Bayesian Fit Indices

Compute posterior distributions of Bayesian fit indices for an INLAvaan model, analogous to blavaan::blavFitIndices().

Usage

bfit_indices(
  object,
  baseline.model = NULL,
  rescale = c("devM", "MCMC"),
  nsamp = NULL,
  samp_copula = TRUE
)

# S3 method for class 'bfit_indices'
summary(object, ...)

# S3 method for class 'bfit_indices'
print(x, ...)

Arguments

object

An object of class INLAvaan.

baseline.model

An optional INLAvaan object representing the baseline (null) model. Required for incremental fit indices (BCFI, BTLI, BNFI).

rescale

Character string controlling how the Bayesian chi-square is rescaled. "devM" (default) subtracts pD from the deviance at each sample. "MCMC" uses the classical chi-square and classical df at each sample.

nsamp

Number of posterior samples to draw. Defaults to the value used when fitting the model.

samp_copula

Logical. When TRUE (default), posterior samples are drawn using the copula method with the fitted marginals. When FALSE, samples are drawn from the Gaussian (Laplace) approximation.

...

Additional arguments passed to methods.

x

An object of class bfit_indices (for print).

Value

An S3 object of class "bfit_indices" containing:

indices

Named list of numeric vectors (one per posterior sample) for each computed fit index.

details

List with chisq (per-sample deviance), df, pD, rescale, and nsamp.

Use summary() to obtain a table of posterior summaries (Mean, SD, quantiles, Mode) for each index.

See also

lavaan::fitMeasures(), blavaan::blavFitIndices(), fitmeasures(), compare()

Examples

# \donttest{
HS.model <- "
  visual  =~ x1 + x2 + x3
  textual =~ x4 + x5 + x6
  speed   =~ x7 + x8 + x9
"
utils::data("HolzingerSwineford1939", package = "lavaan")
fit <- acfa(HS.model, HolzingerSwineford1939, std.lv = TRUE, nsamp = 100,
            verbose = FALSE)

# Absolute fit indices
bf <- bfit_indices(fit)
bf
#> Posterior summary of devM-based Bayesian fit indices (nsamp = 100): 
#> 
#>       BRMSEA    BGammaHat adjBGammaHat          BMc 
#>        0.091        0.957        0.920        0.903 
summary(bf)
#> 
#> Posterior summary of devM-based Bayesian fit indices (nsamp = 100):
#> 
#>               Mean    SD X2.5.  X25.  X50.  X75. X97.5.  Mode
#> BRMSEA       0.091 0.005 0.083 0.088 0.091 0.094  0.101 0.090
#> BGammaHat    0.957 0.004 0.948 0.954 0.957 0.960  0.964 0.958
#> adjBGammaHat 0.920 0.008 0.904 0.915 0.921 0.926  0.933 0.923
#> BMc          0.903 0.010 0.883 0.897 0.904 0.909  0.918 0.907
# }