mvpa.measures.anova

FeaturewiseDatasetMeasure performing a univariate ANOVA.

The comprehensive API documentation for this module, including all technical details, is available in the Epydoc-generated API reference for mvpa.measures.anova (for developers).

OneWayAnova

class mvpa.measures.anova.OneWayAnova(**kwargs)

Bases: mvpa.measures.base.FeaturewiseDatasetMeasure

FeaturewiseDatasetMeasure that performs a univariate ANOVA.

F-scores are computed for each feature as the standard fraction of between and within group variances. Groups are defined by samples with unique labels.

No statistical testing is performed, but raw F-scores are returned as a sensitivity map. As usual F-scores have a range of [0,inf] with greater values indicating higher sensitivity.

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_result: 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

Nothing special to do here.

Parameters:

• 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.

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the OneWayAnova documentation.

Full API documentation of OneWayAnova in module mvpa.measures.anova.