rsa.PDTS multiple regression #373
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| @@ -199,12 +202,15 @@ class PDistTargetSimilarity(Measure): | |||
| If True, return only the correlation coefficient (rho), otherwise | |||
| return rho and probability, p.""") | |||
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| def __init__(self, target_dsm, **kwargs): | |||
| def __init__(self, target_dsm, control_dsms = None, **kwargs): | |||
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very minor nit-picking: there should be no spaces around = within keyword definitions of 'def'.
Also all the optional parameters to this, as you can see above this line, specified as "Parameters".
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benefit is that it then automagically be listed by __str__ and __repr__ of this method
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and now we would need to get some testing done -- look/extend |
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@rystoli Are you interested in finishing this PR up? |
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Hi all, Sorry for the delay. We are trying to sort out whether we should set up something to take into account concerns of certain distance types linearly summing and if that should affect how neural distance is computed when using multiple regression (eg). Also, I actually have to confess I'm at a loss for how exactly these tests are run that Yarik forwarded to me (he asked to to extend on these): mvpa2/tests/test_rsa.py. I am relatively amateur still with my coding chops and am not sure what / how to implement these. Any help would be appreciated! Thanks! |
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example of the distance summation concern: http://biorxiv.org/content/biorxiv/early/2015/10/21/029603.full.pdf |
Adds optional argument to rsa.PDistTargetSimilarity 'control_dsms', allowing RSA with multiple regression - looking at relation between neural and 'target DMs, controlling for other DMs (e.g., you may wish to control for multiple visual model DMs when looking at similarity structure in ventral temporal cortex).