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

From: Quin Wills <>
Date: Thu 27 Jul 2006 - 01:44:17 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.


-----Original Message-----
From: [] Sent: 25 July 2006 09:24 AM
To: Quin Wills
Subject: Re: [R] PCA with not non-negative definite covariance

Hi , hi all,

> Am I correct to understand from the previous discussions on this topic (a
> few years back) that if I have a matrix with missing values my PCA options
> seem dismal if:
> (1) I don’t want to impute the missing values.
> (2) I don’t want to completely remove cases with missing values.
> (3) I do cov() with use=”pairwise.complete.obs”, as this produces
> negative eigenvalues (which it has in my case!).

(4) Maybe you can use the Non-linear Iterative Partial Least Squares (NIPALS)
algorithm (intensively used in chemometry). S. Dray proposes a version of this
procedure at

Hope this help :)


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Received on Thu Jul 27 01:46:59 2006

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