On 6 Dec 2004, at 7:36, Janice Tse wrote:
When Any Liaw answered you (below), he asked you to specify which kind of 'gam' did you use: the one in standard package 'mgcv' or the one in package 'gam'. We should know this to know "what does it mean" to get your error message. If you used mgcv:::gam, it means that you didn't read it help pages which say that you should specify your model as:Thanks for the email. I will check that out....
However when I was doing this : gam(y~s(x1)+s(x2,3), family=gaussian,
data=mydata )it gives me the error :
"Error in terms.formula(formula, data = data) : invalid model formula in ExtractVars"
What does it mean ?
gam(y ~ s(x1) + s(x2, k=3))
Further, it may be useful to read the help pages to understand what it means to specify k=3 and how it may influence your model. Simon Wood -- the mgcv author -- also has a very useful article in the R Newsletter: see the CRAN archive. It may be really difficult to understand what you do when you do mgcv:::gam unless you read this paper (it is possible, but hard). Simon's article specifically answers to your first question 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
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 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 different from that in `gam'. I'm guessing you're referring to
the one in `gam', but please remember to state which contributed package
you're using, along with version of R and OS.]
Cheers, Andy
From: Janice Tse
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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