anova type III test for a GLMM I am fitting a glmer model in the lme4 R package. I'm looking for an anova table with p-value shown therein, but I cannot find any package that fits it. Is it possible to do it in R?
The model I am fitting is of the form:
model1<-glmer(dmn~period*teethTreated+(1|fullName), 
   family="poisson", 
   data=subset(dataset, 
          group=='Four times a year'),
   control=glmerControl(optimizer="bobyqa"))

 A: If you're willing to settle for Wald tests this should work:
library(lme4)
library(car)
gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
                   data = cbpp, family = binomial)
Anova(gm1,type="III")

However, note (from ?Anova) that:

The designations "type-II" and "type-III" are borrowed from SAS,
       but the definitions used here do not correspond precisely to those
       employed by SAS.  Type-II tests are calculated according to the
       principle of marginality, testing each term after all others,
       except ignoring the term's higher-order relatives; so-called
       type-III tests violate marginality, testing each term in the model
       after all of the others. This definition of Type-II tests
       corresponds to the tests produced by SAS for analysis-of-variance
       models, where all of the predictors are factors, but not more
       generally (i.e., when there are quantitative predictors).  Be very
       careful in formulating the model for type-III tests, or the
       hypotheses tested will not make sense.

I would check your results very carefully to make sure they make sense!
Alternatively, you can use afex::mixed to get analogous tables via likelihood ratio test or parametric bootstrap; the latter is the most accurate, but also the slowest by far.
See ?pvalues in the lme4 package for more general discussion of p-value computation in the context of GLMMs.
