acfa: Fit an Approximate Bayesian Confirmatory Factor Analysis Model

Description

Fit an Approximate Bayesian Confirmatory Factor Analysis Model

Usage

acfa(
  model,
  data,
  dp = blavaan::dpriors(),
  marginal_method = c("skewnorm", "asymgaus", "marggaus", "sampling"),
  nsamp = 500,
  test = "standard",
  marginal_correction = c("shortcut", "hessian", "none"),
  sn_fit_logthresh = -6,
  sn_fit_temp = NA,
  control = list(),
  verbose = TRUE,
  debug = FALSE,
  add_priors = TRUE,
  optim_method = c("nlminb", "ucminf", "optim"),
  numerical_grad = FALSE,
  ...
)

Value

An S4 object of class INLAvaan which is a subclass of the lavaan::lavaan class.

Arguments

model: A description of the user-specified model. Typically, the model is described using the lavaan model syntax. See model.syntax for more information. Alternatively, a parameter table (eg. the output of the lavParTable() function) is also accepted.
data: An optional data frame containing the observed variables used in the model. If some variables are declared as ordered factors, lavaan will treat them as ordinal variables.
dp: Default prior distributions on different types of parameters, typically the result of a call to dpriors(). See the dpriors() help file for more information.
marginal_method: The method for approximating the marginal posterior distributions. Options include "skewnorm" (skew normal), "asymgaus" (two-piece asymmetric Gaussian), "marggaus" (marginalising the Laplace approximation), and "sampling" (sampling from the joint Laplace approximation).
nsamp: The number of samples to draw for all sampling-based approaches (including posterior sampling for model fit indices).
test: Character indicating whether to compute posterior fit indices. Defaults to "standard". Change to "none" to skip these computations.
marginal_correction: Which type of correction to use when fitting the skew normal or two-piece Gaussian marginals. "hessian" computes the full Hessian-based correction (slow), "shortcut" (default) computes only diagonals, and "none" (or FALSE) applies no correction.
sn_fit_logthresh: The log-threshold for fitting the skew normal. Points with log-posterior drop below this threshold (relative to the maximum) will be excluded from the fit. Defaults to -6.
sn_fit_temp: Temperature parameter for fitting the skew normal. If NA, the temperature will be included in the optimisation during the skew normal fit.
control: A list of control parameters for the optimiser.
verbose: Logical indicating whether to print progress messages.
debug: Logical indicating whether to return debug information.
add_priors: Logical indicating whether to include prior densities in the posterior computation.
optim_method: The optimisation method to use for finding the posterior mode. Options include "nlminb" (default), "ucminf", and "optim" (BFGS).
numerical_grad: Logical indicating whether to use numerical gradients for the optimisation.
...: Additional arguments to be passed to the lavaan::lavaan model fitting function.

Details

The acfa() function is a wrapper for the more general inlavaan() function, using the following default arguments:

int.ov.free = TRUE
int.lv.free = FALSE
auto.fix.first = TRUE (unless std.lv = TRUE)
auto.fix.single = TRUE
auto.var = TRUE
auto.cov.lv.x = TRUE
auto.efa = TRUE
auto.th = TRUE
auto.delta = TRUE
auto.cov.y = TRUE

For further information regarding these arguments, please refer to the lavaan::lavOptions() documentation.

Examples

Run this code

# The famous Holzinger and Swineford (1939) example
HS.model <- "
  visual  =~ x1 + x2 + x3
  textual =~ x4 + x5 + x6
  speed   =~ x7 + x8 + x9
"
utils::data("HolzingerSwineford1939", package = "lavaan")

# Fit a CFA model with standardised latent variables
fit <- acfa(HS.model, data = HolzingerSwineford1939, std.lv = TRUE, nsamp = 100)
summary(fit)