Reply-To: | | [log in to unmask][log in to unmask]> wrote:
> Hi Andy, > > Just as a small note: If you are interested in the value of the posterior > probabilities of the parameters after BMA, you could estimate them as > follows: > > Pp = 1 - spm_Ncdf(0,abs(mEp-pE),sEp^2) > > where mEp is the posterior mean, pE is the prior mean and sEp is the > standard deviation. > > Hope that helps. > > Best, > Stefan > > > > Quoting "Zeidman, Peter" <[log in to unmask]>: > > Hi Andy, >> Posterior probabilities aren’t stored in the BMA. You could use the >> posterior means and SDs to perform t-tests against zero (although the >> priors aren't exactly zero, so the test wouldn't be perfect). And you could >> perform paired t-tests to compare parameters if that's of interest. Note >> that the ideal method for testing the presence or absence of a connection >> is via model comparison - i.e. the step before the BMA. >> >> Best, >> Peter. >> >> From: Andy Yeung [mailto:[log in to unmask]] >> Sent: 30 January 2015 01:57 >> To: Zeidman, Peter >> Cc: SPM >> Subject: Re: [SPM] DCM parameters per subject extraction >> >> Dear Peter, >> >> Thank you for your guidance. May I know which item in the BMS.mat is >> storing data for probability of posterior estimates? >> I know how to extract mean and SD of the PEs. >> >> Best, >> Andy >> >> On Wed, Jan 28, 2015 at 10:45 PM, Zeidman, Peter < >> [log in to unmask]<mailto:[log in to unmask]>> wrote: >> Dear Andy, >> In general it’s better to divide your models into families, if that’s >> compatible with your experimental design. I would not base conclusions on >> that one model alone. >> >> Best, >> Peter. >> >> From: Andy Yeung [mailto:[log in to unmask]<mailto: >> [log in to unmask]>] >> Sent: 28 January 2015 00:35 >> To: Zeidman, Peter >> Cc: SPM >> >> Subject: Re: [SPM] DCM parameters per subject extraction >> >> Dear Peter and all, >> >> Thanks for guiding me to use BMA. Should I use BMA at model level or at >> family level? Because if I combine my 6 models into 3 families, this >> 'strong trend' model belongs to a familiy with exceedance probability of >> 1.0. Or you think it's better to report posterior estimates for only the >> 'strong trend' model? >> >> Best, >> Andy >> >> On Tue, Jan 27, 2015 at 10:46 PM, Zeidman, Peter < >> [log in to unmask]<mailto:[log in to unmask]>> wrote: >> Hi Andy, >> The exceedance probability *is* the test of which model stands out >> statistically. Personally, I don’t think I would accept an XP of 0.8045 to >> be a “winning” model. It’s a strong trend, but I think I’d want 0.90 or >> 0.95. Therefore, to look at parameters, I would recommend taking a weighted >> average of your models (weighted by the posterior probability of each >> model). This is called Bayesian Model Averaging, which can be switched on >> in the batch editor when you specify the BMS. You’ll then find posterior >> e,1a |