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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, ...)



An object of class blavaan.


Character. What needs to be inspected/extracted? See Details for Bayes-specific options, and see lavaan's lavInspect() for additional options. Note: the what argument is not case-sensitive (everything is converted to lower case.)


lavaan arguments supplied to lavInspect(); see lavaan.


Below is a list of Bayesian-specific values for the what argument; additional values can be found in the lavInspect() documentation.


A list of starting values for each chain, unless inits="jags" is used during model estimation. Aliases: "starting.values", "inits".


Each parameter's potential scale reduction factor for convergence assessment. Can also use "psrf" instead of "rhat"


Each parameter's estimated lag-10 autocorrelation.


Each parameters effective sample size, taking into account autocorrelation.


An object of class mcmc containing the individual parameter draws from the MCMC run. Aliases: "draws", "samples".


The underlying run.jags or stan object that resulted from the MCMC run.


The number of chains sampled.


The approach used for estimating covariance parameters ("srs" or "fa"); these are only relevant if using JAGS.


Default prior distributions used for each type of model parameter.


Estimated posterior mode of each free parameter.


Estimated posterior mean of each free parameter.


Estimated posterior median of each free parameter.


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.


A matrix of mean factor scores (rows are observations, columns are variables). Use the additional level argument in the same way.


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.

See also


if (FALSE) {
# 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)