Principal coordinate analysis using the alpha-distance {Compositional} | R Documentation |
Principal coordinate analysis using the α-distance.
alfa.mds(x, a, k = 2, eig = TRUE)
x |
A matrix with the compositional data. Zero values are allowed. |
a |
The value of a. In case of zero values in the data it has to be greater than 1. |
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. |
The function computes the α-distance matrix and then plugs it into the classical multidimensional scaling function in the "cmdscale" function.
A list with the results of "cmdscale" function.
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
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 M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf
x <- as.matrix(iris[, 1:4]) x <- x/ rowSums(x) a <- esov.mds(x)