R: Design Matrix Mahalanobis Distance

designMD {fit.models} | R Documentation

## Design Matrix Mahalanobis Distance

### Description

Returns the squared Mahalanobis distance of all rows in the design (model) matrix X and the sample mean vector μ of the columns of X with respect to the sample covariance matrix Σ. This is (for vector x' a row of X) defined as

d^{2} = (x - μ)' Σ^{-1} (x - μ)

where

μ = colMeans(X)

and

Σ = cov(X).

### Usage

    designMD(object, ...)

### Arguments

`object` | a fitted model object with a `model.matrix` method.
`...` | additional arguments are ignored.

### Value

a numeric vector containing the squared Mahalanobis distances.

### Examples

    stack.lm <- lm(stack.loss ~ ., data = stackloss)

    # Mahalanobis distance (not squared)
    sqrt(designMD(stack.lm))

[Package fit.models version 0.5-14 Index]