single_SBS_calibrate {HDCD}

Generates threshold \pi_T for Sparsified Binary Segmentation for single change-point detection

Description

R wrapper for function choosing empirical threshold \pi_T using Monte Carlo simulation for single change-point Sparsified Binary Segmentation. More specifically, the function returns the empirical upper tol quantile of CUSUMs over p time series, each of length n, based on N number of runs.

Usage

single_SBS_calibrate(
  n,
  p,
  N = 100,
  tol = 1/100,
  rescale_variance = TRUE,
  debug = FALSE
)

Arguments

n

Number of observations

p

Number time series

N

Number of Monte Carlo samples used

tol

False positive probability tolerance

rescale_variance

If TRUE, each row of the data is rescaled by a MAD estimate

debug

If TRUE, diagnostic prints are provided during execution

Value

Threshold

Examples

library(HDCD)
n = 50
p = 50
set.seed(101)

# Simulate threshold
pi_T_squared = single_SBS_calibrate(n=n,p=p,N=100, tol=1/100, rescale_variance = TRUE)
pi_T_squared


# Generating data
X = matrix(rnorm(n*p), ncol = n, nrow=p)
# Adding a single sparse change-point:
X[1:5, 26:n] = X[1:5, 26:n] +1

# Run SBS
res = single_SBS(X,threshold=sqrt(pi_T_squared),rescale_variance=TRUE)
res$pos