[R-sig-ME] Convergence in glmmTMB but not glmer

Daniel Wright d@n|e|@wr|ght @end|ng |rom uconn@edu
Wed Oct 21 16:20:16 CEST 2020 

Switching to negative binomial and selecting an optimizer via allFit did
the trick.
Thanks for the help!

Dan

On Wed, Oct 21, 2020 at 9:48 AM Mollie Brooks <mollieebrooks using gmail.com>
wrote:

> *Message sent from a system outside of UConn.*
>
>
> > On 20Oct 2020, at 20:48, Daniel Wright <daniel.wright using uconn.edu> wrote:
> >
> > " It's often a good idea when using an offset such as log(nights) to
> > *also* (alternatively) try using log(nights) as a predictor: using
> > log(nights) assumes that the number of counts is strictly proportional
> > to the number of nights measured (log(counts) ~ log(nights) + <stuff> ->
> > counts ~ nights*exp(stuff) , whereas using log(counts) allows for some
> > saturation effects (log(counts) ~ alpha*log(nights) + <stuff> -> counts
> > ~ nights^alpha*exp(stuff)) "
> >
> > Hi Ben, to respond to your comments I think it's necessary to explain a
> bit
> > about my dataset if you don't mind.
> >
> > For my research, I've collected bat acoustic data and invertebrate
> samples
> > at 26 regenerating forest stands. Each site was monitored for
> > a minimum of two consecutive nights, three when weather permitted. On the
> > last night of each monitoring effort, nocturnal flying insects
> > were collected to observe the influence of prey biomass on activity in
> > selected sites. In order to include invertebrate biomass as a variable
> > in model selection, I've averaged passes per night as a general measure
> of
> > activity and used the single night of invertebrate sampling
> > as representative of available prey biomass. Bat activity in a single
> > location is notoriously variable from night to night, and
> > activity is typically average across sampling nights.
> >
> > I will try log(counts) as per your suggestion. I appreciate the help.
>
> Was there possibly a miscommunication here? I think Ben was just using
> log(counts) in reference to math, not the formula.
> Continue to use counts as the response, but try log(nights) as a predictor
> rather than offset.
>
> cheers,
> Mollie
>
> >
> > I apologize if my response was too lengthy for this platform. This will
> be
> > my first contribution to the e-sig-mixed-models mailing list.
> >
> >
> >
> > On Tue, Oct 20, 2020 at 2:21 PM Ben Bolker <bbolker using gmail.com> wrote:
> >
> >> *Message sent from a system outside of UConn.*
> >>
> >>
> >> On 10/20/20 2:02 PM, Thierry Onkelinx wrote:
> >>> Daniel sent me the data in private.
> >>>
> >>> A couple of remarks on the dataset.
> >>> - the response is non-integer. You'll need to convert it to integer
> >>> (total number) and use an appropriate offset term (log(nights)).
> >>> - make sure the factor covariate is a factor and not an integer.
> >>
> >>   If the response is non-integer, that makes my comment about
> >> overdispersion not necessarily relevant (check again after re-fitting).
> >>
> >>   It's often a good idea when using an offset such as log(nights) to
> >> *also* (alternatively) try using log(nights) as a predictor: using
> >> log(nights) assumes that the number of counts is strictly proportional
> >> to the number of nights measured (log(counts) ~ log(nights) + <stuff> ->
> >> counts ~ nights*exp(stuff) , whereas using log(counts) allows for some
> >> saturation effects (log(counts) ~ alpha*log(nights) + <stuff> -> counts
> >> ~ nights^alpha*exp(stuff))
> >>
> >>
> >>>
> >>> Please see if that solves the problem. What happens if you use a nbinom
> >>> distribution as Ben suggested?
> >>>
> >>> Personally, I don't like to "standardise" covariates. It makes them
> much
> >>> harder to interpret. I prefer to center to a more meaningful value than
> >>> the mean. And rescale it by changing the unit. E.g. Age ranges from 1
> to
> >>> 15 with mean 6.76. I'd use something like AgeC = (Age - 5) / 10. This
> >>> gives a similar range as the standardisation of Age. But one unit of
> >>> AgeC represents 10 year. And the intercept refers to Age = 5. Making
> the
> >>> parameters estimates easier to interpret IMHO.
> >>
> >>   Yes, although 'strict' standardization (scaling by predictor SD or
> >> 2*predictor SD) allows direct interpretation of the parameters as a kind
> >> of effect size (Schielzeth 2010), whereas 'human-friendly'
> >> standardization trades interpretability for the comparison of magnitudes
> >> being only an approximation.
> >>
> >>
> >>>
> >>> Best regards,
> >>>
> >>> ir. Thierry Onkelinx
> >>> Statisticus / Statistician
> >>>
> >>> Vlaamse Overheid / Government of Flanders
> >>> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
> >>> AND FOREST
> >>> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> >>> thierry.onkelinx using inbo.be <mailto:thierry.onkelinx using inbo.be>
> >>> Havenlaan 88 bus 73, 1000 Brussel
> >>> www.inbo.be <http://www.inbo.be>
> >>>
> >>>
> >>
> ///////////////////////////////////////////////////////////////////////////////////////////
> >>> 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
> >>>
> >>
> ///////////////////////////////////////////////////////////////////////////////////////////
> >>>
> >>> <https://www.inbo.be>
> >>>
> >>>
> >>> Op di 20 okt. 2020 om 19:40 schreef Ben Bolker <bbolker using gmail.com
> >>> <mailto:bbolker using gmail.com>>:
> >>>
> >>>        As Thierry says, the data would allow us to give a more detailed
> >>>    answer.  However:
> >>>
> >>>        * the overall goodness-of-fit is very similar (differences of
> >>>    ~0.001
> >>>    or less on the deviance scale)
> >>>
> >>>        * the random-effects std deve estimate is similar (2%
> difference)
> >>>        * the parameter estimates are quite similar
> >>>        * the standard errors of the coefficients look reasonable for
> >>>    glmmTMB
> >>>    and bogus for lme4 (in any case, if there's a disagreement I would
> be
> >>>    more suspicious of the platform that gave convergence warnings)
> >>>
> >>>        There's also strong evidence of dispersion (deviance/resid df >
> >> 6);
> >>>    you should definitely do something to account for that (check for
> >>>    nonlinearity in residuals, switch to negative binomial, add an
> >>>    observation-level random effect ...)
> >>>
> >>>         You might try the usual set of remedies for convergence
> problems
> >>>    (see ?troubleshooting, ?convergence in lme4), e.g. ?allFit.  Or try
> >>>    re-running the lme4 model with starting values set to the glmmTMB
> >>>    estimates.
> >>>
> >>>        Overall, though, I would trust the glmmTMB results.
> >>>
> >>>    On 10/20/20 12:56 PM, Daniel Wright wrote:
> >>>> Hello,
> >>>>
> >>>> I'm having convergence issues when using glmer in lme4, but not
> >>>    glmmTMB.
> >>>> I'm running a series of generalized linear mixed effect models
> >>>    with poisson
> >>>> distribution for ecological count data. I've included a random
> >>>    effect of
> >>>> site (n = 26) in each model. All non-factor covariates are
> >>>    standardized.
> >>>>
> >>>> The coefficient estimates of models run in glmer and glmmTMB are
> >> very
> >>>> similar, but models run in glmer are having convergence issues.
> >>>    Any advice
> >>>> would be appreciated, as I'm not sure if I can rely on my results
> >>>    from
> >>>> glmmTMB.
> >>>>
> >>>> Attached are example of outputs from glmmTMB vs glmer:
> >>>>
> >>>>
> >>>> _______________________________________________
> >>>> R-sig-mixed-models using r-project.org
> >>>    <mailto:R-sig-mixed-models using r-project.org> mailing list
> >>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>>>
> >>>
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> >>>
> >>
> >> _______________________________________________
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> >>
> >
> >
> > --
> > ----------------------------------------------------------------
> > Daniel Wright, Graduate Research Assistant
> > Wildlife and Fisheries Conservation Center
> > Depart. Natural Resources and the Environment
> > <
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> >
> > University of Connecticut
> > Phone: 413-348-7388
> > Email: daniel.wright using uconn.edu
> >
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> >
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> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>

-- 
----------------------------------------------------------------
Daniel Wright, Graduate Research Assistant
Wildlife and Fisheries Conservation Center
Depart. Natural Resources and the Environment
<https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.nre.uconn.edu%2F&data=02%7C01%7C%7Cba31f0d133a24848eb3208d614ebb2f0%7C17f1a87e2a254eaab9df9d439034b080%7C0%7C0%7C636719399881397445&sdata=l3Lhp0QtBoRy5xpfyem%2FzYHmGZU0%2FHfPkq4mELHdRqE%3D&reserved=0>
University of Connecticut
Phone: 413-348-7388
Email: daniel.wright using uconn.edu

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