Models are specified using lavaan syntax, and prior
distribution specification has already been covered on the previous page. The specified model can then be
estimated via the bsem() command, with other models being
estimated via bcfa(), bgrowth(), or
blavaan(). Regardless of the command, there are many
arguments that allow you to tailor the model estimation to your needs.
We discuss here some of the most popular arguments, as well as some
easy-to-miss arguments.
Primary arguments
Primary arguments to the model estimation commands include
burnin, sample, n.chains, and
target. The burnin and sample
arguments are used to specify the desired number of burn-in iterations
and posterior samples for each of n.chains chains (and the
burnin argument controls the warm-up iterations in
Stan). The target argument, on the other hand, is used to
specify the MCMC strategy used for estimation. The default,
target = "stan", tends to be fastest and most efficient.
Other options are slightly more flexible, including
target = "stanclassic" and target = "jags".
Both of these approaches sample latent variables as if they are model
parameters, whereas target = "stan" marginalizes out the
latent variables. For more detail of these approaches, see the JSS paper.
Secondary arguments
Noteworthy secondary arguments include save.lvs,
mcmcfile, mcmcextra, and
inits.
The save.lvs argument controls whether or not latent
variables are sampled during model estimation. It defaults to
FALSE because the latent variable sampling can take a large
amount of memory, and can slow down some post-estimation summaries. But
setting save.lvs = TRUE allows for model summaries of
latent variables and observed variable predictions using
blavPredict() and other functions.
By setting mcmcfile = TRUE, users can obtain the Stan
(JAGS) code and data for the specified model. These files are written to
the lavExport folder within a user’s working directory. One
file has extension .jag or .stan, and the second file is an R data file
(extension .rda). The rda file can be loaded in R (via
load()) and will be a list including elements
data, monitors, and inits. These
elements can be supplied to stan() for model estimation
outside of blavaan.
The mcmcextra argument is used to supply extra
information to Stan or JAGS. Users can supply a list with element names
monitor, data, syntax, or
llnsamp. These elements are respectively used to specify
extra parameters to monitor, extra data to pass to the model estimation,
extra syntax to include in the model file (JAGS only), and the number of
importance samples for likelihood approximation (which is only relevant
to models with ordinal variables).
The inits argument is used to control the starting
values for MCMC estimation. It can sometimes salvage a model that
immediately crashes. The default, inits = "simple",
initializes model parameters to 0 and 1 in fashion similar to
lavaan’s use of this argument. A second option,
inits = "prior", draws initial values from the prior
distributions. The user can also specify a list of their own initial
values via this argument, though the required list format is somewhat
cumbersome. We recommend exporting the model and data using
mcmcfile = TRUE, loading the resulting rda file, and
looking at the format of the initial values that blavaan
created there.
Parallelization
Speed is always an issue when we sample via MCMC, especially using
software like Stan or JAGS. For computers with multiple cores, the
estimation can be sped up by sending each MCMC chain to a separate core.
This is accomplished with the bcontrol argument, which is a
list whose elements correspond to stan() or
run.jags() arguments. For parallelizing the chains in Stan,
we would want to use the argument
bcontrol = list(cores = 3). Many other arguments are
available here to control other aspects of estimation; see
?stan or ?run.jags for all the
possibilities.
Parallelization can also be helpful to speed up post-estimation computations. The future package controls this parallelization, which requires an extra command prior to estimation. The most common commands would be