identify_predictors_numeric: Identify Valid Numeric Predictors

In collinear: Automated Multicollinearity Management

Identify Valid Numeric Predictors

Description

Returns the names of valid numeric predictors. Ignores predictors with constant values or with near-zero variance.

Usage

identify_predictors_numeric(df = NULL, predictors = NULL, decimals = 4)

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
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

Value

character vector: names of numeric predictors

Author(s)

Blas M. Benito, PhD

See Also

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

Examples

if (interactive()) {

data(
  vi,
  vi_predictors
)

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

numeric.predictors

}

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