Determining the Number of Factors in Exploratory Factor Analysis

---

Documentation for package ‘EFAfactors’ version 1.2.4

• 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.DAPCS	20-item Dependency-Oriented and Achievement-Oriented Psychological Control Scale (DAPCS)
data.datasets.DNN	Subset Dataset for Training the Deep Neural Network (DNN)
data.datasets.LSTM	Subset Dataset for Training the Long Short Term Memory (LSTM) Network
data.scaler.DNN	the Scaler for the pre-trained Deep Neural Network (DNN)
data.scaler.LSTM	the Scaler for the pre-trained Long Short Term Memory (LSTM) Network
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.FF	Extracting features According to Goretzko & Buhner (2020)
extractor.feature.NN	Extracting features for the pre-trained Neural Networks for Determining the Number of Factors
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.NN	Load the the pre-trained Neural Networks for Determining the Number of Factors
load.scaler	Load the Scaler for the pre-trained Neural Networks for Determining the Number of Factors
load.xgb	Load the Tuned XGBoost Model
MAP	Minimum Average Partial (MAP) Test
model.xgb	the Tuned XGBoost Model for Determining the Number of Facotrs
NN	the pre-trained Neural Networks for Determining the Number of Factors
normalizor	Feature Normalization for the pre-trained Neural Networks for Determining the Number of Factors
PA	Parallel Analysis
plot	Plot Methods
plot.CD	Plot Methods
plot.CDF	Plot Methods
plot.EFAhclust	Plot Methods
plot.EFAkmeans	Plot Methods
plot.EFAscreet	Plot Methods
plot.EFAvote	Plot Methods
plot.EKC	Plot Methods
plot.FF	Plot Methods
plot.Hull	Plot Methods
plot.KGC	Plot Methods
plot.MAP	Plot Methods
plot.NN	Plot Methods
plot.PA	Plot Methods
plot.STOC	Plot Methods
predictLearner.classif.xgboost.earlystop	Prediction Function for the Tuned XGBoost Model with Early Stopping
print	Print Methods
print.CD	Print Methods
print.CDF	Print Methods
print.EFAdata	Print Methods
print.EFAhclust	Print Methods
print.EFAscreet	Print Methods
print.EFAvote	Print Methods
print.EKC	Print Methods
print.FF	Print Methods
print.Hull	Print Methods
print.KGC	Print Methods
print.MAP	Print Methods
print.NN	Print Methods
print.PA	Print Methods
STOC	Scree Test Optimal Coordinate (STOC)