obscure warning lme4 using lmer in optwrap

Question

Using lmer I get the following warning:

Warning messages:
1: In optwrap(optimizer, devfun, x@theta, lower = x@lower) :
  convergence code 3 from bobyqa: bobyqa -- a trust region step failed to reduce q

This error ois generated after using anova(model1, model2) . I tried to make this reproducible but if I dput the data and try again I the error does not reproduce on the dput data, despite the original and new datarames have the exact same str.

If have tried again in a clean session, and the error reproduces, and again is lost with a dput

I know I am not giving people much to work with here, like i said I would love to reproduce the problem. Cayone shed light on this warning?

Answer 1

(I'm not sure whether this is a comment or an answer, but it's a bit long and might be an answer.)

• The proximal cause of your difficulty with reproducing the result is that lme4 uses both environments and reference classes: these are tricky to "serialize", i.e. to translate to a linear stream that can be saved via dput() or save(). (Can you please try save() and see if it works better than dput()?
• In addition, both environments and reference classes use "pass-by-reference" semantics, so operating on the saved model can change it. anova() automatically refits the model, which makes some tiny but non-zero changes in the internal structure of the saved model object (we are still trying to track this down).
• @alexkeil's comment is wrong: the nonlinear optimizers used within lme4 do not use any calls to the pseudo-random number generator. They are deterministic (but the two points above explain why things might look a bit weird).

To allay your concerns with the fit, I would check the fit by computing the gradient and Hessian at the final fit, e.g.

library(lme4)
library(numDeriv)
fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
dd <- update(fm1,devFunOnly=TRUE)
params <- getME(fm1,"theta") ## also need beta for glmer fits
grad(dd,params)
## all values 'small', say < 1e-3
## [1] 0.0002462423 0.0003276917 0.0003415010
eigen(solve(hessian(dd,params)),only.values=TRUE)$values
## all values positive and of similar magnitude
## [1] 0.029051631 0.002757233 0.001182232

We are in the process of implementing similar checks to run automatically within lme4.

That said, I would still love to see your example, if there's a way to reproduce it relatively easily.

PS: in order to be using bobyqa, you must either be using glmer or have used lmerControl to modify the default optimizer choice ... ??