cls.draw1.5

<p>Output of a Dirichlet process mixture model with normal components fitted to the data set <code>Ysim1.5</code>.
True clusters are given by <code>rep(1:8,each =50).</code></p>

 datasets

Implements methods for processing a sample of (hard)
clusterings, e.g. the MCMC output of a Bayesian clustering
model. Among them are methods that find a single best
clustering to represent the sample, which are based on the
posterior similarity matrix or a relabelling algorithm.

Arno Fritsch

mcclust

Process an MCMC Sample of Clusterings

cls.draw1.5 function

<p>matrix with 500 rows and 400 columns. Each row contains a clustering of the 400 observations.</p>

cls.draw1.5: Sample of Clusterings from Posterior Distribution of Bayesian Cluster Model

Description

Usage

Arguments

Format