boxcox {bestNormalize} | R Documentation |

## Box-Cox Normalization

### Description

Perform a Box-Cox transformation and center/scale a vector to attempt normalization

### Usage

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

### Arguments

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

`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, epsilon) |

`object` |
an object of class 'boxcox' |

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

`inverse` |
if TRUE, performs reverse transformation |

### Details

`boxcox`

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

function.

The function will return an error if a user attempt to transform nonpositive data.

### Value

A list of class `boxcox`

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

Box, G. E. P. and Cox, D. R. (1964) An analysis of transformations. Journal of the Royal Statistical Society B, 26, 211-252.

### See Also

### Examples

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

*bestNormalize*version 1.9.1 Index]