Class For Representing A (Fitted) Bayesian Latent Variable Model

The blavaan class contains the lavaan class, representing a (fitted) Bayesian latent variable model. It contains a description of the model as specified by the user, a summary of the data, an internal matrix representation, and if the model was fitted, the fitting results.

Objects from the Class

Objects can be created via the bcfa, bsem, bgrowth or blavaan functions.

Slots

version:

The lavaan package version used to create this objects

call:

The function call as returned by match.call().

timing:

The elapsed time (user+system) for various parts of the program as a list, including the total time.

Options:

Named list of options that were provided by the user, or filled-in automatically.

ParTable:

Named list describing the model parameters. Can be coerced to a data.frame. In the documentation, this is called the `parameter table'.

pta:

Named list containing parameter table attributes.

Data:

Object of internal class "Data": information about the data.

SampleStats:

Object of internal class "SampleStats": sample statistics

Model:

Object of internal class "Model": the internal (matrix) representation of the model

Cache:

List using objects that we try to compute only once, and reuse many times.

Fit:

Object of internal class "Fit": the results of fitting the model. No longer used.

boot:

List. Unused for Bayesian models.

optim:

List. Information about the optimization.

loglik:

List. Information about the loglikelihood of the model (if maximum likelihood was used).

implied:

List. Model implied statistics.

vcov:

List. Information about the variance matrix (vcov) of the model parameters.

test:

List. Different test statistics.

h1:

List. Information about the unrestricted h1 model (if available).

baseline:

List. Information about a baseline model (often the independence model) (if available).

external:

List. Includes Stan or JAGS objects used for MCMC.

Methods

coef

signature(object = "blavaan", type = "free"): Returns the estimates of the parameters in the model as a named numeric vector. If type="free", only the free parameters are returned. If type="user", all parameters listed in the parameter table are returned, including constrained and fixed parameters.

vcov

signature(object = "lavaan"): returns the covariance matrix of the estimated parameters.

show

signature(object = "blavaan"): Print a short summary of the model fit

% \item{plot}{\code{signature(object = "blavaan")}: S4 method for % creating plots. Also see \code{?plot.blavaan}.}

summary

signature(object = "blavaan", header = TRUE, fit.measures = FALSE, estimates = TRUE, ci = TRUE, standardized = FALSE, rsquare = FALSE, std.nox = FALSE, psrf = TRUE, neff = FALSE, postmedian = FALSE, postmode = FALSE, priors = TRUE, bf = FALSE, nd = 3L): Print a nice summary of the model estimates. If header = TRUE, the header section (including fit measures) is printed. If fit.measures = TRUE, additional fit measures are added to the header section. If estimates = TRUE, print the parameter estimates section. If ci = TRUE, add confidence intervals to the parameter estimates section. If standardized = TRUE, the standardized solution is also printed. Note that SEs and tests are still based on unstandardized estimates. Use standardizedSolution to obtain SEs and test statistics for standardized estimates. If rsquare=TRUE, the R-Square values for the dependent variables in the model are printed. If std.nox = TRUE, the std.all column contains the the std.nox column from the parameterEstimates() output. If psrf = TRUE, potential scale reduction factors (Rhats) are printed. If neff = TRUE, effective sample sizes are printed. If postmedian or postmode are TRUE, posterior medians or modes are printed instead of posterior means. If priors = TRUE, parameter prior distributions are printed. If bf = TRUE, Savage-Dickey approximations of the Bayes factor are printed for certain parameters. Nothing is returned (use lavInspect or another extractor function to extract information from a fitted model).

References

Edgar C. Merkle, Ellen Fitzsimmons, James Uanhoro, & Ben Goodrich (2021). Efficient Bayesian Structural Equation Modeling in Stan. Journal of Statistical Software, 100(6), 1-22. URL http://www.jstatsoft.org/v100/i06/.

Edgar C. Merkle & Yves Rosseel (2018). blavaan: Bayesian Structural Equation Models via Parameter Expansion. Journal of Statistical Software, 85(4), 1-30. URL http://www.jstatsoft.org/v85/i04/.

Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. URL http://www.jstatsoft.org/v48/i02/.

See also

bcfa, bsem, bgrowth

Examples

if (FALSE) { # \dontrun{
HS.model <- ' visual  =~ x1 + x2 + x3
              textual =~ x4 + x5 + x6
              speed   =~ x7 + x8 + x9 '

fit <- bcfa(HS.model, data=HolzingerSwineford1939)

summary(fit, standardized=TRUE, fit.measures=TRUE, rsquare=TRUE)
coef(fit)
} # }