analysis.granger

Module: analysis.granger

Inheritance diagram for nitime.analysis.granger:

Inheritance diagram of nitime.analysis.granger

Analyzers for the calculation of Granger ‘causality’

GrangerAnalyzer

class nitime.analysis.granger.GrangerAnalyzer(input=None, ij=None, order=None, max_order=10, criterion=<function bayesian_information_criterion>, n_freqs=1024)

Bases: nitime.analysis.base.BaseAnalyzer

Analyzer for computing all-to-all Granger ‘causality’

Attributes

Methods

__init__(input=None, ij=None, order=None, max_order=10, criterion=<function bayesian_information_criterion>, n_freqs=1024)

Initializer for the GrangerAnalyzer.

Parameters:

input: nitime TimeSeries object

ij: List of tuples of the form: [(0, 1), (0, 2)], etc.

These are the indices of pairs of time-series for which the analysis will be done. Defaults to all vs. all.

order: int (optional)

The order of the process. If this is not known, it will be estimated from the data, using the information criterion

max_order: if the order is estimated, this is the maximal order to

estimate for.

n_freqs: int (optional)

The size of the sampling grid in the frequency domain. Defaults to 1024

criterion:

XXX

autocov()
causality_xy()
causality_yx()
error_cov()
frequencies()
model_coef()
order()
simultaneous_causality()
spectral_matrix()
nitime.analysis.granger.fit_model(x1, x2, order=None, max_order=10, criterion=<function bayesian_information_criterion>)

Fit the auto-regressive model used in calculation of Granger ‘causality’.

Parameters:

x1,x2: float arrays (n)

x1,x2 bivariate combination.

order: int (optional)

If known, the order of the autoregressive process

max_order: int (optional)

If the order is not known, this will be the maximal order to fit.

criterion: callable

A function which defines an information criterion, used to determine the

order of the model.