Alpha generalised correlations between two compositional datasets | R Documentation |
α generalised correlations between two compositional datasets.
acor(y, x, a, type = "dcor")
y |
A matrix with the compositional data. |
x |
A matrix with the compositional data. |
a |
The value of the power transformation, it has to be between -1 and 1. If zero values are present it has to be greater than 0. If α=0 the isometric log-ratio transformation is applied. If more than one valuesare supplied the distance or canonical correlation are computed for all values. |
type |
The type of correlation to compute, the distance correlation ("edist"), the canonical correlation ("cancor") or "both". |
The α-transformation is applied to each composition and then the distance correlation or the canonical correlation is computed. If one value of α is supplied the type="cancor" will return all eigenvalues. If more than one values of α are provided then the first eigenvalue only will be returned.
A vector or a matrix depending on the length of the values of α and the type of the correlation to be computed.
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
G.J. Szekely, M.L. Rizzo and N. K. Bakirov (2007). Measuring and Testing Independence by Correlation of Distances. Annals of Statistics, 35(6): 2769-2794.
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
acor.tune, aeqdist.etest, alfa, alfa.profile
y <- rdiri(30, runif(3) ) x <- rdiri(30, runif(4) ) acor(y, x, a = 0.4)
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