Dear Fahimeh,
I'm not sure your definition of RV is correct. The one I'm using is:
RV( = sum(spm_vec(DCM.R).^2)./sum((spm_vec(DCM.H)+spm_vec(DCM.R)).^2);
I've recently looked at a large number of models of different subjects
and there is no clear threshold to separate the ones with reasonable
fit from the ones without. If your RV is below 0.2 this is very good
but also there were some examples with RV of 0.5 or above where I
wouldn't say that DCM failed to fit the data and model comparison is
meaningless.
So there is no way around of just visually looking at the 'ERPs
(mode)' and 'Response' displays in DCM results and seeing whether the
model prediction captures what you think are the main features of the
data or not.
Best,
Vladimir
On Thu, Sep 29, 2011 at 2:02 PM, Fahimeh Mamashli <[log in to unmask]> wrote:
> Dear DCM experts,
>
> Thanks for your attention. I have two questions concerning correspondence between data and model. one well established measure is the R2 value.
>
> 1) if a model converged, does it mean that its R2 measure is high enough to explain the data? e.g. being larger than 80%, independence of SNR?
>
> R2=1-(sum(data-model).^2)/(sum(data-mean(data)).^2)
>
> 2) would you suggest other measures except R2 to do the data-model comparison?
>
> currently I have used R2 measures as a function of time, based on the modes as follows:
>
> % observed mode
> datamode=DCM.H{1,1};
>
> % predicted mode
> modepred=DCM.R{1,1};
>
>
> % R2 measure
> surat=(sum((datamode-modepred).^2));
> makhraj= (sum((datamode-mean(datamode)).^2));
>
> gofhigh=1-(surat./makhraj);
>
>
>
> I am looking forward to your answer.
>
> Kind regards,
> Fahimeh
> ------------------------
> PhD student
> Max Planck Institute for Human Cognitive and Brain Sciences
> Stephanstraße 1A
> 04103 Leipzig
> Germany
>
> Tel: +49 341 9940 - 2570
>
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