mvpa2.measures.glm.GLM

Inheritance diagram of GLM

class mvpa2.measures.glm.GLM(design, voi='pe', **kwargs)

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.

Notes

Available conditional attributes:

  • calling_time+: Time (in seconds) it took to call the node
  • null_prob+: None
  • null_t: None
  • pe: Parameter estimates (nfeatures x nparameters).
  • raw_results: Computed results before invoking postproc. Stored only if postproc is not None.
  • training_time+: Time (in seconds) it took to train the learner
  • zstat: Standardized parameter estimates (nfeatures x nparameters).

(Conditional attributes enabled by default suffixed with +)

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_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

null_dist : instance of distribution estimator

The estimated distribution is used to assign a probability for a certain value of the computed measure.

auto_train : bool

Flag whether the learner will automatically train itself on the input dataset when called untrained.

force_train : bool

Flag whether the learner will enforce training on the input dataset upon every call.

space: str, optional :

Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.

postproc : Node instance, optional

Node to perform post-processing of results. This node is applied in __call__() to perform a final processing step on the to be result dataset. If None, nothing is done.

descr : str

Description of the instance

is_trained = True

Indicate that this measure is always trained.

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NITRC-listed