[pymvpa] Searchlights and additional measures

[John Clithero]

Hi, Yaroslav,
Thanks.  Perhaps I was not clear.
I would certainly save all performance rates, I am familiar with
chance-distributions in the brain :-)
What I meant is that if the searchlight function is not currently set
up to allow me to save all the additional parameters what I want for a
large number of searchlights (e.g., weights, confusion matrices), then
it might be easier to create an ROI containing my voxels of interest
and then rerun those classifiers without the searchlight function.
But, your suggestion of mapper.getInId makes sense.
Thanks!
John

On Sun, Aug 23, 2009 at 2:31 PM, Yaroslav
Halchenko<debian at onerussian.com> wrote:
>
> On Sun, 23 Aug 2009, John Clithero wrote:
>> If I ran a searchlight analysis like the one I proposed, what would be
>> the best way to save the peak performing (or other voxels of interest)
>> voxel's coordinate/index and the index for the searchlight's
>> neighboring voxels? I could then, I suppose, rerun the classifier on
>> that searchlight ROI (without using the searchlight function) and
>> harvest other properties (e.g., weights and confusion matrix) that I
>> want.
>> Again, many thanks for this, I am trying to learn quickly!
> Sorry John, I do not quite understand what is the problem if any ;) you
> just store your results in whatever structure you like (e.g. list or
> ndarray), if you went through all indexes incrementally - you can simply
> then use mapper.getInId to get coordinates... or just map all results
> back into original 3D space and work there and get feature id for
> interesting voxel with getOutId.
>
> I would advise to store all performances through the whole brain/ROI
> since it might give you better sense of 'by chance' distribution (all
> those in the left tail of below-chance performances)
> --
>                                  .-.
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