Description Usage Arguments Value Examples
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).
1 |
object |
a fitted model object with a |
... |
additional arguments are ignored. |
a numeric vector containing the squared Mahalanobis distances.
1 2 3 4 |
1 2 3 4 5 6 7 8
2.2536034 2.3247448 1.5937124 1.2718978 0.3033573 0.7728947 1.8526614 1.8526614
9 10 11 12 13 14 15 16
1.3606218 1.7459966 1.4657021 1.8415044 1.4826491 1.7787851 1.6902415 1.2919339
17 18 19 20 21
2.7000165 1.5031545 1.5932205 0.8070539 2.1767610
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