| binary.segmentation {HDcpDetect} | R Documentation |
binary.segmentation
Description
Detects change points in time series data using a binary segmentation algorithm.
Usage
binary.segmentation(data_M,alpha=.05,power_enhancement=TRUE,M_threshold=0.05)
Arguments
data_M |
An nxp matrix representing a times series of length n with p dimensions. |
alpha |
The critical value for the hypothesis testing procedure. |
power_enhancement |
Indicates whether to add a power enhancement term to the test statistic. |
M_threshold |
Value used as a threshold to estimate temporal dependence by determining how small of a standardized difference is indistinguishable from zero. |
Details
The power enhancement term reduces type II error but slows the algorithm.
Value
The returned value is a list with the following components
Foundlist |
The estimated locations of the change points |
pvalues |
The p values corresponding to each change point estimate |
Author(s)
Jun Li, Jeffrey Okamoto, and Natasha Stewart
References
Li, J., Li, L., Xu, M., Zhong, P (2018). Change Point Detection in the Mean of High-Dimensional Time Series Data under Dependence. Manuscript.
Examples
library(HDcpDetect)
HAPT2 <- as.matrix(HAPT[1:35,])
binary.segmentation(data_M=HAPT2,power_enhancement=FALSE)