Package index • measr

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