[pymvpa] Sensitivity analysis with GNB

[Thomas Nickson]

Oh wait. I think you just mean that I can calculate the f-test of the
dataset and this will give me the sensitivity measure, regardless of the
result of the GNB classifier. Is this correct?

On Mon, Dec 15, 2014 at 11:52 AM, Thomas Nickson <thomas.nickson at gmail.com>
wrote:
>
>
>
> On Fri, Dec 12, 2014 at 3:30 PM, Thomas Nickson <thomas.nickson at gmail.com>
> wrote:
>>
>> On Fri, Dec 12, 2014 at 3:17 PM, Nick Oosterhof <
>> n.n.oosterhof at googlemail.com> wrote:
>>
>>>
>>> On 12 Dec 2014, at 16:13, Thomas Nickson <thomas.nickson at gmail.com>
>>> wrote:
>>>
>>> >> >    # XXX Later come up with some
>>> >> >    # could be a simple t-test maps using distributions
>>> >> >    # per each class
>>> >>
>>> >> yeah -- something like that might prove being useful, but probably not
>>> >> too far from omnibus or ad-hoc Anovas which we have.  So that is why
>>> >> they were never implemented
>>> > You mean using ANOVAs to calculate the sensitivity? How do I do that?
>>>
>>> See e.g.
>>> http://www.pymvpa.org/generated/mvpa2.measures.anova.OneWayAnova.html
>>>
>>> Since the GNB classifier works feature-wise (does not consider any
>>> covariance across voxels) the F value is a measure of how sensitive a
>>> feature is in discriminating between the classes.
>>>
>>
>>
> What's the best way to use this module for this? Do I have to take the
> GNB.means and then make a dataset where the targets are just [0,1] or is
> there a better way?
>
> Thanks.
>
>
>
>>
>>
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>>>
>>
>>
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