[R-sig-eco] Zero inflated data on some levels of a random factor in mixed models

[Krista Takkis]

Dear all,



I have a set of data on nectar volumes from four plant species. Two
species have ample zeroes in the data (for one species almost 1/3 of
the flowers had no nectar), but two species don’t have excessive
zeroes in the data and have a normal distribution. I am trying to find
out, what would be the correct way to model the trait responses in
this situation. I would like to analyse all four species in one mixed
model, but should I try to account for the zero inflated data, if the
problem is only with half of the species? And if so, then how could I
do it properly?

   An answer to an earlier question on the topic of zero inflated data
(https://stat.ethz.ch/pipermail/r-help/2014-May/374444.html) suggested
to model the zero and non-zero data separately. With not too many
zeroes in case of two species and wishing to combaine all four
species, I probably cannot use this method in this case or could it be
possible somehow? Till now I have used function glmmPQL (MASS) to
model this trait with species/plant/flower as a random factor.
However, as far as I know, this function does not allow to account for
the zero inflated data. I found that MCMCglmm and glmmADMB would allow
to account for zero inflated data, but before learning to use a new
package I wanted to ask, whether this would be the correct way to
approach this kind of data in the first place and whether there might
be a way to do this using glmmPQL function?

   Could you give me some suggestions, what might be the best way to
deal with this kind of data?



Thank you in advance,



Krista Takkis

Department of Geography

University of the Aegean