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EFAfactors (version 1.2.2)

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

190

Version

1.2.2

License

GPL-3

Maintainer

Haijiang Qin

Last Published

May 1st, 2025

Functions in EFAfactors (1.2.2)

EFAkmeans

K-means for EFA
EFAscreet

Scree Plot
EFAhclust

Hierarchical Clustering for EFA
EFAsim.data

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

the Comparison Data (CD) Approach
EFAindex

Various Indeces in EFA
DNN_predictor

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

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

Check and Install Python Libraries (numpy and onnxruntime)
af.softmax

An Activation Function: Softmax
data.datasets

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

25 Personality Items Representing 5 Factors
Hull

the Hull Approach
GenData

Simulating Data Following John Ruscio's RGenData
KGC

Kaiser-Guttman Criterion
PA

Parallel Analysis
data.scaler

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

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

Plot Comparison Data for Factor Analysis
model.xgb

the Tuned XGBoost Model for Determining the Number of Facotrs
factor.analysis

Factor Analysis by Principal Axis Factoring
load_xgb

Load the Tuned XGBoost Model
extractor.feature.DNN

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

Plot DNN Predictor Classification Probability Distribution
plot.EFAhclust

Plot Hierarchical Cluster Analysis Dendrogram
normalizor

Feature Normalization
plot.Hull

Plot Hull Plot for Factor Analysis
plot.FF

Plot Factor Forest (FF) Classification Probability Distribution
extractor.feature.FF

Extracting features According to Goretzko & Buhner (2020)
print.EFAvote

Print Voting Method Results
print.CD

Print Comparison Data Method Results
predictLearner.classif.xgboost.earlystop

Prediction Function for the Tuned XGBoost Model with Early Stopping
plot.EFAvote

Plot Voting Results for Number of Factors
plot.EKC

Plot Empirical Kaiser Criterion (EKC) Plot
plot.CDF

Plot Comparison Data Forest (CDF) Classification Probability Distribution
print.FF

Print Factor Forest (FF) Results
print.EFAscreet

Print the Scree Plot
print.EFAkmeans

Print EFAkmeans Method Results
print.EKC

Print Empirical Kaiser Criterion Results
print.KGC

Print Kaiser-Guttman Criterion Results
print.PA

Print Parallel Analysis Method Results
plot.KGC

Plot Kaiser-Guttman Criterion (KGC) Plot
print.EFAdata

Print the EFAsim.data
print.EFAhclust

Print EFAhclust Method Results
load_DNN

Load the Trained Deep Neural Network (DNN)
print.Hull

Print Hull Method Results
print.CDF

Print Comparison Data Forest (CDF) Results
plot.PA

Plot Parallel Analysis Scree Plot
print.DNN_predictor

Print DNN Predictor Method Results
plot.EFAkmeans

Plot EFA K-means Clustering Results
plot.EFAscreet

Plots the Scree Plot
EKC

Empirical Kaiser Criterion
EFAvote

Voting Method for Number of Factors in EFA
CDF

the Comparison Data Forest (CDF) Approach