mvpa2.measures.anova.CompoundOneWayAnova¶
- class mvpa2.measures.anova.CompoundOneWayAnova(space='targets', **kwargs)¶
Compound comparisons via univariate ANOVA.
This measure compute an ANOVA F-score per each feature, for each one-vs-rest comparision for all unique labels in a dataset. Each F-score vector for each comparision is included in the return datasets as a separate samples. Corresponding p-values are avialable in feature attributes named ‘fprob_X’, where X is the name of the actual comparision label. Note that p-values are only available, if SciPy is installed. The comparison labels for each F-vectore are also stored as ‘targets’ sample attribute in the returned dataset.
Notes
Available conditional attributes:
- calling_time+: Time (in seconds) it took to call the node
- null_prob+: None
- null_t: None
- 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
(Conditional attributes enabled by default suffixed with +)
Parameters : space : str
What samples attribute to use as targets (labels).
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.
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
