| Multivariate Pattern Analysis in Python

### Navigation

- index
- next |
- previous |
- ExpPsy Project |
- NeuroDebian Project |
- PyMVPA 0.4.x Home |
- PyMVPA Home |
- Documentation »
- Module Reference »

This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.

# measures.anova

## Module: `measures.anova`

Inheritance diagram for `mvpa.measures.anova`:

FeaturewiseDatasetMeasure performing a univariate ANOVA.

## Classes

### `CompoundOneWayAnova`

class `mvpa.measures.anova.``CompoundOneWayAnova`(combiner=<function SecondAxisSumOfAbs at 0xa990dbc>, **kwargs)

  Bases: `mvpa.measures.anova.OneWayAnova`

  Compound comparisons via univariate ANOVA.

  Provides F-scores per each label if compared to the other labels.

  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:

  `OneWayAnova`

  Initialize

  Parameters:

  - 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.
  - 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
  - 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.

### `OneWayAnova`

class `mvpa.measures.anova.``OneWayAnova`(combiner=<function SecondAxisSumOfAbs at 0xa990dbc>, **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_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:

  - 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.
  - 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
  - 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.

### Navigation

- index
- next |
- previous |
- ExpPsy Project |
- NeuroDebian Project |
- PyMVPA 0.4.x Home |
- PyMVPA Home |
- Documentation »
- Module Reference »