Scale space: the statistic across bandwidths

Sweeps a multiscale detector's bandwidth and returns the statistic at every (location, bandwidth) pair. The resulting heatmap answers the question a single-bandwidth fit cannot: at which resolutions does this feature exist? A change that is significant only at a wide bandwidth is a slow shift; one that appears only at a narrow bandwidth is a spike.

Usage

cpt_scale_space(x, bandwidths = NULL, method = c("mosum", "npmojo"), ...)

ggcpt_scale_space(x, bandwidths = NULL, method = c("mosum", "npmojo"), ...)

Arguments

x

A numeric vector (or, for method = "npmojo", a matrix with rows as time points), or a ggcpt object produced by a multiscale engine (subset(cpt_methods(), scale_space)$method).

bandwidths

Integer vector of bandwidths to sweep. Defaults to a geometric grid between max(5, n/50) and n/4.

method

Which engine to sweep: "mosum" (default) or "npmojo". Taken from x when it is a ggcpt produced by one of them.

...

Additional arguments passed to the engine at each bandwidth.

Value

cpt_scale_space() returns a tibble with index, bandwidth, statistic, threshold, significant (the statistic at that location and bandwidth exceeds the threshold) and detected (the engine reported a changepoint there at that bandwidth). The two differ: a location can clear the threshold without surviving the engine's own pruning. ggcpt_scale_space() draws the heatmap with the accepted changepoints overlaid.

See also

cpt_statistic().

Examples

set.seed(2026)
x <- c(rnorm(300), rnorm(300, 2))
ss <- cpt_scale_space(x, bandwidths = c(20, 40, 80))
head(ss)
#> # A tibble: 6 × 6
#>   index bandwidth statistic threshold significant detected
#>   <int>     <int>     <dbl>     <dbl> <lgl>       <lgl>   
#> 1     1        20     0.618      3.91 FALSE       FALSE   
#> 2     2        20     0.375      3.91 FALSE       FALSE   
#> 3     3        20     0.193      3.91 FALSE       FALSE   
#> 4     4        20     0.194      3.91 FALSE       FALSE   
#> 5     5        20     0.500      3.91 FALSE       FALSE   
#> 6     6        20     1.65       3.91 FALSE       FALSE