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,
  ...
)

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 (e.g., 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 for the different types of model parameters; a named character vector as returned by priors_for().
test: Character indicating which post-estimation quantities to compute. Defaults to "standard": posterior fit indices (PPP and DIC), plus – for models supported by the casewise machinery and fitted with a mean structure – the WAIC (reusing the fit's posterior draws, when nsamp >= 100) and a full leave-one-out cross-validation whenever its predicted serial cost is within a 10-second budget; both are stored with the fit (see loo() and waic()). "none" skips all of these. Include "loo" (e.g. test = c("standard", "loo"), or test = "loo" alone) to force the full LOO regardless of the budget.
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.

Value

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

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

# 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)
#> 'data.frame':	400 obs. of  10 variables:
#>  $ t1: num  1.726 -1.984 0.32 0.777 0.449 ...
#>  $ t2: num  2.142 -4.401 -1.269 3.531 -0.773 ...
#>  $ t3: num  2.77 -6.02 1.56 3.14 -1.5 ...
#>  $ t4: num  2.516 -7.0296 2.8685 5.3637 0.0785 ...
#>  $ x1: num  -1.16 -1.75 0.92 2.36 -1.09 ...
#>  $ x2: num  0.174 -1.577 -0.142 0.708 -1.01 ...
#>  $ c1: num  -0.0277 -2.032 0.0524 0.0191 0.6524 ...
#>  $ c2: num  0.555 0.125 -1.258 0.647 0.731 ...
#>  $ c3: num  0.254 -1.564 -1.803 -0.432 -0.754 ...
#>  $ c4: num  -1.0064 1.2293 -0.3273 -1.0324 -0.0275 ...

fit <- agrowth(mod, data = Demo.growth, nsamp = 100)
#> ℹ Mode finding and Hessian computation.
#> ✔ Posterior mode and Hessian. [200ms]
#> 
#> ℹ Performing VB correction.
#> ✔ VB correction; mean |δ| = 0.045σ. [173ms]
#> 
#> ⠙ Fitting 0/17 skew-normal marginals.
#> ✔ Fit 17/17 skew-normal marginals. [843ms]
#> 
#> ℹ Adjusting copula correlations (NORTA).
#> ✔ Adjust copula correlations (NORTA). [83ms]
#> 
#> ⠙ Posterior sampling and summarising.
#> ⠹ Computing WAIC.
#> ✔ Summarise 100 posterior draws. [628ms]
#> 
#> ℹ Fit measures: PPP, DIC, LOO, WAIC.
summary(fit)
#> INLAvaan 0.2.5.9004 ended normally after 83 iterations
#> 
#>   Estimator                                      BAYES
#>   Optimization method                           NLMINB
#>   Number of model parameters                        17
#> 
#>   Number of observations                           400
#> 
#> Model Test (User Model):
#> 
#>    Marginal log-likelihood                   -2565.934 
#>    PPP (Chi-square)                              0.940 
#> 
#> Information Criteria:
#> 
#>    Deviance (DIC)                             4997.459 
#>    Effective parameters (pD)                    17.555 
#> 
#> Parameter Estimates:
#> 
#>    Marginalisation method                     SKEWNORM
#>    VB correction                                  TRUE
#> 
#> Latent Variables:
#>                    Estimate       SD     2.5%    97.5%     NMAD    Prior       
#>   i =~                                                                         
#>     t1                1.000                                                    
#>     t2                1.000                                                    
#>     t3                1.000                                                    
#>     t4                1.000                                                    
#>   s =~                                                                         
#>     t1                0.000                                                    
#>     t2                1.000                                                    
#>     t3                2.000                                                    
#>     t4                3.000                                                    
#> 
#> Regressions:
#>                    Estimate       SD     2.5%    97.5%     NMAD    Prior       
#>   i ~                                                                          
#>     x1                0.608    0.060    0.491    0.726    0.000    normal(0,10)
#>     x2                0.604    0.064    0.478    0.730    0.000    normal(0,10)
#>   s ~                                                                          
#>     x1                0.262    0.029    0.206    0.318    0.000    normal(0,10)
#>     x2                0.522    0.031    0.462    0.582    0.000    normal(0,10)
#>   t1 ~                                                                         
#>     c1                0.144    0.050    0.046    0.242    0.000    normal(0,10)
#>   t2 ~                                                                         
#>     c2                0.289    0.046    0.199    0.379    0.000    normal(0,10)
#>   t3 ~                                                                         
#>     c3                0.328    0.045    0.240    0.415    0.000    normal(0,10)
#>   t4 ~                                                                         
#>     c4                0.331    0.059    0.216    0.445    0.000    normal(0,10)
#> 
#> Covariances:
#>                    Estimate       SD     2.5%    97.5%     NMAD    Prior       
#>  .i ~~                                                                         
#>    .s                 0.069    0.045   -0.019    0.158    0.006       beta(1,1)
#> 
#> Intercepts:
#>                    Estimate       SD     2.5%    97.5%     NMAD    Prior       
#>    .t1                0.000                                                    
#>    .t2                0.000                                                    
#>    .t3                0.000                                                    
#>    .t4                0.000                                                    
#>    .i                 0.580    0.062    0.459    0.702    0.000    normal(0,10)
#>    .s                 0.958    0.030    0.900    1.015    0.000    normal(0,10)
#> 
#> Variances:
#>                    Estimate       SD     2.5%    97.5%     NMAD    Prior       
#>    .t1                0.590    0.080    0.441    0.756    0.003 gamma(1,.5)[sd]
#>    .t2                0.605    0.055    0.504    0.721    0.001 gamma(1,.5)[sd]
#>    .t3                0.488    0.056    0.386    0.603    0.001 gamma(1,.5)[sd]
#>    .t4                0.544    0.097    0.362    0.741    0.012 gamma(1,.5)[sd]
#>    .i                 1.099    0.114    0.889    1.335    0.000 gamma(1,.5)[sd]
#>    .s                 0.229    0.027    0.180    0.284    0.001 gamma(1,.5)[sd]
#>

Fit an Approximate Bayesian Growth Curve Model

Usage

Arguments

Value

Details

See also

Examples