step_lincomb creates a specification of a recipe
step that will potentially remove numeric variables that have
linear combinations between them.
step_lincomb( recipe, ..., role = NA, trained = FALSE, max_steps = 5, removals = NULL, skip = FALSE, id = rand_id("lincomb") )
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
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
The number of times to apply the algorithm.
A character string that contains the names of
columns that should be removed. These values are not determined
A logical. Should the step be skipped when the
recipe is baked by
A character string that is unique to this step to identify it.
An updated version of
recipe with the new step added to the
sequence of any existing operations.
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
tidy() this step, a tibble with column
terms (the columns
that will be removed) is returned.
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 #> 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 × 2 #> terms id #> <chr> <chr> #> 1 NA lincomb_HEnNH tidy(lincomb_filter_trained, number = 1) #> # A tibble: 2 × 2 #> terms id #> <chr> <chr> #> 1 new_1 lincomb_HEnNH #> 2 new_2 lincomb_HEnNH