Convert, retrieve, or verify a model object

Usage

as.model(object, ...)

# Default S3 method
as.model(object, ...)

# S3 method for class 'tidycpt'
as.model(object, ...)

is_model(x, ...)

Arguments

object: A tidycpt object, typically returned by segment()
...: currently ignored
x: An object, typically returned by fit_*()

Value

as.model() returns a mod_cpt model object

is_model() a logical vector of length 1

Details

tidycpt objects have a model component. The functions documented here are convenience utility functions for working with the model components. as.model() is especially useful in pipelines to avoid having to use the $ or [ notation for subsetting.

When applied to a tidycpt object, as.model() simply returns the model component of that object. However, when applied to a segmenter object, as.model() attempts to converts that object into a mod_cpt model object.

is_model() checks to see if a model object implements all of the S3 methods necessary to be considered a model.

Examples

# Segment a time series using PELT
x <- segment(CET, method = "pelt")

# Retrieve the model component
x |> 
  as.model()
#> List of 6
#>  $ data         : Time-Series [1:362] from 1 to 362: 8.87 9.1 9.78 9.52 8.63 9.34 8.29 9.86 8.52 9.51 ...
#>  $ tau          : int [1:5] 55 57 309 311 330
#>  $ region_params: tibble [6 × 3] (S3: tbl_df/tbl/data.frame)
#>   ..$ region           : chr [1:6] "[0,55)" "[55,57)" "[57,309)" "[309,311)" ...
#>   ..$ param_mu         : num [1:6] 8.8 9.04 9.23 9.48 9.49 ...
#>   ..$ param_sigma_hatsq: Named num [1:6] 0.3744 0.16 0.3575 0.0256 0.1518 ...
#>   .. ..- attr(*, "names")= chr [1:6] "[0,55)" "[55,57)" "[57,309)" "[309,311)" ...
#>  $ model_params : NULL
#>  $ fitted_values: num [1:362] 8.8 8.8 8.8 8.8 8.8 ...
#>  $ model_name   : chr "meanvar"
#>  - attr(*, "class")= chr "mod_cpt"

# Explicitly convert the segmenter to a model
x |>
  as.segmenter() |>
  as.model()
#> List of 6
#>  $ data         : Time-Series [1:362] from 1 to 362: 8.87 9.1 9.78 9.52 8.63 9.34 8.29 9.86 8.52 9.51 ...
#>  $ tau          : int [1:5] 55 57 309 311 330
#>  $ region_params: tibble [6 × 3] (S3: tbl_df/tbl/data.frame)
#>   ..$ region           : chr [1:6] "[0,55)" "[55,57)" "[57,309)" "[309,311)" ...
#>   ..$ param_mu         : num [1:6] 8.8 9.04 9.23 9.48 9.49 ...
#>   ..$ param_sigma_hatsq: Named num [1:6] 0.3744 0.16 0.3575 0.0256 0.1518 ...
#>   .. ..- attr(*, "names")= chr [1:6] "[0,55)" "[55,57)" "[57,309)" "[309,311)" ...
#>  $ model_params : NULL
#>  $ fitted_values: num [1:362] 8.8 8.8 8.8 8.8 8.8 ...
#>  $ model_name   : chr "meanvar"
#>  - attr(*, "class")= chr "mod_cpt"

# Is that model valid? 
x |>
  as.model() |>
  is_model()
#> [1] TRUE
  

# Fit a model directly, without using [segment()]
x <- fit_nhpp(CET, tau = 330)
is_model(x)
#> [1] TRUE