logLik-method

logLik,SingleGroupClass-method

logLik,MixtureClass-method

logLik,MultipleGroupClass-method

logLik,MixedClass-method

logLik,DiscreteClass-method

Extract the observed-data log-likelihood.

Analysis of discrete response data using
unidimensional and multidimensional item analysis models under the Item
Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>).
Exploratory and confirmatory item factor analysis models
are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory
bi-factor and two-tier models are available for modeling item testlets using
dimension reduction EM algorithms, while multiple group analyses and
mixed effects designs are included for detecting differential item, bundle,
and test functioning, and for modeling item and person covariates.
Finally, latent class models such as the DINA, DINO, multidimensional latent class,
mixture IRT models, and zero-inflated response models are supported, as well
as a wide family of probabilistic unfolding models.

Phil Chalmers

mirt

Multidimensional Item Response Theory

Joshua Pritikin

Alexander Robitzsch

Mateusz Zoltak

KwonHyun Kim

Carl F. Falk

Adam Meade

Lennart Schneider

David King

Chen-Wei Liu

Ogreden Oguzhan

Samuel Huang

logLik-method function

<dl><dt>object</dt>
<dd>an object of class <code>SingleGroupClass</code>,
<code>MultipleGroupClass</code>, or <code>MixedClass</code></dd></dl>

Arguments

Extract log-likelihood — logLik-method

<dl>

<dt>object</dt>
<dd>an object of class <code>SingleGroupClass</code>,
<code>MultipleGroupClass</code>, or <code>MixedClass</code></dd>

</dl>

Extract log-likelihood

logLik-method: Extract log-likelihood

Description

Usage

Arguments

References

Examples