Feature normalization step using total sum scaling

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, ...)

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

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 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.

x

A step_rownormalize_tss object.

Value

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

Author

Antoine Bichat

Examples

rec <-
  recipe(Species ~ ., data = iris) %>%
  step_rownormalize_tss(all_numeric_predictors()) %>%
  prep()
rec
#> 
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> outcome:   1
#> predictor: 4
#> 
#> ── Training information 
#> Training data contained 150 data points and no incomplete rows.
#> 
#> ── Operations 
#> • TSS normalization on: Sepal.Length and Sepal.Width, ... | Trained
tidy(rec, 1)
#> # A tibble: 4 × 2
#>   terms        id                    
#>   <chr>        <chr>                 
#> 1 Sepal.Length rownormalize_tss_ieexo
#> 2 Sepal.Width  rownormalize_tss_ieexo
#> 3 Petal.Length rownormalize_tss_ieexo
#> 4 Petal.Width  rownormalize_tss_ieexo
bake(rec, new_data = NULL)
#> # A tibble: 150 × 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#>  1        0.5         0.343        0.137      0.0196 setosa 
#>  2        0.516       0.316        0.147      0.0211 setosa 
#>  3        0.5         0.340        0.138      0.0213 setosa 
#>  4        0.489       0.330        0.160      0.0213 setosa 
#>  5        0.490       0.353        0.137      0.0196 setosa 
#>  6        0.474       0.342        0.149      0.0351 setosa 
#>  7        0.474       0.351        0.144      0.0309 setosa 
#>  8        0.495       0.337        0.149      0.0198 setosa 
#>  9        0.494       0.326        0.157      0.0225 setosa 
#> 10        0.510       0.323        0.156      0.0104 setosa 
#> # ℹ 140 more rows