Dear DCM experts
Previous posts suggested to look at the variance explained by models in DCM (DCM.R - in using DCM Diagnostics or spm_dcm_fMRI_check) as an indication for model convergence in DCM_post_hoc. As far as I understand this assumes that the variance explained by the full model is the upper limit for the the variance explained by any reduced model.
We have now specified and inverted 16 DCM models to use in a standard BMS (not post-hoc). When looking at the variance explained by these models we find that in some individuals, there are models that explain more variance than the full model.
My question is:
1) How can that be possible? How can the inclusion of additional parameters reduce the variance explained by the model?
2) If this is possible - how can this criterion (variance explained by the full model) be used as exclusionary criterion for participants from DCM_post_hoc?
Thanks
Tali Bitan
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