Square root transformation

step_sqrt() creates a specification of a recipe step that will apply square root transform to the variables.

step_sqrt(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  skip = FALSE,
  id = rand_id("sqrt")
)

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.

columns

A character string of the selected variable names. This field is a placeholder and will be populated once prep() is used.

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.

Value

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

Tidying

When you tidy() this step, a tibble is returned with columns terms and id:

terms

character, the selectors or variables selected

id

character, id of this step

Sparse data

This step can be applied to sparse_data such that it is preserved. Nothing needs to be done for this to happen as it is done automatically.

Case weights

The underlying operation does not allow for case weights.

See also

Other individual transformation steps: step_BoxCox(), step_YeoJohnson(), step_bs(), step_harmonic(), step_hyperbolic(), step_inverse(), step_invlogit(), step_log(), step_logit(), step_mutate(), step_ns(), step_percentile(), step_poly(), step_relu()

Examples

set.seed(313)
examples <- matrix(rnorm(40)^2, ncol = 2)
examples <- as.data.frame(examples)

rec <- recipe(~ V1 + V2, data = examples)

sqrt_trans <- rec |>
  step_sqrt(all_numeric_predictors())

sqrt_obj <- prep(sqrt_trans, training = examples)

transformed_te <- bake(sqrt_obj, examples)
plot(examples$V1, transformed_te$V1)


tidy(sqrt_trans, number = 1)
#> # A tibble: 1 × 2
#>   terms                    id        
#>   <chr>                    <chr>     
#> 1 all_numeric_predictors() sqrt_IhS7o
tidy(sqrt_obj, number = 1)
#> # A tibble: 2 × 2
#>   terms id        
#>   <chr> <chr>     
#> 1 V1    sqrt_IhS7o
#> 2 V2    sqrt_IhS7o