| mahaldis {FD} | R Documentation |
Mahalanobis Distance
Description
mahaldis measures the pairwise Mahalanobis (1936) distances between individual objects.
Usage
mahaldis(x)
Arguments
x |
matrix containing the variables. |
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 \mathbf{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.
Author(s)
Pierre Legendre pierre.legendre@umontreal.ca
http://adn.biol.umontreal.ca/~numericalecology/
Ported to FD by Etienne Laliberté.
References
Legendre, P. and L. Legendre (2012) Numerical Ecology. 3nd English edition. Amsterdam: Elsevier.
See Also
mahalanobis computes the Mahalanobis distances among groups of objects, not individual objects.
Examples
mat <- matrix(rnorm(100), 50, 20)
ex1 <- mahaldis(mat)
# check attributes
attributes(ex1)