Post hoc test for object of class glmerMod in R? I am running generalised linear mixed effects models in R using the lme4 package. I am wondering if there are any post-hoc tests available for models built using the glmer function?
I know the package lmerTest has a function for post hoc testing lmer models (class merMod), but it won't work for objects of class glmerMod (method given in the lmerTest manual).
My models have either a numeric or integer response variable, two fixed effects (both a factor with 2 levels), and two random effects (both a factor, one nested within the other). I have used either a Gamma (for numeric response) or poisson (for integer response) error family. I want to know which combinations of levels of the fixed effects are statistically different to other combinations.


*

*Are there any options for post hoc testing this type of model?

*If not, can anyone recommend another method for performing generalised linear mixed effects models in R that do allow for post hoc testing?


Edit--
The model I am running for ANCOVA analysis is:
m1<-glmer(data=mydata,FLWR_MASS~BASE_MASS*F1TREAT*SO+
    (1 |LINE/MATERNAL_ID),family=Gamma(link=log))

Where FLWR_MASS and BASE_MASS are numeric, and F1TREAT and SO are both factors each with 2 levels.
The code I am using for the post hoc testing of the slope is:
testInteractions(m1, custom=list(F1TREAT='control', SO=c(1,-1),
    slope='BASE_MASS', adjustment="none"))
testInteractions(m1, custom=list(F1TREAT='stress', SO=c(1,-1),
    slope='BASE_MASS', adjustment="none"))
testInteractions(m1, custom=list(SO='s', F1TREAT=c(1,-1),
    slope="BASE_MASS", adjustment="none"))
testInteractions(m1, custom=list(SO='o', F1TREAT=c(1,-1),
    slope="BASE_MASS", adjustment="none")) 

However, as I've mentioned I get the same output regardless of what I specify as the slope (even if it is a term not included in the model)
 A: The functions testInteractions and testFactors in the R package phia allow you to run various post hoc testing through Wald chi-square test. Read this webpage regarding the limitations of the testing strategy.
P.S.: 

Is it still valid to use these functions of a model of class glmerMod?

Yes, package phia works fine with glmerMod.

if I have factor 'treat' with levels '1' and '2', and factor 'method'
  with levels 'a' and 'b', how can I get the comparisons: treat:1 -
  method:a, treat:1 - method:b, treat:2 - method:a, treat:2 - method:b.

I'm not so sure about the exact comparisons you want to perform. Assuming that the model object is called 'fm', see if the following is what you're looking for:
testInteractions(fm, custom=list(treat='1', method=c(1,-1), adjustment="none")
testInteractions(fm, custom=list(treat='2', method=c(1,-1), adjustment="none")


Can I do the same thing as this, except rather than test for
  differences in the mean of the response, test for differences in the
  slope of the response to a covariate?

Suppose your covariate is 'age', do the following:
testInteractions(fm, custom=list(treat='1', method=c(1,-1), slope='age', adjustment="none")

A: I have solved the issue of trying to calculate the differences in the slope.
Supposing the covariate is age, @bluepole suggested
testInteractions(fm, custom=list(treat='1', method=c(1,-1),
    slope='age', adjustment="none")

The 'slope' term needs to be placed outside of the 'custom' brackets
Instead:
testInteractions(fm, slope='age', custom=list(treat='1',
    method=c(1,-1)), adjustment="none")

So simple, and yet it took me so long to see it
