[R] semi-parametric (partial linear?) regression

[Chong Gu]

   From: pauljohn at ukans.edu
   Date: Sun, 06 May 2001 21:30:48 -0500
   Organization: KU
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   I just heard a talk about a semi-parametric model.  I was quite excited
   by the idea. This model is fitted

   y= xB + g(z) + e

   where x is a data matrix, B a column vector, z is another data matrix,
   and g is a smooth model fitted by a Kernel Smoothing regression (I got
   the idea any smoother would do as well).

   The speaker said that when z is considered as a "control" variable, and
   there is no reason to assume linearity, then one can estimate this model
   and the B estimates are (in some sense I cannot say exactly) better,
   perhaps converging more quickly to the true value as the sample size
   increases.

   I got interested in doing this and wondered if in R it is possible.  In
   R's MASS package I find the modreg library, which has several smoothing
   tools, but I don't find a way to estimate B at the same time. 
   (Incidentally, I'm rather overwhelmed by the many different flavors of
   smoothers!) 

   Does an R package exist for estimating this semi-parametric model?  

   If this is a bad idea, you can tell me, my feelings won't be hurt :)

   pj

   ps. I just found that SAS has at least one procedure for this, called
   tpsplines (thin-plate splines), so I know I wasn't misunderstanding this
   fellow's lecture.
   -- 
   Paul E. Johnson                       email: pauljohn at ukans.edu
   Dept. of Political Science            http://lark.cc.ukans.edu/~pauljohn
   University of Kansas                  Office: (785) 864-9086
   Lawrence, Kansas 66045                FAX: (785) 864-5700
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You can use ssanova in the gss package for this, where xB is the
"partial" term.
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