Alexandre,
Both RDA and MRM are useful methods but they address different questions.
The R2 value from RDA quantifies the proportion of the variance in species
abundances that can be explained with environmental or spatial gradients. In
other words, the response variables in the analysis are the
Hi Steve,
Thank you for your response to my message and for the suggestion.
We are also performin RDA-based variance partitioning. Reading the literature
on community composition variance partition, my impression was that there is a
turmoil and the field is divided into two main fields in
Dear friends,
My name is Alexandre and I am trying to analyze a dataset on floristic
composition of tropical coastal vegetation by means of variance partition,
according to the outlines of a Tuomisto's recent papers, specially
Tuomisto, H., Ruokolainen, L., Ruokolainen, K., 2012. Modelling
Hi,
That seems a bit odd: can you provide a reproducible example, off-list
if necessary?
Sarah
On Wed, Dec 4, 2013 at 12:50 PM, Alexandre Fadigas de Souza
alexso...@cb.ufrn.br wrote:
Dear friends,
My name is Alexandre and I am trying to analyze a dataset on floristic
composition of
Hi,
Not only odd, but impossible. If you have a model y ~ x1, and you *add* a new
explanatory variable, you cannot get worse in raw R2. You can get worse in
adjusted R2. You can also get worse if you add variables to a matrix for which
you calculate distances. So dist(y) ~ dist([x1]) can have
Alexandre,
I'll leave it to Sarah to advise you on MRM (and I agree with Jari that
the method you're describing is not going to work). I'll just add that it
is not clear to me why the predictors (even geographic distance) have to
be treated as distances to partition the variance in composition.
