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

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/aout.conttab.R


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.


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



a matrix or data.frame. The contingency table to be examined.


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 "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.


an atomic vector. Determines the maximum amount of probability mass the outlier region may contain. Defaults to 0.1.


boolean. Returns the outlier-free data if set to TRUE. Defaults to FALSE.


boolean. Returns \hat{λ} for each cell if set to TRUE. Defaults to FALSE.


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


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.

See Also, aout.pois


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)

alphaOutlier documentation built on May 30, 2017, 8:11 a.m.