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)
# }