analysis.granger¶
Module: analysis.granger
¶
Inheritance diagram for 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’
- __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.