plot_acf: Plot autocorrelation functions

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

Plot autocorrelation functions

Description

Creates autocorrelation function (ACF) plots for selected parameters. High autocorrelation indicates slow mixing and low effective sample size.

Usage

plot_acf(fit, pars = NULL, lags = 25, n_pars = 6)

Arguments

fit	A tulpa_fit object.
pars	Character vector of parameter names. If NULL, selects worst-mixing parameters based on ESS.
lags	Maximum number of lags to compute (default: 25).
n_pars	Maximum number of parameters to plot (default: 6).

Details

Ideal ACF plots show rapid decay to zero. Slow decay indicates high autocorrelation, which reduces effective sample size and may indicate poor mixing.

Value

A ggplot object (if ggplot2 available) or base R plot (invisible).

See Also

plot_ess(), diagnostics()

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_acf(fit)
plot_acf(fit, lags = 10)

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