[R] how to use the basis matrix of "ns" in R? really confusedby multi-dim spline filtering?

[Berton Gunter]

Michael:

I do not think Andy mischaracterized, for you initially said:

" Books mention about basis matrix, design matrix, model
 matrix, data matrix, etc. I got lost. "

This also seems to me a statement that you do not understand the basic
principles and concepts -- hence Vito's and Andy's advice.  The
documentation does require that you understand the underlying mathematical
ideas for splines and merely explains how R inmplements them -- R's syntax.
If you need help translating the mathematical ideas into practice, which
**is** an issue of basic understanding in my opinion, than perhaps you need
a more extensive tutorial, which the spline man page certainly does not
provide. I would suggest that you search around the web until you find one.
(I think Friedman/Hastie/Tibshirani's "STATISTICAL LEARNING THEORY" contains
a nice overview, but you may disagree). After such homework and if you still
wish to use splines, R's man page should be fully comprehensible, I believe.
If not, I think you should consider following Andy's advice.

-- Bert Gunter
Genentech Non-Clinical Statistics
South San Francisco, CA
 
 

> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch 
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Michael
> Sent: Monday, February 27, 2006 2:31 PM
> To: Liaw, Andy
> Cc: R-help at stat.math.ethz.ch
> Subject: Re: [R] how to use the basis matrix of "ns" in R? 
> really confusedby multi-dim spline filtering?
> 
> I think you mis-understood. And perhaps you read in a haste, 
> or you were
> just in a bad mood today.
> 
> I think on this mailing-list, many people come for help on a R related
> issue, they know something theoretically, but since they are 
> new to R, they
> want to use R, they want to use R to help do applied things.
> 
> For me, there is a missing link between my understanding of 
> splines from the
> books(which are abstract) and a usage example in R. That's 
> something I am
> looking for. I believe it is very appropriate.
> 
> If you don't know, or don't want to say anything helpful, 
> please just don't
> say anything. It is not about a gun pointing, it is about a 
> learning process
> which is very specific to R.
> 
> Please kindly try not to say negative things, and try to 
> discourage people
> from learning new things.
> 
> On 2/27/06, Liaw, Andy <andy_liaw at merck.com> wrote:
> >
> > If you do not understand what ns() outputs, nor 
> descriptions of splines in
> > books, perhaps it's not an appropriate tool for you.  Look 
> for something
> > that you understand (or can understand after some reading). 
>  No one is
> > pointing a gun to your head and tell you to use splines, I hope.
> >
> > If you have a hard time understanding what you read in books, it's
> > unrealistic to expect a mailing list about a software to teach you.
> >
> > Andy
> >
> > From: Michael
> > >
> > > Have you seen an example on how to do it in R? I found no 
> practical
> > > examples...
> > >
> > > On 2/27/06, vito muggeo <vmuggeo at dssm.unipa.it> wrote:
> > > >
> > > > Dear Micheal,
> > > >
> > > > > the output of the "ns" function in R is "basis 
> matrix", but then
> > > > Yes you are right, the output of the ns(x, df) is the basis
> > > matrix of a
> > > > natural cubic spline with df degrees of freedom. See 
> ?ns (in package
> > > > splines) on how to specify df or knots or ..
> > > >
> > > > Fitting y~ns(x,df) yields a smooth curve given by a linear
> > > combination
> > > > of the basis functions (the single colums of the basis
> > > matrix) by the
> > > > estimated coefficients (returned by the fitted model).
> > > >
> > > > As far as I know, a tensor product is usually employed to
> > > > multidimensional smoothing and the multidimensional basis
> > > is formed via
> > > > the kronecker product of the marginal bases.
> > > >
> > > > Finally, last but not least: Probably you need some statistical
> > > > backaground on spline fitting..
> > > > Please, read some statistical papers/books on such topic
> > > (for instance
> > > > see references in packages splines, mgcv)
> > > >
> > > > best,
> > > > vito
> > > >
> > > > Michael wrote:
> > > > > Hi all,
> > > > >
> > > > > Could anybody recommend some easy-to-understand and 
> example based
> > > > > notes/tutorials on how to use cubic splines to do filtering on
> > > > > multi-dimension data?
> > > > >
> > > > > I am confused by the 1-dimensional case, and more confused by
> > > > > multi-dimensional case.
> > > > >
> > > > > I found all the books suddenly become very abstract when
> > > it comes to
> > > > this
> > > > > subject.
> > > > >
> > > > > They don't provide examples in R or Splus at all.
> > > > >
> > > > > Specifically, I don't know how to provide data "x" to the
> > > "ns" function
> > > > in
> > > > > R,
> > > > >
> > > > > and I don't understand what should be the output matrix,
> > > and how to use
> > > > the
> > > > > output matrix to "filter" data?
> > > > >
> > > > > Books mention about basis matrix, design matrix, model
> > > matrix, data
> > > > matrix,
> > > > > etc. I got lost.
> > > > >
> > > > > I presume the output of the "ns" function in R is "basis
> > > matrix", but
> > > > then
> > > > > how do I use it?  How to form tensor-product?
> > > > >
> > > > > I don't understand it at all.
> > > > >
> > > > > Please help me!
> > > > >
> > > > > Thank you very much!
> > > > >
> > > > >       [[alternative HTML version deleted]]
> > > > >
> > > > > ______________________________________________
> > > > > R-help at stat.math.ethz.ch mailing list
> > > > > https://stat.ethz.ch/mailman/listinfo/r-help
> > > > > PLEASE do read the posting guide!
> > > > http://www.R-project.org/posting-guide.html
> > > > >
> > > >
> > > > --
> > > > ====================================
> > > > Vito M.R. Muggeo
> > > > Dip.to Sc Statist e Matem `Vianelli'
> > > > Universit` di Palermo
> > > > viale delle Scienze, edificio 13
> > > > 90128 Palermo - ITALY
> > > > tel: 091 6626240
> > > > fax: 091 485726/485612
> > > > ====================================
> > > >
> > >
> > >       [[alternative HTML version deleted]]
> > >
> > >
> >
> >
> >
> > 
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