Liu, Feng wrote:
Dear list,
I am trying to use a PCA to predict new dataset, I know how to get the
variance explained for the original PCA, but how could I get the
percent of variance explained by each axis for this new PCA ordination?
Thanks a lot.
x1 <- matrix(rnorm(30), 6, 5)
x2 <- matrix (rnorm(40), 8, 5)
pca1<-prcomp(x1, retx=TRUE)
pca1$sdev
pred <- predict(pca1, x2)
Feng Liu
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I think the following will give you what you want:
(pca1$sdev)^2 / sum(pca1$sdev^2)
# Or cumulative
cumsum((pca1$sdev)^2) / sum(pca1$sdev^2)
The $sdev part of the prcomp object gives the standard deviation of the
pca axis, taking the square gives the variance.
cheers,
Paul
--
Drs. Paul Hiemstra
Department of Physical Geography
Faculty of Geosciences
University of Utrecht
Heidelberglaan 2
P.O. Box 80.115
3508 TC Utrecht
Phone: +3130 274 3113 Mon-Tue
Phone: +3130 253 5773 Wed-Fri
http://intamap.geo.uu.nl/~paul
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
