Deviance Information Criterion for a spifa Model

Computes the Deviance Information Criterion (DIC) for a fitted spifa model, useful for comparing candidate models (e.g. different numbers of factors or different restriction structures) fitted to the same data.

Usage

# S3 method for class 'spifa'
dic(x, burnin = 0, thin = 1, ...)

Arguments

x

A fitted spifa object, as returned by spifa.

burnin

Number of initial iterations to discard.

thin

Thinning interval applied after discarding burn-in.

...

Further arguments passed to methods (currently unused).

Value

A one-row tibble with columns mean_deviance (posterior mean of the deviance), p_eff (effective number of parameters), and dic.

Author

Erick A. Chacón-Montalván

Examples

# \donttest{
data(ipixuna)
nitems <- ncol(ipixuna$items)
nfactors <- 3

# discrimination constraint: start with every item free to load on every
# factor, then restrict a few items per factor based on what each item is
# meant to measure (0 = no relationship, 1 = free parameter to estimate)
A <- matrix(1, nitems, nfactors)
A[c(4, 8), 1] <- 0
A[c(2, 4, 5, 6, 7, 8, 10), 2] <- 0
A[c(5, 6), 3] <- 0
samples <- spifa(
  items ~ 1, data = ipixuna, nfactors = nfactors, ngp = 0,
  niter = 20, standardize = FALSE,
  constraints = list(discrimination = A))
dic(samples)
#> # A tibble: 1 × 3
#>   mean_deviance p_eff   dic
#>           <dbl> <dbl> <dbl>
#> 1          993.  135. 1128.
# }