# yeojohnson

##### Yeo-Johnson Normalization

Perform a Yeo-Johnson Transformation and center/scale a vector to attempt normalization

##### Usage

`yeojohnson(x, eps = 0.001, ...)`# S3 method for yeojohnson
predict(object, newdata = NULL, inverse = FALSE, ...)

# S3 method for yeojohnson
print(x, ...)

##### Arguments

- x
A vector to normalize with Yeo-Johnson

- eps
A value to compare lambda against to see if it is equal to zero

- ...
Additional arguments that can be passed to the estimation of the lambda parameter (lower, upper)

- object
an object of class 'yeojohnson'

- newdata
a vector of data to be (reverse) transformed

- inverse
if TRUE, performs reverse transformation

##### Details

`yeojohnson`

estimates the optimal value of lamda for the Yeo-Johnson
transformation. This transformation can be performed on new data, and
inverted, via the `predict`

function.

The Yeo-Johnson is similar to the Box-Cox method, however it allows for the transformation of nonpositive data as well.

##### Value

A list of class `yeojohnson`

with elements

transformed original data

original data

mean of vector post-YJ transformation

sd of vector post-BC transformation

estimated lambda value for skew transformation

number of nonmissing observations

Pearson's P / degrees of freedom

##### References

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

Max Kuhn and Hadley Wickham (2017). recipes: Preprocessing Tools to Create Design Matrices. R package version 0.1.0.9000. https://github.com/topepo/recipes

##### See Also

##### Examples

```
# NOT RUN {
x <- rgamma(100, 1, 1)
yeojohnson_obj <- yeojohnson(x)
yeojohnson_obj
p <- predict(yeojohnson_obj)
x2 <- predict(yeojohnson_obj, newdata = p, inverse = TRUE)
all.equal(x2, x)
# }
```

*Documentation reproduced from package bestNormalize, version 0.2.2, License: GPL-3*