winsorize: Outliers: Winsorize
In laresbernardo/lares: Analytics & Machine Learning Sidekick
winsorize R Documentation
Outliers: Winsorize
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, thresh = c(0.05, 0.95), na.rm = FALSE)
Arguments
x
Numeric vector. Distribution to be winsorized.
thresh
Numeric vector. Lower and upper quantiles thresholds.
Set values within [0,1].
na.rm
Boolean. Should NA be omitted to calculate the quantiles?
Note that NA in x are preserved and left unchanged anyway.
Value
Numeric vector transformed.
See Also
Other Outliers:
outlier_turkey(),
outlier_zscore(),
outlier_zscore_plot()
laresbernardo/lares documentation built on Oct. 23, 2024, 12:05 p.m.
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| winsorize | R Documentation |
Outliers: Winsorize
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, thresh = c(0.05, 0.95), na.rm = FALSE)
Arguments
x |
Numeric vector. Distribution to be winsorized. |
thresh |
Numeric vector. Lower and upper quantiles thresholds. Set values within [0,1]. |
na.rm |
Boolean. Should |
Value
Numeric vector transformed.
See Also
Other Outliers:
outlier_turkey(),
outlier_zscore(),
outlier_zscore_plot()
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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