agrowth: Fit an Approximate Bayesian Growth Curve Model

Description

Fit an Approximate Bayesian Growth Curve Model

Usage

agrowth(
  model,
  data,
  dp = priors_for(),
  test = "standard",
  vb_correction = TRUE,
  marginal_method = c("skewnorm", "asymgaus", "marggaus", "sampling"),
  marginal_correction = c("shortcut", "shortcut_fd", "hessian", "none"),
  nsamp = 1000,
  samp_copula = TRUE,
  sn_fit_ngrid = 21,
  sn_fit_logthresh = -6,
  sn_fit_temp = 1,
  sn_fit_sample = TRUE,
  control = list(),
  verbose = TRUE,
  debug = FALSE,
  add_priors = TRUE,
  optim_method = c("nlminb", "ucminf", "optim"),
  numerical_grad = FALSE,
  cores = NULL,
  ...
)

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.
test: Character indicating whether to compute posterior fit indices. Defaults to "standard". Change to "none" to skip these computations.
vb_correction: Logical indicating whether to apply a variational Bayes correction for the posterior mean vector of estimates. Defaults to TRUE.
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).
marginal_correction: Which type of correction to use when fitting the skew-normal or two-piece Gaussian marginals. "hessian" computes the full "shortcut" (default) computes only diagonals via central differences (full z-trace plus Schur complement correction), "shortcut_fd" is the same formula using forward differences (roughly half the cost, less accurate), "hessian" computes the full Hessian-based correction (slow), and "none" (or FALSE) applies no correction.
nsamp: The number of samples to draw for all sampling-based approaches (including posterior sampling for model fit indices).
samp_copula: Logical. When TRUE (default), posterior samples are drawn using the copula method with the fitted marginals (e.g. skew-normal or asymmetric Gaussian), with NORTA correlation adjustment. When FALSE, samples are drawn from the Gaussian (Laplace) approximation. Only re
sn_fit_ngrid: Number of grid points to lay out per dimension when fitting the skew-normal marginals. A finer grid gives a better fit at the cost of more joint-log-posterior evaluations. Defaults to 21.
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. Defaults to 1 (weights are the density values themselves). If NA, the temperature is included as an additional optimisation parameter.
sn_fit_sample: Logical. When TRUE (default), a parametric skew-normal is fitted to the posterior samples for covariance and defined parameters. When FALSE, these are summarised using kernel density estimation instead.
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. Defaults to FALSE to use analytical gradients.
cores: Integer or NULL. Number of cores for parallel marginal fitting. When NULL (default), serial execution is used unless the number of free parameters exceeds 120, in which case parallelisation is enabled automatically using all available physical cores. Set to 1L to force serial execution. If cores > 1, marginal fits are distributed across cores using parallel::mclapply() (fork-based; no parallelism on Windows).
...: Additional arguments to be passed to the lavaan::lavaan model fitting function.

Details

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

meanstructure = TRUE
int.ov.free = FALSE
int.lv.free = TRUE
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

Examples

Run this code

# Linear growth model with a time-varying covariate
mod <- "
  # Intercept and slope with fixed coefficients
    i =~ 1*t1 + 1*t2 + 1*t3 + 1*t4
    s =~ 0*t1 + 1*t2 + 2*t3 + 3*t4

  # (Latent) regressions
    i ~ x1 + x2
    s ~ x1 + x2

  # Time-varying covariates
    t1 ~ c1
    t2 ~ c2
    t3 ~ c3
    t4 ~ c4
"
utils::data("Demo.growth", package = "lavaan")
str(Demo.growth)

fit <- agrowth(mod, data = Demo.growth, nsamp = 100)
summary(fit)