[pymvpa] Searchlight statistical inference

[Roni Maimon]

Thank you so much Richard! This was super helpful!
One last question, do you know if the averaging can be done using the
command line without sparse ROI's? Maybe by using --scatter-rois 0? or is
it the default regardless to the input of scatter-rois?

And just to make sure I understand the scatter option: by using the same
value here and in the neighborhood size  the value of a centroid in the
original map is simply the accuracy of it's neighborhood since a centroid
of a calculated neighborhood can never(?) be a part of a different
neighborhood?

On Wed, Aug 12, 2015 at 5:36 PM, Roni Maimon <ronimaimon at gmail.com> wrote:
>
> Yaroslav, Thank you very much for the input.
>
> Richard, in the code you referred to it is stated:
> "The values mapped onto each voxel represent the mean accuracy across all
classification (spheres)
>
> a voxel was included in."
>
>
> How is this achieved? I scanned the code and nothing popped out but I
must be missing something.
> Thanks!
>
>
>
> On Wed, Aug 12, 2015 at 3:05 AM, Roni Maimon <ronimaimon at gmail.com> wrote:
>>
>> 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 <ronimaimon at 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 <ronimaimon at 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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