Multivariate Model Selection

[morphmet_moderator]

----- Forwarded message from "Fabio de A. Machado" <macfa...@gmail.com> -----

Date: Sun, 23 Feb 2014 09:12:51 -0300
From: "Fabio de A. Machado" <macfa...@gmail.com>
Reply-To: "Fabio de A. Machado" <macfa...@gmail.com>
Subject: Multivariate Model Selection
To: morphmet morphmet <morphmet_modera...@morphometrics.org>

Dear all,

I'm trying to implement a model selection protocol for multivariate morphometrics and I'm having some trouble with model selection criteria.

I intended to use AIC to select the best model, but in any real dataset that I have tried this, the best model (lowest AIC) is always the one with the most independent variables.

For nested models, I've tried to check the results using MANOVA procedures (selecting only the significant independent variables) and Canonical Correlate Analysis and both procedures are very similar (significant variables have the highest scores on CCoA). Also, when I use the chi-square approximation to test the difference between linear models, I come up with fairly similar results from the MANOVA procedure. But if I inspect the AIC of those reduced models, they are far higher then the most complex model, sometimes \DeltaAIC>1000, which seems very far from the  \DeltaAIC<2 for similar models. 

Is this some inherent problem of AIC estimation for multivariate data?

Best,

-----------------------------------------------------
Fabio Andrade Machado
Laboratório de Evolução de Mamíferos 
Departamento de Genética e Biologia Evolutiva
Universidade de São Paulo
Rua do Matão, trav.14, nº277, 
Edificio Minas Gerais 
05422-970
f.mach...@usp.br ; macfa...@gmail.com 
+55 11 3091-8758
+55 11 982-631-029
skype: fabio_a_machado

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----- End forwarded message -----