LD_param_est

A function that extracts only the estimation of graph parameters
after the rank estimation is completed.

Implements comprehensive test data engineering methods as described in
Shojima (2022, ISBN:978-9811699856). Provides statistical techniques for
engineering and processing test data: Classical Test Theory (CTT) with
reliability coefficients for continuous ability assessment; Item Response
Theory (IRT) including Rasch, 2PL, and 3PL models with item/test information
functions; Latent Class Analysis (LCA) for nominal clustering; Latent Rank
Analysis (LRA) for ordinal clustering with automatic determination of cluster
numbers; Biclustering methods including infinite relational models for
simultaneous clustering of examinees and items without predefined cluster
numbers; and Bayesian Network Models (BNM) for visualizing inter-item
dependencies. Features local dependence analysis through LRA and biclustering,
parameter estimation, dimensionality assessment, and network structure
visualization for educational, psychological, and social science research.

Koji Kosugi 

exametrika

Test Data Engineering

LD_param_est function

<dl><dt>tmp</dt>
<dd>tmp</dd>
<dt>adj_list</dt>
<dd>adj_list</dd>
<dt>classRefMat</dt>
<dd>values returned from emclus</dd>
<dt>ncls</dt>
<dd>ncls</dd>
<dt>smoothpost</dt>
<dd>smoothpost</dd>
<dt>beta1</dt>
<dd>Beta distribution parameter 1 for prior density. Default is 2.</dd>
<dt>beta2</dt>
<dd>Beta distribution parameter 2 for prior density. Default is 2.
Unlike the other network models (which default to 1), the default of 2
follows the original Mathematica implementation of LDLRA.</dd>
<dt>fit_only</dt>
<dd>If TRUE, return only the BIC (list with element <code>BIC</code>),
skipping the posterior distribution, IRP, and full TestFit construction.
Used by the LDLRA_PBIL fitness loop, where only the BIC is consumed.
Requires <code>bench</code>.</dd>
<dt>bench</dt>
<dd>Cached benchmark-model statistics from <code>BNM_bench_stats()</code>,
consumed only when <code>fit_only = TRUE</code>. They depend only on the data, so the
structure-search loop computes them once instead of re-running <code>TestFit()</code>
per candidate.</dd></dl>

Arguments

LDparam set — LD_param_est

<dl>

<dt>tmp</dt>
<dd>tmp</dd>


<dt>adj_list</dt>
<dd>adj_list</dd>


<dt>classRefMat</dt>
<dd>values returned from emclus</dd>


<dt>ncls</dt>
<dd>ncls</dd>


<dt>smoothpost</dt>
<dd>smoothpost</dd>


<dt>beta1</dt>
<dd>Beta distribution parameter 1 for prior density. Default is 2.</dd>


<dt>beta2</dt>
<dd>Beta distribution parameter 2 for prior density. Default is 2.
Unlike the other network models (which default to 1), the default of 2
follows the original Mathematica implementation of LDLRA.</dd>


<dt>fit_only</dt>
<dd>If TRUE, return only the BIC (list with element <code>BIC</code>),
skipping the posterior distribution, IRP, and full TestFit construction.
Used by the LDLRA_PBIL fitness loop, where only the BIC is consumed.
Requires <code>bench</code>.</dd>


<dt>bench</dt>
<dd>Cached benchmark-model statistics from <code>BNM_bench_stats()</code>,
consumed only when <code>fit_only = TRUE</code>. They depend only on the data, so the
structure-search loop computes them once instead of re-running <code>TestFit()</code>
per candidate.</dd>

</dl>

LD_param_est: LDparam set

Description

Usage

Arguments