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.
step_depth(recipe, ..., class, role = "predictor", trained = FALSE,
metric = "halfspace", options = list(), data = NULL,
skip = FALSE, id = rand_id("depth"))# S3 method for step_depth
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 that will be used to create the new features. See
selections() for more details. For the tidy
method, these are not currently used.
A single character string that specifies a single categorical variable to be used as the class.
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.
A logical to indicate if the quantities for preprocessing have been estimated.
A character string specifying the depth metric. Possible values are "potential", "halfspace", "Mahalanobis", "simplicialVolume", "spatial", and "zonoid".
A list of options to pass to the underlying
depth functions. See ddalpha::depth.halfspace(),
ddalpha::depth.Mahalanobis(),
ddalpha::depth.potential(),
ddalpha::depth.projection(),
ddalpha::depth.simplicial(),
ddalpha::depth.simplicialVolume(),
ddalpha::depth.spatial(),
ddalpha::depth.zonoid().
The training data are stored here once after
prep.recipe() is executed.
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_depth 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 class.
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.
This step requires the ddalpha package. If not installed, the step will stop with a note about installing the package.
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_.
# 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, new_data = iris)
dists_to_species
tidy(rec, number = 1)
tidy(rec_dists, number = 1)
# }
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