Changepoint wrapper

This function wraps a number of cpt functions from the changepoint package and the cpt.np() function from the changepoint.np package. It is handy that users can use this function to get the same changepoint results as these functions output individually. Moreover, it returns a tibble that inherits the tidyverse style. Functions from the changepoint package do require data normality assumption by default, yet changepoint.np is a non-parametric way to detect changepoints and let data speak by itself. If user sets change_in as np (or cpt_np), a seed should be set before using the function for the sake of reproducibility. For more details on the changepoint and changepoint.np packages, please refer to their documentation.

Usage

cpt_wrapper(data, change_in = "mean_var", cp_method = "PELT", ...)

Arguments

data

A numeric vector.

change_in

Choice of mean_var, mean, var, and np (or cpt_np for backward compatibility). Each choice corresponds to cpt.meanvar(), cpt.mean(), cpt.var() and cpt.np() respectively. The default is mean_var.

cp_method

A wide range of choices (i.e., AMOC, PELT, SegNeigh or BinSeg). Please note when change_in is np or cpt_np, PELT is the only option.

...

Extra arguments for each cpt function mentioned in the change_in section.

Value

A tibble including which point(s) is/are the changepoint along with raw changepoint value corresponding to that changepoint. Changepoint locations follow the convention of the changepoint package: the last index of the left segment. The upstream cpt object is attached as the "ggcpt_fit" attribute, which is what cpt_detect() stores in the result's $fit.

Standardise the data for a change in mean

With change_in = "mean" the upstream Normal cost assumes a noise standard deviation of 1 and the penalty is compared against the raw residual sum of squares, so a series with wider noise is under-penalised and over-segmented: 29 changepoints instead of 1 at \(\sigma = 3\) in a measured example. Standardise the series first, or use change_in = "mean_var", which estimates a variance per segment and is unaffected. See the scale-sensitivity section of cpt_detect.

References

Killick R, Eckley I (2014). “changepoint: An R package for changepoint analysis.” Journal of statistical software, 58(3), 1–19.

See also

Other changepoint engines: bcp_wrapper(), beast_wrapper(), bfast_wrapper(), binsegrcpp_wrapper(), bocpd_wrapper(), cpm_wrapper(), cpop_wrapper(), decafs_wrapper(), ecp_wrapper(), envcpt_wrapper(), esac_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(2022)
cpt_wrapper(c(rnorm(100,0,1),rnorm(100,0,10)))
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
cpt_wrapper(c(rnorm(100,0,1),rnorm(100,10,1)))
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.225