plot.clusterTree {TDA}

Plots the Cluster Tree

Description

The function plot.clusterTree plots the Cluster Tree stored in an object of class clusterTree.

Usage

## S3 method for class 'clusterTree'
plot(
    x, type = "lambda", color = NULL, add = FALSE, ...)

Arguments

x

an object of class clusterTree. (see clusterTree)

type

string: if "lambda", then the lambda Tree is plotted. if "r", then the r Tree is plotted. if "alpha", then the alpha Tree is plotted. if "kappa", then the kappa Tree is plotted.

color

number: the color of the branches of the Cluster Tree. The default value is NULL and a different color is assigned to each branch.

add

logical: if TRUE, the Tree is added to an existing plot.

...

additional graphical parameters.

Author(s)

Fabrizio Lecci

References

Kent BP, Rinaldo A, Verstynen T (2013). "DeBaCl: A Python Package for Interactive DEnsity-BAsed CLustering." arXiv:1307.8136

Lecci F, Rinaldo A, Wasserman L (2014). "Metric Embeddings for Cluster Trees"

See Also

clusterTree, print.clusterTree

Examples

## Generate data: 3 clusters
n <- 1200  #sample size
Neach <- floor(n / 4) 
X1 <- cbind(rnorm(Neach, 1, .8), rnorm(Neach, 5, 0.8))
X2 <- cbind(rnorm(Neach, 3.5, .8), rnorm(Neach, 5, 0.8))
X3 <- cbind(rnorm(Neach, 6, 1), rnorm(Neach, 1, 1))
XX <- rbind(X1, X2, X3)

k <- 100   #parameter of knn

## Density clustering using knn and kde
Tree <- clusterTree(XX, k, density = "knn")
TreeKDE <- clusterTree(XX,k, h = 0.3, density = "kde")

par(mfrow = c(2, 3))
plot(XX, pch = 19, cex = 0.6)
# plot lambda trees
plot(Tree, type = "lambda", main = "lambda Tree (knn)")
plot(TreeKDE, type = "lambda", main = "lambda Tree (kde)")
# plot clusters
plot(XX, pch = 19, cex = 0.6, main = "cluster labels")
for (i in Tree[["id"]]){
  points(matrix(XX[Tree[["DataPoints"]][[i]], ], ncol = 2), col = i, pch = 19,
         cex = 0.6)
}
#plot kappa trees
plot(Tree, type = "kappa", main = "kappa Tree (knn)")
plot(TreeKDE, type = "kappa", main = "kappa Tree (kde)")