prepare function

Transformation and standardization

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

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").

Returns

Transformed and standardized data.

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.

Examples

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

See Also

scale(), car::powerTransform()

Author(s)

Matthias Templ, modifications by Andreas Alfons