On 12 May 2008, at 14:37, Doran, Harold wrote:
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
First off, let's keep SAS out of this. I never used it, never wanted
to use it and did not mention anywhere I wanted to get SAS-like
results! Although, seeing how easily it creeps up, I can sympathise
with those who have strog feelings about it! [for those with strong
feelings about me, this is meant to be something joke-like]
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.
Thank you very much. I'll read the paper and hopefully get the
answers I was looking for.
Best,
Federico
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",
}
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Federico C. F. Calboli
Department of Epidemiology and Public Health
Imperial College, St. Mary's Campus
Norfolk Place, London W2 1PG
Tel +44 (0)20 75941602 Fax +44 (0)20 75943193
f.calboli [.a.t] imperial.ac.uk
f.calboli [.a.t] gmail.com
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