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
#> 
#> (31 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().