Distances to Class Centroids

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")
)

# 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().
prefix	A character string that defines the naming convention for new distance columns. Defaults to "classdist_". See Details below.
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 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.

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 x 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 x 4
#>   terms                    value class id             
#>   <chr>                    <dbl> <chr> <chr>          
#> 1 all_numeric_predictors()    NA NA    classdist_jisyY
tidy(rec_dists, number = 1)
#> # A tibble: 12 x 4
#>    terms        value class      id             
#>    <chr>        <dbl> <chr>      <chr>          
#>  1 Sepal.Length 5.01  setosa     classdist_jisyY
#>  2 Sepal.Width  3.43  setosa     classdist_jisyY
#>  3 Petal.Length 1.46  setosa     classdist_jisyY
#>  4 Petal.Width  0.246 setosa     classdist_jisyY
#>  5 Sepal.Length 5.94  versicolor classdist_jisyY
#>  6 Sepal.Width  2.77  versicolor classdist_jisyY
#>  7 Petal.Length 4.26  versicolor classdist_jisyY
#>  8 Petal.Width  1.33  versicolor classdist_jisyY
#>  9 Sepal.Length 6.59  virginica  classdist_jisyY
#> 10 Sepal.Width  2.97  virginica  classdist_jisyY
#> 11 Petal.Length 5.55  virginica  classdist_jisyY
#> 12 Petal.Width  2.03  virginica  classdist_jisyY