step_inverse creates a specification of a recipe step that will inverse transform the data.

  role = NA,
  offset = 0,
  trained = FALSE,
  columns = NULL,
  skip = FALSE,
  id = rand_id("inverse")

# S3 method for step_inverse
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.


An optional value to add to the data prior to logging (to avoid 1/0).


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 terms argument.


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_inverse 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 affected.

See also


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 x 2 #> terms id #> <chr> <chr> #> 1 all_numeric_predictors() inverse_ooyvr
tidy(inverse_obj, number = 1)
#> # A tibble: 2 x 2 #> terms id #> <chr> <chr> #> 1 X1 inverse_ooyvr #> 2 X2 inverse_ooyvr