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Dear Stéphane,

I'm afraid you're asking more than FSL can deliver:  You have a design with
a mix of with and between subjects factors.  As Donald McLaren has
expounded upon in many postings, FSL's approach to group modelling only
allows a single measure per subject, *or*, if you have multiple measures
per subject,you have only within subject effects (e.g. a 1-way repeated
measures ANOVA... 1 group of subjects, each subject with k measurements).

This design, with a mix of within- and between-subject factors can't be fit
in Feat or Randomise correctly.

Sorry!

-Tom

On Thu, Feb 9, 2012 at 9:43 AM, Stéphane Jacobs
<[log in to unmask]>wrote:

>  Hello list,
>
> I have a 2x3 factorial design, with one factor with 2 levels, and the
> other with 3 levels. As several persons before me, from what I've seen in
> the archives, I've been having a hard time understanding how to set up my
> analysis, but I think I've figured it out, and would love to have a
> confirmation that what I do is correct before running it.
>
> So, I've used the 2x2 ANOVA and the tripled t-test examples to set up my
> EVs. EV1 corresponds to factor A (2 levels), while EV2 and EV3 correspond
> to factor B (3 levels). Then, I obtain EV4 and EV5 by multiplying EV1 and
> EV2, and EV1 and EV3, respectively. If I'm correct, EV4 corresponds to the
> interaction between factor A and the 2 first levels of factor B (AB[1-2]),
> while EV5 corresponds to the interaction between factor A and level 1 and 3
> of factor B (AB[2-3]). The subsequent EVs then represent each individual
> subject. The following table illustrates this for 2 subjects for the sake
> of simplicity:
>
>
>
>  EV1 EV2
>  EV3
>  EV4
>  EV5
>  EV6
>  EV7
>   A1B1 (s01)
>  -1
>  1
>  1
>  -1
>  -1
>  1
>  0
>   A1B1 (s02)
>  -1
>  1
>  1
>  -1
>  -1
>  0
>  1
>   A1B2 (s01)
>  -1
>  -1
>  0
>  1
>  0
>  1
>  0
>   A1B2 (s02) -1
>  -1
>  0
>  1
>  0
>  0
>  1
>   A1B3 (s01) -1
>  0
>  -1
>  0
>  1
>  1
>  0
>   A1B3 (s02) -1
>  0
>  -1
>  0
>  1
>  0
>  1
>   A2B1 (s01) 1
>  1
>  1
>  1
>  1
>  1
>  0
>   A2B1 (s02) 1
>  1
>  1
>  1
>  1
>  0
>  1
>   A2B2 (s01) 1
>  -1
>  0
>  -1
>  0
>  1
>  0
>   A2B2 (s02) 1
>  -1
>  0
>  -1
>  0
>  0
>  1
>   A2B3 (s01) 1
>  0
>  -1
>  0
>  -1
>  1
>  0
>   A2B3 (s02) 1
>  0
>  -1
>  0
>  -1
>  0
>  1
>
> Then, the contrasts and corresponding F tests to get the classical omnibus
> test would be as follows:
>
>
>  EV1
>  EV2
>  EV3
>  EV4
>  EV5
>  EV6
>  EV7
>
>  F1
>  F2
>  F3
>   A
>  1
>  0
>  0
>  0
>  0
>  0
>  0
>
>   X
>
>
>   B1-B2
>  0
>  2
>  1
>  0
>  0
>  0
>  0
>
>
>   X
>
>   B1-B3
>  0
>  1
>  2
>  0
>  0
>  0
>  0
>
>
>   X
>
>   B2-B3
>  0
>  -1
>  1
>  0
>  0
>  0
>  0
>
>
>   X
>
>   AB[1-2]
>  0
>  0
>  0
>  1
>  0
>  0
>  0
>
>
>
>   X
>   AB1-3]
>  0
>  0
>  0
>  0
>  1
>  0
>  0
>
>
>
>   X
>
>
> Thanks for the help and great support, as always!
>
> Stéphane
>
> --
> Stéphane Jacobs - Chercheur post-doctorant / Post-doctoral researcher
>
> ImpAct - Inserm U1028 - Equipe Pélisson
> Centre de Recherche en Neurosciences de Lyon
> 16 avenue du Doyen Lépine
> 69676 Bron Cedex, France
> Téléphone / Phone: (+33) (0)4-72-91-34-20
>
>


-- 
__________________________________________________________
Thomas Nichols, PhD
Principal Research Fellow, Head of Neuroimaging Statistics
Department of Statistics & Warwick Manufacturing Group
University of Warwick, Coventry  CV4 7AL, United Kingdom

Web: http://go.warwick.ac.uk/tenichols
Email: [log in to unmask]
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