binarize {bestNormalize} | R Documentation |

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.

binarize(x, location_measure = "median") ## S3 method for class 'binarize' predict(object, newdata = NULL, inverse = FALSE, ...) ## S3 method for class 'binarize' print(x, ...)

`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 |

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.

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]