Binomial glmer() with zero-inflated data

On 13Apr 2018, at 13:39, Jessie Barker <jessiebarker at gmail.com> wrote:

Dear mixed-model enthusiasts,

I have a question about how to model zero-inflated data (I have several 1's
too, but more 0's).

The data are observations of bees visiting flowers to collect nectar. A
visit can either be "pollination" or "nectar robbing" (the bee collects
nectar from a hole through the side of the flower). Each bee made a
sequence of visits, and the response variable that I am interested in is
how many times a bee robbed nectar. (In many cases a bee did not rob nectar
at all - hence the zero-inflation - and in a few cases it only robbed
nectar.) I observed each bee only once.

The predictor variable I am interested in is the proportion of flowers on
the plant that have holes in them (thus enabling nectar robbing). I have
multiple measurements of the response variable on each plant, but only one
measurement of the predictor variable. So I will include plant as a random
effect.

I think I need something like this:

model1 <- glmer ( cbind(rob,pollinate) ~ prop.holes + (1|plant),
data=mydata, family=binomial)

But I don't think that can handle the zero-inflation. Does anyone on this
listserv have any advice?

Thanks in advance - I really appreciate any suggestions!

Best wishes,

Jessie Barker

Junior Fellow
Aarhus Institute of Advanced Studies, Denmark
http://aias.au.dk/aias-fellows/jessica-barker/

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