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, [EMAIL PROTECTED] 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: [EMAIL PROTECTED] > Tel: (651) 733-6092 > Fax: (651) 736-3122 > > ______________________________________________ > R-devel@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel > -- Brian D. Ripley, [EMAIL PROTECTED] 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@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
