Check the assumptions behind a segmentation

A short report on whether the result's assumptions look violated for this series, what that does to the answer, and what would model it. It reports and does not act: every automatic correction measured trades true changepoints for false ones, and which side of that trade matters is the analyst's decision.

Usage

cpt_assumptions(fit, lag = NULL)

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

Arguments

fit

A ggcpt object.

lag

Lag for the Ljung-Box test. Defaults to 10, or n / 5 for a short series.

x

A ggcpt_assumptions object.

...

Ignored.

Value

A ggcpt_assumptions tibble with one row per check: component, value, flag (TRUE when the check raises a concern), detail and advice. The components:

residual_dependence

Ljung-Box p-value on the within-segment residuals (and their lag-1 autocorrelation in detail). Flagged below 0.05. Autocorrelated noise is the commonest reason for spurious changepoints.

scale_sensitivity

the noise standard deviation, flagged when the engine's answer depends on the data's units (measured) and the noise is far from unit scale.

expected_false_positives

how many changepoints the engine is expected to report on this much pure noise: n / arl0 for cpm, and the measured null-size table otherwise. Flagged above one.

count_plausibility

reported changepoints per hundred observations, flagged above one: more than n / 100 is more often misconfiguration than a finding.

data_type

what the series looks like (continuous, counts, binary, proportions), flagged when a Gaussian cost is fitted to non-Gaussian data.

See also

cpt_report(), which prints this; cpt_robustness() for the answer under other noise models.

Other inference: cpt_attribute_event(), cpt_effect(), cpt_gof(), cpt_null_power(), cpt_robustness(), cpt_test_at(), cpt_test_null()

Examples

set.seed(1)
ar <- as.numeric(arima.sim(list(ar = 0.7), 300))
cpt_assumptions(cpt_detect(ar, method = "pelt"))
#> ggcpt_assumptions (method: pelt)
#>  ! residual_dependence       Ljung-Box at lag 10; lag-1 autocorrelation 0.54
#>       The residuals are autocorrelated, the commonest cause of spurious
#>       changepoints. cpt_detect(x, method = "kcp") (0.3 spurious, 1.9 of 2
#>       real changes found); cpt_detect(x, method = "bfast") (1.0 spurious,
#>       1.6 of 2 real changes found); cpt_detect(x, method = "nsp", variant =
#>       "selfnorm") (0.8 spurious, 1.2 of 2 real changes found)
#>    scale_sensitivity         noise sd 0.717; this engine's cost assumes unit noise
#>    expected_false_positives  measured on pure noise at n = 1,000 (50 replicates)
#>  ! count_plausibility        4 changepoints in 300 observations (1.33 per hundred)
#>       More than one changepoint per hundred observations is more often a
#>       penalty on the wrong scale or dependent noise than a finding; check
#>       the two rows above.
#>    data_type                 looks continuous; family gaussian