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.
Arguments
- x
A numeric vector (or, for
method = "npmojo", a matrix with rows as time points), or anyggcptobject. The sweep is computed here rather than read off the result, so a fit from any engine is accepted and the scale space is the one belonging to its series: from amosumornpmojoresult the engine is adopted as well (seemethod), and from anything else the default"mosum"sweep is drawn over the same series.- bandwidths
Integer vector of bandwidths to sweep. Defaults to a geometric grid between
max(5, n/50)andn/4.- method
Which engine to sweep:
"mosum"(default) or"npmojo". Taken fromxwhen it is aggcptproduced 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.
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