Picking up an ancient thread (from Oct 2007), I have a somewhat more complex
problem than given in Simon Wood's example below. My full model has more than
two smooths as well as factor variables as in this simplified example:
b <- gam(y~fv1+s(x1)+s(x2)+s(x3))
Judging from Simon's example, my g
Dear Prof. Wood,
Just another quick question. I am doing model selection following Wood
and Augustin (2002). One of the criteria for retaining a term is to see
if removing it causes an increase in the GCV score. When doing this, do
I also need to fix the smooth parameters?
Thanks,
Julian B
Thanks again for your answer, prof. Wood.
And my apologies for the list for my repeated message from yesterday.
Still trying to figure out what happened with my email software.
Julian
Simon Wood wrote:
> I think that your approach is reasonable, except that you should use the same
> smoothing
I think that your approach is reasonable, except that you should use the same
smoothing parameters throughout. i.e the reduced models should use the same
smoothing parameters as the full model. Otherwise you get in trouble if x1
and x2 are correlated, since the smoothing parameters will then ten
Hello fellow R's,
I do apologize if this is a basic question. I'm doing some GAMs using the mgcv
package, and I am wondering what is the most appropriate way to determine how
much of the variability in the dependent variable is explained by each term in
the model. The information provided by sum
Hello fellow R's,
I do apologize if this is a basic question. I'm doing some GAMs using the mgcv
package, and I am wondering what is the most appropriate way to determine how
much of the variability in the dependent variable is explained by each term in
the model. The information provided by
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