scimo (version 0.0.3)

step_rownormalize_tss: Feature normalization step using total sum scaling

Description

Normalize a set of variables by converting them to proportion, making them sum to 1. Also known as simplex projection.

Usage

step_rownormalize_tss(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  res = NULL,
  skip = FALSE,
  id = rand_id("rownormalize_tss")
)
# S3 method for step_rownormalize_tss
tidy(x, ...)

Value

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

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 recipes::selections() for more details.

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.

res

This parameter is only produced after the recipe has been trained.

skip

A logical. Should the step be skipped when the recipe is baked by recipes::bake()? While all operations are baked when recipes::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.

x

A step_rownormalize_tss object.

Author

Antoine Bichat

Examples

rec <-
  recipe(Species ~ ., data = iris) %>%
  step_rownormalize_tss(all_numeric_predictors()) %>%
  prep()
rec
tidy(rec, 1)
bake(rec, new_data = NULL)