esov.mds: Principal coordinate analysis using the Jensen-Shannon...

In Compositional: Compositional Data Analysis

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.

Details

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

Value

A list with the results of "cmdscale" function.

Author(s)

Michail Tsagris.

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

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

x <- as.matrix(iris[, 1:4])
x <- x/ rowSums(x)
a <- esov.mds(x)

Compositional documentation built on Oct. 23, 2023, 5:09 p.m.