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Subject:

Re: repeated measures in flexible factorial

From:

Colin Hawco <[log in to unmask]>

Reply-To:

[log in to unmask][log in to unmask]>
Subject: Bayesian model reduction and empirical Bayes for group (DCM) studies

Dear Peter,

this is Marta Gandolla from Politecnico di Milano, and I'm writing you as you are indicated as corresponding author for the paper mentioned in the subject, published on 2016.

We are currently replicating your approach with our data, however, we would need a confirmation from you.

With respect to 2nd level analysis, the code select itself in independece the model space order (function spm_dcm_bmc_peb).

By reading your paper and information found here and there, we concluded that the model space is indicated in the variable BMC.K, where we can find on the columns the specified second level regressors, and in the rows the models.

We would be greatful if you could confirm or detail our interpretation of the output of your paper.

All the best,
[log in to unmask]

Date:

Mon, 8 Oct 2018 14:27:28 -0500

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (80 lines)

Thanks Donald.

I don't think the main effect of group is very sensible anyways, since  
it is collapsing between time points and therefor of little interest.

The interaction ioss till valid though right?

We were planning a contrast os 0 0 0 0 1 -1 - 1 (the 4 interaction  
terms, skipping the main effects terms) as an F contrast. I'm not 100%  
sue that is right though. I'm not very experienced with F contrasts.

I'd appreciate a second opinion : )

Colin


Quoting "MCLAREN, Donald" <[log in to unmask]>:

> Hi Colin,
>
> (1) You need to add the subject factor as a main effect. Then it will
> appear in your model.
>
> (2) The main effect of group is not a valid contrast in a repeated measures
> design. This is due to the wrong degrees of freedom and the wrong error
> term. The error term of this model is the within-subjects error. For the
> main effect of group, you'd want the between-subjects error term, which is
> not provided with the model.
>
> Best Regards,
> Donald McLaren, PhD
>
>
>
> On Fri, Oct 5, 2018 at 4:46 PM Colin Hawco <[log in to unmask]> wrote:
>
>> Oh and I forgot part 2, my contrasts. Main effects are easy (1 1 -1 -1
>> or 1 -1 1 -)
>>
>> but for interaction, since it put the interaction terms in the model,
>> I think it would be an F contrast of  [0 0 0 0 1 -1 1 -1]
>> Confirmation of this would make me feel a lot better, I've been as
>> confident in f contrasts as I'd like as I so rarely make use of them!
>>
>> best,
>> Colin
>>
>> Quoting [log in to unmask]:
>>
>> > Dear all,
>> >
>> > I'm sure this has been addressed before but my list search didn't
>> > run up a clear answer (a reflection on my poor search skills than
>> > the clarity of past answers, I am sure).
>> >
>> > I am running a repeated measures type ANOVA design, with a group
>> > (between subject) by time/session (pre-post, within subject) design.
>> > After some consideration, flexible factorial seemed the best way to
>> > go.
>> >
>> > I set for main effects of time and session, as well as the
>> > interaction. My design matrix is attached.
>> >
>> > I set independence for no for 'time', but not for group, while I
>> > left variance unequal (after all we expect changes over time, so I
>> > expect possible unequal variance).
>> >
>> > First I wanted to check if this seems OK.
>> >
>> > Second, I wanted to check if maybe we should model subject as an
>> > additional factor? It seems to maybe be already embedded implicitly
>> > in the flexible factorial. If I add this factor, but don't specify a
>> > main effect, it doesn't appear in the design matrix, which I found a
>> > bit surprising (it should still be modeled even if we don't contrast
>> > it, for the effects on the Beta estimation).
>> >
>> > Thanks a lot,
>> > Colin
>>

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