blavaan: blavInspect – R documentation

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blavInspect

Inspect or Extract Information from a fitted blavaan object

Description

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.

Usage

blavInspect(blavobject, what, ...)

blavTech(blavobject, what, ...)

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.)
`...`	Default 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".
"psrf":: Each parameter's Gelman-Rubin PSRF (potential scale reduction factor) for convergence assessment.
"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").
"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.
"lvmeans":: A matrix of mean factor scores (rows are observations, columns are variables).
"hpd":: HPD interval of each free parameter. In this case, an additional argument level can be supplied to specify a number in (0,1) reflecting the percentage of the interval.

Examples

## Not run: 
# The Holzinger and Swineford (1939) example
HS.model <- ' visual  =~ x1 + x2 + x3
              textual =~ x4 + x5 + x6
              speed   =~ x7 + x8 + x9 '

fit <- bcfa(HS.model, data=HolzingerSwineford1939,
            jagcontrol=list(method="rjparallel"))

# extract information
blavInspect(fit, "psrf")
blavInspect(fit, "hpd", level=.9)

## End(Not run)

blavaan

Bayesian Latent Variable Analysis

v0.3-15

GPL (>= 3)

Authors

Edgar Merkle [aut, cre] (<https://orcid.org/0000-0001-7158-0653>), Yves Rosseel [aut], Ben Goodrich [aut], Mauricio Garnier-Villarreal [ctb] (<https://orcid.org/0000-0002-2951-6647>, R/blav_compare.R, R/ctr_bayes_fit.R), Terrence D. Jorgensen [ctb] (<https://orcid.org/0000-0001-5111-6773>, R/ctr_bayes_fit.R, R/ctr_ppmc.R), Huub Hoofs [ctb] (R/ctr_bayes_fit.R), Rens van de Schoot [ctb] (R/ctr_bayes_fit.R)

Initial release