aout.conttab: Find $\alpha$-outliers in two-way contingency tables

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 $\lambda$ of each cell can either be set by the user or estimated. Given the parameters, aout.conttab identifies $\alpha$-outliers in a given contingency table.

Usage

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: $\lambda$. "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 $\hat{\lambda}$ for each cell if set to TRUE. Defaults to FALSE.

Value

is.outlier that flags the outliers with TRUE and a vector named param containing the estimated lambdas.

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.

Examples

Run this code

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)

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