missing data in PCA

Dear all,

I was wondering if you have good options to deal with missing data  
on a PCA in R. I guess I could simply delete those cases, but I  
think there should be better options in terms of predicting them.  
Any hints?

Howdy,

There may be better ways, but they are not easy... Check paragraph on  
missing data in multivariate task view. This lists several  
alternatives of multiple imputation data. About a year ago I tried  
some of them for multivariate analysis, but that was not quite  
straightforward. The problem was summarizing multivariate results.  
Things may have been changed since then, and there may be some canned  
routines.

Here is one link to multivariate task view (but you can use a mirror  
close to you):

http://cran.r-project.org/web/views/Multivariate.html

One thing you should remember: do not replace missing values with means.

Cheers, Jari Oksanen