alphaOutlier (version 1.2.0)

aout.mvnorm: 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

is.outlier that flags the outliers with TRUE. If hide.outliers is set to TRUE, a data frame of the outlier-free data.

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)