sqrt_x {bestNormalize} | R Documentation |

Perform a sqrt (x+a) normalization transformation

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

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

`a` |
The constant to add to x (defaults to max(0, -min(x))) |

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

`...` |
additional arguments |

`object` |
an object of class 'sqrt_x' |

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

`inverse` |
if TRUE, performs reverse transformation |

`sqrt_x`

performs a simple square-root 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), while the base
must be specified beforehand.

A list of class `sqrt_x`

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 |

`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) sqrt_x_obj <- sqrt_x(x) sqrt_x_obj p <- predict(sqrt_x_obj) x2 <- predict(sqrt_x_obj, newdata = p, inverse = TRUE) all.equal(x2, x)

[Package *bestNormalize* version 1.8.0 Index]