lme nesting/interaction advice

I haven't followed this thread carefully, so apologies if I'm too off
base. But, in response to Rolf's questions/issues. First, SAS cannot
handle models with crossed random effects (at least well at all).  
SAS is
horribly incapable of handling even the simplest of models (especially
generalized linear mixed models). I can cite numerous (recent)  
examples
of SAS coming to a complete halt (proc nlmixed) for an analyses we  
were
recently working on. R (and Ubuntu) was the only solution to our
problem

Now, lme is not optimized for crossed random effects, but lmer is.  
That
is why lmer is supported and lme is not really supported much. lmer is
optimized for models with nested random effects and crossed random
effects.

When working with models with nested random effects, and software
optimized for those problems (e.g., HLM, SAS, mlWin) the
variance/covariance matrix forms a special, and simple structure that
can be easily worked with. This is not the case for models with  
crossed
random effects.

Software packages designed for nested random effects can be tricked  
into
handling models with crossed random effects, but this kludge is  
slow and
really inefficient.

If you want complete transparency into the why and how, here is a
citation for your review.

Best
Harold

@article{Doran:Bates:Bliese:Dowling:2007:JSSOBK:v20i02,
  author =	"Harold  Doran and Douglas  Bates and Paul  Bliese and
Maritza   Dowling",
  title =	"Estimating the Multilevel Rasch Model: With the lme4
Package",
  journal =	"Journal of Statistical Software",
  volume =	"20",
  number =	"2",
  pages =	"1--18",
  day =  	"22",
  month =	"2",
  year = 	"2007",
  CODEN =	"JSSOBK",
  ISSN = 	"1548-7660",
  bibdate =	"2007-02-22",
  URL =  	"http://www.jstatsoft.org/v20/i02",
  accepted =	"2007-02-22",
  acknowledgement = "",
  keywords =	"",
  submitted =	"2006-10-01",
}