collinear (version 3.0.1)

f_auto: Automatic selection of predictor scoring method

Description

Internal function to select a proper f_...() function to compute preference order depending on the types of the response variable and the predictors. The selection criteria is available as a dataframe generated by f_auto_rules().

Usage

f_auto(df = NULL, response = NULL, predictors = NULL, quiet = FALSE, ...)

Value

function name

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.

response

(optional, character string) Name of a response variable in df. Default: NULL.

predictors

(optional; character vector or NULL) Names of the predictors in df. If NULL, all columns except responses and constant/near-zero-variance columns are used. 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).

See Also

Other preference_order_tools: f_auto_rules(), f_functions()

Examples

data(vi_smol, vi_predictors, package = "spatialData")
vi_predictors_numeric <- identify_numeric_variables(
  df = vi_smol,
  predictors = vi_predictors
)$valid
vi_predictors_categorical <- identify_categorical_variables(
  df = vi_smol,
  predictors = vi_predictors
)$valid

f_auto(
  df = vi_smol,
  response = "vi_numeric",
  predictors = vi_predictors_numeric
  )

f_auto(
  df = vi_smol,
  response = "vi_binomial",
  predictors = vi_predictors_numeric
  )

f_auto(
  df = vi_smol,
  response = "vi_categorical",
  predictors = vi_predictors_categorical
  )