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Plot diagnostics from nested sampling results

Source: R/plot.R
plot-ernest.Rd

Visualizes key diagnostics from nested sampling outputs as functions of log-prior volume.

Usage

# S3 method for class 'ernest_estimate'
plot(x, which = c("evidence", "weight", "likelihood"), n = 512, ...)

# S3 method for class 'ernest_run'
plot(x, which = c("evidence", "weight", "likelihood"), n = 512, ...)

# S3 method for class 'ernest_estimate'
summary(
  object,
  which = c("evidence", "weight", "likelihood"),
  n = 512,
  width = NULL,
  ...
)

Arguments

x, object

[ernest_run] or [ernest_estimate]
An object containing results from nested sampling.

which

[character()]
One or more diagnostics to display. Must be any of "evidence", "weight", and "likelihood".

n

[integer(1)]
Number of evaluation points along the log-volume axis used to summarize each curve.

...

These dots are for future extensions and must be empty.

width

[numeric()]
Numeric vector of widths for uncertainty intervals. Defaults to the 50%, 80%, and 95% highest mean depth intervals.

Value

For plot, the ggplot object, invisibly. It is also printed as a side effect. For summary, a list with possible elements evidence, weight, and likelihood. Each element is a data frame.

Details

plot() is a visualization wrapper around summary.ernest_estimate(), followed by autoplot(). Use which to select diagnostics:

  • which = "evidence": Estimated marginal likelihood as a function of log-prior volume.

  • which = "weight": Posterior mass concentration across log-prior volume.

  • which = "likelihood": Normalized likelihood across log-prior volume.

If x is an ernest_run, plotting first computes calculate(x, ndraws = 0). In this mode, log_volume and log_weight are deterministic, and evidence uncertainty comes from the analytical normal approximation generated from the original sampling run.

If x is an ernest_estimate generated with ndraws > 0, diagnostics are summarized over simulated log-volume trajectories. For these simulated estimates, uncertainty bands for evidence and weight are computed as interval summaries on interpolated curves.

To get the underlying data frames used for plotting, use summary.ernest_estimate(). This is useful when you want full control over plotting.

Note

Plotting multiple diagnostics with which requires patchwork. Plotting evidence or weight diagnostics requires ggdist.

See also

calculate.ernest_run()

Other visualizations: visualize.ernest_run()

Examples

# Plot diagnostics from a run (analytical uncertainty for evidence).
data(example_run)
plot(example_run)


# Plot diagnostics from simulated log-volume trajectories.
set.seed(123)
est <- calculate(example_run, ndraws = 100)
plot(est)