Box-Cox Transformation for Non-Negative DataSource:
step_BoxCox creates a specification of a recipe
step that will transform data using a simple Box-Cox
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
NULLuntil computed by
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 = TRUEas it may affect the computations for subsequent operations.
A character string that is unique to this step to identify it.
An updated version of
recipe with the new step added to the
sequence of any existing operations.
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.
tidy() this step, a tibble with columns
terms (the selectors or variables selected) and
lambda estimate) is returned.
Sakia, R. M. (1992). The Box-Cox transformation technique: A review. The Statistician, 169-178..
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)) #> Warning: Non-positive values in selected variable. #> Warning: No Box-Cox transformation could be estimated for: `Frost` 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) #> # A tibble: 1 × 3 #> terms value id #> <chr> <dbl> <chr> #> 1 all_numeric() NA BoxCox_Pmb90 tidy(bc_estimates, number = 1) #> # A tibble: 7 × 3 #> terms value id #> <chr> <dbl> <chr> #> 1 Population 0.000966 BoxCox_Pmb90 #> 2 Income 0.524 BoxCox_Pmb90 #> 3 Illiteracy -0.379 BoxCox_Pmb90 #> 4 Life Exp 4.59 BoxCox_Pmb90 #> 5 Murder 0.606 BoxCox_Pmb90 #> 6 HS Grad 1.92 BoxCox_Pmb90 #> 7 Area 0.250 BoxCox_Pmb90