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alanis
2002 Dec 09
2
Principal component analysis
...04h.100.118.06.4.1 28 25 125 79 166
x.HU.24h.Ctr.118.07.24.ctrl 22 20 95 61 128
> pca <- prcomp(m, retx = TRUE)
there are 30 "PC"s displayed (I've truncated the output). Shouldn't tere be
1000 PCs, with the 1st PC beeing the most discriminativePC? In a principal
comp. Alanysis, aren't there as many PCs as dimensions? On the other hand I
thought that PCA somehow collapses dimensionality ... . What is are PCs for
my 30 data points. Afterwards I'd also like to display the results in a
diagram, e.g. in 2 or 3 dimensions, to visualise clusters. I'm not sure I'...