On Tue, 2009-08-25 at 10:00 +0100, Corrado wrote:
> Dear Gavin / Rlings,
>
> thanks for your kind answer and sorry for posting to the dev mailing list.
>
> Concerning the specific of your answer:
>
> I am working with 6 to 36 covariates, and they are all centred and scaled. I
> represented the
Dear Simon,
thanks again.
Concerning the whole 36 variables well, I have run a principal components
analysis, and I am only using part of them (I am running a test with the pc
which cover the 95% of variance and then the 99%). :) so I will possibly
end up with s(x1,,x8). I wonder
Dear Simon,
thanks for your answer.
I am running the model with both s and te smoothing, to compare.
A few questions on your email:
1) Isotropic smoothness: my variables are centred and scaled. I assumed an
isotropic smoother (that is, a smoother that treats all the variables in the
same way)
This will not work...
> 2) y~s(x1, ,x36)
Estimating a 36 dimensional functions reasonably well would require a
tremendous quantity of data, but in any case the 36 dimensional TPS smoothnes
measure will involve such high order derivatives that it will no longer be
practically useful: in fact
> > I am trying to understand the relationships between:
> >
> > y~s(x1)+s(x2)+s(x3)+s(x4)
> >
> > and
> >
> > y~s(x1,x2,x3,x4)
> >
> > Does the latter contain the former? what about the smoothers of all
> > interaction terms?
The first says that you want a model
E(y) = f_1(x_1) + f_2(x_2) + f_3(x
Dear Gavin / Rlings,
thanks for your kind answer and sorry for posting to the dev mailing list.
Concerning the specific of your answer:
I am working with 6 to 36 covariates, and they are all centred and scaled. I
represented the problem with two variables to simplify the question.
So ideally,
[Note R-Devel is the wrong list for such questions. R-Help is where this
should have been directed - redirected there now]
On Mon, 2009-08-24 at 17:02 +0100, Corrado wrote:
> Dear R-experts,
>
> I have a question on the formulas used in the gam function of the mgcv
> package.
>
> I am trying to
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