Mahalanobis Distance
DESCRIPTION:
Returns a vector of the Mahalanobis distances for the rows of a data matrix.
USAGE: 
mahalanobis(x, center, cov, inverted=F)
REQUIRED ARGUMENTS:
x:
matrix of data. Rows represent observations and columns represent variables. Missing values (NAs) are allowed.
center:
vector of the mean of the distribution. The length of center must equal the number of columns in x. Missing values are not accepted.
cov:
matrix giving the covariance matrix for the distribution. This must be square and have the same number of columns as x. This may alternatively be a QR decomposition of the covariance matrix, or the inverse of the covariance matrix (see inverted). Missing values are not accepted.
OPTIONAL ARGUMENTS:
inverted:
logical flag: if TRUE, then cov is taken to be the inverse of the covariance matrix.
VALUE:
a vector, each element of which is the (squared) Mahalanobis distance for the corresponding row of x.
DETAILS:
The result contains missing values for rows of x that contain missing values.

The ith element of the result is equal to (x[i,]-center)%*%solve(cov)%*%(x[i,]-center).

REFERENCES:
The Mahalanobis distance is discussed in many multivariate books such as:

Mardia, K. V., Kent, J. T. and Bibby, J. M. (1979). Multivariate Analysis. Academic Press, London.

SEE ALSO:
dist , qr .
EXAMPLES: 
freeny.cov <- cov.mve(freeny.x)
freeny.mah <- mahalanobis(freeny.x, freeny.cov$center, freeny.cov$cov)