identify_predictors_categorical: Identify Valid Categorical Predictors

In collinear: Automated Multicollinearity Management

Identify Valid Categorical Predictors

Description

Returns the names of character or factor predictors, if any. Removes categorical predictors with constant values, or with as many unique values as rows are in the input data frame.

Usage

identify_predictors_categorical(df = NULL, predictors = NULL)

Arguments

df	(required; data frame, tibble, or sf) A data frame with responses and predictors. Default: NULL.
predictors	(optional; character vector) Names of the predictors to select from df. If omitted, all numeric columns in df are used instead. If argument response is not provided, non-numeric variables are ignored. Default: NULL

Value

character vector: categorical predictors names

Author(s)

Blas M. Benito, PhD

See Also

Other data_types: identify_predictors(), identify_predictors_numeric(), identify_predictors_type(), identify_predictors_zero_variance(), identify_response_type()

Examples

data(
  vi,
  vi_predictors
)

non.numeric.predictors <- identify_predictors_categorical(
  df = vi,
  predictors = vi_predictors
)

non.numeric.predictors

collinear documentation built on April 12, 2025, 1:36 a.m.