plot.dirichletprocess

Logical, indicating whether to plot the likelihood from the dpobj.

likelihood

Logical, indicating whether to draw the posterior from the last iteration or use the full cluster sequence.

single

For a univariate Dirichlet process plot the density of the data with the
posterior distribution and credible intervals overlayed. For multivariate data
the first two columns of the data are plotted with the data points coloured by their
cluster labels.

Perform nonparametric Bayesian analysis using Dirichlet
processes without the need to program the inference algorithms.
Utilise included pre-built models or specify custom
models and allow the dirichletprocess package to handle the
Markov chain Monte Carlo sampling.
Our Dirichlet process objects can act as building blocks for a variety
of statistical models including and not limited to: density estimation,
clustering and prior distributions in hierarchical models.
See Teh, Y. W. (2011)
<https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>,
among many other sources.

plot.dirichletprocess: Plot the Dirichlet process object

Description

Usage

Arguments

Value