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Pareto Scaling Recipe Step

Source: R/recipe_steps_preprocessing.R
step_pareto_scale.Rd

step_pareto_scale() creates a specification of a recipe step that divides each selected column by the square root of its standard deviation, as pareto_scale() does.

Usage

step_pareto_scale(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  scales = NULL,
  columns = NULL,
  skip = FALSE,
  id = recipes::rand_id("pareto_scale")
)

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 the predictors (for example, the spectral channels). See recipes::selections().

role

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

trained

A logical indicating whether the step has been trained.

scales

The scale of each column, stored once the step has been trained.

columns

The names of the selected columns, stored once the step has been trained.

skip

A logical. Should the step be skipped when the recipe is baked by recipes::bake()? Keep the default FALSE, so that new data are corrected with the same filter as the training data.

id

A character string that is unique to this step.

Value

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

Details

The standard deviations are estimated from the training data and applied unchanged to new data. Columns with zero standard deviation are not scaled. The data are not centered; add recipes::step_center() if needed. tidy() returns the selected terms, the scale each column is divided by (NA before the step is trained) and id.

See also

pareto_scale(), step_poisson_scale()

Examples

data(forageLIBS)
# potassium and the K I resonance lines
wl <- suppressWarnings(as.numeric(names(forageLIBS)))
dat <- forageLIBS[c(which(names(forageLIBS) == "K"), which(wl > 760 & wl < 780))]
rec <- recipes::recipe(K ~ ., data = dat[1:300, ]) |>
  step_pareto_scale(recipes::all_predictors())
prepped <- recipes::prep(rec)
recipes::bake(prepped, new_data = dat[301:368, ])
#> # A tibble: 68 × 246
#>    `760.0161416` `760.1001689` `760.1841961` `760.2682233` `760.3522506`
#>            <dbl>         <dbl>         <dbl>         <dbl>         <dbl>
#>  1          142.          143.          139.          138.          136.
#>  2          138.          132.          134.          135.          132.
#>  3          148.          155.          157.          150.          148.
#>  4          139.          144.          147.          141.          138.
#>  5          135.          141.          144.          148.          146.
#>  6          158.          159.          159.          156.          151.
#>  7          145.          152.          150.          145.          141.
#>  8          149.          145.          143.          145.          140.
#>  9          134.          137.          136.          140.          135.
#> 10          146.          151.          147.          150.          151.
#> # ℹ 58 more rows
#> # ℹ 241 more variables: `760.4362778` <dbl>, `760.520305` <dbl>,
#> #   `760.6043323` <dbl>, `760.6883595` <dbl>, `760.7723867` <dbl>,
#> #   `760.856414` <dbl>, `760.9404412` <dbl>, `761.0244684` <dbl>,
#> #   `761.1084957` <dbl>, `761.1925229` <dbl>, `761.2765501` <dbl>,
#> #   `761.3605774` <dbl>, `761.4446046` <dbl>, `761.5286319` <dbl>,
#> #   `761.6126591` <dbl>, `761.6966863` <dbl>, `761.7807136` <dbl>, …