Compute standard errors after fitting

gllvmTMB() computes a TMB TMB::sdreport() at fitting time unless it is told not to (control = gllvmTMBcontrol(se = FALSE)). Skipping it is substantially faster, but it used to be a one-way door: the only way to get standard errors afterwards was to fit the model again. standard_errors() removes that door by computing the same sdreport() on demand from the fitted object.

Usage

standard_errors(fit)

Arguments

fit: A fitted multivariate model returned by gllvmTMB().

Value

The same fit, with its sd_report field populated (and sdreport_error cleared). Standard-error consumers such as summary.gllvmTMB_multi(), getLV(), getREsd(), and confint(method = "wald") work on the returned object.

R semantics matter here: the fit is returned, not modified in place. Assign the result – fit <- standard_errors(fit) – or the standard errors are discarded.

If the fit already has an sd_report, it is returned unchanged; the calculation is not repeated.

Details

Fits made with non-unit likelihood weights use a weighted objective rather than an ordinary likelihood. standard_errors() refuses those fits because the package does not yet provide sandwich-calibrated uncertainty for them; point estimates remain available from the fitted model.

When this helps

The saving is real but bounded: it is worth using when a workflow fits many models and needs standard errors for only some of them (model selection, simulation loops, a grid of candidate structures). For a single fit whose summary you intend to read, the default se = TRUE is simpler and costs nothing extra.

Same-session only

A TMB ADFun object holds external pointers into compiled memory. Those pointers do not survive saveRDS() / readRDS() or a new R session, so standard_errors() can only be called on a fit made in the current session. This is a pre-existing property of every function in this package that reuses fit$tmb_obj (profiling, bootstrapping, getLV()), not a new restriction – but this function names it with a clear error rather than failing obscurely. If you need standard errors from a saved fit, refit it with se = TRUE.

What these standard errors are

Exactly the ones the fit would have produced at fitting time – the same TMB sdreport() call, on the same converged parameter vector, to the bit. Deferring the calculation does not change it, and does not change any honesty caveat that already applies to Wald standard errors from this package.

Examples

if (FALSE) { # \dontrun{
## Fit fast, decide later whether standard errors are needed.
fit <- gllvmTMB(
  traits(y1, y2) ~ 1 + latent(1 | site, d = 1),
  data = dat, family = gaussian(),
  control = gllvmTMBcontrol(se = FALSE)
)

fit <- standard_errors(fit)
summary(fit)
} # }