IRTsim

A simulated dataset that replicates the dataset from CITO.

datasets

Utilizes the 'lme4' and 'optimx' packages (previously the optim()
function from 'stats') to estimate (generalized) linear mixed models (GLMM)
with factor structures using a profile likelihood approach, as outlined in
Jeon and Rabe-Hesketh (2012) <doi:10.3102/1076998611417628> and
Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>.
Factor analysis and item response models can be extended to allow
for an arbitrary number of nested and crossed random effects,
making it useful for multilevel and cross-classified models.

Nicholas Rockwood

PLmixed

Estimate (Generalized) Linear Mixed Models with Factor
Structures

Minjeong Jeon

IRTsim function

A data frame with 2500 rows and 4 variables:<dl>
 <dt>sid</dt>
<dd>Student ID</dd> <dt>school</dt>
<dd>School ID</dd> <dt>item</dt>
<dd>Item ID</dd> <dt>y</dt>
<dd>Response</dd>
</dl>

Format

Simulated multilevel IRT dataset. — IRTsim

A data frame with 2500 rows and 4 variables:<dl>
 <dt>sid</dt>
<dd>Student ID</dd>

 <dt>school</dt>
<dd>School ID</dd>

 <dt>item</dt>
<dd>Item ID</dd>

 <dt>y</dt>
<dd>Response</dd>


</dl>

IRTsim: Simulated multilevel IRT dataset.

Description

Usage

Arguments

Format