Peter Dalgaard BSA
14 Sep 2000 19:16:42 +0200
Douglas Bates <email@example.com> writes:
> I am teaching a graduate course on Statistical Computing this
> semester. A major part of the grade is determined by a project in
> which a student or small group of students produce, test, and document
> some software for statistics. I will encourage those students who are
> developing in S to package their software as an R package.
> I would welcome suggestions of possible projects, especially projects
> that come under the heading of "Useful facilities to be added to R".
> Please keep in mind that the project must be completed by mid-December
> and that not all the students have extensive experience programming in
> S and C.
These are probably too hard and too narrow, but now the topic is up:
- getting predictions to work on new data in cases where model depends
on data set (notably regressions splines with auto knot placement)
- in lme, we can predict at level K would be nice to get SE of
prediction (this also takes levels, extending distinction between
confidence and tolerance intervals)
- conditional tolerance in lme (much too hard I suspect)
- in model.tables.aov, SE's for type="means" are sorely missed.
This is not very hard, but maybe too small (although one will have to
study issues of contrasts and internals of an lm object rather
- extend pairwise.t.test to take a linear model and a factor in the
model as argument.
O__ ---- Peter Dalgaard Blegdamsvej 3
c/ /'_ --- Dept. of Biostatistics 2200 Cph. N
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
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