recipes (version 0.1.7)

step_classdist: Distances to Class Centroids

Description

step_classdist creates a specification of a recipe step that will convert numeric data into Mahalanobis distance measurements to the data centroid. This is done for each value of a categorical class variable.

Usage

step_classdist(recipe, ..., class, role = "predictor", trained = FALSE,
  mean_func = mean, cov_func = cov, pool = FALSE, log = TRUE,
  objects = NULL, skip = FALSE, id = rand_id("classdist"))
# S3 method for step_classdist
tidy(x, ...)

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. For the tidy method, these are not currently used.

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 distances will be used as predictors in a model.

trained

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

mean_func

A function to compute the center of the distribution.

cov_func

A function that computes the covariance matrix

pool

A logical: should the covariance matrix be computed by pooling the data for all of the classes?

log

A logical: should the distances be transformed by the natural log function?

objects

Statistics are stored here once this step has been trained by prep.recipe().

skip

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

id

A character string that is unique to this step to identify it.

x

A step_classdist object.

Value

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), value (the centroid of the class), and class.

Details

step_classdist 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 classdist_.

Note that, by default, the default covariance function requires that each class should have at least as many rows as variables listed in the terms argument. If pool = TRUE, there must be at least as many data points are variables overall.

Examples

# NOT RUN {
# in case of missing data...
mean2 <- function(x) mean(x, na.rm = TRUE)

rec <- recipe(Species ~ ., data = iris) %>%
  step_classdist(all_predictors(), class = "Species",
                 pool = FALSE, mean_func = mean2)

rec_dists <- prep(rec, training = iris)

dists_to_species <- bake(rec_dists, new_data = iris, everything())
## on log scale:
dist_cols <- grep("classdist", names(dists_to_species), value = TRUE)
dists_to_species[, c("Species", dist_cols)]

tidy(rec, number = 1)
tidy(rec_dists, number = 1)
# }