recipes (version 0.1.0)

step_lincomb: Linear Combination Filter

Description

step_lincomb creates a specification of a recipe step that will potentially remove numeric variables that have linear combinations between them.

Usage

step_lincomb(recipe, ..., role = NA, trained = FALSE, max_steps = 5,
  removals = NULL)

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 which variables are affected by the 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.

max_steps

A value .

removals

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

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any).

Details

This step finds exact linear combinations between two or more variables and recommends which column(s) should be removed to resolve the issue. This algorithm may need to be applied multiple times (as defined by max_steps).

See Also

step_nzvstep_corr recipe prep.recipe bake.recipe

Examples

# NOT RUN {
data(biomass)

biomass$new_1 <- with(biomass,
                      .1*carbon - .2*hydrogen + .6*sulfur)
biomass$new_2 <- with(biomass,
                      .5*carbon - .2*oxygen + .6*nitrogen)

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

rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen +
                sulfur + new_1 + new_2,
              data = biomass_tr)

lincomb_filter <- rec %>%
  step_lincomb(all_predictors())
  
prep(lincomb_filter, training = biomass_tr)
# }