mahaldis measures the pairwise Mahalanobis (1936) distances between individual objects.
matrix containing the variables.
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).
an object of class
Pierre Legendre firstname.lastname@example.org
Ported to FD by Etienne Laliberté.
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.
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