HDCD (version 1.0)

single_Inspect: Inspect for single change-point estimation

Description

R wrapper for C function for single change-point estimation using Inspect wang_high_2018HDCD. Note that the algorithm is only implemented for \(\mathcal{S} = \mathcal{S}_2\), in the notation of wang_high_2018;textualHDCD.

Usage

single_Inspect(
  X,
  lambda = sqrt(log(p * log(n))/2),
  eps = 1e-10,
  rescale_variance = FALSE,
  maxiter = 10000,
  debug = FALSE
)

Value

A list containing

pos

estimated change-point location

CUSUMval

projected CUSUM value at the estimated change-point position

Arguments

X

Matrix of observations, where each row contains a time series

lambda

Manually specified value of \(\lambda\) (can be NULL, in which case \(\lambda \gets \sqrt{\log(p\log n)/2}\))

eps

Threshold for declaring numerical convergence of the power method

rescale_variance

If TRUE, each row of the data is re-scaled by a MAD estimate using rescale_variance

maxiter

Maximum number of iterations for the power method

debug

If TRUE, diagnostic prints are provided during execution

References

Examples

library(HDCD)
n = 500
p = 500
set.seed(101)
# Generating data
X = matrix(rnorm(n*p), ncol = n, nrow=p)
# Adding a single sparse change-point:
X[1:5, 201:500] = X[1:5, 201:500] +1

res = single_Inspect(X,rescale_variance=TRUE)
res$pos

# Manually setting the value of \lambda:
res = single_Inspect(X, lambda = 2*sqrt(log(p*log(n))/2))
res$pos