aout.exp {alphaOutlier}

Find \alpha-outliers in exponentially distributed data

Description

Given the parameters of an exponential distribution, aout.exp identifies \alpha-outliers in a given data set.

Usage

aout.exp(data, param, alpha = 0.1, hide.outliers = FALSE, theta = 0)

Arguments

data

a vector. The data set to be examined.

param

an atomic vector. Contains the parameter of the exponential distribution.

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.

theta

an atomic vector. Determines the lower bound of the support of the exponential distribution. Defaults to 0.

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

dexp

Examples

aout.exp(attenu[,5], median(attenu[,5]), alpha = 0.05)