[pymvpa] AttributePermutator: Permute within chunks (& subjects) but only training labels

[Michael Bannert]

Dear all,

I would like to use permutation testing for spatially aligned
across-subject decoding. I have one vector of beta estimates per run
(aka chunks) and per subject. Hence I figured it would be wise to
permute within subjects and runs.

I can achieve this (I think) if I use AttributePermutator in this way:

permutator = AttributePermutator(attr='targets', limit=['subject',
'chunks'], count=n_perm)

According to the debugging information provided when setting the 'APERM'
option, the permutations that are produced look reasonable.

However, I would also like to permute only the training data. How can I
accomplish this?

I tried something like this:

permutator = AttributePermutator(attr='targets', limit={
         'partitions': 1, 'subject': range(n_subj), 'chunks':
['run%02.f' % j for j in range(1, n_runs + 1)]},
          count=1)

... but it doesn't work.

I guess I am not very clear on what the documentation of
AttributePermutator has to say about the limit argument.

Could anyone help?

Thanks & best,
Michael