identify_valid_variables: Find valid numeric, categorical, and logical variables in a...

In collinear: Automated Multicollinearity Management

Find valid numeric, categorical, and logical variables in a dataframe

Description

Returns a list with the names of the valid numeric, categorical, and logical variables in a modelling dataframe.

Usage

identify_valid_variables(
  df = NULL,
  responses = NULL,
  predictors = NULL,
  decimals = 4,
  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
decimals	(required, integer) Number of decimal places for the zero variance test. Smaller numbers will increase the number of variables detected as near-zero variance. Recommended values will depend on the range of the numeric variables in 'df'. Default: 4
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

• numeric: character vector of numeric predictors.

• categorical: character vector of categorical (character and factor) predictors.

• logical: character vector of logical predictors.

Author(s)

Blas M. Benito, PhD

See Also

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

Examples

data(vi_smol, vi_predictors)

x <- identify_valid_variables(
  df = vi_smol,
  predictors = vi_predictors
)

x

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