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Hi, I'm trying to do PCA on a n by p wide matrix (n < p), and I'd like to get more principal components than there are rows. However, svd() only returns a V matrix of with n columns (instead of p) unless the argument nv=p is set (prcomp calls svd without setting it). Moreover, the eigenvalues returned are always min(n, p) instead of p, even if nv is set: > x <-

Hi, I'm trying to plot several lag.plots on a page, however the second plot replaces the first one (although it only takes up the upper half as it should): par(mfrow=c(2,1)) a<-sin(1:100) b<-cos(1:100) lag.plot(a) lag.plot(b) What's the trick to this? I'm using R 2.2.1 (2005-12-20 r36812) on Ubuntu Linux. Thanks, Gad -- Gad Abraham Department of Mathematics and