binary.segmentation: binary.segmentation

Description Usage Arguments Details Value Author(s) References Examples

View source: R/functions.R

Description

Detects change points in time series data using a binary segmentation algorithm.

Usage

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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

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library(HDcpDetect)
HAPT2 <- as.matrix(HAPT[1:35,])
binary.segmentation(data_M=HAPT2,power_enhancement=FALSE)

HDcpDetect documentation built on May 2, 2019, 7:24 a.m.