Description
Usage
Arguments
Details
Value
References
See Also
Examples
Fit a Confirmatory Factor Analysis (CFA) model.
 (, cp = "srs",
dp = , n.chains = 3, burnin, ,
adapt, mcmcfile = , mcmcextra = (), inits = "prior",
convergence = "manual", target = "stan", save.lvs = ,
jags.ic = , seed = , bcontrol = ())

... 
Default lavaan arguments. See lavaan .

cp 
Handling of prior distributions on covariance parameters: possible values are "srs" or
"fa" . Option "srs" is more flexible and better from a
theoretical standpoint, but it is also slower.

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.

n.chains 
Number of desired MCMC chains.

burnin 
Number of burnin iterations, NOT including the adaptive
iterations.

sample 
The total number of samples to take after burnin.

adapt 
The number of adaptive iterations to use at the start of
the simulation.

mcmcfile 
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.

mcmcextra 
A list with potential names syntax and
monitor . The syntax object is a text string containing extra
code to insert in the JAGS/Stan model syntax, and the monitor object
is a character vector containing extra JAGS/Stan parameters to sample.

inits 
If it is a character string, the options are currently
"simple" , "Mplus" , "prior" (default), and
"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 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.
If start is a fitted
object of class lavaan , the estimated values of
the corresponding parameters will be extracted, then perturbed in
the manner described above. If it is a model list,
for example the output of the paramaterEstimates() function,
the values of the est or start or ustart column
(whichever is found first) will be extracted.

convergence 
If "auto" , parameters will be
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).

target 
Desired MCMC sampling, with "stan" (precompiled
marginal approach) as
default. 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.

save.lvs 
Should sample latent variables (factor scores) be saved? Logical; defaults to FALSE

jags.ic 
Should DIC be computed the JAGS way, in addition to the BUGS way? Logical; defaults to FALSE

seed 
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.

bcontrol 
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 bcfa
function is a wrapper for the more general
blavaan
function, using the following default
lavaan
arguments:
int.ov.free = TRUE
, int.lv.free = FALSE
,
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
.
An object of class lavaan
, for which several methods
are available, including a summary
method.
Yves Rosseel (2012). lavaan: An R Package for Structural Equation
Modeling. Journal of Statistical Software, 48(2), 136. URL
http://www.jstatsoft.org/v48/i02/.
Edgar C. Merkle & Yves Rosseel (2018). blavaan: Bayesian Structural
Equation Models via Parameter Expansion. Journal of Statistical
Software, 85(4), 130. URL http://www.jstatsoft.org/v85/i04/.
blavaan
 ## Not run:
# The Holzinger and Swineford (1939) example
HS.model < ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit < (HS.model, =HolzingerSwineford1939)
(fit)
## End(Not run)

ecmerkle/blavaan documentation built on Jan. 26, 2020, 11:05 a.m.