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)

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

Value

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.

Author

Michael Friendly

Examples


summary(Mahalanobis(iris[, 1:4]))
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>  0.3195  2.1871  3.0628  3.9733  4.8053 13.1011 
summary(Mahalanobis(iris[, 1:4], method="mve"))
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>  0.4461  2.5331  4.0110  5.5585  6.8295 25.7951 
summary(Mahalanobis(iris[, 1:4], method="mcd"))
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>  0.4352  2.7301  5.7160 19.2207 36.7121 98.0335