Fit a Growth Curve model.
bgrowth(..., cp = "srs", dp = NULL, n.chains = 3,
burnin, sample, adapt, mcmcfile = FALSE, mcmcextra = list(),
inits = "simple", convergence = "manual", target = "stan",
save.lvs = FALSE, wiggle = NULL, wiggle.sd = 0.1, prisamp = FALSE,
jags.ic = FALSE, seed = NULL, bcontrol = list())An object of class blavaan, for which several methods
are available, including a summary method.
Default lavaan arguments. See lavaan.
Handling of prior distributions on covariance parameters:
possible values are "srs" (default) or "fa". Option "fa" is only available for target="jags".
Default prior distributions on different types of
parameters, typically the result of a call to dpriors().
See the dpriors() help file for more information.
Number of desired MCMC chains.
Number of burnin/warmup iterations (not including the adaptive iterations, for target="jags"). Defaults to 4000 or target="jags" and 500 for Stan targets.
The total number of samples to take after burnin. Defaults to 10000 for target="jags" and 1000 for Stan targets.
For target="jags", the number of adaptive iterations to use at the start of sampling. Defaults to 1000.
If TRUE, the JAGS/Stan model will be written to file
(in the lavExport directory). Can also supply a character
string, which serves as the name of the directory to which files will be written.
A list with potential names syntax (unavailable
for target="stan"),
monitor, data, and llnsamp. The syntax object is a text string containing extra
code to insert in the JAGS/Stan model syntax. The data object
is a list of extra data to send to the JAGS/Stan model. If
moment_match_k_threshold is specified within data the looic of
the model will be calculated using moment matching. The monitor object
is a character vector containing extra JAGS/Stan parameters to
monitor. The llnsamp object is only relevant to models with ordinal
variables, and specifies the number of samples that should be drawn to approximate
the model log-likelihood (larger numbers imply higher accuracy and
longer time). This log-likelihood is specifically used to compute
information criteria.
If it is a character string, the options are currently
"simple" (default), "Mplus", "prior", or "jags". In the first two
cases, parameter values are set as though they will be estimated via
ML (see lavaan). The starting parameter value for
each chain is then perturbed from the original values through the
addition of random uniform noise. If "prior" is used, the starting
parameter values are obtained based on the prior distributions
(while also trying to ensure that the starting values will not crash
the model estimation). If "jags", no starting values are
specified and JAGS will choose values on its own (and this will probably
crash Stan targets). You can also supply
a list of starting values for each chain, where the list format can
be obtained from, e.g., blavInspect(fit, "inits"). Finally,
you can specify starting values in a similar way to lavaan,
using the lavaan start argument (see the lavaan
documentation for all the options there). In this case, you should also set
inits="simple", and be aware that the same starting values
will be used for each chain.
Useful only for target="jags". If "auto", parameters are
sampled until convergence is achieved (via autorun.jags()). In
this case, the arguments burnin and sample are passed to
autorun.jags() as startburnin and startsample,
respectively. Otherwise, parameters
are sampled as specified by the user (or by the run.jags
defaults).
Desired MCMC sampling, with "stan" (pre-compiled
marginal approach) as
default. Also available is "vb", which calls the rstan function
vb(). Other options include "jags", "stancond", and
"stanclassic", which sample latent variables and provide some
greater functionality (because syntax is written "on the fly"). But
they are slower and less efficient.
Should sampled latent variables (factor scores) be saved? Logical; defaults to FALSE
Labels of equality-constrained parameters that should be "approximately" equal. Can also be "intercepts", "loadings", "regressions", "means".
The prior sd (of normal distribution) to be used in approximate equality constraints. Can be one value, or (for target="stan") a numeric vector of values that is the same length as wiggle.
Should samples be drawn from the prior, instead of the
posterior (target="stan" only)? Logical; defaults to FALSE
Should DIC be computed the JAGS way, in addition to the BUGS way? Logical; defaults to FALSE
A vector of length n.chains (for target
"jags") or an integer (for target "stan") containing random
seeds for the MCMC run. If NULL, seeds will be chosen randomly.
A list containing additional parameters passed to
run.jags (or autorun.jags) or stan. See the manpage of those functions for an
overview of the additional parameters that can be set.
The bgrowth function is a wrapper for the more general
blavaan function, using the following default
lavaan 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.th = TRUE, auto.delta = TRUE,
and auto.cov.y = TRUE.
Edgar C. Merkle, Ellen Fitzsimmons, James Uanhoro, & Ben Goodrich (2021). Efficient Bayesian Structural Equation Modeling in Stan. Journal of Statistical Software, 100(6), 1-22. URL http://www.jstatsoft.org/v100/i06/.
Edgar C. Merkle & Yves Rosseel (2018). blavaan: Bayesian Structural Equation Models via Parameter Expansion. Journal of Statistical Software, 85(4), 1-30. URL http://www.jstatsoft.org/v85/i04/.
Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. URL http://www.jstatsoft.org/v48/i02/.
blavaan
if (FALSE) {
## linear growth model with a time-varying covariate
data(Demo.growth, package = "lavaan")
model.syntax <- '
# intercept and slope with fixed coefficients
i =~ 1*t1 + 1*t2 + 1*t3 + 1*t4
s =~ 0*t1 + 1*t2 + 2*t3 + 3*t4
# regressions
i ~ x1 + x2
s ~ x1 + x2
# time-varying covariates
t1 ~ c1
t2 ~ c2
t3 ~ c3
t4 ~ c4
'
fit <- bgrowth(model.syntax, data = Demo.growth)
summary(fit)
}
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