bicoherence: Estimate bicoherence from given time series data.
In rhosa: Higher-Order Spectral Analysis

Description Usage Arguments Value References Examples

Estimate magnitude-squared bicoherence from given real- or complex-valued time series data.

bicoherence(
  data,
  window_function = NULL,
  mc = FALSE,
  mc_cores = getOption("mc.cores", 2L),
  alpha = 0.05,
  p_adjust_method = "BH"
)

`data`	Given time series, as a data frame or matrix with which columns correspond to sampled stretches.
`window_function`	A window function's name for tapering. Defaults to `NULL` ("no tapering"). Currently the following window functions are available: Hamming window ("hamming"), Hann window ("hann"), and Blackman window ("blackman").
`mc`	If `TRUE`, calculation is done in parallel computation. Defaults to `FALSE`.
`mc_cores`	The number of cores in use for parallel computation, passed `parallel::mcmapply()` etc. as `mc.cores`.
`alpha`	The alpha level of the hypotesis test. Defaults to 0.05.
`p_adjust_method`	The correction method for p-values, given to `p.adjust()`. Defaults to "BH" (Benjamini and Hochberg). No correction if a non-character is given.

A data frame including the following columns:

f1:: The first elements of frequency pairs.
f2:: The second elements of frequency pairs.
value:: The estimate of magnitude-squared bicoherence at the respective frequency pair.
p_value:: The (corrected, if requested) p-value for hypothesis testing under null hypothesis that bicoherence is 0.
significance:: TRUE if the null hypothesis of the above hypothesis test is rejected with given alpha level.

Brillinger, D.R. and Irizarry, R.A. "An investigation of the second- and higher-order spectra of music." Signal Processing, Volume 65, Issue 2, 30 March 1998, Pages 161-179.

f <- function(x) {
    sin(2 * x) + sin(3 * x + 1) + sin(2 * x) * sin(3 * x + 1)
}
v <- sapply(seq_len(1280), f) + rnorm(1280)
m <- matrix(v, nrow = 128)
bc1 <- bicoherence(m)
bc2 <- bicoherence(m, "hamming")
bc3 <- bicoherence(m, "hann", mc = TRUE, mc_cores = 1L)