Have you plotted the raw data to have a look at the distribution? You could try another exponential family distribution like tweedie that has a mass at zero but is otherwise similar to poisson/gamma - so you're directly modeling the zeroes. It won't work if you have a lot of high values though. Proportions are tricky. Have a read of the Warton paper (2012/11?) "the arcsine is asinine".
Liz On 02/02/2013, at 6:34 PM, v_coudr...@voila.fr wrote: > Thank you very much for this suggestion. In fact I reconsidered my question > and I am not sure that zero-inflated model is what I need. If I understood it > properly, > a zero-inflated model is best suited when we don't know if zero values are > true or false absences (right?). In my case all zero values are assumed to be > real > absence and are therefore informative. However, fitting quasipoisson on raw > counts or quasibinomial on proportion gives me awful distributions of > residuals and > meaningless results. > > Valérie > > >> Message du 01/02/13 à 17h22 >> De : "Cade, Brian" >> A : v_coudr...@voila.fr >> Copie à : r-sig-ecology@r-project.org >> Objet : Re: [R-sig-eco] proportion data with many zeros >> >> 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_c...@usgs.gov >> tel: 970 226-9326 >> >> >> >> On Fri, Feb 1, 2013 at 1:30 AM, 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 >>> ___________________________________________________________ >>> CAN 2013 : résultats et matchs en direct à suivre sur Voila.fr >>> http://sports.voila.fr/football/can/ >>> >>> _______________________________________________ >>> R-sig-ecology mailing list >>> R-sig-ecology@r-project.org >>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > > ___________________________________________________________ > CAN 2013 : résultats et matchs en direct à suivre sur Voila.fr > http://sports.voila.fr/football/can/ > > _______________________________________________ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology