Plot the Dirichlet process object

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. The additional arguments are not used for multivariate data.

# S3 method for dirichletprocess
plot(x, ...)

plot_dirichletprocess_univariate(x, likelihood = FALSE, single = TRUE,
  data_fill = "black", data_method = "density", data_bw = NULL,
  ci_size = 0.05, xgrid_pts = 100, quant_pts = 100, xlim = NA)

plot_dirichletprocess_multivariate(x)

Arguments

x	Dirichlet Process Object to plot
...	Further arguments, currently ignored.
likelihood	Logical, indicating whether to plot the likelihood from the dpobj.
single	Logical, indicating whether to draw the posterior from the last iteration or use the full cluster sequence.
data_fill	Passed to `fill` in the data geom, for example a color. Defaults to "black".
data_method	A string containing either "density" (default), "hist"/"histogram", or "none". Data is plotted according to this method.
data_bw	Bandwith to be passed either as the binwidth of geom_histogram, or as the bw of geom_density.
ci_size	Numeric, the interval size to use. Defaults to .05.
xgrid_pts	Integer, the number of points on the x-axis to evaluate.
quant_pts	Integer, the number of posterior functions to use to obtain the posterior and its interval.
xlim	Default NA. If a vector of length two, the limits on the x-axis of the plot. If NA (default), the limits will be automatically chosen.

Value

A ggplot object.

Examples

dp <- DirichletProcessGaussian(c(rnorm(50, 2, .2), rnorm(60)))
dp <- Fit(dp, 100)
plot(dp)

plot(dp, likelihood = TRUE, data_method = "hist",
     data_fill = rgb(.5, .5, .8, .6), data_bw = .3)