detectSliding: Change point detection using PCA and sliding method

In detectR: Change Point Detection

Description

Change point detection using PCA and sliding method

Usage

detectSliding(
  Y,
  wd = 40,
  L,
  Del,
  q = "fixed",
  alpha = 0.05,
  nboot = 199,
  n.cl,
  bsize = "log",
  bootTF = TRUE,
  scaleTF = TRUE,
  diagTF = TRUE,
  plotTF = TRUE
)

Arguments

Y	data: Y = length*dim
wd	window size for sliding averages
L	the number of factors
Del	Delta away from the boundary restriction
q	methods in calculating long-run variance of the test statistic. Defaul is "andrew" "fixed" = length^1/3 or user specify the length
alpha	significance level of the test
nboot	the number of bootstrap sample for pvalue. Defauls is 199.
n.cl	number of cores in parallel computing. The default is (machine cores - 1)
bsize	block size for the Block Wild Boostrapping. Default is log(length), "sqrt" uses sqrt(length), "adaptive" deterines block size usign data dependent selection of Andrews
bootTF	determine whether the threshold is calculated from bootstrap or asymptotic
scaleTF	scale the variance into 1
diagTF	include diagonal term of covariance matrix or not
plotTF	Draw plot to see test statistic and threshould

Value

sW The test statistic

L The number of factors used in the procedure

q The estimated vecorized autocovariance on each regime.

crit The critical vlaue to identify change point

bsize The block size of the bootstrap

diagTF If TRUE, the diagonal entry of covariance matrix is used in detecting connectivity changes.

bootTF If TRUE, boostrap is used to find critical value

scaleTF If TRUE, the multivariate signal is studentized to have zero mean and unit variance.

Examples

out4 = detectSliding(changesim, wd=40, L=2, n.cl=1)

detectR documentation built on Feb. 8, 2021, 5:06 p.m.