Mahalanobis: Classical and Robust Mahalanobis Distances

In heplots: Visualizing Hypothesis Tests in Multivariate Linear Models

Classical and Robust Mahalanobis Distances

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
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
...	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 vector of length nrow(x) containing the squared distances.

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 Sept. 8, 2023, 5:32 p.m.