Summarize MCMC convergence diagnostics

Computes common convergence diagnostics for selected parameters from a JAGS/BUGS fit and returns a compact, report-ready table. The diagnostics include Gelman–Rubin $\hat{R}$, Geweke z-scores, Heidelberger-Welch stationarity p-values, and autocorrelation at a user-specified lag.

Usage

mcmcDiag(bml.out, parameters, lag = 50)

Arguments

bml.out: A model fit object containing JAGS output, typically as returned by R2jags::jags(), with component $jags.out$BUGSoutput.
parameters: Character vector of parameter names (or patterns) to extract. These may be exact names or patterns (e.g., a prefix like "b" that matches "b[1]", "b[2]", …).
lag: Integer specifying the lag at which to compute autocorrelation. Default: 50. Lower values (e.g., 10) capture short-range dependence; higher values assess whether the chain has mixed well over longer intervals.

Value

A data.frame with one row per diagnostic and one column per parameter; cell entries are the average diagnostic values across chains. Row names include: "Gelman/Rubin convergence statistic", "Geweke z-score", "Heidelberger/Welch p-value", "Autocorrelation (lag <lag>)".

Details

Internally, the function converts the BUGS/JAGS output to a coda::mcmc.list, then computes per-chain diagnostics and averages them across chains for each parameter:

Gelman–Rubin ($\hat{R}$): coda::gelman.diag(). Values close to 1 indicate convergence; a common heuristic is $\hat{R} \le 1.1$.
Geweke z-score: coda::geweke.diag(). Large absolute values (e.g., $|z|>2$) suggest lack of convergence.
Heidelberger–Welch p-value: coda::heidel.diag() tests the null of stationarity in the chain segment.
Autocorrelation: coda::autocorr() at the specified lag, averaged across chains. Values near zero indicate good mixing; persistent autocorrelation suggests the chain needs thinning or reparameterization.

All statistics are rounded to three decimals. The returned table is transposed so that rows are diagnostics and columns are parameters.

References

Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4), 457-472.

Brooks, S. P., & Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7(4), 434-455.

Author

Benjamin Rosche benrosche@nyu.edu

Examples

if (FALSE) { # \dontrun{
data(coalgov)

# Fit model
m1 <- bml(
  Surv(dur_wkb, event_wkb) ~ 1 + majority +
    mm(id = id(pid, gid), vars = vars(cohesion), fn = fn(w ~ 1/n), RE = TRUE),
  family = "Weibull",
  monitor = TRUE,
  data = coalgov
)

# Check convergence for main parameters
mcmcDiag(m1, parameters = "b")  # All b coefficients

# Check specific parameters
mcmcDiag(m1, parameters = c("b[1]", "b[2]", "shape"))

# Check mm block parameters
mcmcDiag(m1, parameters = c("b.mm.1", "sigma.mm.1"))

# Custom autocorrelation lag
mcmcDiag(m1, parameters = "b", lag = 100)

# Interpreting results:
# - Gelman-Rubin < 1.1: Good convergence
# - |Geweke z| < 2: No evidence against convergence
# - Heidelberger p > 0.05: Chain appears stationary
# - Low autocorrelation: Good mixing
} # }