# winsorize: Winsorize transformation In jeffreyevans/spatialEco: Spatial Analysis and Modelling Utilities (development version)

## Description

Removes extreme outliers using a winsorization transformation

## Usage

 ```1 2``` ```winsorize(x, min.value = NULL, max.value = NULL, p = c(0.05, 0.95), na.rm = FALSE) ```

## Arguments

 `x` A numeric vector `min.value` A fixed lower bounds, all values lower than this will be replaced by this value. The default is set to the 5th-quantile of x. `max.value` A fixed upper bounds, all values higher than this will be replaced by this value. The default is set to the 95th-quantile of x. `p` A numeric vector of 2 representing the probabilities used in the quantile function. `na.rm` (FALSE/TRUE) should NAs be omitted?

## Value

A transformed vector the same length as x, unless na.rm is TRUE, then x is length minus number of NA's

## Note

Winsorization is the transformation of a distribution by limiting extreme values to reduce the effect of spurious outliers. This is done by shrinking outlying observations to the border of the main part of the distribution.

## Author(s)

Jeffrey S. Evans <[email protected]>

## References

Dixon, W.J. (1960) Simplified Estimation from Censored Normal Samples. Annals of Mathematical Statistics. 31(2):385-391

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```set.seed(1234) x <- rnorm(100) x[1] <- x[1] * 10 winsorize(x) plot(x, type="l", main="Winsorization transformation") lines(winsorize(x), col="red", lwd=2) legend("bottomright", legend=c("Original distribution","With outliers removed"), lty=c(1,1), col=c("black","red")) # Behavior with NA value(s) x[4] <- NA winsorize(x) # returns x with original NA's winsorize(x, na.rm=TRUE) # removes NA's ```

jeffreyevans/spatialEco documentation built on Aug. 11, 2018, 1:08 p.m.