step_inverse creates a specification of a recipe
step that will inverse transform the data.
step_inverse( recipe, ..., role = NA, offset = 0, trained = FALSE, columns = NULL, skip = FALSE, id = rand_id("inverse") )
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.
An optional value to add to the data prior to logging (to avoid
A logical to indicate if the quantities for preprocessing have been estimated.
A character string of variable names that will be populated (eventually) by the
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 = TRUEas 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.
tidy() this step, a tibble with columns
terms (the columns that will be affected) is returned.
set.seed(313) examples <- matrix(runif(40), ncol = 2) examples <- data.frame(examples) rec <- recipe(~ X1 + X2, data = examples) inverse_trans <- rec %>% step_inverse(all_numeric_predictors()) inverse_obj <- prep(inverse_trans, training = examples) transformed_te <- bake(inverse_obj, examples) plot(examples$X1, transformed_te$X1) tidy(inverse_trans, number = 1) #> # A tibble: 1 × 2 #> terms id #> <chr> <chr> #> 1 all_numeric_predictors() inverse_ooyvr tidy(inverse_obj, number = 1) #> # A tibble: 2 × 2 #> terms id #> <chr> <chr> #> 1 X1 inverse_ooyvr #> 2 X2 inverse_ooyvr