Description Usage Arguments Value Author(s) References See Also Examples
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.
1 | aout.conttab(data, param, alpha = 0.1, hide.outliers = FALSE, show.estimates = FALSE)
|
data |
a matrix or data.frame. The contingency table to be examined. |
param |
a character string from |
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 |
show.estimates |
boolean. Returns \hat{λ} for each cell if set to |
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.
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)
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