Hi Phil,
Are you using metaMDS in the vegan package? This allows you to determine
the number of random starts, and selects the best. It might help.
Hank Stevens
Dear Phil,
I don't have experiences with Minissa but I know that isoMDS is bad in
some situations. I have even seen situations with
Dear Phil,
I don't have experiences with Minissa but I know that isoMDS is bad in
some situations. I have even seen situations with non-metric
dissimilarities in which the classical MDS was preferable.
Some alternatives that you have:
1) Try to start isoMDS from other initial configurations
Thanks for your message.
I don't know what is Minissa. Sounds like a piece of software. What
is the method it implements? That is, is it supposed to implement
the same method as isoMDS or something else? IsoMDS implements
Kruskal's (and Young's and Sheperd's and Torgeson's) NMDS, but
there
Short answer: you cannot compare distances including NAs, so there is no
way to find a monotone mapping of distances.
If the data really are identical for two rows, you can easily drop one of
them whilst doing MDS, and then assign the position found for one to the
other.
On Tue, 18 Apr 2006,
On Tue, 2006-04-18 at 22:06 -0400, Tyler Smith wrote:
I'm trying to do a non-metric multidimensional scaling using isoMDS.
However, I have some '0' distances in my data, and I'm not sure how to
deal with them. I'd rather not drop rows from the original data, as I am
comparing several
On Wed, 2006-04-19 at 07:46 +0100, Prof Brian Ripley wrote:
Short answer: you cannot compare distances including NAs, so there is no
way to find a monotone mapping of distances.
The original Kruskal-Young-Shepard-Torgerson programme KYST (version 1
from 1973) could handle missing values.
About replacing the zeroes with tiny numbers:
isoMDS works with the rankings of the distances. Therefore replacing
zeroes by tiny values gives them a rank above the real zeroes (distance
to same observation) and below all the non-zero distances. If this makes
sense in your application (in my
Thanks all!
From Christian's explanation I think I will be alright adding small
values to my zero distances. In my application my distances are limited
by the number of primer pairs I use, and it is reasonable to expect that
adding primer pairs would eventually reveal some small genetic
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
On Thu, 2004-09-09 at 04:53, Kjetil Brinchmann Halvorsen wrote:
Mardia, kent Bibby defines the standard transformation from a
similarity matrix to a dissimilarity
(distance) matrix by
d_rs - sqrt( c_rr -2*c_rs + c_ss)
where c_rs are the similarities. This assures the diagonal of the
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
On Thu, 2004-09-09 at 14:25, Doran, Harold wrote:
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.
--- snip ---
Doran,
Your data clarified things. It seems to me now, that your data are not
,] 3.0638025 0.7058540
$stress
[1] 3.233738
Does this help?
-Original Message-
From: Doran, Harold [mailto:[EMAIL PROTECTED]
Sent: September 9, 2004 8:26 AM
To: Jari Oksanen
Cc: Doran, Harold; Prof Brian Ripley; R-News
Subject: RE: [R] isoMDS
Thank you. I use the same matrix on cmdscale as I
On Wed, 8 Sep 2004, Doran, Harold wrote:
1)Can isoMDS work only with dissimilarities? Or, is there a way
that it can perform the analysis on the similarity matrix as I have
described it?
Yes. The method, as well as the function in package MASS. All other
MDS packages are doing a
Distances cannot always be constructed from similarities. This can be done
only if the matrix of similarities is nonnegative definite. With the
nonnegative definite condition, and with the maximum similarity scaled so
that s_ii=1, d_ik=(2*(1-s_ik))^-.5
Check out the vegan package.
Alex
On Wed, 8 Sep 2004, Hanke, Alex wrote:
Distances cannot always be constructed from similarities. This can be done
only if the matrix of similarities is nonnegative definite. With the
nonnegative definite condition, and with the maximum similarity scaled so
that s_ii=1, d_ik=(2*(1-s_ik))^-.5
Distances are
often called disimilarities.
-Original Message-
From: Prof Brian Ripley [mailto:[EMAIL PROTECTED]
Sent: September 8, 2004 11:58 AM
To: Hanke, Alex
Cc: 'Doran, Harold'; '[EMAIL PROTECTED]'
Subject: RE: [R] isoMDS
On Wed, 8 Sep 2004, Hanke, Alex wrote:
Distances cannot
PROTECTED]
Sent: Wednesday, September 08, 2004 9:58 AM
To: Doran, Harold
Cc: [EMAIL PROTECTED]
Subject: Re: [R] isoMDS
On Wed, 8 Sep 2004, Doran, Harold wrote:
1)Can isoMDS work only with dissimilarities? Or, is there a way
that it can perform the analysis on the similarity matrix as I have
: Prof Brian Ripley [mailto:[EMAIL PROTECTED]
Sent: Wednesday, September 08, 2004 9:58 AM
To: Doran, Harold
Cc: [EMAIL PROTECTED]
Subject: Re: [R] isoMDS
On Wed, 8 Sep 2004, Doran, Harold wrote:
1) Can isoMDS work only with dissimilarities? Or, is there a way
that it can perform the analysis
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