aic-bic: Maximum likelihood based information criteria

In measr: Bayesian Psychometric Measurement Using 'Stan'

Maximum likelihood based information criteria

Description

Calculate information criteria for diagnostic models not estimated with full Markov chain Monte Carlo (i.e., with method = "optim"). Available information include the Akaike information criterion (AIC; Akaike, 1973) and the Bayesian information criterion (BIC; Schwarz, 1978).

Usage

aic(x, ..., force = FALSE)

bic(x, ..., force = FALSE)

Arguments

x	A measrdcm object estimated with backend = "optim".
...	Unused.
force	If the criterion has already been added to the model object with add_criterion(), should it be recalculated. Default is FALSE.

Value

The numeric value of the information criterion.

References

Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Csáki (Eds.), Proceedings of the Second International Symposium on Information Theory (pp. 267-281). Akademiai Kiado.

Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/aos/1176344136")}

Examples

model_spec <- dcm_specify(
  qmatrix = dcmdata::mdm_qmatrix,
  identifier = "item"
)
model <- dcm_estimate(
  dcm_spec = model_spec,
  data = dcmdata::mdm_data,
  identifier = "respondent",
  method = "optim",
  seed = 63277
)

aic(model)

bic(model)

measr documentation built on Jan. 14, 2026, 5:08 p.m.