R: Transformation and standardization

prepare {VIM} | R Documentation

## Transformation and standardization

### Description

This function is used by the `VIM` GUI for transformation and standardization of the data.

### Usage

    prepare(
      x,
      scaling = c("none", "classical", "MCD", "robust", "onestep"),
      transformation = c("none", "minus", "reciprocal", "logarithm", "exponential",
        "boxcox", "clr", "ilr", "alr"),
      alpha = NULL,
      powers = NULL,
      start = 0,
      alrVar
    )

### Arguments

`x` | a vector, matrix or `data.frame`.
`scaling` | the scaling to be applied to the data. Possible values are `"none"`, `"classical"`, `MCD`, `"robust"` and `"onestep"`.
`transformation` | the transformation of the data. Possible values are `"none"`, `"minus"`, `"reciprocal"`, `"logarithm"`, `"exponential"`, `"boxcox"`, `"clr"`, `"ilr"` and `"alr"`.
`alpha` | a numeric parameter controlling the size of the subset for the MCD (if `scaling="MCD"`). See `robustbase::covMcd()`.
`powers` | a numeric vector giving the powers to be used in the Box-Cox transformation (if `transformation="boxcox"`). If `NULL`, the powers are calculated with function `car::powerTransform()`.
`start` | a constant to be added prior to Box-Cox transformation (if `transformation="boxcox"`).
`alrVar` | variable to be used as denominator in the additive logratio transformation (if `transformation="alr"`).

### Details

Transformation:

`"none"`: no transformation is used.

`"logarithm"`: compute the the logarithm (to the base 10).

`"boxcox"`: apply a Box-Cox transformation. Powers may be specified or calculated with the function `car::powerTransform()`.

Standardization:

`"none"`: no standardization is used.

`"classical"`: apply a z-Transformation on each variable by using function `scale()`.

`"robust"`: apply a robustified z-Transformation by using median and MAD.

### Value

Transformed and standardized data.

### Author(s)

Matthias Templ, modifications by Andreas Alfons

### See Also

`scale()`, `car::powerTransform()`

### Examples

    data(sleep, package = "VIM")
    x <- sleep[, c("BodyWgt", "BrainWgt")]
    prepare(x, scaling = "robust", transformation = "logarithm")

[Package VIM version 6.0.0 Index]