sim_mst

Simulates data from an extended nominal response model according to an mst design

Conditional Maximum Likelihood Calibration and data management of multistage tests.
Supports polytomous items and incomplete designs with linear as well as multistage tests.
Extended Nominal Response and Interaction models, DIF and profile analysis.
See Robert J. Zwitser and Gunter Maris (2015)<doi:10.1007/s11336-013-9369-6>.

Timo Bechger

dexterMST

CML and Bayesian Calibration of Multistage Tests

Jesse Koops

Ivailo Partchev

Gunter Maris

Robert Zwitser

sim_mst function

<dl><dt>pars</dt>
<dd>item parameters, can be either: 
a data.frame with columns item_id, item_score, beta or a dexter or dexterMST parameters object</dd>
<dt>theta</dt>
<dd>vector of person abilities</dd>
<dt>test_design</dt>
<dd>data.frame with columns item_id, module_id</dd>
<dt>routing_rules</dt>
<dd>output of <code>mst_rules</code></dd>
<dt>routing</dt>
<dd>'all' or 'last' routing</dd></dl>

Arguments

Simulate multistage testing data — sim_mst

<dl>

<dt>pars</dt>
<dd>item parameters, can be either: 
a data.frame with columns item_id, item_score, beta or a dexter or dexterMST parameters object</dd>


<dt>theta</dt>
<dd>vector of person abilities</dd>


<dt>test_design</dt>
<dd>data.frame with columns item_id, module_id</dd>


<dt>routing_rules</dt>
<dd>output of <code>mst_rules</code></dd>


<dt>routing</dt>
<dd>'all' or 'last' routing</dd>

</dl>

sim_mst: Simulate multistage testing data

Description

Usage

Arguments