Thanks for your replies! Francisco, great suggestion, I will absolutely consider that classifier for my next study. For the present study/additional analysis though, I would like to stick with the v4.0 version and the linear SVM as this showed good results (and I preferably don't switch to a different version in between analyses/revisions). Jo, using multiple computers is indeed a good option,
However, in the documentation I read that if there is a strong assumptiom of the data being normally/gaussian distributed (http://v04.pymvpa.org/examples/permutation_test.html), 30 permutations would also suffice. As my data indeed approaches a normal distribution, I would like to try this first. But, unfortunately I did not manage to implement this into the searchlight. In the example, it is clearly described how this permutation test would be done in a 'normal' analysis, but how exactly to combine this with the searchlight is unclear to me. I assume the null_dist = MCNullDist... should be included in the TransferError in the script below, but the searchlight now only returns the error scores for each voxel and no other results. How could I perform the permutation test for each searchlight and let the searchlight output also the p values for testing against the null distribution? cv = CrossValidatedTransferError( TransferError(LinearCSVMC()), NFoldSplitter()) sl = Searchlight(cv, radius=6) sl_map = sl(dataset_total) _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
