Kevin Wright <[EMAIL PROTECTED]> writes:
> 12. Wanted: General-purpose mixed-models function/package
> The nlme library is very nice for mixed-effects models with nested
> effects, but it is not very general-purpose. Even Bates/Pinheiro have said
> several times in posts to R-help/S-news that nlme was designed for nested
> models and using other models can be hard.
> Bates: "highly unintuitive" (crossed effects model)
> Bates: "algorithms for lme are tuned for nested random effects"
> For example, in nlme,
> The syntax for crossed random effects is quite intimidating
> Try removing the variance component for Rep in: random=~1|Rep/WholePlot.
> Try changing an nested effect from random to fixed (or vice-versa).
> Try to extract lsmeans for fixed-effects in a model.
> Try to do a multiple-comparison of fixed-effects estimates.
> Try using AR1xAR1 error structure. The nlme library appears to have
> tools for this, but again is syntactically difficult. I can find no
> examples.
> Most of these tasks would ideally be straightforward in a general-purpose
> mixed-models function (as they are in SAS, Genstat, etc.)
The crossed random effects problem is also in the process of being fixed.
Our recent work on computational methods for mixed-effects models
http://www.stat.wisc.edu/~bates/reports/MixedComp.pdf
shows how to structure the calculations but it takes a long time to
get the code designed, implemented, debugged, debugged again, debugged
again, ..., documented, documented some more, documented some more,
debugged again, ...
I am hopeful that I will be able to come up with a single, unified
data structure, based on sparse matrices, that can be used for nested,
crossed, and partically crossed random effects.
At present I am doing a major redesign of the Matrix package to change
to S4 classes and methods and to incorporate sparse matrices. Once
that is more-or-less stable (I expect a preliminary release by the end
of January) I will work on the implementation of the lme structures.
> The ASREML software is available in S-Plus (and soon R, I'm told) via
> the proprietary 'samm' library. Whereas lme seems excellent for basic
> nested-effects models and difficult for other models, samm excels at
> crossed-effects models, but doesn't have the plethora of useful
> print, plot, extractor, and summary methods that are found in nlme.
It is interesting that ASREML will be available for R.
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