alphaOutlier-package {alphaOutlier}

Obtain \alpha-outlier regions for well-known probability distributions

Description

Given the parameters of a distribution, the package uses the concept of \alpha-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 \alpha-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 \alpha_N = 1 - (1 - \alpha)^{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)