View source: R/blav_object_inspect.R
blavInspect | R Documentation |
The blavInspect()
and blavTech()
functions can be used to
inspect/extract information that is stored inside (or can be computed from) a
fitted blavaan object. This is similar to lavaan's lavInspect()
function.
blavInspect(blavobject, what, ...)
blavTech(blavobject, what, ...)
blavobject |
An object of class blavaan. |
what |
Character. What needs to be inspected/extracted? See Details for Bayes-specific options, and see |
... |
lavaan arguments supplied to |
Below is a list of Bayesian-specific values for the what
argument; additional values can be found in the lavInspect()
documentation.
"start"
:A list of starting values for each chain, unless inits="jags"
is used during model estimation. Aliases: "starting.values"
, "inits"
.
"rhat"
:Each parameter's potential scale reduction factor for convergence assessment. Can also use "psrf" instead of "rhat"
"ac.10"
:Each parameter's estimated lag-10 autocorrelation.
"neff"
:Each parameters effective sample size, taking into account autocorrelation.
"mcmc"
:An object of class mcmc
containing the individual parameter draws from the MCMC run. Aliases: "draws"
, "samples"
.
"mcobj"
:The underlying run.jags or stan object that resulted from the MCMC run.
"n.chains"
:The number of chains sampled.
"cp"
:The approach used for estimating covariance
parameters ("srs"
or "fa"
); these are only relevant if
using JAGS.
"dp"
:Default prior distributions used for each type of model parameter.
"postmode"
:Estimated posterior mode of each free parameter.
"postmean"
:Estimated posterior mean of each free parameter.
"postmedian"
:Estimated posterior median of each free parameter.
"lvs"
:An object of class mcmc
containing latent variable (factor score) draws. In two-level models, use level = 1
or level = 2
to specify which factor scores you want.
"lvmeans"
:A matrix of mean factor scores (rows are observations, columns are variables). Use the additional level
argument in the same way.
"hpd"
:HPD interval of each free parameter. In this case, the prob
argument can be used to specify a number in (0,1) reflecting the desired percentage of the interval.
lavInspect
, bcfa
, bsem
, bgrowth
## Not run:
# The Holzinger and Swineford (1939) example
data(HolzingerSwineford1939, package = "lavaan")
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit <- bcfa(HS.model, data = HolzingerSwineford1939,
bcontrol = list(method = "rjparallel"))
# extract information
blavInspect(fit, "psrf")
blavInspect(fit, "hpd", prob = .9)
## End(Not run)
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