Thank you Brian, very helpful! Obviously, if I run ANOVA for each voxel separately, I treat the trials as "subject" *(based on my analogy).
On Tue, May 6, 2014 at 3:21 PM, Brian Murphy <[email protected]> wrote: > Hello Vadim, > > you're right that the Anova feature selection is univariate. And also > for a two-class problem it gives an identical result to that you would > get with a t-test (F statistic is different from t-statistic, but what > matters is the ranking, which will be identical). I suppose the reason > PyMVPA uses Anova rather than t or z statistics, is so that it also > works for more than two classes. > > > In one-way ANOVA with data analysis we might have subjects as > > between-group factor and treatment as within-group factor. But > > treating voxels as "subjects" does not help me because there is no > > grouping for voxels (grouping is for condition). > > > Hmmm, not sure, but if I read your analogy correctly, I think you should > be thinking of *trials* as being equivalent to subjects. > > best, > > Brian > > > > Any ideas? Any links for explanation would be also appreciated! > > > > > > Thanks, > > Vadim > > > > > > > > > > > > > > > > > > -- > Dr. Brian Murphy > Lecturer (Assistant Professor) > Knowledge & Data Engineering (EEECS) > Queen's University Belfast > [email protected] > > > _______________________________________________ > Pkg-ExpPsy-PyMVPA mailing list > [email protected] > http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa >
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