ROC plots and predictions with glmm?

Dear List,

I am new to GLMM's and am trying to understand if a workflow i have used previously with GLM's will transfer to lmer.

I have a binary response variable - TRUE / FALSE and a series of 6 categorical factors with one interaction, and two random variables which account for phylogenetic structure.

I have run the model -

model1 <-  lmer(response~1+(1|ord/fam) + a*b + c + d +e + f, family=binomial) which works fine.

Now i want to use the model to predict responses and am looking to validate the model with cross validation.

Using GLM my approach was 


	get predicted values using predict()

	construct a ROC plot with ROCR or Epi.

	cross validate with cv.glm

	construct another ROC plot with cross validation lines to get a idea of variation etc.


I am thinking this is a common thing to do and people must want to do it with GLMM's? however with no predict function i don't know how this is possible?

Based on AIC criteria, the model that explains the data "best" for glm was also the best when using glmm, however i am unsure if this can justify using a glm and ignoring the random variables, even though i know they are important?

Any help would be greatly appreciated.

George