divergence: Divergence matrix of compositional data

In Compositional: Compositional Data Analysis

Divergence matrix of compositional data

Description

Divergence matrix of compositional data.

Usage

divergence(x, type = "kullback_leibler", vector = FALSE)

Arguments

x	A matrix with the compositional data.
type	This is either "kullback_leibler" (Kullback-Leibler, which computes the symmetric Kullback-Leibler divergence) or "jensen_shannon" (Jensen-Shannon) divergence.
vector	For return a vector instead a matrix.

Details

The function produces the distance matrix either using the Kullback-Leibler (distance) or the Jensen-Shannon (metric) divergence. The Kullback-Leibler refers to the symmetric Kullback-Leibler divergence.

Value

if the vector argument is FALSE a symmetric matrix with the divergences, otherwise a vector with the divergences.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

Endres, D. M. and Schindelin, J. E. (2003). A new metric for probability distributions. Information Theory, IEEE Transactions on 49, 1858-1860.

Osterreicher, F. and Vajda, I. (2003). A new class of metric divergences on probability spaces and its applicability in statistics. Annals of the Institute of Statistical Mathematics 55, 639-653.

See Also

comp.knn, js.compreg

Examples

x <- as.matrix(iris[1:20, 1:4])
x <- x / rowSums(x)
divergence(x)

Compositional documentation built on Oct. 9, 2024, 5:10 p.m.