R help - May 2010 - How could I get the percent of variance explained by each axis if I use prcomp to predict new dataset?

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

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
>
> ______________________________________________
> R-help at 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.
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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