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Comprehensive Diagnostic Summary

Source: R/plot_diagnostics.R
diagnostic_summary.Rd

Provides a comprehensive diagnostic report for a tulpa model, combining convergence metrics, divergence information, and actionable recommendations.

Usage

diagnostic_summary(fit, quiet = FALSE)

Arguments

fit

A tulpa_fit object.

quiet

Logical; if TRUE, suppress printed output (default: FALSE).

Value

A list with class tulpa_diagnostic_summary containing:

status

Overall status: "PASS", "WARN", or "FAIL"

n_divergent

Number of divergent transitions

divergent_pct

Percentage of divergent transitions

worst_rhat

Data frame of parameters with worst Rhat

worst_ess

Data frame of parameters with worst ESS

e_bfmi

E-BFMI value (HMC only)

pareto_k, quad_ess

approximation fits only: the outer PSIS k-hat, or the grid quadrature ESS when no k-hat was produced

pareto_k_declined

approximation fits only, and only when there is no k-hat: WHY – "not_requested" and "unguessable_axis: <axis>" are benign or permanent, "degenerate_proposal" and "grid_too_small" are signals about the fit, and "internal_inconsistency" is an engine bug and raises the status to "WARN"

inner_skew_max, inner_skew_declined

approximation fits only: the largest scored inner-Laplace |gamma_3|, or why nothing was scored ("coupled_arm" marks arms the inner layer could score neither per observation nor through the cell tensor)

axis_fields_dropped

data frame of grid axes the fit's own resolved path could not read and dropped as engine defaults: one row per field, with the block, family, path and the axis that path integrated instead. Absent whenever every supplied axis was used

recommendations

Character vector of recommendations

See also

check_diagnostics(), diagnostics(), plot_diagnostics()

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))
ds <- diagnostic_summary(fit)
print(ds)
# }