[pymvpa] mvpa2.clfs.transerror.chisquare

[marco tettamanti]

Dear Yaroslav,
thank you for your reply.
I might be wrong in the specific case of MVPA, but I think the 1-dimension Goodness-of-fit test is appropriate in case
you have something like one dice and you are expecting each of the 6 sides to occur with equal frequencies.
The N x N confusion matrix rather reflects the case in which you can a variable with N classes (targets) and you
measure how frequent these classes distribute across the levels of a different variable (predictions). In such a case,
a 2-dimension Pearson's test seems more appropriate.

Best,
Marco

On Thu, 11 May 2017, marco tettamanti wrote:

>/Dear all, />/I apologize if this has been asked before, or else is too trivial. /
>/I have been trying to understand how the the pymvpa2 toolbox calculates />/the chi-square test of a confusion matrix. /
>/In a cross-validation (e.g., cvte.ca.stats), it seems that by default this />/is done by means of a one-dimensional Goodness-of-fit chi-square test with />/expected uniform frequency distribution. /
>/I was wondering whether the bi-dimensional Pearson's chi square wouldn't />/be more appropriate, as it seems to me that this would more closely />/reflect the "predictions vs targets N x N" matrix structure. /
Hi Marco,

might as well be -- I would need to read on/check... IIRC we were just
following instructions on chi-square test to be done on contingency
tables.

-- 
Yaroslav O. Halchenko
Center for Open Neurosciencehttp://centerforopenneuroscience.org
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         


-- 
Marco Tettamanti, Ph.D.
Nuclear Medicine Department & Division of Neuroscience
IRCCS San Raffaele Scientific Institute
Via Olgettina 58
I-20132 Milano, Italy
Phone ++39-02-26434888
Fax ++39-02-26434892
Email: tettamanti.marco at hsr.it
Skype: mtettamanti

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