ESAC wrapper — sparsity-adaptive high-dimensional detection
Source:R/wrap-highdim2.R
esac_wrapper.RdWraps HDCD::ESAC() (Moen, Glad and Tveten, 2023): Efficient
Sparsity Adaptive Changepoint estimation for a change in the mean vector
of a high-dimensional series. Where inspect projects onto a single
estimated sparse direction, ESAC adapts across the whole sparsity range at
once, which is a different regime rather than a refinement of the same one
— it is competitive both when a handful of coordinates change and when all
of them do.
Usage
esac_wrapper(
x,
threshold_d = 1.5,
threshold_s = 1,
empirical = FALSE,
N = 1000,
seed = NULL,
...
)Arguments
- x
A numeric matrix or data frame with rows as time points and columns as coordinates.
- threshold_d, threshold_s
Leading constants of the dense and sparse thresholds. Defaults follow the engine (
1.5and1).- empirical
Calibrate the thresholds by Monte Carlo rather than using the theoretical values? Slower but sharper; defaults to
FALSE.- N
Monte Carlo samples when
empirical = TRUE.- seed
Optional seed (used by the empirical calibration).
- ...
Additional arguments passed to
HDCD::ESAC().
Value
A ggcpt object. The changepoints tibble carries
cusum (the ESAC statistic at each detected location) and
depth (its level in the recursion).
References
Moen PAJ, Glad IK, Tveten M (2024). “Efficient sparsity adaptive changepoint estimation.” Electronic Journal of Statistics, 18(2), 3975–4038. doi:10.1214/24-EJS2294 .
See also
Other changepoint engines:
bcp_wrapper(),
beast_wrapper(),
bfast_wrapper(),
binsegrcpp_wrapper(),
bocpd_wrapper(),
cpm_wrapper(),
cpop_wrapper(),
cpt_wrapper(),
decafs_wrapper(),
ecp_wrapper(),
envcpt_wrapper(),
fabisearch_wrapper(),
fastcpd_wrapper(),
fcov_wrapper(),
fmean_wrapper(),
fpop_wrapper(),
geomcp_wrapper(),
hdcov_wrapper(),
hdreg_wrapper(),
idetect_wrapper(),
inspect_wrapper(),
kcp_wrapper(),
kwc_wrapper(),
mcp_wrapper(),
mosum_wrapper(),
network_wrapper(),
not_wrapper(),
npmojo_wrapper(),
nsp_wrapper(),
ocd_wrapper(),
pilliat_wrapper(),
segmented_wrapper(),
smuce_wrapper(),
sn_wrapper(),
strucchange_wrapper(),
taylor_wrapper(),
tguh_wrapper(),
trend_wrapper(),
var_wrapper(),
wbs2_wrapper(),
wbs_wrapper(),
wbsts_wrapper()
Examples
set.seed(2026)
X <- matrix(rnorm(100 * 20), nrow = 100)
X[51:100, 1:5] <- X[51:100, 1:5] + 3
esac_wrapper(X)
#> ggcpt (changepoint detection result)
#> Method: esac
#> Change in: mean
#> Changepoints found: 1
#> CP convention: left
#> Penalty: threshold = 1.5
#> Series length: 100
#>
#> Changepoints:
#> # A tibble: 1 × 4
#> cp cp_value cusum depth
#> <int> <dbl> <dbl> <int>
#> 1 50 0.426 3.79 1