[R-sig-ME] working around glm.fit: "fitted probabilities numerically 0 or 1 occurred"

[Jack Tanner]

I'm using lme4 0.999999-0 to fit some 0/1 response data with a logistic 
regression and I get glm.fit: "fitted probabilities numerically 0 or 1 
occurred". I've read Ted Harding's explanation 
<http://r.789695.n4.nabble.com/glm-fit-quot-fitted-probabilities-numerically-0-or-1-occurred-quot-td849242.html>, 
but I still don't know how to work around this.

More specifically, this is a Rasch-style model. When I fit a smaller 
model that has only effects (intercepts), not covariates, glmer() 
converges fine. When I add a matrix of covariates, which are mainly 
low-frequency counts (lots of zeros), I get "fitted probabilities 
numerically 0 or 1 occurred". I can fit the model including covariates 
under JAGS, but I was hoping to be able to fit it under lmer because 
MCMC can be very slow.

Perhaps there are some ways I could reparametrize the model?

Another idea is that calling glmer(..., verbose=TRUE) gives the error on 
the very first iteration, i.e.,
   0:           nan: 0.180462 0.0723839 ...
Warning messages:
1: glm.fit: fitted probabilities numerically 0 or 1 occurred
2: In mer_finalize(ans) : gr cannot be computed at initial par (65)

Would it be worth trying to specify good starting values? If so, is 
there a way to extract estimates from an object of class mer that can be 
easily passed to glmer(..., start=...)?