mvpa2.measures.statsmodels_adaptor.GLM

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

Adaptor to the statsmodels-based UnivariateStatsModels

This class is deprecated and only here to ease the transition of user code to the new classes. For all new code, please use the UnivariateStatsModels class.

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

generate(ds)	Yield processing results.
get_postproc()	Returns the post-processing node or None.
get_space()	Query the processing space name of this node.
reset()
set_postproc(node)	Assigns a post-processing node
set_space(name)	Set the processing space name of this node.
train(ds)	The default implementation calls _pretrain(), _train(), and finally _posttrain().
untrain()	Reverts changes in the state of this node caused by previous training

Initialize instance of GLM

Parameters:

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

exog : array-like

Column ordered (observations in rows) design matrix.

model_gen : callable

Callable that returns a StatsModels model when called like model_gen(endog, exog).

res : {‘params’, ‘tvalues’, ...} or 1d array or 2d array or callable

Variable of interest that should be reported as feature-wise measure. If a str, the corresponding attribute of the model fit result class is returned (e.g. ‘tvalues’). If a 1d-array, it is passed to the fit result class’ t_test() function as a t-contrast vector. If a 2d-array, it is passed to the f_test() function as a contrast matrix. In both latter cases a number of common test statistics are returned in the rows of the result dataset. A description is available in the ‘descr’ sample attribute. Any other datatype passed to this argument will be treated as a callable, the model fit result is passed to it, and its return value(s) is aggregated in the result dataset.

add_constant : bool, optional

If True, a constant will be added to the design matrix that is passed to exog.

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.

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

generate(ds)	Yield processing results.
get_postproc()	Returns the post-processing node or None.
get_space()	Query the processing space name of this node.
reset()
set_postproc(node)	Assigns a post-processing node
set_space(name)	Set the processing space name of this node.
train(ds)	The default implementation calls _pretrain(), _train(), and finally _posttrain().
untrain()	Reverts changes in the state of this node caused by previous training