Message-ID: <87ptuascb9.fsf@home.nicht-ich.de>
Date: 2002-10-16T14:11:54Z
From: Christof Meigen
Subject: absurd computiation times of lme
In-Reply-To: <878z0zaarn.fsf@bates5.stat.wisc.edu>
Douglas Bates <bates at stat.wisc.edu> writes:
> You must
> ask yourself if you think that the ways in which these growth curves
> differ has that great a dimensionality. In most cases I think it is a
> more effective modeling strategy to start with a few random effects
> and check residuals to see if the model needs to be made more complex
> instead of starting with an overly complex model.
I discussed that with Jim Ramsay, and he strongly discouraged me
to "guess a reasonable, low dimensional basis", using either
known models for growth curves or something like PCA.
His argument was, that, in the first case, I put too much a priori
assumtions into the model, and in the other, that I use results
from the data to analyze the data, which would be some kind of
statistical "deadly sin".
He suggested, and that seemed plausible to me, to use indeed a
much too complex/flexible model like a quite high-dimensional
spline basis, and use lme to constrain that flexibility.
It seems to turn out that lme is not really made for this ...
> As Peter suggested, if you feel that lme is inadequate for your
> purposes we invite you to write better software.
...which does not mean I'd dare to say that I could write "better"
software, I had a look at the code when I was trying to write
a band-structured pdMat, and I'm really impressed ...
Christof
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