Dear all,

 

Does anyone know about an ME implementation that handles zero-truncated
binomial response variables.

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. Hence the zero
truncation. Furthermore this is a crossed mixed-effects situation as
discovery trials are repeated over testers and flaws.

 

To give an example:

DISCOV:              binary response variable

testerID:             tester trying to find flaws

flawID:                 flaw to be discovered

flawClass:           a factor containing a classification of flaws

testerExp:          experience of tester in #years

 

Two typical research question were: 

(1)    Does effectiveness of testers increases with years of experience?

(2)    Are certain classes of flaws easier to find than others?

 

A general finding of previous research is that testers as well as flaws are
heterogeneous. Some flaws are less visible than others and testers differ in
overall effectiveness. Hence, random effects are needed to account for
overdispersion, right?

 

In lme4 this would look like:

lmer( DISCOV ~ flawClass + testerExp + (1|testerID) + (1|flawID),
family=binomial)

 

However, this would ignore the fact that observations are missing, namely
all discovery trials of flaws that were never discovered.

 

Afaik there are implementations for zero-truncated Poisson and NegBin
(glmmADMB and MCMCglmm), but none for zt binomial responses. Any
suggestions?

 

CU, Martin.


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