BinnedDataCausalityTest

# CausalityTools.CausalityTests.BinnedDataCausalityTest — Type.

BinnedDataCausalityTest(test::CT, binning::AbstractBinnedUncertainValueResampling, n_realizations::Int)
BinnedDataCausalityTest(test::CT, binning::AbstractBinnedSummarisedResampling)

A causality test where the data are binned before applying the test. If binning is some binning scheme that returns an uncertain value for each bin (e.g. BinnedResampling or BinnedWeightedResampling), then the test is applied n_realizations times. If binning returns a single value for each bin (e.g. BinnedMeanResampling or BinnedMeanWeightedResampling, the test is applied only once.

Fields

  • test::CausalityTest. An instance of a causality test, e.g. VisitationFrequencyTest, PredictiveAsymmetryTest, CrossMappingTest or JointDistanceDistributionTest.
  • binning::AbstractBinnedResampling. An instance of a resampling scheme indicating the type of binning, e.g BinnedResampling, BinnedWeightedResampling, BinnedMeanResampling or BinnedMeanWeightedResampling.
  • n_realizations::Int: The number of independent draws of the binned data set over which to compute the causality test. Only taken into consideration of binning is a scheme resulting in each bin being represented by an uncertain value. If bins are summarized, then the test is always applied only once.

Examples

Binned convergent cross mapping test

Let's say we want to bin the data by drawing n_draws = 5000 realiastions of each uncertain data point, then assign the draws to the correct bins, and finally get a kernel density estimate to the distribution of values in each bin.

grid = 0:10:1000 # left bin edges
n_draw = 5000 # sample each point 5000 times and distribute among bins
binning = BinnedResampling(grid, 5000)

ccm_test = ConvergentCrossMappingTest(timeseries_lengths = 15:5:100 |> collect, 
    n_reps = 100, libsize = 100, τ = 1, replace = true)

test = BinnedDataCausalityTest(ccm_test, binning, n_realizations)

Binned transfer entropy test

Let's say we want to bin the data by drawing n_draws = 7500 realiastions of each uncertain data point, then assign the draws to the correct bins, and finally get a kernel density estimate to the distribution of values in each bin.

grid = 0:5:1000 # left bin edges
n_draws = 7500
binning = BinnedResampling(grid, n_draws)

# A transfer entropy causality test using the visitation frequency estimator
state_space_binning = RectangularBinning(5)
te_test = VisitationFrequencyTest(binning = state_space_binning, ηs = 1)

test = BinnedDataCausalityTest(te_test, binning, n_realizations)

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