| Multivariate Pattern Analysis in Python

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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.glm

## Module: `measures.glm`

Inheritance diagram for `mvpa.measures.glm`:

The general linear model (GLM).

## `GLM`

class `mvpa.measures.glm.``GLM`(design, voi='pe', **kwargs)

  Bases: `mvpa.measures.base.FeaturewiseDatasetMeasure`

  General linear model (GLM).

  Regressors can be defined in a design matrix and a linear fit of the data is computed univariately (i.e. indepently for each feature). This measure can report ‘raw’ parameter estimates (i.e. beta weights) of the linear model, as well as standardized parameters (z-stat) using an ordinary least squares (aka fixed-effects) approach to estimate the parameter estimate.

  The measure is reported in a (nfeatures x nregressors)-shaped array.

  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
  - pe: Parameter estimates (nfeatures x nparameters).
  - raw_results: Computed results before applying any transformation algorithm
  - zstat: Standardized parameter estimates (nfeatures x nparameters).

  (States enabled by default are listed with +)

  See also

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

  `FeaturewiseDatasetMeasure`

  Parameters:

  - design (array(nsamples x nregressors)) – GLM design matrix.
  - voi (‘pe’ | ‘zstat’) – Variable of interest that should be reported as feature-wise measure. ‘beta’ are the parameter estimates and ‘zstat’ returns standardized parameter estimates.
  - 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.

### Navigation

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