Question about zero-inflated Poisson glmer
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 Z?rich
>>>>>>>> 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:
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> <
>>>>>>>>>>>
>>>>>>>>>>> >
>>>>>>>>>>>
>>>>>>>>>> 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
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>>>>>>>>>>>> <mailto:R-sig-mixed-models at r-project.org
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