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")
)

# S3 method for step_spatialsign
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 used for the normalization. See selections() for more details. For the tidy method, these are not currently used.

role

For model terms created by this step, what analysis role should they be assigned?

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 variable names that will be populated (eventually) by the terms argument.

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_spatialsign 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 which is the columns that will be affected.

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.

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.

Examples

library(modeldata) data(biomass) 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 x 2 #> terms id #> <chr> <chr> #> 1 carbon spatialsign_rfFce #> 2 hydrogen spatialsign_rfFce
tidy(ss_obj, number = 3)
#> # A tibble: 2 x 2 #> terms id #> <chr> <chr> #> 1 carbon spatialsign_rfFce #> 2 hydrogen spatialsign_rfFce