yeojohnson {bestNormalize} | R Documentation |

## Yeo-Johnson Normalization

### Description

Perform a Yeo-Johnson Transformation and center/scale a vector to attempt normalization

### Usage

```
yeojohnson(x, eps = 0.001, standardize = TRUE, ...)
## S3 method for class 'yeojohnson'
predict(object, newdata = NULL, inverse = FALSE, ...)
## S3 method for class 'yeojohnson'
print(x, ...)
```

### Arguments

`x` |
A vector to normalize with Yeo-Johnson |

`eps` |
A value to compare lambda against to see if it is equal to zero |

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

`...` |
Additional arguments that can be passed to the estimation of the lambda parameter (lower, upper) |

`object` |
an object of class 'yeojohnson' |

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

`inverse` |
if TRUE, performs reverse transformation |

### Details

`yeojohnson`

estimates the optimal value of lambda for the
Yeo-Johnson transformation. This transformation can be performed on new
data, and inverted, via the `predict`

function.

The Yeo-Johnson is similar to the Box-Cox method, however it allows for the
transformation of nonpositive data as well. The `step_YeoJohnson`

function in the `recipes`

package is another useful resource (see
references).

### Value

A list of class `yeojohnson`

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 |

`lambda` |
estimated lambda value for skew transformation |

`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

Yeo, I. K., & Johnson, R. A. (2000). A new family of power transformations to improve normality or symmetry. Biometrika.

Max Kuhn and Hadley Wickham (2017). recipes: Preprocessing Tools to Create Design Matrices. R package version 0.1.0.9000. https://github.com/topepo/recipes

### Examples

```
x <- rgamma(100, 1, 1)
yeojohnson_obj <- yeojohnson(x)
yeojohnson_obj
p <- predict(yeojohnson_obj)
x2 <- predict(yeojohnson_obj, newdata = p, inverse = TRUE)
all.equal(x2, x)
```

*bestNormalize*version 1.9.1 Index]