[R] predicted means on mixed models

[Francisco J. Zagmutt Vergara]

Hi everybody

I am trying to move from SAS to R and there is a couple of things that I 
have not been able to obtain from the nlme3 module (mixed models)
I want to compare the differences between different levels of a categorical 
variable in presence of a non-ignorable interaction compromising this 
variable. To do this I SAS gives you the possibility to extract predicted 
population margins (they call them least squares means or LS means) for each 
level of a categorical fixed effect in the model, so these values can then 
be used to perform multiple comparisons and look for meaningful differences 
between levels of a categorical fixed effect. I guess that in R I could do 
this manually by using the augPred() function for each possible combination 
and then performing multiple comparisons over these values but I just want 
to know if there is and already built-in function to do this.  Also In SAS 
exists an option to "slice" the LSMeans output so you can obtain
separated outputs for each level of the variable (test of simple effects).  
This is very handy when it comes to evaluate levels of nested
and/or interaction terms. Is there sometihing similar available in R?

Thanks for your help!


Francisco





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