alphaOutlier-package: Obtain alpha-outlier regions for well-known probability...

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

Description

Given the parameters of a distribution, the package uses the concept of α-outliers by Davies and Gather (1993) to flag outliers in a data set.

Details

The structure of the package is as follows: aout.[Distribution] is the name of the function which returns the α-outlier region of a random variable following [Distribution]. The names of the distributions are abbreviated as in the d, p, q, r functions. Use pre-specified or robustly estimated parameters from your data to obtain reasonable results. The sample size should be taken into account when choosing alpha, for example Gather et al. (2003) propose α_N = 1 - (1 - α)^{1/N}.

Author(s)

A. Rehage, S. Kuhnt

References

Davies, L.; Gather, U. (1993) The identification of multiple outliers, Journal of the American Statistical Association, 88 423, 782-792.

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

nleqslv, solnp, rq.fit.fnc

Examples

iris.setosa <- iris[1:51, 4]
aout.norm(data = iris.setosa, param = c(mean(iris.setosa), sd(iris.setosa)), alpha = 0.01)
aout.pois(data = warpbreaks[,1], param = mean(warpbreaks[,1]), alpha = 0.01, 
          hide.outliers = TRUE)

alphaOutlier documentation built on May 2, 2019, 3:59 p.m.