From: Prof Brian Ripley <ripley_at_stats.ox.ac.uk>

Date: Thu, 26 Jul 2007 09:11:28 +0100 (BST)

Date: Thu, 26 Jul 2007 09:11:28 +0100 (BST)

The R interface is just a wrapper for those Netlib C/Fortran functions. I don't think anyone is going to be able (or willing) to read and explain those for you.

You do need to understand the loess.control parameters, and I believe they are explained in the White Book. But perhaps you should use the simplest options in R as a baseline.

I don't believe your sketchy description of tricube weights is correct: the White Book has the details.

The default degree is 2, not linear fits.

On Wed, 25 Jul 2007, apjaworski_at_mmm.com wrote:

*>
*

> Hello,

*>
**> I need help with the details of loess prediction algorithm. I would like
**> to get it implemented as a part of a measurement system programmed in
**> LabView. My job is provide a detailed description of the algorithm. This
**> is a simple one-dimensional problem - smoothing an (x, y) data set.
**>
**> I found quite a detailed description of the fitting procedure in the "white
**> book". It is also described in great detail at the NIST site in the
**> Engineering Statistics Handbook. It provides an example of Loess
**> computations. I managed to reproduce their example exactly in R. At each
**> data point I compute a weighted local linear fit using the number of points
**> based of span. Then I predict the values from these local fits. This
**> matches R "loess" predictions exactly.
**>
**> The problem starts when I try to predict at x values not in the data set.
**> The "white book" does not talk about predictions at all. In the NIST
**> handbook in the "Final note on Loess Computations" they mention this type
**> of predictions but just say that the same steps are used for predictions as
**> for fitting.
**>
**> When I try to use "the same steps" I get predictions that are quite
**> different that the predictions obtained by fitting R loess model to a data
**> set and running predict(<model object>, newdata=<grid of x values>). They
**> match quite well at the lowest and highest ends of the x grid but in the
**> middle are different and much less smooth. I can provide details but
**> basically what I do to create the predictions at x0 is this:
**> 1. I append c(x0, NA) to the data frame of (x, y) data.
**> 2. I calculate abs(xi-x0), i.e., absolute deviations of the x values in
**> the data set and a given x0 value.
**> 3. I sort the data set according to these deviations. This way the first
**> row has the (x0, NA) value.
**> 4. I drop the first row.
**> 5. I divide all the deviations by the m-th one, where m is the number of
**> points used in local fitting - m = floor(n*span) where n is the number of
**> points.
**> 6. I calculate the "tricube" weights and assign 0's to the negative ones.
**> This eliminates all the points except the m points of interest.
**> 7. I fit a linear weighted regression using lm.
**> 8. I predict y(x0) from this linear model.
**> This is basically the same procedure I use to predict at the x values from
**> the data set, except for point 4.
**>
**> I got the R sources for loess but it looks to me like most of the work is
**> done in a bunch of Fortran modules. They are very difficult to read and
**> understand, especially since they handle multiple x values. A couple of
**> things that worry me are parameters in loess.control such as surface and
**> cell. They seem to have something to do with predictions but I do not
**> account for them in my simple procedure above.
**>
**> Could anyone shed a light on this problem? Any comment will be
**> appreciated.
**>
**> I apologize in advance if this should have been posted in r-help. I
**> figured that I have a better chance asking people who read the r-devel
**> group, since they are likely to know more details about inner workings of
**> R.
**>
**> Thanks in advance,
**>
**> Andy
**>
**> __________________________________
**> Andy Jaworski
**> 518-1-01
**> Process Laboratory
**> 3M Corporate Research Laboratory
**> -----
**> E-mail: apjaworski_at_mmm.com
**> Tel: (651) 733-6092
**> Fax: (651) 736-3122
**>
**> ______________________________________________
**> R-devel_at_r-project.org mailing list
**> https://stat.ethz.ch/mailman/listinfo/r-devel
**>
*

-- Brian D. Ripley, ripley_at_stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ R-devel_at_r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-develReceived on Thu 26 Jul 2007 - 08:15:11 GMT

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