I am trying to obtain model-predicted means and CI's for a categorical predictor in a GEE model fitted with the geeglm function (geepack package). The model is fitted with no problem, but where I am stuck is when trying to estimate the model-predicted group means. This is something fairly easy to do in other software packages (i.e., SAS and SPSS), but my point is to try to do this in R (also I was a bit disappointed to find no direct way to obtain overall tests for a categorical predictor other than fitting reduced and full models separately and then comparing them; but on the other hand I was able to find an easy way to compute the QIC with the MESS package). Anyway, I searched around to see if perhaps this was done with another package (there's a package called lsmeans, but it seems not compatible with geepack or gee packages). I am surprised I have not been able to find something as common as estimating model predicted means for a GEE model in R, so I was wondering if someone knows a solution for this. I tried contacting the geepack maintainer, but no answer.
Why GEE instead of a mixed model (marginal means for a mixed model can be computed using the lmerTest package)? well, it has fewer assumptions, and is more robust with small samples.
predictmethods are defined for
geeobjects. A quick cheat for getting around this is just to fit the analogous
glmmodel. The coefficients are exactly the same, the standard errors are what differ between the two. $\endgroup$