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Bayesian model comparisons, including WAIC, LOO, and Bayes factor approximation.

Usage

blavCompare(object1, object2, ...)

Arguments

object1

An object of class blavaan.

object2

A second object of class blavaan.

...

Other arguments to loo().

Details

This function computes Bayesian model comparison metrics, including a Bayes factor approximation, WAIC, and LOOIC.

The log-Bayes factor of the two models is based on the Laplace approximation to each model's marginal log-likelihood.

The WAIC and LOOIC metrics come from the loo package. The ELPD difference and SE specifically come from loo::loo_compare().

Value

A list containing separate results for log-Bayes factor, WAIC, LOOIC, and differences between WAIC and LOOIC.

References

Raftery, A. E. (1993). Bayesian model selection in structural equation models. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 163-180). Beverly Hills, CA: Sage.

Vehtari A., Gelman A., Gabry J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 27, 1413-1432.

Examples

if (FALSE) {
data(HolzingerSwineford1939, package = "lavaan")

hsm1 <- ' visual  =~ x1 + x2 + x3 + x4
          textual =~ x4 + x5 + x6
          speed   =~ x7 + x8 + x9 '

fit1 <- bcfa(hsm1, data = HolzingerSwineford1939)

hsm2 <- ' visual  =~ x1 + x2 + x3
          textual =~ x4 + x5 + x6 + x7
          speed   =~ x7 + x8 + x9 '

fit2 <- bcfa(hsm2, data = HolzingerSwineford1939)

blavCompare(fit1, fit2)
}