# maha: Compute the (squared) Mahalanobis distance between two groups... In GenAlgo: Classes and Methods to Use Genetic Algorithms for Feature Selection

## Description

The Mahalanobis distance between two groups of vectors

## Usage

 `1` ```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 krc@silicovore.com, P. Roebuck proebuck@mdanderson.org

## References

Mardia, K. V. and Kent, J. T. and Bibby, J. M.
Multivariate Analysis.

`cov.mve`, `var`

## Examples

 ```1 2 3 4 5``` ```nFeatures <- 40 nSamples <- 2*10 dataset <- matrix(rnorm(nSamples*nFeatures), ncol=nSamples) groups <- factor(rep(c("A", "B"), each=10)) maha(dataset, groups) ```

### Example output

```        [,1]
[1,] 2.58538
```

GenAlgo documentation built on Oct. 23, 2020, 7:28 p.m.