ttm_perplexity

Calculates the perplexity of LDA models over a range of K topics to help
determine the optimal number of topics.

Implements the Topic Testlet Model (TTM) as described by Xiong et al. (2025) <doi:10.1111/jedm.70001>.
The package integrates Latent Dirichlet Allocation (LDA) with the Partial Credit Model to
account for local item dependence in testlets using latent topics from student
textual responses.

Jiawei Xiong

TopicTestlet

A Topic Testlet Model for Calibrating Testlet Constructed
Responses

Cheng Tang

Qidi Liu

ttm_perplexity function

<dl><dt>text_vector</dt>
<dd>A character vector of aggregated student responses (length N).</dd>
<dt>k_range</dt>
<dd>A numeric vector indicating the number of topics to try (e.g., 2:10).</dd>
<dt>seed</dt>
<dd>Integer seed for reproducibility.</dd></dl>

Arguments

Calculate Perplexity for Different Topic Numbers — ttm_perplexity

<dl>

<dt>text_vector</dt>
<dd>A character vector of aggregated student responses (length N).</dd>


<dt>k_range</dt>
<dd>A numeric vector indicating the number of topics to try (e.g., 2:10).</dd>


<dt>seed</dt>
<dd>Integer seed for reproducibility.</dd>

</dl>

ttm_perplexity: Calculate Perplexity for Different Topic Numbers

Description

Usage

Value

Arguments