Thank you. I use the same matrix on cmdscale as I did with isoMDS. I have reproduced
my steps below for clarification if this happens to shed any light.
Here is the original total matrix (see opening thread if you care how this is created)
a b c d e f g h
a 4 4 2 4 1 2 0 0
b 4 4 2 4 1 2 0 0
c 2 2 4 2 3 2 2 1
d 4 4 2 4 1 2 0 0
e 1 1 3 1 4 3 3 2
f 2 2 2 2 3 4 2 1
g 0 0 2 0 3 2 4 3
h 0 0 1 0 2 1 3 4
So, there are 8 items. This matrix indicates that items 1,2, and 4 were always grouped
together (or viewed as being similar by individuals). I transformed this using
tt<-max(t)-t
which results in
a b c d e f g h
a 0 0 2 0 3 2 4 4
b 0 0 2 0 3 2 4 4
c 2 2 0 2 1 2 2 3
d 0 0 2 0 3 2 4 4
e 3 3 1 3 0 1 1 2
f 2 2 2 2 1 0 2 3
g 4 4 2 4 1 2 0 1
h 4 4 3 4 2 3 1 0
When I run isoMDS on this new matrix, it tells me to specify the initial config
because of the NA/INFs/ But when I perform cmdscale on this same matrix I end up with
the following results,
> bt<-cmdscale(tt);bt
[,1] [,2]
a -1.79268634 -0.2662750
b -1.79268634 -0.2662750
c -0.02635497 0.5798934
d -1.79268634 -0.2662750
e 1.08978620 0.6265313
f -0.02635497 0.5798934
g 2.20852966 0.2828937
h 2.13245309 -1.2703869
The results suggest that items 1,2, and 4 have similar locations as is expected. Also
items 3 and 6 have similar locations as would also be expected. So, my results seem to
have been replicated correctly using cmdscale.
I've tried to specify an initial config using isoMDS in a few ways without success, so
I am surely doing something wrong. So far, I have tried the following:
> ll<-isoMDS(tt, y=cmdscale(tt))
which tells me "zero or negative distance between objects 1 and 2"
> ll<-isoMDS(tt, y=cmdscale(tt, k=2))
Again, thanks,
Harold
-----Original Message-----
From: Jari Oksanen [mailto:[EMAIL PROTECTED]
Sent: Thu 9/9/2004 4:26 AM
To: Doran, Harold
Cc: Prof Brian Ripley; R-News
Subject: RE: [R] isoMDS
On Wed, 2004-09-08 at 21:31, Doran, Harold wrote:
> Thank you. Quick clarification. isoMDS only works with dissimilarities.
> Converting my similarity matrix into the dissimilarity matrix is done as
> (from an email I found on the archives)
>
> > d<- max(tt)-tt
>
> Where tt is the similarity matrix. With this, I tried isoMDS as follows:
>
> > tt.mds<-isoMDS(d)
>
> and I get the following error message.
>
> Error in isoMDS(d) : An initial configuration must be supplied with
> NA/Infs in d. I was a little confused on exactly how to specify this
> initial config. So, from here I ran cmdscale on d as
>
This error message is quite informative: you have either missing or
non-finite entries in your data. The only surprising thing here is that
cmdscale works: it should fail, too. Are you sure that you haven't done
anything with your data matrix in between, like changed it from matrix
to a dist object? If the Inf/NaN/NA values are on the diagonal, they
will magically disappear with as.dist. Anyway, if you're able to get a
metric scaling result, you can manually feed that into isoMDS for the
initial configuration, and avoid the check. See ?isoMDS.
> > d.mds<-cmdscale(d)
>
> which seemed to work fine and produce reasonable results. I was able to
> take the coordinates and run them through a k-means cluster and the
> results seemed to correctly match the grouping structure I created for
> this sample analysis.
>
> Cmdscale is for metric scaling, but it seemed to produce the results
> correctly.
>
> So, did I correctly convert the similarity matrix to the dissimilarity
> matrix? Second, should I have used cmdscale rather than isoMDS as I have
> done? Or, is there a way to specify the initial configuration that I
> have not done correctly.
If you don't know whether you should use isoMDS or cmdscale, you
probably should use cmdscale. If you know, things are different.
Probably isoMDS gives you `better'(TM) results, but it is more
complicated to handle.
cheers, jari oksanen
--
Jari Oksanen -- Dept Biology, Univ Oulu, 90014 Oulu, Finland
Ph. +358 8 5531526, cell +358 40 5136529, fax +358 8 5531061
email [EMAIL PROTECTED], homepage http://cc.oulu.fi/~jarioksa/
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