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EFAfactors (version 1.2.1)

Determining the Number of Factors in Exploratory Factor Analysis

Description

Provides a collection of standard factor retention methods in Exploratory Factor Analysis (EFA), making it easier to determine the number of factors. Traditional methods such as the scree plot by Cattell (1966) , Kaiser-Guttman Criterion (KGC) by Guttman (1954) and Kaiser (1960) , and flexible Parallel Analysis (PA) by Horn (1965) based on eigenvalues form PCA or EFA are readily available. This package also implements several newer methods, such as the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017) , Comparison Data (CD) by Ruscio and Roche (2012) , and Hull method by Lorenzo-Seva et al. (2011) , as well as some AI-based methods like Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) and Factor Forest (FF) by Goretzko and Buhner (2020) . Additionally, it includes a deep neural network (DNN) trained on large-scale datasets that can efficiently and reliably determine the number of factors.

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Version

Install

install.packages('EFAfactors')

Monthly Downloads

303

Version

1.2.1

License

GPL-3

Maintainer

Haijiang Qin

Last Published

February 17th, 2025

Functions in EFAfactors (1.2.1)

Subset Dataset for Training the Pre-Trained Deep Neural Network (DNN)

check_python_libraries

Check and Install Python Libraries (numpy and onnxruntime)

Factor Forest (FF) Powered by An Tuned XGBoost Model for Determining the Number of Factors

Kaiser-Guttman Criterion

the Hull Approach

An Activation Function: Softmax

Simulating Data Following John Ruscio's RGenData

25 Personality Items Representing 5 Factors

Parallel Analysis

Load the Tuned XGBoost Model

extractor.feature.DNN

Extracting features for the Pre-Trained Deep Neural Network (DNN)

Plot Comparison Data for Factor Analysis

the Tuned XGBoost Model for Determining the Number of Facotrs

the Scaler for the Pre-Trained Deep Neural Network (DNN)

factor.analysis

Factor Analysis by Principal Axis Factoring

Load the Scaler for the Pre-Trained Deep Neural Network (DNN)

Load the Trained Deep Neural Network (DNN)

Plot Voting Results for Number of Factors

Plot Empirical Kaiser Criterion (EKC) Plot

extractor.feature.FF

Extracting features According to Goretzko & Buhner (2020)

Plot Kaiser-Guttman Criterion (KGC) Plot

Plot Factor Forest (FF) Classification Probability Distribution

predictLearner.classif.xgboost.earlystop

Prediction Function for the Tuned XGBoost Model with Early Stopping

Print Comparison Data Method Results

Print the EFAsim.data

Plot EFA K-means Clustering Results

print.EFAhclust

Print EFAhclust Method Results

Plot Hull Plot for Factor Analysis

Plot Comparison Data Forest (CDF) Classification Probability Distribution

Plots the Scree Plot

Print Factor Forest (FF) Results

Print Hull Method Results

Print Comparison Data Forest (CDF) Results

Plot Parallel Analysis Scree Plot

print.DNN_predictor

Print DNN Predictor Method Results

plot.DNN_predictor

Plot DNN Predictor Classification Probability Distribution

Plot Hierarchical Cluster Analysis Dendrogram

Print Voting Method Results

print.EFAkmeans

Print EFAkmeans Method Results

print.EFAscreet

Print the Scree Plot

Print Kaiser-Guttman Criterion Results

Feature Normalization

Print Parallel Analysis Method Results

Print Empirical Kaiser Criterion Results

Hierarchical Clustering for EFA

Voting Method for Number of Factors in EFA

Various Indeces in EFA

the Comparison Data Forest (CDF) Approach

K-means for EFA

the Comparison Data (CD) Approach

Empirical Kaiser Criterion

A Pre-Trained Deep Neural Network (DNN) for Determining the Number of Factors

Simulate Data that Conforms to the theory of Exploratory Factor Analysis.