The 'factor.model.stat' function (available in the public domain area of http://www.burns-stat.com) fits a principal components factor model to data that can have NAs. You might be able to copy what it does for your purposes. It does depend on there being some variables (columns) that have no missing values.
If that doesn't work for you, then I would guess that doing missing value imputation could be another approach. I'm sure there be dragons there -- perhaps others on the list know where they lie. Patrick Burns [EMAIL PROTECTED] +44 (0)20 8525 0696 http://www.burns-stat.com (home of S Poetry and "A Guide for the Unwilling S User") Birgit Lemcke wrote: >Dear all, >(Mac OS X 10.4.11, R 2.6.0) >I have a quantitative dataset with a lot of Na´s in it. So many, that >it is not possible to delete all rows with NA´s and also not >possible, to delete all variables with NA´s. >Is there a function for a principal component analysis, that can deal >with so many NA´s. > >Thanks in advance > >Birgit > > >Birgit Lemcke >Institut für Systematische Botanik >Zollikerstrasse 107 >CH-8008 Zürich >Switzerland >Ph: +41 (0)44 634 8351 >[EMAIL PROTECTED] > >______________________________________________ >R-help@r-project.org 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. > > > > ______________________________________________ R-help@r-project.org 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.
