[R-lang] Re: False convergence in mixed logit model

[Levy, Roger]

Yes -- the first column of the verbose output is the step number and the second column is the deviance.  If the deviance was still going down and the model stopped, you probably need more iterations.

It could be useful to change the starting value of the model parameters with the "start" argument of lmer and see if you wind up converging to the same parameter estimates regardless of starting value.

More information about the dataset, and example code output, is, of course, always helpful.

Best

Roger


On Nov 29, 2012, at 7:03 AM PST, Laura Suttle wrote:

Hi Roger,

Thanks for the other list suggestion, I'll cross post to there.

Every variable in my data set is categorical, so I can't do that fix. I've tried playing around with the maxIter parameter before, but I'm not sure I was doing it right. Do you have any suggestions for where I can read more about how to interpret the verbose output? I found some things but they weren't very helpful.

Thanks,
Laura


On Thu, Nov 29, 2012 at 1:34 AM, Levy, Roger <rlevy@ucsd.edu<mailto:rlevy@ucsd.edu>> wrote:
Hi Laura,

This is a question that might be better answered on R-sig-ME, but briefly: I would be cautious with a model that reports false convergence; in my experience with this warning (and I am by no means expert on it), it can indicate that the optimization routine that determines the best-fit model parameters got stuck at a parameter estimate that is not near a true optimum, perhaps due to numerical issues.  You might try standardizing any continuous predictor variables you and rerunning the lmer() call.  It would be helpful to set the msVerbose control parameter to TRUE to see what the optimizer is doing.  Also, upping the maxIter and/or maxFN control parameters *might* be helpful.

I do not think that this warning message alone would be justification to omit a random effect.

Best & hope that this helps,

Roger

On Nov 28, 2012, at 8:58 PM PST, Laura Suttle wrote:

Hello all,

I hope this question hasn't been asked before, but the internet isn't being of much help to me.

I am trying to run a mixed logit regression predicting whether participants use a novel verb in a particular construction or not depending on how they were exposed to that novel verb. I dummy coded the three conditions of the experiment into two dummy variables and have added two random effects, one for the motion used for the verb, the other for the verb itself (since these were all counterbalanced).

I can get this model to run fine, the problem is when I try to add any kind of random effect for the subjects themselves. I then get this error message:

Warning message:
In mer_finalize(ans) : false convergence (8)

And all of the effects I had of the exposure type go away.

I've been trying to look up what this means and how to deal with it, but there are no clear solutions or explanations that I can find, but plenty of warning of how I should be skeptical of any output from a model with this warning. One suggestion I did find was that the subjects variable may be overfitting my data and there might be something to this: when participants are exposed to the verb in a certain way, they tend to only use the construction I'm looking for, with no variance in their responses. That said, I'm not sure that's right and I'd love a second opinion on either how I can fix this or whether I can use this as justification to not include the subjects random effect.

Thanks in advance for any help you can give,
Laura Suttle



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