`step_YeoJohnson()`

creates a *specification* of a recipe step that will
transform data using a Yeo-Johnson transformation.

## 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.

- lambdas
A numeric vector of transformation values. This is

`NULL`

until computed by`prep()`

.- limits
A length 2 numeric vector defining the range to compute the transformation parameter lambda.

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

- na_rm
A logical value indicating whether

`NA`

values should be removed during computations.- 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.

## Details

The Yeo-Johnson transformation is very similar to the Box-Cox but does not require the input variables to be strictly positive. In the package, the partial log-likelihood function is directly optimized within a reasonable set of transformation values (which can be changed by the user).

This transformation is typically done on the outcome variable
using the residuals for a statistical model (such as ordinary
least squares). Here, a simple null model (intercept only) is
used to apply the transformation to the *predictor*
variables individually. This can have the effect of making the
variable distributions more symmetric.

If the transformation parameters are estimated to be very
closed to the bounds, or if the optimization fails, a value of
`NA`

is used and no transformation is applied.

## Tidying

When you `tidy()`

this step, a tibble with columns
`terms`

(the selectors or variables selected) and `value`

(the
lambda estimate) is returned.

## References

Yeo, I. K., and Johnson, R. A. (2000). A new family of power
transformations to improve normality or symmetry. *Biometrika*.

## See also

Other individual transformation steps:
`step_BoxCox()`

,
`step_bs()`

,
`step_harmonic()`

,
`step_hyperbolic()`

,
`step_inverse()`

,
`step_invlogit()`

,
`step_logit()`

,
`step_log()`

,
`step_mutate()`

,
`step_ns()`

,
`step_percentile()`

,
`step_poly()`

,
`step_relu()`

,
`step_sqrt()`

## Examples

```
data(biomass, package = "modeldata")
biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]
rec <- recipe(
HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
data = biomass_tr
)
yj_transform <- step_YeoJohnson(rec, all_numeric())
yj_estimates <- prep(yj_transform, training = biomass_tr)
yj_te <- bake(yj_estimates, biomass_te)
plot(density(biomass_te$sulfur), main = "before")
plot(density(yj_te$sulfur), main = "after")
tidy(yj_transform, number = 1)
#> # A tibble: 1 × 3
#> terms value id
#> <chr> <dbl> <chr>
#> 1 all_numeric() NA YeoJohnson_A4Lkd
tidy(yj_estimates, number = 1)
#> # A tibble: 6 × 3
#> terms value id
#> <chr> <dbl> <chr>
#> 1 carbon -0.0225 YeoJohnson_A4Lkd
#> 2 hydrogen 2.10 YeoJohnson_A4Lkd
#> 3 oxygen 1.78 YeoJohnson_A4Lkd
#> 4 nitrogen -0.830 YeoJohnson_A4Lkd
#> 5 sulfur -4.09 YeoJohnson_A4Lkd
#> 6 HHV -0.388 YeoJohnson_A4Lkd
```