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


  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 selections() for more details.


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 until prep() is called.


A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() 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.


An updated version of recipe with the new step added to the sequence of any existing operations.


This step can potentially remove columns from the data set. This may cause issues for subsequent steps in your recipe if the missing columns are specifically referenced by name. To avoid this, see the advice in the Tips for saving recipes and filtering columns section of selections.

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).


When you tidy() this step, a tibble is returned with columns terms and id:


character, the selectors or variables selected


character, id of this step

Case weights

The underlying operation does not allow for case weights.

See also

Other variable filter steps: step_corr(), step_filter_missing(), step_nzv(), step_rm(), step_select(), step_zv()


Max Kuhn, Kirk Mettler, and Jed Wing


data(biomass, package = "modeldata")

biomass$new_1 <- with(
  .1 * carbon - .2 * hydrogen + .6 * sulfur
biomass$new_2 <- with(
  .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 %>%

lincomb_filter_trained <- prep(lincomb_filter, training = biomass_tr)
#> ── Recipe ────────────────────────────────────────────────────────────────
#> ── Inputs 
#> Number of variables by role
#> outcome:   1
#> predictor: 7
#> ── Training information 
#> Training data contained 456 data points and no incomplete rows.
#> ── Operations 
#>  Linear combination filter removed: new_1 and new_2 | Trained

tidy(lincomb_filter, number = 1)
#> # A tibble: 1 × 2
#>   terms                    id           
#>   <chr>                    <chr>        
#> 1 all_numeric_predictors() lincomb_IeIAm
tidy(lincomb_filter_trained, number = 1)
#> # A tibble: 2 × 2
#>   terms id           
#>   <chr> <chr>        
#> 1 new_1 lincomb_IeIAm
#> 2 new_2 lincomb_IeIAm