The checks behind a segmentation, as numbers: for every segment, its length, level and spread, whether its residuals look independent (Ljung-Box) and Gaussian (Shapiro-Wilk), and a flag where the segment is too short for either test to mean anything.
Usage
cpt_gof(fit, lag = NULL)
# S3 method for class 'ggcpt_gof'
print(x, ...)Value
A ggcpt_gof tibble with one row per segment (per segment
and coordinate for a multivariate fit): seg_id, start,
end, n, mean, sd, acf1 (lag-1
autocorrelation of the residuals), ljung_box_p,
shapiro_p, and too_short (fewer than 12 observations,
where the tests are left NA). The whole-series Ljung-Box result
is attached as the "overall" attribute, and print()
summarises what the columns say.
See also
autoplot(fit, type = "diagnostics") draws the same
checks; cpt_assumptions() reports them with the
alternatives.
Other inference:
cpt_assumptions(),
cpt_attribute_event(),
cpt_effect(),
cpt_null_power(),
cpt_robustness(),
cpt_test_at(),
cpt_test_null()
Examples
set.seed(1)
fit <- cpt_detect(c(rnorm(100), rnorm(100, 3, 2)), method = "pelt")
cpt_gof(fit)
#> ggcpt_gof (method: pelt, 5 segment rows)
#> Whole series: Ljung-Box p = 0.00075 at lag 10, lag-1 autocorrelation -0.18
#> Note: 1 segment(s) with non-Gaussian residuals
#> Note: segment spreads differ by a factor of 2.2
#>
#> # A tibble: 5 × 10
#> seg_id start end n mean sd acf1 ljung_box_p shapiro_p too_short
#> <int> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
#> 1 1 1 100 100 0.109 0.898 -0.00365 0.809 0.988 FALSE
#> 2 2 101 133 33 3.13 1.44 -0.117 0.505 0.00375 FALSE
#> 3 3 134 159 26 1.91 1.97 -0.254 0.0687 0.120 FALSE
#> 4 4 160 180 21 4.09 1.99 0.0145 0.637 0.454 FALSE
#> 5 5 181 200 20 2.67 1.81 -0.460 0.197 0.272 FALSE