identify_categorical_variables: Find valid categorical variables in a dataframe

In collinear: Automated Multicollinearity Management

Find valid categorical variables in a dataframe

Description

Identifies valid and invalid character or factor variables. Invalid categorical predictors are those with a single category, or as many categories as cases (full-cardinality).

Usage

identify_categorical_variables(
  df = NULL,
  responses = NULL,
  predictors = 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	(required, character vector) Names of the predictors to identify. 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

list:

• valid: character vector with valid categorical predictor names.

• invalid: character vector with invalid categorical predictor names due to degenerate cardinality (1 or nrow(df) categories).

Author(s)

Blas M. Benito, PhD

See Also

Other data_types: identify_logical_variables(), identify_numeric_variables(), identify_response_type(), identify_valid_variables(), identify_zero_variance_variables()

Examples

data(vi_smol, vi_predictors)

#create an invalid categorical
vi_smol$invalid_categorical <- "a"

x <- identify_categorical_variables(
  df = vi_smol,
  responses = "vi_categorical",
  predictors = vi_predictors
)

x$valid
x$invalid

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