step_depth: Data Depths

Description

step_depth creates a a specification of a recipe step that will convert numeric data into measurement of data depth. This is done for each value of a categorical class variable.

Usage

step_depth(recipe, ..., class, role = "predictor", trained = FALSE,
  metric = "halfspace", options = list(), data = NULL)

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 that will be used to create the new features. See selections for more details.

class

A single character string that specifies a single categorical variable to be used as the class.

role

For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that resulting depth estimates will be used as predictors in a model.

trained

A logical to indicate if the quantities for preprocessing have been estimated.

metric

A character string specifying the depth metric. Possible values are "potential", "halfspace", "Mahalanobis", "simplicialVolume", "spatial", and "zonoid".

options

A list of options to pass to the underlying depth functions. See depth.halfspace, depth.Mahalanobis, depth.potential, depth.projection, depth.simplicial, depth.simplicialVolume, depth.spatial, depth.zonoid.

data

The training data are stored here once after prep.recipe is executed.

Value

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

Details

Data depth metrics attempt to measure how close data a data point is to the center of its distribution. There are a number of methods for calculating death but a simple example is the inverse of the distance of a data point to the centroid of the distribution. Generally, small values indicate that a data point not close to the centroid. step_depth can compute a class-specific depth for a new data point based on the proximity of the new value to the training set distribution.

Note that the entire training set is saved to compute future depth values. The saved data have been trained (i.e. prepared) and baked (i.e. processed) up to the point before the location that step_depth occupies in the recipe. Also, the data requirements for the different step methods may vary. For example, using metric = "Mahalanobis" requires that each class should have at least as many rows as variables listed in the terms argument.

The function will create a new column for every unique value of the class variable. The resulting variables will not replace the original values and have the prefix depth_.

Examples

Run this code

# NOT RUN {
# halfspace depth is the default
rec <- recipe(Species ~ ., data = iris) %>%
  step_depth(all_predictors(), class = "Species")

rec_dists <- prep(rec, training = iris)

dists_to_species <- bake(rec_dists, newdata = iris)
dists_to_species
# }