plot_clusters {anticlust}

Visualize a cluster analysis

Description

Visualize a cluster analysis

Usage

plot_clusters(
  features,
  clusters,
  within_connection = FALSE,
  between_connection = FALSE,
  illustrate_variance = FALSE,
  show_axes = FALSE,
  xlab = NULL,
  ylab = NULL,
  xlim = NULL,
  ylim = NULL,
  main = "",
  cex = 1.2,
  cex.axis = 1.2,
  cex.lab = 1.2,
  lwd = 1.5,
  lty = 2,
  frame.plot = FALSE,
  cex_centroid = 2
)

Arguments

features

A data.frame or matrix representing the features that are plotted. Must have two columns.

clusters

A vector representing the clustering

within_connection

Boolean. Connect the elements within each clusters through lines? Useful to illustrate a graph structure.

between_connection

Boolean. Connect the elements between each clusters through lines? Useful to illustrate a graph structure. (This argument only works for two clusters).

illustrate_variance

Boolean. Illustrate the variance criterion in the plot?

show_axes

Boolean, display values on the x and y-axis? Defaults to 'FALSE'.

xlab

The label for the x-axis

ylab

The label for the y-axis

xlim

The limits for the x-axis

ylim

The limits for the y-axis

main

The title of the plot

cex

The size of the plotting symbols, see par

cex.axis

The size of the values on the axes

cex.lab

The size of the labels of the axes

lwd

The width of the lines connecting elements.

lty

The line type of the lines connecting elements (see par).

frame.plot

a logical indicating whether a box should be drawn around the plot.

cex_centroid

The size of the cluster center symbol (has an effect only if illustrate_variance is TRUE)

Details

In most cases, the argument clusters is a vector returned by one of the functions anticlustering, balanced_clustering or matching. However, the plotting function can also be used to plot the results of other cluster functions such as kmeans. This function is usually just used to get a fast impression of the results of an (anti)clustering assignment, but limited in its functionality. It is useful for depicting the intra-cluster connections using argument within_connection.

Author(s)

Martin Papenberg [e-mail]

Examples

N <- 15
features <- matrix(runif(N * 2), ncol = 2)
K <- 3
clusters <- balanced_clustering(features, K = K)
anticlusters <- anticlustering(features, K = K)
user_par <- par("mfrow")
par(mfrow = c(1, 2))
plot_clusters(features, clusters, main = "Cluster editing", within_connection = TRUE)
plot_clusters(features, anticlusters, main = "Anticluster editing", within_connection = TRUE)
par(mfrow = user_par)