Package 'sdf.test'

EEG Signals

Description

A dataset containing four EEG recordings of a patient under rest or different transcranial Direct Current Stimulations (tDCS).

Usage

eeg_data

Format

A data frame with columns:

index

Integer index of the measurement (frequency of 250 Hz).

condition

Can take four values: "initial_rest", devtools::document() "first_frontal_tDCS", "second_frontal_tDCS", "posterior_tDCS".

signal

Numeric EEG measurement.

Source

Original: Alexis Pomares, "Dissertation preprocessed EEG dataset", https://www.kaggle.com/datasets/alexispomares/dissertation-preprocessed This version has been preprocessed by selecting specific conditions and removing outliers, for details see (Nadin, Kivobokova, Enikeeva; 2026).

Examples

data(eeg_data)
head(eeg_data)
tail(eeg_data)
str(eeg_data)
X1 <- subset(eeg_data, condition == "first_frontal_tDCS")$signal

---

Print test

Description

Custom printing function for the class "test".

Usage

## S3 method for class 'test'
print(x, ...)

Arguments

x	Object of class "test".
...	Further arguments passed to or from other methods.

Value

Invisible x.

Examples

test <- sdf.test(
  X1 = rnorm(50), X2 = rnorm(50),
  q1 = 2, method1 = "GCV", Te = 10, alpha = 0.05, N = 1000
)
print(test)
test

---

SDF Test

Description

Performs the test for equality of spectral densities of two time series.

Usage

sdf.test(
  X1,
  X2,
  Te,
  alpha,
  q1 = 4,
  method1 = "GCV",
  f1_true = NULL,
  q2 = q1,
  method2 = method1,
  f2_true = f1_true,
  N = 10000,
  cores = 1,
  nulldistr = NULL
)

Arguments

X1	Longest time series.
X2	Shortest time series.
Te	Integer. Number of bins.
alpha	Numeric. Significance level for the test.
q1	Integer. Penalisation order for X1, q=1,2,3,4,5,6 are available (default 4).
method1	Character. Method for selecting the smoothing parameter for X1 (default "GCV").
f1_true	Vector or NULL. True regression function evaluated at equi-spaced points. Required only if method1 is set to "GCV-oracle" or "ML-oracle".
q2	Integer. Penalization order for X2 (default q1).
method2	Character. Method for selecting the smoothing parameter for X2 (default method1).
f2_true	Vector or NULL. True regression function evaluated at equi-spaced points. Required only if method2 is set to "GCV-oracle" or "ML-oracle" (default f1_true).
N	Integer. Number of iterations for null distribution computa- tion (default 10000).
cores	Integer. Number of cores for parallel computation of null distribution (default 1).
nulldistr	Vector or NULL. Vector of empirical null distribution (default NULL).

Value

An object of class "test", which is a list containing the following components:

• result: Logical. TRUE if the null hypothesis of equality of spectral densities is accepted by the test, FALSE otherwise.

• S: Value of the statistic.

• quantile: Empirical quantile used for the test.

• p-value: p-value of the test according to the empirical null distribution.

• nulldistr: Vector of empirical null distribution.

References

Nadin, Krivobokova, Enikeeva (2026). Nonparametric two sample test of spectral densities. https://arxiv.org/abs/2602.10774

Examples

# --- Example 1: Using random data ---
test <- sdf.test(
  X1 = rnorm(100), X2 = rnorm(80),
  q1 = 4, method1 = "GCV", Te = 20, alpha = 0.05, N = 1000
)
test

# --- Example 2: Using random data ---

X1 <- arima.sim(list(order=c(1,0,0),ar=0.5),n = 1200,rand.gen = rnorm, sd = 1)
X2 <- arima.sim(list(order=c(1,0,0),ar=0.8),n = 1000,rand.gen = rnorm, sd = 1)
sdf.test(
  X1 = X1, X2 = X2,
  q1 = 4, method1 = "GCV", Te = 176, alpha = 0.05
)


# --- Example 3: Using EEG dataset ---

data(eeg_data)
X1 <- subset(eeg_data, condition == "first_frontal_tDCS")$signal
X2 <- subset(eeg_data, condition == "second_frontal_tDCS")$signal
test <- sdf.test(
  X1 = X1, X2 = X2,
  q1 = 4, method1 = "ML", Te = 2100, alpha = 0.05, cores = 50
)
test

---