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. Author manuscript; available in PMC: 2014 May 20.
Published in final edited form as: Neuroimage. 2013 Jun 27;83:189–199. doi: 10.1016/j.neuroimage.2013.06.056

Figure 4.

Figure 4

As the autoregressive order increases, for moderate timeseries lengths, CGC becomes much more adept at estimating the underlying true bivariate causality than GCCA. For each choice of method, length of timeseries N, and autoregressive order R, the bar is the mean error over simulations and the error bars describe the 95% confidence interval of error. For short timeseries, CGC seems to perform worse, but the mean value and variance of errors show that this drawback is minimal because both CGC and GCCA are equally inadequate at estimating the strength of the true causality.