In the `Detail' section of ?princomp: princomp only handles so-called Q-mode PCA, that is feature extraction of variables. If a data matrix is supplied (possibly via a formula) it is required that there are at least as many units as variables. For R-mode PCA use prcomp.
Andy > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] > Sent: Thursday, October 16, 2003 5:14 AM > To: [EMAIL PROTECTED] > Subject: [R] princomp with more coloumns than rows: why not? > > > As of R 1.7.0, princomp no longer accept matrices with more > coloumns than rows. I'm curious: Why was this decision made? > > I work a lot with data where more coloumns than rows is more > of a rule than an exception (for instance spectroscopic > data). To me, princomp have two advantages above prcomp: 1) > It has a predict method, and 2) it has a biplot method. > > A biplot method shouldn't be too difficult to implement (I > believe I've seen one on R-help). > > A predict method seems to be more difficult, because the > prcomp object doesn't include the means that need to be > subtracted from the new data. Would it break conformance > with S to let prcomp return the means as well? > > -- > Sincerely, > Bjørn-Helge Mevik > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo> /r-help > ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
