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

Philipp Singer killver at gmail.com
Fri Jun 24 15:13:45 CEST 2016 

Update, I tried it like that, but receive an error message.

Warning message:
In nlminb(start = par, objective = fn, gradient = gr): NA/NaN function evaluation

Error in solve.default(hessian.fixed): Lapack routine dgesv: system is exactly singular: U[3,3] = 0
Traceback:

1. glmmTMB(y ~ 1 + x + (1 | b),
  .     data = data, family = "poisson", dispformula = ~1 + x)
2. sdreport(obj)
3. solve(hessian.fixed)
4. solve(hessian.fixed)
5. solve.default(hessian.fixed)

Any ideas on that?

BTW: Is it fine to post glmmTMB questions here, or should I rather use 
the github issue page, or is there maybe a dedicated mailing list?

Thanks,
Philipp

On 24.06.2016 14:35, Philipp Singer wrote:
> It indeed seems to run quite fast; had some trouble installing, but 
> works now on my 3.3 R setup.
>
> One question I have is regarding the specification of dispersion as I 
> need to specify the dispformula. What is the difference here between 
> just specifying fixed effects vs. also the random effects?
>
> On 23.06.2016 23:07, Mollie Brooks wrote:
>> glmmTMB does crossed RE. Ben did some timings in vignette("glmmTMB") 
>> and it was 2.3 times faster than glmer for one simple GLMM.
>>
>>
>>> On 23Jun 2016, at 10:44, Philipp Singer <killver at gmail.com> wrote:
>>>
>>> Did try glmmADMB but unfortunately it is way too slow for my data.
>>>
>>> Did not know about glmmTMB, will try it out. Does it work with 
>>> crossed random effects and how does it scale with more data? I will 
>>> check the docu and try it though. Thanks for the info.
>>>
>>> On 23.06.2016 19:14, Ben Bolker wrote:
>>>>   I would also comment that glmmTMB is likely to be much faster 
>>>> than the
>>>> lme4-based EM approach ...
>>>>
>>>>   cheers
>>>>     Ben B.
>>>>
>>>> On 16-06-23 12:47 PM, Mollie Brooks wrote:
>>>>> Hi Philipp,
>>>>>
>>>>> You could also try fitting the model with and without ZI using either
>>>>> glmmADMB or glmmTMB. Then compare the AICs. I believe model selection
>>>>> is useful for this, but I could be missing something since the
>>>>> simulation procedure that Thierry described seems to recommended more
>>>>> often.
>>>>>
>>>>> https://github.com/glmmTMB/glmmTMB
>>>>> http://glmmadmb.r-forge.r-project.org
>>>>>
>>>>> glmmTMB is still in the development phase, but we’ve done a lot of
>>>>> testing.
>>>>>
>>>>> cheers, Mollie
>>>>>
>>>>> ------------------------ Mollie Brooks, PhD Postdoctoral Researcher,
>>>>> Population Ecology Research Group Department of Evolutionary Biology
>>>>> & Environmental Studies, University of Zürich
>>>>> http://www.popecol.org/team/mollie-brooks/
>>>>>
>>>>>
>>>>>> On 23Jun 2016, at 8:22, Philipp Singer <killver at gmail.com> wrote:
>>>>>>
>>>>>> Thanks, great information, that is really helpful.
>>>>>>
>>>>>> I agree that those are different things, however when using a
>>>>>> random effect for overdispersion, I can simulate the same number of
>>>>>> zero outcomes (~95%).
>>>>>>
>>>>>> On 23.06.2016 15:50, Thierry Onkelinx wrote:
>>>>>>> Be careful when using overdispersion to model zero-inflation.
>>>>>>> Those are two different things.
>>>>>>>
>>>>>>> I've put some information together in
>>>>>>> http://rpubs.com/INBOstats/zeroinflation
>>>>>>>
>>>>>>> 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 12:42 GMT+02:00 Philipp Singer <killver at gmail.com
>>>>>>> <mailto:killver at gmail.com <mailto:killver at gmail.com>>>:
>>>>>>>
>>>>>>> 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 <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>>
>>>>>>>>> <mailto: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
>>>>>>>> <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
>>>>>>>>>
>>>>>>>>> _______________________________________________
>>>>>>>>> R-sig-mixed-models at r-project.org 
>>>>>>>>> <mailto:R-sig-mixed-models at r-project.org>
>>>>>>>>> <mailto:R-sig-mixed-models at r-project.org>
>>>>>>>> <mailto:R-sig-mixed-models at r-project.org
>>>>>>>> <mailto:R-sig-mixed-models at r-project.org>>
>>>>>>>>> <mailto:R-sig-mixed-models at r-project.org
>>>>>>>>> <mailto:R-sig-mixed-models at r-project.org>
>>>>>>>> <mailto:R-sig-mixed-models at r-project.org
>>>>>>>> <mailto:R-sig-mixed-models at r-project.org>>> mailing list
>>>>>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>>>>>> <https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
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