test_sop_bootstrap

docblock

test_sop_bootstrap(data, n_boot, d1, d2; chart_choice=TauTilde(),
  refinement=OrdinaryType(), alpha=0.05, block_size=1)

Compute a bootstrap hypothesis test for spatial ordinal patterns and return an SOPTestResultBoot with the bootstrap critical value, p-value, and reject decision.

For Tau/Kappa charts the raw statistic is used. For entropy charts (Shannon, ShannonExtropy, DistanceToWhiteNoise) the same rescale_sop transformation used by test_sop is applied so that the bootstrap critical value is on the same scale as the asymptotic critical value and the two can be compared directly.

data: the 2D image (M × N matrix).
n_boot: number of bootstrap replications.
d1, d2: row and column delays.
chart_choice=TauTilde(): one of TauHat(), KappaHat(), TauTilde(), KappaTilde(), Shannon(), ShannonExtropy(), DistanceToWhiteNoise(). For Shannon and ShannonExtropy, the logarithm base must be larger than 1. The statistic does not depend on it.
refinement=OrdinaryType(): OrdinaryType() for the classical SOP classification, or one of RotationType(), DirectionType(), DiagonalType().
alpha=0.05: significance level.
block_size=1: set > 1 for a 2D block bootstrap that preserves spatial dependencies.