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Mahalanobis Distance

Source: R/AllGenerics.R, R/dist.R
mahalanobis.Rd

Computes the squared Mahalanobis distance of all rows in x.

Usage

# S4 method for class 'CompositionMatrix'
mahalanobis(x, center, cov, ..., robust = TRUE, method = c("mve", "mcd"))

# S4 method for class 'ILR'
mahalanobis(x, center, cov, ..., robust = TRUE, method = c("mve", "mcd"))

Arguments

x

A CompositionMatrix or an ILR object.

center

A numeric vector giving the mean vector of the distribution. If missing, will be estimated from x.

cov

A numeric matrix giving the covariance of the distribution. If missing, will be estimated from x.

...

Extra parameters to be passed to MASS::cov.rob(). Only used if robust is TRUE.

robust

A logical scalar: should robust location and scatter estimation be used?

method

A character string specifying the method to be used. It must be one of "mve" (minimum volume ellipsoid) or "mcd" (minimum covariance determinant). Only used if robust is TRUE.

Value

A numeric vector.

See also

stats::mahalanobis()

Other statistics: aggregate(), condense(), covariance(), dist, margin(), mean(), pip(), quantile(), scale(), variance(), variance_total(), variation()

Author

N. Frerebeau

Examples

## Data from Aitchison 1986
data("hongite")

## Coerce to compositional data
coda <- as_composition(hongite)

## Mahalanobis distance
mahalanobis(coda)
#> 
#>         H1         H2         H3         H4         H5         H6         H7 
#>  4.6935346  0.4862639  1.8203070  0.9564282  4.6278361  5.6478569  7.0792602 
#>         H8         H9        H10        H11        H12        H13        H14 
#>  8.4724638  1.1452246 22.4936169  1.3734240  5.2150046  5.5223415  7.1744957 
#>        H15        H16        H17        H18        H19        H20        H21 
#>  1.9366162  9.0374393  4.7712623  1.7101898  3.2622612  2.9144878  1.2990165 
#>        H22        H23        H24        H25 
#>  1.6917684 11.1555514  2.9663402  2.0780653