binarize {bestNormalize} R Documentation

## Binarize

### Description

This function will perform a binarizing transformation, which could be used as a last resort if the data cannot be adequately normalized. This may be useful when accidentally attempting normalization of a binary vector (which could occur if implementing bestNormalize in an automated fashion).

Note that the transformation is not one-to-one, in contrast to the other functions in this package.

### Usage

```binarize(x, location_measure = "median")

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

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

### Arguments

 `x` A vector to binarize `location_measure` which location measure should be used? can either be "median", "mean", "mode", a number, or a function. `object` an object of class 'binarize' `newdata` a vector of data to be (reverse) transformed `inverse` if TRUE, performs reverse transformation `...` additional arguments

### Value

A list of class `binarize` with elements

 `x.t` transformed original data `x` original data `method` location_measure used for original fitting `location` estimated location_measure `n` number of nonmissing observations `norm_stat` Pearson's P / degrees of freedom

The `predict` function with `inverse = FALSE` returns the numeric value (0 or 1) of the transformation on `newdata` (which defaults to the original data).

If `inverse = TRUE`, since the transform is not 1-1, it will create and return a factor that indicates where the original data was cut.

### Examples

```x <- rgamma(100, 1, 1)
binarize_obj <- binarize(x)
(p <- predict(binarize_obj))

predict(binarize_obj, newdata = p, inverse = TRUE)
```

[Package bestNormalize version 1.8.0 Index]