Retrieve the name of the model that a segmenter or model used

Retrieve the name of the model that a segmenter or model used

Usage

model_name(object, ...)

# Default S3 method
model_name(object, ...)

# S3 method for class 'character'
model_name(object, ...)

# S3 method for class 'mod_cpt'
model_name(object, ...)

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

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

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

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

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

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

Arguments

object

A segmenter object.

...

currently ignored

Value

A character vector of length 1.

Details

Every segmenter works by fitting a model to the data. model_name() returns the name of a model that can be passed to whomademe() to retrieve the model fitting function. These functions must begin with the prefix fit_. Note that the model fitting functions exist in tidychangepoint are are not necessarily the actual functions used by the segmenter.

Models also implement model_name().

See also

Other model-fitting: fit_lmshift(), fit_meanshift(), fit_meanvar(), fit_nhpp(), model_args(), new_fun_cpt(), whomademe()

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

Examples

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

# Retrieve the name of the model from the segmenter
x |>
  as.segmenter() |>
  model_name()
#> [1] "meanvar"

# What function created the model? 
x |>
  model_name() |>
  whomademe()
#> function (x, tau, ...) 
#> {
#>     if (!is_valid_tau(tau, length(x))) {
#>         stop("Invalid changepoint set")
#>     }
#>     else {
#>         tau <- unique(tau)
#>     }
#>     regions <- split_by_tau(as.ts(x), tau)
#>     region_mods <- purrr::map(regions, ~fit_meanshift_norm(.x, 
#>         tau = NULL))
#>     fitted_values <- purrr::list_c(purrr::map(region_mods, ~c(fitted(.x))))
#>     region_params <- dplyr::mutate(purrr::list_rbind(purrr::map(region_mods, 
#>         purrr::pluck("region_params"))), region = names(regions))
#>     region_params$param_sigma_hatsq <- purrr::map_dbl(region_mods, 
#>         model_variance)
#>     mod_cpt(x <- as.ts(x), tau = tau, region_params = region_params, 
#>         model_params = c(), fitted_values = fitted_values, model_name = "meanvar")
#> }
#> <bytecode: 0x55719275b890>
#> <environment: namespace:tidychangepoint>
#> attr(,"model_name")
#> [1] "meanvar"
#> attr(,"class")
#> [1] "fun_cpt"
model_name(x$segmenter)
#> [1] "meanvar"

# Retrieve the name of the model from the model
x |>
  as.model() |>
  model_name()
#> [1] "meanvar"