elephant.causality.granger.conditional_granger¶
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elephant.causality.granger.
conditional_granger
(signals, max_order, information_criterion='aic')[source]¶ Determine conditional Granger Causality of the second time series on the first time series, given the third time series. In other words, for time series X_t, Y_t and Z_t, this function tests if Y_t influences X_t via Z_t.
Parameters: - signals(N, 3) np.ndarray or neo.AnalogSignal
A matrix with three time series (second dimension) that have N time points (first dimension). The time series to be conditioned on is the third.
- max_orderint
Maximal order of autoregressive model.
- information_criterion{‘aic’, ‘bic’}, optional
- A function to compute the information criterion:
bic for Bayesian information_criterion, aic for Akaike information criterion,
Default: ‘aic’.
Returns: - conditional_causality_xy_z_roundfloat
The value of conditional causality of Y_t on X_t given Z_t. Zero value indicates that causality of Y_t on X_t is solely dependent on Z_t.
Raises: - ValueError
If the provided signal does not have a shape of Nx3.
Notes
The formulas used in this implementation follows [gr3]. Specifically, the Eq 35.