[R] semi-parametric (partial linear?) regression
Prof Brian D Ripley
ripley at stats.ox.ac.uk
Mon May 7 08:07:16 CEST 2001
On Sun, 6 May 2001 pauljohn at ukans.edu wrote:
> 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
You don't! modreg is a package in R (although mainly implemented by the R
of V&R's MASS package).
> 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?
mgcv, gss, sm, ...
--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272860 (secr)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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