> Top combinations:
#>   No.  Variables                          Avg    Max    Size
#>   ------------------------------------------------------------
#>   [ 1] F, B, C, D, E                     0.082  0.185     5
#>   [ 2] A, B, C, D, E                     0.083  0.185     5
as.data.frame(res)
#>                      VarName01 VarName02 VarName03 VarName04 VarName05
#> Subset01 [avg=0.082]         F         B         C         D         E
#> Subset02 [avg=0.083]         A         B         C         D         E

corrSubset(res, df, which = 1)[1:10,]
#>              F          B          C            D           E
#> 1   1.33677667  1.2009654 -2.0009292 -0.004620768  1.33491259
#> 2  -0.41675087  1.0447511  0.3337772  0.760242168 -0.86927176
#> 3   0.32656994 -1.0032086  1.1713251  0.038990913  0.05548695
#> 4   0.58317730  1.8484819  2.0595392  0.735072142  0.04906691
#> 5   0.29182614 -0.6667734 -1.3768616 -0.146472627 -0.57835573
#> 6  -0.11532450  0.1055138 -1.1508556 -0.057887335 -0.99873866
#> 7   1.25744892 -0.4222559 -0.7058214  0.482369466 -0.00243278
#> 8  -0.18188872 -0.1223502 -1.0540558  0.992943637  0.65551188
#> 9   1.69450003  0.1881930 -0.6457437 -1.246395498  1.47684228
#> 10  0.02717808  0.1191610 -0.1853780 -0.033487525 -1.90915279

Forcing variables into all subsets

res2 <- corrSelect(df, threshold = 0.7, force_in = "A")
res2
#> CorrCombo object
#> -----------------
#>   Method:      els
#>   Correlation: pearson
#>   Threshold:   0.700
#>   Subsets:     1 valid combinations
#>   Data Rows:   100 used in correlation
#>   Forced-in:   A
#> 
#> Top combinations:
#>   No.  Variables                          Avg    Max    Size
#>   ------------------------------------------------------------
#>   [ 1] A, B, C, D, E                     0.083  0.185     5

Using a different correlation method

res3 <- corrSelect(df, threshold = 0.6, cor_method = "spearman")
res3
#> CorrCombo object
#> -----------------
#>   Method:      bron-kerbosch
#>   Correlation: spearman
#>   Threshold:   0.600
#>   Subsets:     2 valid combinations
#>   Data Rows:   100 used in correlation
#>   Pivot:       TRUE
#> 
#> Top combinations:
#>   No.  Variables                          Avg    Max    Size
#>   ------------------------------------------------------------
#>   [ 1] F, B, C, D, E                     0.088  0.191     5
#>   [ 2] A, B, C, D, E                     0.090  0.206     5

Matrix Interface (`MatSelect`)

If you already computed a correlation matrix or want to apply the method to precomputed correlations:

mat <- cor(df)
res4 <- MatSelect(mat, threshold = 0.7)
res4
#> CorrCombo object
#> -----------------
#>   Method:      bron-kerbosch
#>   Threshold:   0.700
#>   Subsets:     2 valid combinations
#>   Data Rows:   6 used in correlation
#>   Pivot:       TRUE
#> 
#> Top combinations:
#>   No.  Variables                          Avg    Max    Size
#>   ------------------------------------------------------------
#>   [ 1] F, B, C, D, E                     0.082  0.185     5
#>   [ 2] A, B, C, D, E                     0.083  0.185     5

Selecting subsets:

MatSelect(mat, threshold = 0.5)
#> CorrCombo object
#> -----------------
#>   Method:      bron-kerbosch
#>   Threshold:   0.500
#>   Subsets:     2 valid combinations
#>   Data Rows:   6 used in correlation
#>   Pivot:       TRUE
#> 
#> Top combinations:
#>   No.  Variables                          Avg    Max    Size
#>   ------------------------------------------------------------
#>   [ 1] F, B, C, D, E                     0.082  0.185     5
#>   [ 2] A, B, C, D, E                     0.083  0.185     5

Force variable 1 into every subset:

MatSelect(mat, threshold = 0.5, force_in = 1)
#> CorrCombo object
#> -----------------
#>   Method:      els
#>   Threshold:   0.500
#>   Subsets:     1 valid combinations
#>   Data Rows:   6 used in correlation
#>   Forced-in:   A
#> 
#> Top combinations:
#>   No.  Variables                          Avg    Max    Size
#>   ------------------------------------------------------------
#>   [ 1] A, B, C, D, E                     0.083  0.185     5

Mixed Data Types (`assocSelect`)

df_ass <- 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
res5 <- assocSelect(df_ass, threshold = 0.6)
res5
#> CorrCombo object
#> -----------------
#>   Method:      bron-kerbosch
#>   Correlation: mixed
#>   AssocMethod: numeric_numeric = pearson, numeric_factor = eta, numeric_ordered
#>                = spearman, factor_ordered = cramersv
#>   Threshold:   0.600
#>   Subsets:     1 valid combinations
#>   Data Rows:   15 used in correlation
#>   Pivot:       TRUE
#> 
#> Top combinations:
#>   No.  Variables                          Avg    Max    Size
#>   ------------------------------------------------------------
#>   [ 1] height, weight, group, score      0.174  0.332     4

Changing Correlation Method

By default, corrSelect() uses Pearson correlation. You can choose alternatives with the cor_method argument:

"pearson": linear correlation (default)
"spearman": rank-based monotonic association
"kendall": Kendall’s tau
"bicor": robust biweight midcorrelation (WGCNA::bicor)
"distance": distance correlation (energy::dcor)
"maximal": maximal information coefficient (minerva::mine)

Example:

res6 <- corrSelect(df, threshold = 0.7, cor_method = "spearman")
res6
#> CorrCombo object
#> -----------------
#>   Method:      bron-kerbosch
#>   Correlation: spearman
#>   Threshold:   0.700
#>   Subsets:     2 valid combinations
#>   Data Rows:   100 used in correlation
#>   Pivot:       TRUE
#> 
#> Top combinations:
#>   No.  Variables                          Avg    Max    Size
#>   ------------------------------------------------------------
#>   [ 1] F, B, C, D, E                     0.088  0.191     5
#>   [ 2] A, B, C, D, E                     0.090  0.206     5

Handling Mixed Data Types

The function assocSelect() extends corrSelect() to support mixed data types — including numeric, factor, and ordered variables — by using appropriate association measures for each variable pair.

Instead of a single correlation matrix, it constructs a generalized association matrix using the following logic:

Variable 1	Variable 2	Method Used
numeric	numeric	`pearson` (default; customizable)
numeric	factor	`eta`
numeric	ordered	`spearman` (default; customizable)
factor	factor	`cramersv`
factor	ordered	`cramersv`
ordered	ordered	`spearman` (default; customizable)

The defaults for numeric-numeric, numeric-ordered, and ordered-ordered associations can be changed via arguments:

assocSelect(df_ass,
  method_num_num = "kendall",
  method_num_ord = "spearman",
  method_ord_ord = "kendall"
)
#> CorrCombo object
#> -----------------
#>   Method:      bron-kerbosch
#>   Correlation: mixed
#>   AssocMethod: numeric_numeric = kendall, numeric_factor = eta, numeric_ordered
#>                = spearman, factor_ordered = cramersv
#>   Threshold:   0.700
#>   Subsets:     1 valid combinations
#>   Data Rows:   15 used in correlation
#>   Pivot:       TRUE
#> 
#> Top combinations:
#>   No.  Variables                          Avg    Max    Size
#>   ------------------------------------------------------------
#>   [ 1] height, weight, group, score      0.178  0.332     4

All other combinations use fixed methods (eta or cramersv) appropriate for measuring association strength.

Example with Mixed Types

df_ass <- data.frame(
  height = rnorm(10),
  weight = rnorm(10),
  group  = factor(sample(c("A", "B"), 10, replace = TRUE)),
  score  = ordered(sample(1:3, 10, replace = TRUE))
)

res7 <- assocSelect(df_ass, threshold = 1, method = "bron-kerbosch", use_pivot = TRUE)
res7
#> CorrCombo object
#> -----------------
#>   Method:      bron-kerbosch
#>   Correlation: mixed
#>   AssocMethod: numeric_numeric = pearson, numeric_factor = eta, numeric_ordered
#>                = spearman, factor_ordered = cramersv
#>   Threshold:   1.000
#>   Subsets:     1 valid combinations
#>   Data Rows:   10 used in correlation
#>   Pivot:       TRUE
#> 
#> Top combinations:
#>   No.  Variables                          Avg    Max    Size
#>   ------------------------------------------------------------
#>   [ 1] height, weight, group, score      0.336  0.495     4

Each pairwise association is bounded to [0,1] and treated analogously to correlation.

Theory

Given a symmetric correlation matrix $R \in \mathbb{R}^{p \times p}$ , we seek all maximal subsets $S \subseteq \{1, \dots, p\}$ such that:

$\forall i, j \in S,\ i \neq j: \ |R_{ij}| < t$

for a fixed threshold $t \in (0, 1)$ .

This is equivalent to finding all maximal cliques in the thresholded correlation graph, where:

Nodes represent variables
Edges connect nodes whose absolute correlation is below the threshold

A maximal clique corresponds to a variable subset that cannot be extended without violating the correlation limit.

Algorithms

ELS (Eppstein–Löffler–Strash)

The ELS algorithm efficiently enumerates all maximal cliques in a sparse graph using degeneracy ordering:

Compute a degeneracy ordering $v_1, \dots, v_p$ .
For each $i$ , extend current clique $S$ from $\{v_i\}$ within candidate set $C = \{v_{i+1}, \dots, v_p\}$ .
Recursively build cliques, pruning when no further vertices can be added.

Formally, define:

$\text{extend}(S, C) = \begin{cases} S, & C = \emptyset, \\ \bigcup_{v \in C} \text{extend}(S \cup \{v\},\ C \setminus (N(v) \cup \{v\})), & \text{otherwise}. \end{cases}$

ELS avoids redundant exploration, achieving good performance on typical correlation graphs.

Bron–Kerbosch (with Pivoting)

The classical Bron–Kerbosch algorithm enumerates maximal cliques via recursive backtracking with optional pivoting:

Let $R$ = current clique, $P$ = prospective nodes, $X$ = excluded nodes. Then:

$\text{BK}(R, P, X) = \begin{cases} \text{report}(R), & P = X = \emptyset, \\ \text{for each } v \in P \setminus N(u): \\ \quad \text{BK}(R \cup \{v\},\ P \cap N(v),\ X \cap N(v)), \ \quad P \leftarrow P \setminus \{v\},\ X \leftarrow X \cup \{v\}. \end{cases}$

Choosing a pivot $u \in P \cup X$ and iterating over $P \setminus N(u)$ reduces recursive calls.

Why corrselect?

Most existing R tools:

Filter one variable at a time (e.g. findCorrelation)
Use greedy or backward-selection heuristics
Do not enumerate all valid subsets

corrselect uniquely provides:

Exact enumeration of maximal subsets
Support for multiple correlation measures
Optional forcing of variables
Full inspection via CorrCombo objects
Fast C++ implementations via Rcpp

This makes it ideal for pipelines where interpretability and completeness are essential.

Inspecting Results

Convert results for downstream use:

df_res <- as.data.frame(res)
head(df_res)
#>                      VarName01 VarName02 VarName03 VarName04 VarName05
#> Subset01 [avg=0.082]         F         B         C         D         E
#> Subset02 [avg=0.083]         A         B         C         D         E

Extract individual subsets:

lapply(corrSubset(res, df, which = 1:2), function(x) head(x, 10))
#> $Subset1
#>              F          B          C            D           E
#> 1   1.33677667  1.2009654 -2.0009292 -0.004620768  1.33491259
#> 2  -0.41675087  1.0447511  0.3337772  0.760242168 -0.86927176
#> 3   0.32656994 -1.0032086  1.1713251  0.038990913  0.05548695
#> 4   0.58317730  1.8484819  2.0595392  0.735072142  0.04906691
#> 5   0.29182614 -0.6667734 -1.3768616 -0.146472627 -0.57835573
#> 6  -0.11532450  0.1055138 -1.1508556 -0.057887335 -0.99873866
#> 7   1.25744892 -0.4222559 -0.7058214  0.482369466 -0.00243278
#> 8  -0.18188872 -0.1223502 -1.0540558  0.992943637  0.65551188
#> 9   1.69450003  0.1881930 -0.6457437 -1.246395498  1.47684228
#> 10  0.02717808  0.1191610 -0.1853780 -0.033487525 -1.90915279
#> 
#> $Subset2
#>              A          B          C            D           E
#> 1   1.37095845  1.2009654 -2.0009292 -0.004620768  1.33491259
#> 2  -0.56469817  1.0447511  0.3337772  0.760242168 -0.86927176
#> 3   0.36312841 -1.0032086  1.1713251  0.038990913  0.05548695
#> 4   0.63286260  1.8484819  2.0595392  0.735072142  0.04906691
#> 5   0.40426832 -0.6667734 -1.3768616 -0.146472627 -0.57835573
#> 6  -0.10612452  0.1055138 -1.1508556 -0.057887335 -0.99873866
#> 7   1.51152200 -0.4222559 -0.7058214  0.482369466 -0.00243278
#> 8  -0.09465904 -0.1223502 -1.0540558  0.992943637  0.65551188
#> 9   2.01842371  0.1881930 -0.6457437 -1.246395498  1.47684228
#> 10 -0.06271410  0.1191610 -0.1853780 -0.033487525 -1.90915279

Summarize correlation metrics:

# Number and size of subsets
length(res@subset_list)
#> [1] 2
summary(lengths(res@subset_list))
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>       5       5       5       5       5       5

# Summaries of within-subset correlations
summary(res@max_corr)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   0.185   0.185   0.185   0.185   0.185   0.185
summary(res@avg_corr)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#> 0.08162 0.08185 0.08208 0.08208 0.08232 0.08255

CorrCombo Object Structure

A CorrCombo S4 object contains:

subset_list: list of character vectors (variable names)
avg_corr, min_corr, max_corr: numeric vectors of correlation metrics
threshold, forced_in, search_type, cor_method, n_rows_used
Attribute use_pivot (if applicable)

Inspect slots:

str(res@subset_list)
#> List of 2
#>  $ : chr [1:5] "F" "B" "C" "D" ...
#>  $ : chr [1:5] "A" "B" "C" "D" ...

Session Info

sessionInfo()
#> R version 4.5.1 (2025-06-13 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows 11 x64 (build 26100)
#> 
#> Matrix products: default
#>   LAPACK version 3.12.1
#> 
#> locale:
#> [1] LC_COLLATE=English_United States.utf8 
#> [2] LC_CTYPE=English_United States.utf8   
#> [3] LC_MONETARY=English_United States.utf8
#> [4] LC_NUMERIC=C                          
#> [5] LC_TIME=English_United States.utf8    
#> 
#> time zone: Europe/Luxembourg
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] corrselect_2.0.1
#> 
#> loaded via a namespace (and not attached):
#>  [1] digest_0.6.37     desc_1.4.3        R6_2.6.1          fastmap_1.2.0    
#>  [5] xfun_0.52         cachem_1.1.0      knitr_1.50        htmltools_0.5.8.1
#>  [9] rmarkdown_2.29    lifecycle_1.0.4   cli_3.6.5         sass_0.4.10      
#> [13] pkgdown_2.1.3     textshaping_1.0.1 jquerylib_0.1.4   systemfonts_1.2.3
#> [17] compiler_4.5.1    rstudioapi_0.17.1 tools_4.5.1       ragg_1.4.0       
#> [21] bslib_0.9.0       evaluate_1.0.4    Rcpp_1.1.0        yaml_2.3.10      
#> [25] jsonlite_2.0.0    rlang_1.1.6       fs_1.6.6          htmlwidgets_1.6.4