Hi Pavel,

Please, see attached an example design. Note that each subject is allocated on its own exchangeability block (EB).

- For contrasts C1-C3, use the option "-e design.grp" in randomise.
- For contrasts C4-C6, and for the F-test, use the option "-e design.grp" and also the option "--permuteBlocks" in randomise.

You may need to split the contrast file (*.con) into two, one with just C1-C3, another with just C4-C6, then make the necessary changes to the *.fts file (F-contrast), and run randomise twice (in separate directories to avoid overwriting results).

All the best,

Anderson


2015-01-28 14:41 GMT+00:00 Pavel Hok <[log in to unmask]>:
Dear FSL Experts,

I know that similar questions have been already answered many times, but I could not find any satisfactory conclusions on the forum.

I was trying to run 2x3 repeated measures ANOVA (i.e. 3 groups, 2 timepoints, all subjects have complete data) in FEAT. Since this design is not mentioned in the GLM manual page, I tried to combine two other designs: the factors effect approach (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GLM#ANOVA:_2-factors_2-_.26_3-levels_.282x3_between-subjects_ANOVA.29) and mixed effects ANOVA (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GLM#ANOVA:_2-groups.2C_2-levels_per_subject_.282-way_Mixed_Effect_ANOVA.29).

Unfortunately, the Feat complains about rank deficiency. Is there a way to model such a study design? My second guess would be to get the within-subject contrasts using a FE analysis and then run simple T-test. My original design matrix is attached.

Thanks for any ideas!

Kind regards

Pavel



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