# step_YeoJohnson

##### Yeo-Johnson Transformation

`step_YeoJohnson`

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

- Keywords
- datagen

##### Usage

```
step_YeoJohnson(recipe, ..., role = NA, trained = FALSE, lambdas = NULL,
limits = c(-5, 5), nunique = 5)
```

##### 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 which variables are affected by the 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.recipe`

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

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

##### 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.

##### Value

An updated version of `recipe`

with the
new step added to the sequence of existing steps (if any).

##### References

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

##### See Also

##### Examples

```
# NOT RUN {
data(biomass)
biomass_tr <- biomass[biomass$dataset == "Training",]
biomass_te <- biomass[biomass$dataset == "Testing",]
rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
data = biomass_tr)
yj_trans <- step_YeoJohnson(rec, all_numeric())
yj_estimates <- prep(yj_trans, training = biomass_tr)
yj_te <- bake(yj_estimates, biomass_te)
plot(density(biomass_te$sulfur), main = "before")
plot(density(yj_te$sulfur), main = "after")
# }
```

*Documentation reproduced from package recipes, version 0.1.0, License: GPL-2*