Doubt about CCA and PCA
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.jombart at imperial.ac.uk http://www1.imperial.ac.uk/medicine/people/t.jombart/ http://adegenet.r-forge.r-project.org/
From: r-help-bounces at r-project.org [r-help-bounces at r-project.org] On Behalf Of Francisco Javier Santos Alamillos [fsantos at ujaen.es]
Sent: 23 November 2009 21:43
To: r-help at r-project.org
Subject: [R] Doubt about CCA and PCA
Sent: 23 November 2009 21:43
To: r-help at 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,
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