step_BoxCox
creates a specification of a recipe
step that will transform data using a simple Box-Cox
transformation.
step_BoxCox(
recipe,
...,
role = NA,
trained = FALSE,
lambdas = NULL,
limits = c(-5, 5),
num_unique = 5,
skip = FALSE,
id = rand_id("BoxCox")
)# S3 method for step_BoxCox
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.
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.recipe()
.
A length 2 numeric vector defining the range to compute the transformation parameter lambda.
An integer where data that have less possible values will not be evaluated for a transformation.
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_BoxCox
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
(the
selectors or variables selected) and value
(the
lambda estimate).
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.
Sakia, R. M. (1992). The Box-Cox transformation technique: A review. The Statistician, 169-178..
# NOT RUN { rec <- recipe(~ ., data = as.data.frame(state.x77)) bc_trans <- step_BoxCox(rec, all_numeric()) bc_estimates <- prep(bc_trans, training = as.data.frame(state.x77)) bc_data <- bake(bc_estimates, as.data.frame(state.x77)) plot(density(state.x77[, "Illiteracy"]), main = "before") plot(density(bc_data$Illiteracy), main = "after") tidy(bc_trans, number = 1) tidy(bc_estimates, number = 1) # }