collinear (version 3.0.0)

identify_numeric_variables: Find valid numeric variables in a dataframe

Description

Identifies valid numeric variables and ignores those with constant values.

Usage

identify_numeric_variables(
  df = NULL,
  responses = NULL,
  predictors = NULL,
  decimals = 4,
  quiet = FALSE,
  ...
)

Value

list:

• valid: character vector with valid numeric predictor names.

• invalid: character vector with invalid numeric predictor names due to near-zero variance.

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).

Author

Blas M. Benito, PhD

See Also

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

Examples

data(vi_smol, vi_predictors)

x <- identify_numeric_variables(
  df = vi_smol,
  responses = "vi_numeric",
  predictors = vi_predictors
)

#valid numeric predictors
x$valid

#invalid due to zero variance (none here)
x$invalid