Factor Map and Co-inertia Analysis

From: Culhane, Aedin (a.culhane@ucc.ie)
Date: Mon Feb 18 2002 - 18:42:04 MET

  • Next message: Jean Thioulouse: "Re: Factor Map and Co-inertia Analysis"

    Dear ADElist
    I am trying to reproduce the factor map figures from Thioulouse and Lobry,
    1995 Co-inertia analysis of amino acid physico-chemical properties and
    protein composition with the ADE package, Comput. Appl. Biosci 11(3) 321-9.
    I would be really grateful if you could tell me which results files from the
    co-inertia analysis are required to produce these figures.
    Thanks a million
    Aedin

    -----Original Message-----
    From: Daniel Chessel [mailto:chessel@biomserv.univ-lyon1.fr]
    Sent: 13 November 2001 14:06
    To: adelist@biomserv.univ-lyon1.fr
    Subject: Re: Gaussian ordination

    At 11:40 13/11/2001 +0100, Agustin Lobo wrote:

    > > Non, on ne trouve pas cette méthode dans ADE-4 qui ne contient que des
    > > méthodes basées sur la géométrie euclidienne.
    >
    >May I ask why? Why do we go on pretending things are linear?
    >I have the impression that this type of non-linear
    >analysis is not often used, it's just a matter of
    >complexity in the programming and/or computational requirements
    >or are there other (theoretical) drawbacks?
    >
    >In the paper you mention (in the 70's), it says
    >"The distortion in indirect gradient analysis is a consequence of
    >assumptions in conflict with knowledge of species distributions"

    La question est importante
    Pour moi, une méthode linéaire est une méthode basée sur l'algèbre linéaire
    Les choses ne sont pas linéaire, l'algèbre peut l'être !
    "linear" is "linear algebra"

    Il faut voir le très important article :
    Ter Braak, C. J. F. 1985. Correspondence analysis of incidence and
    abundance data : properties in terms of a unimodal reponse model.
    Biometrics 41:859-873.
    "In this paper correspondence analysis is shown to approximate the maximum
    likelihood solution of explicit unimodal response models in one latent
    variable"

    Etonnant, non ?



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