DescTools (version 0.99.19)

Winsorize: Winsorize

Description

Clean data by means of winsorization, i.e., by shrinking outlying observations to the border of the main part of the data.

Usage

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

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.

Value

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

Details

Consider standardizing (possibly robust) the data before winsorizing. See scale, RobScale

See Also

Winsorize library(robustHD) contains an option to winsorize multivariate data

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))