[R] Doubt about CCA and PCA

[Jombart, Thibaut]

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

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]]

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