Description Usage Arguments Value Author(s) References Examples
Given the parameters of a conditional Gaussian distribution, aout.cg
identifies αoutliers in a given data set.
1 
data 
a matrix. First column: Class of the value, coded with an integer between 1 and d, where d is the number of classes. Second column: The value as a realization of a univariate normal with parameters μ and σ. The data set to be examined. 
param 
a list with three elements:

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 outlierfree data if set to 
Data frame of the input data and an index named is.outlier
that flags the outliers with TRUE
. If hide.outliers
is set to TRUE
, a data frame of the outlierfree data.
A. Rehage
Edwards, D. (2000) Introduction to Graphical Modelling. 2nd edition, Springer, New York.
Kuhnt, S.; Rehage, A. (2013) The concept of αoutliers in structured data situations. In C. Becker, R. Fried, S. Kuhnt (Eds.): Robustness and Complex Data Structures. Festschrift in Honour of Ursula Gather. Berlin: Springer, 91108.
1 2 3 4 5 6 7  # Rats' weights data example taken from Edwards (2000)
ratweight < cbind(Drug = c(1, 1, 2, 3, 1, 1, 2, 3, 1, 2, 3, 3, 1, 2, 2, 3, 1,
2, 2, 3, 1, 2, 3, 3),
Week1 = c(5, 7, 9, 14, 7, 8, 7, 14, 9, 7, 21, 12, 5, 7, 6,
17, 6, 10, 6, 14, 9, 8, 16, 10))
aout.cg(ratweight,
list(p = c(1/3, 1/3, 1/3), mu = c(7, 7, 14), sigma = c(1.6, 1.4, 3.3)))

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