ob.stability: Estimate the stability of a clustering based on...

In bootcluster: Bootstrapping Estimates of Clustering Stability

Estimate the stability of a clustering based on non-parametric bootstrap out-of-bag scheme, with option for subsampling scheme

Description

Estimate the stability of a clustering based on non-parametric bootstrap out-of-bag scheme, with option for subsampling scheme

Usage

ob.stability(x, k, B = 500, r = 5, subsample = FALSE, cut_ratio = 0.5)

Arguments

x	data.frame of the data set where the rows as observations and columns as dimensions of features
k	number of clusters for which to estimate the stability
B	number of bootstrap re-samples
r	integer parameter in the kmeansCBI() funtion
subsample	logical parameter to use the subsampling scheme option in the resampling process (instead of bootstrap)
cut_ratio	numeric parameter between 0 and 1 for subsampling scheme training set ratio

Details

This function estimates the stability through out-of-bag observations It estimate the stability at the (1) observation level, (2) cluster level, and (3) overall.

Value

membership

vector of membership for each observation from the reference clustering

obs_wise

vector of estimated observation-wise stability

clust_wise

vector of estimated cluster-wise stability

overall

numeric estimated overall stability

Smin

numeric estimated Smin through out-of-bag scheme

Author(s)

Tianmou Liu

References

Bootstrapping estimates of stability for clusters, observations and model selection. Han Yu, Brian Chapman, Arianna DiFlorio, Ellen Eischen, David Gotz, Matthews Jacob and Rachael Hageman Blair.

Examples

set.seed(123)
data(iris)
df <- data.frame(iris[,1:4])
# You can choose to scale df before clustering by 
# df <- scale(df)
ob.stability(df, k = 2, B=500, r=5)

bootcluster documentation built on April 3, 2025, 7:45 p.m.