R: Cophenetic Distances for a Hierarchical Clustering

cophenetic {mva}

R Documentation

Cophenetic Distances for a Hierarchical Clustering

Description

Computes the cophenetic distances for a hierarchical clustering.

Usage

cophenetic(x)

Arguments

x

an object of class hclust or with a method for as.hclust() such as agnes.

Details

The cophenetic distance between two observations that have been clustered is defined to be the intergroup dissimilarity at which the two observations are first combined into a single cluster. Note that this distance has many ties and restrictions.

It can be argued that a dendrogram is an appropriate summary of some data if the correlation between the original distances and the cophenetic distances is high. Otherwise, it should simply be viewed as the description of the output of the clustering algorithm.

Value

An object of class dist.

Author(s)

Robert Gentleman

References

Sneath, P.H.A. and Sokal, R.R (1973) Numerical Taxonomy: The Principles and Practice of Numerical Classification, p. 278 ff; Freeman, San Francisco.

Examples

 data(USArrests)
 library(mva)
 d1 <- dist(USArrests)
 hc <- hclust(d1, "ave")
 d2 <- cophenetic(hc)
 cor(d1,d2) # 0.7659

## Example from Sneath & Sokal, Fig. 5-29, p.279
d0 <- c(1,3.8,4.4,5.1, 4,4.2,5, 2.6,5.3, 5.4)
attributes(d0) <- list(Size = 5, diag=TRUE)
class(d0) <- "dist"
names(d0) <- letters[1:5]
d0
str(upgma <- hclust(d0, method = "average"))
plot(upgma, hang = -1)
#
(d.coph <- cophenetic(upgma))
cor(d0, d.coph) # 0.9911