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Functional covariance changepoints

Source: R/wrap-functional.R
fcov_wrapper.Rd

Wraps fChange::fchange() for changes in the covariance operator, eigenstructure or trace of a functional time series — the changes that leave the mean curve untouched.

Usage

fcov_wrapper(
  x,
  target = c("covariance", "trace", "eigenjoint", "eigensingle"),
  statistic = c("Tn", "Mn"),
  critical = c("simulation", "resample", "welch"),
  type = c("segmentation", "single"),
  alpha = 0.05,
  ...
)

Arguments

x

A numeric matrix or data frame with one row per time point and one column per grid location (the curve's resolution).

target

What to test: "covariance" (default), "trace", "eigenjoint" or "eigensingle".

statistic

Test statistic: "Tn" (integrated, the default) or "Mn" (maximum).

critical

How critical values are obtained: "simulation" (default), "resample" or "welch".

type

"segmentation" (default, multiple changes) or "single" (one change).

alpha

Significance level. Defaults to 0.05.

...

Additional arguments passed to fChange::fchange().

Value

A ggcpt object with change_in = "covariance".

References

Aue A, Rice G, Sönmez O (2020). “Structural break analysis for spectrum and trace of covariance operators.” Environmetrics, 31(1), e2617.

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(), esac_wrapper(), fabisearch_wrapper(), fastcpd_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

# \donttest{
set.seed(2026)
X <- matrix(rnorm(60 * 20), nrow = 60)
X[31:60, ] <- X[31:60, ] * 3
fcov_wrapper(X, target = "trace", M = 200)
#> ggcpt (changepoint detection result)
#>   Method:         fcov
#>   Change in:       covariance 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         alpha = 0.05 
#>   Series length:   60 
#> 
#> Changepoints:
#> # A tibble: 1 × 3
#>      cp cp_value p_value
#>   <int>    <dbl>   <dbl>
#> 1    30    0.380    0.05
# }