Spatial sign preprocessing

step_spatialsign() is a specification of a recipe step that will convert numeric data into a projection on to a unit sphere.

step_spatialsign(
  recipe,
  ...,
  role = "predictor",
  na_rm = TRUE,
  trained = FALSE,
  columns = NULL,
  skip = FALSE,
  id = rand_id("spatialsign")
)

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.

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.

na_rm

A logical: should missing data be removed from the norm computation?

trained

A logical to indicate if the quantities for preprocessing have been estimated.

columns

A character string of the selected variable names. This field is a placeholder and will be populated once prep() is used.

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

The spatial sign transformation projects the variables onto a unit sphere and is related to global contrast normalization. The spatial sign of a vector w is w/norm(w).

The variables should be centered and scaled prior to the computations.

Tidying

When you tidy() this step, a tibble is returned with columns terms and id:

terms

character, the selectors or variables selected

id

character, id of this step

Case weights

This step performs an unsupervised operation that can utilize case weights. As a result, only frequency weights are allowed. For more information, see the documentation in case_weights and the examples on tidymodels.org.

Unlike most, this step requires the case weights to be available when new samples are processed (e.g., when bake() is used or predict() with a workflow). To tell recipes that the case weights are required at bake time, use recipe |> update_role_requirements(role = "case_weights", bake = TRUE). See update_role_requirements() for more information.

References

Serneels, S., De Nolf, E., and Van Espen, P. (2006). Spatial sign preprocessing: a simple way to impart moderate robustness to multivariate estimators. Journal of Chemical Information and Modeling, 46(3), 1402-1409.

See also

Other multivariate transformation steps: step_classdist(), step_classdist_shrunken(), step_depth(), step_geodist(), step_ica(), step_isomap(), step_kpca(), step_kpca_poly(), step_kpca_rbf(), step_mutate_at(), step_nnmf(), step_nnmf_sparse(), step_pca(), step_pls(), step_ratio()

Examples

data(biomass, package = "modeldata")

biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]

rec <- recipe(
  HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
  data = biomass_tr
)

ss_trans <- rec |>
  step_center(carbon, hydrogen) |>
  step_scale(carbon, hydrogen) |>
  step_spatialsign(carbon, hydrogen)

ss_obj <- prep(ss_trans, training = biomass_tr)

transformed_te <- bake(ss_obj, biomass_te)

plot(biomass_te$carbon, biomass_te$hydrogen)


plot(transformed_te$carbon, transformed_te$hydrogen)


tidy(ss_trans, number = 3)
#> # A tibble: 2 × 2
#>   terms    id               
#>   <chr>    <chr>            
#> 1 carbon   spatialsign_Geidk
#> 2 hydrogen spatialsign_Geidk
tidy(ss_obj, number = 3)
#> # A tibble: 2 × 2
#>   terms    id               
#>   <chr>    <chr>            
#> 1 carbon   spatialsign_Geidk
#> 2 hydrogen spatialsign_Geidk