aout.norm: Find alpha-outliers in normal data

In alphaOutlier: Obtain Alpha-Outlier Regions for Well-Known Probability Distributions

Find \alpha-outliers in normal data

Description

Given the parameters of a normal distribution, aout.norm identifies \alpha-outliers in a given data set.

Usage

aout.norm(data, param = c(0, 1), alpha = 0.1, hide.outliers = FALSE)

Arguments

data	a vector. The data set to be examined.
param	a vector. Contains the parameters of the normal distribution: \mu, \sigma.
alpha	an atomic vector. Determines the maximum amount of probability mass the outlier region may contain. Defaults to 0.1.
hide.outliers	boolean. Returns the outlier-free data if set to TRUE. Defaults to FALSE.

Value

Data frame of the input data and an index named is.outlier that flags the outliers with TRUE. If hide.outliers is set to TRUE, a simple vector of the outlier-free data.

Author(s)

A. Rehage

References

Gather, U.; Kuhnt, S.; Pawlitschko, J. (2003) Concepts of outlyingness for various data structures. In J. C. Misra (Ed.): Industrial Mathematics and Statistics. New Delhi: Narosa Publishing House, 545-585.

See Also

dnorm

Examples

iris.setosa <- iris[1:51, 4]
# implosion breakdown point:
aout.norm(data = iris.setosa, param = c(median(iris.setosa), mad(iris.setosa)), 
          alpha = 0.01) 
# better:
aout.norm(data = iris.setosa, param = c(median(iris.setosa), sd(iris.setosa)), 
          alpha = 0.01) 

alphaOutlier documentation built on April 27, 2026, 9:07 a.m.