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EFAfactors (version 1.2.3)

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

277

Version

1.2.3

License

GPL-3

Maintainer

Haijiang Qin

Last Published

June 14th, 2025

Functions in EFAfactors (1.2.3)

EFAscreet

Scree Plot
EFAindex

Various Indeces in EFA
EFAsim.data

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

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

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

Parallel Analysis
KGC

Kaiser-Guttman Criterion
extractor.feature.FF

Extracting features According to Goretzko & Buhner (2020)
data.scaler

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

the Hull Approach
extractor.feature.DNN

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

Check and Install Python Libraries (numpy and onnxruntime)
EFAhclust

Hierarchical Clustering for EFA
plot.EFAkmeans

Plot EFA K-means Clustering Results
FF

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

25 Personality Items Representing 5 Factors
GenData

Simulating Data Following John Ruscio's RGenData
plot.FF

Plot Factor Forest (FF) Classification Probability Distribution
plot.CD

Plot Comparison Data for Factor Analysis
plot.CDF

Plot Comparison Data Forest (CDF) Classification Probability Distribution
plot.EFAvote

Plot Voting Results for Number of Factors
model.xgb

the Tuned XGBoost Model for Determining the Number of Facotrs
af.softmax

An Activation Function: Softmax
plot.EFAscreet

Plots the Scree Plot
print.EFAdata

Print the EFAsim.data
print.EFAhclust

Print EFAhclust Method Results
plot.EKC

Plot Empirical Kaiser Criterion (EKC) Plot
plot.KGC

Plot Kaiser-Guttman Criterion (KGC) Plot
normalizor

Feature Normalization
plot.PA

Plot Parallel Analysis Scree Plot
print.EFAkmeans

Print EFAkmeans Method Results
print.CDF

Print Comparison Data Forest (CDF) Results
load_xgb

Load the Tuned XGBoost Model
load_scaler

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

Print the Scree Plot
load_DNN

Load the Trained Deep Neural Network (DNN)
factor.analysis

Factor Analysis by Principal Axis Factoring
plot.DNN_predictor

Plot DNN Predictor Classification Probability Distribution
print.DNN_predictor

Print DNN Predictor Method Results
plot.EFAhclust

Plot Hierarchical Cluster Analysis Dendrogram
plot.Hull

Plot Hull Plot for Factor Analysis
print.EFAvote

Print Voting Method Results
print.EKC

Print Empirical Kaiser Criterion Results
print.KGC

Print Kaiser-Guttman Criterion Results
print.PA

Print Parallel Analysis Method Results
predictLearner.classif.xgboost.earlystop

Prediction Function for the Tuned XGBoost Model with Early Stopping
print.CD

Print Comparison Data Method Results
print.FF

Print Factor Forest (FF) Results
print.Hull

Print Hull Method Results
EKC

Empirical Kaiser Criterion
EFAvote

Voting Method for Number of Factors in EFA
EFAkmeans

K-means for EFA
CD

the Comparison Data (CD) Approach
CDF

the Comparison Data Forest (CDF) Approach