step_center_to {sparseR}

Centering numeric data to a value besides their mean

Description

'step_center_to' generalizes 'step_center' to allow for a different function than the 'mean' function to calculate centers. It creates a *specification* of a recipe step that will normalize numeric data to have a 'center' of zero.

Usage

step_center_to(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  centers = NULL,
  center_fn = mean,
  na_rm = TRUE,
  skip = FALSE,
  id = rand_id("center_to")
)

## S3 method for class 'step_center_to'
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 are affected by the step. See [selections()] for more details. For the 'tidy' method, these are not currently used.

role

Not used by this step since no new variables are created.

trained

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

centers

A named numeric vector of centers. This is 'NULL' until computed by [prep.recipe()] (or it can be specified as a named numeric vector as well?).

center_fn

a function to be used to calculate where the center should be

na_rm

A logical value indicating whether 'NA' values should be removed during computations.

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_center_to' object.

Details

Centering data means that the average of a variable is subtracted from the data. 'step_center_to' estimates the variable centers from the data used in the 'training' argument of 'prep.recipe'. 'bake.recipe' then applies the centering to new data sets using these centers.

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' (the selectors or variables selected) and 'value' (the centers).

See Also

[recipe()] [prep.recipe()] [bake.recipe()]

Examples

data(biomass, package = "modeldata")

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

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

center_trans <- rec %>%
  step_center_to(carbon, contains("gen"), -hydrogen)

center_obj <- recipes::prep(center_trans, training = biomass_tr)

transformed_te <- recipes::bake(center_obj, biomass_te)

biomass_te[1:10, names(transformed_te)]
transformed_te

recipes::tidy(center_trans)
recipes::tidy(center_obj)