NN — Nearest Neighbors Superclass

NN is an abstract S3 superclass for the classes of the objects returned by kNN(), frNN() and sNN(). Methods for sorting, plotting and getting an adjacency list are defined.

Usage

adjacencylist(x, ...)

# S3 method for class 'NN'
adjacencylist(x, ...)

# S3 method for class 'NN'
sort(x, decreasing = FALSE, ...)

# S3 method for class 'NN'
plot(x, data, main = NULL, pch = 16, col = NULL, linecol = "gray", ...)

Arguments

x

a NN object

...

further parameters past on to plot().

decreasing

sort in decreasing order?

data

that was used to create x

main

title

pch

plotting character.

col

color used for the data points (nodes).

linecol

color used for edges.

Subclasses

kNN, frNN and sNN

See also

Other NN functions: comps(), frNN(), kNN(), kNNdist(), sNN()

Author

Michael Hahsler

Examples

data(iris)
x <- iris[, -5]

# finding kNN directly in data (using a kd-tree)
nn <- kNN(x, k=5)
nn
#> k-nearest neighbors for 150 objects (k=5).
#> Distance metric: euclidean 
#> 
#> Available fields: dist, id, k, sort, metric

# plot the kNN where NN are shown as line conecting points.
plot(nn, x)


# show the first few elements of the adjacency list
head(adjacencylist(nn))
#> [[1]]
#>  1  2  3  4  5 
#> 18  5 40 29 28 
#> 
#> [[2]]
#>  1  2  3  4  5 
#> 35 46 13 10 26 
#> 
#> [[3]]
#>  1  2  3  4  5 
#> 48  4  7 13 46 
#> 
#> [[4]]
#>  1  2  3  4  5 
#> 48 30 31  3 46 
#> 
#> [[5]]
#>  1  2  3  4  5 
#> 38  1 18 41  8 
#> 
#> [[6]]
#>  1  2  3  4  5 
#> 19 11 49 45 20 
#> 

if (FALSE) { # \dontrun{
# create a graph and find connected components (if igraph is installed)
library("igraph")
g <- graph_from_adj_list(adjacencylist(nn))
comp <- components(g)
plot(x, col = comp$membership)

# detect clusters (communities) with the label propagation algorithm
cl <- membership(cluster_label_prop(g))
plot(x, col = cl)
} # }