[R] zero random effect sizes with binomial lmer

Gregor Gorjanc gregor.gorjanc at bfro.uni-lj.si
Mon Jan 1 11:51:06 CET 2007

Daniel Ezra Johnson <johnson4 <at> babel.ling.upenn.edu> writes:
> 1) Yes, I have tweaked the data to show as clearly as I can that this is a 
> bug, that a tiny change in initial conditions causes the collapse of a 
> reasonable 'parameter' estimate.

I would not call this a bug, since this is related to data and not to the
software. I might be wrong!

> 2) mcmcsamp() does not work (currently) for binomial fitted models.

Sorry, for wrong pointer. You could try with some other packages if they
have support for binomial models with "random" effects. I would just try
in BUGS --> take a look at R2WinBUGS or Brugs.

> 3) This is an issue of what happens when the sample is too small. For all 
> larger data sets I have gotten a ranef variance between 0.05 and 1.00 or 
> so.
> It makes no sense to say that as the data set gets smaller, the systematic 
> variation between Items goes away. It doesn't, as I've shown. In the data 

I believe that when data gets smaller such parameters are harder to estimate
and you can easily get 0 as MLE.

> above, certain Items were still 10+ times as likely (log-odds wise) to 
> have Response==1 as others.


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