step_BoxCox: Box-Cox Transformation for Non-Negative Data

View source: R/BoxCox.R

step_BoxCoxR Documentation

Box-Cox Transformation for Non-Negative Data


step_BoxCox() creates a specification of a recipe step that will transform data using a Box-Cox transformation.


  role = NA,
  trained = FALSE,
  lambdas = NULL,
  limits = c(-5, 5),
  num_unique = 5,
  skip = FALSE,
  id = rand_id("BoxCox")



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.


A numeric vector of transformation values. This is NULL until computed by prep().


A length 2 numeric vector defining the range to compute the transformation parameter lambda.


An integer to specify minimum required unique values to evaluate for a transformation.


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.


The Box-Cox transformation, which requires a strictly positive variable, can be used to rescale a variable to be more similar to a normal distribution. In this package, the partial log-likelihood function is directly optimized within a reasonable set of transformation values (which can be changed by the user).

This transformation is typically done on the outcome variable using the residuals for a statistical model (such as ordinary least squares). Here, a simple null model (intercept only) is used to apply the transformation to the predictor variables individually. This can have the effect of making the variable distributions more symmetric.

If the transformation parameters are estimated to be very closed to the bounds, or if the optimization fails, a value of NA is used and no transformation is applied.


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


When you tidy() this step, a tibble with columns terms (the selectors or variables selected) and value (the lambda estimate) is returned.

Case weights

The underlying operation does not allow for case weights.


Sakia, R. M. (1992). The Box-Cox transformation technique: A review. The Statistician, 169-178..

See Also

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


rec <- recipe(~., data =

bc_trans <- step_BoxCox(rec, all_numeric())

bc_estimates <- prep(bc_trans, training =

bc_data <- bake(bc_estimates,

plot(density(state.x77[, "Illiteracy"]), main = "before")
plot(density(bc_data$Illiteracy), main = "after")

tidy(bc_trans, number = 1)
tidy(bc_estimates, number = 1)

recipes documentation built on Aug. 26, 2023, 1:08 a.m.