[R] Nested and repeated effects together?

[Stephan Kolassa]

Dear R people,

I am having a problem with modeling the following SAS code in R:

Class ID Gr Hemi Region Gender
Model Y = Gr Region Hemi Gender Gr*Hemi Gr*Region Hemi*Region 
Gender*Region Gender*Hemi Gr*Hemi*Region Gender*Hemi*Region 
Gr*Gender*Hemi*Region
Random Intercept Region Hemi /Subject = ID (Gr Gender)

I.e., ID is a random effect nested in Gr and Gender, leading to 
ID-specific Intercept, Region and Hemi means. We have repeated 
measurements within each ID (one measurement each for the different 
combinations of Hemi and Region, i.e. 2 levels in Hemi: L vs. R; 4 
levels in Region: F vs. C vs. T vs. PO).

I have been trying things like

aov(y~Gr+Region+Hemi+Gender+Gr:Hemi+Gr:Region+Hemi:Region+Gender:Region+Gender:Hemi+Gr:Hemi:Region+Gender:Hemi:Region+Gr:Gender:Hemi:Region+
    Error(ID/(Gr+Gender))

and do get results, but I am very unsure whether this implements the 
right model. An Error term like

    Error((1+Region+Hemi)/(ID_Vp/(Gender+Gr))), data=daten))

in the above model looks intuitively better to me, but (1) again: I'm 
unsure about this, (2) this crashes my R.

Of course, I have been googling for all permutations of "Nested 
effects", "repeated effects", "random effects" and digging through the 
R-help archives, but I can't seem to locate a similar question having 
been answered before.

Thank you all for your time!

Best regards,
Stephan