step_depth | R Documentation |
step_depth
creates 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, prefix = "depth_", skip = FALSE, id = rand_id("depth") )
recipe |
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 |
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 new columns created by this step from the original variables 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 |
data |
The training data are stored here once after
|
prefix |
A character string for the prefix of the resulting new variables. See notes below. |
skip |
A logical. Should the step be skipped when the
recipe is baked by |
id |
A character string that is unique to this step to identify it. |
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 depth 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 by default have the prefix depth_
. The
naming format can be changed using the prefix
argument.
An updated version of recipe
with the new step added to the
sequence of any existing operations.
When you tidy()
this step, a tibble with columns
terms
(the selectors or variables selected) and class
is returned.
The underlying operation does not allow for case weights.
Other multivariate transformation steps:
step_classdist()
,
step_geodist()
,
step_ica()
,
step_isomap()
,
step_kpca_poly()
,
step_kpca_rbf()
,
step_kpca()
,
step_mutate_at()
,
step_nnmf_sparse()
,
step_nnmf()
,
step_pca()
,
step_pls()
,
step_ratio()
,
step_spatialsign()
# halfspace depth is the default rec <- recipe(Species ~ ., data = iris) %>% step_depth(all_numeric_predictors(), class = "Species") # use zonoid metric instead # also, define naming convention for new columns rec <- recipe(Species ~ ., data = iris) %>% step_depth(all_numeric_predictors(), class = "Species", metric = "zonoid", prefix = "zonoid_" ) 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.