[R-sig-ME] Question about zero-inflated Poisson glmer
Philipp Singer
killver at gmail.com
Thu Jun 23 12:42:05 CEST 2016
Thanks! Actually, accounting for overdispersion is super important as it
seems, then the zeros can be captured well.
On 23.06.2016 11:50, Thierry Onkelinx wrote:
> Dear Philipp,
>
> 1. Fit a Poisson model to the data.
> 2. Simulate a new response vector for the dataset according to the model.
> 3. Count the number of zero's in the simulated response vector.
> 4. Repeat step 2 and 3 a decent number of time and plot a histogram of
> the number of zero's in the simulation. If the number of zero's in the
> original dataset is larger than those in the simulations, then the
> model can't capture all zero's. In such case, first try to update the
> model and repeat the procedure. If that fails, look for zero-inflated
> models.
>
> 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 11:27 GMT+02:00 Philipp Singer <killver at gmail.com
> <mailto:killver at gmail.com>>:
>
> 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>
> > <mailto: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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