Thanks Doug for your reply. I forgot that the number of columns is number of classes by number of covariates +1.
So, I only want you to confirm me that the extra zero that I've seen in the FSwiki example is an erratum (see contrasts 7 and 8 in Four Groups (2 Factors, Two Levels), One Covariate. DODS only. QDEC-able) and that I don't have to add any extra zero per covariate when testing the interaction effect.
Many thanks again. Jose
----- Mensaje original ----- De: Douglas N Greve greve@nmr.mgh.harvard.edu Fecha: Martes, 18 de Enero de 2011, 16:53 Asunto: Re: [Freesurfer] contrast matrix for 2 discrete factors and 3 continuous variables A: Jose Luis Cantero Lorente jlcanlor@upo.es CC: freesurfer@nmr.mgh.harvard.edu
Hi Jose, with 6 classes and 3 continuous variables, I think you should have 24 regressors, which means 24 columns in the contrast matrix. Yours only has 18. Otherwise the contrast to test between B and C is correct (just need to add 6 zeros). For the interaction, you would need
0 0 0.5 -0.5 -0.5 0.5 0 0 ....
doug
Jose Luis Cantero Lorente wrote:
Dear Freesurfers,
My experimental design includes 2 discrete factors and 3
continuous variables:
Discrete factors:
- Diagnosis with three
levels (A, B, C)
- Gender with two levels
(Males, Females)
Continuous variables:
- Mean cortical thickness
in the left hemisphere (CTLH)
- Age
- Performance in a memory
test (TASK)
The FSGD file would be as follows:
GroupDescriptorFile 1
Title Thickness-subj
Class AMale
Class AFemale
Class BMale
Class BFemale
Class CMale
Class CFemale
variables ZCTLH ZAGE
Input control_01 AMale -1.04398 0.24789
Input control_02 AMale 1.22853 -0.72343
Input control_03 AFemale .13773 -1.37097
Input control_04 AFemale .41043 0.24789
Input mci_01 BMale -.77128 0.24789
Input mci_02 BMale -2.40749 0.73354
Input mci_03 BFemale -.86218 -1.0472
Input mci_04 BFemale .50133 0.23777
Input ad_01 CMale -.75678 0.22789
Input ad_02 CMale -2.88749 0.63047
Input ad_03 CFemale -.96015 -1.4452
Input ad_04 CFemale .80712 -0.37567 …
We are assuming different offsets and different slopes (DDOS).
The following contrast would test the null hypothesis whether
there is a difference between B and C (T-test) regressing out the effect of the remaining variables.
0 0 0.5 0.5 -0.5 -0.5 0 0 0 0 0 0 0 0 0 0 0 0
Could you help me to build the contrast for testing
interactions in the above experimental design? In the FSwiki examples, you add one column when you have two factors with two levels each, and one continuous variable. I am not sure how many columns should I add in our particular case and why.
Null hypothesis: is there an interaction between diagnosis (B >
C) and gender regressing out the two remaining variables? Assuming that you add one column per variable that is regressed out, the contrast would be as follows:
0 0 0.5 0.5 -0.5 -0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Is this correct? Any help will be very appreciated.
Many thanks in advance.
Best regards,
Jose
Jose L. Cantero, Ph.D. Laboratory of Functional Neuroscience Department of Physiology, Anatomy and Cell Biology University Pablo de Olavide Ctra. de Utrera, Km.1 41013 - Seville
- Spain -
Phone: +34 954 977433 Fax: +34 954 349151 Email: jlcanlor@upo.es http://www.upo.es/neuroaging/en/
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--- Jose L. Cantero, Ph.D. Laboratory of Functional Neuroscience Department of Physiology, Anatomy and Cell Biology University Pablo de Olavide Ctra. de Utrera, Km.1 41013 - Seville - Spain - Phone: +34 954 977433 Fax: +34 954 349151 Email: jlcanlor@upo.es http://www.upo.es/neuroaging/es/