[R] zero random effect sizes with binomial lmer
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
> 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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