plot_diagnostics: Diagnostic plotting functions for tulpa models

In tulpa: Templated Unified Library for Posterior Approximation in Bayesian Hierarchical Models

Diagnostic plotting functions for tulpa models

Description

Visual diagnostic tools for MCMC convergence assessment. All functions provide base R fallbacks when ggplot2/bayesplot are unavailable.

Creates a combined diagnostic figure with Rhat, ESS, trace plot, and energy/ACF panels. Requires the patchwork package for layout.

Usage

plot_diagnostics(fit, pars = NULL)

Arguments

fit	A tulpa_fit object.
pars	Character vector of parameter names for trace plot. If NULL, uses the parameter with worst Rhat.

Details

Creates a 2x2 grid:

• Top left: Rhat plot

• Top right: ESS plot

• Bottom left: Trace for worst parameter

• Bottom right: Energy (HMC) or ACF (other)

Value

A combined plot (ggplot + patchwork) or NULL if requirements not met.

See Also

diagnostic_summary(), plot_rhat(), plot_ess()

Examples

set.seed(123)
df <- data.frame(x = rnorm(60))
df$y <- rpois(60, exp(0.5 + 0.3 * df$x))
fit <- tulpa(y ~ x, data = df, family = "poisson", mode = "hmc",
             control = list(n_iter = 500L, warmup = 250L, n_chains = 2L,
                            seed = 1L))
plot_diagnostics(fit)

tulpa documentation built on Oct. 19, 2026, 5:09 p.m.