mahalanobis(x, center, cov, inverted=FALSE)
x
| vector or matrix of data with, say, p columns. |
center
| mean vector of the distribution or second data vector of length p. |
cov
| covariance matrix (p x p) of the distribution. |
inverted
|
logical. If TRUE , cov is supposed to
contain the inverse of the covariance matrix.
|
x
and the
vector &mu=center
with respect to
Sigma=cov
.
This is (for vector x
) defined as
D^2 = (x - &mu)' Sigma^{-1} (x - &mu)
cov
, var
ma <- cbind(1:6, 1:3) (S <- var(ma)) mahalanobis(c(0,0), 1:2, S) x <- matrix(rnorm(100*3), ncol=3) all(mahalanobis(x, 0, diag(ncol(x))) == apply(x*x, 1,sum)) ##- Here, D^2 = usual Euclidean distances Sx <- cov(x) D2 <- mahalanobis(x, apply(x,2,mean), Sx) plot(density(D2))