Thank you, Jean.

Mateus


2012/7/6 Jean V Adams <jvadams@usgs.gov>

> Mateus,
>
> I see what you mean.
> I can't figure it out.
> I found a message from a few years ago that mentions the same problem.
> http://tolstoy.newcastle.edu.au/R/e8/help/09/12/8165.html
>
> This may be a bug.
> Since hclust() is in the stats package, I am ccing the maintainer,
> R-core@r-project.org, on this e-mail.
>
> Jean
>
> > find("hclust")
> [1] "package:stats"
> > maintainer("stats")
> [1] "R Core Team <R-core@r-project.org>"
>
>
>
> Mateus Teixeira <mateus.teixeira@gmail.com> wrote on 07/04/2012 07:39:39
> AM:
>
>
> > Dear R users,
> >
> > I have noted a difference in the merge distances given by hclust using
> > centroid method.
> >
> > For the following data:
> >
> > x<-c(1009.9,1012.5,1011.1,1011.8,1009.3,1010.6)
> >
> > and using Euclidean distance, hclust using centroid method gives the
> > following results:
> >
> > > x.dist<-dist(x)
> > > x.aah<-hclust(x.dist,method="centroid")
> > > x.aah$merge
> >      [,1] [,2]
> > [1,]   -3   -6
> > [2,]   -1   -5
> > [3,]   -2   -4
> > [4,]    1    2
> > [5,]    3    4
> > > x.aah$height
> > [1] 0.50000 0.60000 0.70000 0.97500 1.36875
> >
> > A calculation by hand results same merges, but at different distances for
> > latter stages:
> >
> > heights:
> >    0.5  => merging 3 and 6  => G1
> >    0.6  => merging 1 and 5  => G2
> >    0.7  => merging 2 and 4  => G3
> >    *1.25  => merging G1 and G2 => G4
> >    1.92  => merging G3 and G4*
>
> >
> > It seems that hclust is not correctly computing the group centroids. Is
> it
> > correct?
> >
> > Best regards,
> >
> > Mateus
>

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