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NearestNeighbourMITest

# CausalityTools.CausalityTests.NearestNeighbourMITest — Type.

NearestNeighbourMITest(; k::Int = 1, l::Int = 1, m::Int = 1, n::Int = 1; τ::Int = 1,
    estimator::NearestNeighbourMI = NearestNeighbourMI(b = 2, k1 = 2, k2 = 3, metric = Chebyshev()),
    ηs::Union{Int, AbstractVector{Int}})

The parameters for a transfer entropy test using the NearestNeighbourMI estimator that estimates mutual information terms using the counting of nearest neighbours.

Mandatory keyword arguments

• ηs: The prediction lags (that gos into the $T_{f}$ component of the embedding).

Optional keyword arguments

• k::Int: The dimension of the $T_{f}$ component of the embedding.
• l::Int: The dimension of the $T_{pp}$ component of the embedding.
• m::Int: The dimension of the $S_{pp}$ component of the embedding.
• n::Int: The dimension of the $C_{pp}$ component of the embedding.
• τ::Int: The embedding lag. Default is τ = 1.
• metric: The distance metric to use for mutual information estimation.
• k1: The number of nearest neighbours for the highest-dimensional mutual information estimate. To minimize bias, choose $k_1 < k_2$ if $min(k_1, k_2) < 10$ (see fig. 16 in [1]). Beyond dimension 5, choosing $k_1 = k_2$ results in fairly low bias, and a low number of nearest neighbours, say k1 = k2 = 4, will suffice.
• k2: The number of nearest neighbours for the lowest-dimensional mutual information estimate. To minimize bias, choose $k_1 < k_2$ if if $min(k_1, k_2) < 10$ (see fig. 16 in [1]). Beyond dimension 5, choosing $k_1 = k_2$ results in fairly low bias, and a low number of nearest neighbours, say k1 = k2 = 4, will suffice.

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