[pymvpa] zscore clarification and question

[Yaroslav Halchenko]

Hi J.A., The Great Thief of a Thread! ;)

I am sorry for our scarce docstring of zscore :-/  

pervoxel=False is actually what you are looking for I believe --
row-wise standartization. 
just be careful with it -- using it for generalization assessment is ok,
but features are not 'voxels' anylonger and you shouldn't look at their
sensitivities from then.


On Tue, 30 Nov 2010, J.A. Etzel wrote:

> Setting perchunk=False and pervoxel=True normalizes the entire
> column: each voxel has a mean of zero and standard deviation of one
> over all volumes in the dataset.

> Setting perchunk=False and pervoxel=False normalizes over all
> columns: all voxels together have a mean of zero and standard
> deviation of one over all volumes in the dataset.

> Setting perchunk=True and pervoxel=False normalizes over all columns
> within each chunk: all voxels together have a mean of zero and
> standard deviation of one over all volumes in each chunk.

> This gives various options for normalizing column-wise, but is it
> possible to normalize row-wise? In other words, normalize so that
> the voxels in each sample (volume) have a mean of zero and standard
> deviation one? Chunks are irrelevant for this case, since each
> sample is normalized separately. If this is already in pyMVPA, can
> you point me in the right direction?

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