`library(rhosa)`

This is a simple example, based on the outline at Figure 1 of (Villa and Tetko 2010), which demonstrates how to use rhosa’s functions to find an obscure relationship between two frequencies in some time series imitated by a generative model.

With four cosinusoidal waves having arbitrarily different phases
(`omega_a`

, `omega_b`

, `omega_c`

, and
`omega_d`

), but sharing a couple of frequencies
(`f_1`

and `f_2`

), we define function
`D(t)`

to simulate a pair of time series: `v`

and
`w`

. We make them noisy by adding an independent random
variate that follows the standard normal distribution.

```
set.seed(1)
<- 0.35
f_1 <- 0.2
f_2 <- function(t) {
D <- runif(1, min = 0, max = 2 * pi)
omega_a <- runif(1, min = 0, max = 2 * pi)
omega_b <- runif(1, min = 0, max = 2 * pi)
omega_c <- runif(1, min = 0, max = 2 * pi)
omega_d <- function(t) cos(2 * pi * f_1 * t + omega_a)
wave_a <- function(t) cos(2 * pi * f_2 * t + omega_b)
wave_b <- function(t) cos(2 * pi * f_1 * t + omega_c)
wave_c <- function(t) cos(2 * pi * f_2 * t + omega_d)
wave_d <- function(t) wave_a(t) + wave_b(t) + wave_a(t) * wave_b(t)
curve_v <- function(t) wave_c(t) + wave_d(t) + wave_c(t) * wave_b(t)
curve_w data.frame(v = curve_v(t) + rnorm(length(t)),
w = curve_w(t) + rnorm(length(t)))
}
```

Both `v`

and `w`

are oscillatory in
principle:

```
<- D(seq_len(2048))
data with(data, {
plot(seq_len(100), head(v, 100), type = "l", col = "green", ylim = c(-3, 3), xlab = "t", ylab = "value")
lines(seq_len(100), head(w, 100), col = "orange")
})
```

It is noteworthy that the power spectrum densities of `v`

and `w`

are basically identical as shown in their spectral
density estimation:

```
with(data, {
spectrum(v, main = "v", col = "green")
spectrum(w, main = "w", col = "orange")
})
```

On the other hand, their bispectra are different. More specifically,
we are going to see that their bicoherence at some pairs of frequencies
are different. rhosa’s `bicoherence`

function allows us to
estimate the magnitude-squared bicoherence from samples.

```
<- replicate(100, D(seq_len(128)), simplify = FALSE)
x <- do.call(cbind, Map(function(d) {d$v}, x))
m_v <- do.call(cbind, Map(function(d) {d$w}, x))
m_w
library(rhosa)
<- bicoherence(m_v, window_function = 'hamming')
bc_v <- bicoherence(m_w, window_function = 'hamming') bc_w
```

In the above code, we take 100 samples of the same length for a
smoother result. The `bicoherence`

function accepts a matrix
whose column represents a sample sequence, and returns a data frame.
Note that an optional argument to `bicoherence`

is given for
requesting tapering with Hamming window
function.

```
library(ggplot2)
<- function(bc) {
plot_bicoherence ggplot(bc, aes(f1, f2)) +
geom_raster(aes(fill = value)) +
scale_fill_gradient(limits = c(0, 10)) +
coord_fixed()
}
```

The axis `f1`

and `f2`

represent normalized
frequencies in unit cycles/sample of range `[0, 1)`

.
Frequency pairs of bright points in the following plot of
`bc_v`

indicate the existence of some quadratic phase
coupling, as expected:

`plot_bicoherence(bc_v)`

In contrast, `bc_w`

has no peaks at frequency pair
`(f_1, f_2) = (0.35, 0.2)`

, etc.:

`plot_bicoherence(bc_w)`

Villa, Alessandro E. P., and Igor V. Tetko. 2010. “Cross-Frequency
Coupling in Mesiotemporal EEG Recordings of Epileptic
Patients.” *Journal of Physiology-Paris*, Neural
Coding, 104 (3): 197–202. https://doi.org/10.1016/j.jphysparis.2009.11.024.