For a fully parametric approach, you might want to use of zero-inflated
beta distribution (e.g., as available in gamlss package), which is designed
for zero-inflated proportions.  Or for a semi-parametric approach, you
could estimated a sequence of quantile regression estimates (e.g., in
package quantreg), where some interval (hopefully not to large) of the
quantiles will be uninformative because they are massed at the zero values.

Brian

Brian S. Cade, PhD

U. S. Geological Survey
Fort Collins Science Center
2150 Centre Ave., Bldg. C
Fort Collins, CO  80526-8818

email:  brian_cade@usgs.gov
tel:  970 226-9326



On Fri, Feb 1, 2013 at 1:30 AM, <v_coudrain@voila.fr> wrote:

> Dear all, I am trying to test how the proportion of pollen of different
> plants found in the brood cells of a wild bee changes over time. I
> conducted 4 sampling sessions
> (thus time is a factor with 4 levels) and collected several pollen samples
> for each time point (300 pollen grains counted for each sample). I thought
> about applying a
> quasi-binomial glm:
>
> y = cbind(total pollen - pollen of plant X, pollen of plant X)
>
> glm(y~time, family=quasibinomial)
>
> The problem is that I have a lot of zero value, because the pollen of some
> plants only occurred rarely or very clumped in time. I thought about
> applying a zero-inflated
> model, but I have never used it and I am not sure if it is suitable for
> proportion data. Additionally I wondered if I have to consider the fact
> that I don't have the same
> number of pollen sample for each date, which makes my design unbalanced.
> Thank you in advance for advice.
>
> Best wishes
> Valérie
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