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,
prefix = "classdist_",
skip = FALSE,
id = rand_id("classdist")
)

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 variables for this step. See selections() for more details.

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.

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().

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 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 = TRUE as it may affect the computations for subsequent operations.

id

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

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

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 by default have the prefix classdist_. The naming format can be changed using the prefix argument.

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.

Tidying

When you tidy() this step, a tibble with columns terms (the selectors or variables selected), value (the centroid of the class), and class is returned.

Case weights

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 tidymodels.org.

Other multivariate transformation steps: step_depth(), 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()

Examples


# 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