diagnose {tidychangepoint} | R Documentation |
Diagnose the fit of a segmented time series
Description
Depending on the input, this function returns a diagnostic plot.
Usage
diagnose(x, ...)
## S3 method for class 'mod_cpt'
diagnose(x, ...)
## S3 method for class 'seg_basket'
diagnose(x, ...)
## S3 method for class 'tidycpt'
diagnose(x, ...)
## S3 method for class 'nhpp'
diagnose(x, ...)
Arguments
x |
A tidycpt object, or a |
... |
currently ignored |
Value
A ggplot2::ggplot()
object
See Also
Other tidycpt-generics:
as.model()
,
as.segmenter()
,
changepoints()
,
fitness()
,
model_name()
Examples
# For meanshift models, show the distribution of the residuals by region
fit_meanshift_norm(CET, tau = 330) |>
diagnose()
# For Coen's algorithm, show the histogram of changepoint selections
x <- segment(DataCPSim, method = "coen", num_generations = 3)
x |>
as.segmenter() |>
diagnose()
# Show various iterations of diagnostic plots
diagnose(segment(DataCPSim))
diagnose(segment(DataCPSim, method = "single-best"))
diagnose(segment(DataCPSim, method = "pelt"))
# Show diagnostic plots for test sets
diagnose(segment(test_set()))
diagnose(segment(test_set(n = 2, sd = 4), method = "pelt"))
# For NHPP models, show the growth in the number of exceedances
diagnose(fit_nhpp(DataCPSim, tau = 826))
diagnose(fit_nhpp(DataCPSim, tau = 826, threshold = 200))
[Package tidychangepoint version 1.0.1 Index]