recipes (version 0.1.0)

step_YeoJohnson: Yeo-Johnson Transformation

Description

step_YeoJohnson creates a specification of a recipe step that will transform data using a simple Yeo-Johnson transformation.

Usage

step_YeoJohnson(recipe, ..., role = NA, trained = FALSE, lambdas = NULL,
  limits = c(-5, 5), nunique = 5)

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 which variables are affected by the 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.

lambdas

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

limits

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

nunique

An integer where data that have less possible values will not be evaluate for a transformation

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any).

Details

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.

References

Yeo, I. K., and Johnson, R. A. (2000). A new family of power transformations to improve normality or symmetry. Biometrika.

See Also

step_BoxCox recipe prep.recipe bake.recipe

Examples

# NOT RUN {
data(biomass)

biomass_tr <- biomass[biomass$dataset == "Training",]
biomass_te <- biomass[biomass$dataset == "Testing",]

rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
              data = biomass_tr)

yj_trans <- step_YeoJohnson(rec,  all_numeric())

yj_estimates <- prep(yj_trans, training = biomass_tr)

yj_te <- bake(yj_estimates, biomass_te)

plot(density(biomass_te$sulfur), main = "before")
plot(density(yj_te$sulfur), main = "after")
# }