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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