calc_MI_FB

Calculate the mutual information using full Bayesian.

Uses the optimal test design approach by Birnbaum (1968, ISBN:9781593119348) and
van der Linden (2018) <doi:10.1201/9781315117430> to construct fixed, adaptive, and parallel tests.
Supports the following mixed-integer programming (MIP) solver packages: 'Rsymphony',
'highs', 'gurobi', 'lpSolve', and 'Rglpk'. The 'gurobi' package is not available from CRAN; see <https://www.gurobi.com/downloads/>.

Seung W. Choi

TestDesign

Optimal Test Design Approach to Fixed and Adaptive Test
Construction

Sangdon Lim

calc_MI_FB function

<dl><dt>x</dt>
<dd>A numeric vector of MCMC sampled theta values.</dd>
<dt>items_list</dt>
<dd>A list of item parameter matrices.</dd>
<dt>ncat</dt>
<dd>a numeric vector specifying the number of response categories in each item.</dd>
<dt>model</dt>
<dd>a numeric vector indicating the IRT models of each item (1: 1PL, 2: 2PL, 3: 3PL, 4: PC, 5: GPC, 6: GR).</dd></dl>

Arguments

Calculate the mutual information using full Bayesian — calc_MI_FB

<dl>

<dt>x</dt>
<dd>A numeric vector of MCMC sampled theta values.</dd>


<dt>items_list</dt>
<dd>A list of item parameter matrices.</dd>


<dt>ncat</dt>
<dd>a numeric vector specifying the number of response categories in each item.</dd>


<dt>model</dt>
<dd>a numeric vector indicating the IRT models of each item (1: 1PL, 2: 2PL, 3: 3PL, 4: PC, 5: GPC, 6: GR).</dd>

</dl>

calc_MI_FB: Calculate the mutual information using full Bayesian

Description

Usage

Arguments