mvpa2.measures.statsmodels_adaptor.UnivariateStatsModels¶

class
mvpa2.measures.statsmodels_adaptor.
UnivariateStatsModels
(exog, model_gen, res='params', add_constant=True, **kwargs)¶ Adaptor for some models from the StatsModels package
This adaptor allows for fitting several statistical models to univariate (in StatsModels terminology “endogeneous”) data. A model, based on “exogeneous” data (i.e. a design matrix) and optional parameters, is fitted to each feature vector in a given dataset individually. The adaptor supports a variety of models provided by the StatsModels package, including simple ordinary least squares (OLS), generalized least squares (GLS) and others. This featurewise measure can extract a variety of properties from the model fit results, and aggregate them into a result dataset. This includes, for example, all attributes of a StatsModels
RegressionResult
class, such as model parameters and their error estimates, Aikake’s information criteria, and a number of statistical properties. Moreover, it is possible to perform tcontrasts/ttests of parameter estimates, as well as Ftests for contrast matrices.Notes
Available conditional attributes:
calling_time+
: Nonenull_prob+
: Nonenull_t
: Noneraw_results
: Nonetrained_dataset
: Nonetrained_nsamples+
: Nonetrained_targets+
: Nonetraining_time+
: None
(Conditional attributes enabled by default suffixed with
+
)Examples
Some example data: two features, seven samples
>>> endog = Dataset(np.transpose([[1, 2, 3, 4, 5, 6, 8], ... [1, 2, 1, 2, 1, 2, 1]])) >>> exog = range(7)
Set up a model generator – it yields an instance of an OLS model for a particular design and feature vector. The generator will be called internally for each feature in the dataset.
>>> model_gen = lambda y, x: sm.OLS(y, x)
Configure the adaptor with the model generator and a common design for all feature model fits. Tell the adaptor to autoadd a constant to the design.
>>> usm = UnivariateStatsModels(exog, model_gen, add_constant=True)
Run the measure. By default it extracts the parameter estimates from the models (two per feature/model: regressor + constant).
>>> res = usm(endog) >>> print res <Dataset: 2x2@float64, <sa: descr>> >>> print res.sa.descr ['params' 'params']
Alternatively, extract tvalues for a test of all parameter estimates against zero.
>>> usm = UnivariateStatsModels(exog, model_gen, res='tvalues', ... add_constant=True) >>> res = usm(endog) >>> print res <Dataset: 2x2@float64, <sa: descr>> >>> print res.sa.descr ['tvalues' 'tvalues']
Compute a tcontrast: first parameter is nonzero. This returns additional test statistics, such as pvalue and effect size in the result dataset. The contrast vector is pass on to the
t_test()
function (r_matrix
argument) of the StatsModels result class.>>> usm = UnivariateStatsModels(exog, model_gen, res=[1,0], ... add_constant=True) >>> res = usm(endog) >>> print res <Dataset: 6x2@float64, <sa: descr>> >>> print res.sa.descr ['tvalue' 'pvalue' 'effect' 'sd' 'df' 'zvalue']
Ftest for a contrast matrix, again with additional test statistics in the result dataset. The contrast vector is pass on to the
f_test()
function (r_matrix
argument) of the StatsModels result class.>>> usm = UnivariateStatsModels(exog, model_gen, res=[[1,0],[0,1]], ... add_constant=True) >>> res = usm(endog) >>> print res <Dataset: 4x2@float64, <sa: descr>> >>> print res.sa.descr ['fvalue' 'pvalue' 'df_num' 'df_denom']
For any custom result extraction, a callable can be passed to the
res
argument. This object will be called with the result of each model fit. Its return value(s) will be aggregated into a result dataset.>>> def extractor(res): ... return [res.aic, res.bic] >>> >>> usm = UnivariateStatsModels(exog, model_gen, res=extractor, ... add_constant=True) >>> res = usm(endog) >>> print res <Dataset: 2x2@float64>
Methods
Parameters: exog : arraylike
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 featurewise measure. If a str, the corresponding attribute of the model fit result class is returned (e.g. ‘tvalues’). If a 1darray, it is passed to the fit result class’
t_test()
function as a tcontrast vector. If a 2darray, it is passed to thef_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
.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 subclass 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 strtuple, 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 postprocessing 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

is_trained
= True¶