Hmm,
why on earth are you using hierclust() from the ORPHANED package
'multiv', when there's hclust() in the core 'stats' package
and 'agnes' in the recommended 'cluster' package ?
To your question "similarities -> dissimilarities"
the textbooks all deal with this.
Assuming similarities s_ij in [0,1] {which you can get by scaling},
things mentioned are
e.g.,
d_ij := 1 - s_ij
d_ij := sqrt(1 - (s_ij)^2)
also d_ij := sqrt(1 - s_ij)
but really, in your situation where you're defining your
similarities yourself, you probably should rather think about
defining your dissimilarities yourself *directly* {i.e. not via
the above formulae}.
Martin Maechler
>>>>> "Xinan" == Xinan Yang <[EMAIL PROTECTED]>
>>>>> on Thu, 10 Jun 2004 09:04:05 +0200 writes:
Xinan> my major is bioinformatics, and i'm trying to cluster ( agglomerate
Xinan> the closest pari of observations ) in R.
Xinan> i have already got my own similarities metric, but do not know how to
Xinan> clust it based on similarities instead of dissimilarities.
Xinan> since the help document of hierclust mentions the parameter "sim",
Xinan> which seems good to me, but it doesn't appear in the code of
Xinan> hierclust() function again? and no sample about it. so could anybody
Xinan> please help me as author?
Xinan> thanks in advance
Xinan> xinan yang
Xinan> [EMAIL PROTECTED]
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