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Classical and Robust Mahalanobis Distances

Source: R/Mahalanobis.R
Mahalanobis.Rd

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

...

other arguments passed to MASS::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.

See also

stats::mahalanobis(), MASS::cov.rob()

Other robust methods: plot.robmlm(), robmlm()

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