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).
Value
A ggplot object (if ggplot2 available) or base R plot (invisible).
Details
Ideal ACF plots show rapid decay to zero. Slow decay indicates high
autocorrelation, which reduces effective sample size and may indicate
poor mixing.
Examples
# \donttest{
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)
# }