pairwise.mahalanobis: Mahalanobis distances for grouped data
In HDMD: Statistical Analysis Tools for High Dimension Molecular Data (HDMD)

Description Usage Arguments Details Value Author(s) See Also Examples

Returns a square matrix of Mahalanobis distances by doing a pairwise comparison of group means using the correlation between variables.

1	pairwise.mahalanobis(x, grouping = NULL, cov = NULL, inverted = FALSE, digits = 5, ...)

`x`	vector or matrix of data with N observations and D variables. If grouping is not specified, the first column is used for grouping observations.
`grouping`	vector of characters or values designating group classification for observations.
`cov`	Covariance matrix (DxD) of the distribution
`inverted`	logical. If TRUE, cov is the inverse of the covariance matrix.
`digits`	number of decimals to keep for the means, cov and distance values
`...`	passed to mahalanobis for computing the inverse of the covariance matrix (if inverted is false).

To determine the distance between group i and group j, the difference of group means for each variable are compared. For a (NxD) data matrix with m groups, a matrix of mxD means and a correlation matrix of DxD values are calculated. pairwise.mahalanobis calculates the mahalanobis distance for all possible group combinations and results in a mxm square distance matrix with m choose 2 distinct pairwise measures.

`means`	(mxD) matrix of group means for each variable
`cov`	(DxD) covariance matrix of centered and scaled data, so it's actually the correlation matrix
`distance`	(mxm) matrix of squared mahalanobis distances

Lisa McFerrin

mahalanobis

data(bHLH288)
grouping = t(bHLH288[,1])
bHLH_Seq = as.vector(bHLH288[,2])
bHLH_pah = FactorTransform(bHLH_Seq, alignment=TRUE)

Mahala1 = pairwise.mahalanobis(bHLH_pah, grouping, digits = 3)
D = sqrt(Mahala1$distance)
D