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measures.corrcoef

Module: measures.corrcoef

Inheritance diagram for mvpa.measures.corrcoef:

FeaturewiseDatasetMeasure of correlation with the labels.

CorrCoef

class mvpa.measures.corrcoef.CorrCoef(pvalue=False, attr='labels', **kwargs)

Bases: mvpa.measures.base.FeaturewiseDatasetMeasure

FeaturewiseDatasetMeasure that performs correlation with labels

XXX: Explain me!

Note

Available state variables:

• base_sensitivities: Stores basic sensitivities if the sensitivity relies on combining multiple ones
• null_prob+: State variable
• null_t: State variable
• raw_results: Computed results before applying any transformation algorithm

(States enabled by default are listed with +)

See also

Please refer to the documentation of the base class for more information:

FeaturewiseDatasetMeasure

Initialize

Parameters:

• pvalue (bool) – Either to report p-value of pearsons correlation coefficient instead of pure correlation coefficient
• attr (basestring) – What attribut to correlate with
• enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
• disable_states (None or list of basestring) – Names of the state variables which should be disabled
• combiner (Functor) – The combiner is only applied if the computed featurewise dataset measure is more than one-dimensional. This is different from a transformer, which is always applied. By default, the sum of absolute values along the second axis is computed.
• transformer (Functor) – This functor is called in __call__() to perform a final processing step on the to be returned dataset measure. If None, nothing is called
• null_dist (instance of distribution estimator) – The estimated distribution is used to assign a probability for a certain value of the computed measure.