Package mvpa :: Package measures :: Module anova :: Class OneWayAnova

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# Class OneWayAnova

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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.

Nested Classes | [hide private]

Inherited from `misc.state.ClassWithCollections`: `__metaclass__`

Instance Methods | [hide private]

_call(self, dataset, labels=None) Computes a per-feature-measure on a given Dataset. | source code

Inherited from `base.FeaturewiseDatasetMeasure`: `__init__`, `__repr__`, `combiner`

Inherited from `base.FeaturewiseDatasetMeasure` (private): `_postcall`

Inherited from `base.DatasetMeasure`: `__call__`, `null_dist`, `transformer`, `untrain`

Inherited from `misc.state.ClassWithCollections`: `__getattribute__`, `__new__`, `__setattr__`, `__str__`, `reset`

Inherited from `object`: `__delattr__`, `__format__`, `__hash__`, `__reduce__`, `__reduce_ex__`, `__sizeof__`, `__subclasshook__`

Class Variables | [hide private]

Inherited from `base.FeaturewiseDatasetMeasure`: `base_sensitivities` Inherited from `base.DatasetMeasure`: `__doc__`, `null_prob`, `null_t`, `raw_results` Inherited from `misc.state.ClassWithCollections`: `_DEV__doc__`, `descr`

Properties | [hide private]

Inherited from `object`: `__class__`

Method Details | [hide private]

### _call(self, dataset, labels=None)

source code

Computes a per-feature-measure on a given Dataset.

Behaves like a DatasetMeasure, but computes and returns a 1d ndarray with one value per feature.

Overrides: base.DatasetMeasure._call
  (inherited documentation)