step_spatialsign {recipes} | R Documentation

## Spatial Sign Preprocessing

### Description

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

### Usage

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

    ## S3 method for class '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.

### 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.

### 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.

### 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

    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)
    tidy(ss_obj, number = 3)

[Package recipes version 0.1.5 Index]