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

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

df

A data frame (or tibble / data.table). May contain any mix of:

numeric / integer (treated as numeric)
ordered factors
unordered factors (character vectors are coerced to factors)

threshold

Numeric in \((0,1)\). Maximum allowed pair-wise absolute association. Default 0.7.

method

Character; the subset-search algorithm. One of "els" or "bron-kerbosch". If NULL (default) the function selects automatically: ELS when force_in is supplied, otherwise Bron–Kerbosch.

force_in

Optional character vector or column indices specifying variables that must appear in every returned subset.

method_num_num

Association measure for numeric–numeric pairs. One of "pearson" (default), "spearman", "kendall", "bicor", "distance", or "maximal".

method_num_ord

Association measure for numeric–ordered pairs. One of "spearman" (default) or "kendall".

method_ord_ord

Association measure for ordered–ordered pairs. One of "spearman" (default) or "kendall".

...

Additional arguments passed unchanged to MatSelect() (e.g., use_pivot = TRUE for Bron–Kerbosch).

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.

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.

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)
#> 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  0.474     4

## 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")
#> CorrCombo object
#> -----------------
#>   Method:      els
#>   Correlation: mixed
#>   AssocMethod: numeric_numeric = kendall, numeric_factor = eta, numeric_ordered
#>                = kendall, factor_ordered = cramersv
#>   Threshold:   0.500
#>   Subsets:     1 valid combinations
#>   Data Rows:   15 used in correlation
#>   Forced-in:   height
#> 
#> Top combinations:
#>   No.  Variables                          Avg    Max    Size
#>   ------------------------------------------------------------
#>   [ 1] height, weight, group, score      0.173  0.474     4