# cosnn: The k-nearest neighbours using the cosinus distance In Directional: A Collection of Functions for Directional Data Analysis

 The k-nearest neighbours using the cosinus distance R Documentation

## The k-nearest neighbours using the cosinus distance

### Description

The k-nearest neighbours using the cosinus distance.

### Usage

``````cosnn(xnew, x, k = 5, index = FALSE, rann = FALSE)
``````

### Arguments

 `xnew` The new data whose k-nearest neighbours are to be found. `x` The data, a numeric matrix with unit vectors. `k` The number of nearest neighbours, set to 5 by default. It can also be a vector with many values. `index` If you want the indices of the closest observations set this equal to TRUE. `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.

### Details

The shortest distances or the indices of the k-nearest neighbours using the cosinus distance are returned.

### Value

A matrix with the shortest distance of each xnew from x, if index is FALSE, or the indices of the nearest neighbours of each xnew from x, if index is TRUE.

### Author(s)

Michail Tsagris.

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

### References

Tsagris M. and Alenazi A. (2019). Comparison of discriminant analysis methods on the sphere. Communications in Statistics: Case Studies, Data Analysis and Applications, 5(4): 467–491.

```dirknn, dirknn.tune ```

### Examples

``````xnew <- rvmf(10, rnorm(3), 5)
x <- rvmf(50, rnorm(3), 5)
a <- cosnn(xnew, x, k = 5)
b <- cosnn(xnew, x, k = 5, index = TRUE)
``````

Directional documentation built on Oct. 12, 2023, 1:07 a.m.