Multivariate quasi-bionomial analysis of proportion data?

Amanda--
I'm not sure I would be convinced by you analyses, as I don't think your
statistical model corresponds to your sampling or data generating process.
But, I'd need to know more information about the response design (data
collection) to make any suggestions.

For binomial or quasi-, you aren't analyzing the ratio of time observed
(DV) to total time observed, you're presumably using the number of minutes
or seconds?  If so, note that you get very different answers depending on
the units, because the binomial response is treating each point observation
as independent.  Depending on the animal and the behaviors, in my
experience not even minute or 10 minute observations are independent.

How long is an individual animal observed in a given bout (period of
consecutive recording)?  Are individuals monitored for more than 1 bout?
How many behaviors does it perform (on average) in one observation bout?
How many times does it switch behavior in a bout?  Even if it only does
behaviors A & B, if it is doing A when you start observing, at some point
it switches to B, and is still doing B when you stop recording, that is
very different than it switching back & forth A B A B A B A B A B in a
single bout.

If you have lots of switching by individual animals in individual bouts,
then there may be a reasonable mixed-model binomial-based approach,
treating individual animals as random subjects.  If not, there are some
approaches to proportional data that might be a better approximation to
your data and components of variation.  But I've already stuck my neck out
far enough guessing about how you might have collected your data, so I'll
stop here unless you provide more information.

I hope that this helps...

Tom 2

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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-------------------------------------------
Tom

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