log_x: Log(x + a) Transformation

In bestNormalize: Normalizing Transformation Functions

Log(x + a) Transformation

Description

Perform a log_b (x+a) normalization transformation

Usage

log_x(x, a = NULL, b = 10, standardize = TRUE, eps = 0.001, warn = TRUE, ...)

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

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

Arguments

x	A vector to normalize with with x
a	The constant to add to x (defaults to max(0, -min(x) + eps)); see bestLogConstant
b	The base of the log (defaults to 10)
standardize	If TRUE, the transformed values are also centered and scaled, such that the transformation attempts a standard normal
eps	The allowed error in the expression for the selected a
warn	Should a warning result from infinite values?
...	additional arguments
object	an object of class 'log_x'
newdata	a vector of data to be (potentially reverse) transformed
inverse	if TRUE, performs reverse transformation

Details

log_x performs a simple log transformation in the context of bestNormalize, such that it creates a transformation that can be estimated and applied to new data via the predict function. The parameter a is essentially estimated by the training set by default (estimated as the minimum possible to some extent epsilon), while the base must be specified beforehand.

Value

A list of class log_x 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
a	estimated a value
b	estimated base b value
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.

Examples

x <- rgamma(100, 1, 1)

log_x_obj <- log_x(x)
log_x_obj
p <- predict(log_x_obj)
x2 <- predict(log_x_obj, newdata = p, inverse = TRUE)

all.equal(x2, x)

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