On 08/02/15 12:27, Amanda Greer wrote:
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.
Ignoring the zeroes problem for the moment, I think (quasi-)binomial
distributions are a distraction: binomials are based on counts of things
(see Petr Keil's post: http://www.petrkeil.com/?p=603). If you're
looking at proportions of times, then it might be better to think in
terms of gamma distributions, which lead to a beta distribution for the
proportion of times spent doing one thing, and a Dirichlet distribution
if you have several items (as you do here).
Once you have to worry about the zeroes, you need to do something more,
for example see this paper:
<http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12122/abstract>
Bob
Thanks,
Amanda
[[alternative HTML version deleted]]
_______________________________________________
R-sig-ecology mailing list
R-sig-ecology@r-project.org
https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
--
Bob O'Hara
Biodiversity and Climate Research Centre
Senckenberganlage 25
D-60325 Frankfurt am Main,
Germany
Tel: +49 69 7542 1863
Mobile: +49 1515 888 5440
WWW: http://www.bik-f.de/root/index.php?page_id=219
Blog: http://blogs.nature.com/boboh
Journal of Negative Results - EEB: www.jnr-eeb.org
_______________________________________________
R-sig-ecology mailing list
R-sig-ecology@r-project.org
https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
