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+: None
• null_prob+: None
• null_t: None
• raw_results: None
• trained_dataset: None
• trained_nsamples+: None
• trained_targets+: None
• training_time+: None

(Conditional attributes enabled by default suffixed with +)

Methods

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.

pass_attr : str, list of str|tuple, optional

Additional attributes to pass on to an output dataset. Attributes can be taken from all three attribute collections of an input dataset (sa, fa, a – see Dataset.get_attr()), or from the collection of conditional attributes (ca) of a node instance. Corresponding collection name prefixes should be used to identify attributes, e.g. ‘ca.null_prob’ for the conditional attribute ‘null_prob’, or ‘fa.stats’ for the feature attribute stats. In addition to a plain attribute identifier it is possible to use a tuple to trigger more complex operations. The first tuple element is the attribute identifier, as described before. The second element is the name of the target attribute collection (sa, fa, or a). The third element is the axis number of a multidimensional array that shall be swapped with the current first axis. The fourth element is a new name that shall be used for an attribute in the output dataset. Example: (‘ca.null_prob’, ‘fa’, 1, ‘pvalues’) will take the conditional attribute ‘null_prob’ and store it as a feature attribute ‘pvalues’, while swapping the first and second axes. Simplified instructions can be given by leaving out consecutive tuple elements starting from the end.

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

Methods