recipes: step_lincomb – R documentation

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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,
  skip = FALSE,
  id = rand_id("lincomb")
)

## S3 method for class 'step_lincomb'
tidy(x, ...)

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. For the `tidy` method, these are not currently used.
`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.
`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.
`x`	A `step_lincomb` object.

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).

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with columns terms which is the columns that will be removed.

Author(s)

Max Kuhn, Kirk Mettler, and Jed Wing

Examples

library(modeldata)
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_numeric_predictors())

lincomb_filter_trained <- prep(lincomb_filter, training = biomass_tr)
lincomb_filter_trained

tidy(lincomb_filter, number = 1)
tidy(lincomb_filter_trained, number = 1)

recipes

Preprocessing Tools to Create Design Matrices

v0.1.16

MIT + file LICENSE

Authors

Max Kuhn [aut, cre], Hadley Wickham [aut], RStudio [cph]

Initial release