medv: Clustering Method of Medvedovic

In mcclust: Process an MCMC Sample of Clusterings

Clustering Method of Medvedovic

Description

Based on a posterior similarity matrix of a sample of clusterings medv obtains a clustering by using 1-psm as distance matrix for hierarchical clustering with complete linkage. The dendrogram is cut at a value h close to 1.

Usage

medv(psm, h=0.99)

Arguments

psm	a posterior similarity matrix, usually obtained from a call to comp.psm.
h	The height at which the dendrogram is cut.

Value

vector of cluster memberships.

Author(s)

Arno Fritsch, [e-mail]

References

Medvedovic, M. Yeung, K. and Bumgarner, R. (2004) Bayesian mixture model based clustering of replicated microarray data, Bioinformatics, 20, 1222-1232.

See Also

comp.psm for computing posterior similarity matrix, maxpear, minbinder, relabel for other possibilities for processing a sample of clusterings.

Examples

data(cls.draw1.5) 
# sample of 500 clusterings from a Bayesian cluster model 
tru.class <- rep(1:8,each=50) 
# the true grouping of the observations
psm1.5 <- comp.psm(cls.draw1.5)
medv1.5 <- medv(psm1.5)
table(medv1.5, tru.class)

mcclust documentation built on May 2, 2022, 5:05 p.m.