Mahalanobis function

Classical and Robust Mahalanobis Distances

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)

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

Returns

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

Details

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

Examples

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

See Also

mahalanobis, cov.rob

Author(s)

Michael Friendly