Update a cluster specification — update.db

If parameters of a cluster specification need to be modified, update() can be used in lieu of recreating the object from scratch.

Usage

# S3 method for class 'db_clust'
update(
  object,
  parameters = NULL,
  radius = NULL,
  min_points = NULL,
  fresh = FALSE,
  ...
)

# S3 method for class 'gm_clust'
update(
  object,
  parameters = NULL,
  num_clusters = NULL,
  circular = NULL,
  zero_covariance = NULL,
  shared_orientation = NULL,
  shared_shape = NULL,
  shared_size = NULL,
  fresh = FALSE,
  ...
)

# S3 method for class 'hier_clust'
update(
  object,
  parameters = NULL,
  num_clusters = NULL,
  cut_height = NULL,
  linkage_method = NULL,
  dist_fun = NULL,
  fresh = FALSE,
  ...
)

# S3 method for class 'k_means'
update(object, parameters = NULL, num_clusters = NULL, fresh = FALSE, ...)

# S3 method for class 'mean_shift'
update(object, parameters = NULL, bandwidth = NULL, fresh = FALSE, ...)

Arguments

object: A cluster specification.
parameters: A 1-row tibble or named list with main parameters to update. Use either parameters or the main arguments directly when updating. If the main arguments are used, these will supersede the values in parameters. Also, using engine arguments in this object will result in an error.
radius: Positive double, Radius drawn around points to determine core-points and cluster assignments (required).
min_points: Positive integer, Minimum number of connected points required to form a core-point, including the point itself (required).
fresh: A logical for whether the arguments should be modified in-place or replaced wholesale.
...: Not used for update().
num_clusters: Positive integer, number of clusters in model.
circular: Boolean, whether or not to fit circular MVG distributions for each cluster. Default TRUE.
zero_covariance: Boolean, whether or not to assign covariances of 0 for each MVG. Default TRUE.
shared_orientation: Boolean, whether each cluster MVG should have the same orientation. Default TRUE.
shared_shape: Boolean, whether each cluster MVG should have the same shape. Default TRUE.
shared_size: Boolean, whether each cluster MVG should have the same size/volume. Default TRUE.
cut_height: Positive double, height at which to cut dendrogram to obtain cluster assignments (only used if num_clusters is NULL)
linkage_method: the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).
dist_fun: A function for calculating the distance between observations. Defaults to philentropy::distance which supports numerous distance metrics via its method argument. The function should accept a matrix or data frame and return a square numeric matrix or an object coercible to one via stats::as.dist(). See silhouette() for further details.
bandwidth: Positive double, kernel bandwidth controlling the size of the neighborhood used to compute the density estimate (required).

Value

An updated cluster specification.

Examples

kmeans_spec <- k_means(num_clusters = 5)
kmeans_spec
#> K Means Cluster Specification (partition)
#> 
#> Main Arguments:
#>   num_clusters = 5
#> 
#> Computational engine: stats 
#> 
update(kmeans_spec, num_clusters = 1)
#> K Means Cluster Specification (partition)
#> 
#> Main Arguments:
#>   num_clusters = 1
#> 
#> Computational engine: stats 
#> 
update(kmeans_spec, num_clusters = 1, fresh = TRUE)
#> K Means Cluster Specification (partition)
#> 
#> Main Arguments:
#>   num_clusters = 1
#> 
#> Computational engine: stats 
#> 

param_values <- tibble::tibble(num_clusters = 10)

kmeans_spec |> update(param_values)
#> K Means Cluster Specification (partition)
#> 
#> Main Arguments:
#>   num_clusters = 10
#> 
#> Computational engine: stats 
#>