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Plot Method for DPprior_fit Objects

Source: R/17_s3_methods.R
plot.DPprior_fit.Rd

Creates visualizations of a prior elicitation result. Multiple plot types are available, including individual distribution plots and comprehensive dashboards.

Usage

# S3 method for class 'DPprior_fit'
plot(
  x,
  type = c("auto", "dashboard", "alpha", "K", "w1", "dual", "comparison"),
  engine = c("ggplot2", "base"),
  ...
)

Arguments

x

A DPprior_fit object.

type

Character; the type of plot to create:

"auto"

(Default) Automatically selects the appropriate plot type. Uses "dual" for dual-anchor fits, "dashboard" otherwise.

"dashboard"

4-panel dashboard showing alpha, K, w1, and summary.

"alpha"

Prior density of the concentration parameter alpha.

"K"

Prior PMF of the number of clusters \(K_J\).

"w1"

Prior density of the first stick-breaking weight w1.

"dual"

Dual-anchor comparison dashboard (for dual-anchor fits).

"comparison"

Same as "dual".

engine

Character; graphics engine to use: "ggplot2" (default) or "base".

...

Additional arguments passed to the underlying plot functions. Common options include:

base_size

Base font size (default: 11)

ci_level

Credible interval level for alpha plot (default: 0.95)

title

Optional title for the dashboard

show

If TRUE, display the plot; if FALSE, return silently

Value

Depends on the plot type and engine:

  • For ggplot2: Returns a ggplot object or gtable (for dashboards)

  • For base: Returns invisible(NULL)

Details

The "auto" type is recommended for most use cases. It automatically detects whether the fit object is from dual-anchor calibration and selects the appropriate visualization.

For dual-anchor fits, the comparison dashboard shows:

  • Alpha prior: K-only vs Dual-anchor

  • K distribution comparison

  • w1 distribution comparison with dominance threshold

  • Summary comparison table

Plot Type Details

dashboard

A 2x2 grid showing: (A) Alpha prior density with CI (B) \(K_J\) prior PMF with mode and mean (C) w1 prior density with dominance shading (D) Summary statistics table

alpha

Gamma(a, b) density with: - Mean line (dashed) - Credible interval (shaded region) - Annotation with moments and CI

K

Bar plot of \(P(K_J = k)\) with: - Target mean line - Achieved mean line - Optional CDF overlay

w1

Density plot with: - Dominance region shading (w1 > 0.5) - Threshold lines - Exceedance probabilities

See also

plot_prior_dashboard, plot_alpha_prior, plot_K_prior, plot_w1_prior, plot_dual_comparison

Examples

# Create a fit object
fit <- DPprior_fit(J = 50, mu_K = 5, var_K = 8)
#> Warning: HIGH DOMINANCE RISK: P(w1 > 0.5) = 48.1% exceeds 40%.
#>   This may indicate unintended prior behavior (Lee, 2026).
#>   Consider using DPprior_dual() for weight-constrained elicitation.
#>   See ?DPprior_diagnostics for interpretation.

# Auto-detect best plot type
plot(fit)

#> TableGrob (2 x 2) "dpprior_dashboard": 4 grobs
#>   z     cells              name           grob
#> 1 1 (1-1,1-1) dpprior_dashboard gtable[layout]
#> 2 2 (2-2,1-1) dpprior_dashboard gtable[layout]
#> 3 3 (1-1,2-2) dpprior_dashboard gtable[layout]
#> 4 4 (2-2,2-2) dpprior_dashboard gtable[layout]

# Specific plot types
plot(fit, type = "alpha")


plot(fit, type = "K")


plot(fit, type = "w1")


plot(fit, type = "dashboard")

#> TableGrob (2 x 2) "dpprior_dashboard": 4 grobs
#>   z     cells              name           grob
#> 1 1 (1-1,1-1) dpprior_dashboard gtable[layout]
#> 2 2 (2-2,1-1) dpprior_dashboard gtable[layout]
#> 3 3 (1-1,2-2) dpprior_dashboard gtable[layout]
#> 4 4 (2-2,2-2) dpprior_dashboard gtable[layout]

# With custom options
plot(fit, type = "dashboard", title = "My Prior Analysis")

#> TableGrob (3 x 2) "dpprior_dashboard": 5 grobs
#>   z     cells              name                grob
#> 1 1 (2-2,1-1) dpprior_dashboard      gtable[layout]
#> 2 2 (3-3,1-1) dpprior_dashboard      gtable[layout]
#> 3 3 (2-2,2-2) dpprior_dashboard      gtable[layout]
#> 4 4 (3-3,2-2) dpprior_dashboard      gtable[layout]
#> 5 5 (1-1,1-2) dpprior_dashboard text[GRID.text.710]

# Dual-anchor comparison
fit_K <- DPprior_a2_newton(J = 50, mu_K = 5, var_K = 8)
fit_dual <- DPprior_dual(fit_K, w1_target = list(prob = list(threshold = 0.5, value = 0.3)))
plot(fit_dual)  # Auto-selects dual comparison

#> TableGrob (3 x 2) "dpprior_dashboard": 5 grobs
#>   z     cells              name                grob
#> 1 1 (2-2,1-1) dpprior_dashboard      gtable[layout]
#> 2 2 (3-3,1-1) dpprior_dashboard      gtable[layout]
#> 3 3 (2-2,2-2) dpprior_dashboard      gtable[layout]
#> 4 4 (3-3,2-2) dpprior_dashboard      gtable[layout]
#> 5 5 (1-1,1-2) dpprior_dashboard text[GRID.text.886]
plot(fit_dual, type = "comparison")  # Explicit

#> TableGrob (3 x 2) "dpprior_dashboard": 5 grobs
#>   z     cells              name                 grob
#> 1 1 (2-2,1-1) dpprior_dashboard       gtable[layout]
#> 2 2 (3-3,1-1) dpprior_dashboard       gtable[layout]
#> 3 3 (2-2,2-2) dpprior_dashboard       gtable[layout]
#> 4 4 (3-3,2-2) dpprior_dashboard       gtable[layout]
#> 5 5 (1-1,1-2) dpprior_dashboard text[GRID.text.1059]