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

[Philipp Singer]

Thanks Thierry - That totally makes sense. Is there some way of formally 
checking that, except thinking about the setting and underlying processes?

On 23.06.2016 11:04, Thierry Onkelinx wrote:
> 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 zero-inflated.
>
> set.seed(1)
> n <- 1e8
> sim <- rpois(n, lambda = 0.01)
> mean(sim == 0)
> hist(sim)
>
> sim.infl <- rbinom(n, size = 1, prob = 0.99) * rpois(n, lambda = 1000)
> mean(sim.infl == 0)
> hist(sim.infl)
>
> 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 Forest
> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> Kliniekstraat 25
> 1070 Anderlecht
> Belgium
>
> 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 at gmail.com 
> <mailto:killver at 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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