to compute species scores using the
function wascores() I have now pondered for 2 days how these scores
are calculated and what their precise meaning would be. Would these
species scores be appropriate to show as vectors in the MDS?
Thanks for any answer...
Gabriel Singer
Dear Jari and Gavin,
thanks a lot, everything clear... with the connection to CCA I now get
the meaning of the species scores, almost trivial after all...
gg
Gavin Simpson wrote:
On Wed, 2009-08-19 at 11:40 +0200, gabriel singer wrote:
Hi sig-ecology!
Here comes a probably stupid
, 2009-09-15 at 17:02 +0200, gabriel singer wrote:
Hi vegan-users and programmers,
Can anybody tell me how the function vectorfit (envfit) computes arrow
lengths (as fits of a metric variable onto an ordination) exactly? I
understand the scaling bit in the end, but have troubles to understand
how
hi gian,
no, there is no such way. A MDS can´t express explained variance.
However, the stress value is the overall measure of quality of fit of
your MDS to the data. There are various measures of stress, but loosely
speaking you can regard the stress as a percentage of variation NOT
Hi everybody,
Anybody has used capscale() in package vegan to compute a PCoA-CDA as
suggested by Anderson and Willis 2003 (Ecology 84: 511 ff) using one or
more factors as predictors?
Then I wonder about:
*) How to interpret interactions of factors? Why are interactions
(specified as
--
Dr. Gabriel Singer
Department of Freshwater Ecology - University of Vienna
and Wassercluster Lunz Biologische Station GmbH
+43-(0)664-1266747
gabriel.sin...@univie.ac.at
Gian Maria Niccolò Benucci wrote:
Hi Gavin and Hi all,
I will not go in front of a bus for sure, I not mad, at least I am
A difference between two communities within a host could still exist and
could make perfect sense, too, when you regard community as a random
factor. Then community may introduce some extra variation (compared to
the within-community variation), experimentally seen interesting and
important,
Hi Jaime,
The interactions are just a matter of defining the formula as such, e.g.
adonis(dist~factor1*factor2).
I suppose, a multiple comparison (with the reasoning of a post-hoc test)
can just be done using adonis() for pairwise comparisons and then use
p.adjust().
Cheers, gabriel
On
dear alida,
legend() should help to get the legends, just ask for help(legend), it´s
pretty easy.
then for the variance explained: with an NMS the only measure of fit you
get is the stress value, there isn´t anything like a percentage of
explained variance. you may want to regard the stress
-ecology@r-project.org
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Dr. Gabriel Singer
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Hi list,
Conducting sort of an opinion poll among list members.
Start with two matrices, one environmental, one species, same sites. I
wondered what people think of defining groups by a cluster analysis
based on the environmental variables (say, hclust or similar). Then
testing for a
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Department of Limnology - University of Vienna
and Wassercluster Lunz Biologische Station GmbH
+43-(0)664-1266747
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Dr. Gabriel Singer
Department of Limnology - University of Vienna
and Wassercluster Lunz Biologische Station GmbH
+43-(0)664-1266747
gabriel.sin...@univie.ac.at
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Department of Limnology - University of Vienna
and Wassercluster Lunz Biologische Station GmbH
+43-(0)664-1266747
gabriel.sin...@univie.ac.at
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hi jakub,
I would suggest starting with standardizing your environmental variables
with scale(), then compute Euclidean distances with e.g. vegdist() in
{vegan} and run a cluster analysis on the distance matrix with hclust().
Choose a cutoff for minimum dissimilarity and group your sites
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