[R] semi-parametric (partial linear?) regression
Chong Gu
chong at stat.purdue.edu
Mon May 7 17:13:32 CEST 2001
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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