This sounds like something I could use..
I'm kind of new with R, meaning I've having some minor troubles all the
time...
Say I have a range of binary(0,1) variables X1 to Xn, with missing data for
different cases.
At the moment my data is a binary indicator matrix; rows representing the i
individu
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 stat
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
To:
r-help@r-project.org
Date:
12/28/2010 08:26 AM
Subject:
[R] Jaccard dissimilarity matrix for PCA
Sent
Flabbergaster 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
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
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