Ben,

thanks for the reply.
So for now (till those tools are available), by 'assess convergence', do you mean just checking whether the results look meaningful and like what I expect from plots?

For convergence, I have a strange result: With Nelder-Mead, my model converges for some factor orders (I mean, the order I put them in the function call), but not with others. This seems to be reproducible (with the given dataset). So, say, my model converges for response ~ A * B * C + random, but not for B * A * C + random. The model converges with all orders using bobyqa. 
I found another report like this (order effect) in a post somewhere, but it didn't seem to have been solved. Order really shouldn't matter, should it? Could this be due to starting values for optimization or something like that?

Tobias 

--
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Tobias Heed, PhD
Biological Psychology and Neuropsychology  |  University of Hamburg
Von-Melle-Park 11, Room 206  |  D-20146 Hamburg, Germany
Phone: (49) 40 - 42838 5831  |  Fax:   (49) 40 - 42838 6591
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On 17.09.2013, at 21:27, Ben Bolker <bbolker@gmail.com> wrote:

> Tobias Heed <tobias.heed@...> writes:
> 
>> 
>> Hello,
>> 
>> I am trying to understand the different options for fitting with glmer.
>> I have been unable to find an overview over which options are 
>> appropriate in which cases.
>> If there is a document out there that explains these things, 
>> I'd be thankful for a link.
> 
>  No (want to write one?)
> 
>> 
>> My specific questions are:
> 
>> 1, what is the difference in using maxIter in the function call
>> vs. using maxfun in glmerControl()? Which one is better or more
>> important to change when a model doesn't converge (i.e., what kind
>> of iteration do they stand for)? Maxiter seems not to be documented
>> in the help of lme4 1.1.0, does this mean it should not be used
>> anymore?
> 
>  maxIter is old/obsolete.
>  maxfun controls the iteration counter in the BOBYQA/Nelder-Mead
> phase (i.e., optimization over the 'theta' (Cholesky factor of
> random-effects variance-covariance matrices) parameter vector)
> 
>> 2, I have a model that does not converge with Nelder-Mead, but does
>> converge with bobyqa -- from googling around, it seems that some
>> people like one or the other better, but are there specific things I
>> should look out for when using the one or the other? Or, are there
>> specific cases in which using one or the other would be more
>> recommendable?
> 
>  We don't know enough about this (yet) to make strong recommendations
> 
>> 3, what kind of result or warning message would indicate that 
>> I should use the restart_edge option?
> 
>   If you get parameters on the boundary (i.e. 0 variances,
> +/-1 correlations) it may be worth trying.  However, I'm not
> sure it's actually implemented for glmer!
> 
>> 4, I got this warning: 2: In commonArgs(par, fn, control,
>> environment()) : maxfun < 10 * length(par)^2 is not recommended.
>> par appears to be the vector with parameters passed to the
>> optimizer. Is it necessary (or just "better", but not imperative) to
>> set maxfun to the value indicated in this equation, or higher? Why
>> is a higher value for maxfun not used automatically when appropriate
>> - does it have any negative consequences? Can I read out par easily
>> somewhere?
> 
>   I believe this is coming from BOBYQA, but I'm not sure. 
> 
> 
>> 5, when a model converges only after tinkering with any of the
>> options (e.g., optimizer, maxfun, restart_edge) or maxiter, does
>> this say anything about the quality or reliability of the fit?
> 
>  I would certainly be more careful to assess convergence in
> these cases.  Do the answers look sensible?  (We hope to add
> some more functionality for checking convergence ...)
> 
>> 6, when reporting a GLMM, should these kinds of options be reported?
>> It doesn't seem that people do, but it would seem appropriate when
>> they are necessary to achieve convergence etc., wouldn't it?
> 
>  Absolutely.  You should always report *everything* necessary
> for someone to reproduce your results (in an appendix or online
> supplement, if necessary).
> 
>  cheers
>    Ben Bolker
> 
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