[R] aov help

[john_heumann at agilent.com]

Please pardon an extremely naive question. I see related earlier 
posts, but no responses which answer my particular question. In 
general, I'm very confused about how to do variance decomposition with 
random and mixed effects. Pointers to good tutorials or texts would
be greatly appreciated.

To give a specific example, page 193 of V&R, 3d Edition, illustrates
using raov assuming pure random effects on a subset of coop:

> raov(Conc ~ Lab / Bat, data=coop, subset = Spc=="S1")

I realize ('understand' would be a bit too strong) that the same analysis, 
resulting in identical sums of squares, degrees of freedom, and residuals 
can be generated in R by doing

> op <- options(contrasts=c("contr.helmert", "contr.poly"))
> aov(Conc ~ Lab + Error(Lab / Bat), data=coop, subset = Spc=="S1")

However, as shown in V&R, raov also equated the expected and observed 
mean squares, to solve for and display the variance components associated
with the random factors, \sigma_\epsilon^2, \sigma_B^2, and \sigma_L^2 in
a column labeled "Est.  Var.". Given the analytical forms of the expected 
mean squares for each stratum, I can obviously do this manually. But is 
there way to get R to do it automatically, a la raov? This would be 
particularly useful for mixed cases in which the analytical formulations 
of the expected mean squares may not be immediately obvious to a novice.

Thanks in advance!

Regards,
-jh-