mahalanobis {stats} | R Documentation

## Mahalanobis Distance

### Description

Returns the squared Mahalanobis distance of all rows in `x` and the vector mu = `center` with respect to Sigma = `cov`. This is (for vector `x`) defined as

D^2 = (x - μ)' Σ^-1 (x - μ)

### Usage

    mahalanobis(x, center, cov, inverted = FALSE, ...)

### Arguments

`x` | vector or matrix of data with, say, p columns.
`center` | mean vector of the distribution or second data vector of length p or recyclable to that length. If set to `FALSE`, the centering step is skipped.
`cov` | covariance matrix (p x p) of the distribution.
`inverted` | logical. If `TRUE`, `cov` is supposed to contain the inverse of the covariance matrix.
`...` | passed to `solve` for computing the inverse of the covariance matrix (if `inverted` is false).

### See Also

`cov`, `var`

### Examples

    require(graphics)

    ma <- cbind(1:6, 1:3)
    (S <-  var(ma))
    mahalanobis(c(0, 0), 1:2, S)

    x <- matrix(rnorm(100*3), ncol = 3)
    stopifnot(mahalanobis(x, 0, diag(ncol(x))) == rowSums(x*x))
            ##- Here, D^2 = usual squared Euclidean distances

    Sx <- cov(x)
    D2 <- mahalanobis(x, colMeans(x), Sx)
    plot(density(D2, bw = 0.5),
         main="Squared Mahalanobis distances, n=100, p=3") ; rug(D2)
    qqplot(qchisq(ppoints(100), df = 3), D2,
           main = expression("Q-Q plot of Mahalanobis" * ~D^2 *
                             " vs. quantiles of" * ~ chi[3]^2))
    abline(0, 1, col = 'gray')

[Package stats version 3.4.2 Index]