Compositional (version 5.0)

Principal coordinate analysis using the Jensen-Shannon divergence: Principal coordinate analysis using the Jensen-Shannon divergence

Description

Principal coordinate analysis using the Jensen-Shannon divergence.

Usage

esov.mds(x, k = 2, eig = TRUE)

Arguments

x

A matrix with the compositional data. Zero values are allowed.

k

The maximum dimension of the space which the data are to be represented in. This can be a number between 1 and \(D-1\), where \(D\) denotes the number of dimensions.

eig

Should eigenvalues be returned? The default value is TRUE.

Value

A list with the results of "cmdscale" function.

Details

The function computes the Jensen-Shannon divergence matrix and then plugs it into the classical multidimensional scaling function in the "cmdscale" function.

References

Aitchison J. (1986). The statistical analysis of compositional data. Chapman & Hall.

Cox, T. F. and Cox, M. A. A. (2001). Multidimensional Scaling. Second edition. Chapman and Hall.

Mardia, K. V., Kent, J. T. and Bibby, J. M. (1979). Chapter 14 of Multivariate Analysis, London: Academic Press.

Tsagris, Michail (2015). A novel, divergence based, regression for compositional data. Proceedings of the 28th Panhellenic Statistics Conference, 15-18/4/2015, Athens, Greece. https://arxiv.org/pdf/1511.07600.pdf

See Also

alfa.mds, alfa.pca,

Examples

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