Hello list members

I'm doing a cluster analysis on a large dataset and to take advantage of
multicore processing I'm trying to use Rclusterpp package.

However in an analysis of a sample matrix (see
http://dl.dropbox.com/u/755659/gillnet.txt) I'm getting different results
from Rclusterpp and hclust, what should not happen since the purpose of the
package is to "deliver identical results as the “stock” stats::hclust
implementation."

Following the example from "An Introduction to Rclusterpp" (
http://cran.r-project.org/web/packages/Rclusterpp/vignettes/Rclusterpp.pdf)
I got:

library(vegan)
library(Rclusterpp)
data <- read.csv("http://dl.dropbox.com/u/755659/gillnet.txt
",sep=",",dec=".",row.names=1)
h <- hclust(dist(data, method="euclidean"), method="ward")
v <- hclust(vegdist(data, method="euclidean"), method="ward")
r <- Rclusterpp.hclust(data, method="ward", distance="euclidean")
identical(h$merge, r$merge) && all.equal(h$height, r$height)
[1] FALSE
identical(h$merge, v$merge) && all.equal(h$height, v$height)
[1] FALSE

If you plot the dendrograms you will see the differences. The results from
h and v are very close but the results from r are quite different.

Am I doing something wrong? Why I'm getting such different results? In the
example of "An Introduction to Rclusterpp" it does not happen.

Thanks in advance for any help.

Best regards,

Antonio Olinto

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