extract_clusters

This function extracts estimated cluster memberships from MCMC samples obtained from a DPCD model fit.

A Bayesian hierarchical model for clustering dissimilarity data using the Dirichlet process. The latent configuration of objects and the number of clusters are automatically inferred during the fitting process. The package supports multiple models which are available to detect clusters of various shapes and sizes using different covariance structures. Additional functions are included to ensure adequate model fits through prior and posterior predictive checks.

Samuel Morrissette

DPCD

Dirichlet Process Clustering with Dissimilarities

Sam Morrissette

extract_clusters function

<dl><dt>mcmc_samples</dt>
<dd>An object of class <code>mcmc</code> or <code>mcmc.list</code> containing posterior samples from a DPCD model fit using <code>run_dpcd()</code>. The variable <code>z</code> must be included in the output parameters.</dd></dl>

Arguments

Extract clusters from MCMC samples — extract_clusters

<dl>

<dt>mcmc_samples</dt>
<dd>An object of class <code>mcmc</code> or <code>mcmc.list</code> containing posterior samples from a DPCD model fit using <code>run_dpcd()</code>. The variable <code>z</code> must be included in the output parameters.</dd>

</dl>

extract_clusters: Extract clusters from MCMC samples

Description

Usage

Value

Arguments

Details

References

See Also

Examples