#this is all assuming you want Bray-Curtis distances, but there are
other distances involved.
library(vegan)
library(labdsv)
dis.bc <- vegdist(your.data)
bc4d.nmds <- nmds(dis.bc,4)
ordcomp(bc4d.nmds,dis.bc,dim=4)
On Sat, Jan 31, 2009 at 6:53 AM, Titus von der Malsburg
wrote:
> Hi Tomek, have a
Hi Tomek, have a look at R News, Volume 3/3, December 2003. There you
find an article about different algorithms that are available in R.
Titus
On Sat, Jan 31, 2009 at 01:36:29AM +0100, Tomek Wlodarski wrote:
> now I see that cmdscale is not the best option for my problem
> So I am wondering i
Dear Stephen,
Thanks a lot!
now I see that cmdscale is not the best option for my problem
So I am wondering if you can advice me other method of MDS or
different approach to my problem:
I have matrix which describes "distances" between object and I would
like to visualise this matrix onto 2D in su
It depends on what you mean? If you would like a goodness of fit of
your ordination to your distance matrix then this is doable and I
would suggest that you look at the labdsv tutorial -
http://ecology.msu.montana.edu/labdsv/R/
On Thu, Jan 29, 2009 at 6:50 PM, Tomek Wlodarski
wrote:
> Dear R d
Dear R developers and users!
I have calculated metric MDS by cmdscale from matrix of distances
(dissimilarities).
I would like to ask you how can I estimate how well this new mapping
represents characteristic features of my data set?
Thank you for any suggestions.
Best,
tomek
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