Fit Measures for a Latent Variable Model estimated using INLA

Fit Measures for a Latent Variable Model estimated using INLA

Usage

# S4 method for class 'INLAvaan'
fitMeasures(
  object,
  fit.measures = "all",
  baseline.model = NULL,
  h1.model = NULL,
  fm.args = list(standard.test = "default", scaled.test = "default", rmsea.ci.level =
    0.9, rmsea.close.h0 = 0.05, rmsea.notclose.h0 = 0.08, robust = TRUE, cat.check.pd =
    TRUE),
  output = "vector",
  ...
)

# S4 method for class 'INLAvaan'
fitmeasures(
  object,
  fit.measures = "all",
  baseline.model = NULL,
  h1.model = NULL,
  fm.args = list(standard.test = "default", scaled.test = "default", rmsea.ci.level =
    0.9, rmsea.close.h0 = 0.05, rmsea.notclose.h0 = 0.08, robust = TRUE, cat.check.pd =
    TRUE),
  output = "vector",
  ...
)

Arguments

object

An object of class INLAvaan.

fit.measures

If "all", all fit measures available will be returned. If only a single or a few fit measures are specified by name, only those are computed and returned.

baseline.model

An optional INLAvaan object representing the baseline (null) model. Required for incremental fit indices (BCFI, BTLI, BNFI). Must have been fitted with test != "none".

h1.model

Ignored (included for compatibility with the lavaan generic).

fm.args

Ignored (included for compatibility with the lavaan generic).

output

Ignored (included for compatibility with the lavaan generic).

...

Additional arguments. Currently supports:

rescale

Character string controlling how the Bayesian chi-square is computed, following blavaan::blavFitIndices(). Options are "devM" (default) which uses the deviance rescaled by pD from DIC, or "MCMC" which uses the classical chi-square ((N-1) * F_ML) and classical degrees of freedom (p - npar) at each posterior sample.

Value

A named numeric vector of fit measures.

See also

bfit_indices(), compare(), diagnostics()

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)

# All available fit measures
fitMeasures(fit)
#>         npar   margloglik          ppp          dic        p_dic       BRMSEA 
#>           21    -3830.737        0.000     7516.943       20.655        0.091 
#>    BGammaHat adjBGammaHat          BMc 
#>        0.957        0.920        0.903 

# Specific measures
fitMeasures(fit, c("npar", "DIC", "pD", "ppp"))
#>  npar   ppp 
#>    21 0.000 
# }