step_log: Logarithmic transformation

In recipes: Preprocessing and Feature Engineering Steps for Modeling

Logarithmic transformation

Description

step_log() creates a specification of a recipe step that will log transform data.

Usage

step_log(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  base = exp(1),
  offset = 0,
  columns = NULL,
  skip = FALSE,
  signed = FALSE,
  id = rand_id("log")
)

Arguments

recipe	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.
role	Not used by this step since no new variables are created.
trained	A logical to indicate if the quantities for preprocessing have been estimated.
base	A numeric value for the base.
offset	An optional value to add to the data prior to logging (to avoid log(0)).
columns	A character string of the selected variable names. This field is a placeholder and will be populated once prep() is used.
skip	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.
signed	A logical indicating whether to take the signed log. This is sign(x) * log(abs(x)) when abs(x) => 1 or 0 if abs(x) < 1. If TRUE the offset argument will be ignored.
id	A character string that is unique to this step to identify it.

Value

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

Tidying

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

terms

character, the selectors or variables selected

base

numeric, value for the base

id

character, id of this step

Case weights

The underlying operation does not allow for case weights.

See Also

Other individual transformation steps: step_BoxCox(), step_YeoJohnson(), step_bs(), step_harmonic(), step_hyperbolic(), step_inverse(), step_invlogit(), step_logit(), step_mutate(), step_ns(), step_percentile(), step_poly(), step_relu(), step_sqrt()

Examples

set.seed(313)
examples <- matrix(exp(rnorm(40)), ncol = 2)
examples <- as.data.frame(examples)

rec <- recipe(~ V1 + V2, data = examples)

log_trans <- rec %>%
  step_log(all_numeric_predictors())

log_obj <- prep(log_trans, training = examples)

transformed_te <- bake(log_obj, examples)
plot(examples$V1, transformed_te$V1)

tidy(log_trans, number = 1)
tidy(log_obj, number = 1)

# using the signed argument with negative values

examples2 <- matrix(rnorm(40, sd = 5), ncol = 2)
examples2 <- as.data.frame(examples2)

recipe(~ V1 + V2, data = examples2) %>%
  step_log(all_numeric_predictors()) %>%
  prep(training = examples2) %>%
  bake(examples2)

recipe(~ V1 + V2, data = examples2) %>%
  step_log(all_numeric_predictors(), signed = TRUE) %>%
  prep(training = examples2) %>%
  bake(examples2)

recipes documentation built on July 4, 2024, 9:06 a.m.