clmstan (version 0.1.1)

extract_acf: Extract ACF values from clmstan object

Description

Computes autocorrelation function (ACF) values for MCMC chains and returns them in a tidy data frame format.

Usage

extract_acf(object, pars = NULL, lags = 20, ...)

Value

A data frame with columns:

• parameter: Parameter name

• chain: Chain number

• lag: Lag value (0, 1, 2, ...)

• acf: Autocorrelation value

Arguments

object

A clmstan object

pars

Character vector of parameter names. If NULL (default), uses beta, c_transformed (except first), and beta0.

lags

Maximum number of lags to compute. Default is 20.

...

Additional arguments (ignored)

Details

The ACF measures how correlated each draw is with previous draws in the same chain. High autocorrelation at many lags indicates slow mixing and the need for more samples or reparameterization.

Ideally, ACF should drop to near zero within a few lags. Persistent high autocorrelation suggests the sampler is exploring the posterior slowly.

Examples

if (FALSE) {
fit <- clm_stan(rating ~ temp, data = wine)
acf_df <- extract_acf(fit)
head(acf_df)

# Plot ACF for specific parameters
library(ggplot2)
acf_df |>
  dplyr::filter(parameter == "beta[1]") |>
  ggplot(aes(x = lag, y = acf, color = factor(chain))) +
  geom_line() +
  geom_hline(yintercept = 0, linetype = "dashed")
}