# winsorize

##### Winsorize transformation

Removes extreme outliers using a winsorization transformation

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.

##### Usage

```
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

##### References

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

##### Examples

```
# NOT RUN {
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
# }
```

*Documentation reproduced from package spatialEco, version 1.3-2, License: GPL-3*