Hi! My question(s) in the end might be silly but I am no expert on this, so here it goes:
Noy-Meir (1973), Pielou (1984) and a few others have pointed to non-centered PCA being in some cases useful. They clearly explain that "it is the case" when multi-dimensional data display distinct clusters (which have zero, or near-zero, projections in some subset of the axes) and the task is (exactly) to separate this clusters among the principal components. I have done my complete work using prcomp() and tested combinations of center=FALSE/TRUE and scale=FALSE/TRUE. I would like to now check this "between-axes" vs "within-axes" heterogeneity of my data and cross-check results with the various tested PCA-versions. Is there any (official or custom) function available in R that could answer this question? Some relative/comparative (preferrable simple and intuitive) measure(s)? Something that would graphically perhaps give an indication without time-consuming clustering, sampling or whatsoever processing? Even though the above mentoined authors mention some measure for the assymetry of the yielded compoenents ( uncentered -> unipolar, centered -> bipolar) I find the concept a bit hard to understand. Isn't there a quick way (function) to just say (with numbers of plots of course) "well, it seems that the data are heterogenous looking at between- axes" or the other way around "it looks like the variables differ within, more than between"? Apologies for repeating the same question (trying to understand the problem myself). Thank you, Nikos ______________________________________________ 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.
