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Joint distance distribution tests

JointDistanceDistributionTest

# CausalityTools.CausalityTests.JointDistanceDistributionTest — Type.

JointDistanceDistributionTest(; distance_metric = SqEuclidean(), B::Int = 10,
    D::Int = 2, τ::Int = 1)

The parameters for a joint distance distribution [1] analysis.

Optional keyword arguments

  • distance_metric: The distance metric used to compute distances. Has to be a instance of a valid distance metric from Distances.jl. Defaults to SqEuclidean().
  • B::Int: The number of equidistant subintervals to divide the interval [0, 1] into when comparing the normalised distances.
  • D::Int: The dimension of the delay reconstructions.
  • τ::Int: The delay of the delay reconstructions.

References

[1] Amigó, José M., and Yoshito Hirata. "Detecting directional couplings from multivariate flows by the joint distance distribution." Chaos: An Interdisciplinary Journal of Nonlinear Science 28.7 (2018): 075302.

source

JointDistanceDistributionTTest

# CausalityTools.CausalityTests.JointDistanceDistributionTTest — Type.

JointDistanceDistributionTTest(; distance_metric = SqEuclidean(), B::Int = 10,
    D::Int = 2, τ::Int = 1, 
    hypothesis_test::OneSampleTTest = OneSampleTTest,
    μ0 = 0.0)

The parameters for a joint distance distribution [1] analysis.

Optional keyword arguments

  • distance_metric: The distance metric used to compute distances. Has to be a instance of a valid distance metric from Distances.jl. Defaults to SqEuclidean().
  • B::Int: The number of equidistant subintervals to divide the interval [0, 1] into when comparing the normalised distances.
  • D::Int: The dimension of the delay reconstructions.
  • τ::Int: The delay of the delay reconstructions.
  • μ0: The hypothetical mean value of the joint distance distribution if there is no coupling between x and y (default is μ0 = 0.0).
  • hypothesis_test: A OneSampleTTest to test whether the joint distance distribution is skewed towards positive values.

References

[1] Amigó, José M., and Yoshito Hirata. "Detecting directional couplings from multivariate flows by the joint distance distribution." Chaos: An Interdisciplinary Journal of Nonlinear Science 28.7 (2018): 075302.

source