Dear James:
From what I can see from spm_dcm_average, it does not report simply an
arithmetic mean (but you already knew that). What it does is to weight each
models posterior mean by the posterior precision. Models with higher
precision will thus contribute more to the average.
I'll leave it to others you've emailed as to whether this is the best
approach for what you want. We have tended to focus more on analyzing the
connections as a random variable using t-tests etc. This of course gives an
arithmetic mean.
Darren
>-----Original Message-----
>From: SPM (Statistical Parametric Mapping)
>[mailto:[log in to unmask]] On Behalf Of James Rowe
>Sent: Thursday, September 14, 2006 9:58 AM
>To: [log in to unmask]
>Subject: [SPM] spm_dcm_average query
>
>Dear Karl, Klaas and colleagues,
>
>I am puzzled by the output from spm_dcm_average. I understand
>from your message 024483 that it does not give the average of
>models in the sense of the arithmetic means of each of the
>connections/modulations in matrices A,
>B and C (although this approach has been advocated on the list by
>yourself and Will in the past, suggesting that one perform
>one-sample t-tests on the non-zero values of interest in
>matrices A,B,C).
>
>In contrast, spm_dcm_average uses a Bayesian FFX analysis
>across the group, to estimate the overall posterior mean for
>each connection/modulation. But, how does this explain the
>following discrepancy, in a very simple model (two regions X
>and Y, connected reciprocally and with intrinsic self
>connections. Area X receives input from an external visual
>stimulus. No moderator variables)
>
> From matrices C, in 18 subjects, we estimated the strength of
>the influence of the visual input on the area X. For all
>subjects, pC were 1.000. The actual value in C (for input
>onto area X) ranged from -0.06 to
>+0.40, mean +0.07. Positive values in 15 / 18 subjects for
>this connection.
>
>But, spm_dcm_average value for this connection was -0.03, pC
>1.000 . I do not understand how the model average could have
>suggested a negative value for this connection, when the
>individual subjects nearly all showed positive effects (and
>the nature of the task and simple model would lead one to also
>expect a positive value).
>
>Any ideas?
>
>thanks in advance,
>
>James Rowe
>
>(PS, this question arose with a more realistic complex model
>with bilinear inputs etc, but these are more tedious to
>describe in text - the issue is still seen with the most basic
>model outlined above)
>
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