aout.mvnorm {alphaOutlier}

Find \alpha-outliers in multivariate normal data

Description

Given the parameters of a multivariate normal distribution, aout.mvnorm identifies \alpha-outliers in a given data set.

Usage

aout.mvnorm(data, param, alpha = 0.1, hide.outliers = FALSE)

Arguments

data

a data.frame or matrix. The data set to be examined.

param

a list. Contains the parameters of the normal distribution: the mean vector \mu and the covariance matrix \sigma.

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.

Value

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.

Author(s)

A. Rehage

References

Kuhnt, S.; Rehage, A. (2013) The concept of \alpha-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.

See Also

dnorm

Examples

temp <- iris[1:51,-5]
temp.xq <- apply(FUN = median, MARGIN = 2, temp)
aout.mvnorm(as.matrix(temp), param = list(temp.xq, cov(temp)), alpha = 0.001)