Compositional (version 5.5)

Divergence matrix of compositional data: 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.

Value

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

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.

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

# NOT RUN {
x <- as.matrix(iris[1:20, 1:4])
x <- x / rowSums(x)
divergence(x)
# }