R: Data standardization

standardize {robustHD} | R Documentation

## Data standardization

### Description

Standardize data with given functions for computing center and scale.

### Usage

    standardize(x, centerFun = mean, scaleFun = sd)

    robStandardize(
      x,
      centerFun = median,
      scaleFun = mad,
      fallback = FALSE,
      eps = .Machine$double.eps,
      ...
    )

### Arguments

`x` | a numeric vector, matrix or data frame to be standardized.
`centerFun` | a function to compute an estimate of the center of a variable (defaults to `mean`).
`scaleFun` | a function to compute an estimate of the scale of a variable (defaults to `sd`).
`fallback` | a logical indicating whether standardization with `mean` and `sd` should be performed as a fallback mode for variables whose robust scale estimate is too small. This is useful, e.g., for data containing dummy variables.
`eps` | a small positive numeric value used to determine whether the robust scale estimate of a variable is too small (an effective zero).
`...` | currently ignored.

### Details

`robStandardize` is a wrapper function for robust standardization, hence the default is to use `median` and `mad`.

### Value

An object of the same type as the original data `x` containing the centered and scaled data. The center and scale estimates of the original data are returned as attributes `"center"` and `"scale"`, respectively.

### Note

The implementation contains special cases for the typically used combinations `mean`/`sd` and `median`/`mad` in order to reduce computation time.

### Author(s)

Andreas Alfons

### See Also

`scale`, `sweep`

### Examples

    ## generate data
    set.seed(1234)     # for reproducibility
    x <- rnorm(10)     # standard normal
    x[1] <- x[1] * 10  # introduce outlier

    ## standardize data
    x
    standardize(x)     # mean and sd
    robStandardize(x)  # median and MAD

[Package robustHD version 0.7.2 Index]