Convergence in glmmTMB but not glmer
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. 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. 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 Havenlaan 88 bus 73, 1000 Brussel 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 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:
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