detectBinary: Change point detection using PCA and binary segmentation

Description Usage Arguments

View source: R/detectBinary.R

Description

This function uses PCA-based method to find breaks. Simultaneous breaks are found from binary segmentation.

Usage

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

Arguments

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 threshould

Input

data: Y = length*dim


mgampe/detectR documentation built on Nov. 17, 2019, 6:01 a.m.