Dear Francisco, CCA and PCA are quite different methods. CCA regresses your 'response' data onto a set of explanatory variables. This needs to invert the matrix of covariances of the predictors, which is only possible if n>p, where n is the number of observations and p the number of explanatory variables.
PCA is defined in any case. The ratio between n and p is then relevant only if you intend to infer principal axes / component of the population (as opposed to using the PA/PC as mere descriptors of the sample). I would recommend reading : Joliffe, I. T. Principal Component Analysis Springer, 2004 which tackles the latter point very clearly. Best regards, Thibaut. -- ###################################### Dr Thibaut JOMBART MRC Centre for Outbreak Analysis and Modelling Department of Infectious Disease Epidemiology Imperial College - Faculty of Medicine St Mary’s Campus Norfolk Place London W2 1PG United Kingdom Tel. : 0044 (0)20 7594 3658 t.jomb...@imperial.ac.uk http://www1.imperial.ac.uk/medicine/people/t.jombart/ http://adegenet.r-forge.r-project.org/ ________________________________________ From: r-help-boun...@r-project.org [r-help-boun...@r-project.org] On Behalf Of Francisco Javier Santos Alamillos [fsan...@ujaen.es] Sent: 23 November 2009 21:43 To: r-help@r-project.org Subject: [R] Doubt about CCA and PCA Dear R community, I'm working with PCA and CCA methods, and I have a theoretical question. Why is it necesary to have more temporal values than variables when the CCA O PCA are going to be used? Could you advise to me some any paper about it? Thanks in advance, [[alternative HTML version deleted]] ______________________________________________ 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.
