step_classdist {recipes} | R Documentation |
step_classdist
creates a 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.
step_classdist(recipe, ..., class, role = "predictor", trained = FALSE, mean_func = mean, cov_func = cov, pool = FALSE, log = TRUE, objects = NULL) ## S3 method for class 'step_classdist' tidy(x, ...)
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 |
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 |
x |
A |
step_classdist
will create a
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
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.
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
.
# 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, newdata = 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)