Cluster a Dataset Three Ways
Produces and plots dendrograms using three similarity measures: Euclidean distance, Pearson correlation, and Manhattan distance on dichotomized data.
cluster3(data, eps=logb(1, 2), name="", labels=dimnames(data)[[2]])
data |
A matrix, numeric data.frame, or
|
eps |
A numerical value; the threshold at which to dichotomize the data |
name |
A character string to label the plots |
labels |
A vector of character strings used to label the items in the dendrograms. |
Invisibly returns the data object on which it was invoked.
Kevin R. Coombes emailkrc@silicovore.com
## simulate data from two different classes d1 <- matrix(rnorm(100*30, rnorm(100, 0.5)), nrow=100, ncol=30, byrow=FALSE) d2 <- matrix(rnorm(100*20, rnorm(100, 0.5)), nrow=100, ncol=20, byrow=FALSE) dd <- cbind(d1, d2) ## cluster it 3 ways par(mfrow=c(2,2)) cluster3(dd) par(mfrow=c(1,1))
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.