R help - Dec 2010 - Jaccard dissimilarity matrix for PCA

Hi
I have a large dataset, containing a wide range of binary variables.
I would like first of all to compute a jaccard matrix, then do a PCA on this
matrix, so that I finally can do a hierarchical clustering on the principal
components. 
My problem is, that I don't know how to compute the jaccard dissimilarity
matrix in R? Which package to use, and so on...
Can anybody help me?
Alternatively I'm search for another way to explore the clusters present in
my data.
Another problem is, that I have cases with missing values on different
variables.

Jacob 
-- 
View this message in context:
http://r.789695.n4.nabble.com/Jaccard-dissimilarity-matrix-for-PCA-tp3165982p3165982.html
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Flabbergaster <jlunding <at> gmail.com>
writes:
> My problem is, that I don't know how to compute the jaccard
dissimilarity
> matrix in R? Which package to use, and so on...
http://rss.acs.unt.edu/Rdoc/library/arules/html/dissimilarity.html

http://cc.oulu.fi/~jarioksa/softhelp/vegan/html/vegdist.html

Jacob,
  You might have a look at the vegan package. It might compute the Jaccard 
distance and it might have some other toolsa that you might be interested 
in.
Dave




From:
Flabbergaster <jlunding@gmail.com>
To:
r-help@r-project.org
Date:
12/28/2010 08:26 AM
Subject:
[R] Jaccard dissimilarity matrix for PCA
Sent by:
r-help-bounces@r-project.org




Hi
I have a large dataset, containing a wide range of binary variables.
I would like first of all to compute a jaccard matrix, then do a PCA on 
this
matrix, so that I finally can do a hierarchical clustering on the 
principal
components. 
My problem is, that I don't know how to compute the jaccard dissimilarity
matrix in R? Which package to use, and so on...
Can anybody help me?
Alternatively I'm search for another way to explore the clusters present 
in
my data.
Another problem is, that I have cases with missing values on different
variables.

Jacob 
-- 
View this message in context: 
http://r.789695.n4.nabble.com/Jaccard-dissimilarity-matrix-for-PCA-tp3165982p3165982.html

Sent from the R help mailing list archive at Nabble.com.

______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide 
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and provide commented, minimal, self-contained, reproducible code.



	[[alternative HTML version deleted]]

jaccard in package prabclus computes a Jaccard matrix for you.

By the way, if you want to do hierarchical clustering, it doesn't seem to 
be a good idea to me to run PCA first. Why 
not cluster the dissimilarity matrix directly without information loss by 
PCA? (I should not make too general statements on this because generally 
how to cluster data always depends on the aim of clustering, the cluster 
concept you are interested in etc.)

prabclus also contains clustering methods for such data; have a 
look at the functions prabclust and hprabclust (however, they are 
documented as functions for clustering species distribution ranges, so if 
your application is different, you may have to think about whether and how 
to adapt them).

Hope this helps,
Christian




On Tue, 28 Dec 2010, Flabbergaster wrote:
>
> Hi
> I have a large dataset, containing a wide range of binary variables.
> I would like first of all to compute a jaccard matrix, then do a PCA on
this
> matrix, so that I finally can do a hierarchical clustering on the principal
> components.
> My problem is, that I don't know how to compute the jaccard
dissimilarity
> matrix in R? Which package to use, and so on...
> Can anybody help me?
> Alternatively I'm search for another way to explore the clusters
present in
> my data.
> Another problem is, that I have cases with missing values on different
> variables.
>
> Jacob
> --
> View this message in context:
http://r.789695.n4.nabble.com/Jaccard-dissimilarity-matrix-for-PCA-tp3165982p3165982.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
*** --- ***
Christian Hennig
University College London, Department of Statistical Science
Gower St., London WC1E 6BT, phone +44 207 679 1698
chrish at stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche