# Inspect or Extract Information from a Fitted blavaan Object

`blavInspect.Rd`

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.

## Arguments

- blavobject
An object of class blavaan.

- what
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`

.

## Details

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.

## Examples

```
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)
}
```