Description Usage Arguments Value Author(s) References Examples
Given the parameters of a conditional Gaussian distribution, aout.cg
identifies α-outliers 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 outlier-free 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 outlier-free data.
A. Rehage
Edwards, D. (2000) Introduction to Graphical Modelling. 2nd edition, Springer, New York.
Kuhnt, S.; Rehage, A. (2013) The concept of α-outliers 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, 91-108.
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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