Recommend a detection method

Turns the capability matrix into an answer. Given what the analyst knows about their problem — how many dimensions, what kind of change, what the noise looks like, how long the series is, whether they need uncertainty or an online alarm — this returns the shortlist of methods that actually fit, with a reason for each and the reference to cite. It is a decision table, not a model: everything it knows is in cpt_methods(), and making that explicit and printable is the point.

Usage

cpt_recommend(
  dimension = c("univariate", "multivariate"),
  change_in = "mean",
  noise = c("iid", "heavy", "autocorrelated", "heteroscedastic"),
  n = NULL,
  need_uncertainty = FALSE,
  online = FALSE,
  installed_only = FALSE
)

# S3 method for class 'ggcpt_recommendation'
tidy(x, ...)

# S3 method for class 'ggcpt_recommendation'
print(x, top = 5, ...)

Arguments

dimension

"univariate" (default) or "multivariate".

change_in

What kind of change is expected: any value accepted by cpt_detect(). Defaults to "mean".

noise

Noise structure: "iid" (default), "heavy" (heavy-tailed), "autocorrelated", or "heteroscedastic".

n

Series length, used to flag methods that are impractical at that size. Optional.

need_uncertainty

Does the answer have to come with a confidence interval or significance region? Defaults to FALSE.

online

Is detection sequential (alarms as data arrive) rather than retrospective? Defaults to FALSE.

installed_only

Restrict to engines that are installed. Defaults to FALSE, so the recommendation names the right method even when it needs an install.

x

A ggcpt_recommendation object (for print()).

...

Ignored.

top

How many candidates to print. Defaults to 5.

Value

A tibble of candidate methods ordered by suitability, with columns method, engine, installed, score, why and caveat, and a print() that reads as advice.

See also

cpt_methods(), cpt_consensus(), cpt_cite().

Examples

cpt_recommend(noise = "autocorrelated")
#> Recommended methods for: univariate series, change in mean, autocorrelated noise
#> 
#> 1. decafs (DeCAFS)
#>    why: handles change_in = "mean"; built for autocorrelated noise
#> 2. envcpt (EnvCpt)
#>    why: handles change_in = "mean"; built for autocorrelated noise
#> 3. mcp (mcp)
#>    why: handles change_in = "mean"; built for autocorrelated noise
#> 4. nsp (nsp)
#>    why: handles change_in = "mean"; built for autocorrelated noise
#> 5. sn (SNSeg)
#>    why: handles change_in = "mean"; built for autocorrelated noise
#> 
#> (27 further candidate(s); the full table is the return value.)
#> 
#> Cite the method you use with cpt_cite(). Cross-check the choice with
#> cpt_consensus() and cpt_sensitivity().
cpt_recommend(dimension = "multivariate", change_in = "covariance")
#> Recommended methods for: multivariate series, change in covariance, iid noise
#> 
#> 1. fcov (fChange)
#>    why: handles change_in = "covariance"
#> 2. hdcov (changepoints)
#>    why: handles change_in = "covariance"
#> 3. kwc (KWCChangepoint)
#>    why: handles change_in = "covariance"
#> 
#> Cite the method you use with cpt_cite(). Cross-check the choice with
#> cpt_consensus() and cpt_sensitivity().