[R] Estimate "between-axes" vs "within-axes heterogeneity of multivariate matrices
Nikos Alexandris
nikos.alexandris at felis.uni-freiburg.de
Wed Dec 22 04:57:17 CET 2010
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
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