Dear Arkan
The Leave-One-Out (LOO) function is intended to check whether your effect sizes (DCM parameters) are large enough to predict subject-level covariates (e.g. group membership or behavioural scores). However, you say you want to predict unseen subjects' effective connectivity parameters, in order to validate DCM. I don't understand that. Please could you clarify the validation you are trying to perform?
All the best
Peter
-----Original Message-----
From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Arkan
Sent: 19 June 2018 16:55
To: [log in to unmask]
Subject: [SPM] Accuracy of DCM model
Dear SPM list,
I know that the spm_dcm_loo function is for distinguishing (classify) between two groups if the subject is unseen.
Here, I would like to validate the spectral DCM model to predict the unseen subject parameters (effective connectivity and hemodynamic) for one group.
How can I use the spm_dcm_loo function in this case?
I would like to plot the expected parameter and the real parameter (not the classification).
Regards,
Arkan
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