Stable: Stable results

In sharp: Stability-enHanced Approaches using Resampling Procedures

Stable results

Description

Extracts stable results for stability selection or consensus clustering.

Usage

Stable(stability, argmax_id = NULL, linkage = "complete")

SelectedVariables(stability, argmax_id = NULL)

Adjacency(stability, argmax_id = NULL)

Clusters(stability, linkage = "complete", argmax_id = NULL)

Arguments

stability	output of VariableSelection, BiSelection, GraphicalModel or Clustering.
argmax_id	optional indices of hyper-parameters. If argmax_id=NULL, the calibrated hyper-parameters are used.
linkage	character string indicating the type of linkage used in hierarchical clustering to define the stable clusters. Possible values include "complete", "single" and "average" (see argument "method" in hclust for a full list).

Value

A binary vector or matrix encoding the selection status (1 if selected, 0 otherwise) in stability selection or stable cluster membership in consensus clustering.

See Also

VariableSelection, BiSelection, GraphicalModel, Clustering

Examples

# Variable selection
set.seed(1)
simul <- SimulateRegression(pk = 20)
stab <- VariableSelection(xdata = simul$xdata, ydata = simul$ydata)
SelectedVariables(stab)
Stable(stab)

# Graphical model
set.seed(1)
simul <- SimulateGraphical(pk = 10)
stab <- GraphicalModel(xdata = simul$data)
Adjacency(stab)
Stable(stab)

# Clustering
set.seed(1)
simul <- SimulateClustering(
  n = c(30, 30, 30),
  nu_xc = 1
)
stab <- Clustering(xdata = simul$data)
Clusters(stab)
Stable(stab)

sharp documentation built on April 11, 2025, 5:44 p.m.