From: Gregor Gorjanc <gregor.gorjanc_at_bfro.uni-lj.si>

Date: Mon 01 Jan 2007 - 10:51:06 GMT

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Mon Jan 01 21:55:56 2007

Date: Mon 01 Jan 2007 - 10:51:06 GMT

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.
*

Gregor

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