The size of every change a fit reports, in the data's own units and standardised: the level before and after, their difference with an interval, the difference in noise standard deviations, the percentage change, and for count and waiting-time data the rate or hazard ratio.
Arguments
- fit
A
ggcptobject.- level
Confidence level for the intervals. Defaults to
0.95.- method
"naive"(the default) measures each effect on the data that located the change."split"locates the changepoints on the odd-numbered observations and measures their effects on the even-numbered ones, which removes the winner's curse at the price of locating on half the data (see below).- seed
Optional seed for
method = "split", scoped to this call.- ...
Further arguments for the re-run
method = "split"makes (overriding what is recovered from the fit).- x
A
ggcpt_effectobject.- object
A
ggcpt_effectobject.
Value
A ggcpt_effect tibble with one row per changepoint (per
changepoint and coordinate for a multivariate fit, per changepoint and
term for a regression fit): cp (and cp_index),
before, after, n_before, n_after,
delta, delta_lower, delta_upper (a Welch
interval), delta_std (the change in noise standard deviations,
the unit cpt_detect(min_effect = ) filters on),
pct_change, sd_before, sd_after,
sd_ratio, the family-specific ratio where it applies
(rate_ratio for Poisson counts, odds_ratio for binary
data, hazard_ratio for exponential waiting times, each with an
interval), selection_adjusted and method. With
print() and autoplot().
The winner's curse
A change measured at a location the same data chose is biased upward:
the detector put the boundary where the two sides differ most, so the
difference it reports is the largest the noise allowed. The bias is
worst for the marginal detections, which are exactly the ones whose size
matters. method = "naive" rows carry selection_adjusted =
FALSE for that reason. method = "split" is honest about the size
(the measuring half never influenced the locations) and less precise
about the location (the locating half has half the data), and it assumes
the noise is independent from one observation to the next, since the
two halves are interleaved.
See also
cpt_test_at() for a change at a date fixed in
advance, cpt_detect()'s min_effect.
Other inference:
cpt_assumptions(),
cpt_attribute_event(),
cpt_gof(),
cpt_null_power(),
cpt_robustness(),
cpt_test_at(),
cpt_test_null()
Examples
set.seed(1)
fit <- cpt_detect(c(rnorm(100, 10), rnorm(100, 12)), method = "pelt")
cpt_effect(fit)
#> ggcpt_effect (method: pelt, naive)
#> Measured where the same data located each change, so the sizes are biased upward
#> (selection_adjusted = FALSE). method = "split" measures on held-out observations.
#>
#> # A tibble: 1 × 15
#> cp before after n_before n_after delta delta_lower delta_upper delta_std
#> <int> <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 100 10.1 12.0 100 100 1.85 1.59 2.11 1.92
#> # ℹ 6 more variables: pct_change <dbl>, sd_before <dbl>, sd_after <dbl>,
#> # sd_ratio <dbl>, selection_adjusted <lgl>, method <chr>
counts <- c(rpois(100, 4), rpois(100, 8))
cpt_effect(cpt_detect(counts, method = "pelt", family = "poisson"))
#> ggcpt_effect (method: pelt, naive)
#> Measured where the same data located each change, so the sizes are biased upward
#> (selection_adjusted = FALSE). method = "split" measures on held-out observations.
#>
#> # A tibble: 1 × 18
#> cp before after n_before n_after delta delta_lower delta_upper delta_std
#> <int> <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 100 4.06 8.3 100 100 4.24 3.55 4.93 2.02
#> # ℹ 9 more variables: pct_change <dbl>, sd_before <dbl>, sd_after <dbl>,
#> # sd_ratio <dbl>, rate_ratio <dbl>, rate_ratio_lower <dbl>,
#> # rate_ratio_upper <dbl>, selection_adjusted <lgl>, method <chr>