# exp_x: exp(x) Transformation In bestNormalize: Normalizing Transformation Functions

## Description

Perform a exp(x) transformation

## Usage

 ```1 2 3 4 5 6 7``` ```exp_x(x, standardize = TRUE, warn = TRUE, ...) ## S3 method for class 'exp_x' predict(object, newdata = NULL, inverse = FALSE, ...) ## S3 method for class 'exp_x' print(x, ...) ```

## Arguments

 `x` A vector to normalize with with x `standardize` If TRUE, the transformed values are also centered and scaled, such that the transformation attempts a standard normal `warn` Should a warning result from infinite values? `...` additional arguments `object` an object of class 'exp_x' `newdata` a vector of data to be (potentially reverse) transformed `inverse` if TRUE, performs reverse transformation

## Details

`exp_x` performs a simple exponential 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.

## Value

A list of class `exp_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 `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

 ```1 2 3 4 5 6 7 8``` ```x <- rgamma(100, 1, 1) exp_x_obj <- exp_x(x) exp_x_obj p <- predict(exp_x_obj) x2 <- predict(exp_x_obj, newdata = p, inverse = TRUE) all.equal(x2, x) ```

bestNormalize documentation built on June 3, 2021, 5:10 p.m.