Inspect: Informative sparse projection for estimating change-points...
In HDCD: High-Dimensional Changepoint Detection

Inspect

R Documentation

Informative sparse projection for estimating change-points (Inspect)

Description

R wrapper for C function implementing a Narrowest-Over-Threshold variant of Inspect \insertCitewang_high_2018;textualHDCD as specified in Appendix C in \insertCitemoen2023efficient;textualHDCD. Note that the algorithm is only implemented for \mathcal{S} = \mathcal{S}_2, in the notation of \insertCitemoen2023efficient;textualHDCD.

Usage

Inspect(
  X,
  lambda = NULL,
  xi = NULL,
  alpha = 1.5,
  K = 5,
  eps = 1e-10,
  empirical = FALSE,
  maxiter = 10000,
  N = 100,
  tol = 1/100,
  rescale_variance = TRUE,
  debug = FALSE
)

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}`)
`xi`	Manually specified value of `\xi` (can be NULL, in which case `\xi \gets 4\sqrt{\log(np)}`)
`alpha`	Parameter for generating seeded intervals
`K`	Parameter for generating seeded intervals
`eps`	Threshold for declaring numerical convergence of the power method
`empirical`	If `TRUE`, the detection threshold `xi` is based on Monte Carlo simulation using `Inspect_calibrate`
`maxiter`	Maximum number of iterations for the power method
`N`	If `empirical=TRUE`, `N` is the number of Monte Carlo samples used
`tol`	If `empirical=TRUE`, `tol` is the false error probability tolerance
`rescale_variance`	If `TRUE`, each row of the data is re-scaled by a MAD estimate using `rescale_variance`
`debug`	If `TRUE`, diagnostic prints are provided during execution

Value

A list containing

`changepoints`	vector of estimated change-points
`changepointnumber`	number of changepoints
`CUSUMval`	vector with the sparse projected CUSUMs corresponding to `changepoints`
`coordinates`	a matrix of zeros and ones indicating which time series are affected by a change in mean, with each row corresponding to the change-point in `changepoints`
`scales`	vector of estimated noise level for each series

References

\insertAllCited

Examples

library(HDCD)
n = 50
p = 50
set.seed(100)
# 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

# Vanilla Inspect:
res = Inspect(X)
res$changepoints

# Manually setting leading constants for \lambda(t) and \gamma(t)
res = Inspect(X, 
              lambda = sqrt(log(p*log(n))/2),
              xi = 4*sqrt(log(n*p))
)
res$changepoints #estimated change-point locations

# Empirical choice of thresholds:
res = Inspect(X, empirical=TRUE, N = 100, tol = 1/100)
res$changepoints

# Manual empirical choice of thresholds (equivalent to the above)
thresholds_emp = Inspect_calibrate(n,p, N=100, tol=1/100)
res = Inspect(X, xi = thresholds_emp$max_value)
res$changepoints

HDCD documentation built on June 22, 2024, 10:53 a.m.