[pymvpa] Cross-participant MVPA and controlling for traits of no interest

[John Clithero]

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
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