Diagnose the fit of a segmented time series

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 model or segmenter

...

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()
#> Registered S3 method overwritten by 'tsibble':
#>   method               from 
#>   as_tibble.grouped_df dplyr

# \donttest{
# For Coen's algorithm, show the histogram of changepoint selections
x <- segment(DataCPSim, method = "coen", num_generations = 3)
#> Warning: `segment_coen()` was deprecated in tidychangepoint 0.0.1.
#> ℹ Please use `segment_ga_coen()` instead.
#> ℹ The deprecated feature was likely used in the tidychangepoint package.
#>   Please report the issue to the authors.
#> 
  |                                                                  
  |                                                            |   0%
  |                                                                  
  |==============================                              |  50%
  |                                                                  
  |============================================================| 100%
x |>
  as.segmenter() |>
  diagnose()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_bar()`).

# }

# 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))