[R-sig-ME] zero-truncated mixed effects logistic regression?

[Martin Schmettow]

> > The problem I have is similar to the capture-recapture approach for
> > estimating abundance. In my case the captured animals are design flaws
> > of software.
> >
> > A given number of testers independently tries to find these flaws,
> > which makes it a binomial problem. However, flaws that were never
> > discovered during the study are not known to the experimenter.
> 
> I may be corrected, but I think your setup is "actually" a Rasch type
model
> with each flaw being an item.  Some flaws are just too difficult to see,
ie the
> item is "too hard".  I presume, given your research questions, you are not
> actually interested in estimating the number of undetected flaws from each
> class, so a missing data type setup is not really needed.
> 
> http://www.jstatsoft.org/v20/a02/paper
> 
> is one paper from our esteemed leader ;)

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.

So, while the above paper merges IRT models with (crossed) mixed effects
models (and that's great!), I would need sth. that merges the latter with
C-R models. C-R models do deal with sort of random effects: the
heterogeneous capturability of animals (denoted "h") and variability in
trials ("t"). So called Mht models could thus be viewed as crossed random
effects models, but: C-R models (afaik) don't deal with predictors and
mixed-effects models require at least missing-at-random, which is not the
case.

Any ideas?

CU, Martin