prediction_cor_to_vif

Dataframe with predicted VIF threshold corresponding to a given correlation threshold..

datasets

Provides a comprehensive and automated workflow for managing multicollinearity in data frames with numeric and/or categorical variables. The package integrates five robust methods into a single function: (1) target encoding of categorical variables based on response values (Micci-Barreca, 2001 (Micci-Barreca, D. 2001 <doi:10.1145/507533.507538>); (2) automated feature prioritization to preserve key predictors during filtering; (3 and 4) pairwise correlation and VIF filtering across all variable types (numeric–numeric, numeric–categorical, and categorical–categorical); (5) adaptive correlation and VIF thresholds. Together, these methods enable a reliable multicollinearity management in most use cases while maintaining model integrity. The package also supports parallel processing and progress tracking via the packages 'future' and 'progressr', and provides seamless integration with the 'tidymodels' ecosystem through a dedicated recipe step.

Blas M. Benito

collinear

Automated Multicollinearity Management

prediction_cor_to_vif function

A dataframe with 901 rows and 2 numeric columns:<dl>
<dt>max_cor</dt>
<dd>Maximum allowed pairwise correlation, from 0.10 to 1.00 in steps of 0.001.</dd><dt>max_vif</dt>
<dd>Predicted VIF threshold corresponding to each <code>max_cor</code>.</dd>
</dl>

Format

Prediction of the model gam_cor_to_vif across correlation values — prediction_cor_to_vif

A dataframe with 901 rows and 2 numeric columns:<dl>
<dt>max_cor</dt>
<dd>Maximum allowed pairwise correlation, from 0.10 to 1.00 in steps of 0.001.</dd>

<dt>max_vif</dt>
<dd>Predicted VIF threshold corresponding to each <code>max_cor</code>.</dd>


</dl>

prediction_cor_to_vif: Prediction of the model `gam_cor_to_vif` across correlation values

Description

Usage

Arguments

Format

Details

See Also

Examples