[R] partially linear models

[Liaw, Andy]

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
R-based answer, either.

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 
> help, either.  However, I wonder if you tried "RSiteSearch" with your 
> favorite key words and phrases?  For example, I just got 107 hits for 
> 'RSiteSearch("wavelets")'.  I wonder if any of them might help you.
> 
> 	  If you'd like further help from this list, please 
> submit another 
> post.  However, before you do, I suggest you read the posting guide! 
> "www.R-project.org/posting-guide.html".  Anecdotal evidence suggests 
> that posts more consistent with the guide tend to receive 
> quicker, more 
> useful replies.
> 
> 	  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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> -- 
> Spencer 
> Graves, PhD
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