Winsorize: Winsorized Mean

In SciencesPo: A Tool Set for Analyzing Political Behavior Data

Description

Compute the winsorized mean, which consists of recoding the top k values of a vector.

Usage

Winsorize(x, k = 1, na.rm = TRUE)

Arguments

x	The vector to be winsorized
k	An integer for the quantity of outlier elements that to be replaced in the calculation process
na.rm	a logical value for na.rm, default is na.rm=TRUE.

Details

Winsorizing a vector will produce different results than trimming it. While by trimming a vector causes extreme values to be discarded, by winsorizing it in the other hand, causes extreme values to be replaced by certain percentiles.

Value

An object of the same type as x

References

Dixon, W. J., and Yuen, K. K. (1999) Trimming and winsorization: A review. The American Statistician, 53(3), 267–269.

Dixon, W. J., and Yuen, K. K. (1960) Simplified Estimation from Censored Normal Samples, The Annals of Mathematical Statistics, 31, 385–391. @references Wilcox, R. R. (2012) Introduction to robust estimation and hypothesis testing. Academic Press, 30-32. Statistics Canada (2010) Survey Methods and Practices.

@note One may want to winsorize estimators, however, winsorization tends to be used for one-variable situations.

@author Daniel Marcelino, [e-mail]

@examples set.seed(51) # for reproducibility x <- rnorm(50) ## introduce outlier x[1] <- x[1] * 10

# Compare to mean: mean(x) Winsorize(x)

SciencesPo documentation built on May 29, 2017, 9:28 p.m.