ob.stability: Estimate the stability of a clustering based on...
In bootcluster: Bootstrapping Estimates of Clustering Stability

Description Usage Arguments Details Value Author(s) References Examples

View source: R/stability_ob.R

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

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

`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

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.

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

Tianmou Liu

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.

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)