collinear (version 3.0.0)

identify_logical_variables: Find logical variables in a dataframe

Description

Identifies logical predictors and excludes those with constant values.

Usage

identify_logical_variables(
  df = NULL,
  responses = NULL,
  predictors = NULL,
  quiet = FALSE,
  ...
)

Value

list:

• valid: character vector with valid logical predictor names.

• invalid: character vector with invalid logical predictor names.

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).

Author

Blas M. Benito, PhD

See Also

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

Examples

data(vi_smol, vi_predictors)

#invalid logical
vi_smol$logical_invalid <- TRUE

#valid logical
vi_smol$logical_valid <- sample(
  x = c(TRUE, FALSE),
  size = nrow(vi_smol),
  replace = TRUE
)

x <- identify_logical_variables(
  df = vi_smol,
  predictors = c(
    vi_predictors,
    "logical_invalid",
    "logical_valid"
  )
)

x$valid
x$invalid