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Surrogate-based significance test for coupling matrices

Source: R/stats-significance.R
surrogateMatrixTest.Rd

Tests each element of a coupling matrix for significance using surrogate testing, with correction for multiple comparisons (FDR or Bonferroni).

Usage

surrogateMatrixTest(
  x,
  y = NULL,
  method,
  n_surrogates = 199L,
  surrogate_type = c("phase", "timeshift", "iaaft", "aaft"),
  correction = c("fdr", "bonferroni", "none"),
  alpha = 0.05,
  channels_x = NULL,
  channels_y = NULL,
  modality_x = NULL,
  modality_y = NULL,
  cores = 1L,
  ...
)

Arguments

x

PhysioExperiment or MultiPhysioExperiment.

y

PhysioExperiment or NULL (when x is an MPE).

method

Character coupling method (same options as couplingAnalysis).

n_surrogates

Integer number of surrogates (default 199).

surrogate_type

Character surrogate generation method: "phase" (default; Fourier phase randomization), "timeshift" (circular time shift), "iaaft" (iterative amplitude-adjusted Fourier transform, preserving the power spectrum and value distribution; Schreiber & Schmitz, 1996), or "aaft" (non-iterative amplitude-adjusted FT).

correction

Character correction method: "fdr" (default), "bonferroni", or "none".

alpha

Numeric significance level (default 0.05).

channels_x, channels_y

Integer vectors of channel indices, or NULL for all channels.

modality_x, modality_y

Character modality names for MPE input.

cores

Integer number of parallel cores (default 1).

...

Additional arguments passed to the coupling function.

Value

A list with components:

matrix

Coupling matrix (observed values).

p_values

Matrix of raw p-values (same dimensions).

p_adjusted

Matrix of corrected p-values.

significant

Logical matrix indicating significance.

correction

Character correction method used.

alpha

Numeric significance level.

References

Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., & Farmer, J. D. (1992). Testing for nonlinearity in time series: the method of surrogate data. Physica D: Nonlinear Phenomena, 58(1–4), 77–94.

Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300.

See also

surrogateTest(), couplingMatrix(), coherenceMatrix()