elephant.causality.granger.conditional_granger¶

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 [gr1]. Specifically, the Eq 35.