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).
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 isFALSE
.
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. doi:10.1214/aos/1176344136
Examples
# example code
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)
#> [1] 707.0866
bic(model)
#> [1] 733.689