Winsorize {DescTools}

R Documentation

Winsorize (Replace Extreme Values by Less Extreme Ones)

Description

Winsorizing a vector means that a predefined quantum of the smallest and/or the largest values are replaced by less extreme values. Thereby the substitute values are the most extreme retained values.

Usage

Winsorize(x, minval = NULL, maxval = NULL, probs = c(0.05, 0.95),
          na.rm = FALSE, type = 7)

Arguments

x	a numeric vector to be winsorized.
minval	the low border, all values being lower than this will be replaced by this value. The default is set to the 5%-quantile of x.
maxval	the high border, all values being larger than this will be replaced by this value. The default is set to the 95%-quantile of x.
probs	numeric vector of probabilities with values in [0,1] as used in quantile.
na.rm	should NAs be omitted to calculate the quantiles? Note that NAs in x are preserved and left unchanged anyway.
type	an integer between 1 and 9 selecting one of the nine quantile algorithms detailed in quantile to be used.

Details

The winsorized vector is obtained by

g(x) = \left\{\begin{array}{ll} -c &\textup{for }x \le -c\\ x &\textup{for } |x| < c\\ c &\textup{for }x \ge c \end{array}\right.

You may also want to consider standardizing (possibly robustly) the data before you perform a winsorization.

Value

A vector of the same length as the original data x containing the winsorized data.

Author(s)

Andri Signorell <andri@signorell.net>

See Also

Winsorize from the package robustHD contains an option to winsorize multivariate data

scale, RobScale

Examples

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

## Winsorize data
x
Winsorize(x)

# use Large and Small, if a fix number of values should be winsorized (here k=3):
Winsorize(x, minval=tail(Small(x, k=3), 1), maxval=head(Large(x, k=3), 1))

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