[R-sig-ME] Question about zero-inflated Poisson glmer
Philipp Singer
killver at gmail.com
Mon Jun 27 22:03:23 CEST 2016
NBinom was not really successfull unitl now, but will try to tune.
Thanks for your help!
One point I forgot to mention was that apart from my excess of zeros,
the lowest data outcome is 10, so there is a gap between zeri and 10.
Could that be somehow a problem?
On 27.06.2016 21:59, Thierry Onkelinx wrote:
> If there is overdispersion, then try a negative binomial model or a
> zero-inflated negative binomial model. If not try a zero-inflated
> Poisson. Adding relevant covariates can reduce overdispersion.
>
> 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
>
> 2016-06-27 17:46 GMT+02:00 Philipp Singer <killver at gmail.com
> <mailto:killver at gmail.com>>:
>
> Well, as posted beforehand the std dev is 9.5 ... so does not seem
> too good then :/
>
> Any other idea?
>
>
> On 27.06.2016 17:31, Thierry Onkelinx wrote:
>> Dear Philipp,
>>
>> You've been bitten by observation level random effects. I've put
>> together a document about it on http://rpubs.com/INBOstats/OLRE.
>> Bottomline you're OKish when the standard devation of the OLRE
>> smaller than 1. You're in trouble when it's above 3. In between
>> you need to check the model carefully.
>>
>> 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
>>
>> 2016-06-27 16:17 GMT+02:00 Philipp Singer <killver at gmail.com
>> <mailto:killver at gmail.com>>:
>>
>> Here is the fitted vs. residual plot for the
>> observation-level poisson model where the observation level
>> has been removed as taken from:
>> https://stat.ethz.ch/pipermail/r-sig-mixed-models/2013q3/020817.html
>>
>> So basically the prediction is always close to zero.
>>
>> Note that this is just on a very small sample (1000 data points).
>>
>> If I fit a nbinom2 to this smalle sample, I get predictions
>> that are always around ~20 (but never zero). Both plots are
>> attached.
>>
>> What I am wondering is whether I can do inference on a fixed
>> parameter in my model which is my main task of this study.
>> The effect is similar in the different models and in general
>> I am only itnerested in whether it is positive/negative and
>> "significant" which it is. However, as can be seen, the
>> prediction looks not too good here.
>>
>>
>>
>>
>> 2016-06-27 15:18 GMT+02:00 Philipp Singer <killver at gmail.com
>> <mailto:killver at gmail.com>>:
>>
>> The variance is:
>>
>> Conditional model:
>> Groups Name Variance Std.Dev.
>> obs (Intercept) 8.991e+01 9.4823139
>>
>>
>>
>> 2016-06-27 15:06 GMT+02:00 Thierry Onkelinx
>> <thierry.onkelinx at inbo.be <mailto:thierry.onkelinx at inbo.be>>:
>>
>> Dear Philipp,
>>
>> How strong is the variance of the observation level
>> random effect? I would trust a model with large OLRE
>> variance.
>>
>> Best regards,
>>
>> Thierry
>>
>> 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
>>
>> 2016-06-27 14:59 GMT+02:00 Philipp Singer
>> <killver at gmail.com <mailto:killver at gmail.com>>:
>>
>> I have now played around more with the data an
>> the models both using lme4
>> and glmmTMB.
>>
>> I can report the following:
>>
>> Modeling the data with a zero-inflated Poisson
>> improves the model
>> significantly. However, when calling predict and
>> simulating rpoissons, I
>> end up with nearly no values that are zero (in
>> the original data there are
>> 96% zero).
>>
>> When I model the data with overdisperion by
>> including an observation-level
>> random effect, I can also improve the model (not
>> surprisingly due to the
>> random effect). When I predict outcomes by
>> ignoring the observation-level
>> random effect (in lme4), I receive bad prediction
>> if I compare it to the
>> original data. While many zeros can be captured
>> (of course), the positive
>> outcomes can not be captured well.
>>
>> Combining zero inflation and overdispersion
>> further improves the model, but
>> I can only do that with glmmTMB and then have
>> troubles doing predictions
>> ignoring the observation-level random effect.
>>
>> Another side question:
>>
>> In lme4, when I do:
>>
>> m = glm(x~1,family="poisson")
>> rpois(n=len(data),lambda=predict(m,
>> type='response',re.form=NA)
>>
>> vs.
>>
>> simulate(1,m,re.form=NA)
>>
>> I receive different outcomes? Do I understand
>> these function wrongly?
>>
>> Would appreciate some more help/pointers!
>>
>> Thanks,
>> Philipp
>>
>> 2016-06-24 15:52 GMT+02:00 Philipp Singer
>> <killver at gmail.com <mailto:killver at gmail.com>>:
>>
>> > Thanks - I started an issue there to answer
>> some of my questions.
>> >
>> > Regarding the installation: I was trying to
>> somehow do it in anaconda with
>> > a specific R kernel and had some issues. I am
>> trying to resort that with
>> > the anaconda guys though, if I have a tutorial
>> on how to properly setup
>> > glmmTMB in anaconda, I will let you know. The
>> install worked fine in my
>> > standard R environment.
>> >
>> >
>> > On 24.06.2016 15 <tel:24.06.2016%2015>:40, Ben
>> Bolker wrote:
>> >
>> >> Probably for now the glmmTMB issues page is best.
>> >>
>> >> When you go there:
>> >>
>> >> - details on installation problems/hiccups
>> would be useful
>> >> - a reproducible example for the problem
>> listed below would be useful
>> >> - dispformula is for allowing
>> dispersion/residual variance to vary
>> >> with covariates (i.e., modeling
>> heteroscedasticity)
>> >>
>> >> cheers
>> >> Ben Bolker
>> >>
>> >>
>> >> On 16-06-24 09:13 AM, Philipp Singer wrote:
>> >>
>> >>> 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 <mailto: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 <mailto: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
>> <mailto: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
>> <mailto: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>>>
>> >>>>>>>>>>>
>> >>>>>>>>>>>> <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
>> <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
>> <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
>> <mailto:R-sig-mixed-models at r-project.org>>>>
>> mailing list
>> >>>>>>>>>>>
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>> >>>>>>>>>>>
>> >>>>>>>>>>>
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>> >>>>>>>>>>> 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>>
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>> >>>>>>>>>>>
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>> <mailto:R-sig-mixed-models at r-project.org>>>
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>> >>>>>>>>> R-sig-mixed-models at r-project.org
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>> >>>>>>>>>
>> <mailto:R-sig-mixed-models at r-project.org
>> <mailto:R-sig-mixed-models at r-project.org>>
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