[pymvpa] z-score before or after removal of junk data

mhampel at uni-osnabrueck.de mhampel at uni-osnabrueck.de
Tue Dec 3 14:00:24 UTC 2013


Thanks a lot! Now I understand why all these parameters are used in the
example scripts.

Best,
Matthias

>
> On Mon, 02 Dec 2013, mhampel at uni-osnabrueck.de wrote:
>
>> Dear all,
>
>> I wondered if there is a good reason to keep not needed data for
>> z-scoring? In the searchlight and sensitivity measures examples the
>> order
>> is like this:
>
>> 1. zscore(dataset, dtype='float32', chunks_attr='chunks')
>> 2. dataset = datatset[dataset.sa.targets != '0']
>
>> But if I reverse the order, my accuracy values are much higher (10-20%)
>> when classifying on whole brain. For searchlights the accuracies are
>> also
>> higher. On a searchlight group level I therefore need a much higher
>> threshold for the p-values, with a higher threshold I get similar
>> results.
>
>> Is it okay to do the z-score after removing the junk data?
>
> it should be perfectly fine and probably advisable if your "junk data"
> is indeed "junk data".
>
> In some cases those "targets == '0'" are meaningful samples (e.g.
> "control condition") which of no direct important for MVPA but could
> provide you with better sense of variability among voxels etc...
> moreover someone might be interested to standardize  !=0 samples against
> baseline's offset/variance, thus specifying that condition to zscore's
> param_est.  That is why 'in general' we kept zscoring before selecting
> only relevant categories.
>
> hope this helps ;)
> --
> Yaroslav O. Halchenko, Ph.D.
> http://neuro.debian.net http://www.pymvpa.org http://www.fail2ban.org
> Senior Research Associate,     Psychological and Brain Sciences Dept.
> Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
> Phone: +1 (603) 646-9834                       Fax: +1 (603) 646-1419
> WWW:   http://www.linkedin.com/in/yarik
>
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