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 useRs,
last week I asked you about a problem related to isoMDS. It turned
out that in my case isoMDS was trapped. Nonetheless, I still have
some problems with other data sets. Therefore I would like to know if
anyone here has experience with how well isoMDS performs in
comparison to
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
Sorry for not threading: I don't subscribe to this list, and the
linking of web browser and email seems to be rudimentary.
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
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
philip.leifeld at uni-konstanz.de wrote:
This was my initial call:
mds - isoMDS(dist, y = cmdscale(dist, k = 2), k=2, tol = 1e-3, maxit
= 500)
I played around a little bit with tol and maxit (adding some
zeros...) and increased the number of dimensions, but it did not
change the
Dear R users,
I have a specific question about isoMDS. Imagine the following (fake)
distance table:
hamburg bremen berlin munich cologne
hamburg 0911982677 424
bremen 911 0293547 513
berlin 982293 0785 875
munich 677
I have a specific question about isoMDS. Imagine the following (fake)
distance table:
hamburg bremen berlin munich cologne
hamburg 0911982677 424
bremen 911 0293547 513
berlin 982293 0785 875
munich 677
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
Hi,
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 datasets (morphology and molecular data) for the same
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
Dear List:
I have a question regarding an MDS procedure that I am accustomed to
using. I have searched around the archives a bit and the help doc and
still need a little assistance. The package isoMDS is what I need to
perform the non-metric scaling, but I am working with similarity
matrices, not
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
-Original Message-
From: Doran, Harold [mailto:[EMAIL PROTECTED]
Sent: September 8, 2004 10:00 AM
To: [EMAIL PROTECTED]
Cc: Doran, Harold
Subject: [R] isoMDS
Dear List:
I have a question regarding an MDS procedure that I am accustomed to
using. I have searched around the archives a bit
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
Happy New Year!
I tried to use isoMDS to present graphically matrix of coefficients of
divergence, and I
have seen error NAs/Infs not allowed in d.
But there no NAs or Inf's in my matrix!
Function `as.vector' (which is applied to test input data with
`!is.finite' ) returns in one case input
Hi,
this is a second try to post this to the R-help mailing list. The first one
has been rejected because of a too large attachment.
Now I ask this without attaching the data. If you want to reproduce the
results, please contact me directly to get the data.
(First mail, rejected:)
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