maha: Compute the (squared) Mahalanobis distance between two groups...

In GenAlgo: Classes and Methods to Use Genetic Algorithms for Feature Selection

Compute the (squared) Mahalanobis distance between two groups of vectors

Description

The Mahalanobis distance between two groups of vectors

Usage

maha(data, groups, method = "mve")

Arguments

data	A matrix with columns representing features (or variables) and rows representing independent samples
groups	A factor or logical vector with length equal to the number of rows (samples) in the data matrix
method	A character string determining the method that should be used to estimate the covariance matrix. The default value of "mve" uses the cov.mve function from the MASS package. The other valid option is "var", which uses the var function from the standard stats package.

Details

The Mahalanobis distance between two groups of vectors is the distance between their centers, computed in the equivalent of a principal component space that accounts for different variances.

Value

Returns a numeric vector of length 1.

Author(s)

Kevin R. Coombes [e-mail], P. Roebuck [e-mail]

References

Mardia, K. V. and Kent, J. T. and Bibby, J. M.
Multivariate Analysis.
Academic Press, Reading, MA 1979, pp. 213–254.

See Also

cov.mve, var

Examples

nFeatures <- 40
nSamples <- 2*10
dataset <- matrix(rnorm(nSamples*nFeatures), ncol=nSamples)
groups <- factor(rep(c("A", "B"), each=10))
maha(dataset, groups)

GenAlgo documentation built on April 12, 2025, 1:44 a.m.