detectSliding {detectR}

Change point detection using PCA and sliding method

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. Default is "fixed" = length^(1/3) or "andrews" implements data adaptive selection, or user specify the length

alpha

significance level of the test

nboot

the number of bootstrap sample for p-value. Default is 199.

n.cl

number of cores in parallel computing. The default is (machine cores - 1)

bsize

block size for the Block Wild Bootstrapping. Default is log(length), "sqrt" uses sqrt(length), "adaptive" determines block size using 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 threshold

Value

sW The test statistic

L The number of factors used in the procedure

q The estimated vectorized autocovariance on each regime.

crit The critical value 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, bootstrap 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)