no_transform {bestNormalize} | R Documentation |

Perform an identity transformation. Admittedly it seems odd to
have a dedicated function to essentially do I(x), but it makes sense to
keep the same syntax as the other transformations so it plays nicely
with them. As a benefit, the bestNormalize function will also show
a comparable normalization statistic for the untransformed data. If
`standardize == TRUE`

, `center_scale`

passes to bestNormalize instead.

```
no_transform(x, warn = TRUE, ...)
## S3 method for class 'no_transform'
predict(object, newdata = NULL, inverse = FALSE, ...)
## S3 method for class 'no_transform'
print(x, ...)
center_scale(x, warn = TRUE, ...)
## S3 method for class 'center_scale'
predict(object, newdata = NULL, inverse = FALSE, ...)
## S3 method for class 'center_scale'
print(x, ...)
## S3 method for class 'no_transform'
tidy(x, ...)
```

`x` |
A 'no_transform' object. |

`warn` |
Should a warning result from infinite values? |

`...` |
not used |

`object` |
an object of class 'no_transform' |

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

`inverse` |
if TRUE, performs reverse transformation |

`no_transform`

creates a identity transformation object
that can be applied to new data via the `predict`

function.

A list of class `no_transform`

with elements

`x.t` |
transformed original data |

`x` |
original data |

`n` |
number of nonmissing observations |

`norm_stat` |
Pearson's P / degrees of freedom |

The `predict`

function returns the numeric value of the transformation
performed on new data, and allows for the inverse transformation as well.

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

[Package *bestNormalize* version 1.9.1 Index]