 |
 |
I mean the former. Vladimir On Tue, Dec 1, 2015 at 1:32 PM, Frederik Van de Steen < [log in to unmask]> wrote: > 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 >>> >>> > > >