If that's the kind of framework you'd like to work in, use locfit, which has the predict() method for evaluating new data. There are several different handwidth selectors in that package for your choosing.
Kernel smoothers don't really fit the framework of "creating a model object, followed by predicting new data using that fitted model object" very well because of it's local nature. Think of k-nn classification, which has similar problem: The "model" needs to be computed for every data point you want to predict. Andy ________________________________ From: Michael [mailto:comtech....@gmail.com] Sent: Thursday, February 23, 2012 10:06 AM To: Liaw, Andy Cc: Bert Gunter; r-help Subject: Re: [R] Good and modern Kernel Regression package in R with auto-bandwidth? Thank you Andy! I went thru KernSmooth package but I don't see a way to use the fitted function to do the "predict" part... data=data.frame(z=z, x=x) datanew=data.frame(z=z, x=x) lmfit=lm(z ~x, data=data) lmforecast=predict(lmfit, newdata=datanew) Am I missing anything here? Thanks! 2012/2/23 Liaw, Andy <andy_l...@merck.com<mailto:andy_l...@merck.com>> In short, pick your poison... Is there any particular reason why the tools that shipped with R itself (e.g., kernSmooth) are inadequate for you? I like using the locfit package because it has many tools, including the ones that the author didn't think were optimal. You may need the book to get most mileage out of it though. Andy ________________________________ From: Michael [mailto:comtech....@gmail.com<mailto:comtech....@gmail.com>] Sent: Thursday, February 23, 2012 12:25 AM To: Liaw, Andy Cc: Bert Gunter; r-help Subject: Re: [R] Good and modern Kernel Regression package in R with auto-bandwidth? Ｉmeant its very slow when I use "cv.aic"... On Wed, Feb 22, 2012 at 11:24 PM, Michael <comtech....@gmail.com<mailto:comtech....@gmail.com>> wrote: Is "np" an okay package to use? I am worried about the "multi-start" thing... and also it's very slow... On Wed, Feb 22, 2012 at 8:35 PM, Liaw, Andy <andy_l...@merck.com<mailto:andy_l...@merck.com>> wrote: Bert's question aside (I was going to ask about laundry, but that's much harder than taxes...), my understanding of the situation is that "optimal" is in the eye of the beholder. There were at least two schools of thought on which is the better way of automatically selecting bandwidth, using plug-in methods or CV-type. The last I check, the jury is still out. Andy > -----Original Message----- > From: r-help-boun...@r-project.org<mailto:r-help-boun...@r-project.org> > [mailto:r-help-boun...@r-project.org<mailto:r-help-boun...@r-project.org>] On > Behalf Of Bert Gunter > Sent: Wednesday, February 22, 2012 6:03 PM > To: Michael > Cc: r-help > Subject: Re: [R] Good and modern Kernel Regression package in > R with auto-bandwidth? > > Would you like it to do your your taxes for you too? :-) > > Bert > > Sent from my iPhone -- please excuse typos. > > On Feb 22, 2012, at 11:46 AM, Michael > <comtech....@gmail.com<mailto:comtech....@gmail.com>> wrote: > > > Hi all, > > > > I am looking for a good and modern Kernel Regression > package in R, which > > has the following features: > > > > 1) It has cross-validation > > 2) It can automatically choose the "optimal" bandwidth > > 3) It doesn't have random effect - i.e. if I run the > function at different > > times on the same data-set, the results should be exactly > the same... I am > > trying "np", but I am seeing: > > > > Multistart 1 of 1 | > > Multistart 1 of 1 | > > ... > > > > It looks like in order to do the optimization, it's doing > > multiple-random-start optimization... am I right? > > > > > > Could you please give me some pointers? > > > > I did some google search but there are so many packages > that do this... I > > just wanted to find the best/modern one to use... > > > > Thank you! > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > R-help@r-project.org<mailto: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<http://www.r-project.org/posting-guide.html> > > and provide commented, minimal, self-contained, reproducible code. > > ______________________________________________ > R-help@r-project.org<mailto: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<http://www.r-project.org/posting-guide.html> > and provide commented, minimal, self-contained, reproducible code. > Notice: This e-mail message, together with any attachments, contains information of Merck & Co., Inc. 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