Distances to Class CentroidsSource:
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.
step_classdist( recipe, ..., class, role = "predictor", trained = FALSE, mean_func = mean, cov_func = cov, pool = FALSE, log = TRUE, objects = NULL, prefix = "classdist_", skip = FALSE, id = rand_id("classdist") )
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
selections()for more details.
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 new columns created by this step from the original variables will be used as predictors in a model.
A logical to indicate if the quantities for preprocessing have been estimated.
A function to compute the center of the distribution.
A function that computes the covariance matrix
A logical: should the covariance matrix be computed by pooling the data for all of the classes?
A logical: should the distances be transformed by the natural log function?
Statistics are stored here once this step has been trained by
A character string for the prefix of the resulting new variables. See notes below.
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked when
prep()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 = TRUEas it may affect the computations for subsequent operations.
A character string that is unique to this step to identify it.
An updated version of
recipe with the new step added to the
sequence of any existing operations.
step_classdist will create a new column for every
unique value of the
The resulting variables will not replace the original values
and by default have the prefix
classdist_. The naming format can be
changed using the
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
tidy() this step, a tibble with columns
terms (the selectors or variables selected),
value (the centroid
of the class), and
class is returned.
This step performs an supervised operation that can utilize case weights.
As a result, case weights are used with frequency weights as well as
importance weights. For more information,, see the documentation in
case_weights and the examples on
# in case of missing data... mean2 <- function(x) mean(x, na.rm = TRUE) # define naming convention rec <- recipe(Species ~ ., data = iris) %>% step_classdist(all_numeric_predictors(), class = "Species", pool = FALSE, mean_func = mean2, prefix = "centroid_" ) # default naming rec <- recipe(Species ~ ., data = iris) %>% step_classdist(all_numeric_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)] #> # A tibble: 150 × 4 #> Species classdist_setosa classdist_versicolor classdist_virginica #> <fct> <dbl> <dbl> <dbl> #> 1 setosa -0.800 4.74 5.21 #> 2 setosa 0.733 4.42 5.04 #> 3 setosa 0.250 4.55 5.08 #> 4 setosa 0.534 4.42 4.95 #> 5 setosa -0.272 4.79 5.22 #> 6 setosa 1.31 4.79 5.21 #> 7 setosa 1.23 4.56 5.04 #> 8 setosa -1.07 4.64 5.12 #> 9 setosa 1.10 4.30 4.89 #> 10 setosa 1.16 4.54 5.05 #> # … with 140 more rows tidy(rec, number = 1) #> # A tibble: 1 × 4 #> terms value class id #> <chr> <dbl> <chr> <chr> #> 1 all_numeric_predictors() NA NA classdist_GJtCN tidy(rec_dists, number = 1) #> # A tibble: 12 × 4 #> terms value class id #> <chr> <dbl> <chr> <chr> #> 1 Sepal.Length 5.01 setosa classdist_GJtCN #> 2 Sepal.Width 3.43 setosa classdist_GJtCN #> 3 Petal.Length 1.46 setosa classdist_GJtCN #> 4 Petal.Width 0.246 setosa classdist_GJtCN #> 5 Sepal.Length 5.94 versicolor classdist_GJtCN #> 6 Sepal.Width 2.77 versicolor classdist_GJtCN #> 7 Petal.Length 4.26 versicolor classdist_GJtCN #> 8 Petal.Width 1.33 versicolor classdist_GJtCN #> 9 Sepal.Length 6.59 virginica classdist_GJtCN #> 10 Sepal.Width 2.97 virginica classdist_GJtCN #> 11 Petal.Length 5.55 virginica classdist_GJtCN #> 12 Petal.Width 2.03 virginica classdist_GJtCN