Description Usage Arguments Value Examples
This function uses PCA-based method to find breaks. Simultaneous breaks are found from binary segmentation.
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Y |
data: Y = length*dim |
Del |
Delta away from the boundary restriction |
L |
the number of factors |
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 threshold |
tstathist The complete history of test tsatistic
Brhist The sequence of breakspoints found from binay splitting
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.
1 | out3= detectBinary(changesim, L=2, n.cl=1)
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