mxPBF_combined: Change Point Detection in Mean Structure using Maximum...

In hdbcp: Bayesian Change Point Detection for High-Dimensional Data

Change Point Detection in Mean Structure using Maximum Pairwise Bayes Factor (mxPBF)

Description

This function detects change points in both mean and covariance structure of multivariate Gaussian data using the Maximum Pairwise Bayes Factor (mxPBF). The function selects alpha that controls the empirical False Positive Rate (FPR), as suggested in the paper. The function conducts a multiscale approach using the function.

Usage

mxPBF_combined(
  given_data,
  nws,
  alps,
  a0 = 0.01,
  b0 = 0.01,
  FPR_want = 0.05,
  n_sample = 300,
  n_cores = 1
)

Arguments

given_data	An (n \times p) data matrix representing n observations and p variables.
nws	A set of window sizes for change point detection.
alps	A grid of alpha values used in the empirical False Positive Rate (FPR) method.
a0	A hyperparameter a_0 used in the mxPBF (default: 0.01).
b0	A hyperparameter b_0 used in the mxPBF (default: 0.01).
FPR_want	Desired False Positive Rate for selecting alpha, used in the empirical FPR method (default: 0.05).
n_sample	Number of simulated samples to estimate the empirical FPR, used in the empirical FPR method (default: 300).
n_cores	Number of threads for parallel execution via OpenMP (default: 1).

Value

A list provided. Each element in the list contains:

Result_cov

A list result from the mxPBF_cov() function.

Result_mean

A list result from the mxPBF_mean() function applied to each segmented data.

Change_points_cov

Locations of detected change points identified by mxPBF_cov() function.

Change_points_mean

Locations of detected change points identified by mxPBF_mean() function.

Examples

nws <- c(25, 60, 100)
alps <- seq(1,10,0.05)
## H0 data
mu1 <- rep(0,10)
sigma1 <- diag(10)
X <- mvrnorm_cpp(500, mu1, sigma1)
res1 <- mxPBF_combined(X, nws, alps)

## H1 data
mu2 <- rep(1,10)
sigma2 <- diag(10)
for (i in 1:10) {
  for (j in i:10) {
    if (i == j) {
    next
    } else {
    cov_value <- rnorm(1, 1, 1)
    sigma2[i, j] <- cov_value
    sigma2[j, i] <- cov_value
    }
  }
}
sigma2 <- sigma2 + (abs(min(eigen(sigma2)$value))+0.1)*diag(10) # Make it nonsingular
Y1 <- mvrnorm_cpp(150, mu1, sigma1)
Y2 <- mvrnorm_cpp(150, mu2, sigma1)
Y3 <- mvrnorm_cpp(200, mu2, sigma2)
Y <- rbind(Y1, Y2, Y3)
res2 <- mxPBF_combined(Y, nws, alps)

hdbcp documentation built on April 3, 2025, 7:39 p.m.