I'm grateful for your kind help. I've clearly got the idea and am relieved.
As for question 1, the value of mgcv.conv$rms is small (less than 1.E-5 while GCV being around 1). For question 2, as I don't have other reasons to doubt the linearity, I guess the result is OK. Sincerely, Ariyo 2007/10/5, Simon Wood <[EMAIL PROTECTED]>: > Actually the answers to you questions may well be linked.... > > On Thursday 04 October 2007 22:11, Ariyo Kanno wrote: > > Dear all, > > > > I'm trying to fit a pure additive model of the following formula : > > fit <- gam(y~x1+te(x2, x3, bs="cr")) > > ,with the smoothing parameter estimation method "magic"(default). > > > > Regarding this, I have two questions : > > > > Question 1 : > > In some cases the value of "mgcv.conv$fully.converged" becomes > > "FALSE", which tells me that the method stopped with a "steepest > > descent step failure". > --- This is not necessarily a problem. What does the mgcv.conv$rms.grad tell > you? If it's near zero then convergence is probably fine. `fully.converged' > is only set to TRUE if the gcv optimization terminates with a Newton step > (and +ve definite hessian). In this circumstance you can be sure that it's > uphill in all directions from the reported optimum. However, there are cases > where the gcv score is flat (horizontal) in some direction, or nearly so. In > this case it may be necessary to use steepest descent and the routine may > terminate by failing to find a better set of smoothing parameters in the > steepest descent direction. The GCV score will be flat w.r.t. changes in a > smoothing parameter that has an optimum value effectively at infinity. > > > So I'd like to modify the arguments of magic() to make it easier to > > converge. But It doesn't seem like that I can do it by modifying the > > gam defaults through gam.control(). Is there any means to set magic() > > arguments from outside ? > > gam.control arguments mgcv.tol, mgcv.half and rank.tol actually get passed > through to magic. > > > Question 2 : > > Sometimes the smoothing parameter for x2 is very large (in the order > > of 1E+8 or 1E+10), while that for x3 is modest(less than 1), and the > > opposite cases also happen. Does this indicate that something is > > wrong, or just that the data is actually linear with respect to x2 or > > x3 ? > > --- large smoothing parameters are fairly normal and simply indicate heavy > penalization, so I would interpret this as indicating linearity w.r.t. x2 or > x3 rather than a problem (unless there is other reason to suspect a problem, > of course.) > > best, > Simon > > > Thank you for your help in advance. > > > > ______________________________________________ > > R-help@r-project.org mailing list > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide > > http://www.R-project.org/posting-guide.html and provide commented, minimal, > > self-contained, reproducible code. > > -- > > Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK > > +44 1225 386603 www.maths.bath.ac.uk/~sw283 > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
