Update and re-fit an INLAvaan model

Re-fit an INLAvaan model with modified arguments, in the style of stats::update(). This is convenient for prior-sensitivity analyses (vary dp or the prior() modifiers), iterative respecification (swap the model or add syntax), and swapping approximation settings (e.g. marginal_method, nsamp) without retyping the whole call.

Usage

# S4 method for class 'INLAvaan'
update(object, model, add, ..., evaluate = TRUE)

Arguments

object: An object of class INLAvaan.
model: Optional replacement model, in the same form accepted by inlavaan() (lavaan model syntax or a parameter table).
add: Optional lavaan syntax appended to the original model. Requires the original model to have been specified as a syntax string, and is ignored when model is supplied.
...: Further arguments passed to inlavaan(), overriding the values in the original call (e.g. dp, marginal_method, nsamp, data).
evaluate: Logical. If TRUE (default) the updated call is evaluated and the re-fitted INLAvaan object is returned; if FALSE the updated, unevaluated call is returned for inspection.

Value

A re-fitted INLAvaan object, or the updated call when evaluate = FALSE.

Details

The method edits the recorded inlavaan() call, replacing model (or extending it via add) and overriding any argument supplied through ..., then re-evaluates it. Because INLAvaan extends the lavaan class, a dedicated method is required: without it, update() would dispatch to lavaan's method and silently return a frequentist lavaan fit instead of a Bayesian one.

When the parameter structure is preserved (no change to model, add, or model.type), the previous posterior mode is passed as the optimiser's starting value (a warm start), so mode-finding is typically much faster than a cold fit. This makes prior sweeps particularly cheap. The warm start only changes the optimiser's starting point, not the fitted result. Supply start explicitly to override it.

Fits produced by INLAvaan versions that did not record their call cannot be updated; re-fit once with inlavaan() (or acfa(), asem(), agrowth()) and update() will work on the new object.

Examples

if (FALSE) { # \dontrun{
model <- "visual =~ x1 + x2 + x3"
fit <- acfa(model, data = lavaan::HolzingerSwineford1939)

# Prior sensitivity: refit under a tighter loading prior (warm-started)
fit2 <- update(fit, dp = priors_for(lambda = "normal(0,1)"))

# Respecify: add a residual covariance
fit3 <- update(fit, add = "x1 ~~ x2")
compare(fit, fit3)

# Swap approximation settings only
fit4 <- update(fit, marginal_method = "sampling", nsamp = 4000)
} # }