Skip to contents

Model estimation

Specification

Specify a diagnostic model, including measurement model, structural model, and prior distributions.

dcm_specify()
Specify a diagnostic classification model (from dcmstan)
get_parameters()
Identify parameters included in a diagnostic classification model (from dcmstan)
prior()
Prior definitions for diagnostic classification models (from dcmstan)
default_dcm_priors()
Default priors for diagnostic classification models (from dcmstan)

Estimation

Estimate the model using Markov chain Monte Carlo or Stan’s optimizer.

dcm_estimate()
Fit Bayesian diagnostic classification models

Model evaluation

Reliability

Estimate the pattern- or attribute-level classification accuracy and consistency.

reliability()
Estimate the reliability of a diagnostic classification model
cdi()
Item, attribute, and test-level discrimination indices

Model fit

Evaluate the fit of the estimated model to the observed data.

fit_m2(<measr::measrdcm>)
Estimate the M2 fit statistic for diagnostic classification models
fit_ppmc()
Posterior predictive model checks for assessing model fit

Model comparisons

Assess the relative fit of two competing models.

aic() bic()
Maximum likelihood based information criteria
loo(<measr::measrdcm>) waic(<measr::measrdcm>) loo_compare(<measr::measrdcm>)
Relative fit for Bayesian models
loglik_array()
Extract the log-likelihood of an estimated model

Add evaluations to model objects

Add reliability, model fit, and model comparison information to an estimated model object.

add_criterion() add_reliability() add_fit() add_respondent_estimates()
Add model evaluation metrics model objects

Model applications

View and use an estimated model.

measr_extract()
Extract components of a measrfit object
score()
Posterior draws of respondent proficiency