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inspect wrapper — high-dimensional changepoints via sparse projection

Source: R/wrap-highdim.R
inspect_wrapper.Rd

Wraps InspectChangepoint::inspect() (Wang and Samworth, 2018). For a \(p\)-variate series whose mean changes in an unknown sparse subset of coordinates, the algorithm computes the CUSUM transformation, finds the optimal sparse projection direction via a convex relaxation, and locates changepoints on the projected univariate series, recursing via wild binary segmentation.

Usage

inspect_wrapper(x, lambda = NULL, threshold = NULL, ...)

Arguments

x

A numeric matrix or data frame with one row per time point and one column per coordinate.

lambda

Regularisation parameter of the sparse projection; when NULL the engine default \(\sqrt{\log(p \log n)/2}\) is used.

threshold

Detection threshold; when NULL it is computed by Monte Carlo (via the engine).

...

Additional arguments passed to InspectChangepoint::inspect().

Value

A ggcpt object. The changepoints tibble carries a strength column (the maximum projected CUSUM statistic). The first coordinate is used for cp_value and the univariate plot line; the full matrix is kept for the faceted multivariate autoplot().

References

Wang T, Samworth RJ (2018). “High dimensional change point estimation via sparse projection.” Journal of the Royal Statistical Society: Series B, 80(1), 57–83.

Examples

set.seed(2026)
X <- cbind(c(rnorm(80), rnorm(80, 3)), c(rnorm(80), rnorm(80, -2)),
           rnorm(160))
res <- inspect_wrapper(X)
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res$changepoints
#> # A tibble: 1 × 3
#>      cp cp_value strength
#>   <int>    <dbl>    <dbl>
#> 1    80    0.785     21.9