Skip to contents

Convert, retrieve, or verify a segmenter object

as.segmenter.Rd

Convert, retrieve, or verify a segmenter object

Usage

as.segmenter(object, ...)

as.seg_cpt(object, ...)

# S3 method for class 'seg_basket'
as.seg_cpt(object, ...)

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

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

# S3 method for class 'ga'
as.seg_cpt(object, ...)

# S3 method for class 'cpt'
as.seg_cpt(object, ...)

# S3 method for class 'wbs'
as.seg_cpt(object, ...)

is_segmenter(object, ...)

Arguments

object

A tidycpt or segmenter object

...

Arguments passed to methods

Value

  • as.segmenter() returns the segmenter object of a tidycpt object. Note that this could be of any class, depending on the class returned by the segmenting function.

  • as.seg_cpt() returns a seg_cpt object

  • is_segmenter() a logical vector of length 1

Details

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

as.segmenter() simply returns the segmenter of a tidycpt object.

as.seg_cpt() takes a wild-caught segmenter object of arbitrary class and converts it into a seg_cpt object.

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

See also

Other tidycpt-generics: as.model(), changepoints(), diagnose(), fitness(), model_name()

Other segmenter-functions: fitness(), model_args(), seg_params()

Examples

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

# Return the segmenter component
x |>
  as.segmenter()
#> Class 'cpt' : Changepoint Object
#>        ~~   : S4 class containing 12 slots with names
#>               cpttype date version data.set method test.stat pen.type pen.value minseglen cpts ncpts.max param.est 
#> 
#> Created on  : Wed Apr 24 21:56:29 2024 
#> 
#> summary(.)  :
#> ----------
#> Created Using changepoint version 2.2.4 
#> Changepoint type      : Change in mean and variance 
#> Method of analysis    : PELT 
#> Test Statistic  : Normal 
#> Type of penalty       : MBIC with value, 23.56658 
#> Minimum Segment Length : 2 
#> Maximum no. of cpts   : Inf 
#> Changepoint Locations : 55 57 309 311 330 
  
# Note the class of this object could be anything
x |>
  as.segmenter() |>
  class()
#> [1] "cpt"
#> attr(,"package")
#> [1] "changepoint"
  
# Convert the segmenter into the standardized seg_cpt class
x |>
  as.segmenter() |>
  as.seg_cpt()
#> List of 8
#>  $ 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 ...
#>  $ pkg         : chr "changepoint"
#>  $ algorithm   : chr "PELT"
#>  $ changepoints: int [1:5] 55 57 309 311 330
#>  $ fitness     : Named num -Inf
#>   ..- attr(*, "names")= chr "MBIC"
#>  $ seg_params  :List of 1
#>   ..$ :List of 3
#>   .. ..$ test_stat     : chr "Normal"
#>   .. ..$ num_cpts_max  : num Inf
#>   .. ..$ min_seg_length: num 2
#>  $ model_name  : chr "meanvar"
#>  $ penalty     : chr "MBIC"
#>  - attr(*, "class")= chr "seg_cpt"

# Is the segmenter valid?
x |>
  as.segmenter() |>
  is_segmenter()
#> [1] TRUE