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.

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`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

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