% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/method-vcov.R
\name{vcov}
\alias{vcov}
\alias{vcov,INLAvaan-method}
\title{Variance-Covariance Matrix for INLAvaan Models}
\usage{
\S4method{vcov}{INLAvaan}(object, type = c("lavaan", "theta"), ...)
}
\arguments{
\item{object}{An object of class \link{INLAvaan}.}

\item{type}{Character. \code{"lavaan"} (default) returns the posterior
covariance matrix of the model parameters computed from posterior
samples (matching lavaan output). \code{"theta"} returns the Laplace
approximation covariance in the internal parameterisation.}

\item{...}{Currently unused.}
}
\value{
A square numeric matrix.
}
\description{
Extract the posterior variance-covariance matrix of model parameters from a
fitted \code{INLAvaan} model.
}
\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)

# Default: posterior covariance of lavaan parameters
vcov(fit)

# Internal parameterisation (Laplace approximation)
vcov(fit, type = "theta")
}

}
\seealso{
\code{\link[=summary]{summary()}}, \code{\link[=coef]{coef()}}, \code{\link[=standardisedsolution]{standardisedsolution()}}
}
