# 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)