# [R-sig-ME] Question about zero-inflated Poisson glmer

Philipp Singer killver at gmail.com
Thu Jun 23 10:07:32 CEST 2016

```Dear group - I am currently fitting a Poisson glmer where I have an
excess of outcomes that are zero (>95%). I am now debating on how to
proceed and came up with three options:

1.) Just fit a regular glmer to the complete data. I am not fully sure
how interpret the coefficients then, are they more optimizing towards
distinguishing zero and non-zero, or also capturing the differences in
those outcomes that are non-zero?

2.) Leave all zeros out of the data and fit a glmer to only those
outcomes that are non-zero. Then, I would only learn about differences
in the non-zero outcomes though.

3.) Use a zero-inflated Poisson model. My data is quite large-scale, so
I am currently playing around with the EM implementation of Bolker et
al. that alternates between fitting a glmer with data that are weighted
according to their zero probability, and fitting a logistic regression
for the probability that a data point is zero. The method is elaborated
for the OWL data in:
https://groups.nceas.ucsb.edu/non-linear-modeling/projects/owls/WRITEUP/owls.pdf

I am not fully sure how to interpret the results for the zero-inflated
version though. Would I need to interpret the coefficients for the
result of the glmer similar to as I would do for my idea of 2)? And then
on top of that interpret the coefficients for the logistic regression
regarding whether something is in the perfect or imperfect state? I am
also not quite sure what the common approach for the zformula is here.
The OWL elaborations only use zformula=z~1, so no random effect; I would
use the same formula as for the glmer.

I am appreciating some help and pointers.

Thanks!
Philipp

```