[R] How could I get the percent of variance explained by each axis if I use prcomp to predict new dataset?

[Paul Hiemstra]

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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> and provide commented, minimal, self-contained, reproducible code.

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
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