Calculate the aggregation of a set of outlier columns and filters the resulting tibble

step_outliers_remove creates a specification of a recipe step that will calculate the score of the row of selected columns using an aggregation function and filter the resulting tibble based on the filter function

step_outliers_remove(
  recipe,
  ...,
  aggregation_function = mean,
  score_dropout = 0.95,
  outliers_indexes = NULL,
  aggregation_results = NULL,
  col_names = NULL,
  role = NA,
  trained = FALSE,
  skip = TRUE,
  id = rand_id("outliers_remove")
)

# S3 method for step_outliers_remove
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 will be transformed. See selections() for more details. For the tidy method, these are not currently used.
aggregation_function: a function that returns a value between 0 and 1 on an applied row
score_dropout: a value between 0 and 1 to decide outliers uses ">=" rule
outliers_indexes: placeholder for the tidy method
aggregation_results: a placeholder for the vector of probabilities
col_names: name of the columns being operated on, after filtering they will be removed
role: not defined for this function
trained: A logical to indicate if the quantities for preprocessing have been estimated.
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.
x: A step_outliers_remove object.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any), with the name on name_mutate and the probabilities calculated. For the tidy method, a tibble with columns index (the row indexes of the tibble), outliers (the filtered outliers), aggregation_results the "probabilities calculated".

Details

All columns in the data are sampled and returned by juice() and bake().

All columns used in this step must be numeric with no missing data.

When used in modeling, users should strongly consider using the option skip = TRUE so that this operation is not conducted outside of the training set.

Examples

library(recipes)
library(tidy.outliers)
rec <-
  recipe(mpg ~ ., data = mtcars) %>%
  step_outliers_maha(all_numeric_predictors()) %>%
  step_outliers_remove(contains(r"(.outliers)")) %>%
  prep(mtcars)

bake(rec, new_data = NULL)
#> # A tibble: 28 × 11
#>      cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb   mpg
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1     6  160    110  3.9   2.62  16.5     0     1     4     4  21  
#>  2     6  160    110  3.9   2.88  17.0     0     1     4     4  21  
#>  3     4  108     93  3.85  2.32  18.6     1     1     4     1  22.8
#>  4     6  258    110  3.08  3.22  19.4     1     0     3     1  21.4
#>  5     8  360    175  3.15  3.44  17.0     0     0     3     2  18.7
#>  6     6  225    105  2.76  3.46  20.2     1     0     3     1  18.1
#>  7     8  360    245  3.21  3.57  15.8     0     0     3     4  14.3
#>  8     4  147.    62  3.69  3.19  20       1     0     4     2  24.4
#>  9     6  168.   123  3.92  3.44  18.3     1     0     4     4  19.2
#> 10     6  168.   123  3.92  3.44  18.9     1     0     4     4  17.8
#> # … with 18 more rows

tidy(rec, number = 2)
#> # A tibble: 32 × 3
#>    index outliers aggregation_results
#>    <int> <lgl>                  <dbl>
#>  1     1 FALSE                  0.411
#>  2     2 FALSE                  0.374
#>  3     3 FALSE                  0.222
#>  4     4 FALSE                  0.192
#>  5     5 FALSE                  0.124
#>  6     6 FALSE                  0.350
#>  7     7 FALSE                  0.481
#>  8     8 FALSE                  0.493
#>  9     9 TRUE                   0.985
#> 10    10 FALSE                  0.737
#> # … with 22 more rows