ORQ normalization (orderNorm) for recipes implementation

`step_orderNorm` creates a specification of a recipe step (see `recipes` package) that will transform data using the ORQ (orderNorm) transformation, which approximates the "true" normalizing transformation if one exists. This is considerably faster than `step_bestNormalize`.

step_orderNorm(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  transform_info = NULL,
  transform_options = list(),
  num_unique = 5,
  skip = FALSE,
  id = rand_id("orderNorm")
)

# S3 method for step_orderNorm
tidy(x, ...)

# S3 method for step_orderNorm
axe_env(x, ...)

Arguments

recipe

A formula or 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

For recipes functionality

transform_info

A numeric vector of transformation values. This (was transform_info) is `NULL` until computed by [prep.recipe()].

transform_options

options to be passed to orderNorm

num_unique

An integer where data that have less possible values will not be evaluate for a transformation.

skip

For recipes functionality

id

For recipes functionality

x

A `step_orderNorm` object.

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 lambda estimate).

Details

The orderNorm transformation can be used to rescale a variable to be more similar to a normal distribution. See `?orderNorm` for more information; `step_orderNorm` is the implementation of `orderNorm` in the `recipes` context.

As of version 1.7, the `butcher` package can be used to (hopefully) improve scalability of this function on bigger data sets.

References

Ryan A. Peterson (2019). Ordered quantile normalization: a semiparametric transformation built for the cross-validation era. Journal of Applied Statistics, 1-16.

See also

orderNorm bestNormalize, [recipe()] [prep.recipe()] [bake.recipe()]

Examples

library(recipes)
rec <- recipe(~ ., data = as.data.frame(iris))

orq_trans <- step_orderNorm(rec, all_numeric())

orq_estimates <- prep(orq_trans, training = as.data.frame(iris))

orq_data <- bake(orq_estimates, as.data.frame(iris))

plot(density(iris[, "Petal.Length"]), main = "before")

plot(density(orq_data$Petal.Length), main = "after")


tidy(orq_trans, number = 1)
#> # A tibble: 1 × 3
#>   terms         value id             
#>   <chr>         <dbl> <chr>          
#> 1 all_numeric()    NA orderNorm_EVEeP
tidy(orq_estimates, number = 1)
#> # A tibble: 4 × 3
#>   terms        value        id             
#>   <chr>        <named list> <chr>          
#> 1 Sepal.Length <orderNrm>   orderNorm_EVEeP
#> 2 Sepal.Width  <orderNrm>   orderNorm_EVEeP
#> 3 Petal.Length <orderNrm>   orderNorm_EVEeP
#> 4 Petal.Width  <orderNrm>   orderNorm_EVEeP