# [R] partially linear models

Liaw, Andy andy_liaw at merck.com
Tue Dec 20 04:23:14 CET 2005

This doesn't look like an R question, as I know of no pre-packaged
functionality publicly available that can fit the model that Elizabeth
described, and it doesn't seem like she's particularly interested in an

My gut feeling is that if there is a test of significance for beta in such a
model, it probably shouldn't depend upon how f() is fitted, wavelets or
otherwise.  I.e., any test for the linear component in a partially linear
model ought to do just fine.  The main difference here, from a fully linear
model, is that one no longer can estimate E(y) without bias, even with the
assumption that the model is correct.  What gets messier still is if
data-dependent smoothing/de-noising is done in estimating f(), as that opens
up a whole bucket of nasty creatures.

I could be off, though, so take this with a truck-load of NaCl...

Andy

From: Spencer Graves
>
> 	  I have seen no replies to this post, and I don't know
> that I can
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>
> 	  If you'd like further help from this list, please
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> 	  Best Wishes,
> 	  spencer graves
>
> Elizabeth Lawson wrote:
>
> > Hey,
> >
> >    I am estiamting a partially linear model
> y=X\beta+f(\theta) where the f(\theta) is estiamted using wavelets.
> >
> >   Has anyone heard of methods to test if the betas are
> significant or to address model fit?
> >
> >   Thanks for any thoughts or comments.
> >
> >   Elizabeth Lawson
> >
> > __________________________________________________
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> >
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