# Determining the Number of Factors in Exploratory Factor Analysis

## Documentation for package ‘EFAfactors’ version 1.2.1

- DESCRIPTION file.
- Package NEWS.

## Help Pages

af.softmax | An Activation Function: Softmax
CD | the Comparison Data (CD) Approach
CDF | the Comparison Data Forest (CDF) Approach
check_python_libraries | Check and Install Python Libraries (numpy and onnxruntime)
data.bfi | 25 Personality Items Representing 5 Factors
data.datasets | Subset Dataset for Training the Pre-Trained Deep Neural Network (DNN)
data.scaler | the Scaler for the Pre-Trained Deep Neural Network (DNN)
DNN_predictor | A Pre-Trained Deep Neural Network (DNN) for Determining the Number of Factors
EFAhclust | Hierarchical Clustering for EFA
EFAindex | Various Indeces in EFA
EFAkmeans | K-means for EFA
EFAscreet | Scree Plot
EFAsim.data | Simulate Data that Conforms to the theory of Exploratory Factor Analysis.
EFAvote | Voting Method for Number of Factors in EFA
EKC | Empirical Kaiser Criterion
extractor.feature.DNN | Extracting features for the Pre-Trained Deep Neural Network (DNN)
extractor.feature.FF | Extracting features According to Goretzko & Buhner (2020)
factor.analysis | Factor Analysis by Principal Axis Factoring
FF | Factor Forest (FF) Powered by An Tuned XGBoost Model for Determining the Number of Factors
GenData | Simulating Data Following John Ruscio's RGenData
Hull | the Hull Approach
KGC | Kaiser-Guttman Criterion
load_DNN | Load the Trained Deep Neural Network (DNN)
load_scaler | Load the Scaler for the Pre-Trained Deep Neural Network (DNN)
load_xgb | Load the Tuned XGBoost Model
model.xgb | the Tuned XGBoost Model for Determining the Number of Facotrs
normalizor | Feature Normalization
PA | Parallel Analysis
plot.CD | Plot Comparison Data for Factor Analysis
plot.CDF | Plot Comparison Data Forest (CDF) Classification Probability Distribution
plot.DNN_predictor | Plot DNN Predictor Classification Probability Distribution
plot.EFAhclust | Plot Hierarchical Cluster Analysis Dendrogram
plot.EFAkmeans | Plot EFA K-means Clustering Results
plot.EFAscreet | Plots the Scree Plot
plot.EFAvote | Plot Voting Results for Number of Factors
plot.EKC | Plot Empirical Kaiser Criterion (EKC) Plot
plot.FF | Plot Factor Forest (FF) Classification Probability Distribution
plot.Hull | Plot Hull Plot for Factor Analysis
plot.KGC | Plot Kaiser-Guttman Criterion (KGC) Plot
plot.PA | Plot Parallel Analysis Scree Plot
predictLearner.classif.xgboost.earlystop | Prediction Function for the Tuned XGBoost Model with Early Stopping
print.CD | Print Comparison Data Method Results
print.CDF | Print Comparison Data Forest (CDF) Results
print.DNN_predictor | Print DNN Predictor Method Results
print.EFAdata | Print the EFAsim.data
print.EFAhclust | Print EFAhclust Method Results
print.EFAkmeans | Print EFAkmeans Method Results
print.EFAscreet | Print the Scree Plot
print.EFAvote | Print Voting Method Results
print.EKC | Print Empirical Kaiser Criterion Results
print.FF | Print Factor Forest (FF) Results
print.Hull | Print Hull Method Results
print.KGC | Print Kaiser-Guttman Criterion Results
print.PA | Print Parallel Analysis Method Results