Hello, a proposed solution of Bill Venables is archieved on the S-News mailing list:
http://www.biostat.wustl.edu/archives/html/s-news/2001-07/msg00035.html and if I remember it correctly (and if the variance matrix is estimated from the data), another similar way is simply to use the Euclidean distance of rescaled scores of a pricipal component analysis, e.g.: data(iris) dat <- iris[1:4] # without the species names z <- svd(scale(dat, scale=FALSE))$u cl <- hclust(dist(z), method="ward") plot(cl, labels=iris$Species) #### or alternatively: #### pc <- princomp(dat, cor=FALSE) pcdata <- as.data.frame(scale(pc$scores)) cl <- hclust(dist(pcdata), method="ward") plot(cl, labels=iris$Species) Hope it helps! Thomas P. ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
