Robust Box-Cox and Yeo-Johnson Transformation Recipe Step

step_robust_bcyj() creates a specification of a recipe step that transforms the selected variables toward central normality with the robust Box-Cox or Yeo-Johnson transformation of robust_bcyj(), like recipes::step_BoxCox() or recipes::step_YeoJohnson() but robust to outliers.

Usage

step_robust_bcyj(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  type = "bestObj",
  quantile = 0.99,
  nbsteps = 2,
  standardize = TRUE,
  res = NULL,
  columns = NULL,
  skip = FALSE,
  id = recipes::rand_id("robust_bcyj")
)

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 the predictors (for example, the spectral channels). See recipes::selections().
role: Not used by this step, since no new variables are created.
trained: A logical indicating whether the step has been trained.
type: The transformation: "bestObj" (default; Box-Cox or Yeo-Johnson, whichever fits best, for positive variables), "BC" or "YJ". See robust_bcyj().
quantile: The quantile used for the weights of the re-weighting steps. Default is 0.99.
nbsteps: The number of re-weighting steps. Default is 2.
standardize: A logical: robustly standardize the transformed variables (TRUE, default).
res: The fitted transformation, stored once the step has been trained.
columns: The names of the selected columns, stored once the step has been trained.
skip: A logical. Should the step be skipped when the recipe is baked by recipes::bake()? Keep the default FALSE, so that new data are corrected with the same filter as the training data.
id: A character string that is unique to this step.

Value

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

Details

The transformation parameter \(\lambda\) of each variable is estimated on the training data by re-weighted maximum likelihood (Raymaekers and Rousseeuw, 2021), as in robust_bcyj(), and applied unchanged to new data. With standardize = TRUE, the transformed variables are also centered and scaled with the mean and standard deviation of the training inliers. Variables that cannot be transformed (for example, constant ones) are left unchanged.

tidy() returns a tibble with columns terms, lambda, method ("BC" or "YJ") and id.

Author

Christian L. Goueguel

Examples

library(recipes)
data(forageLIBS)
contents <- forageLIBS[c("Ca", "Mg", "P", "K", "Mn")]
rec <- recipe(~ ., data = contents[1:300, ]) |>
  step_robust_bcyj(all_numeric_predictors())
prepped <- prep(rec)
tidy(prepped, number = 1)
#> # A tibble: 5 × 4
#>   terms  lambda method id               
#>   <chr>   <dbl> <chr>  <chr>            
#> 1 Ca     0.0291 BC     robust_bcyj_xB8WH
#> 2 Mg     0.805  YJ     robust_bcyj_xB8WH
#> 3 P      0.808  YJ     robust_bcyj_xB8WH
#> 4 K      0.871  YJ     robust_bcyj_xB8WH
#> 5 Mn    -0.150  BC     robust_bcyj_xB8WH
bake(prepped, new_data = contents[301:368, ])
#> # A tibble: 68 × 5
#>        Ca      Mg      P      K      Mn
#>     <dbl>   <dbl>  <dbl>  <dbl>   <dbl>
#>  1 -1.35  -1.60   -0.404  0.546  0.465 
#>  2  0.993  2.33   -0.790 -0.137  2.27  
#>  3  0.903  0.0603  1.07   1.22  -0.867 
#>  4  1.00   0.0188 -0.345  0.271 -0.408 
#>  5  1.49   0.798  -0.286  0.389  2.74  
#>  6 -2.41  -1.85   -0.873 -0.782  0.0961
#>  7 -0.854 -0.919  -0.424 -1.12  -0.479 
#>  8  0.660 -0.0441 -0.728 -0.221 -0.479 
#>  9  1.39   1.39    2.56   1.64   1.25  
#> 10  0.898  2.39    2.76   0.681  2.20  
#> # ℹ 58 more rows