Yeo-Johnson Normalization

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

yeojohnson(x, eps = 0.001, standardize = TRUE, ...)

# 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

standardize

If TRUE, the transformed values are also centered and scaled, such that the transformation attempts a standard normal

...

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

Value

A list of class yeojohnson with elements

x.t

transformed original data

x

original data

mean

mean after transformation but prior to standardization

sd

sd after transformation but prior to standardization

lambda

estimated lambda value for skew transformation

n

number of nonmissing observations

norm_stat

Pearson's P / degrees of freedom

standardize

Was the transformation standardized

The predict function returns the numeric value of the transformation performed on new data, and allows for the inverse transformation as well.

Details

yeojohnson estimates the optimal value of lambda 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. The step_YeoJohnson function in the recipes package is another useful resource (see references).

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

Examples

x <- rgamma(100, 1, 1)

yeojohnson_obj <- yeojohnson(x)
yeojohnson_obj
#> Standardized Yeo-Johnson Transformation with 100 nonmissing obs.:
#>  Estimated statistics:
#>  - lambda = -0.6285334 
#>  - mean (before standardization) = 0.4860509 
#>  - sd (before standardization) = 0.2433941 
p <- predict(yeojohnson_obj)
x2 <- predict(yeojohnson_obj, newdata = p, inverse = TRUE)

all.equal(x2, x)
#> [1] TRUE