yeojohnson: Yeo-Johnson Normalization

In petersonR/bestNormalize: Normalizing Transformation Functions

Yeo-Johnson Normalization

Description

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

Usage

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

## S3 method for class 'yeojohnson'
predict(object, newdata = NULL, inverse = FALSE, ...)

## S3 method for class '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

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).

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.

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
p <- predict(yeojohnson_obj)
x2 <- predict(yeojohnson_obj, newdata = p, inverse = TRUE)

all.equal(x2, x)

petersonR/bestNormalize documentation built on March 15, 2024, 9:29 a.m.