Stephen,
Thanks for this very helpful suggestion. Your code modification permitted
the analysis
we needed to run in SPM2.
Andy Saykin
At 08:14 AM 3/9/2005, you wrote:
>On Tue, 8 Mar 2005 18:06:54 EST, John D. West <[log in to unmask]>
>wrote:
>
> >Dear SPM group:
> >
> >We are currently trying to setup an ANCOVA with multiple covariates for
>VBM. Unfortunately, the basic ANCOVA in SPM2 appears to only permit one
>covariate (as a nuisance variable).
>
>Not sure this is what you're looking for but you can modify the SPM code
>to allow multiple covariates.
>
>Here's an example: in spm_spm_ui.m, replace
>
>D = [D, struct(...
> 'DesName','AnCova',...
> 'n', [Inf Inf 1 1], 'sF',{{'repl','group','',''}},...
> 'Hform', 'I(:,2),''-'',''group''',...
> 'Bform', 'I(:,3),''-'',''\mu''',...
> 'nC',[0,1],'iCC',{{8,1}},'iCFI',{{1,1}},...
> 'iGXcalc',[-1,2,3],'iGMsca',[1,-9],'GM',[],...
> 'iGloNorm',9,'iGC',12,...
> 'M_',struct('T',[-Inf,0,0.8*sqrt(-1)],'I',Inf,'X',Inf),...
> 'b',struct('aTime',0))];
>
>with
>
>D = [D, struct(...
> 'DesName','AnCova',...
> 'n', [Inf Inf 1 1], 'sF',{{'repl','group','',''}},...
> 'Hform', 'I(:,2),''-'',''group''',...
> 'Bform', 'I(:,3),''-'',''\mu''',...
> 'nC',[0,Inf],'iCC',{{8,1}},'iCFI',{{1,1}},...
> 'iGXcalc',[-1,2,3],'iGMsca',[1,-9],'GM',[],...
> 'iGloNorm',9,'iGC',12,...
> 'M_',struct('T',[-Inf,0,0.8*sqrt(-1)],'I',Inf,'X',Inf),...
> 'b',struct('aTime',0))];
>
>(Of course, don't simply cut and paste this as it's not certain the
>version to modify is the same. But the idea behind the modification
>should be relevant.)
>
> >
> >We could use any suggestions you might have to setup this analysis.
> >
> >The basics of the VBM analysis we wish to perform are as follows:
> >There are two between subjects factors that we wish to investigate in a 2
>X 3 design yielding 6 independent cells (Factor A with level 1 and 2 and
>Factor B with level 1, 2, and 3).
> >We think that the best design to use would be to combine these factors so
>that we have 6 groups. (11 12 13 21 22 23). We also want to covary for 3
>other potential confounding variables (age, sex, ICV). It seems that we
>could do this by using a multiregression analysis and a combination of
>dummy variables to code for the six groups. However, it seems to us that
>we would lose the ability to look at paired contrasts betwen subgroups
>(e.g. 11 vs 12) and to extract the adjusted data for profiling the
>results. Similar issues have come up with complex fMRI models.
> >
> >Any suggestions on how to compute a complex factorial design with
>multiple covariates would be most appreciated.
> >
> >Thank you in advance.
> >John West
> >Brain Imaging Lab, DHMC
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