Fit an Approximate Bayesian Growth Curve Model
Usage
agrowth(
model,
data,
dp = blavaan::dpriors(),
estimator = "ML",
marginal_method = c("skewnorm", "asymgaus", "marggaus", "sampling"),
nsamp = 3000,
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,
...
)Arguments
- model
A description of the user-specified model. Typically, the model is described using the lavaan model syntax. See
model.syntaxfor more information. Alternatively, a parameter table (eg. the output of thelavParTable()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 thedpriors()help file for more information.- estimator
The estimator to be used. Currently only
"ML"(maximum likelihood) is supported.- 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"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.
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 = TRUEint.ov.free = FALSEint.lv.free = TRUEauto.fix.first = TRUE(unlessstd.lv = TRUE)auto.fix.single = TRUEauto.var = TRUEauto.cov.lv.x = TRUEauto.efa = TRUEauto.th = TRUEauto.delta = TRUEauto.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)
#> ℹ Using MVN log-likelihood.
#> ℹ Finding posterior mode.
#> ✔ Finding posterior mode. [249ms]
#>
#> ℹ Computing the Hessian.
#> ✔ Computing the Hessian. [384ms]
#>
#> ℹ Performing VB correction.
#> ✔ Performing VB correction. [404ms]
#>
#> ℹ Using skew normal approximation.
#> ⠙ Fitting skew normal to 0/17 marginals.
#> ⠹ Fitting skew normal to 11/17 marginals.
#> ✔ Fitting skew normal to 17/17 marginals. [1.5s]
#>
#> ℹ Sampling covariances and defined parameters.
#> ✔ Sampling covariances and defined parameters. [416ms]
#>
#> ⠙ Computing ppp and DIC.
#> ⠹ Computing ppp and DIC.
#> ✔ Computing ppp and DIC. [2.7s]
#>
summary(fit)
#> INLAvaan 0.2.0.9005 ended normally after 85 iterations
#>
#> Estimator BAYES
#> Optimization method NLMINB
#> Number of model parameters 17
#>
#> Number of observations 400
#>
#> Model Test (User Model):
#>
#> Marginal log-likelihood -2565.958
#> PPP (Chi-square) 0.945
#>
#> Information Criteria:
#>
#> Deviance (DIC) 4996.157
#> Effective parameters (pD) 16.903
#>
#> Parameter Estimates:
#>
#> Marginalisation method SKEWNORM
#> VB correction TRUE
#>
#> Latent Variables:
#> Estimate SD 2.5% 97.5% KLD 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% KLD Prior
#> i ~
#> x1 0.608 0.060 0.490 0.727 0.001 normal(0,10)
#> x2 0.604 0.065 0.477 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 0.001 normal(0,10)
#> t1 ~
#> c1 0.144 0.050 0.045 0.242 0.001 normal(0,10)
#> t2 ~
#> c2 0.289 0.046 0.199 0.380 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 0.446 0.000 normal(0,10)
#>
#> Covariances:
#> Estimate SD 2.5% 97.5% KLD Prior
#> .i ~~
#> .s 0.153 0.040 -0.006 0.152 0.001 beta(1,1)
#>
#> Intercepts:
#> Estimate SD 2.5% 97.5% KLD Prior
#> .t1 0.000
#> .t2 0.000
#> .t3 0.000
#> .t4 0.000
#> .i 0.580 0.062 0.458 0.703 0.000 normal(0,10)
#> .s 0.957 0.030 0.899 1.015 0.000 normal(0,10)
#>
#> Variances:
#> Estimate SD 2.5% 97.5% KLD Prior
#> .t1 0.591 0.081 0.441 0.759 0.003 gamma(1,.5)[sd]
#> .t2 0.605 0.055 0.503 0.721 0.007 gamma(1,.5)[sd]
#> .t3 0.488 0.056 0.386 0.604 0.003 gamma(1,.5)[sd]
#> .t4 0.543 0.098 0.367 0.751 0.002 gamma(1,.5)[sd]
#> .i 1.099 0.115 0.891 1.340 0.005 gamma(1,.5)[sd]
#> .s 0.229 0.027 0.181 0.286 0.004 gamma(1,.5)[sd]
#>