INLAvaan (version 0.2.4)

diagnostics: Convergence and Approximation Diagnostics for INLAvaan Models

Description

Extract convergence and approximation-quality diagnostics from a fitted INLAvaan model.

Usage

diagnostics(object, ...)
# S4 method for INLAvaan
diagnostics(object, type = c("global", "param"), ...)

Value

For type = "global", a named numeric vector (class "diagnostics.INLAvaan"). For type = "param", a data frame (class c("diagnostics.INLAvaan.param", "data.frame")).

Arguments

object

An object of class INLAvaan.

...

Currently unused.

type

Character. "global" (default) returns a named numeric vector of scalar diagnostics. "param" returns a data frame with one row per free parameter containing per-parameter diagnostics.

Details

Global diagnostics (type = "global"):

npar

Number of free parameters.

nsamp

Number of posterior samples drawn.

converged

1 if the optimiser converged, 0 otherwise.

iterations

Number of optimiser iterations.

grad_inf

L-infinity norm of the analytic gradient at the mode (max |grad|). Should be ~0 at convergence.

grad_inf_rel

Relative L-infinity norm of the analytic gradient (max |grad| / (|par| + 1e-6)).

grad_l2

L2 (Euclidean) norm of the analytic gradient at the mode.

hess_cond

Condition number of the Hessian (precision matrix) computed from \(\Sigma_\theta\). Large values indicate near-singularity.

vb_kld_global

Global KL divergence from the VB mean correction (NA if VB correction was not applied).

vb_applied

1 if VB correction was applied, 0 otherwise.

kld_max

Maximum per-parameter KL divergence from the VB correction.

kld_mean

Mean per-parameter KL divergence.

nmad_max

Maximum normalised max-absolute-deviation across marginals (skew-normal method only; NA otherwise).

nmad_mean

Mean NMAD across marginals.

Per-parameter diagnostics (type = "param"): A data frame with columns:

param

Parameter name.

grad

Analytic gradient of the negative log-posterior at the mode. Should be ~0 at convergence.

grad_num

Numerical (finite-difference) gradient at the mode. Should agree with grad; large discrepancies indicate a bug in the analytic gradient.

grad_diff

Difference grad_num - grad: should be ~0.

grad_abs

Absolute analytic gradient.

grad_rel

Relative analytic gradient |grad| / (|par| + 1e-6).

kld

Per-parameter KL divergence from the VB correction.

vb_shift

VB correction shift (in original scale).

vb_shift_sigma

VB shift in units of posterior SD.

nmad

Normalised max-absolute-deviation of the skew-normal fit (NA when not using the skewnorm method).

See Also

timing(), fitmeasures(), plot()

Examples

# \donttest{
HS.model <- "
  visual  =~ x1 + x2 + x3
  textual =~ x4 + x5 + x6
  speed   =~ x7 + x8 + x9
"
utils::data("HolzingerSwineford1939", package = "lavaan")
fit <- acfa(HS.model, HolzingerSwineford1939, std.lv = TRUE, nsamp = 100,
            test = "none", verbose = FALSE)

# Global convergence summary
diagnostics(fit)

# Per-parameter table
diagnostics(fit, type = "param")
# }