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!
[[alternative HTML version deleted]]
______________________________________________
R-help@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.
