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
