[RsR] In robust PCA methods, how to get variance explained?

[Valentin Todorov]

library(rrcov)
data(bus)
p <- ncol(bus)
rpca <- PcaHubert(bus, k=p, kmax=p)
summary(rpca)

Hope this helps.
Best regards,
Valentin


On Tue, Apr 24, 2012 at 6:03 PM, Michael <comtech.usa using gmail.com> wrote:

> In robust PCA methods, how to get variance explained?
>
> For example, PcaHubert,
>
> how to get the variance explained which are similar to those concepts in
> traditional PCA?
>
> In traditional PCA, you have a bunch of eigenvalue lambdas...
>
> and you sort the lambdas from the biggest to the smallest,
>
> the lambda_i / (sum of all lambdas) is the variance explained by that
> principal component...
>
> how to obtain the equivalent concepts in PcaHubert?
>
> Thanks a lot!
>
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