[R] PCA with not non-negative definite covariance
h.wickham at gmail.com
Wed Jul 26 17:58:47 CEST 2006
> I suppose that another option could be just to use classical
> multi-dimensional scaling. By my understanding this is (if based on
> Euclidian measure) completely analogous to PCA, and because it's based
> explicitly on distances, I could easily exclude the variables with NA's on a
> pairwise basis when calculating the distances.
I don't think it as straightforward as that because distances
calculated on observations with missing values will be smaller than
other distances. I suspect adjusting for this would be in some way
equivalent to imputation.
Exactly what do you want a low-dimensional representation of your data
set for? (And why are you concerned about negative eigenvalues?)
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