The alpha-distance {Compositional} | R Documentation |
This is the Euclidean (or Manhattan) distance after the α-transformation has been applied.
alfadist(x, a, type = "euclidean", square = FALSE) alfadista(xnew, x, a, type = "euclidean", square = FALSE)
xnew |
A matrix or a vector with new 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. |
type |
Which type distance do you want to calculate after the α-transformation, "euclidean", or "manhattan". |
square |
In the case of the Euclidean distance, you can choose to return the squared distance by setting this TRUE. |
The α-transformation is applied to the compositional data first and then the Euclidean or the Manhattan distance is calculated.
For "alfadist" a matrix including the pairwise distances of all observations or the distances between xnew and x. For "alfadista" a matrix including the pairwise distances of all observations or the distances between xnew and x.
Michail Tsagris
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
Tsagris M.T., Preston S. and Wood A.T.A. (2016). Improved classification for compositional data using the α-transformation. Journal of Classification. 33(2):243–261. https://arxiv.org/pdf/1506.04976v2.pdf
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
library(MASS) x <- as.matrix(fgl[1:20, 2:9]) x <- x / rowSums(x) alfadist(x, 0.1) alfadist(x, 1)