Hi Paul, I skimmed over the pdf.
I have comments on the discusssion about centering. I'm from a completely different field (chemometrics). Of course, I also have to explain centering. However, the argumentation I use is somewhat different from the one you give in your pdf. One argument I have in favour of (mean) centering is numerical stability, depending on the algorithm of course. I generally recommend that if data is centered, there should be an argument why the *chosen* center is *meaningful*, emphasizing that centering actually involves decisions, and that the center can have a meaning. While I agree that a centered model with the center chosen without any thought about its meaning is "exactly the same in every important way" compared to not centering, I disagree with the generality of your claim. A "natural" center of the data may exist. And in this case, using this appropriate center will ease the interpretation. Examples: - In analytical chemistry / chemometrics e.g. we can often use blanks (samples without analyte) as coordinate origin. Centering to the blank removes the influence of some parts of the instrumentation, like sample holders, cuvettes, etc. - Many of our samples (sample in the meaning of physical specimen) have a so-called matrix (a common composition/substance in which different other substances/things are observed), or is measured in a solvent. - I also work with biological specimen. There we often have controls (either control specimen/patients or for example normal tissue [vs. diseased tissues]) which are another type of "natural" coordinate origin. - I can even imagine problems where mean centering is meaningful: if the problem involves modeling properties that are deviations from a mean (I'm thinking of process analytics). However, mean centering will always need careful attention about the sampling procedure. Looking from the opposite point of view, some problems of *mean* centering become apparent. If the data comes from different groups, the mean may not be meaningful (I once heard a biologist arguing that the average human has one ovary and one testicle - this gets your audience awake and usually convinces immediately). And the mean may be influenced by the different proportions of the groups in your data. Which is what you do *not* want: what you want is a stable center. Best, Claudia -- Claudia Beleites Spectroscopy/Imaging Institute of Photonic Technology Albert-Einstein-Str. 9 07745 Jena Germany email: claudia.belei...@ipht-jena.de phone: +49 3641 206-133 fax: +49 2641 206-399 ______________________________________________ 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 and provide commented, minimal, self-contained, reproducible code.
