aout.conttab: Find alpha-outliers in two-way contingency tables In alphaOutlier: Obtain Alpha-Outlier Regions for Well-Known Probability Distributions

Description

This is a wrapper function for aout.pois. We assume that each entry of a contingency table can be seen as a realization of a Poisson random variable. The parameter λ of each cell can either be set by the user or estimated. Given the parameters, aout.conttab identifies α-outliers in a given contingency table.

Usage

 1 aout.conttab(data, param, alpha = 0.1, hide.outliers = FALSE, show.estimates = FALSE) 

Arguments

 data a matrix or data.frame. The contingency table to be examined. param a character string from c("ML", "L1", "MP") or a vector containing the parameters of each cell of the Poisson distribution: λ. "ML" yields the maximum likelihood estimate from the log-linear Poisson model using a suitable design matrix. "L1" yields the L1-estimate from rq.fit.fnc. "MP" yields the Median Polish estimate. If the parameter vector is given by the user, it is necessary that the contingency table was filled byrow = FALSE. 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. show.estimates boolean. Returns \hat{λ} for each cell if set to TRUE. Defaults to FALSE.

Value

Data frame of the vectorized input data and, if desired, an index named is.outlier that flags the outliers with TRUE and a vector named param containing the estimated lambdas.

A. Rehage

References

Kuhnt, S. (2000) Ausreisseridentifikation im Loglinearen Poissonmodell fuer Kontingenztafeln unter Einbeziehung robuster Schaetzer. Ph.D. Thesis. Universitaet Dortmund, Dortmund. Fachbereich Statistik.

Kuhnt, S.; Rapallo, F.; Rehage, A. (2014) Outlier detection in contingency tables based on minimal patterns. Statistics and Computing 24 (3), 481-491.

rq.fit.fnc, aout.pois
 1 2 aout.conttab(data = HairEyeColor[,,1], param = "L1", alpha = 0.01, show.estimates = TRUE) aout.conttab(data = HairEyeColor[,,1], param = "ML", alpha = 0.01, show.estimates = TRUE)