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Deviance for INLAvaan Models

Source: R/method-deviance.R
deviance.Rd

Extract the (Bayesian) deviance of a fitted INLAvaan model. Unlike lavaan, which has no deviance() method, this follows the BUGS/JAGS/Stan convention: "deviance" is \(-2\) times the log-likelihood, summarised over the posterior.

Usage

# S3 method for class 'INLAvaan'
deviance(object, type = c("mean", "plugin"), ...)

Arguments

object

An object of class INLAvaan.

type

Character. "mean" (default) returns the posterior mean deviance \(\bar{D} = E[-2\log p(y \mid \theta)]\), averaged over posterior draws. "plugin" returns the deviance evaluated at the posterior mean point estimate, \(\hat{D} = -2\log p(y \mid \hat\theta)\) (matching -2 * logLik(object, type = "plugin")). Both require the model to have been fitted with test != "none".

...

Currently unused.

Value

A length-one numeric of class inlavaan_deviance, with the effective number of parameters (pD) and DIC attached as attributes.

Details

\(\bar{D}\) and \(\hat{D}\) are the two ingredients of the Deviance Information Criterion, \(DIC = \bar{D} + p_D\) where \(p_D = \bar{D} - \hat{D}\) is the effective number of parameters. Use compare() to compare models by DIC (or Bayes factors, or LOO/WAIC) rather than comparing raw deviances directly.

See also

logLik(), 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,
            test = "standard", verbose = FALSE)

deviance(fit)
#> Deviance: 7531.83
#> # ℹ pD = 20.683, DIC = 7552.512
#> 
attr(deviance(fit), "DIC")
#> [1] 7552.512
# }