Dear Marco,
If you like to do BMA across all models of your model space, it is not
necessary to create families. As far as I remember, you could average
across all models simply by setting the BMA option to 'Winning Family'
while not defining any family.
Hope that helps.
All the very best,
Stefan
Quoting marco tettamanti <[log in to unmask]>:
> Dear Peter,
> thank you for your suggestion, I'll experiment with this solution.
> One problem is that I will need to define families within BMS; but
> maybe I could bypass it by arbitrarily assigning models to e.g. 2
> families and then calculate BMA over 'All Families'?
> If your colleagues at the FIL have other suggestions, please let me know.
> Best!
> Marco
>
> On 08/28/2014 09:06 AM, Zeidman, Peter wrote:
>> Dear Marco, This is a good question. One possibility would be to create all
>> the models you want for a subject (as actual DCM files), and then use
>> spm_dcm_search (or the 'search' item from the DCM menu) to estimate them all
>> at once using the post-hoc scheme. Then repeat this for each subject. You
>> could now do BMS / BMA on the models in the normal way.
>>
>> I'll speak to my colleagues at the FIL to see if anyone has any other ideas.
>>
>> Best, Peter.
>>
> --------------------------------------------------------------------------
> IL MIO 5XMILLE VA AL SAN RAFFAELE DI MILANO
> PERCHE' QUI LA RICERCA DIVENTA CURA.
> CF 07636600962
> SE NON QUI, DOVE?
> Info: [log in to unmask] - http://www.5xmille.org/
>
>
> Disclaimer added by CodeTwo Exchange Rules 2007
> http://www.codetwo.com
>
>> -----Original Message----- From: SPM (Statistical Parametric Mapping)
>> [mailto:[log in to unmask]] On Behalf Of marco tettamanti Sent: 27 August
>> 2014 17:09 To: [log in to unmask] Subject: [SPM] DCM network discovery and
>> comparison between groups
>>
>> Dear all, I have a question regarding the correct manner to perform a
>> between-group comparison within the framework of DCM network discovery.
>>
>> In the framework of BMS, the suggested approach in the presence of more than
>> one experimental group (or family), when the winning models for the
>> different
>> groups differ with respect to parameters/connections, is to use Bayesian
>> Model Averaging (Penny et al. 2010). This provides weighted summary coupling
>> parameters e.g. over the entire model space of each group, and it
>> then allows
>> to perform between-group comparisons, thus avoiding conservative assumptions
>> about any particular model.
>>
>> In one experiment, I have now used DCM network discovery, instead of BMS, to
>> identify a group-specific optimum model for two different experimental
>> groups. The resulting optimum models have different parameter/connection
>> configurations between the two groups, and it is therefore difficult to
>> perform between-group comparisons on the coupling parameters. The problem is
>> that in this case BMA does not seem to be a viable solution since, strictly
>> speaking, I only have one model per group (i.e. the winning model), instead
>> of a model space constituted by several models whose parameters can be
>> averaged. Are there any recommended approaches to overcome this problem in
>> this case?
>>
>> I could e.g. manually specify and estimate a set (or even the full set?) of
>> alternative models to the optimum model, then perform BMS to actually verify
>> that the optimum model is indeed the winning model, and, contextually, also
>> calculate BMA. But this does not seem a very efficient solution. Also, I
>> would need to specify families to calculate BMA, and I do not think that
>> there is any particular rationale to do so here.
>>
>> Another solution that I have explored is, given that for each subject I have
>> both the fully connected model entered in the DCM network discovery and the
>> output optimum model, I can in principle perform BMS entering the two models
>> for each subject and arbitrarily assigning the fully connected model to
>> family 1 and the optimum model to family 2, and in such a way calculate BMA.
>> But I am not sure that this is a sensible solution. In addition, in
>> one of my
>> two experimental groups, the optimum model has the same number of
>> connections/parameters as the fully connected model (though e.g. DCM.F
>> differs between the two models). Therefore, BMS/BMA seems to make even less
>> sense to me.
>>
>> Any help would be warmly appreciated!
>>
>> Thank you and best wishes, Marco
>>
>> -- Marco Tettamanti, Ph.D. Nuclear Medicine Department & Division of
>> Neuroscience San Raffaele Scientific Institute Via Olgettina 58 I-20132
>> Milano, Italy Phone ++39-02-26434888 Fax ++39-02-26434892 Email:
>> [log in to unmask] Skype: mtettamanti
>> http://scholar.google.it/citations?user=x4qQl4AAAAAJ
>> --------------------------------------------------------------------------
>> IL
>> MIO 5XMILLE VA AL SAN RAFFAELE DI MILANO PERCHE' QUI LA RICERCA DIVENTA
>> CURA. CF 07636600962 SE NON QUI, DOVE? Info: [log in to unmask] -
>> http://www.5xmille.org/
>>
>>
>> Disclaimer added by CodeTwo Exchange Rules 2007 http://www.codetwo.com .
>>
>
> --
> Marco Tettamanti, Ph.D.
> Nuclear Medicine Department & Division of Neuroscience
> San Raffaele Scientific Institute
> Via Olgettina 58
> I-20132 Milano, Italy
> Phone ++39-02-26434888
> Fax ++39-02-26434892
> Email: [log in to unmask]
> Skype: mtettamanti
> http://scholar.google.it/citations?user=x4qQl4AAAAAJ
|