The strange thing (to me, and to Carlisle) is the behavior of predict.smooth.spline() on the data he posted: part of the predictions are way off from the data. If one just plot prediction at the input data, it looks just fine. What am I missing?
Andy > From: Roger Koenker [mailto:[EMAIL PROTECTED] > > If one repeats the experiments in Craven and Wahba, the paper that > "invented" GCV you find, or at least I found, when I tried to do this > some years ago, that GCV fails in about 10% > of cases rather catastrophically, and this is a fairly innocuous > setting. So one way to interpret Brian's comment would be that maybe > it is GCV that is failing, and another choice of lambda might > do better. > > Obviously, Brian can interpret for himself. I would only add > that in cases > where you really want something with sharp breaks in derivatives then > then the usual L_2 roughness penalties are not very > appropriate however > you choose to do the smoothing. > > url: www.econ.uiuc.edu/~roger/my.html Roger Koenker > email [EMAIL PROTECTED] Department of Economics > vox: 217-333-4558 University of Illinois > fax: 217-244-6678 > Champaign, IL 61820 > > > > ------------------------------------------------------------------------------ Notice: This e-mail message, together with any attachments,...{{dropped}} ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
