Yves Magliulo wrote:
so mgcv package is the one i need! indeed, i want integrated smoothness
selection and smooth interactions rather than stepwise selection. i have
a lot of predictor, and i use gam to select those who are "efficient"
and exclude others. (using p-value)
It is interesting that you
so mgcv package is the one i need! indeed, i want integrated smoothness
selection and smooth interactions rather than stepwise selection. i have
a lot of predictor, and i use gam to select those who are "efficient"
and exclude others. (using p-value)
thanks a lot for those precious information.
> this subject is very intersting for me. I'm using mgcv 0.8-9 with R
> version 1.7.1. i didn't know that there was an another gam version with
> package library(gam). Someone can tell me the basics differences between
> them? I look for an help page on google but i only find "mgcv" help
> pages.
At 10:48 2004-12-06 +0100, Yves Magliulo wrote:
this subject is very intersting for me. I'm using mgcv 0.8-9 with R
version 1.7.1.
You're in need of an update.
i didn't know that there was an another gam version with
package library(gam).
This is the 'classic' GAM implementation by Hastie & Tibshir
-Original Message-
From: Simon Wood [mailto:[EMAIL PROTECTED]
Sent: Monday, December 06, 2004 5:54 AM
To: Janice Tse
Cc: [EMAIL PROTECTED]
Subject: Re: [R] Gam() function in R
> I'm a new user of R gam() function. I am wondering how do we decide on
the
> smooth function to use?
> Th
> I'm a new user of R gam() function. I am wondering how do we decide on the
> smooth function to use?
> The general form is gam(y~s(x1,df=i)+s(x2,df=j)...) , how do we decide
> on the degree freedom to use for each smoother, and if we shold apply
> smoother to each attribute?
I guess you a
> of deciding the smoothness, and explains how elegantly this is done in
> mgcv:::gam (gam:::gam has another set of tools and philosophy).
>
> If you happened to use gam:::gam, then you have to look at another
> explanation.
>
> cheers, jari oksanen
>
> > Fr
Sunday, December 05, 2004 11:34 PM
To: 'Janice Tse'; [EMAIL PROTECTED]
Subject: RE: [R] Gam() function in R
Unfortunately that's not really an R question. I recommend that you
read up
on the statistical methods underneath. One that I'd wholeheartedly
recommend is Prof. Harrell
ks
-Janice
-Original Message-
From: Liaw, Andy [mailto:[EMAIL PROTECTED]
Sent: Sunday, December 05, 2004 11:34 PM
To: 'Janice Tse'; [EMAIL PROTECTED]
Subject: RE: [R] Gam() function in R
Unfortunately that's not really an R question. I recommend that you read up
on the stati
Unfortunately that's not really an R question. I recommend that you read up
on the statistical methods underneath. One that I'd wholeheartedly
recommend is Prof. Harrell's `Regression Modeling Strategies'.
[BTW, there are now two implementations of gam() in R: one in `mgcv', which
is fairly diff
Hi all,
I'm a new user of R gam() function. I am wondering how do we decide on the
smooth function to use?
The general form is gam(y~s(x1,df=i)+s(x2,df=j)...) , how do we decide
on the degree freedom to use for each smoother, and if we shold apply
smoother to each attribute?
Thanks!!
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