That's why I said you need the book. The details are all in the book.
________________________________
From: Michael [mailto:comtech....@gmail.com]
Sent: Thursday, February 23, 2012 1:49 PM
To: Liaw, Andy
Cc: r-help
Subject: Re: [R] Good and modern Kernel Regression package in R with
auto-bandwidth?
Thanks Andy.
I am reading the "locfit" document...
but not sure how to do the CV and bandwidth selection...
Here is a quote about the function "regband": it doesn't seem to be usable?
Basically I am looking for a "locfit" that comes with an automatic bandwidth
selection so that I am essentially parameter free for the local-regression
step...
-------------------------------------
regband
Bandwidth selectors for local regression.
Description
Function to compute local regression bandwidths for local linear regression,
implemented as a front
end to
locfit().
This function is included for comparative purposes only. Plug-in selectors are
based on flawed logic,
make unreasonable and restrictive assumptions and do not use the full power of
the estimates available
in Locfit. Any relation between the results produced by this function and
desirable estimates
are entirely coincidental.
Usage
regband(formula, what = c("CP", "GCV", "GKK", "RSW"), deg=1, ...)
2012/2/23 Liaw, Andy <andy_l...@merck.com<mailto:andy_l...@merck.com>>
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<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
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> PLEASE do read the posting guide
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> and provide commented, minimal, self-contained, reproducible code.
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