sqrt_x {bestNormalize} R Documentation

## sqrt(x + a) Normalization

### Description

Perform a sqrt (x+a) normalization transformation

### Usage

```sqrt_x(x, a = NULL, standardize = TRUE, ...)

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

## S3 method for class 'sqrt_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))) `standardize` If TRUE, the transformed values are also centered and scaled, such that the transformation attempts a standard normal `...` additional arguments `object` an object of class 'sqrt_x' `newdata` a vector of data to be (potentially reverse) transformed `inverse` if TRUE, performs reverse transformation

### Details

`sqrt_x` performs a simple square-root 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), while the base must be specified beforehand.

### Value

A list of class `sqrt_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

```x <- rgamma(100, 1, 1)

sqrt_x_obj <- sqrt_x(x)
sqrt_x_obj
p <- predict(sqrt_x_obj)
x2 <- predict(sqrt_x_obj, newdata = p, inverse = TRUE)

all.equal(x2, x)

```

[Package bestNormalize version 1.8.0 Index]