collinear: Smart multicollinearity management

In collinear: Automated Multicollinearity Management

Smart multicollinearity management

Description

Automates multicollinearity management in datasets with mixed variable types (numeric, categorical, and logical) through an integrated system of five key components:

Target Encoding Integration (opt-in)

When responses is numeric, categorical predictors can be converted to numeric using response values as reference. This enables VIF and correlation analysis across mixed types. See target_encoding_lab.

Intelligent Predictor Ranking (active by default)

Three prioritization strategies ensure the most relevant predictors are retained during filtering:

• User-defined ranking (argument preference_order): Accepts a character vector of predictor names or a dataframe from preference_order. Lower-ranked collinear predictors are removed.

• Response-based ranking (f): Uses f_auto, f_numeric_glm, or f_binomial_rf to rank predictors by association with the response. Supports cross-validation via preference_order.

• Multicollinearity-based ranking (default): When both preference_order and f are NULL, predictors are ranked from lower to higher multicollinearity.

Unified Correlation Framework (active by default)

Computes pairwise correlations between variable types using Pearson (numeric–numeric), target encoding (numeric–categorical), and Cramer's V (categorical–categorical). See cor_df, cor_matrix, and cor_cramer.

Adaptive Filtering Thresholds (active by default)

When max_cor and max_vif are both NULL, thresholds are determined from the median correlation structure of the predictors.

Dual Filtering Strategy (active by default)

Combines two complementary methods while respecting predictor rankings:

• Pairwise Correlation Filtering: Removes predictors with Pearson correlation or Cramer's V above max_cor. See cor_select.

• VIF-based Filtering: Removes numeric predictors with VIF above max_vif. See vif_select, vif_df, and vif.

This function accepts parallelization via future::plan() and progress bars via progressr::handlers(). Parallelization benefits target_encoding_lab, preference_order, and cor_select.

Usage

collinear(
  df = NULL,
  responses = NULL,
  predictors = NULL,
  encoding_method = NULL,
  preference_order = NULL,
  f = f_auto,
  max_cor = NULL,
  max_vif = NULL,
  quiet = FALSE,
  ...
)

Arguments

df	(required; dataframe, tibble, or sf) A dataframe with responses (optional) and predictors. Must have at least 10 rows for pairwise correlation analysis, and 10 * (length(predictors) - 1) for VIF. Default: NULL.
responses	(optional; character, character vector, or NULL) Name of one or several response variables in df. Default: NULL.
predictors	(optional; character vector or NULL) Names of the predictors in df. If NULL, all columns except responses and constant/near-zero-variance columns are used. Default: NULL.
encoding_method	(optional; character or NULL) One of "loo", "mean", or "rank". If NULL, target encoding is disabled. Default: NULL.
preference_order	(optional; character vector, dataframe from preference_order, or NULL) Prioritizes predictors to preserve.
f	(optional; unquoted function name or NULL) Function to rank predictors by relationship with responses. See f_functions. Default: f_auto.
max_cor	(optional; numeric or NULL) Maximum allowed pairwise correlation (0.01–0.99). Recommended between 0.5 and 0.9. If NULL and max_vif is NULL, it is selected automatically. Default: NULL.
max_vif	(optional; numeric or NULL) Maximum allowed VIF. Recommended between 2.5 and 10. If NULL and max_cor is NULL, configured automatically. Default: NULL.
quiet	(optional; logical) If FALSE, messages are printed. Default: FALSE.
...	(optional) Internal args (e.g. function_name for validate_arg_function_name, a precomputed correlation matrix m, or cross-validation args for preference_order).

Value

A list of class collinear_output with sublists of class collinear_selection. If responses = NULL a single sublist named "result" is returned; otherwise a sublist per response is returned.

Adaptive Multicollinearity Thresholds

When both max_cor and max_vif are NULL, the function determines thresholds as follows:

1. Compute the 75th percentile of pairwise correlations via cor_stats.

2. Map that value through a sigmoid between 0.545 (VIF~2.5) and 0.785 (VIF~7.5), centered at 0.665, to get max_cor.

3. Compute max_vif from max_cor using gam_cor_to_vif.

Variance Inflation Factors

VIF for predictor a is computed as 1/(1-R^2), where R^2 is the multiple R-squared from regressing a on the other predictors. Recommended maximums commonly used are 2.5, 5, and 10.

VIF-based Filtering

vif_select ranks numeric predictors (user preference_order if provided, otherwise from lower to higher VIF) and sequentially adds predictors whose VIF against the current selection is below max_vif.

Pairwise Correlation Filtering

cor_select computes the global correlation matrix, orders predictors by preference_order or by lower-to-higher summed correlations, and sequentially selects predictors with pairwise correlations below max_cor.

References

• David A. Belsley, D.A., Kuh, E., Welsch, R.E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. John Wiley & Sons. DOI: 10.1002/0471725153.

• Micci-Barreca, D. (2001) A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification and Prediction Problems. SIGKDD Explor. Newsl. 3, 1, 27-32. DOI: 10.1145/507533.507538

See Also

Other multicollinearity_filtering: collinear_select(), cor_select(), step_collinear(), vif_select()

Examples

data(vi_smol, vi_predictors_numeric)
x <- collinear(df = vi_smol[, vi_predictors_numeric])

collinear documentation built on Dec. 8, 2025, 5:06 p.m.