Dear r-sig-ecology listers,
I am involved in a study whose objective is the see if there relationships
between the functional diversity of the fish community and environmental
factors of the sample site for a number of sites in a bounded environment.
Specifically, we are looking at the parameters
Dear friends,
I have just looked at vegan:::varpart2, and it seems to me that in
estimation of degrees of freedom for adjusted R2 calculation, it just
uses number of columns in the constraining matrix/dataframe.
Am I right?
Is this appropriate if the constraining dataframe involves factors with
Dear Marc,
Try the permutation of trait values! Such permutation does not change
any of the patterns that you mentioned and probably wanted to be
constrained.
On the other hand, it removes the correlation between traits and
environment, and also correlation between traits and abundance.
On 23/10/2014, at 18:17 PM, Gavin Simpson wrote:
On 22 October 2014 17:24, Chris Howden ch...@trickysolutions.com.au wrote:
A good place to start is by looking at your residuals to determine if
the normality assumptions are being met, if not then some form of glm
that correctly models the
This all looks bogus to me; you've fit the data perfectly by fitting a
saturated model - there are no residual degrees of freedom and
(effectively) zero residual deviance. Things are clearly amiss because you
have huge standard errors. You have 24 data points and fit a model with 23
coefficient
I think there are actually 4 data points per level of some factor (after
seeing some of the other no-threaded emails - why can't people use emails
that preserve threads?**); but yes, either way this is a small data set and
trying to decide if residuals are normal or not is going to be nigh on
Hi Kendra,
Thanks for the question. It is great to see you looking at the relative
size/significance of univariate tests as a way to gauge which species most
strongly respond to some environmental gradient (/treatment) - this is a much
more reliable way to do things than using something like
Hi Kendra,
Which values are in uni.test depends which test statistic you choose to use (in
the test argument) - the default is log-likelihood ratio statistics (not
sum-of-LR, but the original LR's that later get summed to make a multivariate
statistic).
For something in the output that tells
