Convergence in glmmTMB but not glmer

On 20Oct 2020, at 20:48, Daniel Wright <daniel.wright at 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.

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 at 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 at inbo.be <mailto:thierry.onkelinx at inbo.be>
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be <http://www.inbo.be>

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Op di 20 okt. 2020 om 19:40 schreef Ben Bolker <bbolker at gmail.com
<mailto:bbolker at 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:

   <mailto:R-sig-mixed-models at r-project.org> mailing list

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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 at uconn.edu

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