plot_clusters {anticlust} | R Documentation |
Visualize a cluster analysis
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 )
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 |
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 |
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 |
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
.
Martin Papenberg martin.papenberg@hhu.de
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)