mleirt

Overview

mleirt is an R package that provides interactive Shiny applications for demonstrating Maximum Likelihood Estimation (MLE), Expected A Posteriori (EAP), and Maximum A Posteriori (MAP) estimation methods in Item Response Theory (IRT). The package offers visual and interactive tools for understanding parameter estimation in various IRT models, including the Rasch model, 2PL (Two-Parameter Logistic), and 3PL (Three-Parameter Logistic) models. Individual plots were originally developed by Metin Bulus and Wes Bonifay, whose work forms the foundation of the visualizations included here.

Features

• Interactive Shiny Interface: Explore IRT concepts through dynamic visualizations
• Multiple IRT Models:
  • Rasch Model (person location estimation)
  • 2PL Model (item discrimination and difficulty estimation)
  • 3PL Model (item discrimination, difficulty, and guessing parameter estimation)
• Parameter Estimation Methods:
  • Maximum Likelihood Estimation (MLE)
  • Expected A Posteriori (EAP)
  • Maximum A Posteriori (MAP)
• Real-time Visualization: Interactive 3D plots showing likelihood surfaces and parameter relationships
• Educational Tool: Ideal for teaching and learning IRT fundamentals

Installation

You can install the development version of mleirt from GitHub:

# Install devtools if you haven't already
install.packages("devtools")

# Install mleirt
devtools::install_github("sedzinfo/mleirt")

Dependencies

The package requires: - shiny - shinydashboard - htmltools

These will be automatically installed when you install mleirt.

Usage

Launch the interactive Shiny application with a single command:

library(mleirt)
mleirt()

This will open an interactive dashboard in your default web browser with multiple tabs:

MLE Rasch Tab

Demonstrates person location estimation using MLE in the Rasch model. Features include: - Adjust raw scores (0-5) - Set starting values for the estimation - Step through iterations to see how MLE converges - Visualize likelihood functions

MLE 2PL Tab

Explores item parameter estimation in the 2PL model with: - Interactive discrimination and difficulty parameter controls - 3D likelihood surface visualization - Rotatable plots to examine the likelihood from different angles

MLE 3PL Tab

Extends to the 3PL model, incorporating guessing parameters alongside discrimination and difficulty estimation.

Examples

# Launch the main application
library(mleirt)
mleirt()

# The application will open in your browser
# Navigate between tabs to explore different IRT models
# Adjust sliders to see how parameters affect the likelihood functions

Educational Context

This package is particularly useful for: - Students learning Item Response Theory - Instructors teaching psychometric methods - Researchers exploring IRT model behavior - Anyone interested in understanding MLE, EAP, and MAP estimation visually

The implementations replicate examples from educational resources, including tables from de Ayala (2009).

Credits

• Package Development: Dimitrios Zacharatos
• Original Plot Development: Metin Bulus & Wes Bonifay

License

GPL (>= 2)

References

• Bulus, M., & Bonifay, W. (2022). irtDemo R Package: Pedagogical Interactive Web Applications for Estimation, Scoring, and Multi Dimensionality in Item Response Theory. Anadolu University Journal of Education Faculty, 6(1), 92-108. https://doi.org/10.34056/aujef.913781
• de Ayala, R. J. (2009). The Theory and Practice of Item Response Theory. Guilford Press.

Contact

For questions, issues, or contributions, please contact: - Dimitrios Zacharatos: dzacharatos@yahoo.com - GitHub Issues: https://github.com/sedzinfo/mleirt/issues

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.