Thanks for so many thoughts!
Arne: I could use Condition() for that but something like
capscale(communitydistances ~ environmentalvar+Condition(region))
would not attempt what I need, because I need to fit a model only with
"region", extract residuals, and then do another analysis (ISA) with these
residuals.
However, if this kind of formulation is correct, it could also be useful to
see the effects of environmental factors while controlling for the effect
of region
Indicator species analysis could be done for each region separately. - yes,
but I would like to try both approaches, ISA for all regions (but removing
the region effect) and ISA for each region separately, and compare the
results
Jari: OK I guess that even if they could be calculated, residuals from the
adonis() point of view are not suitable for me, as they are
dissimilarities. Sorry if I was confusing, what I need are residuals of raw
data, as you say. So I guess the residuals() method of rda() should work.
Yes, I know that ISA needs non-negative values, I was attending to
transform these residuals to make them all positive.
Pierre: I checked manyglm() function in the mvabund package and I think it
could be useful too, you can indeed get residuals from a mutivariate GLM
with family=binomial.
Ivailo: I think you maybe misunderstood my approach, I want to perform an
ISA based on differences in species composition, but I want to focus on the
part of these differences which is not caused by the region studied. I want
to identify indicator species for different environmental conditions and
caracteristics of forest patches (like landscape management type, forest
age and others), but I am not interested in finding the characteristic
species for the different regions. To make it clearer, I have data from
different regions, into each of the regions there are a series of forest
patches which vary in management type, age and other factors. So the
cluster I am using in multipatt() is a classification into management
types, age groups, etc. As I said before, I am not interested in use
"regions" for site classification. I hope this makes the analysis easier to
understand now!
So my main doubts now are: for this kind of purpose, 1) what is your
opinion on using a community data table in the ISA which contains not the
actual presence/absence, but residuals form a previous model with region
effect?; and 2) Which kind of residuals do you find more appropiate to use
here (rda residuals of multivariate GLM residuals)?
Cheers,
Alicia
2014-03-18 16:23 GMT+01:00 Ivailo <ubuntero.9...@gmail.com>:
> On Tue, Mar 18, 2014 at 5:02 PM, Alicia Valdés
> <aliciavaldes1...@gmail.com> wrote:
> ...
> > However, what I attempt to do is to perform an indicator species analysis
> > (ISA) with these residuals. I want to see if I can find species which are
> > indicators for different environmental conditions, but first I would like
> > to remove the differences in species composition due to the study region
> ...
>
> Isn't an indicator species analysis *based* on differences in species
> composition? How could one "remove the difference in species
> composition due to region" and then still hope to identify indicator
> species for the regions that are "equalized"?
>
> Cheers,
> Ivailo
> --
> "The cure for boredom is curiosity. There is no cure for curiosity." --
> Dorothy Parker
>
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