# mahaldis

From FD v1.0-12
0th

Percentile

##### Mahalanobis Distance

mahaldis measures the pairwise Mahalanobis (1936) distances between individual objects.

Keywords
multivariate
##### Usage
mahaldis(x)
##### Arguments
x
matrix containing the variables. NAs are not tolerated.
##### Details

mahaldis computes the Mahalanobis (1936) distances between individual objects. The Mahalanobis distance takes into account correlations among variables and does not depend on the scales of the variables.

mahaldis builds on the fact that type-II principal component analysis (PCA) preserves the Mahalanobis distance among objects (Legendre and Legendre 2012). Therefore, mahaldis first performs a type-II PCA on standardized variables, and then computes the Euclidean distances among (repositioned) objects whose positions are given in the matrix $G$. This is equivalent to the Mahalanobis distances in the space of the original variables (Legendre and Legendre 2012).

##### Value

an object of class dist.

##### References

Legendre, P. and L. Legendre (2012) Numerical Ecology. 3nd English edition. Amsterdam: Elsevier.

mahalanobis computes the Mahalanobis distances among groups of objects, not individual objects.

• mahaldis
##### Examples
mat <- matrix(rnorm(100), 50, 20)

ex1 <- mahaldis(mat)

# check attributes
attributes(ex1)

Documentation reproduced from package FD, version 1.0-12, License: GPL-2

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