Perform Lambert's W x F transformation and center/scale a vector
to attempt normalization via the LambertW package.
lambert(x, type = "s", standardize = TRUE, warn = FALSE, ...)# S3 method for lambert
predict(object, newdata = NULL, inverse = FALSE, ...)
# S3 method for lambert
print(x, ...)
A list of class lambert with elements
transformed original data
original data
mean after transformation but prior to standardization
sd after transformation but prior to standardization
estimated parameters of LambertW::Gaussianize
number of nonmissing observations
Pearson's P / degrees of freedom
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.
A vector to normalize with Box-Cox
a character indicating which transformation to perform (options are "s", "h", and "hh", see details)
If TRUE, the transformed values are also centered and scaled, such that the transformation attempts a standard normal
should the function show warnings
Additional arguments that can be passed to the LambertW::Gaussianize function
an object of class 'lambert'
a vector of data to be (reverse) transformed
if TRUE, performs reverse transformation
lambert uses the LambertW package to estimate a
normalizing (or "Gaussianizing") transformation. This transformation can be
performed on new data, and inverted, via the predict function.
NOTE: The type = "s" argument is the only one that does the 1-1 transform
consistently, and so it is the only method currently used in
bestNormalize(). Use type = "h" or type = 'hh' at risk of not having
this estimate 1-1 transform. These alternative types are effective when the
data has exceptionally heavy tails, e.g. the Cauchy distribution.
Additionally, sometimes (depending on the distribution) this method will be unable to extrapolate beyond the observed bounds. In these cases, NaN is returned.
Georg M. Goerg (2016). LambertW: An R package for Lambert W x F Random Variables. R package version 0.6.4.
Georg M. Goerg (2011): Lambert W random variables - a new family of generalized skewed distributions with applications to risk estimation. Annals of Applied Statistics 3(5). 2197-2230.
Georg M. Goerg (2014): The Lambert Way to Gaussianize heavy-tailed data with the inverse of Tukey's h transformation as a special case. The Scientific World Journal.
Gaussianize
if (FALSE) {
x <- rgamma(100, 1, 1)
lambert_obj <- lambert(x)
lambert_obj
p <- predict(lambert_obj)
x2 <- predict(lambert_obj, newdata = p, inverse = TRUE)
all.equal(x2, x)
}
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