summary.SNSeg_HD: Summary of SN-based change-point estimates for...

In SNSeg: Self-Normalization(SN) Based Change-Point Estimation for Time Series

Summary of SN-based change-point estimates for high-dimensional time series with dimension greater than 10

Description

Summary method for objects of class SNSeg_HD

Usage

## S3 method for class 'SNSeg_HD'
summary(object, ...)

Arguments

object	a SNSeg_HD object
...	not in use

Details

Provide information about estimated change-point locations, the parameter tested by SN-based procedures, the confidence level, the grid_size, and the critical value of the SN-based test.

Examples

n <- 500
p <- 50
nocp <- 5
cp_sets <- round(seq(0,nocp+1,1)/(nocp+1)*n)
num_entry <- 5
kappa <- sqrt(4/5)
mean_shift <- rep(c(0,kappa),100)[1:(length(cp_sets)-1)]
set.seed(1)
ts <- matrix(rnorm(n*p,0,1),n,p)
no_seg <- length(cp_sets)-1
for(index in 1:no_seg){
  tau1 <- cp_sets[index]+1
  tau2 <- cp_sets[index+1]
  ts[tau1:tau2,1:num_entry] <- ts[tau1:tau2,1:num_entry] +
    mean_shift[index]
}

# grid_size defined
result <- SNSeg_HD(ts, confidence = 0.9, grid_size_scale  = 0.05,
                   grid_size = 40)
# summary method
summary(result)

SNSeg documentation built on June 22, 2024, 10:50 a.m.