Diagnostic plots for dirichletprocess objects

Source: R/diagnostic_plots.R

Plot several diagnostic plots for dirichletprocess objects. Because the dimension of the dirichletprocess mixture is constantly changing, it is not simple to create meaningful plots of the sampled parameters. Therefore, the plots focus on the likelihood, alpha, and the number of clusters.

DiagnosticPlots(dpobj, gg = FALSE)

AlphaTraceplot(dpobj, gg = TRUE)

AlphaPriorPosteriorPlot(dpobj, prior_color = "#2c7fb8",
  post_color = "#d95f02", gg = TRUE)

ClusterTraceplot(dpobj, gg = TRUE)

LikelihoodTraceplot(dpobj, gg = TRUE)

Arguments

dpobj

A dirichletprocess object that was fit.
gg

Logical; whether to create a ggplot or base R plot (if gg = FALSE). For DiagnosticPlots, this means that the plots will be given one-by-one, while base plots can be arranged in a grid.
prior_color

For AlphaPriorPosteriorPlot, the color of the prior function.
post_color

For AlphaPriorPosteriorPlot, the color of the posterior histogram.

Value

If gg = TRUE, a ggplot2 object. Otherwise, nothing is returned and a base plot is plotted.

Functions

  • AlphaTraceplot: Trace plot of alpha.

  • AlphaPriorPosteriorPlot: Plot of the prior and posterior of alpha.

  • ClusterTraceplot: Trace plot of the number of clusters.

  • LikelihoodTraceplot: Trace plot of the likelihood of the data for each iteration.

Examples

dp <- Fit(DirichletProcessGaussian(rnorm(10)), 100)
DiagnosticPlots(dp)