Description Usage Arguments Details Value References See Also Examples
Fit a Growth Curve model.
1 2 3 4 
... 
Default lavaan arguments. See 
cp 
Handling of prior distributions on covariance parameters: possible values are 
dp 
Default prior distributions on different types of
parameters, typically the result of a call to 
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 
mcmcextra 
A list with potential names 
inits 
If it is a character string, the options are currently

convergence 
If 
target 
Desired MCMC sampling, with 
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 
bcontrol 
A list containing additional parameters passed to

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
.
An object of class blavaan
, 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/.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  ## Not run:
## linear growth model with a timevarying covariate
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
# timevarying covariates
t1 ~ c1
t2 ~ c2
t3 ~ c3
t4 ~ c4
'
fit < bgrowth(model.syntax, data=Demo.growth)
summary(fit)
## End(Not run)

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