Hi all. I have a question about methods that's not really about pymvpa per se
-- if this is too inappropriate for this mailing list, please let me know. I
have a structural dataset with one volume per subject and a lot of redundant
voxels (i.e., two voxels that have the same value for all subjects). I have a
preprocessing step outside python that creates a mask that effectively strips
the redundancy (i.e., masks out all but one of each set of identical voxels,
separately within each ROI). Then I use shogun SVR in PyMVPA to get an error
measure for each voxel. My naive assumption was that removing redundant voxels
would never result in a larger error. But while this is true in most cases, a
substantial minority (about a quarter) of the ROIs do benefit from including
the redundant voxels. The difference in error is relatively small (mostly in
the 1-3% range), but I was surprised it happens so often. So I was hoping I
could get an expert opinion on whether or not I should be alarmed by this
(i.e., it means I'm doing something wrong). Just for the record, I wasn't
planning to do anything with the analyses that included the redundant voxels, I
just forgot to create the stripped masks the first time through.
The most relevant lines of the script are below. Any thoughts would be greatly
appreciated. Thanks,
dan
cc=sg.SVM(svm_impl='libsvr', kernel_type='linear', regression=True)
for mask in masklist:
myds=NiftiDataset(samples=lmap,mask=mask,labels=n.array(labels))
cv=CrossValidatedTransferError(
TransferError(cc,errorfx=RMSErrorFx()),
NFoldSplitter())
res=cv(myds)
print "error for %s is [%g]" % (mask,res)
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