R: Create Clustering Object

create_clustering {ClustAssess}

R Documentation

Create Clustering Object

Description

Creates a Clustering object from the output of a clustering method.

Usage

create_clustering(clustering_result, ...)

## S4 method for signature 'numeric'
create_clustering(clustering_result, alpha = 0.9)

## S4 method for signature 'integer'
create_clustering(clustering_result, alpha = 0.9)

## S4 method for signature 'character'
create_clustering(clustering_result, alpha = 0.9)

## S4 method for signature 'factor'
create_clustering(clustering_result, alpha = 0.9)

## S4 method for signature 'matrix'
create_clustering(
  clustering_result,
  alpha = 0.9,
  ppr_implementation = "prpack",
  row_normalize = TRUE
)

## S4 method for signature 'Matrix'
create_clustering(
  clustering_result,
  alpha = 0.9,
  ppr_implementation = "prpack",
  row_normalize = TRUE
)

## S4 method for signature 'hclust'
create_clustering(
  clustering_result,
  alpha = 0.9,
  r = 1,
  rescale_path_type = "max",
  ppr_implementation = "prpack",
  dist_rescaled = FALSE
)

Arguments

`clustering_result`	The clustering result, either: A numeric/character/factor vector of cluster labels for each element. A samples x clusters matrix/Matrix::Matrix of nonzero membership values. An hclust object.
`...`	This argument is not used.
`alpha`	A numeric giving the personalized PageRank damping factor; 1 - alpha is the restart probability for the PPR random walk.
`ppr_implementation`	Choose a implementation for personalized page-rank calculation: "prpack": use PPR algorithms in igraph. "power_iteration": use power_iteration method.
`row_normalize`	Whether to normalize all rows in clustering_result so they sum to one before calculating ECS. It is recommended to set this to TRUE, which will lead to slightly different ECS values compared to clusim.
`r`	A numeric hierarchical scaling parameter.
`rescale_path_type`	A string; rescale the hierarchical height by: "max" : the maximum path from the root. "min" : the minimum path form the root. "linkage" : use the linkage distances in the clustering.
`dist_rescaled`	A logical: if TRUE, the linkage distances are linearly rescaled to be in-between 0 and 1.

Value

A Clustering object.

Methods (by class)

numeric: Create Clustering Object from Numeric Vector
integer: Create Clustering Object from Integer Vector
character: Create Clustering Object from Character Vector
factor: Create Clustering Object from Factor Vector
matrix: Create Clustering Object from base matrix
Matrix: Create Clustering Object from Matrix::Matrix
hclust: Create Clustering Object from hclust

[Package ClustAssess version 0.3.0 Index]