Detection delay and false-alarm rate

Scores an online result the way the sequential literature does: how long after each true change did the first alarm arrive, and how many alarms were raised with no change behind them. cpt_metrics() is the wrong tool for an online detector — it asks whether the location was recovered, which a sequential procedure never claims — and warns if you point it at one.

Usage

cpt_delay(object, truth, max_delay = Inf)

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

# S3 method for class 'ggcpt_delay'
glance(x, ...)

# S3 method for class 'ggcpt_delay'
print(x, ...)

# S3 method for class 'ggcpt_delay'
autoplot(object, ...)

Arguments

object

A ggcpt_delay object (for autoplot()).

truth

Integer vector of true changepoint positions, on the same clock as the alarms. When object is a monitor built by cpt_replay() the baseline offset is applied automatically.

max_delay

Alarms further than this after a change are treated as false alarms rather than late detections. Defaults to Inf.

x

A ggcpt_delay object.

...

Ignored.

Value

A ggcpt_delay object: a list with per_change (one row per true change: truth, alarm, delay, detected), false_alarms, and the summary statistics mean_delay, median_delay, n_false_alarms and arl (mean observations per false alarm).

See also

cpt_monitor(), cpt_replay().

Examples

set.seed(2026)
mon <- cpt_replay(c(rnorm(200), rnorm(200, 3)), method = "edetector")
cpt_delay(mon, truth = 200)
#> ggcpt_delay
#>   True changes:      1
#>   Detected:          1
#>   Mean delay:        16
#>   Median delay:      16
#>   False alarms:      4
#>   Average run length: 100
#> 
#> # A tibble: 1 × 4
#>   truth alarm delay detected
#>   <int> <int> <dbl> <lgl>   
#> 1   200   216    16 TRUE