[R-sig-eco] Multivariate quasi-bionomial analysis of proportion data?

[Amanda Greer]

Hi All,

I am trying to best analyse a set of foraging ecology data with >10
behaviour categories (DVs) and 3 levels of IV (season, sex, age). The time
which an animal spent engaged in a behaviour was recorded and then divided
by the total time spent in sight of the observer, so my data are
proportional. As is typical, not all animals engaged in all behaviours and
there are a large number of zeros in my dataset which is severely
over-dispersed. I had initially analysed all the data using the glm
function (family = quasibinomial, followed by anova. The intention was then
to use the false discovery rate alpha to account for the large number of
analyses. However, it was pointed out to me that a multivariate approach
might be better so I have been trying to figure out (a) if it's possible to
run a quasi-binomial multivariate analysis of proportion data  (b) how to
go about it.

In the R Documentation quasi-binomial family function page (
http://artax.karlin.mff.cuni.cz/r-help/library/VGAM/html/quasibinomialff.html
) it is stated that if multivariate response = TRUE the response matrix
should be binary. This seems a pretty straightforward indictment of my idea
to run this analysis on my proportion data, but I am wondering why - is
this just not possible, or is there a particular package that could help?
If anyone could provide me with an answer or some much needed guidance on
this topic I would be very grateful.

Thanks,

Amanda

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