R help - Nov 2013 - Error in MuMIn "models are not all fitted to the same data"

I'm pretty new to GLMMs and model averaging, but think I'm getting some
understanding of it all through lots of reading. However, I keep receiving
an error message when trying to average models that I don't understand and
can't find any resources about. I'm doing science education research
trying
to evaluate population demographic factors that predict biology student
math performance. I have a lot of factors and so I tested a lot of models.
6 of my models had pretty similar AIC values (and evidence ratios of less
than 2.7) so I'm trying to average them. I keep receiving an error message
that says the models are not fitted to the same data, but I have no idea
how this is possible because all the models are from the same set of data
(same file and same variables)...strangely it seems to work when I try to
average MEx7, MEx10, & MEx22 only OR MEx24, MEx29, and MEx47 only. My code
is below. Any ideas? Thanks for any advice you can offer!!

library(MuMIn)
MEx7=lmer(cbind(c.score, w.score) ~ year + transfer + gender + p.math +
(1|section) + (1|quarter), family=binomial, data=survey.full, REML=F)
MEx10=lmer(cbind(c.score, w.score) ~ transfer + gender + p.math + Pmajor +
(1|section) + (1|quarter), family=binomial, data=survey.full, REML=F)
MEx22=lmer(cbind(c.score, w.score) ~ year + transfer + p.math + (1|section)
+ (1|quarter), family=binomial, data=survey.full, REML=F)
MEx24=lmer(cbind(c.score, w.score) ~ transfer + gender + p.math +
(1|section) + (1|quarter), family=binomial, data=survey.full, REML=F)
MEx29=lmer(cbind(c.score, w.score) ~ transfer + p.math + Pmajor +
(1|section) + (1|quarter), family=binomial, data=survey.full, REML=F)
MEx47=lmer(cbind(c.score, w.score) ~ transfer + p.math + (1|section) +
(1|quarter), family=binomial, data=survey.full, REML=F)
MExAvg=model.avg(rank=AIC, MEx24, MEx7, MEx10, MEx47, MEx29, MEx22)

Error in model.avg.default(rank = AIC, MEx24, MEx7, MEx10, MEx47, MEx29,  :
  models are not all fitted to the same data
Lilly Dethier

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