model-diagnosticslisted

# Model Diagnostics ## Key Convergence Metrics | Metric | Good Value | Concern | |--------|------------|---------| | Rhat | < 1.01 | > 1.1 indicates non-convergence | | ESS bulk | > 400 | < 100 unreliable estimates | | ESS tail | > 400 | < 100 unreliable intervals | | Divergences | 0 | Any indicates geometry issues | | Max treedepth | 0 hits | Hitting limit = slow exploration | ## Stan Diagnostics (cmdstanr) ```r library(cmdstanr) fit <- mod$sample(data = stan_data, ...) # Quick check fit$cmdstan_diagnose() # Summary with diagnostics fit$summary() # Detailed diagnostics fit$diagnostic_summary() # Extract specific metrics draws <- fit$draws() rhat <- posterior::rhat(draws) ess_bulk <- posterior::ess_bulk(draws) ess_tail <- posterior::ess_tail(draws) # Divergences np <- fit$sampler_diagnostics() sum(np[,,"divergent__"]) # Treedepth sum(np[,,"treedepth__"] == 10) # Default max ``` ## JAGS Diagnostics (R2jags) ```r library(R2jags) library(coda) fit <- jags(...) # Summary (includes Rhat, n.eff) print(fit) fit$BUGSoutput$summary # Rhat max(fit$BUGSoutput$summary[,"Rhat"]) # Effective sample size min(fit$BUGSoutput$summary[,"n.eff"]) # Convert to coda mcmc_obj <- as.mcmc(fit) # Gelman-Rubin gelman.diag(mcmc_obj) # Autocorrelation autocorr.diag(mcmc_obj) autocorr.plot(mcmc_obj) # Geweke diagnostic geweke.diag(mcmc_obj) ``` ## Visual Diagnostics ### Trace Plots ```r # Stan (bayesplot) library(bayesplot) mcmc_trace(fit$draws(), pars = c("mu", "sigma")) # JAGS t