Thank you for your ideas. 

You seem to suggest that I use a C-R model to estimate the number of missing
values per category and then impute these values?

Sounds feasible, although I was in hope for a more straight forward way to
deal with zero trunc. binomial data like seemingly is available for Poisson
data.

Since zero trunc. Poisson  is available in glmmADMB, would it also be an
option to treat binomial data as Poisson by using a per class offset?

 

CU, Martin.

 

From: David Duffy [mailto:David.Duffy@qimr.edu.au] 
Sent: Friday, January 20, 2012 12:42 AM
To: Martin Schmettow; r-sig-mixed-models@r-project.org
Subject: RE: [R-sig-ME] zero-truncated mixed effects logistic regression?

 

 

> In one of my works on that topic I, indeed, viewed this as a Rasch type
> model. And I well remember how excited I got when reading above paper,
> because that would allow me to deal with predictors in a straight forward
> way.

> However, the number of undetected flaws is crucial by itself as it means
to
> go on testing. Capture-recapture models are a good way to estimate these,
> but they don't allow for predictors. So, if I run a crossed mixed effects
> logistic regression on my data, I have missing values.

I am quite ignorant of these matters, but guess that you can run your GLMM
on the complete data to
estimate the "capture probabilities" for each type of fault for each tester,
and in a separate step
use these a la Horvitz-Thompson to estimate total number of each fault type
conditional on
your current set of testers.  My first thought was there is a closed
population of faults, but I
just had a sudden dizzying image of a hierarchy of more and more subtle
problems ;)





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