Rik and Pengxu,
They don't have to equal 0, for example if you had 4 condition and 20
subjects, (conditions then subject factors in the design matrix), then
the contrast for condition 1 would be:
1 0 0 0 ones(1,20)/20
While this contrast works, it is not valid for statistical inference
because you end up using the wrong error term because the error term
is for the within-subject errors. However, it is useful for getting
the mean response to condition1 across subjects if you just want to
plot the results.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain
PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
and which is intended only for the use of the individual or entity
named above. If the reader of the e-mail is not the intended recipient
or the employee or agent responsible for delivering it to the intended
recipient, you are hereby notified that you are in possession of
confidential and privileged information. Any unauthorized use,
disclosure, copying or the taking of any action in reliance on the
contents of this information is strictly prohibited and may be
unlawful. If you have received this e-mail unintentionally, please
immediately notify the sender via telephone at (773) 406-2464 or
email.
On Wed, Apr 6, 2011 at 8:43 AM, Rik Henson <[log in to unmask]> wrote:
>
>
> You can’t do this via SPM’s GUI, because it forces contrast weights to sum
> to zero for rank deficient design matrices (inestimable contrasts with
> respect to columns of interest).
>
>
>
> You can however estimate the equivalent contrast via calling SPM’s
> spm_FcUtil function directly, which will re-parametrise your contrast to
> make it estimable with respect to all columns in the design matrix, eg:
>
>
>
> SPM.xCon(end+1) = spm_FcUtil('Set',’Any_name’,'T','c',c',SPM.xX.xKXs);
>
> spm_contrasts(SPM);
>
>
>
> where c is your original contrast vector (eg [1 0 0 0]), which will be
> reprojected to include contributions from the subject effects. For further
> information, try Christophe Pallier’s document here:
>
>
>
> http://www.pallier.org//ressources/glm_anova/glm_anova2.pdf
>
>
>
> BW,R
>
>
>
> -------------------------------------------------------
>
> Dr Richard Henson
>
> Assistant Director, Neuroimaging
>
> MRC Cognition & Brain Sciences Unit
>
> 15 Chaucer Road
>
> Cambridge, CB2 7EF, UK
>
>
>
> Office: +44 (0)1223 355 294 x522
>
> Mob: +44 (0)794 1377 345
>
> Fax: +44 (0)1223 359 062
>
>
>
> http://www.mrc-cbu.cam.ac.uk/people/rik.henson/personal
>
> -------------------------------------------------------
>
>
>
> From: Pengxu Wei [mailto:[log in to unmask]]
> Sent: 06 April 2011 11:52
> To: Rik Henson
> Subject: a simple question about one-way within-subjects ANOVA
>
>
>
> Dear Dr. Rik Henson,
>
>
>
> I am reading your paper "ANOVAs and SPM". One of my data is just the same of
> Figure 5: Design matrix for one-way (1x4) within-subjects ANOVA. The first 4
> columns are treatment effects and the last 12 are subject effects, except 20
> instead of 12 subjects in my study.
>
>
>
> In SPM5 I get this design matrix through 'Flexible factorial'
> specification. I want to check activation of each of the 4 conditions
> through this model, and then use inclusive masking or conjunction to explore
> the activation areas in common between each pair of the 4 treatment effects.
> But I failed when using the contrast like [1 0 0 0]. Only contrast with a
> zero sum(eg.1 -1 0 0) can be used. Would you like to show me a clue?
>
>
>
>
>
> Best regards,
>
>
>
> Pengxu Wei
> Director, Chinese Medicine and Rehabilitation Dept.
> National Rehabilitation Hospital,
> National Research Center for Rehabilitation Technical Aids, China
|