Overview
OptimalFactor is a comprehensive R package for optimal factor model refinement in both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) using machine-learning inspired boosting algorithms.
Installation
You can install the latest version of OptimalFactor from GitHub:
if (!require("devtools")) {
install.packages("devtools")
}
devtools::install_github("jventural/OptimalFactor")Main Features
EFA-Boosting Algorithm
The efa_boosting() function implements an advanced
iterative optimization algorithm for EFA:
- Greedy and global search strategies for optimal item selection
- Adaptive composite fit indices (RMSEA, SRMR, CFI) with weights based on df x N
- Automatic Heywood case and near-Heywood detection
- Cross-loading identification and removal
- Minimum items per factor enforcement
- Interfactor correlation verification
- Optional AI-assisted conceptual analysis via GPT models
CFA Optimization
The cfa_boosting() function provides CFA
optimization:
- Modification indices-based model improvement
- Automatic covariance addition
- Fit index monitoring
Quick Start
Basic EFA-Boosting
library(OptimalFactor)
# Run EFA-Boosting
result <- efa_boosting(
data = your_data,
name_items = "item",
n_factors = 3,
verbose = TRUE
)
# View results
print(result$final_structure)
print(result$fit_indices)Theoretical Background
The EFA-Boosting algorithm uses adaptive fit indices based on the research by Kenny and McCoach (2003) and Shi, Lee, & Maydeu-Olivares (2019), which demonstrates that traditional cutoff values for fit indices depend on model complexity (degrees of freedom) and sample size.
The adaptive weights are calculated as:
- RMSEA weight: Higher for larger models (more df)
- SRMR weight: Stable across model sizes
- CFI weight: Higher for smaller models (fewer df)
This adaptive approach ensures appropriate model evaluation across different scales and sample sizes.
References
Kenny, D. A., & McCoach, D. B. (2003). Effect of the number of variables on measures of fit in structural equation modeling. Structural Equation Modeling, 10(3), 333-351.
Shi, D., Lee, T., & Maydeu-Olivares, A. (2019). Understanding the model size effect on SEM fit indices. Educational and Psychological Measurement, 79(2), 310-334.