Mahalanobis: Classical and Robust Mahalanobis Distances

In heplots: Visualizing Hypothesis Tests in Multivariate Linear Models

Description

This function is a convenience wrapper to mahalanobis offering also the possibility to calculate robust Mahalanobis squared distances using MCD and MVE estimators of center and covariance (from cov.rob)

Usage

Mahalanobis(x, center, cov, method = c("classical", "mcd", "mve"), nsamp = "best", ...)

Arguments

x	a numeric matrix or data frame with, say, p columns
center	mean vector of the data; if this and cov are both supplied, the function simply calls mahalanobis to calculate the result, ignoring the method argument.
cov	covariance matrix (p x p) of the data
method	estimation method used for center and covariance, one of: "classical" (product-moment), "mcd" (minimum covariance determinant), or "mve" (minimum volume ellipsoid).
nsamp	passed to cov.rob, just to make this argument explicit
...	other arguments passed to cov.rob

Details

Any missing data in a row of x causes NA to be returned for that row.

Value

A numeric vector of squared Mahalanobis distances corresponding to the rows of x.

Author(s)

Michael Friendly

See Also

mahalanobis, cov.rob

Examples

summary(Mahalanobis(iris[, 1:4]))
summary(Mahalanobis(iris[, 1:4], method="mve"))
summary(Mahalanobis(iris[, 1:4], method="mcd"))

heplots documentation built on May 31, 2017, 4:54 a.m.