Computes the Aitchison distance between two observations, between two data sets or within observations of one data set.
1 2 3
a vector, matrix or data.frame
a vector, matrix or data.frame with equal dimension as
This distance measure accounts for the relative scale property of the
Aitchison distance. It measures the distance between two compositions if
y are vectors. It evaluates the sum of the distances between
y for each row of
y are matrices or data frames. It computes a n times n distance matrix (with n
the number of observations/compositions) if only
x is provided.
The underlying code is partly written in C and allows a fast computation also for
large data sets whenever
y is supplied.
The Aitchison distance between two compositions or between two data sets, or a distance matrix in case codey is not supplied.
Matthias Templ, Bernhard Meindl
Aitchison, J. (1986) The Statistical Analysis of Compositional Data Monographs on Statistics and Applied Probability. Chapman and Hall Ltd., London (UK). 416p.
Aitchison, J. and Barcelo-Vidal, C. and Martin-Fernandez, J.A. and Pawlowsky-Glahn, V. (2000) Logratio analysis and compositional distance. Mathematical Geology, 32, 271-275.
Hron, K. and Templ, M. and Filzmoser, P. (2010) Imputation of missing values for compositional data using classical and robust methods Computational Statistics and Data Analysis, vol 54 (12), pages 3095-3107.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
data(expenditures) x <- xOrig <- expenditures ## Aitchison distance between two 2 observations: aDist(x[1, ], x[2, ]) ## Aitchison distance of x: aDist(x) ## Example of distances between matrices: ## set some missing values: x[1,3] <- x[3,5] <- x[2,4] <- x[5,3] <- x[8,3] <- NA ## impute them: xImp <- impCoda(x, method="ltsReg")$xImp ## calculate the relative Aitchsion distance between xOrig and xImp: aDist(xOrig, xImp) data("expenditures") x <- expenditures[, 1] y <- expenditures[, 2] iprod(x, y)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.