gdist: Pairwise distance between groups
In VBmix: Variational Bayesian Mixture Models

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

dist computes distances between pairs of elements: gdist takes two arguments, and returns the matrix of distances wrt every possible pair with one argument from each group. General Mahalanobis metrics are also allowed.

1	gdist(g1, g2, metric = NULL, norm=FALSE)

`g1`	n1 x d matrix with n1 data elements.
`g2`	n2 x d matrix with n2 data elements.
`metric`	If NULL, defaults to identity (i.e. Euclidean distance). A d x d matrix is assumed as a sample covariance matrix, and its inverse is used to compute distances (i.e. Mahalanobis distance). Likewise, a list of n2 d x d matrices can be provided, yielding distances specific to each row in g2.
`norm`	If TRUE, a ln det term is added to distances in order to mitigate the prevalence of metrics reflecting large variance.

This function is especially useful to make algorithms such as k-means (or mkmeans in the package) more efficient - rows in g1 are then generally the data set, and in g2 respectively cluster centres.

n1 x n2 matrix of distances

P. Bruneau

dist, mkmeans