Thanks. 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.
Quin -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Sent: 25 July 2006 09:24 AM To: Quin Wills Cc: [email protected] 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 dont want to impute the missing values. > (2) I dont 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 http://pbil.univ-lyon1.fr/R/additifs.html. Hope this help :) Pierre -------------------------------------------------------------------------- Ce message a été envoyé depuis le webmail IMP (Internet Messaging Program) -- No virus found in this incoming message. -- ______________________________________________ [email protected] 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.
