Dear DCM experts,
We can choose to compute BMA (Bayesian Model Averaging) for the winning family in conducting the family comparison. And then a file 'BMS.mat' which contains BMA results of each subject and of all subjects is generated. Mean DCM parameters in the group level are stored in BMS.DCM.rfx.bma.mEp in this file.
The posterior probabilities corresponding to mean DCM parameters for winning family are regarded as the indicators of significance in some papers. That is, a mean parameter with the posterior probability bigger than 0.95 in group level will be regarded as the significant one.
I would like to know how to calculate the posterior probability of each mean DCM parameter for the winning family in the group level.
Besides, my DCM.B is a 3X3X2 matrix with three brain regions and two experimental effects. What I am interested in is the difference between two experimental effects on the modulatory parameters. That is, the difference between DCM.B(:,:,1) and DCM.B(:,:,2) is my interest.
Do you know how to test the difference between two experimental effects on modulatory parameters for the winning family if we don't use T-test?
That is, how do we know whether the parameters in BMS.DCM.rfx.bma.mEp.B(:,:,1) is significantly different from those in BMS.DCM.rfx.bma.mEp.B(:,:,2)?
I think some kinds of Bayesian test are needed. But I don't know how to do these.
Thank you very very much!