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
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 qu
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 val
