scimo (version 0.0.3)

step_select_wilcoxon: Feature selection step using Wilcoxon test

Description

Select variables with the lowest (adjusted) p-value of a Wilcoxon-Mann-Whitney test against an outcome.

Usage

step_select_wilcoxon(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  outcome = NULL,
  n_kept = NULL,
  prop_kept = NULL,
  cutoff = NULL,
  correction = "none",
  res = NULL,
  skip = FALSE,
  id = rand_id("select_wilcoxon")
)
# S3 method for step_select_wilcoxon
tidy(x, ...)

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

...

One or more selector functions to choose variables for this step. See recipes::selections() for more details.

role

Not used by this step since no new variables are created.

trained

A logical to indicate if the quantities for preprocessing have been estimated.

outcome

Name of the variable to perform the test against.

n_kept

Number of variables to keep.

prop_kept

A numeric value between 0 and 1 representing the proportion of variables to keep. n_kept and prop_kept are mutually exclusive.

cutoff

Threshold beyond which (below or above) the variables are discarded.

correction

Multiple testing correction method. One of p.adjust.methods. Default to "none".

res

This parameter is only produced after the recipe has been trained.

skip

A logical. Should the step be skipped when the recipe is baked by recipes::bake()? While all operations are baked when recipes::prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.

id

A character string that is unique to this step to identify it.

x

A step_select_wilcoxon object.

Author

Antoine Bichat

Examples

rec <-
  iris %>%
  dplyr::filter(Species != "virginica") %>%
  recipe(formula = Species ~ .) %>%
  step_select_wilcoxon(all_numeric_predictors(), outcome = "Species",
                       correction = "fdr", prop_kept = 0.5) %>%
  prep()
rec
tidy(rec, 1)
bake(rec, new_data = NULL)