double_reverse_log {bestNormalize} | R Documentation |

First reverses scores, then perform a log_b(x) normalization transformation, and then reverses scores again.

```
double_reverse_log(
x,
b = 10,
standardize = TRUE,
eps = diff(range(x, na.rm = TRUE))/10,
warn = TRUE,
...
)
## S3 method for class 'double_reverse_log'
predict(object, newdata = NULL, inverse = FALSE, ...)
## S3 method for class 'double_reverse_log'
print(x, ...)
```

`x` |
A vector to normalize with with x |

`b` |
The base of the log (defaults to 10) |

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

`eps` |
The cushion for the transformation range (defaults to 10 percent) |

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

`...` |
additional arguments |

`object` |
an object of class 'double_reverse_log' |

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

`inverse` |
if TRUE, performs reverse transformation |

`double_reverse_log`

performs a simple log transformation in the
context of bestNormalize, such that it creates a transformation that can be
estimated and applied to new data via the `predict`

function. The parameter
a is essentially estimated by the training set by default (estimated as the
minimum possible to some extent epsilon), while the base must be specified
beforehand.

A list of class `double_reverse_log`

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 |

`b` |
estimated base b value |

`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.

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

[Package *bestNormalize* version 1.9.1 Index]