standardize: 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

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.

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.

Details

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

Examples

Run this code

# NOT RUN {
## 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
# }