Hi Jo - Thanks for your response. The features are structural data, so yes, they are voxel values. It just so happens that a behavioral trait is also predictive of whether or not participants are in Group A or Group B. Since the behavioral trait is not of the same "type" as the features, it seems incorrect to simply add it to the feature space. Still, though, I would like to "control" for the predictability of that trait in the MVPA. Does that make more sense?
Cheers, John On Tue, Jul 12, 2011 at 11:25 AM, J.A. Etzel <[email protected]>wrote: > To clarify a bit: what are your features for the classification? I take it > they're not voxel values/imaging data but rather some sort of behavioral > measures? > > As a general strategy I'd work hard to make sure the feature you don't want > driving the classification is not present in the training data, rather than > trying to adjust for it afterwords. > > Jo > > > > On 7/11/2011 3:39 PM, John Clithero wrote: > >> Hi PyMVPAers - >> >> I have a bit of a thought problem (but also hopefully an implementation >> one): >> >> I am performing cross-participant classification (do they belong to >> group A or group B?) and that classification works quite well (I've >> tried several different algorithms and the leave-one-out CVs are all >> significant). However, there is a trait X that we wish to control for >> (using the trait X - which is something we are not interested in and >> would prefer to have no effect on prediction - as a univariate >> predictor, it also performs significantly well for predicting group A or >> group B in a simple logistic regression), and I am hoping for some help >> in determining the best option. >> >> One option that I've thought of involves running SVM regression on the >> residuals from the logistic regression (so, instead of SVM on 0s and 1s, >> give it the continuous variable of the residuals and run SVM >> regression). This would (I think) effectively ask if a multivariate >> analysis can predict the variance that remains in individual binary >> classification after we have accounted for trait X. Does this sound >> reasonable, or can an option be thought of to adjust CV post-hoc that >> takes trait X into account? >> >> And, if that does sound reasonable, is there a straightforward way to >> implement this test in PyMVPA? >> >> Thanks for humoring me and my thought problem. >> >> Cheers, >> John >> >> >> >> ______________________________**_________________ >> Pkg-ExpPsy-PyMVPA mailing list >> Pkg-ExpPsy-PyMVPA@lists.**alioth.debian.org<[email protected]> >> http://lists.alioth.debian.**org/cgi-bin/mailman/listinfo/** >> pkg-exppsy-pymvpa<http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa> >> > > ______________________________**_________________ > Pkg-ExpPsy-PyMVPA mailing list > Pkg-ExpPsy-PyMVPA@lists.**alioth.debian.org<[email protected]> > http://lists.alioth.debian.**org/cgi-bin/mailman/listinfo/** > pkg-exppsy-pymvpa<http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa> >
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