> On Thu, 27 Feb 2003 13:52:50 -0500, "Wiener, Matthew" <[EMAIL PROTECTED]> wrote > regarding > "[despammed] RE: [R] multidimensional function fitting" > > 8-) Take a look at package mgcv. Hope this helps. --Matt > 8-) > > Thank you, I just did. It may indeed be what I'm looking for (I haven't >quite understood everything about it...), but: > > 1) The best fits I obtain with a formula like z~s(x,y) ; but this I cannot > possibly transport into the C programme where I need it! Maybe I wasn't > clear on this aspect? - Yes, this won't be entirely straightforward, but note that the underlying code in mgcv is written in C, so it would be possible...
> > 2) It is very memory hungry, esp. when using the s() function: I have > 192Mb with 256Mb swap (not a lot, but reasonable I'd say), and I've > never had to kill R as often as when trying gam()... > - do you have a very large number of data? The way mgcv works it first finds an "optimal" basis for smoothing and this will involve formation of a matrix of size n^2 where n is your number of data.... The last couple of examples in the ?gam help file show how to avoid this using the "knots" argument to gam: basically you find a "near optimal" basis for a random subset of your data, and then use this basis to do the smoothing on the whole data set. (Can you let me know if this solves the problem/isn't the issue). best, Simon _____________________________________________________________________ > Simon Wood [EMAIL PROTECTED] www.stats.gla.ac.uk/~simon/ >> Department of Statistics, University of Glasgow, Glasgow, G12 8QQ >>> Direct telephone: (0)141 330 4530 Fax: (0)141 330 4814 ______________________________________________ [EMAIL PROTECTED] mailing list http://www.stat.math.ethz.ch/mailman/listinfo/r-help
