The k-nearest neighbours using the alpha-distance {Compositional} R Documentation

## The k-nearest neighbours using the α-distance

### Description

The k-nearest neighbours using the α-distance.

### Usage

```alfann(xnew, x, a, k = 10, rann = FALSE)
```

### Arguments

 `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. `k` The number of nearest neighbours to search for. `rann` If you have large scale datasets and want a faster k-NN search, you can use kd-trees implemented in the R package "RANN". In this case you must set this argument equal to TRUE. Note however, that in this case, the only available distance is by default "euclidean".

### Details

The α-transformation is applied to the compositional data first and the indices of the k-nearest neighbours using the Euclidean distance are returned.

### Value

A matrix including the indices of the nearest neighbours of each xnew from x.

### Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

### References

Michail Tsagris, Abdulaziz Alenazi and Connie Stewart (2021). Non-parametric regression models for compositional data. https://arxiv.org/pdf/2002.05137.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

```alfa.knn, comp.nb, alfa.rda, alfa.nb, link{aknn.reg}, alfa, alfainv ```

### Examples

```library(MASS)
xnew <- as.matrix(fgl[1:20, 2:9])
xnew <- xnew / rowSums(xnew)
x <- as.matrix(fgl[-c(1:20), 2:9])
x <- x / rowSums(x)
b <- alfann(xnew, x, a = 0.1, k = 10)
```

[Package Compositional version 5.2 Index]