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

Thierry Onkelinx thierry.onkelinx at inbo.be
Thu Jun 23 11:04:45 CEST 2016

Dear Philipp,

Do you have just lots of zero's, or more zero's than the Poisson
distribution can explain? Those are two different things. The example below
generates data from a Poisson distribution and has 99% zero's but no
zero-inflation. The second example has only 1% zero's but is clearly

n <- 1e8
sim <- rpois(n, lambda = 0.01)
mean(sim == 0)

sim.infl <- rbinom(n, size = 1, prob = 0.99) * rpois(n, lambda = 1000)
mean(sim.infl == 0)

So before looking for zero-inflated models, try to model the zero's.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2016-06-23 10:07 GMT+02:00 Philipp Singer <killver op gmail.com>:

> 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
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