step_YeoJohnson creates a specification of a
recipe step that will transform data using a simple Yeo-Johnson
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 where data that have less possible values will not be evaluated for a transformation.
A logical value indicating whether
NAvalues should be removed during computations.
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 Yeo-Johnson transformation is very similar to the Box-Cox but does not require the input variables to be strictly positive. In the 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.
Yeo, I. K., and Johnson, R. A. (2000). A new family of power transformations to improve normality or symmetry. Biometrika.
data(biomass, package = "modeldata") biomass_tr <- biomass[biomass$dataset == "Training", ] biomass_te <- biomass[biomass$dataset == "Testing", ] rec <- recipe( HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur, data = biomass_tr ) yj_transform <- step_YeoJohnson(rec, all_numeric()) yj_estimates <- prep(yj_transform, training = biomass_tr) yj_te <- bake(yj_estimates, biomass_te) plot(density(biomass_te$sulfur), main = "before") plot(density(yj_te$sulfur), main = "after") tidy(yj_transform, number = 1) #> # A tibble: 1 × 3 #> terms value id #> <chr> <dbl> <chr> #> 1 all_numeric() NA YeoJohnson_Rwq5X tidy(yj_estimates, number = 1) #> # A tibble: 6 × 3 #> terms value id #> <chr> <dbl> <chr> #> 1 carbon -0.0225 YeoJohnson_Rwq5X #> 2 hydrogen 2.10 YeoJohnson_Rwq5X #> 3 oxygen 1.78 YeoJohnson_Rwq5X #> 4 nitrogen -0.830 YeoJohnson_Rwq5X #> 5 sulfur -4.09 YeoJohnson_Rwq5X #> 6 HHV -0.388 YeoJohnson_Rwq5X