element_agreement: Element-Wise Average Agreement Between a Set of Clusterings

In Core-Bioinformatics/ClustAssess: Tools for Assessing Clustering

Element-Wise Average Agreement Between a Set of Clusterings

Description

Inspect how consistently of a set of clusterings agree with a reference clustering by calculating their element-wise average agreement.

Usage

element_agreement(
  reference_clustering,
  clustering_list,
  alpha = 0.9,
  r = 1,
  rescale_path_type = "max",
  ppr_implementation = "prpack",
  dist_rescaled = FALSE,
  row_normalize = TRUE
)

Arguments

reference_clustering	The reference clustering, that each clustering in clustering_list is compared to. It can be 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.
clustering_list	The list of clustering results, each of which is 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.
alpha	A numeric giving the personalized PageRank damping factor; 1 - alpha is the restart probability for the PPR random walk.
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.
ppr_implementation	Choose a implementation for personalized page-rank calculation: "prpack": use PPR algorithms in igraph. "power_iteration": use power_iteration method.
dist_rescaled	A logical: if TRUE, the linkage distances are linearly rescaled to be in-between 0 and 1.
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.

Value

A vector containing the element-wise average agreement.

References

Gates, A. J., Wood, I. B., Hetrick, W. P., & Ahn, Y. Y. (2019). Element-centric clustering comparison unifies overlaps and hierarchy. Scientific reports, 9(1), 1-13. https://doi.org/10.1038/s41598-019-44892-y

Examples

# perform k-means clustering across 20 random seeds
reference.clustering <- iris$Species
clustering.list <- lapply(1:20, function(x) kmeans(iris[, 1:4], centers = 3)$cluster)
element_agreement(reference.clustering, clustering.list)

Core-Bioinformatics/ClustAssess documentation built on Nov. 4, 2024, 1:05 p.m.