[R] PCA with not non-negative definite covariance

Quin Wills quin.wills at googlemail.com
Wed Jul 26 17:44:17 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.


-----Original Message-----
From: bady at univ-lyon1.fr [mailto:bady at univ-lyon1.fr] 
Sent: 25 July 2006 09:24 AM
To: Quin Wills
Cc: r-help at stat.math.ethz.ch
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
algorithm (intensively used in chemometry). S. Dray proposes a version of
procedure at http://pbil.univ-lyon1.fr/R/additifs.html.

Hope this help :)


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