Zero Variance Filter

Source: R/zv.R

step_zv creates a specification of a recipe step that will remove variables that contain only a single value.

step_zv(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  removals = NULL,
  skip = FALSE,
  id = rand_id("zv")
)

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 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.
removals

A character string that contains the names of columns that should be removed. These values are not determined until prep.recipe() is called.
skip

A logical. Should the step be skipped when the recipe is baked by bake.recipe()? While all operations are baked when prep.recipe() 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.

Value

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

Details

When you tidy() this step, a tibble with column terms (the columns that will be removed) is returned.

See also

Other variable filter steps: step_corr(), step_lincomb(), step_nzv(), step_rm(), step_select()

Examples

library(modeldata)
data(biomass)

biomass$one_value <- 1

biomass_tr <- biomass[biomass$dataset == "Training",]
biomass_te <- biomass[biomass$dataset == "Testing",]

rec <- recipe(HHV ~ carbon + hydrogen + oxygen +
                    nitrogen + sulfur + one_value,
              data = biomass_tr)

zv_filter <- rec %>%
  step_zv(all_predictors())

filter_obj <- prep(zv_filter, training = biomass_tr)

filtered_te <- bake(filter_obj, biomass_te)
any(names(filtered_te) == "one_value")
#> [1] FALSE

tidy(zv_filter, number = 1)
#> # A tibble: 1 × 2
#>   terms id      
#>   <chr> <chr>   
#> 1 NA    zv_mQoHJ
tidy(filter_obj, number = 1)
#> # A tibble: 1 × 2
#>   terms     id      
#>   <chr>     <chr>   
#> 1 one_value zv_mQoHJ