majority_rule_mxPBF: Majority Rule for Multiscale approach using mxPBF Results

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

Majority Rule for Multiscale approach using mxPBF Results

Description

This function implements a majority rule-based post-processing approach to identify common change points across multiple window sizes from mxPBF results.

Usage

majority_rule_mxPBF(res_mxPBF)

Arguments

res_mxPBF

A list of results from mxPBF_mean() or mxPBF_cov().

Value

A vector of final detected change points that are common across multiple windows based on majority rule.

Examples

n <- 500
p <- 200
signal_size <- 1
pre_value <- 0.3
pre_proportion <- 0.4
given_data <- generate_mean_datasets(n, p, signal_size, pre_proportion, pre_value,
single_point = 250, multiple_points = c(150,300,350), type = 5)
nws <- c(25, 60, 100)
alps <- seq(1,10,0.05)
res_mxPBF <- mxPBF_mean(given_data, nws, alps)
majority_rule_mxPBF(res_mxPBF)

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