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

  role = NA,
  trained = FALSE,
  max_steps = 5,
  removals = NULL,
  skip = FALSE,
  id = rand_id("lincomb")

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



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.


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


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


A value.


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


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


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


A step_lincomb object.


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.


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


Max Kuhn, Kirk Mettler, and Jed Wing


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
#> Data Recipe #> #> Inputs: #> #> role #variables #> outcome 1 #> predictor 7 #> #> Training data contained 456 data points and no missing data. #> #> Operations: #> #> Linear combination filter removed new_1, new_2 [trained]
tidy(lincomb_filter, number = 1)
#> # A tibble: 1 x 2 #> terms id #> <chr> <chr> #> 1 NA lincomb_HEnNH
tidy(lincomb_filter_trained, number = 1)
#> # A tibble: 2 x 2 #> terms id #> <chr> <chr> #> 1 new_1 lincomb_HEnNH #> 2 new_2 lincomb_HEnNH