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")
)An updated version of recipe with the new step added to the
sequence of any existing operations.
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 1/0).
A logical to indicate if the quantities for preprocessing have been estimated.
A character string of the selected variable names. This field
is a placeholder and will be populated once prep() is used.
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.
When you tidy() this step, a tibble with columns
terms (the columns that will be affected) is returned.
The underlying operation does not allow for case weights.
Other individual transformation steps:
step_BoxCox(),
step_YeoJohnson(),
step_bs(),
step_harmonic(),
step_hyperbolic(),
step_invlogit(),
step_logit(),
step_log(),
step_mutate(),
step_ns(),
step_percentile(),
step_poly(),
step_relu(),
step_sqrt()
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)
tidy(inverse_obj, number = 1)
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