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") ) # 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
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
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 existing steps (if any). For the
tidy method, a tibble with columns
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 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_HEnNHtidy(lincomb_filter_trained, number = 1)#> # A tibble: 2 x 2 #> terms id #> <chr> <chr> #> 1 new_1 lincomb_HEnNH #> 2 new_2 lincomb_HEnNH