| yeojohnson {bestNormalize} | R Documentation |
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)