[pymvpa] OneWayAnova for feature selection: how it works?

[Vadim Axel]

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 <brian.murphy at qub.ac.uk> 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
> brian.murphy at qub.ac.uk
>
>
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