lambert {bestNormalize} | R Documentation |

## Lambert W x F Normalization

### Description

Perform Lambert's W x F transformation and center/scale a vector
to attempt normalization via the `LambertW`

package.

### Usage

```
lambert(x, type = "s", standardize = TRUE, warn = FALSE, ...)
## S3 method for class 'lambert'
predict(object, newdata = NULL, inverse = FALSE, ...)
## S3 method for class 'lambert'
print(x, ...)
```

### Arguments

`x` |
A vector to normalize with Box-Cox |

`type` |
a character indicating which transformation to perform (options are "s", "h", and "hh", see details) |

`standardize` |
If TRUE, the transformed values are also centered and scaled, such that the transformation attempts a standard normal |

`warn` |
should the function show warnings |

`...` |
Additional arguments that can be passed to the LambertW::Gaussianize function |

`object` |
an object of class 'lambert' |

`newdata` |
a vector of data to be (reverse) transformed |

`inverse` |
if TRUE, performs reverse transformation |

### Details

`lambert`

uses the `LambertW`

package to estimate a
normalizing (or "Gaussianizing") transformation. This transformation can be
performed on new data, and inverted, via the `predict`

function.

NOTE: The type = "s" argument is the only one that does the 1-1 transform
consistently, and so it is the only method currently used in
`bestNormalize()`

. Use type = "h" or type = 'hh' at risk of not having
this estimate 1-1 transform. These alternative types are effective when the
data has exceptionally heavy tails, e.g. the Cauchy distribution.

Additionally, sometimes (depending on the distribution) this method will be unable to extrapolate beyond the observed bounds. In these cases, NaN is returned.

### Value

A list of class `lambert`

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 |

`tau.mat` |
estimated parameters of LambertW::Gaussianize |

`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.

### References

Georg M. Goerg (2016). LambertW: An R package for Lambert W x F Random Variables. R package version 0.6.4.

Georg M. Goerg (2011): Lambert W random variables - a new family of generalized skewed distributions with applications to risk estimation. Annals of Applied Statistics 3(5). 2197-2230.

Georg M. Goerg (2014): The Lambert Way to Gaussianize heavy-tailed data with the inverse of Tukey's h transformation as a special case. The Scientific World Journal.

### See Also

### Examples

```
## Not run:
x <- rgamma(100, 1, 1)
lambert_obj <- lambert(x)
lambert_obj
p <- predict(lambert_obj)
x2 <- predict(lambert_obj, newdata = p, inverse = TRUE)
all.equal(x2, x)
## End(Not run)
```

*bestNormalize*version 1.9.1 Index]