generate_cov_datasets {hdbcp}R Documentation

Generate Simulated Datasets with Change Points in Covariance Matrix

Description

This function generates simulated datasets that include change points in the covariance matrix for change point detection. Users can specify various parameters to control the dataset size, dimension, size of signal, and change point locations. The generated datasets include datasets with and without change points, allowing for comparisons in simulation studies.

Usage

generate_cov_datasets(
  n,
  p,
  signal_size,
  sparse = TRUE,
  single_point = round(n/2),
  multiple_points = c(round(n/4), round(2 * n/4), round(3 * n/4)),
  type = c(1, 2, 3, 4, 5)
)

Arguments

n

Number of observations to generate.

p

Number of features or dimensions for each observation.

signal_size

Magnitude of the signal applied at change points.

sparse

Determines if a sparse covariance structure is used (default is TRUE).

single_point

Location of a single change point in the dataset (default is n/2).

multiple_points

Locations of multiple change points within the dataset (default is quartiles of n).

type

Integer vector specifying the type of dataset to return. Options are as follows: - 1: No change points (H0 data) - 2: Single change point with rare signals - 3: Single change point with many signals - 4: Multiple change points with rare signals - 5: Multiple change points with many signals

Value

A 3D array containing the generated datasets. Each slice represents a different dataset type.

Examples

# Generate a default dataset
datasets <- generate_cov_datasets(100, 50, 1)

null_data <- datasets[,,1]
single_many_data <- datasets[,,3]


[Package hdbcp version 1.0.0 Index]