Package: EFAfactors 1.1.1

Haijiang Qin

EFAfactors: Determining the Number of Factors in Exploratory Factor Analysis

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) <doi:10.1207/s15327906mbr0102_10>, Kaiser-Guttman Criterion (KGC) by Guttman (1954) <doi:10.1007/BF02289162> and Kaiser (1960) <doi:10.1177/001316446002000116>, and flexible Parallel Analysis (PA) by Horn (1965) <doi:10.1007/BF02289447> 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) <doi:10.1037/met0000074>, Comparison Data (CD) by Ruscio and Roche (2012) <doi:10.1037/a0025697>, and Hull method by Lorenzo-Seva et al. (2011) <doi:10.1080/00273171.2011.564527>, as well as some AI-based methods like Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) <doi:10.3758/s13428-023-02122-4> and Factor Forest (FF) by Goretzko and Buhner (2020) <doi:10.1037/met0000262>. Additionally, it includes a deep neural network (DNN) trained on large-scale datasets that can efficiently and reliably determine the number of factors.

Authors:Haijiang Qin [aut, cre, cph], Lei Guo [aut, cph]

EFAfactors_1.1.1.tar.gz
EFAfactors_1.1.1.tar.gz(r-4.5-noble)EFAfactors_1.1.1.tar.gz(r-4.4-noble)
EFAfactors.pdf |EFAfactors.html✨
EFAfactors/json (API)
NEWS

# Install 'EFAfactors' in R:
install.packages('EFAfactors', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • data.bfi - 25 Personality Items Representing 5 Factors
  • data.datasets - Subset Dataset for Training the Pre-Trained Deep Neural Network
  • data.scaler - The Scaler for the Pre-Trained Deep Neural Network
  • model.xgb - The Tuned XGBoost Model for Determining the Number of Facotrs

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.48 score 153 downloads 23 exports 118 dependencies

Last updated 1 months agofrom:0c6498c80c. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-linux-x86_64OKNov 20 2024

Exports:af.softmaxCDCDFDNN_predictorEFAhclustEFAindexEFAkmeansEFAscreetEFAsim.dataEFAvoteEKCextractor.feature.DNNextractor.feature.FFfactor.analysisFFGenDataHullKGCload_DNNload_scalerload_xgbnormalizorPA

Dependencies:askpassbackportsbase64encBBmiscbitbit64bslibcachemcheckmateclicliprcolorspacecommonmarkcpp11crayoncrosstalkcurldata.tableddpcrdigestdplyrDTevaluateevdfansifarverfastmapfastmatchfontawesomefsgenericsggplot2glueGPArotationgtableherehighrhmshtmltoolshtmlwidgetshttpuvhttrineqisobandjquerylibjsonlitekernlabknitrlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixmemoisemgcvmimemixtoolsmlrmnormtmunsellnlmeopensslparallelMapParamHelperspillarpkgconfigplotlyplyrpngprettyunitsprogresspromisesproxypsychpurrrR6rangerrappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppTOMLreadrreticulaterlangrmarkdownrprojrootsassscalessegmentedshinyshinydisconnectshinyjsSimCorMultRessourcetoolsstringistringrsurvivalsystibbletidyrtidyselecttinytextzdbutf8vctrsviridisLitevroomwithrxfunxgboostXMLxtableyaml

Readme and manuals

Help Manual

Help pageTopics
An Activation Function: Softmaxaf.softmax
the Comparison Data (CD) ApproachCD
the Comparison Data Forest (CDF) ApproachCDF
25 Personality Items Representing 5 Factorsdata.bfi
Subset Dataset for Training the Pre-Trained Deep Neural Network (DNN)data.datasets
the Scaler for the Pre-Trained Deep Neural Network (DNN)data.scaler
A Pre-Trained Deep Neural Network (DNN) for Determining the Number of FactorsDNN_predictor
Hierarchical Clustering for EFAEFAhclust
Various Indeces in EFAEFAindex
K-means for EFAEFAkmeans
Scree PlotEFAscreet
Simulate Data that Conforms to the theory of Exploratory Factor Analysis.EFAsim.data
Voting Method for Number of Factors in EFAEFAvote
Empirical Kaiser CriterionEKC
Extracting features for the Pre-Trained Deep Neural Network (DNN)extractor.feature.DNN
Extracting features According to Goretzko & Buhner (2020)extractor.feature.FF
Factor Analysis by Principal Axis Factoringfactor.analysis
Factor Forest (FF) Powered by An Tuned XGBoost Model for Determining the Number of FactorsFF
Simulating Data Following John Ruscio's RGenDataGenData
the Hull ApproachHull
Kaiser-Guttman CriterionKGC
Load the Trained Deep Neural Network (DNN)load_DNN
Load the Scaler for the Pre-Trained Deep Neural Network (DNN)load_scaler
Load the Tuned XGBoost Modelload_xgb
the Tuned XGBoost Model for Determining the Number of Facotrsmodel.xgb
Feature Normalizationnormalizor
Parallel AnalysisPA
Plot Comparison Data for Factor Analysisplot.CD
Plot Comparison Data Forest (CDF) Classification Probability Distributionplot.CDF
Plot DNN Predictor Classification Probability Distributionplot.DNN_predictor
Plot Hierarchical Cluster Analysis Dendrogramplot.EFAhclust
Plot EFA K-means Clustering Resultsplot.EFAkmeans
Plots the Scree Plotplot.EFAscreet
Plot Voting Results for Number of Factorsplot.EFAvote
Plot Empirical Kaiser Criterion (EKC) Plotplot.EKC
Plot Factor Forest (FF) Classification Probability Distributionplot.FF
Plot Hull Plot for Factor Analysisplot.Hull
Plot Kaiser-Guttman Criterion (KGC) Plotplot.KGC
Plot Parallel Analysis Scree Plotplot.PA
Prediction Function for the Tuned XGBoost Model with Early StoppingpredictLearner.classif.xgboost.earlystop
Print Comparison Data Method Resultsprint.CD
Print Comparison Data Forest (CDF) Resultsprint.CDF
Print DNN Predictor Method Resultsprint.DNN_predictor
Print the EFAsim.dataprint.EFAdata
Print EFAhclust Method Resultsprint.EFAhclust
Print EFAkmeans Method Resultsprint.EFAkmeans
Print the Scree Plotprint.EFAscreet
Print Voting Method Resultsprint.EFAvote
Print Empirical Kaiser Criterion Resultsprint.EKC
Print Factor Forest (FF) Resultsprint.FF
Print Hull Method Resultsprint.Hull
Print Kaiser-Guttman Criterion Resultsprint.KGC
Print Parallel Analysis Method Resultsprint.PA