Re: [R] PCA with not non-negative definite covariance

From: hadley wickham <>
Date: Thu 27 Jul 2006 - 01:58:47 EST

> 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?)

Hadley mailing list PLEASE do read the posting guide and provide commented, minimal, self-contained, reproducible code. Received on Thu Jul 27 02:03:04 2006

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