Plot the posterior distribution of an ernest_run

Create a plot of the posterior distributions from a nested sampling run, or trace the evolution of discarded live points along the log prior volume.

Usage

# S3 method for class 'ernest_run'
visualize(x, ..., type = c("density", "trace"), vars = NULL, plot = TRUE)

Arguments

x

An ernest_run object.

...

Arguments passed on to as_draws.ernest_run

units

Case-sensitive string. The scale for the sampled points:

• "original": Points are on the scale of the prior space.

• "unit_cube": Points are on the (0, 1) unit hypercube scale.

radial

Logical. If TRUE, returns an additional column .radial containing the radial coordinate (i.e., the Euclidean norm) for each sampled point.

type

Case-sensitive string. The type of plot to create:

• "density": Shows the posterior density of each parameter.

• "trace": Shows the distribution of points along estimates of the log prior volume.

vars

<tidy-select> Variables to plot from the run. If NULL, all variables are plotted.

plot

Logical. If TRUE, returns a ggplot of the visualisation; if FALSE, returns a tibble of the data used to create the plot.

Value

A ggplot object if plot = TRUE, otherwise a tibble.

See also

plot() for visualising evidence estimates from an ernest_run.

Examples

# Load example run
library(ggdist)
data(example_run)

# Plot posterior distributions of the parameters
visualize(example_run, type = "density")


# Plot the trace of the radial coordinate in unit scale
visualize(
  example_run,
  type = "trace",
  vars = ".radial",
  units = "unit_cube",
  radial = TRUE
)