Data frame with known relationship between responses and predictors useful
to illustrate multicollinearity concepts. Created from <a href="/link/vi?package=collinear&version=2.0.0" data-mini-rdoc="collinear::vi">vi</a> using the code
shown in the example.

datasets

Effortless multicollinearity management in data frames with both numeric and categorical variables for statistical and machine learning applications. The package simplifies multicollinearity analysis by combining four robust methods: 1) target encoding for categorical variables (Micci-Barreca, D. 2001 <doi:10.1145/507533.507538>); 2) automated feature prioritization to prevent key variable loss during filtering; 3) pairwise correlation for all variable combinations (numeric-numeric, numeric-categorical, categorical-categorical); and 4) fast computation of variance inflation factors.

Blas M. Benito

collinear

Automated Multicollinearity Management

toy function

Data frame with 2000 rows and 5 columns.

Format

Data frame with known relationship between responses and predictors useful
to illustrate multicollinearity concepts. Created from <a href='https://rdrr.io/r/utils/edit.html'>vi</a> using the code
shown in the example.

toy: One response and four predictors with varying levels of multicollinearity

Description

Usage

Arguments

Format

Details

See Also