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.
Thank you and very best wishes, Marco -- 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.ma...@hsr.it Skype: mtettamanti
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