Dear Vladimir,
Thanks for you reply. I have an additional question. Do you mean
correlating the observed effects with BMA parameters? Or predicted
effects? If the later how can i get the predicted effect with BMA
parameters?
best
Citeren Vladimir Litvak <[log in to unmask]>:
> Dear Frederik,
>
> Why don't you just correlate your effect magnitudes across subjects with
> the parameter values obtained by doing a BMA of all models within subject?
> This sounds like a more standard strategy.
>
> Best,
>
> Vladimir
>
> On Tue, Dec 1, 2015 at 12:40 PM, Frederik Van de Steen <
> [log in to unmask]> wrote:
>
>> Dear DCM experts,
>>
>> I am conducting a DCM study for ERP's . More specifically, one of the
>> things i would like to do is to link DCM (modulatory) parameters with two
>> temporal (early and late effect) ERP effects. I already fitted a quite
>> large model space (400 models). My idea of linking parameter to these two
>> effects was to calculate a mean (across subject) predicted effect
>> (channels) for every fitted model for both temporal effects. Then, for each
>> temporal effect, i would take 10% (or 50%) of the models with the smallest
>> predicted effect and 10% of the models with the largest predicted effect
>> (sort of 'data' driven post hoc family partitioning). Than i would use BMA
>> for these models with lowest and highest effect. After I would simple use
>> classical paired t-tests on these BMA parameters to see which parameters
>> are significantly different.
>>
>> Is this a valid analysis strategy? I would be grateful for comments/advice
>> regarding this issue.
>>
>> Kind regards,
>>
>> Frederik Van de Steen
>>
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