View source: R/plot.clusterTree.R
plot.clusterTree | R Documentation |
The function plot.clusterTree
plots the Cluster Tree stored in an object of class clusterTree
.
## S3 method for class 'clusterTree'
plot(
x, type = "lambda", color = NULL, add = FALSE, ...)
x |
an object of class |
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 |
add |
logical: if |
... |
additional graphical parameters. |
Fabrizio Lecci
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"
clusterTree
, print.clusterTree
## 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)")
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