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Fit a Structural Equation Model (SEM).


bsem(..., cp = "srs",
     dp = NULL, n.chains = 3, burnin, sample,
     adapt, mcmcfile = FALSE, mcmcextra = list(), inits = "simple",
     convergence = "manual", target = "stan", save.lvs = FALSE,
     wiggle = NULL, = 0.1, prisamp = FALSE, jags.ic = FALSE,
     seed = NULL, bcontrol = list())



Default lavaan arguments. See lavaan.


Handling of prior distributions on covariance parameters: possible values are "srs" (default) or "fa". Option "fa" is only available for target="jags".


Default prior distributions on different types of parameters, typically the result of a call to dpriors(). See the dpriors() help file for more information.


Number of desired MCMC chains.


Number of burnin iterations, NOT including the adaptive iterations.


The total number of samples to take after burnin.


The number of adaptive iterations to use at the start of the simulation.


If TRUE, the JAGS/Stan model will be written to file (in the lavExport directory). Can also supply a character string, which serves as the name of the directory to which files will be written.


A list with potential names syntax (unavailable for target="stan"), monitor, data, and llnsamp. The syntax object is a text string containing extra code to insert in the JAGS/Stan model syntax. The data object is a list of extra data to send to the JAGS/Stan model. If moment_match_k_threshold is specified within data the looic of the model will be calculated using moment matching. The monitor object is a character vector containing extra JAGS/Stan parameters to monitor. The llnsamp object is only relevant to models with ordinal variables, and specifies the number of samples that should be drawn to approximate the model log-likelihood (larger numbers imply higher accuracy and longer time). This log-likelihood is specifically used to compute information criteria.


If it is a character string, the options are currently "simple" (default), "Mplus", "prior", or "jags". In the first two cases, parameter values are set as though they will be estimated via ML (see lavaan). The starting parameter value for each chain is then perturbed from the original values through the addition of random uniform noise. If "prior" is used, the starting parameter values are obtained based on the prior distributions (while also trying to ensure that the starting values will not crash the model estimation). If "jags", no starting values are specified and JAGS will choose values on its own (and this will probably crash Stan targets). You can also supply a list of starting values for each chain, where the list format can be obtained from, e.g., blavInspect(fit, "inits"). Finally, you can specify starting values in a similar way to lavaan, using the lavaan start argument (see the lavaan documentation for all the options there). In this case, you should also set inits="simple", and be aware that the same starting values will be used for each chain.


Useful only for target="jags". If "auto", parameters are sampled until convergence is achieved (via autorun.jags()). In this case, the arguments burnin and sample are passed to autorun.jags() as startburnin and startsample, respectively. Otherwise, parameters are sampled as specified by the user (or by the run.jags defaults).


Desired MCMC sampling, with "stan" (pre-compiled marginal approach) as default. Also available is "vb", which calls the rstan function vb(). Other options include "jags", "stancond", and "stanclassic", which sample latent variables and provide some greater functionality (because syntax is written "on the fly"). But they are slower and less efficient.


Should sampled latent variables (factor scores) be saved? Logical; defaults to FALSE


Labels of equality-constrained parameters that should be "approximately" equal. Can also be "intercepts", "loadings", "regressions", "means".

The prior sd (of normal distribution) to be used in approximate equality constraints. Can be one value, or (for target="stan") a numeric vector of values that is the same length as wiggle.


Should samples be drawn from the prior, instead of the posterior (target="stan" only)? Logical; defaults to FALSE


Should DIC be computed the JAGS way, in addition to the BUGS way? Logical; defaults to FALSE


A vector of length n.chains (for target "jags") or an integer (for target "stan") containing random seeds for the MCMC run. If NULL, seeds will be chosen randomly.


A list containing additional parameters passed to run.jags (or autorun.jags) or stan. See the manpage of those functions for an overview of the additional parameters that can be set.


The bsem function is a wrapper for the more general blavaan function, using the following default lavaan arguments: = TRUE, = FALSE, auto.fix.first = TRUE (unless = TRUE), auto.fix.single = TRUE, auto.var = TRUE, = TRUE, = TRUE, = TRUE, and auto.cov.y = TRUE.


An object of class lavaan, for which several methods are available, including a summary method.


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

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

Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. URL

See also


if (FALSE) {
## The industrialization and Political Democracy Example
## Bollen (1989), page 332
model <- '
  # latent variable definitions
     ind60 =~ x1 + x2 + x3
     dem60 =~ y1 + a*y2 + b*y3 + c*y4
     dem65 =~ y5 + a*y6 + b*y7 + c*y8

  # regressions
    dem60 ~ ind60
    dem65 ~ ind60 + dem60

  # residual correlations
    y1 ~~ y5
    y2 ~~ y4 + y6
    y3 ~~ y7
    y4 ~~ y8
    y6 ~~ y8

## unique priors for mv intercepts; parallel chains
fit <- bsem(model, data=PoliticalDemocracy,