Dear list,

I have 1340 observations of animal survival (binomial), and 10 variables in
my full model (with no interactions between variables). Using lmer() this
full model didn't converged, then I removed 2 variables that were highly
correlated (r=0.75 and 0.49) with two other, and this model presented no
convergence problems.

I standardized variables dividing by 2SD, in the "arm" package, and used
the dredge() function of "MuMIn" to perform all combinations of variables,
then selected those with deltaAIC <= 4. But from the 27 models with this
deltaAIC, 19 presented false convergence warnings.

I believe that my random factor (animals from the same litter) is causing
it, as family sizes are unbalanced. Even some simple models with only 2
fixed effects had false convergence.

Then, to get model averaged parameters, should I average all models
selected based on the AIC, or discard those 19 models that didn't converged?


(I am basically following the guidelines provided by Grueber et al. 2011
(doi: 10.1111/j.1420-9101.2010.02210.x), and lme4 version 0.999375-42.)

Any help or literature would be appreciated.

Thank you in advance.


Diego

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