bestLogConstant: Calculate and perform best normalizing log transformation...

In bestNormalize: Normalizing Transformation Functions

Calculate and perform best normalizing log transformation (experimental)

Description

Similar to bestNormalize, this selects the best candidate constant for a log transformation on the basis of the Pearson P test statistic for normality. The transformation that has the lowest P (calculated on the transformed data) is selected. This function is currently in development and may not behave as expected.

See details for more information.

Usage

bestLogConstant(x, a, standardize = TRUE, ...)

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

## S3 method for class 'bestLogConstant'
print(x, ...)

Arguments

x	A vector to normalize
a	(optional) a list of candidate constants to choose from
standardize	If TRUE, the transformed values are also centered and scaled, such that the transformation attempts a standard normal. This will not change the normality statistic.
...	additional arguments.
object	an object of class 'bestLogConstant'
newdata	a vector of data to be (reverse) transformed
inverse	if TRUE, performs reverse transformation

Details

bestLogConstant estimates the optimal normalizing constant for a log transformation. This transformation can be performed on new data, and inverted, via the predict function.

Value

A list of class bestLogConstant with elements

x.t	transformed original data
x	original data
norm_stats	Pearson's Pearson's P / degrees of freedom
method	out-of-sample or in-sample, number of folds + repeats
chosen_constant	the chosen constant transformation (of class 'log_x')
other_transforms	the other transformations (of class 'log_x')

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

See Also

bestNormalize, log_x,

bestNormalize documentation built on Aug. 18, 2023, 9:08 a.m.