So the full design is I have 4 conditions in 8 runs. 5 blocks of each
condition in each run.
All runs have all the conditions but I'm interested only in two
classifications and the differences between these classifications.
The order of trials is different across runs.
Some recommend I only permute the labels within runs, is this what you're
referring to? Is there a quick way to do that in pyMVPA?
On Wed, Aug 12, 2015 at 2:14 AM, Roni Maimon <ronimai...@gmail.com> wrote:
> Hi,
>
> Yaroslav and Richard, thank you so much for the quick and very helpful
> reply!
>
> Though I only received it through the daily summary, so I am sure this is
> the wrong way to reply.
>
> Yaroslav, regarding the permutator "dance", is it necessary in cases where
> I have several betas in each run?
>
> Thanks again for all the help.
>
> On Tue, Aug 11, 2015 at 8:18 PM, Roni Maimon <ronimai...@gmail.com> wrote:
>
>> Hi all,
>> I'm rather new to pyMVPA and I would love to get your help and feedback.
>> I'm trying do understand the different procedures of statistical
>> inference, I can achieve for whole brain searchlight analysis, using pyMVPA.
>>
>> I started by implementing the inference at the subject level (attaching
>> the code). Is this how I'm supposed to evaluate the p values of the
>> classifications for a single subject? What is the differences between
>> adding the null_dist to the sl level and the cross validation level?
>> My code:
>> clf = LinearCSVMC()
>> splt = NFoldPartitioner(attr='chunks')
>>
>> repeater = Repeater(count=100)
>> permutator = AttributePermutator('targets', limit={'partitions': 1},
>> count=1)
>> null_cv = CrossValidation(clf, ChainNode([splt,
>> permutator],space=splt.get_space()),
>> postproc=mean_sample())
>> null_sl = sphere_searchlight(null_cv, radius=3, space='voxel_indices',
>> enable_ca=['roi_sizes'])
>> distr_est = MCNullDist(repeater,tail='left', measure=null_sl,
>> enable_ca=['dist_samples'])
>>
>> cv = CrossValidation(clf,splt,
>> enable_ca=['stats'], postproc=mean_sample() )
>> sl = sphere_searchlight(cv, radius=3, space='voxel_indices',
>> null_dist=distr_est,
>> enable_ca=['roi_sizes'])
>> ds = glm_dataset.copy(deep=False,
>> sa=['targets','chunks'],
>> fa=['voxel_indices'],
>> a=['mapper'])
>> sl_map = sl(ds)
>> p_values = distr_est.cdf(sl_map.samples) # IS THIS THE RIGHT WAY??
>>
>> Is there a way to make sure the permutations are exhaustive?
>> In order to make an inference on the group level I understand I can
>> use GroupClusterThreshold.
>> Does anyone have a code sample for that? Do I use the MCNullDist's
>> created at the subject level?
>>
>> Thanks,
>> Roni.
>>
>
>
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